Proceedings of the 25th international transport & air pollution (TAP) and the 3rd shipping & environment (S&E) conferences.
Date: 2024-02-28
Institutions: Joint Research Centre
Subjects: EU environmental policy, air quality, clean technology, conference proceedings, energy use, environmental impact, environmental law, maritime shipping, product life, reduction of gas emissions, research and development, research results, substitute fuel, sustainable mobility, transport infrastructure

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How to cite this report: European Commission, Joint Research Centre, Proceedings of the 25th International Transport & 
Air Pollution (TAP) and the 3rd Shipping & Environment (S&E) Conference, Sjödin, Å., Moldanova, J., Laurelin, M., Cha, Y., 
Lundström, H. and Fontaras, G. editors, Publications Office of the European Union, Luxembourg, 2024, 
https://data.europa.eu/doi/10.2760/564701, JRC136825. 

Contents 

Abstract ...................................................................................................................................................................3 

Acknowledgements .................................................................................................................................................4 

1 

Introduction .......................................................................................................................................................5 

1.1  Message of the Chairs ..............................................................................................................................5 

1.2  Organisation ..............................................................................................................................................7 

1.2.1 

General information ......................................................................................................................7 

1.2.2 

Local organisers ............................................................................................................................7 

1.2.3 

Collaborators .................................................................................................................................8 

1.2.4 

Funders/sponsors ...........................................................................................................................8 

1.2.5 

Scientific Committees ....................................................................................................................9 

1.2.6  Organising Committee ................................................................................................................ 10 

2  Conference Proceedings  Extended abstracts ............................................................................................... 11 

2.1  TAP.01. New emission control concepts, testing conditions and pollutants, non-exhaust emission ... 15 

2.2  TAP.02. On-board monitoring and diagnostics, emission tampering and deterioration. ...................... 42 

2.3  TAP.03. In-service conformity and new concepts for enhanced emission testing in PTI. .................... 69 

2.4  TAP.04. Remote sensing of vehicle emissions. ...................................................................................... 94 

2.5  TAP.05. Vehicle greenhouse gas emissions, energy consumption, vehicle and fuel life-cycle analysis.

150 

2.6  TAP.06. Vehicle-emission modelling and measurements and impact assessment of emission 
regulations. ................................................................................................................................................... 201 

2.7  TAP.07. Non-road emissions. ............................................................................................................... 254 

2.8  JS.08. Air pollutant and GHG emissions, water contaminants, ambient and underwater noise and 
vessel-induced mixing. ................................................................................................................................. 275 

2.9  JS.09. Atmospheric processes and air quality impact studies: Modelling impacts of transport on air 
pollution, climate, health and ecosystems. .................................................................................................. 343 

2.10 JS.10. Compliance monitoring technological and legal frameworks; experimental studies; theoretical 
impact studies. ............................................................................................................................................. 442 

2.11 JS.11. Reduction measures for GHG emissions   alternative fuels, electrification, energy use and 
optimization; inner-disciplinary and cross-sector studies. .......................................................................... 469 

2.12 JS.12. Scenarios and policy options for sustainable transport ........................................................... 477 

2.13 SE.13. Marine processes   fate of pollution from shipping in the marine environment impact studies 
on ecotoxicology, eutrophication and acidification, energy pollution including underwater noise & induced 
mixing experimental work, modelling studies of dispersion, transport, and chemical and biological 
processes in marine waters. ........................................................................................................................ 483 

2.14 SE.14. Holistic assessment of shipping impacts on the environment, shipping in the marine spatial 
planning. ....................................................................................................................................................... 490 

3  Conference Proceedings   Short abstracts .................................................................................................. 497 

3.1  TAP.02. On-board monitoring and diagnostics, emission tampering and deterioration. .................... 502 

3.2  TAP.03. In-service conformity and new concepts for enhanced emission testing in PTI. .................. 505 

1 

3.3  TAP.04. Remote sensing of vehicle emissions. .................................................................................... 507 

3.4  TAP.05. Vehicle greenhouse gas emissions, energy consumption, vehicle and fuel life-cycle analysis.

512 

3.5  TAP.06. Vehicle-emission modelling and measurements and impact assessment of emission 
regulations. ................................................................................................................................................... 514 

3.6  TAP.07. Non-road emissions. ............................................................................................................... 517 

3.7  JS.08. Air pollutant and GHG emissions, water contaminants, ambient and underwater noise and 
vessel-induced mixing. ................................................................................................................................. 522 

3.8  JS.09. Atmospheric processes and air quality impact studies: Modelling impacts of transport on air 
pollution, climate, health and ecosystems. .................................................................................................. 529 

3.9  JS.10. Compliance monitoring technological and legal frameworks; experimental studies; theoretical 
impact studies. ............................................................................................................................................. 534 

3.10 JS.12. Scenarios and policy options for sustainable transport. .......................................................... 538 

3.11 SE.13 Marine processes   fate of pollutions from shipping in the marine environment: impact studies 
on ecotoxicology, eutrophication and acidification, energy pollution including underwater noise & induced 
mixing, experimental work, modelling studies of dispersion, transport, and chemical and biological 
processes in marine waters. ........................................................................................................................ 544 

3.12 SE.14. Holistic assessment of shipping impacts on the environment, shipping in marine spatial 
planning. ....................................................................................................................................................... 552 

3.13 PS.1. Poster session 1 (TAP sessions) ................................................................................................. 557 

3.14 PS.2. Poster session 2 (Joint and S&E sessions) ................................................................................. 575 

2 

Joint TAP and S&E Conference 2023 

Abstract 

This publication contains the proceedings of the 25th International Transport & Air Pollution (TAP) and 
the 3rd Shipping & Environment (S&E) conference that took place in Gothenburg, Sweden, from the 25th 
to the 28th of September, 2023. The two joint conferences collected the main research developments 

multiple transport areas. Topics covered research on air pollutant emissions and greenhouse gases from 
transport and clean shipping, emission measurements, remote sensing, emissions modelling and control, 
enhanced  air  quality  modelling  tools,  and  impacts  of  transport-related  air  pollution  on  health  and 
ecosystems.  Additional  focus  areas  included  reduction  measures  for  emissions  of  GHGs  and  air 
pollutants such as electromobility, fuels and energy decarbonisation, low emission engines and vehicles, 
air  pollution/climate  change  trade-offs,  holistic  assessment  of  shipping  impacts  including  the  marine 
environment, as well as scenarios, policy options for sustainable transport. The publication is addressed 
to researchers, engineers and policymakers, as well as to all those interested in developing more efficient 
and cleaner transportation systems. 

3 

Joint TAP and S&E Conference 2023 

Acknowledgements  

We would like to thank all the people involved in the successful organisation of the 25th International 
Transport & Air Pollution and the 3rd Shipping & Environment conference, and in particular the members 
of the Scientific and Organizing Committees for their invaluable efforts. Above all we would like to thank 
all  presenter  and  authors  for  their  high-quality  contributions  to  the  conference  and  the  conference 
proceedings.  

4 

  
 
 
Joint TAP and S&E Conference 2023 

1 

Introduction 

1.1  Message of the Chairs 

The 25th International Transport and Air Pollution Conference (TAP) and the 3rd Shipping & Environment 
Conference (S&E) were jointly co-organized in Gothenburg, Sweden, from 25 to 28 September, 2023, by 
IVL  Swedish  Environmental  Research  Institute  together  with  Chalmers  University  of  Technology, 
University of Gothenburg and Swedish Institute for the Marine Environment, with financial support from 
the  Swedish  Transport  Administration,  the  Swedish  Environmental  Protection  Agency,  the  European 
Commission Joint Research Centre and Chalmers Area of Advance Transport. 

The two joint conferences provided a forum to discuss a wide variety of questions related to the impacts 
of  the  transport  sector  on  all  parts  of  the  environment,  including  atmosphere,  climate,  terrestrial 
ecosystems and the marine environment. At a time when a fast transition to carbon neutral transport is 
essential, this joint event facilitated along with the traditional areas of TAP and S&E conferences also 
holistic studies investigating multimodal transport and/or impacts on several areas of the environment. 

For the TAP part of the conference the topics were: new emission control concepts, testing conditions 
and  pollutants,  non-exhaust  emissions,  onboard  monitoring  and  diagnostics,  emission  tampering  and 
deterioration, in-service conformity and new concepts for enhanced PTI emission testing, remote sensing 
of vehicle emissions, greenhouse gas emissions, energy use, fuel life cycle analysis, emission modelling 
and measurements and impact assessment of emission regulations, non-road emissions. 

For  the  Joint  TAP&SE  part,  focussing  on  shipping,  the  topics  were:  water  contaminants,  ambient  and 
underwater  noise  and  vessel-induced  mixing,  atmospheric  processes  and  air  quality  impact  studies, 
modelling impacts of transport on air pollution, climate, health and ecosystems, compliance monitoring: 
technological  and  legal  frameworks;  experimental  studies;  theoretical  impact  studies,  reduction 
measures for GHG emissions - alternative fuels, electrification, energy use optimization; inter-disciplinary 
and cross-sector studies and scenarios and policy options for sustainable transport.  

For  the  S&E  part  topics  were:  Marine  processes  -  fate  of  pollution  from  shipping  in  the  marine 
environment, impact studies on ecotoxicology, eutrophication and acidification, energy pollution including 
underwater noise & induced mixing; experimental work, modelling studies of dispersion, transport, and 
chemical and biological processes in marine waters, and holistic assessment of shipping impacts on the 
environment and marine spatial planning. 

Taken together the two joint conferences were attended by close to 200 participants, to whom about 70 
scientific papers were presented as oral presentations and about 90 as poster presentations.  

The  TAP  part  of  the  conference  mirrored  the  increasing  research  interest  in  non-exhaust  particle 
emissions and road vehicle CO2 emissions, fuel consumption and energy use, the latter related to the 
growing fleet of battery electric vehicles in recent years. It also showed many new developments in the 
field of remote emission sensing techniques and their applications in the field of new emission legislation 
impacts,  market  surveillance  and  detection  of  high-emitters,  due  to  e.g.,  deliberate  tampering  of  the 
vehicle emission control systems. The needs and options to renew and improve inspection maintenance 
programs (PTIs) were also highlighted in several presentations. 

The Joint TAP&SE part of the conference showed new developments in investigation of emissions from 
shipping and in compliance monitoring, where combustion of traditional and alternative fuels is expected 
to remain a significant source of air pollution affecting communities in coastal regions also in the future. 
Challenges of the current legislation targeting shipping it terms of enforcement, efficiency in emissions 
reduction, as well as trade-offs between impacts on different parts of the environment were discussed 
along with new developments in modelling tools for assessment of impact of transport sector on air 
quality. Several modelling studies showed potential of the mitigation options in road transport, shipping 
and  the  entire  transport  system  to  meet  the  air  quality  and  GHG  emission  goals  on  different  time 
horizons.  

The marine part of the conference focused on impact of shipping on the marine environment and 
holistic studies taking all environmental impacts into consideration. Important topic of this part was 
impact of large increase of use of scrubbers, the exhaust gas aftertreatment enabling continuation of 
use of high-sulphur fuels containing metals and other toxic substances, which are transferring 

5 

  
 
 
Joint TAP and S&E Conference 2023 

emissions to the atmosphere targeted by IMO legislation, directly to the sea, bringing a serious threat 
to the marine environment.  

The present report includes the proceedings of the joint 25th TAP and 3rd S&E conference (2023) and 
is divided into two parts: Volume 1 contains all extended abstracts submitted to the two conferences, 
whereas Volume 2 contains all the short (one page) abstracts that were not submitted as extended 
abstracts. 

6 

  
 
 
 
 
Joint TAP and S&E Conference 2023 

1.2  Organisation 

1.2.1  General information  

Conference Logo 

Conference Title 

25th International Transport & Air Pollution and  
3rd Shipping & Environment Conference 

Date 

Venue 

25 -28 September 2023 

Wallenberg Conference Centre, Gothenburg, Sweden 

1.2.2  Local organisers 

IVL Swedish Environmental Research Institute  

University of Gothenburg  

Chalmers University of Technology 

Swedish Institute for the Marine Environment 

European Commission Joint Research Centre 

7 

With the support of:  

  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1.2.3  Collaborators  

1.2.4  Funders/sponsors  

Joint TAP and S&E Conference 2023 

Laboratory of Applied Thermodynamics 

Aristotle University of Thessaloniki  

Graz University of Technology  

EMPA Swiss Federal Laboratories for 

Materials Science & Technology  

Université Gustave Eiffel 

Institut Francilien des Sciences Appliquées 

Swedish Transport Administration  

Swedish Environmental Protection Agency 

Chalmers University of Technology                 

Area of Advance Transport  

8 

  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

1.2.5  Scientific Committees 

1.2.5.1  TAP Scientific Committee 

Miriam Elser 

EMPA (CH) 

Asif Faiz  

Faiz&Assoc. LLC (US) 

Salah Khardi 

IFSA/University Eiffel (F) 

Jens Borken-Kleefeld 

IIASA/Technical University Dresden (AT/DE) 

Benedikt Notter   

INFRAS (CH) 

James Tate 

Åke Sjödin 

ITS/University of Leeds (UK) 

IVL (SE) 

Georgios Fontaras 

JRC (INT) 

Gary Haq 

JRC (INT) 

Zissis Samaras 

LAT/AUTh (GR) 

Leonidas Ntziachristos 

LHTEE/AUTh (GR) 

Nicolas Moussiopoulos 

LHTEE/AUTh (GR) 

Jonathan Andersson 

Ricardo (UK) 

Nikolas Hill 

Robin Smit 

Norbert Ligterink  

Robin Vermeulen  

Ye Wu 

Wen Yifan 

Ricardo (UK) 

TEER (AU) 

TNO (NL) 

TNO (NL) 

Tsinghua University (CN) 

Tsinghua University (CN) 

Stefan Hausberger 

IVT/TU Graz (AT) 

Martin Rexeis 

IVT/TU Graz (AT) 

1.2.5.2  S&E Scientific Committee 

Erik Fridell  

Elisa Giubilato 

Julia Hansson 

Lars Hole 

IVL (SE) 

Univ. of Venice (IT) 

IVL (SE) 

Met Norway (NO) 

Christa Maradino 

Gemoar (DE) 

Volker Matthias 

Hereon (DE) 

Johan Mellqvist 

Chalmers (SE) 

Jana Moldanova 

IVL (SE) 

Leonidas Ntziachristos 

AUTh (GR) 

Danielle van Dinther 

TNO (NL) 

9 

  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

1.2.6  Organising Committee 

The  members  of  the  Organising  Committee  (OC)  of  the  conference  undertook  tasks  related  to  the 
organisation of the  conference, the reception,  organisation and allocation of the  scientific papers for 
review,  the  notification  of  authors,  the  identification  of  the  thematic  areas,  the  formulation  of  the 
program  and  the  in-
Committee, as well as their affiliations are provided below: 

  Åke Sjödin (TAP Chairman), IVL Swedish Environmental Research Institute (SE) 

 

 

Jana Moldanova (S&E Chairman), IVL Swedish Environmental Research Institute (SE) 

Yingying Cha (Conference Assistant), IVL Swedish Environmental Research Institute (SE) 

  Helena Lundström (Conference Assistant), IVL Swedish Environmental Research Institute (SE) 

 

 

Ida-Maja Hassellöv (S&E Vice Chairman), Chalmers University of Technology (SE) 

Eva-Lotta Sundblad (S&E Conference Assistant), Swedish Institute for the Marine Environment 
(SE) 

10 

  
 
 
 
 
Joint TAP and S&E Conference 2023 

2  Conference Proceedings  Extended abstracts 

The present chapter includes collection of all submitted extended abstracts that were presented during 
the  conference.  Short  one-page  abstracts  of presentation for which  the  extended  abstracts  have  not 
been prepared are available in Volume 2 of the Proceedings.  

Papers of this section are: 

 

 

 

structured based on the thematic sessions of the TAP&SE 2023, as defined in the final program 
of the conference, 

listed in the order that were presented during the conference, 

included i
implementation dates of TAP&SE 2023. 

Table 1 lists the submitted extended abstracts by session/topic of TAP&SE 2023. 

Table 1. Submitted extended abstracts by session/topic of TAP&SE 2023.  

es poster presentations. 

Session/Paper 

Corresponding author, Title 

ID 

TAP.01 

New emission control concepts, testing conditions and pollutants, non-
exhaust emissions 

TAP.01.PS.1.5 

Kulovuori, S. 

TAP.01.PS.1.9 

Colombo, A. 

TAP.01.PS.1.13 

Tsakonas, G. 

TAP.01.PS.1.15 

Dimaratos, A. 

Influence  of  vehicle  weight  on  PM  emissions  from  studded  and 
studless winter tyres - laboratory and on-road results 
First  application  of  a 
protocol 
characterization of the nanoparticulate. 
Comparison of two  Air  Liquid Interface  (ALI)  systems:  lung cells 
exposure to vehicle exhaust 
Design  of  a  sampling  system  for  brake  particles  on-road 
measurement   A computational preliminary study 

physico-chemical 

for 

On-board monitoring and diagnostics, emission tampering and deterioration 

TAP.02 

TAP.02.3 

Frateur, T. 

TAP.02.PS.1.27 

Svensson, N. 

TAP.02.PS.1.31 

Matzer, C. 

TAP.02.PS.1.33 

Mehel, A. 

Real world particle number emission factors from plume chasing 
data 
Effects of geofencing on exhaust emissions and noise: A combined 
test track and traffic simulation study 
Feasibility study for future on-board NOx monitoring of passenger 
cars 
Simultaneous in-cabin and on-road CO2 concentrations on-board 
measurements 

TAP.03 

In-service  conformity and new  concepts for  enhanced emission testing in 

s 

TAP.03.1 

Melas, A. 

TAP.03.3 

Wang, Y. 

TAP.03.PS.1.35 

Franzetti, J. 

TAP.03.PS.1.48 

Ktistakis, M. 

Solid  particle  number  (SPN)  measurements  during  the  periodic 
technical inspection (PTI) of vehicles 
In-use NOx emission trends of diesel trucks in China informed by 
large-sized inspection data 
Assessment of Hot Idling Test Procedure for NOx Measurement in 
Periodic Technical Inspection (PTI) of Vehicles 
Statistical evaluation of Conformity of Production of road vehicles   

TAP.04 

TAP.04.2 

Remote sensing of vehicle emissions 

Yang, Z. 

Single-blind test on the effectiveness of the Gumbel distribution 
method in detecting high-emitters in remote sensing data 

11 

  
 
 
 
 
Joint TAP and S&E Conference 2023 

TAP.04.6 

Chu, M. 

TAP.04.7 

Buhigas, J. 

Applications of multi-sensor roadside networks for fleet emission 
source apportionment and single-vehicle EF determination 
Development and testing of a novelty Remote Sensing Device for 

TAP.04.PS.1.43 

Moroni, S. 

TAP.04.PS.1.47 
TAP.04.PS.1.49 

Cha, Y. 
Mahesh, S. 

TAP.04.PS.1.50 

Piasecki, C. 

TAP.04.PS.1.51 

Schaffer, P. 

TAP.04.PS.1.52 

Rushton, C. 

in multilane roads 
Advanced Air quality sensors and Remote Sensing to investigate 
vehicular traffic emissions in Milan: CARES H2020 Project results  
Remote Sensing Measurements of Vehicle Emissions in Sarajevo 
Emissions  from  in-use  vehicles  in  Dublin  using  on-road  remote 
sensing 
Remote Sensing Emission Measurements on a German Motorway 

 Insights and initial Results 

Simulations of a NIR TDLAS Sensor for Stand-Off Measurement of 
Carbon Dioxide for Remote Emission Sensing 
A modern, flexible cloud-based database and computing service 
for storing and analysing vehicle emission measurements 

TAP.05 

Vehicle greenhouse gas emissions, energy consumption, vehicle and fuel life 
cycle analysis 

TAP.05.1 

Tansini, A. 

TAP.05.2 

Komnos, D. 

TAP.05.3 

Hausberger, S. 

TAP.05.5 

Mellios, G. 

TAP.05.6 

Broekaert, S. 

TAP.05.7 
TAP.05.PS.1.10  Wang, F. 

Frobert, A. 

TAP.05.PS.1.18 

Lejri, D. 

From  physical  testing  to  on-board  fuel  consumption  monitoring 
and  telemetry:  a  pilot  project  for  capturing  the  real-world  fuel 
consumption of vehicles 
CO2  emissions  performance  comparison  of  Australian  and 
European SUVs  
Temperature Effects on Energy Consumption from Battery Electric 
Vehicles 
Prediction of fuel consumption for truck planning based on VECTO 
simulations 
Experimental  validation  of  battery  electric  truck  simulation  in 
VECTO 
Emission Monitoring for used cars: Evaluation of On-Road Testing 
Multi-sectoral drivers of decarbonizing battery electric vehicles in 
China 
Vehicle 
assessment at the scale of an urban area. 

fleet  electrification:  electric  energy  consumption 

TAP.06 

Vehicle emission modelling and measurements and impact assessment of 
emission regulations 

TAP.06.3 

Tirico, M. 

TAP.06.4 

Roldin, P. 

TAP.06.5 

Cox, B. 

TAP.06.6 
TAP.06.PS.1.22 

Johansson, E. 
Norman, M. 

TAP.06.PS.1.28  Wærsted, E. 

TAP.06.PS.1.30 

Philips, I. 

Assessing  pollutant  emissions  on  an  urban  area  using  different 
traffic  simulation  approaches:  multi-agent  modelling  and 
microscopic modelling 
Secondary  aerosol  formation  from  real  vehicle  exhausts 
Development  of  an  equivalent  total  particle  emission  index 
module for air quality modelling applications  
Emission  savings  potential  of  ecodriving  based  on  over  1500 
hours of driving data from 5 countries. Findings from the uCARe 
project 
Impact of uCARe measures on air quality in selected cities 
Nanoparticles  in  different  environment  in  Stockholm,  the  nPETS 
project 
Quantification of temperature dependence of NOx emissions from 
road traffic in Norway using air quality modelling and monitoring 
data 
The impact of UK Clean Air Zones (CAZs) on the observed vehicle 
fleet 

12 

  
 
 
 
Joint TAP and S&E Conference 2023 

TAP.06.PS.1.34 

TAP.06.PS.1.36 

Troncoso 
Lamaison, F. 
Quaassdorff, C. 

Estimation of mobility and traffic emissions based on Cell Phone 
Data 
Input 
the  microscale  spatial 
distribution of emission hotspots based on real-world measured 
vehicle activity 

requirements  for  modeling 

TAP.07 

TAP.07.3 
TAP.07.4 

Non-road emissions 

Demuynck, J. 
Gustafsson, M. 

TAP.07.PS.1.38 

Schmidt, C. 

Real-world NOx emissions of Stage IV and V machines 
PM10 emissions and rubber content from different tyre types in 
relation to rubber hardness and road surface type 
NRMM  Real  Operation  NOx  Emission  Measurements  with  Plume 
Chasing 

Air  pollutant  and  GHG  emissions,  water  contaminants,  ambient  and 
underwater noise and vessel-induced mixing 

JS.08 

JS.08.2 
JS.08.3 

JS.08.9 

Jalkanen, J. 
Weigelt, A. 

Streibel, T. 

JS.08.10 
JS.08.PS.2.1 

Kuittinen, N. 
Grigoriadis, A. 

JS.08.PS.2.13 
JS.08.PS.2.17 

Grigoriadis, A. 
Wang, G. 

JS.08.PS.2.19 

Heikkilä, M. 

JS.08.PS.2.21 

Krause, J. 

JS.09 

JS.09.1 

JS.09.4 

JS.09.6 

Uhrner, U. 

Megaritis, A. 

Fink, L. 

JS.09.7 

Ioannidis, G. 

JS.09.8 
JS.09.PS.1.46 

Broman, L. 
Hafs, N. 

JS.09.PS.1.57 

Chekrouba, K. 

JS.09.PS.2.29 

Piccoli, A. 

JS.09.PS.2.31 

Engardt, M. 

JS.09.PS.2.33 

Schlesinger, D. 

Underwater noise emissions from ships during 2014-2020 
Size  resolved  particle  emission  behaviour  for  different  types  of 
vessels 
Effects of sulfur scrubbers on particulate emissions from a marine 
diesel engine 
Methane slip from LNG engines - review and on-board study 
Particulate  and  gaseous  emissions  from  a  large  2-stroke  slow 
speed marine engine equipped with open-loop scrubber under real 
sailing conditions 
A new set of Emission Factors for ships 
Automatic Classification of Aerosol Particles using Single-Particle 
Mass Spectrometry and Machine Learning 
Gentlemen,  do  not  start  your  engines:  The  association  between 
vessel  departures  and  air  pollution  in  Helsinki  port  area  2016-
2021 
Maritime  Greenhouse  Gas  Emission  Reduction  Scenarios 
Extension of DIONE model 

in 
the 
region 

impact  of  shipping  on  PM2.5  species 

Specification of Zero-Impact Vehicle Emissions & Demonstration 
of Zero Impact 
The impact of shipping emissions to urban air quality in Europé - 
A port/city analysis  
Potential 
Mediterranean 
- a multi-model evaluation 
A  numerical  CFD  model  to  quantify  traffic-related  pollutant 
concentrations in urban scale. 
Health benefits if air pollution goals achieved along highways 
Assessing the Impact of Car Cabin Filters mileage on In-Vehicle Air 
Quality: Results from Controlled Environment 
A numerical study of particle dispersion in the wake of a static and 
rotating cylinder 
The assessment of a Zero Emission Zone air quality and human 
health impacts in the metropolitan city of Milan 
Improving  3-day  deterministic  air  pollution  forecasts  using 
machine learning algorithms 
Effects  of  noise  barriers  on  population  exposure  to  and  health 
impacts of air pollutants downwind of highways 

13 

Atmospheric processes and air quality impact studies: Modelling impacts of 
transport on air pollution, climate, health and ecosystems 

  
 
 
 
JS.09.PS.2.39 

JS.09.PS.2.43 

JS.09.PS.2.45 

JS.09.PS.2.47 

JS.09.PS.2.50 

JS.10 

JS.10.1 

JS.10.PS.2.2 
JS.10.PS.2.6 

JS.10.PS.2.8 

JS.11 

Joint TAP and S&E Conference 2023 

Schwarzkopf, D.  Transition  to  cleaner  and  carbon-free  marine  fuels  and  their 
potential impacts on air quality in the North and Baltic Sea in the 
future 
The  impact  of  data  splitting  in  air  quality  modelling  on  the 
possibilities of interpretation of the results 
Particle  number  box-model  calculations  in  a  street  canyon  and 
comparison to measurements 
Temporal  distribution  of  national  emission  data  for  dispersion 
calculations with chemical transport models 
Transport emission footprint in the Slovak economy 

Toenges-
Schuller, N. 
Toenges-
Schuller, N. 
Horváth, J. 

Compliance monitoring: technological and legal frameworks; experimental 
studies; theoretical impact studies 

Badeke, R. 
Rieker, M. 

Ntziachristos, L.  Shipping  emissions  monitoring  with  on-board  and  remote 
techniques  and  impacts  on  air  quality:  The  SCIPPER  project 
summary and results 
Application of a 24-hour ship plume forecasting system 
SEICOR  -  Ship  Emission  Inspection  with  Calibration-free  Optical 
Remote sensing 
Experiences with sensor based continuous emission monitoring for 
demonstration of maritime emissions compliance 

Verbeek, R. 

Reduction measures for GHG emissions - alternative fuels, electrification, 
energy use optimization; inter-disciplinary and cross-sector studies 

JS.11.PS.2.48 

Norman, M. 

Active traffic management for improved air quality and reduced 
climate impact 

JS.12 

Scenarios and policy options for sustainable transport 

JS.12.PS.2.22 

Pagels, J. 

Solid Oxide Fuel Cells for Reduced Health and Climate Impact of 
Ship Emissions 

SE.13 

Marine  processes  -  fate  of  pollution  from  shipping  in  the  marine 
impact  studies  on  ecotoxicology,  eutrophication  and 
environment: 
acidification, energy pollution including underwater noise & induced mixing; 
experimental work, modelling studies of dispersion, transport, and chemical 
and biological processes in marine waters 

SE.13.2 

Picone, M. 

Ecotoxicological  effects  of  exhaust  gas  cleaning  system  (EGCS) 
discharge water on marine copepods 

SE.14 

Holistic  assessment  of  shipping  impacts  on  the  environment,  shipping  in 
marine spatial planning 

SE.14.5 

Johansson, T. 

Financial incentives for ship underwater noise mitigation 

14 

  
 
 
 
 
 
Joint TAP and S&E Conference 2023 

2.1  TAP.01.  New  emission  control  concepts,  testing 
conditions and pollutants, non-exhaust emission 

15 

  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

Influence of vehicle weight on PM emissions from studded and studless 
winter tyres - laboratory and on-road results 

S. Kulovuori1 and M. Gustafsson2 

1Metropolia University of Applied Sciences, Department of Automotive and Mechanical Engineering, Vantaa, 00160, 
Finland, Sami.Kulovuori@metropolia.fi 

2Swedish National Road and Transport Research Institute (VTI), Linköping, 581 95 Sweden.  

Introduction 

Automobiles are becoming progressively heavier, both as a result of customer preferences, such as the 
rise of SUVs, and the fact that electric vehicles are generally heavier than their fossil-fuel counterparts. 
There  are  also  ongoing  initiatives  at  the  European  level  for  longer  and  heavier  heavy-duty  vehicles 
motivated by lower emissions per ton of goods (e.g. (de Saxe et al., 2018)). Weight-dependent tyre and 
pavement deterioration (Beddows and Harrison, 2021; Low et al., 2023) and road dust resuspension pose 
a  risk of heavier vehicles producing more non-exhaust emissions. In Nordic countries, where studded 
tyres are used, the risk of road deterioration and consequently PM emissions from roads is particularly 
high. In this study, the effects of weight on PM emissions in the laboratory and field were investigated. 

Methods 

The tests were carried out on the VTI road simulator (Figure 1A, (Gustafsson et al., 2009)) and on the 
1B, (Pirjola et al., 2009)). The VTI Road simulator is 
carousel-like equipment with four wheels that run on a circular pavement ring (Figure 1). Any type of 
pavement or light-duty vehicle tyres can be used. The tests can be run up to 70 km/h, and the hall is 
temperature-controlled.  In  the  tests,  studded  tyres  (two  Nokian  Hakkapeliitta  9  and  two  Kumho 
Wintercraft Ice) and the tests were performed at a start temperature of -5°C at 40 and 60 km/h on a 
mixed asphalt concrete pavement. Three different axle loads were used in the road simulator: 350, 450, 
and 550 kg/axle, resulting in total loads of 1400, 1800, and 2200 kg. Particle mass concentrations were 
measured using TEOM 1400a (Tapered Element Oscillating Microbalance, Rupprecht & Pataschnik Co.) 
and size distributions using APS 3321 (Aerodynamic Particle Sizer, TSI Inc.) and SMPS (Scanning Mobility 
Particle Sizer, TSI Inc.) PM10 concentrations were corrected for changes in stud protrusion during the 
tests. 

Figure 1: A: The VTI road simulator. B: Sniffer vehicle. Photos by Mats Gustafsson, VTI, and 
Sami Kulovuori, Metropolia. 

On-road measurements were performed on road number 130 in Nurmijärvi, Finland (60°28'26

at  three  different  speeds  (30,  50,  and  70  km/h)  using  studded  and  friction  tyres  from  the  same 
manufacturer  (Continental  VanContact  Ice  and  VanContact  Viking).  Tests  were  conducted  in  summer 
conditions, where the dust load on the road surface was low, and in spring conditions, where the dust 
load on the road surface was higher because of the use of winter tyres.  

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Joint TAP and S&E Conference 2023 

The ambient temperature and relative humidity were measured using temperature and humidity probes 
(Model HMP45A, Vaisala) during both campaigns. During spring measurements, ambient temperatures 
were between 7.4 15.5 °C and relative humidity was between 27 and 58 %, with a mean temperature 
and relative humidity of 12.3 °C and 41 %, respectively. In the summer measurements, the temperature 
and relative humidity were 16.1 26.7 °C and 28 63 %, respectively, and the mean temperature and 
relative humidity were 21.7 °C and 41 %, respectively. The road surface temperature and moisture were 
not  measured;  however,  in  both  campaigns,  the  road  surface  was  dry,  and  the  ambient  dew  point 
temperature was lower than the measured ambient temperature. Changes in ambient temperature and 
relative humidity were not considered to significantly impact the results. 

The tyre pressure for each weight/tire combination was set to 4.0 bar and the sample inlet height from 
the ground was adjusted with an air suspension of 8 cm, with each weight setting during the tests. The 
particle mass concentrations were measured behind the left rear wheel using a TEOM1405d (Tapered 
Inc.), and the particle 

size distribution using an OPS 3330 (Optical Particle Sizer, TSI Inc.). 

The collected measurement data were divided into groups corresponding to each tyre, weight, speed, and 
test  combination,  resulting  in  48  individual  groups.  For  each  group,  stratified  random  sampling  was 
applied (sample size: N = 300) to ensure an equal number of observations in each group. Each group 
was filtered by ± 3 km/h of the group target speed and inner quartile range (IQR) to remove possible 
outliers  in  each  group.  The  normality  of  the  observations  was  checked  using  the  Shapiro-Wilk  test 
(Shapiro and Wilk, 1965) and visually with Q-Q plots for each group. A nonparametric Kruskal-Wallis test 
(Kruskal and Wallis, 1952) was applied to the data to check if the median was equal between all groups. 

difference between the medians of the groups and which groups were in question in both the spring and 
summer measurements. 

Results and discussion 

Both  test  results  showed  an  increase  in  PM10  emissions  with  increasing  speed  and  weight.  In  the 
simulator, the data reflected direct emissions, mainly from the road wear of studded tyres, whereas the 
on-road data reflected both direct and resuspended emissions. 

The PM10 emission factors from the road simulator showed a clear positive correlation with both load 
and speed, with a stronger load effect at higher speeds (Figure 2). The calculated emission factors are 
high compared to normal driving, which is normally of the order of a few hundred mg/vkm (for example, 
(Ferm and Sjöberg, 2015). This is likely owing to the high turn-slip in the contact between the tyre and 
pavement in the simulator. 

Figure 2: PM10 emission factors for studded tyre tests as functions of axle load and speed. 

17 

  
 
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

The particle size distributions revealed a peak at approximately 20 30 nm for all the loads. The particle 
number  concentration  increased  with  the  load  (Figure  3A).  The  particle  mass  size  distributions  were 
bimodal with a preliminary peak at 2 3 µm and a secondary peak at 5 µm. The concentrations at 350 
kg and 450 kg did not differ significantly in the APS data, whereas 550 kg generated considerably higher 
concentrations (Figure 3B). 

Figure 3: Particle number (A) and mass (B) size distributions at different loads measured using SMPS 
and APS at 60 km/h. 

The road measurements in spring and summer showed increased measured PM10 concentrations with 
both tyres when vehicle weight and speed increased (Figure 4 A and B). In summer measurements, the 
behaviour of the measured PM10 concentration was as suspected, because the dust load on the road 
surface was lower than in spring measurements; therefore, the resuspension caused by vehicles was 
lower, making direct emissions from studded tyres the dominant factor compared to friction tyres.  

Figure 4: Mean measured PM10 concentrations of the Sniffer as a function of different weights, tyres, 
and speeds. (A = summer measurement with friction tyre, B = spring measurement with friction tyre, C 
= summer measurement with studded tyre, D = spring measurements with studded tyre; the number 
above bars = group number in the statistical tests). 

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Joint TAP and S&E Conference 2023 

In spring measurements, the dictating factor was resuspension, which was caused by the higher road 
surface  dust  load  caused  by  studded  tyre  usage;  therefore,  the  measured  PM10  concentrations  were 
somewhat  the  same  magnitude  for  both  tyres.  The  mean  concentrations  of  PM10  in  the  spring 
measurements (Figure 4B and 4D) showed a clear increase  when the speed of the vehicle increased 
from 30 or 50 km/h to 70 km/h for each weight class and tyre. The same effect was also observed with 
studded tyres in the summer measurements (Figure 4C) for each weight class.  

The particle mass size distributions measured on-road (Figure 5) revealed similar observations as in the 
VTI road simulator (Figure 3B), where two peaks were observed. Figure 5 shows two distinctive peaks at 
4-5  µm  and  8-9  µm  for  both  measurements  and  both  tyres,  which  differ  slightly  from  the  VTI  road 
simulator  observations,  although  the  peak  at  approximately  5  µm  in  the  road  simulator  was  also 
observed  in  Sniffer.  Other  tyre,  weight,  and  speed  combinations  showed  similar  shapes,  as  shown  in 
Figure  5.  The  effect  of  the  road  surface  dust  load  on  resuspension  can  be  observed  from  the  size 
distributions when summer measurements with a friction tyre were compared with spring measurements 
(Figure 5A and 5B).  

Figure 5: Mean particle mass size distributions with the lowest and highest vehicle weights and speeds 
for both tyres (A= summer measurements (low dust load) and B = spring measurements (high dust 
load). 

The statistical tests performed for the summer and spring measurements showed that the observations 
of the individual groups were not normally distributed, and the p-values of the Shapiro-Wilk test for each 
group  were  <  0.05.  The  Kruskal-Wallis  test  indicated  that  there  were  differences  between  the  group 
medians (p-value < 0.05) for both summer and spring measurements. The Dunn test showed statistically 
significant differences between the groups, i.e., summer measurements with a friction tyre at speed of 
30 km/h. There were no statistically significant differences in the group medians when vehicle weight 
was 4050 kg and compared to vehicle weight of 4600 kg (p-value:0.073). All the statistically insignificant 
comparisons are presented in Table 1. 

Overall, statistical tests indicate that increasing vehicle weight and speed have statistical significance in 
measured PM10 concentrations for both tyres, although 5.3 % and 1.5 % of the total amount of pairwise 
comparisons  were  not  significant  in  summer  and  spring  measurements,  respectively.  Similar 
observations of PM10 concentrations were observed for all instruments, although some variations were 
observed in absolute PM10 concentration values due to the different techniques used to measure PM10 
concentrations.  

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Joint TAP and S&E Conference 2023 

Table 1: Results of the Dunn test show instances in which statistical significance was not present (p-
value > 0.05). (Group ID = where the groups are compared, speed = driven speed within groups, tyre = 
used tyre within groups, weight = vehicle weight assigned to the group). 

Test 

Groups  speed  tyre 

weight 

p-value 

Summer  2-3 

Summer  5-6 

Summer  5-8 

Summer  6-8 

Summer  10-11 

Summer  14-15 

Summer  15-16 

Summer  17-18 

Summer  18-19 

Summer  18-20 

Summer  19-20 

Summer  22-23 

Summer  22-24 

Summer  23-24 

30 

50 

50 

50 

70 

30 

30 

50 

50 

50 

50 

70 

70 

70 

friction 

friction 

friction 

friction 

friction 

4050-4600  0.073 

3500-4050  1.000 

3500-5150  1.000 

4050-5150  1.000 

4050-4600  0.613 

studded 

4050-4600  0.398 

studded 

4600-5150  0.051 

studded 

3500-4050  0.852 

studded 

4050-4600  0.852 

studded 

4050-5150  0.195 

studded 

4600-5150  1.000 

studded 

4050-4600  0.083 

studded 

4050-5150  0.074 

studded 

4600-5150  0.876 

Spring 

Spring 

Spring 

Spring 

27-38 

30 

friction/studded  4050-4050  0.971 

28-39 

30 

friction/studded  5150-4600  0.964 

28-40 

30 

friction/studded  5150-5150  0.971 

39-40 

30 

studded 

4600-5150  0.365 

Conclusions 

The trend towards larger and heavier passenger and heavy-duty vehicles carries the risk of increasing 
road deterioration and PM emissions in Nordic countries, where winter maintenance and studded tyres 
are used during winter and spring. This study examined how an increase in vehicle weight affects PM 
emissions in the laboratory and field.  

Laboratory  and  on-road  measurements  showed  a  clear  correlation  between  weight,  speed,  and  PM10 
emissions/concentrations  in both the  road  simulator  testing  and  on-road  measurements.  The  particle 
mass size distribution was bimodal, with peaks around 2-3 µm and 5 µm in the simulator test and around 
4-5 µm and 8-9 µm in on-road measurements. The particle number and mass concentrations increased 
with increasing axle load, indicating that heavier vehicles led to higher emissions.  

On-road measurements revealed the seasonal effects of emissions. In summer, direct emissions from 
studded tyres dominated because of a lower road surface dust load, whereas in spring, resuspension 
from a higher road surface dust load was the primary factor  of emissions from studded and friction 

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Joint TAP and S&E Conference 2023 

tyres. The statistical analysis identified statistically significant differences in PM10 concentrations based 
on speed,  tyres,  and weight. This  confirms the effect of increased  vehicle weight and speed  on PM10 
concentrations.  

It  is  discerned  that  trends  towards  heavier  vehicles  could  lead  to  higher  emissions  under  the  given 
conditions, presenting a concern, particularly for regions following similar winter maintenance practices 
and studded tyre usage.  

Acknowledgements  

This work was funded by the NordFoU project, Nordic Road Dust Project Phase II (NorDust II). The authors 
are grateful to the ever-helpful technical staff Dennis Hydén and Tomas Halldin at the VTI road simulator 
facility and to staff members Harri Nordlund, Kari Heikkilä and Kari Leskinen from Metropolia University 
of Applied Sciences for giving a helping hand with the on-road measurements. 

References 

Beddows,  D.C.S.,  Harrison,  R.M.,  2021.  PM10  and  PM2.5  emission  factors  for  non-exhaust  particles  from  road 
vehicles: Dependence upon vehicle mass and  implications  for  battery electric  vehicles. Atmospheric Environment 
244, 117886. https://doi.org/10.1016/j.atmosenv.2020.117886 

de Saxe, C., Kural, K., Kharrazi, S., Schmidt, F., Geem, C., Berman, R., Woodrooffe, J., Cebon, D., 2018. FALCON III: 
Defining a Performance-Based Standards framework for high capacity vehicles in Europe. 

Dunn, O.J., 1961. Multiple Comparisons among Means. Journal of the American Statistical Association 56, 52 64. 
https://doi.org/10.1080/01621459.1961.10482090 

Ferm, M., Sjöberg, K., 2015. Concentrations and emission factors for PM2.5 and PM10 from road traffic in Sweden. 
Atmospheric Environment 119, 211 219. https://doi.org/10.1016/j.atmosenv.2015.08.037 

Gustafsson, M., Blomqvist, G., Gudmundsson, A., Dahl, A., Jonsson, P., Swietlicki, E., 2009. Factors influencing PM10 
emissions 
4699 4702. 
https://doi.org/10.1016/j.atmosenv.2008.04.028 

Environment 

Atmospheric 

pavement 

wear. 

from 

road 

43, 

Kruskal, W.H., Wallis, W.A., 1952. Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical 
Association 47, 583 621. https://doi.org/10.1080/01621459.1952.10483441 

Low, J.M., Haszeldine, R.S., Harrison, G.P., 2023. The hidden cost of road maintenance due to the increased weight of 
battery and hydrogen trucks and buses a perspective. Clean Technologies and Environmental Policy 25, 757 770. 
https://doi.org/10.1007/s10098-022-02433-8 

Pirjola,  L.,  Kupiainen,  K.J.,  Perhoniemi,  P.,  Tervahattu,  H.,  Vesala,  H.,  2009.  Non-exhaust  emission  measurement 
4703 4713. 
the  mobile 
system 
https://doi.org/10.1016/j.atmosenv.2008.08.024 

Environment 

Atmospheric 

laboratory 

SNIFFER. 

43, 

of 

611. https://doi.org/10.1093/biomet/52.3-4.591 

21 

  
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

First  application  of  a  protocol  for  physico-chemical  characterization  of  the 
nanoparticulate generated by brakes. 

A.  Colombo1*,  C.E.  Campiglio2,  S.  Conti2,  A.  Mancini3,  B.  Tsyupa3,  S.  Petromelidou4,5,6,  A.  Besis5,6,  D. 
Margaritis5 and D. Lambropoulou4,5 

1

- IRCCS, Via Mario 

Negri 2, 20156 Milano, Italy, andrea.colombo@marionegri.it 
2

- IRCCS, Via Stezzano 87, 24126 Bergamo, Italy   

3Materials Engineering & Laboratories, Brembo Spa, Viale Europa 2, 24040 Stezzano (BG), Italy  
4Aristotle University of Thessaloniki / School of Chemistry, Environmental Pollution Control Laboratory, University 
Campus, 54124, Thessaloniki, Greece 
5Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Balkan Center, Thessaloniki, GR-57001 
6Centre for Research and Technology-Hellas (CERTH), Hellenic Institute of Transport (HIT), 6th km Charilaou-Thermi 
Rd, P.O. Box 60361, GR 57001 Thermi, Thessaloniki, Greece 

1. Introduction 

Although many efforts have been provided to monitor and reduce air pollutants in the last decades there 
still exists concern about air quality and adverse health consequences among the populations exposed 
[WHO, 2006, 2015, 2018, European Council, 2008, European Commission, 2012, European Environment 
Agency, 2021]. 
Many legislation activities were focused on particulate matter (PM), which adverse effects are related to 
their size and chemistry [WHO,2915mPope et al., 2002, Pope and Dockery, 2002, Valavanidis et al 2008, 
Kim et al., 2015]. The major fraction of outdoor ultrafine particles is traffic generated from road, rail, air, 
and sea transportation, each one representing a transport source. For these reasons the chemical and 
morphological  characterization  of  nano-particulates  produced  by  different  exhaust  and  non-exhaust 
emission sources related to the transport sector is one of the aims of the nanoParticle Emissions from 
the Transport Sector (nPETs) project.  
Therefore,  to  better  understand  the  mechanisms  behind  adverse  risks  posed  by  different  types  and 
sources of the identified sub 100 nm particles, a reliable procedure for physico-chemical characterization 
of  the  sub  100  nm  emissions  was  developed.  In  this  paper,  the  results  from  the  investigation  of 
nanoparticle emissions generated by brakes are reported as example. More in detail, the investigated 
nano-particulate has been sampled in controlled environment involving a friction couple composed by a 
gray  cast-iron  (GCI)  braking  disc  and  an  ECE  R90  Copper-free  friction  material,  representative  of  a 
standard original equipment for European automotive market.   

2. Experimental 

2.1 Materials & NanoParticulate Collection 

Brake-related nanoparticle emissions are generated by a friction pair composed by a fully pearlitic gray 
cast iron braking disc (BD) coupled against an ECE R90 Copper-free friction material (FM). The nominal 
elemental composition of the materials involved in the study is reported in Table 1 for reference. 

22 

  
 
 
 
 
 
 
 
 
Table 1: Nominal composition of BD and FM. Only elements with concentration higher than 0.1 wt% are 
reported for brevity. 

BD 

FM 

Joint TAP and S&E Conference 2023 

Element 
C 
Si 
Mn 
Cu 
Cr 
Fe 
Others 

[wt%] 
3.8 
1.9 
0.5 
0.2 
0.1 
balance 
traces 

Element 
C 
Fe 
O 
Zn 
Mg 
Al 
Sn 
S 
Cr 
Si 
K 
Ca 
Cu 
Mn 
Others 

[wt%] 
31.5 
23.8 
14.8 
6.1 
5.5 
4.1 
3.9 
3.0 
2.3 
2.2 
2.0 
0.4 
0.3 
0.1 
traces 

The  generated  nanoparticulate  sample  is  collected  at  a  dyno-bench  designed  for  emissions  tests 
performed  in  controlled  conditions  [Perricone  et  al.,  2015,  2016].  The  procedure  used  to  collect  the 
particulates is the WLTP-Brake Cycle [Mathissen et al., 2018]: the PM material is collected both during 
the bedding cycles (5 WLTP-Brake repetitions) and during the cycle typically used for the evaluation of 
the  emissions  (i.e.,  the  6th  WLTP-Brake  cycle  repetition)  to  maximize  the  amount  of  collected  nano-
particles. For all the tests, the braking corner is composed by: i) a four pistons fixed Aluminum caliper 
with pistons diameter of 44 mm; ii) a vented braking disc with a diameter of 342 mm and a thickness 
of 32 mm; and iii) a couple of pads with a surface of 89.1 cm2. During the emission test, a controlled 
particle-free  air  flux  of  245  m3  h-1  enters  the  brake  enclosure  to  match  the  temperature  targets  as 
suggested  by  the  most  recent  guidelines  from  the  Particle  Measurement  Programme  (PMP)  Informal 
Working Group and the United Nations Global Technical Regulation (UN GTR) on Laboratory Measurement 
of  Brake  Emissions  for  Light-Duty  Vehicles  [UN  GRPE,  2020,  2023].  The  entering  air  flux  is  filtered 
through a HEPA-H13 filter, which ensures an filtration efficiency higher than 99.95%. The collection of 
the PM emissions is carried out by an isokinetic sampling-probe, equipped with sharp-ended nozzles to 
ensure  a  high  efficiency  sampling.  The  nano-particulate  is  sampled  through  a  Dekati  Gravimetric 
e 
-off is placed before the inlet to provide the first 
dimensional cut. Greased Aluminum substrates are used for the upper four stages, while a Teflon filter 
(pores diameter of 3 µm) is used as backup. In this paper, the material collected on the first collection 
substrate (S1: 130 < d50 < 150 nm) represents the target sample for the study.  

2.2. Organic Chemical Characterization 

Organic  chemical  characterization  protocol  was  developed  according  to  the  different  EPA  methods 
[USEPA,  1994,  2010],  to  obtain  from  a  single  extraction  multiple  runs  by  high-resolution  mass 
spectrometry (HRGC HRMS) [Mariani et al., 2009]. 
Briefly, samples were spiked with 13C-labelled standard (PCDD/Fs, PCBs, BDEs and PCNs) obtained from 
Wellington  Laboratories  (Canada)  and  deuterated  PAHs,  NPAHs  and  OPHAs  obtained  from  Dr. 
Ehrenstorfer (Augsburg, Germany). After spinking samples were submitted to a single extraction, carried 
out by Pressurized Liquid Extraction (PLE), using an accelerated solvent extraction instrument (ASE 350, 
Dionex,  Sunnyvale,  CA,  USA)  with  dichloromethane  followed  by  a  second  extraction  with  acetone-
dichloromethane for more polar chemicals. PLE conditions encompass a 5 min preheat time, followed by 
three static cycles of 15 min at 1500 psi. The flush volume is 60%, followed by a purge with gaseous 
nitrogen during 100 s. Finally, the two extracts were combined and concentrated to 5 ml and injected 
into a 5ml loop of an automated Gel Permeation Cromatography (GPC) system (AccuPrep MPSTM, J2 
Scientific). The GPC column was 2.5 cm x 43 cm filled with 40 g of BioBeads SX-3 resin working at a 
flow rate of 5 ml/min using dichloromethane.  

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Joint TAP and S&E Conference 2023 

The 20% of the sample collected (1 ml) was concentrated to the final volume of 50 µl and submitted to 
PAH, OPAH and NPAH instrumental analysis. The 80% of the sample (4 ml) was concentrated until 0.5 
ml  and  diluted  with  hexane  to  4  ml.  These  4  ml  were  submitted  to  an  automated  clean-up  system 
(Dioxin/SPE module) using acid silica/neutral silica (Si/5G-44%H2SO4/Si, Supelco, USA) and basic alumina 
cartridges (manually packed with 7 g of active Aluminium oxide 90 basic, Supelco USA). The sample is 
loaded on an acid silica/neutral silica cartridge connected to a basic alumina cartridge and eluted with 
75 ml of n-hexane. This fraction was discarded. The basic alumina column is eluted with 60 ml of 98/2 
n-hexane/dichloromethane, and this fraction was collected for PCBs and PCNs analysis. Then the basic 
alumina is eluted with 120 ml of a 50/50 n-hexane/dichloromethane mixture, collected for non-ortho 
PCBs, PCDDs/Fs, and PBDE analysis. 
The quantitative determination was performed by a TRACE GC 2000, Thermo Finnigan (Thermo Fisher 
Scientific), coupled with a high resolution Mat 95 XP Mass Spectrometer, operating in the electron impact 
ionization (EI+) mode at 48 eV with a resolution >10,000 and a temperature source of 280 °C. The MS 
is operated in Selected Ion Monitoring mode (SIM) for all compounds. For PCDD/Fs, PCBs, PCNs, PAHs, 
NPAHs and OPAHs, the capillary column was a BPX-DXN (60 m length, 0.25 mm i.d., 0.25 film thickness), 
while for PBDEs the capillary column was a Rtx-1614 (15 m length, 0.10 mm i.d., 0.10 film thickness). 
Compound identification was performed by comparison with the chromatographic retention time and 
mass spectra of authentic standards, reported mass spectra and the mass spectral library of the GC/MS 
data system. Moreover, for halogenated compounds peaks were accepted if the isotopic ratio was within 
15% of the corresponding internal standard (IS) ratio. LODs were calculated individually for each sample 
on the basis of a 3:1 signal-to-noise ratio, while LOQs were calculated for each sample on the basis of 
a 10:1 signal-to-noise ratio. 

2.3 Inorganic Chemical Characterization 

The inorganic fraction is characterized by the combination of the Energy Dispersive Spectroscopy (EDS) 
and the Raman Spectroscopy (RS) to investigate both the elemental and the phase composition of the 
collected  nanoparticulate.  Scanning  Electron  Microscopy  (SEM)  imaging  is  used  in  order  to  verify 
qualitatively the granulometric profile of the collected material, while the Energy Dispersive Spectroscopy 
(EDS) analysis is used to unveil the elemental composition of the sample. The analysis is carried out by 
means of a Zeiss MA EVO10 scanning electron microscope, equipped with a 10 mm2 active area INCA X-
act silicon-drift detector (Oxford Instruments). The investigated sample is stripped from the collection 
substrates  after  dissolution  of  the  collection  grease  in  acetone  and  following  sonication  in  1  mL  of 
isopropyl alcohol at 35 kHz for 2 minutes. The suspended particles are then recollected by centrifugation 
at 3000 rpm for 5 minutes and dried overnight in mild vacuum conditions (20 mbar) before the chemical 
analysis. The obtained powder is deposited onto an Aluminum stub for electron microscopy, previously 
covered with conductive carbon tape. The particulate is always manipulated with a small metallic yet 
non-magnetic spatula and stocked or deposited in glass containers in order to minimize the potential 

time of 500 s and a beam intensity of 300 pA. The elemental concentrations obtained in each region of 
interest  are  statistically  averaged  in  order  to  obtain  final  compositional  results.  The  experimental 

surface).  The  elemental  composition  is  probed  by  back-scattered  electrons  (BSE)  to  maximize  the 
interaction volume of the incident beam with the sample.  

Raman  Spectroscopy  (RS)  is  used  to  unveil  information  on  the  phase  composition  of  the  collected 
nanoparticulate, i.e. on the main compounds composing the nano-powders. Raman analysis is carried out 
by the mean of the Horiba LabRAM HR, equipped with a solid-
power is set nominally to 12.5 mW, since higher power values were found to damage the sample, leaving 
burnt micro-areas after the measurement. All the spectra are acquired with 50x objective, which was 
found to be best compromise to balance magnification, resolution and signal/noise ratio. The acquisition 
range is set in the range of 50-2000 cm-1, while high intensity grating (600 gr mm-1) is used to maximize 
the collected signal. For each spectrum, five acquisition integrations of 10 sec are merged along the 
whole investigated frequency range.  

24 

  
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

2.4 Particle Size Distribution and Morphological Characterization 

Dimensional and morphological characterization of nanoparticle emissions is carried out by means of 
Scanning Electron Microscopy (SEM   GEMINI column Cross-Beam 1540EsB Carl Zeiss GmbH). Samples 
collected were marked on stubs and coated with a deposition of a thin layer of gold (1 minute, 15 mA) 
in a sputter coater (Agar Scientific Ltd, Stansted, England). To visualize nanoparticles, samples have been 
observed using backscattered electrons detector. Particle size distribution was analysed using the plugin 
Trainable Weka Segmentation of ImageJ software. 

3. Results 

3.1 Organic Chemical Characterizazion 

The  above-described  procedure  has  been  used  for  the  analysis  of  the  organic  chemicals  in  nano-
particulate samples collected from laboratory environment conditions. on the DGI Stage 1 (S1) collection 
substrate  (d50  <  150  nm).  Table  2  summarizes  PAHs  and  OPAHs  concentrations  obtained  for  the 
nanoparticle emission collected on the DGI S1 collection substrate PAHs and OPAHs concentrations were 
186.8 and 37.4 µg g-1 respectively, while NPAHs and halogenated chemical concentrations were lower 
than the LOQ (data not reported).  

Table 2: PAH and OPAH concentration values obtained for the nanoparticle emission collected on the DGI 
S1 collection substrate. 

25 

  
 
 
 
Compound 

Naphthalene 

Acenaphthylene 

Acenaphtene 

Fluorene 

Phenantrene 

Anthracene 

1-mPhenantrene 

2-mPhenantrene 

3-mPhenantrene 

9-mPhenantrene 

Fluoranthene 

Pyrene 

Benz[a]anthracene 

Triphenylene 

Chrysene 

Benzo[b]fluoranthene 

Benzo[k]fluoranthene 

Benzo[j]fluoranthene 

Benzo[e]pyrene 

Benzo[a]pyrene 

Indeno[1.2.3-cd]pyrene 

Benzo[ghi]perylene 

Dibenz[a.h]anthracene 

Coronene 

Polyciclic Aromatic Hydrocarbons 

1.4-naphtoquinone 

9-fluorenone 

Acenaphtoquinone 

Antraquinone 

2-nitrofluorenone 

Benzo(a)fluorenone 

Benzo(a)anthrone 

1.2 benzoantraquinone 

Oxygenated Polyciclyc Aromatic Hydrocarbons 

Joint TAP and S&E Conference 2023 

Abbreviation 

Concentration (µg g-1) 

Np 

Acy 

Ace 

F 

Ph 

An 

1-MEPh 

3-MEPh 

2-MEPh 

9-MEPh 

Fl 

Py 

B[a]An 

TP 

Chr 

B[b]Fl 

B[k]Fl 

B[j]Fl 

B[e]Py 

B[a]Py 

I[1.2.3-cd]Py 

B[ghi]Pe 

dB[a.h]An 

COR 

(1.4)O2NAP 

9-OFLN 

AceNQ 

(9.10)O2ANT 

2-N-9-OFLN 

BaOFLN 

BAN 

O2BAA 

41.8 

5.1 

4.4 

2.6 

32.8 

<0.2 

<0.2 

<0.2 

4.7 

3.0 

33.7 

26.9 

1.3 

2.1 

3.7 

2.5 

0.7 

1.5 

1.7 

<0.1 

2.2 

7.6 

0.3 

7.6 

186.8 

1.0 

35.8 

<0.1 

<0.1 

<0.1 

<0.1 

<0.1 

<0.1 

37.4 

Figure 1 reports the distribution of PAHs and OPAHs for the material collected on the DGI S1 collection 
substrate,  showing  that  the  composition  of  the  investigated  nanoparticle  emission  is  dominated 
Naphthalene, Acenaphtoquinone, Fluoranthene, Phenanthrene, Pyrene, Benzo[ghi]perylene and Coronene. 

26 

  
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

Figure 1: Distribution of PAHs and OPAHs for the material collected on the DGI S1 collection substrate. 

3.2 Inorganic Chemical Characterization 

The elemental composition of the nanoparticle emission. in the form of the material collected on the DGI 
Stage 1 (S1) collection substrate (d50 < 150 nm). is investigated by means of the EDS probe. Average 
elemental  concentration  values  are  reported  in  Table  3.  together  with  the  corresponding  standard 
deviations. Figure 1 shows the elemental distributions over five different measurements. together with 
the average elemental composition. 

Table 3: Average elemental concentration values obtained for the nanoparticle emission collected on the 
DGI S1 collection substrate. The standard deviation (SD) over five independent observations is reported 
for each element. Elements with concentrations lower than 0.1 wt% are not reported for brevity. 

SD 

1.7 

0.7 

1.0 

0.2 

< 0.1 

0.1 

0.1 

< 0.1 

< 0.1 

< 0.1 

< 0.1 

< 0.1 

< 0.1 

- 

S1: 130 < d50 < 150 nm 

Element 

[wt%] 

Fe 

C 

O 

Sn 

Zn 

S 

Mg 

Si 

Al 

K 

Mn 

Cr 

Cu 

37.9 

25.5 

24.7 

2.6 

2.1 

1.6 

1.5 

1.4 

1.3 

0.3 

0.3 

0.3 

0.3 

Others  

traces 

27 

  
 
 
 
 
Joint TAP and S&E Conference 2023 

Figure  2:  Distribution  of  elements  as  measured  for  the  material  collected  on  the  DGI  S1  collection 
substrate:  in  the  five  independent  observations  (blue  histograms)  and  averaged  results  (green 
histograms). 

The results reported in Table 3 and Figure 2 show that the elemental composition of the investigated 
nanoparticle emission is dominated by the presence of three main elements. namely Iron. Carbon and 
Oxygen. They account together for more than 85 wt% of the total particulate mass. Iron is an unspecific 
proxy for the wear of both sides of the tribological interface. i.e. it can arise from the wear and tribo-
oxidation  of  both  the  cast  iron  brake  disc  and  from  the  Iron-based  compounds  contained  inside  the 
friction  mix  of  the  brake  pads.  In  turn. Carbon  is  a  more  specific  marker  for  the  consumption  of the 
friction material. since several Carbon-based compounds are commonly used in friction composites. such 
for  example  organic  resins as  binders.  graphite  and  cokes  as  solid  lubricants.  inorganic  carbides  and 
carbonates as abrasives and fillers [Dante. 2015]. Similarly to Iron. Oxygen arises from both sides of the 
friction couple: it  is indeed a marker  for  both the  oxidation of brake  disc  Iron and material from the 
friction composite. since several inorganic oxides. carbonates and silicates are commonly used in brake 
pads  as  abrasives  and  fillers  [Dante.  2015].  Finally.  the  nanoparticle  emission  features  the  minor 
presence of several  secondary  and trace elements. which are  typically  found in concentrations  lower 
than 5 wt%. Together. these elements account for slightly more than 10 wt% of the total nanoparticulate 
mass. The presence of these elements relates closely to the specific friction material formulation and. 
secondary. to alloying elements from the worn cast iron.  

Phase  composition  of  the  collected  nanoparticle  emissions  is  investigated  via  Raman  Spectroscopy. 
Figure 3 shows the Raman spectrum acquired directly from the particulate obtained from the DGI S1 
collection substrate. As can be observed. some characteristic features are detected. In particular. two 
characteristic frequency ranges host Raman peaks: i) the low frequencies. from 50 to 700 cm-1. hosting 
signals  from  Iron  oxides;  and  ii)  the  high  frequencies.  from  1200  to  2000  cm-1.  where  signals  from 
elemental Carbon typically reside. In detail. the collected spectrum exhibits two peaks at about 215 and 
280  cm-1.  which  are  associated  with  the  Hematite  (Fe2O3)  phase.  In  addition.  it  is  also  possible  to 
recognize two additional broad features at about 380 and 590 cm-1. which are also coherent with the 
Hematite identification. Moving towards the higher frequencies. two peaks at about 1355 and 1570 cm-
1 can be observed. which testify the presence of elemental Carbon (i.e.. graphites and cokes) inside the 
investigated sample. All the described peaks are extremely broad and characterized by low intensities: 
this observation suggests an overall low degree of crystallinity of the identified compounds. which is 
coherent with the nature of the sample. Notably. the evaluation of the phase composition is in excellent 
agreement with observations from the elemental composition analysis.         

28 

  
 
 
 
Joint TAP and S&E Conference 2023 

Figure 3 : Raman spectrum of the collected nanoparticulates. 

3.3 Particle Size Distribution and Morphological Characterization 

By SEM observation, we observed that the particles are homogenously distributed onto substate (Figure 
4a).  They  present  irregular  shapes  and  most  of  them  appears  agglomerated  into  nonhomogeneous 
structures. 

The particle size distribution analysis allowed to identify major and minor axis size (Figure 4b). We can 
appreciate that most of the particles show a dimension included in the range 100   300 nm. The presence 
of numerous agglomerates could affect the measurement. 

Figure 4   (a) SEM micrograph of nanoparticles collected. (b) Major and minor axis size distribution in 
nm. 

Acknowledgements  

This work was supported by the 
under grant agreement No 954377. 

29 

  
 
 
 
 
 
 
 
 
 
References 

European Council. 

74 (2015): 136-143. 

Joint TAP and S&E Conference 2023 

 - 2021 Report.  Luxembourg. 2021. 

Mariani G.. Amalfitano L.. Manni A. Mueller A.. Skejo H.. Umlauf G. (2009) Organohalogen Compd. 71: 2796-2801. 

Mathissen. M.. Grochowicz. J..  Schmidt. C.. Vogt. R.. et al.. 

-World Braking Cycle for Studying Brake Wear 

-415. 219-226. 2018. 

Particle Measurement Programme Informal Working Group. Informal document GRPE_81_12. 81st GRPE. 9-11 June 
2020. Agenda Item 7. 

Perricone.  G..  Wahls

8.2015. 

Perricone. G.. Alemani. M.. Metinoz. I.. Matejka. V.. et al.. 
car Brakes   

to Fine 

-1141. 

rne Particle Emissions from 
-17. 2016. 

-Term Exposure 

Assoc. 56 (2002): 709-742. 

GRPE/87/Add.3. 2023. Geneva. 

on Laboratory Measurement of Brake Emissions for Light-

USEPA 1613: Tetra- through Octa-Chlorinated Dioxins and Furans by Isotope Dilution HRGC/HRMS. 1994. 

USEPA 1614: Brominated Diphenyl Ethers in Water. Soil. Sediment. and Tissue by HRGC/HRMS. 2010. 

USEPA 1668c: Chlorinated Biphenyl Congeners in Water. Soil. Sediment. Biosolids. and Tissue by HRGC/HRMS. 2010. 

USEPA 3640A: Gel Permeation Clean-up.1994. 

ne  Particulate  Matter  and  Human  Health:  Toxicological 
Assessment  and  Importance  of  Size  and  Composition  of  Particles  for  Oxidative  Damage  and  Carcirogenic 

-362. 

ir Quality Guidelines for Particulate Matter. Ozone. Nitrogen Dioxide and Sulfur 

Dioxide - 

eva. 2015 

30 

  
 
 
 
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

Comparison of two Air Liquid Interface (ALI) systems: lung cells exposure to 
vehicle exhaust  

Tsakonas, G.1, Stamatiou, R.2, Kontses, A.1, Baltzopoulou, P.3, Papaioannou, E.3, Deloglou D. 3, Lazou, A.2 
and Samaras, Z.1 

1Aristotle University, Laboratory of Applied Thermodynamics, GR-54124, Thessaloniki, Greece 

2 Physiology Laboratory, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece 

3Chemical Process and Energy Resources Institute (CPERI), Centre for Research and Technology Hellas 
(CERTH), 57001, Thessaloniki, Greece 

Introduction 

Air pollution is a major public health concern worldwide, with studies linking it to numerous premature 
deaths and adverse health effects (European Environment Agency, 2020). One significant source of air 
pollution  is  road  traffic,  which  generates  large  amounts  of  emissions  from  exhaust  fumes.  Several 
researchers carried out extensive characterizations including toxicity analysis of particles emitted from 
gasoline engines (e.g., Durga et al., 2014, Bisig et al., 2018). Although their toxic effects, especially those 
of  GDI  engines,  are  not  yet  well  understood,  their  mutagenicity  cannot  be  overlooked.  To  better 
understand the impact of vehicle emissions on human health, researchers have utilized a method of 
exposing airway cells to diluted exhaust via Air Liquid Interface (ALI) (Asimakopoulou et al., 2013). This 
method has been shown to be a valid research technique for studying the harmful effects of vehicle 
emissions on human health (Paur et al., 2011). Cell exposure to a known concentration of pollutants can 
provide information about the way cells respond and even have quantification of this the effect.  

In 2023, Lau et al aimed to investigate the effect on human cells of different vehicle emission standards 
under  various  driving  conditions  utilizing  an  ALI  system.  They  specifically  examined  the  vehicles' 
performance  using  the  New  European  Driving  Cycle  (NEDC),  idle  cycle,  and  steady  state  cycle.  The 
researchers observed that as the emission standards became stricter, the impact on cell viability was 
decreased. In another study (Bisig et al., 2018), gasoline passenger cars with Euro5b and Euro6b gasoline 
direct injection (GDI) were tested using the Worldwide harmonized Light vehicles Test Cycle (WLTC). The 
purpose was to assess the potential induction of oxidative stress and activation of pro-inflammatory 
pathways.  The  study  did  not  find  any  statistically  significant  increase  in  oxidative  stress  markers  or 
activation of pro-inflammatory pathways, indicating that the specific vehicle technologies did not affect 
these  cellular  responses,  under  the  tested  conditions.  Studies  such  as  the  above  mentioned  provide 
valuable insights regarding the effect of emission standards and specific vehicle technologies on cellular 
health, under controlled test conditions. However, the relationship between toxicity, real-world driving 
conditions and driving dynamics still needs to be scrutinized. It is important to examine further aspects 
of emission effects in order to have a more comprehensive view of their potential correlation to cell 
toxicity. 

In this context, the present study aims to assess the cell toxicity of exhaust emissions of a state-of-the-
art vehicle utilizing two different air liquid interface (ALI) systems. The vehicle was tested on the chassis 
dynamometer using two driving cycles, a mild and a more dynamic one. To assess the cellular toxicity 
due to emissions, human epithelial A549 cells were used.  Cells were exposed to diluted exhaust gases 
and cell viability was measured using the ALAMAR blue method. The particles that the cells were exposed 
were equivalent to normal air inhalation in an urban environment. 

Methodology 

In the experiments a gasoline port fuel injection (PFI) hybrid vehicle Euro 6d compliant was tested on a 
chassis  dynamometer.  Chassis  dynamometer  offers  a  controlled  driving  environment,  allowing  the 
performance of vehicles to be monitored and evaluated in various operating conditions, incl. Real driving, 
in a fully repeatable way. The vehicle exhaust pipe was connected to a constant volume sampling system 

31 

  
 
 
 
 
 
Joint TAP and S&E Conference 2023 

(CVS). The gaseous emissions were monitored with a portable gas emission measurement system (AVL 
M.O.V.E gas PEMS). Throughout the driving cycles, two cell exposure to gaseous and particulate pollutants 
systems were sampling through the CVS from sampling points 1 and 2. Sampling point 1 was free of 
particles larger  than 450nm; with these  particles being removed using a  cyclone  (for  pre-removal of 
particles >2.5 µm) and an impactor (cut-off 450 nm at 1.5 l/min). At the same sampling point (after the 
impactor),  a  condensation  particle  counter  (CPC)  recorded  total  (solid  and  volatile)  particle  number 
emissions  with  a  cut-off  at  10  nm.  Since  the  particle  size  recorded  at  the  entrance  of  the  exposure 
system is limited to the range of 10-450 nm, we will hereafter refer to these recordings as nTPN10 and 
the particles to which the cells  were exposed as  nanoparticles.  The measurement set-up  is shown in 
figure 1.  

Table 1 Vehicle characteristics 

Manufacturer  Model 

Technology 

Engine 
displacement 

Euro 

Aftertreatment 

Mileage 

[km] 

Vehicle 

Toyota 

CH-R 

Gasoline 
Hybrid-PFI 

1800 

6d 

TWC 

23000 

The driving cycles used in this test campaign are presented in Figure 2. These test cycles were derived 
from  on-road  tests  under  real-world  operation  conditions  and  were  designed  in  order  to  combine 
different driving characteristics (e.g., in terms of percentage of urban, rural, and motorway parts and 
driving dynamics). All tests have a duration of 2h. The target for the temperature in the engine test cell 
was to remain below 37oC during all testing.  

Figure 1 Measurement set-up 

The main characteristics of the test cycles are as follows: 

  mRDE (moderate real driving emissions) test: This test comprises an urban, rural and motorway 

part following the current RDE regulation. It is characterized by mild driving dynamics. This test ran 
twice in a row in each measurement to fulfil the 2-h duration. 

  Combined: This is a combination of smaller cycles (or parts of cycles) to reach the two-hour target. 
It contains longer high-speed sections compared to the mRDE, while urban parts between the high-
speed parts were used to maintain the test cell temperature at the target levels.  

32 

  
 
 
 
 
 
Joint TAP and S&E Conference 2023 

Figure 2 Test driving cycles 

The most important difference between the two ALI systems used is the direction of aerosol flow towards 
the cells. The first ALI system (ALI 1) has six cell exposure chambers, four of which are used for cell 
exposure to both particulates and gaseous emissions while the other two filter out the particulates, thus 
allowing only gaseous pollutants to reach the cells. The particle deposition was monitored with two CPCs. 
CPC 3750 was recording the inlet particle concentration and CPC 3776 (set at a 10 µm cut-off) was 
sampling at the outlet of the exposure chambers. Thus, the particles were charged with a radioactive 
source (TSI 3077 ) and an electrostatic field was provided to the exposure chambers, in order to increase 
deposition efficiency through electrostatic precipitation (Brown, et al., 2005). To simulate the conditions 
inside the human lungs the aerosol was tempered (37°C) and humidified (about 90% RH), before entering 
the  cellular  chambers.  In  the  second  ALI  system  (ALI  2) 
  namely,  Multiculture  in-vitro  Cell exposure 
Chamber (MEC) (Asimakopoulou et al., 2013) - the flow is parallel to the biological samples and allows 
diffusional  deposition  of  fine  particulate  matter.  The  chamber  was  designed  with  a  geometrical 
arrangement of exposed cell cultures to accommodate uniform testing of up to six identical positions for 
culture plates (each one able to hold up to 6 exposure inserts).  

A549 human epithelial cells were used for testing, since the assessment of this cell-line viability is a 
well characterized model to assess nanoparticle-induced toxicity (Croasdell et al., 2021). Furthermore, 
the  airway  epithelial  cells  are  actually  both  a  barrier  preventing  microorganisms  from  invading  the 
respiratory system and a key-player in lung homoeostasis. Reduced viability of lung epithelial cells can 
lead to decreased inflammation resolution and impaired tissue repair due to exposure to acids, hypoxia, 
or chronic irritants (Schroeter et al., 2006). 

The cells were cultured to approximately 80% confluency. Then, they were recultivated on inserts suitable 
for 6-well plates (Falcon) at a concentration of 2x105 cells/well and incubated for 24 hours at 37°C with 
5%  CO2  in  Dulbecco's  Modified  Eagle  Medium  (DMEM)-  High  glucose  supplemented  with  10%  Fetal 
Bovine Serum (FBS), 5% penicilin-streptomycin (PS) (100 U/ml, 100 µg/ml), and 1 mM Sodium pyruvate 
(complete  medium).  The  medium  from  the  upper  side  of  the  insert  was  removed,  and  inserts  were 
transferred  and  placed  at  each  ALI  system,  while  a  control  experiment  remaining  in  incubation, 
unexposed each time. Among the inserts of ALI 1, two were exposed only to gaseous emissions, wherein 
the emission sample passed through a HEPA filter, while the remaining four inserts were exposed to 
unfiltered sample that also contained nanoparticles. In ALI 2 the inserts were divided into two triads each 
placed in a different exposure position (A and B). Following a 2-hour exposure, the cells were incubated 
overnight  in  complete  medium  and  then  underwent  a  3-hour  incubation  with  Alamar  blue  stain.  The 
number  of  cells  was  estimated  by  photometrical  evaluation  of  the  Alamar  Blue  assay  is  based  on 
fluorometric  detection  of  metabolic  mitochondrial  activity  of  cells.  The  results  are  presented  as 
percentages of the control experiments that were not exposed to emissions, and comparisons were made 
between cells exposed under different conditions and unexposed control cells. 

The formula that was applied to calculate the dose D in particles deposited per cell per hour was: 

𝐷 =

𝐶𝑇𝑊𝐴×𝑄𝑒𝑥𝑝
𝐶𝑒𝑙𝑙 𝑑𝑒𝑛𝑠𝑖𝑡𝑦× 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑒𝑑 𝑠𝑢𝑟𝑓𝑎𝑐𝑒 

  × 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦   

𝐶𝑇𝑊𝐴 =

𝑡=𝑒𝑛𝑑𝑜𝑓𝑐𝑦𝑐𝑙𝑒
∫
𝑡=1𝑠

𝐶𝑖

𝑡𝑐𝑦𝑐𝑙𝑒

33 

(1) 

(2) 

  
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
Where: C=particle concentration in the diluted exhaust [p/cm3], TWA=Time Weighted Average and Qexp= 
supply of diluted exhaust for exposure [cm3/s] 

Joint TAP and S&E Conference 2023 

Results  

Cell exposures to both ALI systems were performed at realistic particle concentrations (realistic dose: 2-
300 p/cell/h according to Paur et al, 2011) and in both ALI systems a decrease in cell viability compared 
to control was observed. Experimental data from ALI 1 confirm that part of the reduction in cell viability 
is due to nanoparticle emissions since there is a statistically significant difference between cell viability 
of particle and non-particle exposures. In ALI 2 no statistical difference was detected between the two 
exposure positions, verifying repeatability. In addition, the data show that an increase in driving dynamics 
leads to a further decrease in cell viability due to the particles (Figure 3).  

Figure 3 A. Particle dosage B. cell viability estimated after Alamar Blue staining presented as a 

percentage of control cell viability, Mann Whitney test statistical significance (p-value): 

#p<0,05, ##p<0,01 particle exposure compared to non-particle exposure  
*p<0,05 compared to the same exposure type in different driving dynamics  

The toxicological effects presented using the methods and systems employed in this study should be 
interpreted  as  indicative  of  the  potential  effects  of  exposure  to  exhaust  in  controlled  experimental 
conditions.  

Summary/Conclusions 

Two ALI systems were used to assess the toxicological effects of a modern gasoline car exhaust. ALI 1 
provided the possibility to identify the effect on cell viability due to gaseous emissions alone or the effect 
induced by the total exhaust fumes. On the other hand, the second ALI, equipped with multiple exposure 
chambers, allowed to replicate the experiment several times at the same cycle. In both exposure systems 
a  statistically  significant  reduction  of  cell  viability  was  observed,  due  to  emissions  exposure.  The 
presented results could indicate that the exhaust fumes can have a negative effect on human lung cells, 
while showing some dynamic driving cell viability correlation. The possibility that particles deriving from 
modern gasoline exhaust can be harmful to the human respiratory system needs to be further evaluated. 

34 

  
 
 
 
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

References 

1.  Asimakopoulou, A., Daskalos, E., Lewinski, N., Riediker et al., Development of a dose-controlled 

multiculture cell exposure chamber for efficient delivery of airborne and engineered nanoparticles. 
Journal of Physics: Conference Series, 429, 012023, 2013, https://doi.org/10.1088/1742-
6596/429/1/012023  

2.  Bisig, C., Comte, P., Güdel, M., Czerwinski, J., et al., Assessment of lung cell toxicity of various 

gasoline engine exhausts using a versatile in vitro exposure system. Environmental Pollution, 235, 
263 271, 2018, https://doi.org/10.1016/j.envpol.2017.12.061  

3.  Brown, J. S., Wilson, W. E., & Grant, L. D., Dosimetric comparisons of particle deposition and 

retention in rats and humans. Inhalation Toxicology, 17(7 8), 355 385, 2005, 
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4.  Croasdell Lucchini A, Gachanja NN, Rossi AG, Dorward DA, Lucas CD. Epithelial Cells and 
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5.  Durga, M., Soundararajan N., Rajasekar A., Thiyagarajan D., Effects of ultrafine petrol exhaust 

particles on cytotoxicity, oxidative stress generation, DNA damage and inflammation in human 
A549 lung cells and murine RAW 264.7 macrophages, Environmental Toxicology and 
Pharmacology, 38, 2, 2014, https://doi.org/10.1016/j.etap.2014.08.003  

6.  European Environmetal Agency (2023). Road transport. https://www.eea.europa.eu/en/topics/in-

depth/road-transport  

7.  Lau, Y.-S., Poon, H.-Y., Organ, B., Chuang, H.-C., et al., Toxicological effects of fresh and aged 

gasoline exhaust particles in Hong Kong. Journal of Hazardous Materials, 441(441, preprint no. 
129846.), 2023, https://doi.org/10.1016/j.jhazmat.2022.129846  

8.  Paur, H.-R., Cassee, F. R., Teeguarden, J., Fissan, H., Diabate, S., Aufderheide, M., Kreyling, W. G., 
Hänninen, O., Kasper, G., Riediker, M., Rothen-Rutishauser, B., & Schmid, O. (2011). In-vitro cell 
exposure studies for the assessment of nanoparticle toxicity in the lung A dialog between 
aerosol science and biology. Journal of Aerosol Science, 42(10), 668 692. 
https://doi.org/10.1016/j.jaerosci.2011.06.005 

9.  Schroeter, J. D., Kimbell, J. S., Bonner, A. M., Roberts, et al., Incorporation of tissue reaction kinetics 
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Toxicological Sciences: An Official Journal of the Society of Toxicology, 90(1), 198 207, 2006, 
https://doi.org/10.1093/toxsci/kfj072 

Contact Information 

Name: Samaras Zissis  

Email address: zisis@auth.gr 

Telephone: 0030 2310996014 

Acknowledgments 

This work was supported by the Horizon 2020 project nPETS, under grant agreement No 954377. 

35 

  
 
 
 
 
 
Joint TAP and S&E Conference 2023 

Design of a Sampling System for Brake Particles On-Road Measurement   A 
Computational Preliminary Study 

A. Katsinos,1, A. Dimaratos1, A. Kontses1, V. Noutsou1, G. Christou1, A. Tomboulides1, Z. Samaras1* 

1 Laboratory of Applied Thermodynamics, Department of Mechanical Engineering, Aristotle University of 
Thessaloniki, Thessaloniki, 54124, Greece, zisis@auth.gr 

Introduction 

Brake wear particles are currently a major source of particle emissions, especially in urban environments. 
Their  relative  contribution  is  continuously  increasing  due  to  the  great  improvement  of  the  emission 
control  systems  of  combustion  engines  (thus,  reduction  of  exhaust  particles)  and  the  penetration  of 
electric vehicles in the fleet (Grigoratos and Martini, 2015; Air Quality Expert Group, 2019). The latter 
account for 13% of new registrations in the EU during the first half of 2023, versus almost 10% in the 
same period of 2022 (ACEA, 2023) and have great potential to reduce brake particle emissions owing to 
their regenerative braking function (Hicks et al., 2023).  

In  this  context,  brake  wear  emissions  attract  increased  interest  from  all  the  relevant  stakeholders, 
including regulatory authorities, the industry and the scientific community, around the globe (EuroBrake, 
2021;  Vanherle  et.  al.,  2021;  Wang  et  al.,  2022).  This  is  highlighted  in  the  most  explicit  way  by  the 
inclusion of emission limits for brake particle emissions (together with another source of non-exhaust 
particles,  i.e.,  tyres)  in  the  proposed  Euro  7  regulation  for  the  first  time  ever  (European  Commission, 
2022). 

Therefore, the detailed characterisation of brake particle emissions is a very  important topic. A brake 
particle  measuring  system  needs  to  address  various  challenges,  with  the  most  important  being  the 

combustion engine. The brake components on a vehicle form a system open to the environment and the 
sampling  system  needs  to  ensure  separation  of  brake  particles  from  other  sources,  such  as  tyres, 
resuspension of road particles etc. As summarised by Wang et al. (2022), six methods can be identified 
for  sampling  and  measuring  brake  particles,  ranging  from  simple  laboratory  test  stands  to  highly 
sophisticated systems for real-world on-road measurements. 

For regulatory purposes, a measurement methodology and protocol for particle emissions from brake 
systems in laboratory conditions was developed by the Particle Measurement Programme, which defined 
the  exact  test  stand  specifications  and  measuring  procedure  (PMP,  2021).  In  parallel,  a  number  of 
prototype systems have been developed for sampling and measuring brake particles under real-world 
driving  conditions  and  can  be  differentiated  by  the  level  of  isolation  of  the  brake  system  from  the 
external environment. In this context, there are the open systems, using sampling hoses close to the 
brakes (e.g., Oroumiyeh and Zhu, 2021), the semi-enclosed systems that isolate only partially the brakes 
from  the environment,  still  sucking  ambient  air  (e.g.,  Farwick  Zum  Hagen  et  al.,  2019)  and  the  fully-
enclosed systems that encapsulate the brake components and apply a forced flow of conditioned air 
through the capsule (e.g., Feißel et al., 2020). Each system is characterised by particular advantages and 
disadvantages, in terms of particle separation from different sources, particle transport efficiency, effect 
on the temperature of the brake system components etc (Wang et al., 2022). 

The current work makes a preliminary study of one semi-enclosed sampling concept with a cone-type 
sampler  (Farwick  Zum  Hagen  et  al.,  2019).  To  that  aim,  CFD  calculations  are  performed,  in  order  to 
examine a braking event of a mid-sized (segment C) passenger car running at 60 km/h. The overall target 
at this stage is to setup a computational tool for the simulation of the relevant phenomena in the brake 
particle sampling system and run a preliminary sensitivity analysis. At the next step, experimental data 
will be produced with a system that is currently being manufactured and the tool will be validated and 
further developed for more extended parametric analyses towards the optimisation of the brake particle 
sampling system. 

36 

  
 
 
 
Joint TAP and S&E Conference 2023 

Methodology   Simulation setup 

The developed CFD tool uses the commercial solver STAR-CCM+ for the simulation of the flow and other 
relevant  phenomena  in  the  brake  particle  sampling  system.  Figure  1  shows  the  control  volume,  that 
consists  of  the  full  wheel  assembly  (power  shaft,  brake  disc  and  pads,  calliper  etc.)  and  the  particle 
collection cone, which is then connected to the pipe of the sampling system. The calculation domain is 
defined by the cavity enclosed by the wheel and the cone, while the flow enters from the inner side of 
the wheel and exits the domain at the cone outlet (Figure 1). Only the area which is in contact with the 
airflow is meshed, so no conjugate heat transfer is considered between the air and the solid parts. The 
mesh consists of around 2 million polyhedral cells, after a grid independence study that was performed. 
The inlet boundary condition is set to constant velocity with a uniform profile and the outlet as a pressure 
boundary equal to the ambient. The volumetric flow rate of the system is controlled by the magnitude 
of the inlet velocity and varies for the different cases of the sensitivity analysis, as shown and discussed 
in the results section. During the actual measurements, these operational parameters will be controlled 
with the blower of the sampling system. 

A surface heat flux is introduced at the brake disc and the pads to account for the heat generated during 
the brake event. The magnitude of this heat flux is calibrated so that the predicted temperature matches 
the measured one on the brake disc. This was done by estimating the kinetic energy dissipation during 
braking and thus the actual heat generated during the brake event at 60km/h. Based on an order of 
magnitude analysis, the heat flux applied to match the measured temperature is about two to four times 
lower than the total kinetic energy dissipation estimated during braking, which given the uncertainties of 
the approach is deemed satisfactory. The temperature at the outer surface of the cone is set equal to 
the ambient. The standard k-
-layer RANS turbulence model is used to simulate the flow turbulence. 
A moving reference frame is used to account for the rotating wheel and the rate of rotation imposed 
corresponds to the angular velocity of the wheel at 60 km/h, as mentioned earlier. Lagrangian particles 
are injected tangentially through line injectors lying on the pad-disc interface (produced by their friction), 
for both pads (one per each side of the disc). At this first setup phase, particles of constant diameter are 
considered,  with  only  a  one-way  coupling  with  the  flow.  At  a  later  development  stage,  particle  size 
distributions will also be considered. Steady-state simulations, including a turbulent particle dispersion 
model are performed in a full three-dimensional setup. 

Figure 1: Left: Control volume for the CFD calculations. Right: Section cut of the wheel-cone assembly. 

Methodology   Testing 

As mentioned above, a system with a cone sampler is considered and simulated. Figure 2 (top) illustrates 
the layout of the complete system, which will measure brake particle number, mass and size distribution. 
Ambient particle concentration will be also measured as background and in order to estimate the number 
of particles coming solely from the brakes. The air flow will be adjusted with the blower and will be kept 
constant  through  a  closed-loop  control  with  a  flow  sensor.  Additional  measurements  will  include  the 
temperature of the brake components (disc and pads), the pressure of the brake fluid and vehicle OBD 
data. A first experimental setup comprising the latter set of parameters has been already developed and 
brake  disc  temperature  measurement  has  been  conducted,  as  shown  in  Figure  2  (bottom-left). 
Consecutive braking events from 60km/h to complete stop were performed, until reaching a constant 

37 

Inlet flowOutlet flowCollection coneWheel  
 
 
 
 
Joint TAP and S&E Conference 2023 

temperature  profile  of  the  system  during  braking,  allowing  thus  for  steady-state  simulations.  The 
recorded temperature value at the end of the braking event is used for the calibration of the surface 
heat flux magnitude on the disc. Figure 2 (bottom-right) illustrates the evolution of disc temperature 
during these consecutive events, along with the brake pressure and the brake light voltage signals. The 
temperature drop around 1050 s results from the longer stop between two consecutive braking events. 

Figure 2: Top: Schematic layout of the brake particle sampling and measuring system. Bottom-left: Brake 
disc  temperature  and  brake  pressure  measurement.  Bottom-right:  Disc  temperature  evolution 
consecutive braking events at 60 km/h. 

Results 

In  the  context  of  this  preliminary  study,  a  sensitivity  analysis  is  performed  in  order  to  evaluate  the 
characteristics  of  the  sampling 

system. 

38 

NozzleAmbientBrake Temperature & PressureFlow MeasurementConeSampling TunnelBlowerOutlet TubeParticle Size DistributionPM MeasurementPN MeasurementOBD DataPN Measurement AmbientDisc Temperature MeasurementBrake Pressure Measurement02040608010012001002003004005006000300600900120015001800Vehicle speed (km/h), Brake pressure (bar), Brake light voltage (V)Temperature ( C)Time (s)Disc temperatureVehicle SpeedBrake pressureBrake light  
 
 
 
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

For the base case of this sensitivity analysis, the volumetric airflow rate of the sampling system is equal 
to 100 m3/h. As the work done in each braking event (deceleration from 60km/h until complete stop in 6 
seconds)  is  the  same,  independently  of  the  presence  of  the  cone  or  the  volumetric  flow  rate  of  the 
sampling system, the boundary condition for the brake disc and pads is set to a constant surface heat 
flux.  As  explained  above,  the  heat  flux  was  calibrated  so  that  the  disc  temperature  matches  the 
experimental one, for this base case. This way, the temperature of the disc is obtained as a solution by 
the CFD calculations.  

Three sensitivity cases were examined with varying volumetric airflow rate by +30% and +50% around 
the base case to investigate its effect on the disc temperature. Figure 3 presents the variation of the 
disc temperature with the volumetric flow rate of the sampling system. As expected, the increase of the 
airflow rate leads to a temperature drop, due to the more intense heat transfer. However, as can be 
observed  in  Figure  3,  a  50%  increase  in  the  flow  rate  causes  only  a  moderate  decrease  of  the  disc 
temperature from 510 to 490 °C, i.e., less than 5%.  

Figure 3: Effect of volumetric airflow rate on disc temperature. 

At a next step, particles were introduced to the flow as described in the methodology section, at a mass 
flow  rate  of  0.167  mg/s.  First  the  effect  of  particle  diameter  (in  the  range  of  1  nm  to  10  m)  was 
investigated  on  total  particle  mass  flow  rate  collected  by  the  sampling  system  at  the  cone  outlet 
compared to the particle mass flow injected. For the examined range of volumetric airflow rates, the 
st zero., i.e., large particles are stuck inside the cone 
or other internal surfaces of the system (e.g., wheel). For volumetric airflow rates lower than 120 m3/h, 
the  particle  mass  flow  rate  at  the  outlet  of  the  cone  reaches  a  constant  value  for  all  particles  with 

As  the  outlet  mass  flow  rate  is  a 
combination of the number of particles that flow through the exit of the domain and of their mass (or 
diameter for the case of constant density), this value peaks at 3  m and 2  m for of 120 m3/h and 150 
m3/h airflow, respectively. For larger particles, although the mass per particle increases, the total outlet 
mass flow rate decreases, as less particles follow the trajectory of the flow, and finally reaches zero at 
around 10 m. As particle size decreases below the diameter where the peaks are observed, the mass 
flow rate varies until it finally reaches a constant value, as the trajectory of the particles is not altered 
any further.  

e.g., Nosko and 
Olofsson, 2017), the subsequent runs, aimed at investigating the effect of volumetric airflow rate on 
particle mass flow rate at the cone outlet, were all performed with 10 nm particles. Because the flow is 
confined, the behaviour of the particles as they reach the solid surfaces is critical. If all particles rebound, 
then the mass flow rate at the cone outlet is significantly increased, as shown in Figure 5, i.e., more 
particles are collected. With the increase of the air flow, the percentage of collected particles further 
increases. However, it does not reach 100% as the number of the particle tracking steps in the calculation 
is finite. This limit is therefore exceeded either by some particles that are trapped into vortices from 
where  they  never  escape or  by  some  particles  that  follow  unrealistically  long  trajectories  due  to  the 
rebound boundary condition that was set. 

39 

470480490500510520100130150Disc temperature ( C)Volumetric flow rate (m3/h)CalculatedExperimental  
 
 
 
Joint TAP and S&E Conference 2023 

In the case that all particles stick on the solid surfaces, the percentage of collected particles at the outlet 
of the cone is as low as 1%. It is important to mention that if turbulent dispersion is deactivated, the 
percentage of particle mass collected increases even for sticking solid surfaces above 80%, for the range 
of the volumetric airflow rate examined here. This is due to the stochastic gas velocity components that 
are added to the flow-particle interaction by the turbulent particle dispersion model and which may drive 
them towards solid surfaces where they stick, instead of following the flow streamlines as indicatively 
shown in Figure 6. 

Figure 4: Effect of particle size on the mass flow rate at the cone outlet. 

Figure 5: Percentage of particle mass collected at the outlet of the cone (particle diameter 10 nm). 

Figure 6: Indicative illustration of the streamlines followed by particles. 

40 

0.000.020.040.060.080.100.120.140.160.180246810Outlet mass flow rate (mg/s)Particle diameter (μm)120 m3/h130 m3/h150 m3/hInlet particle mass flow rate = 0.167 mg/s66%75%81%81%83%93%100130150Normalized particle mass flow rateVolumetric airflow rate (m3/h)Rebound at solid surfacesWithout Turbulent Dispersion  
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

Summary & Next Steps 

In the context of the present work, a brake particle collection system is examined, which is aimed for use 
in  real-world  on-road  measurements  on  light-duty  vehicles.  A  semi-enclosed  sampling  concept  is 
considered with a cone-type particle collector. At this first phase, a computational tool was set up for 
the simulation of the relevant phenomena and a sensitivity analysis was conducted. A braking event of 
a mid-sized (segment C) passenger car from 60 km/h to complete stop was examined. Experimental data 
concerning brake disc temperature are produced with a targeted test campaign. The energy model was 
calibrated  in  order  to  match  the  measured  disc  temperature  and  the  effect  of  the  airflow  rate  was 
examined.  At  the  next  step,  particles,  generated  by  the  friction  between  brake  disc  and  pad,  are 
introduced  at  a  constant  mass  rate  in  the  flow.  The  computational  tool  was  used  to  examine  the 
behaviour of particles under different assumptions. With the current setup (cone and wheel design) and 
in the range of examined air flow rates, particles la
percentage  of  particle  mass  collected  at  the  cone  outlet  (compared  to  the  inlet  mass)  stabilizes  for 
diameters less than 1-
at solid surfaces, the collected particle mass percentage at the cone outlet varies from practically zero, 
if particles are sticking to solid surfaces, to 66%-81% if particles rebound on solid surfaces. In case that 
turbulent dispersion is deactivated, the percentage of particle mass collected increases even for sticking 
solid surfaces above 80%. 

The computational tool will be further developed in the next phases, including the implementation of 
more  realistic  boundary  conditions  for  the  interaction  of  particles  with  solid  surfaces,  making  a 
probabilistic selection between sticking and rebound. On the experimental side, next activities include the 
finalization of manufacturing of the sampling system and the execution of measurements of particle 
mass, number and size distribution. Using the so produced experimental data, the computational tool 
will be further developed and validated. It will then be further applied for the deeper investigation of the 
relevant phenomena in the system, particularly as concerns particle flow, as well as for the optimisation 
of the particle sampling system. 

References 

ACEA, 2023. Press releases 

 New car registrations. https://www.acea.auto/pc-registrations/new-car-registrations-

15-2-in-july-battery-electric-13-6-market-share/ 

Air Quality Expert Group, 2019. Non-Exhaust Emissions from Road Traffic. DEFRA. 

EuroBrake 

2021: 

Brake 

emissions 

regulation 

imminent 

as 

non-exhaust 

emissions 

surge. 

https://www.fisita.com/post/eurobrake-2021-brakeemissions-regulation-imminent-as-non-exhaust-emissions-
surge 

European Commission, Internal Market, Industry, Entrepreneurship and SMEs, Euro 7 standard proposal, November 

2022, https://single-market-economy.ec.europa.eu/publications/euro-7-standard-proposal_en 

Farwick Zum Hagen F.H., Mathissen, M., Grabiec, T., Hennicke, T., Rettig, M., Grochowicz, J., Vogt, R., Benter, T. (2019) 

On-road vehicle measurements of brake wear particle emissions. Atmos Environ 217, 116943. 

Feißel,  T.,  Hesse,  D.,  Augsburg,  K.,  Gramstat,  S.  (2020).  Measurement  of  Vehicle  Related  Non-Exhaust  Particle 

Emissions Under Real Driving Conditions. EuroBrake 2020. 

Grigoratos, T, Martini, G. (2015) Brake wear particle emissions: a review. Environ Sci Pollut R 22, 2491-2504. 

Hicks, W., Green, D.C., Beevers, S. (2023) Quantifying the change of brake wear particulate matter emissions through 

powertrain electrification in passenger vehicles. Environ Pollut 336, 122400. 

Nosko, O., Olofsson, U. (2017) Effective density of airborne particles from car brake materials. J Aerosol Sci 107, 

94-106. 

Oroumiyeh, F., Zhu, Y. (2021) Brake and tire particles measured from on-road vehicles: Effects of vehicle mass and 

braking intensity Atmos Environ X 12, 100121. 

PMP  IWG,  Brake  Emissions  Task  Force  2  (TF2).  Minimum  specifications  for  measuring  and  characterizing  brake 

emissions. July 2021. 

Vanherle, K., Lopez-Aparicio, S., Grythe, H., Lukewille, A., Unterstaller, A., Mayeres, I., Eionet Report   ETC/ATNI 2020/5, 
March 2021. Transport Non-exhaust PM-emissions   An overview of emission estimates, relevance, trends and 
policies. 

Wang, Y., Yin, H., Yang, Z., Su, S., Hao, L., Tan, J., Wang, X., Niu, Z., Ge, Y. (2022) Assessing the brake particle emissions 

for sustainable transport: a review. Renew Sust Energ Rev 167, 112737 

41 

  
 
 
 
 
Joint TAP and S&E Conference 2023 

2.2  TAP.02.  On-board  monitoring  and  diagnostics, 

emission tampering and deterioration. 

42 

  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

Real world particle number emission factors from plume chasing data 

T. Frateur1*, N.E. Ligterink1, J.P. Lollinga2, D. Pöhler3,4, C. Schmidt3,4, M. Knoll5 

6 

1 Department of Sustainable Transport and Logistics, TNO, The Hague, South Holland, 2595 DA, NL, 
thomas.frateur@tno.nl 
2 Department of Environmental Modelling, Sensing & Analysis, TNO, Utrecht, Utrecht, 3584 CB, NL 
3 Airyx GmbH, Justus-von-Liebig-Straße 14, Eppelheim, 69214, Germany 
4 Institute of Environmental Physics , University of Heidelberg, INF 229, Heidelberg, 69120, Germany 
5 Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, 8010 Graz, Austria 
6 CVUT, Czech Technical University Prague, Praha, 166 07, Czech Republic 

Introduction 

Modern diesel vehicles are equipped with diesel particulate filters (DPF) to reduce particle emissions on 
the  road.  DPFs  can  crack  or  be  removed  from  vehicles  causing  very  high  particle  emissions  and  the 
associated health effects. Remote sensing techniques enable measuring a high volume of vehicles to 
gain statistical information and aid authorities in targeted inspections (Borken-Kleefeld, 2018). There 
are different measurement techniques to detect particle emissions. Commercial light-based RES systems 
are capable of determining statistically acceptable emission factors of gases, but they  lack accuracy 
measuring particles. A more direct measurement technique is therefore preferred. Plume chasing is a 
remote sensing technique where a measurement vehicle, following vehicles, is equipped to analyse the 
exhaust plume of a chased subject vehicle. The technique is already well described in i.a. (Pöhler, 2021). 
Most of the existing knowledge on plume chasing concerns NOx emission factors. Only a few studies are 
available which investigate particle number (PN) emission measurements for plume chasing (i.a. Järvinen 
et  al.,  (2019),  Leinonen  et  al.,  (2023)  and 
  introduce  a  PN 
application of plume chasing in a controlled test environment. They show PN emission factors correlate 
well with PEMS tests under controlled conditions. However, the second-by-second variation of PN/CO2 
ratios is typically larger than for NOx/CO2 due to other sources polluting the signal, requiring more data 
for a robust evaluation of the vehicle emission performance. On the other hand, differences between 
high and low emitters should be more pronounced for PN emissions. 

. 

Figure 1: CARES experimental plume chase vehicle. 

Real world PN plume chase measurements of approximately 220 Euro VI heavy duty vehicles are used 
to show the validity of this technique on the road by determining the PN emission factor distribution of 
these vehicles and comparing different PN instrument results. Measurement data is obtained with an 
experimental TNO plume chase vehicle measuring HDV highway traffic in the Czech Republic in the CARES 
research  program.  Among  other  instruments,  the  measurement  data  used  here  are  from  a  TEN  AEM 
particle counter as a low-cost PN measurement device, a TSI SMPS+CPC combination as state of the art 
PN reference instruments, and an Airyx ICAD for CO2 data. 

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Averaged PN emission factors (PN divided by CO2 concentration) are calculated per vehicle. The different 
particle instruments result in different mean PN measurements due to differences in particle size ranges 
measured  and  measurement  principles.  They  are  however  found  to  generate  very  similar  emission 
distribution results with only marginal differences in the relative measurement variation. Results from 
the TEN AEM particle counter are used as leading factors to enable comparison with PTI-PN results.  

CARES campaigns 

CARES is a European Union sponsored research project aimed at investigating contactless measurement 
of vehicle exhaust emissions. During several CARES campaigns throughout Europe, several contactless 
measurement methods were used to measure the exhaust emissions from passing or followed vehicles 
to validate their suitability for monitoring pollutant emissions and improving city air quality. During a 
testing  campaign  in  Prague,  among  other  measurement  methods,  the  plume  chasing  method  was 
demonstrated for both NOx and particle number emissions in an experimental plume chase vehicle. NOx 
plume chasing was also performed in a market ready measurement setup by Airyx.  

Method 

Real world data acquisition 

Particle number (PN) emission data is gathered from vehicles using plume chasing techniques. In plume 
chasing,  a  chase  vehicle  is  equipped  with  a  front  mounted  sampling  system,  exhaust  gas  analysing 
instruments and a data acquisition system. Vehicles are chased by the chase vehicle at a normal driving 
distance to position the sampling system in the exhaust plume of the followed vehicle. Exhaust gas is 
sampled  and  routed  through  the  analysers  in  the  back  of  the  vehicle  for  analysis.  Unprocessed 
measurement results are shown directly to the vehicle operators on a screen for vehicle selection and 
plume  alignment.  A  schematic  representation  of  the  instrumentation  setup  used  for  this  analysis  is 
shown in Figure 2. 

Figure 2: Schematic representation of instrument and data acquisition connections. 

The relevant emission measurement instruments used for this analysis are listed in Table 1. Additional 
emission measurement instruments were present in the experimental vehicle but are not used in this 
work.  Note  that  the  TEN  AEM  particle  counter  device  is  originally  designed  for  PTI  checks  of  diesel 
particulate filters (DPF), in the Netherlands. The device is used here as a cheap PN instrument alternative 
and to enable linking of plume chase results to PTI measurements, focussing at 90 nm particle sizes. 
Instruments used to monitor measurement conditions are listed in Table 2. 

44 

  
 
 
 
 
 
Table 1: Exhaust gas plume chase instruments 

Instrument name 

Measurement 
parameters 

Units 

Comments 

Joint TAP and S&E Conference 2023 

Airyx ICAD 

NOx / CO2 

ppb / ppm 

TEN AEM 

TSI SMPS + CPC 

PN 

PN 

k#/cm3 

#/cm3 

A secondary ICAD was 
used on some occasions 
for background 
measurements 

PTI particle device used 
as a cheap alternative 
for real world PN 
measurements. 

State of the art PN 
reference instrument 
with SMPS size selection 
of 90nm. 

Table 2: Additional plume chase instruments 

Instrument name 

Measurement parameters 

Comments 

GPS 

Position, velocity 

ANPR camera 

License plates 

Continental radar 

Vaisala weather station 

Relative position and 
velocity of the chased 
vehicle 

Relative windspeed, 
pressure, temperature & 
humidity 

Used to couple vehicle 
properties to measurement 
data. 

All the relevant instruments were logged on a Delphi computer to enable aligned logger files with a static 
delay. Measurement of individual vehicles is manually started and stopped by the operator trough an 
Airyx user interface to enable measurement ID assignment to the different chased vehicles.  

During the CARES campaign, approximately 220 heavy duty Euro VI vehicles were successfully chased 
by the experimental plume chase vehicle to obtain measurement data on particle number emissions. 
Measurement data is saved to measurement logs for all instruments per measurement day. 

Signal postprocessing 

Background signals for all measured signals are determined on the bulk dataset of each measurement 
day. The background level is determined by taking the lower bound of each signal in 60 second windows 
and smoothing the resulting datapoints. The datasets are then split to vehicle specific datasets based 

performed on each vehicle dataset separately. 

Measured  PN  concentrations  are  normalised  with  a  reference  gas  concentration.  Similar  to  emission 
factor calculations of NOx measurements (Pöhler, 2021), CO2 is used as the reference gas for PN plume 
chasing.  Different  methodologies  are  used  in  calculating  emission  factors  from  measurement  data. 
However, most methods such as total peak surface integration discard a lot of information about the 
quality and consistency of the signal. Instantaneous emission factor calculation attains this detail in the 
results. To ensure accurate results, the peaks of the particle and CO2 measurements should however be 
properly aligned with this method. Due to noise in the signal, conventional peak alignment is hard to 
implement robustly, as multiple peaks may occur during measurements. A rising edge alignment method 

45 

  
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

is  used to  cope  with  variability and noise in the  signals,  during the measurement  period for a  single 
vehicle. As the rising edge of PN and CO2 measurements of high emitters is observed to be steep, this 
method should result in good alignment between measurements.  

A  schematic  example  of  the  rising  edge  alignment  is  shown  in  Figure  3.  Only  data  where  CO2 
concentrations are well above the background value are used in the calculations. The reference threshold 
for CO2 is therefore defined at 50 ppm above the background. For other measurement, the threshold is 
defined  with  the  threshold  ratio  between  the  CO2  maximum  peak  value  (𝑝𝑚𝑎𝑥)  and  the  reference 
threshold (𝑇𝑟𝑟𝑒𝑓). 

𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 𝑟𝑎𝑡𝑖𝑜 =

𝑝𝑚𝑎𝑥
𝑇𝑟𝑟𝑒𝑓

Peaks are aligned on the t1 line (Figure 3) of each signal. If a t1 line is not available, e.g., due to noise, 
peaks can be aligned on the t2 line (falling edge alignment).  

Figure 3: Schematic example of rising edge peak selection. 

For  all  measurement  points  where  the  CO2  signal  is  larger  than  the  reference  threshold  above  the 
background,  the instantaneous particle number emission factor is calculated according to  the carbon 
mass balance method (Hansen and Rosen, 1990) and as shown by the equation below. 

𝑃𝑁𝑒𝑓[

𝑘#
𝑘𝑔

] = 1012 ∙

𝑃𝑁  [

𝑘#
𝑐𝑚3] 
𝐶𝑂2[𝑝𝑝𝑚] ∙ 𝜌𝐶𝑂2 [

𝑘𝑔
𝑚3]

Here  the  density  of  CO2  is  taken  to  be  constant,  in  ambient  conditions1,  with  𝜌𝐶𝑂2 = 1.84  [
𝑚3].  The 
particle  number  emission  factor  for  the  vehicle  is  calculated  as  the  average  over  the  instantaneous 
emission factors of all valid measurements (CO2 above threshold). In addition, the standard deviation in 
these  instantaneous  results  is  calculated.  These  steps  are  performed  for  both,  the  particle  number 
measurements of the TEN AEM instrument and the CPC measurements of 90 nm size-selected particles. 

𝑘𝑔

1 At 15 degree Celsius and 1 atmosphere pressure 

46 

  
 
 
 
 
 
 
                                                       
 
Joint TAP and S&E Conference 2023 

Results 

TEN AEM particle number emission factors 

Approximately 220 Euro VI HD vehicles are measured with the experimental plume chase vehicle during 
the 2023 CARES Prague campaign. The PN emission factors from these vehicles as measured with the 
TEN  AEM  particle  counter  are  shown  in  Figure  4  (left)  as  a  histogram.  Approximately  74.5%  of  the 
measured vehicles fall below the average PN measurement result of 2.76e11 [k#/kgCO2], indicating a 
clear right skew distribution in the measured emission factors. This distribution form is expected under 
the assumption that the majority of vehicles is compliant with the imposed emission regulation. The tail 
to the right of the distribution accounts for vehicles with smaller or larger defects in the aftertreatment 
system or tampered vehicles.  

Figure 4: Binned PN emission factors (left) and mean standard deviations of individual measurements 
(right) from approximately 220 HD vehicles (Euro VI) using a PTI particle counter. Measurements above 
4e11 [k#/kgCO2] are grouped to the right of the histogram, the average of this bin is 9.45e11 [k#/kgCO2]. 

In normal use, PN emission factors vary substantially during driving with variation increasing for high 
emitters. This is shown in the standard deviation ranges for individual measurements (Figure 1, right) 
which indicates the standard deviation in instantaneous emission factors obtained for each measured 
vehicle. Large variation in PN/CO2, per vehicle, is consistent with similar findings in emission testing. The 
absolute emission values measured with the TEN AEM particle counter are however very high for Euro 
VI  HD  vehicles.  The  PN  limit  imposed  on  these  vehicles  in  Euro  VI  legislation  of  9.78e11  [#/kWh]  is 
equivalent to approximately 14.4e8 [k#/kgCO2]2. The high measurements of the TEN AEM particle counter 
can most likely be explained by the non-linearity of particle measurements with this device, based on 
the diffusion charging principle. Especially larger particles are overcounted significantly as can be seen 
in Figure 5. It is likely that concentrations of larger particles increase with distance from the source as 
particle number and particle sizes are not conserved quantities over time. Note that also differences in 

2 Assuming a typical HD highway CO2 per kWh ratio of 680 [g/kWh] 

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Joint TAP and S&E Conference 2023 

background  estimation  for  both  CO2  and  PN  have  a  large  influence  on  the  absolute  values  of  the 
calculated emission factors. 

Figure 5: TEN AEM PN validation results with TSI CPC on size selected exhaust samples from a diesel 
generator  set.  Averaged  measurements  are  obtained  by  averaging  stabilised  PN  results  of  5  minute 

measurements on the selected particle size and normalising TEN AEM results with TSI CPC results. 

The validation measurements of the TEN AEM shown in Figure 5 are obtained from measurements on a 
diesel generator set with both a TEN AEM PTI particle counter and a TSI CPC connected to a TSI SMPS 
ents on 
several particle size selections. The data fit shown in this figure indicates the particle counter is only 
accurate in a limited range around the calibration particle size of the device. Some smaller deviations 
between the instruments with absolute particle concentrations levels is noted. This is not investigated 
further. Future work will look at the possibility of removing larger particles from the sample flow before 
the measurement to obtain more accurate PN results, for exhaust emissions in ambient conditions, with 
the TEN AEM device. 

CPC particle number emission factors 

The  same  emission  factor  distribution  as  shown  above  is  made  using  90  nm  size  selected  particle 
measurements from a TSI SMPS+CPC combination. Note that size selection drastically limits the specific 
particle  concentration  in  the  sample.  As  such,  much  lower  emission  factors  are  expected  with  this 
approach. The calculated emission factor distribution of these measurements is shown in Figure 6. A 
similar right skew distribution is found in the emission factors as seen in the emission factors from the 
TEN  AEM  data.  Also  similar  behaviour  in  the  standard  deviation  of  the  measurements  is  found  with 
standard deviations remaining at the same order of magnitude as the mean emisison factor. It can also 
be  observed  that  the  standard  deviation  increases  with  higher  emission  factor  measurements,  note 
however  that  this  effect  might  also  be  due  to  a  different  sample  size  of  the  bin.  Future  work  could 
possibly look into this effect by analysing similar sized bins for all emission factors. 

48 

  
 
 
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Figure 6: Binned PN emission factors (left) and mean standard deviations of individual measurements 
(right) from approximately 220  HD vehicles  (Euro VI)  of 90nm size selected  particles. Measurements 
above  1e9  [k#/kgCO2]  are  grouped  to  the  right  of  the  histogram,  the  average  of  this  bin  is  1.58e9 
[k#/kgCO2]. 

Discussion 

Similar to remote emisson factor measurements for NOx emissions in real world conditions, PN emission 
factors can be measured on-road using plume chase techniques. From data analysis on 220 HD Euro VI 
vehicles  in  highway  conditions,  a  clear  right  skew  PN  emission  distribution  is  found.  A  right  skew 
distribution is expected when the majority of vehicles complies with the legislative limits with a lower 
fraction of vehicles showing small to larger defects or tampering of the DPF. The similar right skew trend 
found in the data from two separate instruments used in this analysis indicates the distributions found 
are not influenced significantly by different instrument techniques. Both measurement methods can be 
used in detecting PN emissions under real world conditions. 

While emission factor distributions are largely similar, emission factor magnitudes differ significantly 
between the two measurement methods. While the magnitude difference between total particle count 
and size selected  count is to be expected, the magnitudes found using the TEN AEM PTI counter are 
higher than expected. From testing performed on the PTI instrument after the plume chase measurement 
campaign, an overestimation of PN, up to a factor 3.5 on  large exhaust particles of 200 nm, is found. It 
can be assumed the higher PN magnitudes in the plume chase results are a result of this overestimation. 
As larger particles are not necessarilly originating from exhaust emissions only, future work should look 
into the possibilities to separate these larger particles from the sample before the measurement as to 
prevent false positive high emitter identification under dusty road conditions. 

Moreover,  particle  numbers and  particle sizes  are  not  conserved  quantities  over  time.  Measurements 
earlier and closer to  the engine find more and smaller  particles than measurements further  away. A 
problem that led to a distance specification in the official PMP method. In plume chasing the distance is 
substantially larger and may lead to an increased particle size, possibly affected by volatiles.  

No definitive threshold for high emitter detection is determined from this analysis. From the observed 
measurement results, direct correlation to the legislative PN limit is not representative. This is partially 
explained from the large particle sensitivity explained above, however, PN emission factor results are 
also  heavily  influenced  by  background  determination  and  possibly  weather  effects.  To  enable 
classification, proper PN bandwidths for high emitters should be determined from comparison between 
TEN AEM data and PEMS PN data in future work. 

49 

  
 
 
 
 
Acknowledgements  

This work was supported by the EU H2020 CARES Project No. 814966 and TNO research funds. 

Joint TAP and S&E Conference 2023 

References 

Borken-Kleefeld, J., Dallmann, T. (2018). Remote Sensing of Motor Vehicle Exhaust Emissions. White Paper. 

Hansen, A. D. and Rosen, H. Individual measurements of the emission factor of aerosol black carbon in automobile 
plumes, Journal of the Air and Waste Management Association, 40, 1654 1657. 

Järvinen,  A.,  Timonen,  H.,  Karjalainen, P., Bloss,  M.,  Simonen,  P.,  Saarikoski,  A.,  Kuuluvainen,  H.,  Kalliokoski,  J.,  Dal 
Maso, Miikka., Niemi, J.V., Keskinen, J., Rönkkö, T. (2019). Particle emissions of Euro VI, EEV and retrofitted EEV city 
buses in real traffic. Environmental Pollution, 250, 708-716. 

-road black carbon and 
particle  number  emission  factors  and  comparison  between  mobile  and  stationary  measurements,  Atmospheric 
Measurement Techniques, 14. 

Knoll, M., Lang, B., & Bergmann, A. (2021). Performance of Black Carbon Instruments for Extractive Remote Emission 
Sensing, AAAR 39th Annual Conference. 

Leinonen, V., Olin, M., Martikainen, S., Karjalainen, P., Mikkonen, S. (2023). Challenges and solutions in determining 
dilution ratios and emission factors from chase measurements of passenger vehicles, Athmospeheric Measurement 
Techniques, 26 

Pöhler,  D.  (2021).  Heavy  Duty  Vehicle  (HDV)  NOx  emission  measurement  with  mobile  remote  sensing  (Plume 
Chasing) and subsequent inspection of high emitters, Ribe: Danish Road traffic Authority 

50 

  
 
 
 
 
 
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Effects of Geofencing on Exhaust Emissions and Noise: A combined Test 
Track and Traffic Simulation Study 

Nina Svensson1*, Anders Genell1, Mats Gustafsson1, Johan Olstam1, Rihanna Gebrehiwot1, Kinjal 
Bhattacharyya1, Jonas Sjöblom2 

1VTI (Swedish National Road and Transport Research Institute), 583 30, Linköping, Sweden,  

2 Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96, Gothenburg, 
Sweden 

*corresponding author: Nina Svensson, nina.svensson@vti.se 

Introduction 

Speed-limiting geofencing has been suggested as a mean to improve both traffic safety and to reduce 
emissions from road transport. However, there is limited knowledge of the effect of geo-fencing on air 
quality and noise. In this project we have performed a combined measurement and modelling study to 
evaluate the effects of geofencing on traffic performance, traffic safety (described elsewhere), exhaust 
and noise emissions. 

Method  

The study consists of a combination of measurements on a single car and modelling of a typical car 
fleet for the same case. The case is based on trajectory measurements using video from a real urban 
street with speed limit of 50 km/h with a ~200 meter long local speed limit section of 30 km/h. From 
the video measurements, typical driving behaviours were extracted corresponding to a median driver and 
an aggressive driver. In addition, a geofenced driving style was defined, were the driver assist system 
was assumed to keep all the speed limits and ensure a moderate acceleration. 

Measurements of exhaust emissions (CO, CO2, NOx, PN) using a PEMS (Portable Emissions Measurement 
System, Figure 1) from AVL (AVL 492 Gas PEMS iS, AVL 496 PN PEMS and AVL 495 EFM) and noise 
emissions using stationary microphones were performed using a diesel-powered passenger car (Volvo 
V60, Figure 1) on a test track, which was set up to resemble the real site. The microphones were placed 
on the 50 km/h section, 100 m before the 30 km/h traffic sign, and 80 m into the 30 km/h section (see 
Figure 2). The microphones were placed 7.5 m from the centre of the driving lane at a height of 1.2 m 
above the road surface. The test driver conducted 10 18 repetitions for each driving style. 

Figure 1. PEMS system and test car. Photo: Mats Gustafsson, VTI 

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Joint TAP and S&E Conference 2023 

A  microscopic  traffic  simulation  model  (using  PTV  Vissim)  was  developed  for  the  road  stretch  in  the 
scenario. The simulated road stretch consisted of a two-lane arterial with three segments with speed 
limits of 50 km/h, 30 km/h, and 50 km/h, respectively. Calibration of the model was performed using the 
trajectory data from the video-based traffic measurements. Two scenarios were explored: one with and 
one without the presence of a signalized pedestrian crosswalk. Apart from this, the experiment design 
considered different acceleration and deceleration behavior of non-geofenced vehicles in response to 
the speed limit sign, different total demand levels (number of vehicles in one direction), and different 
penetration  rates  (0-100  %)  of  geofenced  vehicles.  Each  combination  was  replicated  10  times  in 
simulations with different random seeds to account for stochasticity. The tailpipe emissions (CO, CO2, 
NOx,  HC  and  PM)  were  estimated  by  feeding  the  trajectories  from  the  traffic  simulation  into  the 
microscopic emission model PHEM (Hausberger et al. 2019) using a mix of emission legislative classes 
representative  for  Sweden,  extracted  from  HBEFA  (Hausberger  et  al.  2009).  In  order  to  avoid 
overestimation  of  the  tailpipe  emissions  due  to  unrealistically  quick  changes  in  acceleration  in  the 
simulated trajectories, the trajectories were first treated with the smoothing function T4235H (Velleman, 
1980) which have been applied in the work with development of the new WLTP driving cycles (UNECE, 
2009). The average emissions per vehicle were measured in terms of g/h and g/km. 

Results 

Emission measurements 

Figure 2 shows the vehicle speed and emissions of CO2, NOx and PN along the route for the three driving 
styles. The left column shows the instantaneous emissions along the route and the right column shows 
the total emissions in the different sections of the route and the whole route. The aggressive and median 
driver both drove faster than the speed limit and accelerated more often compared to the geofenced 
driver. NOx and PN emissions were clearly lowest for the geofenced driver. The median value was reduced 
by 44 % and 59 % respectively compared to the median driver and 62 % and 68 % compared to the 
aggressive driver. A large emission reduction of NOx and PN was seen in section 2 and 3 of the route, 
where  only  the  median  and  aggressive  drivers  were  accelerating,  showing  the  benefit  of  keeping  a 
constant speed to reduce emissions. For CO2, there was a reduction compared to the aggressive driver 
of 16 %, while no reduction compared to the median driver. 

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Joint TAP and S&E Conference 2023 

Figure  2:  Speed  and  emissions  from  PEMS  measurements  along  the  route.  The  location  of  the 
microphones for noise measurements are shown in the speed figure. 

Noise measurements 

The  noise  measurements  show  a  strong  potential  for  reducing  noise  emission  because  of  the  lower 
vehicle speed. Figure 3 shows momentary (timestep 125 ms) a-weighted sound pressure level (SPL) for 
the two microphone positions along the road sections (marked in Figure 2). The black curve represents 
the  50  km/h  section  and  the  red  curve  represents  the  30  km/h  section.  The  LAFmax  (maximum  a-
weighted sound level) values represents the highest momentary level for each microphone and the area 
under the curve above the background level, which is about 40 dB(A), represents the sound energy level 
LAE. When considering LAFmax, the difference between geofenced vehicles and non-geofenced vehicles 
is 5.6 10.6 dB(A) depending on the driving style and speed limit. As can be seen in the left half of Figure 
4, LAFmax for the median driver is slightly lower than for the aggressive driver, and the level difference 
between the 30 km/h and 50 km/h sections is slightly larger than the corresponding level difference for 
the  extreme  driver.  The  geofenced  driver  scenario  shows  significantly  lower  maximum  level  for  both 
sections and also a significantly increased level reduction for the 30 km/h section compared to the 50 
km/h section. The difference in sound energy level (LAE) between geofenced and non-geofenced vehicles 

53 

  
 
 
 
 
Joint TAP and S&E Conference 2023 

is correspondingly 4.2 7.9 dB(A) depending on driving style and speed limit. The right half of Figure 4 
shows how the different driver categories affect LAE for the two road sections. For the extreme driver 
there is little difference in sound energy level between the 30 km/h section and the 50 km/h section. For 
the median driver the difference between 30 km/h and 50 km/h sections is also small but clearly present. 
The geofenced driver scenario shows both significantly reduced overall sound energy level, and also that 
physically limiting the vehicle speed to the current speed limit greatly increases the sound energy level 
reduction for low-speed road sections. 

Figure 3: Momentary a-weighted sound pressure level vs time. Red curve represents the 30 km/h section 
and the black curve represents the 50 km/h section. 

Figure  4:  Maximum  sound  level  and  total  sound  energy  level  as  a  function  of  vehicle  speed  for  the 
different driving styles. Red markers represent the 30 km/h section and black markers represents the 50 
km/h section. 

When studying the effect of vehicle acceleration (Figure 5), no effect could be identified for any driver 
or any scenario, indicating that the engine noise does not contribute much to the overall noise level in 
the current study. One reason might be that the a-weighting underestimates the low frequency sound 
from the diesel engine. Another reason may be that the mounted PEMS equipment reduced the exhaust 
noise significantly during measurements. 

54 

  
 
 
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

Figure 5: Maximum sound level and total sound energy level as a function of vehicle acceleration for the 
different driving styles. Red markers represent the 30 km/h section and black markers represents the 50 
km/h section. 

Traffic and emission simulation  

The results  from  the  combined  traffic  and  emission simulations  show  a  clear  lowering  effect  on  the 
cumulative  speed  distribution,  mean  speed  and  85-percentile  speed  when  introducing  geofenced 
vehicles. As expected, a higher penetration rate of geofenced vehicles is required at lower traffic flows 
to reach a substantial effect on the total traffic. On the other hand, high penetration rates (70 80 %) 
are required to get the 85-percentile close to the speed limit. The analysis also revealed a clear reduction 
in tailpipe emissions per unit time (g/h), which improved with increasing penetration levels. However, an 
increase in emissions per unit distance (g/km) was observed when no signalized pedestrian crosswalk is 
present,  while  there  was  no  change  in  the  presence  of  the  signalized  crosswalk.  CO2  emission 
performances for the scenario without a signalized pedestrian crosswalk and with different penetration 
rates of geofenced vehicles are illustrated in Figure 6. The results for NOx, PM and PN follow a similar 
trend as the results for CO2.The reason for the increased emissions per unit distance  may be attributed 
to a combination of two aspects: one, since the geofenced vehicles were more compliant to the speed 
limits, they accelerated from 30 km/h to 50 km/h once the speed restrictions were relaxed while the non-
geofenced vehicles, being less compliant to the 30 km/h rule, accelerated (in average) only from 40 km/h 
eo-fenced vehicles need longer travel time for each km, i.e. gains in 
emissions per time unit were lost due to longer travel time. On the other hand, introducing a signalised 
pedestrian crossing forces the vehicles to decelerate or accelerate depending on the traffic light. Thus, 
the acceleration behaviour of geo-fenced and non-geo-fenced vehicles become similar which has led to 
similar level of emission per unit distance regardless of the penetration rate of geo-fenced vehicles.  

Figure 6: Average CO2 emissions (g/h and g/km) per vehicle along the route with confidence interval as 
a function of penetration rates of geofenced vehicles (without signalized pedestrian crossing) 

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Joint TAP and S&E Conference 2023 

Discussion and conclusions 

The emission and noise measurements both show a clear reduction for the geofenced driver compared 
to  the  other  driving  styles.  The  emission  reduction  is  mainly  due  to  decreased  acceleration  of  the 
geofenced vehicles, while the noise reduction is mainly due to the decreased speed of the geofenced 
vehicles. Noise mapping is often used as a tool for investigating road traffic noise exposure, and mostly 
the noise emission is calculated based on posted speed limit. This study indicates that to evaluate effects 
on noise from countermeasures such as geofencing, the vehicle speed distribution should be taken into 
account. 

The simulation  results  show  a  clear  improvement  in  speed  compliance  with  increased  penetration  of 
geo-fenced vehicles but there is no decrease in total emissions along the route. The main reason for this 
may be attributed to the perfect speed compliances of geofenced vehicles, in comparison to reduced 
speed  compliances  of  non-geofenced  vehicles,  resulting  in  increased  acceleration  duration  for  the 
geofenced  vehicles  at  speed  transition  zones.  In  contrast,  the  emission  measurements  from  the  test 
track study show high emissions even during the small speed changes of the aggressive and median 
driver, resulting in higher total emissions.  

While,  the  field  measurements  only  covered  one  specific  car,  the  simulations  covered  a  fleet 
representative for Sweden, which may account for the differences. The driving behaviour of each vehicle 
in the simulations was also stochastic in nature and not exactly the same as the three specific driving 
styles defined for the test track study. Thus, the present study shows that it can be important to use 
both  field  measurements  and  simulations  to  comprehensively  assess  the  traffic  and  environmental 
impacts of a new strategy, such as speed geofencing in this case. 

Acknowledgements  

This work was funded by The Swedish Transport Administration (TRV dnr 2021/52944). The authors are 
grateful to Robert Buadu at Chalmers University of Technology who managed the PEMS measurements 
and to Henrik Biswanger at ASTA ZERO proving ground for valuable assistance during the track tests. 

References 

Hausberger, S., Rexeis, M., Dippold, M. (2019). PHEM User guide for Version 13. TU Graz. 

Hausberger, S.,  Rexeis, M.,  Zallinger, M., & Luz, R. (2009).  Emission Factors from the Model PHEM for the  HBEFA 
Version 
thermodynamics. 
and 
https://www.hbefa.net/e/documents/HBEFA_31_Docu_hot_emissionfactors_PC_LCV_HDV.pdf 

combustion 

institute 

engines 

Graz 

for 

TU 

3. 

PTV, PTV VISSIM 2022 user manual. PTV AG: Karlsruhe, Germany, 2022. 

UNECE.  (2009).  Experience  of  the  Development  of  a  Test  Cycle  -  Data  Collection,  Processing  and  Assessment. 
URL: 
Working 
https://unece.org/DAM/trans/doc/2009/wp29grpe/WLTP-02-12e.pdf 

WLTP-02-12. 

Informal 

WLTP. 

Group 

paper 

GRPE 

No. 

Velleman, P. F. (1980). Definition and comparison of  robust nonlinear data smoothing algorithms. Journal of the 
American Statistical Association, 75(371), 609-615. 

World  Health  Organization.  (WHO,  2018).  Environmental  noise  guidelines  for  the  European  region.  World  Health 
Organization. Regional Office for Europe. 

56 

  
 
 
 
 
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

Feasibility study for future on-board NOx monitoring of passenger cars 

C. Matzer1, S. Hausberger1, S. Lipp1, U. Ellmers2, J. Blassnegger3, A. Prosenc4 

1 Forschungsgesellschaft für Verbrennungskraftmaschinen und Thermodynamik (FVT), Graz, 8010, Austria 
2 Federal Highway Research Institute (BASt), Bergisch Gladbach, 51427, Germany 
3 Technisches Büro Blassnegger (TBB), Wolfsberg, 9400, Austria 
4 Austrian Automobile, Motorcycle and Touring Club (ÖAMTC), Vienna, 1030, Austria 
Presenting author email: matzer@ivt.tugraz.at 

Introduction 

Due to the high efficiency of modern emission control systems, the identification of malfunctions and 
tampering  is  gaining  high  importance.  On-board  emission  monitoring  can  support  this  target  and  is 
planned  to  be  introduced  with  Euro  7.  On-board  fuel  consumption  monitoring  (OBFCM)  is  already 
mandatory for all new passenger cars from 2021. FVT, TBB und ÖAMTC have conducted a feasibility 
study on how NOx emissions of passenger cars can be monitored in real vehicle operation in future. The 
-board diagnostics (OBD): Analysis of the OBD with regard to emission data available in future 

for  the  Periodic  Technical  Inspection  (PTI)
contract.  In  this  feasibility  study,  a  method  was  developed  to  monitor  NOx  emissions  in  real-world 
operation  in  the  future.  For  the  method  development,  a  total  of  around  4,500  vehicle  kilometres  of 
measurement  data  were  collected  with  different  driving  situations,  which  were  recorded  with  a  data 
logger. The tests were carried out both on the chassis dynamometer and on the road. Tests with specially 
generated  faulty  exhaust  emission  control  system  were  also  recorded.  The  measurement  campaign, 
method development and application of the proposed method are described in the following chapters. 

The Measurement Campaign 

The measurement campaign formed the basis for the development of the method. The tests were carried 
out with a modern Euro 6d diesel passenger car. The exhaust gas aftertreatment system of the test 
vehicle consists of exhaust gas recirculation, an oxidation catalytic converter, a particulate filter (DPF) 
and two SCR catalytic converters including ammonium slip catalyst (Figure  1). Three NOx sensors are 
also installed in the test vehicle as standard. One NOx sensor is installed at the end of the exhaust line 
and can therefore detect whether the NOx exhaust gas aftertreatment system is working properly.  

Measurements were carried out with the test vehicle on the chassis dynamometer and on the road. A 

Figure 2: Schematic illustration of the exhaust gas aftertreatment system of the test vehicle 

total of around 4,500 vehicle kilometres of measurement data were collected and recorded using a data 
logger.  For  the  most  part,  the  data  includes  conventional  driving  on  the  road  (approx.  3,500  vehicle 
kilometres). However, it also includes measurement data in which the exhaust emission control system 
was modified (a total of approx. 450 vehicle kilometres) in order to be able to test the method for future 
NOx monitoring. For this purpose, the following modifications were made to simulate a defect exhaust 
emission control system: 

57 

  
 
 
 
 
  AdBlue feed lines to the dosing valves disconnected and AdBlue fed into collection tank 

Joint TAP and S&E Conference 2023 

  AdBlue tank emptied 

  AdBlue tank filled with distilled water 

 

Exhaust gas recirculation valve unplugged 

Measurement data was also collected for driving situations that are challenging for the exhaust emission 
control system (approx. 250 vehicle kilometres). Driving uphill and downhill with and without load as well 
as long traffic jams were classified as challenging driving situations. Figure 2 shows the NOx emissions 
of  the  NOx  sensor  installed  by  the  vehicle  manufacturer  (OEM  NOx  sensor)  end-of-tailpipe  of  the 
individual tests, which are sorted in ascending order in the figure. The NOx measurement data for the 
110 tests range from 1 mg/km to 150 mg/km. The data also includes tests where periodic particulate 
filter regenerations happened, which can lead to higher NOx emissions. Changes to the exhaust emission 
control  system  and  challenging  driving  situations  also  lead  to  higher  NOx  emissions.  The  tests  were 
carried out between -15 °C and 35 °C ambient temperature. The average speed of the tests was 58 km/h, 
the maximum speed 170 km/h. 

Figure 3: NOx measurement data of the test vehicle of the individual tests arranged in 

ascending order (measurement data from each point in time from which the OEM NOx 
sensor was active) 

The  OEM  NOx  sensor  data  was  also  compared  to  the  chassis  dynamometer  measurement  data.  The 
comparison showed that the NOx sensor data matched that of the chassis dynamometer, taking into 
account sensor tolerances in the range of +/- 10 to 12 ppm for NOx < 100 ppm. 

Method development for future NOx monitoring 

All measurement data were used for development of the method. The following boundary conditions 
were taken into account when developing the method for future NOx monitoring in real vehicle operation: 

 

 

The method should be able to detect faults in the NOx exhaust emission control system. For this 
purpose,  signals  can  be  used  which  are  already  available  in  modern  passenger  cars  with 
sufficient accuracy via the OBD interface of the vehicle. 

The application of the method should be possible for diesel as well as gasoline passenger cars. 
In the course of this project, the focus was on diesel passenger cars. 

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Joint TAP and S&E Conference 2023 

 

It should be taken into account that higher NOx emissions in certain driving situations, which are 
challenging for the exhaust emission control system, shall not be identified as a fault, provided 
that the exhaust emission control system is working properly. 

When developing the method, a reference of the NOx emissions to the kilometres driven (g/km) did not 
appear to be useful, since driving situations (e.g. with very high engine power) can lead to higher NOx 
emissions in g/km, even if there is no fault in the exhaust emission control system (Notter et al. 2021). 
A reference of NOx emissions to engine power therefore seems to be more appropriate. In many cases 
engine power is not available as a standard signal or is not available with sufficient accuracy via the 
OBD interface. However, since engine power is directly related to fuel mass flow (the relationship is also 
known as the Willans line), fuel consumption can also be used as a reference. The NOx sensor and fuel 
consumption  signal  could  be  retrieved  from  the  test  vehicle  via  the  OBD  interface  with  appropriate 
accuracy.  It  is  assumed  that  these  signals  are  also  available  in  modern  passenger  cars  from  other 
manufacturers. In future emission regulations,  access to  the relevant OBD signals  can be  defined as 
mandatory  and  checks  for  function  and  accuracy  in  the  course  of  vehicle  tests  can  be  introduced  to 
ensure  the  validity  of  these  OBD  signals.  The  following  method  proposal  was  derived  from  these 
considerations: 

  Assessment of NOx exhaust emission control system by the ratio gNOx/kgFuel. If the calculated 
ratio is above a threshold, there is a high probability for a fault in the NOx exhaust emission 
control system (Figure 3). The gNOx/kgFuel ratio, if above the threshold, could lead to an "occasion-
based" PTI or be  indicated as  a  fault  with the  Malfunction Indicator  Light  (MIL)  in the driver 
information display or be read out during the regular PTI. It would then make sense for the OBD 
system to also be able to define a source of error to enable efficient repairs. 

Figure 4: Schematic illustration of the proposal 

Application of the proposed method 

The method was applied to the measured data. In a first step, the ratio gNOx/kgFuel was calculated for each 
trip from which OEM NOx sensor data were available at end-of-tailpipe. Figure 4 shows the result for the 
individual  tests  with  the  test  vehicle  plotted  over  the  fuel  consumption  in  l/100 km.  Each  data  point 
represents the average value of a trip, calculated from the time when the OEM NOx sensor was active. 
Data points outlined in red are those tests in which the exhaust emission control system was modified. 
Data points that are outlined in black are those tests in which DPF regeneration took place during the 
test.  Tests  with  driving  situations  that  are  challenging  for  the  exhaust  emission  control  system  are 
outlined in yellow. It can be clearly seen that tests with a faulty exhaust emission control system have 
a higher gNOx/kgFuel ratio than normal tests without DPF regeneration. It can also be seen that tests with 
DPF  regeneration  and  tests  with  challenging  driving  situations  can  have  higher  gNOx/kgFuel  ratios.  The 
challenging driving situations included several uphill and downhill trips. During the uphill runs, high NOx 
emissions but also high fuel consumption were present, which is why the gNOx/kgFuel ratio can again be 
classified as non-critical. During downhill runs, fuel consumption can be very low due to the longer drag 
phases, so that even with low NOx emissions per kilometre, the gNOx/kgFuel ratio can be in the range of a 
faulty exhaust emission control system (e.g. the three data points outlined in yellow shown in the left 
area in Figure 4). 

59 

  
 
 
 
 
Joint TAP and S&E Conference 2023 

Figure 5: Application of the proposed method to the measurement data of the test vehicle 

Applying the method to longer distances can compensate for possible uncertainties that may lead to 
higher gNOx/kgFuel ratios for short tests, but where there is no fault in the exhaust emission control system. 
The investigations were carried out for 100 km sections. For this purpose, the measurement data of the 
test  vehicle,  which  were  recorded  from  the  OEM  NOx  sensor  on  the  road,  were  strung  together  and 
evaluations were performed over 100 km sections. Figure 5 shows the calculated ratios plotted against 
l/100 km fuel consumption. Each data point represents the ratio over a 100 km section. In the course of 
the evaluation, it was found that tests with DPF regeneration phases can still lie in the areas of trips 
with faulty exhaust emission control system. Therefore, route sections with DPF regeneration phases 
were filtered out before calculating the gNOx/kgFuel ratios on 100 km sections. It can be clearly seen that 
the tests made with properly working exhaust emission control system and without DPF regeneration 
phases are below a gNOx/kgFuel ratio of 1. This also includes tests with challenging driving situations, such 
as  uphill  and downhill  driving. In contrast  to  Figure  4,  the challenging driving situations do not show 
conspicuous emission levels, since the downhill tests compensate with other tests over 100 km sections. 
However, should a downhill drive over 100 km take place, the gNOx/kgFuel ratio could lie in a range in which 
values from faulty exhaust emission control system also lie. To prevent this, a filter excluding long phases 
with low fuel flow was also developed as part of the project. In summary, it can be recommended that 
the gNOx/kgFuel ratios should not be considered on a cycle-specific basis, but rather over sections in the 
range of 100 km or longer. 

Based on the measurement data of the test vehicle, a possible threshold value was also derived and 
plotted in Figure 5. Below this threshold value, a properly working exhaust emission control system can 
be  assumed,  at  least  for  the  test  vehicle  analysed  in  this  study.  If  the  gNOx/kgFuel  ratio  is  above  the 
threshold value, a fault in the exhaust emission control system is very likely. This can also be confirmed 
with the 3-times standard-deviation from the test data using a log-normal distribution. If phases with 
DPF regeneration are not taken into account and evaluations are performed over 100 km sections as 
proposed, the measured values of tests with properly working exhaust emission control system are 99.7 
% below 1.3 gNOx/kgFuel (compared to the individual tests with properly working exhaust emission control 
system, 99.7 % are below 4.5 gNOx/kgFuel). Therefore, a gNOx/kgFuel ratio of 2 was derived as the threshold 
value  in  order to  have also an additional  margin against  false detections. The data point  at  which a 
relevant defect first occurred during the 100 km interval would not yet be detected with a gNOx/kgFuel ratio 
of just under 2, but if the error persisted, the error would become visible in the next 100 km interval. 
One may also apply 100 km moving average windows for monitoring for an earlier detection. With a 
faulty exhaust emission control system over 100 km, the gNOx/kgFuel ratio for the test vehicle is around 3, 
meaning that clear identification is possible. The extent to which this threshold value is also valid for 
other vehicle models would still have to be validated with measurement data from other vehicle models.  

60 

  
 
 
 
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Figure 6: Possible threshold for distinguishing a faulty and properly working exhaust emission 

control system for Euro 6d vehicle emission levels 

In the course of drafting the Euro 7 implementing acts, discussions are currently ongoing about whether 
vehicles need to comply NOx limits at operating points under most driving situations in the planned Euro 
7 legislation. Such a proposal was published by the EU Commission during the work on this project (EU 
COMMISSION 2022). This means that NOx emissions would have to be below the limit values at almost 
all driving conditions, which is why NOx emissions in real-world operation could also be monitored via 
g/km. Just a filter for the valid temperature and altitude ranges and possible additional boundaries for 
valid Euro 7 Real Driving Emissions (RDE) tests would be needed3. The NOx emission limit in g/km with a 
safety  margin  for  NOx  sensor  uncertainty  could  then  be  used  as  the  threshold  value.  However,  the 
application  of  the  developed  method  with  gNOx/kgFuel  would  of  course  still  be  possible.  It  is  therefore 
recommended to validate the proposed method with gNOx/kgFuel after the relevant boundary conditions of 
the Euro 7 legislation are decided and to optimize the best fitting method if necessary. 

3 With more than 40 parameters the boundary conditions for valid Euro 6 RDE tests are rather too complicated and too selective 

to be applied in the Engine Control Units (ECUs) as filters. 

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Joint TAP and S&E Conference 2023 

Conclusion 

In developing the method for future NOx monitoring in real vehicle operation, consideration was given to 
the fact that higher NOx emissions in certain driving situations, which are challenging for the exhaust 
emission control system, shall not be identified as a fault, provided that the exhaust emission control 
system is working properly. The developed method assesses the functionality of NOx emission control 
systems by the ratio gNOx/kgFuel. The NOx sensor and fuel consumption signals could be retrieved from the 
OBD interface on the test vehicle with appropriate accuracy. It is assumed that these signals are also 
available  in  modern  passenger  cars  from  other  manufacturers.  If  the  determined  ratio  is  above  a 
threshold  value,  there  is  a  high  probability  that  there  is  a  fault  in  the  NOx  exhaust  emission  control 
system. If the ratio is below the threshold value, it can be assumed that there is no fault. The gNOx/kgFuel 
ratio, if calculated above the threshold, could lead to an "occasion-based" PTI or be indicated as a fault 
with the MIL in the driver information display or be read out during the regular PTI. The method was 
tested using measurement data from 4,500 vehicle kilometers of a modern Euro 6d diesel passenger 
car.  Tests  with  faulty  exhaust  emission  control  system  could  be  clearly  identified.  Longer  evaluation 
distances, e.g. 100 km sections, lead to a better differentiation of tests with and without malfunctions 
compared to shorter evaluation distances. 

In course of drafting Euro 7 implementing acts, it is currently being discussed whether vehicles will have 
to comply with NOx limits at operating points under almost any driving situations in the planned Euro 7 
legislation. Monitoring in real operation could then also be carried out via g/km as alternative. However, 
the  application  of  the  developed  method  with  gNOx/kgFuel  would  still  be  possible.  It  is  therefore 
recommended  to  validate  and  optimize  the  most  suitable  method  with  gNOx/kgFuel  after  the  boundary 
condition of the Euro 7 RDE test conditions are defined. 

Acknowledgements  

The study presented in this paper was designed and funded by BASt as part of the research contract 
-board diagnostics (OBD): Analysis of the OBD with regard to emission data available in future for 

the project and for the opportunity to make the results of the study available for this paper. 

References 

EU COMMISSION (2022). Proposal for a Regulation of the European Parliament and the Council on type approval of 
motor vehicles and engines and of systems, components and separate technical units for such vehicles with respect 
to their emissions and battery durability (Euro 7), COM (2022) 586final; Brussels, 10.11.2022. 

Notter B., Cox B., Hausberger S., Matzer C., Weller K., Dippold M., Politschnig N., Lipp S., et al.  (2021). HBEFA 4.2 - 
Documentation of updates, I-36/21/Wel EM Inst-21/04/20-670. 

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Simultaneous In-cabin and On-road CO2 Concentrations On-board 
Measurements 

A. Mehel 1* and N. Hafs 1  

1 
* amine.mehel@estaca.fr 

 Paris-Saclay, F-78180 Montigny-le-Bretonneux, France  

Introduction  

Vehicle in-cabin could be subject of high pollution depending on external parameters such as road type, 
traffic condition (Hudda et al., 2012), the weather or even the distance with upstream vehicles (Mehel et 
al., 2023). But also on vehicle internal parameters such as its speed, age or ventilation settings (Knibbs 
et  al.,  2010;  Hudda  et  al., 2012).  The  pollutants  infiltrate  vehicle  in-cabin  through the  air  intake  and 
hence the Heating, ventilation and Air Conditioning (HVAC) system, but also from different leakages (Lee 
at al., 2015). Under certain situations, gaseous and particulates concentrations are higher in the car cabin 
in comparison to the on-road concentrations (Mehel et al., 2023).  

In this situation, the passengers could be exposed to high in-cabin concentrations which could induce 
health risks. Indeed, several toxicological and epidemiological studies have associated the exposure to 
high levels of such toxic pollutants to the enhancement of respiratory inflammation, allergy and Asthma 
(Li  et  al.,  2003)  and  numerous  long-term  health  problems  including  lung  cancer  and  cardiovascular 
diseases (Delfino et al., 2005; Silverman et al., 2012) and could even alter the placental function with a 
possible intergenerational effect (Valentino et al., 2016)  

The most used solutions to reduce the in-
as the active carbon and/or High Efficiency Particulate Air (HEPA) filters (Xu et al., 2010). On the other 
hand, the use of the recirculated air ventilation mode (RC) allows reducing the infiltration of on-road 
pollutants into car-cabin however it induces CO2 concentration accumulation inside the car cabin (Grady 
et al., 2013, Mehel et al., 2023).  

This accumulation of C02 in the car cabin under RC ventilation is the result of passengers exhalation. 
When the ventilation is switched to outdoor air mode (OA), on-road CO2 could infiltrate the car-cabin. We 
need to assess then what is its contribution to the global in-cabin CO2, that is the objective of the present 
study.  

In the present study, we conducted simultaneous in-cabin and on-road CO2 concentration measurements 
during mobile on-board campaign around and in Paris. 

2. Methodology 

2.1 Car model and instrumentation  

A  diesel-powered  SUV-type  vehicle  model  with  28000  km  mileage  was  used  for  the  on-board 
measurement campaign. The HVAC system is equipped with new OEM filters including a High Efficiency 
filter and an activated carbon filter of 0 km. 

Concerning the instrumentation, we used two TSI IAQ-Calc 7525 allowing for the measurement of CO2 
concentration,  temperature,  and  Relative  Humidity.  The  CO2  concentration  range  allowed  was  0-
5000ppm, while the resolution was 1ppm with an accuracy of 3% of reading. The internal measurements 
probe was positioned between the front headrests (Figure 1a), while the external probe was positioned 

Figure 1b). 

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Joint TAP and S&E Conference 2023 

In-cabin probe  

On-road probe  

Figure 1: CO2 concentration measurements probe positioning in (a) In-cabin and (b) On-road.  

64 

  
 
 
 
 
 
Joint TAP and S&E Conference 2023 

2.2 Ventilation settings  

In the present study we wanted to investigate the on-road contribution of CO2 to the in-cabin global CO2 
concentration. Hence, the ventilation mode was set to outdoor air (OA) for all the tests. The temperature 
was set to 22° and the air-conditioning was off. The fan speed was set to medium (4/8) or high (8/8). 

2.3 Route type and test conditions 

The route that was mainly used was of RDE-type with urban and freeways parts of the road. It included 
tunnels and ring road for a total length of 27.5 km. The test consisted of a round-trip with a stop in the 
centre of Paris with a complete aeration of the in-cabin at the arrival place for around 10 min. Average 
duration of the global journey was about150 min for 55 km distance.    

Figure 2: the used route for simultaneous in-cabin and on-road CO2 concentrations, measurements. 

During the test, only the driver is in the vehicle for the entire test duration. 

3. Results and discussion 

First, we conducted one-off test that showed the fast increase of the in-cabin CO2 concentration when 
using the recirculated air (RC) ventilation mode (Figure 3). Indeed, when switching on the RC ventilation 
mode, we could notice in-cabin CO2 concentration accumulation due to the driver exhalation. Hence, in 
RC ventilation mode, the contribution of on-road CO2 to the in-cabin CO2 is negligible. 

Next  tests  were  conducted  both  in  static  and  driving  modes.  To  compare  the  on-road  and  in-cabin 
concentrations, we calculated the ratio of the averaged In-cabin to On-road (I/O) concentrations: 

𝑅𝐼 𝑂 ⁄   =

𝐶𝑖𝑛
𝐶𝑜𝑢𝑡

    (1) 

Static measurements were achieved without passengers under OA ventilation and medium fan speed 
(4/8) at  Estaca  outdoor  parking next an intersection. The  results are presented in Figure  4. We could 
notice that both curves of the time evolution of the CO2 concentration on-road and in the car-cabin show 
similar trends with less intensity of the in-cabin concentration peaks. This similar trend is not surprising 
since there is no passenger in the car cabin, the on-road CO2 infiltrates the cabin and constitutes the 
main source. But it reveals that the active carbon has a negligible effect on CO2 filtration.  

The  average  in-cabin  and  on-road  concentrations  and  the  ratio  of  the  average  in-cabin  to  on-road 
concentrations are given in Table 1. The ratio is approximately equal to one which means that there is 
no buffering effect of the car cabin (ratio not larger than one). And inversely the on-road CO2 infiltrates 
entirely the car cabin.(the ratio not smaller than one). 

65 

  
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

Figure 3 : In-cabin vs on-road CO2 concentrations under RC ventilation mode with fan speed 4/8. 

Figure 4 Time evolution of in-cabin and on-road CO2 concentrations under OA ventilation with fan speed 
4/8 during static measurements. 

Table 1: In-cabin and on-road CO2 mean concentrations and the ratio of the average concentrations: 

𝐶𝑖𝑛 (ppm) 

𝐶𝑜𝑢𝑡 (ppm) 

𝑅𝐼 𝑂 ⁄   

534 

526 

1,01 

66 

010020030040050060070080090008:24:0008:31:1208:38:2408:45:3608:52:4809:00:0009:07:12CO2 (ppm)timeIn-cabinOn-road  
 
 
 
 
 
 
 
Joint TAP and S&E Conference 2023 

Figure 5 Time evolution of in-cabin and on-road CO2 concentrations under OA ventilation with fan speed 
4/8 outward and 8/8 during the return (Morning measurements). 

The results obtained during mobile measurements following the route presented in Figure 2 are displayed 
in Figure 5. The curves are partitioned into two parts, the first one corresponds to the outward, followed 
by the stop stage in Paris center with a complete aeration of the car cabin by opening the doors (between 
10H03 and 10H10). The final stage corresponds to the return trip. The gray boxes correspond to the road 

We could notice that in-cabin and on-road CO2 concentrations curves measured during mobile test have 
similar  trends  with  more  dynamics  for  the  on-road  curve.  As  for  static  measurements,  the  same 
conclusion could be drawn, that is, when the OA ventilation mode was selected and one passenger was 
onboard,  the  main  contributor  to  in-cabin  CO2  was  from  on-road  sources.  Indeed,  among  points  that 
illustrate this behavior are the peaks that appeared in ring road where important traffic were found. 
These peaks are enclosed in the yellow boxes, during the outward at 9H45, while it is found at 10H30 
during the return. These peaks were found simultaneously on-road and in the car cabin.  

of 

the 

ratio 

The 
concentrations  
to 
𝑅𝐼 𝑂 ⁄   were about 1.09 during the outward (OA, fan speed 4/8), while it was about 1.04 during the return 
stage (OA, fan speed 8/8). These ratios were obtained from numerous test repetitions. They show that, 
the  on-road  CO2  infiltrated  completely  the  in-cabin  and  depending  on  fan  speed,  the  passenger 
exhalation could contribute to the increase of the global CO2 concentration.  

average 

in-cabin 

road 

on 

Conclusion 

In  this  study  we  investigated  the  relevance  of  conducting  simultaneous  in-cabin  and  on-road  CO2 
concentration measurements during on-board tests. The results showed that the ratio is greater than 
one for all cases, i.e., OA and RC ventilation mode and for all-fan speed, even the higher speed where 

are not designed  to  filter  the CO2. This  investigation could be  used from a  practical point  of view  of 
conducting on-board mobile measurements.  Indeed,  the question that arose was  if it  is necessary to 
conduct simultaneous on-road to in-cabin concentration measurements. It seems that, even if the on-
road CO2 infiltrates entirely the car cabin, its contribution to the global in-cabin concentrations is of the 
level of background pollution. Even in road sections with important pollution concentration, such tunnels 
or ring roads, the CO2 concentration is below 1000 ppm which is the average recommended threshold 
for indoor air in numerous countries. Consequently, the contribution of on-road CO2 is limited. 

Acknowledgements  

We are grateful to the French Environmental and Energy Management Agency (ADEME) for providing 
financial support for the present research. 

67 

010020030040050060070080090009:07:1209:21:3609:36:0009:50:2410:04:4810:19:1210:33:3610:48:0011:02:24CO2 (ppm)timeIn-cabinOn-roadA13 tunnelParis Ringrd  
 
 
 
 
 
Joint TAP and S&E Conference 2023 

References 

Delfino, R.J., Malik, S., and Sioutas, C., 2005. Potential role of ultrafine particles in associations between airborne 
particle mass and cardiovascular health. Environmental Health Perspectives 113, 934-946. 

Grady, M., Jung, H., Kim, Y., Park, J. et al. (2013). SAE Technical Paper, 2013-01-1494. 

Hudda, N., Eckel, S. P., Knibbs, L. D., Sioutas, C., Delfino, R. J., and Fruin, S. A. (2012). Linking in-vehicle ultrafine particle 
exposures to on-road concentrations. Atmospheric Environment 59, pp. 578-586. 

Lee ES, Stenstrom MK and Zhu YF. (2015). Ultrafine particle infiltration into passenger vehicles, Part I: Experimental 
evidence. Transport Research D, 38, pp. 156 165. 

Li, N.; Hao, M.Q.; Phalen, R. F.; Hinds, W.C.; Nel, A. E. (2003). Particulate air pollutants and asthmas A paradigm for 
the role of oxidative stress in PM-induced adverse health effects. Clinical Immunology, 109, pp. 250-265. 

Mehel, A., Deville-Cavellin, L., Joly, F., Sioutas, C., Murzyn, F., Cuvelier, Ph and Baudic, A. (2023). Atmospheric Pollution 
Research, 14 (2), 101673. 

Silverman, D.T., Samanic, C.M., Lubin, J.H., Blair A.E., Stewart, P.A., Vermeulen, R., Coble J.B., Rothman, N., Schleiff P.L., 
Travis, W.D., Ziegler, R.G., Wacholder, S. and Attfield M.D (2012). The Diesel Exhaust in Miners study: a nested case-
control study of lung cancer and diesel exhaust. Journal of the National Cancer Institute;104 (11), pp. 55-68.  

Valentino, S.A., Tarrade, A., Aioun, J., Mourier, E., Richard, C., Dahirel, M., Rousseau-Ralliard, D., Fournier, N., Aubrière, 
M.-C., Lallemand, M.-S., Camous, S., Guinot, M., Charlier, M., Aujean, E., Al Adhami, H., Fokkens, P.H., Agier, L., Boere, 
J.A., Cassee, F.R., Slama, R., Chavatte-Palmer, P. (2016). Maternal exposure to diluted diesel engine exhaust alters 
placental function and induces intergenerational effects in rabbits. Particle Fibre Toxicology, 13 (39). 

Xu, B., Liu, S., Liu, J. and Zhu, Y. (2010). Effects of cabin filter on in-cabin to on-roadway ultrafine particle ratios. 
Aerosol Science and Technology, Aerosol Science and Technology, 45, pp. 215 224. 

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Joint TAP and S&E Conference 2023 

2.3  TAP.03.  In-service  conformity  and  new  concepts  for 

enhanced emission testing in PTI. 

69 

  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Solid particle number (SPN) measurements during the periodic technical 
inspection (PTI) of vehicles 

Joint TAP and S&E Conference 2023 

A. Melas1*, R. Suarez-Bertoa1 and B. Giechaskiel1*  

1 Joint Research Centre, European Commission, 21027, Ispra, Italy  

Anastasio.Melas@ec.europa.eu; Barouch.Giechaskiel@ec.europa.eu 

Introduction 

In the European Union (EU), after the introduction of a vehicle in the market, its roadworthiness is checked 
at  authorized  testing  centers  via  a  mandatory  periodic  technical  inspection  (PTI)  that  promotes  road 
safety and environmental protection (Directive 2014/45/EU). In respect to the environment, the proper 
operation of the engine and after-treatment devices are checked with simplified tests. PTI emission tests 
can identify high polluting vehicles and potentially improve air quality. Particulate emissions of Diesel 
vehicles are checked with an opacity test. 

Modern Diesel vehicles are equipped with particulate filters in order to comply with solid particle number 
(SPN)  limits  imposed  during  their  type-approval.  Several  studies  questioned  the  efficiency  of  opacity 
tests to identify malfunctioning or even manipulated Diesel particulate filters (DPFs) (Boveroux et al., 
2019;  Yamada,  2019).  Considering  that  a  small  fraction  of  vehicles  with  malfunctioning  or  severely 
damaged  DPFs  may  dominate  the  SPN  emissions  of  the  fleet  (Burtscher  et  al.,  2019),  there  is  an 
emerging  necessity  of  introducing  a  new  particle  test.  On  account  of  the  doubts  on  the  opacity 
measurement  efficiency,  several  European  member  states  apply  or  are  planning  to  introduce  a  new 
methodology in their PTI for Diesel vehicles; the measurement of SPN concentration of particles with 
diameter  approximately  larger  than  23  nm  particle  size,  at  low  idling  (herein  after  called  SPN-PTI) 
(Bainschab et al., 2020; Vasilatou et al., 2022). 

The Netherlands, Belgium, and Switzerland currently apply a SPN-PTI test at their PTI while Germany will 
soon  introduce  it.  National  regulations  differ  on  several  aspects  like  the  applicability  (homologation 
standard),  the  limit,  the  procedure,  and  the  metrological  requirements  of  the  respective  SPN 
measurement  instrumentation.  Table  1  presents  measurement  procedure  requirements  in  different 
regulations/recommendations.  

Table 1: SPN-PTI measurement procedures in different regulations/recommendations 

Country 

Engine 
conditions 

Test duration 
(s) 

Limit (#/cm3) 

Application 

Netherlands, Belgium 

Colda or hot 

15 

106 

Euro 5 and Euro 6b  

Germany 

Hot (Engine 
coolant >60 
oC) 

Switzerland 

Hot 

30 (x 3 
repetitions) 

5 (x 3 
repetitions) 

2.5 x 105 

Euro 6 

105 or 2.5 x 105 

DPF-equipped 

EU recommendation 

Colda or hot 
(Engine 
coolant >60 
oC) 

At least 15 s 
total duration 

2.5 x 105 

Euro 5b and Euro 6 

a 
homologated before Euro 5 can be tested 

b  Depending  on  the  region,  also  vehicles  equipped  with  DPF  and 

European  Commission  recently  published  a  recommendation  for  SPN  measurements  during  PTI  that 
harmonizes the different approaches proposing some minimum requirements that ensure the robustness 
of the methodology. Moreover, in the upcoming revision of the Directive 2014/45/EU, the SPN-PTI test 
will be considered. 

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Joint TAP and S&E Conference 2023 

In  this  work,  we  present  an  overview  of  the  main  experimental  results  obtained  by  JRC  at  different 
experimental campaigns performed from 2021 to 2023 with the participation of different companies 
producing SPN-PTI instruments. The experimental campaigns aimed to establish the correlation between 
low idling concentrations and type-approval emissions and define metrological-procedural requirements 
that guarantee accurate SPN measurements  (Melas et al., 2023, 2022, 2021; Vasilatou et al., 2023) 
considering the uncertainty of the methodology as well as the challenges that arise from the place of 
testing (garage). 

Experimental 

The experimental campaigns performed by JRC involved seven different instruments used for SPN-PTI 
tests. These instruments are typically composed of a sampling line, a pre-conditioning unit that removes 
volatiles and/or dilutes exhaust particles, and a particle detector. The entire instrument has a cut-off size 
at approximately 23 nm, similarly to particle number counters used for vehicle type-approval testing in 
EU (Giechaskiel et al., 2021). Table 2 presents the PTI instruments that were tested. The manufacturers 
that provided the PTI devices were in alphabetical order: CAPELEC (Montpelier, France) and PEGASOR 
(Tampere,  Finland),  DEKATI  (Kangasala,  Finland),  MAHLE  (Stuttgart,  Germany),  SENSORS  (Erkath, 
Germany), TEN (Baambrugge, The Netherlands), and TSI (Aachen, Germany). The SPN-PTI instrument #6 
was owned by JRC and it was the NPET of TSI.  

Table 2: Sampling and measurement technologies used by the PTI instruments. 

PTI23 
instrument 
code 

Sampling line  Dilution 

Volatile particle 
remover 

Particle detector 

#1 

#2 

#3 

#4 

#5 

#6 

#7 

Heated 

No 

Thermal denuder *   Diffusion charger 

Heated 

Heated 

Heated 

Not heated 

Not heated 

Heated 

Venturi 

Evaporation tube 

Diffusion charger 

No 

200:1 

20:1 

10:1 

10:1 

Evaporation tube 

Diffusion charger 

Evaporation tube 

Condensation Particle Counter 

Catalytic stripper 

Condensation Particle Counter 

Catalytic stripper 

Condensation Particle Counter 

Evaporation tube 

Condensation Particle Counter 

* in some tests catalytic stripper 

In  total,  11  vehicles  were  used  in  this  study;  9  Diesel-  and  2  gasoline-fueled.  For  all  of  them,  SPN 
emissions were measured at low idling while V1, V3, V7, V8, V9, V10 were also tested according to type-
approval driving cycles. Table 3 lists information on the vehicles. 

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Table 3: Main characteristics of tested vehicles. 

Code 

Euro 

V1 

V2 

V3 

V4 

V5 

V6 

V7 

V8 

V9 

6b 

6b 

4 

6d 

5b 

3 

6d 

6c 

6d-TEMP 

V10 

V11 

6d 

6d 

Fuel 

Diesel 

Gasoline PFI 

Diesel 

Diesel 

Gasoline DI 

Diesel 

Diesel 

Diesel 

Diesel 

Diesel 

Diesel 

Mileage 
(km) 

Engine displacement 
(cm3) 

Power 
(kW) 

Particulate 
filter 

23 800 

20 000 

211 000 

5 000 

158 800 

286 000 

26 700 

125 500 

11 600 

5 000 

4 100 

1560 

1400 

1997 

1968 

1197 

2933 

2933 

1968 

1997 

1950 

1999 

88 

70 

100 

110 

77 

150 

210 

110 

107 

143 

132 

Yes 

No 

Yes 

Yes* 

No 

No 

Yes 

Yes 

Yes 

Yes 

Yes 

* For this vehicle the DOC and the DPF were bypassed and a DPF efficiency of 90% was simulated. 

Two reference systems were used in the testing campaigns; a solid particle number measurement system 
down  to  23  nm  (SPN23)  and  down  to  10  nm  (SPN10).  A  total  particle  number  system  (both  solid  and 
volatile) down to 10 nm (TPN10) was also used. Solid particle number measurements were performed 
with  the  Advanced Particle Counter (APC) (AVL, Graz, Austria).  APC removed volatile particles using a 
catalytic stripper. The reference instrumentation was calibrated less than one year from the tests. Total 
particle measurements were done with two cold dilution stages and a CPC 3792 (TSI, Shoreview, MN, 
USA) with cut-off size at 10 nm. 

Cold vs. hot idling concentrations 

The low idling SPN23 emissions of vehicles V1 to V5 were measured with PTI instruments #1 to #7. Tests 
were performed both with cold (engine coolant <25◦C) and hot (engine coolant >60◦C) engine in a room 
conditioned at approximately 23◦C. The measurement duration varied from 15 to 90 s according to the 
different measurement protocols of European countries, i.e. 15 s in the Netherlands and Belgium, 90 s 
in Germany. 

Figure 1a plots the relative (%) increase of SPN emissions when sub-23 nm particles are considered (i.e. 
the contribution of sub-23 nm particles to the final concentration) while Figure 1b, the relative increase 
when volatile particles are considered (i.e. the contribution of volatile particles to the total concentration). 

Figure 1: (a) Relative increase of SPN23 when sub-23 nm particles are considered (from Melas et al., 
2022), (b) Relative increase of SPN10 when volatile particles are considered (from Melas et al., 2022).  

Vehicles V1 and V3 presented very high sub-23 nm particle concentrations (Figure 1a). At cold conditions 
the relative increase was 1580 % for V1 and 845 % for V3 which at hot engine conditions sub-23 nm 
decreased to 725 % and 430 %, respectively. For V3 the sub-23 nm fraction at a type-approval cycle 

72 

  
 
 
 
 
Joint TAP and S&E Conference 2023 

(worldwide harmonized light vehicles test cycle - WLTC) was much lower, ~20 %. High sub-23 nm solid 
particle  emissions  at  low  idling  were  observed  also  in  previous  studies  and  have  been  attributed  to 
polyaromatic hydrocarbons (PAHs) that do not evaporate at 350◦C or to lubrication oil particles (De Filippo 
and Maricq, 2008; Lähde et al., 2014).  

The highest volatile particle concentrations were observed for V4 (~370 % more than SPN10) while the 
engine temperature did not influence the volatile particles formation. A possible reason for high volatile 
particles emissions for V4 was that DOC was bypassed. 

Figure 2 summarizes the cold (Figure 2a) and hot (Figure 2b) engine low idling measurements performed 
with PTI instruments against the reference instrumentation SPN23.The red dashed lines indicate the SPN-
PTI  in  Germany,  250,000  #/cm3,  and  divide  the  graphs  in  four  rectangles.  Points  in  the  up  and  left 
rectangle are higher than the limit when measured with a PTI instrument but lower when measured with 

rectangle would be a 
x 104 #/cm3 (whichever is greater) according to the maximum permissible error in the NL regulation. The 
green dotted lines indicate the DE maximum permissible error, i.e., ± 50 % but at least 5 x 103 #/cm3. In 
Figure 2a and 2b we also plot a linear fit (black solid line) by setting the intercept zero and we also report 
the slope and the R2.   

SPN concentrations down to 23 nm of 5 vehicles at low idling measured with seven PTI23 
Figure 2: 
instruments and compared to a reference system (SPN23) (from Melas et al., 2022): (a) cold engine, (b) 
hot engine. The red dashed lines indicate the German SPN-PTI limit, the purple dash dotted lines the NL 
maximum permissible error, the green dotted lines the DE regulation and the black solid line is a linear 
fit with intercept set at zero.  

Linearity between PTI instruments and reference instrumentation was not good when the engine was 
cold. Specifically, the slope was 1.6 and R2=0.58. In most cases the PTI instruments overestimated SPN 
concentrations.  The  highest  differences  were  observed  for  V1  and  V3  which  also  had  the  highest 

shows the importance of these nucleation particles in PTI instruments deviations. Instead, for V4, with 
high volatile particles concentrations, the agreement was good and specifically ± 40 %. 

When the engine was hot, the agreement between PTI instruments and reference instruments was very 
good, the slope being 1.12 and R2
for the better agreement is the reduction of sub-23 nm particles. 

Correlation of type-approval SPN emissions with SPN concentrations during PTI 

Figure  3  plots  the  SPN  emissions  down  to  23  nm  of  different  Diesel  vehicles  at  type-approval  tests 
against hot low idle SPN concentrations. The horizontal dashed line indicates the Euro 5b and Euro 6 
limit of 6 x 1011 #/km. The dotted line is a linear fit of the results obtained in this and previous studies. 
Specifically,  TNO  study  results  were  presented  in  (Kadijk  et  al.,  2017)  and  previous  JRC  results  in 
(Giechaskiel et al., 2020). Dashed yellow lines are a ± 65% which include all measurements near the 
limit 6 x 1011 #/km. The correlation factor between PTI and type-approval was found to be 8.2 x 106 
(km/cm3) and R2=0.90. By applying the 65% uncertainty for values near the type-approval limit, we obtain 
that a PTI limit of ~1 x 105 #/cm3 

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Joint TAP and S&E Conference 2023 

Figure 3: 
emissions. Dashed lines are a ± 65 % uncertainty (from Melas et al., 2022).  

Solid particle number emissions at type-approval driving cycle against hot low idling 

Conclusions 

This study examined the accuracy of instruments designed for SPN-PTI measurements as well as the 
correlation of the SPN-PTI methodology with the vehicle type-approval testing. When vehicle engine was 
cold, we obtained high differences between PTI23 and SPN23. The agreement was much better when the 
-PTI tests are recommended to be performed with an engine coolant 
temperature of at least 60◦C. Finally, a very good correlation between SPN-PTI and type-approval SPN23 
emissions were observed showing that the SPN-PTI methodology can detect high SPN emitters. 

Acknowledgements  

Authors would like to thank T. Selleri, F. Forloni, V. Valverde, C. Gruening for the discussions and D. Luseur, 
P. Le Lijour, A. Migneco, J. Franzetti, L. Bigozzi, F. Forni, P. Canevari for the experimental support. 

References 

Bainschab,  M.,  Schriefl,  M.A.,  Bergmann,  A.,  2020.  Particle  number  measurements  within  periodic  technical 
inspections: A first quantitative assessment of the influence of size distributions and the fleet emission reduction. 
Atmospheric Environment: X 8, 100095. https://doi.org/j.aeaoa.2020.100095 

Boveroux, F., Cassiers, S., Buekenhoudt, P., Chavatte, L., De Meyer, P., Jeanmart, H., Verhelst, S., Contino, F., 2019. 
Feasibility study of a new test procedure to identify high emitters of particulate matter during periodical technical 
inspection. SAE Technical Paper 2019-01 1190. https://doi.org/10.4271/2019-01-1190 

Burtscher, H., Lutz, Th., Mayer, A., 2019. A new periodic technical inspection for particle emissions of vehicles. Emiss. 
Control Sci. Technol. 5, 279 287. https://doi.org/s40825-019-00128-z 

De Filippo, A., Maricq, M.M., 2008. Diesel nucleation mode particles: Semivolatile or solid? Environ. Sci. Technol. 42, 
7957 7962. https://doi.org/10.1021/es8010332 

Giechaskiel, B., Lähde, T., Suarez-Bertoa, R., Valverdre, V., Clairotte, M., 2020. Comparisons of laboratory and on-road 
type-approval cycles with idling emissions. Implications for periodical technical inspection (PTI) sensors. Sensors 20, 
5790. https://doi.org/10.3390/s20205790 

Giechaskiel,  B.,  Melas,  A.,  Martini,  G.,  Dilara,  P.,  2021.  Overview  of  vehicle  exhaust  particle  number  regulations. 
Processes 9, 2216. https://doi.org/10.3390/pr9122216 

Kadijk, G., Elstgeest, M., Ligterink, N.E., Van der Mark, P.J., 2017. Investigation into a Periodic Technical Inspection 
(PTI)  test  method  to  check  for  presence  and  proper  functioning  of  Diesel  Particulate  Filters  in  light-duty  diesel 
vehicles   part 2. TNO report TNO 2017 R10530. 

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Lähde, T., Virtanen, A., Happonen, M., Söderström, C., Kytö, M., Keskinen, J., 2014. Heavy-duty, off-road diesel engine 
low-load  particle  number  emissions  and  particle  control.  J.  Air  Waste  Manag.  Assoc.  64,  1186 1194. 
https://doi.org/10.1080/10962247.2014.936985 

Melas, A., Selleri, T., Suarez-Bertoa, R., Giechaskiel, B., 2022. Evaluation of measurement procedures for solid particle 
number (SPN) measurements  during the periodic technical inspection (PTI) of vehicles. Int. J.  Environ. Res.  Public 
Health 19, 7602. https://doi.org/10.3390/ijerph19137602 

Melas, A., Selleri, T., Suarez-Bertoa, R., Giechaskiel, B., 2021. Evaluation of solid particle number sensors for periodic 
technical inspection of passenger cars. Sensors 21, 8325. https://doi.org/10.3390/s21248325 

Melas, A., Vasilatou, K., Suarez-Bertoa, R., Giechaskiel, B., 2023. Laboratory measurements with solid particle number 
instruments  designed  for  periodic  technical 
inspection  (PTI)  of  vehicles.  Measurement  215,  112389. 
https://doi.org/10.1016/j.measurement.2023.112839 

Vasilatou, K., Kok, P., Pratzler, S., Nowak, A., Waheed, A., Buekenhoudt, P., Auderset, K., Andres, H., 2022. New periodic 
technical inspection of diesel engines based on particle number concentration measurements. OIML Bulletin LXIII. 

Vasilatou, K., Wälchli, C., Auderset, K., Burtscher, H., Hammer, T., Giechaskiel, B., Melas, A., 2023. Effects of the test 
aerosol  on  the  performance  of  periodic  technical  inspection  particle  counters.  J.  Aerosol  Sci.  172,  106182. 
https://doi.org/10.1016/j.jaerosci.2023.106182 

Yamada, H., 2019. Improving methodology of particulate measurement in periodic technical inspection with high-
sensitivity techniques: laser light scattering photometry and particle number method. Emiss. Control Sci. Technol. 5, 
37 44. https://doi.org/10.1007/s40825-019-0108-z 

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In-use NOX emission trends of diesel trucks in China informed by large-sized 
inspection data 

Joint TAP and S&E Conference 2023 

Y.Wang2, H.Cheng2, S.Zhang1*, Y.Wu1 and Z.Ran2 

1 School of Environment, Tsinghua University, Beijing, China, zhsjun@tsinghua.edu.cn 

2 Clean Air Asia China Office, Beijing, China 

Introduction 

With the advancement of economic and social development, as well as the prominence of air pollution, 
Nitrogen Oxides (NOX) emissions from anthropogenic sources have become a matter of great concern. 
As critical precursors of ozone (O3) and fine particulate matter (PM2.5), NOX (mainly NO2 and NO) emissions 
may cause severe environmental issues such as photochemical smog and acid rain and pose a high risk 
to human health (Chen et al., 2012; Li et al., 2019; Pye et al., 2022; Richter et al., 2005; Shaw and Van 
Heyst, 2022; Wei et al., 2019). Among the anthropogenic sources of NOX emissions, on-road vehicles are 
the largest contributor. Specifically, diesel trucks account for more than 80% of the NOX emissions from 
on-road  vehicles,  although  they  account  for  only  11%  of  the  on-road  vehicle  stock,  highlighting  the 
importance  of  controlling  NOX  emissions  from  diesel  trucks  (Li  et  al.,  2023;  Ministry  of  Ecology  and 
Environment, 2022).  

Since the 11th Five-Year Plan, China has adopted robust regulations and policies to control NOX emissions 
from  in-use  diesel  trucks.  On  the  one  hand,  China  has  been  progressively  improving  its  emission 
standards for new diesel trucks, known as China IV, China V, and China VI. Especially, the implementation 
of China VI standard, Limits and measurement methods for emissions from diesel fueled heavy-duty 
vehicles (GB 17691-2018), which requires diesel trucks to be equipped with remote on-board diagnostic 
(OBD) system to ensure that NOX emissions satisfy stringent criteria, has pushed the emission control of 
new vehicles to an international level (Zhang et al., 2020). On the other hand, since the launch of Action 
Plan for Diesel Truck Pollution Control in 2018, China has gradually developed a Sky-Ground-Vehicle-
Personnel (SGVP) integrated emission control system that integrates multiple emission monitoring tools, 
such as roadside remote sensing, on-board monitoring, and periodic emission inspection (Hao et al.,2022; 
MEE  et  al.,  2018).  Required  by  the  active  regulations,  diesel  trucks  must  undergo  periodic  emission 
inspections at least once a year. Inspections should be conducted based on the Limits and measurement 
methods for emissions from diesel vehicles under free acceleration and lugdown cycle (GB 3847-2018), 
and non-compliant trucks must be repaired or scrapped according to the Inspection and Maintenance 
(I/M) program. 

NOX emission concentration data of lug-down test in emission inspections can provide comprehensive 
coverage of diesel trucks with various emission standards, vehicle types, and vehicle weights. However, 
only  a  few  studies  have  focused  on  NOX  emission  analysis  using  inspection  data.  This  study  utilized 
representative and high-quality inspections data, which was validated using multi-source test data, to 
analyze the effectiveness of current emission control programs in reducing NOX emissions from diesel 
trucks. Additionally, this study presents the emission reduction effects of a scenario implementing stricter 
NOX emission limits in the lug-down test. The findings will provide valuable recommendations for the 
formulation  and  implementation  of  relevant  policies  aiming  to  reduce  O3  and  PM2.5  emission 
synergistically and lower their harmful impacts on the environment and public health. 

Data and methods 

Owing to the heterogeneity in emission performance, diesel trucks with varying emission standards, gross 
vehicle  weight  (GVW),  and  vehicle  types were  included  in  this  study.  In  terms  of emission  standards, 
China III, IV, V, and VI diesel trucks are chosen and further classified into four GVW categories: mini trucks 
 1800 kg), light-duty trucks (LDTs) (1800 kg < GVW < 4500 kg), medium-duty trucks (MDTs) 
(GVW 
(4500 kg 
 12000 kg). Straight, dump, public 
service, refrigerated, and tractor trucks, the most prevalent vehicle types in the Chinese market, were 
included in this study. 

 GVW < 12000 kg), and heavy-

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Although various emission monitoring techniques exist for diesel trucks, including the lug-down test used 
for periodic emission inspection, portable emission measurement system (PEMS), OBD, plume-chasing 
measurement, and remote sensing, most monitoring techniques either lack large samples or lack data 
reliability.  However,  periodic  emission  inspections  cover  a  high  proportion  of  diesel  truck  fleets  while 
ensuring good data reliability and accuracy, which has been validated in the consistency analysis section. 
The Regulations of the People's Republic of China on the Implementation of the Road Traffic Safety Law 
mandates that trucks registered for less than 10 years must undergo an annual inspection and those 
registered for more than 10 years must undergo a twice-yearly inspection. Periodic emission inspection, 
a mandatory program for annual truck inspection, covers representative and large in-use diesel trucks 
that can be analyzed to evaluate the NOX emission performance over many years, providing excellent 
data for assessing the effectiveness of diesel truck emission reduction strategies. 

This  study  assessed  the  NOX  emission  performance  of  in-use  diesel  trucks  during  periodic  emission 
inspections from 2019 to 2021 in a systematic and comprehensive manner using valid lug-down test 
data.  In the selection of vehicle samples, this study used a random sampling method  to select truck 
samples that underwent their first emission inspection of the year, using the sampling parameters listed 
in Table 1. The sample size for each vehicle model was determined using Eq. (1), and the vehicles models 
covered in this study accounted for more than 90% of the in-use truck models. 

2
)

𝜎2

𝑍𝛼
(
2

𝐸2

𝑛 ≈

                                                                                                                                      (1) 

2  is  the  variance  of  the  observed  NOX  emission 
concentrations  for  each  vehicle  model,  E  is  the  sampling  error,  and  Z /2  is  the  reliability  factor  that 
indicates the confidence interval, with 1.96 used for the 95% confidence interval and 1.645 used for the 
90% confidence interval. 

Table 1:  Summary of sample size and sampling parameters in this study 

Year 

2019 

2020 

2021 

Emission Standard 

China III; 
China IV; 
China V; 
China VI 

China V;  
China VI 

China IV;  
China V;  
China VI 

Sampling Error (lug-down test) 

10% 

5% 

Confidence Level (lug-down test) 

95% 

95% 

10% (China IV; China 
V) 
5% (China VI) 

90% (China IV; China 
V) 
95% (China VI) 

Number of models (lug-down test) 

5,683 

2,827 

5,996 

Number of vehicles (lug-down test) 

123,465 

390,000 

356,185 

Number of vehicles (PEMS) 

140 

Number of vehicles (OBD) 

Number of vehicles (plume-chasing) 

800 

933 

6,503 

3,070 

933 

6,262 

1,791 

The emission inspection standard, GB 3847-2018, set two NOX limits for the lug-down test: limit A, 1500 
ppm and limit B, 900 ppm. However, limit B has not been adopted in any province or city in China (MEE 
and  SAMR,  2018).  In  this  study,  to  avoid  abnormal  values  caused  by  high-emission  trucks,  the  NOx 
emission control performance was assessed using the lug-down test data of diesel trucks compliant with 
limit A, if not specified.  

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This study also employed PEMS, OBD, and plume-chasing data from vehicle models covered by periodic 
emission inspections to validate the accuracy and reliability of the lug-down test data. The validity of 
the plume-chasing test on low- and high-emission vehicles has been demonstrated previously (Wang et 
al., 2020). In the consistency analysis of the multi-source test data, the units of NOX emissions were all 
converted to ppm based on the relationship between NOX (g/kg-fuel) and NOX (ppm) built using the PEMS 
data, which validated the strong correlation with a correlation factor of 0.97 (R2). The detailed test data 
sizes are presented in Table 1. 

Results and discussions 

NOX emissions control effectiveness of new vehicle emission standards upgrade 

Since the end of the 11th Five-Year Plan, China's new vehicle emission standards have been upgraded 
thrice, from China III to China VI, with a clear benefit to the reduction of NOX emission from diesel trucks. 
Particularly, the NOX emissions from diesel trucks were significantly reduced with the implementation of 
China VI standard. By comparing the NOX concentrations of China IV, V, and VI diesel trucks tested in the 
emission  inspections    during  2019 2021,  it  was  found  that  China  IV  trucks  have  comparable  NOX 
concentration levels to China III trucks, the NOX concentration of China V trucks was at most 25% lower 
than that of China IV trucks, and the NOX concentration of China VI trucks was more than 50% lower 
than that of China V trucks. 

NOX emissions control effectiveness of in-use vehicle emission control programs 

To investigate the NOX emission control effect of in-use vehicle emission control programs, we evaluated 
the variation in the NOX concentration of China IV and China V diesel trucks tested from 2019 to 2021, 
which were grouped by registration year to limit the impact of deterioration. The NOX concentrations in 
China IV diesel trucks increased yearly, significantly for those registered in the early years, with higher 
deterioration levels. Taking the China IV diesel trucks registered in 2015 as an example, the average NOX 
concentration of trucks tested in 2021 was 9.6% higher than that of trucks tested in 2019. However, the 
NOX emission trend of China V diesel trucks with varied registration years were opposite to that of China 
IV trucks. As shown in  

Figure 1, there is a clear downward trend in the NOX concentrations of the China V trucks tested from 
2019 to 2021, regardless of the registration year. Considering the China V trucks registered in 2017 as 
an example, the NOX concentration decreased on average annually from 1.7% to 10.5%, depending on 
the GVW categories and vehicle types, which could be explained by the retrofitting of remote OBD system 
on in-use China V trucks.  

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Figure 1: 
tested in emission inspections from 2019 to 2021. 

Distribution of NOX concentrations in China V diesel trucks with varied registration years 

Furthermore, we compared the NOX concentrations of China V diesel trucks that were tested two years 
after registration but had various registration years. It was found that the NOX emission control of China 
V trucks registered after 2018, the initial launch year of the Action Plan for Diesel Truck Pollution Control, 
outperformed trucks registered earlier. Considering HDTs as an example, the average NOX concentrations 
of  trucks  registered  in  2018  were  5.8%  lower  than  those  of  trucks  registered  in  2017,  whereas  the 
average  NOX  concentrations  of  trucks  registered  in  2019  were  12.1%  lower  than  those  of  trucks 
registered in 2018. Therefore, in-use vehicle emission control programs are effective in controlling NOX 
emissions from China V diesel trucks but have limit efficiency in controlling NOX emissions from China 
IV diesel truck. 

NOX emissions control effectiveness of implementing lug-down test limit B 

In  the  case  of  currently  implemented  limit  A,  less  than  3%  of  the  diesel  trucks  tested  in  the  2021 
emission inspection were identified as high-emission vehicles, that is, non-compliant vehicles, regardless 
of  the  emission  standard.  This  suggests  that  limit  A  was  too  lax  for  identifying  high-emission  diesel 
trucks. Limit B would have a higher efficacy in identifying high-emission vehicles, with 34% of China IV 
trucks, 18.5% of China V trucks, and 4.8% of China VI trucks screened as high-emission vehicles. The 
high-emission vehicle identification efficacy of limit B in lug-down test is consistent with that of plume-
chasing test, which is illustrated in Figure 2. As more high-emission trucks were identified, more trucks 
must be repaired or scrapped according to the requirements of the I/M program, thus lowering the NOX 
concentration level and total NOX emissions of the diesel truck fleet. If the NOX concentration in high-
emission vehicles identified by limit B is lowered to the limit level (900 ppm) after repair, the average 
NOX concentrations in China IV and China V diesel trucks will decrease by 9.6% and 5.8%, respectively. 
If the high-emission China IV vehicles identified by limit B were replaced by China VI vehicles or battery-
electric vehicles, the total NOX emissions of the diesel truck fleet in 2021 would be reduced by 5.3%
9.7%. Therefore, the implementation of limit B needs to be expedited because of its important role in 
optimizing truck fleet structure and minimizing NOX emissions.  

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Joint TAP and S&E Conference 2023 

Identification efficacy in high-emission China V diesel trucks of lug-down and plume-

Figure 2: 
chasing tests. There were two high-emission determination thresholds of plume-chasing test: plume-
chasing and plume-chasing (10%), which were derived from full test data and the top 10% value, 
respectively, by balancing the accuracy and efficiency in identifying high-emission China V diesel 
trucks. 

Consistency analysis 

As demonstrated in Figure 3, there was good emission trend consistency among the multi-source test 
data, regardless of emission standards, GVW categories, or vehicle type. The average NOX concentration 
of China VI vehicles was 56% 92% lower than that of China V trucks, the NOX concentration of HDTs 
was 60% 246% higher than that of LDTs, and the NOX concentration of tractor trucks was 30% 75% 
greater than that of straight trucks.  

The NOX concentration derived from the lug-down tests was consistently the highest across the datasets 
for both the China V and VI trucks. This is largely because of truck idling prior to emission inspection, 
where  lower  selective  catalytic  reduction  (SCR)  system  temperatures  are  not  conducive  to  emission 
control,  as  well  as  high-load  conditions  during  test,  which  result  in  higher  emissions.  Additionally, 
compared  with  China  V  trucks,  China  VI  trucks  demonstrated  a  smaller  gap  between  the  NOX 
concentrations of LDTs and HDTs, which could be attributed to distinct aftertreatment technology routes, 
with a portion of China V LDTs employing EGR and China V HDTs employing SCR, whereas China VI diesel 
trucks adopted the same SCR technology regardless of LDTs and HDTs, resulting in a smaller variance in 
emission  performance.  Furthermore,  the  average  NOX  concentration  of  tractor  trucks  with  the  same 
emission standard was 30% 75% higher than that of straight trucks, which could be attributed to the 
comparably lower operating efficiency of SCR systems in tractor trucks.  

Figure 3: 
standards, GVW categories, and vehicle types. 

NOX emissions trend from multi-source test data of diesel trucks grouped by emission 

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Conclusions 

In this study, the NOX data from the lug-down test were validated with good consistency across many 
data  sources,  including  PEMS,  OBD,  and  plume-chasing  tests.  Based  on  valid  lug-down  test  data, 
adequate samples were selected to evaluate the effectiveness of NOX emission control programs for 
diesel  trucks,  with  a  focus  on  two  aspects:  emission  standard  upgrades  for  new  vehicles  and  the 
enhancement of in-use vehicle emission control programs.  

New  vehicles  emission  standards  upgrade,  particularly  the  implementation  of  the  China  VI  standard, 
have  resulted  in  a  significant  decrease  in  the  NOX  emission  concentrations  of  diesel  trucks.  The  NOX 
concentrations of China V diesel trucks are up to 25% lower than those of China VI trucks, whereas the 
concentrations of China VI trucks are more than 50% lower than those of China V trucks. In-use vehicle 
emission control programs have effectively reduced NOX emissions from China V diesel trucks. Based on 
a time-series comparison, it was found that the average NOX concentrations of China V diesel trucks 
decreased on average annually by 1.7% 10.5% across the different GVW categories and vehicle types. 
Furthermore, the NOX concentrations in trucks registered after 2018 were significantly lower than those 
registered before 2018. However, the NOX emissions from China IV diesel trucks are too challenging to 
regulate in the context of the current in-use vehicle emission control programs.  

Limit A of the lug-down test in periodic emission inspections is deemed ineffective for controlling NOX 
emissions, as less than 3% of diesel trucks with different emission standards are identified as high-
emission  vehicles.  Limit  B,  however,  identifies  more  high-emission  vehicles,  with  a  high-emission 
proportion of 34% for China IV trucks, 18.5% for China V trucks, and 4.8% for China VI trucks, implying 
that  more  vehicles  would  be  repaired  to  be  compliant  or  replaced  with  cleaner  vehicles.  If  the  high-
emission vehicles identified by limit B were repaired to decrease the NOX concentration to the limit of 
level B (900 ppm), the average NOX concentrations of China IV and China V diesel trucks would decrease 
by 9.6% and 5.8%, respectively. If the high-emission trucks identified by limit B are replaced with China 
VI trucks or battery-electric trucks, the total NOX emissions of diesel trucks in 2021 would be reduced by 
5.3% 9.7%. Currently, several provinces and cities in China have issued plans to phase out in-use China 
IV vehicles. In these areas and others with large stocks of China IV diesel trucks, the implementation of 
limit B needs to be expedited to serve as an enabling tool to optimize the fleet structure and minimize 
NOX  emissions,  thus  further  contributing  to  the  synergistic  reductions  in  PM2.5  and  O3  as  well  as  the 
improvement of urban air quality in China.  

In the future, the lug-down test in identifying high-emission vehicles in diesel trucks with gradually lower 
NOX  concentrations  as  the  emission  standards  upgrade,  should  be  continually  validated  using  multi-
source test data, to ensure the high-efficiency of in-use vehicle emission control programs in reducing 
NOX emissions from diesel trucks. 

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Hao L.J., Yin H., Wang J. F., et al. (2022). Research on Analysis Method of Remote Sensing Results of NO Emission 
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82 

  
 
 
 
 
Joint TAP and S&E Conference 2023 

Assessment of a NOx Measurement Procedure for Periodic Technical 
Inspection (PTI) of Light-Duty Diesel Vehicles 

Jacopo Franzetti 1, Tommaso Selleri 1,2,*, Christian Ferrarese 1, Anastasios Melas 1, Dario Manara 1, 
Barouch Giechaskiel 1 and Ricardo Suarez-Bertoa 1,*  

1 Joint Research Centre (JRC), European Commission, 21027, Ispra, Italy 
2 European Environment Agency (EEA), 1050 Copenhagen, Denmark 

ricardo.suarez-bertoa@ec.europa.eu; tommaso.selleri@eea.europa.eu 

Introduction 

Periodic technical inspection (PTI) of vehicles promotes road safety and environmental protection. Indeed, 
PTI  is  also  used  to  verify  the  proper  functioning  of  the  vehicle  aftertreatment  system  (ATS)  over  its 
lifetime. During PTI, the emissions of every vehicle circulating are checked for opacity (if it is a Diesel 
vehicle) or CO levels (in case of spark ignition engines). The compliance with PTI requirements are the 
responsibility of the car owner. While the current Directive 2014/45/EU, which covers the PTI, does not 
require  NOx  emissions  measurement,  the  ongoing  revision  of  the  roadworthiness  package  aims  at 
including new methods for measuring exhaust NOx and particle number (PN) emissions.  

PTI  tests  are  required  to  be  simple,  quick,  inexpensive  and  effective.  Due  to  the  variety  of  de-NOx 
aftertreatment technologies (Selleri et al. 2021) in the current Diesel fleet, it becomes difficult to define 
n of the 

overall ATS.  

Herein, a new procedure for the identification of selective catalytic reduction (SCR) unit malfunctioning 
in  modern  Diesel  vehicles  during  PTI  is  assessed.  The  focus  is  on  post-RDE  (real-driving  emissions) 
regulation  light-duty  Diesel  vehicles  (Euro  6d  and  6d-TEMP)  where  SCR  units  are  ubiquitous.  The 
procedure is based on the functioning principles of the SCR and its capability to efficiently reduce NOx 
emissions at low idling when appropriately conditioned. 

Experimental 

Seven vehicles were tested at Vehicle Emissions Laboratories (VELA) of the Joint Research Centre (JRC) 
of the European Commission. All of them met post-RDE emission standards. Table 1 summarises the 
main characteristics of the vehicles tested. Vehicle #1 was tested a second time, at a later stage, with a 
malfunction in the urea injector (referred to as vehicle #1b). This condition was exploited to assess a real 
case of SCR system malfunctioning. 

A summary of the NOx measurement equipment used and their working principle is presented in Table 
2.  Laboratory  equipment  included  a  MEXA  7400  bench  analyser  and  a  MEXA  7100  bench  analyser 
(HORIBA, Kyoto, Japan), a MEXA ONE bench analyser (HORIBA, Kyoto, Japan) and an AMA i60 bench (AVL, 
Graz, Austria). All the analysers were equipped with a chemiluminescence detector (CLD). In addition to 
laboratory grade equipment, Portable Emission Measuring Systems (PEMS) were also used, including: an 
AVL MOVE (AVL, Graz, Austria) PEMS with a non-dispersive ultra-violet sensor (NDUV) and an HORIBA 
OBS ONE PEMS equipped with a CLD. 

83 

  
 
 
 
 
 
 
Table 1: Summary of vehicles tested. 

#  Fuel Displacement (cm3) Registration Emission control system  Vehicle 
category 
EGR, DOC, DPF, SCR  N1 Class III Euro 6d-TEMP-EVAP-ISC 

Euro standard 

1  B7 

1968 

2019 

2  B7 

3  B7 

4  B7 

5  B7 

6  B7 

1995 

1997 

2184 

1968 

1499 

2021 

2019 

2021 

2020 

EGR, DOC, DPF, SCR  N1 Class III 

Euro 6d-ISC-FCM 

EGR, DOC, DPF, SCR 

M1 

Euro 6d-TEMP-EVAP-ISC 

EGR, DOC, DPF, SCR  N1 Class III 

Euro 6d-ISC-FCM 

EGR, DOC, DPF, SCR 

M1 

Euro 6d-ISC-FCM 

2020 

EGR, DOC, DPF, LNT, pSCR  M1 

Euro 6d-TEMP-EVAP-ISC 

1998 

7  B7 
EGR:  exhaust  gas  recirculation,  DOC:  diesel  oxidation  catalyst,  DPF:  diesel  particulate  filter,  SCR:  selective  catalytic 
reduction, LNT: lean NOx trap, pSCR: passive SCR. 

EGR, DOC, DPF, LNT, SCR 

Euro 6d-ISC-FCM 

2020 

M1 

Table 2: Summary of the measurement equipment used. On-vehicle NOx sensors were used when available from OBD 
port. 

Vehicle # 

1 

2 

3 

4 

5 

6 

7 

Laboratory 

 HORIBA 
MEXA 7400 
- CLD 

PEMS 

OBD NOx 
sensors 

AVL MOVE 
- NDUV 

AVL MOVE 
- NDUV 

AVL MOVE 
- NDUV 

X 

X 

HORIBA 
MEXA ONE 
- CLD 
HORIBA 
OBS ONE - 
CLD 

X 

 HORIBA 
MEXA 7400 
- CLD 

AVL AMA 
i60 - CLD 

 HORIBA 
MEXA ONE 
- CLD 

X  

* CLD: chemiluminescence detector, OBD: On board diagnostics, NDUV: non-dispersive ultra-violet sensor. 

Hot idling procedure 

The  procedure  assessed  in  this  work  aims  at  identifying,  during  a  PTI,  modern  Diesel  vehicles  that  present  a 
malfunctioning SCR unit and therefore excess emissions of NOx. A malfunctioning SCR system would result in undesired 
high NOx emissions. The test procedure is also presented schematically in Figure 1 for better understanding and it 
consists of two main phases:  

1) 
activity;  

vehicle warm-up phase, in order to ensure that SCR unit temperature is high enough to trigger NOx reduction 

2) 

hot idling phase, where NOx emissions are measured at tailpipe.  

84 

 
 
 
 
  
  
  
  
  
  
  
  
 
Figure 1: 
displayed are indicative. 

Simplified schematic representation of the hot idling procedure. NOx  concentration and engine speed 

During the hot idling phase, unless there is a SCR unit malfunction, NOx tailpipe level are expected to be low for few 
minutes where the SCR keeps reducing the NOx emissions (referred as low NOx phase   from hot idle start till tailpipe 
NOx concentration >1 ppm). During the low NOx phase, the NOx reduction takes place due to their reaction with NH3, 
which is available because the urea injection continues during vehicle hot idling or because it remains stored on the 
catalyst during the warm-up phase (Czerwinski et al., 2019; Selleri et al., 2022, Giechaskiel et al., 2022). Both cases 
would suggest an SCR that is properly functioning, allowing to exclude a malfunctioning of the system. After the low 
NOx  phase  a  gradual  NOx  increase  at  tailpipe  is  expected  with  time,  since  NH3  stored  previously  in  the  SCR  is 
progressively consumed and/or SCR unit temperature is not sufficiently high to have a reaction even though there is 
still stored NH3. 

Functioning vs. Malfunctioning SCR system 

Figure 2 shows the NOx emission profiles of vehicles #1 and #1b during the hot idling test.  Figure 2a shows NOx 
emission profile for vehicle #1, equipped with a well-functioning SCR unit: low NOx tailpipe concentration lasted ca. 81 
sec. Then, NOx concentration gradually increased reaching values (~86 ppm) comparable with the levels observed at 
engine out steady state emission (99 ppm). Figure 2b plots NOx emission profile for vehicle #1b, with urea injection 
system not working: NOx concentration increased by more than 45 ppm right since the idling start, not following the 
pattern observed for the same vehicle with a properly functioning SCR.  

NOx emission profiles of vehicle #1 (a) and vehicle #1b 

Figure 2: 
 vehicle #1 with Diesel Exhaust Fluid (DEF) 
injector not working - (b) during the hot idling test. NOx tailpipe concentration measured by laboratory-grade CLD NOx 
analysers  (solid  light  green  line)  and  vehicle  NOx  engine  out  (solid  dark  green  line)  concentrations  are  displayed 
together with vehicle engine speed (dashed grey line) and exhaust temperature (solid blue line line) for both vehicles. 

The average low NOx duration (<1 ppm) for all tests was 363 sec, ranging from 81 sec for vehicle #1 to 544 sec for 
vehicle #2 (see Table 3). To provide additional insight on the NOx increase time, Table 3 also presents the time required 
for the NOx tailpipe concentration to reach 10 ppm and 20 ppm from idle start. For all the vehicles the time needed 
increased. For example, for vehicle #1 it increased to 199 sec to reach 10 ppm of tailpipe NOx concentration and to 
245 sec for 20 ppm.  

85 

 
 
 
 
 
 
The low NOx duration obtained from the tested vehicles (81   544 sec), together with the immediate increase of NOx 
at tailpipe for the malfunctioning vehicle #1b, suggest that a short measurement time span (e.g., between 15 and 45 
seconds) would suffice to distinguish between a vehicle with a properly operating or a malfunctioning SCR. Such a 
short measurement time would support the PTI criteria of having a quick test. 

Table 3: Low NOx phase duration, time required for NOx tailpipe concentration to reach 10 ppm and 20 ppm (except 
for vehicle #3 for which the test was stopped before those concentrations were reached) and NOx engine out average 
concentration measured during hot idling. 

 Vehicle 

#1 

#2 

#3 

#4 

#5 

Test 1  Test 1 

Test 2  Test 1  Test 1  Test 2  Test 1 

Low NOx phase < 1 ppm (sec)  81 

460 

544 

246 

496 

301 

412 

NOx < 10 ppm (sec) 

199 

591 

709 

598 

573 

421 

571 

NOx < 20 ppm (sec) 

245 

671 

812 

610 

542 

671 

NOx  Engine  Out  ave.  conc. 
(ppm)* 

99 

118 
(81) 

135 
(100) 

187 
(46) 

224 
(41) 

96 
(63) 

* Engine out values were recorded at the OBD port for those vehicle where the NOx engine out sensor signal was 
available (except for vehicle #5 where NOx at engine out was measured with a laboratory analyser). For those vehicles 
where engine out concentration changed during the idle two values are reported: the maximum concentration period 
average  and  in  brackets  the  lowest.  When  two  tests  were  performed  on  the  same  vehicle  the  results  were  both 
reported. 

Challenges 

Figure 3 shows the NOx emission profiles of vehicles #6 and #7 during the hot idling test. In particular, Figure 3a 
shows vehicle #7 NOx tailpipe emission profile and engine speed, together with the NOx sensor signal at the LNT outlet 
and  Figure  3b  shows  the  NOx  tailpipe  emission  profile  and  engine  speed  for  vehicle  #6  following  the  hot  idling 
procedure. At a first glance, vehicle #7 NOx concentration profile at the LNT outlet is analogous to the one discussed 
above, low NOx values were observed at the hot idle start, <1 ppm for 340 sec. However, this behaviour is related to 
a different principle, namely NOx adsorption (Forzatti et al., 2015). The NOx increase at the LNT outlet is damped by 
the operation of the two SCRs and the effect is visible on the NOx concentration at the tailpipe only delayed in time. 
Vehicle #6 is equipped with an LNT and a passive SCR unit (pSCR), relying on the NH3 emitted by the LNT during its 
regeneration (Hou et al., 2012). The trend illustrated in Figure 3b for vehicle #6 is analogous to the one presented 
above in case of active SCR unit (with DEF injection, commercially known in Europe as AdBlue). Also the NOx values 
observed are consistent with the ones described in Table 3: low NOx values were observed at the hot idle start, <1 
ppm for 639 sec. NOx concentration then gradually increase up to ~148 ppm. 

Finally, the presence of an LNT in the ATS could, under certain situations, limit determining whether or not the SCR unit 
has DEF dosing malfunction under the hot idling procedure studied for two main reasons: (i) LNT may adsorb NOx 
emissions during the length of the hot idle test keeping the concentrations low; (ii) even without DEF dosing the SCR 
unit could be active reducing NOx using the NH3 released by LNT. 

86 

 
 
 
 
  
  
 
Figure 3: 
NOx concentration profiles at tailpipe (solid light green line) from vehicle #7 (a) and #6 (b) during hot 
idling test. (a) also includes NOx sensor signal at the LNT outlet (solid dark green line), available at the OBD port. Engine 
speed (dashed grey line) is reported on the secondary axis. 

Conclusions 

Seven light-duty vehicles fulfilling post-RDE Euro 6 requirements and equipped with selective catalytic reduction SCR 
unit/s were tested following a hot idling procedure designed for NOx Periodical Technical Inspections (PTI). The test 
consists of two phases: (i) a vehicle warm-up phase, ensuring that the SCR unit temperature is high enough to trigger 
NOx reduction activity and (ii) a hot idling phase where NOx emissions are measured at the tailpipe.  The aim is to 
identify malfunctioning SCR units that would result in high NOx emitting vehicles. For well-functioning SCR units, a 
consistent NOx pattern was observed: low tailpipe NOx concentration at the hot idle start, then gradually increasing 
with time to values comparable to engine out levels. A well-functioning SCR unit will continue to reduce NOx as long 
as the temperature is high enough and that there is ammonia in the system to reduce them. Malfunctioning SCR unit 
resulted instead in high NOx tailpipe emissions, closer to engine out levels, from the idling phase start. This suggests 
that  hot  idling  procedure  could  be  effectively  used  to  discriminate  between  functioning  and  malfunctioning  SCR 
systems. Finally, the average low NOx concentration duration (<1 ppm) for all tests was 363 sec, the minimum 81 sec 
and the maximum 544 sec. Shorter time spans (e.g. from 15 to 45 sec) may also be used as PTI measurement period. 
Future steps will focus on: the investigation of the vehicle conditioning (warm-up phase definition), the detection of 
actual  malfunctioning  vehicles  equipped  with  ATS  including  an  LNT  in  their  configuration  and  the  definition  of  an 
effective and robust pass/fail criteria. 

Acknowledgements  

The authors would like to acknowledge the support and collaboration of JRC VELA technical staff. 

References 

Czerwinski, J., Comte, P., Engelmann, D., Mayer, A. et al. (2019). Considerations of Periodical Technical Inspection of Vehicles with 
deNOx Systems, SAE Technical Paper 2019-01-0744. 

Forzatti, P., Lietti, L. & Castoldi, L. (2015). Storage and Reduction of NO x Over LNT Catalysts. Catal Lett, 145, 483 504.  

Giechaskiel, B., Selleri, T., Gioria, R., Melas, A.D., Franzetti, J., Ferrarese, C., Suarez-Bertoa, R. (2022). Assessment of a Euro VI Step E 
Heavy-

ystem. Catalysts, 12, 1230 

Hou, X., Schmieg, S. J., Li, W., & Epling, W. S. (2012). NH3 pulsing adsorption and SCR reactions over a Cu-CHA SCR catalyst. Catalysis 
today, 197.1, 9-17.  

Selleri, T., Melas, A.D., Joshi, A., Manara, D., Perujo, A., Suarez-Bertoa, R. (2021). An Overview of Lean Exhaust deNOx Aftertreatment 
Technologies and NOx Emission Regulations in the European Union. Catalysts, 11, 404. 

Selleri, T., Gioria, R., Melas, A.D., Giechaskiel, B., Forloni, F., Mendoza Villafuerte, P., Demuynck, J., Bosteels, D., Wilkes, T., Simons, O., 
et al. (2022). Measuring Emissions from a Demonstrator Heavy-Duty Diesel Vehicle under Real-World Conditions Moving Forward 
to Euro VII. Catalysts, 12, 184. 

87 

 
 
 
 
 
 
 
 
Statistical evaluation of Conformity of Production of road vehicles4 

M.A. Ktistakis1, J. Suarez2, J. Pavlovic1, D. Komnos2, B. Ciuffo2, A. Marotta2, and G. Fontaras2* 

1 FINCONS Group, Vimercate, 20871, Italy 
2 European Commission, Joint Research Centre (JRC), Ispra, 21027, Italy, *georgios.fontaras@ec.europa.eu 

Introduction 

European Union (EU) requires that road vehicles are certified for their CO2 emissions over a laboratory test cycle (a 
process that is part of vehicle type approval). The certified CO2 emissions values are used for monitoring and emissions 
target compliance purposes in addition to user information, taxation and other purposes, hence they play an important 

Driving  Cycle  (NEDC)  (European  Commission,  2007)  until  2020  and  has  since  been  replaced  by  the  Worldwide 
Harmonized  Light  Vehicles  Test  Procedure  (WLTP)  (European  Union,  2017).  Initially,  this  approval  process  involves 
testing a limited number of vehicle prototypes. When a group of vehicles successfully meets the stipulated criteria, 
they are granted type approval, validating their compliance with EU regulations and allowing them to enter the market. 

To  ensure  the  accuracy  and  representativeness  of  certified  CO2  emissions  and  energy  consumption  data  for  EU 
vehicles,  the  European  Commission  has  instituted  validation  mechanisms.  Among  them  are  the  Conformity  of 
Production (CoP) tests, an ongoing process ensuring quality and standards throughout the vehicle production lifecycle. 
A central objective of the CoP is to verify that the CO2 emissions measured in production vehicles align with the values 

are interpolated from the type approval tests conducted on the best and worst case prototype vehicle of the same 
vehicle CO2 family. Notably, manufacturers have the option to declare CO2 emissions that exceed those measured in 
the type approval procedure. These declared emissions are every year reported to the Commission for each new vehicle 
registered in the EU and used to assess manufacturers' compliance with annual targets (European Union, 2019). 

Until the end of 2022, the CO2 emissions of Light-Duty Vehicles (LDVs) under the CoP framework have been evaluated 
using  a  sequential  sampling  schema.  This  method,  while  operational,  exhibits  an  inherent  flaw:  the  pass  and  fail 
thresholds  almost  coincide.  As  a  result,  pass or  fail  decisions  are  almost  invariably determined with  the  minimum 
sample size of just 3 vehicles. This characteristic hinders the sample mean from effectively converging towards the 
population mean. Moreover, manufacturers have often opted to over-declare CO2 emissions by up to 5% (Chatzipanagi 
et al., 2020), a strategy aimed at reducing the risk of CoP failure. This practice has cast a shadow on the efficacy of 
EC's annual targets, manifesting notably in an artificial increase in post-2020 CO2 targets. 

In view of these concerns, the Joint Research Centre (JRC) has formulated an enhanced sequential sampling schema 
to address the shortcomings identified in the statistical assessment of CoP tests. This advanced method has been 
formally incorporated into the Regulation and, has been in practice since 2023, endorsed by the authorities responsible 

broader integration across various dimensions of the EU's regulatory framework, thereby establishing a comprehensive 
approach  to  CO2  compliance.  The  scope  of  application  encompasses  both  LDVs  and  Heavy-Duty  Vehicles  (HDVs). 
Certain proposals have already been embraced, exemplified by the CO2 accuracy requirements of On-Board Fuel and 
Energy  Consumption  Monitoring  (OBFCM)  devices  within  LDV  CoP.  Other  initiatives  are  progressing  towards  final 
implementation stages, notably including the validation of CO2 emissions for in-service  LDV vehicles. Furthermore, 
initial strides have been taken in other part of vehicle certification regulation such as the In-Service Verification (ISV) 
statistical evaluation for HDVs.. This study explores the application of the sequential sampling method in verifying CO2 
emissions during the CoP process for LDVs within the EU, highlighting its significance in improving CoP accuracy. By 
exploring  the  method's  inception,  evolution,  and  impact,  a  more  comprehensive  understanding  of  CO2  compliance 
assessment is achieved. 

4 The opinions expressed in the paper are purely those of the authors and shall not be considered an official European Commission position under 

any circumstance. 

88 

 
 
 
 
 
 
                                                       
 
Methodology  

The former problematic methodology utilised in the CoP for LDVs and the currently adopted approach both incorporate 
sequential sampling methods. Sequential sampling is distinct from conventional sampling methods due to its unique 
characteristic of not being reliant on a predetermined sample size. Instead of waiting for a fixed number of samples 
to  accumulate,  sequential  sampling  continuously  evaluates  emerging  data,  allowing  for  more  efficient  resource 
utilisation  and  heightened  efficiency.  This  approach  strikes  a  balance  between  statistical  precision  and  practical 
efficiency,  making  it  a  valuable  tool  in  decision-making  processes  and  quality  control.  Furthermore,  this  dynamic 
approach allows for adjustments to sample size and decision-making criteria through ongoing data analysis, ensuring 
both  efficiency  and  well-informed  outcomes.  Thresholds,  depending  on  the  sample  size,  are  established  to  guide 
decision-making.  If  the  data  strongly  support  a  particular  decision,  the  sampling  process  concludes;  otherwise,  it 
continues until conclusive evidence is obtained. A predefined initial and maximum sample size provide flexibility within 
a structured framework. 

The CoP procedure for LDVs operates at a group level, organised into CoP families that correspond to planned annual 
production volumes of over 1,000 vehicles. Furthermore, the flexibility to consolidate smaller families is permitted. 
The sampling frequency is tailored to production scale, a design ensuring a systematic and effective assessment of 
CO2 emissions conformity. This approach guarantees a comprehensive evaluation, contributing to the overall quality 
and precision of CO2 compliance verification within the LDV manufacturing process. 

Previous statistical framework for CoP  

For each family, the process started with an initial random sample of 𝛮 = 3 vehicles selected from the production 
line. These vehicles underwent testing, where their CO2 emissions and/or electric consumption were measured in a 
laboratory setting. Subsequently, these measured values underwent normalisation by dividing them by each vehicle's 
official/declared  value.  The  decision-making  criteria  relied  upon  both  the  mean  value  (𝑋𝑡𝑒𝑠𝑡𝑠)  and  the  sample 
variance (𝑉𝑎𝑟) of the normalised values. The outcomes encompassed three possibilities: 

1. 

If 𝑋𝑡𝑒𝑠𝑡𝑠 < 𝐴 − 𝑉𝑎𝑟, the family passed. 

2. 

If 𝑋𝑡𝑒𝑠𝑡𝑠 > 𝐴 −

𝑁−3

13

× 𝑉𝑎𝑟, the family failed. 

3.  Otherwise, another vehicle underwent testing, increasing the sample size by 1, and subsequently recalculating 

the mean and variance. 

This iterative procedure continued until a conclusive pass or fail decision was reached. Notably, the maximum sample 
size was capped at 16 vehicles, beyond which the pass and fail boundaries aligned at A. Here, A was set at 1.01, 
serving as a safety margin to account for the measurement inaccuracies. 

However, this method suffers from a significant drawback: the variance of the normalised values is notably small, 
typically ranging from 10−3 to 10−4. As a result, the pass and fail boundaries nearly coincide, leading in an almost 
binary decision pattern involving just three vehicles. To elaborate, if the average ratio surpasses 1.01, the result is a 
fail, while a value below triggers a pass (as illustrated in Figure 1). Consequently, a decision is nearly always reached 
with only three vehicles. This scenario renders the adoption of a sequential sampling method unnecessary and prevents 
the sample mean from effectively converging to the population mean, given the consistent three-vehicle outcome. 
Moreover, a critical issue emerges: there exists a substantial likelihood that if the manufacturer truthfully declares the 
actual CO2 emissions and adheres to EC regulations during the type approval procedure, the family of vehicles might 
fail. This predicament arises due to the error margin, denoted by the factor A, which is set at 1%, potentially falling 
short when compared to the often-higher variability observed in production lines. 

Consequently, OEMs over-declared the CO2 emissions during the type approval process, thereby minimising the chances 
of both a family's failure and the testing of more than three vehicles in the CO2 emissions CoP procedure. The statistical 
limitations of the previous methodology are visually represented in Figure 1, where the pass and fail boundaries are 
illustrated. The blue line on the graph portrays the distribution of the sample mean of three vehicles from a simulated 
dataset. This dataset is assumed to follow a normal distribution with a mean equal to 0.95 and a standard deviation 
of 2%. The pass and fail boundaries are so close in the graph that the difference between them is not even visible, 
demonstrating  the  challenge  of  making  decisions  based  on  a  small  sample  size  and  leading  to  an  almost  binary 
outcome in many cases. 

89 

 
 
 
Figure 1:      Illustration of the old method.  

Innovative statistical framework for CoP 

To overcome this, JRC has developed an improved sequential sampling approach that addresses the shortcomings 

 

Statistical Soundness: Pass and fail decisions are grounded in robust statistical criteria. 

  Balanced Risk: Optimal balance between consumers' and manufacturers' risks is achieved by controlling false 

fail and false pass rates. 

 

 

Testing  Efficiency:  The  testing  burden  is  minimised  by  reducing  the  number  of  vehicles  tested,  while 
maintaining procedure robustness. 

Resilience  against  Excess  CO2:  The  method  safeguards  against  scenarios  where  CO2  emissions  measured 
within CoP (𝐶𝑂2𝐶𝑜𝑃) significantly exceed the respective declared values (𝐶𝑂2𝐷𝑉):  
𝐶𝑂2𝐷𝑉   𝑓𝑎𝑖𝑙 ⇔   𝑖𝑓 

𝐼𝑓 𝐶𝑂2𝐶𝑜𝑃 ≫

≫ 1   𝑓𝑎𝑖𝑙 

𝐶𝑂2𝐶𝑜𝑃
𝐶𝑂2𝐷𝑉

The proposed validation criteria of this method are rooted in the expected statistical variability of a limited sample of 
observations'  mean  value,  as  it  converges  towards  the  population  average  with  increasing  tests.  This  decision 
procedure recognises the uniqueness of each family by demarcating distinct pass and fail regions, dependent on its 
variability, leading to diverse decisions. Similar to the previous method, the measured to declared CO2 emissions ratio 
is examined, with vehicles categorised into CoP families. The family criteria for pass and fail are defined as follows:  

The pass and family criteria are the following: 

1. 

If 𝑋𝑡𝑒𝑠𝑡𝑠 ≤ 𝐴 − (𝑡𝑃1,𝑖 𝑡𝑃2,𝑖)∙ , then the family passes. 

2. 

If 𝑋𝑡𝑒𝑠𝑡𝑠 > 𝐴 + (𝑡𝐹1,𝑖 𝑡𝐹2,𝑖)∙ , then the family fails. 

3.  Otherwise, another vehicle undergoes testing, increasing the sample size by 1, followed by recalculating the 

sample mean (𝑋𝑡𝑒𝑠𝑡𝑠) and standard deviation ( ). 

Key factors influencing the pass and fail rates are: 

  Maximum  Number  of  Tested  Vehicles:  The  maximum  allowable  sample  size  before  a  decision  is  reached 
significantly impacts the conclusion's speed, with lower limits accelerating decisions at the expense of reduced 
confidence and increased risk. 

  A Factor: The A factor is crucial, accounting for uncertainties like measurement inaccuracies and the potential 

high costs of failure. It sets the pass/fail boundary after testing the maximum number of vehicles. 

 

Pass and fail coefficients (𝑡𝑃1,𝑖, 𝑡𝑃2,𝑖, 𝑡𝐹1,𝑖 𝑎𝑛𝑑 𝑡𝐹2,𝑖) play a pivotal role in determining the decision speed 
the number of vehicles tested before a pass or fail decision.  

90 

 
 
 
 
 
 
 
 

 

Family  Mean  Value  (Bias):  The  average  difference  between  CO2  emissions  measured  within  CoP  and  the 
official values, encompassing the average over-declaration. 

Family Standard Deviation: This factor reflects the variability of the ratio, influenced by measurement errors 
and repeatability. 

Following an extensive cost-benefit analysis utilising data provided by type approval authorities and simulations, the 
following parameters were selected: a maximum allowable sample size of 16 vehicles, an A factor set at 1.01, and 
specific pass and fail coefficients detailed in Table 2 of the Annex. Furthermore, a minimum sample size of three was 
retained, as this represents the threshold for achieving a meaningful standard deviation. 

Figure 2:      Illustration of the new method 

The fail and pass thresholds are illustrated in Figure 2. If the calculated average falls below the pass threshold (denoted 
by the green region) or exceeds the fail threshold (indicated by the red region), the procedure concludes, resulting in a 
family  pass  or  fail,  respectively.  Alternatively,  if  the  calculated  average  falls  within  the  grey  region,  an  additional 
vehicle  is  incorporated  into  the  sample,  and  the  process  continues  until  a  conclusive  pass  or  fail  determination  is 
reached. Figure 2 demonstrates the boundaries for a representative family with a population standard deviation of 
2%. The boundaries are depicted for a theoretical example where the sample standard deviation is always 2%, which 
will  not  be  exactly  the  case,  however  it  should  fluctuate  around  this  variability.  Conspicuously,  greater  variability 
expands the grey region, indicating a more prolonged decision-making process. Regardless of variability, the pass and 

distribution (blue line) and the boundaries which in turn are dependent of the sampling standard deviation of the tested 
vehicles. 

Table  1  presents  the  pass  rates  for both  the  previous  methodology  and  the  proposed  one.  Apparently,  the  former 
approach consistently rendered pass or fail verdicts with just three or four vehicles. This is evident from the fact that 
the cumulative pass rate achieved with four tests is equivalent to the overall pass rate obtained from 16 tests. For 
instance, under the assumption of a 1% over-declaration for a family with a 2% standard deviation, the old method 
yielded a total pass rate of 96% (which represents the pass rate after all vehicles are tested). Strikingly, this same 
pass rate of 96% is achieved even when testing only 3 or 4 vehicles using the previous approach. In contrast, the new 
proposal  gradually  escalates  pass  and  fail  outcomes.  With  a  1%  over-declaration  and  reasonable  variability, 
manufacturers  have ensured passes (100%  total  pass  rate). This strategy  minimises the occurrence  of false  fails. 
Conversely, even a 1% under-declaration causes the total pass rate to drop below 50%, an unacceptable scenario for 
manufacturers. Consequently, the false pass rate is also managed, ensuring a low level of risk for consumers. 

91 

 
 
 
 
 
Table 1:      Pass rates for a population with standard deviation of 2% 

Mean (bias) 

Cumulative vehicles 

Old method 

New method 

0.95  
(5% over-declaration) 

0.99 
(1% over-declaration) 

1.00 
(0% over-declaration) 

1.01 
(1% under-declaration) 

Discussion  

4 

16 

4 

16 

4 

16 

4 

16 

100 

100 

96 

96 

80 

80 

49 

49 

98 

100 

42 

100 

16 

95 

3 

38 

The  sequential  sampling  approach  introduces  pass  and  fail  criteria  that  effectively  balance  consumer  and 
manufacturer risks, reducing false fail and false pass rates. This approach dynamically adjusts sample size through 
continuous data analysis, optimising resource utilisation while maintaining decision-making reliability. 

The new methodology significantly enhances CO2 compliance assessment across regulatory aspects, spanning both 
Light-Duty  and  Heavy-Duty  Vehicles.  The  study  lays  the  groundwork  for  addressing  evolving  challenges  such  as 
pollutant emissions and energy consumption. It is important to acknowledge that this overview captures key insights 
while omitting the intricate stages and complexities involved in the actual process. Furthermore, the extensive dataset 
provided by type approval authorities, which played a pivotal role in validating and refining the method, is not presented 
here. 

The analysis of the new method reveals notable improvements over the previous approach. As elucidated in Table 1, 
even  a  1%  over-declaration  leads  to  a  total  pass  rate  approaching  100%,  showcasing  a  remarkable  equilibrium 
between effective risk management and safeguarding consumer interests. Conversely, it is noteworthy that a minor 
1%  under-declaration  can  lead  to  considerably  low  pass  rates,  underscoring  the  fine  line  manufacturers  tread  in 
ensuring compliance. In conclusion, the advanced approach enhances CO2 emissions compliance evaluation, benefiting 
both manufacturers and consumers, while acknowledging the need for ongoing refinement and validation processes. 

References  

Chatzipanagi, A., Pavlovic, J., Fontaras, G., et al., (2020). Impact of WLTP introduction on CO2 emissions from M1 and N1 vehicles : 
evidence from type-approval and 2018 EEA data, Publications Office of the European Union, Luxembourg.  

European Commission, (2007). Regulation (EC) No 715/2007 of the European Parliament and of the Council of 20 June 2007 on 
type approval of motor vehicles with respect to emissions from light passenger and commercial vehicles (Euro 5 and Euro 6) and 
on access to vehicle repair and maintenance information. Official Journal of the European Union 

European Union, (2017). Commission Regulation (EU) 2017/1151 of 1 June 2017 supplementing Regulation (EC) No 715/2007 of 
the European Parliament and of the Council on type-approval of motor vehicles with respect to emissions from light passenger and 
commercial  vehicles  (Euro  5  and  Euro  6)  and  on  access  to  vehicle  repair  and  maintenance  information,  amending  Directive 
2007/46/EC of the European Parliament and of the Council, Commission Regulation (EC) No 692/2008 and Commission Regulation 
(EU) No 1230/2012 and repealing Commission Regulation (EC) No 692/2008. Official Journal of the European Union 

European Union, (2019). Regulation (EU) 2019/631 of the European Parliament and of the Council of 17 April 2019 setting CO2 
emission performance standards for new passenger cars and for new light commercial vehicles, and repealing Regulations (EC) No 
443/2009 and (EU) No 510/2011. Official Journal of the European Union 

Suarez, J., Komnos, D., Ktistakis, M.A., Fontaras, G., (2023). 2025 and 2030 CO2 emission targets for Light Duty Vehicles, Publications 
Office of the European Union, Luxembourg. 

92 

 
 
 
 
 
 
Annex 

Table 2:  Pass and fail coefficients. 

Tests (i) 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

PASS 

FAIL 

𝑡𝑃1,𝑖 

1.686 

1.125 

0.850 

0.673 

0.544 

0.443 

0.361 

0.292 

0.232 

0.178 

0.129 

0.083 

0.040 

0.000 

𝑡𝑃2,𝑖 

0.438 

0.425 

0.401 

0.370 

0.335 

0.299 

0.263 

0.226 

0.190 

0.153 

0.116 

0.078 

0.038 

0.000 

𝑡𝐹1,𝑖 

1.686 

1.177 

0.953 

0.823 

0.734 

0.670 

0.620 

0.580 

0.546 

0.518 

0.494 

0.473 

0.455 

0.438 

𝑡𝐹2,𝑖 

0.438 

0.438 

0.438 

0.438 

0.438 

0.438 

0.438 

0.438 

0.438 

0.438 

0.438 

0.438 

0.438 

0.438 

93 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2.4  TAP.04. Remote sensing of vehicle emissions. 

94 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Single-Blind Test on the Effectiveness of the Gumbel Distribution Method in Detecting 
High-Emitters in Remote Sensing Data 

Z. Yang1*, J.E. Tate2, C.E. Rushton2, J. Borken-Kleefeld3, M. Qiu4, Å. Sjödin5 

1 School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 611756, China 

2 Institute for Transport Studies, University of Leeds, Leeds, West Yorkshire, LS2 9JT, England 

3 Transportation Ecology, Technische Universität Dresden, 01062 Dresden, Germany 

4 Department of Earth System Science, Stanford University, Stanford, CA 94305 2004, United States 

5 IVL Swedish Environmental Research Institute, Stockholm, SE-10031, Sweden 

1. Introduction 

Gumbel distribution has been used to find the cut-off point between normally-behaving vehicles and high-emitters 
(HEs) with faulty, deteriorated, or tampered emission after-treatment system based on remote sensing measurements 
(Rushton et al., 2021; Yang et al., 2022). Vehicles with extremely high emission values that do not follow the Gumbel 

-

capture a snapshot sample of emission rates, whether these vehicles are true HEs have not been validated. In this 
paper, a single-blind test was designed to investigate whether the Gumbel HE identification method applied only on 
instantaneous emission rates can effectively screen out vehicles whose average emission values are higher than a 
certain level.  

2. Materials and Methods: 

2.1 Data Preparation 

Following  Qiu  and  Borken-Kleefeld  (2022),  data  sets  of  25  fleets  were  prepared  based  on  PEMS  and  Chassis 
measurements, with known HEs in the mix. Each set includes single/multiple sampled instantaneous NOx emissions 
(g/kg) of 5,000 diesel cars as well as other information: fleet ID, vehicle ID, speed and acceleration, vehicle specific 
power (VSP), average NOx emission rates (over a complete test cycle in the PEMS and Chassis measurements). The 
data  are  from  tests  under  normal  conditions  with  temperature  between  20  and  25C,  and  the  quality  of  the 
measurements has been checked.  

Data from three emission standards are prepared: Euro 5, Euro 6a/b and Euro 6d-temp. In this trial, a slightly-HE is 
defined as a vehicle whose average emission factor is 2 times above the limit value but 5 times below the limit value 
(2 * limit < slightly-HE < 5 * limit). And a super-HE is a vehicle with an average NOx emission rate 5 times or more 
above the emission limit value (super-HE  5 * limit). The emission limit value is the NEDC limit converted to the unit 
g-kg using an average (real world) fuel efficiency, arriving at 3.516 g (NOx)/Kg (fuel) for Euro 5 diesel cars, and 1.563 
g (NOx)/Kg (fuel) for Euro 6a/b and Euro 6d-temp diesel cars. 

2.2 Study Design 

Researchers were divided into three groups in this trial. Group A prepared the data sets, they had the full information 
onducted the Gumbel HE identification method and screened out the 

Gumbel distribution in detecting HEs. The detailed trial process is stated as follows: 

 intermediary to check the effectiveness of the 

Step 1: Group A prepared data sets of 25 fleets and each fleet includes full information of every record (fleet ID, 
vehicle ID, instantaneous NOx emission rate, instantaneous speed and acceleration, vehicle specific power, average 
NOx emission rates of the vehicle); 

95 

 
 
 
 
 
 
 
 
 
Step 2: Group A transmitted the data excluding average NOx emission rates to Group B; Group A transmitted the list 
of true HEs (including slightly-HEs and super-HEs) and the corresponding average NOx emission rates to Group C. 

Step 3: Group B fitted the Gumbel distribution into each fleet, screened out candidate HEs, and passed the detailed 
list of candidate HEs to Group C. 

Step 4: Group C compared the true HEs provided by group A with candidate HEs provided by Group B and calculated 
the accuracy rate of using the Gumbel distribution to identify HEs. 

2.3 the Gumbel Distribution HE Identification Method 

Rushton et 
probability density function (pdf) f(x) and cumulative density function (cdf) F(x) of a Gumbel distribution are given as: 

 𝑓(𝑥) =

1

𝑏

𝑥−𝑎
𝑏

− 

+𝑒

𝑒−(

𝑥−𝑎

𝑏 ) 

 𝐹(𝑥) = 1 − 𝑒−𝑒

𝑥−𝑎
𝑏  

Eq. 1 

Eq. 2 

Where a is the location parameter and represents the highest observation frequency in a dataset; and b is the scale 
parameter and represents the spread of the dataset. 

The Gumbel distribution identifies the candidate HEs by dynamic threshold (cut-
emission  performance,  meaning  the  candidate  HEs  identified  by  the  Gumbel  distribution  are  not  above  a  fixed 
e Figure 1). However, when conducting the single-
blind test, Group B with known HEs can only provide vehicles above a fixed threshold (i.e., 2 or 5 times above the NEDC 
emission  limit  value).  As  a  result,  the  candidate  HEs  identified  by  the  Gumbel  distribution  and  HEs  with  average 
emission above a certain threshold are not directly comparable. This study therefore compares the percentage of true 
HEs (including slightly-HEs and super-HEs) with candidate HEs identified by the Gumbel distribution. 

Figure 1: Using the Gumbel distribution to identify candidate HEs in Euro 6a/b van fleet (Yang et al., 2022) 

3. Results 

3.1 Results from Fleets with Single Records 

 

Percentage of slightly-HEs and super-HEs in every fleet 

The prepared 25 fleets have increasing share of HEs (including slightly-HEs and super-HEs) from 0% to 100% (see 
left plot on Figure 2). The predicted HE shares by the Gumbel distribution is shown on the right plot of Figure 2. The 
predicted HE shares and true HE shares are more consistent when the fleets have more clean vehicles (share of true 
HEs below 50%). However, the Gumbel distribution estimates there is only 1% of HEs in every fleet while the share of 
true HEs accounts for 55%-100% of the fleet. Similar results have been found in Yang et al. (2022), where there are 

-

to have lots of HEs (Carslaw et al., 2011). The assumption is that most Euro 5 diesel vans have high emissions, so the 
measurements are fitted to the Gumbel distributions with relatively high location and scale parameters and left very 
96 

 
 
 
 
 
 
 
 
 
-

slightly-HEs and super-HEs within fleets that have a very high share (>50%) of true HEs. 

Figure 2: Share of true HEs and super-HEs in sampled Euro 5 fleets (left), share of predicted HEs identified by the 
Gumbel distribution (right) 

In addition, the R2 value (goodness-of-fit) of observed and theoretical quantiles of the Gumbel distribution is calculated 
for each fleet, fleets with R2 values higher than 98% are considered as more reliable results, and these fleets are fleet 
15, 18, 16, 2, 23, 7, 25, 1, 13, which are fleets with either the lowest or the highest share of true HEs. Whether fleets 
with higher R2 value have more accurate prediction of true HEs is discussed as follows. 

 

Error of commission 

Error of commission is the amount of vehicles that the Gumbel distribution identifies as HEs whereas they are actually 
clean vehicles. Within the 1% of candidate HEs identified in the Euro 5 fleets 2, 23, 7, 25, 1, 13 (these fleets are 
considered as a good fit of dirty fleets by the Gumbel), 99% are true HEs. In detail, 38% of vehicles are super-HEs ( 
5*limit value) and 61% are slightly-HEs (2*limit < slightly-HE < 5*limit). Within the 8%/10% of candidate HEs in fleet 
15, 18, 16 (these fleets are considered as good fit of clean fleets by the Gumbel), 25% are true HEs. In detail, only 
21% of the vehicles are super-HEs and 5% are slightly-HEs. This indicates that the error of commission in clean fleets 
are very high. For fleets with lower R2 values, 77% of the candidate HEs are true HEs. In detail, 53% are super-HEs 
and 24% are slightly-HEs. 

Figure 3: the share of super-HEs and slightly-

-

3.2 Results from Fleets with Multiple Records 

97 

 
 
 
        
 
 
 
 
 
This section analyses the effectiveness of the Gumbel distribution in detecting HEs by using multiple records fleets. 
Within each fleet, there are 5,000 vehicles in total and for each vehicle there are 7 random selected records based on 
PEMS and Chassis measurements. It is expected that multiple records fleets, the Gumbel HE identification method will 
have improved performance. 

 

Percentage of slightly-HEs and super-HEs in every fleet 

(To follow) 

 

Error of commission 

(To follow) 

4 Summary and Conclusions 

This is an area of ongoing research in Autumn 2023, repeating this analysis with fleets of Euro 6b and Euro 6d-temp 
diesel cars as well as multiple records fleets, following the same process as reported for the Euro 5 fleets. The full 
results will help to verify the effectiveness of the Gumbel distribution in detecting HEs. 

Acknowledgements 

We are grateful for the provision of RS data from the H2020 CARES project and for modal chassis and PEMS data 
from the ERMES dbEFA.  

References 

Carslaw, D.C., Beevers, S.D., Tate, J.E., Westmoreland, E.J. and Williams, M.L. 2011. Recent evidence concerning higher 
NOx  emissions  from  passenger  cars  and  light  duty  vehicles.  Atmospheric  Environment.  45(39),  pp.7053-7063. 
https://doi.org/10.1016/j.atmosenv.2011.09.063 
Qiu, M. and Borken-Kleefeld, J. 2022. Using snapshot measurements to identify high-emitting vehicles. Environmental 
Research Letters. 17(4), p044045 
Rushton, C.E., Tate, J.E. and Shepherd, S.P. 2021. A novel method for comparing passenger car fleets and identifying 
high-chance  gross  emitting  vehicles  using  kerbside  remote  sensing  data.  Science  of  The  Total  Environment.  750, 
p142088. https://doi.org/10.1016/j.scitotenv.2020.142088 
Yang, Z., Tate, J.E., Rushton, C.E., Morganti, E. and Shepherd, S.P. 2022. Detecting candidate high NOx emitting light 
commercial  vehicles  using  vehicle  emission  remote  sensing.  Science  of  The  Total  Environment.  823,  p153699. 
https://doi.org/10.1016/j.scitotenv.2022.153699 

98 

 
 
 
 
 
 
 
 
 
 
Applications of multi-sensor roadside networks for fleet emission source 
apportionment and single-vehicle EF determination  

Mengyuan CHU1, Peter Brimblecombe2, Dane Westerdahl1, Ning Zhi*1  

1 Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, zhining@ust.hk 
2 Department of Marine Environment and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan 

Introduction 

Vehicular emissions make a significant contribution to air pollution in cities. Hong Kong is one of the most densely 
populated cities in the world, with many people living, working, and commuting in areas that are directly exposed to 
these emissions. These pollutants may harm people and more information is needed on these pollutants to assess and 
mitigate the sources (Sun et al., 2019). It is crucial to identify and quantify the emission of the wide range of vehicles, 
especially high emitters' emission factors (EFs) for urban air quality management (Huang et al., 2022).  

Different methods have been applied to investigate in-use vehicle emissions. The Hong Kong Environmental Protection 
Department uses Remote Sensing (RS) as an enforcement tool for detecting high-emitting vehicles beginning in 2014. 
However, this RS driven enforcement program does not include diesel vehicles - a primary source of NOx emissions, 
which is problematic in Hong Kong (Huang et al., 2022). Portable emissions measurement system (PEMS) are also used 
to measure emissions from many types of vehicles, but measure only one vehicle at a time, making it less suitable to 
represent an in-use traffic fleet. Chasing methods have also been applied to assess emissions and estimate the EF of 
in-use vehicles but also require a considerable workforce and elaborate monitoring protocols. Further, PEMS and RS 
measurements do not provide actual concentration required for exposure estimation at the heavily occupied roadside 
in urban area. Our study extends and compliments existing in-use emissions measurement methods. We used a high-
time-resolution  kerbside  microsensor-based  network,  capturing  plumes  of  passing  vehicles  to  determine 
concentrations and calculate EFs of NOx and CO. 

Method 

Instrumentation 

The sensor we used in the study was based on the High Speed Sensor system (HSS, Sapiens). It is a microsensor-based 
monitor customized for portable and continuous air pollution measurements. With a pump inside, it can actively sample 
and measure the concentration of NO, NO2, CO and CO2 with 1Hz time resolution. In addition to local storage (backup), 
real-time, direct output of pollutant concentrations and system status data were transmitted to a cloud server for 
online data processing. An automated plate capture and recognition  system were developed and applied to obtain 
vehicle information. Supplementary measurements including temperature, relative humidity, wind speed and direction 
were conducted using a mini-meteorological station (Hongyue HY-WDC5). 

Sites 

We conducted two measurement campaigns. The first was over 4-days in a busy street canyon in Hennessy Road, 
Causeway Bay, with ~3300 vehicles/day (from 9am   7pm). The site was within a street canyon, where buildings on 
either side of the roadway range between 20 and 40 storeys (∼120 220 m) forming a relatively confined space. The 
impact of the opposite lanes was largely separated by the wall of the tram tracks station and the footbridge near the 
site (Figure 1a). Seven HSSs were positioned on both sides and top of the carriageway to capture the exhaust plume 
and at elevated locations to be more representative of air quality at the canyon bottom. The L1, L2, R1, R2 are sensors 
set at the kerb were ~40 cm above the ground, to be close to tailpipe height, which enable the sensors capture the 
fasting changing vehicle plumes. The elevated sensor nodes were denoted H for high. The one on the lamp post on the 
left were named as LH. The one on the exterior of the overpass over the center of the carriageway was marked as CH.  
The one outside the stairway that provides access to the tram stop right (RH) was about 2m about the road. The set 
up can be found in Figure 1a (Brimblecombe, et al., 2021).  

99 

 
 
 
 
Figure 1. (a) Photograph illustrating the site on Hennessy Road, looking west (b) Photograph illustrating the site on 

, looking north (© Google, June 2023) (c) Layout of system. 

A second monitoring campaign was conducted at the roadside of a rural highway (Hiram's Hwy) in Sai Kung, have been 
found around 1200 vehicle passed from 9am   7pm in average in the 5 days of monitoring. The monitored section of 
the  highway  is  characterized  by  its  uphill  gradient  and  its  dual-lane,  one-way  configuration.  The  area  is  further 
distinguished by the presence of trees flanking both sides of the highway. We deployed 4 sensors on each side of the 
road, with R1, L1 closest to the tailpipe, ~40 cm above the ground, and R2, L2 1.5 m away from R1 and L1 with the 
same height. Like the Causeway site, the elevated sensor nodes were denoted H for ~ 1.5m high (Figure 1b).  

Sensor Calibration  

Laboratory calibration of the pollutant sensors took place before and after the measurements. Standard CO (100 ppm 
CO  and  10%  CO2/N2,  Linde  HKO  Ltd.,  Hong  Kong,  China)  concentrations  came  from  a  dynamic  calibrator  (T700U, 
Teledyne, Sauzend Oaks, CA, USA) combined with a zero-gas generator (701, Teledyne, Sauzend Oaks, CA, USA). The 
NO2 and  NO  were  generated  with  a  NO2/NO/O3 calibration  source  (714,  2B  Technology,  Boulder,  CO,  USA).  During 
calibration and linearity checks, the gas flow rate was constant at 0.6～0.8 L min , which was validated by a flow 
meter  (Defender  520,  Mesa  Labs,  Lakewood,  CO,  USA).  Prior  to  the  calibrations,  both  the  T700U  and  2B  714 
instruments were warmed up for 60 min, while the pollutant sensors were warmed up for 12h to ensure a steady 
state.  

Data Analysis 

The roadside environment can highly variable,  thus  short  response  time  of sensors are required  to  track the fast-
changing concentration fluctuations of observed plumes. Thus, a novel deconvolution algorithm was developed and 

seconds, allowing further analysis of the plume data (Chu et al., 2022; Brimblecombe et al., 2022) (Figure 2).  

Figure 2.  (a) Imposed changes in NO concentration in laboratory experiments (dotted line), concentration 

measurements as black dots and deconvoluted concentrations as gray line. (b) similar plots for NO2. Adapted 
Atmospheric Environment, 

2/NOx and primary NO2 from individu

Volume (295), 295, 119562. Copyright (2023) by Elsevier. 

Then the NOx, CO, and CO2 plumes selected with criteria were matched with specific vehicles based on the time the 
vehicle captured by our camera. Algorithms were developed to estimate the EFs of individual vehicles based on the 
carbon  balance  principle  and  identify  high  emitters  among  a  fleet  from  the  snapshot  of  vehicle  emissions.  The 
algorithm was written using python.  

Results 

Overview of concentrations 

The sensor network as illustrated in Figure 1a offers an opportunity to delve into the intricate dispersion process within 
the urban street canyon of Causeway Bay. Prior to scrutinizing the EF of individual vehicles, we utilized a variety of 

100 

 
 
 
 
 
 
 
statistical tools including Fourier transforms, auto-correlation function, cross-correlation function, and non-parametric 
test to investigate the fluctuation of pollutant concentration in the street canyon micro-environment.  

The kerbside concentrations showed cyclic changes that corresponded to passing traffic. To discern patterns at lower 
frequencies, we employed Fourier analysis for the examination, which revealed weak cycles at 60s and 110s (Figure 
3 f-j) which matches with the traffic light interval. Generally, the vehicles from crossroad near the site wait 60-s for a 
green light, then go straight ahead or turn left and pass the site. The traffic flow along Hennessy Road followed another 
cycle  of  110  s.  These  two  cyclic  periods  were  clearest  in  the  data  from  node  L1,  which  was  close  to  the 
crossroad.  (Brimblecombe et al., 2021). 

Figure 3. The autocorrelation function (ACF) of 1-s NOx concentrations for the seven nodes for the four days: (a) 12
21, (b) 12 22, (c) 12 28 and (d) 12 29. (e) Average autocorrelation of 1-s CO across the four nodes for 
the entire campaign. Fourier transforms for NOx on 12 21 at the kerbside nodes, (f) L1, (g) L2 and (h) R2, 
and the elevated node on the overhead walkway (i) CH, along with the transform for (j) kerbside CO at L1. 
Note the spectral power takes the units ppb2 Hz , with the quantity at the top of each figure (f j) shown 
by the small numerals. Adap
Environments, Volume 8(12), 137. 

Autocorrelation is the correlation of a signal with a delayed copy of itself as a function of delay. It was used in previous 
study to examine the fluctuations of pollutant concentration in a street canyon. Researchers noted that autocorrelation 
persists in the stagnant zones of air that lie outside the vortices in urban canyons (Pavageau and Schatzmann, 1999). 
In our study, autocorrelation of 1-s NOx concentrations at nodes R1 and R2, which were closest to the tailpipe and the 
turbulence  caused  by  the  passing  vehicles,  generally  showed  a  rapid  decline  (Figure  3a d).  Node  L2  on  the  less 
turbulent left-hand lane often showed a decline that was slower than that of nodes R1 and R2. The autocorrelation 
function at the more elevated nodes LH, CH and RH also decayed more slowly; this was probably associated with less 
turbulent  air  and  less  variation  in  pollutant  concentrations.  The  average  autocorrelation  of  1-s  CO  concentrations 
across the four days is shown in  Figure 3e. Similarly, it was more persistent at the elevated nodes on the walkway.  

The application of cross-correlation analysis has been demonstrated to be instrumental in elucidating the variations 
in airflow between an enclosed street canyon and its intersections (Richmond-Bryant et al., 2009). In this study, we 
employed cross-correlation function to explore the lags between the appearance of pollutants at various locations, 
with node R1 taken as the reference. Across the entire campaign, the cross-correlation functions of NOx were slightly 
delayed at the other nodes (Figure 4a). A similar picture emerged for CO (Figure 4b). The lag time was longer for the 
elevated nodes (Figure 4c), indicating that the pollutant signal moved more slowly in the vertical direction. The signals, 
and presumably the plumes of passing vehicles, moved more rapidly in the horizontal direction, as the kerbside nodes 
were separated by less than 10 m, representing just a few seconds of vehicles travel along Hennessy Road. However, 
it took slightly longer for the concentration signal to cross the carriageway from the right-hand kerb to that on the left 
(nodes L1 and L2). The signal took some tens of seconds to reach the elevated nodes, e.g., the signal observer on the 
walkway (CH) was often delayed by more than 20 s. 

101 

 
 
 
 
Figure 4. (a) The cross-correlation function (CCF) of 1-s NOx concentrations from the other six nodes with R1 on 12
28 and (b) CO. (c) The lag of each node for both NOx and CO with node R1 determined for the four days. 

Environments, Volume 8(12), 137. 

The 1-min average concentrations are compared with distinct vehicle flows in Figure 5 as box and whisker plots. The 
averaged  kerbside  NOx  related  to  double-decker  bus  frequency,  indicating  the  main  NOx  contributors  were  diesel-
fueled double-decker buses (Figure 5a). The Kruskal-Wallis test suggested the concentrations at different flows of 
buses was not the same (p < .0001). While CO concentration increasing with the flow of EURO 4 or lower-emission 
standard  vehicles  (Figure  5b),  most  of  which  were  private  cars  (PC)  and  taxis  (Chu  et  al.,  2022).  This  trend  was 
significant (Kruskal-Wallis p < .001). 

Figure 5. Box and whisker plots and mean values (triangles) for (a) 1-min averaged roadside NOx concentration and 
number of double-decker buses per minute (nBus) and (b) 1-min averaged roadside CO concentration and 
number of EU4 (n<=EURO4) and lower standard vehicles per minute. Note: measurements from node R2 
omitted values during congestions. The box represents the interquartile range or IQR (25 75%) of the data. 
The horizontal line through the center of the box is the median. The whiskers represent 1.5 × IQR. 

Z., 2022, Atmospheric Environment, Volume (271), 118878. Copyright (2023) by Elsevier. 

., Wei, P., Liu, C. H., Du, X., & Ning, 

Emission factors 

We applied a carbon-balance, differential method to estimate the EF from clear isolated peaks of CO2 and pollutants 
including NOx and CO (Chu et al., 2022). After the alignment of plumes of different pollutants, The EF is determined 
from the best-fit to the slope of Cp against CCO2: 

EFp = wcdCp/dCCO2 

Instead of using only the maximum and minimum points of the peak, the EF was estimated by all the points throughout 
the plume. The benefit of this method is it avoiding the error 
cause big error in EF estimation (Georgopoulos& Seinfeld, 1986). The EFs extracted from the snapshot of the vehicles 
can be paired with a specific vehicle from information extracted from photo recognition of the vehicle registration 
plate and matched to non-private registration information including engine size, vehicle type, fuel used and registration 
year. Such information only available for the Causeway Bay project, therefore, all subsequent discussion will be limited 
to the Causeway Bay project. 

The differences in EFNOx from the five typical vehicle types are shown in Figure  6a, buses > middle goods vehicles 
(MGV)  >  private  car  (PC)  >  light  good  vehicles  (LGV)  >taxis,  and  the  EFs  appear  not  to  be  drawn  from  the  same 
distribution (p<0.005). It's worth mentioning that the buses in this project are diesel double-decker buses, and most 
LGV are diesel 5-seats vans. Further, there appears to be a slight increase in EFNOx with diesel engine size (Figure 6b). 
The Kruskal-Wallis test reveals that the samples were not all drawn from the same distribution (p<0.05). The fuel type 
(Figure 6.cd) suggests the EFNOx fall in the order diesel > petrol > LPG. The Kruskal-Wallis test suggests again that 

102 

 
 
 
 
 
these are not drawn from the same population (p<0.05). For EFCO, LPG vehicles, mainly mini-bus and taxis, had the 
highest value. Our EF estimates were in the range of previous studies (Chu et al., 2022).  

Figure 6. Emission factors calculated from individual plume segments for various elements of the fleet: Double 

Decker buses (Bus), medium goods vehicle (MGV), private car (PC), light goods vehicle (LGV) and taxi (a) 
EFNOx for vehicle types (b) EFNOx for various engine sizes (c) EFNOx for individual vehicles using different fuels 
(diesel, petrol, and liquid petroleum gas: LPG) and (d) EFCO for individual vehicles using different fuels. 

Z., 2022, Atmospheric Environment, Volume (271), 118878. Copyright (2023) by Elsevier. 

., Wei, P., Liu, C. H., Du, X., & Ning, 

EF from different sensor nodes 

The  benefit  of  the  multi sensor  network  is  we  can  measure  the  different  part  of  the  plume  and  estimate  the  EF 
accordingly. The Figure 7a shows a pair of typical plume segment detected using raw data from sensors L2 and LH. 
The Figure 7b compares the EF estimated from L2 and LH of the plumes in a scatter plot, the scatter shows a close to 
one slope (1.08 for NOx and 0.97 for CO) and R2 (0.91 and 0.96 for NOx and CO respectively), indicating a consistence 
value of EF in the dispersion process of plumes.  

Figure 7. (a) A typical plume segment detected using raw data from sensors L2 and LH showing concentration peaks 
NOx and EFCO determined from the plume segment detected at sensor LH compared with 

that determined from sensor L2. 

Discussion 

This  study  has  demonstrated  the  feasibility  of  using  roadside  gas  sensors  to  derive EFs  from  individual  plume 
segments. We captured data from many vehicles and developed a database for real-world on-road EF. Importantly, 
this method is particularly effective in identifying high emitters, given that low emitters may not release sufficient 
quantities for detection by the sensors. This cost-effective approach could be particularly advantageous for developing 
countries. In fact, it has already been successfully implemented in India (Raparthi et al., 2023). While the method may 
have lower accuracy, this limitation can be mitigated through repeated measurements, which is an avenue for future 
research.The technique could also be extended to other pollutants such as PM, BC, or toxic organic substances and can 
readily be expanded into more sites.  

103 

 
 
 
 
 
Acknowledgements  

This  work  was  supported  by  European  Union  (EU) 
  Hong  Kong  Research  and  Innovation  Cooperation  Co-funding 
Mechanism by Research Grants Council (RGC) (Ref.: E-HKUST601/19), Collaborative Research Fund (CRF) for 2018/19 
(Ref No. C7064-18G), Hong Kong Environment and Conservation Fund (ECF/21/2021). 

References 

Brimblecombe, P., Chu, M. Y., Liu, C. H., & Ning, Z. (2021). NOx and CO fluctuations in a busy street canyon.  Environments, 8(12), 
137. 

Brimblecombe,  P.,  Chu,  M.,  Liu,  C.  H.,  Fu,  Y.,  Wei,  P.,  &  Ning,  Z.  (2023).  Roadside  NO2/NOx  and  primary  NO2  from  individual 
vehicles. Atmospheric Environment, 295, 119562. 

Chu, M., Brimblecombe, P., Wei, P., Liu, C. H., Du, X., Sun, Y., & Ning, Z. (2022). Kerbside NOX and CO concentrations and emission 
factors of vehicles on a busy road. Atmospheric Environment, 271, 118878. 

Georgopoulos, P. G., & Seinfeld, J. H. (1986). Instantaneous concentration fluctuations in point‐source plumes. AIChE journal, 32(10), 
1642-1654. 

Huang, Y., Lee, C. K., Yam, Y. S., Mok, W. C., Zhou, J. L., Zhuang, Y., & Chan, E. F. (2022). Rapid detection of high-emitting vehicles by 
on-road remote sensing technology improves urban air quality. Science Advances, 8(5), eabl7575. 

Pavageau,  M.,  &  Schatzmann,  M.  (1999).  Wind  tunnel  measurements  of  concentration  fluctuations  in  an  urban  street 
canyon. Atmospheric environment, 33(24-25), 3961-3971. 

Raparthi, N., Barudgar, A., Chu, M., Ning, Z., & Phuleria, H. C. (2023). Estimating individual vehicle emission factors from near-road 
measurements in India. Atmospheric Environment, 308, 119869. 

Richmond-Bryant, J., Eisner, A. D., Hahn, I., Fortune, C. R., Drake-Richman, Z. E., Brixey, L. A., & Ellenson, W. D. (2009). Time-series 
analysis to study the impact of an intersection on dispersion along a street canyon. Journal of Environmental Monitoring, 11(12), 
2153-2162. 

Sun, S., Stewart, J. D., Eliot, M. N., Yanosky, J. D., Liao, D., Tinker, L. F., ... & Wellenius, G. A. (2019). Short-term exposure to air pollution 
and incidence of stroke in the Women's Health Initiative. Environment international, 132, 105065. 

104 

 
 
 
 
 
 
Development and testing of a novelty Remote Sensing Device for the simultaneous 

ating in Multilane Roads 

J. Buhigas1and J. Muñoz1 

1Opus RS Europe SL, Madrid, 28015, Spain  

Introduction  

Remote Sensing (RS) plays a vital role in road traffic emissions monitoring, providing effective and efficient means of 
evaluating the real-world emissions of motor vehicles. Remote Sensing Devices (RSD) can remotely detect and measure 

for  market  surveillance,  air  quality  modelling  and  implementing  targeted  measures,  such  as  emission  reduction 
programs  and  enhanced  vehicle  inspection  programs.  Data  from  the  RSDs  helps  policymakers  to  design  fair  and 
efficient  policies  and  allocate  resources  effectively.  By  monitoring  traffic  emissions,  RSDs  can  also  identify  highly 
polluting vehicles (known as high-emitters, HE) for selective action. Most of the cases in the world use portable RSDs, 
capable of being easily placed on the sides of the road without complicated interventions or installations. In this way, 
the devices can be moved frequently to cover many points of a territory with few instruments. However, there are 
applications where a long-term measurement at a specific site might be desirable. Similarly, being able to measure 
more than one lane of the road at a time would increase the number of sites where these systems can be placed, and 
thus reduce operating cost in long-
we  have  developed  a  new  device,  called  LRSD,  based  on  interband  and  quantum  cascade  lasers,  with  a  modular 
architecture  based on fibre optic for guiding the  lasers  radiation,  with the  intention of measuring at  least  2  lanes 
simultaneously,  using  a  single  main  system.  This  paper  describes  the  instrument  and  evaluates  this  multilane 
capability, as well as its drawbacks and barriers identified during testing. In the last part of the paper, an alternative 
dual-lane system is evaluated by adapting a commercial RSD. 

State of the art 

Vehicle  emissions  RS  has  been  used,  validated  and  improved  for  decades  and  there  is  extensive  literature  on  the 
subject. The classic RS approach employs infrared (IR) and ultraviolet (UV) radiation to quantify the presence of the 
targeted pollutants by measuring the amount of radiation that every single pollutant absorbs through the optical path. 
The amount of each pollutant is obtained by calculating the gas column density thought the Lamber-Beer law (equation 
1). 

− ln (

𝐼(𝜆)
𝐼0(𝜆)

) = 𝑘(𝜆) 𝐶  (1) 

Where: I is the intensity of the received radiation as a lambda function; I0 is the emitted intensity of the radiation as a 
lambda function; k is the absorption coefficient for a specific gas as a lambda function; and C is the column density 
(product of gas concentration and length of the gas column). 

The principle of the vehicle emissions RS measurement is to calculate ratios of pollutants to CO2 present in the exhaust 
gas plume of each analysed vehicle. The pollutant ratio is a good indicator of whether the vehicle is more or less 
polluting  and  can  be  transformed  into  fuel-specific  emissions  by  applying  the  combustion  equation,  for  further 
estimating  the  travelled-distance  emissions  of  a  vehicle  and,  for  instance,  comparing  measured  values  to  type-
approval limits. The underlying principles are detailed and reviewed in the relevant literature (Bishop, B.A., 1996), 

There are different technologies, architectures and providers, but portable or semi-fixed remote sensing devices (RSD) 

-

-

emissions from the vehicles, but their high operating costs and installation complexity make them difficult to use. For 
a top-down RSD to be beneficial compared to a cross-road RSD, its installation and operation must be much simpler 
and they must provide multi-lane measurement capabilities. 

Description of the LRSD 

The proposed Laser Remote Sensing Device (LRSD) applies Tuneable Diode Laser Absorption Spectroscopy (TDLAS) to 
measure CO2, CO, NO, NO2, NH3 and HC (unburned hydrocarbons, typically specified as propane). The gas column density 
is calculated by a regression between the measured absorptance and the chemical model HITRAN (https://hitran.org/). 

range. When performing a measurement for a vehicle passage, the system sequentially activates each of the 5 lasers 

105 

 
 
 
 
to emit 5000 measurement profiles in 0.5 seconds. In other words, the device takes 1.5 million measurements in 0.5 
exhaust plume) record. The 

-

-

subtraction of both measurements provides a value of the plume concentrations only. 

The LRSD uses interband and quantum cascade lasers (ICL and QCL, respectively) for each of the targeted species (a 
total  of  6  lasers).  A  QCL  is  a  novelty  technology  that  can  be  applied  in  infrared  spectroscopy.  It  has  layers  of 
semiconductors forming a high quantum well, which transform the electronic energy intro electromagnetic radiation 
(Chen and Furukawa, 2023). The QCL can emit in the spectral region from 4 to 10 microns. The ICL is a coherent optical 
source for the mid-wave infrared band (defined from 3 to 6 microns) (Graydon et al., 2018). ICL are less powerful, but 
are two of the most difficult issues in this 
kind of systems, so all the lasers are grouped in the same module, and custom-made electronics have been developed 
to centralize the lasers temperature and current control, and to synchronize the emission of the lasers. The LRSD has 
been designed to be used in a cross-road configuration or in a top-down configuration. For the latest, the protype has 
a modular architecture (Figure 1), with the intention of measuring more than 1 lane simultaneously. It is composed of 
a) a laser control and emission module, b) individual detector and processing modules (one for each lane), c) a splitter 
module,  d)  a  fiber optic bundle for guiding the  lasers  radiation from the emission module  to  the detectors  and e) 
processing units over each of the road lanes. With this design the LRSD can simultaneously measure several lanes, 
thus evaluating up 7000 vehicles per hour, increasing the capabilities of the RS technology to massively monitor real-
world emission from road transport. Fiber optics make it possible to guide all the emitted radiation to the detector and 
processing units on the top of each lane. The detector and processing units have a collimation optics for sending each 
laser radiation to the road´s embedded retroreflector, returning the light to the detector module on the gantry, once 
it has passed through the exhaust plume of the vehicle. 

Figure 1: Schematic of LRSD for multilane vehicle emissions measurement. 

System performance results 

Prior to an evaluation of the system to measure multiple lanes, the prototype was sent to Ispra's Joint Research Centre 
(JRC) for an evaluation of its performance. The tests were conducted on May 10, 11 and 12, 2022. The LRSD system 
was placed on a quiet road, with light positive slope of approx. 1.3º, with the main module on one side of the track and 
the reflector module on the opposite side of the road. PEMS-equipped vehicles were used as "Reference" to check the 
emissions measurements of the LRSD against a known method. To obtain instantaneous vehicle emissions at the RSD 
location, a set of five reference vehicles were all equipped with PEMS. PEMS measure the gaseous concentrations in 
and  the  mass  flow  of  the  exhaust  of  the  vehicles  within  a  known  uncertainty  range  and  were  considered  as  the 
reference within this study. Calibrated systems from AVL and Horiba were used during the campaign. Each vehicle had 
its PEMS installed and pre- and post-test checks following the RDE (Real Driving Emission) regulation were performed. 
The systems were recording measurement data continuously with 1 Hz while the vehicles were driving on the JRC site 
and through the E-RSD setup about 20 times each. Details on PEMS measurements as regularly performed at the JRC 
can be found in Bonnel P. et al.  2022. The characteristics of the tested vehicle are described in Table 1. 

Table 1: PEMS-equipped vehicles. 

106 

 
 
 
 
 
Vehicle ID 

Model 

Brand 

Engine type 

Fuel type 

Emissions std 

1 

2 

3 

4 

5 

Forester 

Subaru 

ICE 

Gasoline 

Q3 

Audi 

OVC-HEV 

Gasoline 

Euro 6d 

Euro 6d 

F-Pace 

Jaguar 

ICE 

Diesel 

Euro 6d-temp 

Golf 

VW 

NOVC-HEV 

Gasoline 

Euro 6d-temp 

Boxer 

Peugeot 

ICE 

Diesel 

Euro 3 

Year 

2021 

2021 

2019 

2020 

2006 

The results were presented in TRA2022, Lisbon (Buhigas et al., 2022) and a summary is shown in  Figure 2. Linear 
regression revealed very good agreement of LRSD with PEMS data: CO/CO2 slope = 1.02, R2 = 0.78; NO/CO2 slope = 
0.94, R2 = 0.93 and NO2/CO2 slope = 0.82, R2 = 0.95. 

Figure 2: Scatter plots for NO, NO2 and CO to CO2 ratios (ppm/%) between PEMS (x-axis) and LRSD (y-axis) 
measurements for the different reference vehicles. 

Top-down installation and performance assessment 

-down configuration in Madrid, Spain. The first test was done 
in October 2022, in a private test area of the company INDRA (www.indracompany.com), which collaborated in the 
project, installing the LRSD on a 1-lane gantry, with the reflector on the ground (Figure 3). The second test was done 
in December 2022, on a public 2-lane road, on a fully equipped gantry managed also by INDRA (Figure 4). 

Figure 3: First deployment of the LRSD on a gantry in a test road. a) Retroreflector system installed on road. b) and 
c) System installed on the gantry. 

107 

 
 
 
 
 
 
 
Figure 4: Second deployment of the LRSD on a gantry in a public road. a) General overview. b) Detail on the laser 
module and one of the detectors over one lane. 

The instrument alignment and measurement capabilities were tested in both top-down setups. Although there were 
no  PEMS  vehicles  to  repeat  previous tests,  there  was  a  qualitative  analysis  on the measured  concentrations.  24/7 
uninterrupted monitoring was successfully tested too, with the system operating several days without interruption. The 
fibre optics for guiding the radiation from the laser module to the lane detectors worked well too. Fibre optics losses 
were measured, finding losses (including insertion losses and attenuation on the fibre optics), between 3.5 dB and 4.5 
dB, depending on the wavelength and laser beam diameter for 2 m fibre length. The initial power output of the laser 
sources is between 20mW and 250mW. The system is capable to measure with signal around 3 mW per lane (in the 
worst cases). Therefore, considering also the splitting losses, at least two lanes can be measured with a single set of 
lasers, as tested in this experiment. A larger number of lanes could be measured if alternative laser radiation sources 
are found for those with lower output power or implementing more than one laser module. 

However, although the experiment was successful, the researchers found several critical challenges for a top-down 
remote sensing system, which are described below: 

 

Installation complexity: The installation of a top-down system was found to be extremely challenging, especially 
for a multilane deployment. Installing equipment above the road requires complying with strict road safety rules. 
The reflector on the asphalt requires modifying the road itself, which is a separate and difficult procedure for road 
environment and road safety too. Additionally, a multilane deployment requires closing traffic lanes. All this makes 
the installation permits difficult and costly to get. 

  Equipment  maintenance:  Any  maintenance  on  the  overhead  modules  requires  overcoming  stringent  safety 
requirements,  which  is  a  barrier  to  establishing  long-term,  low-cost  monitoring  programs.  Similarly,  the  road 
reflector has proven to be an even greater problem. The need for a clean reflector represents a critical problem 
due to dust and water accumulation, as well as durability issues caused by passing vehicles. These problems are 
more acute if the measurement site receives regular rainfall (as is the case in many parts of the world) and/or if 
the traffic intensity is high or with a high share of heavy vehicles passing by. Our findings suggest that any road 
reflector will need regular maintenance and sporadic replacement. This would require, again, solving challenging 
permitting and executing civil works, including closing lanes. These drawbacks increase the operational costs for a 
long-term monitoring. 

Therefore, because of the shortcomings found above, a top-down measurement system may be less favourable than 
a  cross-road  system  for  continuous  and  long-term  monitoring,  which  was  precisely  one  of  the  objectives  of  this 
research.¨ 

Cross-road RSD adaptation for multilane measurement 

In order to find a solution to the problems identified above, the RSD 5700 is adapted to measure on two-lane roads 
with a horizontal (cross-road) deployment. Although this type of setup has been tested in the past, it had never been 
implemented in a robust way. The system is upgraded with a double camera system, capable of identifying the passage 
of vehicles in the right and left lanes, reading their license plates associating them with each lane. The limitation of a 
dual-lane cross-road RSD is that if two vehicles pass parallel (or very close to each other) in the two lanes at the 
measurement point, the acquired column density refers to the sum of the pollutants emitted by the two vehicles. To 

108 

 
 
 
 
prevent this phenomenon, a logic is implemented that marks the measurements of events of this type as invalid. The 
software identifies the passage of each vehicle in each lane, with a timestamp and a vehicle speed. By applying a rule 
that leaves enough space between one vehicle and the next, the system can seamlessly identify isolated plumes of 
vehicles preventing cross-contamination, and flag passes from very close vehicles as invalid. With this methodology, 
considering typical traffic intensities on European roads, it is believed that these events would be very unusual in most 
locations. In other words, a dual-lane cross-road remote sensing approach can be efficient and trustworthy. 

This new system has been tested in a two-lane test area. In this case the tests have been carried out in Teesdorf, 
Austria, in a test area of KAPSCH (www.kapsch.net) who is also a partner of the NEMO project. The tests were conducted 
in June 2023. 

Figure 
5: 

Deployment of the RSD5700 on dual-lane road. Left: deployed components. Right: a photograph of 
four different vehicles circulating on both lanes. 

A total of 2,202 records were collected over 3 days of measurements. 1,843 of those records were related to known 
vehicles (cars and trucks). Car drivers were instructed to circulate with and without acceleration, to capture small and 
large plumes. In all scenarios vehicles drove with a large or very close separation between them. Table 2 summarizes 
the percentage of valid measurements for the different tested scenarios. In the worst-case scenario (vehicle circulating 
in 2-lanes simultaneously and drivers were not accelerating at the measurement point) the validity rates are 49% and 
76% for cars and trucks respectively, so there is still a high share of records with valid measurements even if vehicles 
drive very close on purpose. 

Table 2: Valid measurement rates (% from total) in each tested scenario and vehicle type. 

Scenarios 

Validity rate for cars 

Validity rate for trucks 

1-lane driving no acceleration 

1-lane driving with acceleration 

2-lane driving no acceleration 

2-lane driving with acceleration 

71% 

98% 

49% 

82% 

68% 

-- 

76% 

-- 

The results are also analysed based on the vehicle-to-vehicle interval time, since it is the critical factor to ensure that 
the  plumes  can  be  measured  in  isolation.  Figure  6  summarizes  these  results.  Each  bar  shows  the  total  valid 
measurements (in percentage) of each scenario, breaking down between passes of vehicles that are very  close or 
separated. The results show that when vehicles are very close, the validity rates are 7% to 27%, as many of these 
events are discarded by the dual-lane algorithm. It is also observed that if vehicles pass by the system with some 
acceleration, the rates of vehicles with valid measurements are higher, whether the vehicles are closer together or 
further apart. 

109 

 
 
 
 
 
 
Figure 6: Valid measurement rates in each scenario and by vehicle-to-vehicle interval time. Dark blue: less than 2 
seconds between one vehicle and the next. Light blue: more than two seconds. 

Conclusions 

 

 

The  developed  LRSD  system  can  measure  vehicular  pollutant  emissions  remotely  and  unassisted  and  can  be 
installed in different road infrastructures to measure in a top-down manner the passing vehicles, but the technical 
and operational complexity of this configuration has proved to be very high. One of the reasons for considering a 
vertical approach is to facilitate unattended and cost-effective competitive monitoring in the long term, but the 
installation and maintenance costs of this deployment type can be insurmountable. 

The RSD 5700 in dual-lane mode can measure all types of vehicles traveling on dual-lane roads without installing 
any elements on the asphalt or over the lanes, which suggests that it can greatly facilitate both the installation 
and  maintenance  of  the  system,  making  it  more  viable  than  a  vertical  setup  in  the  long-term.  Thanks  to  the 
installation of internal cells in the RSD, the system can regularly self-audit itself, without the need for the use of 
any external gas cylinder, and therefore also operate 24/7 without any human assistance. 

  Dual-lane measurements with a cross-road setup is feasible and reliable if the system marks as invalid records 
where there is an overlap of plumes (measured or potential). Taking into account that most of the kilometres of 
road built in the world are two-lane, this amplifies the possibilities for cross-road systems. 

Acknowledgements  

This project was funded by the European Commission within the H2020 programme in the NEMO project (ref. 860441). 
The authors thank the VELA team of the JRC for their work evaluating the s
sincere thanks to the ITS companies INDRA and KAPSCH for their collaboration in the testing of the systems in Spain 
and Austria. 

References 

Bernard Y., German J., Muncruief R. (2019). Worldwide use of remote sesing to measure motor vehicles emissions, ICCT whitepaper. 

Bishop, G.A.; Stedman, D.H. (1996). Measuring the Emissions of Passing Cars. Accounts of Chemical Research, 29: 489-495. 

Bonnel. P. et al. (2022). European market surveillance of motor vehicles - Results of the 2020-2021 European Commission Vehicle 
Emissions Testing programme, EUR 31030 EN, Publications Office of the European Union, doi:10.2760/59856, JRC128360. 

Buhigas et al. (2022). Performance of a remote sensing device based on a spectroscopic approach for the remote measurement of 
vehicle emissions, Transport Research Arena (TRA) Conference. 

Chen J., Furukawa H. (2023). Rapid and non-invasive detection of high-thickness glucose solutions concentrations using quantum 
cascade laser-based transmission infrared spectroscopy, Infrared Physics & Technology, volume 131. 

Graydon O. (2018). Interband cascade laser. Nature Photon 12, 568. 

110 

 
 
 
 
 
 
 
 
 
Advanced Air quality sensors and Remote Sensing to investigate vehicular traffic 
emissions in Milan: CARES H2020 Project results 

S. Moroni1*, F. Cruz Torres1,8, R. Pedrini1, P. Palomba1, S. Belmuso1, M. Bedogni1, S. Casadei2, T. Rossi2, G. Migliavacca2, M. Knoll3, C. 
Schmidt4,5, H. Juchem4, D. Pöhler4,5, Y. Bernard6 and A. Sjodin7 

1 AMAT Agenzia Mobilità Ambiente e Territorio s.r.l., Department of Environmental Transition, Milan, 20134, Italy 
2 Innovhub - 
3 Graz University of Technology, Institute of Electrical Measurement and Sensor Systems, Graz, 8010, Austria 
4 University of Heidelberg, Institute of Environmental Physics, Heidelberg, 69120, Germany 
5 Airyx GmbH, Eppelheim, 69214, Germany 
6 ICCT International Council on Clean Transportation, Berlin, 10178, Germany 
7 IVL Swedish Environmental Research Institute, Box 530 21, SE-400 14 Gothenburg, Sweden 
8 Politecnico di Milano, Department of Energy, 20156 Milan, Italy 

Corresponding author: silvia.moroni@amat-mi.it 
Keywords:   sensors, air quality, remote sensing, vehicular traffic emissions. 

1. Introduction 

To support urban air quality (AQ) policies, sensor technologies entailing lower costs than reference instruments have 
recently become available and may complement traditional AQ reference instruments (Kang et al., 2022). One of the 
cities currently equipped with such AQ sensors is the city of Milan, Italy. In particular, Milan is equipped with advanced, 
i.e.,  near-reference,  AQ  sensors  through  AMAT,  an  agency  belonging  entirely  to  the  Municipality  of  Milan.  Such 
technologies (Moroni et al., 2022) seamlessly collect information at vulnerable sites to support the implementation of 
the recently adopted local Air Quality and Climate Plan. 

5 (CARES). 
Such project investigated real-world emissions to help improve urban AQ as well as enforce pollutants limits in three 
European cities, including Milan. Emissions from over 45,000 real-driving vehicles were monitored in Milan during the 
fall  2021.  Measurements  were  carried  out  by  means  of  remote  emission  sensing  (RES)  techniques,  including 
commercial  systems  and  an  innovative,  lower  cost,  point  sampling  (PS)  system.  The  latter  may  constitute  a  more 
Congestion Charge Zones 
Area C and Area B. Additionally, advanced AQ sensors may provide an easily 
and Low Emission Zones 
deployable system to determine airborne pollutant concentrations, thus providing city administrations with a more 
complete effectiveness evaluation of their AQ policies. 

Assessments of advanced AQ sensors performance, which differ from low-cost sensors (cf. Castell et al., 2017), are 
still few to date (Karagulian et al., 2019) and the CARES project provided the unique opportunity to assess a subset of 
-

related emissions, including from high polluting vehicles, was analysed. 

2. Material and methods 

location of Via Madre Cabrini (Milan, Italy). This street well exemplifies urban street canyons and develops along the 
south-west to north-east direction.  

In the next sections the methodology followed by this research is outlined (Section 2.1) and the related instrumentation 
is described (Section 2.2). 

2.1 Methodology 

As  a  first  step  of  the  measurement  campaign,  the  compact  AQ  station  equipped  with  advanced  (near-reference) 
sensors technology (Sun et al., 2016), deployed by AMAT, and a reference mobile AQ cabin, deployed by Innovhub-SSI, 
were collocated. The collocation enabled simultaneous, parallel measurements  of the same set of pollutants. Such 
collocation  made  it  possible  to  determine  the  accuracy  of  the  advanced  sensors  compared  to  the  reference 
instruments. 

At this stage, observations were filtered so that data collected during precipitation events (> 0 mm), windy events 
(wind intensity > 1.5 m/s) and when wind comes through the street canyon opening (wind intensity > 0.2 m/s and 

5 https://cares-project.eu/ 

111 

 
 
 
 
                                                       
 
direction from 303° to 346°) were excluded. The number of observations before weather-related filtering amounted 
to 25,688. Remaining datapoints after data filtering amounted to 22,335 (83%), whereby datapoints excluded are 
2,315 (9%) for wind intensity and 1,038 (4%) for rainfall. This filtering operation made it possible to limit the undesired 
effect of the urban canyon opening and to only consider data during convenient weather conditions for AQ monitoring.  

As a second step, the compact AQ station was deployed in combination with the RES systems in the typical urban 
street  canyon  of  via  Madre  Cabrini  for  eleven  days  (from  27.09.2021  to  07.10.2021)  to  detect  traffic-related  air 
pollutant concentrations (e.g., NO2, NO, CO, CO2, PM10, PM2.5, PM1, particle number (PN) and black carbon (BC)). 

At this stage, AQ data were collected by means of the advanced AQ sensors at a 1-minute resolution and compared 
with measurements from the RES point sampling system deployed by Graz University of Technology and Airyx GmbH. 
Such RES point sampling measurements were gathered at a 1-second resolution and averaged over 1-minute intervals, 
so as to consistently compare them with 1-minute AQ data. Additionally, 5-minute running means were calculated to 
determine statistical correlation between the two data series. 

Finally, an analysis of variance (ANOVA) test was performed to verify the hypothesis that advanced AQ sensors are 
capable of detecting 
 Traffic data was used to categorize 
each minute with the number of vehicles that passed through the street in that time frame. Subsequently, an ANOVA 
was carried out to test whether the mean value of each advanced AQ sensor pollutant is different among the categories 
of vehicular transits. 

2.2 Instrumentation 

The advanced sensors compact station (Mod. MAS-AF300, Sapiens Env. Technology Co., Ltd) deployed by AMAT was 
calibrated with reference instruments and is equipped with: 

Electrochemical sensors for detection of gaseous pollutants (NO, NO2, O3, CO), 
Photoionization detector for Total Volatile Organic Carbon (TVOC), 

 
 
  Non-Dispersive Infrared - NDIR detector for CO2, 
  Optical Particle Counter - OPC equipped with 50 channels with length between 0.3 𝜇m and 10.0 𝜇m for 

PM10, PM2.5, PM1 and Particle Number (PN), 

  A 5 wavelength aethalometer, including 880 nm and 375 nm absorption analysis for BC monitoring. The 

measurement of BC is referred to the PM2.5 fraction. 

The reference AQ monitoring station deployed by Innovhub-SSI is a relocatable cabin equipped with: 

 
SO2 and CO analyzers, HORIBA model APSA-360 and HORIBA model APMA-360, respectively,  
  An NOx analyzer, HORIBA model APNA-360, which uses cross-modulated chemiluminescence. 
  An Ozone analyzer, a HORIBA model APOA-360, utilizing non-dispersive UV absorption with cross-modulated 

flow.  

  A methane and non-methane hydrocarbons analyzer, a HORIBA model APHA-360, based on flame ionization 

with cross-modulated flow.  

  An ammonia analyzer CNH3S2, which relies on the chemiluminescence principle and is equipped with an 
additional NH3 to NO converter consisting in a quartz tube filled with quartz wool heated to 980°C.  
The Aethalometer® Model AE33 by MAGEE Scientific is the analyzer used for Black Carbon monitoring. 

 

The  instruments  used  in  the  reference  Innovhub  cabin  for  meteorological  parameters  detection  are  produced  by 
MICROS. They are: 

 
 
 
 
 
 

a Relative humidity sensor MICROS model SRHS/C,  
an Atmospheric Pressure MICROS model SRHS/C SDVD.0/2,  
an Air temperature MICROS model STEP.0/2V,  
a Rain gauge MICROS model PLUV,  
a Total solar radiation sensor (pyranometer) MICROS PIR model,  
a Wind direction and speed sensor: MICROS model SDVD.0/2.  

The sensors are fixed on top of an extensible pole by means of a support, consisting of a stainless-steel bar fitted with 
two collars. 

Meteorological  parameters  (ambient  temperature,  pressure,  precipitation,  wind  and  speed  direction)  could  both  be 
monitored with compact sensors integrated in the AQ relocatable station and reference instruments. In the current 
analysis, however, only meteorological data measured by reference instruments were considered. 

112 

 
 
 
 
3. Results 

A first indication of the level of agreement between the advanced AQ sensors and the reference instruments can be 
seen in Figure 1 for NO2 and Figure 2 for BC. When considering a 1-minute time resolution, a good agreement between 
advanced AQ sensors and AQ reference instruments can be noticed, despite the different principles of measure. 

Figure 1. NO2 concentration measurement collocation results. 

Figure 2. BC concentration measurement collocation results. 

A more rigorous degree of agreement between advanced AQ sensors and the reference instruments was determined 
for each pollutant monitored via the coefficient of determination (R2) and the regression model. The relative results 
(Figure 3) indicate that the advanced AQ sensors reach good agreement with the reference AQ instruments in detecting 
airborne  traffic  tracers  such  as  NO2  (R2  =0.81),  NO  (R2  =0.74)  and  Black  Carbon  (R2=0.79)  despite  the  different 
measuring principle. Also, Ozone (O3), an important interfering pollutant for airborne NO2 measurements, presents good 
agreement between AQ sensors and AQ reference instruments data (R2 =0.80). Carbon monoxide (CO) concentrations 
feature a lower correlation (R2=0.40). This is probably due to the very low absolute airborne values near the lower 
detection limit, which leads to a higher noise variance in the data. 

With regard to emissions, a significant positive correlation was observed between the advanced AQ sensors data and 
the RES Point sampling detections of gaseous traffic-related pollutants such as NO2 (R2> 0.60) and CO2 (R2> 0.60). 
Such results need to be viewed with due consideration of the existing distance (i.e., 15 m ca.) and height difference 

113 

 
 
 
 
 
 
 
between the RES system and the AQ monitoring site. Lower correlation coefficients are found for BC (R2> 0.30) probably 
due to the non-gaseous nature of the pollutant (gravity force, particle-particle interaction) (Figure 4). 

The coefficient of determination R2 relative to NO2 measurements by advanced AQ sensors and by RES Point sampling 
system  ranges  between  0.6  and  0.8.  In  particular,  the  higher  R2  (0.8)  is  obtained  when  5-minute  average  AQ 
concentrations 
ones.  
Moreover, as the level of NO2 emission increases, as measured by the point sampling sensors, a less than proportional 
increase in the NO2 concentration, as measured by the AQ sensors, can be expected. In particular, with the increase of 
1 ppb (1.91 µg/m3) of NO2 measured by the RES Point sampling sensors, an increase of 0.5-0.6 ppb (0.96-1.15 µg/m3) 
is observed on average in the measurements made by the AQ sensors. 

considered, 

1-minute 

instead 

are 

of 

The coefficient of determination R2 relative to BC measurements by advanced AQ sensors and by RES Point sampling 
system ranges between 0.3 and 0.4. In particular, a higher R2 is obtained when 5-minute average AQ concentrations 
are considered, instead of 1-minute ones. In addition, conversely to NO2, as the level of BC emission increases, as 
measured by the RES Point sampling sensors, a less than proportional increase in the BC concentration, as measured 
by the AQ sensors, can be expected only when considering a 1-minute concentrations average. 

The coefficient of determination R2 relative to CO2 measurements by advanced AQ and by RES point sampling sensors 
ranges between 0.7 and 0.8. In particular, the higher R2 (0.8) is obtained when 5-minute average AQ concentrations 
are considered, instead of 1-minute ones. As a result, a clear agreement between the advanced AQ and RES point 
sampling sensors can be observed.  

R2 = 0.81 
y = 0.56x + 9.41 

R2 = 0.79 
y = 0.93x + 1.57e2 

Figure 3. Agreement between advanced AQ sensors and reference instruments in the case of NO2 [ppb] (left), and BC 
[ng/m3] (right). 

R2 = 0.61 
y = 0.48x + 10.70 

R2 = 0.68 
y = 0.77x + 13.37 

114 

 
 
 
 
 
 
 
 
 
 
 
 
 
R2 = 0.32 
y = 0.77x + 13.37 

Figure 4. Relationship between advanced AQ sensors and RES Point Sampling measurements for NO2 [ppb] (top left), 
CO2 [ppm] (top right), and BC [ng/m3] (bottom). 

With regard to traffic, ANOVA results show that advanced AQ sensors are capable of detecting incremental transits of 
vehicles (Figure 5). Indeed, as a consequence of an incremental vehicle transit, an increase in NO2 concentration was 
observed.  Such  increase  shows  to  be  statistically  significant  for  the  incremental  transit  from  zero  to  every  other 
condition, also from one to two and from five to more than five. 

25

24

23

22

21

20

19

18

r
o
s
n
e
s
d
e
c
n
a
v
d
A
Q
A
2
O
N

* 

* 

* 

S
P
n
o
i
s
s
i

m
E
2
O
C
/
X
O
N

0.11

0.10

0.09

0.08

0.07

0.06

0.05

0.04

* 

* 

* 

0

1

4
3
2
Vehicle / Minute

5

>5

0

1

2

3

4

5

>5

Vehicle / Minute

Figure 5. Impact of vehicle transits [Vehicle/Minute] on NO2 concentrations [ g/m3] as measured by the advanced AQ 
sensor  (left)  and  NOx  emission  factor  (NOx/CO2)  monitored  by  Point  sampling  (right)  (statistically  different  means 
marked with *). 

4. Conclusions 

results demonstrate that advanced (near-reference) sensors technologies 
are capable of detecting exposure to traffic-related emissions (NO2, BC, CO2) in local environmental monitoring. Such 
sensors, recently available for AQ monitoring in the city of Milan, are able to detect exposure to high polluting vehicles 
as well. Moreover, the analysis of variance (ANOVA) test results show that advanced AQ sensors are capable to detect 
local traffic volume variations. 

These results are important for the City of Milan and large European cities still not in compliance with the European 

* 

continuously collect information at vulnerable sites, can support the Air Quality and Climate Plan recently adopted by 
the City Council, attempting to counteract vehicular traffic-related air pollution. For European large cities, these results 
suggest the adoption of lower cost technologies, both emission- (e.g., RES Point Sampling) and concentration-detecting, 
to better assess traffic policy effectiveness such as Limited Traffic Zones. 

115 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Acknowledgements  

This  project  received  funding  from  the  EU Horizon  2020  research  and  innovation  program  under  grant  agreement 
No.814966 (CARES Project) and by the Agenzia Mobilità Ambiente e Territorio, a totally funded agency of the City of 
Milan. 

References 

Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., Broday, D., & Bartonova, A. (2017). Can commercial low-cost 
sensor  platforms  contribute  to  air  quality  monitoring  and  exposure  estimates?  Environment  International,  99,  293 302. 
https://doi.org/10.1016/j.envint.2016.12.007 

Kang, Y., Aye, L., Ngo, T. D., & Zhou, J. (2022). Performance evaluation of low-cost air quality sensors: A review. Science of The Total 
Environment, 818, 151769. https://doi.org/10.1016/j.scitotenv.2021.151769 

Karagulian, F., Gerboles, M., Barbiere, M., Kotsev, M., Lagler, A., & Borowiak, A. (2019). Review of sensors for air quality monitoring. 
Publications Office. https://data.europa.eu/doi/10.2760/568261 

Moroni, S., Cruz Torres, F., Palomba, P., Santo, U. D., & Colombi, C. (2022). Near-Reference Air Quality Sensors Can Support Local 
Planning:  A  Performance  Assessment 
Italy.  Environmental  Sciences  Proceedings,  19(1),  Article  1. 
https://doi.org/10.3390/ecas2022-12814 

in  Milan, 

Sun, L., Wong, K., Wei, P., Ye, S., Huang, H., Yang, F., Westerdahl, D., Louie, P., Luk, C., & Ning, Z. (2016). Development and Application 
of  a  Next  Generation  Air  Sensor  Network  for  the  Hong  Kong  Marathon  2015  Air  Quality  Monitoring.  Sensors,  16(2),  211. 
https://doi.org/10.3390/s16020211 

116 

 
 
 
 
 
Remote Sensing Measurements of Vehicle Emissions in Sarajevo 

Y. Cha and Å. Sjödin 

IVL Swedish Environmental Research Institute, Stockholm, 10031, Sweden.  

Yingying.cha@ivl.se  

Introduction 

Road traffic is often the main source of poor air quality in urban areas. Although stricter emission requirements and 
improved  emission  control  technology  in  Europe  have  led  to  reduced  emissions  from  petrol-powered  vehicles,  in 
particular since the early 1990s, problems with high emissions of nitrogen oxides (NOX) and particulate matter (PM) 
from diesel  vehicles  remain. This  is in part  due to  major shortcomings  in EU exhaust  legislation alongside  historic 
deceptions perpetrated by car manufacturers (e.g.  "dieselgate"). As a result, emissions during real-world driving in 
traffic  are  many  times  higher  than  in  driving  cycles  used  to  set  legal  requirements.  Consequently,  air  quality  in 
European cities has not improved in proportion to increasingly toughened legal requirements. As a result, interest in 
measuring emissions in real traffic has increased sharply in recent years, both by national authorities and individual 
cities.  

A comprehensive investigation of air pollutant emissions from traffic in real-world environments can help to better 
understand the impact of transport on urban air quality and to support regulations and techniques to mitigate future 
emissions. Remote sensing (RS) technology is a well-established technique to monitor real-driving traffic emissions 
and  has  been used  extensively  throughout  the  world. RS  is  well  suited  to  measuring  different  types  of  pollutants, 
especially gaseous species. The data handling and processing of RS is also well-developed which makes it easy and 
efficient to apply for tests at different scales.  

In this study, an intensive vehicle emission campaign was conducted with OPUS RS devices at four sites in Sarajevo, 
the capital of Bosnia and Herzegovina, in the summer of 2022 to measure air pollutant emissions from on-road traffic. 
The pollutants measured include gaseous species; oxides of nitrogen NOX, nitrogen dioxide NO2, hydrocarbon HC, and 
carbon  monoxide  CO,  as  well  as  particulate  matter  PM.  Around  25,000  vehicle  passages  were  recorded  of  which 
approximately 14,000 vehicles were analysed. These covered passenger cars (PC), light commercial vehicles (LCV), 
buses, trucks, and motorcycles.  

This  report  describes  the  Sarajevo  measurement  campaign  and  summarizes  the  main  findings  from  the 
measurements.  Emissions of NOX, PM, CO, and HC from passenger cars were analysed in terms of the fuel-specific 
and  distance-specific  emissions.  The  emissions  of  NOX  and  PM  from  the  Sarajevo  measurement  campaign  were 
compared with corresponding results derived from the CONOX database from measurements carried out in Switzerland 
(Sintermann et al., 2021) and Italy (Bernard et al., 2023) in the year of 2021. 

In  addition,  distance-specific  emissions  were  evaluated  against  the  corresponding  type-approved  regulatory  limits. 
Findings from the measurements of other type of vehicles, such as LCV, buses, trucks, and motorcycles are presented. 
The main findings are highlighted in the conclusion section and outlooks for future work are provided. 

Overview of measurements 

Measurement site  
The  measurement  campaign  was  carried  out  at  four  sites  in  Sarajevo  city:  site  KSL012  (Longitude  18.329452o, 
Latitude 43.84063o), located on Kurta Schorka and near the intersection of Kurta Schorka and Laticka; site KSL03 
(Longitude  18.365916o,  Latitude  43.85169o),  located  on  street  Radenka  Abazovica  between  Safeta  Zajke  and 
Dzemala Bijedica; site KSL09 (Longitude 18.39115o Latitude 43.85841o), located on street Hamdije Cemerlica near 
the intersection of Hamdije Cemerlica and Put zivota; and site BETANIJA (Longitude 18.407006o, Latitude 43.8717o), 
located  on  street  Betanija  at  the  intersection  of  between  Alipasina  and  Betanija.  (KSL012,  KSL03,  KSL09,  and 
BETANIJA) 

Distribution of samples  
There were 25,079 vehicle passages recorded in total, of which 15,409 (61.4%) were identified as passenger cars 
(PC),  1,787  (7.1%)  were  light  commercial  vehicles  (LCV),  197  (0.8%)  were  buses,  134  (0.5%)  were  trucks,  and 80 
(0.3%) were motorcycles. For 7,472 (29.8%) records, the vehicles could not be identified. The measurements comprised 
approximately 14,000 unique vehicles, as some vehicles were measured more than once. The fuel used for PC was 
77.4% diesel and 22.6% petrol. For LCV it was 85.3% diesel and 14.7% petrol, for buses 59% diesel and 41% with 
other  type  of  fuel  (such  as  CNG  and  LPG),  for  trucks  98%  diesel  and  2%  petrol,  and  motorcycles  were  all  petrol-
powered.  The  measurements  of  heavy-duty  trucks  were  less  successful  due  to  less  effective  vehicle  license  plate 
identification. 

117 

 
 
 
 
Results  

Passenger cars: fuel-specific emissions  

Of  the  15,409  remote  sensing  measurements  recorded  for  passenger  cars,  11,928  cars  were  diesel-powered  and 
3,477 were petrol-powered. In this section, the fuel-specific emissions of PM, NOX, NO2, HC and CO are presented. The 
NOX  emission results  from the Sarajevo  measurement were  compared with  corresponding data  retrieved  from the 
CONOX database. Distance-specific emissions of PM, NOX, HC, and CO were also evaluated to compare to the legislative 
emission limits.  

The valid numbers of remote sensing measurements for the emissions of PM, NOX, NO2, HC, and CO by fuel type and 
emission standard are summarized in Table 1. The most common vehicles were Euro 3, Euro 4, Euro 5 and Euro 6, and 
consisted  of  about  96%  of  all  the  diesel  vehicles  and  94%  of  all  petrol  vehicles.    For  PM,  the  average  measured 
emission decreased from Euro 1 to Euro 6 for both diesel and petrol cars. The average PM emission for Euro 6 was 
0.024 g/kg fuel for diesel vehicles, and -0.0005 g/kg fuel for petrol cars. Negative average values indicate the majority 
of the emissions measured were below the detection limit of the remote sensing instrument. Much lower PM emissions 
were observed for petrol cars compared to diesel cars for Euro 1-4, while for Euro 6 the difference between diesel and 
petrol vehicles was small. A large reduction can also be seen for NOX emissions from Euro 2 (18 g/kg fuel) to Euro 6 
(close to 0 g/kg fuel) for petrol vehicles, while for diesel vehicles the change is slower varying from around 17 g/kg 
fuel (Euro 2) to 8.5 g/kg fuel (Euro 6).  

Higher emissions of HC and CO were measured for petrol cars compared to diesel cars. However, remarkable reductions 
were observed with increasing emission standard from Euro 1 to Euro 6 for petrol vehicles, with a reduction ratio of 
approximately 95% for CO and 96% for HC. 

Table 1. Summary of the number of pollutant emission measurements for passenger cars by emission standard and 
fuel type (diesel and petrol). 

Standard 

Diesel counts (share) 

Petrol counts (share) 

Euro 1 

Euro 2 

Euro 3 

Euro 4 

Euro 5 

Euro 6 

73 (0.6%) 

217 (1.8%) 

2,459 (20.6%) 

3,661 (30.7%) 

3,516 (29.5%) 

1,831 (15.4%) 

Unknown -- 

171 (1.4%) 

22 (0.6%) 

155 (4.5%) 

633 (18.2%) 

1,079 (31%) 

723 (20.8%) 

817 (23.5%) 

48 (1.4%) 

Total 

11,928 (100%) 

3,477 (100%) 

Figure 1 and Figure 2 show the average measured fuel-specific NOx and PM emissions, respectively, of passenger cars 
in grams of pollutant emitted per kilogram of fuel burned (g/kg) by fuel type and emission standard. The emissions 
were compared with the CONOX database, which were included in these figures. In the CONOX datasets, Euro 6 includes 
Euro 6, Euro 6b, Euro 6c, and Euro 6d. Average emissions of NOx for Euro 2, Euro 3, Euro 4 and Euro 5 petrol cars from 
the Sarajevo measurements were remarkably higher, by a factor of 1.5 - 3, compared to the CONOX data. For diesel 
cars, unlike for the petrol cars, the NOX emissions measured in Sarajevo were about 20-30% lower than the average 
levels obtained from CONOX.  This is true for most Euro standards (Euro 1-5), except for Euro 6, for which the average 
NOx emission in Sarajevo was about 40% higher than according to the CONOX-2021 dataset.   

118 

 
 
 
 
Figure 1. Average fuel-specific NOx emissions from diesel and petrol passenger cars by emission standards for Sarajevo 
and CONOX database (2021). Whiskers represent the 95% confidence interval of the mean. 

Euro standard 

As can be seen in Figure 2, for petrol cars, PM emissions were substantially higher in Sarajevo compared to the CONOX 
data for all Euro standards, especially prior to Euro 5, except for Euro 6 in which case the average emissions of both 
datasets were close to 0 g/kg, i.e., below the detection limit of the RS instrument. The average PM emissions for diesel 
cars  in  Sarajevo  were  higher  than  those  of  the  CONOX  dataset  for  all  Euro  standards  except  for  Euro  6,  but  the 
differences were not statistically significant. 

Euro standard 

Figure 2. Average fuel-specific PM emissions from diesel and petrol passenger cars by emission standards for Sarajevo 
and CONOX database (measurements done in 2021). Whiskers represent the 95% confidence interval of the mean. 

Passenger cars: distance-specific emissions  

In addition to the fuel specific emissions of pollutants analysed above, the tailpipe pollutant concentration ratios were 
converted to distance-specific estimates in grams per kilometre (g/km), by combining the average pollutant emissions 
derived by RS with fuel consumption factors presented in Table 2.  

119 

 
 
 
 
 
 
 
 
 
 
Table 2. Fuel consumption factors for passenger cars (Borken-Kleefeld et al., 2018) 

Diesel PC 

Petrol PC 

FC kg/km 

FC kg/km 

0.0504 

0.0576 

0.0485 

0.0565 

0.0622 

0.058 

0.0597 

0.0575 

Euro 1 

Euro 2 

Euro 3 

Euro 4 

Figure 3 presents the average distance-specific NOx, PM, CO and HC emissions by fuel type and emission standard for 
the Sarajevo measurements. The overall trend shows that the emissions of all pollutants (NOx, PM, CO, and HC) have 
decreased with increasing emission standard for both petrol and diesel and cars. NOx and PM emissions from petrol 
cars are lower than those from diesel cars, especially for the latest emission standards Euro 5 and Euro 6. This is 
contrary  to  the  emissions of CO and HC which  have reduced  more slowly or levelled off. In comparison to the EU 
emission regulation limits for each stage, the real-world NOx and PM emissions from diesel passenger cars greatly 
exceed the regulatory limits for vehicle groups including Euro 2-Euro 5, which were found to have real-world emissions 
of NOx and PM about 40% to 220%, and about 80% to 200%, respectively, higher than the respective laboratory type-
approval limits. For petrol vehicles with emission standards prior to Euro 6, the real-world measured values were 2-6 
times higher for NOx emissions (excluding Euro 1), 2-6 times higher for CO emissions, and 2-5 times higher for HC 
emissions, than the laboratory type-approval limits, respectively. CO and HC emissions from diesel cars were found to 
be lower than  the limits for all emission standards, with  only Euro 3 and Euro 4 found to have slightly higher  CO 
emissions than the limits.   

The share of NOx and PM emissions (by g/km) from PCs and the share of measurements by fuel type and Euro standard 
were estimated. The results indicate that Euro 3, Euro 4, and Euro 5 diesel vehicles contribute the highest shares of 
total NOx emissions from all passenger cars with a total share of about 74% for NOx emissions, which together account 
for about 63% of the total number of measurements. For petrol vehicles, Euro 3, Euro 4, and Euro 5 petrol vehicles 
make up 16% of the total measurements and they together only contribute 13% of the total NOx.  Similarly, Euro 3 
and Euro 4 diesel vehicles together are responsible for the greatest share of total PM emissions from all passenger 
cars, which are responsible of 74% of total PM emissions, with a share of only 40% of total measurements. 

Euro 6 diesel and petrol cars were found to have lower real-world emissions of PM, CO, and HC than the regulation 
limits. For NOx emissions, Euro 6 petrol cars were measured with an average level lower than the limit, while Euro 6 
diesel vehicles had approximately 5 times higher emissions of NOx than the regulation limit. This result indicates that 
the  latest  Euro  6  standard  implementation  step  has  significantly  lowered  real-world  emissions  of  all  regulated 
pollutants (NOx, PM, CO, and HC) for petrol cars and greatly reduced the emissions of the PM, CO and HC for diesel 
cars. NOx emissions are the only exception, which remain high for diesel cars. It should be noted that while Euro 6 
petrol cars comprised 5% of all measurements, they produced negligible NOx and PM emissions (with both levels under 
the  detection  limit  of  the  RSD).  Euro  6  diesel  cars  contributed  approximately  9%  and  0.4%  of  total  NO  x  and  PM 
emissions,  respectively,  while  comprising  12%  of  all  measurements.  Based  on  these  findings,  the  replacement  of 
previous Euro standard vehicles with Euro 6 vehicles will remarkably reduce the contribution of emissions from PCs to 
urban air pollution. 

120 

 
 
 
 
 
 
 
 
(a) NOx emissions 

(b) PM emissions 

(c) CO emissions 

(d) HC emissions 

121 

 
 
 
 
 
 
 
 
Figure 3. Average distance specific NOx, PM, CO, and HC (gram per kilometre) emissions for diesel and petrol passenger 
cars by Euro standard. Whiskers represent the 95% confidence interval of the mean. Regulation limits for each emission 
substances are indicated with orange dash lines (petrol regulation limits) and blue dash lines (diesel regulation limits). 

Emissions from LCVs, Buses, and Trucks 

In total 1,787 measurements were obtained for LCV (7.1% of the total number of measurements), among which 1,524 
(85%)  were diesel-powered,  262  were  petrol-powered, and one powered  by  other  type  of fuel. Around 90%  of all 
diesel-powered LCVs were type-approved to Euro 4, Euro 5 and Euro 6. Of all petrol-powered LCVs, 57% were type-
approved to Euro 6, with Euro 4 and Euro5 constituting the remaining 42%. The average fuel-specific NOx, PM, HC, and 
CO emissions were analysed. For petrol LCVs only Euro 4, Euro 5 and Euro 6 were detected in the Sarajevo campaign, 
and  for  diesel  LCVs  the  total  attempted  measurements  of  Euro  1,  Euro  2  and  EEV  was  less  than  20,  which  was 
considered too small to draw a representative conclusion. By looking at the results of Euro 4, Euro 5 and Euro 6 LCVs 
with  both  fuel  types,  decreasing  trends  were  shown  for  NOx,  PM,  HC  and  CO  emissions  as  the  emission  standard 
increased, especially for petrol vehicles, although the average CO emission increased for diesel vehicles.  

197 measurement records were identified as for buses (0.8% of total measurements), among which 116 (59%) buses 
were powered by diesel and 81 were powered by other types of fuel. For the diesel buses detected, the average fuel-
specific emissions of NOx and PM by emission standard were estimated. Approximately 49% of the diesel buses were 
Euro 2 and Euro 3, 33% were Euro 6, 7% were EEV, and the remaining were Euro 4 and Euro 5. The average NOx 
emission of Euro 6 diesel bus was 13 g/kg fuel, which was slightly lower (around 10%-65%) than the buses with older 
Euro standards from Euro 3 to Euro 5. In comparison to other Euro standard stages, a significantly lower average PM 
emission (about 0.02 g/kg fuel) was found for Euro 6 buses, which was up to 30 times lower than Euro 2 buses.  

There are in total 134 measurements (0.5% of total measurements) obtained for trucks, among which 131 (98%) 
were diesel-powered and 3 were petrol-powered. The measurements of heavy-duty trucks were less successful, due 
to vehicle license plate capture, as previously stated. By reviewing a random sample of 300 photographs where license 
plates were not identified, about 13% could be manually identified as trucks, indicating the proportion of trucks initially 
identified was heavily underestimated. Around 85% of all diesel trucks were Euro 3-6, with the remaining 15% made 
up of older emissions standards than Euro 3. Despite the small sample, a significant reduction in both NOx and PM 
emissions, especially for PM emission, can be seen for the trucks with the latest Euro 6 emission standards.  

Conclusions 

The  one-week  remote  sensing  pilot  study  in  Sarajevo  successfully  recorded  around  25,000  vehicle  passages  from 
which emission measurements of PM, NOX, HC and CO were made, split into different vehicle types (PC, LCV, trucks, 
buses and motorcycles), fuel types (mainly diesel and petrol) and Euro standards (Euro 1-6).  

Data capture for heavy-duty trucks was less successful, due to less effective vehicle number plate identification, which 
is a well-known limitation of remote sensing in this context. A higher capture rate could be achieved with a different 
positioning of the video camera, but due to other problems (e.g., elevated tailpipes, connected trailers affecting the 
exhaust plume) associated with measuring exhaust plumes from trucks, capture rates will always be lower than for 
light-duty vehicles.  

As observed in many other recent European remote sensing studies, the PM emissions from the Sarajevo vehicle fleet 
have reduced substantially for all vehicle types (on the order of 80-90%), as Euro standards increase from Euro 1 to 
Euro 6. In contrast, NOx emissions from diesel vehicles have only reduced on the order of around 50%, despite the 
stricter Euro standards. 

Euro 3   5 diesel passenger cars contributed 74% of all NOx emissions from all passenger cars, while accounting for 
63% of the number of all passenger cars measured. Similarly, Euro 3 and Euro 4 diesel cars together are responsible 
for 74% of total PM emissions from all passenger cars, while representing only 40% of all passenger cars measured. 

The emission performance of the Sarajevo vehicle fleet deviates from that of other European fleets, mainly for petrol 
cars.  Emissions  of  PM,  NOx,  HC,  and  CO  were  substantially  higher  for  pre-Euro  5  cars  in  Sarajevo,  i.e.,  older  cars, 
compared to those in other European cities where similar remote sensing measurements have also been performed. 
The main reason for this observation is the substantially higher share of high-emitting cars within the Sarajevo petrol 
car fleet. 

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The latter observation raises the question whether a lower quality of petrol fuel has been (and still is) used in Sarajevo 
than in the EU, e.g., with higher sulphur content poisoning the three-way catalyst, poor maintenance and/or tampering 
of the cars, possibly in combination with an inefficient inspection and maintenance program (PTI). 

Based on the results from this study, the implementation of a low emission zone which impose restrictions on pre-
Euro 6 diesel vehicles would significantly reduce the contribution of air pollution emissions from road traffic.  

Future  measurements  should  also  consider  a  focus  on  heavy-duty,  typically  diesel  fuelled,  vehicles  as  their  data 
capture were relatively low, while their relative contribution to NOX remains high. 

Acknowledgements  

We  gratefully  acknowledge  the  support  of  Opus  RSE  for  both  providing  valuable  input  regarding  the  selection  of 
measurement sites in Sarajevo and and carrying out the remote sensing measurements. We gratefully acknowledge 
our colleagues at the Ministry of Traffic and the Ministry of Spatial Planning, Civil Engineering and Environment at the 
Canton of Sarajevo for investigating/assessing/documenting many different locations in Sarajevo allowing us to select 
the most appropriate measurement sites for this study. The support from IVL colleagues Helena Lundström, Anders 
Roth, and Tomas Wisell are highly appreciated. 

References 

Borken-Kleefeld, J., Hausberger, S., McClintock, P., Tate, J., Carslaw, D., Bernard, Y., Sjödin, Å. (2018). Comparing emission rates 
derived from remote sensing with PEMS and chassis dynamometer tests - CONOX Task 1 report. IVL Report No. C 293. 
https://www.ivl.se/english/ivl/publications/publications/comparing-emission-rates-derived-from-remote-sensing-with-pems-and-
chassis-dynamometer-tests---conox-task-1-report.html. 

J. Sintermann, G. M. Alt, M. Götsch, F. Baum, V. Delb (2021) Langjährige Abgasmessungen im realen Fahrbetrieb mittels Remote 
Sensing 
https://www.zh.ch/content/dam/zhweb/bilder-dokumente/themen/umwelt-tiere/luft-
strahlung/luftschadstoffquellen/verkehr/abgasmessungen-rsd/rsd_bericht_2021.pdf  

Ber
content/uploads/2023/06/CARES-814966-D3.4-Summary-report-on-partner-cities-measurement-campaigns.pdf. 

-project.eu/wp-

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Emissions from In-use Vehicles in Dublin using On-road Remote Sensing  

S. Mahesh1*, B. Ghosh1, A. McNabola1, W. Smith2, D. Timoney2, B. Fowler3, and P. Willis3 

1Department  of  Civil,  Structural  and  Environmental  Engineering,  School  of  Engineering,  Trinity  College  Dublin,  Dublin,  Ireland, 
Srinath.nda@gmail.com 

2School of Mechanical and Materials Engineering, University College Dublin, Dublin, Ireland 

3Ricardo Energy and Environment, Oxfordshire, United Kingdom 

Introduction and background 

Air pollution causes approximately 7 million deaths 
live in places where air quality exceeds World Health Organisation (WHO) guideline limits (WHO, 2016). At present, it 
is the greatest environmental risk to our health and environment.  In Europe, approximately 364,200 premature deaths 
annually are attributable to fine particulate matter (PM2.5), ozone (O3) and nitrogen dioxide (NO2) exposure (EEA, 2021). 
The  transport  sector  and  more  particularly  road-transport  emission  is  a  major  source  of  these  key  pollutants  and 
greenhouse gases. In Ireland, the emission share from transport is about 20%, of which 95.8% is caused by on-road 
vehicles (EPA, 2018). Due to high density of on-road vehicles and due to proximity of pollutant generation to high 
density urban dwellings the impact of air pollution is higher in urban areas.  

In Europe, air quality has been a priority and through policy development, legislation, standardisation and compliance, 
transport sector has successfully reduced emissions of some pollutants. However, reductions in emissions have been 
lower than originally anticipated over the last two decades. This is due to growth in road-transport, especially diesel 
vehicles,  and  the  fact  that  the  levels  of  'real-world  emissions'  particularly  from  diesel  passenger  cars  and  vans, 
generally exceed the permitted European emission (Euro) standards, which define the limits for exhaust emissions of 
new vehicles sold in the EU Member States (EEA, 2019). 

Since 1996, the introduction of European emission legislation, NEDC (New European Driving Cycle) has been used for 
vehicle type-approval tests in the EU. Several studies (Ntziachristos & Samaras, 2012; Dey et al., 2018) indicated that 
these laboratory tests using NEDC or similar cycles underestimate the exhaust emissions from in-use vehicles during 
real-world driving conditions due to low acceleration pattern, constant speed cruising and high number of idling events 
and other factors. The discrepancies between type approval and real-world emissions is significant. 

In  addition  to  the  test  cycle,  laboratory  tests  are  generally  conducted  using  new  and/or  well-maintained 
engines/vehicles, which may not represent the conditions of a large proportion of most vehicle fleet. In reality, the level 
of vehicle maintenance has a significant effect on emissions (Bishop et al., 1996). The type of after-treatment used 
also affects the emission levels especially NOx (Carslaw et al., 2011). The differences in emissions highlighted the 
need to include real-driving emission (RDE) testing to accurately estimate the emissions from in-use vehicles (Donateo 
and Giovinazzi, 2018; Carslaw et al., 2011). The main approaches used to measure emission rates from in-use vehicles 
under  real-world  conditions  are  principally  Portable  Emission  Measurement  Systems  (PEMS)  and  vehicle  emission 
Remote Sensing (RS). Typically, the PEMS is used in the RDE procedure and is the basis of the European RDE Regulations 
(European  Commission,  2018).  PEMS  provides  the  advantage  of  providing  detailed  emissions  information  from 
individual vehicles over whole drive cycles in real conditions. Currently, PEMS is quite frequently used for generation 
of EFs (emissions factors) to models. It requires that individual vehicles to be instrumented which is costly and time 
consuming and can test only a small number of vehicles. The RS technology developed by Bishop and Stedman (Bishop 
and Stedman, 1996) utilises short-duration road-side measurement of individual vehicle plumes using IR long-path 
photometry. A large number of measurements are captured using RS to derive statistically significant measurements 
of emission estimates from different types of vehicles. 

Remote sensing has been successfully used to distinguish between emission trends of petrol and diesel vehicles. In a 
study of light duty vehicles in Zurich during 2000 2012, it was seen that unlike petrol vehicles, the diesel NOx emissions 
[g/kg  fuel]  had  actually  increased  although  emission  limits  had  been  progressively  tightened  (Chen  and  Borken-
Kleefeld, 2014). Similarly, Carslaw et al. (2011) and Carslaw and Rhys-Tyler (2013) used RS in London and found that 

124 

 
 
 
 
 
 
 
 
 
only petrol vehicles showed a reduction in NOx/CO2 over the period of 1985 2012, while diesel vehicles, including those 
with after-treatment systems designed to reduce NOx, showed little evidence of NOx/CO2 reduction. 

Data collection and analysis 

Remote  sensing  (RS)  instruments  measure  tail-pipe  emissions  from  passing  vehicles  under  real-world  driving 
conditions.  RS  instruments  use  an  open-path  optical  measurement  system  that  consists  of  ultra-violet  (UV)  and 
infrared  (IR) light  sources  that  are  directed  across  a  single  lane  carriageway  to  a  mirror  and  then  returned  to  the 
detection system within the remote sensing unit. The light is absorbed by pollutants as it passes through the exhaust 
plume and measured in relation to the absorption of carbon dioxide (CO2) to provide pollutant emissions as ratios to 
CO2  emissions.  In  conjunction  with  the  vehicle  emissions  measurements,  a  camera  records  the  number  plates  of 
passing vehicles, and a pair of light gates record the vehicle speed and acceleration. In this study, the camera was 
placed to capture the rear license plate of the vehicle. Vehicle details, such as make, model and fuel type, was later 
obtained from the number plate of each vehicle and merged to the emissions measurements for the vehicle. 

In  this  study,  a  comprehensive  remote  sensing  campaign  spanning  sixteen  weeks  was  conducted.  The  campaign 
involved collection of real-world data in two phases: winter and summer across six different locations using Opus RSD 
5000. The instrument is capable of measuring emissions of nitric oxide (NO), nitrogen dioxide (NO2), particulate matter 
(PM),  ammonia  (NH3),  carbon  monoxide  (CO)  and  hydrocarbons.  Emissions  are  measured  as  ratios  to  CO2  and  the 
analysis  routine  uses  combustion  equations  to  calculate  fuel-specific  emissions  in  units  of  grams  of  pollutant  per 
kilogram of fuel (g/kg fuel). Distance specific emissions can be estimated from fuel-specific emissions by estimating 
the amount of fuel burnt per kilometre travelled (kg/km). The vehicle emissions presented in the subsequent section 
are in units of g/kg fuel. The number plate of each vehicle was matched to a set of vehicle details obtained from either 
the Department of Transport (Ireland), or derived from DVLA and SMMT databases in the UK, including, where available, 
vehicle type (i.e., car, van, bus, heavy goods vehicle (HGV)), fuel type, Euro standard, engine size, vehicle weight, date 
of registration, and the odometer reading at last mandatory vehicle testing.  

The RSD 5000 is always calibrated internally prior to each data collection session, in a process that only takes a few 
minutes.  During  the  session,  the  RSD  5000  was  audited  each  hour,  to  verify  the  system  is  performing  within 
specifications and does not need re-alignment and/or re-calibration. This process involves measurements of gases of 
known concentrations through the RSD5000. As data is collected, exhaust plume verification software reviews each 
measurement in real-time to ensure it is of adequate strength, that the exhaust plume decayed in a manner consistent 
with warm loaded-mode vehicle operations, and that the prevailing background levels are stable and can be accurately 
determined. Each session
made at six locations with an aim to capture different vehicle types: (1) Southbound on Templeogue Road, directly 
after the traffic light-controlled crossroads, (2) Mayor Street Lower, a single lane road next to the tram lines, (3) College 
Green, on the bend of the R137 road, forking off the R138, (4) Southbound on Beach Road (Strand Road), directly after 
od Hill Road, underneath the Chapelizod Bypass bridge, 
and (6) Northbound on Richmond Street, next to the cycle lane. Sites at Mayor Street lower and Richmond Street were 
later omitted as they were found to be unsuitable due to less traffic or lack of acceleration when the vehicle passed 
the remote sensing setup. 

In total, more than 136,000 emission tests were conducted which included cars, taxis, vans, and buses of different 
Euro  emission  standards  and  fuel  types  (predominantly  diesel  and  petrol).  The  dataset  included  vehicles  of  five 
different fuel types with 59.1% diesel vehicles and 28,128 (29.2%) petrol vehicles. The share of hybrid and plug-in 
hybrid vehicles was 9.5% and the share of electric vehicles was 2.2%. This indicates that the vehicle fleet in Dublin is 
predominantly diesel fuelled and the share of hybrid / electric vehicles is low.  

The vehicles were classified into different Euro classes based on their year of registration. Emission factors (in g/kg 
fuel) were determined for different Euro emission standards including Euro 6b, 6c, 6d-temp, and 6d.  

125 

 
 
 
 
 
 
 
 
 
 
 
Results and discussion 

From Figure 1, we see that the CO emission factor decreases with evolution in the emission standard for all vehicle 
types. For cars, there is a significant reduction from Euro 4 to Euro 5 (28%), but a relatively smaller reduction from 
Euro 5 to Euro 6b (11.36%), and a decreasing trend is seen from Euro 6b to 6d(t). In the case of vans, the mean 
emission factor value reduces by 10.6% for Euro 5 compared to Euro 4 and by 16.6% for Euro 6b compared to Euro 
5. A significant reduction (29.4%) is seen for Euro 6 buses compared to the Euro 5 buses. Comparing the different 
vehicle types with same emission standard, vans have the highest emission factors. Figure 2 shows the mean HC 
emission factors (g/kg fuel) for the different vehicle types and Euro classes within each vehicle type. Clearly, the HC 
emission factor decreases with improvement in the emission standard for all vehicle types. For cars, there is a 
significant reduction from Euro 4 to Euro 5 (35.7%), and a relatively smaller reduction from Euro 5 to Euro 6b 
(21.3%). In the case of taxis, a significant reduction is observed from Euro 4 to Euro 5 (35%) and also from Euro 5 to 
Euro 6b (22.5%). Comparing the different vehicle types with same emission standard, vans have the highest 
emission factor for Euro 4. Buses have the lowest HC emission factors among all the vehicle types. 

The variation of the NO emission factors is shown in Figure 3. The trends are similar for cars, vans and taxis with 
Euro 6 having the lowest value and Euro 5 having the highest. Also, among the three vehicle types, taxis have 
slightly higher emission factor when compared to cars and vans of the same emission standard. This could be due to 
the relatively higher mileage and assertive driving style for taxis compared to cars and vans. In the case of buses, a 
significant reduction is observed for Euro 6 (0.5 g/kg fuel) compared to Euro 5 (25.6 g/kg fuel). 

The variation of the NO2 emission factors is shown in Figure 4. The trends are similar for cars and vans with Euro 6 
having the lowest value and Euro 4 having the highest. In the case of taxis, the Euro 4 taxis had a significantly lower 
mean value (1.3 g/kg fuel) than Euro 5 taxis (2.1 g/kg fuel). Both Euro 5/6 buses have significantly low NO2 
emissions (< 1 g/kg fuel). 

Figure 1: EF of CO (g/kg fuel) for different diesel vehicle types based on the Euro class. The point represents the 
mean and the error bars indicate one standard deviation on either side of the mean. 

126 

 
 
 
 
 
 
 
Figure 2: EF of HC (g/kg fuel) for different diesel vehicle types based on the Euro class. The point represents the 
mean and the error bars indicate one standard deviation on either side of the mean. 

Figure 3. EF of NO (g/kg fuel) for different diesel vehicle types based on the Euro class. The point represents the 
mean and the error bars indicate one standard deviation on either side of the mean. 

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Figure 4: EF of NO2 (g/kg fuel) for different diesel vehicle types based on the Euro class. The point represents the mean 
and the error bars indicate one standard deviation on either side of the mean. 

Figure 5: Emission factors (g/kg fuel) for petrol cars based on emission standards. The points represent the mean 
and the error bars indicate one standard deviation on either side of the mean. 

The trends in NO EF with year of registration for petrol cars is shown in Figure 6. Similar trends are seen for both 
summer and winter plots. Comparing the winter and summer EFs for the same year of registration, the mean EF values 
are not significantly different. However, the variability is higher for the values before 2015 and lesser from 2016 
onwards. The trends in NO2 EFs are shown in Figure 7. The mean EF values are significantly lower in winter compared 

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to the summer values. The EFs in winter are less than 0.5 g/kg fuel for most of the vehicles and the values seem to 
have stabilized beyond 2015 in both summer and winter cases. 

Figure 6: Trends in the NO EF with year of registration for summer and winter, petrol cars 

Figure 7: Trends in the NO2 EF with year of registration for summer and winter, petrol cars 

To summarize, the RS technique for measuring emissions of key pollutants (including NO, NO2, and PM) from in-use 
vehicles was successfully applied for the first time in Dublin, Ireland. The data collection campaign, conducted in two 
phases, provided a comprehensive dataset comprising of different vehicle types (cars, taxis, vans, and buses) of varying 
makes, mileage, fuel type, and Euro classes. The dataset was used to determine the emission factors of CO, HC, NO, 
and NO2 from the fleet of vehicles in Dublin. The Euro classes of the vehicles ranged from Euro 3 to Euro 6 with a 
large number of the latest Euro 6 vehicles in the dataset. Thus, the dataset can be considered to be representative of 
the current fleet of vehicles in Dublin, and the findings provide evidence for benefits of fleet renewal. However, since 
the study was restricted to Dublin, which may have a newer fleet of vehicles compared to other areas of Ireland, the 

129 

 
 
 
 
 
 
 
 
older vehicles may be underrepresented. Based on the findings from this study, it is recommended that similar remote 
sensing-based emission measurement studies should be carried out in other cities of Ireland, which would help in the 
quantification  of  emissions  from  in-use  vehicles  more  accurately.  This  can  form  the  basis  for  suitable  emission 
mitigation measures for that particular city. 

The trends in NO EFs were similar for diesel cars, vans, and taxis with Euro 6 having the lowest value and Euro 5 
having the highest. Also, among the three vehicle types, taxis have slightly higher emission factor when compared to 
cars and vans of the same emission standard. This could be due to the relatively higher mileage and assertive driving 
style for taxis compared to cars and vans. In the case of buses, a significant reduction in NO EF was observed for Euro 
6 (0.5 g/kg fuel) compared to Euro 5 (25.6 g/kg fuel). It is recommended that Euro 3 and Euro 4 compliant cars, and 
Euro 5 compliant buses are phased out as soon as possible. The NO emission factor reduced by 43.5% for Euro 6 
relative to Euro 5. The HC emission factor increased from Euro 3 to Euro 4 and then decreased with a significantly low 
value for Euro 6 (1.0 g/kg fuel). The NO2 emission factors were similar for Euro 5 and Euro 6 cars and significantly 
lower relative to Euro 3 and Euro 4. In general, an overall decreasing trend of emission factors was observed for NO 
and NO2 with improving emission standards. 

Conclusions 

The analysis of the real-world emissions from different vehicle types using RS provided several important findings. A 
decreasing trend in the mean emission factor values of NOx is seen for all the vehicle types. Decreasing trends were 
also observed for CO and HC with improvement in emission standards. The trends in EFs were compared for summer 
and winter and different trends were observed. The NOx emissions from diesel cars decreased with registration year 
after  2015.  This  is  likely  a  result  of  more  stringent  emissions  limits  for  Euro  6  vehicles.  The  comparison  of  NOx 
emissions for summer and winter campaign showed similar values with only slight increases for some registration 
years during the summer campaign. The NOx emissions from buses dropped considerably after 2013, likely due to the 
introduction of Euro VI buses towards the end of 2013.  In the case of petrol cars, the CO emission factor was similar 
for Euro 3 and Euro 4 cars and decreases significantly for Euro 5 and Euro 6 (41.3% less than Euro 5). A similar trend 
was observed for the NO emission factor with 43.5% reduction for Euro 6 relative to Euro 5. The HC emission factor 
increases from Euro 3 to Euro 4 and then decreases with a significantly low value for Euro 6 (1.0 g/kg fuel). The NO2 
emission factors  were similar  for Euro 5  and Euro 6  cars  and significantly lower relative  to  Euro 3 and Euro  4. In 
general, an overall decreasing trend of emission factors was observed for CO, NO, and NO2 with improving emission 
standards. 

The findings from this study provide evidence that fleet renewal measures could lead to significant air quality benefits. 
The results also indicate the presence of a small number of vehicles which pollute significantly higher than others. 
Identifying and penalizing those vehicles would also an impact of the air pollution levels, especially along the major 
arterial roads. The results can also be useful for evaluating the effect of introducing low emission zones on local air 
quality. 

Acknowledgements  

This work was funded by the Environmental Protection Agency of Ireland and Department of Transport Ireland through 
the REDMAP project (No-209974/Award no-16217). The authors acknowledge Dublin City Council for providing permits 
for on-road data collection and Department of Transport for providing the vehicle registration data. 

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Bishop, G. A., Stedman, D. H. and Ashbaugh, L. L. (1996). Motor vehicle emissions variability. Journal of Air and Waste Management 
Association, 46: 667 675. 

Carslaw, D. C., & Rhys-Tyler, G. (2013). New insights from comprehensive on-road measurements of NOx, NO2 and NH3 from vehicle 
emission remote sensing in London, UK, Atmospheric Environment, 81, 339-347. 

Carslaw,  D.  C.,  et  al.  (2011).  Recent  evidence  concerning  higher  NOx  emissions  from  passenger  cars  and  light  duty  vehicles, 
Atmospheric Environment 45.39: 7053-7063. 

Chen, Y., & Borken-Kleefeld, J. (2014). Real-driving emissions from cars and light commercial vehicles Results from 13 years remote 
sensing at Zurich/CH. Atmospheric Environment, 88, 157-164. 

Dey,  S.,  Caulfield,  B.  and  Ghosh,  B.  (2018).  The  potential  health,  financial  and  environmental  impacts  of  dieselgate  in  Ireland, 
Transportation Planning and Technology, 41.1: 17-36. 

Donateo, T., and Giovinazzi, M. (2018). Some Repeatability and Reproducibility Issues in Real Driving Emission Tests. No. 2018-01- 
5020. SAE Technical Paper. 

EEA. (2021). Air quality in Europe   2021 report. European Environment Agency, EEA Report No 15/2021. 

EEA. (2019) Progress of EU transport sector towards its environment and climate objectives 

EPA. (2018). Air Quality in Ireland 2017. Key Indicators of Ambient Air Quality. Environmental Protection Agency, Wexford, Ireland. 

European Commission. (2018). European Commission - Fact Sheet. Testing of emissions from cars. Brussels, 4 May 2018. Accessed 
on June 26, 2019. Accessed https://ec.europa.eu/growth/sectors/automotive/environment-protection/emissions_en on 09/07/2018 

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World Health Organization. (2016). Ambient air pollution: A global assessment of exposure and burden of disease. 

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Remote  Sensing  Emission  Measurement  on  a  German  Motorway 
Results 

  Insights  and  initial 

C. Piasecki1*,  

1 Federal Highway Research Institute (BASt), Bruederstrasse 53, 51427 Bergisch Gladbach, Germany, piasecki@bast.de 

Introduction 

The admission of motor vehicles to road traffic is regulated by the EU-wide type approval process. The effort linked 
with this permission is rather extensive and covers, among other things, the examination of the exhaust gas emissions 
under challenging conditions, laid down in EU Legislation (Reg. (EC) No 715/2007 and Reg. (EC) No 692/2008). Complex 
and standardized laboratory dynamometer test runs in defined driving cycles and on-road emission measurements 
with  mobile  measurement  equipment  (PEMS 
  Portable  Emission  measurement  Systems)  are  carried  out  for  this 
purpose. If the test results comply with the specified emission limit values under relevant driving conditions, the vehicle 
type is approved evidenced by a certain certificate. Besides the type approval procedure, there are different measures 
in operation to ensure that an approved vehicle meets the prescribed technical specifications over a defined period 
(usually  defined  as  a  specific  lifetime  durability  or  mileage  requirements).  Periodical  technical  inspections,  market 
surveillance programs and roadside inspections should be mentioned here.  

Independent emission measurements, however, installed along the roadside in running traffic have not been embedded 
in  current  regulations  yet  and  have  therefore  only  been  carried  out  very  occasionally  and  for  research  purposes 
(Ghaffarpasand et al., 2023). Due to partly supposed inconsistencies in the emission behavior of approved vehicles on 
the  road  compared  to  emission  characteristics  determined  in  the  type  approval  procedure,  such  roadside 
measurements are becoming increasingly important. The project presented in this study is intended to use an over-
head  remote  sensing  measurement  system  "Emission  Detection  and  Reporting"  (EDAR)  (Gruening  et  al.,  2019), 
(Dallmann  et  al.,  (2019)  from  Hager  Environmental  and  Atmospheric  Technologies  (HEAT)  for  measuring  vehicles 
tailpipe emissions contactless on a federal motorway in Germany for the first time, see figure 1.  

Figure  1:  Emission  Detection  and  Reporting  System  (EDAR)  by  HEAT  (Hager  Environmental  &  Atmospheric 
Technologies); roadside construction on German Motorway A61. 

This procedure differs from the measurements applied in the type approval procedure in a way that it is only a selective 
and contactless  measurement inside  the exhaust plume of passing vehicles  at  a  fixed location. The emissions are 
recorded in a specific vehicle operation point (with regard to e. g. vehicle speed, acceleration) and the measurement 
system is able to carry out a large sample of measurements within a comparatively short period of time. The data 
sets generated within this procedure can be used for in-depth emission analyzes and this large sample of data may 
represent a basis for initializing further emission investigations in future, if necessary.  

Since the recording of exhaust emissions on a test bench or under real driving conditions (RDE) does not cover any 
vehicle  operation  points,  the  non-contact  measurement  of  exhaust  emissions  in  moving  traffic  is  an  additional 
instrument for getting insights into the real-world vehicle fleet emission characteristic. Due to the large number of 
vehicles recorded, the measurement method can, for example, discover aging effects of a specific engine and vehicle 
type. It is also possible to detect unusual emission characteristics or so called high-emitting vehicles. Measurements 

132 

 
 
 
 
with  the  EDAR  system  are 
  according  to  the  current  state  of  the  art  -  no  high-precision  vehicle-individual 
measurements that would allow deriving expert statements on the individual vehicle emission behavior, however, such 
measurements are suitable for cluster formation of vehicle classes with similar technologies. So, initial statements 
regarding the fleet emissions and their changes over time can be derived. In addition to applications of remote sensing 
systems in the traffic sector, such data sets can also form a basis for analyzes and measures in the field of air quality 
issues, as ambient pollution concentrations are continuously recorded, too. 

Project description and goal 

In the framework of this project, a contactless emission measurement system for the recording of emissions in the 
exhaust plume of motor vehicles on public roads was installed and evaluated. The measurements were implemented 
in the moving traffic without affecting the traffic flow. Hereby, no adaption of the driving behavior in terms of artificial 
braking or changing lanes was provoked. The EDAR measuring system applied in this project recorded the relevant 
exhaust gas emission components on a German Autobahn spot (BAB) in a speed range between 80 km/h and 100 
km/h. The location was selected by considering system-specific criteria, among others, vehicles had to drive under 
engine  load at  the  measurement spot  to generate  specific exhaust gas  flows  that  can be  measured 
  realized by 
choosing a spot with a positive road gradient. An extensive approval process for setting up the measuring system 
including a test run was successfully completed before the measurements began. Measurements were then carried 
out on the BAB 61 in Rhineland Pfalz for twelve days in June 2022. The emission data measured were linked with 
relevant engine and vehicle specific characteristics (e. g. emission class, vehicle type, engine type, registration year 
etc.) from the official registration database of the Federal Motor Transport Authority (KBA). Therefore, a data protection 
concept was drawn up for this project, which focused primary on legal aspects like license plate detection and personnel 
data processing. Finally, the emission behavior of the vehicle fleet at the abovementioned spot was measured and 
analyzed regarding different criteria and vehicle characteristics like, among other things, vehicle category, emission 
classes  as  well  as  country  of  registration.  As  part  of  the  research  project,  HEAT  worked  together  with  DTV-
Verkehrsconsult GmbH and Prof. Borken-Kleefeld (Dresden University) in a consortium, who have already been active 
in this work area in several applications (Rauterberg-Wulff et al., 2021). 

The overall goal of this research project presented here is to demonstrate the feasibility of a remote sensing emission 
measurement system on a federal motorway. Furthermore, the possibilities and limits of this measuring system for 
recording exhaust emissions in road traffic on motorways including legal and organizational aspects should also be 
shown. This includes, among other things, data protection requirements and the combination of emission data with 
vehicle features provided by the KBA. Even the main concern here was evaluating the feasibility and practicability of 
the measurement, the data obtained here should also be processed and analyzed in an exemplary evaluation. Hereby, 
it  should  become  apparent  in  which  specific  areas  remote  sensing  data  might  become  a  basis  for  dealing  with 
environmental related questions and for deriving subsequent legal measures. 

In addition, the interaction of the responsibilities for the process of a remote sensing measurement of different legal 
actors  should  be  tested  including  the  German  Federal Office  for  Logistics  and  Mobility  (BALM),  the  police  and the 
Federal Motor Transport Authority (KBA). These three authorities are also responsible for market surveillance, roadside 
inspection  and  manipulation  detection  in  the  road  traffic  sector.  It  should  be  stated  at  this  point  that  the  project 
presented  in  the  study  focused  explicitly  on  the  goals  outlined  above  and  is  not  intended  to  uncover  possible 
inconsistencies in the emission behavior of individual road users and their vehicles. 

Location  

Certain requirements have to be met for emission measurement with the EDAR system on public roads. These primarily 
include the general option of installing the system next to or above the motorway and access to the installation site, 
as well as the necessity for motor vehicles to be driven under engine load so that a relevant exhaust plume is generated 
at the measuring point. The mandatory specification in this project was the measurement on a federal highway. An 
extensive  road  selection  process  was  carried  out  for  this  purpose.  From  the  BISSTRA  database  (Federal  street 
information  system  guidanced  by  the  Federal  Highway  Research  Institute  in  Germany)  some  locations  for  the 
measuring system in the federal trunk road network were successfully determined. 

System-related limitations occurred by the need for an electric power supply, the application and subsequent removal 
of a reflector strip on the road surface without leaving any residue, and the possibility of mounting the measuring 
system vibration-free. As a result, there were only very few locations on the motorways in North Rhine-Westphalia 

133 

 
 
 
 
 
and Rhineland-Pfalz declared as suitable in relation to the large number of kilometers of motorways in general. The 
traffic density at the measurement point also had to be considered to ensure that a sufficiently large sample of vehicle 
emission measurements could be collected. Opposed to motorways, finding a suitable location on rural or inner-city 
roads seems to be easier, as safety criteria and power supply issues can be enforced in a more manageable way. 
Finally, a location on the A 61 motorway was chosen for this project that suited best with abovementioned criteria. 
Here the measuring system was set up with a traverse system behind the crash barrier on an incline at operational 
kilometer 187,40 (see figure 2).  

Figure  2:  Location  of  BASt-coordinated  emission  measurement  campaign  via  remote  sensing  technology 
(www.openstreetmap.org). 

Data protection concept 

The emission data recorded by the EDAR system need to be linked to technical specification of the measured vehicles 
in order to carry out a purposeful evaluation. These specifications include, among other things, criteria like vehicle class 
and  -type,  manufacturer,  emission  class,  date  of  first  registration,  fuel  type  or  energy  source  and  unladen  and 
maximum weight. This data is made available by the KBA. Therefore, it is necessary to record the license plates of the 
vehicles as a vehicle-unique feature and to match it with the centralized vehicle database. Even if number plates are 
recorded only for a short time, it is essential to create a comprehensive data protection concept due to the regulations 
laid down in the General Data Protection Regulation (GDPR, 2016). The creation of the concept in close cooperation 
with the Federal Commissioner for Data Protection and Freedom of Information (BfDI) was necessary in order to fulfill 
the high data protection requirements with regard to the protection of individual data of vehicle owners. Depending on 
the system and process, this has to be considered individually for each measuring system in which camera-based 
systems are applied. The creation process for the individual data protection concept used in this  study was finally 
accepted by relevant authorities and can serve as a basis for further applications of the EDAR system in Germany. 
Depending on the location, however, the data protection concept must be individually adapted then. 

Measurement 

The  measurement  on  the  BAB  A61  with  the  EDAR  measurement  setup  on  a  specially  designed  traverse  was 
successfully carried out in June 2022. The installation took a few hours with the appropriate preparation. However, the 
installation process on motorway is always associated with a lane closure, on the one hand for the application of the 
reflector strip on the road and on the other hand according to safety aspects. Once installed, the system works more 
or less autonomously, provided that a power supply is available. The measurements were carried out within the planned 
time slot without any interruptions. The weather conditions were favorable, there was no rain or strong winds during 
the  measurement,  that  might  affect  the  accuracy  of  the  system.  In  total,  approximately  124.000  vehicles  were 
recorded within 12 days. The EDAR system was dismantled in the reverse order to the assembly. Here, too, the right 
lane had to be closed to remove the reflector strip and to uninstall the traverse mounting. 

Results 

Approximately 84% of the 124.000 recorded data sets contained all relevant information required for an in-depth 
emission evaluation. As already described, the focus of this pilot project was not the pure collection and evaluation of 
measurement  data  itself,  instead,  questions  concerning  the  feasibility  of  remote  sensing  systems  on  German 
motorways in general including legal aspects were prioritized. Nevertheless, an initial evaluation of measured data 
sets  was  carried  out  of  which  selected  results  are  presented  in  this  study.  Figure  3  allocates  the  measurements 
134 

 
 
 
 
according  to  EU-vehicle  classes  and,  additionally,  vehicles  with  non-German  number  plates  are  displayed.  It  is 
noticeable, that the share of foreign-registered vehicles is disproportionally high, accounting for almost one third of 
all valid measurements. The reason for this can be found in the commercial traffic (N3), which takes place particularly 
on the right-hand lane in Germany, which was the only lane measured here and contains a high share of foreign heavy-
goods  vehicles.  By  measuring  the  entire  one-direction  traffic  an  increase  in  passenger  car  data  can  be  expected 
resulting in a reduced share of foreign registered vehicles. 

Valid measurements according to EU-
vehicle categories / foreigners

s
e
l
c
i
h
e
V

40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
0

Figure  3:  EDAR  measurements  differentiated  according  to  EU-vehicle  categories  (Reg.  (EC)  No  2018/858)  and  by 
foreigner registrations. 

With regard to the invalid measurements, it is assumed that either the measured pollutant concentration is too low, 
different exhaust gas plumes overlap and the measurement is therefore declared as invalid, or that the number plate 
was not recorded and assigned correctly. Concerning the emission behavior of the German passenger car vehicle fleet 
the following evaluation provides insights into the capabilities of the remote sensing system. The average nitrogen 
oxide (NOX) emission values   indicated in grams / kg fuel   for Euro 6 approved diesel passenger cars with different 
registration  years  are  shown,  see  fig.  4.  For  comparison  purposes  the  type  approval  limit  value  of  Euro  6  diesel 
passenger cars (80 mg / km, correspond to approximately 1,6 g/kg fuel) is displayed. A strong decrease of measured 
average NOX values depending on the vehicles registration year and depending on the ongoing Euro 6 standard (from 
Euro 6b to Euro 6d) becomes apparent.  

Development of German diesel passenger cars 
NOx emissions differentiated by registration 
year (g/kg fuel consumption)

)
l
e
u
f
g
k
/
g
(

x
O
N
Ø

10

8

6

4

2

0

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023

Figure 4: Development of German passenger cars (Euro 6, diesel) NOx emissions differentiated by registration year (in 
g/kg fuel consumption) and by record numbers (bubble size represents the number of vehicles measured). 

One has to state out here, that the average emission values indicated in figure 4 comprise all different vehicle and 
exhaust  after-treatment  technologies  of  Euro  6  diesel  passenger  cars 
  a  further  distinction  could  be  made  by  a 
detailed assignment of vehicle identification number to technology standard. The bubble size represents the number 
of records per registration year   
in  2019.  Comparable statements regarding the average  emission behavior could also be  made  for  other  pollutant 
components (such as, among others, carbon monoxide, particles or hydrocarbons) and 
 as far as enough data sets 
are available - for other vehicle categories. However, one has to state out that average fleet evaluations require an 
135 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
adequate  number  of  records  per  vehicle  segment  in  order  to  generate  robust  and  meaningful  statements  and  to 
minimize the effects of major exceptions. In the powered two-wheeler segment, for instance, the data base does not 
provide an explicit picture. Approximately 90 records of the measured data sets are available and declared valid for 
motorcycles (L3e category), which in turn can be allocated to different Euro emission standards (mainly Euro 3 to Euro 
5) and different technologies. In particular, the wide variation of vehicle and engine concepts in this vehicle category 
makes it difficult to derive clear statements concerning the emission behavior of certain segments. One can assume, 
that motorcycles are partly under-recorded in this campaign as they probably do not drive primary on right lanes on 
motorways 
 this applies particularly for heavy machines. For vehicle segments representing a high share to total 
traffic   e. g. passenger cars and heavy-good vehicles - reliable results can be achieved with the system in question 
even  with  relatively  short  measurement  periods.  For  other  segments,  either  the  measurement  period  should  be 
extended or measurements should be taken on all road lanes. 

Outlook and Conclusion 

As part of this research project, a remote sensing emission measurement system was successfully used for the first 
time on a motorway in Germany. Emission measurements were carried out over a period of two weeks in the summer 
of 2022.  Data  protection issues were clarified  in advance  of the  measurement  and suitable  locations for possible 
further measurements were identified. Depending on the share of the vehicle segment to the entire vehicle fleet the 
measurement data provide a very good snapshot of the fleet emission behavior. Detailed analyzes of the change in 
fleet emissions over time can be derived. In addition, particularly strong emitting vehicles could be identified. In order 
to be able to make reliable statements for less strongly represented vehicles segments such as motorcycles or historic 
vehicles, it is advisable either to extend the measurement period or to measure across all road lanes. Increasing costs, 
however, have to be considered in any case and weighted against possible benefits. The use of temporary remote 
sensing devices as a pre-selection of high emitting vehicles for subsequent technical inspections is conceivable. 

References 

Commission Regulation (EC) No 715/2007 of the European Parliament and of the Council of 20 June 2007 on type approval of 
motor vehicles with respect to emissions from light passenger and commercial vehicles (Euro 5 and Euro 6) and on access to vehicle 
repair and maintenance information, Official Journal of the European Union L 171/1, 2007. 

Commission  Regulation  (EC)  No  692/2008  of  18  July  2008  implementing  and  amending  Regulation  (EC)  No  715/2007  of  the 
European Parliament and of the Council on type-approval of motor vehicles with respect to emissions from light passenger and 
commercial  vehicles  (Euro  5  and  Euro  6)  and  on  access  to  vehicle  repair  and  maintenance  information,  Official  Journal  of  the 
European Union L 199/1, 2008 

Commission Regulation (EU) 2018/858 of the European Parliament and of the Council of 30 May 2018 on the approval and market 
surveillance of motor vehicles and their trailers, and of systems, components and separate technical units intended for such vehicles, 
amending  Regulations  (EC)  No  715/2007  and  (EC)  No  595/2009  and  repealing  Directive  2007/46/EC,  Official  Journal  of  the 
European Union L 151/1, 2018. 

Dallmann, T., Bernard, Y., Tietge, U., Muncrief, R. (2019). Remote sensing of motor vehicle emissions in Paris, Final Report. 

Ghaffarpasand, O.,  Ropkins, K., Beddows, David C. S., Pope, Francis D. (2023). Detecting high emitting vehicle subsets using emission 
remote sensing systems, Science of The Total Environment, Volume 858, Part 2, 159814. 

Gruening, C., Bonnel, P., Clairotte, M., Giechaskiel, B., Valverde, V., Zardini, A., Carriero M. (2019). Potential of Remote Sensing Devices 
(RSDs) to screen vehicle emissions  - Assessment of RSD measurement performance. EUR 29871 EN,  Publications Office of  the 
European Union, Luxembourg, ISBN 978-92-79-11820-6, doi:10.2760/277092, JRC117894. 

Rauterberg-Wulff,  A.,  Schmidt,  W.,  Düring,  I.,  Borken-Kleefeld,  J.,  Laxenburg,  A.  (2021).  Bestimmung  der  Real-Emissionen  von 
Kraftfahrzeugen im Berliner Straßenverkehr mit Remote Sensing Detection und Vergleich mit den Emissionsfaktoren des HBEFA 4.1. 
Immissionsschutz, Volume 26, Issue Number: 3. pp 131-5. ISSN: 0039-2219. 

https://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-bisstra.html 

Map data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org. 

Datenschutz-Grundverordnung (DSGVO); EU) 2016/679; https://dsgvo-gesetz.de/ 

136 

 
 
 
 
 
 
Simulations of a NIR TDLAS Sensor for Stand-Off Measurement of Carbon Dioxide for 
Remote Emission Sensing 

Paul Schaffer1, Hafiz Hashim Imtiaz1, Benjamin Lang1, Martin Kupper1 and Alexander Bergmann1 

1Institute of Electrical Measurement and Sensor Systems, University of Technology, Graz, Austria, paul.schaffer@tugraz.at 

Introduction 

Over the course of the last two decades emission limits for type approval of vehicles in the EU and worldwide have 
steadily tightened. This led to lower emissions in the whole on-road transport sector from compliant vehicles. Vehicles 
with  damaged  or  inoperable  exhaust  aftertreatment  systems  do  not  comply  with  the  imposed  emission  limits. 
Therefore, the challenge of measurement and identification of those high-emitting vehicles emerged. The impact of 
such high emitting vehicles to road pollution is substantial, as 15 % of vehicles on the road can be classified as high-
emitters, causing over 90 % of total pollution (Bainschab et. al. (2020)). 

Remote Emission Sensing (RES) has proven to be a promising emission measurement method for the on-road transport 
sector. The characteristics of RES systems is the measurement of passing vehicles by a measurement device installed 
directly at the roadside. By its non-invasive nature, a RES system can monitor the emissions of thousands of vehicles 
under real-world driving conditions. This allows for the identification of high-emitting vehicles and the transmission of 
the respective license plate to legislative authorities, which can impose further investigations, such as an inspection of 
the vehicle in a workshop. By design, RES systems are employed in unmanned 24/7 operation and allow the installation 
of the system in various locations, such as city center driveways, highways or other highly frequented roads. 

RES systems commonly rely on spectroscopic methods for the determination of emission factors (gas concentrations 
relative to a stoichiometrically calculated CO2 value) of passing vehicles, such as passenger cars, trucks, etc. With the 
increasing  availability  of  diode  lasers,  Tunable  diode  laser  absorption  spectroscopy  (TDLAS)  has  been  shown  as  a 
promising approach also for direct emission measurement in RES systems (Lin et. al. (2023)), emission factors can be 
calculated. TDLAS offers high accuracy combined with fast sampling, making it an ideal method for RES. However, 
TDLAS measurements are currently employed only for the measurement of emission factors. 

In this work we present a TDLAS based RES system that allows for a direct concentration measurement of CO2 over 
an  open  path  length  of  10  m.  The  TDLAS  system  is  combined  with  a  gas  imaging  technique  that  enables  the 
determination of the exhaust plume size at the moment of the spectroscopic measurement. The system design can 
easily be adapted to include more analytes, which allows the RES system to measure e.g. CO, NO, NO2 or NH3 in future 
applications. 

The TDLAS measurement presented here targets the absorptions lines of CO2 in the wavelength range between 1601.0 
nm and 1601.6 nm. The targeted absorption lines in this wavelength range show several benefits over other commonly 
used absorption lines with similar line strengths. Interfering absorption lines from other, commonly encountered gases 
in RES, such as H2O or CO show only minor influence on the CO2 absorption, as the line strengths are much smaller. 
Furthermore, the shape of the absorption lines of CO2 at 1601 nm can be considered to have a good temperature 
stability, as the line shape is well preserved even at elevated temperature of 300 °C. 

The aforementioned gas imaging technique used to determine the size of the exhaust plume is based on schlieren 
imaging.  Using  a  defined  arrangement  of  the  system  setup  therefore  enables  the  calculation  of  the  absorption 
pathlength in the exhaust plume.  

The  proposed  TDLAS  system  design  was  evaluated  using  simulations  with  a  numerical  model  for  two  different 
measurement approaches. The evaluation and validation of both approaches and the system design in the laboratory 
is ongoing. The simulations were used as an excellent starting point for the design of the spectroscopic setup. 

Laser Absorption Spectroscopy 

TDLAS as a spectroscopic measurement principle relies on the absorption of parts of the incident laser intensity by the 
targeted gaseous medium. The absorption of intensity is described by the Beer-Lambert law  

𝐼 = 𝐼0𝑒−𝜒(𝜆)⋅𝑐⋅𝐿.  

(1) 

The received laser intensity I is affected by the emitted laser intensity I0, the wavelength dependent absorption cross 
, the number concentration of the absorbing gas species c and the absorption path length L.  
section 

When  employing  TDLAS  to  an  open-path  measurement,  such  as  in  setups  used  for  RES,  the  targeted  gas  volume 
extends only over parts of the total measurement path. In the case of a roadside application of a RES system, the total 
pathlength would cover the full width of the road, but the targeted gas volume, in this case the exhaust plume, extends 

137 

 
 
 
 
only over a relatively small length. To measure the gas concentration in the exhaust plume, the knowledge of the size 
of the exhaust plume along the measurement path is necessary. 

-
project  which  allows  us  to  present  a  new  solution  to  this  challenge.  The  RES  system  employs  Synthetic  Schlieren 
Imaging to capture real-time images of the exhaust plume emitted by passing vehicles. In this technique a high-quality 
camera placed on one side of the road is focuses on a checker patter board situated on the opposite side of the road. 
The exhaust plume emitted by passing vehicles alters the refractive index in parts of the volume between camera and 
board,  resulting  in  small  distortions  in  the  captured  image  of  the  board.  By  capturing  and  subtracting  consecutive 
images the relative changes between the images are calculated, generating Synthetic Schlieren Images. Furthermore, 
advanced  image  processing  techniques  are  applied  to  the  images  the  highlight  the  exhaust  plume  against  the 
background and eliminate displaced pixels. An object detection algorithm employing Convolutional Neural Networks 
(CNN) then identifies the exhaust plume. The extension of the exhaust plume is then determined by considering the 
plume size in pixels, the camera sensor size, the distance from the camera and the focal length, giving the absorption 
path length in the exhaust plume. 

Tunable Diode Laser Absorption Spectroscopy 

TDLAS relies on the wavelength tuning of a diode laser to scan the targeted absorption feature. This wavelength tuning 
is commonly realized by a periodic modulation of the injection current supplied to the laser diode, often also referred 
to as sweeping. Usually this modulation is applied as a triangular waveform, which is the approach followed in this 
work. Depending on the type of laser diode and driver the resulting tuning range can be up to several nm. The increase 
in injection current also results in an increase in optical power, leading to the so-called Residual Amplitude Modulation 
(RAM). 

In addition to the direct absorption technique another method, called Wavelength Modulation Spectroscopy (WMS) can 
be  applied.  In  this  technique  an  additional  sinusoidal  modulation  is  superimpose  onto  the  triangular  sweep.  This 
modulation is of much smaller amplitude, but higher frequency. Due to this modulation, the absorption modulation is 
encoded in an amplitude modulation at the modulation frequency f, decreasing the influence of many occurring noise 
sources. The detected amplitude modulated signal is then processed by Lock-In algorithms, extracting the absorption 
information at different harmonics of the modulation frequency f. In the presented application the first and second 
harmonic signals are extracted. The realization approach and the chosen components for this work are described below. 

Simulations of the Direct Absorption Setup 

As a starting point for the design of the spectroscopic setup of the RES system simulations of a direct absorption setup 
were done. In this simulation, critical components of the system, such as the laser diode and the photodetector were 
simulated  using  their  real-world  characteristics  as  reported  by  their  corresponding  datasheets.  The  gas  specific 
parameters for absorption were considered by connecting the simulation with the HITRAN database (Gordon et. al. 
(2022))  through  the  RADIS  python  package  (Pannier  et.  al.  (2019)).  By  simulating  the  absorption  process  intensity 
spectra are recorded. The spectra are then analyzed using a multi component, multi variable least-squares fit to obtain 
both the gas concentration and the gas temperature.  

138 

 
 
 
 
 
 
The proposed signal analysis algorithm for the direct absorption spectroscopy was tested by simulating different gas 
compositions at different temperatures. Here we present exemplary results of 0.1 % CO2 in ambient air, assuming an 
open  absorption  pathlength  of  10  m  at  standard  ambient  conditions.  The  resulting  baseline  corrected  intensity 
spectrum is displayed in Figure 1. 

Figure 7: 
Simulated transmittance of a CO2 measurement. A concentration of 0.1 % CO2 
was  simulated  in  ambient  air  and  ambient  conditions.  The  top  graph  shows  the  simulated, 
baseline corrected intensity signal (blue) and the intensity profile calculated with the described 
fitting algorithm. The bottom graph shows the residual between the calculated and real intensity 
profile. 

The simulated intensity spectrum is used to extract both temperature and concentration information. The calculated 
deviations  between set and retrieved  gas properties are below 5 %  for  the gas  temperature  and 2  %  for the gas 
concentration. 

Simulations of a Wavelength Modulation Spectroscopy Setup 

The proposed TDLAS system design is simulated using a wavelength modulation approach to assess performance. The 
laser diode is wavelength tuned using a triangular modulation of the injection current between 32.518 mA and 74.686 
mA at a frequency of 1 Hz. This modulation allows a sweep over the CO2 absorption feature located at 1601.12 nm. 
Superimposed on this triangular modulation is a sinusoidal modulation at a frequency of 1 kHz with an amplitude of 
2.588 mA.  

Absorption is simulated in a similar way to the direct absorption case for two different detection schemes, to compare 
the  system  performance.  The  simulated  intensity  signal  is  then  detected  by  two  different  types  of  amplified 
photodetectors:  a  single  photodetector  (PDA30B2,  Thorlabs  Inc.,  similar  to  the  direct  absorption  simulation)  and  a 

Figure 2: 
Figure 3: 
CO2 concentration of 14000 ppm. 
CO2 concentration of 50 ppm. 

The simulated demodulated 2f-signal of the balanced detection scheme for a 
The simulated demodulated 2f-signal of the balanced detection scheme for a 

139 

 
 
 
 
 
 
 
 
balanced photodetector (2017 Nirvana Auto-Balanced Optical Receiver, Newport Cooperation). To remove the residual 
amplitude modulation for the single photodetector a high-pass filter with a cut-off frequency of 500 Hz is simulated 
after signal detection on the photodetector. To simulate an ADC front end of a DAQ system the detected signal is then 
down sampled to 14-bit precision (ALPHA250, Koheron SAS). Further, the signal was demodulated at both 1f and 2f 
(1 kHz and 2 kHz respectively) using a digital Lock-In detector.  

For assessing the system performance two edge cases are simulated, a low CO2 concentration, similar to ambient 
background and a high CO2 concentration, as expected in vehicle exhaust. The concentration used are 50 ppm for the 
low  concentration case and 14000 ppm for  the high  concentration case respectively.  The conditions are as stated 
above, 10 m absorption pathlength at ambient conditions. The simulation result for the high concentration case in the 
balanced detection scheme is depicted in Figure 2, while Figure 3 shows the low concentration case. The demodulated 
2f-signal of the high concentration case is well resolved, whereas the 2f-signal in the low concentration case shows 
significant disturbances originating from quantification noise from the analog to digital conversion. However, further 
signal processing would be still possible in this case. 

The results of the simulated case of a single photodetector are presented in Figure 4 for the high concentration and 
in Figure 5 for the low concentration. While the demodulation of the simulated signal in the high concentration case 
recovers the 2f-signal properly, the results of the low concentration case show no detectable signal. The characteristic 
2f-signal vanishes in the noise background completely in this case and further signal processing would not be possible. 

Figure 5: 
concentration of 50 ppm. 
Figure 4: 
concentration of 14000 ppm. 

The simulated demodulated 2f-signal of the single photodetector for a CO2 

The simulated demodulated 2f-signal of the single photodetector for a CO2 

In summary, both simulated detection schemes show equal performance at high target gas concentrations, making 
them  both  suited  for  an  application  in  a  RES  system  for  high-emitter  detection.  When  investigating  lower 
concentrations in the simulation, the single photodetector setup is not able to recover the absorption signal from the 
simulated low concentration case. The balanced detection scheme shows a visible influence of noise on the signal 
while still allowing further signal processing to be done. 

Preliminary Measurements in the Wavelength Modulation Setup 

Using a setup similar to the simulated setup described in the previous section measurements to verify the operation 
of the system design are conducted in the laboratory. The laser diode (NLK1L5GAAA, NTT Electronics) is installed in a 
laser driver (CTL101-2-B-200, Koheron SAS). The modulation of the injection current is provided by the DAQ system 
(ALPHA250, Koheron SAS). The fiber coupled output of the laser diode is collimated using a mirror collimator (RC02APC-
P01 Thorlabs Inc.) and directed to the multi-pass gas cell (MGC1C/M-P01, 1 m pathlength, Thorlabs Inc.). After the gas 
cell the laser beam is focused and directed onto the photodetector (PDA30B2, Thorlabs Inc.) using an off-axis parabolic 
mirror (MDP129-M01, Thorlabs Inc.). The detected signal output from the photodetector is high-pass filtered using a 
custom made 1st order high-
C = 4.98 kHz). Digitization of the signal is done using 
the RF front-end of the DAQ system. 

140 

 
 
 
 
 
 
For the validation of the system design the gas cell was filled with 100 % CO2 at ambient pressure. The injection 
current is modulated at frequencies of 244.14 Hz for the triangular, and 105.05 kHz for the sinusoidal modulation. 
The  triangular  modulation  was  selected  from  50  mA  to  130  mA  to  sweep  one  absorption  feature  while  the 
superimposed sinusoidal modulation was set to 20 mA to yield a high signal amplitude after high-pass filtering. The 
recorded signal is then demodulated using two digital Lock-In amplifiers detecting at the 1f and 2f frequencies (105.05 
kHz and 210.10 kHz respectively). 

In Figure 6 an exemplary measurement of the 2f amplitude is shown. The recovered shape of the 2f signal meets the 
expectation and thus proves the proper operation of the system design in the case of WMS with a single photodetector. 
Further investigations using varying gas concentrations as well as a balanced photodetector will be conducted in the 
future. 

Summary 

In this paper we present a system concept for a TDALS based CO2 measurement for a RES system. The proposed 
setup was simulated for a direct absorption measurement to analyze the system components. A specially designed 
fitting algorithm for evaluating gas concentration and temperature from the recorded intensity spectra was tested. 
In the presented test conditions the algorithm was able to recover gas concentration and temperature within 2 % 
error for the concentration and 5 % error for the temperature respectively. 

Further simulations were done to investigate the implementation of a wavelength modulation technique (WMS). Two 
different detection schemes, a single photodetector and an auto-balanced photodetector were simulated to assess 
their performance over the expected concentration range. While the balanced photodetector was able to recover the 
signal for both concentration cases, the single photodetector only provided reasonable output for the high 
concentration case. The setup was then realized in a laboratory setup to perform initial feasibility tests for a WMS 
approach. The recorded signals using a gas concentration of 100 % CO2 show good agreement with the simulations. 

Conclusion and Outlook 

Based on the results from the preliminary laboratory measurements with the WMS setup further measurements will 
be done. The direct correlation between signal amplitude and CO2 concentration will be evaluated. A setup using a 
balanced photodetector will be realized to further verify the simulation results. 

The presented  TDALS  system will further be  included into the  mentioned RES system  alongside the  described  gas 
imaging system to allow direct concentration measurements of pollutants of passing vehicles. 

Acknowledgements  

This work is funded by the Aus
by AVL List GmbH. 

Figure 6: 
path length of 1 m. 

Recorded 2f-amplitude of a measurement with 100 % CO2 at an absorption 

141 

 
 
 
 
 
 
 
 
References 

Bainschab M, Schriefl M. A., and Bergmann A. (2020). Particle number measurements within periodic technical inspections: A first 
quantitative assessment of the influence of size distributions and the fleet emission reduction. Atmospheric Environment: X, Volume 
8, p. 100095, https://doi.org/10.1016/j.aeaoa.2020.100095 

Lin, H Wu, K Zhang T, Lai X, Mei L. (2023).  Remote sensing of vehicle emitted carbon oxides employing wavelength modulation 
spectroscopy  and  a  segment  modulation  method.  Microwave  and  Optical  Technology  Letters.  Volume  65,  pp.  1162-1168, 
https://doi.org/10.1002/mop.33214 

Gordon I. E., et. al. (2022). The HITRAN2020 molecular spectroscopic database. Journal of Quantitative Spectroscopy and Radiative 
Transfer. Volume 277, pp. 107949, https://doi.org/10.1016/j.jqsrt.2021.107949 

Pannier E., Laux C. O. (2019). RADIS: A nonequilibrium line-by-line radiative code for CO2 and HITRAN-like database species. Journal 
of Quantitative Spectroscopy and Radiative Transfer. Volumes 222 223, pp. 12-25, https://doi.org/10.1016/j.jqsrt.2018.09.027 

142 

 
 
 
 
 
A  Modern,  Flexible  Cloud-Based  Database  and  Computing  Service  for  Storing  and 
Analysing Vehicle Emission Measurements 

C.E. Rushton*1, J.E. Tate1, M. Callaghan2, M. Knoll3, and A. Sjodin4 

1 Institute for Transport Studies and 2School of Computing, University of Leeds, Leeds, LS2 9JT, UK  

3 Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, Inffeldgasse 33/I, 8010 Graz, Austria 

4 IVL Swedish Environmental Research Institute Ltd. P.O Box 210 60, S-100 31 Stockholm, Sweden 

Keywords:   remote sensing, point sampling, data science, vehicle emissions.      

Presenting author email: c.e.rushton@leeds.ac.uk 

Introduction 

Data dashboards (Sarikaya et al, 2018) offer an opportunity to streamline various processes associated with analysis 
of large and complicated data sets (Stadler et al, 2016). They are often deployed at city levels to aid data driven 
decision and policy making (Kourtit & Nijkamp, 2018).  Dashboards offer advantages over traditional data presentation 
methods because they can be made more widely available, are interactive and engaging, present up-to-date and near 
real-time information, and can generate support the publication of evolving documents, unlike more traditional static 
papers and reports.  Intelligently designed dashboards can improve user engagement and the accessibility of more 
advanced scientific methods and analysis techniques, whilst limiting the potential for unintentional or malicious misuse 
of data and statistics. Data sets can be updated and expanded, new algorithms can be added and existing algorithms 
can be improved in close to real time, meaning that the end user has access to the latest data, methodology and 
insights possible.  The impact of this approach to this field is significant as air pollution is a quickly evolving topic and 
resources from a few years ago may be out of date relatively quickly.  This is mitigated by a database that is frequently 
updated and a dashboard that gives most users some access to the most up to date data in a clear and approachable 
package. 

The overarching objective of the CARES project was to remove or reduce barriers to implementation of remote sensing 
device  (RSD)  measurements.    An  online  data  dashboard  coupled  to  a  persistent  database  allows  researchers  and 
citizens alike the opportunity to access and interact with the data collected as part of the CARES project.  A historical 
barrier to entry for RSD data analysis is cost.  The devices are expensive to purchase, require complex installation and 
specialist expertise to analyze the data.  The lower-cost point sampling (PS) devices advanced as part of the CARES 
project (described elsewhere by Knoll et al., 2023) has significantly helped make remote sensing vehicle emissions 
more accessible and affordable. The data dashboard has also helped, removing the barrier for specialist data analysis 
skills to extract relevant insights from measurement campaigns. 

This research paper describes a modern, flexible cloud-based database and computing service for storing and analyzing 
vehicle emission measurements. 

Methodology 

There are two key strands to this work: a database and a web-app.  The two work together to deliver reliable data in 
a data dashboard that is accessible to most users. 

Database: The Microsoft CosmosDB database platform, part of the Azure cloud computing platform6 was chosen for 
this task as it provides an enterprise level suite of tools for data science, app deployment and database construction 
and  maintenance.    The  Azure  platform  is  used  worldwide  and  across  multiple  sectors,  with  users  including  banks, 
energy firms and telecom businesses e.g. ABN-AMRO (https://www.abnamro.com), Centrica (https://www.centrica.com/), 

6 https://cosmos.azure.com/  

143 

 
 
 
 
 
 
 
 
 
 
                                                       
 
and  Vodafone  (https://www.vodafone.co.uk/).  Azure  provides  Infrastructure  as  a  Service  through  a  range  of  virtual 
machine, storage and network offerings that allow a user to build and manage their own virtual infrastructure in the 
er to develop and deploy web-applications, mobile applications, 
and APIs without worrying about underlying infrastructure. PaaS is useful for building new applications quickly and 
without the need to worry about managing infrastructure.  Data and analytics services include Cosmos DB and SQL 
Databases,  Machine  Learning  etc.    The  analytics  services  are  useful  for  building  applications  that  can  collect  and 
analyse and process large volumes of data in the cloud which can be used to build intelligent applications that can 
make data-driven decisions and gain insight from large data sets. The CosmosDB database is a document style, NoSQL 
database that uses JavaScript Object Notation (JSON) style documents to store data.  The documents are organized 
into containers and the containers are organized into databases.  The containers are schema-agnostic, meaning that 
no standard schema is required or enforced by the database.  The flexibility that a schema-agnostic system offers is 
of paramount importance to the data storage process and was a key factor in the decision to use this approach. 

Each measurement is entered into the database in its own document.  The document format consists of key: value 
pairs.    A  value  may  take  on  different  data  types:  Boolean,  numeric  and  character,  as  well  as  lists  of  data  and 
dictionaries.  The values used for remote sensing are typically single valued, but storing data in a key list may also be 
done for much longer time series data.  More complex documents can be created such imported GeoJSON files.   

The documents created for the CARES measurements use four keys to organize the data.  These keys are required for 
most documents but not all.  Meta-data documents do not require measurement data.  The keys are capitalized to 
signify their necessity and are:   

TIMESTAMP_MS 

 
  MEASUREMENT_OBSERVED_VALUE 
  MEASUREMENT_DESCRIPTION 
  DB_DATA_TYPE  

TIMESTAMP_MS is an integer typed UNIX timestamp in milliseconds.  This column indicates the time the measurement 
was taken.  It is set to the hour of measurement (e.g., 23:45:59 would be set to 23:00:00) for GDPR7 compliance.  This 
obscuration of time is considered adequate to meet GDPR compliance for studies less than two weeks in length, whilst 
still being adequate for most scientific analysis purposes in line with other related air quality and traffic flow data sets 
that typically aggregate to an hour-long time interval.  A UNIX timestamp was chosen over a traditional YMD/HMS date 
format to make querying time intervals easier. 

MEASUREMENT_OBSERVED_VALUE: is the measurement taken or the value observed.  This is typically a floating point 
variable and corresponds to the thing of interest for this particular measurement. 

MEASUREMENT_DESCRIPTION: is the description of the number in the previous key pair and usually includes the units 
if available.  The format of these descriptions is {description}_{unit}. 

DB_DATA_TYPE: is the data type being measured.  For the CARES project this is typically the instrument type but can 
also include metadata such as site location. 

A sample measurement document is shown below. 

{ 

7 https://gdpr-info.eu/  

144 

 
 
 
 
 
 
 
 
 
 
 
 
                                                       
 
    "campaign_id": "PL_2021_1", 

    "measurement_id": *******, 

    "site_id": "JOSEFA", 

    "instrument_id": 5059, 

    "make_raw": "SKODA", 

    "model": "FABIA COMBI", 

    "build_year": 2007, 

    "eng_disp": 1896, 

    "eng_power": 74, 

    "co2_ta": 129, 

    "emission_standard_raw": null, 

    "fuel_type_1": "diesel", 

    "ptr_type": "ICEV", 

    "emission_standard_calc": "Euro 4", 

    "TIMESTAMP_MS": 166**********, 

    "DB_DATA_TYPE": "rsd_heat_data", 

    "MEASUREMENT_DESCRIPTION": "speed_kmh-1", 

    "MEASUREMENT_OBSERVED_VALUE": 63.3735802, 

} 

the entire contents of the container it is 

eturn emission rates only for all Euro 4 
Skoda diesels measured by the RSD HEAT instrument in a given container.  Well designed queries are more efficient 
than downloading the entire data set whenever a single calculation is required. 

The schema-agnostic containers mean that different measurements do not require the same key and value structure.  
The  flexibility to  use  different  instruments  that  may  have  different  output  data  structures  is critical  to  the  CARES 
project and the document style database allows for that.  It is reasonable to store RS data in the same container as 
point sampling or other data sets from different instruments collected as part of the CARES project. 

Web App:  A suite of web applications were developed as front-ends to the database.  The web apps were developed 
in  Python  using  Streamlit8  as  a  front  end,  Altair  as  a  data  visualization  package  and  a  range  of  standard  Python 
libraries for data analysis including NumPy9, SciPy10 and Pandas.  A relatively user-friendly user interface allows more 
streamlined access to different areas of interest for different users without them having to have prior knowledge of 
the database, its structure, or querying with third party tools.  The apps also do not require any analysis to be done on 
the part of the user as all figures are pre-developed and displayed within the app.  Three apps were developed: 

  CARES Public App 
  CARES City App 

8 https://streamlit.io  
9 https://numpy.org  
10 https://scipy.org  

145 

 
 
 
 
 
 
 
                                                       
 
  CARES Science App 

The CARES public app is available at https://cares-public-app.azurewebsites.net and was designed for the public.  The 
data collected throughout the CARES project was funded by EU and UK taxpayers, so is made open and accessible.  
The City App presents the same figures as in the Public App, but with a wider population of cities that have remote 
sensing data available. Access to the city app is only available to registered users on a request basis.  Neither the 
Public nor City apps reveal the raw data used to create the figures so reduce the risk of misuse of data.  The Science 
App was designed for registered experienced remote sensing developers.  Deeper analysis is available on all data sets 
and the raw, annonymised data is available to download. 

Outputs 

City / Public App: The three CARES cities included in the CARES Public-App are Milan, Prague and Krakow.  A range of 
different figures may be created using the CARES Public and City App.  These include: 

 

Fleet composition 
o  Euro class 
o  Vehicle type 
o  Manufacturer 
o  Euro class and fuel type 

  Vehicle dynamics 

o  Speed and acceleration 
o  Vehicle specific power (VSP) 

  Vehicle emissions 

o  Euro class (Figure 1) 
o  Year of registration 
o  Manufacturer 
o  Summary 

  Weather 

o  Temperature 
o  Pressure 
o  Humidity 
o  Wind speed 
o  Wind direction 

CARES Science App: More detailed analysis can be performed using the Science App. These include: 

 

Point Sampling 

o  Simple (single channel) point sampling 
o  Complex (multi-channel) point sampling 
o  Automatic triggered point sampling 

 

Remote Sensing 

o  Remote sensing summary (Figure 2) 
o  High emitter detection 

Impact 

The  CARES  app  suite  and  in  particular  the  public-App  go  some  way  to  increasing  the  visibility  and  accessibility  of 
remote sensing data, which was a key objective of the CARES project. It has also modernized and gone a long-way to 
future proofing the pan-European vehicle emission remote sensing database and data architecture initially created in 
the CONOX initiative (Sjödin et al, 2018). 

More EU and UK citizens and other interested parties can now begin to understand the emissions of the vehicles in 
some EU cities and think about how they might best use the newly accessible information and  resource to help design 
and realise their own air pollution mitigation policies. For example, the diesel facet of Figure 2 shows that there is an 

146 

 
 
 
 
 
 
 
 
 
improvement in overall emission factors for Euro 6d (yellow) and Euro 6d-temp (purple) vehicles compared to Euro 6 
(a/b/c - orange).  This suggests that policies that target diesel vehicles below these emission standards will be more 
impactful than policies that include non-d /d-temp Euro 6 vehicles.  The information that backs up this claim is now 
publicly accessible and free to anyone who is interested in looking it up. 

Figure 8: Vehicle emissions by Euro class for NO2: CO2 ratios for Milan from the CARES Public App 

Future Work 

The platform used for these apps was selected with the future in mind.  Whilst the data presentation is an innovative 
step, the data collection and storage follow well-established processes that can reduce the immediacy of the impact 
of this work.  Automating well understood processes on the cloud can lead to a quicker and more cost-effective turn-
around of RS data and its insights from scientific experts to policy makers.   

As  more  sensors  become  connected  to  the  Internet  of  Things  (IoT)  then  more  real-time  data  dashboards  may  be 
developed.  This work may serve as a basis for any future IoT enabled vehicle emission measurement systems.  When 
deployed at scale, particularly the lower-cost point sampling devices tested during other phases of the CARES project, 
these devices connected to a real-time data platform will be able to provide a close to real-time picture of the status 
of vehicle emissions in the measurement region. 

The real-time data stream could be integrated into machine learning algorithms to predict future trends and potentially 
identify areas of concern before they become problematic.  This extra knowledge could help decision-making processes 
for city authorities tasked with reducing the exposure to air pollution of their citizens.  

147 

 
 
 
 
 
 
Figure 9: Normalized histogram of emission factors for NO2 fuel mass measurements in Milan, faceted by 

fuel type and colored by Euro standard, taken from the CARES Science App. 

Beyond  air  pollution there  is a  wealth of publicly available data  that is often difficult  to  parse or  analyze  without 
specialist knowledge.  The use of interactive dashboard type web applications to improve the visibility and accessibility 
of public health  related  data and other publicly available  data  remains a  relatively  untapped  resource.   It may  be 
possible to apply the lessons learned through the CARES project to a wide range of useful and impactful data sources 
that are currently publicly available but not publicly accessible. 

148 

 
 
 
 
 
References 

Knoll,  M.,  Penz,  M.,  Juchem,  H.,  Schmidt,  C.,  Pöhler,  D.,  &  Bergmann,  A.  (2023).  Large-scale  automated  emission 
measurement of individual vehicles with point sampling. EGUsphere, 2023, 1-46. 

Kourtit, K., & Nijkamp, P. (2018). Big data dashboards as smart decision support tools for i-cities An experiment on 
stockholm. Land use policy, 71, 24-35. 

Sarikaya,  A.,  Correll,  M.,  Bartram,  L.,  Tory,  M.,  &  Fisher,  D.  (2018).  What  do  we  talk  about  when  we  talk  about 
dashboards?. IEEE transactions on visualization and computer graphics, 25(1), 682-692. 

Sjödin,  Å.,  Borken-Kleefeld,  J.  Carslaw,  D.,  Tate,  J.,  Alt,  G.-M.,  De  la  Fuente,  J.,  Bernard,  Y.,  Tietge,  U.,  McClintock,  P., 
Gentala, R., Vescio, N., Hausberger, S (2018) Real-driving emissions from diesel passenger cars measured by remote 
sensing and as compared with PEMS and chassis dynamometer measurements - CONOX Task 2 report. On behalf of 
the  Federal  Office  of  Environment,  Switzerland,  https://www.bafu.admin.ch/bafu/en/home/topics/air/publications-
studies/studies.html  

Stadler, J. G., Donlon, K., Siewert, J. D., Franken, T., & Lewis, N. E. (2016). Improving the efficiency and ease of healthcare 
analysis through use of data visualization dashboards. Big data, 4(2), 129-135

149 

 
 
 
 
2.5  TAP.05.  Vehicle  greenhouse  gas  emissions,  energy 

consumption, vehicle and fuel life-cycle analysis. 

150 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
From physical testing to on-board fuel consumption monitoring and telemetry: a 
pilot project for capturing the real-world fuel consumption of vehicles11  

A. Tansini1, J. Suarez1, N. Aguirre2, A. Laverde3 and G. Fontaras1 *  

1

* georgios.fontaras@ec.europa.eu  

2Nestor Aguirre, nfaguirrec@gmail.com  

3Universitat Politècnica de València, VRAIN, València, Spain  

Introduction 

The European Union (EU) is committed to an ambitious strategy to reduce CO2 emissions from various sectors 
including  road  transport.  This  commitment  is  deeply  tied  to  international  agreements,  such  as  the  Paris 
Agreement (Paris Agreement, 2015), and EU regulations, such as the climate law (EU Regulation 2021/1119), 
setting a comprehensive target to achieve carbon neutrality by 2050. Road transport sector is one of the main 
contributors to Greenhouse Gas (GhG) emissions and has come under increased scrutiny due to its substantial 
carbon footprint. In this context, monitoring and understanding real-world fuel consumption and CO2 emissions 
from vehicles have emerged as a paramount objective. GhG emissions reduction measures need to be effective 
in real world operation that is continuously changing. For decades, the standard approach involved vehicle tests 
on a chassis dynamometer within controlled laboratory conditions. These tests quantified CO2 emissions and 
fuel consumption over predefined drive cycles. Recent regulatory enhancements introduced with the Worldwide 
harmonised Light vehicles Test Procedure (WLTP) and EU Regulation 2017/1151 have refined these laboratory 
tests to more accurately mirror on-road scenarios, thereby narrowing the gap between laboratory and real-
world emissions (Pavlovic, 2020). Despite these improvements, no laboratory test can always fully replicate the 
diversity of on-road operations (e.g. ambient conditions and vehicle usage pattern) and energy consumption 
demands  (e.g.  use  of  auxiliaries)  (Fontaras,  2017).  Acknowledging  their  limitations,  data  sourced  from  real-
world contexts emerges as a method to grasp the full spectrum of fuel consumption variability (Pavlovic, 2018). 
This approach complements the standardised certification processes and enhances the understanding of road 
transport  energy  demand  and  carbon  impact,  ultimately  favouring  consumer  awareness  (EU  Regulation 
2019/631). As of January 2021, EU vehicles must incorporate On-Board Fuel Consumption Monitoring (OBFCM) 
devices to enable real-time readout and internal storage of fuel consumption metrics. These devices, among 
other  things,  accumulate  distance  and  fuel  consumption  data  over  a  vehicle's  lifetime,  offering  a 
straightforward  way  to  obtain  real-world  average  fuel  consumption.  These  values  are  subject  to  accuracy 
requirements and can be extracted from the vehicle's On-Board Diagnostics (OBD) system.  

This paper describes a pilot project initiated by the European Commission's Joint Research Centre (JRC) aimed 
at collecting OBFCM and vehicle telemetry data from private and corporate vehicles across Europe using OBD-
dongles with internet connectivity. Upon the beginning of a new trip, these devices automatically start recording 
instantaneous parameters such as speed, engine RPM, engine fuel rate and ambient temperature. Additionally, 
they gather OBFCM lifetime distance and fuel data, transmitting this comprehensive dataset to a centralised 
repository for storage and analysis. Vehicles are monitored for a long time, as much as the vehicle owner/driver 
agrees to, therefore supporting data collection throughout all seasons and driving conditions. This innovative 
approach enables a holistic understanding of real-world vehicle performance and fuel efficiency, empowering 
regulatory advancements and informed decision-making. This short scientific paper thus seeks to present the 
preliminary  results  obtained  in  this  pilot  project  and  the  opportunities  it  brings  to  understand  the  interplay 
between real-world conditions, driving style, vehicle technology and environmental impact. By shedding light on 
these  aspects,  this  study  offers  valuable  insights  into  the  ongoing  efforts  to  reshape  the  landscape  of 
sustainable mobility and drive the EU towards a greener future.  

11 The views expressed in this paper are purely those of the authors and shall not be interpreted as an 
official position of the European Commission under any circumstance  

151 

 
 
 
                                                       
 
Materials and methods 

The project collects real-world OBFCM from vehicles in use. It is composed of two main blocks: (a) production 
of vehicle data and transfer to central server (b) data storage and manipulation in the data centre.  

Block  (a)  encompasses  OBD-dongles  preparation,  testing,  installation  in  vehicles  and  check  of  their  correct 
operation. Installation requires that these devices are connected to the car's OBD port. Data production relies 
on collecting OBD data and other measurements from the dongle, an on open-hardware and open-software 
device  featured  by  an  Arduino  programmable  board,  OBD  port  and  chip,  GNSS,  motion  sensor  and  cellular 
module. A few upgrades were applied to the device under both hardware software perspectives, focusing on 
OBD and OBFCM data collection performance. OBD is a vehicle system to monitor and manage various aspects 
of their performance, emissions, and overall health. It consists of both hardware and software parts complying 
with the technical requirements and communication protocols defined by the Society of Automotive Engineering 
(SAE)  and  ISO  standards.  Initially  introduced  for  emissions  control,  OBD  has  evolved  into  a  comprehensive 
diagnostic tool integrated into modern vehicles. The hardware consists of sensors and Electronic Control Units 
(ECUs)  distributed  throughout  the  vehicle, collecting data from different  components and devices.  This  data 
includes  information  about  engine  performance,  emission  levels,  fuel  efficiency,  and  operating  parameters. 

also plays a role in real-time emissions monitoring, which is crucial for environmental regulations. OBD systems 
can detect anomalies in emissions control systems and alert the driver or technician if a potential problem could 
affect the vehicle's emissions performance. Building on OBD capabilities, EU Regulation 2017/1151 added the 
requirement for European vehicles to be fitted with OBFCM devices as of 1st January 2021 for new models and 
as  of 1st  January 2022  for all models. OBFCM data consists  of both instantaneous and lifetime cumulative 
signals  and  is  retrievable  through  OBD  protocols  (Society  of  Automotive  Engineering,  2023).  The  list  of 
instantaneous and lifetime signals covered by OBFCM is presented in Table 1.   

Table 1:  

mandatory data on OBFCM-compliant vehicles  

Instantaneous signals 
(all vehicles)  

Lifetime signals  
(all vehicles)  

Additional lifetime signals  (OVC-HEVs 
only)  

vehicle speed (km/h)  

total distance travelled (km)   total  distance  travelled 

in  charge 
depleting  operation  with  engine  off 
(km)  

engine fuel rate (lt/hr)   total fuel consumed (lt)  

engine fuel rate (gr/s)     

vehicle fuel rate (gr/s)  

total distance travelled in charge 
depleting operation with engine 
running (km)  

total distance travelled in driver-
selectable charge increasing operation 
(km)  

total fuel consumed in charge 
depleting operation (lt) total fuel 
consumed in driver-selectable 
charge increasing operation (lt) 
total grid energy into the battery 
(kWh)  

In addition to these signals, the following OBD data is collected within our project: accelerator pedal position, 
engine speed, engine load, engine coolant temperature, ambient temperature and vehicle odometer. The OBD-
dongle collects instantaneous signals and internal measurements on a second-by-second basis, and lifetime 

152 

 
 
 
 
 
  
  
  
  
  
values every 30 seconds to limit the workload and network usage. Data is transmitted Over-The-Air (OTA) to a 
central server for convenient and robust data management (see Figure 1).  

Block (b), on the other hand, encompasses all the operations required on the data centre side to secure and 
prepare the data  for later analysis.  The raw  data, i.e.  the  trips  timeseries  collected  by  the OBD-dongles are 
stored in a non-relational MongoDB database to ensure scalability and flexibility of the data schema. In addition 
to the raw data from devices, the database also holds a table with vehicle specifications (powertrain type, fuel 
type,  others)  to  enable  deeper  analysis  per  vehicle  or  group.  The  dataset  enable  analysis  from  different 
perspectives  and  with  different  level  of  complexity:  simple  assessment  on  the  most  recent  lifetime  values, 
inspection of the instantaneous and lifetime signals in a trip, analysis of the aggregation of data per trip or per 
other segment/group as defined by the data analyst: per season, per month, per vehicle, per powertrain type. 
An additional criteria for OVC-HEVs could be aggregating data between charging events (leveraging the total 
grid energy into the battery lifetime parameter).  

Figure 1:  

over-the-air collection of vehicle data (OBFCM and telemetry) through OBD-dongles  

A very insightful and simple way of analysing the data that was explored in this study is to aggregate data per 
trip,  obtaining  for  each  of  them  the  total  distance  travelled,  the  total  fuel  consumed,  the  average  fuel 
consumption in litres/100km together with context data to study its variability: average ambient temperature, 
average trip speed, share of urban, rural or motorway driving, trip dynamicity and other metrics. The numbers 
and  plots  presented  in  the  next  section  are  obtained  by  aggregatind  data  per  trip.  The  results  are  to  be 
considered preliminary since the data collection experienced some issues in these early stages of the project, 
with trips getting divided into two or more segments because of network, device or server problems. Ways to 
solve  this  problem  are  under  investigation,  raw  data  as  received  by  the  server  shall  be  fixed  and  trips 
reconstructed as they took place in reality, identifying and concatenating in a robust way all their segments. 
Such a fix is going to be applied in follow-up investigations on the same data. For the time being, data are being 
processed and presented in a relatively independent way from the trips identification as it came in the raw data. 
The data processing aimed at calculating the travelled distance and the consumed fuel by computing deltas on 
the total distance and total fuel OBFCM lifetime parameters. It was impossible for some vehicles to read the 
lifetime parameters due to incompatibilities between the OBD-dongles pre-implemented OBD library and the 

these  vehicles  were  used  instead  (vehicle  speed  to  calculate  total  distance,  fuel  rates  to  calculate  total 
consumed fuel).  

Preliminary results 

An overview of the data captured until the writing of the paper (August 2023) on OBFCM vehicles since the start 
of the project in late 2021 is provided in Table 2. The number of vehicles, the sum of distance and the sum of 
days per vehicle is presented for each powertrain technology covered (conventional, mild hybrid, full hybrid and 
plug-in hybrid). The distance here presented is calculated by considering only the trips where the OBD-dongles 
actively log data, therefore cumulating any trip it was possible to capture after the installation. Anyhow, the 
OBFCM  lifetime  parameters  enable  us  to  obtain  the  average  fuel  efficiency  from  the  entire  vehicle  history, 
therefore also covering the travelled distance before this moment. This implies that from the captured data, it 
is possible to derive the average fuel efficiency for even longer total distances than the values reported in Table 

153 

 
 
  
  
2;  this  additional  value  is  nevertheless  missing  the  context  data  that  telemetry  could  bring  (vehicle  speed, 
ambient temperature, engine load, etc.). The data aggregation process described in the materials and methods 
section generated a population of trip average fuel efficiencies expressed in litres per 100 kilometres. These 
results are presented in Figure 2 in the form of distributions complemented by median value, 5% and 95% 
percentiles. The subplots on the left show the simple distribution of trips average fuel consumption, wherein 
each trip counts the same regardless of the travelled distance.  The subplots on the right show the same average 
fuel consumption values but after distanceweighting process to assign higher significance to longer trips (for 
their higher relevance in the vehicles lifetime). The distance-weighting is also to return the true value of average 
vehicles lifetime fuel efficiency through the median. 

Table 2:  

Captured data overview (until Aug. 2023)  

Type 

Count (-) 

Sum of distance (km) 

Sum of days per vehicle (-) 

Conventional 

Mild hybrid 

Full hybrid 

Plug-in hybrid 

All 

13 

8  

4  

7  

32  

135602 

48548  

11379  

53663  

249194  

1760 

832  

303  

1169  

4064  

Figure 2:  
distributions (right) per technology (conventional, mild, full and plug-in hybrid)  

Per  trip  fuel  consumption  distributions  (left)  and  distance-weighted  fuel  consumption 

Different  distributions  are  returned  for  the  same  technology  from  the  two  different  representations,  with 
different median values and percentiles. For plug-in hybrid in particular the difference is striking. The simple 
distribution returns a median trip average fuel consumption of about 0.6 litres/100km, which is unrealistically 
low, whereas the distance-weighted distribution returns more trustworthy values of about 4.0 litres/100km. The 

154 

 
 
 
 
 
  
 
  
explanation of the very different median value is found in the twofold nature of plug-in hybrids, which can drive 
trips in pure electric mode in addition to fuel-supported trips (as any other engine vehicle). From the captured 
data,  it  is  clear  that  for  plug-in  hybrids  pure  electric  trips  outnumber  the  fuel-supported  ones,  bringing  the 
median  value  of  the  simple  distribution  close  to  zero.  These  numerous  purely  electric  trips  are  though 
cumulating a limited part of the vehicle lifetime distance and have limited significance when looking into the 
average  fuel  efficiency  over  a  long  period.  The  distance-weighting  of  trips  is  therefore  highly  significant  to 
obtain the best representation of vehicles average fuel efficiency. In conclusion, focusing on the subplots on 
the right of the figure, a gradual decrease in fuel consumption is identified with increasing electrification degree 
(moving from top, conventional vehicles, to bottom, plug-in hybrids), as expected. By comparing the median 
values  between    the  minimum  and  maximum  electrification  degree  one  can  see  that  the  average  fuel 
consumption drops from 6.1 to 4.0 litres/100km. Nevertheless, these results largely depend on the vehicles and 
drivers in our sample, which is not big enough to generalise conclusions. Furthermore, the plug-in hybrids sample 
also covers some corporate u
the  average  fuel  consumption  of  the  entire  group.  A  much  bigger  sample,  or  more  careful  analysis  and 
discussion of the results, is required to comment on plug-in hybrids' real-world fuel-saving potential.  

possible  to  produce  through OBD and telemetry,  which serves as  invaluable  context information to the fuel 
consumption  values  provided  by  OBFCM.  More  information  could  be  produced  based  on  the  available  OBD 
signals or the measurements that can be carried out and delivered by the OBD-dongles. From the figure, one 
can derive the trip average speed (subplots on the left) and the trip average ambient temperature (subplots on 
the right) to put the fuel efficiency data into perspective for each vehicle and usage condition.  

Figure 3:  
technology (conventional, mild, full and plug-in hybrid)  

Distributions  of  trip  average  speed  (left)  and  trip  average  ambient  temperature  (right)  per 

The results in this section showcase how the methodology enables the collection of real-world fuel consumption 
data, complemented by context data, for different vehicle technologies , for a significant amount of distance 
and with relatively little effort. No laboratory, professional measurement instrument or trained technician/driver 
is involved in producing this data, which comes straight from the vehicle monitoring systems to our data centre, 
opening the road to the collection of very large amounts of driving and fuel consumption data from all Europe. 
The realworld vehicles usage patterns and efficiency can therefore be obtained and analysed considering the 
boundary conditions found in each region. 

155 

 
 
  
  
 
 
  
Acknowledgements 

The authors acknowledge Fabrizio Forloni, Lorenzo Maineri, Kostis Anagnostopoulos, Thomas Vliagkfotis, Athina 
Mitsiara for their invaluable help  in kickstarting, upgrading and supporting the project. The authors are also 
grateful  to  the  project  volunteers  who  agreed  in  sharing  their  driving  data  and  make  our  data  collection 
campaign possible.  

References 

COMMISSION  REGULATION  (EU)  2017/1151 of  1  June  2017 supplementing  Regulation  (EC)  No  715/2007  of  the  
European  Parliament  and  of  the  Council  on  type-approval  of  motor  vehicles  with  respect  to  emissions  from  light  
passenger  and  commercial  vehicles   (Euro   5   and   Euro   6)   and   on   access   to   vehicle   repair   and   maintenance   
information,   amending   Directive   2007/46/EC   of   the   European   Parliament   and   of   the   Council,   Commission   
Regulation   (EC)   No   692/2008   and   Commission   Regulation   (EU)   No   1230/2012   and   repealing   Commission  
Regulation  (EC)  No  692/2008. Official Journal of The European Union.  

Fontaras G., Zacharof N. G., Ciuffo B. (2017). Fuel consumption and CO2 emissions from passenger cars in Europe - 
Laboratory versus real-world emissions, Progress in Energy and Combustion Science, 60, 97131.  

Pavlovic J., Anagnostopoulos K., Clairotte M. et al. (2018). Dealing with the Gap between Type-Approval and In-Use Light 
Duty  Vehicles  Fuel  Consumption  and  CO2  Emissions:  Present  Situation  and  Future  Perspective,  Transportation  Research 
Record, 2672(2), 23 32.  

Pavlovic J., Fontaras G., Ktistakis M. et al.  (2020) Understanding the origins and variability of the fuel consumption gap: 
lessons learned from laboratory tests and a real-driving campaign. Environ Sci Eur 32, 53.   

REGULATION (EU) 2019/631 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 17 April 2019 setting CO2 emission 
performance standards for new passenger cars and for new light commercial vehicles, and repealing Regulations (EC) No 
443/2009 and (EU) No 510/2011. Official Journal of The European Union.  

REGULATION (EU) 2021/1119 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 30 June 2021 establishing the 
framework for achieving climate neutrality and 

Official Journal of The European Union.  

Society of Automotive Engineering (2023). J1979-DA, Digital Annex of E/E Diagnostic Test Modes.  

UNFCCC, The Paris Agreement, Paris Climate Change Conference November 2015, COP 21.  

156 

 
 
 
 
 
CO2 emissions of Australian and European Sport Utility Vehicles12  

D. Komnos1, J. J. Gómez Vilchez13, R. Smit3, L. Ntziachristos1 and G. Fontaras2*2  

1 
2 
3 

Aristotle University, Thessaloniki, 54124, Greece  
European Commission, Joint Research Centre (JRC), Ispra, 21027, Italy  
Transport Energy/Emission Research (TER), Launceston, Tasmania, 7249, Australia  

Introduction 

To address the increasing emissions from road transport and mitigate climate change, in 2009 the first CO2 
standards were adopted in European Union (EU), setting intermediate fleet-wide targets for the next decade   
130 g/km for 2015, and 95 g/km for 2020   and providing time for the technology to mature and the car fleets 
to  adapt  (Regulation  (EC)  No  443/2009).  Multiple  studies  indicated  an  increasing  discrepancy  between  the 
officially reported CO2 values and the ones realised under real-world (RW), reaching in 2017 a gap of 40% 
(Pavlovic et al., 2020; Tietge, 2019). The main reason was the flexible definition of the certification procedure 
used for efficiency benchmarking, the New European Driving Cycle (NEDC). Consequently, the NEDC-based CO2 
and  fuel  consumption  values  continuously  decreased,  but  without  improvement  in  RW  conditions.  They 
undermined  the  efforts  to  produce  cleaner  vehicles  and  reduce  the  CO2  footprint  of  road  transport.  The  EU 
regulators' reaction was to introduce a new certification procedure, the Worldwide Harmonised Light Vehicles 
Test Procedure (WLTP) to address the RW gap issue and to provide more detail in the manufacturers' CO2 and 
fuel consumption data. The new test protocol corresponded better to RW conditions resulting to a substantial 
drop of the gap 
 between certified and the RW values (Fontaras et al., 2017; Pavlovic et al., 2020; 
TER, 2019). In 2019, new regulations integrated the new protocol and created a path for CO2 standards in the 
following decade, also putting in place provisions to ensure the CO2 gap is not growing again (Regulation (EU) 
2019/631).   

 to half 

The evolution of EU regulation increased confidence in the stability of the CO2 framework, which aligned with a 
larger climate ambition in the EU (European Commission, 2019). In 2023 the EU voted for more ambitious CO2 
targets for 2030 and a reduction of 100% for 2035 (Regulation (EU) 2023/851). These actions intend to bring 
public  attention  and  progressively  decarbonise  the  sector.  It  is  already  visible  that  vehicle  electrification  is 
rapidly increasing, providing a cleaner and more efficient on-road fleet. However, this is not the case worldwide, 
even among developed countries. In Australia (AU) light-duty vehicles are still being certified using the NEDC, 
and the growth of the sales of electric vehicles is limited compared to EU (TER, 2019). Lacking CO2 emissions 
standards,  in  parallel  to  continuous  growth  in  the  sales  of  heavier  and  more  energyconsuming  sport-utility 
vehicles (SUV) and utes, vehicle manufacturers have no incentive to introduce fuel-efficient vehicles into the 
AU fleet. Moreover, the vehicles are less fuel efficient than identical makes and models available overseas (Smit 
et al., 2022, 2021). As a result, the AU passenger vehicle fleet is moving towards higher absolute CO2 emissions 
and a likely widening RW CO2 gap. A recent study showed that the AU on-road fleet will only achieve a 35-45% 
reduction in (well-to-wheel) GHG emissions in road transport in 2050 compared to 2019 levels, even if a delayed 
and ambitious EU EV penetration scenario is followed (Smit, 2023).  

The present paper shows the preliminary results of the simulations performed to analyse SUVs' RW consumption 
and CO2 gap and provides insights by comparing a regulated (EU) versus a relatively non-regulated market (AU). 
The EU is the reference market where CO2 targets have been established for over a decade and where up-to-
date test procedures have been adopted, while the Australian market provides the unregulated example with 
the continued use of an outdated test protocol and a lack of (mandatory) CO2 or fuel efficiency regulation.  

Methodology 

Overview. A schematic representation of the approach followed to simulate the annual CO2 gap of the SUV 
fleet for EU and AU is shown in Figure 1. Technical characteristics of indicative SUVs from both regions were 
collected and combined with representative RW trips and representative ambient temperatures to build realistic 
RW scenarios. Combinations of all the different parameters, i.e., representative vehicles, trips, and temperatures, 

12 The views are purely those of the authors and should not be considered as an official EU  
Commission position  
13 Corresponding author georgios.fontaras@ec.europa.eu   

157 

 
 
                                                       
 
were simulated with a vehicle simulator to derive each individual vehicle's RW CO2 gap from the certified values. 
For the present research, the study team has used the PyCSIS tool that is a light version of CO2MPAS (Fontaras 
et al., 2018). PyCSIS performs detailed simulations because its physical models are sensitive to the individual 
vehicle characteristics and the environmental parameters used as inputs. It has proven its accuracy in simulating 
laboratory tests (following both NEDC and WLTP certifications) and RW driving in several studies, with the most 
recent assessing the EU fleet gap of 2018 and 2019 (Komnos et al., 2022).To derive the SUV fleet annual RW 
consumption  and  gap,  the  individual  vehicle  RW  gaps  simulated  were  then  intersected  with  the  below 
distributions:   

1.  Certified CO2 distributions of SUVs of one registration year for both EU and AU.  
2.  RW trip distance distributions of annual usage.  
3. 

Population-weighted average ambient temperature distributions.  

For each of these distributions, 10,000 Monte Carlo simulations were performed following their characteristic 
shapes. Then, each Monte Carlo simulation was compared to the values previously selected as representatives. 
This procedure was followed to assign a percentage correspondence to the full distribution. For example, each 
vehicle selected reflects a specific percentage of vehicles in the fleet in CO2 terms. For this purpose, the distance 
between each representative value and each Monte Carlo simulated value was calculated; then, every value of 
the 10,000 Monte Carlo is distributed to the representative value that their distance is the minimum. 

Figure 1:  

Schematic workflow of the process followed.   

The following subparagraphs provide detailed explanations for distributions and the representative values used 
for the simulations.  

EU and AU and representative SUVs and fleets. Finding representative EU and AU market data for SUVs 
was a crucial step. For the EU market, the Joint Research Centre (JRC) database that collects the vehicle technical 
characteristics of the vehicles tested each year for the EU Market Surveillance (Bonnel et al., 2022) was used, 
and  four  indicative  vehicles  were  selected.  Four  SUVs  tested  under  real-world  conditions  (PEMS)  and 
representative  of  typical  SUVs  sold  in  AU  (Smit  et  al.,  2022)  were  selected  for  AU.  In  Table  1,  essential 
characteristics for both vehicle sets are provided. Each set of vehicles is ordered by their certified CO2 value. 
The AU vehicles are a mix of diesel and gasoline vehicles and have larger engine capacities and higher weights 
as compared with the EU SUVs. Altogether, the first indication is that the AU SUVs are bigger vehicles than the 
ones most frequently sold in EU. Regarding the SUV fleets of both regions, it was decided to study the registered 
vehicles in the year 2020. The 2020 EU registered SUVs were collected by combining the 2020 EU monitoring 
dataset for passenger vehicles reported by the European Environment Agency (European Environment Agency, 
2023), with a database created by collecting data from online sources. This provided new vehicle sales numbers 
by make and model and with associated NEDC and WLTP-certified CO2 values. The dataset was used to identify 
which vehicles are SUVs. In the EU 3,192,369 vehicles were identified as new SUVs sold in 2020. 

158 

 
 
 
 
 
 
  
Table 1: Characteristics of the sampled EU and AU representative SUVs. Gas: Gasoline; Dies: Diesel; T: Turbo 
engine; A: Naturally aspirated engine.  

EU  

AU  

Veh 1   Veh 2   Veh 3   Veh 4   Veh 1   Veh 2   Veh 3   Veh 4  

Fuel  

Gas T   Dies T Gas T  

Gas T   Gas N   Dies T  

Dies T   Gas N  

Gear box  

Auto   Auto  

Auto  

Auto  

Auto   Auto  

Auto  

Auto  

Engine capacity   1.2  

2.0  

1.4  

2.0  

2.5  

3.0  

2.8  

3.5  

Rated power  

96  

110  

110  

141  

126  

130  

130  

218  

Vehicle mass  

1208   1728   1592   1645   1560   2142  

2315   2045  

Year  

2021   2021   2017   2019   2020   2018  

2019   2019  

The AU Green Vehicle Guide (DITRDCA, 2020) was used collect the NEDC certified CO2 values of all the SUVs 
present in the AU market for the period 2018-2021. AU sales data were retrieved from online sources for the 
100 top sold vehicles in 2020. Combining the two sources 376,655 vehicles were identified as new SUVs in 
2020 for AU, which is about 80% of total SUV sales in 2020 (TER, 2022). Figure 2 shows the CO2 distributions 
for both regions and the two sets of representative vehicles. Distributions were produced by fitting the fleet 
data to lognormal and gamma distributions for EU and AU CO2 values, respectively. The selected probability 
distributions for each fleet provided the best fit and lowest errors.    

Figure 2:  
CO2 certified values for EU (left) and for AU (right). The vertical lines indicate the certified 
CO2 values of the representative vehicle selected. The thick lines correspond to the distributions fitted to the 
data.   

In the Figure 2 (left), two distributions appear for the new EU SUVs because 2020 was the last year that 
regulation allowed for a smooth transition from NEDC to WLTP. Comparing the EU with the AU distributions 
and their average values (Figure 2), it is evident that even with the more representative protocol of WLTP, the 
EU SUV fleet is, on average, more efficient by 21 g/km of CO2. The accuracy of the EU distributions is 
considered acceptable since they were produced using official information reported by the EU member states. 
This is not the case for the AU distributions. However, comparison with a detailed study of the 2018 AU sales 
and emissions data (TER, 2019) confirms good agreement and provides confidence in the initial results for AU.  

Real-world  set  up.  Previous  studies  (Fontaras  et  al.,  2017)  split  the  factors  in  vehicle-related  factors, 
environmental factors and traffic/driving pattern related factors.  Vehicle-related  factors  are reflected  in the 
vehicle characteristics of the representative vehicles selected for the study. The impact of the road factors is 
introduced  by  utilising  trips  recorded  in  a  one-year  RW  campaign  (Pavlovic  et  al.,  2020).  To  reduce  the 
computational cost of the simulations, representative trips falling to similar average trip speeds as in the 4 
phases of the WLTP cycle (Low   D1, Medium D2, High   D3, Extra-High   D4) were selected. For the Extra-High 
phase,  two  different  speed  profiles  were  used  to  understand  the  influence  of  the  highway  trips  in  the  fuel 
consumption and the total annual milage driven (D4 case 1 and case 2). More details are shown in Table 2. For 

159 

 
 
  
 
 
 
 
  
 
  
this initial investigation, it has been assumed that RW driving conditions are comparable in the EU and AU, but 
future work will include AU driving behaviour in more detail.  

Table 2:  

Distances of the trips selected for the simulations.  

D1   D2   D3   D4 case 1   D4 case 2  

RW distances [km]  

3   10   27  

130  

216  

Annual average ambient temperatures were collected for all EU Member States and states and territories in AU 
via web scraping and combined with population figures. This showed that the two regions have different range 
of  ambient  temperatures  that  can  impact  the  RW  vehicle  usage  (e.g.,  need  for  heating,  ventilation  and  air 
conditioning of the cabin), the vehicle efficiency (e.g., coldstart). To understand the variability, in addition to of 
the population-weighted mean, also the temperatures corresponding to the 1st, 25th, 75th, and 99th percentiles 
of the distributions were simulated (hereafter referred to temperatures T1 to T5): 3oC (T1), 8oC (T2), 10.5oC (T3), 
14oC (T4) and 17oC (T5) for EU, and 11oC (T1), 14oC (T2), 17oC (T3), 19oC (T4) and 22oC (T5) for AU. Besides the 
population weighted mean, another way to produce representative ambient temperatures is by applying vehicle-
kilometres-travelled (VKT) weight average. Comparing the two methods we see that the population-weighted 
mean calculated for AU (17oC) is close to the VKT weighted temperature estimated in previous study, that was 
18.2oC (Smit, 2014).  

Results 

Model  validation.  PyCSIS  has  been  validated  under  different  vehicle  configurations  and  simulation 
environments, but validation was performed also for the indicative vehicles used for the present study. The 
validation  included  an  assessment  if  the  selected  vehicles'  certified  values  are  reproduced  and  if  their  RW 
performance is replicated in the case of the AU representative vehicles. It should be noted the simulation of the 
certified values followed the respective test cycles, the initial vehicle conditions (e.g., initial engine temperatures) 
and test cell temperature. For the EU vehicles the official test mass and road load coefficients were used. For 
the  AU  vehicles,  road  load  coefficients  were  obtained  from  coast-down  tests  (Smit  et  al.,  2022)  and  were 
assumed  to  be  WLTP  representative.  The  translation  to  NEDC  representative  values  followed  the EU  NEDC-
WLTP correlation work (Regulation (EU) 2017/1153). Regarding the AU RW trips used for the validation, the 
PyCSIS simulated CO2 signals and aggregated values were compared to measured emissions during on-road 
trips performed in Sydney using portable emissions measurement systems. Table 3 shows that all the simulated 
cases show acceptable accuracy, giving confidence for the next steps. 

Table 3:  

Sampled vehicle validation.  

Veh 1   Veh 2   Veh 3   Veh 4   Average  

AU certification (NEDC)  -3.1  

-3.8  

4.9  

15.6  

AU RW trips  

1.9  

6.1  

1.3  

0.5  

EU certification (WLTP) 2.4  

8.2  

6.1  

4.1  

3.4  

2.5  

5.2  

Global  representations  and  CO2  gap.  In  this  subparagraph  using  the  global  representations  from  the 
distributions derived from the SUV fleets, the annual trip distances, and average ambient temperatures, the 
previous results will be expanded to produce annual SUV fleet values. The fleet shares in Table 4 provide the 
share of each individual vehicle in the fleet. The shares sum up to the total number of SUVs registered in the 
two regions in 2020. The trip shares sum up to 10,671 km for case 1 and 11,770 km for case 2, with the half 
of them driven in the highway (trips D4). Regarding the temperatures selected, only the extremes appear to 
have lower contribution than the others. In further work, EU trip data will be compared with AU trip data to 

160 

 
 
 
  
 
 
  
  
 
 
further refine these initial estimates. To understand the possible influence of the vehicle to the CO2 gap, Figure 
3 shows the annual RW gap simulated for the four EU and four AU SUVs. The average annual CO2 gap in EU 
(WLTP-based) ranges between 7 and 19%, showing a declining trend as SUV are getting bigger. Similar trends 
appear for AU when the estimated WLTP-based CO2 values are examined. The NEDC-based gap is considerably 
larger, reaching values of up to 45%. The CO2 gap ranges, either WLTP-based or NEDC-based are in line with 
the scientific literature (Ktistakis et al., 2021; TER, 2019). 

Table 4:  

Global representations.  

Fleet shares [%]    

Trip shares [%]  

Temperature shares [%]  

EU  

AU     

  Case 1   Case 2    

V1  

40  

66  

D1  

34  

34  

T1  

V2  

26  

17   D2    

33  

32  

T2  

V3  

19  

10   D3    

24  

28  

T3  

V4  

15  

7   D4    

9  

6  

T4  

EU  

14  

27  

27  

20  

T5  

12 

AU  

15  

22  

21  

17  

25 

Figure 3:  
share case 2 and the top of the boxes are produced with the trips share case 1.  

Individual vehicles CO2 gaps. Left: EU; Right: AU. The bottom of each box represents the trips 

Combining the individual vehicles' annual gaps with the fleet shares data. The calculated EU SUV fleet CO2 gap 
ranges between 12%-18% (WLTP). The calculated AU SUV fleet CO2 gap ranges between 26%-32% (NEDC). 
Applying the WLTP certification procedure in AU, the certification values would be closer to the RW driving, thus 
the CO2 gap would reduce to 11% to 17%.   

Discussion and Conclusions 

Two different markets were assessed for the year 2020: the EU market as the regulated market and AU as the 
unregulated market. Since there has been an upturn of SUVs worldwide the recent years, the study focused on 
these vehicles. A methodology is proposed to assess the two markets, combining representative vehicles, fleet 
data  and  environmental  conditions  from  both regions  with a  simulation-based  RW  framework  to  perform a 
detailed analysis. The preliminary results quantify the performance of the SUVs in EU and AU, clearly showing 
a higher fuel efficiency of the EU vehicles. Using the simulation framework, the RW consumption was derived 
and also the CO2 gap. The EU WLTP-based CO2 gap is calculated to be around 18% and the AU NEDC-based 
CO2  gap  ranges  up  to  32%,  with  both  figures  being  in  line  with  existing  literature.  With  the  support  of  the 

161 

 
 
 
  
 
 
 
 
 
 
 
simulation tool, the WLTP-equivalent CO2 values for AU were derived, and the annual SUV fleet CO2 gap dropped 
considerably and in the same levels as in EU. It should be acknowledged that other factors than regulation will 
shape  a  countries  on-road  fleet,  and  this  will  be  further  explored  in  future  work.  Despite  any  fallbacks 
experienced in the last decade, the EU fleet is transforming fast, while the opposite appears to be the case for 
AU. Furthermore, the WLTP protocol provides more realistic CO2 and consumption values and supports building 
public awareness and making better choices for private commuting.   

References 

Bonnel, P., Clairotte, M., et al., 2022. European market surveillance of motor vehicles Results of the 20202021 European 

Commission Vehicle Emissions Testing Programme. https://doi.org/10.2760/59856 DITRDCA, 2020. Green Vehicle Guide. URL 
https://www.greenvehicleguide.gov.au/.  

European Commission, 2019. EU climate action and the European Green Deal. Climate Action - European Commission. URL 
https://ec.europa.eu/clima/policies/eu-climate-action_en.  

European  
Agency,   2023.   Datasets  
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European  

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Fontaras, G., Ciuffo, B., et al., 2017. The difference between reported and real-world CO2 emissions: How much improvement 
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Fontaras,  G.,  Valverde,  V.,  et  al.,  2018.  The  development  and  validation  of  a  vehicle  simulator  for  the  introduction  of 
Worldwide Harmonized test protocol in the European light duty vehicle CO2 certification process. Applied Energy 226, 784
796. https://doi.org/10.1016/j.apenergy.2018.06.009  

Komnos, D., Tsiakmakis, et al., 2022. Analysing the real-world fuel and energy consumption of conventional and electric cars 
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Ktistakis, M., Pavlovic, J., et al., 2021. Understanding the fuel consumption gap using self-reported user and smartphone 
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Pavlovic, J., Fontaras, G., et al., 2020. Understanding the origins and variability of the fuel consumption gap: lessons learned 
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https://www.transport-e-research.com/publications (2023c)  

Smit,  R.,  2014.  Australian  Motor  Vehicle  Emission  Inventory  for  the  National  Pollutant  Inventory  (NPI).  C01772. 
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Temperature Effects on Energy Consumption from Battery Electric Vehicles 

S. Hausberger1, G. Silberholz1, S. Lipp1, M. Opetnik1 

1 Institute for Thermodynamics and Sustainable Propulsion Systems, University of Technology, Graz, Styria, 8010, Austria 

Introduction 

Fuel consumption and CO2-emissions per km from conventional cars proved to be on average much higher in 
real world driving than in type approval. Studies such as (Eisenmann, 2021) and (Weller, 2019) indicate for 
conventional  cars  ca.  +10  to  20%  compared  to  the  WLTP  (+35%  to  40%  compared  to  NEDC).  Reasons  are 
auxiliary power demands not considered in type approval, higher road loads due to side wind, wet road, roof 
racks, trailers etc. but also engines optimised for the load profiles of the type approval cycles. For PHEVs the 
ratio real world/type approval is even worse (Plötz, 2022). In a  study for UBA Germany,  (Helms, 2022)], we 
analysed the real world effects for the energy consumption of Battery Electric Vehicles (BEVs). Based on this 
study, the paper analyses following questions:  

  What is the difference between real world and type approval energy consumption? 

  What are the main parameters for possibly higher energy consumption in real world? 

 

Is there a need to adjust type approval testing? 

Methods and data used for the study 

The fleet average differences between real world and type approval energy consumption were analysed using 
real world energy consumption according to spritmonitor.de (data 2019 to 2022) with the type approval energy 
consumption  for  the  same  fleet.  For  detailed  analysis  of  the  parameters  influencing  the  real  world  energy 
consumption we measured three BEVs, also referred to as pure electric vehicles (PEVs): a VW ID.3, a Renault 
Zoe  and  a  Tesla  Model  Y.  The  tests  were  driven  on  the  road  but  with  different  pre-conditioning  on  the 
temperature  test  stand  of  the  University  of  Technology,  Graz  (TUG),  with  different  real  world  routes  and 
different  settings  of the heating,  ventilation and air  conditioning (HVAC)  system. The test  data was used to 
parametrise the vehicle emission and energy consumption model PHEM from TUG for each measured vehicle 
to meet the measured energy consumption values in all tests.  

The energy consumption of the HVAC system and of other auxiliaries was simulated with the Software AuSyn 
from TUG, which considers inter alia sun radiation, ambient temperature, passenger heat release and a user-
defined mix of fresh and recirculated air for an energy balance to calculate the cooling and heating energy 
demand for any ambient condition. To assess the related electric energy demand, a combined cooling circuit 
and heat pump is simulated. The model was applied for different temperatures and for the European average 
conditions (Table 1).  

Table 1:  Solar radiation in [W/m²] (left) and average temperature in [°C] (right) per month and time slots for 

Frankfurt 

The average conditions in Table 1 are based on hourly weather data weighted according to traffic volumes over 
a year in Frankfurt. We also performed an extensive literature review on energy consumption from auxiliaries 

163 

Month23-5:005:00-11:0011:00-17:0017:00-23:0010.0037.72160.340.0120.0090.30305.151.4630.00168.53431.5218.0440.00317.89581.2848.5150.28445.05730.4894.2861.26471.06733.06101.5070.35451.45776.92114.9780.00387.56673.3465.9890.00238.69484.3123.90100.00132.96320.201.63110.0067.66162.980.00120.0034.65114.440.00Month23-5:005:00-11:0011:00-17:0017:00-23:001-0.80-1.062.971.252-0.51-0.494.341.9832.463.188.806.1245.337.7013.7310.1759.0412.2618.3215.32611.6815.6521.4618.02714.4117.5123.3020.57813.8716.5723.0219.44911.2912.9919.1915.79107.948.5814.0011.14112.863.187.525.07120.300.163.921.98 
 
 
  
 
 
Figure 1 shows a schematic picture of the vehicle emission model PHEM from TUG. PHEM calculates the power 
demand at the wheels for given driving cycles using the equations of longitudinal dynamics. The motor power 
demand and rotational speed is calculated from the transmission ratios and losses in the axle and gear box. 
For  BEVs  a  battery  model  is  implemented  which  simulates  the  charging  and  discharging  losses  from  the 
resulting voltage and current with defined internal resistance values depending on the battery capacity and 
voltage. A simple map depictures the electric power demand over mechanical power output and rpm of the 
electric motor. The auxiliary power demand is an input value, which was simulated here with the model AuSim 
as described before. 

Figure 1: 

Schematic picture of the vehicle emission model PHEM from TUG  

The model PHEM is also used to calculate exhaust gas emissions and energy consumption values for all vehicle 
categories in the Handbook on Emission Factors (HBEFA, www.hbefa.net). The BEV model used here however, is 
different to the HBEFA 4.2 model, since the HBEFA BEV car model was designed around 2018. 

Type approval test procedure for BEVs 

The type approval procedure to measure the energy consumption and the electric range of BEV cars is described 
in Regulation (EU) 2017/1151. BEVs are driven in a special test cycle starting with full battery (100% state of 
charge, SOC) on the chassis dynamometer until vehicle stops with 0% SOC. Constant speed phases (CSS) are 
used  to  discharge  the  battery  over  the  entire  test  completely  in  a  driver  friendly  manner.  The  net  energy 
consumption from the battery is measured during the test. Then the battery is charged to 100% SOC with a 
charging method according to OEM choice. The ratio between recharged energy and consumed energy in the 
test provides the charging loss factor, which is considered in the type approval energy consumption values: 

ECWLTP [Wh/km] = EC(DS1&DS2)  x charging loss factor 

Where EC(DS1&DS2) is the net energy consumption in [Wh/km] measured at the battery during the two DS phases 
shown in Figure 2. The DS (dynamic segment) phases consist of the WLTC with a repetition of the two low speed 
phases and thus represent higher shares of urban driving (almost 50% of the distance) compared to the WLTC 
for conventional vehicles. In the type approval value for the electric range the charging losses are not considered 
since they do not influence the battery capacity if a trip is started with 100% SOC. The DS sub-cycle shows in 
the simulation a ca. 10% lower energy consumption than the WLTC due to the higher low speed shares. The 
entire test has typically even lower energy consumption due to the CSS phases.  

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Figure 2:  Schematic picture of the BEV type approval test (Regulation (EU) 2017/1151) and energy 

consumption simulated for the generic compact BEV car in the WLTC cycle, in the DS1 part and in 
the entire BEV type approval test 

Deviation real world versus type approval 

Figure  3  shows  the  results  from  the  comparison  of  the  average  energy  consumption  recorded  for  BEVs  in 
spritmonitor.de compared to the related type approval energy consumption values and the deviation between 
these values. The analysis included only the makes and models where type approval data was available (Helms, 
2022). A main uncertainty in the spritmonitor.de data is the handling of charging losses since it is not defined 
if users fill in the energy consumption provided by the vehicle dash board or the one read from the charging 
station. Since type approval data includes charging losses, also the real world data should be based on the 
charged energy. We decided to assume, that ca. 50% of the real world data includes charging losses and thus 
corrected the spritmonitor.de values with a factor of 1.1. The average charging losses found in the study are 
ca.  17%,  (Helms,  2022).  The  assumption,  that  50%  of  the  data  include  the  charging  losses  may  rather 
underestimate  real  world  energy  consumption  since  users  may  rather  read  the  kWh/km  from  the  vehicles 
dashboard than using the total charged energy which needs to be divided by the mileage driven since the last 
(full) charging. Consequently, the real world energy consumption of BEVs may currently be on average ca. 10 
to 25% higher than the type approval values. Reasons for the higher deviation for the registration year 2021 
were not identified. We found a tendency for higher deviations with increasing battery capacity but no significant 
correlation between deviation and rated power of the vehicles. 

Figure 3: Average energy consumption recorded for BEVs in spritmonitor.de compared to the related type 

approval energy consumption values and deviation between these values 

Since BEVs currently may be used more often for commuting and for urban trips than the average car, the real 
world  energy  consumption  of  BEVs  may  increase  in  future  with  increasing  fleet  penetration  since  then  the 
relative  share  of  leisure  and  holiday  trips  from  BEVs  may  increase.  The  latter  have  more  passengers  and 
luggage in the vehicle, roof racks, more and faster highway driving etc.  

Relevant parameters for higher real world energy consumption 

165 

 
 
 
 
 
 
Overall,  the  measurements  and  simulations  for  warm  conditions,  i.e.  between  ca.  20°C  and  30°C  indicated 
excellent energy efficient for the BEVs. While we had to increase real world rolling and air resistances as well 
as  the  average  loading  and  auxiliary  power  demand  to  meet  reported  real  world  fuel  consumptions  of 
conventional  cars  in  the  simulations  we  performed  in  (Eisenmann,  2021),  we  had  to  reduce  the  generic 
parameters  for  the  BEVs  to  meet  the  values  measured  at  the  three  tested  BEVs.  Obviously,  the  target  of 
sufficient electric range motivates to optimise the BEVs also in real world driving.  

Beside  the  (unavoidable)  extra  driving  resistances  due  to  cross  wind,  wet  road  etc.,  the  main  parameters 
increasing the BEVs energy consumption compared to the type approval value are: 

 

Low temperature driving with a main parameter being the initial energy demand to heat the battery 
cells up to 15°C to 20°C 

  Heating and cooling of the passenger compartment 

  High speed driving due to exponential increase of air drag and dropping motor efficiency at high rpm 

(revolutions per minute) 

  Charging losses possibly different to the ones with the charging system used in type approval. 

We calibrated the AuSym model for the base auxiliary power demand from the BEVs with the test trips with 
deactivated HVAC system where also the blower was switched off. The model for the additional HVAC power 
demand was calibrated with tests with activated HVAC both in stand still and different driving conditions. Tests 
covered ambient conditions between 0°C and 26°C.  

The left picture in Figure 4 shows the results for stationary thermal conditions, i.e. after the initial heating or 
cooling  of  the  battery  and  of  the  passenger  compartment.  The  square  shows  the  result  for  the  European 
temperature and sun radiation variation over a year with Frankfurt weather conditions, the circles show the 
results for constant temperature conditions with the corresponding average solar radiation. The initial heating 
energy for the battery was calculated for a target temperature of 20°C from the thermal capacity of the cells. 
Figure 4 shows the measured heating energy at a cold start at 4°C, which was used to calibrate the specific 
heat capacity value of the generic battery cells. 

Figure 4:  Auxiliary power demand with and without HVAC in thermal stationary driving conditions (left) and 

electric energy consumption for initial battery heating after cold start at 4°C (right) 

Figure  5 
temperatures compared to the BEV-WLTP test for the full DS1 phase. Average urban driving at 20°C leads to 
even lower energy consumption compared to the type approval values. Towards low temperatures the energy 
demand for initial heating of the battery and the passenger compartment is getting more relevant. Due to the 
short distance, the initial heating energy per km is getting quite high. This can lead to more than double energy 
consumption per km compared to a similar trip at 20°C. The energy consumption for HVAC at thermal stable 
vehicle  conditions  after  the  initial  heating  has  comparable  lower  impact.  Since  the  driver  does  not  see  the 
charging losses on the dashboard while they are included in type approval values, drivers may recognize in 
urban driving between 10 and 30°C a similar or even lower energy consumption compared to the average value 
from type approval. 

166 

 
 
 
Figure 5:  Energy consumption simulated for the generic medium size BEV for type approval (DS1 of WLTP) 

and for short distance urban driving  

Figure 6 shows the results for long distance motorway driving with an average speed of 119 km/h. The initial 
heating of the battery needs the same absolute value in terms of kWh as in city driving but the division by the 
high number of km driven after the initial heating leads to a low contribution in the kWh/km result of the total 
trip. Due to the higher air resistance and the lower efficiency of the electric motor at higher rpm, the energy 
consumption of the electric motor is clearly higher in the motorway trip than in the WLTP-DS 1 sub-cycle. Lower 
speed  on  the  motorway  and  a  gear  box  with  two  or  more  gears  would  help  to  reduce  the  BEVs  energy 
consumption on motorways. 

Figure 6:  Energy consumption simulated for the generic medium size BEV for long distance highway driving 

and for type approval (DS1 of WLTP) 

Today, BEVs may be used rather for urban trips and for commuting which can be used as argument for the 
higher share of urban driving in the BEV type approval compared to the WLTC for conventional cars. The average 
real world mix of driving situations from BEVs is unknown, also the entries in spritmonitor.de do not provide 
information on the typical vehicle use profile. 

However, with sharply increasing shares of BEVs in new registrations, the usage of BEVs will most likely align 
with the current user profile of conventional passenger cars.  

Summary 

We have updated the simulation model for BEV cars in our software tool PHEM and calibrated the input data 
with measurements of three BEVs in various driving and ambient conditions. The simulation results indicate:  

  Current BEVs have already a high energy efficiency in warm conditions. 

167 

 
 
 
 
  Average real world energy consumption for driving in central Europe is ca. 10 to 25% higher than the 
type approval value, depending on the driving mix. Both, colder or hotter climates increase the real 
world energy consumption. 

 

The BEV type approval cycle (DS phases) has higher urban shares than the WLTC cycle, which leads to 
a ca. 10% lower energy consumption compared to the conventional WLTC. 

  Main reasons for higher real world energy consumption are: 

o  Battery  conditioning  at  temperatures  below  ca.  15°  to  20°C  to  minimize  deterioration  is  not 

relevant in type approval, which starts with 20°C, 

o  HVAC and other auxiliaries are not engaged in the type approval test, 

o  Effects of side wind, partly wet and snowy roads, small shares of roof boxes etc. and possibly 
also  a  different  driving  mix  than  the  WLTP-DS  phases  for  BEVs  most  likely  also  contribute  to 
higher  real  world  consumption  values.  A  quantification  of  the  average  impacts  was  yet  not 
possible. 

From the results, some adjustments for the BEV type-approval test can be considered: 

  A low temperature test (e.g. at 0°C) should be added in future type approval of BEVs to incentivize a 
low energy demand for initial battery conditioning. The type approval value may then be a weighted 
average of the 23°C and the low temperature test. 

 

The  WLTP  cycle  for  BEVs  may  be  adjusted  to  the  one  for  conventional  cars,  i.e.  to  eliminate  the 
additional low speed phases in the evaluation since in the long term, BEVs will be used similar and 
instead of conventional cars.  

References 

Eisenmann C., Kuhnimhof T., Tietge U., Dornoff J., Díaz S., Mock P., Allekotte M., Heidt C., Knörr W., Althaus H.-J., 
Notter B., Oberpriller Q., Läderach A., Hausberger S., Matzer C. (2021): Erarbeitung einer Methode zur Ermittlung 
und Modellierung der CO2-Emissionen des Kfz-Verkehrs, Forschungskennzahl 3716 58 180 0 

Helms H., Bruch B., Räder D., Hausberger S., Lipp S., Matzer C. (2022): Energieverbrauch von Elektroautos (BEV), 
Projektnummer 1711964, UBA-Texte |160/2022 

Plötz P., Link S., Ringelschwendner H., Keller M., Moll C., Bieker G., Dornoff J., Mock P. (2022): REAL-WORLD USAGE 
OF PLUG-IN HYBRID VEHICLES IN EUROPE A 2022 UPDATE ON FUEL CONSUMPTION, ELECTRIC DRIVING AND 
CO2 EMISSIONS; Fraunhofer, ICCT; berlin June 2022 

Weller K., Lipp S., Röck M., Matzer C., Bittermann A., Hausberger S. (2019): Real World Fuel Consumption and 
Emissions From LDVs and HDVs; Engine and Automotive Engineering, Frontiers in Mechanical Engineering 5:45., 
doi: 10.3389/fmech.2019.00045 

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Prediction of fuel consumption for truck planning based on VECTO simulations 

N. Kousias1, F. Kyriakidis1, K. Agavanakis2, R. Quittard2 and G. Mellios1 

1Emisia SA, Thessaloniki, GR55535, Greece, Corresponding author email: nikos.kousias@emisia.com 
2ADD, Avenir Développent Durable, Marseille, 13007, France 

Introduction 

Globally, emissions from freight transport account for 8-10% of Greenhouse Gases (GHG), with road transport 
being responsible for 62% of all freight emissions worldwide (Punte et al., 2022). In the EU, a similar pattern 
emerges, with road transport accounting for 75% of total freight transport, while Heavy-Duty Vehicles (HDVs) 
contribute to 25% of road transport GHG emissions and 5% of the EU's total CO2 emissions (Eurostat, 2021). 
Beyond  their  environmental  impact, these  emissions  directly  influence  fuel  costs  for  carriers,  establishing  a 
clear industry-driven incentive to explore strategies for reducing carbon footprint. 

Significant  efforts  are  underway  to  enhance  the  reporting  and  reduction  of  GHG  emissions  from  freight 
transport. However, addressing this challenge within the road transport sector proves particularly challenging 
due to its fragmentation, characterized by a multitude of small carriers. This inherent complexity impedes the 
monitoring and harmonization of practices. In an attempt to address this, initiatives like the GLEC Framework 
have  emerged,  aiming  to  include  all  transport  modes  (road,  air,  sea,  rail)  under  a  single  methodology  that 
remains feasible even with limited data (Punte et al., 2022). These simplified methodologies can be integrated 
into  fleet  management  software  to  facilitate  consumption  calculations  and  promote  harmonization. 
Nonetheless,  these  methodologies  lack  consideration  for  specific  vehicle  and  route  technicalities,  thereby 
limiting the value of predictions, especially for the planning of new routes. 

Modelling tools offer a promising way of generating digital twins that significantly enhance fuel consumption 
predictions (Agavanakis et al., 2022). Specifically, a digital twin for road transport emissions prediction can be 
constructed through vehicle modelling software. Various solutions, such as the GT-suite, AVL Boost, and VECTO, 
are available. Notably, the VECTO tool emerges as a good candidate for developing a digital twin for freight 
transport  planning  due  to  its  demand  for  less  extensive  technical  information  compared  to  other  tools. 
Additionally, it is tailored for simulating HDVs and holds official recognition from the EU for the type approval 
of new trucks. However, its complexity still presents a barrier for direct use by non-experts, especially in the 
context of accurate route planning. Consequently, a gap remains in achieving seamless integration of VECTO 
with limited truck data for effective route planning. 

In this paper, we present a novel tool designed to predict fuel consumption for new routes. Our approach involves 
the generation of a VECTO digital twin model rooted in basic information and historical data from a specific 
truck.  Subsequently,  a  routing  tool  is  deployed  to  explore  alternative  routes  between  any  given  origin  and 
destination points. Each alternative route is assessed using the digital twin model to calculate fuel consumption, 
facilitating the identification of the most fuel-efficient option. Central to our methodology is the utilization of 
historical data, which in this case study is harnessed by Fleetenergies (FE). As a provider of fleet management 
systems, FE possesses access to technical specifications and real-time data for numerous trucks, enriching the 
accuracy of our digital twin predictions. 

Methodology 

A VECTO-based model serves as a digital twin for a truck, drawing from three primary sources. 

 

 

 

Firstly, we leverage key truck parameters provided by FE. Table 1 summarizes the input parameters 
required, along with their corresponding values for six vehicles selected for the analysis. These inputs 

(Database, 2021). 
Secondly, we extract pertinent information concerning the truck from the EEA database that is not 
available from FE, such as air drag and gearbox data. 
Lastly, we calculate the truck engine's fuel consumption for each operational point by adopting the 
methodology in Zacharof et al., 2019. 

Complementing the technical dataset provided by FE, we incorporate real-world historical timeseries data for 

169 

 
 
 
mileage,  mean  velocity,  altitude,  fuel  consumption,  and  total  weight  (including  payload).  The  truck's  fuel 
consumption is derived from in-tank volume measurements, further refined using AI algorithms for increased 
accuracy. The utilization of such precise measurements contributes to the robust performance of our tool. The 
historical data is processed to extract short trips connecting consecutive stops, ensuring a constant payload 
throughout each trip, which can be seamlessly integrated into the VECTO model. 

Table 1. Main vehicle data 

Parameter 

Model 

Vehicle 
Type 

Gross Vehicle 
Weight 

Axles 

Engine Rated 
Power 

Vehicle #1 

MAN TGS 

Rigid 

Vehicle #2 

DAF CF480 

Tractor 

Vehicle #3 

DAF CF480 

Tractor 

Vehicle #4 

MAN TGX 

Tractor 

Vehicle #5 

Ford F-MAX 

Tractor 

Vehicle #6 

Volvo FM 

Rigid 

26 t 

44 t 

44 t 

44t 

44 t 

32 t 

6x2 

4x2 

4x2 

4x2 

4x2 

6x2 

470 kW 

483 kW 

483 kW 

510 kW 

500 kW 

520 kW 

The historical data play a key role in constructing and fine-tuning the digital twin's fuel consumption. This fine-
tuning process involves adjusting the truck's engine map to increase or decrease fuel consumption. To ensure 
data integrity, a few trips characterized by significant variance in total truck weight were excluded from the 
analysis. Figure 1 illustrates the total weight timeseries for two trips 
 one included in the analysis and one 
excluded due to substantial weight fluctuation. Notably, a total of 677 routes were used in our analysis and the 
average trip distance across all routes for the six vehicles stands at 54 km. 

Figure 1: Timeseries of the gross vehicle weight for Vehicle #1 including one trip that was used in the analysis 
and one that was excluded due to large variance of the weight measurement. 

Subsequently, the digital twin is employed to replicate actual trips undertaken by the truck, utilizing the recorded 
speed and altitude profiles. Predicted fuel consumption (in liters) is then compared to actual consumption for 
each trip. Additionally, we assess the digital twin's fuel consumption (in liters per 100 km) against actual data 
for different truck weights, substantiating the twin's accuracy in forecasting fuel consumption for individual 
trips. 

Following digital twin validation, we harness its capabilities for planning purposes. Leveraging the HERE API 
(HERE, 2023), we identify potential routes for specified origin and destination pairs. The API accommodates 
various parameters, including vehicle dimensions, and offers essential route information for conducting VECTO 
simulations, such as altitude and vehicle speed profiles. Importantly, we fine-tune the API's speed profile output, 
adjusting  it  for  distinct  road  types  (e.g.  highway,  residential).  This  fine-tuning,  conducted  across  five  road 
categories, ensures alignment between predicted mean speeds and actual mean speeds derived from historical 

170 

 
 
 
 
data. To validate HERE's speed profile accuracy, we force the API to replicate the actual route, comparing its 
average speed profile against measured data and plotting both profiles for a single trip. 

Finally, building upon this evaluation, we employ the HERE API to generate alternative routes for the same origin 
and destination. Up to four alternative routes are identified for each trip of every vehicle, with truck dimension 
limits set at: length of 8 m or greater, width of 2.5 m or greater, height of 4 m or greater, and weight of 25 t 
or  greater.  These  identified  routes  are  subsequently  subjected  to  the  digital  twin  for  fuel  consumption 
calculation.  The  route  with  the  lowest  fuel  consumption  is  then  determined,  compared  against  actual 
consumption, and used to quantify potential fuel savings. In Figure 2, the actual route and three alternatives 
identified  by  HERE  for  the  mentioned  trip  in  Figure  1  are  depicted.  Notably,  the  real  route  corresponds  to 
alternative 3, exclusively utilizing the highway. Alternatives 1 and 2 encompass smaller roads for segments of 
the route and exhibit shorter distances. 

Figure 2: Real route and HERE alternative routes for one trip of Vehicle #1. 

Results 

Digital twin performance 

To assess the predictive capability of the digital twin, we start by demonstrating its performance in forecasting 
real fuel consumption in liters. Figure 3 provides a visual representation of the correlation between the actual 
measured and the VECTO simulated fuel consumption for each individual vehicle. Meanwhile, Figure 4 employs 
a bar plot to compare the actual measured and VECTO simulated fuel consumption in liters per 100 km across 
varying payload categories. 

Figure 3: Correlation plots of the real and the digital twin consumed fuel in litres. 

A good correlation is observed between the actual and predicted fuel consumption for individual trips across all 
vehicles. The digital twin of Tractors (Vehicles #2 to #5) outperforms Rigid trucks (Vehicles #1 and #6) in terms 
of predictive accuracy. Among these, Vehicle #4's digital twin stands out, with an exceptionally high R2 value. It 
is noteworthy that Vehicle #4 undertook longer trips, averaging around 105 km, approximately twice the mean 
distance covered by all six vehicles combined. In contrast, Vehicle #6's digital twin exhibits the lowest R2 values, 
displaying scattered data points around the linear fit. Vehicle #6's trips were notably shorter than the average 
(40  km),  and  it  possessed  the  lowest  payload-to-empty  weight  ratio.  Vehicle  #5's  digital  twin  showcases  a 
robust correlation across most data points, despite an outlier trip. This outlier's measured fuel consumption (9 
liters for 250 km, with a total truck weight of 41 t) seems erroneous, as opposed to the VECTO prediction of 

171 

 
 
 
 
 
90.5 liters, which aligns coherently with the linear fit. While this case represents an extreme discrepancy, it 
underscores  the  potential  for  slight  errors  in  measured  fuel  data  across  various  trips,  contributing  to  an 
enhanced R2 value in the correlation analysis. 

Figure 4: Bar plot of the real and digital twin fuel consumption in litres per 100 km for various truck weight 
classes for each one of the six simulated vehicles 

Moving forward, Figure 4 compares the actual measured and VECTO simulated truck consumption in liters per 
100 km, categorized by varying total truck weight classes. The anticipated trend of increasing consumption with 
increasing  truck  weight  is  evident  across  all  vehicles.  Importantly,  any  secondary  impacts  stemming  from 
diverse speed or altitude profiles between distinct trips are mitigated through averaging, as each bar shows the 
average of several trips. This averaging effect substantiates the robust performance of the digital twins. Fuel 
consumption aligns closely with the measured values for nearly all vehicles and truck weights. An overprediction 
is noticeable for very heavy-loaded trucks in the case of Vehicle #3, albeit this anomaly is attributed to the 
small number of trips included in this bar. Another instance of overprediction appears during empty running of 
Rigid trucks (Vehicles #1 and #6). This discrepancy is attributed to these trucks exhibiting an empty weight lower 
than their technical specifications. Such deviations can be rectified through suitable modifications to the digital 
twin. 

HERE Routing - Speed profile evaluation 

To evaluate the performance of the HERE speed profile, we present the results in Table 2, showing the average 
speed in km/h for both the actual real routes, the HERE profiles based on the default speed table, and the HERE 
profiles based on the calibrated speed table. This identical table is applied across all six vehicles, with HERE 
constrained to replicate the precise routes undertaken by each truck. 

Table 2: Comparison of the average speed for each vehicle based on the real speed profile, the HERE default 
speed profile, and the HERE calibrated speed profile. 

Real speed (km/h) 
HERE   default (km/h) 
HERE   calibrated (km/h) 

Vehicle 
#1 
54.1 
35.0 
48.7 

Vehicle 
#2 
49.6 
34.9 
46.6 

Vehicle 
#3 
54.3 
37.1 
49.2 

Vehicle 
#4 
66.2 
53.9 
65.1 

Vehicle 
#5 
69.7 
55.5 
66.6 

Vehicle 
#6 
55.8 
37.7 
50.2 

Examination of the data reveals that the default speed table within HERE leads to a substantial underestimation 
of  the  truck's  average  speed.  In  contrast,  the  calibrated  speed  profile  is  significantly  closer,  particularly  for 
Vehicles #4 and #5, which predominantly travel on highways, yielding higher average speeds. However, a minor 
underestimation of average speed is apparent for Vehicle #1, displaying the greatest deviation from real speed 
values. This divergence can be attributed to the calibrated speed table, primarily constructed based on larger 
tractors frequenting highways, whereas Vehicle #1 stands as the smallest in weight among the six vehicles. 

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Figure 5 shows the real measured speed, the HERE default speed profile, and the HERE calibrated speed profile 
for the retained trip highlighted in Figure 1. Both HERE profiles show a similar trend, with the default speed 
profile consistently lagging behind the calibrated counterpart across most of the route. Moreover, the default 
profile consistently maintains speeds below 70 km/h, an observation that appears relatively conservative even 
for a heavy-duty vehicle. The calibrated profile, by contrast, looks very similar to the real profile, although with 
a small divergence around the 40th km mark, where the calibrated profile somewhat underestimates the truck's 
speed.  Additionally,  it's  evident  that  the  real  profile  displays  a  smoother  trend  compared  to  the  profiles 
generated by HERE. This distinction largely results from the interval between consecutive data points. While the 
real data points span between 1 and 2 km, the HERE-generated profiles show much shorter intervals, typically 
a few hundred meters at most. 

Figure 5. The real speed profile, the HERE speed profile for the default speed table and the HERE profile for the 
calibrated speed table for the first trip of Vehicle #1. 

Figure 6 illustrates the measured fuel consumption alongside the predictions from the digital twin based on 
both the default and calibrated HERE speed profiles. Even with the provision of the exact truck route to HERE, 
minor deviations in travelled distances arise due to inherent errors in latitude and longitude coordinates within 
the recorded data. To ensure an equitable comparison of fuel consumption, we shift our analysis to liters per 
100 km, accounting for these variations. Evidently, the default HERE speed profile leads to an underestimation 
of fuel consumption, while the calibrated speed profile aligns well with the actual consumption figures. This 
underprediction can be attributed to the lower velocity, acceleration, and deceleration inherent to the default 
profile. Only Vehicle #1 experiences a modest overprediction of 6.5% for the calibrated speed profile and 4.5% 
for the default speed profile. 

Figure 6. Fuel consumption for each vehicle based on the real data and the HERE speed profiles 

HERE Routing   Identify the best route 

The final phase of our investigation entails leveraging the HERE API to explore alternative routes for identical 
origin and destination points. The quantity of routes identified varies across trucks, with some having only one 
route, while others including up to four alternatives. In each instance, the most fuel-efficient route is selected. 
Subsequently, we compute the overall distance traveled and the aggregate fuel consumption for each vehicle 
based  on  these  optimal  routes.  Table  3  summarizes  the  outcomes  of  this  analysis,  alongside  the  actual 
measured data for reference. 

From this  data, it's apparent  that  the total  travel  time estimated  by  HERE tends  to  be  higher than the  real 
values. This alignment with the mean velocities highlighted in Table 2 implies that while the projected travel 

173 

 
 
time  might  appear  to  increase,  the  actual  impact  might  be  negligible  or  only  marginally  increased.  Fuel 
consumption, measured in liters per 100 km, shows a minor reduction across all vehicles, except for Vehicle #1. 
However, the total distance traveled along the optimal routes perfectly aligns with the actual distance. On the 
route level, we observe that shorter routes are generally favored as expected to reduce fuel consumption. Yet, 
there are instances where all alternatives are longer than real routes. This results from certain roads not being 
classified as suitable for trucks, prompting the routing tool to explore other (longer) alternatives. 

The difference in the total consumed fuel for each vehicle ranges from 0 to 3.5 % with Vehicles #1 and #4 
showing the highest potential for fuel savings. Vehicle #1 due to selection of shorter routes, and Vehicle #4 due 
to identification of more fuel-efficient ones. The total cumulative savings across all six vehicles are 140 liters, 
translating to a 1.2% reduction in fuel consumption. 

Table 3: Comparison of main route data for the real and the HERE optimised alternative routes 

Real distance 
(km) 
4404 
2145 
6589 
10851 
4831 
7949 

Real travel 
time (h) 
80.8 
42.2 
122.1 
162.6 
68.2 
142.0 

Real fuel 
(lt/100km) 
30.5 
34.3 
31.9 
30.1 
30.9 
40.8 

HERE distance 
(km) 
4069 
2138 
6705 
10839 
4947 
8075 

HERE travel 
time (h) 
86.4 
43.4 
127.5 
160.8 
74.9 
161.1 

HERE fuel 
(lt/100km) 
31.9 
33.7 
30.8 
30.1 
29.2 
40.2 

Vehicle #1 
Vehicle #2 
Vehicle #3 
Vehicle #4 
Vehicle #5 
Vehicle #6 

Discussion 

In this study, we have successfully demonstrated the creation and validation of VECTO-based digital twins using 
limited input data, that can be easily used even by non-experts. Through the generation of six digital twins, we 
showcased their robust performance against real-world data, establishing their potential as reliable predictive 
tools.  Furthermore,  we  harnessed  the  HERE  API  to  identify  truck-suitable  alternative  routes  and  construct 
realistic  speed  profiles,  thereby  enabling  accurate  fuel  consumption  calculations  for  each  route.  This 
methodology led to the identification of more fuel-efficient routes, yielding potential fuel savings of up to 3.5% 
for specific vehicles. 

The proposed approach exhibits flexibility and potential for refinement. Further optimization of the routing tool's 
calibration could address instances where routes are wrongly classified as unsuitable for trucks. It's important 
to acknowledge that the presented analysis is individualised for to a particular digital twin, implying that the 
optimal route may vary across different vehicles. The methodology, while demonstrated within the context of 
diesel trucks, can readily adapt to accommodate various powertrain configurations. 

Importantly, while high-quality historical data undoubtedly enhances predictive accuracy, it is not an absolute 
prerequisite for the tool's functionality. Carriers lacking extensive or detailed data can still achieve good results 
using lower resolution data.  For  carriers  without any data, generic vehicle profiles or non-fine-tuned VECTO 
models can be employed. Despite potential accuracy compromises, such models surpass methods relying on 
generic emission factors, exemplifying the tool's robustness in diverse data scenarios. 

The tool's output, referred to as the "optimum" route in this context, can be further tailored to user requirements. 
For instance, if time-sensitive deliveries are important, additional criteria can be incorporated into the route 
suggestion process. With such refinements, our methodology extends beyond fuel savings. 

In conclusion, our study introduces an innovative framework that combines digital twin technology, real-world 
data, and advanced routing capabilities to optimize freight transport fuel consumption. By enabling carriers, 
including  non-experts,  to  enhance  route  selection  and  reduce  environmental  impact,  this  approach  may 
significantly contribute to the broader sustainability goals of the freight industry. Further investigations and 
refinements  have  the  potential  to  amplify  the  impact  of  this  methodology  across  various  freight  transport 
scenarios and powertrain technologies. 

174 

 
 
 
 
 
 
Acknowledgements  

The study was self-funded. 

References 

Agavanakis, K., et al., 2022. " Telemetry transformation towards industry 4.0 convergence - A fuel management 
solution for the transportation sector based on digital twins ", AIP Conference Proceedings 2437, 020083 

EEA Database, 2021, "Monitoring of CO2 emissions from heavy-duty vehicles - Regulation (EU) 2018/956", Prod-

ID: DAT-251-en 

Eurostat, 

2021, 

"Freight 

transport 

statistics 

-  modal 

split,". 

Available 

https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Freight_transport_ 
_modal_split, Last accessed: 20th July 2023 

online 

at: 
statistics_-

HERE, © 2023, 2023, Calculate Route Endpoint, https://developer.here.com/ 

Punte, S., Greene S., Lewis A. et al., 2022. GLECl Framework Version 2.0 

-Data 

Generation Model for Creating Fleet-
Paper 2019-01-1280 

175 

 
 
 
 
 
Experimental Validation of Battery Electric Truck Simulation in VECTO14 

S. Broekaert1, E. Bitsanis1 and G. Fontaras1*  

1European Commission, Joint Research Centre, Directorate for Energy, Mobility and Climate, Ispra, Lombardy, 21027, Italy, 
Georgios.Fontaras@ec.europa.eu  

Introduction 

The European Union (EU) aims for climate neutrality by 2050. This can be achieved by reducing greenhouse gas 
emissions in all sectors, including transport. Therefore, the EU has set CO2 emission reduction targets for new 
heavy-duty vehicles (HDVs) (EU, 2019), requiring the reduction of the average CO2 emissions by 15% and 30% 
for 2025 and 2030, respectively. A more ambitious proposal was put forward by the European Commission in 
2023. It increases the CO2 emission reduction target to 45% for 2030 and introduces reduction targets of 65% 
and 90% for 2035 and 2040, respectively. Moreover, it introduces a zero-emission vehicle target of 100% by 
2030 for urban buses. 

An increasing number of battery electric lorries and buses are being introduced to the market in recent years to 
gradually meet the CO2 emission reduction targets. To inform the customer about the energy consumption and 
operating range of battery electric vehicles, these properties are to be determined as of 2024 as part of the 
vehicle type approval (EU, 2022). The approach is similar to the one for determining the CO2 emissions and fuel 
consumption of HDVs powered by an internal combustion engine (EU, 2017). Each new vehicle is simulated over 
different  mission  profiles  with  the  Vehicle  Energy  Consumption  Calculation  Tool  (VECTO),  a  software  tool 
developed  by  the  European  Commission  (Fontaras,  2013).  Component  models  and  operating  strategies  are 
currently being added to the tool, to be able to accurately simulate the energy consumption of battery electric 
HDVs (Silberholz, 2022).  

The Joint Research Centre has tested a battery-electric, rigid lorry on the road and on the chassis dyno. The 
vehicle  was  equipped  with  wheel  torque  transducers  and  current  and  voltage  sensors  in  the  powertrain  to 
measure energy consumption. On the chassis dyno, the regional delivery VECTO mission profile was replicated 
under regulatory conditions to allow for a direct comparison to the VECTO declaration results. The on-road trip 
replicated typical vehicle operating conditions and allowed for validating the real world representativeness of 
VECTO.  The  positive  electric  work  of  the  electric  machine  was  simulated  within  5  and  6%  for  the  regional 
delivery and on-road cycle, respectively. 

Methods 

Test vehicle 

The experiments were conducted with a 27 tonne gross vehicle weight rigid lorry. The vehicle powertrain consists 
of  2  electric  machines  (EM)  combined  with  a  2-speed  transmission.  Table  1  summarises  the  main  vehicle 
specifications.  

The vehicle was instrumented with current and voltage sensors at the battery terminals and at the DC-side of 
 the energy demand in the powertrain. Wheel torque transducers were installed 
on the driven wheels to measure the instantaneous wheel torque. The wheels with the torque sensors were 
lifted from the ground so that the applied torque was zero, at the start and the end of each test. The transducers 
were zeroed at the start of each test, and the residual sensor drift was recorded at the end of each test. The 
recorded  drift  values  were  used  to  correct  linearly  for  torque-drift.  The  sensor  data  and  relevant  operating 

to 2Hz for the analysis. 

14  The  views  expressed  are  purely  those  of  the  authors  and  shall  not  be  considered  as  an  official  Commission  position  under  any 

circumstance. 

176 

 
 
 
 
 
                                                       
 
Table 1: Vehicle specifications. 

Axle configuration 

Chassis configuration 

TPMLM [tonnes] 

Powertrain architecture 

Transmission 

Battery size [kWh] 

Motor max. power [kW] 

Laboratory tests 

6 x 2 

Rigid 

27 

E2 

2-speed 

448 

400 

The  regional  delivery  cycle  was  performed  under  regulatory  conditions,  with  the  reference  payload,  on  the 
Vehicle Emissions Laboratory chassis dyno (VELA 7). The driving cycles prescribe speed as a function of distance. 
Therefore, a VECTO simulation was performed to obtain the speed as a function of time, taking into account 
the vehicle performance and the VECTO driver model and replicating that on the chassis dyno. The measured 
speed and slope profiles are shown in Fig. 1. The characteristics of the cycle are listed in Table 2. The cycle was 
repeated  3  times  and  the  relative  standard  deviation  (RSD)  of  the  wheelwork  and  battery  energy  (Table  2) 
indicates a good repeatability of the experiments. 

Figure 1: Speed and slope profile of the regional delivery cycle. 

Table 2: Regional delivery properties. 

Distance [km] 

Urban distance share [%] 

Rural distance share [%] 

Motorway distance share [%] 

Start SoC [%] 

RSD wheel work [%] 

RSD battery energy [%] 

100 

9 

14 

77 

80 

0.6 

1.0 

177 

 
 
 
 
 
 
On-road tests 

On-road tests were performed in the vicinity of the Joint Research Centre site in Ispra, Italy to obtain data of 
the vehicle under real world operating conditions. The vehicle was loaded to 90% of its maximum payload and 
a warm-up was conducted before the start of the test, compliant with the Verification Testing Procedure (EU, 
2017). The measured speed and slope profile are shown in Fig. 2. The characteristics of the cycle are listed in 
Table 3. The cycle was repeated 3 times and the RSD of the wheel work and battery energy (Table 3) indicate 
a good repeatability of the experiments. 

Figure 2: Speed and slope profile of the on-road trip. 

Table 3: On-road trip properties. 

Distance [km] 

Urban distance share [%] 

Rural distance share [%] 

Motorway distance share [%] 

Start SoC [%] 

RSD wheel work [%] 

RSD battery energy [%] 

VECTO simulations 

139 

8 

9 

83 

98 

0.7 

1.2 

A model of the vehicle was created in VECTO, with the tyre rolling resistance and air drag properties determined 
according to (EU, 2017). The EM and transmission are integrated (Integrated Electric Powertrain Component), 
but were treated as separate components and their properties were characterised with available engineering 
data. The official regional delivery cycle, the measured speed trace during the regional delivery cycle and the 
measured speed trace during the on-road trip were simulated in VECTO version 0.7.7.2547. The measured speed 
traces were converted to cycles with the speed as a function of distance. 

Results 

The relative error of the simulation compared to the measurements is show in Figure 3, for the official regional 
delivery mission profile, the regional delivery cycle with a measured speed trace and the on-road trip with a 
measured speed trace. The wheel work is overestimated in the simulation for the laboratory tests. This can be 
attributed to the speed-dependent yaw angle correction of the air drag value that is applied in VECTO compared 

178 

 
 
 
 
 
to the fixed road load coefficients that the chassis dyno imposes. The opposite can be observed for the on-road 
trips, where the wheel work is underestimated. This discrepancy can originate from various sources where the 
idealized conditions in the simulation deviate from the real world, e.g. road surface friction or wind speed.The 
error of the wheel work results in a similar error of the mechanical and electrical work done by the EM for 
propulsion (positive). The recuperated energy by the EM (negative work), on the other hand, is underestimated 
by VECTO.  

Figure 3: Relative error of the simulation. 

The efficiency of the powertrain during propulsion and recuperation can be defined as follows: 

𝜂𝑝𝑟𝑜𝑝𝑢𝑙𝑠𝑖𝑜𝑛 =  

𝑤ℎ𝑒𝑒𝑙 𝑤𝑜𝑟𝑘𝑝𝑟𝑜𝑝𝑢𝑙𝑠𝑖𝑜𝑛

⁄

𝐸𝑀 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑎𝑙 𝑤𝑜𝑟𝑘𝑝𝑟𝑜𝑝𝑢𝑙𝑠𝑖𝑜𝑛

𝜂𝑟𝑒𝑐𝑢𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 =  

𝐸𝑀 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑎𝑙 𝑤𝑜𝑟𝑘𝑟𝑒𝑐𝑢𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛

⁄

𝑤ℎ𝑒𝑒𝑙 𝑤𝑜𝑟𝑘𝑟𝑒𝑐𝑢𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛

The simulation error of both efficiencies is shown in Fig.4. Errors of 2 and 3% for the propulsion powertrain 
efficiency are found for the regional delivery and on-road trip (Table 4). Hence, VECTO is able to accurately 
simulate these event under the same simulated boundary conditions (wheel work). The powertrain efficiency 
during recuperation, on the other hand, is underestimated.   

179 

 
 
 
 
 
 
 
 
 
Figure 4: Error of the simulated error 

Table 4: Relative error between simulation results and experimental measurements. 

Cycle 

EM positive electric work (%) 

Powertrain efficiency  (%) 

Regional Delivery 
declaration 

Regional Delivery 
measured 

On-road 

+4.7 

+5.7 

-4.2 

-1.7 

-1.5 

-3.3 

As  the  vehicle  is  equipped  with  a  2-speed  transmission,  the  gearshift  model  in  VECTO  will  select  the  most 
appropriate gear in every time step. The distance share where the simulation and the measurement have the 
same or a different gear is shown in Fig. 5. The gear is correctly simulated by VECTO. 

Conclusions 

Figure 5: Error of the simulated gear 

A test campaign was performed that allows the validation of VECTO under regulatory and real world operating 
conditions.  It  shows  that  VECTO  is  able  to  accurately  simulate  the  energy  consumption  during  propulsion. 
However, the simulation of brake energy recuperation needs to be improved further.  

The next steps will be to repeat the validation with newer VECTO development versions and to simulate the 
cycles with the speed as a function of time and with the wheel power as input. The latter will remove errors 
introduced by the environment and will allow validating the vehicle powertrain by itself. 

180 

 
 
 
 
 
Acknowledgements 

The  authors  like  to  acknowledge  Daimler  Truck  for  providing  the  vehicle,  for  technical  support  during  the 
measurement campaign and for conducting the VECTO simulations. The authors greatly acknowledge the work 
done by the staff of the Vehicle Emissions Laboratory. 

Disclaimer 

The  opinions  expressed  in  this  manuscript  are those of  the authors  and  should  in  no  way  be  considered  to 
represent  an  official  opinion  of  the  European  Commission.  The  mentioning  of  trade  names  or  commercial 
products does not constitute endorsement or recommendation by the authors or the European Commission. 

References 

European Union. (2017). Commission Regulation (EU) 2017/2400 of 12 December 2017 implementing Regulation (EC) No 
595/2009  of  the  European  Parliament  and  of  the  Council  as  regards  the  determination  of  the  CO2  emissions  and  fuel 
consumption of heavy-duty vehicles and amending Directive 2007/46/EC of the European Parliament and of the Council 
and Commission Regulation (EU) No 582/2011. 

European Union. (2019). Regulation (EU) 2019/1242 of the European Parliament and of the Council of 20 June 2019 setting 
CO2 emission performance standards for new heavy-duty vehicles and amending Regulations (EC) No 595/2009 and (EU) 
2018/956 of the European Parliament and of the Council and Council Directive 96/53/EC. 

European Union. (2022). Commission Regulation (EU) 2022/1379 of 5 July 2022 amending Regulation (EU) 2017/2400 as 
regards the determination of the CO2 emissions and fuel consumption of medium and heavy lorries and heavy buses and 
to introduce electric vehicles and other new technologies. 

Fontaras, G., Rexeis, M., Dilara, P., Hausberger, S. et al. (2013). The Development of a Simulation Tool for Monitoring Heavy-
Duty Vehicle CO2 Emissions and Fuel Consumption in Europe, SAE Technical Paper 2013-24-0150. 

Silberholz G., Rexeis M., Quaritsch M., Present S. (2022).VECTO: Extension to hybrids and further  technical support, Final 
report for Service Contract Number 340201/2018/789690/SER/CLIMA.C.4. 

181 

 
 
 
 
 
 
 
Emission Monitoring for used cars: Evaluation of On-Road Testing 

P. Dégeilh, J. Kermani, S. Rodriguez1 and A. Frobert2 

1 IFP Energies nouvelles, Institut Carnot IFPEN TE, 1-4 avenue de Bois-Préau, 92852 Rueil-Malmaison, France 
2 IFP Energies nouvelles, Institut Carnot IFPEN TE, Echangeur de Solaize, 69360 Solaize, France 

Keywords:   Emissions, Used Cars, Embedded Measurement, On-Road, PTI.      

Presenting author email: arnaud.frobert@ifpen.fr 

Introduction 

Air quality remains a major health issue. Road transport has an important contribution to the emissions of local 
pollutants as particulate matter (PM), nitrogen dioxide (NO2), and ground-level ozone (O3). These local pollutants 
have  a  significant  impact  on  the  health  of  the  European  population,  particularly  in  urban  areas  (European 
Envrionment Agency,  2020).  This  point  has been highlighted  during the lockdown of spring 2020, when the 
traffic  has  been  drastically  reduced  due  to  COVID-19  pandemic.  That  unprecedented  situation  led  to  a 
significant reduction of the local air pollutants in cities, in particular NO2 (Donzelli et al., 2020 ; Fu et al., 2020 ; 
Kumari et al., 2020). For a more standard situation, among the road transport contributors, it has been proven 
that the fraction of the most polluting vehicles has a major contribution to the overall pollution. It is estimated 
from plume chasing measurements that only 10% of the vehicles are at the origin of respectively 38%, 69% 
and 54% of NOx, CO and PN emissions from the rolling stock (Wang et al., 2015). The Euro6d latest generation 
vehicles are cleaner even in severe condition (Dégeilh et al., 2020 ; Giechaskiel et al., 2021), notably thanks to 
the entry into force of the RDE standard and the growing electrification of the fleet. That status will even be 
improved with the future entry into force of Euro7 (European Commission, 2022), but as the renewal of the 
fleet remains a slow phenomenon, the current rolling stock must be closely considered. Acting effectively on 
the reduction of transport emissions over the next 15 years therefore involves monitoring actions for the current 
fleet to pinpoint and repair the most polluting vehicles. 

For  that,  the  two  main  levers  currently  available  are  In  Service  Conformity  (ISC)  and  Periodical  Technical 
Inspection (PTI). The latest is the more widespread one, as it concerns 100% of used cars (after 1 to 4 years, 
depending on the kind of vehicle and the local regulation). The emissions tests are currently performed in the 
workshop with a range from OBD-sensing only to (on some non-European countries) chassis-dyno testing. As 
an intermediate, most of emission tests are operated on steady-state conditions on some specific indicators 
(Opacity  for  Diesel  cars,  CO  and  A/F  ratio  for  Gasoline  cars).  Even  if  that  protocol  is  evolving,  e.g.  with  the 
addition of a PN measurement in some European countries as Netherlands, Belgium, Germany (
Ministry  of  Infrastructure  and  Water  Management,  2021),  some  pollutants  as  NOx  are  still  not  properly 
considered despite their impact on human health. 

The  NOx  performance  is  something  very  difficult  to  assess  in  steady-state  or  low-load  conditions,  as  it  is 
strongly multi-factorial (engine speed, load, ambient temperature and pressure...) and it depends on numerous 
organs, especially for diesel (air loop, EGR, Turbocharger, Aftertreatment system). Information from the On-
ion is to 
-dyno  does  not  seem 
realistic.  The  other  way  is  to  drive  the  car  outside  of  the  PTI  workshop  to  have  a  complete  answer  on  the 
emission level of the vehicles on the roads. It raises numerous technical challenges as the measuring device 
must be cost-efficient, easy to handle and give an intelligible result in as-short-as-possible trip.  

-

Based  on  these  findings,  that  paper  proposes  an  analysis  of  the  current  and  potential  future  protocols  for 
measuring  vehicle  emissions  for  large-scale  monitoring,  including  on-road  tests  based  on  the  in-house  IFP 
Energies Nouvelles REAL-e solution. 

182 

 
 
 
 
Panel & Protocol 

Vehicle Panel 

The study is focused on passenger cars and the first challenge is to define and find a representative panel of 
the French fleet (French Ministry of the Ecological Transition, 2021). For that, a sample of 33 vehicles (Euro3 
to Euro 6dTemp) are selected from about 54 personal vehicles of volunteer IFP Energies nouvelles employees 
(Figure 1). 

- by standard: 
 1 Euro 1, 
 2 Euro 3, 
 6 Euro 4, 
 10 Euro 5 (Diesel-only), 
 14 Euro 6 (b, c and d-Temp). 

- By fuel : 

 1 E85 (not presented), 
 1 GPL (not presented), 
 16 gasoline, 
 15 Diesel. 

These  vehicles  are  representative  of 
numerous  classes:  urban  car,  sedan, 
4WD, SUV, van. No distinction has been 
made  between  standard  intermediate 
steps, to keep representative samples. 

Figure 1: 

Panel of 33 vehicles tested, sorted by standard, Fuel, mileage and model-year 

Test Protocol 

Each vehicle is borrowed for 2 business days. It allows to perform during the first day: the devices setup, the 
steady tests and a first drive (hot conditions). The second day is devoted to a second drive with a cold start and 
the vehicle check-out. The different test performed are described hereafter: 

Static Tests 

Several tests are performed in steady state conditions, in a dedicated workshop. The gas measurement systems 
are stock devices, supplied by CAPELEC and described in Table 1. 

Table 1: Devices for steady-state measurements 

Device 

Outputs 

CAPELEC  CAP3600 
standard version 

OBD, CO, CO2, lambda 
(O2), opacity 

CAPELEC  CAP3600 
additional features 

HC 
(NO) 

(propane),  NOx 

183 

 
 
 
 
 
 
 
 
 
CAPELEC CAP3070 

PN (+ 23 nm) 

The first protocol is the French PTI emission test, performed with a CAPELEC CAP3600 station, following the 
official guideline (lambda and CO for gasoline cars, opacity for diesel cars). OBD test is made as well. This non 
discriminating results will not be developed here. 

The second protocol is named 
all available PTI emission measurements with an exhaustive protocol. The proposed protocol associates 7 
low-idle phases, 3 accelerated idles (phase 2 -1500 rpm, phase 4   2500 rpm and phase 6   3500 rpm) and 
3 free accelerations for a total test duration of less than 10 minutes. An example is given in Figure 2. 

Figure 2: 

On-Road Tests 

For on-road tests, the in-house embedded system REAL-e has been used (Dégeilh et al., 2019). It provides a 
raw measurement of: 

  CO, CO2, O2, NO, NO2, NH3, PN, HC (propane) 
  GPS (Speed, altitude, position) 
  OBD (Vehicle speed, Engine speed, Coolant temperature, engine load) 

Raw data are continuously 
the amounts of pollutants in mass per distance and RDE indicators. 

-

Concerning the chosen trip, it has been designed to be short (30 min) but exhaustive. Main characteristics are 
summarized in Table 2. That drive is not compliant with the Euro 6d RDE package 4 (European Commission, 
2018) because of its duration, its urban / rural / motorway share and its elevation. Nevertheless, the conditions 
meet the latest Euro 7 proposal (European Commission, 2022). 

Table 2: Main characteristics of the chosen trip 

Distance 
[km] 

Duration 
[min] 

23 
Urban: 11,8 (51 %) 
Rural: 6,3 (28 %) 
Motorway: 4,8 (21%) 

30 
Urban: 22 (74 %) 
Rural: 5 (17 %) 
Motorway: 3 (9 %) 

Average 
Vehicle Speed 
[km/h] 

Pos. Elevation 
[m] 

Stop share 
[%] 

44 

950 

10 

184 

 
 
 
 
 
 
 
 
 
Results, Discussions & Analysis 

Main results by protocol 

French PTI Emission test 

All the vehicles tested passed the emission part of the French PTI protocol, with a comfortable margin, for both 
diesel  (opacity test)  and gasoline  cars  (air-fuel ratio  and CO emissions). This  result  is not surprising, as  the 
selected vehicles own a valid PTI certificate   mandatory to borrow vehicle in a legal framework. 

Enhanced static test 

The following results are presented using boxplots, allowing a statistic representation of the results. 

Figure 3 gives an overview of the raw NOx emissions by volume during the 13 phases of the proposed protocol. 
For diesel cars, no trend can be deducted whatever the test phase (low idle, accelerated idle, accelerations). 
Moreover, the results are very sensitive to the test conditions: accessory state (AC on / AC off), EGR shut-off for 
long low-idles, SCR and combustion chamber temperature (depending of the previous test or drive). 

Concerning the gasoline cars, the average level is lower, except for some Euro 6 car during the acceleration 
phases, probably due to a poor transient air-fuel ratio management. 

Figure 3: 

 Up : Diesel cars, Down : Gasoline cars 

Short On-Road tests 

Concerning NOx, the average emissions of the diesel vehicles are of 975, 1012 and 747 mg/km, respectively 
for Euro 4, Euro 5, and Euro 6b/c/d vehicles. One can notice that the only post-RDE Euro 6d vehicle is the lower 
diesel emitter with 154 mg/km. 

For gasoline cars, the average NOx results are 194, 423 and 100 mg/km, respectively for Euro 3, Euro 4 and 
Euro 6 vehicles (no Euro 5 gasoline car is available is our panel). The Euro 4 results, built from a reduced panel 
of 3 vehicles are upsized by a high-emitting vehicle, with results close to 1 g/km.  

185 

 
 
 
 
The cold start effect has been evaluated during that test campaign. For the complete panel, the impact is +21%: 
+15% for the diesel cars and +59% for the gasoline cars. This general trend is strongly variable between the 
vehicles, with even some inverted results. 

The results are illustrated in Figure 4 hereunder, with a boxplot representation of the complete panel for diesel 
car on the left and gasoline cars on the right. The results are shared by standard; the red dots are used for 
warm tests and the blue dots for cold starts. 

The results are in line with numerous studies, for instance ICCT 2014 (Posada, 2014) with a huge gap between 
the standard and the real-life measurement. The short track protocol leads to higher NOx levels compared to 
the RDE one, due to a high severity of the trip with short distance, high elevation, and a dynamic driving.  

Concerning  CO  (Figure  5),  the  diesel  results  are  very  low  with  few  exceptions.  Gasoline  engines  are  mainly 
represented by Euro 6 cars. For Euro 4 (1 car) and Euro 5 (3 cars), results are not statistically relevant. A huge 
disparity is demonstrated between Euro 6 cars, with CO emission up to 2 g/km, even for warm tests. These 
results must be moderated considering the severe test conditions. 

Figure 4: NOx mass emissions during the short drive test for Diesel (left) and Gasoline (right) cars, sorted by 
homologation standards. 

186 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5: CO  mass  emissions  during  the  short  drive  test  for  Diesel  (left)  and  Gasoline  (right)  cars,  sorted  by 
homologation standards. 

Protocols comparison & Impact of the trip duration 

 to give an exhaustive comparison of the protocols for 

each pollutant. The more important results concern the NOx status, as static tests are not able to give a 
proper answer. This is illustrated in Figure 6 where each dot represents one vehicle. The Euro 4 NOx limit (250 
mg/km) is added for illustration. Low-idle static results have been kept for this illustration, but results are 
similar for accelerated idles or free acceleration: no clear correlation can be identified between static and 
road tests. Moreover, depending on the vehicle, huge errors can be made: for instance, the best diesel car on 
the road (the only Euro6d of the diesel panel) has one of the worst results during low-idle. It leads to a very 
damageable false positive result. On the contrary, several diesel and a gasoline vehicles have a poor test on 
the road (>1000 mg/km) and decent results during low-idle (<100ppm) (False negative). 

Figure 6: 
(yellow) and gasoline (green) cars 

Comparison of static NOx results in ppm (during low idles) vs. on-road ones  in mg/km for diesel 

As the road test appears to be a more discriminative test, a specific study has to be carried out to identify the 
shortest required trip. 

187 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The question of the minimum amount of data required for robust diagnostics arises, as it is not feasible to carry 
out large-scale tests lasting more than 90 min / 80 km, as specified for a valid RDE test. 

Figure 7 provides an analysis of the effect of the amount of data available (trip distance) on diagnostic errors. 

Figure 7: 
threshold set at 5*CF (Conformity Factor) 

Error Rate (false positives and false negatives) due to the trip distance reduction   Defect 

To carry on the investigation, at first, a reference status is set for each vehicle (compliant or non-compliant) 
from the data over the entire trip (15 km). This is established by comparing the emissions level over the journey 
with  the  vehicle's  normative  emissions  threshold  multiplied  by  a  compliance  factor  of  5.  On  Figure  7,  no 
diagnostic errors (false positives or false negatives) are visible for 15km trips, as this distance was used to set 
the reference status. 

A status is then computed with shorter sample runs: 10 km, then 8 km, 6 km 4 km and down to 2 km, using the 
same methodology, comparing the emission levels on these runs with the normative emission threshold. 

Finally, the diagnostic error rate is established by comparing these statuses with the reference status: 

  Carrying out 2 km trips results in a false positive rate (vehicle judged compliant when it failed the 15 

km test) of 12%. 

  Carrying out 2 km trips results in a false negative rate (vehicle judged to be faulty when it was 

compliant at the 15 km test) of 5%. 

These error rates are significant, in order to keep the false positive and false negative rate low, the minimum 
distance should be magnified to at least 8km according to Figure 7. However, to compute the vehicle status, 
the test conditions are not considered: the tolerated threshold (5 times the standard) is constant whatever the 
experimental conditions. Taking into account the behavior of a nominal vehicle on this particular test, through 
modelling, could improve the status quality and help to lower the minimum distance.  

Work is currently underway to propose a dynamic conformity factor that depends on the conditions (ambient 
temperature,  engine  temperature,  driving  style,  gradient).  It  would  be  calibrated  to  represent  the  nominal 
behavior  of  vehicles  (for  example,  it  has  been  observed  and  therefore  tolerated  that  Euro  6d  diesel  fueled 
vehicles nominally over-emit 4-5 times more than the norm after 2 km when starting from cold) (Dégeilh et al., 
2020). This work should make it possible to maintain acceptable diagnostic error rates while reducing minimum 
test times, by taking test conditions into account. 

Conclusions & Perspectives 

That work gives an overview of different emission measurement protocols that could be operated during a PTI 
test, to better identify malfunctioning vehicles. 

188 

 
 
 
 
 
 
The  French  PTI  protocol  for emission has not  been  discriminating  for  our  panel, as  it  has  been  designed  to 
pinpoint heavy engine or aftertreatment failures. In the framework of this project, an enhanced static procedure 
has been developed. It provides a more detailed and exhaustive status for CO, PN or opacity but is not helpful 
to identify NOx issues. 

It has also been proven that short trips (approximately 20min / 15km) on the road with a dedicated embedded 
measurement system are realistic and give a more reliable picture of the emissions performance. Nevertheless, 
the results are strongly influenced by the nature and the duration of the trip. The first sensitivity study shows 
that the error rate decreases when the trip duration increases, with a reasonable compromise found with a 8 
km drive. The remaining challenge stands in the objectivation of the trip whatever the profile and the duration 
(with potential minimum requirements) and the determination of related alert and failure thresholds. For that, 
another  experimental  study  with  a  larger  panel  of  trips  and  vehicle  is  required  to  fit  a  more  statistically 
representative digital model. 

Acknowledgements  

This  work  was  supported  by  The  French  Agency  for  Ecological  Transition  (ADEME)  in  the  framework  of  the 
SESAME project (2021-2022)   Agreement 2166D0010. 

References 

Dégeilh P., Kermani J., Rodríguez S., Frobert A. et al. ; REAL-e: Robust and affordable IoT solution for market surveillance. 

23rd Transport and Air Pollution Conference, Thessaloniki, 2019 

Dégeilh P., Kermani J., Sery J., Michel P., Study of Euro 6d-TEMP emissions - IFPEN for DGEC: Summary report. [Research 

Report], French Ministrary of the Ecological Transition, 2020. ⟨hal-03632915⟩ 

Donzelli, G.; Cioni, L.; Cancellieri, M.; Llopis Morales, A.; Morales Suárez-Varela, M. The Effect of the Covid-19 Lockdown on 

Air Quality in Three Italian Medium-Sized Cities. Atmosphere 2020, 11, 1118, doi:10.3390/atmos11101118. 

European Commission, 

the European Parliament and of the Council, Commission Regulation (EC) No 692/2008 and Commission Regulation (EU) 
2017/1151 for the Purpose of Improving the Emission Type Approval Tests and Procedures for Light Passenger and 
Commercial Vehicles, Including Those for in-Service Conformity and Real-Driving Emissions and Introducing Devices for 
27.11.2018,  1  (2018), 

http://data.europa.eu/eli/reg/2018/1832/oj 

European Environment Agency. Air Quality in Europe: 2020 Report; Publication Office of the European Union: Luxembourg, 

2020; ISBN 978-92-9480-292-7. 

European commission, Proposal 2022/0365 for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on 
type-approval of motor vehicles and engines and of systems, components and separate technical units intended for 
such vehicles, with respect to their emissions and battery durability (Euro 7) and repealing Regulations (EC) No 715/2007 
and (EC) No 595/2009, 10th November 2022 

French Ministry of the Ecological Transition, WebSite « Service des données et études statistiques (SDES) », « Données sur 
le parc automobile français au 1er janvier 2021 », 2021 : https://www.statistiques.developpement-durable.gouv.fr/parc-
et-immatriculations-des-vehicules-routiers 

Fu, F.; Purvis-Roberts, K.L.; Williams, B. Impact of the Covid-19 Pandemic Lockdown on Air Pollution in 20 Major Cities around 

the World. Atmosphere 2020, 11, 1189, doi:10.3390/atmos11111189. 

Giechaskiel B et al., « Comparisons of Laboratory and On-Road Type-Approval Cycles with Idling Emissions. Implications for 

Periodical Technical Inspection (PTI) Sensors », Sensors 2020, 20, 5790; doi:10.3390/s20205790 

Giechaskiel, B.; Valverde, V.; Kontses, A.; Suarez-Bertoa, R.; Selleri, T.; Melas, A.; Otura, M.; Ferrarese, C.; Martini, G.; Balazs, A.; 
et al. Effect of Extreme Temperatures and Driving Conditions on Gaseous Pollutants of a Euro 6d-Temp Gasoline Vehicle. 
Atmosphere 2021, 12, 1011. https://doi.org/10.3390/atmos12081011 

Kumari, P.; Toshniwal, D. Impact of Lockdown on Air Quality over Major Cities across the Globe during Covid-19 Pandemic. 

Urban Clim. 2020, 34, 100719. 

Regulation of the State Secretary for Infrastructure and 
Water  Management,  of  12  January  2021  No.  IENW  /  BSK-2020/125046,  amending  the  Vehicle  Regulation  for  the 
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189 

 
 
 
 
 
Suarez Bertoa, R., Valverde Morales, V., Clairotte, M., Pavlovic, J., Giechaskiel, B., Franco, V., Kregar, Z. and Astorga-Llorens, 
M.,  On-road  emissions  of  passenger  cars  beyond  the  boundary  conditions  of  the  real-driving  emissions  test, 
ENVIRONMENTAL RESEARCH, ISSN 0013-9351, 176, 2019, p. 108572, JRC114583. 

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Multisectoral drivers of decarbonizing battery electric vehicles in China 

F. Wang1*, S. Zhang1 and Y. Wu1 

1 School of 
wangfang18@mails.tsinghua.edu.cn.  

Introduction 

As  the  end-use  sector  with  the  highest  reliance  on  fossil  fuels,  the  global  transport  sector  (8.3  Gt)  was 
responsible for ∼23% of total combustion carbon dioxide (CO2) emissions before the Covid-19 pandemic (IEA, 
2021).  Among  all  transport  modes,  light-duty  vehicles,  predominantly  passenger  cars,  were  the  largest 
contributor to the total transport CO2 emissions (IEA, 2021). Electrifying passenger cars are regarded as one 
predominant solution to deliver deep mitigation of their CO2 emissions in the future. China has made great 
progress in the electrification of passenger cars, and the sales of battery electric vehicles (BEVs) have reached 
20% (IEA, 2023). 

The  potential  environmental  and  climate  benefits  of  BEVs  provide  an  important  motivation  for  China's 
supportive policies in developing BEVs (Liang X. et al., 2019). However, considering the high reliance on coal-
based electricity generation, there remains ongoing public debate on the actual impacts of BEVs on mitigating 
CO2 emissions. Life-cycle assessment (LCA) methods then were developed to address this issue, which could be 
typically categorized into two types according to the boundary setting, well-to-wheels [WTW, only focusing on 
the fuel cycle; (Huo et al., 2010; Wu et al., 2012; Shen et al., 2019; Gan et al., 2021)] or cradle-to-grave [C2G, 
further including production and recycling processes of automotive components materials; (ICCT, 2021; Wu et 
al., 2019)].  

The influence of multiple complex factors apart from the electricity mix not comprehensively analyzed. In this 
study,  we  emphasize  that  BEVs  and  internal  combustion  engine  vehicles  (ICEVs)  at  fleet  level as  compared 
components, and summarise their recent huge progress on C2G CO2 emissions by dissecting multiple-sectoral 
drivers  from  electricity,  vehicle  fuel  economy,  Li-ion  battery,  and  major  automotive  metals  with  real-world 
investigated  data.  Furthermore,  we  synthesized  all  the  drivers  to  evaluate  the  potential  C2G  CO2  emissions 
reduction in 2030 with each factors dissected. 

Scope 

Figure 1: Scope of cradle-to-grave analysis of CO2 emissions for BEVs in China 

We selected model years (MY) of 2015, 2020, and 2030 as the past, present, and future study years to compare 
C2G CO2 emissions between BEVs and ICEVs. C2G CO2 emissions are expressed in the CO2 emissions per unit 
distance traveled during the vehicle lifespan. The LCA study consists of two main aspects the fuel cycle (i.e. 
WTW) and the vehicle cycle and thus the C2G emissions are calculated as Eq. (1).  

𝐸𝐶2𝐺 =

𝐹𝐶×𝐸𝐹𝑊𝑇𝑊÷100
𝐸𝑓𝑓𝑐ℎ𝑎𝑟𝑔𝑖𝑛𝑔

+

𝐸𝑚𝑎𝑗𝑜𝑟 𝑚𝑒𝑡𝑎𝑙𝑠+𝐸𝑏𝑎𝑡𝑡𝑒𝑟𝑦+𝐸𝑜𝑡ℎ𝑒𝑟𝑠
𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛

 (1) 

191 

 
 
 
Where FC refers to fuel consumption for ICEV and BEV in the unit of L 100 km-1 and kWh 100 km-1, respectively. 
𝐸𝐹𝑊𝑇𝑊  is  the  CO2  emission  factor  for  gasoline  (g  L-1)  or  electricity  (g  kWh-1),  and  Effcharging  represents  the 
charging efficiency of chargers, which depicts the proportion available for batteries from the output of chargers 
(80% in 2015, 86% in 2020, and 92% in 2030).  

Results and Discussion 

Figure 2: WTW CO2 

-grids (2015, 2020, and 2030). 

BEVs are estimated to mitigate WTW CO2 emissions by 57% in 2020 with the national-average grid (102 g km-
1 for BEVs, 155 g km -1 for HEVs, and 238 g km-1 for ICEVs) and could deliver WTW CO2 emission mitigations 
across all sub-grid regions. In the most coal-dependent region, the North sub-grid (a coal-based share of 79%), 
BEVs could still reduce 44% WTW CO2 emissions compared with the ICEV counterparts there. In cleaner sub-
grid regions, such as the South and Southwest, the mitigation percentages could be enlarged to 71% and 82%. 

Figure  3:  Cradle-to-grave  CO2  emissions  of  ICEV  and  BEV  during  2015-2030  with  the  national-average 
electricity mixes. The error bars represent the variability of WTW CO2 emissions due to the different electricity 
mixes across various sub-grids.  

The estimated C2G CO2 emissions of NCM-BEVs, LFP-BEVs and ICEVs in 2020 are 165 g km-1, 153 g km-1, and 
280  g  km-1,  respectively.  According  to  the  national-average  electricity  mix,  BEVs  have  already  shown 
approximately a 40% reduction in C2G CO2 emissions compared with ICEVs in 2020 and the reduction would 
further increase to 53% in 2030, despite only a 23% reduction in 2015. As far as sub-grid regions are concerned, 
the C2G CO2 mitigations of NCM-BEVs range between 30% and 62%. This indicates that even in the North grid 
still with a high proportion of coal-fired electricity, BEVs can achieve considerable emission reduction benefits. 

192 

 
 
 
 
Figure 4: Drivers on decarbonizing NCM-BEVs from 2015 to 2030. The drivers are categorized into two phases 
(WTW and vehicle cycle) and four major parts: P: Power generation; V: Vehicle performance (e.g., vehicle weight, 
energy consumption (EC)); B: Battery performance (e.g. battery size, energy density); and M: Metal production. 
See Fig. S1 for the results of LFP-BEVs. 

With  the  accelerated  decarbonization  of  the  power  system  during  2020-2030,  we  estimate  that  the  future 
cleanness  of  the  electricity  mix  will  be  the  major  driver  to  reduce  C2G  CO2  emissions  for  BEVs.  The  total 
reductions then will reach 43% (72 g km -1 and 66 g km-1) for the two types of BEVs. For NCM-BEVs, the cleaner 
electricity will lead to 40 g km-1 of C2G CO2 emission mitigation. The improved operating and charging efficiency 
would be other drivers to reduce WTW CO2 emissions by 11 g km-1 (thus jointly reducing the WTW CO2 emissions 
by 51 g km-1). The improvement in the energy density of the battery is still significant, resulting in 12 g km-1 
C2G CO2 mitigation. The decarbonized production of metal materials can also contribute to emission mitigation 
by 5 g km-1. Battery recycling can bring additional emissions mitigation of 2 g km-1.  

The European Union's (EU) proposed battery regulation aims to enhance sustainability across the battery value 
chain, including introducing a carbon footprint limit for electric vehicle (EV) batteries. This aligns with a growing 
trend  emphasizing  the  pivotal  role  of  life  cycle  assessment  (LCA)  in  climate  mitigation.  Addressing  data 
limitations,  our  study  provides  primary  industry-based  data  from  China's  battery  and  automotive  sectors, 
enhancing reliability in assessing carbon footprints. By incorporating real-world data, including China's carbon 
peaking  and  neutrality  policies,  we  analyze  key  drivers  for  decarbonizing  passenger  vehicle  emissions.  This 
informs  policymakers  of  the  complexities  in  reducing  EV  emissions  during  electrification,  emphasizing 
technology enhancement and supply chain decarbonization. Amid the global imperative to limit warming, our 
study  demonstrates  BEVs'  potential  to  significantly  reduce  CO2  emissions  compared  to  ICEVs  by  2030, 
supporting China's climate targets. While advanced strategies like smart charging and battery reuse can further 
amplify benefits, focusing on the entire BEV fleet underscores potential uncertainties. Improved battery life-
cycle  data,  evolving  smelting  technologies,  and  renewable  energy  integration  offer  untapped  mitigation 
avenues. Although plug-in hybrid electric vehicles are not considered here, broader decarbonization strategies 
hold promise for them as well. Harnessing these factors through a comprehensive supply-chain approach can 
enhance BEV carbon mitigation within future policy frameworks. 

Acknowledgements 

This  work  was  sponsored  by  the  National  Key  Research  and  Development  Program  of  China  (grant  No. 
2022YFC3703600) and the National Natural Science Foundation of China (grant No. 42261160645). 

193 

 
 
 
 
 
 
References 

Gan Y, et al.  (2021). Provincial greenhouse gas emissions of gasoline and plug-in electric vehicles in China: comparison 
from the consumption-based electricity perspective. Environ Sci Technol. 55, 6944 6956. 

Huo H, Zhang Q, Wang MQ, Streets DG, He K. (2010). Environmental implication of electric vehicles in China.  Environ Sci 
Technol. 44, 4856 4861. 

ICCT (2021). A global comparison of the life-cycle greenhouse gas emissions of combustion engine and electric passenger 
cars.  

IEA (2021). Net zero by 2050, IEA, Paris. 

IEA (2023). Global EV Outlook 2023, IEA, Paris.  

Liang X, et al. (2019). Air quality and health benefits from fleet electrification in China. Nat Sustain. 2, 962 971. 

Shen W, Han W, Wallington TJ, Winkler SL. (2019). China Electricity generation greenhouse gas emission intensity in 2030: 
implications for electric vehicles. Environ Sci Technol. 53, 6063 6072. 

Wu Y, et al.  (2012). Energy consumption and CO2 emission impacts of vehicle electrification in three developed regions of 
China. Energy Policy. 48, 537 550. 

Wu Z, et al.  (2019). Assessing electric vehicle policy with region-specific carbon footprints. Appl Energy. 256, 113923. 

194 

 
 
 
 
Vehicle fleet electrification: electric energy consumption assessment at the scale 
of an urban area. 

D. Lejri*1, B. Jeanneret2, C. Bécarie2 and R. Trigui2 

1LICIT-ECO7, Univ Lyon, Univ Eiffel, ENTPE, F-69675 Lyon, France, delphine.lejri@entpe.fr 

2LICIT-ECO7, Univ Eiffel, Univ Lyon, ENTPE, F-69675 Lyon, France 

Introduction 

In the field of transportation, one of the European strategies being considered to mitigate climate change 
is the deployment of electric vehicles (EVs). In France, fleet electrification is rather slow, but it seems to be 
intensifying: EVs and Plug in HEV account for 22% of new car sales in France in 2022. Indeed, electric mobility 
has proven to be a powerful technology for decarbonization of the transportation sector (Kouridis, 2022). In 
comparison to conventional vehicles, EVs have either zero or lesser tailpipe greenhouse gas emissions. However, 
their carbon emissions are shifted from the exhaust pipe to the electric generators. Therefore, EVs are more 
eco-friendly the lower the carbon intensity of the electricity generation mix (De Angelis, 2020).  

As more electric vehicles are introduced into the fleet, the consumption of electric energy increases. The 
amount of electric energy consumed is often simply assessed by considering that a typical personal electric car 
consumes  20kWh  per  100km  on  average.  However,  some  papers  focus  on  the  evaluation  of  EVs  electric 
consumption  (EC):  (Zhang,  2015)  proposes  an  estimate  from  driving  cycles,  (Al-Wreikat,  2021)  analyses 
experimental data to understand the relevant factors that influence EC during operation, (Fiore, 2019) looks at 
impact of traffic conditions on fuel and electric consumption by modelling it from various speed profiles. 

In this paper, the issue is to assess EC at the scale of an urban network with a fleet of vehicles including 
an  increasing  share  of  EV.  Is  a  flat  rate  estimate  sufficient?  What  is  the  appropriate  description  of  traffic 
conditions in order to provide a more accurate estimate? Our objective is to compare on a large urban area, 
namely L63V (3 districts in the metropole of Lyon) (i) an estimate using detailed speed profiles directly derived 
from simulated trajectories and (iii) an estimate using average flow velocity per road section, (ii) an estimate 
using average flow velocity per Iris zone. 

Case study: Lyon63V 

The case study is the Lyon63V15 network, a 15 km2 urban area divided into 75 sub-areas (fig.1). The 
supply specification covers the various transportation modes: light, heavy vehicles, and public transport (metro, 
tram and bus). The demand specifications are defined dynamically on morning peak hour (from 6.30 am to 
10.30 am), for each vehicle category and each OD pair. This baseline demand profile has been calibrated on 
loop detectors measurements.   

Figure 1. L63V network : road sections and 75 IRIS areas (uniform subdivision of the popultaion for statistical 
purposes, INSEE16) 

15 3rd and 6th districts of Lyon and Villeurbanne 
16 French national institute of statistics and economic studies 

195 

 
 
 
 
 
 
                                                       
 
The traffic microsimulation was implemented in the SYMUVIA platform17. Vehicle movements at the microscopic 
scale are governed by a set of rules, including car-following modelling (Leclercq, 2007a, 2007b), lane-changes 
(Laval  and  Leclercq,  2008)  and  specific  movements  at  intersections  (Chevallier  and  Leclercq,  2007).  The 
platform  also  copes  with  the  cohabitation  on  the  network  of  vehicles  with  different  kinematics,  including 
passenger cars, buses and heavy-duty vehicles.  

In this first study, we have decided to focus on passenger cars. We will consider three levels of information in 
order to study the impact of traffic dynamics level of description on the evaluation of electricity consumption: 
(i) vehicle trajectories, (ii) traffic variables at link scale and (iii) at IRIS zone scale.  In the first case, we consider 
each individual vehicle, so the output of the traffic simulation is characterized by its instantaneous speed profile. 
In the other cases, a spatio-temporal element is considered (i.e., the road section for 10min or the zone for 
10min) and the micro-simulation outputs are aggregated to obtain the associated average speeds and distance 
travelled. 

According to the fleet composition published by Citepa18 in December 2022, the share of electric vehicles is 
0.64% of passenger cars in France. However, this share is constantly growing. In 2023, 23.5% of new vehicle 
registrations  were  for  electric  vehicles  (European  Alternative  Fuels  Observatory19).  For  the  purposes  of  this 
study,  we  assume  that  all  vehicles  are  electric,  in  order  to  avoid  introducing  biases  by  randomly  selecting 
vehicles. We will therefore focus on the relative differences between the proposed options. The passenger car 
electric fleet considered is composed of Tesla model 3, Renault Zoe and Citröen C0, three EVs with different 
sizes and autonomy ranges. For more realistic case study, one must consider the actual French EVs share.  

Finally, two different simulations are used to evaluate and compare the energy consumption of the EV fleet in 
the considered area. In the first option, accurate EV simulation energetic models are used based on VEHLIB 
software description and the different generated trajectories (Vinot, 2008), while for the second and the third 
options, a curve of EC in function of the average speed has been previously built using the same models and 
speed profiles databases. 

Electrical consumption assessment 

Energy  consumption  of  electric  vehicles  depends  on  several  internal  (vehicle  features)  and  external 
(environment) parameters. The most impacting vehicle characteristics are the weight, the aerodynamic drag, 
the  frictions  and  the  efficiency  of  the  components  such  as  the  battery  and  the  electric  drive  systems.  The 
environment parameters that influence the energy consumption are related to the infrastructure (road type, 
slopes,  lights),  the  traffic  and  the  weather  conditions.  Consequently,  a  precise  calculation  of  the  consumed 
energy during a trip is a complex task. 

The Accurate Dynamic Model (ADM) 

For  more  than  two  decades,  LICIT-ECO7  lab  has  been  developing  VEHLIB,  an  energy  consumption  software 
dedicated to all types of vehicles, including EVs. VEHLIB is now an open-source software available on the Gitlab 
platform (Jeanneret & al, 2021). Two types of simulation are possible, namely forward simulation and backward 
simulation. The first is based on Simulink blocks and allows to simulate in a causal way the power flow in each 
component of the vehicle using speed control and a driver model. The second uses Matlab scripts and an inverse 
causality, i.e upstream from the effect to the cause (e.g. from the speed to the forces that cause it). Both need 
the instantaneous speed profile as input and allow the calculation at each time of the power going through the 
different systems of the vehicle. As no driver model and control loop is needed in the backward model, this 
makes it faster and fairer for comparison as the speed profile is supposed to be exactly followed. The forward 
model is on the other hand more adapted to develop components and vehicle controls and is often used in real 
time experiments such as hardware in the loop tests. We suggest in this paper to use the backward model for 
irst  option)  and  this  will  be  considered  as  the  reference  for  the  energy 

comparison of the different simulation options because of its proven validity when compared to vehicle test. 

17 https://github.com/licit-lab/Open-SymuVia 
18 https://www.citepa.org/en/ 
19 https://alternative-fuels-observatory.ec.europa.eu 

196 

 
 
 
                                                       
 
The pre-processed interpolated model (PIM) 

The  previous  model  requires  as  input  the  instantaneous  speed  profile,  at  least  second  by  second,  and  the 
detailed vehicle parameters data. For a global calculation of a whole fleet of vehicle in a given area and during 
significant period of time this imposes two constraints that are firstly the availability of the huge amount of 
instantaneous data and secondly the computation time that can exceed the capacity of standard computers. 

Therefore, one solution to accelerate simulation, in order to consider larger spatial and time scales, could be 
the use of the accurate model to generate pre-processed consumption results for each vehicle. The approach 
is to use ADM VEHLIB based model of the EV and a representative set of speed profiles (here we considered 
data from Hyzem, Artemis and WLTC speed profiles). The speed data so gathered is divided into sequences, 
each one represents an elementary speed profile from the vehicle start until the next stop. The ADM model of 
the EV is then executed on each sequence to calculate the corresponding EC. In this paper, the described method 
is applied to the three electric vehicles considered (Tesla, Zoe and C0). The EC of each sequence is plotted in 
blue  function  of  the  average  speed  of  the  sequence  for  the  three  vehicles  (fig  2).  To  obtain  a  mathematic 
function  of  the  EC  to  be  used  in  the  aggregated  options  (option  2  and  3),  we  considered  an  exponential 
interpolation function plotted in red on the same figures. 

         a)                                                  b)                                                c)  

Figure 2. Energy consumption according to the average speed and the vehicle model: (a) Tesla model3, b) 
Renault Zoe, c) Citroen C0    

Results and discussion 

Global energy consumption 

Using the traffic simulation software SYMUVIA and the two energy models (ADM for option1 and PIM for options 
2 and 3) the total energy consumed during the simulation period (from 6.30 am to 10.30 am) is calculated for 
each vehicle then for the whole fleet. The vehicle fleet is supposed to be composed equally of Tesla, Renault 
Zoe and Citroen C0. The results are depicted in Table 1. As total energy consumption is the energy consumed 
by the battery, we add a column corresponding to the total energy need that includes the energy lost during 
charging with a charge efficiency value of 90%.  

197 

 
 
 
 
 
 
 
 
Table 1: Global Electric Energy Consumption of the three simulation options  

[Simulation 
Options] 

Distance  
(km) 

Total EC 
(kWh) 

Total Energy 
 need (kWh) 

  Error in % 
Ref option1 

Mean EC 
(kWh/100km) 

Error in % 
Ref option1 

Option 1 

323202 

47758 

  53 064 

      0 

Option 2 

326012 

44742 

  49 713 

     -6  

Option 3 

328754 

42260 

  46 955 

     -11 

16.4 

14.9 

14.3 

      0 

     -9 

    -13 

We  can  notice  that  the  three  options  do  not  lead  exactly  to  the  same  distance  as  this  variable  calculation 
method is different from an option to another (integral of speed for option 1 and sum of the length of different 
segments for option 2 and 3).  

As shown in Table 1, options 2 and 3 underestimate the total energy requirement by 6 and 11% respectively. 
If we include the distance calculation effect, the average difference in energy consumption reaches 9 and 13% 
error compared to the reference option1. 

In terms of computation time, option 1 runs in a parallel mode with 4 workers on a laptop I7-7820HQ CPU @ 
2.90GHz processor and lasts almost 5 hours to achieve the whole simulation for the 3 vehicles. Options 2 and 
3 need only one worker and last less than one second on the same computer. 

As the PIM model is average speed dependant, and in order to understand the error origin for the options 2 and 
3, we plotted in figure 3 the speed distribution according to the distance for the 3 options.   

a)  Option1                                 b) Option2                                  c) Option3 

Figure 3. Speed distribution according to the distance for the three options of simulation 

As shown in figure 3 a), the option 1 with instantaneous speed distribution includes two main classes around 
50 and 90 km/h corresponding to the two speed limits encountered (urban and rapid loop speeds). These classes 
have less weight in option 2 and almost equal to the other classes in option 3. This is due to the average speed 
estimation on each link for the two options (full link and iris zone use respectively). As the PIM model uses these 
speeds to determine the energy consumption according to the mathematic model presented in figure 2, it seems 
coherent that EC is lower for option 2 and the lowest for option 3 (90 km/h has high EC). 

Impact of the traffic  

One advantage of the proposed model coupling (SYMUVIA+VEHLIB) is to evaluate the impact on the energy 
consumption of the traffic intensity according to the time of the day. Figure 4 presents a temporal simulation 
in the morning (from 6.30 am to 10.30 am) in the considered area (L63V) using option 2 methodology. Although 
the effect of a higher traffic density around the peak hours (8.40 to 9.40) is clearly registered on the energy 
consumption (without charging efficiency), the difference between the peak value and the lowest value of the 
consumed energy is only about 3 %. 

198 

 
 
 
 
 
Figure 4. Electric Energy Consumption per vehicle and per 100km of the Evs fleet simulated on L63V 
according to the time. a) tesla model 3 vehicle b) average of the three vehicles 

Conclusion 

In  order  to  assess  energy  consumption  in  a  large  urban  area  considered  infrastructure  network  and  traffic 
constraints  a  new  methodology  based  on  traffic  simulation  tool  and  energetic  vehicle  modeling  coupling  is 
presented. Three options using this coupling have been set and compared. The first one, taken as reference, 
uses instantaneous description of the speed profile and a high-fidelity energetic vehicle model. It gives in theory 
the most reliable results. The second and the third option are derived from the traffic description with mean 
speed values for each segment. The third option, compared to the second, is based on area subdivisions to 
further  reduce spatial  description.  The simulation results showed  a  deviation from the  baseline  of the  total 
energy consumed with options 2 and 3 of 6 and 11% respectively. Depending on the objective of the various 
studies, these values could be considered satisfactory or not because, on the other hand, options 2 and 3 allow 
a drastic reduction in the calculation time. This opens up prospects for assessing energy consumption on a large 
scale as: 

- 

Enlarging  the spatial  area  to  cover  a  whole  town  or  metropolitan  area  and  further study  the scale 
effect.   

-  Using large scale traffic simulations or real census data. 

However, studying power constraints on components is important for EVs. For example, the battery is a key 
component  and  its  thermal  behavior  during  use  impacts  its  lifetime.  In  this  case  the  option  1  is  the  only 
alternative to highlight and further control these phenomena. 

References 

Al-Wreikat, Y., Serrano C., Ricardo Sodré, J. (2021) Applied Energy, https://doi.org/10.1016/j.apenergy.2021.117096 

Chevallier, E., Leclercq, L., (2007). A macroscopic theory for unsignalized intersections,  Transp. Res. Part B Methodol. 41, 
1139 1150. https://doi.org/10.1016/j.trb.2007.05.003 

De Angelis, E., Turrini, E., Carnevale, C., Volta, M. (2020) IFAC-PapersOnLine, https://doi.org/10.1016/j.ifacol.2020.12.784 

Fiori, C., Arcidiacono, V., Fontaras, G., Makridis, M. Mattas, K., Marzano, Thiel, C., Ciuffo, B. (2019) Transportation Research 
Part D, https://doi.org/10.1016/j.trd.2018.11.018. 

Jeanneret, B., Kabalan, B., Redondo-Iglesias, E. Vinot, E., Trigui, R., VEHLIB2 software, 01/10/2021 

https://gitlab.univ-eiffel.fr/eco7/vehlib 

Kouridis, C., Vlachokostas, C. (2022) Renewable and Sustainable Energy Reviews, https://doi.org/10.1016/j.rser.2021.111775 

Laval,  J.A.,  Leclercq,  L.  (2008).  Microscopic  modeling  of  the  relaxation  phenomenon  using  a  macroscopic  lane-changing 
model, Transp. Res. Part B Methodol. 42, 511 522, https://doi.org/10.1016/j.trb.2007.10.004 

199 

 
 
  
 
 
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Transp.  Res.  Part  B 

Leclercq, L. (2007b). Bounded acceleration close to fixed and moving bottlenecks, Transp. Res. Part B Methodol. 41, 309
319, https://doi.org/10.1016/j.trb.2006.05.001 

Vinot, E., Scordia, J., Trigui, R., Jeanneret, B. and Badin, F. (2008)  International Journal of Vehicle Systems Modelling and 
Testing, https://doi.org/10.1504/IJVSMT.2008.023835 

Zhang, R., Yao, R. (2015) Trans. Res. Part D, https://doi.org/10.1016/j.trd.2015.10.010 

200 

 
 
 
 
2.6  TAP.06.  Vehicle-emission  modelling  and  measurements 

and impact assessment of emission regulations. 

201 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Assessing pollutant emissions on an urban area using different traffic simulation 
approaches: multi-agent modelling and microscopic modelling 

M.Tirico1, V. Le Bescond2, D. Lejri1, P. Gastineau2, A. Can3 

1LICIT-ECO7, Univ Lyon, Univ Eiffel, ENTPE, F-69675 Lyon, France  

2AME-SPLOTT, Université Gustave Eiffel, Bouguenais, 44344, France 

3UMRAE, Université Gustave Eiffel, CEREMA, Bouguenais, 44344, France 

Abstract 

Human exposure to traffic-related air pollution is widely acknowledged as a major health hazard worldwide. 
Computational models are estimated to be valuable tools to assess the exposure of humans to pollutants in an 
urban context. Promising outputs have been obtained by modelling chain of traffic models, emission models, 
and dispersion models. In this paper, we focus on the impact of traffic models on the emissions obtained. We 
are coupling two  traffic models, the agent-based  MATSim and the  microscopic  simulator  Symuvia, with  the 
emission model HBEFA. Our analysis comprises three main comparisons: the traffic conditions, the statistical 
distribution of emission and the spatial distribution of the gaps between models. Although emissions computed 
with the multi-agent model are higher than emissions obtained with the microscopic model, we observed some 
correlations between total air pollutant emissions. A correlation can be found spatially. However, we observed 
some significant gaps that needed to be investigated in more depth. This preliminary work is the first step in 
assessing human exposure to traffic-related emissions using different modelling approaches. 

Introduction 

Exposure to traffic-related air pollution, such as the frequency of pollutant events that are dangerous to human 
health,  is  a  significant risk to  humans. According to  the Air  Quality  in Europe  report (European Environment 
Agency, 2022), 96% of the urban population is exposed to unaccepted levels of air pollutants. Evidence supports 
a  causal  relationship  between  traffic  exposure  and  the exacerbation  of  asthma  in  children  (Institute  Health 
Effects, 2022), colon cancer (Roswal, 2023), and mortality (Beelen, 2008). Particulate matter (PM) inhalation 
induces respiratory and cardiovascular diseases (Apte, 2018). A correlation in terms of mortality has been shown 
between human home addresses and the proximity of congested roads (Beelen, 2008). High exposure is also a 
source  of  social  inequalities  and  escalates  urban  segregation  (Gurram,  2019).  Emission  abatement  may 
decrease  the impacts of pollutants on human health, but it often involves economic and social costs. Therefore, 
improving air quality in urban areas is a major challenge nowadays. 

The contribution of traffic to individual exposure is difficult to quantify. Despite recent sensor improvements 
showing promise for measuring pollutant concentrations, detailed information about pollutant concentration 
cannot  be  obtained  by  interpolating  data  from  sparsely  distributed  sensor  measurements.  The  European 
Environment  Agency  report  (2022)  encourages  the  use  of  computational  models  to  estimate  air  quality  to 
support planners and policy makers. In this field, the combination of traffic, emission and dispersion models 
has become an extended practice to estimate human exposure. Predicting models have become an essential 
tool  for  human  exposure  from  traffic-related  assessment.  Evaluating  human  exposure  to  traffic  requires 
detailed  information  on  the  nature  and  distribution  of  pollutants'  emissions  and  dispersion.  To  this  end,  we 
should  look  for  the  improvement  of  two  aspects:  (1)  improving  our  understanding  of  complex  interaction 
dynamics between traffic emission, air pollutant dispersion and human exposure, and (2) developing decision 
support tools to estimate exposure and reduce human nuisances via traffic mitigation strategies. 

In this context, the Symexpo project (SYstemic approach for assessing the impact of urban Mobility on EXposures 
to  environmental  Pollutions)  aims  to  investigate  human  exposure  to  traffic-related  emissions  in  an  urban 
context. The  main expected output  of the project  is to formalize  a  framework for estimating traffic-related 
emissions,  computing  air  diffusion  of  pollutants  and  estimating  human  exposure  pollutants  at  different 
spatiotemporal scales. The developed modelling chain will also enable proposing and evaluating the benefit of 
mitigation  strategies.  To  this  end,  this  paper  aims  to  set  up  preliminary  bricks  of  the  Symexpo  project  by 
investigating traffic air emission at two urban scales using two modelling chains.  

202 

 
 
 
Methodology  

Traffic models 

Estimating traffic-related air pollutant emissions is challenging. Generally speaking, a solid approach consists 
of  coupling  static  or  dynamic  traffic  models  with  emission  models  (Fallah,  2015).  Despite  their  realistic 
macroscopic  description  of  traffic  conditions,  static  models  do  not  provide  a  detailed  spatio-temporal 
description of traffic conditions over the transportation network. For this reason, here we only consider dynamic 
traffic models.  

A variety of dynamic models has been proposed. They can be resumed in three main classes: (1) in aggregated 
traffic  models,  based  on  the  Macroscopic  Fundamental  Diagramm  (Daganzo,  2007),  traffic  conditions  are 
measured  with  fluid  dynamics-inspired  equations  at  the  level  of  sub-urban  areas  (called  reservoirs),  (2)  in 

kinematics are described wit

An essential advantage of MFD models is the low computational cost, which allows the development of real-
time network monitoring systems for traffic emissions. However, the definition of network partitioning might 
influence the total exhaust emissions of the network (Batista, 2022). Moreover, congestion conditions, which 
are  supposed  to  be  responsible  for  the  most  dangerous  hot  spot  for  human  health,  are  aggregated  at  the 
reservoir level and do not emerge at the street level. Therefore, microscopic and mesoscopic models are more 
adapted for estimating human exposure. To quantitatively assess the impact of those models, we will compare 
emission rates obtained with the HBEFA model and traffic simulations, derived from the microscopic model 
Symuvia (Leclercq, 2007) and the mesoscopic model MATSim (Horni, 2016).  

Several differences can be found between the microscopic model Symuvia and the mesoscopic model MATSim. 
Symuvia is a vehicle-centered model, where the route traffic choice is governed by a dynamic traffic assignment 
model that minimises the vehicles' travel time. A set of rules governs the vehicles' dynamics, including the laws 
of pursuit, lane change and intersection crossing. The main focus of this model is on understanding vehicles' 
interactions. Therefore, intermodality (the possibility of an individual changing his transportation mode along 
his trip) is not implemented. Due to the detailed modelling of vehicle interactions, the traffic situation can be 
measured by aggregating individual kinematics. Therefore, this approach is expensive regarding computational 
costs, limiting case studies on a few roads or neighbourhoods and for a few hours of a day simulation. On the 
other hand, MATSim is an individual-centered model where each agent of a synthetic population (representative 
of  the  real  population)  is  associated  with  some  daily  activities.  The  route  traffic  choice  is  the  result  of  the 
maximisation of each utility agent associated with different options, which aim to represent the profitability 
after each trip. Therefore, intermodality is a crucial feature of the MATSim approach. Outputs are aggregated 
at the level of links of the transportation network. In other words, we know the traffic condition at each link as 
a congestion level. The computational requirement of this approach is less than that of the Symuvia approach, 
allowing it to simulate the daily evolution of traffic conditions in wide urban areas.   

Emission model 

To estimate the real impact of the difference between these models on emissions, we coupled them with the 
same emission model HBEFA (Kickhöfer, 2016). This latter is a European database of emission factors for all 
current vehicle categories. The model is based on traffic situations: emission factors are associated with five 
traffic conditions (from free flow to congested) and the characteristics of the streets. Therefore, we obtained 
each link's traffic condition by measuring the average speed with the Symuvia and MATSim models, funding 
the  related  emission  factor  in  the  HBEFA  model  and  computing  the  associated  emissions  for  each  link.  We 
investigate the impacts of model mechanisms on emissions rates by comparing the outputs of each modelling 
chain. 

Our  modelling  chains  have  been  designed  as  independently  as  possible.  Concerning  the  demand  input,  the 
origin-destination matrices were obtained from similar data (household and travel surveys, census data, etc.), 
but using different processing methods specific to each modelling approach. Furthermore, the transportation 
networks came from two different geographical datasets (from the French geographical dataset BDTOPO for 
Symuvia and from the open-access OpenStreetMap for MATSim). Due to those essential differences between 
modelling chains, we group our outputs by a spatial tessellation. Several kinds of tessellation can be proposed. 
In this paper, we start with an administrative tessellation.   

203 

 
 
 
 
 
Results 

The study case of this paper is the L63V neighbourhood: this urban area covers the 1st, 3rd and 6th districts of 
Lyon and Villeurbanne urban area (France). The area covers 15 km² of the North-East Lyon metropolitan area, 
which covers 75 administrative subareas (IRIS). In the following, the IRIS tessellation will be used to investigate 
the spatial distribution of our outputs. While the MATSim simulation covers a day of simulation, the Symuvia 
simulation covers 4 hours (between 6:00 am and 10:00 am). Our analysis is over this last time range, and our 
results are aggregated for 15 min time slots.  

Figure 1 depicts the traffic condition for each 15 minutes time slot. Although the total distance of vehicles at 
each time slot is similar (figure 1, left), the number of vehicles currently over the networks is 3 times more in 
MATSim simulation than in the Symuvia simulation (figure 1, middle). Intuitively, the average vehicle speed is 
less in MATSim (figure 1, right). 

As expected, the different traffic patterns impact PM10, CO2 and NOx emissions (figure 2). The shape of the 
emission  evolution  obtained  from  the  MATSim  and  Symuvia  simulations  are  similar  (Gaussian-like  shape). 
However, for all pollutants, MATSim overestimates pollutant emissions compared with Symuvia.  

Figure 3 depicts the correlation between the total emissions obtained from MATSim and Symuvia models, while 
Figure 4  depicts  the spatial distribution of the total gap between emissions obtained from both models.  As 
previously mentioned, the total emissions are overestimated by MATSim model compared to that of Symuvia. 
However, in Figure 3 we observe that low values are more correlated for the three pollutants than higher values.  

Figure 10: Traffic condition estimation: average speed (left), number of vehicles (middle) and 
total distance travelled by vehicles (right). 

Figure 11: Total emission for 15 min slots of PM10 (left), NOx (middle) and CO2 (right). 

204 

 
 
 
 
 
 
 
Figure 3: total emissions correlation between Symuvia and MATSim models for PM10 

(left), NOx (middle) and CO2 (right). 

Figure 4: Spatial distribution of the gap of PM10 (left), CO2 (middle) and NOx (right) between 
the emissions computed from Symuvia and MATSim.   

Discussion and conclusion 

The first step to conceptualizing a modelling chain to investigate human exposure to traffic-related air pollution 
is coupling a traffic model with an emission model. Several kinds of models can be taken into consideration. 
Among the different aspects to consider, the traffic representation is a crucial feature. This paper compares 
traffic-related emissions computed from two completely independent models: the microscopic (Symuvia) and 
the  mesoscopic (MATSim) traffic simulator.  In order  to avoid being affected  by uncertainty  due  to emission 
models, we coupled both models with the emission model HBEFA, and we estimated the corresponding pollutant 
emissions. 

First, we observe that traffic conditions are quite different between our models. The average speeds of vehicles 
are lower in Symuvia than in MATSim, and the number of vehicles over the network is higher in Symuvia that 
in MATSim. The total distance of vehicles for each 15 min time slot is similar. Those differences found reasons 
for the model concept of our models. In MATSim, each street is modelled as a queue in which vehicles must 
wait at least for the free travel time. The flow rate and storage capacity are limited to reproduce the congested 

designed. Average speeds are computed from instantaneous kinetics. 

Statistical  and  spatial  analyses  show  patterns  and  heterogeneities  of  total  emission  over  the  study  area. 
Concerning the spatial distribution of pollutant emissions, we observe that the gap between our models differs 
for each pollutant. For example, in the north-east of our study area, we observe greater variation in PM10 and 
CO2 emissions. This area represents the Lyon ring road, for which traffic behaviour is very different from that 
of the central zones. This area is also the limit of our study: that of the Symuvia simulation, but that of an 
extraction from the MATsim simulation. These two points need to be considered in greater detail to explain such 
differences. Pollutant emissions are higher for MATSim simulation and, for lower values, strongly correlated.  

This work is based on a preliminary brick that assesses the impacts of traffic mitigation strategies on human 
exposure  to  traffic-related  air  emissions.  Here  our  purpose  is  to  explore  the  impact  of  two  types  of  traffic 
models on emission rates. However, this work is open to plenty of perspectives. First, we want to improve our 
spatial  analysis  by  testing  different  tessellations  (e.g.,  regular  grid  and  network-centered)  to  avoid  spatial 
incoherencies.  Second,  in  the  following,  we  will  consider  time  variations  over  the  spatial  tessellation  to 

205 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
understand the gap between models on the impact of different traffic conditions on emission rates. Finally, we 
will validate our results by comparing the completed modelling chain with empirical measurements. 

Reference 

Apte, Joshua S., Michael Brauer, Aaron J. Cohen, Majid Ezzati, and C. 

Estimating Network-

Beelen, Rob, Gerard Hoek, den Brandt Piet A. van, R. Alexandra Goldbohm, Paul Fischer, Leo J. Schouten, Michael Jerrett, 
Edward Hughes, Ben Armstrong, and 
-Term Effects of Traffic-Related Air Pollution on Mortality 
in a Dutch Cohort (NLCS-

202. 

51. 

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quality-in-europe-2022. 

-

Fallah Shorshani, Masoud, Michel André, Céline Bonhomme, and Christian Seigneur. 

Software 64 (February): 102 23. 

Urban Air Pollution from Transportation: Exposure Disparities and Impacts of High-
and Urban Systems 75 (May): 22 34. 

-Based Modeling to Estimate Exposures to 

Horni,  Andreas,  Kai  Nagel,  and  Kay  W.  Axhausen.  2016.  The  Multi-Agent  Transport  Simulation  MATSim.  Ubiquity  Press. 
Ubiquity Press. 

2022. 

healtheffects.org/annual-report-

-Agent Transport Simulation MATSim. 

Whitham

Research Part B: Methodological 41 (7): 701 9. 

Roswall,  Nina,  Jesse  D.  Thacher,  Mikael  Ögren,  Andrei  Pyko,  Agneta  Åkesson,  Anna  Oudin,  Anne  Tjønneland,  et  al.  2023. 

-Term  Exposure  to  Traffic  Noise  and  Risk  of  Incident  Colon  Cancer:  A  Pooled  Study  of  Elev

Environmental Research 224. 

206 

 
 
 
 
 
Primary and Secondary Aerosol Formation from Real Vehicle Exhausts   
Development of an Equivalent Total Particle Emission Index Module for Air 
Quality Modelling Applications 

P. Roldin1,2*, M.P. Rissanen3, L. Pichelstorfer4, S. Iyer3, P. Karjalainen3, T. Olenius5, J. Pagels6, A. Oudin7, H. Timonen8, 
T. Rönkkö3, P. Aakko-Saksa9 

1Swedish Environmental Research Institute IVL, Malmö, 211 19, Sweden, pontus.roldin@ivl.se 

2Department of Physics, Lund University, Lund, 221 00, Sweden 

3Aerosol Physics Laboratory, Physics Unit, Tampere University, Tampere, 33720, Finland 

4pi-numerics, Neumarkt am W., 5202, Austria 

5 Swedish Meteorological and Hydrological Institute, Norrköping, 601 76, Sweden 

6Ergonomics and Aerosol Technology, Department of Design Sciences, Lund University, Lund, 221 00, Sweden 

7Planetary Health, Lund University, Lund, 221 00, Sweden 

8Aerosol Composition Research, Finnish Meteorological Institute, Helsinki, 00101, Finland 

9Emission Control and Sustainable Fuels Team, VTT Tech. Res. Centre of Finland Oy, Espoo, 02150, Finland 

Introduction 

In the Horizon Europe project PAREMPI (https://parempi.eu/) we will develop a vehicle emission equivalent total 
particle emission index module (ePMI). ePMI will be used for European and global scale chemistry transport 
model applications for improved air pollution population exposure and health impact assessments. 

ePMI is intended to be a generally applicable module that can predict the atmospheric primary and 
secondary  aerosol  formation  (number  and  mass  concentrations)  for  a  wide  range  of  transport  applications 
(road, marine, non-road, aviation), considering the ambient conditions, fuel and engine technologies. 

This requires comprehensive knowledge about the tailpipe exhaust composition (primary particles and 
secondary aerosol precursors) during real driving conditions. In PAREMPI we are reviewing existing emission 
measurements and fill experimental gaps, especially for European wintertime conditions. 

The real vehicle exhaust experiments are also complemented with molecular level oxidation flow-tube 
experiments for representative VOCs (e.g. benzene, toluene, xylene, naphthalene, dodecane, etc.) at high and 
low-NOx conditions. 

Method 

The development and evaluation of the ePMI module build on four connected modelling/experimental tasks: 

1. 
Detailed molecular cluster dynamics and aerosol dynamics simulations of controlled exhaust dilution 
experiments. Vehicle exhaust particle toxicity have been linked to lung-deposited particle surface area, which is 
governed by the initial second to minute tailpipe to urban background primary particle number size distribution 
evolution. 

2. 
Development of explicit gas-phase chemistry and secondary organic aerosol formation mechanisms 
for  ~50  vehicle  exhaust  representative  VOCs.  This  is  based  on  dedicated  laboratory  oxidation  flow-tube 
experiments and a theoretical framework for automated VOC oxidation mechanism generation (Pichelstorfer et 
al., 2023; Iyer et al., 2023). 

3. 
Simulation  of  real  vehicle  exhaust  oxidation  flow  reactor  experiments,  targeting  the  potential  total 
aerosol mass formation for different vehicle/engine/fuel setups. This task relies on the experimental data and 
mechanisms developed in task 1 and 2. 

4. 
Simulation  of  primary  and  secondary  aerosol  formation  during  real  atmospheric  conditions  with  a 
complex  mixture  of  anthropogenic  and  biogenic  aerosol  precursors.  The  results  are  evaluated  against 
atmospheric observations. 

207 

 
 
 
 
 
 
 
In the three first tasks the primary model tool used is the aerosol dynamics, gas- and particle-phase 

chemistry model for laboratory chamber experiments ADCHAM (Roldin et al., 2014; Roldin et al., 2019). 

In task 1, we simulate of the molecular cluster dynamic (H2SO4-H2O-NH3) leading to new nanoparticles 
and aerosol dynamics (condensation, evaporation, coagulation) during the controlled vehicle exhaust dilution 
experiments (e.g. Karjalainen et al., 2016; Timonen et al., 2017). For this we use the novel cluster dynamics 
plugin  module  ClusterIn  (Olenius  and  Roldin,  2022)  in  ADCHAM.  The  exhausts  organic  condensable  vapor 
properties  are  constrained  by  comprehensive  observations  of  gaseous  compounds,  semi-volatiles  and  non-
volatiles, complemented by specific volatility basis set (VBS) distributions when needed, and optimized using 
ADCHAM  to  capture  the  full  condensable  primary  organic  aerosol  dynamics  (i.e.  the  semi-volatile  primary 
particle formation, growth and evaporation dynamics during the complete exhaust dilution and cooling stages 
from the tailpipe to urban background conditions.  

In task 2, explicit VOC gas-phase chemistry mechanisms are developed using the novel autoPRAM-fw 
mechanism generator (Pichelstorfer et al., 2023). AutoAPRAM-fw calculates the alkoxy/peroxy radical reaction 
pathways,  rate  coefficients  and  condensable  vapor  pure-liquid  saturation  vapor  pressures  using  existing 
structural  activity  relationships.  The  generate  alkoxy/peroxy  radical  autoxidation  mechanisms  (APRAM)  are 
coupled to the Master Chemical Mechanism (MCM) (Jenkin et al., 1997; Saunders et al., 2003; Bloss et al., 2005; 
Jenkin et al., 2015) and evaluated using flow-tube and smog chamber experiments (Roldin et al., 2019; Iyer et 
al., 2023; Pichelstorfer et al., 2023). The complete APRAM+MCM schemes are then used in ADCHAM to reproduce 
the  observed  secondary  organic  aerosol  formation  (composition,  number  and  mass  concentrations)  during 
existing and new VOC-oxidation experiments, in smog chambers and oxidation flow reactors (OFRs), covering a 
wide range of atmospheric relevant temperatures, UV-light intensities and NOx levels. 

In task 3 we I will use the complete MCM-APRAM vehicle exhaust mechanism (> 50 secondary organic 
aerosol precursors) from task 2 in ADCHAM and simulate real vehicle exhaust OFR experiments. The aim is to 
close  the  gap  between  the  observed  and  modelled  secondary  aerosol  formation  (organic  and  inorganic) 
formation in vehicle exhaust exposed to different stages of atmospheric ageing (cumulative OH exposure). The 
generate comprehensive model result datasets will be used to train a machine learning method to emulate the 
explicit model results on the time dependent formation of ultrafine primary and secondary aerosol particles 
and condensable vapors. The end product will be the equivalent total particle emission index module ePMI. ePMI 
will  predict  the  secondary  organic  and  inorganic  PM2.5  and  non-volatile  and  semi-volatile  nano-particle 
formation rates based on a limited number of input variables ([NOx], [O3], temperature, UV-light, VOC mixtures, 
[NH3]  etc.),  which  are  standard  variables  provided  by  regional  and  global  scale  chemistry  transport  models 
(Figure 1). 

In  task  4  ePMI  will  be  implemented  and  evaluated  with  the  Lagrangian  chemistry  transport  model 
ADCHEM  (Roldin  et  al.,  2011;  Roldin  et  al.,  2019).  We  will  run  ADCHEM  both  with  the  detailed  near-explicit 
secondary aerosol formation chemistry schemes (MCM+APRAM) and with the simplified ePMI approach along 
air mass trajectories arriving at selected ACTRIS stations in Europe, covering the conditions during all seasons. 
The modelled secondary and primary aerosol particle mass and number concentrations will be evaluated with 
observations at the targeted stations. From this we will be able to judge the performance of ePMI against the 
benchmark MCM+APRAM secondary aerosol formation schemes, for realistic atmospheric conditions in Europe. 

Finally, we will implement ePMI into the global chemistry transport model TM5 (Huijnen et al., 2010) 
and run the model for selected years, with and without emissions from road traffic, shipping, aviation and off-
road  vehicles.  The  modelled  monthly  average  primary  and  secondary  PM2.5,  PM10  and  particle  number 
concentrations from the different model simulations will be used to estimate the vehicle fleet contribution to 
the PM and particle number concentration levels in Europe and globally. These results will also be used for 
health  impact  assessment  and  socio-economic  cost  analysis  of  PM2.5  originating  from  vehicle  exhausts  in 
Europe. 

208 

 
 
 
 
 
 
 
Figure  1:  Illustration of the  intended implementation of the  equivalent total particle  emission index module 
(ePMI) into a chemistry transport model.  

Previous and initial results 

Based  on  the  methodology  presented  in  task  2  and  4  we  have  previously  developed  near-explicit 
mechanism that are fully capable of capturing the observed highly oxygenated organic molecule (HOM) gas-
-carene at a wide range of 
atmospheric conditions (temperatures, NOx levels etc) (Roldin et al., 2019; Nie et al., 2023). In PAREMPI we are 
now  focusing  on  the  HOM  and  SOA  potential  of  anthropogenic  VOCs. The  global  emissions  of  the  aromatic 
compounds benzene, toluene and xylenes account for >20 % of the total anthropogenic non-methane volatile 
organic compound (NMVOC) emissions (Yan et al., 2019). We have finalized a complete APRAM mechanism for 
benzene (Pichelstorfer et al., 2023). Based on flow reactor experiments, quantum chemical calculations and 
ADCHAM simulations we have recently shown that the molecular rearrangement of bicyclic peroxy radicals for 
aromatic compounds is a key route to HOM and secondary organic aerosol formation for toluene and that this 
likely also apply for many other aromatic compounds (Iyer et al., 2023). Including this new knowledge, APRAM 
mechanism for several aromatic compounds will be developed.  

-

According to our simulations alkoxy/peroxy radical autoxidation can explain up to 100% of the benzene-
SOA formed during the OH oxidation of benzene under low-NOx conditions (Pichelstorfer et al., 2023) and for 
toluene at least 30-40 % of the observed SOA mass formation during OH oxidation of toluene can be explained 
by HOM formed rapidly after the molecular rearrangement of bicyclic peroxy radicals. 

During the conference we will present the general methodology behind the ePMI module development 
and show preliminary results from simulations of SOA formation from representative aromatic VOCs (benzene, 
toluene)  and  molecular  cluster  dynamics  and  aerosol  dynamics  simulations  of  controlled  exhaust  dilution 
experiments from previous projects. 

209 

 
 
 
 
 
 
 
Acknowledgements 

grant agreement No 01096133 (PAREMPI: Particle emission prevention and impact: from real-world emissions 
of traffic to secondary PM of urban air). 

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210 

 
 
 
Emission savings potential of ecodriving based on over 1500 hours of driving 
data from 5 countries. Findings from the uCARe project 

Brian Cox1*, Benedikt Notter1, Martin Opetnik2

3 

*brian.cox@infras.ch 
1INFRAS, Bern, 3012, Switzerland 
2Institute of Thermodynamics and Sustainable Propulsion Systems, TU Graz, Graz, 8010, Austria 
3ETEC Department & MOBI Research Group, Vrije Universiteit Brussel, Brussels, 1050, Belgium 

Introduction 

Ecodriving  training  can  be  an  effective  way  to  achieve  reductions  in  average  driving  emissions  and  energy 
consumption  on  a  timescale much  faster  than  fleet  renewal.  In  this  contribution,  we  estimate  the potential 
emission savings achievable by large-scale training of European drivers in 10 different ecodriving interventions. 
This analysis is based on over 1500 hours of measured driving data from 5 countries, including measurements 
and surveys taken from the same drivers before and after ecodriving training. We examine the willingness and 
ability  of  drivers  to  implement  ecodriving  practices  to  reduce  their  emissions.  We  also  use  these  inputs  to 
generalise  results  to  estimate  best-case  and  most-likely  emission  savings  potentials  for  average  European 
vehicles and driving situations. 

This work was done within work package 4 of the uCARe project, which was a Horizon 2020 project funded by 
the European Union that ran between 2019 and 2022 (www.project-ucare.eu). 

Methods 

Description of interventions 

Table  1  lists  the  interventions  included  in  the  uCARe  project.  We  refer  to  the  implementation  of  these 

only fuel consumption and CO2 emissions, but also reducing pollutant emissions.  

211 

 
 
 
 
Table 1 Summary of individual interventions 

Intervention 

Description 

Default 

Normal or default driving by untrained drivers 

Tire 

Load 

AC 

Shift 

Speed 

Drivers are encouraged to monitor their tire inflation and to use winter tires only 
when appropriate. They are also asked to consider the most efficient tires possible 
when choosing replacements. 

Drivers are encouraged to remove unneeded roof boxes and bicycle racks etc. from 
their vehicles. Furthermore, drivers are asked to remove unneeded things from their 
vehicle, as they increase the mass that needs to be carried around, making the 
vehicle work harder. 

Drivers are asked to reduce their use of air conditioning and other auxiliary devices, 

Drivers are given tips on how to improve their shifting behavior to minimise fuel 
consumption and pollutant emissions. 

Drivers are asked to keep their maximum speed below 120 km/h. 

Acceleration 

Drivers are encouraged to accelerate strongly (but not maximally) until speeds of 
around 50 km/h and to limit their acceleration above this speed.  

Drivers are asked to brake gently and to use engine brakes where possible. 
Furthermore, drivers are asked to look ahead and try to avoid slowing down only to 
have to speed up again. For example, when approaching a red light that is expected 
to turn green s
emissions are reduced and the need to reaccelerate is reduced. 

Drivers are asked to turn their engines off when they expect to remain stopped for 
longer than 30 seconds. 

Drivers are encouraged to plan their routes considering how many other cars are on 
the road. Perhaps the trip can wait until the traffic is reduced, or perhaps a different 
route can be taken where traffic flows more smoothly. 

Drivers are asked to chain their trips together to reduce the number of cold starts 
that that the vehicle makes. 

Brake 

Idling 

Traffic 

Cold start 

Input from measured driver behaviour 

To measure the ability of drivers to implement these interventions, pilots were run with a total of 72 drivers in 
Cyprus, Austria, Belgium, the Netherlands, and the UK, collecting over 1700 hours of second-by-second driving 
data20.  Before  starting  the  pilots,  drivers  were  first  asked  to  fill  out  a  survey  to  provide  a  baseline  of  their 
motivation and understanding of ecodriving. At this time a device was installed into their vehicle that measures 
their  driving  behavior.  This  device  allowed  1 Hz  resolution  recording  of  driving  parameters  such  as  location, 
speed, acceleration, and gear choice, and uploaded the data to an online platform, the uCARe website. After 
this, drivers were recorded for a certain amount of time while behaving normally. This provides the baseline 
against which the drivers performance after ecodriving training can be compared. 

At the end of the base period, the drivers were provided with training in the ecodriving interventions listed in 
Table 1. They were also given access to a cell phone application and the uCARe website, which showed them a 
log of all their trips and provided feedback on their driving style and how they could improve their driving to 
reduce  emissions  even  further.  After  the  ecodriving  training  the  drivers  were  asked  to  implement  the 
interventions  listed  in  Table  1  while  going  about  their  daily  driving  and  to  regularly  consult  the  website  to 
monitor their progress in implementing the principles of ecodriving. After several weeks (or, in some pilots, much 
longer) the pilot ended, and the driver was given a final survey that asked them about their experiences. They 
were asked questions about how well they thought they implemented ecodriving as well as how difficult they 
found it to implement the interventions. 

20 Out of 72 total participants, 41 drivers completed both the before and after surveys. Driving data from both before and after driver 

training was successfully gathered for 45 drivers, totalling over 1500 hours of useable data. 

212 

 
 
 
                                                       
 
For more information about the pilots, see uCARe deliverables 3.4 (Geivanidis, Jamson, & Forward, 2022), 3.5 
(Geivanidis, Fragkiadoulakis, & Bidios, 2022), and 3.6 (Geivanidis, Jamson, & Forward, 2022) from the uCARe 
project (www.project-ucare.eu/). 

Modelling potential emission savings from changes in driver behaviour 

To be able to make the step from changes in driver behaviour to changes in driving emissions, the PHEM tool is 
used. The PHEM tool is a vehicle simulation tool developed by TU Graz. PHEM stands for  Passenger car and 
Heavy-duty Emission Model and the tool has been in development since the late 1990s. In a nutshell, the PHEM 
tool takes information describing a vehicle (such as weight, rolling resistance, air resistance, and specific engine 
emission maps), and driving information (such as velocity, engine speed, road gradient) to calculate the fuel 
consumption and emissions associated with that trip as driven by that vehicle. 

The main idea in this part of the uCARe project was to create the uCARe PHEM tool  (Opetnik & Hausberger, 
2022). This update to the tool allows calculation of emissions from trips as if they were driven by a professional 
driver practicing perfect ecodriving: the maximum acceleration is reduced, maximum speed is limited to 120 
km/h,  and  braking  behaviour  and  gear  selection  are  optimised  to  reduce  emissions  and  fuel  consumption. 
Calculation input parameters such as vehicle weight, aerodynamic drag and tyre rolling resistance coefficients, 
start-stop  behaviour,  and  auxiliary  energy  demand  can  also  be  modified  to  represent  other  ecodriving 
interventions. 

The  emissions  savings  potential  of  ecodriving  interventions  can  be  estimated  by  comparing  the  emissions 
results  of  the  uCARe  PHEM  tool  with  default  inputs  to  those  calculated  with  input  parameters  modified  to 
represent  ecodriving  interventions.  For  each  intervention,  we  model  both  a  best-case  and  a  most-likely 
implementation of ecodriving. The best-case implementation is based on the upper limit of what is reasonably 
possible for a driver to implement. The most-likely case is based on experience gained from the pilots regarding 
how willing and successful drivers were in implementing each intervention.  

Where  possible,  we  use  measured  pilot  data  for  the  creation  of  the  most-likely  implementations21.  For 
interventions  where  direct  measurements  were  impossible  (for  example,  we  were  unable  to  take  daily  tire 

own compliance to the ecodriving protocols. In some cases, we also extended the pilot survey results with other 
surveys performed in the uCARe project (Geivanidis, Jamson, & Forward, 2022). 

Interested readers are pointed to deliverable 4.2 from the uCARe  project (D'Amore, Costa, Messagie, Cox, & 
Notter, 2022), which describes this methodology in much more detail. 

Generalising results 

To generalise the knowledge gained in the pilots, we used the uCARe PHEM tool to calculate emissions from 
representative driving cycles driven by a wide variety of passenger vehicle types with and without ecodriving 
interventions. For the calculations with ecodriving interventions we used both the best-case and the most-likely 
implementations as described above. 

27 passenger car types (petrol, diesel, and CNG fuel types with emission standards ranging from pre-Euro to 
Euro 6d) were modelled in the uCARe PHEM tool for each intervention, as well as combinations of interventions. 
To  ensure  that  a  complete  and  representative  set  of  driving  situations  were  considered,  all  2345  possible 
combinations of driving cycles and road gradients from the Handbook of Emission Factors for Road Transport 
(HBEFA) (INFRAS, TUG, ifeu, HSDAC, WSP, 2021) were used as the calculation basis in the uCARe PHEM tool. 
These vehicles and driving cycles are also used as the basis to calculate the emission factors for the HBEFA 
database. The driving cycles are used to represent different traffic situations, and these traffic situations are 
weighted for each country to represent average driving conditions. 

We  estimate  the  European  average  shares  of  driving  on  each  road  type,  gradient,  speed  limit,  and  level  of 
service22 based average traffic situation data from the Handbook for Emission Factors (HBEFA) version 4.2. This 
data  source  includes  highly  detailed  information  on  the  vehicle  fleets  and  shares  of  traffic  situations  of 

21 For example, the best-

PHEM Tool. For the most-likely implementation, we created a new  maximum acceleration curve using the 90th percentile of what 
trained drivers were able to accomplish in the pilots. 

22 Level of Service (LOS) defines the level of congestion on a road, which has a significant influence on driving behaviours such as average 
speed, average acceleration, and time spent idling. The LOS ranges from Freeflow, which represents low traffic volume and smoothly 
flowing traffic at speeds near the speed limit, to Stop+go_II, which represents gridlock conditions. 

213 

 
 
                                                       
 
Germany, France, Austria, Sweden, Norway, and Switzerland. We assume that the weighted average shares of 
traffic situations of the six countries in HBEFA can be used as an approximation for the European average.  

We also estimate the average shares in the total mileage of the European passenger car fleet using detailed 
information from the HBEFA version 4.2. This detailed fleet information is scaled to be more representative for 
Europe as a whole by using data from EuroStat (eurostat, 2022) to correct for the average age distribution of 
the fleet as well as shares of gasoline, CNG, and diesel vehicles across Europe. 

Results 

The overall relative emission savings potential of ecodriving compared to normal driving are shown in Figure 1. 
These results are for an average European car in the year 2020 under an average mix of driving conditions, 
with all ecodriving interventions applied. These results show that the most-likely implementation of ecodriving 
(orange  bars) could lead to  10-20%  savings for emissions of CO2,  NOx,  CO,  and PM10. In the  best-case,  the 
savings could be up to 30-40% for most pollutants, and over 50% for non-exhaust PM10 emissions. We note 
that the uncertainty for non-exhaust PM10 emissions is large as the method is still in development. 

Figure 1 Relative emission savings potential of ecodriving compared to normal driving if all interventions are 
implemented. Results are for an average European vehicle in 2020 under average driving conditions. 

Figure 2 compares the relative emission savings potential of each intervention for an average European vehicle 
in 2020 under an average mix of driving conditions. Each panel shows results for a different substance. The 
first five panels show direct exhaust emissions from the vehicle, while the bottom right panel shows PM10 non-
exhaust emissions, which include emissions from both brake and tire wear. The y-axis represents the emission 
savings  potential  of  each  individual  intervention  relative  to  normal  driving,  with  positive  values  indicating 
emission savings compared to untrained drivers. The blue and orange bars show the results for the best-case 
and most-likely implementations of each intervention respectively. The results for the implementation of all 
interventions simultaneously are shown in Figure 123. Results for different vehicle types are shown in Figure 3. 

Reduction potential for CO2 emissions and fuel consumption are found mostly for the interventions Tyre, AC24, 
Load, and Speed. The Tyre and AC interventions contribute well over half of the improvements in the most-likely 
case. NOx reductions are found to be largest for the interventions Tire, Speed, and Load, with contributions from 
Acceleration  and  AC.  CO  emission  improvements  come  nearly  exclusively  from  the  interventions  Speed  and 
Acceleration. Reductions in HC emissions in the best-case are most affected by reducing the number of cold 
starts, but no changes in this driver behaviour were found in the pilots, so the most-likely improvements to HC 
emissions remain quite small. Reductions in PM10 emissions are found to result from small improvements across 
seven out of the ten interventions examined, with the largest improvements coming from the Tyre intervention. 
Non-exhaust PM10 emission reductions are found to come mostly from the Load and Brake interventions. In the 
best-case implementation, some improvements are also expected due to the intervention to avoid heavy traffic, 
as this reduces driving situations where lots of braking is required. 

23 We note that the overall improvement potential is slightly less than the sum of the relative improvements, as some effects overlap. For 
example, the improvement potential of removing roof racks is slightly reduced when drivers avoid driving at very high speeds  and 
vice versa. 

24  The  emissions  savings  potential  for  the  best-case  AC  intervention  for  more  recent  Euro  classes  are  very  optimistic  and  should  be 

considered an upper bound of potential savings. 

214 

 
 
                                                       
 
Figure 2 Relative improvement of each intervention compared to normal driving for an average 2020 European 

vehicle in average driving conditions. The blue and orange bars represent results for the best-case and most-
likely implementations of each intervention respectively. 

Figure 3 shows the absolute emissions in grams per kilometer for normal driving (bars) overlaid with results for 
the best-case (light dots) and most-likely (dark dots) implementation of all interventions applied together. The 
six panels show results for different pollutants, and the x-axis contains selected vehicle types and the fleet 
averages in 2020, 2025, and 2030. The blue colors represent average European vehicles in the years 2020, 
2025, and 2030, while the orange and green colors represent diesel and gasoline vehicles, respectively. 

215 

 
 
Figure 3 Emissions per kilometer in g/km if all interventions are applied concurrently. Bars represent default 
driving, while dots represent the best-case and most-likely implementation of each intervention. 

The overall CO2 emission improvement potentials are similar across vehicle types: around 50-60 g/km in the 
best-case, and 15-20 g/km in the most-likely case. The relative contribution of each intervention varies across 
vehicle types. For example, earlier vehicles generally show less benefit from reducing AC use (as air conditioning 
and other  power consuming auxiliaries are less  common among these vehicles) but show  relatively  greater 
potential improvements due to interventions that reduce maximum speed (due to generally higher aerodynamic 
drag  coefficients)  or  improvement  regarding  acceleration  and  shifting.  Complete  results  disaggregated  by 
vehicle type, intervention, and traffic situation are available in Appendix C of uCARe deliverable 4.2 (D'Amore, 
Costa, Messagie, Cox, & Notter, 2022). 

The improvement potential for pollutant emissions depends strongly on the vehicle fuel type, as well as the 
Euro  class.  For  example,  diesel  vehicles  with  emission  standards  prior  to  Euro  6d-temp  show  the  largest 
potential  improvement  for  NOx.  The  improvement  potential  for  carbon  monoxide  (CO)  and  unburned 
hydrocarbons (HC) is largest for gasoline vehicles, especially earlier Euro classes. The largest driver in reducing 
carbon  monoxide  emissions  is  found  to  be  the  improvement  in  acceleration  behaviour  and  reduction  of 
maximum  speed.  For  hydrocarbon  emissions,  the  improvement  potential  is  found  to  be  overwhelmingly 
dominated by the potential to reduce the number of cold starts by trip chaining and other measures. The overall 
improvement potential for CO and HC emissions from diesel vehicles is found to be near zero or even slightly 
negative. This is because the ideal shifting intervention slightly increases the emissions of these two pollutants 
for diesel vehicles25. While an increase in the emission of these pollutants is certainly not desirable, the modified 
shifting performance leads to significant reductions in CO2, NOx and PM10 emissions for these vehicles at the 
expense of small increases in CO and HC emissions.  

25 The optimal shifting intervention is designed to seek out the optimum gear to minimize the sum of costs associated with fuel and the 

external costs of the emissions. See uCARe deliverable 2.2 for more information. 

216 

 
 
 
                                                       
 
Absolute  exhaust  PM10  emission  reduction  potential  is  dominated  by  earlier  diesel  vehicles  without  particle 
filters. The overall PM10 emissions for all European passenger cars could have been reduced by over 15% by 
improving the shifting behaviour of only drivers of older diesel cars without particle filters in the best-case. 
However, drivers were found to have difficulty in meeting the ideal shifting criteria, so the overall improvement 
due  to  shifting  behaviour  was  less  than  2%  in  the  most-likely  case.  This  is  still  a  significant  improvement 
potential considering that the share of these vehicles in the overall fleet is comparatively small. Averaged over 
all vehicle types and considering all interventions, drivers are expected to be able to reduce their PM10 emissions 
by about 10% on average in the most-likely case. 

The improvement potential for PM10 non-exhaust emissions is dominated by potential reductions in brake wear 
emissions  due  to  improved  braking  performance.  The  best-case  implementation  of  this  intervention  is  very 
optimistic and assumes that all heavy braking events can be avoided, which is unlikely to be possible during 
real  driving.  In  the  pilots,  real  drivers  were  found  to  be  able  to  achieve  less  than  15%  of  the  ideal  braking 
reduction potential, which explains the large difference between the results for the best-case and most-likely 
implementations of this intervention. 

It is noted that the fleet average reduction potentials for all pollutants are found to decrease in the future. This 
is because, on the one hand, the future fleet has a higher share of more modern vehicles, which generally have 
lower improvement potentials, and, on the other hand, because the future fleet has a higher share of alternative 
powertrains, which have no improvement potential for exhaust pollutant and CO2 emissions. However, the non-
exhaust  emissions  and  energy  consumption  (not  calculated  here)  can  still  be  improved  by  following  these 
interventions with alternative drivetrain vehicles. 

Discussion 

While the method of modelling emission reductions using representative driving cycles provides an excellent 
way  to  estimate  the  best-case  emission  savings  potential  of  ecodriving,  we  faced  one  major  limitation  in 
determining the most-likely implementations that should be discussed. This limitation is that the changes to 
the input parameters to the uCARe PHEM tool reflecting most-likely changes to driving style must be determined 
correctly.  For  interventions  where  driver  performance  can  be  directly  calculated  from  driving  data  this  is 
comparatively straight forward. However, for several interventions, we were forced to rely on self-assessments 
from the drivers, which were found in this project to be unreliable when these self-assessments could be verified 
with measured data. Not only were drivers unable to reliably determine if they had made positive improvements 
in their driver training, but it was also impossible to quantify the magnitude of this improvement. This forced 
us to rely heavily on expert judgement in determining the most-likely improvements to driving. This is one area 
where further research is needed. We further note that the two interventions that generally show the highest 
promise of emission savings in the most-likely case: Tire and AC, could not be quantified using the driving data 
from the pilots. Although we conducted an additional survey to try to get an accurate estimation of potential 
behavioural  change  for  these  interventions,  uncertainty  in  the  real-world  behavioural  change  remains  high, 
which means that the uncertainty of the most-likely emission savings potential also remains high. 

Conclusions 

In the best-case, ecodriving could reduce average emission factors of European passenger cars by 30-40% for 
most pollutants, However, in the most-likely case this improvement is expected to be only 10-20%. The potential 
emission savings in the most-likely case are lower than the best-case emission savings because of two main 
factors. The first is that not all drivers are willing to change their driving behavior. An example of this could be 
that some drivers really enjoy the feeling of hard accelerations and high engine speeds and are not willing to 
give up this driving experience in exchange for a reduction in fuel consumption and emissions. In general, we 
found that two-thirds to three-quarters of drivers in the pilots were willing to change their driving behavior and 
showed quantifiable performance improvements in most of the measured metrics. We note that the drivers 
willing to participate in the pilots probably show above average motivation to improve their driving behavior 
and that achieving this participation rate among all European drivers could be difficult. The second, and perhaps 
more important, reason that the most-likely savings are lower than the best-case is that drivers are not always 
able to reach the theoretical optimal performance of ecodriving. Examples of this could be that achieving the 
ideal shifting performance is too complex for some drivers or that it is very difficult to achieve optimal braking 
behaviour  in  the  uncertain  conditions  of  real  driving  and  that  some  hard  braking  events  are  unavoidable. 
Moreover, the frequency of such events in real traffic conditions are unknown. We found that the drivers in the 
pilots often had great difficulty achieving the optimal driving styles that would be associated with best-case 
emission savings in real life driving situations. In many cases, drivers were able to achieve less than 10% of 
the improvements that would be associated with the theoretical ideal. 

While the uCARe project provided many useful insights into the potential of ecodriving to reduce emissions, 
there is still much room for further research into how to make ecodriving training more effective to A) motivate 

217 

 
 
a  greater  share  of  drivers  to  implement  ecodriving  practices  and  B)  to  help  drivers  move  their  ecodriving 
performance closer to the theoretical ideal 

Acknowledgements  

This work was completed in the uCARe project (GA 815002). The co-authors would like to acknowledge the 
contribution of other participants in the uCARe project, especially: Stefan Hausberger, Martin Dippold, Savas 
Geivanidis, Pavlos Fragkiadoulakis, Samantha Jamson, Daniele Costa, Michal Vojtisek, Mats Gustafsson, Norbert 
Ligterink, Geoff Holmes, and Paul Tilanus. 

This extended abstract is adapted from Deliverable D4.4 from the uCARe project (Cox & Notter, 2022). 

References 

Cox, B., & Notter, B. (2022). D4.4: Report targeted towards policy makers, on the impact of measures. Project uCARe.  

D'Amore, L., Costa, D., Messagie, M., Cox, B., & Notter, B. (2022). D4.2: Updated ranking of measures, reflecting likeliness of 
success. Project uCARe. 

eurostat. 
https://ec.europa.eu/eurostat/web/main/data/database 

statistics 

eurostat 

(2022). 

database. 

(European 

Comission) 

Retrieved 

2022, 

from 

Geivanidis, S., Fragkiadoulakis, P., & Bidios, P. (2022). D3.5: Final results of the pilot projects in terms of achieved emission 
reduction. Project uCARe. 

Geivanidis, S., Jamson, S., & Forward, S. (2022). D3.4: Final description of intervention strategies and methodological details 
for implementation of measures. Project uCARe. 

Geivanidis, S., Jamson, S., & Forward, S. (2022). D3.6: Final results of the pilot projects in terms of user feedback and user 
awareness. Project uCARe. 

INFRAS, TUG, ifeu, HSDAC, WSP. (2021). HBEFA. Handbook of emission factors for road transport 4.2. 

Opetnik, M., & Hausberger, S. (2022). Deliverable D2.2: Software for online and for trip analysis in post-processing with car 
owner guidelines. Project uCARe. 

218 

 
 
 
 
Impact of uCARe measures on air quality in selected cities  

E. Johansson1*, R. van Gijlswijk2, B. Schäppi3 and B. Notter3 

1 IVL Swedish Environmental Research Institute, Gothenburg, SE-41133, Sweden, emelie.mm.johansson@ivl.se  
2 Netherlands Organisation for Applied Scientific Research TNO, 2595DA The Hague, The Netherlands  
3 INFRAS, Consulting, Analysis & Research, 8045 Zurich, Switzerland 

Introduction 

With approximately four million annual premature deaths due to outdoor pollution, improvement of air quality 

have a large effect on air quality, specifically passenger cars and commercial vehicles and to a lesser extent 
non-road  mobile  machinery.  While  technical  improvements  and  more  stringent  legislation  have  led  to  a 
significant  improvement  of  air  quality,  traffic  and  transport  emissions  are  still  a  major  contributor  to  air 
pollutant concentrations in cities, and air quality along roads with heavy traffic is still poor. Although the use of 
electric and other zero-emission propulsion technologies may drastically reduce the pollutant exhaust emissions 
from traffic, the slow introduction of such vehicles as well as the trend of increasing vehicle lifetimes means 
that vehicles with internal combustion engines are expected to dominate the fleet beyond 2030. In the uCARe 
project, opportunities are investigated to reduce emissions from vehicles, not by improving vehicle technology, 
but by actively involving vehicle users and enabling their contribution to clean driving by giving feedback on 
their driving style and vehicle use pattern. In this deliverable, the effect of these behavioural measures on local 
air quality in three different cities is quantified.  

Study areas and scenarios 

The three studied cities were Zurich, Gothenburg and Amsterdam. The number of inhabitants, and also the total 
number of vehicle kilometres, is largest in Amsterdam, then Gothenburg and Zurich. Large emission sources are 
for all three cities road transportation, industrial activities, and households. Gothenburg and Amsterdam also 
have large harbours, which contribute to the emissions. For each city, three different emission scenarios were 
investigated for two different years, resulting in six scenarios for each city. The studied years are 2019/2020 
(2019 for Gothenburg and 2020 without the pandemic for the other cities) and 2030.  

The emission scenarios consist of a baseline scenario (which assumes business as usual), a best-case scenario, 
which  assumed  an  ideal  implementation of  the  studied  uCARe  measures,  and a  most-likely scenario,  which 
assumed a more likely implementation of the measures. 

The studied pollutants were nitrogen dioxide (NO2) and particulate matter (PM10 and PM2.5). For Gothenburg, 
PM10 was not included, due to lack of emission factors. Therefore, NO2 and PM2.5 will be the focus here. 

Models 

INFRAS  and  Meteotest)  was  applied.  The  latest  version  stems  from  July  2020  (Heldstab  et  al.,  2020).  For 
Gothenburg,  ADMS-Urban  was  used  (Cambridge  Environmental  Research  Consultants,  2020),  and  for 
Amsterdam, the air quality model applied was an interactive version of SRM-1 and SRM-
and Wesseling, 2020). Since all the models are Gaussian/quasi-Gaussian, the emission dispersion was modelled 
in the same way for all cities. 

For Gothenburg, the only emissions included were road transport emissions, while for Zurich and Amsterdam, 
other emission sources such as industrial processes and households were also accounted for. This could lead 
to  an  underestimation  of  the  air  pollutant  concentration  in  Gothenburg,  but  the  contribution  of  these  other 
emissions is assumed to be relatively small along the road network. The road emissions are specified on a grid, 
and this grid has a finer resolution for Zurich and Amsterdam, than for Gothenburg. For Zurich, the resolution is 
20 m for road transportation and 100 m for all other emission sources. For Amsterdam, the resolution in 10 m 
next to urban roads and larger in other areas, while the resolution is 50 m for Gothenburg. For Amsterdam, the 
effect of street canyons was included, meaning that emission dispersion could be limited by buildings. 

All three models take roughly the same meteorological input data, and the chemistry scheme is the same (the 
photochemical reactions between NO, NO2 and O3). 

219 

 
 
 
 
Road transport input data 

Two different road transport emission models were used to calculate the emission factors. For Gothenburg and 
Zurich, the Handbook of Emission Factors for Road Transport, Version 4.2 (Notter et al., 2022) was used. For 
Amsterdam, VERSIT+ was used, emission factors SRM version 2022-03-29, method description report version 
2022 (Geilenkirchen et al., 2022). 

were derived, where the relative improvements 
are expressed in % of the default average emission factors for passenger cars, for the two evaluated scenarios. 
spond to 

-

-

scenario, and to a more like
patterns before and after training in the uCARe pilot studies and on surveys.  

-

-

Emission factors from HBEFA 4.2 were corrected by considering effects from aging and temperature, and then 
the delta EFs were applied to these corrected emission factors for each road link, resulting in absolute emission 
factors for each road link.  

Table 1: Summary of individual interventions and intervention groups. 

Intervention 

Abbrevation 

Intervention group 

Overall intervention 

Default driving by untrained drivers  Default 

Default 

Default 

Correct 
efficient tires 

tire  pressure/use  most 

Tire 

Maintenance 

Best-case, Most-likely 

Remove roof boxes, no extra load etc.  Load 

Best-case, Most-likely 

Reduce A/C use 

Optimal shifting  

Avoid excessive speed 

AC 

Shift 

Speed 

AC 

Best-case, Most-likely 

Ecodrive 

Best-case, Most-likely 

Best-case, Most-likely 

Accelerate smoothly 

Acceleration 

Best-case, Most-likely 

Brake gently/use engine brakes 

Brake 

Best-case, Most-likely 

Do not idle more than 30 seconds 

Idling 

Idling 

Best-case, Most-likely 

Avoid heavy traffic 

Traffic 

Traffic 

Best-case, Most-likely 

Avoid cold starts 

Cold start 

Cold start 

Best-case, Most-likely 

Avoid unnecessary driving 

Drive less 

Purchase  new  cleaner  and  more 
efficient car 

New car 

Results 

In Figure 1, a comparison of the relative reduction of NOx and PM2.5 road transport emissions is shown. The 
reduction  is  largest  for  Zurich,  followed  by  Gothenburg,  and  smallest  in  Amsterdam.  The  largest  emission 
reductions are seen for the best-case scenario, with a -31% change in NOx emissions in Zurich 2020 and a -
49% change in PM2.5 emissions in 2030, also for Zurich.  

220 

 
 
 
 
 
 
 
 
 
 
 
The emission reductions also resulted in a reduction in air pollutant concentration, see Figure 2. The largest 
reductions are seen for Zurich and for the best-case scenario for 2020 (NO2: -14% and PM2.5: -7%) and the 
smallest differences are seen for Amsterdam. 

Figure 1: 
and years. 

Comparison of reduction in road transport emissions in the three cities for different scenarios 

Figure 2: 
scenarios. 

Comparison of reduction in average air pollutant concentration for three cities for different 

In the baseline scenario for 2019/2020, the annual average concentration exceeds the limit value for NO2 (40 
µg/m3,  30  µg/m3  in  Switzerland)  in  some  locations  for  all  three  cities  (mostly  near  the  road  networks).  The 
reductions  that  are  observed  for  the  other  scenarios  are  most  noticeable  along  the  road  networks  and  will 
prevent most of these occurrences. Regardless of limit values, any reduction of long-term average concentration 
levels of NO2 and particulates is beneficial to human health.  

The difference in air pollutant concentrations compared to the baseline scenario is visualised in Figure 3 for 
NO2 and in Figure 4 for PM2.5. Focusing on the 95th percentile, the maximum differences for NO2 are 5.6 µg/m3 
(Zurich),  2.1  µg/m3  (Gothenburg)  and  0.53  µg/m3  (Amsterdam).  For  PM2.5,  the  maximum  differences  are  1.2 
µg/m3  (Zurich),  0.26  µg/m3  (Gothenburg)  and  0.06  µg/m3  (Amsterdam).  For  both  pollutants,  the  maximum 
differences are seen for scenario best-case minus baseline for 2019/2020. 

The reductions are typically higher for Zurich than for the other cities. In the model for Zurich, the effect of 
measures  on  the  background  concentration  is  considered.  This  is  believed  to  be  one  reason  to  the  higher 
reduction in the Zurich case.  As mentioned earlier is the effect of street canyons considered for Amsterdam. 
This could lead to a large reduction within the street canyon, while it is smaller in the surrounding area that is 
shielded by the buildings. In addition, the general air pollutant concentration levels are lower in Gothenburg 
than in Zurich. Therefore, an equal relative change will result in a smaller absolute change in Gothenburg.  

221 

 
 
 
 
(a) 

(b) 

Figure 3: 
2019/2020 (a) and 2030 (b). 

Differences in NO2 concentrations with respect to the baseline scenario for all three cities in 

222 

 
 
 
 
 
 
(a) 

(b) 

Figure 4: 
2019/2020 (a) and 2030 (b). 

Differences in PM2.5 concentrations with respect to the baseline scenario for all three cities in 

Discussion 

The impact of the considered consumer behaviour measures on average annual air pollutant concentrations 
was modelled for the cities of Zurich, Gothenburg and Amsterdam. The results show varying effects for these 
cities and this variation can partially be accounted for by differences in the models used.  The models differ 
primarily  in  the  following  aspects:  the  impact  of  interventions  on  background  concentration  and  how  street 
canyons are considered. 

If  measures  are  implemented  in  neighbouring  cities  and  other  countries,  the  measures  will  affect  the 
background  concentration  as  well.  For  Zurich,  it  is  assumed  that  the  share  of  road  transportation  in  the 
background concentration is the same as the share of road transportation in the total direct emissions. This 
contribution is then taken into account when the new background concentration is calculated. This is a somewhat 
simplified assumption, which need to be assessed in more detail to better understand the effect of measures 
on background concentration and how imported air pollutants affect the background levels. 

Street canyons generally trap air pollution at the street level, which leads to higher concentrations along the 
roads  and  lower  concentrations  further  away.  In  the  modelling  for  Amsterdam,  this  was  included,  and  in  a 
simplified way for Zurich by using three classes of building density. For Gothenburg, it was not included at all. 
In the results for Amsterdam, it could be seen that the effect of the measures is mainly visible along the main 
roads, since there is where the pollution concentration is highest. 

223 

 
 
 
 
Conclusions and recommendations 

With the uCARe measures, the models show reductions along the road network of up to 5.6 µg NO2/m3 and up 
to 1.2 µg PM2.5/m3. Even the most-likely scenario shows substantial reductions along the main roads in all study 
areas.  These  study  areas  focus  on  the  city  centres  of  the  three  cities,  which  for  all  three  cities  is  densely 

exposure significantly. 

By just comparing the baseline scenarios between 2019/2020 and 2030, a significant reduction of both NO2 
and  PM2.5  is  observed.  The  reason  for  that  is  the  expected  change  in  the  vehicle  fleet,  to  cars  with  lower 
emissions. This means that the uCARe measures will be more effective in the short term than in the long term. 

This calls for a rapid implementation of the proposed measures and interventions. The idea of the uCARe project 
was to elaborate measures which can be implemented quickly, since new emission limits for cars, such as the 
expected EURO 7, take rather long to show full benefits due to the time needed for fleet renewal.  

This study focused on potential changes in emissions due to reduction measures performed by the driver. Part 
of these reductions may also be achieved by traffic measures (such as enabling smoother driving with fewer 
stops at red lights).  

Furthermore,  this  study  shows  the  importance  of  applying  different  types  of  models.  To  accurately  assess 
impacts  of  measures  on  air  pollutant  concentrations,  the  model  needs  to  account  for  the  effect  of  street 
canyons, and an accurate assessment of the impact on background concentrations is necessary.  

Acknowledgements  

This work was supported by the European Union, grant number (H2020 LC-MG-1-1-2018). 

References 

Cambridge 
[http://www.cerc.co.uk/environmental-software/assets/data/doc_userguides/CERC_ADMS-Urban5.0_User_Guide.pdf] 

Environmental 

ADMS-Urban 

Consultants 

Research 

(2020). 

Guide, 

User 

Cambridge. 

Deliverable D4.2 of the uCARe EU research program, GA 815002. Vrije Universiteit Brussels (VUB), INFRAS, Brussels and 
Berne.  

Geilenkirchen G. et al. (2022): Methods for calculating the emissions of transport in The Netherlands., The Hague, PBL 
Netherlands Environmental Assessment. [https://www.pbl.nl/sites/default/files/downloads/pbl-2022-methods-for-
calculating-the-emissions-of-transport-in-nl-4923_0.pdf] 

Heldstab J., Schäppi B., Künzle T. (2020). NO2-, PM10- und PM2.5-Immissionen Schweiz / Liechtenstein - Aktualisierung des 
PolluMap-Modells  für  2015,  2020  und  2030,  on  behalf  of  the  federal  office  for  the  environment  of  Switzerland. 
[https://www.bafu.admin.ch/dam/bafu/de/dokumente/luft/externe-studien-berichte/no2-pm10-pm2-5-immissionen-
schweiz-liechtenstein-aktualisierung-des-pollumap-modells-fuer-2015-2020-und-
2030.pdf.download.pdf/PolluMap_Technischer-Bericht.pdf] 

Notter B., Cox B., Hausberger S., Matzer C., Weller K., Dippold M., Politschnig N., Lipp S., Knörr  W., Allekotte M., André M., 
Gagnepain  M.,  Hult  C.,  Jerksjö  M  (2022).  HBEFA  4.2.  Documentation  of  updates.  Bundesamt  für  Umwelt  BAFU  (CH), 

(FR), Trafikverket (SE), Miljødirektoratet (NO). [https://www.hbefa.net/e/documents/HBEFA42_Update_Documentation.pdf] 

Visser  S.  and  Wesseling  J. 
[https://rivm.openrepository.com/handle/10029/624458] 

(2020).  Actualisering  en  addenda  SRM-1  en  SRM-2,  RIVM  report  2020-0118. 

World  Health  Organization 
sheets/detail/ambient-(outdoor)-air-quality-and-health] 

  Ambient 

(2022). 

(outdoor)  air  pollution. 

[https://www.who.int/en/news-room/fact-

224 

 
 
 
Nanoparticles in different environments in Stockholm, the nPETS project 

M. Norman1*, S. Silvergren1, D. Schlesinger1, M. Tu2, U. Olofsson2 

1 SLB-analys, Environment and Health Administration, City of Stockholm, S-104 20, Sweden 
2 KTH Royal Institute of Technology, Industrial Technology and Management, S-100 44, Stockholm, Sweden 

Presenting author email: michael.norman@slb.nu 

Introduction 

Nanoparticles  has  gained  increased  attention  during  recent  years.  The  World  Health  Organization  (WHO) 
summarized  the  knowledge  of  their  heatlth  effects  in  the  latest  report  (WHO,  2021).  Several  studies  have 
documented short-term effects  of exposure  to  nanoparticles,  including mortality,  respiratory  symptoms and 
long-term  effects  on  mortality  and  several  types  of  morbidity.  Based  on  this  WHO  suggests  distinguishing 
between high and low particle number concentrations (PNC). Low PNC should be considered less than 1000 
particles  [N]  per  cm3  as  24-hour  mean  and  high  PNC  as  above  10 000  [N]/cm3  as  24-hour  mean  or  above 
20 000 [N]/cm3 as hourly mean. WHO also recommends more measurements of PNC as well as measurements 
of  particle  number  size  distribution  (PNSD)  in  areas  were  people  are  expected  to  be  exposed  to  high 
concentrations. In the new EU proposal there is also suggestion for mandatory measuremenst of PNC in larger 
cities across Europe (EU, 2022). 

In Stockholm measurements of nanoparticles have been running for almost two decades, resulting in one of 
the longest available time series of ultrafine particles in Europe. The nPETS project (nanoparticles Emissions 
from the Transport Sector) aims to study the life of the sub 100 nm particles from emission to its path to 
humans. The nPETS project in Stockholm includes measurement of PNC, black carbon (BC), nitrogen oxides (NOx) 
and particle number size distribution (PNSD) among other component. Measurements of PNC and NOx were 
conducted in four different environments in Stockholm including a road tunnel (Söderledstunneln), a densely 
trafficked street  (Sveavägen), a subway station (Tekniska högskolan) and urban background (roof top) during 
both summer and winter. In addition, BC and PNSD were measured at the roadtunnel, subway station and urban 
backgound. 

Method 

Measurements of nanoparticles in Stockholm has been carried out using condensation particle counters from 
TSI (model 3022, 3775, 3752). All measurements were carried out for total particle number meaning there 
were no upper size limit on the inlet. The measurement using the model 3022 has a lower cut-off 7 nm while 
the  more  modern  models  3775  and  3752  has  a  lower  cut-off  4  nm.  All  data  from  the  3022  has  been 
recalculated to 4 nm using parallel measurements.  BC was measured by using multiwavelength aethalometers 
(Magee Scientific, model AE31 and AE33). Particle number size distribution measurements was made by a home 
built  differential  particle  sizer  (DMPS)  for  particles  with  sizes  between 10  and 450  nanometers.    Additional 
PNSD was measured with an ELPI+ at the subway, road tunnel and urban background. NOx was measured using 
Environnement SA model AC32M. 

Results 

The trend of PNC in Stockholm, Figure 1, shows a clear decreasing trend, especially in road traffic environments. 
The decrease in PNC have been shown to closely correlate with change in vehicle fleet composition and their 
exhaust emissions (Krecl et al., 2017). A more recent study also shows the differences in PNC and BC in relation 
to gasoline or diesel car and light- and heavy-duty vehicles respectively (Krecl et al., 2023). 

225 

 
 
 
Figure 1. Trend in PNC in Stockholm 

Despite the clear downward trend in PNC from Stockholm in Figure 1, concentrations above the WHO guideline 
values still occur for more than 12-hours per day as seen in Error! Reference source not found. This also 
highlight the importance of the traffic exhaust emission for the observed PNC concentrations in Stockholm. 
During  year  2022  there  were  142  days  with  average  daily  PNC  that  exceeded  the  WHO  guideline  for  high 
particle number concentrations of 10 000 /cm3. This shows the importance of monitoring the nanoparticle in 
several environments, including traffic. 

Figure 2: 
WHO guidelines.  

Average diurnal variation in PNC at an urban street (Svevägen) in Stockholm in comparison to 

Within  the  nPets  project  measurements  were  performed  in  different  environments  during  both  winter  and 
summer. The PNC was more than an order of magnitude higher in the road tunnel compared to the subway and 
urban background environments, Figure 3. The urban street data was about twice as high as subway and urban 
background, Figure 3. This shows that road traffic is the most significant source of nanoparticles in Stockholm, 
which also is supportetd by the strong correlation between PNC and NOx in the road tunnel, the urban street 
and to some extend also at the urban backgound site. In both road tunnel and urban streets both the measured 
PNC  and  NOx  concentrations  were  higher  during  winter  compared  to  summer.  This  is  probably  due  to 
meteorological situation with more stable conditions in winter. Higher PNC in winter could also be due to the 
higher particle number concentrations being produced from traffic exhaust at colder temperatures, which has 
been observed earlier in Stockholm (Olivares et al., 2007). The PNC in the subway was found to be similair to 
that in the urban background and significantly lower than road tunnel and urban street, indicating subway trains 
to be a less important source of nanoparticles than road traffic. Both the NOx and the BC (Figure 5) was higher 

226 

 
 
 
 
in winter than in summer. This is most likely due to the difference in ventilation at the subway station between 
summer and winter, with less ventilation in winter. 

The PNC was higher in summer at the urban background, in contrast to the two traffic sites, this is discussed 
more below. 

Figure 3. PNC (>4 nm) in four different environments in Stockholm. Median with 25 and 75 percentile. 

Figure 4. NOx in four different environments in Stockholm. Median with 25 and 75 percentile. 

Black carbon (BC) measurements showed a different pattern,  Figure 5, with high concentration in both road 
tunnel and subway and much lower concentrations in the urban background. The BC in the road tunnel is clearly 
from the road traffic due to the strong correlation with NOx (NOx shown in Figure 4, but the correlation is not 
shown). Traffic is probably also the dominating source for BC at the urban backgound site due to the correlation 
with NOx, but other sources are more important in the urban background. High concentration of BC is also found 
in  the  subway  environment  although  the  NOx  there  is  low  (Figure  4).  This  could  be  explained  by  dark 
mechanically  generated  paricles  in  the  subway  that  is  measured  by  the  Aethelometer  as  BC.  The  BC 
concentration was higher during winter for all environments. 

227 

 
 
 
 
 
 
Figure 5. BC in three different environments in Stockholm. Median with 25 and 75 percentile.  

The PNSD from a DMPS (differential mobility particle sizer) in the urban background environment,  Figure 6, 
exhibits a difference between the summer and winter measurements. The maximum PN was found at sizes 50-
60 nm during summer, while the winter maximum peak was found at 30-40 nm. Also the PNSD showed higher 
concentration in summer which is in line with PNC in Figure 3. The reason for the difference in median size 
distribution and the higher number in summer is not known. Some potential factors could be a larger influence 
from  biogenic  particles  in  summer  at  larger  sizes  than  exhaust  particles.  Volatile  organic  carbons  and 
photochemistry in summer could also influence as well as meteorological factors, such as relative humidity. 

Figure 6. Particle number size distribution from DMPS at urban background in Stockholm. Median with 25 and 
75 percentile. 

The  PNSD  at  the  urban  background  occasionally  show  strong  variability  during  short  timescales.  This  is 
illustrated with the PNSD during two days in October in 2022 in Figure 7. Between those two days a strong 
decrease in particles was observed around 100 nm and instead a significant peak at smaller sizes around 30 
nm occurs. During the 16th of October a heavy rainfall occurred which could be caused by a change in air mass. 
The >100 nm particles were either washed out by the rain or new air mass caused the decrease. When the 
>100 nm particles vanished the primary particles and gases from the local traffic likely produced particle that 
resulted in at peak at around 30 nm. 

228 

 
 
 
 
Figure 7. Particle number size distribution at urban background in Stockholm during 15th and 17th October 
2022. Median with 25 and 75 percentile. 

The PNSD at the urban background occasionally indicated new particle formation as shown in Figure 8. A sudden 
increase in particles around 10 nm occurs in the morning of the 3rd of May that is followed by an increase in 
both size and particle number during the day. 

Figure 8. Evolution of the PNSD during the 3rd and 4th of May 2022 in urban background. 

Additional  PNSD  was  measured  by  an  ELPI+  at  the  subway,  the  road  tunnel  and  urban  background  during 
summertime. The PNSD obtained with the ELPI+ measured in the roadtunneln and in the urban background 
shows a peak around 15 nm. This indicates the same source and that the urban background to a large extent 
is influenced by road traffic. The size distribution from the subway shows much lower concentrations but also 
relatively larger fraction of particles larger than 100 nm compared to the other sites. The particles in the subway 
are mechanically generated in contrast to the particles in the exhaust fron traffic which causes the PNSD to be 
shifted towards larger sizes (Tu, 2023). 

The PNSD from the DMPS and the ELPI+ are slightly different from each other at the urban background were 
they measured in parallell. This difference is not analysed in detail, but the different measurement techniques 
of the instruments as well as different inlets could be some reasons. 

229 

 
 
 
 
Figure 9. PNSD measured by the ELPI+ during summer at the road tunnel (Road), subway (Metro) and urban 
background. 

Summary 

Nanoparticles  from  the  transport  sector  has  been  studied  in  Stockholm  within  the  nPets  project.  The 
concentration  of  nanoparticle  has  decreased  significantly  in  Stockholm  the  last  20  years.  Despite  this  are 
concentrations above  the WHO guidelines still common in Stockholm. The measurements showed that  road 
traffic  exhaust  is  the  major  source  for  nanoparticles  in  Stockholm  and  significantly  higher  particle  number 
concentrations were measured in road traffic environments in comparison to for example subway and urban 
background. The nanoparticles in the subway also showed a different size distribution indication other major 
source.  Black  carbon  and  NOx  are  also  emitted  from  road  traffic  exhaust  and  correlates  with  the  particle 
numbers measured. High BC concentrations were measured in the subway and is explained by dark mechanically 
generated paricles in the subway that is measured as BC. 

Acknowledgements  

References 

(2022). 

EU 
https://environment.ec.europa.eu/publications/revision-eu-ambient-air-quality-legislation_en 

Proposal 

Ambient 

revision 

the 

for 

of 

a 

Air 

Quality 

Directive. 

Krecl P., Johansson C., Creso A., Ström J., and Burman L. (2017). Trends in black carbon and size-resolved particle number 
concentrations and vehicle emission factors under real-world conditions. Atmospheric Environment 165 (155-168). 

Krecl P., Johansson C., Norman M., Silvergren S., and Burman L. (2023). Long-term trends of black carbon and particle 
number concentrations and vehicle emission factors in Stockholm. Extended abstracts, Transport and Air pollution 
Conference, Gothenburg 2023. 

nPets project, https://www.npets-project.eu/ 

Olivares G., Johansson C., Ström J., and Hansson H.-C. (2007). The role of ambient temperature for particle number 
concentrations in a street canyon. Atmospheric Environment 41 (2145-2155).  

Tu M., and Olofsson U. (2023). Train type effect on nanoparticles on an underground metro platform in Stockholm. Extended 
abstracts, Transport and Air pollution Conference, Gothenburg 2023. 

WHO (2021). WHO global air quality guidelines. https://www.who.int/publications/i/item/9789240034228 

230 

 
 
 
 
 
 
 
 
 
Quantification of Temperature Dependence of NOx emissions from road traffic in 
Norway using Air Quality Modelling and Monitoring data 

E. G. Wærsted1*, I. Sundvor2, B. R. Denby1 and Q. Mu1+ 

1 Norwegian Meteorological Institute, Henrik Mohns plass 1, 0313, Oslo, Norway, eivindgw@met.no 
2 Norwegian Institute of Transport Economics, Gaustadalléen 21, 0349, Oslo, Norway 
+ 
Suzhou, China 

-Liverpool University, 215123, 

Introduction 

This study investigates the temperature dependence of NOx emissions from road traffic in Norway by comparing 
modelled and observed concentrations at roadside air quality monitoring stations in the period 2016 2019. The 
results suggest that the emissions are 3.3 times higher at temperatures below -13 °C than at high temperatures, 
and 2.7 times higher at -7 °C, with the increase starting at around +12 °C. The temperature range and magnitude 
of  this  temperature  dependence  are  consistent  with  the  existing  literature  on  emission  measurement 
experiments performed on various models of diesel vehicles. This extended abstract is an abbreviated version 
of the paper Wærsted et al. (2022) published in Atmospheric Environment X. 

Emissions of nitrogen oxides (NOx) from road traffic are dependent on a range of factors including vehicle type, 
speed, driving patterns and engine temperature (e.g. Söderena et al., 2021). Recently a number of studies have 
indicated that ambient air temperature plays an important role in vehicle NOx emissions, mainly due to various 
technical challenges of exhaust after-treatment systems of diesel vehicles of Euro 4/IV and newer that occur 
at low ambient temperatures (e.g. Grange et al., 2019; Ko et al., 2017; Weber et al., 2019) (see Wærsted et al. 
(2022) for a more comprehensive discussion). 

This study aims to derive a correction formula to account for this temperature dependence when calculating 
emissions from road traffic in Norway at hourly resolution. NOx emissions from the Norwegian traffic fleet are 
dominated by diesel vehicles of Euro 4/IV and newer: we found they comprise ca. 82 % of the emissions even 
without accounting for an increase in low temperatures (see Wærsted et al. (2022) for details). Thus, based on 
the studies performed on such vehicles, we expect to see an increase in NOx emissions of a factor 2 4 at -7 °C 
relative to high temperatures (23 °C). To find out if this occurs in practice, we study NOx observations at traffic 
sites combined with an air quality model. 

Methodology 

To find empirical evidence for the temperature dependence of emissions from the whole vehicle fleet, we use 
measured hourly NOx concentrations in the period 2016 2019 at 46 sites dominated by road traffic sources. 
We  then  compare  with  the  NOx  concentrations  calculated  with  a  chemistry-transport  model  without  any 
temperature dependence in its road traffic emissions and study the bias as function of temperature. 

We use NOx observations from the network of air quality monitoring stations in Norway, including all road traffic 
stations (39) and all urban background stations except two stations that are importantly influenced by shipping 
or  industry  (7  stations).  Monitoring  data  have  been  quality  controlled  and  aggregated  to  hourly  mean 
concentrations by the Norwegian National Air Quality Reference Laboratory, and we obtained them through a 
public API (https://api.nilu.no). 

NOx concentrations are modelled for each hour at the location of each monitoring station using the EMEP/uEMEP 
modelling system. The model setup is similar to the one used in the Norwegian operational air quality forecasts, 
which is described in section 4 of Denby et al. (2020). In short, the concept is to use Gaussian plume modelling 
(uEMEP model) for nearby emission sources (within a 10 km x 10 km square around the station) and gridded 
concentrations from the EMEP MSC-W chemistry transport model (EMEP model) (Simpson et al., 2012) at 2.5 
km  resolution  for  sources  further  away.  In  this  way,  the  model  system  can  capture  fine-scale  hot-spots  in 
concentrations around emission sources such as roads. 

We apply detailed national datasets for four GNFR emission sectors: industry (B), residential combustion (C), 
road traffic (F) and shipping (G). Only the emissions from these sectors are included in the Gaussian plume 
modelling. Emissions from all other sectors are taken from the CAMS-REG-AP_v1.1 dataset. The road traffic 

231 

 
 
 
emissions are based on traffic volumes of light and heavy vehicles from the national road database (NVDB) 
and constant emission factors calculated for the whole country using the HBEFA emission model. 

Hourly  temperature  is  interpolated  to  each  station  from  the  gridded  temperature  analysis  dataset  of  the 
Norwegian Meteorological Institute at 1 km resolution (Met Nordic analysis). 

See Wærsted et al. (2022) for a more detailed presentation of the methodology and more references. 

Results 

Figure 1 compares modelled and observed concentrations of NOx over the four years. The model underestimates 
NOx  concentrations  significantly  at  most  stations,  particularly  at  the  traffic  stations.  The  bias  has  a  clear 
seasonal  cycle:  the  NOx  concentrations  are  higher  in  winter  than  in  summer  both  in  the  model  and  in 
observations, but the magnitude of this seasonal cycle is much stronger in the observations. In summer there 
is only a weak negative bias, while this bias is about a factor two in winter. 

Figure 1a also shows how much of the modelled NOx is locally emitted (within 10 km x 10 km) by each emission 
sector. The majority comes from local road traffic exhaust (78 %), while local industry, shipping and wood-
burning contribute very little. The category "EMEP non-local" is the second-most important contribution to NOx 
(19 %) and comprises all emissions outside the 10 km x 10 km domain as well as GNFR sectors other than B, 
C, F and G. 

Statistics of observed and modelled NOx concentrations in the period 2016 2019: Subplot a 

Figure 1: 
shows the monthly time series of observed concentration, and the modelled concentration, which is divided 
into sector contributions, and temperature from Met Nordic analysis. Data have been aggregated by averaging 
over all stations with available observations at each hour, and then taking the monthly mean. Subplot b 
shows the observed vs. modelled concentration averaged over the 4-year period at each station and 
indicating the type of station and data coverage (stations with less than 25 % coverage are not shown). 

Figure 2a shows the NOx concentration plotted against temperature in the model and observations, averaged 
over all data at all the stations. There is a clear temperature dependence in the observations in the range from 
about -10 °C to +10 °C. The modelled concentrations also have a temperature dependence in this range, but it 
is much weaker. 

The ratio of observed to modelled mean concentration at each temperature is plotted in Figure 2b. Above ca. 
15 °C this ratio is close to 1 and constant with temperature. Below 15 °C the ratio increases close to linearly 
with  decreasing  temperature  until  ca.  -14  °C,  below  which  the  increase  is  much  weaker.  At  the  lowest 
temperatures the ratio is close to 3. An indication of the variability of the observed-to-modelled concentration 
ratio in the dataset within each temperature bin is also shown in Figure 2b. It confirms the gradual increase 
when temperatures decrease that is seen in the ratio of mean concentrations. 

232 

 
 
 
 
Statistics of observed and modelled NOx concentration vs. analysis temperature in 2016 2019 

Figure 2: 
at 46 monitoring stations: (a) For each 2 °C temperature interval ([-20, -18), [-18, -16), etc.), all data with 
analysis temperature within that range have been averaged, weighing each hour with data at each station 
equally. The average observed NOx concentration is shown as a circle for each temperature bin. The mean 
modelled NOx concentration in each bin is indicated with the solid line, under which the shaded areas indicate 
the sectoral contributions. (b) For each temperature bin, the black dot shows the ratio of the observed and 
modelled mean NOx concentrations. The shaded area indicates the interquartile range (IQR) of observed-to-
modelled ratio of single hourly data values in each bin. (c) The number of hourly data values contained in 
each temperature bin. Only bins with at least 100 data values are plotted in (a) and (b). 

Discussion 

The large deviation between modelled and observed NOx concentrations at lower temperatures can be the result 
of a number of factors. If it is chiefly the result of increased road traffic emissions of NOx at low temperatures, 
then  the  pattern  in  Figure  2b  corresponds  well  with  the  effect  of  more  and  more  vehicles  having  reduced 
efficiency  of  the  exhaust  aftertreatment  system  and  driving  more  in  cold-phase.  The  levelling-off  at  high 
temperature is interpreted to happen because the exhaust aftertreatment systems function well for all vehicles 
when it is sufficiently warm, and the levelling-off at low temperature because below a certain temperature all 
the vehicles will have reached the most sub-optimal situation. 

We investigated the temporal and spatial variability of the temperature-dependent bias and found a similar 
pattern for different years, seasons and stations, although there is some variability (for details, see Wærsted 
et al., (2022)). We concluded that the temperature signal in NOx bias is really related to temperature and not to 
spatial or temporal patterns that correlate with temperature. 

An alternative explanation for the temperature-dependent bias is that the model systematically overestimates 
dilution at low temperature, leading to underestimated concentrations even with no bias in emissions. Dilution 
of pollutants can be reduced on cold days because low temperatures often occur as a result of high-pressure 
situations in the winter when clear skies and little wind lead to a stable boundary layer that inhibits turbulent 
mixing. In areas where pollution is emitted near the surface, both low wind speed and little vertical mixing will 
limit dilution and lead to elevated concentrations. Gryning et al. (1987) pointed out the difficulties in Gaussian 
plume modelling under stable conditions and this remains a problem for these types of models. 

233 

 
 
 
 
To check this hypothesis, we investigated the stability regime and the wind speed bias as alternative explanatory 
variables. In Figure 3, we have categorised the dataset into periods with stable, neutral and unstable conditions, 
using the modelled Obukhov length. Assuming that the stability regime is correctly modelled, we see that for 
temperatures below 6 °C, the underestimation of NOx by the model is slightly larger in stable conditions than in 
neutral or unstable conditions. The occurrence of stable conditions also rises with decreasing temperature. This 
might indicate that some of the temperature-dependent bias stems from too much vertical mixing in stable 
conditions; however, it could also be that the model overestimates the vertical mixing in unstable conditions. It 
should also be noted that below -10 °C, the underestimation of the NOx concentration is as strong in the unstable 
and neutral regimes as in the stable regime. By comparing the observed and modelled wind speed as function 
of temperature at synoptic stations near the air quality stations, we found there was no systematic bias in wind 
speed at low temperatures (see Wærsted et al. (2022) for details). These results indicate that overestimated 
dilution at low temperatures is not the main cause for the underestimation of NOx concentrations, so most of 
the signal should come from temperature dependence in NOx traffic emissions. 

Figure 3: 
modelled Obukhov length L: Stable (0 m < L < 200 m), neutral (|L| > 200 m), unstable (-200 m < L < 0 m). 

Same as Figure 2b-c, but plotted separately for different stability regimes, defined by the 

Yet another way to check dilution vs. emissions as a cause for the bias is to consider the coarse particulate 
matter  (sizes  between  2.5  and  10  µm,  PMco).  In  Norway,  road  traffic  non-exhaust  particles  are  the  most 
important local source for PMco, particularly at traffic stations. We carried out a similar analysis for PMco as for 
NOx and found no temperature-dependent bias (see details in Wærsted et al. (2022)). 

Temperature correction formula 

If we attribute the temperature-dependent bias to increased road traffic emissions at low temperatures, we 
can derive a temperature-dependent correction factor to be applied to the road traffic emissions. Since not all 
the NOx comes from road traffic, we first need to isolate the road traffic contribution. This contribution is the 

traffic. 

-

In a separate model run where we studied contributions from abroad, we found that sources outside Norway 
contribute ca. 1 µg/m3 to NOx on average (i.e. about 15 % of the non-local). In another run where we used a 
larger  and  more  detailed  source  tracking  area,  we  found  that  road  traffic,  shipping,  industry  and  off-road 
machinery all give contributions of similar importance to the non-local contribution from within Norway. Based 
on these two runs, we estimate that about 20 % of the non-local contribution comes from road traffic in Norway. 

The NOx sources other than road traffic in Norway are assumed to have no temperature-dependent bias. We 
also neglect any road traffic contribution to the small negative bias that can be seen at high temperatures in 
Figure 2a. From these assumptions, we can calculate the observed (Otra) and modelled (Mtra) total road traffic 
contributions to NOx in each temperature interval in Figure 2: 

𝑀𝑡𝑟𝑎 = 𝑀𝑡𝑟𝑎,𝑙𝑜𝑐𝑎𝑙 + 𝑓𝑡𝑟𝑎,𝑛𝑙𝑀𝑛𝑙 

234 

 
 
 
 
𝑂𝑡𝑟𝑎 = 𝑂𝑡𝑜𝑡 − (𝑀𝑡𝑜𝑡 − 𝑀𝑡𝑟𝑎 − 𝜀𝑤𝑎𝑟𝑚) 

Where Mtot and Otot are the total modelled and observed NOx concentrations, respectively, Mtra,local and Mnl are 
the modelled local road traffic and EMEP non-local contributions, respectively (shaded in Figure 2a), ftra,nl = 0.2, 

warm = -4.25 µg/m3 is the mean bias at temperatures above 14 °C. 

Figure 4 shows Otra and Mtra plotted in each temperature interval and the ratio between them. We applied curve-
fitting to the line in Figure 4b by imposing a linear decrease between two temperatures T0 and T1, from a value 
f0 for temperatures T < T0 to exactly 1 for T > T1. Optimized values for the parameters are f0 = 3.28, T0 = -12.9 
°C, and T1 = 12.4 °C. Road traffic NOx emissions can be multiplied by this parametrization in order to correct for 
the temperature dependence. 

Figure 4: 
instead of the total observed and modelled concentrations. 

As Figure 2a-b, but plotting only the estimated road traffic contributions to NOx (Otra and Mtra) 

The correction implies that road traffic emissions are 2.7 times higher at -7 °C than at the highest temperatures. 
The correction is applied to the emissions from the vehicle fleet as a whole. If we included only the Euro 4/IV or 
newer  vehicles,  which  contributes  82  %  of  road  traffic  emissions  in  warm  conditions,  then  the  increase  in 
emissions at -7 °C for these vehicles must be a factor 3.1 in order to produce the same effect. This increase is 
within the range of 2 4 that was expected based on the experimental studies. 

Conclusion 

By comparing observed and modelled NOx concentrations over a 4-year period at 46 Norwegian monitoring 
stations, we found a clear temperature dependence in the bias. The average observed-to-modelled ratio of NOx 
concentration increases with decreasing temperature, from 1.09 above +14.0 °C to 2.90 below -13.4 °C. Both 
below and above this range the ratio is approximately constant with temperature. This temperature dependence 
is quite similar between stations, seasons and years. Our analysis indicates that road traffic emissions must 
cause most of the temperature dependence, rather than dilution bias in the model. 

Based on these results, a correction formula for the NOx traffic emissions was derived. The correction factor is 
1 at high temperatures and 3.28 at low temperatures, with a linear increase in the range from +12.4 °C to -
12.9 °C. This implies that road traffic emissions are 2.7 times higher at -7 °C than in warm conditions, and that 
the emissions continue to increase with decreasing temperature also below -7 °C. Since the correction is derived 
empirically, it might not be directly transferable to other time periods or areas, but if the vehicle fleet is similar 
it is likely to be a good approximation. 

Acknowledgements  

This work was supported financially by the Norwegian Public Roads Administration (Statens Vegvesen). 

235 

 
 
 
 
 
References 

Denby,  B.R.,  Gauss,  M.,  Wind,  P.,  Mu,  Q.,  Wærsted,  E.G.,  Fagerli,  H.,  Valdebenito,  A.,  Klein,  H.  (2020).  Description  of  the 
uEMEP_v5 downscaling approach for the EMEP MSC-W chemistry transport model, Geoscientific Model Development, 13, 
6303 6323, https://doi.org/10.5194/gmd-13-6303-2020 

Grange, S.K., Farren, N.J., Vaughan, A.R., Rose, R.A., Carslaw, D.C. (2019). Strong temperature dependence for light-duty diesel 
vehicle NOx emissions, Environmental Science & Technology, 53, 6587 6596, https://doi.org/10.1021/acs.est.9b01024  

Gryning, S.E., Holtslag, A.A.M., Irwin, J.S., Sivertsen, B. (1987). Applied dispersion modelling based on meteorological scaling 
parameters, Atmospheric Environment (1967), 21, 79 89, https://doi.org/10.1016/0004-6981(87)90273-3  

Ko, J., Jin, D., Jang, W., Myung, C.L., Kwon, S., Park, S. (2017). Comparative investigation of NOx emission characteristics from 
a Euro 6-compliant diesel passenger car over the NEDC and WLTC at various ambient temperatures, Applied Energy, 187, 
652 662, https://doi.org/10.1016/j.apenergy.2016.11.105  

Simpson,  D.,  Benedictow,  A.,  Berge,  H.,  Bergström,  R.,  Emberson,  L.D.,  Fagerli,  H.,  Flechard,  C.R.,  Hayman,  G.D.,  Gauss,  M., 
Jonson, J.E., Jenkin, M.E., Nyíri, A., Richter, C., Semeena, V.S., Tsyro, S., Tuovinen, J.P., Valdebenito, Á., Wind,  P. (2012). The 
EMEP  MSC-W  chemical  transport  model 
  technical  description,  Atmospheric  Chemistry  and  Physics,  12,  7825 7865, 
https://doi.org/10.5194/acp-12-7825-2012  

Söderena, P., Laurikko, J., Weber, C., Tilli, A., Kuikka, K., Kousa, A., Väkevä, O., Venho, A., Haaparanta, S., Nuottimäki, J. (2020). 
Monitoring  Euro  6  diesel  passenger  cars  NOx  emissions  for  one  year  in  various  ambient  conditions  with  PEMS  and  NOx 
sensors. Science of the Total Environment, 746, https://doi.org/10.1016/j.scitotenv.2020.140971  

Weber, C., Sundvor, I., Figenbaum, E. (2019). Comparison of regulated emission factors of Euro 6 LDV in Nordic temperatures 
and  cold  start  conditions:  diesel-  and  gasoline  direct-injection,  Atmospheric  Environment,  206,  208 216, 
https://doi.org/10.1016/j.atmosenv.2019.02.031  

Wærsted, E.G., Sundvor, I., Denby, B.R., Mu, Q. (2022). Quantification of temperature dependence of NOx emissions from road 
traffic  in  Norway  using  air  quality  modelling  and  monitoring  data,  Atmospheric  Environment:  X,  13,  100160, 
https://doi.org/10.1016/j.aeaoa.2022.100160  

236 

 
 
 
 
 
The impact of UK Clean Air Zones (CAZs) on the observed vehicle fleet. 

1Institute for Transport Studies (ITS), University of Leeds, Leeds, West Yorkshire, LS2 9JT, England 

Ian Philips1; James Tate1. 

Introduction 

Local  Authorities  (LAs)  in  the  UK  with  persistent  exceedances  of  NO2  (nitrogen  dioxide)  concentrations  are 
required to develop and implement Local Plans to reduce these concentrations.  Local Plans include charging 
measures in the form of Clean Air Zones (CAZs) of different classes covering different vehicle types.  The classes 
 heavy goods vehicles (HGVs) and 

- 

- vans, minibuses 

standards  are Euro  6/VI for diesel vehicles and Euro  4/IV petrol.  Additionally  non-charging policies  such  as 
support to upgrade vehicles and speed limit reductions are being implemented. A multi-year central evaluation 
of these local plans is being carried out (IPSOS MORI and ITS Leeds, 2023). It includes before and after studies 
of air quality concentrations, traffic flow data, and fleet composition data.  This paper presents the findings 
from the analysis of the fleet composition data derived from ANPR observations and changes over time to the 
end of September 2022.  Newer vehicles with higher Euro standard have lower emissions.  Changes in fleet 
composition are expected to be a factor in improved air quality, along with total flow.   

Data and Methods 

Each LA implementing a CAZ has to submit a 1-week sample of data from a representative subset of its ANPR 
cameras prior to the CAZ launch as a baseline sample.  It then submits quarterly samples.  LAs not implementing 
a CAZ are asked to submit a 1-week sample of data on an annual basis.  The Department for Transport Vehicle 
Statistics  Team  join  the  ANPR  sample  data  to  the  DVLA  stock  table  database  (Google  Cloud  Platform  SQL 
database with R used as an interface) to which they have access, using the VRM (number plate) as the linking 
field.  To establish the Eurostandard, they cross reference vehicle make and model from the DVLA database to 
other  databases,  firstly  the  Society  of  Motor  Manufacturers  and  Traders  (SMMT)  dataset  purchased  under 
licence26.  The Allgemeiner Deutscher Automobil-Club (ADAC) vehicle classification database27, which allows the 
specific Euro 6 designation of light-duty vehicles to be established (Euro 6a/b, Euro 6c, Euro 6dtemp and Euro 
6d). In the event no matches are found on these databases, year of first registration and body type are used to 
infer Eurostandard.  Some vehicles (Mainly Busses and HGVs) have been retrofitted to reduce tailpipe emissions.  
Retrofitted vehicles are classed as Euro 6/IV for CAZ compliance purposes.  Retrofit status is established by 
referencing the Clean Vehicle  Retrofit Accreditation Scheme  (CVRAS)28
report vehicles registered as taxis or private hire vehicles29. 

Data is anonymised by replacing the VRM with a unique identifier for each 1-week sample, unique IDs are not 
carried over between quarterly samples.  The data used in this paper are based on the anonymised fleet data 
(AFD)  and  not  raw  ANPR  data.  AFD  data  are  processed  using  www.R-project.org.  tidyverse,  data_table  and 
ggplot2 are the principal packages used to process and visualise the changing trend in compliance. 

The Emissions factor Toolkit (EFT) (DEFRA, 2021) used the observed fleet data, the AFD, as an input to estimate 
the relative change in traffic related emissions. In the absence of speed data for specific links the average 
30.  Average emissions per vehicle km 
driven  (Emission  Factor,  EF  grams.km-1)  are  calculated  for  weekdays  and  weekends  and  a  weighted  7  day 
average calculated. Suitable data was available for Bath, Birmingham, Leeds and Portsmouth. 

Results 

26 Further information at: www.smmt.co.uk/vehicle-data  
27 Further  information at www.adac.de  
28 Further information at www.energysavingtrust.org.uk/service/clean-vehicle-retrofit-accreditation-scheme  
29 

Regulation 2019: 

www.gov.uk  

ales) 

30 5 Department for Transport statistics, Table CGN0503a, Monthly and 12 month rolling average speeds on local 'A' roads in England, 
2022. 

average 
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1123837/cgn0503.ods  

September 

January 

'Urban 

roads' 

of 

to 

237 

 
 
 
                                                       
 
The evolution of the vehicle fleets in Cities/Towns/Local Authorities of Bath (Bath and North East Sommerset - 
BANES), Basildon, Birmingham, Bradford, Black Water Valley (BWV), Coventry, Farnham, Leeds, Portsmouth and 
Southampton are illustrated in Figure 1. The fleet changes are summarised as the % compliance with a Clean 
Air Zone (CAZ) policy, whether a CAZ is being implemented or not. The minimum CAZ standards are Euro 6/VI 
for diesel vehicles and Euro 4/IV for light-duty petrol vehicles (passenger cars and light-goods vehicles). 

The Cities of Bath, Birmingham, Bradford, Bristol, Portsmouth and Southampton have introduced CAZs and have 
provided at least one sample of data post-launch. 

rates across 

82%  to  95%,  rigid  HGVs  71%  to  82%,  LGVs  47%  to  67%.  Passenger  car  not  impacted  by  the  CAZ  policy 
improved with in step with the UK average 67% to 71%. A broadly similar before/after response was observed 
 to 
the  charging  envelope,  a  step-change  increase  in  their  compliance  rate  was  observed  from  72%  to  87%. 
-changes across Heavy-
. These findings clearly illustrate CAZ policies of different classes 
are largely only impacting the respective vehicle types they are targeting. There is only one sample (baseline) 
sample available ahead of these CAZ launches. It is expected ahead of these first samples, fleet operators, 
businesses and the public were preparing their fleets for the imminent CAZ launch dates. The full impact of the 
CAZs is therefore expected to be greater than reported. 

In  the  proceeding  samples  each  quarter  further  behavioural  responses  in  response  to  the  CAZ  policies  and 
under-lying natural fleet turn-over are observed, with Bus and Coach compliance greater than 96% in the Bath, 
Birmingham  and  Portsmouth  CAZ  samples  from  late  summer  2022,  whereas  Local  Plan  areas  not/  not  yet 
implementing a CAZ policy were in the 70   85% range. After the CAZ launches in Bath and Birmingham there 
was a strong, continued improvement in the LGV compliance rates from June/July 2021 to late summer 2022. 
In Bath for example LGV compliance rates post-CAZ launch rose from 67% to 79% over this period. 

Leeds withdrew its plan to implement a CAZ in Autumn 2020 with a fall back in bus compliance from over 90% 
in  May  2020  to  less  than  85%  in  June  2021.    Articulated  vehicles  are  used  intensively  and  high  levels  of 
reliability are required and have a high level of compliance in the CAZ areas and non-CAZ Leeds (over 90%).  
Rigid HGV compliance has increased in the CAZ LAs since implementation (Over 90% in Bath Birmingham and 
Portsmouth). Compliance has increased at a similar rate in Southampton but from a lower base.  In Leeds (non-
CAZ) there has been an improvement in compliance to just under 90%.  A strong increase in LGV compliance is seen 
in Bath and Birmingham (from around 50% pre-launch to over 75%).  Leeds has a less dramatic increase in LGV compliance 

improvement in LGV compliance has been modest and in Southampton it appears to have declined.  Birmingham is the 
only  LA  where  passenger  cars  are  subject  to  charge.    Birmingham  has  a  much  more  dramatic  increase  in 
passenger car compliance than other LAS (72% in April 2021 
 over 85% post launch).  Cars which were not 
targeted by the CAZ improved compliance in line with the UK fleet average from 67% to 71%. 

The EFT modelling of changes in average emission factor from vehicles are shown in the stacked area plots 
below (Figure 2).  Emissions reductions are more noticeable in Bath (BANES Bath and North East Somerset Local 
Authority) and Birmingham.  In Both LAs there was a clear fall in average emissions per vehicle km following 
launch in 2021, (-17.1% and -
-22%, but, in 
Birmingham the fall continues (-28.3% in July 2022). 

238 

 
 
 
 
Bus & Coach 

TAXI 

HGV articulated 

HGV rigid 

LGV 

Passenger Car 

Figure 1: Graphical representation of the fleet composition changes, the % compliance with a CAZ policy 
across LAs by vehicle type from January 2020 to end of September 2022 (where data is available). 
Note: Dashed blue lines horizontal lines annotate the EFT % compliance for end of calendar year 2021 and 2022.  (Source IPSOS MORI 
and ITS Leeds, 2023) 

239 

 
 
 
 
 
 
 
 
 
 
 
 
Figure  2:  Stacked  area  plots  showing  change  in  weekly  average  (7-day)  vehicle  NOx  emission  factor  (per 
vehicle.km-1) from the first AFD sample (taken as the baseline) as a percentage (%), segregated by vehicle type. 
(Source IPSOS MORI and ITS Leeds, 2023) 

240 

 
 
 
 
 
 
 
 
 
 
Conclusion 

CAZ implementation is associated  with  an improvement in vehicle  fleet Euro  standard for the vehicle types 
subject to the CAZ.  Modelling using the EFT based on the observed fleet suggests a significant reduction in air 
quality emissions (NOx) due to CAZs.  Baseline data may not have fully captured all fleet change that occurred 
in  the  lead  up  to  CAZ  implementation,  so  actual  improvements  in  Eurostandard  and  air  quality  may  be 
underestimated.   

The  scope  of  this  evaluation  is  examining  fleet  composition  within  Local  plan  areas,  so  it  would  not  be 
appropriate to extrapolate these findings to entire local authority districts.  Covid may act as both a confounder 
on results but also offer insights to changes in air quality under disruption (e.g. Ropkins and Tate, 2021). 

To further examine changing fleet composition trends at fine spatial scales not only within Local Plan Areas but 
more broadly may it may be useful make use of data products currently being developed, which make use of 
existing administrative data (DVLA and DVSA records) to provide spatio-temporal research ready data which 
can  be  used 
(e.g.  https://environment.leeds.ac.uk/dir-record/research-
projects/1794/cars-connecting-administrative-vehicle-data-for-research-on-sustainable-transport).  

evaluation 

purposes. 

for 

Acknowledgements 

We would like to acknowledge the support of colleagues in DEFRA / DfT working with the Joint Air Quality Unit 
particularly Anthony Walker, Thomas Parry and James Relph. 

References 

DEFRA, 2021. Emissions Factors Toolkit v11.0 User Guide. Department for Environment Food and Rural Affairs, 
London. 

IPSOS MORI, ITS Leeds, 2023. Local NO2 Plans Evaluation 2022 Annual Report. Ipsos  / University of Leeds, 
London. 
https://randd.defra.gov.uk/ProjectDetails?ProjectID=20688&FromSearch=Y&Publisher=1&SearchText=AQ0851
%20&SortString=ProjectCode&SortOrder=Asc&Paging=10#Description 

Ropkins, K., Tate, J.E., 2021. Early observations on the impact of the COVID-19 lockdown on air quality trends 
across the UK. Sci. Total Environ. 754, 142374. https://doi.org/10.1016/j.scitotenv.2020.142374 

241 

 
 
 
 
 
Estimation of Mobility and Traffic Emissions Based on Cell Phone Data 

F. Troncoso Lamaison1*, T. Vieira da Rocha1, D. Collet2, G. De Nunzio2, M. Seppecher3, J.M. André1, S. Moukhtar1 
and J. Boutang1 

1 Department of Atmospheric Pollution, Mobility & Territories, Citepa, Paris, 75 010, France 
2 IFP Energies nouvelles, Rond-point de l'échangeur de Solaize, BP 3, 69 360 Solaize, France 
3 Université Gustave Eiffel, Université Lyon, ENTPE, LICIT, F-69 518, Lyon, France 

Introduction 

Urban  air  quality  is  a  crucial  environmental  and  health  issue.  This  issue  is  also  central  in  the  fight  against 
climate change and adapting cities to this change. Road traffic and chronic congestion contribute significantly 
to atmospheric emissions in urban areas. Therefore, joint monitoring of road traffic and related emissions is an 
essential  support  for  urban  public  decision-making.  Indeed,  public  authorities  need  methods  for  quickly 
evaluating transport policies according to environmental criteria. 

Coupling dynamic traffic models with traffic-related emission estimation models is a suitable response to this 
need.  However,  integrating this  solution into  decision support tools requires characterizing urban mobility  in 
near real-time. In the context of continuously increasing cell phone usage, the data they generate, and Call 
Detail Records (CDR), appear to be an interesting alternative to traditional data, such as origin/destination traffic 
survey.  In fact,  CDR  data are  rich,  massive, with high penetration rates, in a universal format and available 
worldwide.  They  have  already  supported  critical  mobility  analysis,  such  as  origin-destination  (OD)  matrices 
estimations (Iqbal et al., 2014). However, their use for systematic traffic characterization has remained limited. 
It is mainly due to the low spatial resolution of these data and their temporal sampling rate, which is sensitive 
to communication behaviours. 

The objective of this work is to develop a mobility modelling tool based upon CDR analysis. The aim is to predict 
the variation of air pollutants and GHG emissions realistically and quickly according to traffic variables. These 
traffic  variables are  necessary to  capture  specific events  or  transport  regulation  measures  implemented by 
authorities. The tool will allow assessing ex-ante or explicit ex-post variation in emissions for each regulatory 
measure (temporary or structural) or disruptive event. The first results of the project have been successfully 
obtained for the city of Santiago de Cali (Colombia), using extensive and detailed CDR data provided by CLARO 
mobile  provider.  The  methodological  framework  for  estimating  the  traffic  variables,  needed  to  calculate 
emissions, OD matrix and total travel distances, is presented below. The CDR data have been anonymized to 
respect European Union Data Protection Rules (GDPR). 

Methodology 

This section presents the methodology to estimate air emissions related to road traffic from CDR data. The 
methodology  is  first  based  on  the  mobility  reconstruction  at  city  scale  (Santiago  de  Cali),  as  detailed  by 
Seppecher (2022). It describes the urban traffic according to dynamic OD matrices, as well as distances travelled 
by  cell  phone  users.  Then  the  flows  of  users  from  OD  matrices  are  separated  according  to  the  means  of 
transport they use, according to a methodology of Transport Mode Detection (TMD). Finally, the air emissions 
associated  with  road  traffic  from  motorized  mobility  are  estimated.  These  emissions  are  represented  in  a 
spatiotemporal resolution. 

CDR data 

The democratization of cell phone data has enabled mobility analysis based on communication data, particularly 
Call Detail Records (CDR) which collect communication events like calls, texts, or data connections. CDR data 
offers high representativity of population mobility patterns and spatial coverage due to widespread cell phone 
usage. CDR data is less intrusive from a privacy perspective compared to GPS data as for example itprovides 
protective  spatial  resolution.  In  fact,  CDR  data  is  stored  at  the  base  station  scale,  which  means  the  spatial 
resolution of the data depends on the density of the base station network. Another aspect of CDR data that 
tion behaviours. 

Though some studies have analysed CDR data, reconstructing traffic variables from it remains limited due to 
spatial and temporal constraints. To address this, researchers have focused on estimating OD flows and users' 
distances travelled to explain traffic variables, mostly considering regular mobility patterns of residents and 

242 

 
 
 
 
commuters. However, to fully characterize mobility, non-regular users must also be included (Chen et al., 2019; 
Jiang et al., 2016; Zhao et al., 2021). 

The proposed methodology aims to include non-regular users in reconstructing road traffic variables such as 
total travelled distances from CDR data at a city scale, with the modelled traffic used to estimate related air 
emissions. The methodology classifies users into residents, commuters, and visitors based on presence patterns. 
Scaling factors are estimated to represent the overall population for each category. Users are further classified 
into regular (residents and commuters) and irregular (visitors and occasional travellers) mobility patterns based 
on their entropy. The methodology is further developed in (Seppecher2022). 

For  regular  users,  their  daily  mobility  footprint  is  enriched  using  activity-based  representation  (an  activity 
corresponding to a period during which a user is identified at a specific position) and completed with missing 
activities using historical data. Trip map-matching and path assignment are then used to reconstruct spatial 
trips.  For  irregular  users,  a  subsample  of  statistically  representative  users  is  selected  based  on  daily 
completeness (minimum threshold above the collected mobility data from a user is considered complete over 
a  day),  and  their  travel  distances  are  estimated  using  a  hybrid  distance  approach  with  detour  ratios.  This 
particular methodology for irregular user is developed in (Seppecher et al., 2023). 

This methodology provides insights into both regular and irregular users' mobility patterns, enabling a more 
comprehensive  understanding  of  urban  mobility  and  its  environmental  impact.  In  order  to  estimate  the 
emissions generated from traffic variables, a transport mode detection combined with an air emissions model 
is necessary. 

Transport Mode Identification 

Transport Mode Detection (TMD) is a mandatory step to separate the observed flows of individuals according 
to the means of transport they use. In fact, this is crucial for an accurate estimation of the CO2 and pollutant 
emissions  of mobility  on a territory because  such emissions  are  very different  from one transport mode to 
another. Errors in the modal share estimation for mobility flows may lead to significant errors in the mobility 
emissions estimation.  

There is an extensive literature on Transport Mode Detection (TMD) with GPS data (Wu et al., 2016), which can 
be coupled with other data available from mobile phone sensors (such as inertial mass unit, magnetometer, 
gyroscope,  or  barometer),  Geographic  Information  System  (GIS)  data, or  based  solely  on  the  GPS  trajectory 
containing information on the position, velocity, and acceleration of individuals throughout their trips. However, 
CDR data contain much less information than GPS data, because the raw information contained in CDR consists 
of highly uncertain asynchronous positions. Therefore, GIS data is an extra dataset easily available likely to be 
coupled with CDR data, in order to add precious geographical context information. The philosophy of this TMD 
algorithm is to find the most likely portion of the transport network used during a trip, and consequently the 
most likely associated means of transport. This is done by projecting (or map matching) the reconstructed CDR 
trips per user on the different transport networks, corresponding to the different transport modes available on 
the territory under study. 

Once the trip has been map-matched over the networks for every transportation mode, it is possible to assess 
which one is the most likely to have been taken by the user. To assess this, a twofold cost function, denoted by 
𝐽, is considered: 

𝐽 = 𝐽𝑠𝑝𝑎𝑡 + 𝐽𝑚𝑜𝑣𝑒𝑚𝑒𝑛𝑡 

Where 𝐽𝑠𝑝𝑎𝑡 is a cost function assessing the similarity in space, which is small when the similarity in space 
between the trip measured and the trip mapmatched on GIS data is small, and  𝐽𝑚𝑜𝑣𝑒𝑚𝑒𝑛𝑡 is a cost function 
assessing  the  similarity  in  the  movement,  which  is  small  when  the  movement  of  the  measured  and  map-
matched trips are similar. In practice, for the CDR trips, a vector of time instants associated with each measured 
position is considered. Then, this is compared with time vectors estimated differently depending on the mode 
of transport, using for instance traffic information in the GIS data for personal transport modes (such as cars), 
and timetable data for public transport. Eventually, the TMD algorithm presented hereby aims at finding the 
transportation mode minimizing the cost function 𝐽 when compared to the corresponding measured trip. 

As  already  mentioned,  TMD  is  of  primal  importance  in  the  proposed  pollutant  estimation  strategy  because 
modal share per Origin-Destination (OD) flow is needed. To estimate the mode per OD pairs of zones, a sample 

243 

 
 
of 𝑁 trips from an OD pair 𝐴𝐵 is considered, where the 𝑖-th trip considered is denoted by 𝑥𝑖
The function 𝑇𝑀𝐷(𝑥) allows us to estimate the transport mode of the trip 𝑥. The modal part 𝜇𝑀
for the transport mode 𝑀 is thus the following: 

𝐴𝐵, with 1 ≤ 𝑖 ≤ 𝑁. 
𝐴𝐵 of the OD 𝐴𝐵 

𝐴𝐵 =

𝜇𝑀

1
𝑁

𝑁

∑ 1𝑇𝑀𝐷(𝑥𝑖

𝐴𝐵)=𝑀

𝑖=1

Where the function 1𝑥=𝑦 yields 1 when the Boolean expression 𝑥 = 𝑦 is true and 0 otherwise. 

Air emissions estimations 

After estimating the traffic variables and the modal share per OD pairs of zones, air emissions are estimated 
based on the emissions factors used in the local inventory of Cali from (CVC, 2018). These emissions factors, 
expressed in g/km for different pollutants and transport modes are presented in the following table. 

Table 1: Emission factors by air pollutant and transport mode from (CVC, 2018) 

TM 

CO (g/km) 

VOC (g/km) 

NOx (g/km) 

SOx (g/km) 

PM (g/km) 

Vehicle 

Bus 

Truck 

18,00 

8,59 

11,8 

Motorcycle 

16,00 

0,78 

1,65 

2,53 

5,00 

1,17 

15,01 

20,40 

0,99 

0,05 

0,69 

0,97 

0,02 

0,01 

0,67 

1,34 

0,21 

The emissions are then calculated in two steps. First, we calculate the emissions of regular users as: 

̅̅̅̅̅̅̅̅̅̅
𝑬𝒎𝒊𝒔𝒔𝒕,𝒎𝒐𝒅𝒆 = 𝑭𝒍𝒐𝒘𝑶𝑫𝒕 × 𝑳𝒆𝒏𝒕𝒉𝒔

𝑶,𝑫 × 𝒎𝒐𝒅𝒂𝒍 𝒔𝒉𝒂𝒓𝒆𝑶𝑫,𝒎𝒐𝒅𝒆 × 𝑭𝑬𝒎𝒐𝒅𝒆 

Where:  

  𝑬𝒎𝒊𝒔𝒔𝒕,𝒎𝒐𝒅𝒆 : emissions by mode as a function of time t,  
  𝑭𝒍𝒐𝒘𝑶𝑫𝑻 : ODT matrix from regular patterns, with T being the time, 
̅̅̅̅̅̅̅̅̅̅
  𝑳𝒆𝒏𝒕𝒉𝒔
  𝒓𝒂𝒕𝒊𝒐𝒎𝒐𝒅𝒆 : modal share per OD pair from the TMD, 
  𝑭𝑬𝒎𝒐𝒅𝒆 : Emission factors by mode from the CVC. 

𝑶,𝑫 : average OD distances resulting from the study of road network, 

The emissions of irregular users are then added to comparison with the local inventory:  

𝑬𝒎𝒊𝒔𝒔𝒕,𝒎𝒐𝒅𝒆 = 𝑻𝑻𝑫𝒊𝒓𝒓𝒆𝒈 × 𝒎𝒐𝒅𝒂𝒍 𝒔𝒉𝒂𝒓𝒆𝑶𝑫,𝒎𝒐𝒅𝒆 × 𝑭𝑬𝒎𝒐𝒅𝒆 

Where 𝑻𝑻𝑫𝒊𝒓𝒓𝒆𝒈 is the daily total travelled distance from irregular users. 

Case study: City of Cali, Colombia 

The CDR data applied in the case of this study was provided by the American communication provider CLARO. 
More  than  two  years  of  data  were  collected  but  only  two  months'  analysis  of  mobility  and  emissions  are 
presented here. The historical data length is two months of the pre-COVID-19 period (January to February 2020) 
for irregular users. For regular users, whose mobility patterns allows a daily data reconstruction, traffic variables 
were estimated for three months (January to March 2020). 

The data structure was compressed for privacy protection and transfer efficiency. The compression consisted 

communication sequence entry. The final structure is then characterized by the user id, the base station (with 
the associated location provided on a different table), the timestamps of the first and last event occurred in the 
same sequence and the number of events occurred during the sequence, as described Table 1. 

244 

 
 
 
 
Table 2: Compressed data structure example 

User ID 

Base Station 

First timestamp 

Last timestamp 

Nb of events 

A 

A 

BS1 

BS2 

09:30 

20:30 

12:15 

20:31 

4 

1 

Results 

  Origin-destination matrices 

The regular mobility methodology allowed us to determine the daily trips for the period studied. The daily trips 
are then aggregated into OD matrices that can be spatially distributed on the region level (an aggregation of 
Voronoi cells according to a certain population density and area thresholds) as on a higher scale, at the comuna 
scale as illustrated Figure 1. 

Figure 1: 

Daily Origin-Destination matrix obtained from the mobility estimation based on CDR data. 

Daily trips can be temporally distributed as illustrated in Figure 2. One can observe the impact of COVID-19 
lockdown, which started on March 23, 2020. The figure shows that there was probably some anticipation of the 
population before the first lockdown starts (from March 21). 

245 

 
 
 
 
 
Figure 2: 
The number of trips per quarter-hour for the week before and after COVID-19 (15 March 2020 
to 25 March 2020 included). Green band represents weekends and red band represents the beginning of the 
lockdown. 

  Traffic variables by mode 

In Figure 3, the modal share of the flow on one considered OD found by the TMD algorithm is illustrated.  

Figure 3: Illustration of the overall modal share in percent for one considered OD pair (between two comunas). 

  Daily and annual emission estimation 

The following figure shows the daily emissions per pollutant for the regular mobility only. The results presented 
here relate only to air pollutants. The finest granularity is 15 minutes. The CO emissions are found to be the 
highest followed by NOx emissions. This can be attributed to the high CO emission factors for motorcycles and 
vehicles, coupled with their significant modal share. 

246 

 
 
 
 
 
Figure 4: Illustration of the overall modal share in percent for one considered OD pair (between two comunas). 

Total air emissions of regular and irregular users were then summed and extrapolated for the whole year by 
multiplying total emissions over a pre-COVID week by the number of weeks in a year. By comparing it with the 
local inventory (CVC, 2018), the following differences by pollutant are found: 

Table 3: Comparison of annual emissions modelled with the local inventory.  

CO (t/year) 

COV (t/year) 

NOx (t/year) 

PM (t/year) 

SOx (t/year) 

-78% 

-75% 

-26% 

-50% 

-21% 

References 

Chen, G., Viana, A. C., Fiore, M., & Sarraute, C. (2019). Complete trajectory reconstruction from sparse mobile phone 

data. EPJ Data Science, 8(1). https://doi.org/10.1140/epjds/s13688-019-0206-8 

CVC. (2018). Actualizacion del inventario de emisiones de Santiago de Cali. 

Iqbal, M. S., Choudhury, C. F., Wang, P., & González, M. C. (2014). Development of origin-destination matrices using 
mobile  phone  call  data.  Transportation  Research  Part  C:  Emerging  Technologies,  40,  63 74. 
https://doi.org/10.1016/j.trc.2014.01.002 

Jiang, S., Ferreira, J., & Gonzalez, M. C. (2016). Activity-Based Human Mobility Patterns Inferred from Mobile Phone 
on  Big  Data,  3(2),  208 219. 
IEEE 

Transactions 

Data:  A  Case  Study 
of  Singapore. 
https://doi.org/10.1109/tbdata.2016.2631141 

Seppecher, M. (2022). Mining call detail records to reconstruct global urban mobility patterns for large scale emissions 

calculation. https://theses.hal.science/tel-03703230 

Seppecher, M., Leclercq, L., Furno, A., Rocha, T. V. da, André, J.-M., & Boutang, J. (2023). Identification of Aggregate 
Urban Mobility Patterns of Nonregular Travellers from Mobile Phone Data. Future Transportation 2023, Vol. 3, 
Pages 254-273, 3(1), 254 273. https://doi.org/10.3390/FUTURETRANSP3010015 

Wu,  L.,  Yang,  B.,  &  Jing,  P.  (2016).  Travel  Mode  Detection  Based  on  GPS  Raw  Data  Collected  by  Smartphones:  A 
Information  2016,  Vol.  7,  Page  67,  7(4),  67. 

Systematic  Review  of  the  Existing  Methodologies. 
https://doi.org/10.3390/INFO7040067 

Zhao,  Z.,  Koutsopoulos,  H.  N.,  &  Zhao,  J.  (2021).  Identifying  Hidden  Visits  from  Sparse  Call  Detail  Record  Data. 

http://arxiv.org/abs/2106.12885 

247 

 
 
 
 
 
 
 
Input Requirements for Modelling the Microscale Spatial Distribution of Emission 
Hotspots based on Real-World Measured Vehicle Activity 

C. Quaassdorff 1,2*, L. Pillai1, T. Khan1, and H.C. Frey1 

1 Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, North Carolina 
27606, United States of America.  
2 Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid. 28006 Madrid, Spain, 
c.quaassdorff@upm.es 

Introduction  

Quantification of spatial variability in on-road vehicle emission rates helps to identify locations of emission 
hotspots, which affect near-road air quality and human exposure. Quantification of the spatial distribution in 
vehicle emission rates is needed to identify emission hotspots (locations with higher pollutant emissions) and 
provide  insight  regarding  why  emission  hotspots  occur  (Quaassdorff  et  al.,  2022b).  However,  the  spatial 
resolution of many traffic and emissions models is mesoscopic, based on road links between intersections or 
junctions.  Link-based  models  lack  resolution  to  accurately estimate  emissions hotspots  at  specific  locations 
such as near intersections. The aim of this work is to define the input requirements of a microscale vehicle 
tailpipe emissions model to support estimation of on-road emission rates at high spatial resolution. The specific 
objective of this work is to develop and demonstrate procedures for allocating measurements of second-by-
second (1 Hz) vehicle activity to high resolution segments and prepare this information to estimate segment 
average emission rates by means of a microscale emission model. 

Methods 

One Hz data for a sample of 10 light duty gasoline vehicles are used to demonstrate a method for creating 
segment-by-segment trajectory data files for input to a vehicle emissions model.  

Measurements 

One  Hz  data  were  collected  in  previous  work  by  North  Carolina  State  University  (NCSU)  using  an  on-board 
diagnostic  scantool,  global  positioning  system  receivers  with  in-built  barometric  altimeters  and  Portable 
Emission Measurement Systems (PEMS). From the measurements, 1 Hz (second-by-second) vehicle activity and 
emissions were quantified, including speed, acceleration, road grade, vehicle specific power (VSP), and emissions 
of carbon dioxide (CO2), nitrogen oxides (NOX), hydrocarbons (HC) and carbon monoxide (CO) (Khan et al., 2020). 

Each  vehicle was  measured over 8  one-way 
designated  study  routes  between  NCSU, 
North  Raleigh  (NR),  and  Research  Triangle 
Park  (RTP),  in  North  Carolina  (NC)  United 
States of America (USA) and divided into 4 out 
routes from NCSU to NR and from NR to RTP, 
and 4 in routes from RTP to NR and from NR 
to NCSU (Figure 1). The total length of the trip 
over the 8 routes is 177 km long. These routes 
have a broad coverage of road types, speed 
limits and road grades.  

Figure 1. Measured 8 one-way routes between NCSU, 

North Raleigh and Research Triangle Park, NC. 

This study illustrates the process for allocating measurements of second-by-second (1 Hz) vehicle activity to 
high resolution segments and prepare this information to estimate segment average emission rates for a set 
of selected 10 measured light duty gasoline vehicles (LDGV). These are Tier 2 gasoline passenger cars with 
sedan body type as detailed in Table 1. To model high resolution segments based on 1Hz vehicle activity the 
high  resolution  emission  model  MOVES3  has  been  selected  (details  in  the  Model  section).  There  are  some 
factors, such as the model year or age of the vehicle, that can be represented in MOVES3. Other factors such 

248 

 
 
 
 
 
 
as rated horse power, curb weight, rated fuel economy, and mileage, are not considered in the model but can 
influence variability in measured emission rates. 

Table 1. Vehicle characteristics of ten selected Tier 2 gasoline passenger cars with sedan body type. 

Vehicle 

Model inputs 

Vehicle characteristics that are not modelled 

Model 
Year 

Age when 
measured 
(years) 

Rated 
Horse 
Power 
(hp) 

Curb 
weight 
(kg) 

Rated Fuel 
Economy 
(L/100 km) 

Mileage 
(km*1000) 

Toyota Camry 

2005 

Toyota Camry 

2012 

Kia Forte 

2013 

Honda Accord 

2012 

Ford Fusion 

2016 

Toyota Corolla 

2009 

Hyundai Elantra 

2010 

Mazda 6 

Honda Civic 

2006 

2011 

Hyundai Sonata 

2009 

Segmented trajectories 

7 

0 

0 

2 

0 

7 

7 

12 

7 

10 

160 

1435 

178 

1470 

156 

1266 

185 

1487 

175 

1556 

132 

1245 

132 

1246 

160 

1436 

140 

1284 

175 

1481 

11.3 

10.1 

9.7 

10.9 

10.9 

9.4 

9.7 

12.3 

9.7 

11.3 

171 

31 

14 

48 

0 

280 

119 

278 

101 

230 

The 8  routes  were  previously  divided  into a  total  of  450  directional  road  segments  that  average  0.4  km  in 
length. The segments were determined in previous work based on locations of intersections, entrance and exit 
points of ramps, and vertical curves, considering constant road grade, speed limits and road types (Khan et al., 
2020). Dividing those routes into smaller segments provides the opportunity to consider variations of vehicle 
activity in small distances for the emission estimations. As an example of segmented data, Figure 2 includes 
the outbound trip segment average road grade. ArcGIS 10.3.1 is used to generate microscale maps of vehicle 
activity. 

249 

 
 
 
 
Figure 2. Segment average road grade (%) on outbound trips. 

The second-by-second data from the measurements are spatially joined to the segments using ArcGIS so that 
each segment can be assigned the corresponding second-by-second data from the vehicle trajectory. For that, 
one single .csv file containing the trajectory data of each vehicle and route is created as input for ArcGIS. This 
file contains the second-by-second measured information for one vehicle over one route with location (latitude 
and longitude) of each measured second. Each of these second-by-second files for each of the 8 routes and 
for each of the 10 vehicles are uploaded into ArcGIS as dot layer to perform a spatial join with the line layer of 
the corresponding segmented route. The spatial join is based on proximity (type closest), which finds the closest 
segment in a route for each instantaneous measurement. Thus, each measured data is assigned to the closest 
segment.  

Figure 3 is an example of segmentation of the second-by-second speed trajectory of one vehicle (blue line), in 
this example a Ford Fusion, over route A-out with 42 segments. Each of the high resolution microscale segments 
(numbers in the top of the figure) has been assigned constant road grade (dotted brown line) based on linear 
regression of elevation versus distance data from GPS measurements. 

Figure 3. Second by second vehicle speed and segment average road grade for an example vehicle trajectory 
(Ford Fusion) over route A-out (42 segments). 

250 

 
 
 
 
 
Model 

In this case, for the segmented trajectories, the U.S. E.P.A. Motor Vehicle Emission Simulator version 3 (MOVES3) 
is used. MOVES3 is widely used to characterize emissions for the U.S. vehicle fleet. The model does not calculate 
individual vehicles but a vehicle fleet based on the vehicle type, vehicle model year, ambient conditions on the 
day of the measurement, and second-by-second speed and road grade data from the measured segmented 
trajectories.  

To run MOVES3, each vehicle requires a Run Specification (run spec), an input database and an output database. 
The run spec file contains information on spatial resolution (i.e. microscale), time of the day when the vehicle 
was  measured,  geographic  bounds  (state  and  county),  type  of  vehicle  (passenger  car  or  passenger  truck), 
pollutants, a link to vehicle-specific data, and the name of the output database where the model outcomes are 
stored. Vehicle-specific data include vehicle age, fuel type, ambient conditions, segmented 1Hz vehicle speed 
and road grade data, and segment details. Each of these vehicle-specific data are stored as single files in one 
input database. A description of the vehicle-specific data files included in the input database is given Table 2. 

Table 2. MOVES emission model set of vehicle-specific data input files required for each vehicle input database. 

Files required for 
MOVES 
input 
database 

File description 

File generation 

User defined 

User defined 

Model default data 
based on user provided 
vehicle information 

age distribution 

Defines the vehicle age 

meteorology data 

Contains information on the meteorology. Includes 
temperature and relative humidity. 

fuel 

links 

link drive schedule 

link source types 

Includes information on fuel used (supply, formulation, 
usage fraction, subtype). 

Contains an enumeration of all the links (segments) 
measured for a vehicle, and includes the info of county, 
zone, road type and segment length. 

Contains a second-by-second enumeration of the links the 
vehicle has been driven on, and the information on speed 
(mi/h) and road grade (%) for each second. 

Contains a second-by-second enumeration of the links the 
vehicle has been driven on, vehicle type and fraction per 
vehicle type. The number of second-by-second links must 
match the link drive schedule. 

User defined 

User defined 

User defined 

To set up and run groups of run specs, MOVES batch mode was used. In the batch mode, a MOVES3 run for one 
vehicle with 450 segment trajectories takes 300 seconds. To simplify the input of data into the model and 
minimize potential human errors, a LabVIEW code was developed to run the model in batch calculation mode 
iterating the calculation process from vehicle to vehicle with the final aim of generating microscale segment 
average emission rates per distance (g/km) for each single vehicle. The use of the batch mode provides the 
opportunity to run groups of single vehicles at the same time in an automatic way. Five main steps for running 
the batch mode are specified in Figure 4. 

251 

 
 
 
 
Figure 4. Flow diagram of the model batch calculation process.  

Model outputs include fleet average segment based pollutant emission rates per distance (g/km) for CO2, CO, 
NOX, HC and Total Energy Consumption (g/J) corresponding to the model year and age of each vehicle, from 
Table  1,  and  segment.  Those  outputs  are  stored  in  an  output  database  and  are  useful  to  identify  potential 
th percentile) 
em
of segments average emission rates for each pollutant. ArcGIS 10.3.1 is used to generate microscale maps of 
vehicle activity and segment fleet average emission rates. 

Results and discussion 

The outcomes of this methodology are segment average emission rates for each of the modelled vehicles and 
pollutants  over  the  450  segments.  Quantification  of  spatial  variability  in  segment  average  emission  rates 
enables identification of potential emission hotspots. By aggregating and averaging the emission rate estimates 
for a fleet (in this case represented by 10 vehicles) hot-spot locations can be identified.  

As example and due to health effects related to exposure to NO2 (U.S. EPA, 2016), Figure 5 highlights the location 
of NOx emission hotspots for the segment average emission rate of the ten selected vehicles. As highlighted in 
the black circles (a and b) in the figure, hotspot segments can be differentiated from lower emission segments 
that are spatially very close together. The location of the hotspots is influenced by the factors such as positive 

factors, and interactions among them, can be involved in the generation of a hotspot (Khan, et al., 2020). Further 
analysis to depict the main factors involved in the spatial distribution of modelled emission rates is needed and 
is recommended for future work. This will help to provide detailed information on the location of hotspots and 
why those occur.  

Modelled segment average emission rates are used as part of an ongoing project to assess the ability of a 
microscopic  vehicle emissions  model  to  accurately  predict  hotspots  in comparison to high  spatial  resolution 
tailpipe exhaust measurements (Quaassdorff, et al., 2022a; Quaassdorff, et al., 2023). 

252 

 
 
 
 
Figure 5. Ten-vehicle modelled segment average NOx emission rates (g/mi) for outbound trips, with hotspot (red 
thicker lines) areas higlighted in black circles (a and b). 

Conclusions 

Second-by-second (1 Hz) vehicle activity is a required input for microscale vehicle tailpipe emissions models to 
support estimation of on-road emission rates at high spatial resolution. In this work a procedure is given for 
allocating measurements of 1 Hz vehicle activity to high resolution segments.  Estimation of segment average 
emission rates by means of a microscale emission model has been successfully demonstrated for a set of 10 
vehicles. This methodology is useful to identify microscale emission hotspots locations at high spatial resolution, 
and will be applied to a larger dataset to support a more comprehensive assessment of spatial variability in 
real-world emissions and more robust identification of emissions hotspots.  

Acknowledgements  

This  work  was  supported  by  the  IRTEMS  project  (H2020-MSCA-IF-2019/896417/EU/IRTEMS)  funded  by  the 
-Curie grant 
agreement No 896417; and the Glenn E. and Phyllis J. Futrell Distinguished University Professorship at North 
Carolina  State  University.  Víctor  de  la  Cruz  Salmerón  helped  developing  the  LabVIEW  code.  Disclaimer:  The 
information here presented reflects the author(s) view and does not necessarily reflect the views or policy of 
the European Commission and REA, those of the NC State University nor those of any other sponsor which are 
not responsible for any use that may be made of the information it contains. 

References 

Khan, T., Frey, H. C., Rastogi, N., Wei, T. (2020). Geospatial Variation of Real-World Tailpipe Emission Rates for Light-Duty 

Gasoline Vehicles. Environ. Sci. Technol

Quaassdorff, C., Khan, T., Frey, H. C. (2022a). Accuracy of the predictions of modeled emission hotspots based on real-world 
measured vehicle activity and emissions. CRC Real-World Emission Workshop. San Diego, California, USA. March 13-
16, 2022. 

Quaassdorff, C., Smit, R., Borge, R., Hausberger, S. (2022b).  Comparison of microscale traffic emission models for urban 

networks. Environ. Res. Lett. 17:094030. 

Quaassdorff, C., Khan, T., Pillai, L., Frey, H. C. (2023). Evaluation of model predictions of real-world emission hotspots based 
on measured vehicle activity and emissions. CRC Real-World Emission Workshop. Long Beach, California, USA. March 
26-29, 2023.  

U.S.  EPA.  Integrated  Science  Assessment  (ISA)  for  Oxides  of  Nitrogen 

  Health  Criteria  (Final  Report,  Jan  2016).  U.S. 

Environmental Protection Agency, Washington, DC, EPA/600/R-15/068, 2016. 

253 

 
 
 
 
 
 
 
2.7  TAP.07. Non-road emissions. 

254 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Real-world NOx emissions of Stage V NRMM  

R. Vermeulen1

, J. Demuynck 2*, P. Paschinger1, D. Bosteels2 

1TNO, The Hague, the Netherlands, robin.vermeulen@tno.nl 

2Assocation for Emissions Control by Catalyst, AECC aisbl, Brussels, Belgium, joachim.demuynck@aecc.eu 

Abstract 

This work investigates the real-world NOx emissions of stage V engines used in a variety of Non-Road Mobile 
Machinery (NRMM) applications. The purpose is to understand the real-world usage patterns in the fleet and 
resulting emissions under a variety of operating conditions. The monitoring data of thirteen Stage IV and V 
machines  is  analysed.  NOx  emissions  were  measured  during  real-world  operation  with  Smart  Emissions 
Measurement Systems (SEMS). 

It  is  observed  that  engine  load  patterns  vary  between  machines,  even  when  they  are  of  the  same  type.  In 
general, the average engine load is relatively low or can have intermittent periods of working and stand-by 
periods.  A  variety  in  average  emissions  performance  can  be  observed.  Several  machines  have  average  NOx 
emissions below the limit, some around the limit and others are significantly above the limit. Impact on air 
quality  is  not  only  determined  by  the  average  emissions,  as  specific  usages  seems  associated  with  higher 
emissions.  The  study  therefore  investigated  the  distribution  of  emissions  as  well,  based  on  the Stage  V  In-
Service Monitoring (ISM) post-processing procedures. In contrast to Euro 6/VI for on-road vehicles which has 
requirements for testing In-Service Conformity with PEMS, Stage V for NRMM includes testing with Portable 
Emissions Measurement Systems (PEMS) for monitoring purposes only, called In-Service Monitoring (ISM). 

For  five  out  of  eight  of  the  monitored  Stage IV  and  V  machines  (56 
  560kW, 
respectively), real-world NOx emissions are reasonably low, in the range of the limit value, due to the usage of 
Selective Catalytic Reduction (SCR) systems. There are, however, events with higher emissions, which are often 
categorised as non-working event or invalid window in the ISM procedures. For three of the eight machines of 
these categories higher emissions are observed during the monitoring programme, mainly due to insufficient 
thermal management under low-load operation. Real-world NOx emissions of low powered (<56 kW) and high-
powered (>560 kW) categories are high due to the higher NOx limits, which do not require the use of SCR. 

  P  <  130 kW and  130 

  P 

Introduction 

NRMM (Non-Road Mobile Machinery) is a broad category of machinery which includes mobile machinery and 
transportable equipment which are not primarily used for transporting people or goods over the road and most 
of which are fitted with an internal combustion engine (diesel mainly). As such, the machinery is to be regarded 
as a source of pollutant and greenhouse gas emissions. For instance, NRMM in the construction sector can be a 
significant contributor to local air pollution (NOx, PM), acidification and eutrophication of nearby nature reserves 
(NOx, NH3). EU regulation has become more stringent over time with the aim to reduce pollutant emissions of 
the engines for most power categories. The regulation uses standardized tests to verify the emissions under 
controlled  laboratory  conditions.  In-service  emissions  are  only  checked  by  means  of  monitoring  with  PEMS 
(Portable  Emissions  Measurement  System),  but  this  is  limited  to  a  selective  part  of  the  operation  of  the 
machinery.   

Objectives 

The objectives of the study at hand were to increase the understanding of real-world NOx of stage IV/V engines 
applied in NRMM. More specifically, their usage patterns, dependencies of the emissions, fleet composition and 
contributions to total emissions were investigated. Furthermore, possible opportunities for improving regulatory 
requirements were identified and emissions put in perspective with air quality contributions. Finally, hot spots 
(largest contributing categories) and possible white spots in knowledge and data were identified.  

Approach 

The approach taken was to collect and analyse the real-world emissions and usage data which was measured 
on a number of Stage IV and V certified NRMM in the testing programs conducted by TNO for the Dutch 
Ministry of Infrastructure and Water management. 

255 

 
 
 
NRMM 

In the Netherlands 

To be able to show the significance of emissions from NRMM and to get a view of the fleet of NRMM, the 
situation in the Netherlands was used as an example. In the Netherlands the EMMA model [6] is used to 
estimate annual emissions. Presently, 87 different types of machines are considered in the model, from 
chainsaws to pile drivers. Probably, there are still types in-use which are not identified and included. 
Regarding the fleet of NRMM in the Netherlands it should be noted that there are large uncertainties in terms 
of numbers and types of NRMM due to lack of registration, e.g., a large underestimation was unveiled by the 
mandatory registration of 350,000 agricultural tractors, which is almost five times the initially estimated 
number of 71,000. Also, there is a large uncertainty of the usage of the machinery. 

Annual NOx emissions [8] are at approximately the same level compared to inland shipping, somewhat higher 
than passenger cars, light-, or heavy-commercial vehicles and about 44 % of total traffic NOx emissions. 
Annual PM10 emissions of NRMM are as high as the total of road traffic. Annual NOx emissions are estimated 
to decrease slightly from 2022 to 2030 due to the substantial increase of the share of stage V engines. This 
includes some growth of the fleet. The EMMA model estimates that the contribution to total NOx emissions is 
highest for medium powered categories (75 560 kW). This may shift to the lower power category with less 
stringent emissions limits. This may account for particulate matter as well. 

Figure 1: Total annual NOX emissions in the Netherlands per sector for the year 2021. 

EU Regulation 

The EU NRMM Regulation [3] defines emission limits for NRMM for different power ranges and applications. It 
also lays down the procedures engine manufacturers must follow in order to obtain type-approval of their 
engines   which is a prerequisite for placing their engines on the EU market. The Regulation defines which 
applications fall under the scope. In scope are applications such as NRE (Non-Road Engines), NRG (Non-Road 
Gensets), IWP (Propulsion engines of Inland Waterway Vessels), RLL (Rail Locomotives) and RLR (Railcars). 
Test cycles for engine tests are to be conducted on an engine test bed. They consist of a transient test, the 
NRTC, and several steady-load tests, such as the NRSC (mode tests C1, E2, E3, etc). Emissions limits are 
differentiated by net power categories, with for each power category a different set of limits for the 
substances CO, HC, NOx, PM (Particulate Matter) and PN (Particulate Number). For Stage V the power 
categories between 19 kW and 560 kW have more stringent PM and PN limits and the power categories 
between 56 and 560 kW also have a more stringent NOx limit. In-service monitoring was initially applicable 

 kW and 130 

 P 

 560kW, respectively) but is extended to lower and higher 

power categories as well, entering into force December 2022. For the power categories with more stringent 
limits the requirements lead to application of a DPF (Diesel Particle Filter) to comply to the respective PM and 
PN limits and SCR (Selective Catalytic Reduction) to comply to the NOx limits.  

256 

 
 
 
 
 
Measuring real word emissions: method 

To be able to determine real world emission levels of NRMM, the emissions and usage data of the NRMM were 
measured using a Smart Emissions Measurement System (SEMS). SEMS is a sensor-based system developed 
by TNO [4]. SEMS  has been used in various programmes to measure and analyse the tailpipe NOx, NH3 and CO2 
emissions. Moreover, the fuel consumption during daily operation and a range of vehicle/engine parameters are 
monitored. The purpose is to characterize the typical operation of the vehicles or machinery. In this way, for the 
group of test subjects, weeks up to months of data were collected per subject. The SEMS uses a calibrated 
(periodically checked and adjusted) automotive NOx sensor which is combined with an O2 sensor, an ammonia 
sensor, GPS, and a data-acquisition system to record the sensor data and data from the vehicle and engine at 
a sample rate of 1 Hz. 

Figure 12: Smart Emissions Measurement System (SEMS). Left, emissions sensors in the tail pipe of an 
NRMM. Right, data recorder and transmitter to read, store and transmit sensor data, engine 
data and GPS data to a central server.    

Mass emissions and instantaneous engine power are calculated combining sensor data and engine and vehicle 
data,  such  as  manifold  air  flow,  fuel  rate,  engine  torque,  engine  speed  and  sensor  O2  concentration  where 
available. Depending on availability of signals, the best of the following methods is chosen in terms of expected 
accuracy: carbon-balance, mass air flow and fuel flow, speed density, emission-over-CO2 ratio and estimate of 
specific fuel consumption.  

Data from four projects was collected and reprocessed to calculate and depict emissions and usage parameters 
of  thirteen  NRMM  used  in  real  operation  in  their  daily  duty.  In  the  programmes  fifteen  individual  machines 
fulfilled the criterion of having either stage IV or V certification. Two machines were rejected from the sample 
because one ran only for a very short duration (3 h) during a long test period and another one had a faulty NOx 
sensor signal. In Table 1 an overview is given of the machinery which was used in the analysis. 

257 

 
 
 
 
 
Table 1: Overview of measured Stage IV and V NRMM available for analysis. 

Type 

Brand 

Type 

Power 
[kW] 

EU NRMM Stage 

Kubota 
Excavator 
Hitachi 
Excavator 
Takeuchi 
Excavator 
Excavator 
Takeuchi 
Wheeled Excavator Takeuchi 
Excavator 
Wheel loader 
Wheel loader 
Asphalt Roller 
Paver 
Pile driving rig 
Terminal Swap 
body transporter 
Tractor  

Caterpillar  
Volvo 
Caterpillar  
Bomag 
Dynapac 
ABI 

Kamag 

Valtra 

KX027-4 
ZX85US-6 
TB290-2 
TB 2150 R 
TB295W 
326 
L70h 
950M 
174AP-4AM 
SD2500C 
TM18/22B 

PM 

N4 

Machine 
running 
hours at 
beginning of 
test 

Total test 
hours 

1716 
n.a. 
1648 
2322 
n.a. 
n.a. 
3635 
405 
2665 
2096 
888 

8 
83 
163 
53 
45 
144 
211 
468 
587 
275 
516 

18.5 
42.4 
51.6 
85 
85 
152 
127 
171 
55.4 
129 
563 

V (IID) 
V (NRE-v-4) 
V (NRE-v-4) 
V (NRE-v-5) 
V (NRE-v-5) 
IV (R) 
IV (R) 
V (NRE-v-6) 
V (NRE-v-4) 
V (NRE-v-5) 
V (NRE-v-7) 

115 

V (NRE-v-5) 

4390 

1278 

114 

IV (R) 

n.a. 

55.3 

Figure 13: Pictures of the types of NRMM that were part of the analyses.  

NOx emissions of 13 Stage IV and V NRMM 

EU emissions regulation aims to limit the brake specific emissions, but for air quality the time-based 
emissions are important. Therefore, for the thirteen NRMM both the average work specific as well as the 
average hourly mass emissions were determined, see Figure 4 and Figure 5, respectively. Real-world NOx 
emissions of cat. NRE 5 and 6 (56 
such as excavators and wheel loaders: 0.2   1.8 g/kWh, but are higher for some other regular cases, the 
wheeled excavator (2.4 g/kWh) and the tractor (IV, 1.7 g/kWh) and are high for a special case, the swap body 
transporter (6.7 g/kWh). 

 P < 560 kW), which all use SCR, are reasonably low in most regular cases 

Real-world NOx 
high due to the higher NOx limits which do not require the use of SCR. The same may apply for NRE-1 and 3 
which have not been monitored. Real-world NOx emissions of a category NRE-7 machine, the pile driving rig, 

258 

 
 
 
are very high (7.3 g/kWh) due to the higher NOx limit which does not require the use of SCR and are also high 
in g/h due to the high power of the engine and running at low load.  

Figure 4: Average work specific NOx emissions of 13 NRMM in real world operation and the NOx limit according 
to the EU NRMM Regulation which applies for the prescribed laboratory test cycles. 

Figure 5: Average hourly NOx emissions of 13 NRMM in real world operation. 

Impact on local air quality is not only determined by the average emissions. The study at hand therefore looked 
into the distribution of emissions as well. The data is analysed according to the Stage V In-Service Monitoring 
(ISM) procedures, using the Moving Average Windows (MAW) methodology. This method averages emissions in 
a window with a fixed amount of engine work of which the duration is thus determined by the level of engine 
power. Criteria are defined to exclude MAW (invalid MAW, eg. average engine power <20%), short sequences 
and non-working events (eg. average engine power <10%, take off from the ISM evaluation. Figure 6 illustrates 
the  frequency  of  the  MAW  NOx  values  measured  for  the  wheeled  excavator  of  85 kW  and  the  swap  body 
transporter.  The  blue  bars  represent  the  data  which  remains  after  applying  the  ISM  procedure  (containing 
working events and valid MAW), the orange bars show data which are excluded (non-working events and invalid 
MAW). The example of the wheeled excavator shows that 42 % of the MAWs are in the lowest NOx bin. A long 
tail  is  however  observed,  with  some  MAWs  going  up  to  10 g/kWh  and  where  higher  fractions  of  MAW  are 
excluded. For the swap body transporter almost all MAW are excluded. The peak of the distribution lies around 
7 g/kWh.  Figure 7 shows that the ISM method leads to different shares of valid, invalid MAW and exclusions 
depending on the NRMM and its usage. The pile driving rig and swap body transporter have a low share of valid 
MAW.  Both  NRMM  run  at  a  very  low  power  of  about  11 %  of  maximum  rated  power  on  average.  Also,  the 
wheeled excavator which shows 48 % of valid MAW runs at a low average power of 16 %. 

259 

 
 
 
 
 
 
Figure 6: Examples of the distribution of work specific NOx emissions in Moving Averaging Windows (MAW) of 
two NRMM. This for all valid MAW and MAW which are dropped (invalid and excluded) due to the in-service 
monitoring rules.  

Figure 7: Shares of valid and invalid Moving Averaging Windows (MAW) and exclusions (non-working events, 
cold starts) after applying the calculation rules for in-service monitoring. 

Analysis of the distribution of engine power reveals that for most machines the power distribution is on the low 
side, with a high share below 30 % of maximum engine power. With exception of four machines, most machines 
show a distribution of exhaust gas temperatures above 200°C which indicates that for those applications SCR 
working temperatures is reached most of the times, despite the in some cases lower loads below 30 %. The 
small excavator (18,5 kW, no SCR) shows larger shares of low exhaust gas temperatures, and so do the swap 
body transporter (115 kW, SCR) and the pile driving rig (563kW, Exhaust Gas Recirculation   EGR   only) which 
resulted in high NOx emissions. 

260 

 
 
 
 
 
 
 
Figure 11 shows the percentage of total NOx emissions that can be avoided by switching it off in idle. On 
average 18 % of total NOx can be avoided by switching off the engine after 5 minutes of stand-by, with a 
variation being between 3 44 %. 

Figure 8: Percentage of total NOx emissions which can be avoided when the engine is switched off at different 
times after a working event.     

Discussion 

Engine load patterns vary between machines of the same type and between different types of machines. In 
general, the average engine load is relatively low or can have intermittent periods of working and stand-by 
events. Despite the low power, with a few exceptions most tested NRMM are able to maintain sufficiently high 
SCR working temperatures to reduce engine out NOx emissions for most of the running time, in the case 
NRMM are equipped with SCR. Generally, on average higher or temporarily higher NOx emissions for these 
NRMM are caused by average low load operation, intermittent load operation and high stand-by shares of the 

NRMM in the low (<56 kW) and high-power (>560 kW) categories have less stringent NOx limits, hence SCR is 
not needed to comply to the EU Regulation. This results in high absolute emissions for the low power 
categories (P<56 kW) and very high NOx emissions for the high powered NRMM (>560 kW). No PN limit and a 
less stringent PM limit for the engine category  P < 19 kW and P > 560 kW , might not require the use of a 
DPF which probably results in high PN and PM emissions. Engine power sizing is pushed to categories with 
less stringent NOx and/or PN/PM limits, e.g., >560 or <19 kW/<56 kW where no emissions abatement is 
needed to achieve the test cycle emission limits. For example, several machines were identified with an 
engine power just outside the boundary (e.g., 18.5, 55 and 563 kW). 

Representativeness of test cycles; test cycles do not cover the spread of usage in terms of engine load 
patterns observed. There is a lack of control of real-world emissions from normal (varying) usage with stand-
by/idling, low load, intermitted loads, which are all not well-covered in test cycles. The high average load over 
the test cycle does not require low temperature SCR light off. The NRTC has higher speeds and loads than 
the WHTC test used for heavy-duty engines. For NRMM this results in faster reaching the operating 
temperatures for exhaust aftertreatment in the test cycle. The transient operation of NRTC does resemble the 
typical part of transient engine operation of certain construction machinery while working, such as wheel 
loaders and excavators. In real use, working events can however be followed or preceded by stand-by or non-

261 

 
 
 
 
 
 
 
 
monitoring. ISM is limited to conditions comparable to the NRTC, i.e., working events with well-functioning SCR 
and high load, excludes low load and idling and was only applicable for medium powered NRE, while stand-by 
NOx emissions and NOx from intermittent load cycles are significant. For multiple machines which were 
monitored, a substantial part of the real-world data needed to be excluded because it was considered as a 
non-working event or not valid. The absence of signals from on-board control units makes it difficult to 
calculate the engine load in some cases. Furthermore, the calculation method of engine power is not always 
clear due to lack of standardization.  

Conclusions 

NRMM contributes significantly to the total PM and NOx emissions. Due to the lack of stringent emission limits 
for the low and high-powered engines, the incomplete coverage of normal operation of NRMM in test 
procedures and the use of older machinery with a long lifetime, the contribution of NRMM will remain 
significant in the upcoming years. In contrast, the emissions of road transport will decrease further due to the 
effective Euro VI- step E and Euro 6d and introduction of Euro 7.  

There are many NRMM types. For the ones tested usage patterns vary. 

For the latest generation of NRMM (Stage IV with SCR and stage V) real-world NOx emissions vary from 0.2 to 
7.3 g/kWh and depend on usage and the NRE power category. The large variation is caused by: 

 

The applicable type-approval test procedures which only partly covers normal conditions of use, but 
does  not  cover  typical  use  such  as  stand-by,  intermittent  load  and low  load  operation.  As  a  result, 
NRMM  regulation  does  not  consider  a  substantial  share  of  the  real  working  conditions.  The  ISM 
evaluation also excludes substantial test data of normal use, such as low engine load conditions and 
stand-by/idle. This while NRMM often remain on one location to do their work for a period of time, 
hence all usage and related emissions matter for local air-quality and nitrogen deposition.  
The fact that higher NOx emissions are allowed for Stage V NRE in the low and high-power categories. 
Similar issues for control of PM/PM emissions are expected for NRE in the low and high-power categories with 
absence of a PN and a stringent PM limit. 

 

This can be tackled by more stringent NOx limits for P<56 kW and >560 kW, a PN limit for low (<19 kW) and 
high-powered engines (>560 kW), better coverage of real-world usage in tests and for ISM, extension of 
window of control, covering a larger part of real-world usage, including low load operation. 
Technical solutions include automatic engine shut off, NOx abatement for low and high-powered diesel 
engines (EGR, SCR), increasing low temperature SCR performance and DPF for low and high-powered diesel 
engines and electrification depending on the use case. 

Acknowledgements 

For this work data was used of projects funded by the Dutch Ministry of Infrastructure and Water management.  

262 

 
 
 
 
 
 
 
 
 
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paper for the Transport and Air-Pollution Conference 2014, Graz, Austria 

[8]  Vermeulen, R.J., Ligterink, N.E., van der Mark, P.J., Real-world emissions of non-road mobile machinery, 

TNO report TNO 2021 R10221, 11 February 2021 

[9]  Vermeulen, Paschinger, NRMM stage IV/V real-world NOx emissions: status quo and perspectives, TNO 

2023 P10252, 24 January 2023 

263 

 
 
 
 
PM10 emissions from, and rubber content in, different tyre types in relation to 
rubber hardness 

M. Gustafsson1, P. Tromp2, N. Svensson1 

1Swedish National Road and Transport Research Institute (VTI), Linköping, 581 95 Sweden. mats.gustafsson@vti.se 

2TNO - Department EMSA, Utrecht, 3584 CB, The Netherlands 

Introduction 

Tyre wear particles (TWP) are a source to both air-borne particles and microplastic pollution of soil, water and 
sediments  (Baensch-Baltruschat  et  al.,  2020).  The  total  wear  of  tyres  in  Europe  has  been  estimated  to  1.3 
million tonnes/year (Wagner et al., 2018), making it one of the largest sources to microplastics. The tyre rubber 
mix consists of about 40 60% synthetic and/or natural rubber, depending on tyre type. The rest is reinforcing 
fillers (30 35 %), plasticizers (15 %), vulcanization agents (2 5 %) and other additives (5 10 %) (Wagner et 
al.,  2018).  Winter  tyres  need  to  have  softer  rubber  mixes  than  summer  tyres  to  maintain  friction  at  low 
temperatures, when rubber gets stiffer. Therefore, winter tyres normally contain a higher share of natural rubber 
than summer tyres. 

Except being an important source for general microplastics, defined as polymer containing particles smaller 
than 5 mm, tyre wear also contributes to airborne particles (e.g. Fussel et al., 2022). Since non-exhaust particles 
from  tyres,  brakes  and  road  surface  are  not  yet  regulated,  their  emissions  increase  with  increasing  traffic 
amounts.  Also,  increasing  emissions  of  tyre  and  road  wear  particles  are  promoted  by  vehicles  continuously 
getting  heavier  as  a  result  of  trends  (e.g.  SUV:s)  and  vehicle  propulsion  developments  (electrification)  (e.g. 
Beddows and Harrison, 2021).  

Information about tyre wear emissions and how their size and physico-chemical characteristics are affected by 
material properties and use is scarse, but highly relevant for future mitigation. In this paper, results from the 
Horizon Europe project Nº 955387 Low particle Emissions and lOw Noise Tyres (LEON-T) are presented. In the 
project particle emissions from six sets of tyres (summer, all-season and winter) run in a road simulator have 
been investigated, characterized, and analysed in relation to tyre properties. Data from similar tests of summer 
and winter tyres in a governmental assignment (GA) at VTI are also presented. 

Methods 

The VTI Road simulator is a carousel-like equipment with 4 wheels running on a circular pavement ring (Figure 
1). Any kind of pavement and light duty vehicle tyres can be used. The maximum speed is 70 km/h and the hall 
is temperature controlled. 

Figure 1. The VTI road simulator 

Tyres used in the LEON-T experiments were sets of three summer tyres, one 4-season tyre and one winter tyre. 
In the GA, four sets of summer tyres and four sets of winter tyres for Nordic conditions were used. All tyres in 

264 

 
 
 
 
LEON-T were tested on a cement concrete pavement in the same temperature co
the GA, tyres were tested on an asphalt concrete pavement in the same temperature but at a speed of 60 km/h. 
Axle  load  was  450  kg  and  inflation  pressure  2.5  bars  in  both  tests.  PM  and  particle  size  distributions  were 
measured using TEOM, FIDAS AQguard and Aerodynamic particle sizer (APS) and analysed in relation to tyre 

To acquire the rubber content (SBR, NR) and total TWP content of PM, chemical analysis was performed using 
TED-GCMS following a similar approach as Eisentraut et al. (2019). The analysis is a two-step method: Thermo 
Extraction (step 1) and Desorption (step 2). In summary, a sample is first decomposed in a thermogravimetric 
analyzer  (TGA)  and  the  gaseous  decomposition  products  are  then  trapped  on  a  solid-phase  adsorber. 
Subsequently,  the  solid-phase  adsorber  is  analyzed  with  thermal  desorption  gas  chromatography  mass 
spectrometry. The instrument that was used is a TGA-TED-GCMSMS; decomposition products were sampled with 
Tenax-TA as a solid-phase adsorber at a temperature between 290-550°C. Dipentene and 4-phenylcyclohexene 
were used as markers for qualification and quantification of natural rubber (NR) and styrene-butadiene rubber 
(SBR). Two quantification techniques were used: (1) a direct calculation of TWP based on an average conversion 
factor  from  approximately  40  reference  car  and  truck  tyres  and  (2)  an  indirect  method,  based  on  the 
quantification of SBR and NR rubber and an estimation of the amount of rubber in car and truck tyres (appr. 
50%).  

Results and discussion 

Despite lower speed, the resulting concentrations (and consequently emissions) are markedly higher in the GA 
project compared to the results in the LEON-T project (Figure 2). A probable reason for this is differences in 
road surface properties and how consecutive tests where run. In the GA project, every third test on the asphalt 
road surface was made with studded tyres, while studded tyres where not used in the LEON-T test sequence at 
all. Studded tyres will roughen the road surface. A rougher surface will both abrade the next set of studless 
tyres tested but also, the road surface itself will more easily emit mineral particles since tyre rubber will polish 
the  aggregates  in  the  surface.  In  LEON-T,  the  road  surface  is  consistently  being  polished  by  summer  and 
studless winter tyres, why emissions from both tyres and road surface are lower. 

Therefore,  for  easier  comparison  between  projects,  in  Figure  2,  the  normalised  PM10  emissions  from  both 
projects are shown. PM10 emissions are negatively correlated to rubber hardness. As can be seen, winter tyres, 
which has softer rubber mixes, emit more PM10 in both data sets, compared to summer tyres and the only 4-
season tyre. The higher PM10 levels in the normalised (to mean values of each data set) LEON-T data set are 
likely to be related to the higher speed used in tests. Cement concrete pavements have, exposed to studded 
tyre wear, been shown to emit more PM10 due to the contribution from the cement matrix (Gustafsson et al, 
2019). If this is a contributing factor also for studless tyres is not known. 

Figure 2. Relationship between rubber hardness and PM10 emissions in LEON-T project and the governmental 
assignment (GA) project. S=summer tyre, W=winter tyre, 4=4-season tyre. Letters A-G represent different tyre 
brands. 

Results from the rubber analyses of the LEON-T PM10 samples, show that only a small fraction of PM10 is 
TWP, the remaining PM most likely originates from the concrete pavement (Table 1 and Figure 3). Possibly also 
other emission mechanisms (evaporation & condensation of semi-volatile organic components in tyres) play a 

265 

W:A4:AS:BS:AS:CS:AS:BS:CS:DW:AW:BW:CW:D00.20.40.60.811.21.41.61.82455055606570Normalized PM10Hardness (Shore)LEON-TGA 
 
 
  
role, as the carbonaceous content in PM, observed with electron microscope, seems higher than the analysed 
TWP concentrations. The winter tyre generates a five-fold TWP concentration in the PM10 (Table 1). 

Table 1. Concentration SBR, NR, TWP in PM10 (µg/m3) and percentage TWP in PM10 

Tyre type 

SBR 

NR 

%NR 

TWP 

%TWP 

Winter 

1,6 

0,15 

4 Season 

0,26 

0,02 

9% 

6% 

3,5 

0,54 

4.9% 

1.3% 

Summer 

0,14 

0,02 

10% 

0,31 

0.8% 

Considering the low concentration of TWP in PM10, it can be concluded that the obvious correlation between 
PM10  emission  and  rubber  hardness  depends  on  higher  tyre  wear  at  lower  hardness,  but  mostly  in  higher 
emissions of road wear. Since the road surface in the simulator do not accumulate dust being overrun very 
frequently, it can be argued that softer rubber may cause higher slip forces and thus generate higher wearing 
of the cement concrete as well. Note that the cement binder itself, which is softer and more brittle than the 
rock ballast aggregates, also is a possible source to PM10 in this pavement type (Gustafsson et al., 2019). 

Figure 3. Shares of road and tyre wear particles in the normalized emission from the winter, 4-season and 
summer tyres analysed. S=summer tyre, W=winter tyre, 4=4-season tyre. Letters A and B represent different 
tyre brands. 

In Figure 4, calculated emission factors for PM10 from the AQguard instrument and simultaneous gravimetrical 
sampling as well as emission factor for TWP during the LEON-T experiments are shown as function of rubber 
hardness. The emission factors are very low compared to literature, which could be attributed to a high degree 
of polishing of the road surface. 

266 

00.20.40.60.811.21.41.61.82W:A4:AS:Bnorm ef PM10RWPTWP 
 
 
 
 
Figure 4. Emission factors for PM10 om mass per vehicle kilometre from AQguard instrument and gravimetric 
measurements and emission factor for TWP during the LEON-T experiments. 

In Figure 5, PM10 size distributions as measured by APS of a winter, summer and 4-season tyre from the same 
manufacturer are shown. The mass size distributions reveal that the winter tyres have a markedly finer and 
5 µm. 
This  is  in  agreement  with  the  TED-GCMS  analysis  of  PM10,  PM2.5  and  PM1.0.  For  winter  tyres  a  higher 
percentage of TWP is in the smaller size fraction. For example, for winter tyres approximately 45% of TWP is in 
the PM2.5 fraction, while for the summer tyres this is approximately 20%. 

Figure 5. Particle number size distribution and mass size distribution of three tyre types from the same brand. 

Conclusions 

From this study it can be concluded that the tyres with lower hardness relates to higher yields of PM10. The 
winter tyres tested yields higher amounts of TWP content and a finer mass size distribution compared to the 
summer and 4-season tyres. The contribution of tyre wear to the PM10 produced is between 1 5%, while the 
dominant part is minerals. The higher PM10 emissions from the rougher asphalt may be a result of higher tyre 
wear as well as of higher wear of the pavements rougher surface. 

267 

R² = 0.6868R² = 0.9025R² = 0.9144051015202530354045500.00.10.20.30.40.50.60.70.80.91.0565860626466687072EF TWP (µg/vkm)EF PM10 (mg/vkm)Hardness (shore)PM10-AQPM10-gravTWP-PM1005101520253035400.1110100dM/dlogDp (µg/m3)Aerodynamic diameter (µm)Winter4-seasonSummer 
 
 
 
 
 
 
 
 
Acknowledgements  

This work was funded by the European Commission through the Horison Europe project Nº955387 Low particle 
Emissions and lOw Noise Tyres (LEON-T). The authors are grateful to the ever-helpful technical staff Dennis 
Hydén and Tomas Halldin at the VTI road simulator facility and to Johan Esveld and Paul Fokkens for sampling 
set-up. 

References 

Baensch-Baltruschat, B., Kocher, B., Stock, F., Reifferscheid, G., 2020.  Tyre and road wear particles (TRWP)  - A review of 
generation, properties, emissions, human health risk, ecotoxicity, and fate in the environment. Sci Total Environ 733, 137823, 
https://doi.org/10.1016/j.scitotenv.2020.137823. 

Beddows,  D.C.S.,  Harrison,  R.M.,  2021.  PM10  and  PM2.5  emission  factors  for  non-exhaust  particles  from  road  vehicles: 
Dependence  upon  vehicle  mass  and  implications  for  battery  electric  vehicles.  Atmospheric  Environment  244, 
https://doi.org/10.1016/j.atmosenv.2020.117886. 

Dahl, A., Gharibi, A., Swietlicki, E., Gudmundsson, A., Bohgard, M., Ljungman, A., Blomqvist, G., Gustafsson, M., 2006. Traffic-
generated  emissions  of  ultrafine  particles  from  pavement-tire  interface.  Atmospheric  Environment  40,  1314-1323, 
https://doi.org/10.1016/j.atmosenv.2005.10.029. 

Eisentraut  P,  Dümichen  E,  Ruhl  AS,  et  al.  Two  Birds  with  One  Stone  -  Fast  and  Simultaneous  Analysis  of  Microplastics: 
Microparticles  Derived 
from  Thermoplastics  and  Tire  Wear.  Environ  Sci  Tech  Lett.  2018;5(10):608-613. 
https://doi.org/10.1021/acs.estlett.8b00446 

Gustafsson,  M.,  Kraft,  L.,  Olofsson,  U.,  2019,  Moderna  betongbeläggningar:  utveckling  mot  mindre  slitage  och 
transportforskningsinstitut, 
Statens 
partikelemission, 
http://urn.kb.se/resolve?urn=urn:nbn:se:vti:diva-13697 

03476030 

(ISSN), 

väg- 

och 

Fussell, J.C., Franklin, M., Green, D.C., Gustafsson, M., Harrison, R.M., Hicks, W., Kelly, F.J., Kishta, F., Miller, M.R., Mudway, I.S., 
Oroumiyeh,  F.,  Selley,  L.,  Wang,  M.,  Zhu,  Y.,  2022.  A  Review  of  Road  Traffic-Derived  Non-Exhaust  Particles:  Emissions, 
Physicochemical  Characteristics,  Health  Risks,  and  Mitigation  Measures.  Environmental  Science  &  Technology, 
https://doi.org/10.1021/acs.est.2c01072. 

Mathissen, M., Scheer, V., Vogt, R., Benter, T., 2011. Investigation on the potential generation of ultrafine particles from the 
tire-road interface. Atmospheric Environment 45, 6172-6179. 

Wagner,  S.,  Hüffer,  T.,  Klöckner,  P.,  Wehrhahn,  M.,  Hofmann,  T.,  Reemtsma,  T.,  2018.  Tire  wear  particles  in  the  aquatic 
environment  -  A  review  on  generation,  analysis,  occurrence,  fate  and  effects.  Water  Res.  139,  83-100, 
https://doi.org/10.1016/j.watres.2018.03.051. 

268 

 
 
 
 
NRMM Real Operation NOx Emission Measurements with Plume Chasing  

C. Schmidt1,2*, D. Pöhler1,2, S. Schmitt2, C. Heidt3, F. Münch3, U. Platt1,2 

1Institute 
of 
cschmidt@iup.uni-heidelberg.de 

Environmental 

2Airyx GmbH, Eppelheim, Germany 

Physics, 

University 

of 

Heidelberg, 

Heidelberg, 

Germany,  

3ifeu   Institut für Energie- und Umweltforschung Heidelberg gGmbH, Heidelberg, Germany 

Introduction 

Non  road  mobile  machinery  (NRMM)  contributes  significantly  to  pollution  (Helms  and  Heidt,  2014).  The 
introduction of emission limits in Directive 97/68/EC for new engines (e.g., Stage II: 8000 mg/kWh and Stage 
IIIA: 7500 mg/kWh) has significantly reduced emissions in recent years. With the introduction of the EU Stage 
V emission standard (Regulation EU 2016/1628) in 2019, monitoring of real-world emissions during operation 
is carried out for the first time as part of an in-service monitoring (Regulation EU 2017/655). The current data 
on real-world emissions from NRMM is still incomplete and mostly based on PEMS measurements, which are 
rare  and  costly.  To  obtain  a  greater  knowledge  of  the  emissions  of  machines  under  different  operating 
conditions, it is advisable to use a mobile device that can easily determine reliable emission values. A feasibility 
study applying Plume Chasing / Point Sampling for NOx emissions using an ICAD-NOx device (Horbansky et al., 
x emissions from construction machinery were 

measured and compared with the NOx limits for the investigated construction vehicles. 

Methods 

In  this  study,  a  combination  of  Plume  Chasing  and  Point  Sampling  is  used.  Plume  Chasing  is  an  emission 
measurement method where the plume is traced, while Point Sampling is a stationary method which measures 
the exhaust plume when vehicles pass by. The measurements were conducted next to the construction site 2   
8 m from the investigated vehicles. The exhaust plume was sampled at about 2 m above the ground with a 
tripod. The ICAD-NOx measurement device was placed in a bicycle trailer. With this mobile device it was possible 
to change the measurement position easily depending on the wind conditions and the working position of the 
construction vehicle. The plume was measured several minutes per construction vehicle and operation condition. 
The measurement principle is shown in Figure 1.  

Figure 1: Measurement principle Plume Chasing of NRMM. 

Assuming that the ratio ER between NOx and CO2 concentrations remains the same as the gas becomes diluted 
after leaving the exhaust pipe (Eq. 1), the specific emissions in g/kWh can then be calculated from the NOx to 
𝐸 . A motor efficiency 
CO2 ratio in the sampled air with the assumption of the CO2 emissions of the vehicle 𝐶𝐶𝑂2
of 40% is assumed.  

269 

 
 
 
𝐸𝑅 =

𝐸
𝐶𝑁𝑂𝑥
𝐸 =  
𝐶𝐶𝑂2

𝐵𝐺
𝑀 − 𝐶𝑁𝑂𝑥
𝐶𝑁𝑂𝑥
𝐵𝐺  
𝑀 − 𝐶𝐶𝑂2
𝐶𝐶𝑂2

( 1) 

𝐵𝐺  and 𝐶𝑁𝑂𝑥

𝐵𝐺  of CO2 and NOx, respectively, are determined using a 5 s 
The background (BG) concentrations 𝐶𝐶𝑂2
moving average of the time series of the measured signals. The BG values are determined as the lowest CO2 
value within a 120 s time interval prior to each individual measurement point, along with its corresponding NOx 
value. To ensure that only emissions from the plume of the chased NRMM are considered, a threshold of 30 
𝑀  
ppm was applied for CO2. The measurement data point was considered valid only if the CO2 concentration 𝐶𝐶𝑂2
exceeded the BG value by more than 30 ppm.  

Investigated vehicles 

Four different NRMM were investigated. Vehicle characteristics, including manufacturer, model, type, and Euro 
stage, are presented in Table 1. Among these NRMM, two are older construction vehicles of stage I and II (O&K 
L6.5 and Takeuchi TB135), measured at a rural location apart of other traffic (Figure 2, left). The investigations 
of the remaining two vehicles of stage IIIA (Gehl AL340, Takeuchi TB228) took place next to a busy road (Figure 
2, right).  

Table 1: Characteristics  of  investigated  NRMM  with  time  and  location  of  measurements  as  well  as  weather 
conditions. 

Manufacturer 

O&K 

Takeuchi 

Gehl 

Takeuchi 

Model 

Type 

L 6.5 

TB135 

AL 340 

TB228 

Wheel loader 

Mini-excavator 

Wheel loader 

Excavator 

Motortype 

Perkins 704.30 

Vanmar 3TNV88-
QTB 

Yanmar 3TNV88 
Interim Tier IV 

3TNE82A 

Fuel type 

diesel 

Euro stage 

EU I 

NOx limit (g/kWh) 

9.2 

diesel 

EU II 

8 

diesel 

EU IIIA 

7.5 

diesel 

EU IIIA 

7.5 

Day 
measurement 

of 

03.03.21 

03.03.21 

24.03.21 

24.03.21 

Wind conditions 

occasional light breeze from the south, 
south-west/east, ~2-4 m/s 

from the south,  
~0-2 m/s 

Measurement 
location 

rural, country road  
(no traffic) 

next to federal highway B3  
(main traffic) 

270 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Results and Discussion 

Examples of the time series of the NOx and CO2 signal along with their respective determined BG (NOx_BG) and 
BG-corrected  time  series  (NOx_BGcorr),  are  depicted  in  the upper  two panels  of  Figure  3  and  Figure  4.  The 
concentrations in Figure 3 are declining from 14:11 onwards after the Takeuchi TB135 mini-excavator changed 
the working position. This movement occurred on the downwind side within a field as the excavator was engaged 
in digging a shaft, as illustrated in  

Figure  15:  Takeuchi  TB135  mini-excavator  with  Plume  Chasing  setup  at  03.03.2021 
during  digging  a  shaft  (left)  and  Takeuchi  TB228  excavator  during  idling  at 
24.03.2021 (right). 

Figure 2 (left). While the measurement device and sampling position remained stationary in this situation, the 
mini-excavator moved further away. The concentrations dropped rapidly, leading to a situation where hardly 
any plume was detectable, and no data points exceeded the CO2 threshold. This underscores the significance of 
a mobile measurement setup like the bicycle trailer, which can be easily relocated even in uneven terrain. This 
flexibility  allows  the  setup  to  track  the  NRMM  plume  based  on  its  work  location  and  the  prevailing  wind 
conditions. For example, between 13:58 and 14:11 the measurement position was established at a distance of 
6 to 8 m from the excavator in the northeast direction (measuring height ~2 m) resulting in good emission 
signal. Note that between 13:59:30 and 14:02, work operations were momentarily paused.  

Figure 14: NOx and CO2 timeseries, 03.03.2021, Takeuchi TB135 mini-excavator (Stage II) 

digging a shaft; measuring height: ~2 m. 

271 

 
 
 
 
Figure 16: NOx and CO2 timeseries, 24.03.2021, Takeuchi TB228 excavator during idling and 

drilling; Measuring position: ~4 m behind the excavator, measuring height: 1.6 m. 

In Figure 4, the emission measurements took place while idling of the Takeuchi TB228 excavator. Only from 
~12:34 to 12:35 it was drilling. Due to the measurements being conducted near a busy road (see  Figure 2, 
right), the road's impact on the background (BG) values was examined further. This investigation was carried 
out during timeframes when no plume was emitted from the construction vehicles, but significant traffic was 
present on the main road. Given that the wind direction aligned with the main road from the south, little variation 
in BG was observable in the concentrations at the measurement height of approximately 2 m, the plume CO2 
concentrations were most of the time below 10 ppm and always below 30 ppm. Nevertheless, it's important to 
recognize  that  at  construction  sites  proximate  to  external  emission  sources,  careful  consideration  of  wind 
conditions and measurement positioning is crucial to ensure the correct plumes are detected. The lower panels 
in Figure 3 and Figure 4 illustrate the 2-second NOx emission values computed using Eq. 1. These 2-second 
31. Different colours show 
values are time-
different CO2 thresholds. The smaller the CO2 threshold the more valid data points are captured. The mean NOx 
𝐸 , power weighted, comparable to PEMS mean values) are indicated 
values (𝑁𝑂𝑥_𝐵𝐺𝑐𝑜𝑟𝑟
by a horizontal line.  

̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ / 𝐶𝑂2_𝐵𝐺𝑐𝑜𝑟𝑟

̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ∙ 𝐶𝐶𝑂2

The mean NOx values and the median of the 2 s NOx values are plotted in Figure 5 together with the distribution 
of the emission values for the different measured NRMM. The results show that the calculated emission values 
are consistent and plausible within the expected ranges. For the stage I (NOx limit 9200 mg/kWh) wheel loader 
(Figure 5, top left) an average emission value of 8682 mg/kWh was determined during idling. The stage II mini-
excavator (NOx limit 8000 mg/kWh) displayed an average emission value of 3181 mg/kWh during earth removal 
(Figure 5, top right) and 2959 mg/kWh while shaft excavation (Figure 5, middle). The distributions show, that 
there is not a big difference between these two operation conditions. The two stage IIIA (NOx limit 7500 mg/kWh) 
NRMM exhibited average emission values of 4394 mg/kWh and 8396 mg/kWh during idling, respectively, for 
the excavator (Figure 5, bottom left) and the wheel loader (Figure 5, bottom right). 

31 

EU-guideline 

582/2011, 

appendix 

1, 

chapter 

4  

( https://eur-lex.europa.eu/legal-content/DE/TXT/PDF/?uri=CELEX:32011R0582&from=ES ) 

272 

 
 
 
 
 
                                                       
 
 
Figure 5: Emission measurements of O&K L6.5 wheel loader (stage I) idling (top, left), Takeuchi TB135 mini-
excavator (stage II) during earth removal (top, right) and digging a shaft (middle), Takeuchi TB228 
excavator (stage IIIA) idling (bottom, left) and Gehl AL340 wheel loader (stage IIIA) idling (bottom, 
right). 

Conclusions 

The optimal measurement position and distance is dependent on wind conditions and the specific NRMM being 
measured. For this study, the optimal measurement distance is greater than 3 m, ensuring that measurements 
are taken at a non-disturbing distance from the NRMM's work area. Direct measurements within 3 m of the 
machinery  are  not  recommended  due  to  difficulty  in  tracking  exhaust  plumes  and  disruption  to  NRMM 
operations.  Emission  signals  could  still  be  detected  at  8  m  with  light  wind.  Despite  potential  disturbances, 
measurements close to roads with heavy traffic appear to work well. However, converting NOx/CO2 ratios to NOx 
emissions using a 40% engine efficiency assumption can lead to underestimating NOx emissions, particularly 
for smaller, older, or low-load machinery as a lower efficiency would lead to a higher calculated emission factor. 
So far, no ECU data was used. This data could be added in future measurements with the use of on-board 
diagnostics  (OBD).  To  further  enhance  the  understanding  of  NRMM  emissions,  measurements  should  also 
include particle emissions. Particle instruments could also be used to simplify the localization of the exhaust 
plume  of  the  construction  vehicles  due  to  their  high  sensitivity.  Within  the  EU  CARES  project  (www.cares-
project.eu), different particle instruments are tested for remote emission sensing techniques like plume chasing 
or point sampling which could probably also be used for NRMM measurements.  

273 

 
 
 
 
 
 
 
 
 
 
Acknowledgements  

This work was supported by Beißwenger Garten- und Landschaftsbau GmbH. 

References 

Helms H., Heidt C. (2014). Erarbeitung eines Konzepts zur Minderung der Umweltbelastung aus NRMM (non-road mobile 
machinery) unter Berücksichtigung   aktueller Emissionsfaktoren und     Emissionsvermin-derungsoptionen für den Bestand, 
Umweltbundesamt (Hrsg.). 

Horbanski, M., Pöhler, D., Lampel, J., and Platt, U. (2019). The ICAD (iterative cavity-enhanced DOAS) method, Atmospheric 
Measurement Techniques, 12, 3365 3381 

274 

 
 
 
 
2.8  JS.08.  Air  pollutant  and  GHG  emissions,  water 
contaminants, ambient and underwater noise and vessel-
induced mixing. 

275 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Underwater Noise Emissions From Ships During 2014-2020   

Jukka-Pekka Jalkanen1,*, Lasse Johansson1, Mathias H. Andersson2, Elisa Majamäki1, Peter Sigray3  

1 

2 

Atmospheric Composition Research, Finnish Meteorological Institute, P.O. Box 503, FI-00110 Helsinki, Finland, jukka-
pekka.jalkanen@fmi.fi  

Underwater Technology, Division of Defence Technology, Swedish Defense Research Agency, Stockholm, Sweden  

3  Marine Robotics Laboratory, Engineering Mechanics, Royal Institute of Technology, Stockholm, Sweden  

Introduction 

The disruption of shipping after the notorious September 11th attack on the World Trade Center in 2001 was 

North American sea regions (Rolland et al., 2012). In 2020, global COVID-19 pandemic brought with it a global 
disruption, which changed the traffic patterns of shipping and ground almost the whole cruise sector to a halt. 
It is expected that this global disruption of movement of goods and passengers have a widespread effect in 
the  shipping  sector.  Regional  lockdown  periods  and  travel  restrictions  reflect  strongly  on  ship  movements, 
especially  those  concentrated  on  passenger  traffic.  While  the  Sep  11th  2001  events  led  to  marine  traffic 
restrictions  mostly  concentrated  in  North  American  coastal  regions,  the  COVID-19  pandemic  had  global 
consequences. It is not currently known how marine life reacted to this unexpected change in shipping intensity 
of 2020, but it is widely recognized that noise impact on marine life ranges from masking of communication, 
stress to behavioral changes and may ultimately lead to adverse effects on population level (Duarte et al., 
2021).  

The current study was recently published (Jalkanen et al., 2022).  

Materials and methods 

The Ship Traffic Emission Abatement Model (STEAM) of Finnish Meteorological Institute (FMI) was used in this 
work (Jalkanen et al., 2012, 2018, 2009; Johansson et al., 2013, 2017). Input data for the model, the vessel 
activity  and  fleet  description,  were  obtained  from  Automatic  Identification  System  (AIS)  data  provided  by 
Orbcomm Ltd. and IHS Markit, respectively. The STEAM model predicts instantaneous vessel power use, based 
on ship identity, vessel description and speed indicated by AIS position reports. The model describes the overall 
state of the vessels and their engines considering relevant environmental regulations. Previously, this approach 
has been used to estimate emissions to air, discharges to the sea and underwater noise emissions. 

STEAM estimates vessel noise source levels using the Wittekind noise source model (Jalkanen et al., 2018; 
Wittekind, 2014) which describes low- and high frequency cavitation and machinery contributions separately. 
In the Wittekind model, vessel speed affects the noise source levels (in dB re 1 
significant  increase  if  cavitation  inception  speed  (CIS)  is exceeded.  Throughout  this  paper,  the  noise  source 
energy emitted by a ship to the sea is considered as noise emissions and are reported in energy units (Joules) 
because the duration of the noise signal is also considered. The key benefits of the used modeling approach 
include: a) the use of transponder data from AIS, which describes the ship activity as a function of time; b) 
updates of global underwater noise emission inventories, which can be reported annually; c) realistic description 
of noise as a function of vessel physical and technical description and d) construction of noise scenarios, which 
allow testing of vessel based mitigation options.  

The source level is defined at 1 m distance the energy is calculated by integration of the source level on a 
sphere using the source level. Both the source level and the energy are thus hypothetically placed in an infinite 
uniform lossless ocean, following the definition in ISO18405 (2017). When instantaneous noise is integrated 
over time, a noise source energy map is obtained  

(Jalkanen  et  al.,  2018)  which  can  be  used  to  understand  the  geospatial  distribution  of  vessel  noise  source 
energy. This is a cumulative noise source energy assessment with an integrating period of one year (total noise 
source energy) or one day. The work reported in this paper involves description of noise sources and their time 
integration as an anthropogenic environmental pressure, which can be used as a basis for further work but 
should not be taken as a description of environmental state.  

276 

 
 
 
Results and discussion 

This work is based on the global modeling of noise source energy output of individual ships. In the results, the 
noise source energy is aggregated to daily grids with a resolution of 0.1  (WGS84 coordinate system). The noise 
emissions were calculated as Gigajoules (1E9) of total emitted acoustic energy over a year and a specified 
area, in a specified frequency band (Jalkanen et al., 2018). These gridded data were produced for 63, 125 and 
2000 Hz center frequencies of 1/3 octave bands and the data generated are available for further study. All 
modeling was done at vessel level, which enabled studies of noise emissions by vessel type, age, flag state or 
size. In Figure 1, the geographical distribution of global underwater noise source energy emissions from ships 
(63 Hz 1/3 octave band) to the sea is presented. The main shipping lanes, e.g. the ones from China via the 
Malacca Strait and Red Sea to Europe, have the highest noise contributions from shipping.  

Figure 1 Global map of underwater noise emissions from ships in 2019 (63 Hz 1/3 octave band, in 
Gigajoules). The labeled areas are 1: Baffin Sea with Milnemining operations; 2: Kara Sea with Yamal gas 
fields; 3: Palmer basin research stations; 4: Galapagos Islands; 5: Socotra Island.  

In Figure 2, the difference of annual total noise source energy from ships between years 2014 and 2019 is 
shown. This has been done simply by subtracting the annual totals of 2019 from the totals of 2014 (63Hz 1/3 
octave band data). Therefore, negative values indicate a reduction of noise source energy and positive values 
an increase, respectively. It can be seen from Figure 2 that in most areas the annual shipping noise emissions 
have  increased.  An  increase  in  emitted  noise  was  predicted  for  the  South  China  Sea,  Yellow  Sea,  and  the 
Mediterranean  Sea.  Despite  some  regional  differences  in  underwater  noise  emission  patterns,  increased 
emissions were discovered in most sea areas from 2014 to 2019. At global level, the underwater noise emitted 
by ships has doubled in the period of 11.5 years for this frequency band, which is in line with the often quoted 
observations of +3 dB/decade rate, and corresponding to doubling of energy, for the  

Northeast Pacific (McDonald et al., 2006). The observed +3 dB/decade rate is not directly associated with noise 
emissions, but our results indicate non-uniform regional development of noise emissions. 

277 

 
 
 
 
 
Figure 2 Changes in underwater noise source energy emissions, 2014 2019, at 63Hz 1/3 octave band (in 
Megajoules). This difference map illustrates the changes during this period. Red areas indicate increase in 
shipping noise and blue areas signal a decrease. 

The increasing trend observed for the 2014 2019 period for the global domain was broken by the COVID-19 
pandemic. This caused a disruption in shipping activities, which, in turn, resulted to a decrease in reduced the 
noise  emissions  from  ships  nearly  to  2017  levels.  Recently,  studies  reporting  decreased  shipping  noise  in 

understand changes the pandemic introduced to underwater noise in different areas. It is expected that this 
decrease of underwater noise is only temporary and upon the recovery of the world economy, noise emissions 
will  be  increased  again.  This  is  probable  unless  vessel  operation  and  fleet  size  changes  as  a  response  to 
greenhouse gas (GHG) reduction efforts. 

In Figure 3, the reduction of underwater noise emissions from ships (at 63Hz frequency of 1/3 octave band) is 
visible on major shipping lanes between China and the EU (Arabian Sea: -8%, Red Sea: -5%, Mediterranean 
Sea: -9%). The emissions of shipping noise on Eastern China Sea (-3%) were only slightly changed and in some 
sub-regions,  like  the  Gulf  of  Thailand,  noise  emissions  increased  (+13%)  despite  the  pandemic.  The  noise 
emissions from the Baltic Sea and the Mediterranean Sea shipping had a temporary increase in autumn 2020, 
after  the  June July  2020  minimum,  but  decreased  again  towards  the  end  of  2020.  In  the  North  Sea,  a 
decreasing trend was predicted throughout the year 2020. The decrease in noise emissions towards the end of 
2020 coincides with the start of the second wave of lockdowns in Europe (Looi, 2020). 

278 

 
 
 
 
Figure 3 Changes in underwater noise source energy (63 Hz 1/3 octave band) emitted from ships in 2020
2019 (in Megajoules). This image is a difference plot of annual noise source energy emitted in 2020 and 2019. 
Noise source energy emissions are given in units of megajoules per grid cell. The COVID-19 pandemic decreased 
the underwater noise emissions significantly in major shipping routes. 

Figure 4 Regional trends of underwater noise source energy emitted by ships in 63Hz frequency at 1/3 octave 
band. Increasing noise emissions are observed in most sea areas. Open symbols are noise energies for same 
sea regions in 2020, but these have not been included in the estimation of the trend. Note, that a parabolic 
function leads to a better fit than a linear trendline in some cases.  

279 

 
 
 
 
 
 
 
Previous  measurements  in  Northeast  Pacific  over  four  decades  indicated  an  increasing  3  dB/decade  trend 
(doubling of noise every ten years), which has been viewed as moderate growth of shipping noise (McDonald 
et al., 2006). For comparison, we have computed the annual noise source energy emitted from ships in selected 
sea regions 
 including the Pacific - during 2014  2019 (Figure 4) using the sea area definitions from the 
International Hydrographic Organization (IHO). The long-term development of noise is different in various areas 
and a single number, like the 3dB/decade, does not describe the heterogeneous trends very well.  

The increase of regional ship underwater noise emissions in the 2014 2019 period was found to be diverse in 
various parts of the world. Based on the global modeling of ship underwater noise emissions, the global trend 
from 2014 to 2019 indicates that noise emissions may double every 11.5 years, but regional variations of 
noise increase are large (Figure 4).  

Based on the trends shown in Figure 4 it is possible to estimate the time which it takes to double the underwater 
noise source energy emissions. Investigation of slope of the trendline indicates the steepness of the change, 
and for some sea areas, doubling of shipping noise emissions may occur in a surprisingly short period. The 
regional differences in noise emission doubling are evident from Figure 5. The high latitudes clearly stand out, 
but also the Pacific Ocean and several European seas are areas of concern. 

Figure 5 Underwater noise emissions trend in various sea areas. Shades of blue = Doubling of shipping noise 
emissions takes more than 10 years, or it has decreased over time; Light yellow = noise emissions double 
within 7.5 10 years; Light red = noise emission doubling within 5 7.5 years; Dark red = noise emissions 
doubling in less than 3 years; It should be noted that Arctic shipping noise emissions in 2014 started at a very 
low level and modest increase in Arctic shipping easily doubled the noise emissions from ships. 

Conclusions 

A major result of this modeling study is the quantified increase of underwater noise emissions from shipping 
and its regional heterogeneity. At the current rate, the global shipping noise emissions double every 11.5 years. 
The COVID-19 pandemic has temporarily disrupted this increasing trend, but it is expected that noise emissions 
will increase again once the world economy recovers. In this paper, a rapid increase of shipping noise emissions 
in  near  pristine  areas,  like  the  Arctic  was  found,  but  starting  from  a  low  level.  Mining  operations,  oil/gas 
extraction and vessel routing through Arctic areas will lead to increased shipping noise in these regions. 

280 

 
 
 
 
 
 
 
 
 
Acknowledgements 

responsible for any use that may be made of the information it contains.  

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Size resolved particle emission characteristics for different types of vessels. 

A. Weigelt1*, J. Beecken1,2, D. van Dinther3, S. Griesel1, J. Mellqvist4 and L. Ntziachristos5 

1Federal Maritime and Hydrographic Agency (BSH), Hamburg, Germany, Andreas.Weigelt@bsh.de 

2now at Explicit ApS, Copenhagen, Denmark 

3Netherlands Organisation for Applied Scientific Research (TNO), Petten, the Netherlands 

4Chalmers University of Technology, Gothenburg, Sweden 
5Aristotle University Thessaloniki, Thessaloniki, Greece  

Introduction 

In addition to various gaseous pollutants, vessels do emit a large number of exhaust aerosol particles (Beecken 
et al., 2015). This is a result of both high particle numbers per unit of fuel used as well as an overall high fuel 
consumption  rate.  Therefore,  seagoing  vessels  are  a  very  important  anthropogenic  source  of  atmospheric 
aerosols (e.g. Ausmeel et al., 2019) and do have a significant impact on public health and climate effects (Sofiev 
et al., 2018). Nevertheless, to date, there are no legal limits for particulate emissions from seagoing vessels. 
Moreover,  the  understanding  of  the  particle  emission  characteristics  of  vessels  under  real-life  conditions  is 
limited, as only few data are available
to  Inland  Pollution  Push  for  the  Enforcement  of  Regulations  (www.scipper-project.eu)  long-term  particle 
measurements were carried out to close this gap and to provide recommendations for future regulations and 
monitoring mechanisms. Thereby, for nearly two years the particle size distribution was measured in individual 
plumes of bypassing vessels. Within the present study the total particle emission, as well as the size resolved 
particle  emission  characteristics  for  different  types  of  vessels  are  presented.  A  side-by-side  comparison  of 
different particle measurement methods was also carried out within SCIPPER but is presented separately by 
van Dinther et al (2023). 

Measurement and analysis Method 

In 2020 and 2021 the German Federal Maritime and Hydrographic Agency (BSH) installed two combined particle 
size spectrometers at their ship emission monitoring sites in Wedel (approach to Hamburg harbour) and the Kiel 
fjord (approach to Kiel and Kiel Canal). At both locations vessels pass the measurement sites in a distance of 
about 300 to 800 meter (cf. Fig.1). The measurements are ongoing. Both combined particle size spectrometers 
are composed of a TSI Fast Mobility Particle Sizer (FMPS, Model TSI 3091) and a TSI Optical Particle Sizer (OPS, 
Model TSI 3330). The FMPS measures the particle size distribution with 32 logarithmic equidistant size channels 
in the size range of 5.6 nm < dp < 560 nm. The OPS measures the particle size distribution with 16 logarithmic 
equidistant size channels in the size range of 0.3 µm < dp < 10 µm. Both systems can measure the full size 
range  with  a  response  time  of  1s.  This  means  every  second  from  each  combined  system  a  particle  size 
distribution from 5.6 nm to 10 µm with 

Figure 1: Location of the measurement sites at the Elbe River in Wedel, about 10 km in front of the entrance to 
the port of Hamburg (left) and in the Kiel fjord north of the Kiel Canal lock (right). Satellite images were taken 
from Google maps. 

282 

 
 
 
 
Figure 2: Combined particle size spectrometer (TSI FMPS 3091 + OPS 3330) installed into a climate-controlled 
shelter (left). Sample air is dried with a Nafion counter flow dryer. The PM-10 inlet prevents particles larger 
than 10 µm to enter the system. Particle size distribution (right) is measured with 1 Hz resolution with 32 (FMPS, 
blue) and 16 (OPS, yellow) size channels between 5.6 nm and 10µm with an overlap between 300 and 500 nm. 

a 48 size channel resolution is available. The setup is shown in Figure 2. The instruments are installed in air-
conditioned  shelters  so  that  they  can  be  operated  at  constant  ambient  conditions  and    independent  of  the 
weather. To ensure comparable measurements at low relative humidity, the sample air is dried by using Nafion 
counterflow dryers. The PM-10 inlet head removes particles with a diameter larger than 10 µm. As it can be 
seen in Fig. 2 on the right graph, there is an overlap between the size distributions measured by FMPS and OPS. 
Because the FMPS is known to be less accurate in the largest size channels (above 300 nm), from that size only 
the OPS data were used. 

Besides  the  particle  size  distribution,  at  both  sites  the  concentration  of  CO2,  SO2  and  NOx,  as  well  as  the 
meteorological conditions (T, p, RH, wind direction and speed, global radiation) are continuously measured with 

Automated Identification Signals (AIS) are recorded. By combining the measured vessel plumes (identified peaks 
in  the  time  series  of  trace  gas  and  aerosol  measurements)  with  the  vessels  positions  (AIS)  and  wind  data 
(calculation  of  trajectories  between  vessel  and  measurement  station),  the  measured  plumes  can  be 
automatically allocated to individual vessels. A Matlab algorithm was developed for this allocation. 

Figure 3 gives an exemplary insight into the temporal development of the integral particle concentration (Fig. 
3a) and the particle size distribution (Fig. 3b) at the Wedel station. Vessel plumes can be easily identified by 
the enormous peaks in both graphs. All peaks were assigned to passing vessels (vessel type indicated in Fig. 
3b). It is obvious that the emission characteristics observed for various vessels differ, both in particle number 
concentration and particle size. This will be further investigated in the below results chapter. Furthermore, from 
Fig 2 and 3b it is apparent that the FMPS and the OPS measure similar particle number concentration at the 
crossover point between the instruments.  

283 

 
 
 
 
Figure 3: Time series of combined integral particle number concentration (a) and particle size distribution (b), 
measured in September 2020 at the BSH ship emission measurement site in Wedel/Hamburg. In Figure (a) the 
total particle concentration is indicated in blue (contribution by FMPS) and yellow (contribution by OPS). The 
crossover  from  FMPS  to  OPS  is  indicated  in  (b)  as  black  line.  Plume  allocated  vessel  types  and  length  are 
displayed in Fig (b). 

By  subtracting  the  background  concentration  detected  before  and  after  a  plume  peak  event  from  the 
concentration  measured  during  the  plume  passage,  the  contribution  from  the  vessel  can  be  isolated.  When 
normalizing the individual plume pollutant concentration with the plume CO2 concentration and assuming the 
y converted to CO2, pollutant individual emission factors 
in  amount  of  pollutant  per  kg  of  burned  fuel  can  be  derived  (Schlager  et  al.,  2006;  Agrawal  et  al.,  2008; 
Moldanova et al., 2009; Balzani Lööv et al., 2014; Beecken et al., 2014, 2015; Kattner et al., 2015). This allows 
comparing,  combining  and  statistically  analysing  size  resolved  particle  emission  factors  from  different 
measurements, regardless of the degree of dilution of the individual measurement. Similar methods are used 
2 and NOx emissions and are described in detail e.g. by Beecken et al., 2014, 2015, Kattner et 
al., 2015, van Roy et al., 2022a,b). For the present study, size resolved emission factors from more than 10.000 
plumes have been analysed and allocated to individual vessels. 

Results 

On average, approximately 1.4·1016
 particles per kg of burned fuel (#/kg fuel) are emitted to the atmosphere by 
seagoing  vessels  at  the  measurement  sites  in  Wedel  and  Kiel.  However,  significant  different  emission 
characteristics were observed for different types of vessel, both in terms of number concentration (cf. Fig. 4) 
and particle size (cf. Fig. 5). For example, with 1.64·1016 #/kg fuel the mean emission factors of dredgers, small 
cargo vessels (< 100m) and medium-size tankers are found to be four times larger than that of inland vessels 
(1.51  and  5.07·1015  #/kg  fuel).  Furthermore,  small  cargo  vessels  were  found  to  have  an  average  particle 
emission factor 60% higher than the largest cargo vessels (>300m) and at the same time, however, the largest 
variability in the measured emission factors (difference between 10 and 90% percentile). Sea-going passenger 
vessels, i.e. ferries and cruise liners, show a relatively low total particle emission factor of 9.41·1015
  #/kg fuel. 
However, this is still 3 times more than the average particle emission factor of inland vessels. 

284 

 
 
 
 
 
 
 
Figure 4: Integral particle emission factors for different vessel types, measured between September 2020 and 
April 2022 at the two BSH measurement sites in Wedel/Hamburg and the Kiel fjord. The numbers above the 
boxes indicate how many analysed plumes were used for the respective category to calculate the given median 
and percentiles. 

As  already  indicated  above,  with  the  presented  measurement  and  analysis  method  size  resolved  particle 
emission factors can be derived for different vessel types. Even though for particles larger than 200 nm the 
emission factors looks rather similar for the different vessel types (Fig. 5a), it was found that the emission of 
coarse mode particles (dp > 1000 nm) is on average five orders of magnitude smaller than for particles in the 

emitted particles become apparent (Fig. 5b).The majority of particles emitted by dredgers are on average 75% 
larger than particles emitted from all other types of investigated vessels (dp, max=50 nm). It is noticeable that 
for all other types of seagoing vessels the size of the emitted particles is very similar (distribution of the maxima 
of the curves on the X-axis), even if there are significant differences in the number of emitted particles per unit 
of burned fuel (height of the peaks on the Y-axis). Apart from the dredgers, the inland vessels show a different 
emission characteristic with regard to both, particle size and number by having lower emission factors for all 
size classes, except below 20 nm (grey lines Fig 5 a, b). This means that the majority of particles emitted by 
inland vessels is below 20 nm and therefore smaller than for all seagoing vessels. 

The reasons for the different emission characteristics of the various vessel types probably lie in the difference 
of (I) installed engines, (II) fuels in use and (III) vessel operation modes and needs to be investigated further to 
obtain detailed particulate emission characteristics. The elapsed time between emission at the vessel's stack 
and  measurement  at  the  station  (in  this  study  mostly  between  5  and  12  minutes)  may  also  influence  the 
determined  emission  factors  and  should  be  taken  into  account  in  future  studies.  As  the  measurements  are 
ongoing at both locations, a unique dataset will be created to perform statistically sound analysis on the particle 
emission characteristics of vessels under real-life conditions. 

285 

 
 
 
 
Figure 5: Integral particle emission factors for different vessel types, measured between September 2020 and 
April  2022  at the  two  BSH measurement sites  in  Wedel/Hamburg  and  the Kiel  fjord.  The  Number of  vessel 
plumes used for each vessel type is given in Fig. 4. 

Acknowledgements  

programme under grant agreement Nr.814893. 

286 

 
 
 
 
 
 
 
 
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van Roy, W., Schallier, R., van Roozendael, B., Scheldeman, K., van Nieuwenhove, A., and Maes, F. (2022a). Airborne monitoring 
of compliance to sulfur emission regulations by ocean-going vessels in the Belgian North Sea area, Atmospheric Pollution 
Research,13-6,https://doi.org/10.1016/j.apr.2022.101445. 

van Roy, W., Scheldeman, K., van Roozendael, B., van Nieuwenhove, A., Schallier, R., Vigin, and L., Maes, F. (2022b). Airborne 
monitoring of compliance to  NOx emission regulations from  ocean-going vessels in the Belgian North Sea,  Atmospheric 
Pollution Research, 13-9, https://doi.org/10.1016/j.apr.2022.101518. 

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Effects of Sulfur Scrubbers on Particulate Emissions from a Marine diesel engine 

T. Streibel 1,2*, S. Jeong3, J. Bendl3, M. Saraji-Bozorgzad3, M. Sklorz1, C. Gehm5, L. Anders1, J. Passig1,2, J. 
Schade3, U. Etzien4, T. Adam3, B. Buchholz4, Detlef E. Schulz-Bull5, and R.Zimmermann6 

1 Joint Mass Spectrometry Center (JMSC) at Comprehensive Molecular Analytics, Department Environmental Health, 
Helmholtz Zentrum München, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany 
2 Joint Mass Spectrometry Center (JMSC) at Chair of Analytical Chemistry, Institute of Chemistry, University of Rostock, 
Albert-Einstein-Strasse 27, 18059, Rostock, Germany 
3 University of the Bundeswehr Munich, Faculty for Mechanical Engineering, Institute of Chemical and Environmental 
Engineering, Werner-Heisenberg-Weg 39, 85577, Neubiberg, Germany 
4 Chair of Piston Machines and Internal Combustion Engines, Faculty of Mechanical Engineering and Marine Technology, 
University of Rostock, Albert-Einstein-Strasse 2, 18059, Rostock, Germany 
5 Leibniz Institute for Baltic Sea Research, Marine Chemistry, Seestrasse 15, 18119, Rostock, Germany 

thorsten.streibel@uni-rostock.de 

Introduction 

The International Maritime Organization capped the fuel sulfur content of marine fuels outside ECA zones to 
0.5 % in 2020. Consequently, either low-sulfur fuels or additional exhaust gas cleaning devices for the reduction 
in  sulfur  dioxide  (SO2)  emissions  became  mandatory.  Although  a  wet  scrubber  reduces  the  amount  of  SO2 
significantly, there is still a need to consider the reduction in particle emissions and organic pollutants. 

When assessing potential damage to the environment due to emissions from shipping, the focus is mainly on 
sulfur oxides, nitrogen oxides, heavy metals and particle mass concentration. Particle number and particle size 
ntribute little to mass, 
but have very large number concentrations. In addition, organic pollutants are bound to the particles and enter 
the environment with the particles or are inhaled by humans. These organic components, which include, for 
example,  polycyclic  aromatic  hydrocarbons  (PAHs)  with  a  large  number  of  toxic  and  carcinogenic 
representatives, have also received little attention to date. A precise evaluation of the size distributions and 
number concentrations of the small particle fractions with scrubber operation has not yet taken place, nor has 
a comprehensive chemical characterization of the particulate phase. The effects on these physical and chemical 
parameters by the additional installation of particulate filter technologies in addition to the scrubber have also 
not yet been researched. 

We  present  data  on  the  particle  removal  efficiency  of  a  scrubber  regarding  particle  number  and  mass 
concentration with different marine fuel types, viz. marine gas oil and two heavy fuel oils (HFOs) with sulfur 
contents of 1.3 % and 2.4 %, respectively. An open-loop sulfur scrubber was installed in the exhaust line of a 
marine diesel test engine. Fine particulate matter with diameters below 2.5 µm (PM2.5) was comprehensively 
characterized in terms of its physical and chemical properties.  

Influence of a sulfur scrubber system on the properties of emitted particles 

Table 1: Exhaust PM mass in mg kWh-1 with 60 kW engine load before and after the sulfur scrubber (upstream 
and downstream) 

MGO 

HFO 1.3   HFO 2.4 

PM 
upstream 

mass 

59±3 

275±12 

201±8 

PM 
downstream 

mass 

39±1 

201±23 

218±13 

An important question was the effect of the use of a sulfur scrubber on the physical properties of the emitted 
particles, in particular their size distribution and mass concentration. The latter was determined for all particles, 
and the contents of sulfate particles and soot particles were also measured. With the engine operating at 75% 
of maximum power (60 kW) and the scrubber in open loop mode, the emitted particles were compared for MGO, 
HFO with 1.3% sulfur and HFO with 2.4% sulfur. The measured values are calculated as emission factors with 
reference  to  the  energy  output  of  the  engine  in  kWh  (Table  1).  MGO  exhibits  much  smaller  numbers  in 
comparison to the heavy fuel oils. The HFO with the lower sulfur content showed a slightly higher mass emission 
factor compared to the HFO with 2.4 % sulfur. Furthermore, it was revealed to contain a larger amount of PAHs 

288 

 
 
 
after analysis by two-dimensional gas chromatography/mass spectrometry. Consistently, in terms of total mass 
concentration, it can be observed that the reduction by the sulfur scrubber is relatively small (38% for MGO, 
27% for HFO with 1.3% sulfur), and there is even a slight increase of 8% for HFO with 2.4% sulfur. This can be 
explained by the new formation of sulfate particles due to the high sulfur content in the exhaust gas, while the 
soot particles decrease to a comparable extent as the total concentration. 

Another important parameter for the particles is the number concentration, which is determined together with 
the size distribution (Figure 1). 

Figure 1: 
Particle size distributions referring to energy output at 60 kW load of the engine for MGO and 
the heavy fuel oils with 1.3 % and 2.4 % sulfur, respectively (after Jeong et al., Environmental Pollution 316 
(2023) 120526)  

Again, MGO shows much smaller numbers than the heavy fuel oils. A shift in the distribution towards larger 
particles is seen for the heavy fuel oils after the scrubber, while the shape of the curve remains the same for 
MGO. For all fuels, there is a reduction in the number concentration, but this is also relatively low, comparable 
to the mass concentrations (40% for MGO, 25% for HFO with 1.3% sulfur, 30% for HFO with 2.4% sulfur). 
Accordingly, the scrubber alone will not be sufficient to achieve future restrictions on particulate emissions in 
the nanoparticle range; additional downstream filter systems will be needed for this purpose.  

The first test measurements with such particle filters were also carried out as part of the engine trials. It quickly 
became  apparent  that  reliable and targeted operation in the  exhaust gas of marine diesel engines  poses a 
major challenge to the design and operation of the filters. Well-known concepts from the automotive industry 
cannot simply be transferred directly. Although a wet electrostatic precipitator achieved an almost complete 
reduction of the total particle concentration of 99 % with HFO 2.4 % sulfur and 60 kW, continuous operation 
was only possible for a short period of time. A lot of development and optimization work is still necessary here 
in the future. 

In addition to the physical properties of the emitted particles, their chemical composition and its changes as the 
particles pass through the sulfur scrubber is also of interest. In this context, Polycyclic Aromatic Hydrocarbons 
(PAH) are of particular importance due to their health-damaging potential. Single particle mass spectrometry 
provides an insight into this. In a basic evaluation approach of the detected single particle mass spectra, the 
number of particles containing PAHs or no PAHs can be determined. This is shown in Figure 2. 

289 

 
 
 
 
 
Figure 2: Number of PAH-containing particles in the exhaust before and after the Scrubber (Open Loop) when 
running with HFO 2.4 % sulfur. 

The figure shows the number of particles detected in one hour for the two engine loads under investigation. 
Overall, about half of the particles emitted from the engine show clear PAH signals. The scrubber reduces the 
total particle count at 60 kW, but to a greater extent for the particles without PAHs. This is likely due to less 
effective collection for particles with non-polar compounds by the scrubber. For the 20 kW load condition, an 
increase  in  the  total  particle  count  can  be  seen  due  to  smaller  instabilities  in  the  engine  run.  However,  the 
decrease in the proportion of particles without PAHs with a simultaneous increase in particles containing PAHs 
is also clear here. When looking at the accompanying single mass spectra themselves, an important result is 
that the relatively strong signals of alkylated phenanthrenes, which actually always occur with PAH-containing 
particles, are only slightly influenced by the sulfur scrubber. The scrubber therefore does not ensure a significant 
reduction of these three-ring PAHs on the particles. 

A quantitative approach to the changes in the concentration of larger PAHs (4-ring systems and above) on the 
particles by the scrubber is made possible by two-dimensional gas chromatography with mass spectrometry as 
a  detector.  For  this,  particles  collected  on  filters  were  analysed.  These  quantitative  measurements  were 
performed for a selected number of major PAH compounds using the heavy fuel oils with high sulfur content. 
The first thing to notice from these measurements is an up to a factor of 10 higher PAH content in the raw 
exhaust gas for the HFO with 1.3% sulfur compared with the higher sulfur HFO. This reflects the results from 
the analyses of the fuels, where the HFO 1.3% sulfur stood out due to an exceptionally high aromatic content. 
Since a large proportion of the PAHs enters the exhaust gas as unburned fuel, and is subsequently adsorbed on 
the surface of the particles, this difference in PAH content can be explained. 

Furthermore, it was found that the PAH concentrations were higher at 20 kW than at 60 kW load, a tendency 
that also holds for particle mass concentrations. Moreover, at 20 kW the emission factors of the PAHs increase 
after the scrubber, while they decrease at 60 kW. This result, also in conjunction with the results of the single 
particle mass spectrometry, indicates that it is not possible to make any sweeping statements that are generally 
valid for all conditions with regard to the changes in PAH content. In order to come to a final conclusion, further 
experiments are necessary. 

An interesting question with respect to the operation of a wet sulfur scrubber is the transfer of gaseous exhaust 
constituents to the wash water of the scrubber. For the investigation of this problem, an online measurement 
technique capable of monitoring PAHs in aqueous systems was applied. For this purpose, a newly developed 
mass spectrometric detection method was used, in which the scrubbing water is continuously passed over a 
silicone membrane. PAHs (and also other organic compounds) can pass from the water though the membrane 
into a mass spectrometer, whereas the water itself is unable to do so. 

Figure 3 shows the PAH pattern in the wash water during open-loop operation with an engine load of 20 kW. 
Two more fuels were used in this experiment for further comparison, a heavy fuel oil with 0.5 % sulfur (so 
called  compliant  fuel  eligible  for  ship  engine  operation  without  a  scrubber)  and  diesel  fuel.  For  all  fuels, 
naphthalenes and phenanthrenes are clearly detectable in the wash water. Naphthalene was quantified and 
found to be between 47 and 125 µg/l in the heavy fuel oils. Comparison with GC-MS measurements show a 

290 

 
 
 
slight overestimation of the concentration level, but it is clear that PAHs are transferred to the wash water in 
greater amounts for the heavy fuel oils, while the distillate fuels show only a minor transition. 

Figure 3: Real-time monitored PAHs in the discharged wash water of the scrubber in Open-Loop operation with 
five different fuels with 20 kW load point. 

Conclusion 

The sulfur scrubber leads to only a slight reduction of up to 30% in the number of particles and the emitted 
particulate mass in the ultrafine particle range; at some operating points, a slight increase in the particulate 
mass  concentration  was  even  recorded  with  higher-sulfur  fuels  due  to  the  formation  of  sulfate-containing 
particles. The PAH concentrations emitted with the particles and their change due to the scrubber depend very 
much on the load points and can lead to both an increase and a decrease. No general conclusions can be drawn 
here. However, the number of PAH-containing particles is not reduced by the sulfur scrubber and may even get 
higher after passing the device. A further reduction of the total particle mass and the number of particles was 
only achieved with a particle filter downstream of the sulfur scrubber. Thus, complete removal of the particles 
from the exhaust gas is possible in principle with modern filter technology, but the relevant experiments have 
shown that additional research is required for stable long-term operation of such filter systems. 

A transfer of PAHs from the exhaust gas into the scrubbing water of the sulfur scrubber could be observed in 
real time by online mass spectrometry. For open-loop operation of the scrubber that would lead to a transfer 
of PAHs into the seawater. 

An  unilateral  focus  on  the  removal  of  the  sulfur-containing  components  of  exhaust  gas  is  not  sufficient  to 
minimize negative impacts of ship emissions on air quality, climate and health. In contrast, it is recommended 
to focus on a more complete reduction of particulate emissions. 

Acknowledgements 

This work was supported by the Federal Ministry for Economic Affairs and Climate Action by the project SAARUS 
(grant number 03SX483D). The research is also supported by dtec.bw   Digitalization and Technology Research 
Center  of  the  Bundeswehr  (project  LUKAS  and  MORE).  Dtec.bw  is  funded  by  the  European  Union 
NextGenerationEU. Support by the companies Saacke, RVT, Gea, AVL, and Sult is gratefully acknowledged. 

References 

Jeong, S., Bendl, J., Saraji-Bozorgzad, M., Käfer, U., Etzien, U., Schade, J., Bauer, M., Jakobi, G., Orasche, J., Fisch, K., Cwierz, 
P.P., Rüger, C.P., Czech, H., Karg, E., Heyen, G., Krausnick, M., Geissler, A., Geipel, C., Streibel, T., Schnelle-Kreis, J.,  Sklorz, M., 
Schulz-Bull, D.E., Buchholz, B., Adam, T., Zimmermann, R. (2023). Aerosol emissions from a marine diesel engine running on 
different fuels and effects of exhaust gas cleaning measures, Environmental Pollution, 316, 120526  

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Methane slip from LNG engines   review and on-board study  

N. Kuittinen1*, M. Heikkilä2, H. Vesala1, M. Karppanen1, P. Koponen1, P. Piimäkorpi1, J.-P. Jalkanen2 and K. 
Lehtoranta1  

1 VTT Technical Research Centre of Finland, P.O. Box 1000, FI-02044, Finland, niina.kuittinen@vtt.fi 
2 Finnish Meteorological Institute, FI-00560 Helsinki, Finland 

Introduction 

Following the regulations set by the International Maritime Organization for emissions of nitrogen oxides (NOx) 
and fuel sulphur content, the use of liquefied natural gas (LNG) as shipping fuel has increased in recent years. 
According to a recent report, about 20% of the total vessel orders in 2021 were LNG-fuelled (Plevrakis et al., 
2022). Vessels using LNG as fuel enable one transition pathway from fossil to non-fossil fuels and utilization 
in dual-fuel engines fuel flexibility for the ship operators. Compared to conventional liquid fuels, LNG has direct 
effects, namely benefits, on air quality and human health. The use of LNG as marine fuel can also reduce the 
emissions of NOx and particulate matter including black carbon, relative to operation on marine gas oil.  

LNG  is  mainly  composed  of  methane  (CH4)  which  has  higher  hydrogen-carbon  ratio  and  energy  content 
compared to liquid fuels, leading to lower emissions of carbon dioxide (CO2). However, unburned methane is a 
greenhouse gas (GHG) with a global warming potential of 28-30 times higher than CO2. Thus, while the use of 
LNG has benefits in terms of CO2 emissions and local air pollutants, slip of unburnt methane to atmosphere 
remains a concern. Currently, the European Union is in the process of issuing amendments for directives that 
regulate  maritime  transport  as  part  of  the  Green  Deal  and  Fitfor55  environmental  packages.  Two  specific 
regulations will affect methane emissions from ships: the Emissions Trading System and the FuelEU Maritime, 
which both will include CO2, CH4, and nitrous oxide GHG emissions. Currently, the emission data of methane slip 
from vessels utilizing LNG is limited. In this study, results from a review considering methane slip and other 
emissions from LNG engines are presented, together with comparison to new results collected during on-board 
campaign on a modern RoPax ferry. 

Methods 

A literature review was conducted to collect emission factors of brake-specific methane slip and other emissions 
from LNG engines from published literature and from ship owner data for engines from recent years. From the 
literature, published methane emission results could be found for engines from years 2010 (and older / newer), 
2012,  2013  (and  newer),  2016,  2016  (retrofitted),  and  2021.  In  addition,  ship  owner  data  was  received, 
including results for engines designed and constructed in the recent years (2019-2022).  

The LNG engines could be divided to four categories according to the engine technology they apply. Lean Burn 
Spark Ignited (LBSI) engines are typically 4-stroke (4-S) high or medium-speed engines which use only natural 
gas with spark plug ignition. Low Pressure Dual Fuel engines include both 4-stroke (4-S) and 2-stroke (2-S) 
engines,  in  which  natural  gas  is  injected  in  low  pressure  during  compression  stroke  and  injection  of  small 
quantity of pilot fuel is used for ignition. Fourth engine type are 2-S High Pressure Dual Fuel engines where 
natural gas is injected in high pressure at the end of the compression stroke, simultaneously with the liquid 
pilot fuel injection. The choice of engines for a vessel is dependent at least on the ship type, size, and operational 
parameters. Large container ships often use 2-stroke engines and mainly operate in deep-sea regions where 
they may apply constant engine load for long periods after exiting the harbour. On the other hand, ferries or 
cruise  ships  are  typically  equipped  with  4-stroke  engine  and  operate  on  coastal  areas  where  engine  load 
changes  may  be  more  frequent  (Schuller  et  al.,  2021).  To  consider  these  varying  operational  patterns  with 
different engine types, the methane slip values are presented as function of engine load percentage. 

To complement the review, emission measurements on a state-of-the-art LNG ferry were conducted on-board 
 and Umeå 

autumn 2021. Aurora Botnia is equipped with four Wärtsilä 31DF 4-S medium-speed dual-fuel engines with 8 
cylinders and power of 550 kW per cylinder. Exhaust was sampled from one of the engines with normal 31DF 
setup (ME4) and an engine which was piloting a new combustion concept (ME3). Both engines were operated at 
load points of 10%, 25%, 50%, 75% and 90% and the loadings were realized with the accuracy of ±2 %-units. 
The engines used LNG with high methane content of 95.1% and marine diesel oil (MDO) with very low sulphur 
content of 0.01% was used as pilot fuel. The contribution of pilot fuel to the total fuel flow depended on the 

292 

 
 
 
engine (3-13% with ME4) and (10-28% with ME3) and load condition with higher shares of MDO utilization at 
the lowest load conditions. 

On-board,  raw  exhaust  gas  sample  was  drawn  through  a  sonde  installed  in  the  exhaust  duct  few  meters 
downstream the engine and then divided to separate gaseous and particulate instruments applying different 
sample  conditioning.  A  gas  chromatograph  (Agilent  MicroGC)  was  applied  to  study  methane  concentrations. 
Additionally,  methane  was  measured  with  Fourier  transform  infrared  spectroscopy  (FTIR,  Gasmet  DX4000) 
together with water, NO, NO2, and formaldehyde. The FTIR as well as the sampling line and the filter prior to 
the  FTIR  spectrometer  were  heated  to  180°C.  NOx  was  also  measured  by  standard  method  with 
chemiluminescence detector (CLD). CO2 and carbon monoxide (CO) were measured with a nondispersive infrared 
(NDIR) analyzer. For particle emissions, particle number (PN) was studied according to the procedure mandated 
by EU Stage V regulation for inland waterway vessels which considers non-volatile particles with a diameter 
greater than 23 nm (PN>23nm). In addition, smaller particles with a diameter exceeding 10 nm (PN>10nm) were 
studied with same methodology applying a Dekati Engine Exhaust Diluter (DEED) for sample conditioning. The 
system consists of two ejector diluters, providing a total dilution ratio of 1000:1. The temperature of the first 
ejector was ∼200 °C, and the temperature at the outlet of the DEED unit was below 35 °C. Condensation particle 
counters  (Airmodus  A23  CPC  and  Airmodus  A20  CPC)  were  used  to  determine  PN>23nm  and  PN>10nm 
concentrations,  respectively.  Measurements  were  conducted  similarly  for  both  engines  and  carbon  balance 
method (described e.g., in ISO 8178 and NOx technical code) was used to calculate emission factors in kWh-1 
basis. The fuel consumption measured during the onboard studies was provided by the vessel operator together 
with  the  engine  loading  data  (power  in  kW)  and  LNG  bunkering  report  including  fuel  composition.  Pilot  fuel 
sample was received from the vessel and was further analysed for C, H and N, to include in the calculation of 
the exhaust gas mass flow rate by the carbon balance method. 

Results & discussion 

The methane slip data regarding LNG engines is limited and relies largely on measurement data from test-bed 
(Lehtoranta et al., 2019; Ushakov et al., 2019) or data provided by engine manufacturers (Lindstad et al., 2020; 
Mærsk  Mc-Kinney  Møller  Center  for  Zero  Carbon  Shipping,  2022;  Pavlenko  et  al.,  2020;  Rolls-Royce,  2012; 
Schuller  et  al.,  2021;  Stenersen  &  Thonstad,  2017)  although  a  handful  of  studies  (Anderson  et  al.,  2015; 
Balcombe et al., 2022; Corbin et al., 2020; Peng et al., 2020; Sommer et al., 2019; Stenersen & Thonstad, 2017; 
Ushakov  et  al.,  2019,  Rochussen  et  al.  2023)  which  report  original  research  data  collected  during  on-board 
measurements could be recognized. For the purposes of this study, ship owner data from 6 LPDF 2-S and 5 
LPDF 4-S engines from years 2019-2022 were received to complement the data with methane slip information 
from recently build engines.  

Majority of the found data considers methane slip from 4-stroke and 2-stroke LPDF engines, except few studies 
which  also  included  measurements  of  LBSI  engines.  For  HPDF  engines,  the  results  originate  from  engine 
manufacturers and no data including dependence of engine load was found, however, methane slip from HPDF 
engines is generally considered small due to the injection and combustion strategy of the engines (0.2-0.28 
g/kWh estimated for load ranges between 25-85% (Mærsk Mc-Kinney Møller Center for Zero Carbon Shipping, 
2022). 

Figure 1 shows brake specific methane slip emission factors for 19 LPDF 4-S engines, 8 LPDF 2-S engines, and 
8 LBSI engines for which data was available. For LPDF 4-S engines, the methane slip ranged between 2.1-10 
g/kWh at 100%, 3.1-10.1 g/kWh at 75% load, 2.6-16.7 g/kWh at 50% load, 6.1-70.2 at 25% load and 12.2-
123 g/kWh at 10% load. Respectively, for LBSI engines, corresponding values were 2.5-4.2 g/kWh at 100% load, 
3.3-5 g/kWh at 75% load, 4.1-7.2 g/kWh at 50% load, and 6.4-42 g/kWh at 10% load. In the case of LPDF 2-S 
engines, methane emissions ranged between 1.9-2.5 g/kWh for operation at 100% load, 2.4-2.9 g/kWh at 75% 
load, 2.4-5.1 g/kWh at 50% load and 2.8-7.2 g/kWh at 25% load.  

293 

 
 
 
Figure  1.  Break-specific  emission  factors  as  function  of  engine  load  for  LPDF  4-S,  LBSI  4-S,  and  LPDF  2-S 
engine.  Figure  includes  data  from  reviewed  literature  as  well  as  ship  owner  data.  (Data  sources:  LPDF  4-S 
engines (Anderson et al., 2015; Balcombe et al., 2022; Corbin et al., 2020; Lehtoranta et al., 2019; Peng et al., 
2020; Sommer et al., 2019; Ushakov et al., 2019; Rochussen et al. 2023; ship owner data), LBSI engines (Rolls-
Royce, 2012; Ushakov et al., 2019); LPDF 2-S engines (Balcombe et al., 2022; WinGD according to Pavlenko et 
al., 2020; ship owner data). For details of data sources, see Kuittinen et al. (2023). 

Increase in brake specific methane emissions as function of decreasing engine load could be observed for all 
engine types. In case of LPDF 2-S engines, the load dependency was suppressed, although it should be noted 
that data was limited to load conditions of 25% or above. In the case of LPDF 2-S engines all data originates 
from relatively new engines (~2018-2021), whereas for LBSI engines, newest published data could be found 
for  engine  from  2015.  Largest  amount  of  data  was  found  for  LPDF  4-S  engines  with  results  available  for 
engines  from  2012  until  2021.  For  LPDF  4-S  engines,  it  could  be  noted  that  methane  emission  results  for 
engines from 2016-2019 were equal or higher than those from engines built earlier at all engine loads. For the 
newest  engines,  emission  factors  obtained  from  ship  owner  data  measured  at  test-bench  show  decreased 
values compared to engines from 2016-2019, especially at 50% load and below. For on-board studies, results 
from two on-board studies indicate higher values (especially below 50% load) whereas in one case (Rochussen 
et al. 2023) where engine from 2016 was tested with new engine control calibration, methane slip values were 
among the lowest and agreed with test-bed values for the new engines. 

Taking a  closer look to the on-board results  from LPDF 4-S engines  (Figure 2), 8 studies conducted on 5-6 
vessels were found, covering methane and, in some cases, additional gaseous and particle emissions. These 
results could be complemented with recent on-board results where two engines ME4 (standard engine setup 
build in 2021) and ME3 (engine piloting a novel combustion concept) (see Lehtoranta et al., 2023 for details) 
were studied. Including the recent on-board results shows that methane slip from recently build engines can 
exhibit lower values than majority of the currently available literature suggests, especially at the low engine 
loads. At loads exceeding 25%, similar methane levels were observed for ME4 that in the case of 2016 engine 
fitted with new engine calibration. For the ME4 engine, methane slip varied between 3.3-3.6 g/kWh at 50-85% 
loads, and methane slip of 7.6 g/kWh was observed at 25% load and 12.4 g/kWh at 10% load. In the case of 
ME3, employing the new combustion concept, corresponding values of 1.4-1.6 g/kWh were seen at 50-85% 
loads, 1.5 g/kWh at 25% load, and 3.9 g/kWh at 10% load. From the previous studies, very low values of 0.9 
g/kWh at 70 and 90% loads have been reported for a 7.6MW engine with larger cylinder (Anderson et al., 2015). 

294 

 
 
 
Figure 2: Methane slip emission factors for LPDF 4-S engines studied on-board from literature and recent on-
board measurements (Lehtoranta et al., 2023).  

Considering other gaseous emissions (Figure 3), the results obtained from the on-board study add to the current 
data of CO2, nitrogen oxides (NOx), carbon monoxide (CO), and formaldehyde (HCHO) emission factors. 

Figure 3: Emission factors of CO2, NOx, CO, and HCHO for LPDF 4-S engines studied on-board from literature 
and recent on-board measurements (Lehtoranta et al., 2023). 

Together with the current literature values, it is seen that CO2 levels observed from ME4 and ME3 somewhat 
align with Anderson et al. (2015) and Rochussen et al. (2023), together showing 390-550 gCO2/kWh when load 
decreases from 90% to 25%. Similar trend of increased emissions at 10% load is seen as in Balcombe et al. 
(2022) while their study shows generally higher CO2 level between 500-660 gCO2/kWh (25-100% load) and 
1100  g/kWh  (at  10%  load).  NOx  emissions  were  covered  for  six  different  engines  in  the  included  literature, 
showing  generally  low  values  of  0.5-4.3  gNOx/kWh  over  the  load  range  from  25%  to  100%  and  1.9-6.7 
gNOx/kWh at loads below 25%. The results from ME4 mainly align with these values, except at 10% load when 
18.2 gNOx/kWh was observed. For ME3, NOx emission factors were on a low level of 0.2-0.4 gNOx/kWh for all 
load conditions between 10-100%. In case of CO, the results from ME3 (0.7-1.2 gCO/kWh) and ME4 (1.3-1.4 
gCO/kWh) aligned with emission levels of 1.4-2.1 gCO/kWh at 75-100% loads from three on-board studies. At 
25-50%  loads,  ME3  and  ME4  showed  somewhat  lower  values  (0.7-1.8gCO/kWh  and  1.7-4.2gCO/kWh)  than 

295 

 
 
 
 
 
previous studies (2.1-7 gCO/kWh). In the literature, formaldehyde was included in one on-board study where 
120-470  mgHCHO/kWh  were  reported  at  25-100%  loads  and  significantly  increased  value  of  2500 
mgHCHO/kWh at idle. Results from ME4 (90-550 mgHCHO/kWh) and ME3 (50-210 mgHCHO/kWh) indicate lower 
formaldehyde emissions from the modern engine, especially in the case of the new combustion concept.  

Non-volatile  particle  number  (PNnv)  has  been  reported earlier  from  two  on-board  studies,  where  PN  (>6nm) 
emission factors of 1.0-3.1×1012 1/kWh (25-90% loads) together with 2.3-3.0×1012 1/kWh (53-90% loads) and 
1.4×1014-3.0×1015  1/kWh  (6-50%  loads)  were  reported.  The  results  from  recent  on-board  measurements 
showed lower values of 0.14-3.4×1012 1/kWh (>23nm) and 0.39-26×1012 1/kWh (>10nm) for ME4 with increase 
towards low load conditions. For ME3, the results were 0.65-23×1012 1/kWh and 2.6-43×1012 1/kWh, aligning 
with  the  previous  results  at  high  loads  but  remaining  one  to  two  orders  of  magnitude  lower  than  the 
observations by Corbin et al., 2020. Different cut-points of the measurement instruments however complicate 
the comparison and instead of differences between engines may indicate high number of smallest particles in 
the exhaust.  

Figure 4: Non-volatile PN emission factors for LPDF 4-S engines studied on-board from literature and recent 
on-board measurements (Lehtoranta et al., 2023). 

Conclusions 

Review  of  literature  presenting  methane  slip  emission  factors  for  LNG  engines  showed  limited  amount  of 
available data for different LNG engine types. For HPDF engines, methane slip is considered low (0.2-0.3 g/kWh) 
and no load-dependent data was found. For other engine types, brake specific emissions increased towards low 
load conditions and varied between 2-26 g/kWh for LBSI, 2-70 g/kWh for LPDF 4-S, and 2-7 g/kWh for LPDF 2-
S at 25-75% loads. In addition to NOx, other gaseous and particle emissions were reported for LPDF 4-S engines. 
These emissions from on-board studies were complemented with recent measurements of two recently build 
engines  on-board  a  modern  RoPax  ferry.  Together  they  showed  that  reduced  methane  slip  levels  can  be 
achieved  with  state-of-the-art  LNG  engine  (3-7  g/kWh  at  25-90%  loads)  and  especially  new  combustion 
concept (1.4-1.6 g/kWh at 25-90% loads). NOx, CO, CO2, formaldehyde, and PN results from on-board studies 
show generally low levels for LPSF 4-S engines, but increased concentrations of especially PN and formaldehyde 
at low loads were shown.  

Acknowledgements  

This work was supported by the EU HORIZON project GREEN RAY (Grant number: 101056642). 

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Particulate and gaseous emissions from a large 2-stroke slow speed marine 
engine equipped with open-loop scrubber under real sailing conditions 

Achilleas Grigoriadis1, Nikolaos Kousias1, Anastasios Raptopoulos1, Sokratis Mamarikas1, Anastasios Kontses1, 
Zisimos Toumasatos1, Håkan Salberg2, Anna Lunde-Hermansson3, Jana Moldanová2, Leonidas Ntziachristos1 

1 Department of Mechanical Engineering, Aristotle University, P.O. Box 458, 54124 Thessaloniki, Greece, *leon@auth.gr  

2 IVL Swedish Environmental Research Institute, PO Box 5302, SE 400 14 Goteborg, Sweden 

3 Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, SE-41296 

1. Introduction 

Maritime sector is responsible for transporting over 80 % of the global trade by volume (UNCTAD, 2022), but 
the drawback of this activity is the significant adverse contribution to air pollution, air quality degradation and 
human health effects. Ships emit high quantities of harmful pollutants, such as Sulphur Oxides (SOx), Nitrogen 
Oxides (NOx) (Lehtoranta et al., 2019b) and Particulate Matter (PM) (Viana et al., 2014), as well as emissions 
contributing  to  climate  warming  (CO2,  BC,  etc)  (IMO  2020).  PM  properties  such  as  particle  Number  (PN) 
concentration and Particle Size Distribution (PSD) are also of significant importance, due to severe consequences 
of PM on human health, since most of the emitted particles by combustion in ships are found in the ultrafine 
region (below 100 nm) (Kuittinen et al., 2020). Typical marine residual fuel (Heavy Fuel Oil - HFO), containing 
high sulphur and ash-forming components, instead of distillate (Marine Gas Oil  - MGO) in high power rated 
engines increases energy specific PM formation (Grigoriadis et al., 2021), deterioration air pollution, especially 
in port areas. 

The  International  Maritime  Organization  (IMO)  has  taken  actions  to  reduce  shipping  greenhouse  gas  (GHG) 
emissions, which were projected to reach up to 17 % of global CO2 emissions by 2050. NOx are regulated in the 
context of engine certification and are subject to further geospatial restrictions in low emission areas, called 
Nitrogen Emission Control Areas (NECA) (IMO, 1997). IMO has regulated SOX emissions by the introduction of 
fuel sulphur content (FSC) reduction from 3.5 % to 0.5 % on a mass basis at a global scale, as from 1st January 
2020  (IMO,  2016).  In  addition,  a  stricter FSC  limit  of  0.1  %  is  applicable  in Sulphur  Emission  Control Areas 
(SECA). 

The drawback of using low sulphur fuels is the rise in fuel expenditure. An alternative enabled by IMO and often 
adopted  is  utilization  of  high-sulphur  HFO  in  combination  with  exhaust  gas  cleaning  systems,  so-called 
scrubbers. Scrubbers can decrease exhaust SO2 emissions, typically by up to 99 % (Lehtoranta et al., 2019b), 
so to equivalently meet low sulphur fuel criteria. Three types of scrubbers are technically feasible and most 
often  installed  in  ships:  open  and  closed  loop  scrubbers  and  hybrid.  Open-loop  scrubbers  use  the  natural 
alkalinity of seawater to clean the exhaust, while closed-loop ones utilize fresh water with an added alkaline 
chemical.  Hybrid  scrubbers  can  operate  on  both  configurations,  depending  on  the  restrictions  imposed  over 
different sailing areas. Although scrubbers are used to primarily abate SO2 emissions, PM are indirectly affected 
through the reduction of sulphates (Winnes et al., 2020), organics and elemental carbon (Fridell and Salo, 2016). 

Number of scrubber installations on ships have increased dramatically since the introduction of 0.5 % global 
sulphur  cap  (Comer  et  al.,  2020),  which  results  in  increasing  number  of  ships  using  high-sulphur  HFO  in 
combination with scrubber operate in ports and near populated areas. Hence, the imperative to characterize the 
emission  performance  of  these  emission  controls  is  steadily  rising.  To  evaluate  emissions  downstream  of 
scrubbers,  a  few  studies have  performed  on-board  measurements  on  ships  equipped  with  scrubbers,  under 
real-world conditions (Winnes et al., 2020), (Fridell and Salo, 2016), (Yang et al., 2021), as well as on engines 
with scrubbers placed on test beds (Zhou et al., 2017), (d. Santos et al., 2022) (Jeong et al., 2023). All three 
types of scrubbers have been tested in literature: open-loop (Fridell and Salo, 2016), (Karjalainen et al., 2021) 
closed-loop (Winnes et al., 2020), (Zhou et al., 2017) and hybrid ones (Lehtoranta et al., 2019a), (Johnson et al., 
2018). Exhaust was characterised for gases and particles both upstream and downstream of scrubbers in the 
mentioned  literature  studies,  in  order  to  compare  emission  levels  and  evaluate  scrubber  performance. 
Additionally, Zhou et al., (2017), Winnes et al., (2020), Karjalainen et al., (2021) and (d. Santos et al., 2022) also 
measured  emissions  at  the  same  engine  operating  with  a  low  sulphur  fuel  (scrubber  was  deactivated),  to 
compare its emissions with those downstream of scrubber as well as to evaluate the fuel effect (high versus 
low sulphur fuels). Sampling has been conducted based on the ISO 8178 method in several studies (Winnes 
and Fridell, 2010), but this method is not applicable when FSC is greater than 0.8 % (Ntziachristos et al., 2016). 
Winnes et al., (2020) and Fridell and Salo, (2016) measured PN and PSDs both upstream and downstream of 

298 

 
 
the scrubber. In addition, both teams applied thermal treatment to the sample, with the application of a TD, in 
order to vaporize volatile particles. 

Shipping  exhaust  particle  emissions,  and  especially  those  from  scrubber  outlets,  have  not  been  thoroughly 
investigated,  in  comparison  to  particles  from  other  sources,  such  as  automotive.  Understanding  emission 
performance downstream of scrubbers is of importance, in order to evaluate the effect of shipping on air quality 
and human health, in particular near populated areas. Levels and characteristics of particle emissions in the 
outlet of scrubbers depend on both engine operation and scrubber performance parameters (Yang et al., 2021). 
Such  parameters  correspond  to  engine  load  (Winnes  et  al.,  2020),  engine  age  (Johnson  et  al.,  2018), 
geographical location of cruising (ECA/other particular regional sulphur restrictions or global IMO FSC), water 
alkalinity  (seawater  or  added  chemical)  and  specific  operation  of  scrubber  by  the  ship  crew,  as  well  as 
combination of these parameters under actual sailing, however their specific contribution to emissions is not 
well defined. 

Given the ongoing discussions on fuel change to low-sulphur or alternative fuels, and the increasing utilization 
of  scrubber  on  retrofit  or  newbuild  ships,  understanding  the  scrubber  and  fuel  impact  on  emissions  and 
especially those of PM becomes crucial. This paper intends to present the particulate and gaseous emissions of 
an  actual  vessel  equipped  with  a  slow  speed  2-stroke  engine  and  an  open  loop  scrubber  during  real-world 
operation over a 7-day measurement campaign. The objective is to collect real performance data in order to 
feed emission and air pollution modelling tools with emission factors (EF) downstream of a scrubber both for 
open seas operation but especially for operation near inhabited port regions. 

2. Materials and methods 

2.1 Measurement campaign 

The on-board measurement campaign was performed on a 300 m long container vessel, between 16 and 24 
November 2021, on a voyage from the port of Rotterdam, Netherlands to the port of Gebze, Turkey. The ship 
was built in 2002 and was equipped with a 2-stroke slow speed diesel (SSD) engine as a main engine (ME), with 
a nominal power of 62 MW at 98 rpm. The ship was travelling at an average speed of 18.4 knots during the 
cruising phase of the trip, similar to its indicated service speed, while the engine load at the same period was 
c.a 36.2 %. The ship is equipped with an open-loop exhaust gas cleaning system, in order to comply with the 
global  0.5  % FSC  cap and the stricter 0.1  %  FSC  limit inside  SECAs and ports, when fuel with  high  sulphur 
content is used. The exhaust lines of both ME and AEs enter the same scrubber and exit on a common funnel. 
The physical dimensions of the scrubber tower are 13.8 m in height and 4.5 m in diameter. Three pumps for 
seawater feeding are used to maintain the necessary seawater flow inside the scrubber. The seawater flow 
rate ranges from 200 to 1200 m3/h, depending on total exhaust flowrate that has to be cleaned. A dedicated 
software  is  also  installed  on  the  ship  from  the  scrubber  manufacturer  to  measure  and  monitor  several 
parameters, including those required by regulations, i.e. SO2/CO2 ratio in the exhaust, pH and turbidity in the 
water effluent. 

2.2 Fuel 

-board campaign. Two batches of HFO fuel (further 
called HFO1 and HFO2) were used, having FSCs of 2.64 % and 2.45 %, respectively, and one ULSFO (0.1 % 
FSC). Ship started its voyage by using HFO1 and after two days, on 18/11, HFO2 was inserted in the fuel system. 
At the final stage of the trip, on 23/11, the fuel was changed to ULSFO, while the fuel transition from HFO2 to 
ULFSO lasted for 5 hours. 

2.3 Testing and sampling 

The emitted particles were examined with a variety of sampling systems and conditions. Two different units of 
the same dilution system (Dekati eDiluter Pro) were utilized for sampling of the exhaust gas from the stack 
through an L-shaped probe, and its dilution. The eDiluter Pro is a two-stage dilution system operating on cleaned 
pressurised air, where the first stage is heated and the second stage remains at room temperature. The eDiluter 
was connected to the sampling probe, which was heated in case of downstream-scrubber sampling, by a heated 
transfer line maintained at 150 °C. The heated transfer line was insulated throughout its whole length, but heat 
was not applied on the section close to scrubber, due to the risk of malfunctioning the heating resistances from 
the  major  sea  water  leakage  of  scrubber.  However,  temperature  was  also  maintained  at  this  first  section 
because of the high induced exhaust temperatures of c.a 300 °C. The diluted sample was led through a 12-

299 

 
 
meter  insulated  copper  line,  to  the  instrument  room,  where  the  sample  was  split  and  fed  to  the  individual 
instruments  by  Tygon  tubing.  The  first  eDiluter  Pro  (Dilution  system  1)  was  used  for  collection  of  grabbed 
samples on PM filters and absorbents for off-line analyses: PM gravimetry and chemical characterisation of the 
exhaust. The second eDiluter (Dilution system 2) was connected to the online instrument for PN measurements. 

The sampling line for the off-line characterisation used dilution with DR of c.a. 25 and temperature of 40 °C 
and the diluted sample was led by non-heated Tygon line of length of c.a. 5 m to a filter holder followed by a 
vacuum  pump  and  gas  meter.  The  gas  flow  through  the  filter  unit  was  c.a.  20 l/min  and  the  collected  PM 
corresponded to the total suspended PM. Samples collected on polytetrafluoroethylene membrane filters (Teflo 
filters - Pall Corporation), with mean pore size of 2 µm, were used for PM collection for gravimetric analysis. 
The  analysis  was  performed  under  45-50  %  relative  humidity  and  19-21  °C.  The  uncertainty  for  the  mass 
determination was 20 µg with 95 % confidence interval. 

PSDs and PN concentrations were measured with a Scanning Mobility Particle Sizer (TSI SMPS model 3080), 
comprising an impactor of 0.071 cm, a Differential Mobility Analyzer (TSI DMA model 3081) and a Condensation 
Particle Counter (TSI CPC model 3776), covering a particle range from 5.9 to 225 nm. 

Three alternative sample conditioning schemes were studied to characterize total (volatile and non-volatile) and 
non-volatile particles alone, with two different thermal treatment configurations. The eDiluter Pro was used as 
the main dilution system for all three sampling schemes. The eDiluter has a first dilution stage, using heated 
dilution air that varied between 150 and 250 °C, followed by a second, non-heated (cold) dilution stage (25 °C). 
The total Dilution Ratio (DR) after the second stage ranged between 25 and 100, based on the measurement 
requirements. The DR was monitored and verified with simultaneous CO2 concentrations measurements.  

In the first sampling scheme, the diluted sample was directly fed to the instruments after the second dilution 

(Amanatidis et al., 2018) was applied after the second dilution stage of the eDiluter. The TD comprises a heating 
stage at 300 °C to vaporize semi-volatile particles and the vaporized material is then collected on activated 
carbon  wall  surfaces.  Hence,  only  particles  not  volatile  at  300  °C  penetrate  the  TD.  This  is  denoted  as 

dilution. The third dilution scheme utilized a commercially custom-made Catalytic Stripper (CS) with oxidation 
and sulphur storage capabilities, identical to the one that was used by Amanatidis et al., (2018). The CS was 
designed  to  operate  along  with  an  Ejector  Diluter  (ED)  providing  a  DR  of  12.  To  maintain  a  relatively  high 
concentration, despite the additional dilution, the sample for the CS + ED was taken from the first dilution stage 
of the eDiluter, at nominal DR ranging between 5 to 10. The CS + ED was heated at 350 °C, to evaporate volatile 
complete 
and  semi-
sampling scheme of the measurement campaign is demonstrated in Figure 1. 

Gaseous emissions were measured in raw exhaust by a Horiba PG 350 E multi-gas analyzer and by a Bernath 
Atomic BA 3006 flame ionization detector (FID) for THC (Total Hydrocarbons), which both operate on 1-s time 
resolution.  Non-dispersive  infrared  (NDIR)  analyzer  was  utilized  to  measure  SO2,  CO2  and  CO  and  a 
chemiluminescence analyzer (CLA) for NOX in the Horiba system. The exhaust sample was extracted by a probe 
connected to a ceramic filter and then led to the instruments with a sampling line heated to 180 °C. The length 
of the heated sampling line was 15 m.  

Figure 1: Layout of the measurement setup 

300 

 
 
 
Samples were extracted from two different locations, upstream and downstream of scrubber tower, which are 
designated as Sampling Point Upstream (SPU) and Downstream (SPD), respectively, illustrated in Figure 1. The 
sampling method followed, dilution systems and instrumentation were the same for the two sampling points 
with exception of some modifications of the sample extraction: The probes for PM sampling were heated in SPD 
to avoid condensation of the wet and cold exhaust on the probe, and the heated transfer lines, connecting the 
eDiluter to the sampling probe, were spanned for 2.5 m and 1 m at SPU and SPD, respectively, due to lack of 
suitable  space  and  ease  of  accessibility  in  each  case,  without  affecting  the  set  temperature.  On  SPU,  raw 
exhaust from the engine was measured, while on SPD scrubbed exhaust was collected. Therefore, the effect of 
scrubber from the same engine with the use of HFO can be evaluated. Measurements were also performed at 
ULSFO operation at SPD but with the scrubber deactivated, so, in this case, we consider this to be an equivalent 
sampling condition to SPU used for HFO. Thus, the effect of fuel (HFO and ULSFO) from the same engine can 
be assessed, as well as an emissions comparison between ULSFO and HFO combined with scrubber can also be 
performed. 

PN concentrations and size distributions were corrected for losses, primarily occurring in the eDiluter, the rather 
long sampling copper transfer lines, the TD, the CS sampling lines and the CS + ED. Losses from sampling lines 
and  the  rest  instruments  were  considered  per  size  range,  based  on  the  SMPS  size  range  (Figure  2).  For 
estimating  sampling  line  losses,  a  SMPS  Nanoscan  (TSI  NanoScan  SMPS  model  3910)  was  utilized,  which 
measured particles at various size ranges, initially at a position downstream of the eDiluter and then after the 
12-meter transfer line, upstream of the SMPS. Therefore, particle losses produced into the sampling transfer 
lines can be assessed per size range. Losses induced from the eDiluter, TD and CS were estimated based on 

Figure 2: Dilution system, sampling copper transfer line, thermodenuder, catalytic stripper sampling lines and 
catalytic stripper + ejector diluter losses in relation to particle size diameter 

As seen from Figure 2, losses were found to increase on smaller particles. Specifically, particle losses up to 40 
%  from  sampling  lines  were  observed,  which  were  mainly  attributed  to  diffusion,  and  were  decreased  with 
increase in particle diameter. In the sampling line connecting the first stage of the eDiluter to the CS and the 
line connecting the CS to the ED, losses were estimated to be constant at 20 % for the whole size range, which 
occurred due to thermophoresis, since these lines were not insulated and were exposed to room temperature, 
while hot exhaust was passing through. The eDiluter losses were mainly arose due to the turbulent flow and 
the high residence time of the sample to the system. TD losses, mainly attributed to diffusion in the adsorber 
section, were reduced with the increase of particle diameter and remained constant at 25 %. Losses up to 14 
%  were  found  at  smaller  particles  on  CS,  due  to  diffusion  and  thermophoresis,  and  were  minimized  with 
diameter particle increase. In general, particle penetration losses found increased on smaller particles, due to 
diffusion and thermal losses (Amanatidis et al., 2018). 

301 

 
 
 
 
 
 
 
 
3.Results and discussion 

3.1 Overview of gaseous emissions 

The mean SO2 NOX, CO and THC emission levels (in g/kgfuel) are displayed in Figure 3, where the fuel used, the 
sampling point and mean engine load are also depicted. The results, in Figure 3, present the mean of emission 
rate values, measured in 1-s resolution, for the sampling point (upstream and downstream scrubber and with 
ULSFO). Error bars in Figure 3 correspond to the standard deviation of the measurements per each sampling 
point. 

Figure 3: Emission levels (in g/kgfuel) of SO2, NOX, CO and THC*10 at different sampling points (upstream and 
downstream the scrubber), fuel type (HFO, ULSFO) and main engine load. Error bars correspond to two standard 
deviations. Equivalent SO2 for 0.5 % and 0.1 % FSC values are represented by red and green horizontal lines, 
respectively, on the left panel. 

As  expected,  much  higher  SO2  was  measured  upstream  of  scrubber  (31.8  g/kgfuel)  than  downstream 
(7.63 g/kgfuel), which decreased SO2 emissions by 75 %, to a level complying with the 0.5 % global sulphur 
limit (10 g/kgfuel equivalent   red dashed line). Scrubber was not operated on its full capacity, since the actual 
seawater flow rate ranged between 640 - 725 m3/h, during the cruising phase. ULSFO resulted to the lowest 
SO2 levels, since this fuel actually had sulphur content similar to the stricter 0.1 % limit (2 g/kgfuel equivalent 
 green dashed line). Lehtoranta et al., (2019b), Winnes et al., (2020) and Fridell and Salo, (2016) reported 
scrubber  efficiencies  of  approximately  99  %  in  SO2  reduction.  In  our  study,  the  calculated  SO2/CO2  ratios 
according to MEPC (2015) were always lower than 21.5, which represents the global 0.5 % IMO FSC limit. This 
was also cross-checked with the scrubber monitoring equipment recordings, that were provided by the vessel 
crew. As a result, since regulation compliance is accomplished, there is no need for the ship operator to further 
increase the scrubbing performance, which would in-line increase the energy and fuel consumption. 

NOX levels vary slightly (approximately ±4 % in average) between the different operating conditions. Specifically, 
based  on  results  shown  in  Figure  3,  scrubber  operation  seems  to  increase  NOX  emission  levels  by  4.3  % 
compared  to upstream, while  ULSFO decrease NOX emissions by 4.3 %  compared to HFO. Grigoriadis et  al., 
(2021) showed that lower engine load leads to higher NOX emissions, while fuel does not play a critical role in 
influencing NOX emissions, since NOX formation is mainly determined by combustion setting parameters, rather 
than fuel properties for typical liquid marine fuels (residual and distillate). Winnes et al., (2020), Yang et al., 
(2021) and Jeong et al., (2023) observed minor or negligible differences in NOX upstream and downstream of 
a  scrubber,  and concluded  that  any  differences were related  to  the  different  engine loads. Fridell and Salo, 
(2016) observed a 12 % NOX reduction downstream of scrubber compared to upstream, when measured on a 
2-stroke SSD engine at 51 % engine load. Lehtoranta et al., (2019b) showed a 5 % decrease in NOX emissions 
over  the  scrubber,  supposing  that  probably  NOX  are  transferred  into  the  effluent.  Combining  our  emission 
observations with the existing knowledge from literature, scrubber impact on NOX is not well established, but it 
is considered negligible for NOX regulation compliance. 

CO  emissions  upstream  and  downstream  of  scrubber  and  with  ULSFO  were  found  at  2.01,  2.35  and  2.75 
g/kgfuel when measured at 37 %, 34 % and 11 % engine load, respectively. Similar to CO, HC emission levels 
upstream and downstream of scrubber and with ULSFO were observed at 0.364, 0.412, 0.588 g/kgfuel when 
measured at 37 %, 34 % and 11 % engine load, respectively. Our approach is that these pollutants are mainly 
combustion related and therefore scrubber and fuel were not affecting their emissions performance.  Engine 
load emerges as the dominant parameter affecting these pollutants, as evidenced by a substantial increase in 
CO  and  HC  emission  levels.  Specifically,  at  lower  engine  load  (11%),  CO  emissions  rose  by  37%,  and  HC 
emissions surged by  62%  compared to  the  levels observed at higher engine load (37%). Similar trend with 

302 

 
 
 
engine load for CO and THC is also observed by Grigoriadis et al., (2021). Winnes et al., (2020) and Lehtoranta 
et al., (2019b) reported about 20 % CO reduction downstream of scrubber on 4-stroke MSD engines, while Yang 
et  al.,  (2021)  did  not  find  any  statistically  significant  differences  in  CO  EFs,  when  compared  upstream  and 
downstream  of  scrubber,  on  a  2-stroke  engine.  Fridell  and  Salo,  (2016)  showed  a  200  %  increase  in  CO 
downstream of scrubber, without providing a specific reason for that. Compared to our results, where we found 
13 % increase of THC downstream scrubber, Winnes et al., (2020) measured THC reduction over scrubber of 20 
% to 50 % depending on the engine load, while Fridell and Salo, (2016) only a slight decrease. In our case, 
scrubber seems to adversely impact CO and HC emissions, but since engine load was not remained constant 

evaluated. Literature findings in conjunction with our observations are controversial in terms of scrubber impact 
on CO and HC, and therefore more research is needed in this direction. 

High error bars depicted in NOX, CO and THC for measurements conducted downstream of the scrubber were 
most  probably  related  to  more  measurements  performed  over  different  operating  conditions,  compared  to 
upstream  and  ULSFO  measurements.  In  total,  38  hourly  downstream  scrubber  measurements  have  been 
performed,  while  this  is  only  5  hourly  measurements  upstream  of  the  scrubber  and  7  hourly  ULSFO 
measurements, over the 7-day voyage. Since these pollutants are combustion related (Grigoriadis et al., 2021), 
operating conditions and mainly engine load variance, corresponds to a critical parameter that affects their 
performance. 

3.2 Particulate emissions 

3.2.1 Scrubber and fuel effects 

The mean PM levels (in g/kgfuel), derived from filter-based measurements, are demonstrated in Figure 4, in 
relation to the fuel, sampling point and mean engine load. The results, in Figure 4, represent the average of 
emission rate values based on the sampling point. Error bars in Figure 4 correspond to the standard deviation 
of the measurements per each sampling point. 

In our study, PM downstream of the scrubber was found 39 % higher compared to upstream levels, while ULSFO 
operation reduced PM emissions compared to HFO engine-out emissions by 68 % (Figure 4). Lehtoranta et al., 
(2019a) measured 21-45 % and 8-17 % PM reduction over the scrubber in high (75 %) and low (40 %) engine 
load conditions, respectively, from a 4-stroke MSD engine. Winnes et al., (2020) showed that PM was reduced 
over scrubber by 34 % and 42 % at 76 % and 49 % engine load, respectively, from a 4-stroke MSD engine. 
They also examined the emissions of low sulphur fuel (LSF) with 0.1 % FSC and found that PM was 59 % and 
36 % lower than PM downstream of the scrubber at 75 % and 48 % engine load, respectively. Fridell and Salo, 
(2016) also reported reduction of PM through scrubber, with high removal efficiency (75 %). Karjalainen et al., 
(2022)  observed  70  %  decrease  of  PM  emissions  downstream  of  the  scrubber,  however  the  ship  was  also 
equipped with a Diesel Oxidation Catalyst (DOC) just upstream of the scrubber. They also presented 26 % PM 
decrease when the ship used MGO instead of using scrubber with HFO at 17 % engine load. Yang et al., (2021) 
tested a 2-stroke SSD engine and showed 37 % and 16 % reduction of PM over scrubber at 4 % and 70 % 
scrubber capacity, respectively, while no statistical changes were seen at 48 % and 85 % scrubber capacity. PM 

lowest efficiency (Zhou et al., 2017). They also used a LSF with 0.1 % FSC and found 90 % PM reduction of LSF 
compared to HFO and scrubber. Jeong et al., (2023) tested a small 4-stroke HSD engine, which was mounted 
on  a  test-bench,  and  reported  an  unclear  pattern  on  PM  scrubber  removal  performance,  because  of  the 
inconsistent measured decreases and increases over the scrubber at different engine loads and fuel batches. 
Another laboratory measurement study on a small 4-stroke HSD engine designates that MGO (FSC<0.03 %) 
reduced PM by 21 % compared to a non-compliant to IMO FSC regulation fuel (0.86 % FSC). When a laboratory 
scale scrubber was used in conjunction with the non-compliant fuel, PM was reduced by 8 % to 47 %, depending 
on the combustion conditions variability, compared to the upstream case (d. Santos et al., 2022). 

303 

 
 
Figure 4: PM mean emission levels (in g/kgfuel) at different sampling points (upstream and downstream of the 
scrubber), fuel type (HFO, ULSFO) and main engine load. Error bars correspond to two standard deviations. 

The effect of scrubber and fuel on the PN levels and the corresponding PSDs are demonstrated in Figure 5. 
These measurements were conducted on the so-
were obtained, including volatile and non-volatile ones. Mean PN levels and size distributions are presented in 
each case, together with the individual PSD along with the corresponding uncertainty range that corresponds to 
one standard deviation on each side of the mean value. 

Figure 5: 
upstream and downstream of the scrubber with HFO and deactivated scrubber with ULSFO. Individual mean PN 
size distributions per each sampling point (right panels) are depicted along with error bars that correspond to 
standard deviation. 

PSD upstream of scrubber appeared trimodal with one peak at 6 nm, a second at 14 nm and a third larger peak 
at 76 nm, while PSD downstream of scrubber was quadrimodal with the first peak at 6 nm, a second peak at 
10 nm, a third one at 31 nm and the fourth one at 66 nm (Figure 5a). ULSFO led to a bimodal PSD with a first 
peak at 6 nm and a second higher at 53 nm. PN levels downstream of the scrubber were found higher than 
upstream of the scrubber by 150 %, while the lowest PN levels were observed for ULSFO use (Figure 5b). The 
soot  mode,  which  represents  the  non-volatile  carbonaceous  part  of  particles  (Amanatidis  et  al.,  2018),  was 
found at the same level, both in terms of PN level and PSD between upstream and downstream points. Soot 
emissions are mainly attributed to engine performance, combustion quality and fuel type (Ntziachristos et al., 
2016). Particles downstream of the scrubber were dominated by nanoparticles in the size range between 10 
and 40 nm, that were formed from nucleation of volatized fuel ash species (Kasper et al., 2007), (Ntziachristos 
et al., 2016), as seen in Figure 5a. Hot exhaust of c.a 300 °C, containing high amounts of SO2 and SO3 is inserted 
into the scrubber, where high humidity conditions exist, and rapidly cools down to 40 °C, due to the presence of 
seawater. In such conditions, SO3 reacts with water vapour molecules to form sulphuric acid nucleating into 
particles in the nucleation size range (Yang et al., 2021), (d. Santos et al., 2022). These nuclei-sized sulphuric 

304 

 
 
 
 
acid  particles  are  not  captured  by  coalescence  with  the  seawater  droplets  and  therefore  a  new  high 
concentration particle mode is generated downstream of scrubber. Thus, the increased PN downstream of the 
scrubber  (Figure  5b)  is  probably  to  the  enhanced  nucleation  mode  that  seems  to  be  produced  by  scrubber, 
through nucleation or condensation on smaller particles than the measured size range upstream of the scrubber. 
The increased PN levels downstream of scrubber is also strengthened by the fact that scrubber was inefficient 
in removing soot mode particles. 

On the right side of Figure 5, the mean PSDs of upstream and downstream of the scrubber, as well as with 
ULSFO  use,  of  Figure  5a  are  separately  demonstrated,  along  with  the  corresponding  uncertainty  ranges, 
expressed by one standard deviation. Deviations were emerged, due to utilization of the two HFO batches and 
variable  DRs,  for  both  upstream  and  downstream  of  scrubber  measurements,  as  well  as  different  dilution 
temperature upstream of scrubber and variable engine load when downstream of scrubber. On the ULSFO case, 
ship  was  cruising  at  slow  speed  (about  12  knots)  and  engine  load  was  too  low  (10  %),  which  created 
measurement uncertainties. 

Fridell and Salo, (2016) observed a bimodal PSD forming upstream of the scrubber with a first high peak at 
approximately 10  nm and a  second lower one at  about 50  nm,  and a  monomodal PSD  downstream  of the 
scrubber with a peak at around 50 nm. The sampling scheme comprised by dilution system that diluted the 
exhaust gas at a ratio of 64 to 109 and two online instruments with range of 5.6 to 560 nm and 0.3 to 10  m, 
respectively. PN emission levels over scrubber were reduced by 84 % compared to engine out, when HFO with 
2.3 % FSC used. Kuittinen et al., (2021) measured a bimodal PSD upstream of the scrubber, that was dominated 
by ultrafine particles in the size range of 20 to 40 nm and a lower in concentration soot mode between 30 and 
100 nm. They also reported that the scrubber reduced particle concentration in the nucleation mode, mainly 
due to volatile species of smaller particles, and increased accumulation mode particles in the size range of over 
50 nm. The sampling system utilized a porous tube diluter (PTD) along with an ejector diluter with nominal DR 
of 12 and 8, respectively, and a SMPS for PSD measurements with a size range of 10 
 414 nm. A 92 % PN 
reduction was observed when utilizing scrubber with 0.7 % FSC HFO. Monomodal PSD at about  50 nm was 
found from d. Santos et al., (2022) upstream of scrubber, while bimodal PSD with a first peak at about 20 nm 
and a second one at 60 nm downstream of scrubber was identified, due to the formation of a primary mode 
sulphuric  acid  particles  induced  by  scrubber.  The  research  team  used  a  2-stage  dilution  system  at  a  ratio 
between 60 and 126 and a SMPS measuring particles at a range of 15.1 to 661.2 nm. The use of ULSFO leads 
to change in PSD compared to upstream scrubber and HFO, moving nucleation and soot mode to smaller particle 
diameters (Figure 5a). This is in contrast to the findings of Ntziachristos et al., (2016), who observed similar 
PSDs between a HFO and a low fuel oil (LFO) on a 4-stroke engine tested at 25 % load. Low sulphur fuels or 
MGO produced 70 % lower PN levels compared to downstream of scrubber under same engine conditions, as 
shown by Karjalainen et al., (2022). This was similar to our study, where we found 82 % decrease of PN with 
ULSFO, compared to the one downstream of the scrubber. 

The  integration  of  literature  findings  with  our  own  experimental  observations  reveals  a  certain  level  of 
controversy regarding the impact of scrubbers on PN emissions. PN emission levels and PSDs discrepancies 
between our results and the existing literature findings may be attributed to a multitude of factors. These may 
include variations in engine types and specifications, engine load operations, engine age, the type of fuel utilized, 
and its respective sulfur content. Additionally, the target FSC equivalent, based on the area of sailing, can also 
contribute to differences. Other relevant factors encompass water alkalinity, engine and scrubber wear, as well 
as the specific operation of the scrubber by the crew, particularly in terms of controlling the seawater flow rate. 
Such a complex interplay of factors underscores the necessity for comprehensive research to ensure a holistic 
understanding of the scrubber influences on PN emissions and PSDs in various operating conditions. Another 
critical element contributing to discrepancies between research studies is the variability in sampling schemes 
and  conditions  and  instrumentations  employed.  The  choice  of  sampling  locations,  sampling  lines  and  their 
length, the proper heat line application, DR and dilution temperature can significantly influence the obtained 
results. Additionally, variations in instrumentation sensitivity, accuracy and size range can also lead to differing 
measurements.  To  ensure  more  consistent  and  reliable data,  it  is  crucial  for  future  research  to  standardize 
particle sampling methodologies and adopt advanced, precise instrumentation in PN emission experiments. 

The transition from HFO2 to ULSFO took place as the ship approached the Turkish territorial waters, due to 
restrictions in the use of open-loop systems. PN concentrations were also measured during the fuel transition 
process. Mean fuel transition PN is of the same magnitude as most of the downstream scrubber PN levels, but 
higher than ULSFO. In Figure 6, PN emission levels at the fuel transition phase are presented over time of the 
along with the ship speed (in knots) and the engine load (%). On the fuel change over process, both 

305 

 
 
HFO and ULSFO were supplied to the ME, with decreasing HFO flow and increasing ULSFO, in parallel to scrubber 
operation, until the HFO was completely consumed from the fuel pipes. The ship decreased its speed at this 
stage, from 17 to 12 knots and the engine load was fluctuation between 28 % and 17 %, further contributing 
on the transient effect. Based on data provided from ship operator, fuel change procedure lasted for 5 hours, 
so this time was required for ULSFO to be stabilized for combustion in the ME. In Figure 6, only two hours of 
PN fuel transition time series are depicted, due to malfunction  of the eDiluter heater and therefore the last 
three-hour measurement data was considered invalid. 

Figure 6: 
to ship speed (knots) and engine load (%) 

3.2.1 Effects of sample conditioning 

Effects of sample conditioning on mean PN size distributions (left panels) and mean PN emission levels (right 

the primary eDiluter, as well as with the utilization of TD and CS + ED removing volatile part of the PM at given 
conditions of the respective conditioning instrument. Therefore, both total and non-volatile particles have been 
examined  throughout  the  campaign,  upstream  and  downstream  of  the  scrubber,  as  well  as  at  the  ULSFO 
operation.  According  to  our  knowledge,  our  study  is  the  first  in  the  worldwide  literature  that  observes  the 
findings of non-volatile particles over scrubber with the utilization of both TD and CS + ED. Results have been 
grouped in relation to the sampling points, upstream (Figure 7a) and downstream (Figure 7b) of the scrubber, 
and with ULSFO and deactivated scrubber (Figure 7c). 

The PSD of fresh sample upstream of the scrubber was trimodal with a first peak at 11 nm, a second one at 
20  nm and  a  third  peak  of higher  concentration  at  88  nm  (Figure  7a).  The  use  of  the  TD,  upstream  of  the 
scrubber shifted the PSD to smaller particles, but the distribution was altered to bimodal with a first mode at 
10.5 nm and the second larger peak at 68.5 nm. The CS + ED completely changed the PSD to monomodal with 
a peak at 41 nm. The TD actually increased PN by 80 % than on fresh sample upstream of scrubber, due to the 
artifact formation of particles in the nucleation mode. TD soot mode remained at the same level compared to 
fresh, both in terms of concentration and PSD, only a slight shift was observed. This artifact creation of particles 
was clearly described by Amanatidis et al., (2018), who observed re-nucleation of smaller particles, starting 
from 5 nm, downstream of a similar TD when the temperature exceeded a certain threshold (approximately 
250 °C) under high sulphur conditions (>200 ppbv). This marginal temperature is specific to the TD and depends 
on the TD physical dimensions, flow rate and cooling capacity of the denuding section. 

306 

 
 
 
Figure 7: 

Mean PN size distributions (left panels) and mean PN emission levels (right panels) in (#/kgfuel) 

with HFO, b) downstream scrubber with HFO and c) deactivated scrubber with ULSFO. Error bars correspond to 

s PN/10. 

Use of a CS + ED to isolate non-volatile particles upstream of the scrubber had the opposite effects than this 
intention, as the resulting PN concentration was over 65-times fold higher than in the fresh sample. Amanatidis 
et al., (2013) showed that in presence of oxygen and high sulphur conditions, such as combustion of marine 
of 
sulphate  particles,  which  grow  from  5  nm  to  50  nm  in  a  short  time  period,  when  exposed  to  high  sulphur 
conditions. Particle generation also depends on the CS geometry, design and storage capabilities. In our case, 
the  increased  SO2  concentration  upstream  of  scrubber,  due  to  the  utilization  of  fuels  with  high  FSCs, 
accompanied by high quantities of NOX (approximately 1500 ppm), reduced the adsorbent ability of CS and 
therefore sulphate and nitrate particles were generated. Specifically, we believe that the CS adsorbent material 
(BaO) had reached its nitrogen and sulphur storage capacity during the first CS measurement, downstream of 
scrubber, which lasted about 2 hours. This CS measurement was not considered valid, due to malfunction on 
the  dilution  system.  We  then  observed  that  the  PN  concentration  was  increased  by  2000  %  on  the  first  5 
minutes of the second CS testing, upstream of scrubber, meaning that the CS + ED configuration was creating 
artifact particles. 

The PSD of the fresh sample downstream of the scrubber (Figure 7b) is the same to the one of Figure 5a. Use 
of TD downstream of the scrubber produced a trimodal PSD, similar to the fresh sample, but with a slight shift 
of each mode to smaller particles. The TD PN was higher than fresh sample by 12 %, mainly due to re-nucleation 
of particles in the size range between 10-30 nm, when exposed to high sulphur conditions (Amanatidis et al., 
2018). Even downstream of scrubber, sulphur is found in increased concentrations of various sulphur-based 
components  (SO2,  SO3,  sulphates),  compared  to  the  use  of  low  sulphur  fuels,  since  the  scrubber  cannot 
completely abate sulphur particle species (Yang et al., 2021). Moreover, the high operating temperature of 300 
°C probably decreased the efficiency of TD and promoted the onset of re-nucleation, due to the reduction of 
residence time of species on low temperature, that permits adsorption in the denuding section (Amanatidis et 
al., 2018). CS + ED downstream scrubber PSD displayed a bimodal pattern, with a larger first peak at 20 nm 
and lower second peak at 70 nm, shifting the PSD to smaller particles. Initially, the CS size distribution and 
concentration at soot mode downstream of scrubber were found reduced by over 50 % compared to fresh ones, 
indicating a significant decrease of non-volatile particles at the size range of 60 nm to 100 nm. At this size 
range the fraction of volatiles is generally low, while CS could not effectively reduce non-volatiles. As a result, 
a  dilution system  malfunction was  suspected  to have occurred during the CS  measurement downstream of 

a dilution correcting factor should be applied, which was determined to 50 %. Therefore, the corrected CS + ED 
PN emission level was 37 % lower compared to fresh sample one, meaning that CS + ED eliminated the majority 
of volatile and semi-volatile particles downstream of scrubber. 

307 

 
 
 
Winnes et al., (2020) used a TD, which heated the sample at 300 °C, to vaporize volatile particles, when they 
measured upstream and downstream scrubber with  HFO.  They showed that upstream of scrubber PSD was 
bimodal for all engine loads tested (32 %, 49 %, 76 %), with first peak at smaller than 10 nm particles and a 
second higher peak at 50 nm, but higher loads produced higher PN concentrations. TD seems to remove the 
majority  of  volatiles,  up  to  85  %,  in  contrast  to  our  study,  where  formation  of  particles  was  observed. 
Downstream  scrubber  PSD  seems  bimodal  and  trimodal  at  higher  (76  %)  and  lower  (48  %)  engine  load, 
respectively, but with higher concentrations compared to upstream ones (Winnes et al., 2020). On downstream 
scrubber  case,  the  effect  of  TD  on  reduction  of  volatiles  was  controversial,  since  concentrations  were 
comparable before and after the TD. 

Fresh  sample  PSD  of  ULSFO  and  deactivated  scrubber  is  identical  to  the  one  presented  in  Figure  5a.  TD 
presented bimodal PSD with a first peak 6 nm and a second one at 53 nm, while CS + ED was trimodal with a 
first peak at 6 nm, a second one at 10 nm and a third one at 51 nm (Figure 7c). The PN levels with TD and CS 
+ ED were reduced by 14 % and 38 %, respectively, compared to fresh sample PN. This means that volatiles 
were diminished by the TD and were further eliminated by the CS + ED, due to the higher heated temperature 
(350  °C).  High error bars  that  were depicted  on fresh sample and CS +  ED with ULSFO were related  to the 
unstable sailing conditions of the ship, which was slow steaming (12 knots) and the engine was working at 10 
% engine load during these measurements, therefore affecting the PSDs and PN emission levels. 

Conclusions 

This study provides the particulate and gaseous emission levels, along with the PSDs from a large 2-stroke SSD 
engine equipped with open-loop scrubber. These values were derived from a 7-day measurement campaign 
conducted on a large containership, which performed a scheduled voyage between two international ports. This 
is the first time in worldwide literature that real sailing conditions measurements conducted on an open-loop 
scrubber used to abate emissions from such a large engine with maximum power output of 62 MW. Another 
special and unique element of this study is the first application of both TD and CS + ED on a marine engine 
environment during on-board testing. 

Despite the notable rise in the installation of scrubbers on newbuilding and retrofit ships worldwide, scrubber 
emission performance is quite controversial, since they can effectively abate SO2 emissions but their effect on 
particles is still bound to uncertainty. Therefore, more research is needed on this direction to comprehensively 
.  The  developed  EFs  downstream  of 
scrubbers can be fed to emission and air pollution models to identify their overall effect on the environment 
and human health, and thus to support in promoting new effective emission control policies and regulations. 
This study also highlights the importance of the appropriate particle testing conditions on marine engines under 
various DR, temperature and thermal treatment configurations, as well as the additional complexity induced by 
scrubber. This should be further investigated in future publications as the necessity of creating a standardized 
marine measurement protocol is obvious for accurate shipping emissions estimation. 

Acknowledgements  

This work was conducted in the framework of EMERGE project. The EMERGE project has received funding from 
innovation programme under grant agreement Nr.874990. 

This extended work will be published in Fuel journal during 2023. 

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A new set of Emission Factors for ships 

Achilleas Grigoriadis1, Sokratis Mamarikas1, Giannis Ioannidis1, Elisa Majamäki2, Jukka-Pekka Jalkanen2, 
Leonidas Ntziachristos1* 

1 Department of Mechanical Engineering, Aristotle University, P.O. Box 458, 54124 Thessaloniki, Greece, *leon@auth.gr  

2 Finish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland 

1. Introduction 

The  maritime  sector  remains  a  challenge  in  terms  of  pollutants  emission  reductions,  compared  to  other 
transportation sectors where substantial reductions have been achieved (EEA, 2020). Shipping was responsible 
for 2,89% of total manmade Greenhouse Gases (GHG) in 2018 and this share is expected to increase towards 
2050 (IMO, 2020). Shipping is also characterized by high emissions of harmful pollutants, such as Sulfur Oxides 
(SOX),  Nitrogen  Oxides  (NOX)  and  Particulate  Matter  (PM),  causing  a  significant  deterioration  of  air  quality, 
especially in coastal areas (Corbett et al., 2007), (Viana et al., 2014). Lehtoranta et al. (2019b) estimated that 
vessels produce 15% of NOX and 5-8% of SOX emissions of all anthropogenic activities. Viana et al. (2014) 
found that maritime PM contribution in European coastal areas can reach 7% of total ambient PM10, and 14% 
of total PM2,5. Moldanova et al. (2009) reported that global PM from maritime amounts 0,9-1,7 Mton annually, 
exposing population living in close vicinity to coastal areas to high PM concentrations. Notably, (Kuittinen et al., 
2020) calculated that Particle Number (PN) emissions from vessels are at the same level with all continental 
anthropogenic activities together, quantified to 1,2x1028 particles in 2016. 

Eff
most recent regulation of 0,5% fuel sulphur content limit being globally effective since January 2020 (IMO, 
2016).  NOX  are  regulated  in  the  context  of  the  certification  of  marine  diesel  engines  and  are  subject  to 
restrictions with geospatial characteristics within low emission zones, which are currently applicable to only few 
parts of the world where relevant Emission Control Area (ECAs) have been  established (IMO, 1997). Existing 
NECAs are located in the United States and in the Baltic and North Sea. CO2 is currently being systematically 
monitored and reported by ship operators at EU and international levels (EC, 2013), (IMO, 2011) and energy 
efficient design obligations for specific ship categories have started to be applied, in the context of a detailed 
carbon emission reduction strategy (IMO, 2018). 

As  a  reaction  to  the  increasingly  stringent  emissions  regulatory  framework,  the  maritime  sector  has  been 
strongly investing in new technology and fuels. Technological measures include vessel design and operational 
changes  (Balcombe  et  al.,  2019),  engine  optimization  techniques  including  exhaust  gas  recirculation  (EGR) 
(Raptotasios  et  al.,  2015),  and  the  installation  and  operation  of  exhaust  aftertreatment  devices  such  as 
scrubbers and selective catalytic reduction (SCR) systems (Nova and Tronconi, 2014). Fuel changes involve the 
use of low sulphur fuel (Sarvi et al., 2008), (Sarvi et al., 2011) and, most recently, the use of alternative fuels 
(i.e. LNG, methanol, NH3) followed by all engine modifications these may require (Anderson et al., 2015), (Fridell 
et al., 2020), (Hansson et al., 2020). 

This  range  of  available  options  requires  reliable  and  diversified  Emission  Factors  (EFs)  to  calculate  the 
contribution  of  shipping  to  current  emission  inventories  and  to  project  future  developments  under  different 
policy scenarios. Existing EF datasets for ships, such as the IMO ship emissions toolkit (IMO, 2018), the EMEP/EEA 
Air Pollutant Emission Inventory Guidebook (AEIG) (Trozzi 
EPA,  2009),  and  the  model  STEAM  (Jalkanen  et  al.,  2012),  and  other  commercial  datasets  require  constant 
maintenance and updates to be in-line with latest developments and allow users to make correct assessments 
related to vessel emissions. 

A  number  of  research  and  development  activities  can  be  used  to  produce  new  emission  information  from 
vessels, using a multitude of techniques and available tools. Measurements are conducted on marine engines 
in the laboratory (Ntziachristos et al., 2016) or in field campaigns on operational vessels (Winnes and Fridell., 
2010).  Techniques  utilized  range  from  in-funnel  measurements  using  suitable  devices  (Winnes  and  Fridell., 
2010), to airborne (Murphy et al., 2009) or coast-
emissions  detection  by  satellite  monitoring  (Georgoulias  et  al.,  2020),  being  one  of  the  latest  state-of-art 
techniques. Our literature review came up with more than 53 published research papers focusing on vessels 
emissions characterization since 2015. 

310 

 
 
In  this  context,  the  target  of  the  present  paper  is  to  collect,  analyze,  process  and  develop  EFs  for  various 
pollutants, ship engine and fuel types, based on existing datasets and latest findings available in the public 
domain. To offer significant resolution and versatility for use under different operation conditions, the developed 
EFs are expressed as a function of the ship engine load. The outputs intend to provide evidence on the existing 
environmental performance of ships, enhancing the overall effort to develop accurate and up-to date EFs for 
the sector and supporting existing models and emission inventory databases. 

2. Status of emission factors and emission measurements of ships 

2.1 Emission measurement methods 

Emission rates, in the context of real-world measurement campaigns, can be extracted by measuring exhaust 
emissions in relation to fuel consumption or energy output of the vessel engines. The measurement process 
mainly includes the involvement of high-end pollutant analyzers or more simplified sensors, which are placed 
in the ship exhaust line (before or after any emission control system). For PM and PN, a dilution system is also 
required  prior  to  the  PM/PN  measurement  instrumentation,  whereas  particle  collection  in  filters  is  also 
accomplished.  

Emission factors can be composed in relation to specific vessel activity. Activity can be differentiated according 
to engine operating points or ship operating mode (i.e. maneuvering/hoteling, cruising). In this process, emission 
factors  become  specific  to  conditions  that  can  be  quantified  during  the  emission  inventorying  process.  For 
example, one can discern the different phases of operation of a vessel in open-sea or in-port operation and 
assign  a  proper  emission  factor  to  each  of  the  individual  phases.  This  overall  process  is  schematically 
represented in Figure 1. 

Figure 1: Schematic representation of Emission Factor development process through on-board measurements 

Exhaust  emission  measurements  can  also  be  performed  on  engine  test-beds  in  the  laboratory,  thus  linking 
emission rates with specific and well-determined engine load points. In such tests, the engine is placed on the 
dynamometer and is operated at various constant loads at will. Engine loads are often selected from the ISO 
8178  loading  points.  The  sampling  system  is  similar  to  the  one  used  in  real-world  on-board  emission 
measurements  (Ntziachristos  et  al.,  2016b).  In  the  past  few  years,  in-plume  measurements  have  been 
conducted via remote sniffer technologies, which are installed either in fixed positions where increased ship 
activity exists (ports/harbor/bridges) (Ekmektizoglou et al., 2020) or on mobile means that chase ship plumes 
(vans, boats, aerial means) (Pirjola et al., 2014), (Murphy et al., 2009). New techniques are continuously being 
applied, such as drones carrying small sniffer systems that chase plumes and measure emissions. 

311 

 
 
 
 
 
 
2.2 Current study objective 

Due  to  the  recent  developments  in  availability  of  emissions  measurements  from  different  vessels  and  the 
introduction of stringent environmental regulations for ships at a global level, the target of the current study is 
twofold. First, to develop new EFs based on literature findings through statistical processing and, second, to 
quantify  the  effect  of  emission  control  regulations  in  emission  rates.  In  particular,  the  following  questions 
become relevant in such an analysis: 

  How do parameters such as the fuel type, nominal engine power and nominal rotational speed affect 

power-based EFs of gaseous pollutants and PM/PN? 

  What is the effect of emission standard regulations in EF trends over time? 

  How power-based EFs for gaseous pollutants vary in relation with the engine load? 

3. Materials and Methods 

3.1 Data collection and organization 

The suitability of engine load as a variable on which to base emission factors is justified by the fact that the 
number  of  engine  load  points  of  ships  during  real  world  operation  is  limited,  as  ships  spend  most  of  their 
operating time at specific steady state loads. It is indicative that, on major ship classes, 94-99% (depending on 
vessel  type)  of  the  total  operating  time  is  on  cruising  or  hoteling/anchoring,  in  which  conditions  the  engine 
operates in steady state. In contrast, maneuvering is dominated by engine load changes (transient operation), 
but represents only 1-6% of the voyage time (Comer et al., 2017). Thus, the engine load is not a complicate 
explanatory variable for shipping emissions and can be easily estimated according to phase of operation. 

The  methodology  for  developing  EFs  is  primarily  based  on  a  Literature  Review  (LR),  which  resulted  in  the 
collection of 157 papers, studies and reports focusing on vessel emissions, from which finally 59 were utilized, 

on-board measurements (37 measurements) and marine engine placed on test-beds (19 measurements). Also 
2 identified in-plume measurements and 1 on-board and in-plume measurement were considered. For each 
test configuration, various emission rates, including gaseous pollutants (NOX, CO, SO2, THC, etc.), greenhouse 
gases (CO2) and PM/PN, as well as energy consumption rates have been collected. All these data have been 
organized in a unique database and divided in category groups (fuel type, engine type, ship category etc.). 

3.2 Development of load-dependent emission factors 

The  individual  emission  values  collected  were  used  to  deliver  an  average  emission  performance  and  the 
dependence of emission rate with engine load. As a first step, a single engine load point had to be selected in 
order  to  use  this  as  a  reference.  This  reference  load  point  per  pollutant  was  selected  to  be  the  one  most 
frequently observed in the available literature. Depending on engine type and pollutant, the most frequent load 
point encountered was found to be at 50% of engine load for the vast majority of cases or 75% for only a 
handful of pollutants and engine types. Average values for each pollutant (named Base EFs - BEFs) have been 
calculated for the three categories of marine engines (slow, medium, high speed) and for different fuel types 
(residual and distillate) for this reference load point.  

The emission rates found in other load points than the reference one, have been normalized over the emissions 
rate  at  the  reference  load  point.  This  normalization  process  has  been  separately  applied  for  each  single 
literature source. This produces a family of points which are centered around 1 for every single literature source. 
By  using  this  method  over  all  literature  sources  and  combining,  one  obtains  the  relative  effect  of  load  on 
emissions  regardless  of  the  absolute  value  of  emission  reported  in  each  study.  A  single  function  of  load 
correction is then produced by means of regression over the combined set of values. 

The overall process for estimating the absolute emission levels and the emission dependance with the engine 
load is schematically explained in Figure 2. 

312 

 
 
 
Figure 2: Schematic representation of the Emission Factor development process 

4. Results 

4.1 Baseline Emission factors 

Averaging of emission rates from various sources over the reference engine load point led to the estimation of 
the  baseline  emission  factor  for  various  gaseous  pollutants  (NOX,  CO,  HC)  and  the  baseline  specific  fuel  oil 
consumption factor (SFOC). Table 1 provides the absolute level of emissions and consumption depending on 
engine  type,  together  with  the  number  of  measurements  and  the  standard  deviation  of  each  value.  It  is 
reminded that these reference values correspond to 50% engine load. The same table also provides the grand 

is suggested to be used in case the engine type is not known. 

Table 1: Base  Emission  or  Consumption  Factor  together  with  the  standard  deviation  and  the  number  of 
individual values in parenthesis per engine type at 50% engine load. 

Pollutant / 
Consumption 

NOX 

(g/kWh) 

CO 

(g/kWh) 

HC 

(g/kWh) 

SFOC 

(MJ/kWh) 

Any type 

SSD 

MSD 

HSD 

13,2 ± 3,98 
(35) 

14,4 ± 3,60 
(15) 

12,4 ± 4,08 
(15) 

11,7 ± 3,68 (5) 

0,898 ± 0,571 
(38) 

0,714 ± 
0,631 (14) 

0,974 ± 0,530 
(18) 

1,10 ± 0,361 
(6) 

0,440 ± 0,350 
(20) 

0,358 ± 
0,234 (7) 

0,405 ± 0,204 
(9) 

0,662 ± 0,599 
(4) 

8,83 ± 0,841 
(10) 

8,48 ± 0,266 
(3) 

8,42 ± 0,493 
(4) 

9,74 ± 0,891 
(3) 

The calculated mean values for CO and HC show that emissions are higher for high-speed engines. This is most 
probably the result of the fact that slow speed engines operate at higher air-to-fuel ratios and provide more 
time for oxidation of species after combustion than faster engines do. In terms of fuel consumption, it seems 
that SSD and MSD engines little differ at 50% load, while fuel efficiency drops significantly for HSD engines. 

The impact of engine type on NOX is rather opposite than CO, HC and SFOC. In this case, SSD exhibit higher NOX 
emission rates, again as a result of the higher oxygen and time availability. In particular NOX from vessels are 
controlled over different so-called Tier levels, depending on their date of manufacture. Hence, Table 1 values 
are only to be used when the vessel age or Tier level are not known. Base NOX emission factors split according 

313 

 
 
 
 
to Tier and engine type are presented in Table 2. In all engine types, baseline emission factors drop as the Tier 
level  advances,  a  finding  that  shows  the  positive  impact  of  regulatory  control  on  actual  emission  levels.  In 
particular Tier III appears to be some 90% lower than pre-Tier levels, at least for SSD and MSD engines. In 
particular,  all  NOX  Tier  III  values  correspond  to  SCR-equipped  vessels.  Although,  in  principle,  Tier  III  can  be 
achieved with EGR alone all the measurements found in literature were obtained on SCR-equipped engines so 
the actual performance of EGR on real world emission rates cannot be estimated. 

Table 2: Base NOX emission factors, standard deviation (and number of individual values in parenthesis), per 
engine type and TIER standards for 50% engine load. 

NOX BEF (g/kWh) 

TIER 0 

TIER I 

TIER II 

TIER III 

SSD 

MSD 

HSD 

17,7 ± 2,14 
(20) 

14,4 ± 2,75 
(11) 

11,3 ± 2,53 
(14) 

2,00 (1) 

10,8 ± 1,60 
(6) 

10,5 ± 2,01 
(27) 

8,28 ± 0,148 
(4) 

1,02 ± 0,815 
(9) 

8,53 ± 2,71 
(4) 

7,41 ± 1,72 
(6) 

5,95 ± 1,15 
(3) 

1,25 ± 0,150 
(2) 

CO2 and SO2 can be calculated from SFOC values if the fuel energy content and speciation in carbon and sulphur 
are known. If such values are not known, the emission rates in Table 3 can be used instead. These have been 
derived on the basis of Table 1. SFOC values in combination with averaged fuel characteristics found in the 
literature. Specifically, the SO2 calculation also takes into account the fact that part of the SO2 is converted to 
SO4 and corrects for this (more on this correction follows with the discussion around Figure 5). 

Table 3: CO2 and SO2 Base Emission Factor, per engine and fuel type for 50% engine load 

Pollutant 

Fuel type 

All types 

SSD 

Residual 

678  

651 

MSD 

646 

HSD 

747 

CO2* 

(g/kWh) 

CO2* 

(g/kWh) 

SO2** 

(g/kWh) 

SO2** 

(g/kWh) 

Distillate 

645 

619 

615 

711 

Residual 

4,43 

4,31 

4,29 

4,70 

Distillate 

0,372 

0,358 

0,355 

0,410 

*CO2 has been calculated by the SFOC, considering a fuel carbon content of 86,8% and LHV of 41,5 MJ/kg for residual fuel 
and 86,5% and 43,4 MJ/kg for distillate. .**SO2 has been calculated by the SFOC, considering FSC of 1,42% and LHV of 41,5 
MJ/kg for residual fuel and 0,0931% and 43,4 MJ/kg for distillate. 

CO2 is directly proportional to the SFOC and therefore high-speed engines produce higher emissions, due to 
lower  efficiency,  compared  to  medium  and  slow  speed.  The  higher  heating  value  of  distillate  fuels  in 
combination  with  the  slightly  lower  carbon  content  than  residual  fuels,  leads  to  lower  CO2  emissions.  For 
estimating  SO2  emissions,  the  distillate  fuel  that  on  average  contains  0,09%  sulphur  produces  significantly 
lower SO2 emissions than residual, where average FSC is estimated at 1,42%. It is clarified that these values 
are relevant for residual fuels when no exhaust gas cleaning systems (scrubbers) are used. 

For PM, the engine type was not found to have a significant impact on specific emission rate. On the contrary, 
PM was largely affected by the use of residual or distillate fuels that were shown to have an impact on all 
individual PM components (elemental carbon, organic mass, ash and sulphates). In particular for the contribution 
of sulphates, total PM has been calculated on the basis of hydrated sulphates (SO4 + 6,5H2O) or as dry PM, 
which  includes sulphates but no addition of water. Total PM values would be relevant  if one would  wish to 
compare the calculated values in the current study with measured values collected after typical dilution ratios 
(e.g. 100:1). However, in plume simulation studies, the dry PM would be the starting point and then the model 

314 

 
 
would account for the partition of water between the particulate and the gaseous phases as particles age in 
the atmosphere. Calculated BEFs for PM (total and dry) are presented in Table 4. 

Table 4: PM Base Emission Factors, per engine and fuel type for 50% engine load 

Pollutant 

Fuel type 

All types 

SSD 

Residual 

0,960 

0,934 

MSD 

0,930 

HSD 

1,03 

PM*** 

(g/kWh) 

PM*** 

(g/kWh) 

Dry PM**** 

(g/kWh) 

Dry PM**** 

(g/kWh) 

Distillate 

0,217 

0,215 

0,215 

0,221 

Residual 

0,637 

0,624 

0,622 

0,669 

Distillate 

0,197 

0,196 

0,196 

0,199 

***PM has been calculated by the sum of organic mass, elemental carbon, ash, hydrated sulphates (SO4 +6,5H2O). ****Dry 
PM has been calculated by the sum of organic mass, elemental carbon, ash and dry sulphates (SO4). 

Table 5 provides further speciation of PM to individual components and some average physical characteristics 
of particle distributions. As PM is constituted by EC and OM, a further analysis of its speciation was performed 
on the basis of the statistical analysis of emission rates of these two groups. For the purposes of this analysis, 
EC and BC were considered equivalent and were used interchangeably. Although it is well recognized that EC 
and BC are measured with different techniques, the range of values encountered in the literature were largely 
overlapping and this allowed pooling them together. Particle number (PN) distinguished to total and non-volatile 
(solid) particles is included in Table 5, without distinction to fuel type due to lack of data. Base EFs for OC and 
EC are indicated at 50% engine load, while SO4 conversion rate, total PN and solid PN are provided at 75% load. 

Table 5: OC,  EC  and  PN  Base  Emission  Factors,  standard  deviation  (and  number  of  individual  values  in 
parenthesis). OC and EC are provided for 50% engine load, while SO4 conversion rate, PN (total and non-volatile) 
at 75%. 

Pollutant 

OC 

(g/kWh) 

EC***** 

(g/kWh) 

SO4  conversion  rate 
(%) 

PN 

(#/kWh) 

PN non-volatile 

(#/kWh) 

All types 

Residual  

Distillate  

0,232 ± 0,158 (14) 

0,248 ± 0,163 
(12) 

0,138 ± 0,0617 (2) 

0,0182 ± 0,0181 (16) 

0,0206 ± 0,0193 
(13) 

0,00827 ± 0,00319 
(3) 

3,94 ± 1,50 (6) 

4,56 ± 0,627 (5) 

0,827 (1) 

3,58E+15 ± 1,87E+15 
(3) 

N/A 

6,33E+14 ± 2,67E+14 
(2) 

N/A  

N/A 

N/A 

From Table 5, it can be observed that residual fuel leads to more OM, EC and SO4 compared to distillate fuel 
use. 

315 

 
 
 
 
4.2 Load dependent functions 

The dimensionless dependence of emissions rates on engine load for various pollutants (NOX, CO, HC) and SFOC 
is depicted in Figure 3. As earlier explained, the most frequent engine load point for which emission rates were 
available in the literature was the 50% load, therefore all other emission rates have been normalized over the 
emission rates at this engine load. Figure 3 presents all individual normalized points together with the best-fit 
regression line. Emission rates from all engine types (SSD, MSD, HSD) have been included in the same pool, as 
our analysis could not identify specific engine type effects that would give substantially differentiated results. 

Figure 3: 
Consumption at 50% load. 

Engine load impact for normalized emission rates (NOX, CO, HC) and Specific Fuel Oil 

It is observed that the general emission trends described in section 2.2 of the current study are reflected to the 
shape of the regression lines derived. SFOC, which depends on the efficiency of marine engines, shows to drop 
as load increases towards a minimum at 75% and then this increases again. This is typical behavior for diesel 
engines and in particular for marine ones that are optimized for optimum fuel efficiency at cruise conditions 
that occur around 75% load (Yang et al., 2019). 

CO  emissions  strongly  increase  as  load  drops  below  30%.  This  behavior  can  be  explained  by  the  lower 
  the  CO  to  CO2  conversion  is  known  to  be  very  strongly  linked  to 
combustion  temperature  at  low  loads 
temperature (Sippula et al., 2014). The behavior of HC is similar to that of CO but of lesser value as the HC 
oxidation is not as strongly dependent on temperature as that of CO. One may also observe some slight increase 
in HC for 100% load. Although there is large scatter of data at this load point, slight increase of HC may be 
expected  due  to  the  long  duration  of  injection  at  100%  load  and  the  increased  probability  of  hydrocarbon 
interaction with the piston and cylinder walls (Yang et al., 2019). 

For NOX the trend shows that emissions decrease with engine load, as this approaches 50%. For higher loads 
than 50%, a constant level against engine load is seen within uncertainty. At lower loads, NOX specific emissions 
may increase because of leaner air fuel mixtures (Kalender and Ergin., 2017) and the lower fuel efficiency at 
this engine operating area (Gysel et al., 2017) which is the denominator. Nevertheless, at higher loads (above 
50%) the statistical processing verifies the contrasting findings regarding the NOX increase or decrease with 
the engine load, already recognized in the literature review section. There is a respective number of emission 
rates either above or below the formed steady line above 50%. This behavior indicates that NOX emissions may 
be influenced by parameters that counteract each other, as previously stated in the review section. The NOX 
increase effect due to the temperature rise at high loads may in some cases overtake the influence of NOX 
decrease, because of the enrichment of the fuel mix at high loads, while in other cases the reduction of the air 
to  fuel  ratio  seems  to  overcome  the  effect  of  temperature.  This  finding  leads  to  the  consideration  that 
parameters such as the engine age, technology maintenance and specific engine tuning further play a vital role 
in NOX performance (Fridell et al., 2008). Therefore, an additional analysis has been conducted to investigate 

316 

 
 
 
the influence of Tier standard regulations on NOX load dependence. Emissions are separated to three categories, 
according to the Tier classes I, II and to engines built before the application of Tier regulation (referred as Tier 
0  -  constructed  before  2000).  This  classification  is  considered  convenient  to  reflect  the  effect  of  age  and 
technology evolution 
X 
performance differentiation. The formed trends for all three types of engines are presented in Figure 4. 

Figure 4: 
technology Tier standards 

Engine load formed trend of pollutants for NOX normalized emission rates for three engine 

The comparison of the formed trends over the emission rates for the three engine technology levels (Tiers 0, I, 
II) verifies the hypothesis that newer engines exhibit a different NOX behavior at higher loads compared to the 
older ones. In particular, for Tiers 0 and I energy-specific emissions increase at higher engine loads, while in 
newer  the  load  rise  is  accompanied  with  a  reduction  of  emissions.  The  contradictory  findings  of  literature 
therefore seem to be dependent on the engine type technology, while further investigation should be made in 
order to understand the actual mechanism that leads to the different performance, a fact which is out of the 
scope of the present paper. 

The  dependance  of  SO2  on  engine  load  is  related  to  the  respective  dependencies  of  fuel  consumption  and 
sulphur conversion rate for the formation of SO4. More specifically, the conversion rate to SO4 in relation with 
the engine load is provided in Figure 5. The referred SO  represent the total sulphur emissions. The conversion 
rate shows the amount of SO4 emissions in total SOX quantities. As evident the remaining part of sulphur found 
in fuel is converted to SO2. 

Figure 5: 

Normalized SO4 conversion ratio in relation with the engine load 

The expression of the conversion rate in relation to engine load shows that less SO4 is produced at low loads, 
while the percentage of SO4 in total emitted SOX emissions increases with load. The overall contribution of SO4 
to total SOX is estimated to be between 2-5%, depending on the engine operating condition. The SO2 dependence 
on engine load is actually a combination of the dependencies of the SO2 to SO4 conversion rate and the SFOC. 
This relationship varies with fuel type (residual or distillate) as a result of different fuel properties. The trend is 
depicted in Figure 6, where the SO2 and SO4 load dependent functions have been calculated for these two main 
fuel categories, based on the SFOC, their % FSC and the SO4 conversion rate. 

317 

 
 
 
 
Figure 6: 

SO2 & SO4 load dependent functions for residual and distillate fuels 

In particular, for the distillate fuel where the FSC is low, the SO2 trend follows approximately the one of the 
SFOC. With residual fuel use, the SO2 dependance on engine load is different than that of SFOC due to the 
substantial formation of SO4 at high loads. SO4 load dependency, also displayed in Figure 6, as expected follows 
the convention rate trend in combination with the SFOC. The establishment of a conversion rate dependance on 
engine load is not only important for the estimation of SO2 in the emitted gaseous pollutant mix, but also for 
the quantification of sulphates which contribute to total PM.  

PM, apart from sulphates, is also constituted by OM, EC and ash. Already the SO4 dependency has presented in 
Figure 6, while the respective correlations of EC and OC are provided in Figure 7. 

Figure 7: 

OC & EC engine load dependencies 

The emission rates of OC and EC with the engine load are quite similar, presenting a gradual improvement of 
these emissions with the load increase. Thus, OC and EC are minimized at full load because of the more efficient 
oxidation due to high combustion temperatures at this load range. For the remaining component of PM, ash is 
based on the fuel ash content and SFOC. Specifically, the fuel ash content, which is a fuel property, appears to 

Figure 8: 

Ash content (%) in relation with the FSC.  

318 

 
 
 
 
 
 
The  total  particle  mass,  dry  or  hydrated,  as  already  stated  is  calculated  as  the  sum  of  species;  hydrated 
sulphates (water and SO4) or  dry sulphates (SO4),  OM,  EC  and ash,  at a  specific engine load. Therefore, the 
dependency of the pollutant with the engine load is a combination of the partial dependencies of each substance 
(Figure 9 for residual and distillate). 

Figure 9: 

Hydrated PM load dependance for residual and distillate fuel types 

The overall calculation for two fuel types reveals that for distillate fuel, PM is reduced with the increase of 
engine  load  due  to  the  major  contribution  of  the  OM  in  the  PM,  while  the  dominant  presence  of  hydrated 
sulphates at the residual fuel use leads to higher emissions at higher engine loads. 

Particle emissions  are also characterized  by  their  number.  The identified measurements were conducted  by 
equipment capable of measuring particles with a diameter higher than 5nm. The dependency of the number 
with engine load as regards the total and the non-volatile part of particles is displayed in Figure 10. 

Figure 10: 

PN (total & solid) performance in relation with the engine load 

PN is reduced at full load due to the complete combustion conditions that are being formed, while the increase 
of  PN  at  low  loads  may  be  attributed  to  lower  temperature  conditions  at  this  load  area.  However,  the  PN 
correlations  are  less  strong  than  the  respective  of  PM  because  of  less  available  studies  that  examined  the 
particle number compared to those of considering the particle mass. 

PN load dependance is influenced by the OC and sulphates (SO4). In the low load area, the existence of lower 
quantities of SO4 leads to a lower PN increase, compared to the increase of OC. Also, in higher loads the decrease 
of PN with the load increase is lower than the respective decrease of OC as the SO4 drastically increases at this 
load area. 

Conclusions 

The  study  provided  mean  emission  rates  and  their  engine  load  dependence  for  key  pollutants  produced  by 
marine diesel engines. These values have been derived by combining information from a variety of literature 
sources  in  a  consistent  way.  The  final  values  and  functions  produced  can  be  used  in  the  framework  of 
inventorying  activities  for  shipping  emissions  if  knowledge  of  engine  type,  fuel  type  and  Tier  standard  are 
available.  In  particular  for  PM,  emission  factors  were  expressed  as  composite  values  of  individual  key  PM 
categories (elemental carbon, organic mass, ash and sulphates) in order to retain consistency and provide values 
that can be proven useful to emission and air quality modelers. Emission rates for all pollutants were found to 

319 

 
 
 
 
 
strongly depend on engine load with g/kWh values dropping as load increased. Tier standard was found as the 
most significant variable for the estimation of NOX emissions with Tier III values appearing at a fraction of 10% 
of pre-Tier level. Slow speed engines were also found to be higher emitters of NOX than faster ones. PM, on the 
other hand, mostly depended on fuel use rather than on engine type or Tier standard. 

Despite  efforts  to  use all  available engine  sources  and  to  combine  those  in  the  same  pool  of  data,  marine 
engines emission performance is still bound to uncertainty and more research is required to understand fuel, 
engine and operation mode impacts on emission profiles. In particular, this study identified some inconsistency 
when  expressing  emission  factors  according  to  their  load  dependence  or  operation  mode  dependence.  This 
should  be  further  clarified  in  future  studies  as  this  is  potentially  important  for  correctly  estimating  in-port 
emission contribution of vessels. 

Acknowledgements  

This work was supported by the SCIPPER project. The SCIPPER project has received funding from the European 

been published in the Atmospheric Environment X journal: doi.org/10.1016/j.aeaoa.2021.100142. 

320 

 
 
 
 
 
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Robustness Analysis for Classification of Aerosol Particles using Machine 
Learning with Two Different Single-Particle Mass Spectrometry Datasets 

Guanzhong Wang1, Heinrich Ruser1, Julian Schade2, 3, 5, Johannes Passig3, 4, 5,  

Thomas Adam2, 4, Günther Dollinger1, and Ralf Zimmermann3, 4, 5 

1Institute for Applied Physics and Measurement Technology, University of the Bundeswehr Munich, Neubiberg, 85577, 
Germany, heinrich.ruser@unibw.de 

2Institute of Chemistry and Environmental Engineering, University of the Bundeswehr Munich, Neubiberg, 85577, Germany 

3Joint Mass Spectrometry Centre, Chair of Analytical Chemistry, University of Rostock, Rostock, 18059, Germany  

4Joint Mass Spectrometry Centre, Helmholtz Zentrum München, Neuherberg, 85764, Germany 

5Department Life, Light & Matter, Interdisciplinary Faculty, University of Rostock, Rostock, 18059, Germany 

Abstract 

The chemical composition of aerosol particles in the air is successfully used to determine their origins, e.g., 
traffic emissions, biomass burning, or ship emissions. Single-Particle Mass Spectrometry (SPMS) is a sensitive 
measurement  technique  to  analyze  the  chemical  composition  in  real-time.  The  current  mainstream 
classification  methods  in  the  SPMS  community  for  handling  these  data  require  intensive  manual  post-
processing, making an online analysis impossible. A few studies have demonstrated that supervised learning 
can perform automated classification of SPMS data with high accuracy, enabling selective air quality monitoring 
in real-time. However, the generalizability and reliability of those algorithms using SPMS data from different 
sources (e.g., different SPMS instruments, sampling locations, or weather conditions) are still key issues to be 
solved.  This  work  investigates  the  classification  generalization  capacity  (or  robustness)  of  a  multilayer 
perceptron network using two different datasets of SPMS data. The results show that the model trained on one 
dataset is sensitive to the disparate characteristic features of the other dataset, causing its prediction accuracy 
to decrease significantly. On the contrary, the model trained with data from both datasets performs strong 
robustness  and  adaptation  to  both  datasets,  with  over  96 %  correct  classifications.  The  presented  results 
underscore  the  feasibility  and  practicability  of  a  uniform  approach  for  automated  profiling  of  data  from 
different sources. 

1. Introduction 

Single-particle mass spectrometry (SPMS) is a real-time measurement technique that after ionizing individual 
aerosol particles with a strong UV laser pulse 
 a process called laser ionization and desorption, LDI   reveals 
their chemical composition as bipolar mass spectra of the separated anions and cations (Passig et al., 2021). 
The mass spectra represent the intensity distribution of positive and negative ions with respect to their mass-
to-charge  ratio  (m/z)  and  contain  important  chemical  information  related  to  the  origin  of  the  particles.  For 
example, the main source of particles rich in Elemental Carbon (EC) are traffic emissions 
2006; Shields et al., 2007); Elemental Carbon and Organic Carbon (EC-OC) and Potassium-rich (K-rich) classes 
are good trackers of biomass combustion (Toner et al., 2006; Li et al., 2014; Shen et al., 2019); Calcium ions 
et 
are often an important marker for additives in ship engine lubricants 
al., 2021); Vanadium ions are the signature substance from ships using heavy oil fuels (Ault et al., 2009; Passig 
et  al.,  2021);  Iron  ions  may  come  from  ships  using  distillate  fuels  (Passig  et  al.,  2022);  The  abundance  of 
sodium- and chloride-related ions in the spectra indicates that these particles are from sea salt 
Harrison, 2006; Passig et al., 2022). In Table 1, the seven aerosol particle classes used in this study and their 
characteristic ion markers are summarized. 

Popular  methods  to  classify  the  measured  aerosol  particles  according  to  their  bipolar  mass  spectra  are 
clustering  algorithms,  which  calculate  the  geometric  relationships  between  mass  spectra,  such  as  K-means 
using Euclidean Distance (Healy et al., 2010; Arndt et al., 2021) and ART-2a using Cosine Similarity (Song et al., 
. Clustering algorithms are relatively easy to implement and require only a 
low number of parameters to be set. However, to obtain meaningful results, extensive manual post-processing 
of  their  results  (selecting  and  merging  of  clusters)  is  needed.  This  manual  post-processing  may  have  its 
significance  as  it  allows  the  researcher  to  discover  new  features  of  particle  spectra,  but  it  cannot  be 
automatized.  Moreover,  the  accuracy  of  the  obtained  classification  results  is  based  on  clusters  rather  than 
individual particles, and some clusters may contain thousands of particles. 

323 

 
 
 
In contrast, supervised machine learning algorithms can accomplish automatic pattern recognition of different 
mass  spectra  with  trained  models,  and  their  accuracy  is  based  on  individual  particles.  However,  supervised 
algorithms  require  large  amounts  of  labeled  data  for  training,  and  the  process  of  creating  labels  based  on 
expert knowledge is usually expensive and time-consuming. A few studies to automatically classify SPMS data 
have been published (Christopoulos et al., 2018), (Wang et al., 2023a, 2023b). However, these studies were 
performed  on  a  single  dataset  and  did  not  consider  the  generalizability  and  reliability  of  the  trained 
classification models when facing data from different sources. Different measurement campaigns will differ in 
the settings and locations of the measurement devices, and the environments, causing significant differences 
among the measured mass spectra even for the same particle class. Figure 1 shows in two columns exemplary 
normalized mass spectra from two measurements, assigned to three different particle classes. A comparison 
of the two columns reveals that the mass spectra assigned to the same class show different characteristics 
when  coming  from  different  measurements.  These  disparities  cause  questionable  robustness  of  supervised 
learning models trained on data from one campaign and used to classify data from another campaign, as those 
models can produce dramatically different results when facing slight perturbations in the data. This seriously 
affects the potential of supervised algorithms for applications in environmental studies like aerosol particle 
classification. 

Figure 1: 
Examples for differences in mass spectra from two measurement campaigns,  Datasets A (left 
column) and B (right column), assigned to the same particle class. As Dataset B was sampled near the coast, 
many particles are in mixed state with sea salt (pink signals). 

In this study, to reliably classify SPMS data from different measurement campaigns, a highly adaptive Multilayer 
Perceptron (MLP) network architecture is proposed. Relevant strategies for the hyperparameter tuning are given 
and the robustness of the MLP network for the classification of seven particle classes on data from the two 
campaigns is compared. Finally, means to further enhance the robustness of the models are suggested. 

2. Methodology 

2.1 Sampling sites and equipment 

To investigate the composition and possible sources of aerosol particles, two measurement campaigns were 
performed in the urban area of the coastal city of Rostock, Germany: Campaign 1, from which Dataset A was 
derived,  was  carried  out  from  June  26  to  July  2,  2018,  in  the  southern  part  of  the  city  (54°04'41.5''N, 
12°06'30.6''E, ~35 m above sea level, ~12 km south of the coast line) (Passig et al., 2021); Campaign 2, from 
which Dataset B was obtained, took place from July 1 to August 3, 2022, close to the Baltic Sea (54°10'14.8''N, 
12°06'24.7''E, ~7 m above the sea level, ~1.5 km north of the port), see Figure 2. The measurement device to 
obtain the bipolar mass spectra from individual aerosol particles (size range approx. from 250 nm to 2.5 µm) 
was a Single-Particle Time-of-Flight (TOF) Mass Spectrometer with Z geometry, manufactured by Photonion, 
Inc., Germany. Particles are detected by a continuous wave laser and ionized with a 248 nm KrF excimer laser 
(laser desorption/ionization, LDI), see (Passig et al., 2020, 2021) for more details. 

324 

 
 
 
 
Figure 17:  Maps of the locations of the 
two measurement campaigns (from June 
2018 and July 2022) in the coastal city of 
Rostock,  Germany,  close  to  the  Baltic 
Sea,  where  bipolar  mass  spectrometric 
data for ship emissions monitoring were 
measured and used for this study. 

2.2 Dataset Description 

Of all mass spectra measured during the two measurement campaigns mentioned above, we used selected 
parts to investigate the generalization capacity of several trained MLP models to reliably classify mass spectra 
measured  during  different  campaigns.  Dataset  A  contains  approx.  24,000  mass  spectra,  assigned  to  eight 
different  classes  (Wang  et  al.,  2023a).  Dataset  B  contains  approx.  37,000  mass  spectra  classified  into  13 
different classes (Wang et al., 2023b). Since in 2022 (Dataset B), the sampling site was closer to the coast than 
in  2018  (Dataset  A),  many  more  particles  are  in  mixing  state  with  sea  salt  and  more  particles  from  ship 
emissions  were  collected.  In  Dataset  B,  several  subsets  of  iron-containing  particles  associated  with  ship 
emissions can be distinguished, based on different degrees of aging. Freshly emitted particles are in the Fe-EC 
class; moderately aged particles are in the Fe-Sulfate-Nitrate, Fe-EC-Nitrate and Fe-Nitrate classes and aged 
particles are in the Fe-dominate class. 

For comparison of the applicability of a certain MLP model on two datasets, it is essential to keep the classes 
of both datasets identical. Therefore, we removed and merged several classes to obtain seven identical particle 
classes in both datasets. Table 1 details the list of the classes used in this study along with the most significant 
ion markers to recognize the mass spectra of the different classes and the number of samples in each class 
for both  datasets. Although the chemical  composition of the particles conforms  to  the  rules  in Table  1,  the 
characteristic spectra of particles assigned to the same class might differ for different datasets, see Figure 1. 

2.3 Training and Evaluation 

Figure 3 illustrates the adaptive MLP network architecture for aerosol particle classification. To keep the data 
from Datasets A and B standardized, as learning features for training we used positive and negative LDI mass 
spectra ranging from  m/z  -120  to +120 and normalized according to their  maximum intensity. These  mass 
spectra  are  then  mapped  into  1x240  vectors  and  fed  into  three  fully  connected  hidden  layers  (each  layer 
consists of 512 neurons), using ReLU non-linear function for information transformation. Finally, SoftMax is 
used to predict the probability of classification. During training, we used cross-entropy as loss function to obtain 
the  difference between the true  and predicted labels, and Adam optimizer to minimize the loss so that  the 
output of the model continuously approaches or reaches the optimal value. The learning rate was set to start 
at 0.001 and decrease by 10% every 100 epochs, for a total of 300 epochs to ensure the convergence of the 
network. Dropout was used to prevent overfitting during the training process. 

325 

 
 
 
 
 
Table 1: Overview of the particle classes used in the study with likely sources of emissions and characteristic 
ion markers and number of samples (#) in both datasets. 

Class 

Source 

Ion makers 

EC 

traffic emissions 

EC-OC 

biomass burning 

K-rich 

biomass burning 

Ca-rich 

lubricating oil of ship 
engines 

EC (12nCn

±): 12C±, 24C2

±, ..., 120C10

± 

OC: 27[C2H3]+, 37[C3H]+, 39[C3H3]+, 43[C4H7]+, 
51[C4H3]+, 63[C5H3]+, etc; EC 

39/41K+; EC; OC; Sulfate: 97[HSO4]-; Nitrate: 
46[NO2]-, 62[NO3]- 

# in Dataset 
A 

# in Dataset 
B 

4,671 

816 

4,000 

3,383 

3,998 

3,300 

40Ca+, 56[CaO]+; EC; Sulfate; Nitrate 

1,365 

3,238 

Fe-rich 

ship fuel emissions 

54/56Fe+, 73[FeOH]+; EC; Sulfate; Nitrate 

1,729 

16,222 

V-rich 

ship fuel emissions 

51V+, 67[VO]+; 54/56Fe+; 60Ni+; EC; Sulfate; Nitrate  3,879 

943 

Na-rich 

sea salt 

sum  

23Na±, 39[NaO]+, 46[Na2]+, 62[Na2O]+, 63[Na2OH]+, 
81/83[Na2Cl] +, 35/37Cl-, 59/61[NaCl]-, 93/95[NaCl2]-; 
Sulfate; Nitrate 

3,848 

6,600 

23,490 

37,406 

Figure 3: 
and fed into an MLP with three hidden layers, each with 512 neurons. 

Schematic diagram of the used MLP architecture. The input spectra are mapped as 1x240 vectors 

3. Results and Discussion 

We applied a grid search strategy to determine the hyperparameters of the MLP models and a stratified 5-fold 
cross-validation to evaluate the performance of the network since the number of samples in different classes 
in both datasets are imbalanced. Table 2 presents the experiments and their results. In all experiments, the 
architecture shown in Figure 3 was used. Experiments 1 and 3 give the performance of the models MA and MB 
trained separately on each single dataset, both with an accuracy of around 95 %. If the model MA or MB was 
used to predict the spectra from the other dataset (Experiments 2 and 4), the accuracy dropped significantly. 
Finally, in Experiment 5 model MC was trained with the combined Dataset A&B, indicating the best performance. 
In Figure 4, the normalized confusion matrices for Experiments 2, 4, and 5 are presented. 

Some observations: EC particles are often mixed with OC signals, and when the OC signals are weak, we labeled 
them as EC. However, the thresholds for determining the OC signal strength were more relaxed in Dataset B 
than in Dataset A. For example, the model MB trained with Dataset B incorrectly predicts 40.7% of the EC-OC 

326 

 
 
 
 
 
 
 
particles from Dataset A as EC particles, see element (2,1) of the confusion matrix in Figure 4 (b). This is because 
according to the determination criteria used to create Dataset B, these misidentified EC-OC particles in Dataset 
A fit the criteria of EC particles in Dataset B. 

Table 2: Classification results of five experiments. In Experiment 1, 3 and 5, 80% of the data were used for 
train and 20% for test. In Experiment 2 and 4, all data from the other dataset was used for test. 

Experiment 

Train dataset  Model 

Test dataset 

Accuracy  
in % 

Recall 
in % 

Precision 
in % 

1 

2 

3 

4 

5 

A (80%) 

A (80%) 

B (80%) 

B (80%) 

MA 

MA 

MB 

MB 

A (20%) 

96.1 ± 3.2 

96.1 ± 3.3 

96.2 ± 2.0 

B (100%) 

80.3 

76.6 

78.8 

B (20%) 

94.0 ± 1.2 

92.3 ± 2.2 

90.6 ± 3.4 

A (100%) 

84.6 

86.2 

84.8 

A&B (80%)  MC 

A&B (20%) 

96.7 ± 1.5 

96.5 ± 2.1 

96.6 ± 2.5 

In Experiment 2, a significant number of particles from Dataset B is incorrectly identified as sea salt (class Na-
rich), see the last column of Figure 4 (a). Most of these particles are in a mixed state with sea salt (similar to 
the spectra in the right column of Figure 1) and exhibit strong sea salt characteristics in addition to their own 
signature chemical composition. This result proves that the model cannot adapt to such data discrepancies. 

Experiment 5 shows that when using the merged Dataset A&B, the robustness can be significantly improved. 
The model MC overcomes the problems encountered by the first two models and predicts very good results for 
all classes, see Figure 4 (c). This indicates that model MC learned not only the internal differences of spectra 
from different sources but also the different criteria introduced during human annotation. 

Normalized  confusion  matrix  displaying  the  accuracy  rates  in  %  of  (a)  Experiment  2  (train  on 
Figure 4: 
Dataset A, test on Dataset B), (b) Experiment 4 (train on Dataset B, test on Dataset A) and (c) Experiment 5 
(train and test on the combined Dataset A&B). The diagonal in the matrix is the rate of correct predictions for 
each class; all other entries are incorrect predictions of class assignments. 

4. Conclusion 

For  the  effective  detection  of  environmental  events,  e.g.  for  compliance  monitoring  of  ship  plumes,  fast 
automated on-line  analyzes are required. We  proposed a  neural-network based  Multilayer  Perceptron (MLP) 
network for the automatic classification of aerosol particles measured by Single-Particle Mass Spectrometry 
(SPMS). Differences in measurement campaigns  (SPMS  instruments,  sampling locations,  weather conditions, 
etc.) lead to differences in mass spectra from one measurement to another, even for particles of the same 
class. This work showed that models trained with data from a single measurement do not generalize well and 
are more sensitive to differences in the characteristics of data from different sources. The robustness of the 
model trained with merged data was substantially improved, indicating that the model exhibits high adaptability 

327 

 
 
 
 
and reliability under the condition of sufficient training data, even if there are internal differences or bias in the 
data.  

The model learned the internal variation of data from different sources and overcame the different criteria 
introduced  by  the  annotation  process.  However,  the  creation  of  a  benchmark  dataset  of  labeled  data  is 
expensive and time-consuming. To save time on labeling and ultimately to enhance the robustness of the model, 
so-called transfer learning can be used to fine-tune a pre-trained model with a small dataset. Furthermore, 
with  data  augmentation  techniques  more  artificial  SPMS  data  can  be  generated  from  a  limited  amount  of 
measured data. This, however, is to be investigated carefully, since the discrepancy between the generated and 
the real data inevitably introduces noise. 

Acknowledgements  

This research is funded by dtec.bw   Digitalization and Technology Research Center of the Bundeswehr (project 

 NextGenerationEU. 

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328 

 
 
 
 
The Association between Vessel Departures and Air Pollution at Helsinki Port 
Terminals 2016-2021 

M. Heikkilä1* 

1 Department of Atmospheric Composition, Finnish Meteorological Institute, Erik Palménin aukio 1, 00560 Helsinki, Finland, 
mikko.heikkila@fmi.fi 

Introduction 

Combustion-based ship air emissions can be divided into two categories based on their effect: (1) greenhouse 
gas emissions (GHG), which contribute to global warming, and (2) air pollutant emissions which contribute to 
air quality. The importance and urgency to decrease greenhouse gas emissions are well known, and despite 
committing to the Paris climate agreement, international shipping GHG emissions are found to be increasing 
(Faber et al., 2020). Air pollution is a major global concern to human health. Among other pollutants, there is 
strong  evidence  of  the  public  health  effects  of  particulate  matter  (PM),  ozone  (O3),  nitrogen  dioxide  (NO2), 
sulphur  dioxide  (SO2)  and  carbon  monoxide  (CO)  (World  Health  Organization,  2021).  The  recent  scientific 
literature  shows  the  detrimental  effect  of  even  lower  concentrations  of  particles  than  what  was  previously 
understood,  and  that  the  burden  of  disease  caused  by  air  pollution  is  somewhat  equal  to  smoking  and  an 
unhealthy diet (World Health Organization, 2021). Both long- and short-term exposure to air pollutants have 
been shown to increase mortality and morbidity. A recent systematic review and meta-analysis concluded the 
combined risk ratio for long term exposure to PM2.5 and natural-cause mortality to be 1.08 (95% confidence 
interval  1.05-1.09)  per  10  g/m3  (Chen  &  Hoek,  2020).  Another  systematic  review  and  meta-analysis  found 
positive associations between all-cause mortality and increased 1 h maximum exposure to PM10, PM2.5, and O3. 
The same correlation was found with increased 24 h exposure to NO2 (Orellano et al., 2020). 

A recent cohort study conducted in Finland revealed that even short-term exposure to air pollutants a week 
before scheduled delivery raises the risk of preterm birth by 67% (95% confidence interval 14-146%) for PM2.5 
and 65% (95% confidence interval 14-137%) for NO2 (Siddika et al., 2020). The new evidence led the World 
Health Organization (WHO) to update their Air Quality Guidelines (AQC) in 2021. All other revisions were made 
stricter except for SO2, which was revised upwards. The European Commission submitted a proposal for a new 
Air Quality Directive in October 2022 (European Commission, 2022). 

Maritime transport is one the major air polluters in the world. It is estimated that shipp

(Englert 
& Losos, 2021). Multiple studies show a positive correlation between increased concentrations of pollutants 
and vessel traffic (Toscano & Murena, 2019). The unequal share between GHG emissions and air pollution is 
because  the  requirements  for  fuel  quality  in  shipping  have  not  been  as  strict  as  for  land-based  energy 
production  or transport.  A  health  impact  assessment  made  in eight  Mediterranean  coastal  cities  found  that 
shipping contributed to 430 (95% confidence interval 220-650) annual premature deaths due to an increased 
exposure to PM2.5 (Viana et al., 2020). A recent modelling study concluded that the NO2 and PM2.5 emissions 
from  inland  ships  sailing  up  and  down  the  Yangtze  River  should  be  focussed on  as  they  carry  a  burden  on 
residential areas (Huang et al., 2022). 

The global sulphur cap in marine fuel was lowered from 3.5% to 0.5% only 1 January 2020. The sulphur cap in 
marine fuel is restricted even more to 0.1% in Sulphur Emission Control Areas (SECA), and the whole Baltic Sea 
has been a SECA since May 2006. A gradual reduction of the fuel sulphur content in the Baltic Sea area towards 
0.1% was made over a decade. The motivation for this restriction was to reduce the impact of ship air emissions 
to public health, but it came with a cost as the global cooling effect of sulphur aerosol formation was also 
reduced (Sofiev et al., 2018), (Balkanski et al., 2010), (Tronstad Lund et al., 2012), (Fuglestvedt et al., 2009), 
(Capaldo et al., 1999). It is also debatable if the cost-benefit of a SECA area is positive or negative and if the 
focus should be on the long or the short-term impacts (Antturi et al., 2016). 

Nitrogen oxide (NOx) emissions have been restricted in specified Nitrogen Emission Control Areas (NECA) and 
the Baltic Sea has been included in the strictest Tier III restrictions since 1 January 2021, but the regulation 

329 

 
 
 
 
 
 
 
concerns only new ships. Like SO2 restrictions, NOx emission restrictions are driven by their impact on public 
health, but also to eutrophication (Gren et al., 2021). Many studies have been published that report the impact 
of ship emissions to the air quality around port areas (Gobbi et al., 2020), (Anastasopolos et al., 2021), (Mousavi 
et al., 2018), and analyses have been run to assess the cost-benefit of connecting ships to shore power while 
alongside (Daniel et al., 2022), (Spengler & Tovar, 2021). Nevertheless, the European Commission has submitted 
a proposal, which will insist ports to have container vessels and passenger ships connected to an onshore power 
supply or similar system by 1 January 2030. 

As with other measures to mitigate the impact from maritime air emissions, connecting ships to shore power 
might not work as planned. Shore power reduces only emissions caused by the auxiliary engines while alongside, 
but what about emissions that are caused by the main engines, when manoeuvring the ship in and out of the 
port?  This  study  aims  to  fill  that  knowledge  gap by  first  assessing  what  is  the  share  of  fuel  combusted  by 
passenger and container vessels that remain alongside for 2 h or more in the port of Helsinki and then analysing 
the effect of vessel arrivals, departures, and time at berth on port area air quality. Analyses were focused on 
two  combustion-based  engine  pollutants:  particulate  matter  (PM2.5)  and  nitrogen  dioxide  (NO2)  as  their 
recommended exposure limits were decreased most in the latest WHO update and due to their contribution to 
detrimental health effects and chemical smog. 

Materials and methods 

Ship arrival and departure data were obtained from the Finnish Transport and Communication 

Agency  Traficom. The dataset  contains information from the Porttraffic system  (www.porttraffic.fi)  which  is 
publicly available. Duplicate records and errors in dates and times were corrected manually when identified. 
Arrivals, departures, vessel movement (arrival and departure) and vessels at berth were allocated to the hour 
of local time. In some of the port terminals, specific vessel movements could also be allocated to a specific 
berth at the terminal. In such cases, these were analysed separately. Ships were classified by type to calculate 
the cumulative berth times and to find out the effect of the shore power regulation. 

Ship fuel consumption data were downloaded from the European Union (EU) Monitoring, Reporting and Verifying 
(MRV) Thetis database (mrv.emsa.europa.eu). At the time of the study, data from the years 2018-2021 were 
available,  and  the  versions  used  were  267  for  2018,  208  for  2019,  166  for  2020  and  90  for  2021.  The 
consumption in port was calculated based on the reported values for total annual fuel consumption, total annual 
CO2 emitted, total annual CO2 emitted while in port and time at sea. 

The hourly mean concentrations data of NO2 and PM2.5 were obtained from the Helsinki Region Environmental 
Services  (HSY).  Measuring  was  performed  with  the  HSY  mobile  unit  in  different  port  areas  in  Helsinki.  The 
location of the mobile unit changes once a year. The NOx measurements were made with a Horiba AP-370 air 
pollution  monitor  and  the  PM  measurements  with  a  Fidas  200  instrument  (Helsinki  Region  Environmental 
Services Authority, 2022). The sampling height with the mobile unit is 4 metres above ground and 6-7 metres 
above sea level. In 2016, the mobile unit was stationed in the Vuosaari port terminal, in 2018 the unit was at 
South Harbour Makasiiniterminaali, in 2019 and 2020 the unit was at West Harbour and in 2021 the unit was 
again at South Harbour but at the Katajanokka terminal. There are no data from 2017 as the unit was based 
at the airport of Helsinki. Negative values in the data were not removed based on discussions with the data 
provider as they compensate for measuring inaccuracies as the instruments are calibrated on 24 h mean values. 

Weather data: wind direction, wind speed, air temperature, relative humidity and atmospheric pressure were 
downloaded  from  the  Finnish  Meteorological  Institute  (FMI)  open  data  (en.ilmatieteenlaitos.fi/download-
observations). The meteorological variables used were taken from the Helsinki Harmaja lighthouse observation 
station to minimise the effect of local pollution sources. 

A multivariable linear regression was used to model the effect of vessel air emissions to the air quality data 
while  adjusting  for  confounders  such  as  meteorological  factors  and  local  time,  which  are  associated  with 
variation of road traffic. As the distributions of the dependent variables (measured hourly mean NO2 and PM2.5) 
were skewed, a natural logarithm of the values was used for the regression analysis (Formula 1). 

ln(𝑌) =   𝑏0 +   𝑏1𝑥1 +   𝑏2𝑥2 + ⋯ + 𝑏𝑛𝑥𝑛 

(1) 

330 

 
 
 
 
 
 
 
where ln(Y) is the natural logarithm of the dependent variable (hourly mean NO2 or PM2.5), b0 is the intercept, 
b1-bn are the regression coefficients for the chosen variables and X1-Xn are the chosen variables. 

The variables were chosen with the backward elimination method based on the statistical 

significance  and  optimum  goodness  of  fit  of  the  regression  model  (Broadhurst  et  al.,  1997),  (Suarez  et  al., 
2017). Variables included wind direction in degrees, wind velocity in metres per second, relative humidity in 
percent, air temperature in degrees centigrade and time of day in hours, which was used as a proxy for road 
traffic. 

For the sensitivity analysis, a subset of the data was used to include only weekends (Saturday and Sunday) to 
minimize  the  effect  of  road  traffic.  Additionally,  when  available,  the  effects  of  different  vessel  types  were 
examined  in  detail.  The  statistical  analyses  were  completed  with  R  using  the  Dplyr  and  Openair  packages 
(Carslaw & Ropkins, 2012). As the dependent variable was normalized by taking the natural logarithm of the 
measured pollutant value, the effect of the vessel movement to the hourly mean concentration in percent was 
calculated using Formula 2: 

𝐸𝑓𝑓𝑒𝑐𝑡(%) = (𝑒𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 − 1) ∗ 100 

(2) 

Results 

The vessel traffic in the port of Helsinki is dominated by passenger vessels and roll-on roll-off (roro) cargo 
transport (Table 1). The COVID-19 pandemic resulted in a significant reduction in 2020 to ship visits (-17.4% 
to the mean 2018-2019) notably with passenger-carrying vessels such as the passenger ferry (Pax ferry) and 
the cruise ships. Ropax vessel visits however did not decrease during the pandemic. 

Table 1: Ship visits to the port of Helsinki 2018-2020 by vessel type. Pax ferry: vessel that carries both roll-on 
roll-off cargo and passengers with the largest parts of the ship dedicated for passengers, ropax: vessel that 
carries both roll-on roll-off cargo with the largest parts of the ship dedicated for cargo, container: container 
vessel, roro: cargo vessel carrying roll-on roll-off cargo, cruise: cruise ship, other: all other vessel types. 

2018 

2019 

2020 

2021 

Type 
Pax ferry 
Ropax 
Container 
Bulk 
Roro 
Cruise 
Other 
All 

Visits 
5181 
892 
766 
367 
357 
283 
251 
8097 

% 
64.0 
11.0 
9.5 
4.5 
4.4 
3.5 
3.1 
100.0 

Visits 
5141 
1181 
708 
292 
330 
300 
320 
8272 

% 
62.1 
14.3 
8.6 
3.5 
4.0 
3.6 
3.9 
100.0 

Visits 
4259 
1340 
660 
266 
297 
0 
146 
6968 

% 
61.1 
19.2 
9.5 
3.8 
4.3 
0.0 
2.1 
100.0 

Visits 
4709 
1460 
611 
272 
359 
15 
200 
7271 

% 
64.8 
14.7 
8.7 
3.9 
4.4 
2.5 
3.2 
100.0 

Passenger ferry vessels spend less time in port than cargo ships which take time discharging and loading. On 
the other hand, passenger vessels consume more fuel while alongside due to their larger power consumption 
which  is  required  for  ship  hotel  operations  such  as  air  conditioning,  provision  cooling  and  restaurants.  The 
COVID-19 pandemic effect can be observed clearly in the mean time spent alongside by the passenger ferry 
vessels  as  many  of  them were  laid  up  during  2020  and  by  the  fact  that  there  were  no  cruise  ship  calls  in 
Helsinki in 2020 (Table 2). 

331 

 
 
 
 
 
 
 
 
 
 
Table 2: Mean time in port in hours (TP), mean fuel consumption in port in metric tons per hour (FP) by vessel 
type  at  port  of  Helsinki  2018-2020.  Time  in  port  by  vessel  type  was  calculated  from  the  port  arrival  and 
departure data and fuel consumption in port from the MRV data. 

Vessel type 
Pax ferry 
Ropax 
Container 
Bulk 
Roro 
Cruise 
All 

2018 
TP 
2.48 
4.94 
16.87 
49.00 
18.55 
8.38 
16.1 

FP 
0.43 
0.19 
0.26 
0.03 
0.16 
0.47 
0.34 

2019 
TP 
2.46 
4.72 
18.65 
51.08 
17.58 
8.51 
20.5 

FP 
0.35 
0.23 
0.12 
0.04 
0.17 
0.36 
0.28 

2020 
TP 
6.39 
3.98 
19.67 
104.58 
21.39 
0.0 
20.1 

FP 
0.23 
0.18 
0.12 
0.03 
0.17 
0.0 
0.20 

According to the MRV data, passenger carrying vessels (pax ferry, ropax and cruise) consume around 67% (64.6-
71.6) of the total fuel combusted by vessels while in port in Helsinki. The second largest consumers are container 
vessels: 19% (15.8-24.2). These vessel types are also mandated to be connected to shore power or a similar 
system while alongside by 2030 for port calls more than 2 h in EU ports as per the EU Green Deal initiative. 
This would lead to a 78% (74.9-80.1) reduction in fuel combusted and CO2 emitted by ships while at berth in 
Helsinki. 

Most of the port terminals examined in this study are situated in the urban populated area (South Harbour and 
West Harbour) whereas the Vuosaari terminal is at the outskirts of the city limits around 15 kilometres from 
the centre. The vessel traffic to Vuosaari consists mainly of cargo ships apart from ropax vessels which carry a 
limited number of passengers mainly to Travemünde in Germany and Muuga in Estonia. The port terminals at 
the city centre however are mainly operated by passenger ferry vessels with regular traffic to Mariehamn, St 
Petersburg,  Stockholm  and  Tallinn  and  the  seasonal  cruise  ships.  Separate  measuring  studies  have  been 
completed in the same areas: both prior to the SECA (Pirjola et al., 2014) and after coming into force (Walden 
et al., 2021). Using HSY continuous measurement data, a long-term association can be established better than 
with short-term measuring campaigns. 

Prevailing winds are from the south-west with a mean velocity of 6.75 metres per second (5.87-7.62). It is likely 
that the winds blow most of the air pollutants away from the port towards the urban areas. 

3 for PM2.5

The annual mean concentrations of NO2 and PM2.5 

3 for NO2 and 25 
3 for PM2.5) except for 
PM2.5 in 2020. Based on the annual means, port workers, ship crews, passengers and urban area populations 
exposed  to  the  port  emissions  are  affected  by  NO2  and  PM2.5  concentrations  that  exceed  the  WHO 
recommendations (Figure 1). 

g/m3 for NO2 

332 

 
 
 
 
Figure 1: Boxplot  of  the  measured  annual  median  (black  line),  interquartile  ranges  and  mean  (stars) 
concentration  of  NO2  2016,  2018-2021  at  various  port  terminals  of  Helsinki.  The  blue  dashed  line  is  the 
Finnish/EU limit of 40 µg/m3, which was in force during the studied years. The red dashed line is the WHO 2021 
recommended limit of 10 µg/m3 and the orange line the EU proposal of 20 µg/m3. 

Consistent results using a multivariable linear regression analysis from four different port terminals and five 
different years indicate that vessel departures significantly increase the hourly mean NO2 concentrations, and 
the effect of arrivals is less significant or does not exist (Figure 2). The mean effect to the hourly mean NO2 
concentrations of departures for all vessels was 47.0% (95% confidence interval 41.6-53.3%) for the whole 
year and 38.2% (95% confidence interval 28.9-48.1%) for the weekends. These results do not directly identify 
if the source of the NO2 were the ships themselves or the cargo loaded on them, but the variability between 
arrivals and departures suggest that emissions caused by the departures are higher than for the arrivals. A 
plausible explanation is the visually identifiable emission plume caused by star
prior to the departure. In general, departures consume more energy than arrivals, when vessel inertia can be 
utilised.  Nitrogen  oxide  emissions  have  been  shown  to  increase  by  150%  during  the  cold  start  of  a  marine 
engine (Chu Van et al., 2019). 

The results from South  Harbour in 2018, West  Harbour in 2019 and South  Harbour in 2021 show that  the 
different types of ships docking at different parts of the harbour basin have a different effect size to the hourly 
mean NO2 concentrations. There are multiple possible explanations for this. It seems that the closest vessel to 
the sampling point causes the largest effect, but the results from West Harbour in 2019 for the whole year also 
indicate that vessels further away but with larger engines (cruise ships compared to passenger ferry vessels) 
might affect the NO2 concentrations more. In addition, combustion-based nitrogen oxide emissions are not all 
nitrogen dioxide, but also nitrogen monoxide, of which some will transform into NO2. Therefore, it is likely that 
a close sampling point does not identify all NO2 caused by the source. The vessel departure effect is detectable 
also on the hourly mean PM2.5 concentrations but the overall effect is much smaller indicating that sources 
other than port activity might be more influential. 

Connecting container and passenger vessels to shore power, as required by future EU regulation, would reduce 
most of the emissions that all the vessels that visit Helsinki produce with the auxiliary engines while alongside. 
As shown in this study, this might not have a significant effect on NO2 or PM2.5 concentrations as the main 
engines seem to have a larger effect. Hybrid electric powered ships that could manoeuvre out of the port without 
starting their combustion engines would probably be a better option to reduce air pollution in urban areas. 

333 

 
 
 
 
Figure 2: Boxplot of vessel movement effect in % on the hourly mean concentration of NO2 (blue) and PM2.5 
(pink) using the whole year data and weekends only (_WE). Boxplot showing median effect as black line with 
interquartile ranges and mean with star (*). 

Discussion 

This study aimed to calculate the effect of connecting container and passenger vessels to shore power while 
alongside in port for >2 h at the port of Helsinki as required by the upcoming EU regulation. The effect, 75-
80%, is surprisingly large when considering that most of the ship visits are shorter than 2 h. Shore power, or 
cold ironing as it is often called, is a large modification to existing infrastructure of both the ports and visiting 
vessels  with  specific  challenges  related  to  retrofitting  old  vessels  (Khersonsky  et  al.,  2007),  (Zis,  2019).  To 
overcome this task in the most efficient way, the feasibility of both the shore power connection and energy 
storage on board should be considered carefully. 

-based air emissions are caused purely by the auxiliary 
engines, and during the arrivals and departures they are caused by the combination of both the main engines 
and the auxiliaries. On the other hand, during the port stay, additional air emissions are caused by the loading 
and discharging activities especially in Helsinki having a large share of roll-on/roll-off type of cargo. Based on 
the results, this study identified that departures seem to have the largest effect on the measured concentrations 
of air pollutants. The multivariable linear regression analysis shows that starting the cold main engines has a 
significant short-term effect on NO2 concentration, but not on PM2.5. This will probably not change by connecting 
the vessels to shore power, but an energy storage on board would, assuming that it has enough capacity to 
allow the vessel to manoeuvre besides providing the power that the auxiliary engines would for the time in port. 
The benefit of a hybrid system to mitigate the air pollution from ships is not limited to fuel oil powered ships, 
as LNG-powered vessels have been found to emit larger amounts of carbon monoxide and formaldehyde (Peng 
et al., 2020). An energy storage unit on board does not mean that shore power becomes obsolete, as it can be 
used to charge the storage unit while the ship is alongside (Kalikatzarakis et al., 2018). 

The  limitation  of  the  findings  is  that  the  linear  regression  models  could  only  explain  20-30%  of  the  NO2 
variation. This can be caused by multiple factors: first, as the measuring unit was placed close to the emission 
source, some of the NOx emitted by the vessel could be in the form of nitrogen monoxide (NO) and therefore 
not detected in the NO2 
the plume much higher than the measuring point, most of the impact may not be visible in these measurements. 
To have a better understanding of this, the location of the measurement site should be considered carefully if 
ship  plumes  were  to  be  studied  specifically.  Third,  some  sources  of  emissions  or  confounding  factors  were 
possibly not identified, and the hour of day was used as a proxy to model the effect of road traffic and other 
urban emission sources. This is not surprising as the sampling locations were in an area that is subjected to 

334 

 
 
 
 
multiple emission sources such as powerplants using coal, natural gas and fuel oil. Lastly, hourly mean values 
were chosen to be used in the analyses, knowing that stronger associations and effects would have probably 
been observed with a shorter time resolution and including both NO and NO2 concentrations. The scope of the 
study was to find whether vessel movement causes significant increases in the measured air pollution values 
that are subject to regulation, specifically to NO2 and PM2.5 as their recommended limit values were recently 
lowered by the WHO. 

Acknowledgements  

This research was produced as part of the European Union 

agreement No 874990. 

  Research  and  Innovation  Framework  Programme  action  under  grant 

The author would like to thank Anu Kousa from the Helsinki Region Environmental Services and Antti Arkima 
from the Finnish Transport and Communications Agency for compiling the data used in this research. 

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Maritime Greenhouse Gas Emission Reduction Scenarios   Extension of DIONE 
model32 

Z. Samos2, J. Krause1**, L. Maineri3, M. Georgakaki2, G. Papadimitriou2, C. Kouridis2 and G.  

Fontaras1  

European Commission Joint Research Centre, 21027, Ispra, Italy, Jette.Krause@ec.europa.eu ,  

Georgios.FONTARAS@ec.europa.eu   

Emisia S.A., Antoni Tritsi 21, Thessaloniki, GR-57001, Greece  
Seidor Italy S.r.l., via Castel Morrone 24, 20125, Milano (MI), Italy  

1 

2 
3 

Introduction 

Maritime transport plays a crucial role in the European transportation system, serving as the primary mode 
for global trade. In 2019, ships weighing over 1,000 gross tonnes carried about 11 billion tonnes, accounting 
for around 80% of the world's trade volume and 70% of its value (UNCTAD, 2019). However, this transport 
sector  presents  significant environmental challenges. In particular, maritime  transportation accounted  for 
about  4%  of  the  EU's  total  greenhouse  gas  emissions  (GhG)  in  2019  (EEA,  2022),  with  emissions  from 
shipping  rising  by  about  36%  over  the  past  three  decades.  Additionally,  the  international  shipping  sector 
significantly contributes to pollutant emissions in the EU, including 15% of total nitrogen oxides (NOx), 3.6% 

emissions.  

Figure 1: Transport emissions in 2019 as share of the EU's total GhG emissions.   

Maritime transport is projected to experience a considerable rise in greenhouse gas emissions, with estimates 
ranging from 90% to 130% of 2008 levels by 2050, considering various plausible long-term economic and 
energy scenarios (IMO, 2020). The maritime sector must play a crucial role in curbing its emissions to align 
with the ambitious goal of achieving climate neutrality in Europe by 2050. To address this challenge, the 
European Commission has introduced the 'Fit for 55' package, which encompasses a series of legislative 
proposals to reduce greenhouse gas emissions by at least 55% by 2030 (versus 1990). These proposals 
include extending the coverage of the EU Emissions Trading System to include maritime transport, imposing 
limits on the greenhouse gas content of energy used by ships when calling at European ports, promoting the 

32 The views expressed in this document are purely those of the authors and shall not be considered 
an official Commission position under any circumstance  

337 

 
 
 
                                                       
 
  
adoption of zero-emission technology during vessel berthing, supporting the development of alternative fuel 
infrastructure, and revising the Renewable Energy Directive and Energy Taxation Directive.  

Given  the  shipping  industry's  significant  contribution  to  global  GhG  emissions,  it  becomes  essential  to 
quantify the potential impact of various measures and policies accurately. It is equally important to assess 
the  effectiveness  of  different  strategies  to  reduce  its  environmental  impact.  To  address  this,  the  Joint 
Research  Centre has created a  comprehensive calculation method and integrated  it into  a  computer tool 
capable of evaluating future scenarios concerning vessel fleet composition, activity, and fuel mix. This tool, 
known as the DIONE maritime fleet emission model, has been developed in collaboration with EMISIA SA. It 
leverages  baselines  derived  from  Eurostat  and  UNFCCC  National  inventory  data,  encompassing  historical 
information up to 2020. To project into the future, these baselines are extended based on observed trends 
in the EU Reference Scenario 2020 (Comission, 2020). The DIONE model thus provides a powerful tool to 
reliably assess and analyse the potential outcomes of various measures and policies in the shipping industry, 
aiding in the pursuit of environmentally responsible practices.  

Methodology 

The DIONE model adopts a methodology that ensures its alignment with the EMEP/EEA Guidebook (EMEP/EEA, 
2019) for emission inventories when estimating emissions from maritime navigation. This approach is based 
on  established  guidelines  and  standards,  aiming  to  ensure  accuracy  and  consistency  in  calculating  the 
environmental impact of waterborne transportation. There are three reporting methods known as Tiers. These 
Tiers vary based on the level of complexity and the quality of input data obtained. Generally, higher Tiers 
with increased complexity are used when more detailed input data is available.  

The baseline of the model was initially implemented using the Tier 2 emission calculation level. However, the 
software model also incorporates a more detailed calculation methodology at the Tier 3 level, which can be 
utilised once all the required data becomes available. This enables the model to be flexible and adaptable, 
allowing for more accurate and comprehensive emission assessments when higher-quality data is accessible. 
Below, the steps included in the Tier2 calculation process are presented, along with the methods used to 
create the baseline.  

The Tier 2 method follows a top-down approach, using final fuel consumption statistics as input to calculate 
individual pollutant emissions. The Tier 2 emission factors are based on average conditions for the entire trip 
and  are  derived  as  a  weighted  sum  of  EFs  at  different  operating  conditions  (i.e.,  hoteling,  manoeuvring, 
cruise). The equation used for the Tier 2 approach in maritime emissions is the following:  

Where:  

: Emissions of pollutant i in kilograms  

: Mass of fuel type m used by vessels with engine type j in tonnes (split in  

domestic and international fuel consumption)   

: Average emission factor for pollutant i by vessels with engine j  

using fuel type m in kilograms /tonnes  

Fuel type m can be either Bunker Fuel Oil (BFO), Marine Diesel Oil (MDO) or Marine Gas Oil  

(MGO). Concerning type of engines, there are five types of engines, namely, Slow Speed Diesel (SSD), Medium 
Speed Diesel (MSD), High Speed Diesel (HSD) engine, Gas Turbine (GT) and Steam Turbine (ST).  

The input data needed from the model are the following:  

• 
• 
• 

Total port arrivals by type of vessel  
Domestic port arrivals by type of vessel  
Fuel sold for all domestic navigation  

338 

 
 
  
  
• 

Fuel sold for all international navigation  

The vessel types utilised in this methodology are presented in Figure 2, which also displays the corresponding 
default engine types and fuel types employed within this approach.  

Figure 2: Default Vessel, Engine and Fuel types in the Tier 2 approach  

As seen from equation 1, the emission factors used to calculate the emissions are for various combinations 
of engine type and fuel type, i.e., SSD MDO/MGO, SSD BFO, MSD MDO/MGO,  

MSD BFO, HSD MDO/MGO, HSD BFO, GT MDO/MGO, GT BFO, ST MDO/MGO, ST BFO. The Table 1 below includes 
the emission factors for selected pollutants per engine type - fuel type combination.  

Table 1: Emission factors for selected pollutants per engine type - fuel type combination.  

EF  

(kg/ton of fuel)  

BC  

CO  

NMVOC   NOx  

PM10  

SSD - MDO/MGO  

0.05   3.24  

1.64  

94.30  

1.07  

SSD - BFO  

0.09   3.10  

1.56  

90.20  

5.20  

MSD - MDO/MGO  

0.05   4.45  

1.86  

57.90  

1.07  

MSD - BFO  

0.09   4.25  

1.78  

55.30  

5.21  

HSD - MDO/MGO  

0.04   4.34  

2.64  

39.60  

0.96  

HSD - BFO  

0.08   4.15  

2.52  

37.90  

5.01  

GT - MDO/MGO  

0.05   3.84  

1.75  

17.15  

0.00  

GT - BFO  

0.09   3.67  

1.67  

17.42  

0.30  

ST - MDO/MGO  

0.09   3.67  

1.67  

6.01  

2.60  

ST - BFO  

1.75  

6.01  

1.00  

The  model  requires  the  total  energy  consumed  in  TJ  for  shipping  in  a  country  as  its  input.  Therefore,  to 
implement  this  method,  it  is  necessary  to  disaggregate  the  user-provided  total  energy  consumption  into 
specific values based on engine type and fuel class combinations.  

To achieve this, the following steps are followed according to the guidebook.  

In particular, total power installed (in kW) for each vessel type is calculated by multiplying the total number 
of  port  arrivals  with  the  reference  estimated  main  engine  power  provided  in  the  guidelines  (Table  2). 
Subsequently, the total installed power for each type of vessel is distributed among different engine types / 
fuel classes using the reference percentage table from the guidebook (Table 3). In this way, the total installed 

339 

 
 
  
 
 
 
power is categorised by engine type and fuel class, which is the sum of the separate power of each vessel 
type.A direct proportionality is assumed between total fuel consumption and installed power categorised by 
engine  type  and  fuel  class  calculated  in  the  previous  step.  Consequently,  based  on  this  proportional 
relationship, the model allocates the statistical fuel consumption values to the different engine types and 
fuel classes.  

Table 2: Estimated average main engine power (total power of all engines) by ship category (kW)  

Liquid 
bulk 
ships  

Dry 
bulk 
carriers  

Container  

General 
cargo  

Ro Ro 
Cargo  

Passenger   Fishing   Other  

Tug  

6,543  

4,397  

14,871  

2,555  

4,194  

10,196  

734  

2,469   2,033  

Table 3: Percentage of installed Main Engine power by engine type/fuel class (%)  

Engine / 
Fuel type  

Liquid 
bulk 
ships  

Dry 

bulk 
carriers  

Container  

General 
cargo  

Ro ro 
cargo  

Passenger    Fishing   Other   Tug  

SSD  

MDO/MGO  

SSD  

BFO  

MSD  

MDO/MGO  

MSD BFO  

HSD  

MDO/MGO  

HSD BFO  

GT  

MDO/MGO  

GT BFO  

ST  

MDO/MGO  

ST BFO  

0.87  

0.37  

1.23  

0.36  

0.17  

0.00  

0.00  

0.48   0.00  

74.08   91.63  

92.98  

44.59   20.09  

3.81  

0.00   30.14   0.00  

3.17  

0.63  

0.11  

8.48  

9.86  

5.68  

84.42   29.54   39.99  

20.47   7.29  

5.56  

41.71   59.82  

76.98  

3.82   19.63   6.14  

0.52  

0.06  

0.03  

4.30  

5.57  

3.68  

11.76   16.67   52.80  

0.75  

0.02  

0.09  

0.45  

2.23  

1.76  

0.00  

2.96   0.78  

0.00  

0.00  

0.00  

0.00  

2.27  

4.79  

0.00  

0.38   0.28  

0.14  

0.00  

0.00  

0.10  

0.00  

3.29  

0.00  

0.20   0.00  

0.00  

0.00  

0.00  

0.00  

0.00  

0.00  

0.00  

0.00   0.00  

0.00  

0.00  

0.20  

0.00  

The model's calculations are carried out independently for domestic and international navigation. The model 
adheres to the following criteria to differentiate between these two types of journeys: if a vessel departs 

340 

 
 
 
 
 
 
 
 
 
from and arrives in the same country, the voyage is labelled as domestic. Conversely, a journey is classified 
as international if the vessel departs from one country and arrives in a different country.  

Reference baseline & Customised scenarios 

Incorporated  within  the  model  is  a  dataset  that  serves  as  a  reference  baseline  scenario.  This  dataset 
encompasses  historical  data  and  future  projections  concerning  activity  and  emissions  in  the  maritime 
navigation sector. It includes information from 27 EU member states, Norway, Turkey, the United Kingdom, 
and aggregated data for EU27. This selection ensures comprehensive coverage and relevant information for 
the model's analysis and forecasting related to the maritime navigation sector.  

For developing this reference baseline scenario, Eurostat was primarily chosen as the most appropriate and 
reliable  source  for  historical  data  up  to  2020,  for  the  required  input  data  to  the  model.  Additionally,  the 
National Inventories for UNFCCC (CRF) were used as a secondary source. The inventories of countries in the 
UNFCCC  were  employed  for  three  specific  reasons.  Firstly,  they  were  used  to  validate  the  reliability  of 
Eurostat data. Secondly, in cases where fuel consumption data was missing, inconsistent, or erroneous in 
Eurostat,  the  UNFCCC  inventories  were  used  as  an  alternative  source  for  fuel  consumption  information. 
Lastly, the GhG countryspecific Emission Factors provided by the UNFCCC were used instead of the default 
GhG Emission Factors given in the EMEP/EEA guidebook.  

In addition to historical years for which statistical data is available from 2000 (in most countries) until 2020, 
the reference baseline scenario includes projections for future years. These projections are based primarily 
on the EU Reference Scenario 2020 and extend all the way to 2050.  

The EU Reference Scenario 2020 estimates the evolution of energy demand for international bunkers, inland 
waterways and domestic maritime up to 2050. These trends (one for domestic and one for international 
navigation) were applied to the evolution of the input data required for the Tier 2 methodology calculation, 
namely fuel consumption and port arrivals.Turkey, the United Kingdom, and Norway are not part of the EU 
Reference Scenario. Projection trends were derived from neighbouring or socioeconomically similar countries 
to account for these countries in the reference baseline scenario. For instance, the projection trend for Norway 
was  based  on  trend  data  from  Sweden.  This  approach  allows  for  the  inclusion  of  these  countries  in  the 
analysis, even though the EU Reference Scenario does not directly cover them.  

The  model  provides  the  flexibility  to  generate  an  alternative  customised  scenario,  different  from  the 
reference baseline scenario. Users can specify new combinations of engines and fuel types along with their 
respective emission factors, thereby enabling the creation of a customised scenario. Additionally, the above-
mentioned  Table 3 should  be  modified  accordingly to  reallocate  the percentage  of installed main engine 
power by engine type/fuel class and include the new engine / fuel type combinations.  

Results 

The model's outcomes consist of a comprehensive range of data, covering both maritime greenhouse gas 
emissions (CO2, CH4, N2O) and a variety of pollutants (NOx, CO, non-methane volatile organic compounds 
(NMVOCs), SO2, particulate matter (PM2.5, PM10), BC, and heavy metals).  

The results are disaggregated into two main categories based on their scope: domestic and international. 
Additionally, they are further categorised by engine type and fuel type, providing a detailed breakdown that 
allows for a more precise analysis and understanding of the environmental impact of shipping activities.  

341 

 
 
Figure 3: Baseline CO2 Emissions from Intra-EU27 Maritime Transport by engine / fuel combination  

Maritime intra-EU CO2 emissions for various engine fuel type combinations up to 2050 for the reference 
baseline scenario are depicted in the Figure 3. As illustrated in the graph, the maritime sector is projected to 
gradually increase its CO2 emissions, reaching approximately 60 Mt by 2050. Moreover, a significant portion 
of these emissions are attributed to Medium-speed diesel BFO vessels.  

References 

Comission, E. (2020). Retrieved from EU Reference Scenario 2020:  

https://energy.ec.europa.eu/data-and-analysis/energy-modelling/eu-reference-scenario2020_en  

EEA. (2022). EEA greenhouse gases 

 data viewer. Retrieved from EEA greenhouse gases 

 data viewer: 

https://www.eea.europa.eu/data-and-maps/data/data-viewers/greenhousegases-viewer  

EMEP/EEA.  (2019).  EMEP/EEA  air  pollutant  emission  inventory  guidebook  2019.  Retrieved  from  Technical 

guidance to prepare national emission inventories:  

https://www.eea.europa.eu/publications/emep-eea-guidebook-2019 

IMO.  (2020). 

Fourth Greenhouse Gas Study.   

UNCTAD. (2019). Beyond Uncertainty Annual Report. Geneva.  

342 

 
 
  
 
 
2.9  JS.09.  Atmospheric  processes  and  air  quality  impact 
studies:  Modelling  impacts  of  transport  on  air  pollution, 
climate, health and ecosystems. 

343 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Specification of Zero-Impact Vehicle Emissions and Demonstration of Zero Impact 

Ulrich Uhrner1*, Nicola Toenges-Schuller2, Raphael Reifeltshammer1, Werner Stadlhofer2 and Stefan 
Hausberger1 

1 Institute of Thermodynamics & Sustainable Propulsion Systems, Graz University of Technology, Austria, 
uhrner@ivt.tugraz.at 
2 AVISO GmbH, 52074 Aachen, Germany 

Introduction 

perspective of air quality. Therefore, aspects of atmospheric processes, road traffic, air quality and guidelines 
have been combined to sp
In addition to regulated air pollutants such as NO2 or PM2.5, a key point of this work is particulate number (PN). 

1.  The road traffic contribution to air quality concentration levels is smaller than monitored at clean rural 

background and untraceable related with state-of-the-art monitoring 

2.  The road traffic contribution at kerbside locations shall be irrelevant according to air quality directives, 

3. 

2) 

For the first definition, background levels at remote monitoring sites in Austria and Germany, literature as well 
as  GAW  data have been reviewed. We looked at  site  characteristics such as forests,  settlements and roads 
around and the elevation of background monitoring stations. The elevation proved to be a suitable parameter 
to characterize a clean urban background for NO2, PM2.5, PM10 and PN. Monitored PN levels were inferred from 
the GUAN network (Sun et al., 2019). 10 years and 5 years background trend analysis revealed that elevated 
stations above 900 m sea level show hardly any trend, i.e. are mainly influenced by atmospheric processes and 
climate.  

Figure 1 summarises the results for option 1) and 2). Option 3) leads to similar emission targets as option 2) 
but  gives  different  thresholds  for  stoichiometric  and  lean  combustion  concepts  and  was  thus  not  pursued 
further. In the end, our zero-
shall be irrelevant compared to the WHO 2006 air quality guidelines, i.e. lower than 3% of these ambitious air 

Therefore,  the  ZIV  PN20-800  criteria  of  650  #/cm³  (in  the  size  range  of  20-800  nm)  was  derived  based  on 
conclusion  by  analogy  using  the  NO2  zero  impact  target  related  to  the  NO2  clean  background  target  (1.2 
µg/m³ : 3.6 µg/m³) and PN20-800 of 2000 #cm-3 monitored at clean background sites, see Figure 1. 

Figure 1: Comparison of zero impact targets, i.e. max kerbside traffic contribution to ambient air  

Current  contributions  of  road  transport  exhaust  gas  emissions  to  the  air  quality  near  roads  in  Europe  were 
analysed to identify the relation between road vehicles emissions and pollutant concentrations measured next 
to the road. From the worst-
the necessary traffic emission reduction rates to meet the zero impact pollutant concentrations  next to the 
road. With these reduction rates and with the corresponding fleet average emissions, the maximum emissions 

specific i.e. traffic related contributions to air quality at kerbside in Europe is rarely available. Therefore, the PM 
target was used and average monitored particulate numbers per emission mass were used to assess PN targets. 

344 

 
 
 
 
 
 
 
 
 
 
 
The resulting ZIV emission targets based on the driving situation at Stuttgart, Neckartor in 2016 are shown in 
Table 1. 

Table 1: Specified emission targets for the ZIV fleet  

Vehicle type 

unit 
(activity) 

EF NOx 
(mg/unit) 

EF PM2.5 
(mg/unit) 

EF PN20-800 
(1011 #/unit) 

PC 

LCV 

HDV 

km 

km 
kWh 

  6.7 

  7.9 
28.1 

0.4 

0.5 
1.6 

1.2 

1.5 
4.8 

- Approach  

The specified levels were inferred based on assumptions such as an appropriately selected monitored worst 
case air quality situation, and the assumption of a linear relationship between  emissions and corresponding 
contributions to ambient concentrations, i.e. the reduction rates were transferred to zero-impact vehicle (ZIV) 

emission factors of a hypothetical 100% ZIV fleet to fulfil these zero impact air quality targets. In addition, 
latest technology fleet scenario i.e. 100 % Euro-6 d light vehicles (LV) and 100 % Euro-VI heavy vehicles (HV) 
have been evaluated. The basis for those two scenarios are well established validated base case simulations. 
The GRAMM/GRAL modelling system (Uhrner et al., 2014, Öttl 2015, Öttl 2019) was used to model detailed flow 
and  air  pollutant  dispersion.  Highly  resolved  source  specific  emission  data  have  been  processed  for  the 
simulations, the main dispersion set-up features are shown in Table 2. The flow around buildings impacting 
upon  dispersion  was  accounted,  except  in  the  Vienna  study.  After  validation  of  the  base  cases,  the  traffic 
exhaust related NO2, PM and PN concentrations was assessed for the base case, Euro-6/VI and ZIV emission 
scenarios (see Table 2). NOx to NO2 conversion was computed using a simple Romberg type empirical conversion 
formula  (Romberg  et  al.,  1996)  for  the  Stuttgart,  Vienna  and  Augsburg  case  studies.  For  the  Graz  study,  a 
pseudo-steady  state  approximation  approach  (Seinfeld  and  Pandis,  1998)  using  ozone  and  solar  radiation 
measurements was used to compute NO to NO2 conversion. 

Table 2: Main set-

Case study 

Stuttgart-Neckartor  

Vienna 

Augsburg CAZ 

Graz 
Plüddemanngasse 

Domain size 

Air pollutant focus 

# Monitoring 

1.4 x 1.7 km² 

  2 m   NOx/NO2 

  2 AQ hotspot & bg 

30 x 24 km² 
4 x 6.2 km² 
1.1 x 0.8 km² 

10 m 
  4 m 
  2 m 

NOx/NO2, PM  
NOx/NO2, PM, PN 
NOx/NO2, PN 

17 AQ stations 
  4 AQ stations, 2 SMPS 
NOx, PN>4nm, PN>23nm, CO2 

In Augsburg, the focus was laid on the central activity zone (CAZ). A traffic model, traffic emissions (HBEFA4.1)  
and  residential  heating  emissions  were  established  within  SMARTAQNET  (Uhrner  et  al.,2020).  SMPS 
measurements from the GUAN network (Sun et al., 2019) were used to monitor the urban background; SMPS 
measurements  were  undertaken  by  TUG  in  the  city  centre  at  Königsplatz  (KP)  next  to  a  busy  road  from 
16.10.2020 till 12.01.2021. PN deposition was accounted, however the impact was negligible. Coagulation was 
neglected as a sink process. 

In Graz, a detailed kerbside monitoring took place on October 20th, for details see accompanying paper Toenges 
et al., 2023). NO, NO2, PN4, PN23 and CO2 were measured at 1 m, 3 m and 5 m distance at a busy road. The 
monitoring interval was 10 minutes each location and the measurements were undertaken over 7 hours, on 
20.10.2021. PN measurements were switched all 10 minutes to distinguish between total particle number (TPN) 
and  solid  particle  number  (SPN)  for  particles  >4 nm  (PN4)  and  particles  >23 nm  (PN23).  However,  the  SPN 
measurement results were highly questionable and were not used. Detailed accompanying traffic monitoring 
was performed as well. Emissions were computed using the software PHEM (Passenger car and  Heavy-duty 

345 

 
 
 
 
 
Emission Model) from TU Graz. These standardized PN emissions relate to solid particles >20 nm. Meteorological 
data for the flow field model forcing and air quality data were only available at 30-min resolution, therefore 
the accompanying NOx and PN simulations were performed as 30-min means around the Plüddemanngasse 
street. NOx 
3 
and  radiation  measurements  from  Graz-Nord,  all  operated  by  the  provincial  government  of  Styria.  The  CO2 
measurements were performed in order to monitor dilution (Toenges et al., 2023). Due to the sampling strategy, 
available  NOx  and  CO2  measurements  are  a  factor  of  two  higher  than  PN  measurements.  Here,  these 
measurements were used together with the NOx measurements to evaluate the plausibility of the (10 min) PN 
measurements.  Therefore,  at  first,  NOx  simulations  were  performed  and  compared  with  10  minutes  NOx 
monitored values. Thereafter, PN23 simulations were performed and compared with selected PN23 monitoring. 
The GRAL model was run in transient mode. 

-

Results 

Stuttgart Neckartor case study NO2 

In Figure 2 the simulated and validated base case annual mean NO2 concentrations 2019 at Stuttgart Neckartor 
is shown (top left). Simulated concentrations are significantly higher than the air quality standard of 40 µg/m³. 
2  from  local  contributions  and 
background is shown for the base case 2019 (top right), the Euro-6/VI traffic NOx Scenario (bottom left) and 
ZIV-scenario (bottom right). Up to 40 µg/m³ are related to traffic near kerbside with the base case, up to 20 
µg/m³ with the Euro-6/VI scenario and up to 1 µg/m³ are related to the ZIV scenario, the aforementioned zero 
impact criteria is fulfilled. 

-

Figure 2: 
(top right), Euro-6/VI traffic NOx Scenario (lower left) and ZIV-scenario (lower right) 

Base case Stuttgart Neckartor AM NO2 simulation (top left), total base case traffic related NO2 

346 

 
 
 
 
 
 
 
 
 
 
 
Graz Plüddemanngasse Case Study 

In Figure 5 the simulated 7 hours mean NOx concentration on 20.10.2021 is shown. NOx measurements were 
used to validate the dispersion set-up. Owing to the monitoring strategy (see accompanying paper Toenges et 
al., 2023) and availability of meteorological and air quality data 30-min simulated means are compared versus 
monitored 10-min NOx mean concentrations (Figure 3 right). A good relation was found, the R² is 0.74 and the 
slope (0.94) is close to 1. 

Figure 3: Graz Plüddemanngasse Mean NOx simulation (left) and comparison mean simulated 30 min NOx vs 
mean monitored 10 min NOx (right) 

Figure 4 shows the simulated spatial total PN23 concentration for 20.10.2021 and a comparison of simulated 
30-min mean traffic related solid PN23 (solid PN23 due to emission monitoring) and monitored 10-min mean 
total PN23 concentrations. The correlation between those different measures is high (R² = 0.85). The slope of 
3.89 may indicate that kerbside total PN23 may be composed of a large fraction of so-called delayed aerosols. 
These particles may be related to nucleation and rapidly growing (condensing) VOCs (Uhrner et al., 2007, Wehner 
-3 may be attributed to the urban background and 

atmospheric new particle formation (Birmili et al., 2003). 

Figure 5 shows the maximum traffic related solid PN23 for Euro-6/VI and ZIV scenario. The target value of 650 
#cm-3 is fulfilled at kerbside with both scenarios. 

Figure 4: Graz Plüddemanngasse mean traffic related PN23 base case (left) and validation of the base case 
(right) 

347 

 
 
 
  
 
  
 
 
 
Figure 5: max. traffic related PN23 (Euro-6/VI) scenario (left) and ZIV scenario (right) 

Augsburg central activity zone case study PN 

In Figure 6 total simulated mean (16.10.20   12.01.21) PN concentrations is shown for the base case (top left). 
20-800, were used as urban background. The simulated increment is dominated by 
residential  heating  emissions  from  solid  fuels.  Simulated  PN20-800  concentrations  next  to  roads  appear 
unrealistically  low.  Multiplying  traffic  related  simulated  exhaust  particles  by  a  factor  of  3.8  yields  the  best 
match with the two monitoring stations and the resulting PN20-800 concentrations look more realistic, see Figure 
6 top right. Traffic exhaust and brake wear related PN concentrations are shown for the base case (left) and 
the ZIV scenario (right) at the lower panel in Figure 6. The target value of 650 #cm-3 is fulfilled at kerbside. 

Figure 6: Base case Augsburg mean total solid PN20-800nm simulation (top left), base case mean total PN20-800nm 
with volatile correction (top right), base case traffic solid PN20-800nm (lower left) and ZIV-scenario (lower right) 

348 

 
 
 
 
 
 
 
 
 
Summary 

-

  was  demonstrated  for  the  inferred  emission  targets  illustrated  in  Table  1.  A 
Lagrangian particle model was used to simulate the dispersion of NOx, PM2.5 and PN in selected urban case 
studies and to see if the zero impact air quality targets can be generally fulfilled. Owing to the non-linearity of 
the NO to NO2 conversion, to fulfil the 1.2 µg/m³ criteria for NO2 kerbside at hot spots with high traffic volumes 
or extreme driving conditions seems to be most demanding zero-impact threshold. PM and PN limits for solid 
particles  can  be  already  fulfilled  with  latest  Euro-6/VI  exhaust  technologies.  However,  the  comparison  of 
measured versus simulated PN concentrations indicated a clear underestimation of simulated PN predominantly 
in the ultra-fine particle range (<100 nm). This mismatch is most likely attributable to even dominating delayed 
aerosol of volatile origin. A clear relation of simulated versus unidentified secondary formed particles was found 
in both particle number case studies. However, accounting for the impact of volatiles in future scenarios bears 
large uncertainties due to complex aerosol dynamic processes within the exhaust plume of a driving car (Uhrner 
et al., 2011). 

Acknowledgements 

This  work  was  funded  by  FVV  (The  Research  Association  for  Combustion  Engines  eV)  in  Germany,  Funding 
number: FVV 601407 

References 

Birmili W., Berresheim H., Plass-Dülmer C., Elste T., Gilge S., Wiedensohler A., Uhrner U (2003). The Hohenpeissenberg aerosol 
formation  experiment  (HAFEX):  a  long-term  study  including  size-resolved  aerosol,  H2SO4,  OH,  and  monoterpenes 
measurements, Atmos. Chem. Phys., 3, 361-376. 

Ött 
Landesregierung, Department 15, Air Quality Control. 

Öttl D. (2015). Evaluation of the revised Lagrangian particle model GRAL against wind-tunnel and field experiments in the 
presence of obstacles, Boundary-Layer Meteorol., No 155, pp.271-287. 

Romberg E.,  Bösinger R.,  Lohmeyer A.,  Runke R.,  Röth F.  (1996).  NO-NO2  -  Umwandlungsmodell  für  die  Anwendung  bei 
Immissionsprognosen für Kfz-Abgase. Gefahrstoffe - Reinhaltung der Luft 56 Nr. 6, S. 215-218. 

Seinfeld, J.H., Pandis S.N. (1998). From Air Pollution to Climate Change, John Wiley and Sons, New York. 

Sun J., Birmili W., Herrmann M., Tuch T., Weinhold K., Spindler G., et al. (2019). Variability of black carbon mass concentrations, 
sub-micrometer  particle  number  concentrations  and  size  distributions:  results  of  the  German  Ultrafine  Aerosol  Network 
ranging from city street to High Alpine locations, Atmos. Environ. 202. 

Toenges-Schuller N., Hausberger S., Schneider C., Stadlhofer W., Uhrner U. (2023). Particle Number Box-Model Calculations 
in a Street Canyon and Comparison to Measurements. TAP&SE Göteborg 2023. 

Uhrner U., Reifeltshammer R.J., Werhahn J., Philipp A., Kunde R., Sturm, P.J., (2020). Innovative Use of Sensor Data to Improve 
Spatiotemporal Emission Inventories, Air Quality Conference, Thessaloniki. 

Uhrner U., Lackner B.C., Reifeltshammer R.J., Steiner M., Forkel R., Sturm P.J. (2014). Inter-Regional Air Quality Assessment, - 
Bridging the Gap between Regional and Kerbside PM Pollution, Results of the PMinter Project, VKM-THD Mitteilungen, Volume 
98, Verlag der Technischen Universität Graz, ISBN: 978-3-85125-364-1. 

Uhrner U., von Löwis S., Zallinger M., Vehkamäki H., Wehner B., Stratmann F. Wiedensohler A. (2011). Volatile nanoparticle 
formation and growth within a diluting diesel car exhaust, J. Air Waste Manag Assoc., 61(4), 399-408. 

Uhrner U., von Löwis S., Vehkamäki H., Wehner B., Bräsel S., Hermann M., Stratmann F., Kulmala M., Wiedensohler A. (2007). 
Dilution and Aerosol Dynamics within a Diesel Car Exhaust Plume   CFD Simulations of On-road Conditions. Atmos. Environ., 
41, 7440   7461. 

Wehner B., Uhrner U., von Löwis S., Zallinger M., Wiedensohler A. (2009). Aerosol number size distributions within the exhaust 
plume of a diesel and a gasoline passenger car under on-road conditions and determination of emission factors. Atmos. 
Environ. 43 (2009), 1235-1245 

WHO: Air Quality Guidelines; Global Update 2004; ISBN 92 890 2192 6; WHO, 2006. 

349 

 
 
 
 
 
 
The impact of shipping emissions to urban air quality in Europe   A port/city 
analysis 

A. Megaritis1*, J.S. Hullegie2, J. Tokaya2, P. Coenen2, and G. Valastro1 

1 Concawe, Environmental Science for European refining, Brussels, Belgium 
Corresponding author: athanasios.megaritis@concawe.eu 

2 Netherlands Organization for Applied Scientific Research (TNO), Utrecht, Netherlands 

1. Introduction 

It is well known that elevated concentrations of atmospheric pollutants can lead to adverse effects on both 
human health and ecosystems. Epidemiological studies have shown that the exposure to pollutants such as fine 
particulate matter (PM2.5) and nitrogen dioxide (NO2) is associated with cardiovascular and respiratory diseases, 
leading to increased sickness, hospital admissions and premature death (Beelen et al., 2014). Moreover, nitrogen 
deposition  in  soils  and  water  bodies  leads  to  eutrophication  and  biodiversity  loss  and  sulphur  dioxide  can 
contribute to acidification which can harm sensitive ecosystems. 

Over the past decades, legislation has been introduced to reduce emissions of these harmful pollutants. These 
efforts to reduce emissions in several sectors have resulted in a decrease in the atmospheric concentrations of 
PM2.5,  PM10,  NO2  and  SO2.  The  most  recognized  example  of  successful  emission  reduction  is  in  SO2.  Due  to 
abatement measures in powerplants and desulphurization of fuels, the atmospheric SO2 concentration in the 
European Union countries declined by around 70% between 2000 and 2017 (Colette & Rouïl, 2020) based on 
aggregated observations.  

The  strong  emissions  reductions  in  some  sectors,  have  inevitably  shifted  the  focus  to  other  less  strongly 
contributing sources, such as shipping whose emissions relative contributions have a growing significance, in 
order to further reduce air pollutant concentrations (Denier van der Gon et al., 2022; Jonson et al., 2015).  

In  this  study  insights  are  gained  with  respect  to  the  contribution of  shipping  emissions  to  the air  quality  in 
Europe  and  major  ports  and  cities.  Using  the  chemical  transport  model  LOTOS-EUROS  and  its  source 
apportionment capabilities, the contribution of international and inland shipping emissions to atmospheric air 
pollutant concentrations in 19 European port cities is computed and being put into context compared to the 
relative contribution of other sources. 

An overview of the study and the main findings derived are presented in the following Chapters. 

2. Methods 

2.1 Model Description 

LOTOS-EUROS  is  a  3-D  chemistry  transport  model.  The  off-line  Eulerian  grid  model  simulates  air  pollution 
concentrations  in  the  lower  troposphere  solving  the  advection-diffusion  equation  on  a  regular  latitude-
longitude-grid with variable resolution over Europe (Manders et al., 2017).  

The vertical transport and diffusion scheme accounts for atmospheric density variations in space and time and 
for all vertical flux components. The vertical grid is based on terrain following vertical coordinates and when 
excluding stacked boundary layer on top extends to 5 km above sea level. The height of the layers on top of 
the 25 m surface layer is determined by heights in the meteorological input data. In the model version exploited 
in this study 12 model layers are used (with 7 stacked boundary layers on top), leading to a resolvent of the 
first km in 7 layers (depending on meteorological conditions). 

Gas-phase chemistry is simulated using the TNO CBM-IV scheme, which is a condensed version of the original 
scheme (Whitten, 1980). LOTOS-EUROS explicitly accounts for cloud chemistry, computing sulphate formation 
as  a  function of cloud liquid water  content  and cloud droplet  pH as  described  in Banzhaf et  al. (2012).  For 
aerosol chemistry the thermodynamic equilibrium module ISORROPIA2 is used (Fountoukis & Nenes, 2007). Dry 
Deposition fluxes are calculated using the resistance approach as implemented in the DEPAC (DEPosition of 
Acidifying  Compounds)  module  (Van  Zanten  et  al.,  2010).  Furthermore,  a  compensation  point  approach  for 
ammonia  is  included  in  the  dry  deposition  module  (Wichink  Kruit  et  al.,  2012).  The  wet  deposition  module 
accounts for droplet saturation following Banzhaf et al. (2013).  

350 

 
 
 
The  model  is  part  of  the  Copernicus  Atmospheric  Monitoring  Service  (CAMS)  regional  ensemble  providing 
operational forecasts and analyses over Europe. In this context the model is regularly updated and validated 
using observations from ground and satellite observations. The model performance is also subject to numerous 
peer-reviewed publications (Escudero et al., 2019; Skoulidou et al., 2021; Timmermans et al., 2022). 

2.2 Source Apportionment 

TNO has  developed a system to  track the impact  of emission categories within a LOTOS-EUROS simulation 
based on a labelling technique (Kranenburg et al., 2013). This technique provides more accurate information 
about the source contributions than using a brute force approach with scenario runs as the chemical regime 
remains unchanged. Another important advantage is the reduction of computational costs with respect to the 
brute  force  approach.  The  source  apportionment  technique  has  extensively  been  used  in  previous  studies 
(Pommier, 2021; Timmermans et al., 2022, Thürkow et al., 2023).  

Besides the total pollutants
calculated.  The  labelling  routine  is  implemented  for  primary,  inert  aerosol  tracers  as  well  as  for  chemically 
active tracers containing a C, N (reduced and oxidized) or S atom, as these are conserved and traceable. 

2.3 Emissions & Meteorology 

The LOTOS-EUROS model is run with ECMWF ERA 5 reanalysis meteorological data (2018). ERA5 provides hourly 
estimates of a large number of atmospheric, land and oceanic climate variables, that are necessary inputs for 
calculations of atmospheric concentrations. Typical inputs required by LOTOS-EUROS are for example surface 
and air temperature, cloud cover, windspeed and direction, precipitation and relative humidity.  

The CAMS-REG inventory emission data for the year 2018 version 5.1 REF2 (Kuenen et al. 2019) is used in this 
study for anthropogenic trace gas emissions. At the time of performing this study, this was the latest available 
data set, while an update with more recent data is expected to be published in 2023. The inventory uses the 
officially  reported  emission  data  by  European  countries.  However,  for  international  shipping  the  dataset  is 
replaced with emissions from the Finnish Meteorological Institute (FMI) STEAM model (Jalkanen et al., 2016). 
This  model  is  based  on  actual  ship  movements  as  registered  by  the  AIS  data  and  moreover  they  are 
geographical  referenced.  This  model  gives  the  best  geographical  distribution  of  the  shipping  emissions  on 
European  seas  (and  the  Atlantic).  For  inland  shipping  the  data  reported  in  the  national  inventories  is 
complemented by the spatial distribution of the emissions as calculated by the STEAM model. 

2.4 Model setup 

Figure 1  shows the different domains which  are  part  of the LOTOS-EUROS simulations. A  coarse resolution 
(circa 25 x 25 km) simulation is performed over Europe (domain shown in blue). Results from this simulation 
are used as boundary condition for two nested simulations over the Mediterranean and a central part of Europe 
(domains shown in red) at a higher resolution (circa 6 x 6 km), covering the following ports that were studied in 
more detail: 

Sea ports: 
1.  Rotterdam (NL)  
2.  Antwerp (BE) 
3.  Hamburg (DE) 
4.  Amsterdam (NL) 
5.  Marseille (FR) 
6.  Bremerhaven (DE)  
7.  Barcelona (ES) 
8.  Le Havre (FR) 
9.  Genoa (IT) 
10.  Piraeus (GR) 
11.  Lisbon (PT) 
12.  Naples (IT) 
13.  Venice (IT) 

Inland ports: 

351 

 
 
 
 
 
1.  Vienna (AU)  
2.  Liege (BE)  
3.  Duisburg (DE)  
4.  Nijmegen (NL)  
5.  London (UK)  
6.  Cologne (DE) 

Figure 1: 
map (orange for sea ports and green for inland ports). 

Display of the simulation setup domains. The port/cities of interest are displayed as dots on the 

3. Results 

3.1 Contribution of shipping emissions to air quality in Europe 

Figure 2 shows the predicted annual average surface concentration of NO2 in 2018 for the European domain 
(left  panel)  together  with  the  source  apportionment  results  of  the  whole  domain  (right  panel).  High  NO2 
concentration values are calculated in the central part of Europe (Benelux, Germany, UK) and in the Po Valley 
(north of Italy) with the biggest contributions being attributed to Road Transport 
 exhaust and International 
Shipping. The relative contribution of inland shipping is < 0.5% and therefore forms a small contribution in the 
whole European domain. 

352 

 
 
 
 
 
 
The annual average NO2 surface concentration for 2018 in the simulation domain of the coarse 

Figure 2: 
(25x25km) resolution LOTOS-EUROS simulation (left panel). The relative contributions from the various 
labelled sectors to the surface concentration of NO2 for the entire simulation domain is also shown (right 
panel). 

For  the  remaining  pollutants  examined  in  the  study,  the  predicted  contribution  of  shipping  emissions  in 
comparison with other sectors are shown in Figure 3. From the results it is evident that shipping has the largest 
relative contribution for NO2 compared to other pollutants (Figures 2, 3). For SO2 a significant contribution from 
international  shipping  is  still  present  (11%),  while  for  PM  the  shipping  contribution  is  relatively  smaller.  In 
addition, inland shipping is predicted to be a neglibible contributor to the atmospheric pollutant concentrations 
on average in Europe. 

Figure 3: 
(left), and PM10 (right) annual average surface concentrations over Europe in 2018. 

The predicted relative contribution from international and inland shipping to SO2 (top), PM2.5 

3.2 Contribution of shipping emissions for each port 

Because the highest contribution of shipping is found for NO2, the results of the calculated shipping contrubtion 
to NO2 levels near the ports are presented and discussed in more detail here. Illustrative examples are used for 
three cases: a) a port located nearby the city centre (i.e. Piraeus), b) a port located close to the city (i.e. Antwerp, 
~10km distance) and c) a port located far from the city (i.e. Rotterdam, ~30km distance). Detailed results for 
all ports and pollutants can be found in the appendix of the Concawe study (Concawe, 2023). For the analyses, 
a representative central location for the port and the city centre was determined for the selected cities. The city 
centre locations are represented as blue dots and the port locations as green dots in Figure 4-Figure 6. For 
these locations of interest, the concentration fields were calculated as a weighted average of the 4 nearest grid 
point in the simulation domain (inversely with distance from the grid point to the coordinates of the location of 
interest). 

353 

 
 
 
 
 
 
3.2.1 Piraeus 

In  Figure  4,  the  absolute  and  relative  contribution  of  international  shipping  to  NO2  annual  average  surface 
concentrations is shown. On the top left panel, it can be seen that the absolute contribution of international 
3 in most of the surrounding areas, and can reach up to 10 µg/m3 or higher at the 
sea part. The port is located at the city centre which causes the green and blue dots in the top panels to coincide 
and the associated pie charts to be the same. International shipping is predicted to contribute 12 µg/m3 (34%) 
to  the  annual  average  surface  NO2  concentration  in  Piraeus  being  the  dominant  source  closely  followed  by 
exhaust emissions from the transport sector. 

The calculated absolute (top left panel) and relative contributions (top right panel) of 

Figure 4: 
international shipping to the annual average surface NO2 concentration in Piraeus in 2018. The pie charts for 
the city centre and the port are the same since the port essentially forms the city centre. Inland shipping is 
not shown in the pie charts as there is no contribution from this sector around Piraeus. 

3.2.2 Antwerp 

Antwerp is by far the biggest Belgian (sea)port, located at the river Scheldt which also features the port of 
Ghent closer to the sea. It plays an important role in the connection between the port of Hamburg and Le Havre 
in nearly all major traffic flows. 

In Figure 5 the contribution of international shipping to the NO2 concentration in Antwerp is shown. The absolute 
contribution of international shipping at the port of Antwerp (green dot in Figure 5) located at the delta of the 
Scheldt  River  is  16  µg/m3  (47%).  The  relative  contribution  from  international  shipping  can  go  up  to  70% 
following the Scheldt River further downstream. The concentration in the port is predicted to receive contribution 
from emissions from ships at berth in the port. This also influences the air quality in the city centre of Antwerp 
situated to the southeast of the port (blue dot). Here international shipping contributes 5.8 µg/m3 (24%) and 
inland shipping contributes 1.0 µg/m3 (4.0%). 

354 

 
 
 
 
 
The calculated absolute (top left panel) and relative contributions (top right panel) of 

Figure 5: 
international shipping to the annual average surface NO2 concentration in Antwerp in 2018. The relative 
contributions of various sectors to the NO2 concentrations in the port (bottom right panel) and city centre 
(bottom left panel) of Antwerp are shown in the pie charts. 

3.2.3 Rotterdam 

The distribution of the contribution of international shipping to the annual average NO2 surface concentration 
in the Rotterdam area for 2018 is shown in Figure 6. The pie chart is showing the relative contributions from 
all labelled source sectors at the main container terminal of the port (bottom right) and in the city centre of 
Rotterdam (bottom left).  

The calculated absolute (top left panel) and relative contributions (top right panel) of 

Figure 6: 
international shipping to the annual average surface NO2 concentration in Rotterdam in 2018. The relative 
contributions of various sectors to the NO2 concentrations in the port (bottom right panel) and city centre 
(bottom left panel) of Rotterdam are shown in the pie charts. 

355 

 
 
 
 
 
The average annual absolute contribution of international shipping is 16 µg/m3 (60%) at the port entrance at 
sea (green dot in Figure 6), while even above the city centre of Rotterdam (blue dot), considerable contributions 
are found of 3.7 µg/m3  (13%).  The absolute contribution however,  decreases between the port and the city 
centre due to dilution upon transport and the lifetime of NO2 (the distance between the port and the city is 
approximately 30 km). On the contrary, the absolute contribution from inland shipping is larger in the city centre 
than at the port location (respectively 8.1 (29%) vs 1.1 (4%) µg/m3) and inland shipping is calculated to be the 
most dominant source in the city centre together with exhaust emissions from the road transport sector. The 
river Rhine (which is the major inland waterway linking the North Sea with industrial areas in Germany and its 
eastern  neighbours  via  the  Rhine-Main-Danube  canal)  ends  in  Rotterdam.  This  leads  to  the  significant 
contribution from inland shipping to the air quality in Rotterdam city. 

A total overview of the absolute contributions from the labelled sectors to the city centre locations is given in 
Figure 7. 

Figure 7: 
to the annual average surface NO2 in 2018 for the city centre of the port cities of interest. 

Stacked bar plot showing the predicted absolute contributions from the various labelled sectors 

4. Conclusions 

The contribution of international and inland shipping to atmospheric pollutant concentrations in Europe were 
assessed using the chemical transport model LOTOS-EUROS and its source appointment feature that allows 
tracing of labelled emitted pollutants. The main findings from the study can be summarised as follows: 

 

International Shipping contributes significantly to NO2 concentrations in Europe (18% on average). 

  The contribution to other species is lower but still considerable (11% for SO2, 2.5-5% for PM2.5/PM10). 

  Locally in the seaports, the influence to NO2 concentrations is higher and reaches up to 60% (Rotterdam). 

  The contribution to NO2 remains significant in all cities located nearby the seaports, while in many cases, 

international shipping contribution can be the dominant source (e.g., Piraeus, Hamburg). 

 

Inland shipping has generally a low influence (0-4%) to NO2, with only a few exceptions (Rotterdam, 
Amsterdam). 

356 

 
 
 
 
 
 
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357 

 
 
 
Potential impact of shipping on PM2.5 species in the Mediterranean region - a 
multi-model evaluation 

Lea  Fink*1,  Matthias  Karl1,  Volker  Matthias1,  Sonia  Oppo2,  Richard  Kranenburg3,  Jeroen  Kuenen3,  Sara 
Jutterström4, Jana Moldanova4, Elisa Majamäki5, Jukka-Pekka Jalkanen5 

1Helmholtz-Zentrum Hereon, Institute of Coastal Environmental Chemistry, 21502 Geesthacht, Germany 

2AtmoSud, Air Quality Observatory in the Provence-Alpes-

3TNO, Netherlands Organization for Applied Scientific Research, 3584 CB Utrecht, The Netherlands 

4IVL, Swedish Environmental Research Institute, 411 33 Göteborg, Sweden 

5FMI, Finnish Meteorological Institute, FI-00560 Helsinki, Finland 

Introduction 

Maritime transport plays an essential role in the international trade of goods of the European Union (EU) with 
almost 90% of the EU import and export freight trade being seaborne (Karl et al. 2019). The use of maritime 
transport for EU trade has increased over the last ten years. The number of active ships in 2030 is expected to 
be twice as high compared to 2016 (Eurostat Press Office, 2016). 

With main routes for short sea and international shipping, the Mediterranean Sea is a region with dense shipping, 
resulting  in  high  air  pollution  at  coastal  regions.  Previous  research  has  found  that  the  increase  in  PM2.5 
(particulate matter < 2.5 µm) concentrations induced by shipping emissions is small in Europe (Viana et al., 
2009).  Nonetheless,  the  relative  ship  influence  on  PM2.5  concentration  in  the  Mediterranean  area  is  high, 
accounting for 5% to 20% of total PM2.5 concentration (e.g., Aksoyoglu et al., 2016, Nunes et al., 2020). Especially 
secondary  formation  of  inorganic  aerosol contributes  the  largest  part  to  ship  contribution  to  ambient  PM2.5. 
Exposure to PM2.5 has been linked to the development of certain respiratory illnesses, cancer, and type 2 diabetes 
(Heusinkveld et al., 2016; Chen et al., 2016; Gao and Sang, 2020). 

Regional air quality modeling is important to evaluate air quality. Results can be used as guidance to enhance 
air quality management strategies, which are important regarding the impact of air pollution on human health 
and the environment. A number of regional scale 3-D chemistry transport model (CTM) systems are available 
for the simulation of the transport, chemical transformation and fate of atmospheric pollutants. Although these 
models are steadily improved, intercomparison studies have shown that they often underestimate the PM2.5 
concentration. Im et al. (2015) reported an underestimation of particulate matter in their intercomparison study 
with largest underestimations observed in the Mediterranean region. 

In the present study, five different chemistry transport models (CAMx, CHIMERE, CMAQ, EMEP, LOTOS-EUROS) 
are used to evaluate and compare modelled concentrations of air pollutants in the Mediterranean Sea. The main 
focus  was  to  compare  the  output  of  the  models  regarding  the  concentration  of  PM2.5 and  to  calculate  the 
potential ship impact to total air pollution concentration. The model performance was quantified by comparing 
modelled data against data measured at background stations in coastal areas of the Mediterranean Sea. As 
- as 
ships contribute mainly to the formation of inorganic particle species, focus was laid on NH4
well as their precursors. 

2-, NO3

+, SO4

Model Setup & Methods  

Emission part of the set-up was the same for all five models: ship emissions were calculated with the Ship 
Traffic Emission Assessment Model (STEAM, version 3.3.0; Jalkanen et al. 2012) and land-based emissions were 
taken from the CAMS-REG v2.2.1 dataset (further development of the inventory described in Kuenen et al. 2022) 
for a domain comprising the Mediterranean Sea on a resolution of 12x12 km² (or 0.1° x 0.1°). Meteorological 
data as well as boundary and initial conditions differed among the model runs as all CTMs used their standard 
set-up. This was done because a change of the meteorological driver of a CTM is often very difficult and might 
be connected with unwanted errors in the simulations. With all models a reference run for the current air quality 

358 

 
 
 
 
 
was 2015. 

-out method was used to determine the potential ship impact. The modelled year 

The models used in the intercomparison are CAMx v6.50 (Ramboll Environ., 2016), CHIMERE 2017r4 (Menut et 
al., 2013), CMAQ v5.2 (Byun and Schere, 2006; Appel et al., 2017), EMEP MSC-W (Simpson et al., 2012; Simpson 
et al., 2020),LOTOS-EUROS v2.0 (Manders et al., 2017). 
Based on the daily files, the annual averaged ensemble mean is derived from the results of all models. All of 
the model run outputs included PM2.5 in µg/m3 at a daily resolution on a 2D grid from the lowest layer and were 
given as a netcdf file in accordance with CF standards. For the intercomparison, concentrations in the lowest 
layer closest to the ground were employed. Depending on the major physical and chemical mechanisms used, 
the CTM systems computed PM2.5 concentrations in various ways. 
Model results for total surface concentrations of PM2.5 from the five CTM simulations are evaluated against 
available measurements of the air quality monitoring network from the European Environment Agency (EEA, 
2021). 

Results & Discussion 

The current work examines and analyses the predictions of five different CTM systems for the dispersal and 
-) in the Mediterranean region. In addition to the 
transformation of PM2.5 and inorganic particles (NH4
total concentration, the potential ship impact is taken into account. The results show that four of the five models 
underestimated  the  actually  measured  PM2.5 concentrations  at  stations  near  the  European  coast  (Table  1).  
Considering the spatial distribution, the potential impacts of PM2.5 from ships simulated by CAMx, LOTOS-EUROS 
and EMEP have the largest ranges with values up to 25% along the main shipping routes in the Mediterranean 
Sea. CMAQ and CHIMERE simulated potential ship impacts of 15% along major shipping lanes near the African 
coast. These impacts are within the range of ship impacts determined in other studies (Figure 1). 

2-, NO3

+, SO4

Table 1: Correlation (R), normalized mean bias (NMB), root mean square error (RMSE) for daily PM2.5 values for 
2015 over all 28 stations. 

CAMx 

CHIMERE 

CMAQ 

EMEP 

Correlation 

NMB 

RMSE  

0.19 

0.02 

0.50 

0.17 

-0.33 

11.5 

0.06 

11.1 

-0.42 

10.7 

-0.33 

12.2 

LOTOS-EUROS 

0.54 

-0.53 

10.9 

Various  reasons for underestimation of PM2.5 in regional  CTM systems could be  attributed to  model-specific 
calculations.  Studies  have  shown  that  these  might  be  linked  to  meteorological  parameters  and  to  the 
overestimate  of  vertical  mixing  in  the  lower  atmosphere.  In  addition,  the  mechanisms  for  calculating  the 
equilibrium  between  the  gas  and  aerosol  phases  can  differ  among  the  CTM  systems  and  lead  to 
underestimations. One other reason for underestimations of PM2.5 by CTMs might be the missing descriptions 
of secondary organic aerosol processes in the model.  

A number of reasons and combinations of reasons can lead to underestimations of PM2.5 in the CTM systems 
used in the present study.  

For a better understanding, the inorganic particle species are considered, since inorganic aerosols account for a 
higher percentage of ship-related PM2.5 concentrations emissions compared to organic aerosols. 

359 

 
 
 
 
 
 
 
Figure  1:  Annual  mean  PM2.5  relative  potential  ship  impact.  (a)  =  CAMx,  (b)  =  CHIMERE,  
(c) = CMAQ, (d) = EMEP, (e) = LOTOS-EUROS 

Sulfate is the main contributor to the total PM2.5 concentration related to ship emissions. For the median of 
2- 
model ensemble mean for absolute ship concentration, SO4
2-, the model 
~0.4 µg/m³: Median PM2.5: ~0.8 µg/m³; Figure 2). With regard to the absolute ship impact on SO4
simulations show similar concentrations and are to a certain degree lower for CMAQ and LOTOS-EUROS.  

2- accounts for about the half of PM2.5 (Median SO4

2- to form (NH4)2SO4, thus less NH4NO3 is formed 
Ammonium in atmospheric aerosols preferentially binds to SO4
in areas above water. Higher concentrations over land than over water are expected due to NH4NO3 formation 
2- conditions. This explains the high values 
in areas characterized by high NH3 and HNO3 conditions and low SO4
for sulfate in the present study, since the domain mainly covers the water areas of the Mediterranean area. 

- differ between models. CAMx and CMAQ results are similar high (~ 0.1 
The potential impacts of ships on NO3
µg/m³) whereas for CHIMERE and EMEP absolute potential ship impact is rather low (~ 0.05 µg/m³). Low nitrate 
levels can be explained by the preferential formation of (NH4)2SO4, which keeps nitrate in the gas phase or 
- in CMAQ, but relatively high values for 
converts it to the coarse mode. These low values  for SO4
EMEP,  CAMx  and  LOTOS  are  reflected  in  the  PM2.5  ship  impacts  and  partially  explain  the  variation  in  PM2.5 
between the models.  

2- and NO3

- 

The preference of NH4
NO3
For example, CHIMERE has an NO3
0.4 µg/m³, while in the CAMx results NO3

relatively 

shows 

+ to bind to SO4
low 
- annual median of about 0.05 µg/m³ and for SO4

2- to form (NH4)2SO4 in atmospheric aerosols explains why in some models 
concentration 
high.  
SO4
2- an annual median of about 

values 

when 

the 

is 

2- 

- is about 0.1 µg/m³ but for SO4

2- around 0.25 µg/m³. 

360 

 
 
 
Figure 2: Boxplots for potential ships impact concentrations of PM2.5, and the PM2.5 components SO4
NO3
calculated as PM2.5 minus the sum of SO4

- and NH4

2-, NO3

-,  NH4

+. 

+ 

2-, 

Acknowledgements 

2020  research  and  innovation  programme  under  grant  agreement  Nr.814893.  AtmoSud  acknowledges  the 
continuous support of CAMx by RAMBOLL and CHIMERE by LMD. The Community Multiscale Air Quality Modelling 
System (CMAQ) is developed and maintained by the USEPA. Its use is gratefully acknowledged. Ronny Petrik 
from  Helmholtz-Zentrum  Hereon  (now  at  Marinekommando  Deutsche  Marine,  Rostock)  is  acknowledged  for 
providing meteorology and boundary conditions for CMAQ runs.  
The computations for the regional modeling using the EMEP-model was enabled by resources provided by the 
Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through 
grant agreement no. 2018-05973.  
  West  of  EMEP  at  Norwegian  Meteorological  Institute, 
Support  from  Meteorological  Synthesizing  Center 
especially Peter Wind and David Simpson, in implementation of emissions and meteorological fields used in this 
paper into EMEP Open Source model is gratefully acknowledged. 

361 

 
 
 
 
 
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363 

 
 
 
 
A numerical CFD model to quantify traffic-related pollutant concentrations in 
urban scale.  

G. Ioannidis1, T. Riedel2, C. Li2, P. Tremper2, L. Ntziachristos1  

1 Laboratory of Applied Thermodynamics, Department of Mechanical Engineering, Aristotle  

University of Thessaloniki, Thessaloniki, 54124, Greece   

2Institue for Telematics, TECO, Karlsruhe Institute of Technology, Karlsruhe, 76131, Germany  

Introduction  

Urban environments face significant challenges in terms of atmospheric pollution. The residents of these areas 
are  directly  impacted  by  anthropogenic  emissions,  leading  to  their  daily  exposure  to  harmful  gaseous  and 
particulate pollutants. Consequently, poor air quality is associated with health issues and respiratory diseases 
(Zhang  &  Batterman,  2013).  Specifically,  in  urban  regions  like  Augsburg,  pollutants  such  as  CO,  NOx,  and 
Particulate Matter are prevalent, primarily originating from vehicular activity. Slemr et al. (2002) revealed that 
traffic activity accounts for 54% of NOx concentrations and 88.5% of CO concentrations in the broader Augsburg 
area. Qadir et al. (2014) conducted a source apportionment study in Augsburg, highlighting that 28% of PM10 
concentrations can be attributed to vehicular activity. Therefore, monitoring the concentrations of these three 
pollutants is crucial for accurately assessing the levels of hazardous contaminants present in the environment.  

The assessment of air quality in urban areas has always been a challenging task. To assess pollution levels of 
different contaminants, Air Quality monitoring stations are positioned throughout cities. These stations have 
limitations as they only measure what enters their path of deduction, thus failing to fully capture the overall 
pollution condition of the wider area. To overcome this limitation, Air Quality models are employed to represent 
and predict pollution in urban regions, considering various pollution sources that contribute to the deterioration 
of the atmosphere. In this study, a CFD model has been developed offering a high spatial resolution compared 
to  measuring  stations.  This  model  has  the  capability  to  construct  reliable  3D  pollution  maps  based  on 
meteorological and emission scenarios.  

The objective of this study is to employ Computational Fluid Dynamics to predict pollutant concentrations at a 
micro scale and validate the model using measurement data. Additionally, the simulated concentrations are 
utilized to evaluate the extent to which the Air Quality stations accurately represent the pollution levels in the 
broader area where they are located. The model uses traffic-related emissions of CO, NOx that were calculated 
using COPERT and PM10 that were estimated using emission factors of that period. All the emission are based 
on traffic activity levels observed in September 2018.  

Digital model and CFD setup  

The study focuses on a specific area measuring 1.6 km x 2 km, which encompasses a significant portion of 
Augsburg's city center in Germany. Within this area, two official Air Quality stations, operated by the Bavarian 
State Office of Environment, monitor various pollutants such as PM10, CO, NO and NO2. Figure 1 illustrates the 
positioning of these stations, with the Karlstraße (KS) station situated in the northern part of the domain and 
the Köningsplatz (KP) station positioned at the center. The KS station is placed at a height of 2.5m above the 
ground,  while  the  KP  station  is  elevated  at  4m.  The  measurements  provided  by  these  stations  offer  hourly 

3.  

The geometric characteristics of the area under investigation were extracted from the  

-source  platform.  To  create  a  computational  mesh  on  the  building  surfaces,  a  pre-
processing procedure was implemented to clean and smooth the geometry. This process involved correcting any 
flawed points in the acquired geometry. Blocken (2015) suggests that the design of the computational domain 
should have a minimum height of 6H and an upstream distance of 5H, where H represents the height of the 
tallest building in the area. Emission sources were then established along the main arterial roads. These sources 
were represented as rectangular elements within the computational domain, corresponding to sections of the 

364 

 
 
  
 
  
road where vehicles travel. The width of these sources was set at 8m, which aligns with the typical road width. 
The  developed  CFD  model  employed  a  tetrahedral  unstructured  mesh  with  a  growth  rate  of  1.2.  The  mesh 
resolution  on  the  buildings  was  set  to  2m,  while  it  was  0.5m  for  the  emission  sources  and  20m  for  the 
boundaries of the domain. In total, the volume mesh comprised of 54 million elements.  

Figure 1. Area of interest containing two official AQ stations  

Figure 2 presents the areas where the Air Quality (AQ) stations are located, shown in the left column, and their 
corresponding representation in the digital model, depicted in the right column. The comparison of the modelled 
concentrations with the measured values is conducted at the exact height and coordinates as the real location. 
This represents a notable advantage of utilizing a CFD model, as it enables the generation of representative 
concentrations, considering the surrounding geometrical characteristics of the area.  

The pollutant dispersion modelling is carried out using the open-source CFD code OpenFOAM. The numerical 
solver utilized in this study is simpleFoam, which employs the RANS equations to solve for the velocity field 
(Peralta et al., 2014). To account for the dispersion of pollutants, the passive scalar transport equation (Eq. 1) 
is incorporated into the modified solver, and the code is compiled into the system accordingly. This approach 
allows for the simulation of passive pollutant dispersion (Elfverson & Lejon, 2021; Miao et al., 2014).  

𝑗 

In equation (1), the term Dt represents the turbulent diffusion coefficient, while Dm represents the molecular 
diffusion coefficient. For  CO,  the molecular diffusion coefficient at  a  temperature of 20°C is reported  to  be 
2.08e-5 m2/s (Cleary et al., 2015). In the case of NOx traffic emissions, which predominantly consist of NO, a 
portion of it is oxidized in the atmosphere to form NO2 (Rivas et al., 2019). For simulation purposes, the NOx 
mixture is simulated as NO2, with a diffusion coefficient of 1.56e-5 m2/s (Cape, 2005). For the RANS simulations, 
the standard k-
loyed, as it has been demonstrated to be the most suitable model for 
urban pollutant dispersion modelling with CFD in various studies (Amorim et al., 2013; Santiago et al., 2013).  

Pantusheva et al. (2022) specifically highlighted that 38% of the examined cases favoured the standard k-
model.  

365 

 
 
  
  
  
  
  
  
  
Regarding  the  atmospheric  velocity  profile,  equation  (2)  defines  the  profile  applied  at  the  inlets  for  each 
set to 
0.41),  and  z0  corresponds  to  the  aerodynamic  roughness  length  at  a  height  of  2m,  specifically  for  urban 
environments  (Irwan  Ramli  et  al.,  2009).  The  simulations  were  conducted  considering  the  prevailing  wind 
direction during September 2018, which was determined to be a south-western wind direction. As a test case, 
the period between 13:00 and 14:00 on September 18, 2018, was selected due to its wind direction of 260 
degrees.  

Results  

The converged simulations of pollutant dispersion provided final concentration values across the entire domain. 
To directly compare these simulated concentrations with measurements, background concentrations obtained 
from  a  background  station  during  the  corresponding  hour  were  subtracted  from  the  indications  of  the  AQ 
stations. This approach helps isolate the urban increment by excluding pollutant concentrations unaffected by 
the urban area (Jeanjean et al., 2017). The background station, located 5km outside the city, remains unaffected 
by urban emissions.  

Figure 3 illustrates the comparison between simulated and observed values. In Figure 3a, the comparison is 
shown for the Karlstraße Air Quality station. The deviation between the measured and simulated values is 29% 
for CO, 31% for NOx, and close to zero for PM10. Figure 3b demonstrates the comparison at the location of the 
sensor for the Köningsplatz station. The simulated concentration of CO deviates by 27% from the measurement, 
while NOx deviates by 41%. For PM10, the modelled concentration shows a deviation of 62%. It is important to 
highlight that the simulated values consistently fall within the same range as the observed values, and also 
indicating that the model results are highly accurate with a precision of two decimal places. However, it should 
be acknowledged that the CFD values consistently underestimate the measurements. This difference could be 
attributed  to  the  fact  that  the  dispersion  model  solely  considers  traffic  emissions,  neglecting  other  urban 
sources like heating, cooking, construction, and waste management, which could potentially contribute to the 
overall concentration levels. Considering that all the values are within the same range and the average deviation 
across all cases is approximately 30%, the model results are considered acceptable for the intended purpose. 
It can be concluded that the model provides correct estimates of traffic-related pollutant concentrations for the 
entire domain.  

366 

 
 
  
  
 
Figure 3. Comparison between simulated and observed values for CO, NOx and PM10 for  

Karlstraße station (a) and Köningsplatz station (b)  

One of the notable advantages of using a CFD model as a dispersion model is its high spatial accuracy, which 
sets it apart from other mesoscale models. In this study, we aim to leverage this advantage by utilizing the 
numerous  data  points  available  in  the  vicinity  of  the  sensor  location,  at  a  height  of  1.5m,  representing  the 
average exposure height of the urban population. To evaluate the representativeness of the Air Quality (AQ) 
station's indication, we consider an area with a radius of 100m centred around the two sensors, as shown in 
Figure  4  specifically  for  the  case  of  CO.  The  criterion  employed  to  assess  the  representativeness  of  the 
simulated concentration within this area is whether it deviates by 20% from the observed indication (Santiago 
et  al.,  2021).  This  criterion  provides  a  measure  of  agreement  between  the  simulated  and  observed  values, 
enabling a thorough evaluation of the model's performance in capturing the pollutant concentrations within the 
immediate vicinity of the AQ station.  

The spatial representativeness of the Air Quality (AQ) stations is calculated for the three pollutants, CO, NOx, 
and  PM10,  within  the  constrained  area  around  the  sensor's  location.  Based  on  the  criteria  established,  the 
representativeness of the Karlstraße station indication is found to be 15% for CO and 10% for NOx, while the 
Köningsplatz  station  demonstrates  a  representativeness  of  14%  for  CO  and  16%  for  NOx.  As  for  PM10,  the 
representativeness is 21% for Karlstraße and 7% for Köningsplatz. Rivas et al. (2019) examined the spatial 
representativeness  of AQ  stations  in  the  city  of  Pamplona  using  CFD for  NO2.  They found  that  for  a  traffic 
monitoring station, the representativeness percentage was 17%, indicating that the findings of the work do not 
deviate greatly from similar studies in literature. It is worth noting that the relatively low representativeness 
values in this study can be attributed to a significant portion of the wider area around the two sensors consisting 
of pedestrian roads. The inclusion of traffic emissions as the only source considered in this study could also be 
a reason of the underestimation of the concentrations.   

Figure 4. Modelled CO concentration from traffic at z=1.5m. Closeup to the wider area of the two official AQ 

stations.  

367 

 
 
  
 
Conclusions  

This study successfully developed and applied a CFD model to assess air quality, specifically focusing on traffic 
emissions.  Two  official  measuring  stations  are  located  within  a  1.6  km  x  2  km  area,  and  the  model's 
performance was validated against the indications provided by these sensors. The average deviation between 
the modelled and measured values for both stations  was 28% for CO, 36% for NOx and 31% for PM10. All the 
simulated values lie within the same range as the measurements. The criterion set for representativeness was 
a maximum deviation of 20% from the measured value, within an area of a radius of 100m. The Karlstraße 
station  demonstrated  a  representativeness  of  16%  for  all  three  pollutants,  while  the  Köningsplatz  station 
showed a representativeness of 13%. This difference in representativeness may be attributed to the presence 
of more roads within the area surrounding the Karlstraße station, whereas the Köningsplatz area includes a 
tram line.  By assessing the spatial representativeness of AQ stations within a constrained area close to the 
sensor location, it is possible to determine if the stations are correctly placed and whether their indications 
accurately reflected the pollution situation of the area.  

Acknowledgments  

topic. The University of GRAZ, specifically Ulrich Uhrner and Raphael Reifeltshammer, is recognized for their 
contribution of traffic activity data. Finally, the open-source software OPENFOAM® is a registered trademark of 
OpenCFD Limited, producer and distributor of the OpenFOAM software via www.openfoam.com.  

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Estimate of health benefits if air pollution goals are achieved along highways 

L. Broman1, B. Lövenheim1, H. Kriit3 and C. Johansson1,2 

1Environment and health administration, SLB-analys, 104 20 Stockholm, Sweden 

2Department of Environmental Science, Stockholm University, 10691 Stockholm, Sweden 

3Departmentof Public Health and Clinical Medicine, Umeå University, Umeå, Sweden 

Presenting author email: lina.broman@slb.nu  

Introduction 

Road traffic emissions are among the most important sources of many air pollutants like NO2 and PM10 and it 
is the most important source of exposure to many pollutants in densely trafficked cities. In Sweden, the road 
authorities are responsible for taking action to reduce air pollution exposure of people living close to roads. 
PM10 is the most problematic air pollutant in comparison to the limit values and objective. Concentrations of 
PM10  exceed  EU  air  quality  directives  along  large  parts  of  the  highways  in  the  county  of  Stockholm.  Also 
exceeded is the environmental quality objective for PM10. The objectives aim to ensure that concentrations of 
air pollutants do not exceed low-risk levels for cancer or target values for protection against diseases or impacts 
on  plants,  animals,  materials,  and  cultural  objects.  The  most  important  actions  to  achieve  limit  values  and 
national objectives include reducing the use of studded winter tyres, reducing speed limits, and actions to reduce 
road traffic. 

The  project  aims  to  estimate  the  health  benefits  for  the  population  if  the  limit  values  and  objectives  were 
achieved along the highways in the Stockholm region.  

Methodology 

We have calculated  

1. 

2. 

3. 

the concentrations of PM10 within 200 m along the highways  

the concentration reductions required to achieve the annual and daily limit values and goals  

the corresponding reduction in population exposure of the population 

4. 

the health benefits 

5. 

the benefits in terms of reduced societal costs associated with the exposure. 

A Gaussian air quality dispersion model (https://www.airviro.com/airviro/) is used to calculate concentrations of 
PM10 within 200 m along the highways and the concentration reductions required to achieve the annual and 
daily limit values, upper assessment threshold, and the national goal 2020 and 2030. Concentrations within 
the road area are not considered   road width is obtained from the National Road database. Exposure of drivers 
along roads is not included. Geographic resolution in the calculations varies depending on the emissions from 
35 m by 35 m to 500 m by 500 m. Contributions to the concentrations from sources outside of the domain are 
taken from measurements at a rural location where local source contributions are negligible.  

Wear of the road asphalt is the most important emission of PM10 and this is taken into account using the 
Nortrip model (Denby et al., 2013). The Nortrip model calculates emissions of PM10 from the wear of roads, 
brakes, and tyres and the suspension of road dust depending on dust load, type of tyres, vehicle speed, road 
surface  moisture,  and sanding/salting. The model has been  validated  by  comparison with  measurements at 
different  sites  including  Hornsgatan  in  Stockholm.  Traffic  amounts,  speed  limits,  number  of  lanes,  etc.  on 
highways are obtained from the National Road Database (https://www.nvdb.se/sv/about-nvdb/).   

Meteorological conditions were based on a climatology created from 21 years of meteorological measurements 
(15 min averages) in a 50 m high mast located in the southern part of Stockholm. A deterministic wind model 
is used to generate a wind field over the whole calculation domain. This model considers variations in surface 
roughness,  land  use,  topography,  etc.  Concentrations  are  calculated  at  2  m  above  the  surface.  Individual 
buildings and street canyons were not resolved but treated using a roughness parameter.  

370 

 
 
 
Population-weighted  exposure  is  estimated  based  on  concentrations  at  residential  addresses  in  the  whole 
county of Stockholm. Total number of people in every 100 x 100 m grid is obtained from Statistics Sweden and 
represents the situation 2020-12-31. 

Health benefits and societal costs are based on estimated PM2.5 exposure according to a recent evaluation for 
Swedish road traffic emissions based on a review of epidemiological studies (Forsberg et al., 2021). The change 
in the population health outcomes is calculated as: 

y = Yo [ 1- e(- c)] 

where 

Yo is the baseline health incidence of the outcome 

ß is the dose-response factor (equal to the logarithm of relative risk) per µg/m3, 

(µg/m3) 

Baseline incidence for different outcomes is taken from national health registers. The cost of health is calculated 
according to the REVSEK project based on typical national population exposure modeling. The cost is 8163 SEK 
per  µg/m3  persons)  (Forsberg  et  al.  (2021).  This  refers  to  2017  and  it  includes  costs  associated  with  e  g 
mortality, stroke, diabetes, and childhood asthma. Premature mortality constitutes the highest cost (61% of the 
total costs) and is calculated considering the value of quality-adjusted life years. 

Population data 

The total number of people living within 200 m of the state highways is ca 393 500, which is 16% of the total 
population in Stockholm (2 386 632). 

Table 1 shows that 335 and 390 people living within this zone were exposed to concentrations above the limit 
value during 2020 and 2030 respectively. Corresponding number of people living in areas with concentrations 
above the national PM10 goal was 16 435 and 22 460 for 2020 and 2030, respectively. 

Table 1: Number of people exposed above the limit value, the upper assessment threshold, and the Swedish 
environmental quality objective in 2020 and 2030. 

AQ daily limit value (50 µg/m3)1 

Upper assessment threshold, daily (35 µg/m3)1 

2020 

335 

3545 

2030 

390 

4950 

Environmental quality objective, annual mean (15 µg/m3) 

16 435 

22 460 

1 Not to be exceeded more than 35 days. 

Required reductions in emissions and concentrations 

Calculations have been performed for Stockholm's entire state road network, divided into smaller road sections 
(between traffic junctions). Figure 1 shows the reduction of the PM10 concentration that is necessary to reach 
limit values along a part of the E4 highway in northern Stockholm (Tpl Bredden -Tpl Rotebro and Tpl Rotebro-
Tpl Häggvik). 

The calculations show that large reductions in studded winter tyre share and/or vehicle speed are required to 
achieve the PM10 limit values in 2030:  

371 

 
 
 
 
 
 
 
- 

- 

The maximum stud
for the two highway stretches respectively.  

The needed reduction in PM10 can also be achieved by reducing the traffic speed by 41 km/h and 37 
km/h respectively. 

Up to 20 µg/m3 lower PM10 concentration is needed to achieve the environmental quality goals for the same 
highway section (Figure 2). 

As  a  single  measure,  neither  a  reduction  of  the  traffic  speed  nor  a  reduction  of  the  studded  tyre  share  is 
estimated  to  be  effective  enough  to  reduce  the  PM10  level  to  the  environmental  quality  goal.  Rather,  a 
combination of measures such as limitation traffic volumes, traffic speed, and reduction of studded tyre share 
could be effective to reach the air quality goals. 

Figure 1: 
environmental air quality limits of annual PM10 concentration 2020 (left) and 2030 (right). 

Concentration reductions needed within 200 m along part of the E4 highway to achieve 

372 

 
 
 
 
     
 
 
Figure 2. Concentration reductions needed within 200 m along part of the E4 highway to achieve the 
environmental quality goal of annual PM10 concentration in 2020 (left) and 2030 (right). 

Health benefits and reduction in societal costs 

If the PM10 goal (15 µg/m3 as an annual mean value) would be achieved along the highways, it is estimated 
that 15 or 20 years of life would be saved in 2020 and 2030, respectively (Table 2). Achieving the PM10 daily 
mean limit value would only save 0.4 and 0.5 life years in 2020 and 2030, respectively. Achieving the upper 
assessment  threshold  would  save  5  years  of  life  in 2020.  Considering  both  effects  on  mortality  as  well  as 
morbidity (including stroke, diabetes, and childhood asthma) the corresponding reduction in societal costs if 
goals were achieved is estimated to be 36 and 52 million SEK per year in 2020 and 2030 respectively. 

373 

 
 
   
 
 
 
 
Table 2: Calculated reductions in population exposure to PM2.5 and associated health costs (2017) and saved 
life years for different scenarios.  

PM10 scenario 

Reduced health costs 

(million SEK) 

of 

Saved 
life 
years  for  the 
population 

Population-
weighted 
exposure 
PM2.5* 

(µg/m3) 

Year 2020  

0.3735 

0.84 

PM10 limit (50 µg/m3 day) achieved  

Year 2020  

0.3729 

11 

PM10  upper  assessment  threshold  (35  µg/m3  day) 
achieved 

Year 2020 

0.3716 

36 

PM10 goal (15 µg/m3 year) achieved  

Year 2030 

0.4035 

1.1 

PM10 limit (50 µg/m3 day) achieved 

Year 2030  

0.4009 

52 

PM10 goal (15 µg/m3 year) achieved 

0.4 

5 

15 

0.5 

22 

*Difference compared to baseline with exceedances of limit values, upper assessment threshold, and goal. 

Discussion 

It should be noted that the calculations include the effects of actions taken on the highways with no influence 
of emissions on other roads. In reality, reducing studded tyres on highways will also lead to reduced use of 
studded  tyres  on  other  roads,  which  will  contribute  to  reducing  concentrations  along  the  highways.  The 
consequences of studded tyre reductions are thus underestimated.  

On the other hand, we have not considered that road wear is higher from electric cars compared to conventional 
cars, due to the higher weight of electric cars compared to conventional cars. According to vehicle fleet statistics 
for  2022  average  electric  cars  weigh  around  400  kg  more  than  average  mix  of  diesel  and  petrol  cars. 
Extrapolating the trend to 2030, this could mean that wear of roads is 50% larger if the share of electric cars 
increases by 2030. Then the reductions in emissions by speed restrictions or restrictions in studded winter tyres 
will not have the same effect as if electric cars had the same wear as conventional cars.  

It is important to note that in the above calculations, it is assumed that the proportion of studded tyres is the 
same for electric cars as for other passenger cars. Now surveys in Trondheim, Helsinki, and Stockholm show 
that the proportion of studded tyres is significantly lower among electric cars. A recent survey of the city of 
Helsinki  in  the  spring  of  2023,  for  example,  shows  that  the  studded  tyre  share  of  electric  cars  was  16% 
compared to the share of petrol cars of 69% (Ana Stojilkovic, Finnish Environmental Centre, SYKE, personal 
contact).  In  that  case,  it  could  mean  that  PM10  emissions  actually  decrease  rather  than  increase  with  an 
increasing proportion of electric cars, even if one expects that the electric cars are heavier. 

Conclusions 

Achieving the PM10 environmental quality objective along densely trafficked highways could have substantial 
health benefits as it would reduce the exposure not only of people living close to the roads but also the exposure 
of people in the whole county. Reductions of emissions along highways can be achieved by reducing the use of 
studded winter tyres, reducing the speeds of vehicles, or reducing the amount of traffic. It is uncertain if the 
objectives can be achieved by 2030, but if they would, we estimate that up to 20 years of life could be saved 
associated with the reduced exposure to PM2.5 in Stockholm County with more than 2 million inhabitants.  

374 

 
 
 
Acknowledgements  

The project was funded by the Swedish Transport Administration.  

References 

Denby, B. R., Sundvor, I., Johansson, C., Pirjola, L., Ketzel, M., Norman, M., Kupiainen, K., Gustafsson, M., Blomqvist, G., Omstedt, 
G. (2013). A coupled road dust and surface moisture model to predict non-exhaust road traffic induced particle emissions 
(NORTRIP). Part 1: road dust loading and suspension modelling Atmos. Environ., 77, 283-300. 

Forsberg et al., 2021. Report in Swedish only Swedish Transport Administration, 2021:253. ISBN: 978-91-7725-988-6. 

375 

 
 
 
 
 
Assessing the Impact of Car Cabin Filters mileage on In-Vehicle Air  

Quality: Results from Controlled Environment   

N. Hafs1, A. Mehel1, G. Fokoua1, H. Er-Rbib2 and P. Chevrier3     

1Department of Mechanical and Environmental Engineering, Air Quality group, ESTACA, Paris Saclay  

Campus, 78180, Montigny-le-Bretonneux, France  

²UTAC, av Georges Boillot, 91310 Linas,   

3ARIAMIS Engineering, 18-

Introduction   

Air quality has become a crucial issue both from a public health and economic perspective. According to the 
World Health Organization (2014), air pollution caused the premature deaths of over 7 million people each year 
(OMS, 2014) . The associated health expenses and income losses are estimated at 225 billion annually  The 
transportation domain is a major source of various pollutants, leading to exposure for individuals in transit. 
Studies have shown that exposure to pollutants remains significant regardless of the mode of transportation 
used, including cars, bicycles, trains, and walking (Knibbs et al., 2011). Recent investigations  (Silverman et al. 
2012; Valentino et al. 2016) have shown that air pollution is responsible for respiratory and cardiovascular 
problems  and  an  increased  risk  of  cancer,  with  even  the  placental  barrier  unable  to  provide  a  healthy 
environment. In the case of an enclosed space, such as the cabin of a car, exposure can, in some cases, be more 
significant due to the temporary accumulation of pollutants. Consequently, numerous studies have focused on 
characterizing  the  air  quality  inside  car  cabins  (Hudda  et  al.  2012)  .These  studies  have  shown  that  key 
parameters such as ventilation rates and the age of the vehicle can significantly affect the quality of air inside 
the cars (Mehel et al, 2019) In France, the average commuting duration by car is 50 minutes, with residents of 
the Paris region having twice the travel time of those in smaller urban areas (68 minutes versus 35 minutes) 
to control the 
parameters that affect the pollution rate in the car cabin to improve air quality and limit passenger exposure 
to pollutants. Due to the increasing harmful effects of air pollution, various studies have investigated the health 
risks  associated  with  population  exposure  to  vehicleemitted  pollutants.  Initially,  the  research  focused  on 
measuring gaseous pollutants and their effects on health (McAdam et al., 2011) .Later, due to the particularly 
harmful effects of observed particulate pollutants, especially after smog caused a large number of deaths (Bell, 
Samet, and Dominici 2004). Many studies have characterized a several  personal exposure in the Netherlands, 
Belgium, and Sydney and have demonstrated that the time spent in traffic significantly contributes to the daily 
average  exposure  to  PM10,  PM2.5  and  ultrafine  particles  (Wichmann  et  al.  2005;(Van  Roosbroeck  et  al. 
2008;(Knibbs et de Dear, 2010;(Int Panis et al. 2010). According to a study made by  (Miao et al. 2015), the 
rush hour commuting leads to incrased the exposure to the air pollution, regardless the transportation mode. 
Add to that studies made in London (Briggs et al. 2008; Kaur, Nieuwenhuijsen, and Colvile 2005), found that 
while walking, the average pollution in the air was significantly higher  than those experienced while driving 
inside the car cabin. Also, these studies found that the transportation type used, route taken, and the commuting 
duration all had an important impact on the exposure of the PM2.5, ultrafine particles, and CO.  

All  knowledge  gained  during  this  period  contributed  to  the  establishment  of  European  standards,  which  set 
emission limits for gaseous and particulate pollutants in terms of mass concentration. With the Euro 5 standard, 
it is  the  first time  that  a  limit of number  concentrations was  set  for the  emissions  of particulate  matter  in 
gasoline  and  diesel  vehicles.  This  first  threshold  was  set  at  1011  particles/km  for  diesel  engines  and  1012 
particles/km for gasoline engines. The problem of air quality inside the cabin is also heightened by the existence 
of a diverse fleet of vehicles, not only in terms of technology but also in age and use. In addition to pollutant 
emissions from exhaust gases from combustion, other sources of toxic emissions related to the braking system 
or wear of tires by friction with the road surface are added. This increases the heterogeneity of pollutants (Kam 
et al. 2012; Pant and Harrison 2013). These pollutants, which are both particulate and gaseous, infiltrate the 
cabins of moving vehicles, exposing passengers to high concentrations, have attracted the attention of many 
scientists. 

376 

 
 
  
 
 
 
The most comprehensive in this area is the study by (Hudda et al. 2012) which developed a model to predict 
ultrafine particle concentrations inside vehicle cabins, while knowing concentration values at the roadside. This 
model depends on vehicle characteristics (age and speed), ventilation parameters, and driving conditions. The 
study  involved  measuring  ultrafine  particle  concentrations  inside  73  vehicles  while  varying  ventilation 
parameters and driving speed. It emerged from the study by (Hudda et al. 2012) that the air exchange ratio 

parameter in determining the ratios between internal and external particle concentrations (I/O Inside-tooutside). 
This ratio is influenced by ventilation parameters (recirculation or external air intake), which include the fan 
speed,  but  also  on  other  parameters  such  as  the  age  or  mileage  of  the  vehicle.  The  other  reference  study 
regarding IAQ is the one conducted by (Zhu et al. 2007), who defined the different ventilation parameters to 
minimize the ultrafine particle concentration rate inside vehicle cabins. One of the experiments performed by 
these authors showed that the particle rate inside the cabin changed (decreased) (30-60s) after activating the 
fans. This shows an air exchange rate (AER) of 60 to 120/h, and the highest recorded protection rate is around 
85% when the fans are activated with the recirculation mode. The same study showed that the size distribution 
of particles [7.5-217] nm inside the cabin was bimodal: two peaks were recorded; the primary peak appears for 
particles  of  size  (10-30)  nm,  and  the  secondary  peak  for  particles  of  size  (30-70nm).  Furthermore,  the 
installation of cabin filters at vehicle air inlets reduces particles of size [5-70] nm by about 50% and particles 
of size [20-200] nm by 20 to 30%.  

The characterization of car cabin air quality is an essential aspect of understanding the exposure of vehicle 
occupants to airborne pollutants, including particulate matter, volatile organic compounds, and gases. However, 
the  most  common  approach  to  measuring  car  cabin  air  quality  involves  conducting  on-road  measurements 
(Szczurek and Maciejewska 2016;)Lim et al. 2021;(Lohani and Acharya 2016) . This approach presents several 
limitations that could impact the accuracy of the results (Franco et al. 2013). One of the main limitations of on-
road measurements is that external parameters, such as weather conditions, traffic congestion, and the distance 
between vehicles, are beyond our control (Mehel et al., 2023). These parameters can significantly affect the 
local on-road concentration of pollutants and hence the representativity of the measurements. For example, 
driving  in  heavy  traffic  or  in  congested  urban  areas  increase  the  local  on-road  concentrations  with  a  rapid 
change that depend on the inter-vehicle distance and also on the type of the front vehicle. This influences the 
process of pollutants infiltration. On the other hand, mobile measurements induce airflow around the probe that 
could influence the accuracy of measurements. Therefore, to overcome these limitations and to develop a better 
understanding  of  car  cabin  air  quality,  there  is  a  need  for  more  controlled  and  standardized  measurement 
techniques. This would involve the use of controlled environments, such as laboratory test chambers, where the 
concentration of pollutants and other parameters can be precisely controlled and monitored (Hafs, 2023).  

In this study, we are engaged in the characterization of fine and ultrafine particle infiltration rates within the 
vehicle cabin, operating within a controlled polluted atmosphere with a homogeneous particle concentration 
environment. In the present investigation we use three distinct cabin filters, each representing different mileage 
intervals: initial (0 km), intermediate (2800 km), and extended use (28000 km). The primary aim of this study 
is to elucidate the impact of filter age on the air quality within vehicular cabins in a controlled environment. This 
comprehensive characterization entails evaluations under both outdoor air intake (OA) and recirculation (RC) 
ventilation modes, across a spectrum of three distinct ventilation flow rates: no flow, medium flow (50%), and 
full  flow  (100%).All  three  filters  under  investigation  are  of  the  identical  activated  carbon  type,  and  this 
characterization is meticulously conducted on the very same vehicle and platform.  

This approach allows the significant advantage of studying the influence of filter aging without the influence 
of variable external parameters such as ambient outdoor pollution levels and meteorological conditions. The 
precision of our measurement setup allows us to meticulously use a controlled polluted environment, in which 
the distribution of pollutants remains consistently uniform. Consequently, between filter age and the air quality 
inside vehicle interiors, eliminating the influence of external factors.  

Methodology: 

The study focuses on characterizing the impact of cabin filter age and mileage on air quality within a controlled 
environment, carefully designed to minimize the influence of external variables. This controlled setup involves 
an enclosed chamber where the vehicle is positioned, allowing the creation of a simulated polluted environment 

377 

 
 
  
  
 
surrounding the vehicle. The testing bench is meticulously controlled to replicate real-world on-road pollution 
concentrations, providing a precise platform for assessing cabin filter efficiency. Importantly, the bench setup 
allow for filter assessment when it is mounted in the vehicle HVAC as for its standard operation., This enables 
the evaluation of pollutant infiltration and the assessment of internal (I) to external (O) pollutant concentrations 
ratios.  

 

The measurement bench and the vehicle: 

The measurement bubble is a dedicated structure designed to investigate the air quality within a car cabin. It is 
a large, closed enclosure. It allows a vehicle with different sizes to be characterized. Its main objective is the 
ability to generate a polluted and controlled environment while we can control the pollution levels and types 
and also the temperature. This original plateform, that we called the bubble, it uses seals to maintain stable 
pollutant concentrations, while allowing various gases and particulate pollutants to be generated at different 
concentration levels. The structure has two doors located in the front and a fan, which is used to distribute 
pollutants evenly throughout the enclosure. To obtain a homogeneous distribution of the particles injected in 
the bubble we use a fan which is positioned in the centre of the bubble. To ensure that the concentration of 

inside the measurement bubble during the characterization of the car cabin.   

ngine is On. And lastly an air conditioning system is used to maintain a stable temperature 

The vehicle used is a suv-type with a mileage of 28000 km.  

Figure 1: The measuement platform (the bubble)s  

 

Instrumentation   

The instrumentation that we used in our study is the TSI Dust track, it is  used for measuring the particulate 
matter mass concentrations in real-time. It uses the optical diffraction  technique to detect and quantify the 
mass concentration of particulate matter in the air. The instrument operates by drawing air through a sample 
inlet, which passes through a laser beam, and the scattered light is then detected and analysed to determine 
the particle size and concentration. The TSI dust track can detect particles in the range of 0.1 to 10 micrometres 
with a acquisition rate of one measurement per second. This instrument is widely used for monitoring indoor 
and outdoor air quality, occupational health, and environmental studies.  

 

The protocol of the measurements:  

The  protocol  for  characterizing  the  car  cabin  involves  several  steps.  First,  the  car  is  introduced  into  the 
is then 
activated to ensure a homogenous distribution of particles. Next, measurement instruments are placed both 

378 

 
 
 
 
 
 
 
 
inside the car cabin and within the measurement bubble to determine the particle concentration levels.. Then 
particles  are  injected  until  a  concentration  of  1  mg/m3  is  achieved  inside  the  measurement  bubble,  which 
simulates external car pollution. The AGK2800 valve is used to regulate the particle injection and attain the 
desired  concentration.  Once  the  pollution  concentration  is  stabilized,  the  car  engine  is  turned  on,  and  the 
concentration of particles infiltrating the car cabin is measured for different ventilation modes, Recycled (RC) 
and Outdoor Air (OA), and ventilation flow rate (0%, 50%, and 100%). The indoor/outdoor concentration ratio is 
calculated for different ventilation modes and flow rates, to characterize the infiltration of the particles. This 
protocol allows for the characterization of the car cabin air quality in relation to external pollution and different 
ventilation modes and flow rates. This characterization process is applied to all three cabin filters of the vehicle: 
the  0  km  filter,  the  2800  km  filter,  and  the  28000  km  filter.  Furthermore,  for  each  individual  filter,  the 
characterization  procedure  is  replicated  five  times.  The  filter  characterization  takes  place  under  consistent 
conditions within a polluted environment, maintaining a stable pollution level of 1 mg/m3.  

Results:  

In  this  section,  we  present  the  time-evolution  of  particle  concentrations:  the  one  generated  within  the 
measurement bubble (blue curve) and the one in the car cabin (Orange curve) across various ventilation modes 
and  ventilation  flow  rates.  Figures  2  to  4  provide  visual  depictions  of  particle  concentrations  within  the  air 
quality testing environment, as well as the particles infiltrating the vehicle cabin. These visualizations are based 
on different ventilation modes and varying ventilation flow rates. Notably, it becomes evident that the pollution 
levels  within  the  controlled  environment  remain  relatively  consistent  throughout  the  duration  of  the 
measurements. However, intriguing disparities arise when examining the particle concentration levels within the 
vehicle cabin, which are notably influenced by the distinct ventilation modes employed. It seems that less fine 

 Particles concentration in the measurment bubble 

 Particles concentration in the car cabin 

Figure 2 : Particles concentration in the measurement bubble and in the car cabin (for 0 km cabin filter)  

379 

                     10  20 30 40 50 60 70 80 90                     
 
 
 
 
 
 
 Particles concentration in the measurment bubble 

 Particles concentration in the car cabin 

Figure 3 : Particles concentration in the measurement bubble and in the car cabin (for 2800 km cabin filter)  

 Particles concentration in the measurment bubble 

 Particles concentration in the car cabin 

Figure 4 : Particles concentration in the measurement bubble and in the car cabin (for 28800 km cabin filter) 

To delve deeper into the implications of these varying ventilation modes on particle concentration levels within 
the cabin, a crucial metric known as the indoor/outdoor (I/O) infiltration ratio is meticulously calculated for each 
unique ventilation configuration (Figure 5-7). This calculation has the objective to quantify the extent to which 
external particles penetrate the cabin space relative to the surrounding external atmosphere. By elucidating this 
I/O infiltration ratio across diverse ventilation settings, we can discern the nuanced interplay between ventilation 
modes  and  the  resultant  cabin  air  particle  dynamics.  Such  insights  not  only  elucidate  the  mechanics  of  air 
exchange within vehicular interiors but also contribute to a more comprehensive understanding of the factors 
influencing cabin air quality in such controlled environment.  

380 

                     60 70 80  10 20 30 40 50                                      90 10 80 70 60 50 40 30 20                      
 
 
 
 
 
 
 
Figure 5 : Particles infiltration rate (I/O) for 0 km filter  

Ventilation mode  

mode) 

mode) 

mode) 

Mode de ventilation  

Figure 6 : Particles infiltration rate (I/O) for 2800km filter  

mode) 

mode) 

mode) 

Mode de ventilation  

Figure 7 : Particles infiltration rate (I/O) for 28800km filter  

381 

      10  15 20  25  30   35 40  45   50  55  60  65  70    recycled mode    recycled mode      recycled mode                      10  15  20  25   30 35  40  45  50  55  60  65   70                   non-recycled    non-recycled    non-recycled      10   15  20 25   30  35  40 45  50  55   60  65  70                   non-recycled    non-recycled    non-recycled  
 
 
 
 
 
 
  
 
 
 
 
  
 
 
Figures 5 to 7 depict the rates of particle infiltration into the vehicle cabin for various ventilation configurations 
using the three different filters. It is evident that, across all three filters, the outside air (OA) mode unfortunately 
promotes a higher degree of particle infiltration into the cabin. Moreover, it is noteworthy that the filter with a 
moderate  age  of  2800  km  exhibits  superior  air  filtration  capabilities,  thereby  contributing  to  enhanced  air 
quality. This is a knowk results from numerous previous studies (Knibbs et al., 2010; Hudda et al., 2012;  Mehel 
et al., 2023)  

A discernible pattern emerges wherein the filter having undergone 2800 km of use effectively restrains particle 
infiltration. This is evidenced by comparatively lower particle concentrations within the cabin, indicating a more 
efficient filtration process. Significantly, this advantageous outcome becomes evident when comparing results 
with both the pristine (0 km) and well-traveled (28000 km) filters. These findings underscore the potential of a 
moderately aged filter, specifically one with 2800 km of usage, to significantly contribute to an improved cabin 

all the results into a single histogram. This approach will facilitate a more effective and meaningful comparison. 
Figure  8  illustrates  the  particle  infiltration  rates  into  the  vehicle  cabin  across  various  cabin  ventilation 
configurations and three distinct filter ages. The characterization of these three filters was conducted under 
identical conditions, with a particle concentration of 1  mg/m³ in the same  vehicle model. The  results reveal 
compelling  insights.  When  considering  the  recirculation  mode,  the  new  filter  exhibits  the  highest  particle 
infiltration  rates,  reaching  levels  of  up  to  15%.  Intriguingly,  the  filter  with  2800  km  of  usage  displays 
significantly  lower  infiltration  rates,  dropping  to  less  than  5%  under  the  same  recirculation  mode  and  a 
ventilation flow rate of 50%.  

Ventilation mode   mode) 

mode) 

mode) 

 Filter 0km 

 Filter 2800km 

 Filter 28880km 

Figure 8 : Comparison of the infiltration rates between the 3 filters and for different ventilation settings  

Shifting the focus to the external air mode (OA), the oldest filter exhibits remarkably elevated particle infiltration 
rates, surpassing 60% for non-recirculated ventilation and a flow rate of 50%, while the I/O is about 40% for 
the 2800 km filter and 33% for the new filter. For the highest fan speed (100%), the I/O are 52%, 33% and 
44% respectively for the 28000 km, 2800 km and 0 km filter mileage respectively. The oldest filter is the one 
that has the greater I/O ratios as the findings of Xu et al. (2011) when filters have been characterised in a 
dedicated bench.  

The filter behaviour that could explain this is that the new filter has the greatest porosity allowing for as higher 
infiltration rate as the fan speed is greater. While the 28000km filter, since it is more clogged, the pressure 
drop is higher which favour infiltration from different leakages.  

Overall, the filter with 2800 km of usage demonstrates the most favourable performance in terms of particle 
infiltration rates among the three filters across various ventilation scenarios.   

These results clearly emphasize the impact of filter age on the air quality within the vehicle cabin. The findings 
also highlight that optimal air quality is not solely dependent on the installation of a new filter.  

382 

 
 
 
 
 
  
Conclusion  

In summary, this study highlights a key factor in the discussion concerning indoor air quality within vehicles: the 
age  of  the  cabin  filter.  Observations  reveal  that  the  effectiveness  of  this  filter  extends  beyond  its  initial 
installation.  Through  controlled  testing  methods,  we  were  able  to  isolate  the  direct  impact  of  filter  age, 
eliminating external factors such as background pollution levels, traffic and weather fluctuations.  

These conclusions emphasize the significance of a comprehensive approach to maintaining clean air inside the 
cabin. By considering the age of the filter along with other factors, we can optimize maintenance and ventilation 
practices, enhancing the driving experience by ensuring greater safety and comfort.  

In the ongoing quest for continuous improvement in vehicle air quality, these findings urge us to consider filter 
efficiency holistically. By accounting for the various influences on cabin air quality, we can design vehicles that 
prioritize the well-being of passengers and drivers. This research thus contributes to the ongoing discourse on 
achieving optimal air quality standards in vehicles, ultimately enhancing the overall driving experience while 
ensuring the health and comfort of all occupants.  

Acknowledgements   

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https://doi.org/10.1016/j.atmosenv.2012.05.021.  

Hafs,  N.M.;  Mehel,  A.;  Fokoua,  G.;  Chevrier,  P.  and  Er-Rbib,  H.  2023.  'Generation  of  a  Controlled  Polluted 

Automation 13(2):17-31.  

https:// DOI:10.17265/2159-5275/2023.02.001  

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https://doi.org/10.1016/j.atmosenv.2010.04.028.  

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385 

 
 
 
 
A numerical study of particle dispersion in the wake of a static and rotating 
cylinder  

1 

 Paris-Saclay, F-78180 Montigny-le-Bretonneux, France     

K. Chekrouba1*, A. Benabed1 and A. Mehel1 

 *khaled.chekrouba@estaca.fr 

Introduction 

Particulate matter emissions from road traffic have been linked to numerous adverse health effects, including 
respiratory and cardiovascular diseases, and can also impact ecosystems and the climate (Ostro et al., 2015). 
The ground vehicles, from their rotating wheels are responsible of particles resuspension which has become an 
important source of dust emissions and dispersion in the air. Therefore, their contribution to air pollution requires 
our understanding  of  the  mechanisms  responsible  for  their  dispersion  in  the  wake of  a  rotating wheel.  The 
aerodynamic behavior of the airflow around an isolated rotating wheel is complex to study experimentally which 
led to a lack of data to validate numerical simulations (Gerardin, 2014). In this case, the flow around a rotating 
cylinder could be an interesting alternative configuration, as a first step, to understand the flow topology impact 
on  particles  dispersion  given  the  geometrical  similarity  with  a  rotating  wheel  of  a  specific  aspect  ratio  i.e., 
cylinder length to its diameter. 

Extensive studies have been carried out on the aerodynamics of static 3D cylinders in the sub-critical regime 
(Re < 3×105). However, there remains a notable gap in our understanding concerning the complex interactions 
between cylinder rotation and flow patterns. This also concerns their possible impact on particle transport in 
the cylinder wake. Although a few studies have addressed particle dispersion behind a static 2D cylinder (Huang 
et al., 2014; Keita et al., 2019). , there is no comprehensive study of these dynamics. To fill this gap, the present 
study uses numerical method to investigate how cylinder rotation influences flow structures and, subsequently, 
particle dispersion in its wake. This research aims to make a significant contribution to the ongoing effort to 
understand these phenomena.  

The flow in the wake of bluff bodies, particularly circular cylinder, is characterized by the development of vortex 
shedding patterns. The process of dispersion is mainly driven by particles-vortex interactions which make it very 
important  to  correctly  predict  the  flow  topology  (Keita  et  al.,  2019).  Within  this  context,  Unsteady Reynolds 
Averaged Navier Stokes (URANS) numerical simulations were performed in this work to study the wake flow 
𝛼 = 0 
and 1 and at 𝑅𝑒 = 5000. Solutions of three different URANS turbulence models Shear Stress Transport (SST), 
Local Correlation-based Transition Model (LCTM), and Reynolds Stress Model_BSL (RSM) were compared with 
available experimental and numerical data. Following that, the study investigates the impact of rotation on the 
configuration of flow and its subsequent impact on particle dispersion within the near and far wake of both 
static and rotating cylinder. 

Numerical approach 

Flow field simulations 

3D numerical simulations of the flow around a cylinder are conducted using Ansys CFX using coupled solver, 
second order upwind scheme for advection term, Second Order Backward Euler implicit scheme for the transient 
term and a coupled algorithm for pressure-velocity coupling. This study is focused on URANS solutions, as this 
remains  by  far  the  most  common approach in air  quality  applications. The system  of equations  consists  of 
momentum equations (Eq 1) and continuity equation (Eq 2): 

𝜕𝒖̅𝑖
𝜕𝑡

+ 𝑢̅𝑗

𝜕𝒖̅𝒊
𝜕𝒙𝑗

= −

+

𝜇
𝜌𝑓

𝜕2𝒖̅𝑖
𝜕𝒙𝑗

2 +

𝜕
𝜕𝒙𝑗

(−𝒖𝑖

′𝒖𝑗

′)  

𝜕𝑝̅
𝜕𝒙𝑖

1
𝜌𝑓
𝜕𝒖̅𝑖
𝜕𝑥𝑖

= 0   

                    (1) 

                    (2) 

′ denotes the Reynolds shear stress tensor terms arising from turbulent fluctuations. 

′𝒖𝒋
𝒖𝒊
𝜌𝑓: fluid density (𝑘𝑔/𝑚3) 
𝒖̅𝒊 : mean velocity components (𝑚/𝑠) 

386 

 
 
 
 
 
 
 
 
′ : fluctuating velocity components (𝑚/𝑠) 

𝒖𝒊
𝑝̅: mean pressure (𝑁/𝑚2) 
𝜇 : fluid dynamic viscosity (𝑘𝑔/𝑚 𝑠) 
𝜇𝑡 : turbulent viscosity (𝑘𝑔/𝑚 𝑠) 
′𝒖𝒊
𝑘 =
𝒖𝒊
𝛿𝑖𝑗 : Kronecker delta 

2

1

′ : turbulent kinetic energy (𝑚2/𝑠2) 

To close this system of equations, three different models were chosen in this study:  

 

 

 

The SST model, developed by (Menter, 1994), it is a two-equation eddy-viscosity model that combines 
the advantages of the k-
fectively captures physics in the near-wall region and 
the  far  field.  However,  like  all  eddy-viscosity  models,  the  SST  model  cannot  accurately  capture 
anisotropic effects of turbulence. 

-

The LCTM is a modification of the standard SST model, developed to better predict laminar-turbulent 
transition  (Menter,  Langtry  and  Völker,  2006).  It  is  based  on  local  correlations  and  handles  the 
intermittency  function  and  transition  onset  criteria  using  additional  equations.  This  model  offers 
improved accuracy in predicting the  transition from laminar  to  turbulent  flow  (Menter,  Langtry  and 
Völker,  2006),  crucial  in  our  case  study.  However,  it  does  come  with  an  increase  of  30  %  in 
computational effort.  

The  RSM  is  a  seven-equation  model  that  solves  transport  equations  for  each  Reynolds  stress 
component and the turbulent dissipation rate. Unlike the eddy-viscosity models, the RSM is a second 
order  closing  formulation  that  can  capture  the  anisotropic  behavior  of  turbulence.  However,  the 
complexity of the model leads to a higher computational cost. The Reynolds stress model chosen is 
RSM BSL (Baseline Reynolds stress model) (Launder, Reece and Rodi, 1975). In this model the transport 
equation of dissipation is modeled as in the BSL k-omega model (similar to k-omega SST model) (D. 
C. Wilcox et al, 1998). 

Case study description 

The geometry to be considered is a cylinder with diameter 𝐷 facing in a uniform air flow. The computational 
domain is a 3𝐷 cylindrical volume with a diameter of 40𝐷 (a proper extension according to Breuer (1999) 
and a spanwise thickness of 1𝐷 (see Figure 1)). This extension was carefully chosen to be 𝑍 = 𝐷 based on 
the study of Breuer (1999) . The center of the cylinder is positioned at the center of this domain, maintaining 
a distance of 20𝐷 from the inlet, outlet, and other boundaries. 

Figure 1:   Computational domain and boundary limits 

387 

 
 
 
The simulations are conducted for Reynolds number 𝑅𝑒 = 5000 based on the cylinder diameter 𝐷 and the 
free stream velocity 𝑈. The choice of this Reynolds number is motivated by the availability of data from 
the literature for simulations validation purposes. Two case studies are considered: static cylinder, and a 
rotating cylinder (Figure 2). In the rotating cylinder case, a rotation ratio of 𝛼 = 1 is employed. This later is 
defined as α = (𝜔/𝑟)/𝑈, where 𝜔 represents the rotational speed of the cylinder around its axis, and 𝑟 is 
the radius of the cylinder. Velocity inlet condition is considered for each case, a non-slip condition is applied 
on the cylinder surface. Two symmetrical conditions on domain walls up and down the cylinder (Figure 1) 
to  separate  the  inlet  from  the  outlet.  An  atmospheric  pressure  condition  is  applied  to  the  outlet  of  the 
domain. Symmetry boundaries are applied to the spanwise walls of the domain. For the rotative cylinder 
case wall rotation boundary condition is imposed on the cylinder surface in the counterclockwise direction 
(Breuer, 2000). 

Figure 2:   Simulation case studies 

A single curvilinear body fitted with O-type grid type mesh was generated based on the studies of  (Breuer, 
2000; Karabelas, 2010). In the cross-sectional plane (𝑥, 𝑦), 152  × 152  points are used, while in the spanwise 
direction  64  points  are  distributed  on  the  1D  extension.  The  generated  mesh  comprising  a  total  of  1.7  M 
elements. Fine grids are considered in the near-wall region to be able to capture the thinnest boundary layer 
and respect the constraints of the three models (Figure 3). 

Figure 3:   Mesh of the computational domain 

The generated mesh respects the constraints of turbulence models used in all cases (𝑦+ < 5) for the automatic 
wall  treatment  to  resolve  the  viscous sublayer. The wall normal  distance can be  evaluated  as  𝑦+ = 𝑢𝜏𝑦/𝑣, 
where 𝑢𝜏 is the friction velocity defined as 𝑢𝜏 = √|𝜏𝑤|/𝜌 and 𝜏𝑤 is the wall shear stress. This will guarantee 
accurate  prediction  of  turbulent  flow  dynamics  near  the  wall,  which  is  crucial  for  the  prediction  of  flow 
separation as the most important feature of the studied cases (Aljure et al., 2015; Pereira et al., 2019). Three 
meshes are used to conduct the mesh independence study: the original mesh and two other meshes with 0.75 𝑀 

388 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
and 5.2 𝑀 elements. The latter were generated by changing only the resolution of the cross-sectional plane 
(𝑥, 𝑦) and maintaining the resolution for the near wall elements. Three dimensionless time steps Δt+ = ΔtU/𝐷 
are considered to study the influence of time step. 

Unsteady simulations of all cases are run till convergence of the results. After that the results obtained from 
each case are compared in terms of, the main mean flow characteristics: Strouhal number St, separation angle 
𝜃𝑠, normalized recirculation length 𝑳𝒓 /𝑫, drag coefficient 𝑪𝑫, lift coefficient 𝑪𝑳 , back pressure coefficient 𝑪𝒑𝒃 
were compared with different literature results. 

Particle phase simulation 

Particles dispersion study was achieved using the Lagrangian approach  (Zhang and Ahmadi, 2005) in which 
particles trajectories are calculated (see Eq 3) based on the flow solution and taking accounting for the external 
forces acting on particles. Some assumptions have been made for the present simulation: 

The volumetric fraction of particles present in the air is sufficiently low <  1% . This allows the use 
of a one way coupling where the effect of particles on the flow is neglected, as well as the potential 
for collisions between particles. 
Particles are supposed to be spherical with a density 𝜌𝑓 = 3950 (kg/m3) corresponding to that of 
Alumina particles, which is much larger than that of the air. Alumina particles have physical 
properties that mimic those of many naturally occurring particulates. Their density can be similar to 
dust and other airborne particulates.  

 

 

 

4) (Ansys, 2021):  

𝑑𝒙𝒑
𝑑𝑡

= 𝒖𝑝

𝑚𝑝

𝑑𝒖𝒑
𝑑𝑡

= 𝐹𝐷(𝒖 − 𝒖𝑝) + (

𝜌𝑝 − 𝜌𝑓
𝜌𝑝

) 𝒈 + 𝑭𝑺 + 𝑭𝑩

(3) 

(4)  

where 𝒖 is the velocity of the fluid phase, 𝒖𝑝 is the particle velocity, 𝜌𝑓 is the fluid density, 𝜌𝑝 is the 
particle density. 𝐹𝐷 (𝒖 − 𝒖𝑝) represents the drag force per unit mass of the particle, defined as: 

𝐹𝐷 = mp (

1
𝜏𝑝

) (

𝐶𝐷𝑅𝑒𝑝
24

)                                                               (5) 

 where 𝑅𝑒𝑝 is the particle Reynolds number, defined as: 

𝑅𝑒𝑝 =

𝜌𝑓𝑑𝑝|𝒖𝒑−𝒖|
𝜇

                                                                      (6) 

𝐶𝐷 represents the drag coefficient and is calculated by CFX based on 𝑅𝑒𝑝 using the Schiller Naumann 
model:  

𝐶𝐷 = 𝑚𝑎𝑥 (

24
𝑅𝑒𝑝

(1 + 0.15𝑅𝑒𝑝

0.687), 0.44) 

𝜏𝑝 is the particle relaxation time defined as the ratio of the particle-to-fluid relative velocity to its 
acceleration under the effect of the Stokes drag force, given by the expression: 

𝜏𝑝 =

2𝐶𝑐
𝜌𝑝𝑑𝑝
18𝜌𝑓𝑣

                                                                        (7) 

where 𝑣 is the kinematic viscosity of the fluid, and 𝑑𝑝 is the particle diameter, respectively, and 𝐶𝑐 is 
the Stokes-Cunningham slip correction factor which depends on the ratio of air molecular mean free 
path 𝜆 to particle diameter. 

389 

 
 
                              
  
 
𝐶𝑐 = 1 +

2𝜆
𝑑𝑝

(1.257 + 0.4𝑒

−(

1.1𝑑𝑝
2𝜆

)

)

(8) 

𝑭𝑩 represents the Brownian force (Eq 9) and was included to consider the Brownian motion particularly for 
small particles. This force is modeled as proposed by (Li and Ahmadi, 1992) as a Gaussian white noise 
random process in every direction: 

𝑭𝑩 = 𝑚𝑝𝑮√

𝜋𝑆0
Δ𝑡

(9) 

where the 𝑮 are zero-mean, unit-variance, independent Gaussian random numbers and Δ𝑡 is the time 
step used in the calculation. 𝑆0 is the spectral intensity given by: 

𝑆0 =

216𝜌𝑓𝑣𝑘𝑏𝑇
2𝑑𝑝

𝜋𝜌𝑝

5𝐶𝑐

(10) 

where 𝑇 is the absolute temperature of the fluid (K) and 𝑘𝑏 = 1.38 × 10−23JK−1 is the Boltzmann 
constant. 

𝑭𝒔  represents the Saffman lift force (Eq 11) which is also considered to include the lift effect on large 
particles in shear regions. Its components are calculated with the following expression:   

𝑭𝒔 = 𝑚𝑝

2𝜌𝑓𝐾𝑣

1
2𝑆𝑖𝑗

1
𝜌𝑝𝑑𝑝(𝑆𝑙𝑘𝑆𝑘𝑙)
4

(𝒖 − 𝒖𝒑)

(11) 

where 𝐾 = 2.594 and 𝑆𝑖𝑗 are the components of the mean strain tensor. 

The instantaneous fluid velocity 𝒖 is decomposed into mean and fluctuating components 𝒖 = 𝒖̅ + 𝒖′.  In this 
approach, the averaged component is given by the URANS computation. The fluctuating component is modeled 
along each particle trajectory using a stochastic dispersion model EIM (Eddy Interaction Model) (Gosman and 
(𝑑𝑝  =
Ioannides,  1983).  A  wide  particle  sizes 
 0.01, 0.1, 2.5, 10, 50 𝜇𝑚) were injected at 7 positions close to the cylinder, on the symmetry plane, as illustrated 
in Figure 4. A single injection of 28000 particles of each size was carried out at time 𝑡 = 0 𝑠 . All particles were 
subsequently  tracked  in  the  wake  of  the  cylinder  over  a  dimensionless  duration  of  𝑇 = 𝑡 × 𝑈/𝐷 = 20, 
equivalent to the convective time up to the exit of the domain. This approach was used by Huang et al., (2014) 
to better understand the dynamics of particles from the release instants until the outlet of the domain. Since 
symmetry conditions are applied to the lateral walls of the domain, particle exiting from one side of the domain 
will  re-enter  from  the  opposite  side.  A  rebound  condition  is  applied  to  the  cylinder  wall  with  a  restitution 
coefficient of 1. This implies that a particle will rebound off the wall while maintaining its original speed. 

including  ultrafine, 

large  particles 

fine  and 

Figure 4:   

390 

 
 
 
 
Results and discussions   

Fluid phase  

In this section, flow characteristics are compared with literature results to validate the numerical approach and 
choose the most suitable turbulence model for this specific regime. The simulations of the flow around a static 
and rotating cylinder, ran for a duration of 𝑇 = 200𝐷/𝑈 which corresponds to 50 cycles of vortex shedding. 
The flow variables were then time-averaged over the last 10 vortex release cycles (𝑇 = 40𝐷/𝑈). Finally, these 
variables were averaged in the Z direction, corresponding to the cylinder length direction, to obtain a mean 2D 
solution (Karabelas, 2010; Aljure et al., 2015). This approach is necessary to compare with existing data for the 
validation procedure of the numerical method. Flow topology is important to accurately simulate the particle 
dynamics.  

Figure 5 shows the averaged velocity streamlines of the three different turbulent models obtained. Accordingly, 
all chosen turbulence models capture the symmetrical topology of the wake as predicted in the literature (Aljure 
et al., 2015) i.e., the two upper and lower separations followed by two shear layers with equal length. Both 
stagnation (S) and (SP) saddle points lie within the horizontal line of symmetry. However, SST and LCTM models 
show two additional tiny vortices in proximity of cylinder wall. This observation was made previously by (Young 
and Ooi, 2007) numerical study at 𝑅𝑒 = 3900. On the other hand, the solution obtained with the RSM model 
shows only two vortical structures and a wider recirculation zone.  

Figure 5:  Time averaged of the mean streamlines around the static cylinder at 𝑅𝑒 = 5000 for: a) 𝑆𝑆𝑇, b) 
LCTM, c) 𝑅𝑆𝑀; SP: stagnation point, S: saddle point 

Table  1  presents  the  computed  global  parameters  of  the  flow  compared  to  the  experimental  results  from 
Norberg, (1993, 1994, 1998) and DNS results from Aljure et al., (2015). The table shows that both SST and 
LCTM models overestimate all indicators except the Strouhal number. In addition, for these two models, relative 
difference  of  the  recirculation  length  𝐿𝑟 /𝐷    with  respect  to  experimental  result,  exceeds  50 %.  These 
discrepancies are due to premature transition to turbulence in the boundary layer which is marked by the large 
values of separation angle and the appearance of the two additional tiny structures in the wake of the cylinder 
(see  Figure  5, a).  On  the other hand,  the RSM model  shows  an acceptable separation angle,  with  a relative 
difference around 7 % compared with experimental separation position, which leads to a better estimation of 
recirculation length. 

Table  1:      Integral  mean  quantities  for  static  cylinder  computed  at  𝑅𝑒  =  5000,  compared  to  literature 
experimental and numerical results  

Model 

SST  

LCTM  

RSM 

𝑆𝑡 

𝐶𝐷  

𝐿𝑟 /𝐷 

𝜃𝑠[°] 

𝐶𝑝𝑏 

0.224 

1.457 

0.79 

95.51 

1.41 

0.219 

1.475 

0.79 

95.97 

1.43 

0.233 

1.160 

1.33 

88.92 

1.08 

DNS Aljure et al., (2015) 

0.210 

1.050 

1.76 

82.20 

0.94 

EXP Norberg (1993, 1994, 1998)   

0.209 

1.020 

1.80 

- 

0.93 

391 

 
 
 
 
 
 
 
 
 
Figure 6 shows the averaged streamlines of the three models in the case of the rotative cylinder. It can be 
observed that the three models predict the same overall topology with minor differences in the two vortex sizes. 
Moreover, the cylinder rotation breaks the symmetry of the flow. Therefore, the recirculation zone shrinks and 
deflects in the direction of rotation leading to a larger upper vortex and a smaller lower one. Consequently, the 
stagnation and saddle points move up and get closer to each other. 

Figure 6:  Time averaged of the mean streamlines around the rotative cylinder at 𝑅𝑒 = 5000 for: a) 𝑆𝑆𝑇 
b) LCTM, c) 𝑅𝑆𝑀; SP: stagnation point, S: saddle point  

Table 2 compare the mean flow characteristics of the three models compared to that of DNS simulations of 
Aljure  et  al.,  (2015)  for  the  rotating  cylinder  case.  Similar  to  the  static  case,  the  SST  and  LCTM  models 
underestimate the recirculation length, while the RSM model predicts accurately this parameter. Nevertheless, 
drag coefficient is well estimated by the LCTM model with an error of 2 %. Both RSM and SST models predicted 
with lower accuracy the drag coefficient with a relative difference of approximately 10 %. The lift coefficient is 
underestimated by the three models with an error of 19%, 24%, and 21% for SST, LCTM, and RSM respectively. 
Additionally, the Strouhal number is well predicted by the three models with a max relative difference of 9 %. 
It  can  be  concluded  that  all  flow  main  characteristics  are  reasonably  predicted  by  the  three  models  in  the 
rotating cylinder case.  

392 

 
 
 
 
 
Table 2:  Integral mean quantities for the rotative cylinder computed at 𝑅𝑒 = 5000, compared to experimental 
and other numerical results. 

Model 

SST  

LCTM  

RSM 

𝑆𝑡 

𝐶𝐷  

𝐿𝑟 /𝐷 

𝐶𝐿  

𝐶𝑝𝑏 

0.231 

0.802 

0.67 

-1.667 

1.16 

0.218 

0.883 

0.53 

-1.732 

1.25 

0.243 

0.810 

0.78 

-1.698 

1.16 

DNS Aljure et al (2015) 

0.223 

0.900 

0.79 

-1.400 

0.99 

To conclude this validation section, by considering both rotating and static cylinder cases, the global quality of 
results is improved with RSM model. This model was chosen to conduct all the particle-laden simulations. 

Particles Phase  

In this work, the Lagrangian approach is employed to track particles. In the model we take into account different 
forces  that  were  mentioned  in  section  2.3,  among  them  turbulent  and  Brownian  diffusions  which  consider 
random  processes  in  their  formulations.  Consequently,  different  samples  of  the same  particles  number can 
yield distinct outcomes. This approach is statistical, and according to (Graham and Moyeed, 2002), the variability 
of computed results diminishes with the increase of both number of repetitions and particles. The CFX solver 
ensures the convergence of particle trajectories through iterative resolution of the equation of motion (Eq 3 
and 4). Through multiple iterations, the solver calculates the average of all the solutions. To obtain reliable 
outcomes, we investigated the impact of sample size on statistical quantities. It was found that a number of 
particles of 28000 particle for each particle size was sufficient to guarantee the convergence of concentration 
of particles (results were not shown here to respect the number of pages). 

Figure 7 shows instantaneous particles distributions at 𝑇 = 20 in the wake of static cylinder (left) and rotative 
cylinder (right) for the different particle sizes. Firstly, for static cylinder case it can be noted that except for 
50 𝜇𝑚 particles, distributions of different particle sizes are symmetrical irrespective the fact that it is periodical. 
This is due to the symmetrical nature of the flow in the wake considering the fact that this symmetry is time-
shifted. Particles of sizes ranging from 0.01 µ𝑚 to 10 µ𝑚 which have a stokes number 𝑆𝑡𝑘  <  0.1 which exhibit 
the same behavior. They diffuse in the vortex cores with a uniformly distributed pattern as well as the thin 
region  connecting  the  vortices.  This  behavior  is  associated  with  the  relatively  low  relaxation  time  of  these 
particles (Yao et al., 2009; Keita et al., 2019). Although, 50 µ𝑚 particles have a stokes number close to that of 
the less inertial particles (𝑆𝑡𝑘 = 0.22), they present a different pattern. These particles interact less with eddies 
and sediment under the effect of gravity. Secondly, for rotative cylinder case (Figure 7, right) it can be noticed 
that independently of Reynolds number and particle size, all distributions are inclined to the direction of rotation 
(𝑦 > 0). This is due to the asymmetric nature of the wake as discussed in the fluid phase section. Subsequently, 
particles exhibit less dispersion in the 𝑦 direction in the wake of the rotating cylinder compared to the static 
cylinder case. 

393 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 7:   Distribution of different size particles at T = 20 in the wake of a: static cylinder (left), rotative 
cylinder (Right)  

394 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Conclusion   

Particle-laden  flow  around  a  static  and  rotative  cylinder  was  investigated  using  CFD  simulations,  based  on 
URANS  approach.  Comparison  between  numerical  results  of  flow  main  characteristics  and  literature  results 
showed that RSM model is the most suitable for accurately reproducing the flow topology.  

Particles with sizes < 50 µ𝑚 diffuse within vortices due to their low relaxation time. Conversely, particles of 
50 µ𝑚  interact  less  with  the  vortical  structures  owing  to  their  relatively  higher  relaxation  time,  leading  to 
sedimentation under gravity. Furthermore, cylinder rotation has two noticeable effects on particle distributions: 
a decrease in particle dispersion in the normal direction  𝑦, and an inclination of particle distributions in the 
direction of rotation 𝑦  >  0.  

As a next step, further studies involve considering additional scenarios of particle simulations under various 
turbulent regimes and different spin ratios to enhance our understanding of the effect of rotation on particle 
dispersion in general. 

Acknowledgements  

All simulations data used in this paper was elaborated within ESTACA lab. 

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396 

 
 
 
 
 
The assessment of a Zero Emission Zone: air quality and human health impacts in 
the metropolitan city of Milan 

A. Piccoli1,2*, V. Agresti2, M. Bedogni3, G. Lonati1 and G. Pirovano2. 

1 Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy 
2 Sustainable Development and Energy Sources, RSE S.P.A., Milan, Italy andrea.piccoli@rse-web.it 
3 Agenzia Mobilità Ambiente e Territorio (AMAT), Milan, Italy 

Introduction 

The  Po  Valley  is  one  of  the  most  polluted  regions  in  Europe  (Pernigotti  et  al.,  2012),  where  the  air  quality 
standards  imposed  by  the  European  Union  are  often  not  respected.    The  city  of  Milan  stands  is  the  most 
populated city in the Po Valley. A recent study conducted by the Institute for Global Health (ISGlobal) has ranked 
the Milan metropolitan area as the fifth-worst European city in terms of NO2-related mortality and the 13th 
worst in terms of PM2.5 (Khomenko et al., 2021). Among the policies to mitigate the impact of air pollution on 
human health in urban areas, several European cities have planned and implemented Zero Emission Zones (ZEZ) 
(Cui et al., 2020). ZEZs are areas where only electric vehicles, pedestrians, and cyclists are allowed unrestricted 
access. Assessing the potential air quality benefits of ZEZs can be done using atmospheric models. In this work, 
we developed a modelling chain composed of a road traffic, an emission, and an air quality model to simulate 
the impacts of mobility scenarios. The modelling chain was applied to assess the potential impact of a ZEZ 
scenario in the city centre of Milan on air quality. 

Methods 

HERMESv3 (Guevara et al., 2020) in the bottom-up version was used to estimate road traffic emissions in Milan, 
starting  from  road  traffic  data.  HERMESv3  allows  to  simulate  both  exhaust  and  non-exhaust  emissions, 
including  the  contribution  of  PM  resuspension  due  to  vehicle  transit.  Accurate  modelling  of  non-exhaust 
emissions is crucial in electric mobility scenario studies, as this PM component also affects electric vehicles. For 
the private traffic sector, we used a macroscopic traffic simulation of a typical working day as the starting point 
for the road traffic emission modelling. Milan's environment and mobility agency (AMAT) provided the traffic 
simulation. Daily traffic count indices for 2017, developed by AMAT, were used to achieve weekly and monthly 
temporal disaggregation. Area specific profiles for the fleet composition were employed to differentiate the 
circulating fleet within the city, thus reproducing the effect of the enforced congestion charging scheme. Open 
data on bus lines geometry and bus trip frequency, available on the Milan's municipality open data portal, were 
used to simulate emissions from the public transport sector. The contribution of all the other emission sources 
necessary for the air quality model was estimated using the classical top-down approach.  

The core of the modelling system is the Comprehensive Air Quality Model with Extensions (CAMx) version 7.1 
(Ramboll, 2020). CAMx was applied over two nested domains (Figure 1). The external domain covers Italy (ITA) 
and a portion of the neighbouring countries with a horizontal resolution of 4 km and an extent of 284x364 
cells. The nested domain (MIL) is centred over the city of Milan, with 70x70 cells and a horizontal resolution of 
1 km. Both domains share the same vertical layers set-up, consisting in 14 terrain following levels of increasing 
depth. The meteorological simulation used to drive the air quality simulation was performed using the Weather 
Research and Forecasting (WRF) model version 3.9 (Skamarock et al., 2008) employing a similar configuration 
of nested domains. The WRF domains are slightly larger than the CAMx ones to limit the influence of border 
effects on air quality simulations. For the top-down emission estimation, we used the Sparse Matrix Operator 
Kernel Emissions (SMOKE) model version 3.5 (UNC, 2013). Over the ITA domain the national Italian emission 
inventory ISPRA 2015 (ISPRA, 2020) was used for Italy and the EMEP 2010 emission inventory was used for 
Europe  and  North  Africa.  In  the  MIL  domain,  we  employed  the  regional  emission  inventory  INEMAR  (ARPA 
Lombardia, 2017) developed by the Lombardy region environmental protection Agency (ARPA). Biogenic and 
sea  salt  emission  were  estimated  using  the  MEGAN  model  (Guenther  et  al.,  2006)  and  the  SEASALT  model 

uil et al., 2009). 

397 

 
 
 
 
Figure 1: 
with the Low Emission Zones. Bottom right: Milan and the LEZ. 

Maps of the study area. Left: ITA and MIL Domain. Upper right: MIL domain and the city of Milan 

The modelling chain is completed by a module that starting from Concentration Response Functions (CRFs) and 
pollutant atmospheric concentration change (between BASE and ZEZ scenario) allows estimating the impact on 
human health of the mobility scenario in terms of avoided mortality and avoided Years of Life Lost (YLL). The 
CRFs employed in this study are based on the most recent Relative Risk (RR) guideline values from the World 
Health  Organization  (World  Health  Organization,  2021).  Both  PM2.5  and  NO2  are  considered  for  long-term 
effects. Additionally, we estimated morbidity indicators, specifically the number of hospital admissions and the 
total hospitalization days attributed to NO2 and PM2.5. For these indicators, we used the CRFs developed within 
the European project HRAPIE (World Health Organization, 2013). 

Definition of the Zero Emission Zone scenario 

The ZEZ scenario implements objectives stated in Milan's municipality's Air Quality and Climate Plan (Piano Aria 
Clima or PAC) intended to improve air quality and combat climate change. The PAC, adopted by the city council 
in 2020, includes the establishment of a Zero Emission Zone (ZEZ) as one of its objectives for 2030. This ZEZ is 
intended to replace the existing Low Emission Zone AREAC in the city centre (Figure 1). In 2019 Milan introduced 
AREAB,  that  covers  most  of  the  municipal  area  and  imposes  some  restriction  to  highly  polluting  vehicles. 
Furthermore, the ZEZ scenario implements the Full Electric plan proposed by the local transport agency for the 
public transport vehicle fleet. Compared to the BASE scenario, the ZEZ scenario considers:  

  Creation of a ZEZ in place of AREAC. 
  Conversion of resident passenger cars in AREAC and already admitted vehicles to electric vehicles (EV). 
  Removal of remaining internal combustion engine vehicles (ICEVs) from the ZEZ. 
  Conversion of light-duty vehicles and mopeds/motorcycles in the ZEZ to EVs. 
  Conversion of the Public Transport to EVs. 
 

Implementation of AREAB 

The fraction of cars circulating in AREAC that is removed amounts to 46%.  

Results 

398 

 
 
 
 
Firstly, we evaluated the reduction in road traffic emissions in ZEZ scenario with respect to the base case 2017 
(BASE). The most significant benefit is the reduction of NOx emissions, with a 19.3% decrease of the annual 
road traffic emissions in Milan (Table 1). Conversely, the reductions for particulate matter fractions are relatively 
smaller, in the order of 10% for PM2.5 and of only 6% for PM10. The lower reduction for PM10 is primarily due 
to higher contribution of non-exhaust emissions of coarse particles from electric cars (Timmers and Achten, 
2016).  As  the  ZEZ  scenario  mainly  affects  a  specific  area  of  the  city,  the  emission  reductions  are  more 
pronounced in the model domain cells covering the city centre compared to the city-wide average. For NOx, a 
100%  emission  reduction  is  expected  for  the  cells  totally  included  in  the  ZEZ,  because  all  the  vehicles  still 
circulating in the area in the ZEZ Scenario will be electric. Similarly, for PM10 and PM2.5 the highest reduction 
levels are expected within the ZEZ, with maximum values of 37% and 48% respectively.  

Table 1: Road traffic emissions (Mg year-1) and relative reductions between base case 2017 and ZEZ scenario 
for Milan Municipality. 

Pollutant 

Scenario  

BASE 

ZEZ 

 Relative 
reduction  

NOX 

4348.32  3506.76 

19.3% 

PM10 

448.50 

423.24 

5.6% 

PM2.5 

271.82 

245.94 

9.5% 

Figure 2 shows the annual reduction in yearly mean concentration for NO2 and PM2.5 in the ZEZ scenario. Due 
to the similarity between PM fractions in terms of both spatial pattern and reduction values, we have chosen 
to display PM2.5 only. Both pollutants exhibit maximum concentration reductions within the ZEZ, with significant 
values also observed within the borders of the newly implemented AREAB. The effects of the scenario measures 
outside of AREAB are close to negligible. As expected, NO2 is the pollutant that benefits the most from the ZEZ 
scenario, with reduction values ranging from 8% to 9% for all the model cells within the ZEZ. 

Figure 2: Spatial distribution of the relative reduction of NO2 and PM2.5 yearly mean concentration in the ZEZ 
scenario compared with BASE scenario.  

The impact of these expected reductions in concentrations levels on the compliance with current and prospected 
EU air quality standards and WHO guidelines has been evaluated at the 8 Air Quality Monitoring Sites (AQMS) 
in Milan, both for long- (i.e. annual) and short-term (i.e. daily and hourly average) reference parameters. As for 
the annual average concentrations, the improvements in air quality allow reaching compliance with the current 
40 µg m-3 limit for NO2 only at one site and with the 25 µg m-3 limit for PM2.5 at two more sites (from four to 
six). For PM10, as compliance with the 40 µg m-3 limit was already achieved in the BASE scenario, the small 
reductions expected will have no effect. However, when comparing the annual average values estimated for 
the ZEZ scenario with the prospected EU limits for 2030 (20 µg m-3 for NO2, 10 µg m-3 for PM2.5, 20 µg m-3 
for PM10) and with the WHO guidelines (10 µg m-3, 5 µg m-3 and 15 µg m-3, respectively), non-compliance is 
expected at all AQMS for both all pollutants.  

399 

 
 
 
 
Regarding the daily average concentrations, the estimated reductions do not impact limit compliance. In both 
the BASE and ZEZ scenarios, the limits are not respected. For PM10, the current 50 µg m-3 threshold is exceeded 
on more than 35 days at all the monitoring sites, with an average number of days (54 for BASE, 52 for ZEZ 
scenario) practically unchanged. The limit proposed for 2030 and the WHO guideline value, both stricter in terms 
of concentration threshold and number of annual exceedances, are not attained in both scenarios, with about 
3.5  and  20  time  higher  exceedances,  respectively.  Despite  the  very  high  number  of  exceedances,  small 
reductions of non-compliant days are estimated in the ZEZ scenario with (-10% with respect to the EU proposal 
limit, -5% to WHO guideline) 

Finally, the hourly limit for NO2 (200 µg m-3). The average number of exceedances for the NO2 hourly limit, 
which is consistent across all regulations and guidelines, decreases from 1.88 to 0.25. With the new scenario, 
all AQMS will comply with the allowed one exceedance for calendar year of the proposed standard for 2030. 

Table 2: Average number of exceedances of the current EU daily limits, of the 2030 EU proposed standards for 
2030, and of the WHO guideline values calculated across all air quality monitoring stations in the city of Milan 
(PM2.5 and NO2 have no daily limit in the current EU regulation). 

Pollutant  

NO2  

PM2.5  

PM10  

EU current limit 

EU proposal 2030 

WHO guideline  

BASE  

ZEZ  

BASE  

ZEZ  

BASE  

ZEZ  

- 

- 

18 exc. 50 µg m-3 

3 exc. 25 µg m-3 

156 

142  

257 

245 

18 exc. 25 µg m-3 

3 exc. 15 µg m-3 

143  

140  

228 

226  

35 exc. 50 µg m-3 

18 exc. 45 µg m-3 

3 exc. 45 µg m-3 

54  

52  

66  

63  

66  

63 

A  comprehensive  evaluation  of  the  impact  on  air  quality  of  the  actions  implemented  in  ZEZ  scenario  was 
developed based on the European Air Quality Index (AQI) (EEA, 2023) computed at two traffic exposed AQMS, 
located inside and outside the proposed ZEZ (Figure 3). The AQI gives information on the potential impacts of 
air quality on human health. and is calculated using daily concentrations of NO2, PM10, and PM2.5. The overall 
daily index is based on the highest score among the three pollutants. 

Figure 3: Comparison of the annual frequencies of the European Air Quality Index estimated at two AQMS 
located inside and outside the proposed ZEZ. 

400 

 
 
  
  
  
  
 
 
 
Inside the ZEZ, there is a 10% increase (from 80% to 90%) in the number of days with acceptable (i.e. moderate 
or better) air quality when considering NO2 only, but overall, the improvement is only 2.7% due to AQI being 
mainly influenced by PM2.5 and PM10. Outside the ZEZ, the impact of the interventions on mobility is lower, 
but a small improvement is still expected: the number of "a
for the overall AQI. However, in both scenarios  50%  of the  year  is characterized  by poor  or even worse air 
quality and the implementation of the ZEZ scenario is expected to provide very limited improvements. 

The air  quality benefits estimated  in the ZEZ scenario have been also analysed  in terms of the impacts  on 
human health through mortality and morbidity indicators computed for the city of Milan (Table 3). 

Table 3: Mortality and morbidity indicators: avoided mortality (number of deaths) and Years of Life Lost (YLL), 
avoided number of Hospital Admissions and Hospital Stay days for the ZEZ scenario with respect to the BASE 
scenario. (95% confidence interval reported in brackets). 

Avoided Mortality 

Avoided YLL 

Avoided Hospital 
Admission 

Avoided Hospital 
Stay 

PM2.5 

NO2 

38 (28-42) 

391 (296-438) 

14 (0-28) 

145 (-2-300) 

42 (21-83) 

427 (214-847) 

72 (46-98) 

804 (514-1092) 

For NO2 the positive impacts of the new scenario amounts to 8.5% circa of the estimated total of the base 
case for both mortality and morbidity indicators, while for PM2.5 this value is only 1.9%. 

Conclusions 

In this work we estimated the potential impact of a ZEZ scenario in the city centre of Milan on both air quality 
and  related  impacts  on  human  health,  thanks  to  bottom-up  a  modelling  chain.  We  present  results  for  road 
traffic emissions, air quality, and human health impacts under the ZEZ scenario, in comparison with the baseline 
case  of  2017.    The  mobility  containment  measure  improves  air  quality  in  terms  of      NO2  atmospheric 
concentration, but it is less effective for reducing particulate matter pollution. Within the ZEZ boundaries, the 
complete transition to electric mobility effectively eliminates NOx emissions, resulting in an overall reduction 
of 19.35% in traffic-related NOx emissions. Thus, we estimated concentration reductions ranging from 8% to 
9% for NO2 within the ZEZ, while the reductions for PM2.5 are less than 2%. The ZEZ implementation is expected 
to only marginally aid in complying with annual and daily air quality standards set or prospected by the European 
Union. To meet the proposed EU air quality standards for 2030 or the more stringent WHO guidelines, further 
efforts  are  necessary.  Finally,  we  analysed  the  effects  of  the  concentration  reductions  on  human  health, 
specifically on mortality and morbidity. The expected reduction in mortality and hospitalization in Milan due to 
NO2 concentrations is estimated to be 8.5%, while for PM2.5 is 1.9%. 

Despite the measures on mobility implemented in the ZEZ scenario, the air quality estimated for Milan remains 
poor for almost half of the year. While significant positive impacts can be achieved for NO2, the effects on 
PM10 and PM2.5 are limited. In our base case estimation, only around 20% of total primary PM10 emissions 
are attributed to exhaust, with non-exhaust and resuspension accounting for the remaining 80%. As noted by 
Timmers and Achten, the total non-exhaust emissions of electric vehicles are higher due to the increased weight 
of such vehicles (Timmers and Achten, 2016), while resuspension is expected to be the same. Therefore, solely 
changing the vehicle fleet without reducing the amounts of circulating vehicles will only marginally affect PM 
concentrations.  Moreover,  although  local  traffic  emissions  likely  contribute  to  a  significant  portion  of  NO2 
concentrations in Milan (Pepe et al., 2019), both modelling and experimental studies suggest that traffic directly 
contributes  to  less  than  20%  of  PM  concentration  in  the  city  (Amato  et  al.,  2016;  Pepe  et  al.,  2019). 
Consequently, a more comprehensive approach to air quality management is needed to significantly improve 
PM concentrations in Milan. This approach should extend beyond a single emission sector or a restricted area, 
encompassing measures that transcend city borders. However, it is important to note that the planned ZEZ will 
have positive benefits in other environmental fields not modelled in this work, such as urban noise reduction 
and city's carbon footprint reduction. 

401 

 
 
 
 
 
 
Acknowledgements  

This  work  has  been  financed  by  the  Research  Fund  for  the  Italian  Electrical  System  under  the  Three-Year 
Research Plan 2022-2024 (DM MITE n. 337, 15.09.2022), in compliance with the Decree of April 16th, 2018. 

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402 

 
 
 
 
 
 
 
Improving 3-day deterministic air pollution forecasts using machine learning 
algorithms 

C. Johansson1,3, Z. Zhang2, M. Engardt3*, M. Stafoggia4 and X. Ma2 

1Department of Environmental Science, Stockholm University, 10691 Stockholm, Sweden 

2KTH Royal Institute of Technology, Dept. of Civil and Architectural Engineering, 100 44 Stockholm, Sweden 

3Environment and health administration, SLB-analys, 104 20 Stockholm, Sweden 

4Department of Epidemiology, Lazio Region Health Service, Rome, Italy  

Presenting author email: magnuz.engardt@slb.nu  

Introduction 

Air  quality  forecasts  can  be  based  on  deterministic  dispersion  modelling,  but  to  be  accurate  this  requires 
process-oriented  dispersion  models  and  detailed  information  on  future  emissions  and  meteorological 
conditions. Application of machine learning (ML) models to predict outdoor air quality is getting more and more 
popular. Studies have used ML to predict both hourly and daily average concentrations of particulate matter 
(PM) as well as gaseous air pollutants using meteorological and traffic data (e.g. Thongthammachart et al., 

a combination of ML, land-use data, dispersion modelling, ground-based and satellite measurements have been 
used to obtain temporally and spatially distributed concentrations (Shtein et al., 2020). Although good prediction 
results have been achieved using ML, the challenges of forecasting air pollution concentrations in a longer-term 
horizon such as a day or even several days have not been investigated and only few studies have combined 
deterministic models and ML in forecasting air pollution levels several days into the future.  

In this paper we demonstrate how ML can help improve the accuracy of 1-, 2- and 3-day deterministic forecasts 
of particulate matter (PM10, particles with diameter less than 10 µm), nitrogen oxides (NOx) and ozone (O3) for 
urban background and street canyon sites in Stockholm, Sweden. The deterministic forecast utilises the CAMS 
ensemble model to account for non-local sources (long-range transport). A Gaussian model is applied over the 
urban area of Stockholm accounting for local emissions and a street canyon model (OSPM) to account for the 
effect of buildings on the dispersion of local traffic emissions along the roads in the central area of the city. 
We compare three different machine learning algorithms; two based on decision trees (random forest (RF) and 
XG Boost (XGB)) and one neural network model (LSTM). Important questions addressed are also if there are 
systematic differences in performance depending on different pollutants and different sites. A more detailed 
presentation of the results from this study is presented by Johansson et al. (2023). 

Air pollution measurements 

Input data (in situ observations) for ML modelling are taken from four monitoring stations in central Stockholm, 
including one urban background site (Torkel Knutssonsgatan, hereafter called UB or urban) and 3 street canyon 
sites (Hornsgatan HO, Folkungagatan FO and Sveavägen SV). They are all located in central Stockholm (Figure 
1). 

403 

 
 
 
Figure 1. Map of central Stockholm showing locations of the urban background site (Torkel Knutssonsgatan) 
and the street canyons traffic sites. 

The Stockholm air quality forecast system 

A system of three nested dispersion models is used to forecast concentrations of air pollution in Stockholm. The 
CAMS ensemble model, part of the Copernicus program (Peuch et al., 2022) is used to obtain forecasts of long-
range  transported  air  pollution  from  outside  of  the  Greater  Stockholm.  CAMS  forecasted  concentrations 
representative of background air, hour by hour, are extracted from a location outside the greater Stockholm 
domain. The contributions to concentrations due to local emissions in the metropolitan area were performed on 
a  100  m 
system 
(https://www.airviro.com/airviro/), modelling domain (Greater Stockholm, 35 by 35 km). The Gaussian model is 
fed  with  meteorological  forecasts  from  the  Swedish  Meteorological  and  Hydrological  Institute  (SMHI).  A 
diagnostic  wind  model  is  used  to  account  for  influences  of  variations  in  topography  and  land-use  on  the 
dispersion  parameters  input  to  the  Gaussian  model.  Finally,  the  Operational  Street  Pollution  Model  (OSPM, 
Berkowicz, 2000), driven by forecasted meteorology from SMHI is applied for the street canyon sites. NOx and 
PM10 are modelled on all scales, whereas O3 is only forecasted by the CAMS ensemble model. 

resolution  using  a  Gaussian  dispersion  model  part  of 

the  Airviro 

Meteorological forecasts 

As an integral part of the Stockholm air quality forecast system, meteorological forecasts for a point in central 
Stockholm are downloaded every morning from SMHI (https://www.smhi.se/data/oppna-data) and MET Norway 
(https://docs.api.met.no/doc/). The meteorological forecasts extend over 10 days and are a combination of output 
from  a  number  of  regional  and  global  numerical  weather  prediction  models.  The  combination  is  based  on 
statistical adjustments as well as manual edits. The meteorology is initially used to drive the models of weather-
dependent PM emissions and the urban- and street canyon air quality modelling. The forecasted meteorological 
data are, finally, also used as predictors in the ML algorithms as detailed below. 

404 

 
 
 
 
 
Machine learning models 

Two decision tree based ML models, RF and XGB, and one deep learning model, LSTM are applied. In addition, 
an ensemble learning approach based on a General Additive Model (GAM), aggregating the above three models, 
is also applied to further optimise the results. Figure 2 provides an overview of input and output data and the 
processing of the data using the MLs for the four sites with separate training data for 1-, 2- and 3-day forecasts 
for  each site  and  pollutant (PM10,  NOx  and  O3).  Note  that  the  input  includes  the  deterministic  forecasts  of 
PM10,  NOx  and  O3,  in  order  to  evaluate  how  much  the  deterministic  forecasts  can  be  improved  by  the  ML 
algorithms. Several common performance metrics have been selected for comparing the prediction results of 
different  machine  learning  models  including  Pearson  correlation  (r)  and  error  measures:  normalised  mean 
average  error  (nMAE),  mean  absolute  percentage  error  (MAPE)  and  normalised  root  mean  squared  error 
(nRMSE). For details on the methods and definition of the statistical performance indicators used, see Johansson 
et al. (2023). 

Figure 2. Schematic showing input data and division between training data sets for the different models, sites 
and pollutants. 

Comparison between deterministic forecasts and ML - urban background 

Figure 3 shows an example of the temporal variations in September 2021 in the forecasts with deterministic 
modelling and GAM in comparison to the observations. For all pollutants the ML tend to improve the variability 
in the observed concentrations compared to the deterministic forecasts, but there are significant deviations. For 
O3  the  minimum  concentrations  observed  are  often  not  forecasted  so  well  and  for  PM10  the  highest 
concentrations are not captured by the models. 

405 

 
 
 
 
 
Figure 3. Temporal variations in hourly mean NOx, PM10 and O3 concentrations at the urban background site 
during September 2021 based on observations, deterministic forecasts and GAM. Mean of 1-, 2- and 3-day 
forecasts. 

Figure 4 presents mean of 1-day, 2-day and 3-day statistical performances as ratios of ML to deterministic 
forecasts. This shows that NOx is consistently improved using whatever ML model for all statistical performance 
indexes,  whereas  for  PM10  and  O3  there  are  improvements  in  nRMSE  and  nMAE,  but  MAPE.  Overall,  the 
difference  in  performance  between  different  models  is  small,  less  than  30%,  but  larger  when  comparing 
different pollutants. 

406 

 
 
 
Figure 4. Ratios of statistical performances for ML versus the deterministic hourly forecasts for the urban site. 
Mean of 1-day, 2-day and 3-day forecasts. 

Importance of features - urban background 

The relative importance of different features depending on model (RF or XGB), pollutant (PM10, NOx, O3) and 
forecast period (1-day, 2-day and 3-day) indicates the processes not captured by the deterministic models. In 
summary regarding importance of features for urban background: 

  NOx. Lagged 24-hour mean concentrations, calendar data, wind speed and local deterministic forecasts 
are  among  the  top-10  most  important  variables  using  RF  and  XGB,  but  it  can  be  noted  that  the 
deterministic forecast is not the most important feature for any model. Of the calendar features hour 
is most important reflecting the importance of regular, diurnal variations in traffic emissions. 

 

PM10. The regional deterministic forecast is the most important feature for PM10 forecasts, both for 
RF  and  XGB  and  for  all  forecast  days.  Also  lagged  measurements,  both  average,  minimum  and 
maximum concentrations is important. Of the calendar features the seasonal variation is reflected in 
the importance of the Julian day.  

  O3.  For  O3  RF  and  XGB  shows  very  similar  characteristics  when  comparing  relative  importance  of 
different features. The regional deterministic forecast is the dominant feature both for RF and XGB, 
and for all forecast days. Also lagged measured maximum concentrations is of some importance. The 

407 

 
 
 
 
relative humidity is important, likely reflecting that O3 concentrations are typically higher during dry, 
clear sky conditions, which may not be completely captured by the deterministic forecasts. 

Discussion 

The performances of the ML models are quite similar for the different sites and forecast days. But there are 
large  differences  in  improvements  for  different  pollutants.  In  general,  our  results  indicate  that  ML  is  more 
effective in improving NOx than PM10 and O3. For PM10 the ML show slight improvement in r but not much 
improvements in relative errors. This difference in improvement is likely associated with the different processes 
controlling  the  concentrations,  such  as  different  sources:  NOx  concentrations  being  mainly  due  to  vehicle 
exhaust emissions which shows regular variations from one day to the next depending on day of the week and 
time  of  day,  while  PM10  is  mainly  due  to  road  dust  emissions  controlled  by  a  combination  of  variations  in 
vehicle volumes and meteorological conditions that affect suspension of coarse particles from street surfaces 
(e g Denby et al., 2013). Road dust is accumulated on the road surfaces during wet road surface conditions and 
suspended by vehicle induced turbulence during dry conditions. 

The improvement of the forecasts of NOx with ML is partly driven by the calendar, hour, day of the week and 
to some degree also Julian day, and different features are similarly important for RF and XGB. For PM10 the 
seasonal variation described by Julian day is the most important feature at the street canyon sites, both for RF 
and XGB. This indicates that the deterministic forecasts are not capable at describing impacts of meteorology 
and  road  dust  emissions  on  PM10,  even  though  parameterisations  of  these  processes  are  included  in  the 
deterministic modelling system. The total mass generated by road wear is a key factor for PM10 emissions and 
these emissions are strongly controlled by surface moisture conditions and this is taken into account by the 
NORTRIP model. But there are periods where surface wetness is not well modelled and it is not known if this is 
the result of input data, e.g. precipitation, or of the model formulation itself.  

The deterministic forecast of O3 underestimates concentrations at the urban site due to the fact that the local 
emissions  of NOx  influencing the photochemistry  is  not properly considered by the CAMS model, but this is 
corrected using the MLs. Despite this the deterministic forecast is the most important feature for both RF and 
XGB but also lagged measured mean and maximum O3 concentrations improve the deterministic forecasts. 

Conclusions 

We have applied different machine learning algorithms to improve 1-, 2- and 3-day deterministic forecasts of 
NOx, PM10 and O3 concentrations for a number of locations in Stockholm, Sweden. It is shown that degree of 
improvement  of  deterministic  forecasts  depend  more  on  pollutant  and  monitoring  site  than  on  what  ML 
algorithm  is  applied.  Deterministic  forecasts  of  NOx  are  improved  at  all  sites,  using  all  models.  Pearson 
correlations increase by up to 80% and errors are reduced by up to 60%. For PM10 more variable results are 
seen, likely reflecting the more complicated processes controlling the road wear emissions which constitute a 
large  fraction  of  PM10.  For  O3  at  the  urban  background  site  deviation  between  deterministically  modelled 
absolute level is correct using ML; nRMSE and nMAE is reduced by on average around 20%, but there is almost 
no improvement in the correlation and MAPE.  

408 

 
 
 
 
Acknowledgements  

The project was funded by ICT 
(contract VF 2021-0082). 

References 

 The next generation and Digital future at KTH Royal Institute of Technology 

Denby, B. R., Sundvor, I., Johansson, C., Pirjola, L., Ketzel, M., Norman, M., Kupiainen, K., Gustafsson, M., Blomqvist, G., Omstedt, 
G. (2013). A coupled road dust and surface moisture model to predict non-exhaust road traffic induced particle emissions 
(NORTRIP). Part 1: road dust loading and suspension modelling Atmos. Environ., 77, 283-300. 

Berkowicz, R. (2020). OSPM - A parameterised street pollution model, Environmental Monitoring and Assessment, 65, 323-
331. 

Chuluunsaikhan, T., Heak, M., Nasridinov, A., Choi, S. (2021). Comparative Analysis of Predictive Models for Fine Particulate 
Matter in Daejeon, South Korea. Atmosphere, 12, 1295. https://doi.org/10.3390/atmos12101295. 

Doreswamy,  Harishkumar  K  S.,  Yogesh,  K.M.,  Gad,  I.  (2020).  Forecasting  Air  Pollution  Particulate  Matter  (PM2.5)  Using 
Machine Learning Regression Models. Procedia Computer Science 171, 2057 2066. 

Johansson, C., Zhang, Z., Engardt, M., Staffogia, M., and Ma, X. (2023). Improving 3-day deterministic air pollution forecasts 
using machine learning algorithms. Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2023-38, in review. 

rations  from  traffic  flow  and 

meteorological conditions. Science of the Total Environment 651, 475 483. 

Krecl et al., 2021. Road dust is accumulated on the road surfaces during wet road surface conditions and suspended by 
vehicle induced turbulence during dry conditions  

Peuch, V.-H. et al. (2022). The Copernicus Atmosphere Monitoring Service: From Research to operations. BAMS 103, E2650
E2668. 

Stafoggia, M., Johansson, C., Glantz, P., Renzi, M., Shtein, A., de Hoogh, K., Kloog, I., Davoli, M., Michelozzi, P., Bellander, T. 
(2020).  A  Random  Forest  Approach  to  Estimate  Daily  Particulate  Matter,  Nitrogen  Dioxide,  and  Ozone  at  Fine  Spatial 
Resolution in Sweden. Atmosphere, 11, 239, 1-19. 

Shtein, A. Kloog, I., Schwartz, J., Silibello, C., Michelozzi, P., Gariazzo, C., Viegi, G., Forastiere, F., Karnieli, A., Just, A.C., Stafoggia, 
M. (2020). Estimating daily PM2.5 and PM10 over Italy using an ensemble 

Thongthammachart,  T., Araki, S., Shimadera, H., Eto, S., Matsuo, T. and  Kondo,  A. (2021) An  integrated  model  combining 
random forests and WRF/CMAQ model for high accuracy spatiotemporal PM2.5 predictions in the Kansai region of Japan. 
Atmospheric Environment 262, 118620. 

409 

 
 
 
 
 
 
Effects of noise barriers on population exposure to and health impacts of air 
pollutants downwind of highways 

D. Schlesinger1* and C. Johansson1,2 

1 SLB-analys, Environment and Health Administration, City of Stockholm, 104 20 Stockholm, Sweden 
2 Department of Environmental Science (ACES), Stockholm University, 10691 Stockholm, Sweden 

*correspondence: daniel.schlesinger@slb.nu 

Introduction 

Road traffic emissions are among the most important sources of many air pollutants like nitrogen oxides (NOx) 
and particulate matter (PM10) and it is the most important source of human exposure to many pollutants and 
noise in densely trafficked cities. Noise barriers are commonly used along highways passing close to populated 
areas to reduce impacts of noise exposure on residents. Previous studies show that air pollutant concentrations 
can be significantly reduced behind noise barriers placed along busy highways depending on, for example, the 
height of the barrier, the distance between the receptor and the barrier, and meteorological conditions.  

estimate air pollutant concentrations in populated areas downwind of highways with noise barriers. Varying 
geometric  and  meteorological  parameters,  sensitivity  of  the  concentration  changes  on  those  parameters  is 
investigated. The model results are then combined with spatial population data to calculate population-weighted 
concentrations and the associated health effects, assuming an established dose-response function. Changes in 
pollutant  concentrations  due  to  the  presence  of  noise  barriers  and  their  geometry  are  in  this  way  used  to 
estimate corresponding health benefits.  

Methods 

For the description of barriers in dispersion simulations we use the RLINEXT source type (Research Line Source 
model  (RLINE)-Extended)  as  part  of  the  line  source  option  of  the  U.S.  EPA  regulatory  air  dispersion  model 
AERMOD (AMS/EPA Regulatory Model, AERMOD, 2021; Snyder et al, 2013). The AERMOD/RLINEXT model (in the 
-empirical dispersion model that provides good descriptions of 
concentrations measured in wind tunnel and tracer field studies in geometries that include a barrier between a 
line source and the receptor (Francisco et al., 2022).  

Figure 1: Parameters to be specified to run the RLINE model: distance to center of the road, road width and 
barrier height. 

We ran sensitivity tests of the RLINE model to identify parameters that affect the results most and to compare 
to those of Computational Fluid Dynamics (CFD) simulations and expected behavior. All sensitivity tests were 
run for an idealized geometry of a long (ca 2.5 km), straight road extending in east   west direction on flat and 
open terrain. A noise barrier of height 2, 4, 6 and 8 m was placed north of the road in a distance of 8, 12 and 
16  m  from  the  center  of  the  road,  respectively.  Apart  from  the  geometric  parameters,  sensitivity  to 
meteorological  conditions,  i.e.  atmospheric  stability  and  windspeed,  was  investigated.  The  computational 

410 

 
 
 
 
 
 
 
 
domain extended 100 m along the road, 300 m from the center of the road in perpendicular direction and 16 
m in height with a computational grid of 1 m resolution in all three spatial dimensions.  

For evaluating the potential health benefit of noise barriers we calculated NOx concentrations with and without 
barriers along highway E20 passing through a 4.5 km x 4.7 km densely populated area (15 325 inhabitants per 
km2) in southern Stockholm. Barriers of 2, 4, 6 and 8 m height were simulated on the northern side, barriers of 
4 and 8 m height on both sides along the roughly 5 km long highway section. The horizontal resolution was set 
to  50  m  (91  x  94  grid  cells)  and  we  used  a  climatology  with  360  weather  cases  based  on  18  years  of 
meteorological measurements from a meteorological mast in Stockholm. The total population exposure was 
calculated as population-weighted concentrations based on annual mean concentrations at 2 m above ground 
and residential address (not accounting for people living at different floors). The change in total exposure was 
used to estimate an expected health impact (Martenies et al., 2015; Oudin et al., 2022): 

∆𝑁 = Γ𝐵𝐿𝐼   ×   (1 –  exp (−

ln(𝑅𝑅)
10

∆𝐶)) 

where N is the change in death rate, RR is the relative risk per 10 µg/m3 and C is the change in annual mean 
population-weighted concentration. We calculated the change in annual mortality applying a RR for NOx of 1.08 
(CI: 1.06-1.11) (Nafstad et al., 2004). The baseline incidence rate, Γ𝐵𝐿𝐼, was set to 1329 deaths per 100 000 
persons per year. 

Sensitivity tests of the RLINE model 

Generally, the concentrations behind barriers are well mixed over a height roughly proportional to the barrier 
height, and this persists over several barrier heights downwind. The barrier causes increased turbulence that 
spreads the plume and vertically lifts pollutants above the top of the barrier.  

We investigate the sensitivity of the RLINE results for concentration changes on meteorological conditions, i.e. 
atmospheric stability and windspeed, the no
center  of  the  road.  The  results  are  presented  as  concentration  change  relative  to  a  situation  without  noise 
barrier, see Figure 2.  

Figure 2: Concentration changes relative to concentrations without barrier. The vertical axis indicates the height 
above ground, the horizontal axis the distance from the center of the road. The above example shows results 
for a barrier located 16 m from the center of the road, a barrier height of 4 m and southerly (i.e. left to right) 
wind at speed 6 m/s.  

The results for the example presented in Figure 2 show the expected uplift of the polluted air volume over the 
barrier and simultaneous dilution with significantly decreased concentration downwind and close to the ground 
and increased concentration higher up. Concentration changes on 2 m height above ground are assumed to be 
most relevant to the exposure of the population and we therefore focus on concentration changes at this height, 
see Figure 3. The calculated concentrations are enhanced just behind the barrier (< 30 m) to rapidly decrease 

411 

 
 
 
 
 
 
below those without barrier for longer distances downwind. Some unstable meteorological conditions can lead 
to slightly enhanced concentrations even at intermediate distances (~100 m) of the barrier, an effect known as 

Figure 3: Variation of concentration changes at 2 m height above ground for different meteorological conditions 
and barrier heights. Meteorological scenarios change from darker colors for stable, to lighter colors for unstable 
stratification.  

Concentrations downwind of the barrier generally decrease with increasing barrier height while the magnitude 
of this decrease strongly depends on the meteorological conditions.  

Sensitivity  tests  with  noise  barriers  placed  at  different  distances  to  the  center  of  the  road  show  that  the 

the concentration change at longer distances. 

Potential  changes  in  pollutant  exposure  and  associated  health  impacts  due  to  noise 
barriers 

Using the RLINE model, we aim to estimate the effect of noise barriers on population-weighted air pollutant 
concentrations and the associated health impacts. In this theoretical scenario, the changes due to noise barriers 
along highway E20 in southern Stockholm are modelled.  

With barriers, annual mean concentrations are found to be higher close to the highway (< 30 m) but lower by 
up to roughly 12 % than without barrier at longer distances (Figure 4).  

Population-weighted concentrations are found to be lower for all barrier heights (Figure 5). Using a relative risk 
of  mortality  of  8  %  per  10  µg/m3  we  estimate  the  change  of  mortality  due  to  the  decrease  in  population-
weighted concentrations that is caused by the presence of noise barriers. Depending on the height of the barrier, 
we  estimate  that  1  to  13  premature  deaths  may  be  saved  per  year  per  100  000  inhabitants  with  barriers 
compared to the scenario without barrier.  

412 

 
 
 
 
 
 
 
Figure 1: Percentage change in annual mean concentrations with distance from the center of the highway 

depending on barrier height (4+4 m means 4 m barriers on both sides of highway; otherwise 
barriers on one side of the highway). 

Barriers on both sides of the highway can further reduce the concentrations significantly compared to barriers 
on one side of the highway. It should be noted that we have not considered if it is actually practically possible 
to install barriers everywhere along the highway and that the calculations do not consider any topographic or 
building effects. More detailed calculations considering these aspects would require CFD modelling. 

Figure 7: Variation of NOx concentrations with the distance from the highway E20 (left diagram) and 
Figure 2: Variation of NOx concentrations with the distance from the highway E20 (left diagram) and 
number of saved premature deaths per year per 100 000 inhabitants with barriers compared 
number of saved premature deaths per year per 100 000 inhabitants with barriers compared 
to without barrier (right diagram). 
to without barrier (right diagram). 

413 

 
 
 
 
 
 
 
Summary 

In this work we use the RLINEXT line source implementation of AERMOD to calculate air pollutant concentrations 
downwind of highways with noise barriers. We run sensitivity tests of the model for different barrier heights, 
distance of the barriers to the center of the road, and for different meteorological scenarios.  

The  model  is  found  to  generate  high  concentrations  close  (<  ca  30  m)  to  the  barrier  and  generally  lower 
concentrations  at  larger  distances  from  the  barrier  than  without  barrier.  Concentrations  at  longer  distances 
downwind  generally  decrease  with  increasing  barrier  height  with  the  magnitude  of  this  decrease  strongly 
depending on the meteorological conditions.   

The results on concentration changes due to the presence of a noise barrier are further used to estimate the 
potential mean changes in the population exposure to air pollutants and associated health impacts in a case 
study  for  highway  E20  in  southern  Stockholm.  The  results  of  this  case  study  show  that,  depending  on  the 
ween 1  and 13  premature  deaths per year per  100 000  inhabitants  could be prevented 

using noise barriers. 

Acknowledgements  

This  work  was  funded  by  the  Swedish  Transport  Administration  (Trafikverket)  as  part  of  the  research  and 
innovation portfolio Bygga. 

References 

AERMOD (2021). User's Guide for the AMS/EPA Regulatory Model (AERMOD) U.S. Environmental Protection Agency, Office 
of Air Quality Planning and Standards, Research Triangle Park, NC (2021) Report No. EPA-454/B-21-001. 
https://www.epa.gov/scram/air-quality-dispersion-modeling-preferred-and-recommended-models#aermod 

Amini S., Enayati Ahangar F., Schulte N., & Venkatram A. (2016). Using models to interpret the impact of roadside barriers 
on near-road air quality, Atmospheric Environment, 138(2016), 55 64. http://dx.doi.org/10.1016/j.atmosenv.2016.05.001 

Francisco D. M., Heist D. K., Venkatram A., Brouwer L. H., Perry S. G. (2022). Observations and parameterization of the 
effects of barrier height and source-to-barrier distance on concentrations downwind of a roadway, Atmospheric pollution 
research, 13(4), 101385. https://doi.org/10.1016/j.apr.2022.101385 

Martenies S. E., Wilkins D., Batterman S. A. (2015). Health impact metrics for air pollution management strategies. 
Environment International 85, pp 84-95. https://doi.org/10.1016/j.envint.2015.08.013 

Nafstad, P., Håheim, L.L., Wisløff, T., Gram, F., Oftedal, B., Holme, I., Hjermann, I. and Leren, P. (2004). Urban air pollution 
and mortality in a cohort of Norwegian men, Environmental health perspectives, 112(5), pp.610-615. 
https://doi.org/10.1289/ehp.6684 

Oudin A., Forsberg B., Malmqvist E. (2022). Hälsovinster med att uppnå miljömålet frisk luft, Umeå universitet, 
Institutionen för folkhälsa och kliniskmedicin, Avdelningen för hållbar hälsa. ISSN-nr 2003-3281 Folkhälsa och klinisk 
medicin i Umeå rapporterar.  

Schulte N., Snyder M., Isakov V., Heist D., Venkatram A. (2014). Effects of solid barriers on dispersion of roadway 
emissions, Atmospheric Environment, 97(2014), 286 295. http://dx.doi.org/10.1016/j.atmosenv.2014.08.026 

414 

 
 
 
 
 
 
Transition to Cleaner and Carbon-free Marine Fuels and their Potential Impacts 
on Air Quality in the North and Baltic Seas in the Future 

D. A. Schwarzkopf1, J. Hahn1, R. Petrik1, V. Matthias1*, L. Ntziachristos2. 

1 Helmholtz-Zentrum Hereon, Geesthacht, Germany, volker.matthias@hereon.de 
2 Aristotle University of Thessaloniki, Thessaloniki, Greece 

Introduction 

With  a  share  of  approximately  3  %  to  global  greenhouse  gas  (GHG)  emissions,  the  shipping  sector  is    a 
significant  contributor  to  global  warming  (IMO,  2020).  To  tackle  this  problem,  the  International  Maritime 

with the goal to reduce ship-related GHG emissions by at least 50 % until 2050 compared to 2008. The precise 
pathway to achieve this goal is not yet clear but a transition to cleaner and carbon-free fuels, such as liquefied 
natural gas (LNG) or ammonia is an essential component. 

The present  study utilizes a novel methodology  for constructing ship emission scenarios  that  is  based on a 
virtual ship fleet using the Modular Ship Emission Modeling System (MoSES, Schwarzkopf et al., 2021, 2023). A 
future ship emission inventory  for 2050 calculated with  this  method, focusing on the application of marine 
ammonia  fuel,  was  used  in  a  chemistry  transport  model  (CTM).  The  results  of  this  simulation  allow  an 
assessment of the potential future impacts of shipping on air quality in northern Europe, including the effects 
of ship-related ammonia emissions. 

Emissions Data 

The expected distribution of ship energy consumption on marine fuels is based on projections described in the 
-GL, 2020). The predictions for the increase in ship numbers and fleet capacity 
are based on the SHEBA Deliverable 1.4 (Fridell, 2016). The basis for the generated scenario shipping fleets 
was  a  reference  fleet  created  from    automatic  identification  system  (AIS)  ship  position  data  for  2015. 
Temporally and spatially resolved scenario ship emissions data were calculated on the basis of these future 
shipping fleets using the MoSES model (Schwarzkopf et al. 2021). Applied emission factors for new fuels as 
well  as  for  traditional  fuel  oils  are  based  on  recent  literature.  For  marine  ammonia  engines,  an  ambitious 
technological development was assumed (Schwarzkopf et al., 2023). 

The total gas and aerosol emissions from ships, calculated with the MoSES model show that CO2 equivalent 
emissions decrease by 44% and NOX by 61% until 2050 compared to 2015. Furthermore, emissions of primary 
aerosols decrease significantly. 

Future  projections  of  land  based  emission  were  calculated  with  the  integrated  assessment  model  REMIND 
(Baumstark et al. 2021). They were consistent with the middle-of-the-road SSP2 scenario (Riahi et al. 2017) 
and assume socio-economic developments and additional climate policy targets. Subsequently, all land-based 
emissions were mapped to GNFR emission sectors and spatially and temporally distributed with the internally 
available Higly Modular Emission Model (HiMEMO). 

Emissions from biogenic sources were calculated using the Model of Emissions of Gases and Aerosols from 
Nature (MEGAN, version 3, Guenther et al. 2012, 2020). 

Chemistry Transport Modeling 

Pollutant concentrations and the impact of shipping on air quality were simulated with the chemistry transport 
model CMAQ v5.3 (Byun, 1999, 2006) for a domain that includes the North and Baltic Seas, as well as northern 
Europe  gridded  to  12  x  12  km²  cells.  The  CMAQ  model  was  built  with  the  carbon  bond  5  photochemical 
mechanism and the AE6 aerosol mechanism (Yarwood et al., 2005; Whitten et al., 2010; Sarwar et al., 2011). 

Meteorologcal forcing data for the year 2015, calculated with the community model COSMO-CLM (version 5.0-
clm15) (Rockel et al., 2008), which is embedded into the COSMO model for numerical weather prediction (Doms 
and Schättler, 2002; Doms et al., 2011; Baldauf et al., 2011), were used for CMAQ simulations. 

415 

 
 
 
 
 
Results and Discussion 

Air pollutants from ship emissions can be transported atmospherically into populated regions and impact the 
air quality. To gain insight into potential future changes in air quality impacts of a major switch from marine 
fossil fuel oils to ammonia fuel, a respective emission scenario for 2050 was developed and used as input to 
the CMAQ chemistry transport model. The absolute impact of ships to air quality was calculated using the zero-
out  method.  Percentage  changes  from  a  reference  run  for  2015  were  calculated  according  to  the  relative 
growth. 

The CTM results for 2050 show that ship-related NO2 
along  the  major  shipping  routes  in  the  North  and  Baltic  Seas.  However,  in  hot  spot  regions,  such  as  the 
-3 (Figure 1, left). In general, 
Rotterdam-Antwerp port cluster, NO2 concentrations remain high, up to 4-
ship-related NO2 concentrations are decreasing by 50 % or more (Figure 1, right), mainly due to the effect of 
the Nitrogen Emission Control Area (NECA), which applies to all vessels sailing in the North and Baltic Seas and 
that were laid-keel in 2021 or later. 

-3

Annual averages of ship-related NO2 

-3] in northern Europe and the North 
Figure 1: 
and Baltic Seas are shown in the left figure. The relative growth [%] from 2015 to 2050 for ship-related NO2 
is shown in the right figure. 

-3 in coastal regions of 
Absolute ship-related PM2.5 
the  southern United  Kingdom  and  Sweden,  northern France  and Germany  and  the  Benelux  countries.  In  the 
-3 (Figure 2, 
vicinity of the Rotterdam-
left). Compared to 2015, ship-related PM2.5 concentrations decrease strongly by 50 to 75 %. Particularly over 
land (Figure 2, right). Over the sea, this decrease is less pronounced, due to additional secondary particulate 
matter that can form from the new ammonia emissions from shipping in combination with nitrogen or sulfur 
oxides.  In  front  of  the  Rotterdam-Antwerp  port  cluster  this  even  results  in  a  slight  increase  of  PM2.5  by 
approximately 10 %. 

416 

 
 
 
 
Annual averages of ship-related PM2.5 

-3] in northern Europe and the North 
Figure 2: 
and Baltic Seas are shown in the left figure. The relative growth [%] from 2015 to 2050 for ship-related PM2.5 
is shown in the right figure. 

The  observations  described  for  PM2.5  are  even  more  pronounced  for  ammonium.  Absolute  ship-related 
-3 in the affected coastal regions and 
-3 close to Rotterdam/Antwerp (Figure 3, left). Compared to 2015, a decrease of 25 to 50 % 
can be observed over land. However, in most marine regions, however, ammonium concentrations increase by 
25 to 75 % due to the newly emerging ship-related ammonia emissions (Figure 3, right). 

Annual averages of ship-related NH4

Figure 3: 
and Baltic Seas are shown in the left figure. The relative growth [%] from 2015 to 2050 for ship-related NH4
is shown in the right figure. 

-3] in northern Europe and the North 
+ 

+ 

The impact of ship emissions on ozone formation changes from an ozone-reducing effect of up to 15 along the 
major 
to 
5  µg  *  m³  (Figure  4,  right).  This  is  due  to  the  reduced  NOX  emissions  from  the  designated  NECA. 

to  a  promoting  effect  by  up 

routes  and  ports 

(Figure  4, 

shipping 

left) 

417 

 
 
 
 
 
Annual averages of ship-related O3 

Figure 4: 
and Baltic Seas are shown in the left figure. The relative growth [%] from 2015 to 2050 for ship-related O3 is 
shown in the right figure. 

-3] in northern Europe and the North 

Acknowledgements 

This research were funded by the German Federal Maritime and Hydrographic Agency (BSH) in the framework 

References 

Baumstark L., et al. (2021). REMIND2.1: transformation and innovation dynamics of the energy-economic system within 
climate and sustainability limits, Geosci. Model Dev., 14.10, 6571 6603. 

DNV-GL (2020). Maritime forecast to 2050 - Energy transition outlook 2020 Ed, Technical report. 

Fridell E., Parsmo R., Boteler B., Troeltzsch J., Kowalczyk U., Piotrowicz J., Jalkanen J.P., Johansson L., Matthias V., Ytreberg 
E. (2016). Sustainable Shipping and Environment of the Baltic Sea Region (SHEBA) Deliverable 1.4, type RE, Technical report, 
IVL. 

Guenther A. B., Jiang X., Heald C. L., Sakulyanontvittaya T., Duhl T., Emmons L. K., Wang X. (2012). The model of emissions 
of gases and aerosols from nature version 2.1 (MEGAN2.1): An extended and updated framework for modeling biogenic 
emissions. Geosci. Model Dev., 5(6), 1471 1492. 

Guenther A., Jiang X., Shah T., Huang L., Kemball-Cook S., Yarwood G. (2020). Model of Emissions of Gases and Aerosol from 
Nature Version 3 (MEGAN3) for Estimating Biogenic Emissions, Air Pollution Modeling and its Application XXVI, 187 192. 

International Maritime Organisation (2020). Fourth IMO GHG Study, Technical report. 

Riahi  K.,  et  al.  (2017).  The  Shared  Socioeconomic  Pathways  and  their  energy,  land  use,  and  greenhouse  gas  emissions 
implications: An overview, Glob. Environ. Change, 42, 153 168. 

Sarwar G., Appel K. W., Carlton A. G., Mathur R., Schere K., Zhang R., Majeed M. A. (2011). Impact of a new condensed toluene 
mechanism on air quality model predictions in the US, Geosci. Model Dev., 4, 183 193. 

Schwarzkopf D.A., Petrik R., Matthias V., Quante M., Majamäki E., Jalkanen, J.P. (2021). A ship emission modeling system with 
scenario capabilities, Atmos. Environ.: X, 12, 100132. 

Schwarzkopf D. A., Petrik R., Hahn J., Ntziachristos L., Matthias V., Quante M. (2023). Future ship emission scenarios with a 
focus on ammonia fuel, Atmosphere, 14, 5. 

Whitten G. Z., Heo G., Kimura Y., McDonald-Buller E., Allen D. T., Carter W. P., Yarwood G. (2010). A new condensed toluene 
mechanism for carbon bond: CB05-TU, Atmos. Environ., 44, 5346 5355. 

Yarwood G., Rao S., Yocke M., Whitten G (2005). Updates to the carbon bond mechanism: CB05, Technical report. 

418 

 
 
 
 
 
 
 
The impact of data splitting in air quality modelling on the possibilities of 
interpretation of the results 

1, J. Kajewska-Szkudlarek2  

1Department of Applied Mathematics, Wroclaw University of Environmental and Life Sciences, Grunwaldzka Street 53, 50-
357 Wroclaw, Poland; ORCID 0000-0002-0157-516X 

2 Institute of Environmental Engineering, Wroclaw University of Environmental and Life Sciences, Grunwaldzki Square 24, 
50-363 Wroclaw, Poland; ORCID 0000-0002-0253-8133 

Introduction  

The issue of air quality modelling is widely discussed in the literature. One of the trends is the analysis of the 
relationship/influence  of  environmental  factors  on  the  concentration  of  pollutants  in  the  air  using  various 
(supervised and unsupervised) machine learning methods.  The main problem of these models is their poor fit 
and  interpretation  potential.  A  key  issue  in  pollutant  concentration  modelling  is  to  determine  explanatory 
variables. The most common predictors are traffic intensity and meteorological conditions 
Shang et al., 2019; Sahu et al., 2020; Xie et al., 2020; Gu, Li and Meng, 2022) or additionally air pollution factors 
(Gu et al., 2020; Menares et al., 2021; Xing et al., 2021). Implemented alone, these provide predictions with 
unsatisfactory  accuracy:  Larkin  et  al.  (2017)  obtained  a  52%  fit,  Sayegh  et  al.  (2016) 
(2018) 
Fig.1.). 

(2020) 69% fit between observed and predicted values (yellow part in 

Figure 1: Examples  of work  using  only  current  or  also  past  predictor  values  and  split  models  in  air  pollution 
modelling 

To improve the quality of the model, researchers began to introduce the past values of the dependent variables 
(blue  part  in  Fig.1.)  Such  models  gain  in  accuracy  because  history  carries  essential  information,  which 
computational intelligence is able to use in forecasting (Yang et al., 2021). Cabaneros et al. (2017) used lagged 
air pollution and meteorological data to predict roadside NO2 concentration using Multilayer Perceptron (MLP) 
alone  and  combined  with  Classification  and  Regression  Trees  (C&RT),  stepwise  regression,  and  principal 
component analysis (PCA). Catalano et al. (2016) created ARIMAX model by considering previous values of NO2 
concentrations, as well as traffic and meteorological data. Sun and Xu (2021) used historical data describing 
weather  and  pollution  conditions  to  forecast  hourly  PM2.5.  Ma  et  al.  (2020)  used  lagged  time  series  of 
meteorological and pollution factors in the PM2.5 model (Lag layer-LSTM-Fully Connected network). Researchers  
analysed pollution concentration as a time series also and considered the impact of number of lagged values 
on the air quality  modelling (Brunello et al., 2019; Espinosa et al., 2021). 

419 

 
 
 
A further way to obtain high accuracy is to split the whole database into subsets and create separate models 
for each subset. However, this strategy requires a large data set to provide enough data sets to train and test 
each of the component  models. The  use of optimized partitioning even for models without past  values has 
improved the quality of modelling 
. Multi-model approach allows one to estimate 
well also peak concentrations (Catalano et al., 2016). Gu et al. (2022) proposed a hybrid interpretable model 
combining  a  deep  neural  network  and  a  Nonlinear  Auto  Regressive  Moving  Average  with  Exogenous  Input 
(NARMAX) for actual PM2.5 prediction. They included historical PM2.5, weather, and seasonal variables, as well as 
their time lags, and built two submodels for typical and peak PM2.5 concentrations. Bai et al. (2022) also used a 
hybrid  machine  learning  technique  (extreme  learning  machine,  ELM)  based  on  PM2.5  outlier  detection  and 
splitting of the database into subseries with different characteristics. Shang et al. (2019) similarly implemented 
split models for PM2.5 concentration prediction using classification and regression tree (C&RT) and ensemble 
extreme learning machine (EELM) techniques.  

The aim of the present research was to create an hourly prediction of NOx levels for the urban area taking into 
account the splitting of the data set into subsets of characteristics due to some feature or set of features. We 
combines C&RT and CA (cluster analysis) to divide the data set into subsets with similar conditions and machine 
learning  methods  such  as  RF,  MLP,  and  SVR  for  NOx  modelling.  The  research  was  conducted  based  on 
-17.17594 E; 51.04278-

51.20996 N). 

Methods 

The scheme of these research is as follows: 

1) selection of predictors for NOx modelling using RF; 

2) splitting the database into subsets in two different ways, using Classification and Regression Tree (C&RT) 
and Cluster Analysis (CA); 

3) creation of Random Forest (RF), multilayer perceptron (MLP), and Support Vector Regression  (SVR) predictive 
models for NOx hourly concentrations. 

All details of methods one can find in 

-Szkudlarek, 2023).  

Results 

We proposed a splitting of the data, which grouped the moments in time in a given way. The 13 variables used 
in the modelling (𝑤𝑠𝑡−2, 𝑤𝑠𝑡−1, 𝑡𝑟𝑡−2,  𝑡𝑟𝑡−1, 𝑡𝑟𝑡 , 𝑡𝑡, 𝑟ℎ𝑡−1,  𝑟ℎ𝑡,  𝑁𝑂𝑥𝑡−2,  𝑁𝑂𝑥𝑡−1,  𝑁𝑂2𝑡−2,  𝑁𝑂2𝑡−1,  ℎ𝑡)33 were 
selected from 23 available by the iterative method using Random Forest (RF).  

We  considered two  splitting methods: decision tree  (DT)  and cluster analysis  (using C&RT algorithm).  In the 
decision tree method, only one variable proved crucial in making the splitting -   𝑁𝑂𝑥𝑡−1. This means that in 
this method, the only splitting criterion is the last measured concentration of 𝑁𝑂𝑥.This method produced eight 
𝑁𝑂𝑥𝑡−1 intervals covering ℝ+, with boundary points at 47.04, 82.1, 117.3, 162.5, 242.6, 355.4, and 526.8 µgm-
3 (Fig.2.)  

As a result of the cluster analysis, 8 clusters were also obtained. In this method, all variables played a role in 
creating subsets. Each cluster describes different ambient conditions that most strongly influence the current 
concentration of 𝑁𝑂𝑥, taking into account traffic volume, meteorological conditions, and time of day (Fig.3.). 

33 𝑤𝑠- wind speed, 𝑡𝑟   traffic flow, 𝑡   air temperature, 𝑟ℎ - relative humidity, ℎ - hour in 24h system (the lower subscript 

indicates the delay in time (t - actual, t-1 from the previous hour, etc.). 

420 

 
 
 
 
                                                       
 
Figure 2: C&RT  regression  decision  tree,  where  M 
Kajewska-Szkudlarek, 2023) 

  mean,  Sd 

  standard  deviation  source: 

Figure 3: Mean values of predictors in clusters source: 

-Szkudlarek, 2023) 

Clusters 1 and 4 are one of the most numerous (10215 and 10374 samples, respectively) and contain the peak 
traffic periods in the afternoon with the highest traffic flow and high concentrations of NOx and NO2 from the 
previous  hours.  They  differ  in  terms  of  weather  conditions.  Cluster  1  represents  high-pressure  summer 
conditions: high air temperature and low humidity, both current and past. Cluster 4 represents low temperature 
and very high humidity   wet spring-autumn weather. In both cases, the wind conditions favour the evacuation 
of pollutants. 

Clusters 2 and 3 contain night times with the lowest traffic flow and current and previous concentrations of 
NOx  and  NO2,  but  the  periods  in  cluster  2  are  characterized  by  favourable  wind  conditions  for  pollutants 
evacuation. Cluster 3 is the most numerous, with 11395 samples. 

Cluster 5 contains evening and night hours with relatively low traffic flow that continues to decrease hour by 
hour,  as  well  as  relatively  high  concentrations  of  NOx  and  NO2  from  preceding  hours  and  average  weather 
conditions. 

421 

 
 
 
Cluster  6 also contains  mainly  afternoon peak periods, but with significantly lower  traffic flows,  suggesting 
weekends and holidays, where the volumes are lower than on working days but higher than during the night. 

Cluster 7 contains morning peak traffic hours, with increasing traffic flow and NOx and NO2 concentrations, and 
average weather conditions. 

Cluster 8 consists of late afternoon and early evening hours, with comparable current and previous traffic levels 
(one and two hours ago) and relatively low wind speeds. The accumulation in the hours preceding the pollutants 
and  unfavourable  weather  conditions  for  the  evacuation  of  the  pollutants  lead  to  the  highest  average 
concentration of NOx equal to 190.9 µgm-3. 

For each of the two received divisions (each time into 8 subsets), we created models using machine learning: 
Random  Forest  (RF),  Artificial  Neutral  Network  (ANN)  and  Support  Vector  Regression  (SVR).  For  comparison 
purposes, we also built models for a full dataset without splitting for each modelling methods. The goodness 
of model fit was measured by 4 popular measures summarized in Tab.1. In each case, the model based on the 
SVR technique was the weakest fit. ANN and RF methods show a similar level of fit. Interesting conclusions can 
be drawn from the analysis of the fit of individual models for each of the subsets.  

Table 1. Goodness of fit measures for analysed models. 

Goodness  of  fit 
measures 

RF 

ANN 

SVR 

𝑅2 

0.869 

0.872 

0.840 

𝑀𝐴𝐷𝐸 [𝜇𝑔𝑚−3]  

𝑀𝐴𝑃𝐸 [%] 

𝑅𝑀𝑆𝐸 [𝜇𝑔𝑚−3] 

21.0 

17.2 

33.9 

20.6 

16.8 

34.0 

24.8 

23.7 

38.1 

𝑅2 

0.842 

0.878 

0.836 

𝑀𝐴𝐷𝐸 [𝜇𝑔𝑚−3] 

𝑀𝐴𝑃𝐸 [%]  

𝑅𝑀𝑆𝐸 [𝜇𝑔𝑚−3] 

21.7 

17.3 

40.4 

21.5 

17.3 

34.3 

27.4 

34.7 

39.7 

𝑅2 

0.852 

0.856 

0.678 

𝑀𝐴𝐷𝐸 [𝜇𝑔𝑚−3] 

𝑀𝐴𝑃𝐸 [%]  

𝑅𝑀𝑆𝐸 [𝜇𝑔𝑚−3] 

21.8 

17.5 

36.5 

22.2 

18.9 

35.8 

42.0 

91.6 

53.5 

C&RT 

CA 

Full dataset 

Model fit for individual leaves (in DT splitting method) and clusters (in CA splitting method) varied (Fig. 2).  

422 

 
 
 
 
 
Figure 4: Mean  Average  Percentage  Errors  for  each  subset  separately  in  CA  (left)  and  C&RT  (right) 
source: 

-Szkudlarek, 2023) 

We  recorded  the  most  uniform  fit  for  C&RT  partitioning,  with  the  model  predicting  the  lowest  and  low 
concentrations  the  weakest  (leaves  6  and  7  in  Fig.4.).  In  contrast,  partitioning  by  complex  environmental 
conditions resulted in a more varied quality of fit for individual clusters. All models obtained the best fit (MAPE 
between 14% and 18%) for afternoon peak traffic periods under different ambient conditions: clusters 1, 4, 
and 6 (Fig. 4.). A much worse fit was recorded for the RF and ANN models constructed for night-time conditions 
(clusters 2 and 3), which featured low 𝑁𝑂𝑥 𝑡−1 values. The least fit models were all for cluster 2, which described 
night  hours  with  favourable  conditions  for  the  evacuation  of  pollutants,  which  gave  the  lowest  pollution 
concentrations. 

Conclusions 

The results show that both approaches to model specification may be effectively used to improve the quality 
of  modelling  of  𝑁𝑂𝑥 concentrations  in  urban  areas.  The  choice  of  approach  should  be  determined  by  the 
purpose of the analysis. If the forecast is to be used only to provide instantaneous information, for example, to 
provide public warnings, then methods based on C&RT will be sufficient. However, if the goal of the analysis is 
to identify particular ambient conditions that favour the achievement of a particular level of air quality, with an 
analysis of the influence of individual factors, and the creation of scenarios for development planning or other 
city management actions, then models based on CA provide the possibility of obtaining such information. 

Acknowledgements  

This work was supported by Department of Applied Mathematics and Institute of Environmental Engineering of 
Wroclaw University of Environmental and Life Sciences (Poland). Traffic data are provided by the Department 
of  Traffic  and  Public  Transport  Management  of  the  Roads  and  City  Maintenance  Board  in  Wroclaw. 
Meteorological  data  are  provided  by  Institute  of  Meteorology  and  Water  Management  -  National  Research 
Institute (PIB). The source of the meteorological data is IMGW (PIB). Data from IMGW (PIB) have been processed. 
Data are publicly available on the portal https://danepubliczne.imgw.pl/. Air pollution data was collected by the 
Provincial Environment Protection Inspectorate in Wroclaw. 

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Catalano, M. et al. 
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Neurocomputing, 468, pp. 123 136. doi:10.1016/J.NEUCOM.2021.09.051. 

-term  air  pollution  effects  based  on  traffic  and 
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423 

 
 
 
 
 
 
 
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Larkin,  A.  et  al. 
Technology, 51(12), pp. 6957 6964. doi:10.1021/acs.est.7b01148. 

Environmental  Science  & 

Ma, J. et al. 

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Shang, Z. et al. 
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Chemosphere, 

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424 

 
 
 
 
 
Particle Number Box-Model Calculations in a Street Canyon and Comparison to 
Measurements 

Nicola Toenges-Schuller1*, Stefan Hausberger2, Christiane Schneider1, Werner Stadlhofer2, Ulrich Uhrner2 

1 AVISO GmbH, 52074 Aachen, Germany, nicola.toenges-schuller@avisogmbh.de, 2 Institute of Thermodynamics & 
Sustainable Propulsion Systems, Graz University of Technology, Austria 

Introduction 

Directly at the tailpipe, motor-related exhaust particles are mostly solid (elemental carbon, organics of very low 
volatility,  ash  components).  Due  to  increasing  use  and  improvements  of  filters  as  part  of  the  exhaust 
aftertreatment systems of cars and other motor vehicles, solid exhaust particles lose importance compared to 
other road traffic emission processes such as abrasion processes, and the formation of secondary particles. The 
latter are formed by nucleation and/or condensation of exhaust compounds, that are in the gas-phase at the 
time of emission, and by cooling, dilution, and oxidation end up in the particle-phase (water, sulfuric acid, other 
 few 
seconds (cooling, dilution) and minutes to days (oxidation of VOCs). Some of these newly formed very small 
particles vanish again quickly by dry deposition or evaporation, as, e.g., can be seen by the measurements of 
Wehner et al. (2009), who measured PN at the rear of a passenger car (inlets of the instruments mounted on 
a modified bicycle rack). 

The limit value for PN emissions at the tailpipe in type approval and in-service testing of vehicles refers to solid 
particles, PN air quality measurements usually include also volatile particles. It is therefore desirable to link 
solid particles emitted from the tailpipe of vehicles to all traffic-induced particles in the street. Giechaskiel et 
al. 
regulations  and  possible 
adaptations/extensions how volatile particles can be accounted for.  

(2022)  give  an  overview  of  existing  vehicle  exhaust  emissions 

To  better  understand  the  underlying  particle  dynamics  and  chemistry  mechanism  in  AQ  modelling,  a 
measurement and simulation campaign was performed as part of a project funded by FVV. In addition to the 
box modelling approach described in the following, dispersion calculations of solid particles were performed by 
Uhrner et al. (2023) using the GRAL model. 

Measurements 

The measurements took place 2021 on October 20th
in Graz, which was chosen because of busy traffic and the street canyon situation there. NO2, NO, and PN were 
measured at three different distances from the road (1m, 3m, 5m). Solid particles (using a thermodenuder) and 
total particles at these distances were measured at alternating time intervals. The schematic measurement 
setup is shown in Figure 1.  

Figure 1: 

Schematic measurement setup and arrangement of the different measuring positions 

During the measurement period traffic, counts were done at Plüddemanngasse, the traffic flow (level of service) 
was determined by measurement runs. 

In Figure 2, for the measurements in Plüddemanngasse on 20.10.2021, hourly mean values for NO and NO2, 
PN4nm  and  PN23nm  (all  particles,  and  solid  particles  (measured  after  denuder),  respectively),  during  the 
measurement period are shown.  

425 

 
 
 
 
 
Figure 2: Measurements in Plüddemanngasse, 20.10.2021: hourly mean values for NO and NO2 (top), PN4nm 
(all particles and solid particles measured after denuder, middle), and PN23nm (all particles and solid particles 
measured after denuder, bottom) during measurement period, each at one, three and five m distance from 
the road and for the spatial average (bold lines) 

Since  the  spatial  concentration  gradient  with  respect  to  distance  from  the  road  is  not  constant  over  time 
(possible reasons: vehicle induced turbulence, mixing in of air parcels of varying origin and composition, effects 
of  single  and  possibly  higher  than  average  emitting  vehicles  in  the  fleet,  measurement  uncertainty),  for 
comparison with model results, measured concentrations were averaged (bold lines in Figure 2). 

Box model calculations 

Box model calculations of particle number (PN) concentrations in a street canyon were done by a coupled aerosol 
(MADE, modal aerosol dynamics model for Europe, Ackermann et al., 1998) and gas-phase chemistry model 

426 

 
 
 
 
(Memmesheimer et al., 2007). Emission, nucleation, and aerosol aging processes are accounted for. Dilution is 
parameterised by a simple two-stage process.  

MADE  is  a  simplified  parametrisation  of  the  very  complex  processes  of  particle  dynamics,  aerosols  are 
represented  by  three  particle  modes  (nucleation  mode,  accumulation  mode  and  coarse  particles)  based  on 
lognormal size distributions. The coarse mode is not evaluated here, since exhaust particles completely fall into 
the size range of the finer modes. MADE distinguishes aerosol particles from primary sources (elemental and 
organic carbon, sand dust, sea salt, and anthropogenic particles of other origin (e.g., from abrasion processes)), 
and secondary particles (formed by oxidation/condensation of gas-phase precursors (SO2, NH3, HNO3, 17 organic 
compounds). Aerosol dynamics in MADE includes nucleation (initiated in the H2SO4-H2O system), coagulation, 
condensation,  evaporation,  and  dry  deposition.  This  model  was  applied  to  PN  in  a  generic  street  canyon  by 
Toenges-Schuller et al. (2015), but not compared to actual PN measurements before.  

Local road traffic emissions were calculated  

 

 

 

 

 

 

based on HBEFA4.1,  

using traffic counts during the measurement period,  

using traffic flow (level of service) derived from measurement runs,  

using  extrapolations  for  the  whole  day  outside  the  measurement  period  based  on  typical  diurnal 
variation curves, 

for the average urban fleet composition for Austria 2021,  

for a fleet consisting of 100% Euro 6d/Euro VI vehicles.  

Compared to the average urban 2021 fleet, in a 100% Euro 6d/Euro VI fleet solid exhaust PN emissions are 
reduced by 99%.  

Hourly values for traffic volume, mean trip speed, and NOX emissions from the 2021 fleet for the day of the 
measurements are shown in Figure 3.  

Further model input data are hourly values of meteorological parameters and background concentrations of 
NO, NO2, O3 and PM2.5, which were taken from urban background measurement stations in Graz.  

Even "sulphur free" fuels contain traces of sulphur. In HBEFA, corresponding emission factors are given as SO2. 
In the atmosphere, SO2 is oxidised to SO3 and further to sulphuric acid, causing particle nucleation. Atmospheric 
SO2 oxidation is a slow process, and relevant on larger spatial and temporal scales, not in the vicinity of the 
street. However, for vehicles equipped with oxidation catalysts, part of the SO2 is already oxidised in the catalyst. 
For various emission concepts, Toenges-Schuller et al. (2015) compiled an overview of SO3 fractions of the 
emitted SO2. For the urban Austrian fleet 2021 and the traffic shares in Plüddemanngasse, this results in an 
overall SO3 fraction of SO2 of ~3%. 

Since  the box model  is well  established to model  NO and NO2  (Toenges-Schuller et al.,  2016), NO and NO2 
measurements were used for model calibration, see Figure 4. To be compatible with the measurements, five 
boxes were coupled in 
clear gradient with the distance from kerbside over time, possibly due to vehicle induced turbulence, variable 
emission  contributions  of  individual  vehicles  and  the  mixing-in  of  air  parcels  of  varying  origin  during  the 
alternating time intervals of the measurements. Therefore, modelled concentrations were averaged over box 
2-5  and  compared  to  measured  averages  over  the  distances  1m,  3m  and  5m  from  kerbside.  Model  box  1 
corresponds  to  the  street  itself,  closer  to  the  emissions  than  the  measurements,  and  is  therefore  treated 
separately.  

427 

 
 
Figure 3: Plüddemanngasse, 20.10.2021: Traffic volume (top), mean trip speed (middle), and NOX emissions 
from the 2021 fleet (bottom). 

Figure 4: Box model calibration: modelled and measured NOX at Plüddemanngasse, 20.10.2021 

In Figure 5, hourly mean measured and modelled total PN concentrations during the measurement period are 
shown. The following can be seen:  

1.  The model captures both the order of magnitude and the trend of the measurements, which is a fairly 

good agreement a first comparison of a model to measurements.  

428 

 
 
 
 
 
2.  Modelled PN concentrations are significantly higher right in the street (box 1) than at kerbside (box 2-

5).  

3.  A high reduction of solid exhaust PN emissions (99%) results in a much lower reduction of modelled 
total PN due to contributions of the background, of secondary particle formation (mostly sulfuric acid, 
water, organic carbon), and of abrasion particles. Because of the simplicity of the approach, this is not 
intended as a quantitative forecast, but to show the conditions qualitatively. 

Modelled hourly PN concentrations in box one and mean of box two to five in comparison with 
Figure 5: 
measurements (all particles, mean of 1m, 3m, 5m) at Plüddemanngasse during measurement period for the 
fleets 2021and Euro 6d/VI. 

Acknowledgements 

This  work  was  funded  by  FVV  (The  Research  Association  for  Combustion  Engines  eV)  in  Germany,  Funding 
number: FVV 601407 

References 

Ackermann I. J. Hass H., Memmesheimer M., Ebel A., Binkowski F. S., Shankar U. (1998). Modal aerosol dynamics model for 
Europe: Development and first applications, Atmospheric Environment vol. 32, No. 17, 1998 

Giechaskiel B., Melas A., Martini G., Dilara P., Ntziachristos L. (2022): Revisiting Total Particle Number Measurements for 
Vehicle Exhaust Regulations. In: Atmosphere 2022, 13, 155. https://doi.org/10.3390/atmos13020155 

Memmesheimer M., Wurzler S. Friese E., Jakobs H. (2007). Long-term simulations of photo-oxidants and particulate matter 
over Europe with emphasis on North Rhine-Westphalia. Developments in Environmental Sciences, Chapter 2.8, Elsevier 

Toenges-Schuller  N.,  Schneider  C.,  Niederau  A.,  Vogt  R.,  Birmili  W.  (2015).  Modelling  particle number  concentrations in  a 
typical  street  canyon 
in  Germany  and  analysis  of  future  trends.  Atmospheric  Environment.  111.  127-135. 
10.1016/j.atmosenv.2015.04.006. 

Toenges-Schuller N., Schneider C., Niederau A., Vogt R., Hausberger S. (2016). Modelling the effect on air quality of Euro 6 
emission factor scenarios, Journal of Earth Sciences and Geotechnical Engineering, vol.6 

Uhrner U., Toenges-Schuller N., Reifeltshammer R., Stadlhofer W., Hausberger S. (2023). Specification of Zero-Impact Vehicle 
Emissions & Demonstration of Zero Impact. Oral presentation TAP&SE Göteborg 2023 

Wehner B., Uhrner U., von Löwis S., Zallinger M., Wiedensohler A.(2009). Aerosol number size distributions within the exhaust 
plume of a diesel and a gasoline passenger car under on-road conditions and determination of emission factors. Atmos. 
Environ. 43 (2009), 1235-1245 

429 

 
 
 
 
 
 
 
Temporal distribution of national emission data for dispersion calculations with 
chemical transport models 

Nicola Toenges-Schuller1*, Stefan Feigenspan2, Stephan Nordmann2, Michael Pelzer1, Christiane Schneider1 

1 AVISO GmbH, 52074 Aachen, Germany, nicola.toenges-schuller@avisogmbh.de, 2 German Environment Agency, 
Department Air, Wörlitzer Platz 1, 06844 Dessau-Roßlau, Germany 

Introduction 

As  all  European  member  states,  Germany  reports  annual  emissions  based  on  national,  European,  and 
international agreements (Gothenburg Protocol of the Convention on Long-range Transboundary Air Pollution 
(CLRTAP))  to  the  EU.  This  reporting  is  executed  by  the German  Environment  Agency  (UBA-DE).  The  reported 
emissions are also essential input data for chemical transport models (CTM), which, on the one hand, are used 
to simulate air pollutant concentrations over Germany, and, on the other hand, are used to model scenarios for 
strategies  and  measures  to  reduce  pollution.  The  latter  are  required  to  be  able  to  assess  the  potential  of 
individual measures to achieve compliance with air quality limit values. They are also a basis for the national 
air pollution control program (NAPCP) within the framework of the NEC Directive 2016/2284/EU. 

Spatially  and  temporally  distributed  emission  data  are  required  for  dispersion  calculations  with  chemical 
transport models. The CTM REM-CALGRID (RCG, see Stern, 2009) currently operated at UBA-DE, but also other 
CTMs, work with static time profiles (annual, weekly, and daily cycles) for emissions at SNAP Level 1 (Selected 
Nomenclature for Air Pollution) category level. 

For  use  in  CTMs,  the  emissions  recorded  as  annual  national  totals  must  be  distributed  spatially  to  the 
corresponding model  grid  and  temporally  to  all hourly  values  of  the  year.  The  quality  of  the  model  results, 
especially  for  scenario  calculations,  is  largely  determined  by  the  quality  of  the  incoming  emission  data  - 
quantity, spatial and temporal distribution. 

The "Gridding Emission Tool for ArcGIS" (GRETA) was developed for the spatial distribution of emission data 
(Schneider  et  al.,  2016).  The  sectoral  annual  emissions  of  the  Central Emissions  System  (ZSE)  are  spatially 
distributed using distribution parameters. With GRETA, it is possible to regionally quantify the contributions of 
individual sectors to air pollution. 

Currently, static time profiles are used for the temporal distribution in RCG, and in many other CTMs established 
in Europe. These are fixed daily, weekly, and annual profiles for each source group (usually: SNAP Level 1 level) 
that do not show any spatial or temporal variance. For example, the annual profiles for emissions from small 
combustion  installations  in  the  Alpine  region  currently  do  not  differ  from  those  in  the  Rhine  Valley,  even  if 
heating is required in the Alps for longer. The static time profiles are also identical for all years: Special events, 
such as the corona-related lockdowns in 2020 or the flood disaster in 2021, during which, for example, road 
traffic volume fell significantly, cannot be considered. 

Therefore, the tool TeResE (Temporal Resolution of Emission data) was developed for the dynamic temporal 
distribution of emissions, in which spatial and/or temporal dependencies of the time profiles are considered 
using local and year-specific input data. 

TeResE 

This tool generates dynamic, regionalized time profiles for the emissions of nitrogen oxides (NOX), particulate 
matter (PM10, PM2.5), ammonia (NH3), non-methane volatile organic compounds (NMVOC), sulphur dioxide (SO2) 
and  carbon  monoxide  (CO),  called  splitting  factors  (SF).  These  splitting  factors  contain  the  share  of  the 
respective emission for each grid box and each source category per hour. The SF depend dynamically on the 
respective  spatial  and/or  temporal  conditions,  e.g.,  different  years  differ  in  terms  of  meteorology:  the  type, 
characteristics and times of the weather conditions that occur vary both spatially and from year to year. This 
directly influences the temporal emission profiles of source groups such as agriculture or small combustion 
installations. In the case of road transport, local conditions and special temporal situations can be considered 
by using hourly data for the traffic volume from the automatic continuous counting stations of the BASt (Federal 
Highway Research Institute). 

The SF are initially generated for each NFR sector.  For this purpose, representative time profiles for annual, 
weekly,  and  diurnal  emissions  are  derived,  based  on  an  hourly  resolution  if  possible.  Regional  aspects  are 

430 

 
 
 
considered,  i.e.,  the splitting factors  are provided regionalized  according to  regions or  grid cells.  TeResE can 
output the regionalized temporal distribution of emissions at both NFR and SNAP level. 

Temporal dependencies 

The meteorological and other dependencies to be considered for the SF and the data sources to be used were 
compiled for all NFR sectors. The most important are: 

 

 

Power plants: Large power plants are included in the spatial distribution of emissions by GRETA as 
point sources. Time series of hourly energy deliveries are available from ENTSO-E (European Network 
of Transmission System Operators for Electricity) for blocks of large power plants. The corresponding 
time  profiles  are  used  directly  for  the  point  sources  in  the  PRTR  (Pollutant  Release  and  Transfer 
Register), which are used in the spatial distribution by GRETA and can be assigned in ENTSO-E34. Mean 
values from the transmission system operators are used for all unassigned point sources and for the 
power plant emissions available as area sources. 

Residential  stationary  Combustion:  Degree  Day  figures  are  used  for  the  time  profiles  of  building 
heating. On heating days, i.e., on days when the outside temperature falls below a certain value, degree 
day  figures  correspond  to  the  difference  between  the  desired  inside  temperature  and  the  outside 
temperature.  The  outside  temperatures  are  calculated  based  on  the  COSMO-DE  meteorology35  for 
different climate regions, which also serves as a model driver for the RCG. 

  NFR sectors of the source groups industry, commerce, energy excluding power plants and transport 
excluding road transport: Data from the Federal Statistical Office36 is used for the annual variation at 
monthly  level  (production  indices,  transport  services,  number  of  take-offs  and  landings  at  major 
airports, etc.). Weekly and daily cycles are each taken from the static default time profiles of RCG. 

 

Road transport: The time behaviour of the emissions from road transport is essentially given by the 
time profile of the traffic volume. The BASt's automatic continuous counting stations37 are used as the 
data  source.  A  distinction  is  made  between  light-  and  heavy-duty  transport  as  well  as  between 
motorways, federal roads, and inner-city roads. The time profiles of the motorways and federal roads 
are spatially averaged over the regions defined for the road traffic census. The time profiles for road 
traffic  on  urban  roads  are  averaged  nationwide.  In  addition,  a  dependency  on  the  temperature  is 
considered for the evaporative emissions and for the nitrogen oxide emissions from light duty vehicles. 

  Agriculture: Functions for the temporal distribution of NH3 emissions according to Skjoth et al. (2004) 
and Gyldenkærne et al. (2005) are used. These were originally derived for Denmark, but the parameters 
can be adjusted. For the temporal distribution of emissions from those NFR sectors of agriculture, that 
cannot be related to NH3, the static default profiles of the RCG are used. 

  Other product applications: This sector is particularly relevant because it includes fireworks, among 
other things. Separate time profiles were created for the relevant sub-sectors, for example the fine 
dust emissions from the New Year's Eve fireworks were placed in the first hour of the year. 

Input and output data 

The following input data is required: 

  Configuration files that contain information and data or boundary conditions for the calculation.  

  Data on the meteorology of the model year (currently used: temperature at 2 m above ground level 
and wind speed at 10 m above ground level), specifically in the form of the COSMO-DE meteorology, 
which is also used in the RCG model calculation for evaluation. As part of this project, the years 2016 

34https://transparency.entsoe.eu/content/static_content/Static%20content/knowledge%20base/SFTP-Transparency_Docs.html 
35 https://www.dwd.de/DE/forschung/wettervorhersage/num_modellierung/ 01_num_vorhersagemodelle/regionalmodell_cosmo_d2.html 
36 https://www.destatis.de/DE/Home/_inhalt.html 
37 https://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/Verkehrszaehlung.html 

431 

 
 
 
 
                                                       
 
and 2017 were evaluated; as long as the structure of the data remains unchanged, any year can be 
processed. 

 

Input data on the spatial location of the climatic regions, the road and rail network, the waterways, the 
point sources, etc. must be available as feature layer files. 

  Other  important  year-specific  input  data  are  the  counting  data  from  the  BASt  permanent  counting 

stations, ENTSO-E data, production indices, etc. 

During the calculation process, first, the calculation grids are set up, then all geometric data are projected onto 
these grids and the quantities derived from the meteorological data are calculated. The splitting factors are 
then calculated for all groups, and finally, output files are written at NFR and SNAP Level 1 level. The currently 
available version of TeResE generates splitting factors for the Nest-2 grid (approx. 2 km x 2 km resolution over 
Germany) of the chemical transport model REM-CALGRID (RCG) currently operated at UBA-DE. 

Evaluation 

To evaluate the SF, two model calculations for 2016 were done with the REM-CALGRID (RCG) chemical transport 
model (Stern, 2009) currently operated by UBA-DE (German Environment Agency)

The spatial distribution of emissions for the evaluation was done by the tool GRETA. 

The  evaluation  was  carried  out 
measurement data. Eight air quality monitoring stations were selected for this comparison, five from the urban 
background and three from unpolluted rural background.  

In Figure 1 (A), the annual mean NO2 concentration 2016 modelled by RCG using static SF (default) is shown. 
The  white  circles  mark  the  positions  of  the  eight  measurement  stations  selected  for  evaluation;  the  colour 
values  inside  correspond  to  the  measured  annual  mean  concentrations.  The  spatial  distribution  of  the  NO2 
concentrations visible on the map corresponds to expectation: It is increased in conurbations, large cities and 
along traffic axes such as the Kiel Canal, the Rhine, and the motorways. 

For  most of the monitoring stations selected  for evaluation,  the comparison of the measured annual  mean 
concentration with the RCG model calculation shows good agreement. Within the resolution of the colour scale, 
there are visible deviations for NO2 at only one station (Emsland, on the map: red-filled white circle in the north-
west); here, the model underestimates the measurement (surroundings of the circle: coloured yellow). A possible 
explanation for the higher NO2 measurement at the Emsland station (classified as urban background) is, that 
this value is partly influenced by emissions from the bypass and the nearby A31. The motorway can be seen 
on the map as a thin line with increased NO2 concentrations, but at the 2 km x 2 km resolution it only stands 
out faintly against the background. 

432 

 
 
 
 
(A): Modelled annual mean NO2 concentration 2016 (RCG model run using static splitting factors 

Figure 1: 
(default); the white circles mark the positions of the measurement stations considered, the colour values 
inside correspond to the measured annual mean concentrations there. (B): Relative difference between the 
annual mean NO2 concentration 2016 modelled by RCG using dynamic SF to the default.  

In Figure 1 (B), the relative difference between the annual mean NO2 concentration 2016 modelled by RCG 
using dynamic splitting factors to the default is shown. Although the total amount of emissions does not change 
for the year, the change in time distribution due to the dynamic SF also leads to changes in the annual mean 
concentrations, which for NO2 range between -23% and +6%. The difference map for annual NO2 shows:  

 

 

 

decreases in agricultural areas with low absolute emissions 
(In sparsely populated areas with little traffic and industry, agriculture is the dominating source for 
NOX, mostly emitting NO. By using dynamic SF, the spring maximum of agriculture emissions is 
moved forward by about one month into a time with lower ambient ozone concentrations. 
Consequently, a lower fraction of NO reacts with ozone to NO2.) 

decreases for some isolated grid cells  
(Most of them contain point sources as airports and power stations emitting into higher model layers 
where concentrations are particularly dependent on the meteorological conditions prevailing at the 
time the emissions are released),  

increases along the motorways 
(By using dynamic SF, road transport emissions are partly shifted from summer to winter, since 

NO2/NOX ratio for road transport emissions is higher than for agriculture emissions, so in this case, a 
shift of NO2 emissions into a time period with lower photolysis rates result in higher annual mean 
NO2 concentrations.) 

For the grid cells in which the eight measuring stations selected for the evaluation are located, time series of 
averaged concentration values were examined. The goal is the best possible match between model calculation 
and measurement for the entire period under consideration (year) and the smallest available averaging interval 
(hour). This time series of annual changes from hourly mean values contains all temporal influences (diurnal 
changes, weekly changes, annual changes). However, it is difficult to isolate possible causes for discrepancies 
between model calculation and measurement using this time series. Therefore, in addition to the annual course 
of the hourly mean values, the following time series were examined to be able to consider diurnal courses, 
weekly courses, and seasonal behaviour separately:  

433 

 
 
 
 

annual cycle from hourly averages, daily averages, and monthly averages, 

  weekly cycle from daily mean values, 

 

different diurnal cycles (all days, Sundays including public holidays) from hourly averages 

This allows conclusions to be drawn about source groups, whose emissions vary particularly strongly on the 
respective time scales. 

These  time  series  were  analysed  quantitatively  using  the  statistical  parameter  R2
coefficient) shows, how strong two variables are linearly related, R2 can be interpreted as the proportion of the 
variance in one variable that can be explained by the other variable.  

dynamic/R2

If the ratio R2
static is larger than one, the model behaviour is improved by using dynamic SF. For NO2, NOX, 
O3 and PM10, these ratios are shown as an example for the stations Berlin-Neukölln (urban background) and 
Schauinsland (remote rural background) in Figure 2 in the form of two hexagonal network diagrams.  

Dynamic to static ratios for the statistical parameter R2 (values > 1 mean an improvement) for 

Figure 2: 
the considered species at the stations Berlin-Neukölln (urban background, left figure) and Schauinsland 
(remote rural background, right figure) in 2016. 

dynamic/R2

In both diagrams, the ratios R2
static for the time series of the annual variation from hourly mean values 
are plotted in the direction of the upper corner. The other five corners correspond, clockwise, to the following 
time  series:  annual  variation  from  daily  mean  values,  annual  variation  from  monthly  mean  values,  weekly 
variation from daily mean values, diurnal variation from hourly mean values and diurnal variation on Sundays 
including public holidays  from hourly  mean values. The black dotted hexagon corresponds to the "y=1" line: 
values outside this hexagon mean an improvement in the model calculation by using dynamic time profiles.  

For the Berlin Neukölln station (urban background), the largest improvements occur for PM10 for the time series 
"year, hourly mean values" and "Sundays, hourly mean values", essentially due to two things: the consideration 
of the New Year's Eve fireworks, and the omission of the morning peak of the traffic diurnal profile on Sundays. 
(In the static default profiles, road traffic emissions are lower on Sundays due to the weekly cycle, but there is 
no special diurnal cycle for each day of the week. So, the profile on Sundays exhibits a morning and an evening 
peak just as on working days, only on a lower level. In the dynamic profiles, working days show a traffic peak 
in the morning and in the evening, Saturdays, and Sundays show only one peak.)  

At the Schauinsland station (remote rural background on a mountain in the Black Forest), correlations between 
model and measurements are generally lower than at the other stations considered,  because this station is 
lying temporarily in the free troposphere. However, the changes in R2 are similar to those at the other stations, 
with  improvements  in  nitrogen  oxides  occurring  in  particular  on  Sundays  and  public  holidays  (hourly  mean 
values). 

In addition to the detailed evaluation at selected stations, both model runs were evaluated with the FAIRMODE 
DELTA-Tool (Thunis et Cuvelier, 2022), using measurement data from all valid background stations in Germany 
2016 (243 stations for NO2, 212 for PM10 and 241 for O3). This evaluation showed a slight improvement of R 
for PM10 and NO2 at urban background stations and hardly any change of R for O3. 

434 

 
 
 
Overall,  it  can  be  concluded  that  the  concentrations  modelled  using  dynamic  SF  match  the  measured 
concentrations better than the concentrations modelled using static time profiles for most stations, pollutants, 
and time scales. 

Acknowledgements  

This  work  was  funded  by  UBA-DE  within  the  framework  of  the  Departmental  research  plan  of  the  Federal 
Ministry for the Environment, Nature Conservation and Nuclear Safety, Forschungskennzahl 3718 52 208 0 

References 

Gyldenkærne S., Skjøth C. A., Hertel O., Ellermann T. (2005). A dynamical ammonia emission parameterization for use in air 
pollution models., Journal of Geophysical Research vol. 110, D07108, doi:10.1029/2004JD005459 

Schneider  C.,  Pelzer  M.,  Toenges-Schuller  N.,  Nacken  N.,  Niederau  A.  (2016).  ArcGIS  basierte  Lösung  zur  detaillierten, 
deutschlandweiten  Verteilung 
zur 
Emissionsberichterstattung, UBA Texte 71/2016 

(Gridding)  nationaler  Emissionsjahreswerte  auf  Basis  des 

Inventars 

Skjøth C. A., Hertel O., Gyldenkærne S., Ellermann T. (2004). Implementing a dynamical ammonia emission parameterization 
in 
large-scale  air  pollution  model  ACDEP,  Journal  of  Geophysical  Research,  vol.  109,  D06306, 
doi:10.1029/2003JD003895 

the 

Stern  R.  (2009).  Das  chemische  Transportmodell  REM-CALGRID,  Modellbeschreibung.  Freie  Universität  Berlin,  Institut  für 
Meteorologie, Troposphärische Umweltforschung, Januar 2009 

Thunis P, Cuvelier C. (2022). 

s 

435 

 
 
 
 
 
 
Transport emission footprint in the Slovak economy 

J. Horváth1,2

1 and J. Szemesová1 

1 Department of Emissions and Biofuels, Slovak Hydrometeorological Institute, Jeséniova 17, Bratislava, Slovakia, 
jan.horvath@shmu.sk 
2 Faculty of Ecology and Environmental Science, Technical University in Zvolen, Ul. T. G. Masaryka 24, Zvolen, Slovakia 

Introduction 

Enhancing the methodology for allocating emissions arising from road transport bears significant implications 
for  the  accuracy  and  robustness  of  Environmental  Air  Emissions  Accounts  (AEA)  reporting,  as  stipulated  by 
Regulation 691/2011 on European environmental economic accounts (European Parliament and Council, 2022). 
The  allocation  of  emissions  to  NACE  rev.2  categories  enables  meticulous  analysis,  unravelling  the  major 
contributors  to  air  emissions  among  various  economic  activities.  NACE  rev.2,  a  standardized  classification 
system  with  6  characters  within  the  European  industry,  holds  binding  significance  within  the  EU  (European 
Parliament and Council, 2006; SK NACE, 2022). Through comprehensive analyses, potential enhancements in 
policies and measures can be identified, facilitating targeted interventions in relevant economic sectors. 

In  relation  to  the  first  paragraph,  along  the  AEA  reporting,  PEFA  accounts  serve  as  a  pivotal  platform  for 
recording the flow of energy between the environment and the economy, encompassing natural inputs, products 
within the economy, and residues back to the environment. PEFA accounts are closely linked to the AEA reporting 
by providing valuable data on the energy-related emissions that contribute to the carbon footprint of economic 
activities. Furthermore, PEFA accounts aid in identifying energy inefficiencies, emission hotspots, and potential 
areas  for  improving  resource  management  within  the  economy.  The  integrated  framework  enables  holistic 
analyses, encompassing environmental, energy, and economic dimensions. By analysing the data from PEFA 
accounts in conjunction with the AEA reporting, stakeholders can identify opportunities to reduce the carbon 
footprint and enhance energy efficiency across different economic sectors. This integration of data helps in 
formulating  strategies  and  initiatives  aimed  at  transitioning  towards  a  greener  and  more  environmentally 
responsible economy. 

The research objectives outlined herein arise from the statistical reporting obligations of the Slovak Republic to 
Eurostat, with a focus on improving the allocation of emissions from road transport within the AEA module to 
NACE  rev.2  categories  and  household  categories.  These  objectives  constitute  an  independent  section  of  the 
questionnaire and align with the National Action Plan, fulfilling the obligations of the Slovak Republic (Horváth, 
et al., 2021). A previous contribution, titled "Allocation of Emissions from Road Transport According to Economic 
Activities (NACE rev.2) and Coherence between AEA and PEFA Modules" (Horváth et al., 2023), communicated 
the action plan in 2021. Rodríguez et  al.  (2016)  in their study  on air pollution and urban structure  linkages 
utilized national air emission accounts alongside their findings to reinforce the hypothesis that urban structure 
has substantial impacts on pollution concentration. The research demonstrated the importance of considering 
both local and national-level data to better understand the relationship between urban characteristics and air 
pollution levels, providing valuable insights for environmental policy and urban planning. 

The  second  objective  concerns  on  refining  the  coherence  between  the  AEA  and  PEFA  modules,  particularly 
focusing on fuels for road transport. The methodologies derived from the primary objective are instrumental in 
correctly allocating fuels in road transport to NACE rev.2 categories and households. 

Methodology and Software 

The original methodology for allocating greenhouse gas emissions (GHG) and pollutants (AP) relied on four key 
parameters: vehicle category (passenger, light commercial vehicle, heavy-duty, and motorcycles), total annual 
mileage, quantity of transported goods (tonne-kilometres), and gross value added (GVA). The resultant matrix, 
while straightforward, neglected the actual number of vehicles in individual NACE rev.2 categories, leading to 
imprecise environmental impact assessments. 

In  contrast,  the  new  methodology  is  grounded  in  a  more  detailed  analysis  of  input  data  (Chapter  2.1).  A 
prototype database system and software tool (Horváth, et al., 2021) were developed, enabling data storage 
and processing with output verification.  

2.1 First phase: methodology and software development 

436 

 
 
 
 
The  initial  phase  entailed  data  collection  from  diverse  sources,  including  the  IS  EVO  vehicle  database,  the 
Technical Control Database, and the Register of Organizations (RO) (Figure 1). IS EVO as the information system 
for vehicle registration, provides essential data on registered vehicles in Slovakia, such as VIN numbers, fuel 
types, engine capacity, and relevant information. The Technical Control Database contributes crucial data on 
technical inspections, and the RO offers organizational data for NACE rev.2 allocation. 

2.2 Second phase: methodology and software development 

Subsequently, the second phase integrated outputs from the COPERT emission model, ton-kilometres according 
to  NACE  rev.2  categories,  and  annual  mileage  according  to  the  COPERT  model  (EMISIA,  2022).  The  COPERT 
model,  partly  funded  by  the  European  Environment  Agency  (EEA),  estimates  greenhouse  gas  and  pollutant 
emissions in road transport, considering various parameters like vehicle type, fuel used, and EURO emission 
standards. 

Figure 1: Schema of the software tool 

2.3 Matrix for AEA and PEFA 

The development of the allocation matrix necessitated the integration of data from two phases of the process: 
the number of vehicles in each NACE rev.2 category and the data related to the number of kilometres driven 
and emissions for the given year. The division of the total number of kilometres driven for each vehicle category 
and fuel type yielded a ratio as follows: 

𝐾𝑀𝑥   ÷   𝑉𝑥   =   𝑘𝑚𝑥 (𝑒𝑞. 1) 

𝑘𝑚 𝑥 ×   𝑉𝑁𝐴𝐶𝐸   =  𝑘𝑚𝑁𝐴𝐶𝐸 (𝑒𝑞. 2) 

𝑘𝑚𝑁𝐴𝐶𝐸   ÷   𝐾𝑀 𝑥 =  𝑅 (𝑒𝑞. 3) 

Where:  

  KMx represents the total number of kilometres driven in the specified vehicle category and fuel type. 
  Vx denotes the total number of vehicles in the specified category and fuel type. 
 
  VNACE represents the vehicles of the specified category and fuel type in the NACE rev.2 category. 

kmx stands for the average number of kilometres per vehicle in the specified category and fuel type. 

437 

 
 
 

 

kmNACE corresponds to the total number of kilometres driven in the NACE rev.2 category for each vehicle 
category and fuel type. 
R represents the ratio for the matrix. 

The resulting allocation matrix features emissions ratios from each economic activity based on the NACE rev.2 
categorization  in  the  rows, with  each  column  representing  a  vehicle  category  with  a  specific  fuel  type.  This 
matrix  offers  a  comprehensive  breakdown  of  the  vehicle  fleet  for  a  specific  year,  along  with  detailed 
disaggregated information for each NACE rev.2 category. It is an integral part of the software tool and, once 
populated with estimated GHG and pollutant emissions, can be exported to AEA (Figure 2). 

Software tool

COPERT 
model

Software tool

AEA 
Questionnare

Figure 2: Process diagram for completing the AEA questionnaire 

Distinguishing  the  AEA  and PEFA  matrices  lies  in  their  respective levels of  data  aggregation  concerning  the 
number of vehicles and, for PEFA, the volume of transported goods, which directly influences fuel consumption. 
PEFA requires aggregation at the EURO emission standard level, while AEA necessitates division into individual 
vehicle segments at a higher level of aggregation. 

Results 

The original methodology allocated emissions to the AEA account primarily based on gross value added and 
expert  estimates.  In  this  allocation,  the  most  significant  category  within  NACE  rev.2  was  category  H 
(Transportation and Storage). 

With the new methodology, emissions from road transport are no longer allocated based on this parameter. 
This  means  that  the  new  methodology  enables  the  fulfilment  of  Slovakia's  commitments  and  improves 
reporting for two inadequate elements, as identified in the self-assessment of the road transport methodology 
in 2018 (Horváth, et al., 2021). 

The change in the allocation of GHG and pollutant emissions introduced in the new methodology is significant. 
Household  emissions  have  increased  significantly,  rising  by  76%  in  2013  in  comparison  to  the  previous 
allocation method. This increase is mainly attributed to the separation of gross value added from emissions 
production, relying solely on precise data and the number of vehicles used and registered for economic activities 
within NACE rev.2. The increase in household emissions is also due to the inclusion of a large number of buses 
and their emissions within households. This is because buses do not have their own identification numbers (ICO) 
assigned in the IS EVO database, making it impossible to allocate them correctly to NACE rev.2. 

Wholesale and Retail Trade; Repair of Motor Vehicles (category G) has also experienced a significant increase 
in emissions, with a rise of 230% in 2019 as indicated by the new allocation method. The overall results and 
changes in emission allocations are available in the interim report on the EUROSTAT project grant (Horváth, et 
al., 2023). 

The  new  methodology  extensively  disaggregates  emissions  and  provides  higher  accuracy  through  various 
databases acquired to achieve the objectives. 

438 

 
 
In  addition  to  significant  changes  in  Households  (HH)  and  category  H  (Figures  3,  4,  5),  there  have  been 
substantial changes in emissions within categories C (Manufacturing) and G. In the case of industrial production 
allocated in category C, emissions have significantly decreased (up to fourfold) since the implementation of the 
new methodology. The initially high share of allocated emissions in this category was due to the high proportion 
of industrial production in total gross value added. By separating emissions from gross value added, emissions 
have shifted and relocated to other parts of the national economy or households. Category G is one of these 
categories. 

Figure 3: Changes in CO2 emissions allocation in kt for the dominant NACE rev.2 categories  

Figure 4: Changes in PM2.5 emissions allocation in kt for the dominant NACE rev.2 categories 

Figure 5: Changes in NOX emissions allocation in kt for the dominant NACE rev.2 categories 

439 

0.00100.00200.00300.00400.00500.00600.002013201420152016201720182019C (2021)C (2022)G (2021)G (2022)0.001 000.002 000.003 000.004 000.005 000.002013201420152016201720182019H (2021)H (2022)HH (2021)HH (2022)00.050.10.152013201420152016201720182019C (2021)C (2022)G (2021)G (2022)00.20.40.60.812013201420152016201720182019H (2021)HH (2021)H (2022)HH (2022)0.000.400.801.201.602.002013201420152016201720182019C (2021)C (2022)G (2021)G (2022)0.005.0010.0015.0020.002013201420152016201720182019H (2021)HH (2021)H (2022)HH (2022) 
 
 
Furthermore, the implementation of the new methodology in AFA has not only resulted in substantial changes 
in emissions, but has also provided valuable insights into shifts in gasoline and diesel usage across economic 
activities. The result of the new matrix for the allocation of physical energy flows in PEFA not only provides a 
new distribution of gasoline and diesel usage in economic activities but also offers insights into likely changes 
within the economy itself. In 2013, the largest consumers of gasoline were categories G, H, and M (Professional, 
Scientific, and Technical Activities), which together accounted for 54% of the total gasoline consumption in the 
national economy. By 2019, the significance of category H had decreased, and it was replaced by category N 
(Administrative  and  Support  Services).  In  2019,  these  three  categories  (G,  H,  and  N)  accounted  for  47%  of 
gasoline consumption. 

When analysing diesel consumption, categories C, G, and H emerged as the largest consumers in 2013. Together, 
they accounted for up to 87% of diesel consumption in road transport. In 2019, change happened, category C 
losing  importance  and  either  deliberately  or  otherwise  reducing  its  diesel  consumption,  while  category  N 
surpassed it in terms of consumption among the top three diesel consumers. There was also a decrease in the 
share of the top three categories in total diesel consumption, reaching 77%. 

These changes and declines suggest that there have been slight shifts in the dynamics of economic sectors and 
even diversification, considering the decrease in the share of the top three consumers between 2013 and 2019, 
whether in gasoline or diesel (Figure 6). 

Figure 6: Fuel allocation and their changes in TJ (dotted line with vertical axis on the right) 

A  more  realistic  and  accurate  allocation  of  emissions  and  fuels  from  road  transport  according  to  economic 
activities and households in Slovakia is the result of the project. By improving the allocation and separating it 
from gross value added, it is possible to conduct deeper and more precise analyses. These analyses identify 
economically productive  areas with relatively low emissions as well  as high-emission areas with low  added 
value. 

440 

14000160001800020000220000200400600800100020132014201520162017201820192020GasolineGHMNHH01000020000300004000050000020004000600020132014201520162017201820192020Diesel oilCGNHHH 
 
The information obtainable through the analysis of AEA and PEFA accounts is a valuable basis for policymakers 
and  decision-makers  at  both  the  EU  and  Slovak  levels.  It  helps  to  properly  target  supportive  and  incentive 
mechanisms for activities with higher added value, while discouraging activities and economic practices with 
negative environmental impact and low added value. 

Acknowledgements 

This project was funded by EUROSTAT scheme ESTAT-2020-PA8-E-ENVACC. 

References 

EMISIA S.A. (2022). COPER model. 

Regulation  (EC)  No  1893/2006  of  the  European  Parliament  and  of  the  Council  of  20  December  2006  establishing  the 
statistical classification of economic activities NACE Revision 2 and amending Council Regulation (EEC) No 3037/90 as well 
https://eur-lex.europa.eu/legal-
EC 
as 
content/EN/TXT/?uri=CELEX:32006R1893 

Accessed 

[Online], 

August 

certain 

Regula 

2023, 

4 

Regulation (EU) No 691/2011 of the European Parliament and of the Council of 6 July 2011 on European environmental 
economic  accounts  (Text  with  EEA  relevance)Text  with  EEA  relevance  [Online],  Accessed  August  4  2023,  https://eur-
lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A02011R0691-20220220 

Horvát
in  AEA  module  and  coherence  between  AEA  and  PEFA  modules  [Conference],  Zborník  príspevkov  z  konferecnia  Ochrana 

. - Bratislava : Kongres Studio 

in AEA Module and Coherence between AEA and PEFA Modules. Deliverable 1.1: Methodology for Allocation of Road Transport 
Emissions [Report]. - [s.l.] : Slovenský hydrometeorologcký ústav 

Rodríguez Miguel Cárdenas, Dupont-Courtade Laura and Oueslati Walid (2016) Air pollution and urban structure linkages: 
Evidence from European cities, Renewable and Sustainable Energy Reviews, Vol. 53, 1-9. 

SK NACE [Online] (2023), http://www.nace.sk/ (accesed on 2 August 2023) 

441 

 
 
 
 
2.10  JS.10.  Compliance  monitoring  technological  and  legal 
frameworks;  experimental  studies;  theoretical  impact 
studies. 

442 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Shipping emissions monitoring with on-board and remote techniques and impacts 
on air quality: The SCIPPER project summary and results 

L. Ntziachristos1*, S. Mamarikas1, V. Matthias2, M. Karl2, L. Fink2, P. Simonen3, J. Keskinen3, M.D. Maso3, E. Fridell4, 
J. Moldanova4, Å. Hallquist4, J. Mellqvist5, V. Conde5, R. Verbeek6, J. Duyzer6, D. van Dinther6, H. Timonen7, J.-P. 
Jalkanen7,  A.-M.  Sundström7,  E.  Majamäki7,  A.  Stylogiannis8,9,  V.  Ntziachristos8,9,  T.  Smyth10,  M.  Yang10,  A. 
Deakin11, R. Proud11, J. Oeffner12, J. Weisheit12, J. Beecken13,16, A. Weigelt13, S. Griesel13, H. Schoppmann13, S. 
Oppo14, A. Armengaud14, B. D'Anna15, G.-M. Lanzafame15, B. Temime-Roussel15, B. Knudsen16, J. Knudsen16, M. 
Irjala17, A. Somero17, T. Rantala17, L. Buckers18, J. van Vliet18 

1AUTH *(leon@auth.gr), 2HEREON, 3TAU, 4IVL, 5CHALMERS, 6TNO, 7FMI, 8TUM, 9HMGU, 10PML, 11eEE, 12CML, 13BSH, 
14ATMOSUD, 15AMU-CNRS, 16EXPLICIT, 17AEROMON, 18ILT 

Introduction 

While vessels exhibit comparatively low fuel consumption per unit of cargo-distance, they still produce high 
specific emissions of nitrogen oxides (NOx), sulphur oxides (SOx) and particulate matter (PM). In an effort to 
decisively reduce the environmental footprint of shipping, new stricter regulations were established by the IMO 
and further adopted by member states (IMO 2016). These primarily target to reduce SOx globally, while impose 
NOx limits when ships sail to specific areas (IMO 2008). To comply with the new status quo in the legislation, 
ship owners and operators have to prominently act, by either using cleaner fuels or equipping their ships with 
appropriate emission control technology. Therefore, any type of environmental regulation applied in maritime 
sector, currently and in the future, can be only effective when ships are regularly checked for compliance.  

In this context, a main research question is related on how authorities will verify that correct fuels or proper 
aftertreatment  technology  are  utilized.  SCIPPER  (Shipping  Contributions  to  Inland  Pollution  Push  for  the 
Enforcement  of  Regulations)  was  a  Horizon  2020  research  project  that  sought  to  answer  this  question, 
addressing the overall need for: a) compliance check of environmental regulations, and b) more evidence on 
monitoring possibilities for low sulphur levels, new pollutants, as well as implications of non-compliant ships to 
air pollution. So, the main objectives of SCIPPER were a) to provide evidence on the performance and capacity 
including quality  in 
measurements and cost efficiency in monitoring, and b) to assess the impacts of shipping emissions on air 
quality, under different regulatory enforcement scenarios. 

To address these challenges in checking and enforcing new emission limits, SCIPPER deployed state-of-art and 
next-generation  measurement  techniques  to  monitor  emissions  of  vessels  under  their  normal  operation. 
Techniques applied include many on-board sensors and a wide spectrum of remote instruments (sniffers, mini 
sniffers  on  drones,  optical  remote,  satellites).  All  this  instrumentation  was  implemented  in  five  real-world 
campaigns in critical European shipping areas (ports of Marseille & Hamburg, shipping line from Gothenburg to 
Kiel, English Channel), where more than a thousand of ship plumes were measured. Together with SOx and NOx, 
which are the current legislative priorities, techniques also characterized PM as a candidate pollutant for future 
regulations.  In  addition,  other  emissions  of  interest  were  considered.  Experimental  information  from  the 
campaigns was combined with advanced plume dispersion and chemical transport models (CTMs) to estimate 
current ship-induced air pollution and predict the impact of various degrees of compliance to major port areas 
in  Europe.  The  present  paper  highlights  main  outcomes  and  results  of  SCIPPER,  both  in  deploying  the 
abovementioned techniques and in assessing the effect of new regulations in improving air quality.  

On board Sensors  

Within SCIPPER, innovative cost-effective sensors were considered and tested for conducting continuous on-
board emissions monitoring. Some sensors are being developed by SCIPPER partners and others are packaged 
to be used as complete monitoring devices for on-board applications. The market of such systems is currently 
primitive and the ones existing were known to still have durability and sensitivity issues. Moreover, the precise 
censoring, data processing and reporting format has to be standardised. However, the arrival of Tier III and the 
new  global  sulphur  regulations  significantly  grows  the  need  of  adopting  such  cost-effective  systems  for 
emissions monitoring, on a ship constant operation basis.  

443 

 
 
 
 
So, SCIPPER has successfully tested, during the two on-board experimental campaigns (Line Gothenburg to Kiel 
and English Channel), five innovative low-cost sensor packages, capable of monitoring NOX, NH3, SOX, CO2, BC, 
PM and PN concentrations. Refer to Verbeek (2023) and SCIPPER D1.6 (2022). Innovative sensors sensitivity 
was  defined  by  comparing  their  exhaust  measurements  with  the  reference  ones  provided  by  high-end 
instrumentation, the accuracy of which is well-proven from their long-term application in the automotive sector. 
Results of the low-cost emissions monitoring for gaseous pollutants showed good agreement with the reference 
instrumentation, especially for NOX, CO2 and CO. This is indicatively depicted in Figure 1, where low-cost sensor 
measurements, presented with solid lines, are compared with the reference ones that appear with dashed, for 
a two-hour continuous monitoring period.  

Figure 1. Cost-effective (solid lines) vs high-end (dashed lines) emissions monitoring performance  

The sensors application for measuring NH3, SO2 was less reliable, showing issues of fast decay, attributed to 
specific sampling conditions. For particles, all sensors operated sufficiently during the measurements, including 
detecting BC, and no fouling or loading issues were noticed. 

Experiments  also  focused  on  the  durability  of  the  sensors,  a  criterion  that  will  reveal  their  potential  for 
continuous unmanned monitoring. Based on tests over a 6-month period, the lifetime of gaseous sensors for 
NOx  constant  monitoring  is  expected  to  range  between  2.000  to  4.000  annual  operating  hours,  while 
adjustments  to  the  sampling  systems  for  controlling  sensors  exhaust  gasses  exposure  can  extend  their 
operational lifetime from one to two years. For particles, the long-term uninterrupted monitoring appears to be 
feasible, but existing aspects of contamination limit their unmanned functioning. 

The overall demonstration of on-board sensors within SCIPPER has led to the following findings:  

 
 

there is a promising potential of sensors to be used for onboard monitoring, 
protection against fouling, in the form of intermitted sampling or air shield, is needed to protect sensors 
- once a year maintenance, including replacement of sensors, will probably be necessary, 

  NOx can be measured in raw exhaust, but for most components; SOx, PM, PN, BC, a dilution system is 
needed - some advice would be to develop a simple mechanical dilution system which is adjusted once 
during installation, 

  use of S-AIS as a real-time data transmission pipeline was demonstrated successfully. 

Remote Sensors 

Techniques were deployed by SCIPPER for the remote monitoring of shipping emissions, including sniffers and 
optical remote sensors. These sensors were placed on fixed stations for checking ships that operate closed to 
ports and were also carried by various means as patrol vessels, drones and satellites, with the ability to extend 
measurements in ships sailing outside of port areas. On a period where stricter SOx mitigation regulations are 
globally enforced,remote techniques can become a valuable and cost-effective tool for authorities to massively 

444 

 
 
 
check ships compliance, in addition to the existing regulatory provision of fuel sampling. Similar challenges also 
arise for NOx within NECAs, as well as for the pollutants that will be regulated in the future.  

So within SCIPPER experimental campaigns, standard and high sensitive sniffers equipped fixed stations and 
patrol  vessels,  while  mini  sniffers  were  mounted  on  drones.  Optical  solutions  (DOAS  and  satellites)  were 
supplementary evaluated as they  are less mature  than the sniffer  systems.  The remote  systems measured 
pollutants  such  as  NOx,  CO2,  SO2,  PN,  PM,  BC,  as  well  as  identified  the  fuel  sulphur  content  (FSC)  of  ships. 
Accuracy of these techniques was then evaluated; quality assurance and harmonization reporting aspects were 
addressed, and their operational particularities in the field were highlighted.  

The level accuracy of remote sniffer instrumentation in identifying the FSC of ships was mainly evaluated in 
relation to fuel samples that were taken from on-board inspections in the port of Hamburg. Results, presented 
in  Figure  2,  revealed  the  sniffers  ability  to  capture  the  content  of  sulphur  in  fuel.  However,  a  slight 
underestimation  is  depicted,  compared  to  the  actual  values  recorded  in  the  reference  sampling,  primarily 
attributed to ambient and calibration conditions, but with further research investigation to be required for safe 
conclusions. The total uncertainties for the different systems were found to be varying between 0.03 and 0.14 
% S m/m at an FSC level of 0.1 % S m/m considering a 95 % confidence interval. 

Figure 2. Sniffer systems FSC detections in relation to fuel sampling results 

SCIPPER examined the potential of checking ship compliance through satellites, using TROPOMI observations. 
So single ships NOx and SO2 emission footprints were tried to be detected. Findings for NOx show that single 
ship plumes are visible in TROPOMI observations. However, more investigation is needed for exploiting satellite 
observations for  compliance check, but global monitoring seems  to  be feasible  for NO2. On  the other  hand, 
existing TROPOMI SO2 retrieval algorithms are not sufficient to make emissions in shipping lanes observable. 
SO2 signal from shipping appears to be below the detection limit of TROPOMI. 

The overall remote techniques deployment and evaluation led to the elaboration of recommendations on how 
to conduct remote measurements of gas-pollutant emissions from ships, including data validation procedures, 
uncertainty calculation and reporting. So SCIPPER proposes that: 

 
 

 

validation is needed that the measurement with remote instrumentation is correctly conducted, 
quality scoring system per technique could be applied, with criteria of weather, signal to noise ratio 
and traceability - scoring should not be part of the reporting, but part of the uncertainty analysis, 
uncertainty  needs  to  be  subtracted  from  measurement  results  when  focus  comes  to  compliance 
monitoring. 

Plume ageing & air quality 

Shipping  induced  pollutants,  after  emitted,  may  spend  a  considerable  time  in  the  atmosphere  and  undergo 
significant transformation before reaching coastal and urban areas. Therefore, SCIPPER linked the atmospheric 
evolution of the fresh emissions with the air quality final impacts imposed by the aged emissions, when these 
reach the shore. The assessment of ageing transformation phenomena was conducted at two stages. The first 
one concerns the period when the plume exits the ship funnel, while the second stage considers the evolution 
in distances far from ships till the shore.  

445 

 
 
Reference exhaust sampling measurements vs ageing experiments were made during the on-board campaign 
to understand the exact differences between fresh and aged plume synthesis. The tested ship operation under 
different  fuels  (Marine  Gas  Oil  -  MGO,  Methanol-MeOH)  and  aftertreatment  technology  activation  phases 
(Selective Catalytic Reduction   SCR: on/off), provided additionally the opportunity to study plume synthesis and 
ageing  phenomena  when  popular  emission  reduction  options  are  applied.  A  snapshot  of  this  assessment  is 
provided in Figure 3, where fresh and aged PM concentration results are presented, along with the absolute and 
relative speciation synthesis.  

Figure 3. Fresh vs aged plume PM concentration together with speciation analysis, for MGO and MeOH fuels, as 

well as for SCR on-off status, at different engine loads. 

Overall, PM increases as the plume ages. For fresh emission, BC dominates PM chemical composition for all 
tested conditions, followed by organics and sulphate. For aged samples, sulphate contributed more to PM mass, 
followed by organic compounds, likely due to oxidation of SO2 and organic compounds. The load or catalyst urea 
injection did not have major impact on composition of particulate emissions or relative concentrations of major 
species. For MeOH, the emissions of all major species were significantly lower. Furthermore, in the aging analysis 
for the particles size distribution, the contribution of nucleation particles increased in the plume, whereas the 
soot mode concentrations were stable. Experiments were repeated during the port of Marseille campaign, with 
plumes sampled remotely close to ships and onshore. Results also here showed an important increase of the 
total PM mass in aged plumes.  

The main conclusions of the plume ageing characterization activities of SCIPPER are: 

 

besides  decreasing  the  NOx  emissions,  the  SCR  system  significantly  also  decreased  THC  and  fresh 
aerosol mass emissions, 

  MeOH fuel significantly decreased BC and PM emissions, as well as the secondary aerosol formation 

 

 

potential, 
ship plumes are a source of secondary aerosol precursors - the secondary aerosol mass is generally 
higher than the fresh aerosol mass, 
on gaussian plume scales, secondary aerosol formation is low enough to be insignificant in the initial 
dispersion phase. 

The impact of shipping on air quality was evaluated on two different scales. Macroscale assessments, conducted 
for  the  areas  of  Mediterranean  Sea,  the North  and  Baltic  Seas.  These  targeted  to  cover  the  contribution  of 
shipping to air pollution in a spatially wide and temporally long-term perspective. Several air quality models of 
macroscopic type were used for this purpose, after cross-validated on a specific domain in the Mediterranean. 
Results for the Mediterranean area showed that shipping impacts on NO2 concentrations are highest in short 
distance (less than approx. 100 km) to the shipping lanes, as indicatively depicted in Figure 4 (right), and PM2.5 
concentrations increase by 10-15% because of shipping emissions in many coastal areas. Similar results were 
observed for North and Baltic Seas. Overall, European coastal areas along the Mediterranean Sea are most 
significantly affected by air pollution from shipping in southern Spain, southern Italy and along the Greek and 

446 

 
 
 
Turkish coastline. In general, the southern North Sea region and the English Channel experience the highest 
pollution from shipping in Europe. 

Figure 4. Right: Impact of shipping on the annual mean NO2 concentration in the Mediterranean Sea in 2015 
estimated by various models (a = CAMx, b = Chimere, c = CMAQ, d = EMEP, e= LOTOS-EUROS); Left: Shipping 
air quality impacts for the port of Marseille, assessed with local-scale (down) and microscale (up) models 

More detailed shipping impacts and precise pollutants distribution in harbour areas were revealed through local 
and microscopic models. Assessments were conducted for the port areas of Marseille, Hamburg, Gothenburg, 
Rotterdam,  Kiel,  and  Thessaloniki.  A  characteristic  example  of  the  application  of  detailed  models  for  the 
Marseille port is provided in Figure 4 (right), where NO2 concentrations were calculated for the port area (down) 
and further analyzed in more detail (up) to better understand more local phenomena. 

The overall air-quality work conducted in SCIPPER also led to the production of Improved air-quality models for 
coastal areas and harbour-scale modelling, including secondary aerosol processes. The main conclusions can 
be summarized in the following points: 

 
 

 

 

 

ship emission model systems were improved with new emission factors, 
regional  chemistry  transport  model  systems  are  well  suited  tools  for  quantifying  the  impacts  of 
shipping emissions on air pollution, 
shipping has a large impact on NO2 concentrations with highest values along the main shipping routes 
- shipping has impacts on O3 leading to increased concentrations on average and in larger distances 
from shipping lanes,  
through  complex  interactions  with  pollutants  from  other  sources  shipping  emissions  also  lead  to 
increased secondary PM2.5 concentrations, 
impacts of shipping emissions on air quality in harbour cities can be quantified with city scale model 
systems, 

  NO2 concentrations in harbour cities increase as a results of shipping emissions while O3 concentrations 

 

 

decrease. Among the investigated cities, largest effects take place in Rotterdam and Hamburg, 
a wide application of ammonia as a shipping fuel in the future will cause significant particle formation, 
unless emissions will be regulated, 
air-quality  model  systems  were  improved  with  respect  to  their  representation  of  ship  plumes,  the 
treatment of organic emissions and the formation of secondary particles. 

Conclusions 

SCIPPER assessed several shipping measurement techniques, through the real-world demonstration of several 
emission measurement systems in five experimental campaigns. The following conclusions can be drawn based 
on this evidence: 

 

emission measurement systems that incorporate mature technology, already applied in other fields 
(automotive, air quality), can be used to effectively check if ships operate under the established limits, 

447 

 
 
 
 
 

 

 

 

on-board systems gather several operational advantages in revealing the actual emission profile of 
ships both to the authorities and ship owners, but standard remote systems also appear particularly 
attractive for authorities as an independent system under a regulatory perspective, 
remote  systems  mounted  in  aerial  means  or  in  patrol  vessels  can  supplement  the  standard  fixed-
station ones, expanding the monitoring capacity of authorities outside of port areas, 
optical  systems  (fixed  station  and  satellites)  present  an  alternative  to  the  sniffer-based  remote 
monitoring;  however,  technology  maturity  parameters  and  operational  aspects  reduce  their 
applicability today, 
cost per ship for on-board monitoring is higher than the respective one of any of the remote systems, 
however on-board sensors in all vessels mean that all vessels can be monitored. 

SCIPPER further synthesized its outputs to provide policy recommendations on next steps in shipping emission 
monitoring,  and  in  achieving  future  effective  regulations.  Refer  to  SCIPPER  D5.5  (2023).  Main  concluding 
recommendations are summarized in the following points: 

 

 

all  ships  to  install  sensor  systems  for  on-board  monitoring  of  NOx,  SOx  and  BC  -  data  should  be 
automatically transferred to a monitoring center, using encrypted format, 
establish a not-to-exceed (NTE) limit, suitable for NOx compliance monitoring using remote sensing 
measurements, 

 
 

  mandate minimum deployment of remote sensing stations to detect when fuel with too high sulphur 
content is used. Use remote sensing to identify ships with non-functioning NOx abatement systems, 
especially in ECA environment, 
develop routines for remote sensing measurements and legal actions, 
establish more NOx Emission Control Areas and establish an emission test cycle for NOx certification 
that also reflects operation at low loads, and require in-use measurements or sensors to verify that 
abatement equipment works, 
develop emission limits and measurement procedures for particle related properties (e.g., PN, PM, BC), 
for NH3   both for SCR systems and when ammonia is used as fuel, for methane - most relevant for 
LNG as fuel, and for N2O - most relevant for ammonia fuel. 

 

Acknowledgements  

The SCIPPER project has received funding from the Eur
under grant agreement Nr.814893 

References 

IMO (2008): IMO Resolution MEPC 177(58), Amendments to the technological code on control of emission of nitrogen oxides 
from marine diesel engines, NOx Technical Code 2008, 10 October 2008. 
IMO. (2016). Marpol Annex VI, Sulphur oxides (SOx) and Particulate Matter (PM)   Regulation 14, Resolution MEPC.280(70)  

SCIPPER D1.6 (2022) Weisheit, J., Verbeek, R., Paschinger, P., Verhagen, V., Irjala, M., Simonen, P., Kousias, N., Moldanova, J., 

Deliverable D1.6. September 2022. EU Horizon 2020 project No. 814893. https://www.scipper-project.eu/library/. 

SCIPPER  D5.5  (2023)  Erik  Fridell,  Ruud  Verbeek,  Volker  Matthias,  Johan  Mellqvist:  Policy  recommendations  related  to 
regulations, monitoring and enforcement. SCIPPER deliverable D5.5, February 2023.  EU Horizon 2020 project No. 814893. 
https://www.scipper-project.eu/library/. 

Verbeek (2023). Experiences with sensor based continuous emission monitoring for demonstration of maritime emissions 
compliance. R. Verbeek, et.al.. TAP & SE conference Gothenburg, September 2023.  

448 

 
 
 
 
 
 
 
 
Application of a 24-hour ship plume forecasting system 

R. Badeke1 and V. Matthias1 

Institute of Coastal Environmental Chemistry, Helmholtz-Zentrum Hereon, Geesthacht, Germany, Ronny.Badeke@hereon 

Introduction 

Shipping emissions represent a significant source of air pollutants, primarily including oxides of nitrogen (NOx), 
particulate matter (PM), secondary ozone (O3), sulphur oxides (SOx), carbon monoxide (CO) and volatile organic 
compounds (VOCs). Exposure to these pollutants is associated with respiratory and cardiovascular diseases, and 
the  deposition  of  nitrogen  and  sulfur  compounds  can  lead  to  the  acidification  and  eutrophication  of  water 
bodies. 

The Baltic Sea has been a focal point for air quality research for decades, driven by the economic importance 
of  ship  traffic  and  the  large  population  residing  in  nearby  coastal  cities.  Adverse  health  effects  have  been 
quantified in exposure studies (Barregard et al., 2019, Ramacher et al., 2019). Ship impact on air quality in the 
Baltic Sea region has been modeled using various approaches, including CAMx (Aksoyoglu et al., 2016), CMAQ, 
EMEP/MSC-W and SILAM (all in Karl et al., 2019a) on the regional scale, or EPISODE-CityChem on the city scale 
(Ramacher et al., 2019). 

The effectiveness of designating the Baltic Sea as an emission control area has been assessed in terms of air 
quality and deposition (e.g., Jonson et al., 2015, Karl et al., 2019b) and has shown significant pollution reduction, 
particularly with the ongoing fleet modernization. However, ensuring compliance with emission regulations is 
essential, and in light of the new air quality recommendations from the World Health Organization (WHO, 2021), 
monitoring coastal air quality is of crucial importance. To further mitigate health issues for individuals residing 
in coastal areas, the implementation of a precise forecasting system for ship pollution, similar to weather or 
pollen forecasts, can be a valuable tool. This study presents and evaluates the initial outcomes of a new local-
scale ship pollution forecasting system, based on the setup of the urban air quality forecast system in Hamburg 
(AQF, 2023). 

Methods 

The focus of this study is the German coast in the Southern Baltic Sea region, specifically the Kadetrinne, a 
heavily trafficked channel between the city Rostock and the islands of Fehmarn and Bornholm (Fig. 1). Shipping 
activity  in  this  area  significantly  contributes  to  coastal  pollution,  particularly  during  north  and  west  wind 
conditions.  

Air quality simulations are conducted using the Eulerian model EPISODE-CityChem v1.8 (Karl et al., 2019c). An 
overview of the model setup is provided in Fig. 2. 

Figure 1:  

of 
Depiction 
Warnemünde, Z = Zingst. 

the 

research 

domain.  D  =  Darßer  Ort,  R-W  =  Rostock- 

449 

 
 
 
 
 
Figure 2:  

Overview of the numerical model system employed in this study. 

reanalyzed data is examined. In the second stage, the forecast itself is evaluated.  

ns based on 

The meteorological data are obtained from either measurements taken at the German Weather Service (DWD) 
station located at Rostock-Warnemünde (see Fig. 1) or forecasts based on ICON D2 data up to 24 hours (DWD, 
2023). The key meteorological parameters in this setup include wind speed, wind direction, temperature and 
atmospheric stability. These parameters undergo preprocessing with the MCWIND v1.3 preprocessor (Hamer et 
al., 2020). Ship numbers and movement information are derived from AIS (Automatic Identification System) 
statistics.  Shipping  emission  data  are  calculated  using  the  Modular  Ship  Emission  modeling  System  MoSES 
(Schwarzkopf  et  al.,  2021).  Background 
measurement station Zingst (see Fig. 1) and from the Copernicus Atmospheric Monitoring Service (CAMS, 2023) 
are utilized. CAMS data are also incorporated for land-based emissions. The calculated concentrations for NO2, 
O3 and PM10 are compared against measurements from the German Environment Agency. 

Results and Discussion 

Figure 3 presents NO2 and O3 modeling (reanalysis) compared to measurement results for two exemplary weeks 
in July 2018 at the Zingst measurement station. Corresponding statistical evaluations of the model quality, 
including  PM10  results,  are  presented  in  Table.  The  biases  are  small,  particularly  for  NO2,  and  the  Pearson 
correlation coefficient indicates a strong agreement between modeled and measured values.  

Figure 3: 

Comparison between modeled and measured NO2 concentrations at the 
measurement location Zingst. 

450 

 
 
 
 
 
 
 
 
 
 
Table 1: 

Statistical performance of the model performance (reanalysis). 

BIAS 

NMB 

R 

NO2 

O3 

PM10 

0.35 µg m-3 

-7.9 µg m-3 

0.1 µg m-3 

10 % 

0.72 

-12 % 

0.89 

1 % 

0.96 

To assess the impact of ship emissions at a coastal background position (Darßer Ort, Fig. 1) a zero-out method 
was  employed,  calculating  differences  between  a  model  run  with  and without  ship  emissions.  Figure  4  and 
Table 2 provide an overview of these results. While the overall average relative ship effect on O3 and PM10 over 
the two-week period is relatively low, it is more significant for NO2. A relative impact of 81.28 % is attainable, 
but this should be cautiously interpreted, as these high percentages mainly occur when comparing already low 
concentrations.  Therefore,  evaluating  the  mean  relative  difference,  which  stands  at  22.77  %  with  a  mean 
absolute  difference  of  0.92  µg  m-3,  is  more  meaningful.  During  hours  of  heavy  ship  traffic,  the  maximum 
absolute NO2 impact reaches nearly 8 µg m-3, simultaneously causing the most pronounced reduction of O3, 
approximately 9 µg m-3. Ozone formation in the Baltic Sea occurs can occur under hot summer day conditions, 
primarily during strong radiation, high temperatures and low wind speed (Aksoyoglu et al., 2016, Badeke et al., 
2023). 

The results are comparable to a study by Ramacher et al. (2019), which also utilized the EPISODE-CityChem 
model, focusing mainly on concentrations within coastal cities, including Rostock. They found a local shipping 
impact on NO2 concentrations of 11 % annually averaged for Rostock and a low impact on PM concentrations, 
similar to the findings of this study. 

Figure 4: 

Ship effect on NO2 and O3 concentrations at the station Darßer Ort for a two week simulation in 
July, 2018. 

451 

 
 
 
 
 
 
 
 
 
 
Table 2: 

Ship effects at the position Darßer Ort for a two week simulation in July, 2018. 

mean diff 

max diff 

min diff 

mean rel diff 

max rel diff 

min rel diff 

NO2 

O3 

PM10 

0.92 µg m-3 

-0.97 µg m-3 

0.02 µg m-3 

7.69 µg m-3 

0.08 µg m-3 

0.33 µg m-3 

-0.07 µg m-3 

-8.97 µg m-3 

0 µg m-3 

22.77 % 

81.28 % 

-1.75 % 

0.26 % 

-25.95 % 

-24.02 % 

0.37 % 

5.17 % 

0 % 

Based  on  these  results,  the  setup  is  deemed  to  have  sufficient  performance  to  calculate  representative  air 
quality results using reanalyzed data. However, achieving a good forecast necessitates accurate predictions for 
several  input  parameters  presented  in  Fig.  2,  particularly  meteorology,  ship  movement,  and  background 
chemistry. 

In the current stage of this study, only the effect of the meteorological forecast on the resulting concentration 
fields is evaluated. Due to limited data for July, the forecast results are calculated for another period - the first 
week of October 2018. Figure 5 illustrates wind speed and wind direction forecast performance and includes 
modeled results from the July reanalysis for comparison. 

The meteorological preprocessor MCWIND is driven by a single meteorological measurement station, specifically 
the measurements or forecasts at the Rostock-Warnemünde station. Statistical parameters for wind speed in 
the reanalysis setup shows a BIAS of 1.1 m s-1 (model overestimation), NMB = 18 %, and R = 0.98, indicating a 
good performance consistent with the model results in Figure 3. 

During the forecast period, wind speed exhibits a variable pattern with changing high and low wind conditions 
as  well  as  short  peaks  of  high  wind  speed.  The  wind  direction  primarily  lies  between  southwest  and  north, 
including a rapid change on October 7, 2018. The model captures the wind direction well, but the wind speed 
forecast demonstrates lower quality. Statistical parameters for the wind speed forecast are BIAS = -0.63 m s-
1, NMB = -10 % and R = 0.48. Overall, the current status of the forecast underestimates wind speed and requires 
further improvement, particularly regarding the correlation coefficient. This enhancement will involve including 

Differences  from  real  measurements  can  also  arise  due  to  differences  in  the  model  layer  reference  height 
(lowest layer height of 17.5 m) and the instrument measurement height (10 m). The results herein provide a 
solid baseline for future improvements. 

452 

 
 
 
 
 
 
Figure 5: 

Comparison  of  wind  speed 
for  measured  and  
(left)  and  wind  direction 
modeled values at the position Rostock-Warnemünde. The upper row are results from the July 
period (reanalysis) and the lower row are results from the October period (forecast). 

(right) 

Figure 6: 

Sum  of  emitted  NOx  from  ships 
given on an hourly resolution. 

inside 

the 

research  area.  Emission  values  are  

An individual ship movement prognosis has not yet been incorporated into the model. However, overall emission 
for both periods has been investigated in Figure 6, illustrating ship activity in the entire research area based on 
  emission  values.  Emissions  mostly  range  from  0.5  to  1.5  tons  per  hour.  Regular  spikes  in  July 
total  NOx
correspond to Sundays and may be linked to cruise ship activities. The effect of variable ship movement on 
forecast quality is anticipated to be significant during individual averaging hours. For daily average pollution, it 
is  assumed  to  exhibit  similar  behavior  from  day  to  day,  therefore,  thus  allowing  the  application  of  a  daily 
average profile to represent ship emission. 

A CAMS forecast for background concentration exists but not yet been applied in this study yet. Nevertheless, 
to  demonstrate  the  potential  impact  of  different  background  concentration  sources  on  the  results,  Figure 7 
presents forecast results for NO2 and O3 with the forecasted wind field, as well as with and with both measured 
background values and reanalyzed CAMS background values. The statistical comparison is provided in Table 3. 

The NO2 forecast shows overall good results when using measured background data. Stronger deviations occur 
at times when the wind forecast is also showing lower performance (days 5 and 7), which is expected. The NO2 

453 

 
 
 
 
 
 
 
forecast  with  CAMS  background  data  shows  a  lower  performance.  For  O3,  the  forecast  with  measured 
background data is sufficient and shows an underestimation similar to the reanalysis. CAMS background give a 
better agreement for the BIAS of O3. The PM forecast is good but not strongly affected by ship emissions. 

Conclusion 

The setup has the potential to be employed as a real-time early warning system, primarily contingent on a high-
quality weather. It will undergo further improvement and investigation, especially for periods after the Baltic 
Sea became a nitrogen emission control area in 2021.  

Figure 7: 

Measured and forecasted values of NO2 and O3 in the first week of October 2018. Two different 
runs with background data from measurement (meas bg) and CAMS (cams bg) are shown. 

Table 3: 

Statistical  performance  of  the  model  forecast  for  the  measurement  position  Zingst. 
Different results are achieved with measured and CAMS background concentration. 

NO2 meas 

NO2 cams 

O3 meas 

O3 cams 

PM10 meas 

PM10 cams 

BIAS 

NMB 

R 

0.18 

3 % 

0.70 

-3.17 

-50 % 

0.35 

-12.37 

-23 % 

0.85 

6.75 

13 % 

0.56 

-0.21 

-2 % 

0.96 

0.51 

4 % 

0.69 

Acknowledgements  

This research is funded by dtec.bw   Digitalization and Technology Research Center of the Bundeswehr. dtec.bw 
is funded by the European Union   NextGenerationEU. 

References 

Aksoyoglu,  S.,  Baltensperger,  U.,  and  Prévôt,  A.  S.  H.  (2016).  Contribution  of  ship  emissions  to  the  concentration  and 
deposition of air pollutants in Europe, Atmos. Chem. Phys., 16, 1895 1906. 

AQF (2023). Urban air quality forecast, https://urbanaqforecast.coastalpollutiontoolbox.org. 

Badeke, R., Schneider, K., and Matthias, V. (2023). Analyzing ozone formation conditions for aged ship plumes in a local scale 
chemistry transport modeling study, EGU General Assembly 2023, Vienna, Austria, 24 28 Apr 2023, EGU23-5758. 

Barregard, L., Molnár, P., Jonson, J. E. and Stockfelt, L. (2019). Impact on population health of baltic shipping emissions, Int. 
J. Environ. Res. Public Health, 16, 1954. 

CAMS 
catalogue/cams-europe-air-quality-reanalyses. 

Cams-europe-air-quality-reanalyses, 

(2023). 

https://www.copernicus.eu/en/access-data/copernicus-services-

DWD 
data.html. 

(2023). 

NWP 

forecast 

data, 

https://www.dwd.de/EN/ourservices/nwp_forecast_data/nwp_forecast_ 

454 

 
 
 
 
 
 
 
 
Hamer, P. D., Walker, S.-E., Sousa-Santos, G., Vogt, M., Vo-Thanh, D., Lopez-Aparicio, S., Schneider, P., Ramacher, M. O. P., and 
Karl, M. (2020). The urban dispersion model EPISODE v10.0 - Part 1: An Eulerian and sub-grid-scale air quality model and 
its application in Nordic winter conditions, Geosci. Model Dev., 13, 4323 4353. 

Jonson, J. E., Jalkanen, J. P., Johansson, L., Gauss, M., and Denier van der Gon, H. A. C. (2015).  Model calculations of the 
effects of present and future emissions of air pollutants from shipping in the Baltic Sea and the North Sea, Atmos. Chem. 
Phys., 15, 783 798. 

Karl, M., Jonson, J. E., Uppstu, A., Aulinger, A., Prank, M., Sofiev, M., Jalkanen, J.-P., Johansson, L., Quante, M., and Matthias, V. 
(2019a). Effects of ship emissions on air quality in the Baltic Sea region simulated with three different chemistry transport 
models, Atmos. Chem. Phys., 19, 7019 7053. 

Karl, M., Bieser, J., Geyer, B., Matthias, V., Jalkanen, J.-P., Johansson, L., and Fridell, E. (2019b). Impact of a nitrogen emission 
control area (NECA) on the future air quality and nitrogen deposition to seawater in the Baltic Sea region, Atmos. Chem. 
Phys., 19, 1721 1752.  

Karl, M., Walker, S.-E., Solberg, S., and Ramacher, M. O. P. (2019c). The Eulerian urban dispersion model EPISODE - Part 2: 
Extensions  to  the  source  dispersion  and  photochemistry  for  EPISODE-CityChem  v1.2  and  its  application  to  the  city  of 
Hamburg, Geosci. Model Dev., 12, 3357 3399.  

Ramacher, M. O. P., Karl, M., Bieser, J., Jalkanen, J.-P., and Johansson, L. (2019). Urban population exposure to NOx emissions 
from local shipping in three Baltic Sea harbour cities - a generic approach, Atmos. Chem. Phys., 19, 9153 9179. 

Schwarzkopf, D. A., Petrik, R., Matthias, V., Quante, M., Majamäki, E., & Jalkanen, J. P. (2021). A ship emission modeling system 
with scenario capabilities. Atm. Environment: X,12, 100132. 

Umweltbundesamt (2023). Air data, https://www.umweltbundesamt.de/en/data/air/air-data/stations/. 

World Health Organization (2021). WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen 
dioxide, 
https://apps.who.int/iris/bitstream/ 
handle/10665/345329/9789240034228eng. pdf?sequence=1&isAllowed=y. 

monoxide, 

dioxide 

carbon 

sulfur 

and 

455 

 
 
 
 
SEICOR   Ship Emission Inspection with Calibration-free Optical Remote sensing 

M. Rieker1*, A. Daubinet1, K. Krause2, F. Wittrock2, A. Richter2, S. Schmitt3, D. Pöhler3, C. Haisch4 

1 HORIBA Europe GmbH, D-61440, Oberursel, Germany, Marcus.Rieker@horiba.com 
2 Institute of Environmental Physics, University of Bremen, D-28359, Bremen, Germany 
3 Airyx GmbH, D-69214, Eppelheim, Germany 
4 Technische Universität München, D-81377, Munich, Germany 

Introduction 

Ship emissions of particles, SO2 and NOx (the sum of NO and NO2) are a significant source of air pollution in 
coastal areas and close to busy in-land shipping routes. National and international regulations have been put 
in place to limit emissions and minimize the impact of shipping on air quality. Monitoring of ship emissions is 
challenging and so far, no automated system is operational for systematic surveillance of the compliance with 
regulations. 

Most  of  the  time  in-situ  instruments,  which  rely  on  the  wind  to  transport  the  emissions  towards  the 
measurement site, are used to monitor the emissions of ships. However, in times with low wind speeds or an 
unfavourable wind direction the emissions cannot be measured. Additionally, regular calibration is needed to 
ensure the quality of the measurements. 

Active Optical Remote Sensing, such as Differential Optical Absorption Spectroscopy (DOAS), allows to measure 
ship  emissions  without  being  dependent  on  wind  and  almost  every  passing  ship  plume  can  be  measured. 
Another  advantage  is  that  calibration  gases  are  not  required  at  all  which  enables  long  term  autonomous 
operation without frequent on-site maintenance or calibration procedures. 

Briefly, an active Optical Remote Sensing system consists of a light source on one side of the river or port, and 
a reflector on the other side (typically in a distance of a few hundred meters to kilometres). The system is set 
up in such a way that the emitted light passes through the plume of passing ships. Using spectroscopic methods, 
the concentrations of pollutants in the plume can be determined. In conjunction with an Automated Identification 
System (AIS), the plumes can be assigned to individual ships and their emissions can be analysed in the context 
of  regulations.  The  capabilities  of  such  an  active  Optical  Remote  Sensing  system  have  been  demonstrated 
already (Krause, et al., 2021)(Krause, et al., 2021) and will be briefly presented in the following. 

The  results  from  this  study  will  serve  as  a  base  for  the  development  of  a  new  Open  Path  system  for  the 
operational monitoring of ship emissions (NO2, NO, SO2, CO2) in the framework of the SEICOR project. 

Measurements of ship emissions using LP-DOAS 

To  measure  ship  emissions  on  the  river  Elbe,  a  long-path  Differential  Optical  Absorption  Spectroscopy  (LP-
DOAS) system has been set up in Wedel, Germany, 10 km downriver of the port of Hamburg, Germany. The Elbe 
serves  as  the entrance  route  to  the  port  of  Hamburg and  sea  and  inland  ships  pass the  measurement  site 
regularly. The measurement site is part of the ship exhaust gas measurement network of the Federal Maritime 
and Hydrographic Agency of Germany (Bundesamt für Seeschifffahrt und Hydrographie, BSH) and is equipped 
with weather station, in-situ instruments to measure pollutants emitted by passing ships (e.g., NOx, SO2 and 
particles) and an AIS receiver. A detailed description of the instruments can be found in Kattner, et al., 2015. 

The characteristics of the used LP-DOAS system are shown in Table 1. The system was set up on the northern 
riverbank and the retroreflector was installed on the southern riverbank. The measurement geometry is shown 
in Figure 1. 

456 

 
 
 
 
 
Table 2: Characteristics of the LP-DOAS system 

Component 

Details 

Light source 

Laser-driven light source Energetiy EQ99 

Optical fibres 

200, 800 µm 

Telescope mirror 

Diameter 30 cm, focal length 150 cm 

Spectrometer 

Acton SpectraPro 300i 

CCD 

2048 x 512 pixel Roper scientific back illuminated 

Measured wavelengths 

280   362 nm, 0.53 nm resolution 

Figure 18:  

a) Satellite image of instrument location, with the LP-DOAS marked as a red dot on the 

northern riverbank and the retroreflector position marked as a yellow dot on the southern 
riverbank. (b) Image of a passing container ship next to the measurement site. (c) Schematic 
overview of the measurement geometry of the LP-DOAS for a passing ship leaving Hamburg 
towards the North Sea, seen from above. (d) Same as panel (c) but seen from the port of 
Hamburg. Note that panels (c) and (d) are not to scale. Adapted from Krause, et al., 2021. 

The system sends out a beam of light across the river which is reflected by the retroreflector and measures the 
received spectra. The total length of the light path through the atmosphere is about 5.74 km. The measured 

457 

 
 
 
spectra contain absorption features according to Lambert-
the light source, which did not pass through the atmosphere and therefore do not show absorption features, 
the measured spectra can be evaluated for trace gases using the DOAS method. 

Figure 19: 

Example time series of the fitted trace gases from 19 August 2018 between 10:30 
and 13:00 UTC. In each plot the blue line shows the fitted time series of the respective trace gas, 
and the orange line shows the respective median detection limit. The grey dashed lines mark 
passing ships that have been assigned to a peak in the time series. The green line shows the 
calculated background. Adapted from Krause, et al., 2021. 

Being restricted to the wavelength range between 280 and 360 nm, the LP-DOAS only measures NO2, while the 
ship emits NOx. Only a portion of the measured NO2 is produced during the combustion process directly, while 
the rest is formed after emission by reaction with ozone. Therefore, the total NOx emissions must be estimated 
from the measured NO2 concentrations. The NO2/NOx-ratio can be obtained by summing the NO2 enhancements 
and the O3 decreases and potting them against measured NOx increases. NO2, NOx and O3 can be measured by 
the in-situ instruments on site. Using these measurements, a mean NO2/NOx ratio of 0.138 ± 0.006 was found 
(see Figure 3), which agrees with previous studies. Using this ratio and the O3 and NO2 measured by the LP-
DOAS allows to estimate the increase in NOx originating from the passing ship. 

458 

 
 
 
 
Figure 20: 

Plot of ΔNO2 + ΔO3 against ΔNOx from peaks measured with the in-situ 

instruments between April 2018 and May 2019. All concentrations have been corrected for 
background concentrations. For this analysis, 220 manually quality-checked peaks were used. 
This results in a slope (a NO2/NOx ratio) of 0.138 with a respective standard error of 0.006. 
Adapted from Krause, et al., 2021. 

An example time series of the measured trace gases can be seen in Figure 2. The time series of the measured 
trace gases show peaks (SO2 and NO2) and dips (O3) when a ship plume passes through the light path. These 
peaks and dips can be assigned to their respective source ships using AIS data. The assignment is based on the 
position and the time of the passing ship. 

The LP-DOAS measures the enhancement of trace gases in the light path and not directly at the stack of the 
ship. To gain absolute emission rates from the measured NOx data, a model has to be applied to estimate how 
the plume evolves in the atmosphere. Generally, a ship is a point source and a simple model to describe the 
plume of a points source is the Gaussian plume model (Pasquill, 1968) (Pasquill, 1968): 

𝐶(𝑥, 𝑦, 𝑧) =  

𝑄
2𝜋𝑈𝜎𝑧𝜎𝑦

exp (

−𝑦2
2𝜎𝑦

2 ) [𝑒𝑥𝑝 (

−(𝑧 − 𝐻)2
2
2𝜎𝑧

) + 𝑒𝑥𝑝 (

−(𝑧 + 𝐻)2
2
2𝜎𝑧

)] 

Where Q is the emission rate, U is the wind speed along the main wind direction (along x), 
y are the 
dispersion  parameters  in  horizonal  and  vertical  direction  and  H  is  the  height  of  the  plume  centreline.  The 
dispersion parameters depend on x, the atmospheric stability, and the surrounding environment. To calculate 
the emission rate of a ship, the model is evaluated with an arbitrary but constant emission rate (Qmodel), using 
the ship`s position as the starting point of the plume. This model run then gives insight into the dispersion of 
the plume in the atmosphere and the concentration measured by the LP-DOAS instrument (Cmeas) is compared 
to the concentration of a virtual light path through the model (Cmodel). Finally, the emission rate can be calculated 
as: 

z and 

459 

 
 
 
 
𝑄𝑚𝑒𝑎𝑠 =

𝐶𝑚𝑒𝑎𝑠
𝐶𝑚𝑜𝑑𝑒𝑙

𝑄𝑚𝑜𝑑𝑒𝑙 

In  total  more  than  7400  ships  have  been  identified  and  evaluated  by  applying  the  plume  model  described 
above.  Each  evaluated  ship  passaged  has  been  quality  controlled  and  cases  where  simple  Gaussian  plume 
model is not able to reflect the real plume were removed from the dataset before further analysis. The resulting 
emission rates were then evaluated  with respect  to  size, speed,  and type  of ships. An example of repeated 
measurements of several ships is shown in Figure 4. 

Figure 21: 

Box plot of SO2 and NOx emission rates in grams per second for individual 

ships; their respective length is given in brackets. Boxes indicate the 25th and 75th percentile, 
the line in the middle is the median, and the bars show minimum and maximum values. Dots 
show individual measurements and are colour coded by corresponding ship speed. Dark grey 
boxes show the median emission rate and total number of observations for this ship. Adapted 
from Krause, et al., 2021. 

Aims of the SEICOR project 

The SEICOR project aims to improve the presented method and to develop a system for automated long-term 
surveillance of emissions from ships and other, similar sources. It will be based on the experiences and tools 
from the study described above. The new system will cover measurements of all necessary gas species (NO2, 
NO, SO2, CO2), data analysis of the emission factors to direct generation of warnings in case of high emissions 
as well as automated reporting to the authorities, port operators and / or ship owners. 

The new Open Path instrument developed in SEICOR will be a combination of a UV/vis system (for NO2 and SO2 
measurements) and an IR system to enable measurements of CO2 and NO. The CO2 can then be used as a proxy 
2 
measurements would then enable to calculate emission factors, e.g., for SO2 where the regulations are based 
on limits for the fuel sulphur content. In combination with the direct measurement of NO, the system will directly 

460 

 
 
 
 
quantify  NOx  and  Sulphur  emission  factors  from  ratios  of  SO2/CO2  and  NOx/CO2,  respectively,  without  the 
uncertainties induces by plume dispersion models. 

Acknowledgements 

This work was supported by the University of Bremen, by the German Federal Maritime and Hydrographic Agency 
and the Federal Ministry for economic affairs and climate action. 

Bibliography 

Kattner, L., Mathieu-Üffing, B., Burrows, J. P., Richter, A., Schmolke, S., Seyler, A., & Wittrock, F. (2015). Monitoring compliance 
with sulfur content regulations  of shipping fuel by in situ measurements of ship emissions. Atmospheric Chemistry and 
Physics, 15, 10087 10092. doi:10.5194/acp-15-10087-2015 

Krause, K., Wittrock, F., Richter, A., Schmitt, S., Pöhler, D., Weigelt, A., & Burrows, J. P. (2021). Estimation of ship emission 
rates at a major shipping lane by long-path DOAS measurements. Atmospheric Measurement Techniques, 14, 5791 5807. 
doi:10.5194/amt-14-5791-2021 

Pasquill, F. (1968). Atmospheric diffusion: The dispersion of windborne material from industrial and other sources (1. publ., 
reprint ed.). D. Van Nostrand. 

461 

 
 
 
 
Experiences with sensor based continuous emission monitoring for demonstration 
of maritime emissions compliance 

Ruud Verbeek1, Pauli Simonen8, Jana Moldanova7, Tim Smyth2, Anthony Deakin3, Richard Proud3, Nikos 
Kousias4, Sokratis Mamarikas4, Leonidas Ntziachristos4, Matti Irjala5, Jonathan Weisheit6 , Andy Chink Nok6, 
Vincent Verhagen1, Pierre Paschinger1, Linda Haedrich9 , Antti Rostedt8, Hilkka Timonen10,  

1TNO, 2PML, 3exactEarth Europe, 4AUTH, 5Aeromon Oy, 6CML, 7IVL, 8TAU, 9HMGU, 10FMI 

Keywords: shipping emissions, maritime transport, enforcement, monitoring 

Abstract 

Exhaust emissions of maritime vessels are important due to their growing contribution in air quality problems 
on land and coastal areas, with reduction of other emission sources and the increase in maritime traffic. For 
this reason, Emission Control Areas (ECA) were introduced in Europe to reduce SOx emissions. This has been 

The EU H2020 SCIPPER project was set-up to study various possibilities for monitoring and enforcement of 
pollutant emissions regulations on vessels. Reported here are sensor-based, onboard monitoring results for 
two vessels: a conventional survey vessel and a ferry RoPax vessel with SCR. Particular good results were 
achieved with NOx, PM and BC monitoring.  Nevertheless, periodic maintenance and intermitted sampling or 
an air shield, is needed to protect sensors and secure sufficient lifetime. It is also concluded that continuous 
onboard monitoring gives much more insight in real sailing emissions than the legislative E2/E3 test cycle.  
Apart from the sensor results, this paper also describes the overall system setup with satellite data 
transmission and simple reporting formats to judge emissions performance over longer periods. 

Introduction 

The  EU  Horizon  2020  project  SCIPPER38  was  set-up  to  study  the  various  possibilities  for  monitoring  and 
enforcement of pollutant emissions from vessels, and how this would contribute to the air quality in coastal 
and port areas. This included continuous monitoring onboard of vessels (in the exhaust stack) and several types 
of  remote  sensing  options  (onshore,  via  drones  and  satellite).  SCIPPER  was  aimed  to  develop  solutions  for 
D5.1 (2019) and SCIPPER D5.5 (2023). The gaps addressed 
in this publication include primarily; the lack of monitoring procedures for NOx abatement equipment for Tier 
III, including procedures to discover SCR deactivation. Moreover, the fact that the simple legislative test cycle 
(E2, E3, etc.) allows for substantial differences between test cycle emissions and Real Sailing Emissions (RSE) 
and the limited robustness of the emission control technologies. 

System description  

In the SCIPPER project, six sensor systems for continuous onboard monitoring were tested. The emphasis in this 
publication is on automotive sensors and NOx monitoring. For a full overview, refer to SCIPPER D1.6 (2022). 
The SCIPPER strategy for onboard monitoring is built around the following principles: 

 

 
 

Reducing the number of monitoring parameters to the bare minimum. This makes simple validation, 
and third party checks possible. 
Increase transparency by continuous monitoring and by publishing average performance.  

, rather than mimicking the formal test cycles (E3, E2, D2), 

with stationary operation at specific engine load points. 

The  (continuous)  onboard  monitoring  concepts  investigated  in  the  SCIPPER  project,  includes  satellite  data 
transmission and collecting this data within a web-based monitoring centre. Refer to figure 1.  

38 EU H2020 project No. 814893. https://www.scipper-project.eu.  SCIPPER stands for  Shipping Contributions to Inland Pollution Push for 

. 

462 

 
 
 
 
 
                                                       
 
Figure 1. A schematic showing the end-to-end emissions reporting service from ship-to-shore and then to the 
cloud-based user access services. 

For onboard monitoring, it is proposed to express the NOx and other emissions primarily in mass ratios with 
CO2  and/or  with  fuel.  This  is  a  simple  and  robust  calculation  for  which  fuel  flow  and  engine  power  are  not 
needed. The only parameters needed are exhaust concentrations (in ppm) and specific CO2 emissions of the 
fuel type used (kg CO2 per kg fuel). These emissions can also simply be converted to approximate g/kWh values 
by multiplying the g/kg fuel values with a load dependent or constant Specific Fuel Consumption (SFC) value at 
sufficient engine load. For further explanation see SCIPPER D1.6 (2023) and also Verbeek (2021). 

Results 

Table 1 provides an overview of the two campaigns with onboard monitoring. The first one was in the English 
Channel, the second one in the Baltic Sea. In both campaigns satellite data transmission was tested. 

Table 1. Overview of campaigns CX and C2 for onboard sensor monitoring

CX English Channel 

C2 Baltic Sea 

Ship 

Sensors 

Plymouth Quest - Research Vessel 

Stena Germanica - RoPax Ferry 

automotive sensors   SEMS 

All sensor systems 

Monitoring period  

2021: May   Aug. 

2021: September to 2022: January 

463 

 
 
 
 
 
 
 
 
 
English Channel 

In the English channel, the monitoring took place with automotive sensors and s-AIS data transmission on the 
Plymouth Quest research vessel. Refer to figure 2 below. 

Figure 2: Sensors attached to the exhaust funnel of the RV Plymouth Quest from the TNO SEMS system (Oxygen, 
NOx, NH3 and temperature probes).    

The  instrumentation  onboard  of  the  Plymouth  Quest  was  simple:  the  exhaust  gas  concentrations  with  the 
automotive NOx and O2 sensor and NH3 sensor were monitored with 1 Hz, and additionally the engine speed 
(RPM) and Speed-Over-Ground (SOG) with a GPS module. Each minute the data was also sent to a webserver 
via  satellite  data  transmission.  The  engine  power  was  calculated  according  to  the propeller  law  and  engine 
speed, and a SFC curve was fitted based on engine data. The SFC was used to calculate g/kWh emissions based 
on NOx/CO2 ratios. Engine power and air or exhaust flow are not needed with this approximation. 

In figure 3, NOx emissions data is shown based on the onboard monitoring during vessel trip #26. This is based 
on the 1 Hz data. The figure shows the NOx/CO2 ratio, NOx in g/kWh, engine speed and engine power. The figure 
shows two periods of around 50 minutes with an engine speed just over 1600 rpm. At this speed, the NOx/CO2 
concentration ratio is around 0.014 and the  NOx is just over 10 g/kWh. In this case, we see also a period with 
an engine speed just over 800 rpm. The NOx/CO2 ratio is then about 30% lower, but due to the lower engine 
efficiency at the lower power level, the NOx in g/kWh drops only a bit to just below 10 g/kWh.   

Figure 3 Emission data and ship parameters from Plymouth Quest Trip 26. Source SCIPPER D1.1.Only based on 
exhaust sensor, engine speed and GPS 

464 

 
 
 
 
 
  
Baltic Sea   RoPax ferry 

Monitoring with NOx and other sensors in the exhaust stack was carried out on the STENA Germanica sailing 
from Gothenburg to Kiel. Automotive NOx/O2 and NH3 sensors were installed both in the engine room, before 
the catalyst, and downstream of the catalyst. Two types of fixtures were tested. The first fixture is a simple 
tube of about 10 cm diameter, which brings the sensors (NOx, NH3 and temperature) about 10cm within the 
exhaust pipe (shown in Figure 4). This fixture was used during the entire testing period (4 months).  In a second 
fixture, a sample tube branched some exhaust gas to just outside of the main exhaust pipe lead along the 
sensor and fed back into the exhaust pipe. This was tested during some 2 months.  

The sensors were not excessively fouled after 4 months of running. It should however be noted, that the NOx 
and NH3 sensors mounted at the engine-out position showed some technical problems (respectively after 4 
months and after 2-3 months). For the automotive NOx sensor a lifetime of at least 6000 hours is estimated 
for the electronics parts based on ambient temperatures in the funnel or engine room of the vessel. The sensor 
itself may be affected by SOX, PM and other impurities due to the fuel composition, uncommon in automotive 
applications.  For  that  reason,  it  is  advised  to  limit  the  exposure  time  to  exhaust  gases,  e.g.  by  intermittent 
exposure. Exposure can be limited by installing the sensor in a bypass duct with a valve, or by an air shield 
system. 

Figure 4.  Stena Germanica with sensor fixture in exhaust pipe (right): NH3, NOx and temperature sensor. 

The  emission  measurements  of  the  automotive  NOx  sensor  were  compared  in  detail  to  the  emission 
measurements  of  the  reference  measurements  (Moldanova,  2022).  The  difference  between  the  automotive 
NOx sensor and the Horiba analyser was generally in the range of 1% to 6%. This is considered as more than 
sufficiently accurate given the objective of onboard monitoring, i.e., real-world effectiveness of emission control 
systems.  

The SCIPPER objective was also to produce simple overviews which give direct insight in the overall emissions 
period  during  a  longer  period  and  also  to  be  able  to  compare  that  with  the  legislative  requirements.  Three 
options studied by SCIPPER are: a) emission maps, b) daily averages and c) location specific emissions. Two 
maps of NOx emissions over a longer period are presented in figure 5 below.  These same figures can also be 
drafted as a function of engine power. 

The satellite data transmission used in SCIPPER has a relatively small bandwidth and low frequency (e.g. about 
10 data transmissions per hour). Additionally, also 1 Hz data is recorded and transmitted via cellular (when 
close to land).  For more details on the satellite data transmissions refer to Smyth (2023) and SCIPPER D1.5 
(2021).   

Figure  6  shows  an  overview  with  simple  daily  average  NOx  emission.  This  is  only  based  on  exhaust  gas 
concentrations of NOx and CO2. The NOx/CO2 ratio (linear proportional with g/kg fuel emissions) is averaged. No 
weighing is applied with respect to engine power or exhaust mass flow. The main advantage of this simple 
average  are  transparency  and  robustness.  Onboard  verification  can  simply  be  done  by  sticking  an  analyzer 
probe in the exhaust gas. This figure also shows the Tier II and Tier III limit values (based on a SFC of 200 g/kWh 
and a maximum engine speed of 600rpm).  

465 

 
 
 
 
 
 
 
 
Figure 5. NOx emission maps of Stena Germanica ferry: NOx in g/kg fuel (left) and in g/kWh (right) 

Figure 6 also shows the correlation of the daily average between the 1 Hz data (line) and the data transmitted 
via satellite. In most cases (days) they are almost identical, however on 5 and 10 October there were some 
differences.  It is concluded  that,  in this case,  the daily averages  are  in line  with  the Tier III limit value.  The 
successful satellite data transmission for the Stena Germanica averaged around 6 messages per hour. For the 
English channel (Plymouth Quest), the average transmission rate was about 18 messages per hour, Refer to 
Smyth (2023) and SCIPPER D1.5 (2021). This demonstrates the capability for using s-AIS binary messaging to 
report emissions data via satellite in real-time. In principle this should work from any location globally.  

Figure 6.  Stena Germanica. Comparison of daily average NOx bases on continuous monitoring (red line) and 
low frequency data via s-AIS satellite transmission (black dots).  

Black Carbon and PN monitoring 

A total of five particulate matter sensors were tested: two PN sensors, two standard optical black carbon sensors 
(ObservAir) and one prototype opto-acoustic BC sensor (AUTH/HMGU). All PN sensors were sampling from an 
eDiluter (Dekati Ltd) connected downstream of the SCR catalyst. Optical sensors report BC mass concentration 
based on light attenuation caused by particles deposited on a filter. With the eDiluter intermittent sampling was 
applied.  In  this  way  the  exposure  time  of  the  particulate  sensors  was  limited  to  extend  the  lifetime  of  the 
sensors. 

Contrary to the NOx sensors, the particulate matter sensors were only tested for a relatively short period (1-2 
weeks). During this period, all particulate matter sensors operated well and no fouling or loading issues were 
noticed. The PN sensors appear as quite feasible for unsupervised monitoring of the number concentration in 
the exhaust line. The optical BC sensors also operated well, but as these require periodic filter media changes, 
they are less feasible for unsupervised use. During the campaign, one filter change per day was required even 
with  intermittent  sampling  used.  The  opto-acoustic  BC  sensor  showed  good  potential  for  unsupervised 
monitoring, although is still in an early stage of development. 

466 

 
 
 
 
 
 
 
Conclusions 

The  SCIPPER  program  demonstrated  the  full  chain  of  sensor-based  onboard  monitoring,  satellite  data 
transmission  and  centralized  reporting  of  emissions  performance.  Standards  for  data  formats  and  simple 
reporting formats were proposed. The best results were achieved with NOx and PM/BC monitoring. Furthermore 
the following conclusions were made: 

 

 

There is a good potential of sensors to be used for onboard monitoring. Automotive NOx sensors in 
combination with build in O2 measurement provide a good bases to calculate NOx in g/kg fuel and 
g/kWh. 
Protection against fouling, in the form of intermitted sampling or air shield, is needed to protect 
sensors. Once a year maintenance, including replacement of sensors, will probably be necessary. 
  NOx can be measured in raw exhaust, but for most components; SOx, PM, PN, BC, a dilution system is 
needed. An advice is to develop a simple mechanical dilution system which is adjusted once during 
installation. 
The PN sensors appear as quite feasible for unsupervised monitoring of the number concentration in 
the exhaust line, although the possible fouling may limit the operation time.  
Several simple reporting formats for NOx emissions were demonstrated. The optimum reporting 
format for NOx, NH3 and PM is dependent on the purpose of the monitoring results: 

 

 

o  Emissions modelling: emissions as function of geographical location (g/kg fuel, g/h) 
o  Vessel emissions in normal operation: daily averaged emissions (g/kg fuel, g/kWh) 
o  Engine compliance with legislation: plot of emissions as function of engine power or vessel 

speed (g/kWh, g/kg fuel). 

  Both experiments on the Quest and the Stena Germanica demonstrated the capability for using s-AIS 

binary messaging to report emissions data via satellite IoT in real-
location (i.e. globally). 

References 

Moldanova (2022) 
Jana Moldanova et.al.: Measurement campaign for characterising and monitoring of emissions from vessel 
with alternative fuels and NOx emission control.  IAC 2022, Athens, Greece 5/9-2022.  

SCIPPER D1.5 (2021) 

Compliance pipeline from ship to shore to web enabled secure data delivery. Tim Smyth, Anthony Deakin, Richard 
Proud. SCIPPER deliverable D1.5, 20 October 2021. EU Horizon 2020 project No. 814893. https://www.scipper-
project.eu/library/. 

SCIPPER D1.6 (2022)  

Weisheit, J., Verbeek, R., Paschinger, P., Verhagen, V., Irjala, M., Simonen, P., Kousias, N., Moldanova, J., Merelli, 

Deliverable D1.6. September 2022. EU Horizon 2020 project No. 814893. https://www.scipper-project.eu/library/. 

SCIPPER D5.1 (2019) 

Hulda  Winnes,  Erik  Fridell,  Ruud  Verbeek,  Jan  Duyzer,  Andreas  Weigelt,  Sokratis  Mamarikas,  Leonidas 
Ntziachristos:  Gaps  in  current  emission  enforcement  regulations  and  impacts  to  real-world  emissions.  EU 
Horizon 2020 project No. 814893. https://www.scipper-project.eu/library/. 

SCIPPER D5.3 (2022)  

Ruud Verbeek, Daniëlle van Dinther, Sokratis Mamarik, Achilleas Grigoriadis, Andreas Weigelt, Jasper van Vliet, 
Tim Smyth, Anthony Deakin, Matti Irjala. Cost-effectiveness of different approaches for compliance Monitoring. 
EU Horizon 2020 project No. 814893. https://www.scipper-project.eu/library/. 

SCIPPER D5.5 (2023) 

Erik  Fridell,  Ruud  Verbeek,  Volker  Matthias,  Johan  Mellqvist:  Policy  recommendations  related  to  regulations, 
monitoring and enforcement. SCIPPER deliverable D5.5, February 2023.  EU Horizon 2020 project No. 814893. 
https://www.scipper-project.eu/library/. 

Smyth (2023).  

Tim Smyth, et.al.: Faster, Better, Cheaper: Solutions to the Atmospheric Shipping 

467 

 
 
Emission 
https://doi.org/10.3390/atmos14030500.  

Compliance 

and 

Attribution 

Conundrum. 

Atmosphere 

2023, 

14, 

500. 

Verbeek (2021)  

24th TAP conference, online, https://www.tapconference.org/. 30 March   1 April 2021  

468 

 
 
 
 
2.11  JS.11.  Reduction  measures  for  GHG  emissions 

fuels,  electrification,  energy  use  and 
alternative 
optimization; inner-disciplinary and cross-sector studies. 

469 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Active traffic management for improved air quality and reduced climate impact 

M. Norman1*, M. Benyamine-Remahl2, J, Archer,2 C. Johansson1,3, M. Elmgren1, L. Burman1 and M. Engardt1  

1SLB-analys, Environment and health Administration, City of Stockholm, S-104 20, Sweden 

2Swedish Road Administration, Sundbyberg, S-172 90, Sweden 

3 Department of Environmental Science, Stockholm University, S-106 91 Stockholm, Sweden 

Introduction 

lly for many years, 
but rarely in Sweden. VSL is primarily a traffic safety measure and rarely targets air quality improvement or 
reductions regarding environmental and health impacts. In the autumn of 2019, the research and innovation 
project: "Active traffic management for improved air quality and reduced climate impact along the state road 
network"  began. The  main  purpose  of  this  project  was  to  investigate  whether  traffic  control  in  the  form  of 
variable speed limits (VSL) could reduce the impact of traffic on air quality and climate related emissions. 

Study area and Method 

The road section studied is located on E4/E20 between the Hallunda intersection and Fittja intersection on the 
outskirts of Greater Stockholm in the Botkyrka municipality, south of Stockholm, Figure 1. The E4/E20 is the 
major road towards Stockholm from the south. The 2.7 km stretch of motorway has a considerable flow of 
traffic  that  passes a  densely  populated area.  The  roadway  has known problems  with  environmental quality 
standards for outdoor air. Accessibility and traffic safety are generally good, but some congestion occurs during 
rush hours. The posted speed limit before the introduction of VSL was 80 km/h. 

Figure 1: The area along E4/E20 south of Stockholm were the VSL has been in use.  

VSL was introduced in March 2021 on the road section in Figure 1. Two air quality monitoring stations have 
been  measuring  on  both  sides  of  the  road  since  2019.  In  addition  to  air  quality  measurements,  detailed 
monitoring of the traffic, including vehicle counts, vehicle speed and fleet composition through license plate 
identification, has also been conducted. During periods with high traffic intensity the speed limit was lowered 
to 60 km/h. Due to some technical problems the VSL only showed 60 km/h about half of the time that the 
algorithm proposed that is should. 

470 

 
 
 
 
 
 
 
Results traffic 

The average traffic and speed during weekdays during 2021 are shown in Figure 2. The average speed was 
~84  km/h  but varies  during the  day  with higher speed during nighttime. The  average daily  traffic flow  was 
~100 000 vehicles per day but with significant peaks in morning and afternoon (rush hours). The major traffic 
flow is northwards towards Stockholm in the morning and southwards in the afternoon. 

Figure 2. Average traffic flow and speed during weekdays during 2021 at the site. Data presented as 15-
minute values and divided into north and south.  

The detailed vehicle composition was analyzed using license plate detection (ANPR) and is shown in Figure 3. 
The vehicle composition was used when analyzing the emissions from HBEFA (HBEFA, 2019). As shown in Figure 
3 is there almost equal fraction of petrol and diesel cars. However, the Euro classes shows that the diesel cars 
to a much larger extent are newer with only small fraction of Euro 3 and older. In contrast a significant part of 
the petrol cars were Euro 3 and older. The major part of the heavy trucks were Euro 6 vehicles. 

Figure 3. The average vehicle composition at the site. 

The  effect  from  VSL  was  studied  from  March  until  Dec  2021.  In  Figure  4  is  the  occurrence  of  hours  with 
displayed 60 km/h shown. Since the VSL was based on traffic density it is not surprising that most of the VSL 
with 60 km/h are found for northbound traffic in the morning and southbound traffic in the afternoon. In total 

471 

 
 
 
 
 
 
were ~2.5 million vehicle passages affected by the VSL 60 km/h, which corresponds to 6.8% of the vehicle 
passages during March to December 2021.   

Figure 4. Number of hours when the VSL has shown 60 km/h. Data dived into north/south and time during the 
day.  

The effect of the VSL60-activations on average speed were determined by comparing similar periods of dense 
traffic where VSL60 was activated and where it should have been activated but was missed due to technical 
problems. From this comparison it could be determined that average speed was reduced by approximately 6.0 
km/h  for  the  northbound  and  5.5  km/h  for  the  southbound  direction,  respectively. The  low  level  of  speed-
compliance reflects, an unwillingness on the part of drivers to adapt their speed to the speed limit and as well 
as lack of speed-enforcement measures. 

Results air quality 

High levels of air pollutant occurred at the two measuring sites next to the road. The number of hours with NO2 
concentration >60 µg/m3 are shown in Figure 5 and number of hours with PM10 50 µg/m3 in Figure 6. The levels 
of 60 µg/m3 NO2 and 50 µg/m3 PM10 were chosen because of those are the daily average air quality limit 
values in Sweden. 

High concentration of NO2 mainly occurs during morning and evening traffic peaks (compare with Figure 2). But 
high NO2 concentration are more likely to occur in the late evening although the traffic numbers then are low. 
This is caused by the more stable meteorological conditions and lower windspeed in evenings and during nights 
in comparison to daytime. 

The  PM10  depicts  a  different  feature  with  high  concentrations  mostly  occurring  in  the  afternoon  and  early 
evening. High PM10 in Scandinavia and Sweden are mainly caused by road dust from studded tyres wear on 
the road and winter road maintenance as salt and sand (Norman et al., 2016). The road dust is only emitted 
into the air when the road surface is dry, while it stays on the road surface during wet conditions. Dry road 
surface  more  commonly  occurs  during  the  afternoon  causing  the-  high  PM10  concentrations  mostly  in  the 
afternoon.  

472 

 
 
 
 
 
Figure 5. Number of hours when the NO2 >60 µg/m3. Data presented from both western and eastern 
measurement station.  

Figure 6. Number of hours when the PM10 >50 µg/m3. Data presented from both western and eastern 
measurement station. 

Effect from Variable Speed Limits, VSL 

The effect from the VSL om emissions of CO2, NOx and PM10 as well as concentrations of NO2 and PM10 were 
calculated and analyzed using three different scenarios for the period of March to December 2021, Table 1 

VSL observed: This scenario corresponds to the reality. In the analysis were the speed reduced with 6 
km/h during the periods when the VSL displayed 60 km/h. 

also during those periods when the VSL system should have displayed 60 km/h, but did not due to 
technical problems. 

- 

- 

- 

60km/h during active VSL, meaning that all vehicles followed the speed regulation. 

473 

 
 
 
 
 
 
 
The  reduced  emissions  of  NOx  and  CO2  with  VSL  was  calculated  using  detailed  vehicle  composition  in 
combination with HBEFA 4.1 (HBEFA 2019). The reduced emissions and concentrations for PM10 were calculated 
using the NORTRIP model (Denby et al., 2013). The effect on air quality was calculated using the same scenarios 
and applied to the observed air quality at the monitoring sites. 

The reduced emissions with the VSL scenarios are presented in Table 1. The VSL observed caused a reduction 
in CO2 emissions with 24 ton, which corresponds to ~59 000 fewer vehicle passages during March   December 
2021 or ~200 fewer per day. With fully active VSL and all vehicles driving 60 km/h the CO2 emission reduction 
would be 113 tons which equal 280 000 less vehicle passages or ~940 per day. 

Table 1: The three scenarios used for the analysis and the reduced amount of CO2- NOx and PM10-emissions 
due to different scenarios of the VSL for Mar   Dec 2021. 

VSL obs. 

Speed (km/h) 

-6* 

-6* 

Vehicle passages 
affected (millions) 

2.5  

3.5  

VSL 
theoretical 
max 

60 

3.5  

Reduced CO2 
emission (tons) 

Reduced NOx 
emissions (kg) 

Reduced PM10 
emissions (kg) 

24  

38  

113  

42 

90 

66 

227 

129 

191 

The positive effect on air quality was limited, Figure 7. During the period March   December 2021 was 272 and 
280 hours with NO2 concentration above 60 µg/m3 (Swedish Environmental Goals) observed at the eastern and 
western  site  respectively.  Only  two  less  hours  with  NO2-concentrations  >60  µg/m3  was  calculated  with  the 
observed VSL. With the maximal theoretical scenario up to 10 less hours with maximum effect scenario. For 
PM10 daily average above 30 µg/m3 (Swedish environmental goals) was there no fewer days for any of the 
scenarios compared to the measured.   

474 

 
 
 
 
 
 
 
Figure 7: Change in number of hours with NO2 > 60 µg/m3 for the different scenarios of VSL Mar   Dec 2021.  

Before VSL was introduced additional scenarios was used to calculate the potential effect of VSL if it was active 
also  during  periods  with  high  concentrations  of  pollutants  and  not  only  dense  traffic.  When  comparing  the 
occasions with VSL based on traffic (Figure 4) with the occasions of high NO2 and PM10 concentrations (Figure 
5 and Figure 6) there is some discrepancy, especially for PM10. Calculations for 2019 and 2020 showed that 
the number of occasions with high PM10- and NO2 concentrations would be significantly reduced if VSL (60 
km/h) was triggered at times with high concentrations of PM10 and/or NO2, (full compliance of speed limits). 
VSL based on air quality would lower the risk of exceeding the EU guidelines along the road. The calculations 
showed that using VSL based on air quality and dense traffic would be much more effective than using VSL 
only for dense traffic. 

Some conclusions 

- 

- 

- 

- 

VSL has the potential in improving air quality along major road and to reduce the CO2 emissions. 

The actual speed reduction was low (~6km/h) although the signed speed was reduced from 80 to 60 
km/h. 

In total 2.5 million vehicle passages were affected by the VSL with 60 km/h during the test period 
March   Dec 2021. This corresponds to 6.8 % of all vehicle passages at the test site.  

The effect on air quality would improve significantly if the VSL showed lower speed also at times with 
bad air quality and not only during dense traffic situations  

Acknowledgements  

This project was funded by the Swedish Transport Administration. 

475 

 
 
 
 
 
 
 
 
References 

HBEFA 4.1., Handbook for Emission Factors 4.1, 2019, www.hbefa.net  

Denby B.R., Sundvor I., Johansson C., Pirjola L., Ketzel M., Norman M., Kupiainen K., Gustafsson M., Blomqvist G., Omstedt G. 
(2013).  A coupled road dust and surface moisture model  to predict non-exhaust road traffic  induced particle emissions 
(NORTRIP). Part 1: Road dust loading and suspension modelling http://dx.doi.org/10.1016/j.atmosenv.2013.04.069 

Norman M., Sundvor I., Denby B.R., Johansson C., Gustafsson M., Blomqvist G., Janhäll S. (2016). Modelling road dust emission 
abatement  measures 
reduction. 
http://dx.doi.org/10.1016/j.atmosenv.2016.03.035 

the  NORTRIP  model: 

studded 

Vehicle 

speed 

using 

tyre 

and 

476 

 
 
 
 
2.12  JS.12.  Scenarios  and  policy  options  for  sustainable 

transport 

477 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Solid Oxide Fuel Cells for Reduced Health and Climate Impact of Ship Emissions   
Emission Analysis  

J. Pagels1, A. Oudin2, R. Rittner2, S. Ansar3, V. Malmborg1, J. Rex1 and A. Kristensson4  

Department of Design Sciences, Ergonomics & Aerosol Technology, Lund University, LTH, Lund, SE             22100 

1. 
Sweden  

2. 

3. 

4. 

Department of Laboratory Medicine, Lund University, Lund, SE 22100 Sweden  

Department of Clinical Sciences, Lund University, Lund, SE 22100 Sweden  

Department of Physics, Lund University, Lund, SE 22100 Sweden  

Introduction  

Air pollution from the shipping sector is responsible for substantial adverse health impact. Brandt et al. (2013) 
estimated that the contribution from international ship traffic was 7 % of the total health-related economical 
external costs in Northern Europe in the year 2000, and increase to 12 % for the year 2020. PM emissions 
from ships are estimated to be responsible for around 600 000 premature deaths annually from 
cardiopulmonary disease and lung cancer in coastal areas of Europe, East Asia and South Asia (Maragkogianni 
et al. 2016). Viana et al. (2020), furthermore also showed substantial health impacts by air pollution from 
shipping, and also showed clearly that the more stringent global sulphur reduction IMO standards from 2020 
would reduce the number of premature deaths attributable to PM2.5 by 15%. Shipping is contributing to 
around 3% of the global anthropogenic CO2 emissions. Shipping also emits several other climate warming 
pollutants such as Methane and Black Carbon (Aakko-Saksa et al. 2023). The greenhouse gas (GHG) strategy 

40% by 2030 and 70% by 2050 compared to 2008.  

There are several new technologies developed to reduce both the climate and health impact of shipping. This 
includes the use of new cleaner fuels in ICEs and the introduction of exhaust aftertreatment systems as has 
previously been done for road transport (Aakko-Saksa et al. 2023). The H2020-project NAUTILUS (Nautical 
Integrated Hybrid Energy System for Long-haul Cruise Ships) evaluates solid oxide fuel cells (SOFCs) 
operating in hybrid with batteries as future clean propulsion technology for passenger shipping. The new 
technology will be implemented using a modular strategy to gradually replace existing Internal Combustion 
Engine (ICE) generators. The short-term goal of the NAUTILUS project is to reach IMO targets for 2030  

(10% increase in energy efficiency, 40% CO2 reduction by using LNG as a fuel) and to reduce Nitrogen 
Dioxides (NOx), PM (particulate matter) and Sulphur Dioxide (SO2) by >99% compared to a baseline case using 
HFO with no exhaust after treatment. Due to their high operating temperature SOFCs offer higher fuel 
flexibility compared to other fuel cell technologies. This opens up possibilities for use of low carbon intensity 
or carbon neutral fuels (van Veldhuizen et al. 2023a, 2023b). A Techno-economic analysis for a range of fuels 
used in the system have been carried out (Pina et al. 2023). Although the potential to reduce air pollutants 
such as PM, NOx, SOx and BC are high for the SOFC concept, there is a lack of direct experimental data to 
fully confirm this. A recent study of a biogas fueled SOFC plant for heat generation showed experimental 
evidence of low emissions (Gandiglio et al. 2020). For some pollutants the emissions were below the 
detection limit of the applied emission analysers. This highlights the need for more sensitive methods for 
example from ambient monitoring to quantify the emissions.    

Here we report on the progress of the emission analysis task of the project where the following activities are 
carried out: Literature survey of ship emissions based on ambient, on-board and laboratory investigations and 
of the toxicity of transport emissions including shipping. A measurement strategy is currently designed and 
tested  to  allow  detailed  emission  measurements  of  the  SOFC  -  post  combustor  system.  Direct  emission 
measurements will later in the project be carried out at a demonstrator unit (60 kWe) that will be constructed 
in  the  project.  PM2.5,  NOx,  Black  Carbon,  SO2  and  select  emerging  pollutants  will  be  measured.  Combining 
baseline emissions from the literature survey with measurements of SOFC emissions will allow quantification 
of the emission reduction potential of the new technology for the key pollutants.  

Finally, a health impact assessment will be carried out for different scenarios of implementation of the new 
technology.   

478 

 
 
Literature Survey: Emissions and Exposure to Air Pollution from Shipping 

The emission survey within Nautilus serves the purpose to evaluate the emission reduction potential of SOFC 
system. The survey is based on laboratory, on-board (L/O) and ambient studies (A). Many of these papers can 
be found in the reviews by Aako-Saksa and Lehtoranta (2019), Di Natale and Caratonuto (2015), Aako-Saksa 
et  al.  (2023)  and  Grigoriadis  et  al.  (2021).    The  L/O  studies  allow  studies  of  individual  ship  engines  and 
consequences  of  engine  load,  fuel  switches  and  exhaust  aftertreatment  devices  on  measured  emissions 
expressed  as  grams  emitted  pollutants  per  kg  fuel  consumption 
  emission  factor  (EF).  Ambient  studies 
normally have the benefit of representing an entire fleet sailing along a ship route, but can also be calculated 
for  individual  ships.  Mainly  data  on  gaseous  pollutants  SO2,  NOX,  formaldehyde,  ammonia,  methane,  CO, 
hydrocarbons, particle number (PN), particulate matter (PM) and BC are available. For the L/O studies, quite 
extensive data is available on use of HFO, MDO/MGO and LNG fuels and some exhaust aftertreatment systems, 
while LNG data is not yet available for ambient studies.  

With the introduction of SOFC technology, a few preliminary studies in the literature show that there is a large 
reduction potential for the emission factors shown for many pollutants, for example SOX, NOX, and PM, being 
several orders of magnitude lower than even ships sailing on LNG fuel.  

Currently a number of emission scenarios are being finalised that will be used for the Life-Cycle Assessment 
carried  out  in  the  project  to  deduce  the  total  environmental  benefit  of  the  SOFC  technology.  Air  pollutant 
exposure  analysis  is  also  underway  in  Nautilus.  The  exposure  takes  place  in  harbours  or  at  coastal  areas 
downwind of shipping routes. It can be estimated from dispersion air pollution modelling or from measured 
exposure plus source/receptor models or quantification of the impact of individually measured ship plumes. 
The modelled exposure is usually overestimated compared to measured, and the reason for this is at present 
unclear (Karl et al., 2019). The exposure analysis will be used as input to the health impact assessment task in 
the project.  

Literature Survey: Toxicity of PM Air Pollution from Transport Sources 

An integrative systematic review of the literature was performed. Literature between 2018-2023 that included 
the toxic and cellular mechanism behind the effects of particulate matter emissions from transportation was 
collected  from  the  databases  Embase,  PubMed  and  Web  of  Science  Toxic  effects  of  PM  emission  include 
oxidative damage, cytotoxicity, genotoxicity and mutagenicity. The particles toxicity depends on their chemical 
composition  and  size  which  is  dependent  on  the  emission  sources.  Smaller  size  particles  are  considered  of 
particular importance for health since they exert greater toxicity due to their large reactive surface area and 
the ability to be inhaled deeply in the alveoli of the lungs and translocate into vital organs. Airways   are lined 
with an epithelial cell layer, which constitutes the barrier between the environment and the organism. Pollution 
penetrating  through  these  barriers  reaches  the  bloodstream  and  can  be  transported  throughout  the  body. 
Endothelial cells line the blood vessels and constitute a second barrier between the circulation and the organs. 
Alterations in barrier function are early predictors of disease including diseases associated with PM air pollution 
exposure  such  as  respiratory,  cardiovascular,  (thrombosis,  ischemic  risk,  atherosclerosis,  and  hypertension), 
neurodegenerative,  metabolic  diseases  and  cancer.  High  and  long-term  exposure  to  PM2.5  and  ultrafine 
particles have been correlated with endothelial and epithelial dysfunction. The reviewed studies demonstrate 
that PM exposure induces oxidative stress leading to gene transcription and activation of a stress response, 
including  the  release  of  pro-inflammatory  cytokines,  chemokines,  adhesion  molecules,  and  vasoconstrictors 
causing endothelial and epithelial dysfunction and impairment of the barrier. This may explain the widespread 
effects of these particles around the body cause. Taken together, the reviewed studies demonstrate that PM 
exposure leads to barrier dysfunction and altered homeostasis, which in turn contribute to the increased risk of 
respiratory,  cardiovascular,  neurodegenerative,  and  metabolic  diseases  as  well  as  cancer.  As  the  toxicity 
depends on emission sources studies have demonstrated that emission from shipping is more toxic compared 
to vehicle transportation. As of June 2023, more than 40% of cruise ship orders are LNG powered globally. 
There are currently also rapid investments into Methanol and Ammonia as ship-fuels. However, the effect on 
health and the underlying pathophysiological mechanisms that emissions from these new fuels may cause are 
still unknown.  

NAUTILUS SOFC Technology 

The Nautilus proof-of-concept utilises a 32 kWe SOFC module, coupled with a lithium-ion battery to allow for 
high  power  transients  that  are  characteristic  for  ship  operation.  It  represents  a  conceptional  genset  that  is 

479 

 
 
evaluated to gradually replace conventional ICE gensets on cruise ships, considering its transient behaviour and 
scalability. A simplified schematic of the SOFC system is given in Figure 1, using methane as fuel. The main 
components  of  relevance  for  pollutant  formation  and  removal  are  the  following:  1)  A  pre-reformer  (steam 
reformer) with the aim to partly convert the natural gas fuel to hydrogen. 2) A large stack module that consists 
of  several  SOFC  stack  towers  and  3)  A  post-combustor  system.  Other  auxiliary  components,  referred  to  as  

anode  and/or cathode off-gas  leaving the LSM/SOFC  unit may  be  recirculated.  The anode off-gas from the 
SOFC consists of water vapor, unreacted hydrogen, carbon monoxide, carbon dioxide and traces of other gases. 
The  role  of  the  post-combustor  unit  is  to  oxidise  carbon  monoxide,  non-reacted  hydrogen  and  other  trace 
pollutants to enable low emissions from the system. As in any combustion process new pollutants may form, 
for example trace levels of NOx.   

Figure 1. Schematic of SOFC system using methane as fuel. Reproduced from van Veldhuizen et al. 2023a under CC BY  

4.0.  

Emission Measurement Strategy for SOFC   Post-combustor Systems   

A set-up has been designed with the purpose to analyse the emissions from SOFC 
 postcombustor systems 
(Figure 2). The system includes the following measurement techniques, an FTIR Spectrometer using a 180 C 
heated sampling head for analysis of a large number of organic and inorganic gases (model atmosFIRt, Protea 
Ltd.). A separate sample was diluted 1:10 using an ejector diluter followed by a set of instruments with high 
sensitivity commonly used for ambient monitoring. This includes a chemiluminescence analyser for NO and NO2 
(model  CLD  700,  Eco  Physics),  an  aethalometer  for  Black  Carbon  emissions  (AE33,  Magee  Sci.)  and  a 
condensation particle counter for particle number emissions (CPC 3775, TSI Inc.).  

Figure 2. Set-up to analyse emissions (right) connected to the simplified reformer-burner system (left) to generate SOFClike emissions.  

480 

 
 
  
  
  
 
A  first  experimental  test  of  the  suitability  of  the  sampling  and  emission  characterisation  techniques  for 
expected  SOFC  emissions was  recently carried out at the  SolydEra site  in Switzerland. Gas compositions  of 
relevance for measurements downstream the burner in SOFC systems were generated using a synthetic gas-
mixture that was passed through a reformer before being fed directly into the postcombustor/burner. The SOFC 
was in this first test bypassed because of practical limitations. A number of operating points were investigated 
by varying the fuel (natural gas) flow rate, the steam flow rate (and thereby the steam to carbon ratio) and the 
pre-reforming ratio. The aim was to investigate the suitability of the emission measurement set-up for SOFClike 
conditions.  Another  aim  was  as  to  investigate  the  limits  of  the  post-combustor  in  terms  of  formation  of 
pollutants or break-through of fuel. The data are currently being analysed.   

Health Impact Assessment of Air Pollution from Shipping - Preliminary Results   

In the Nautilus project, we will estimate premature deaths attributable to ship emissions in the study areas 
Skåne, Southern Sweden, and the Hamburg urban area. We present specific case studies to illustrate the local 
effects of shipping emissions in selected areas of Europe. In Skåne, despite an overall decrease in particle and 
soot/BC emissions from shipping between 2000 and 2011, certain port cities, like Trelleborg, still experience 
significant air pollution from shipping activities. Similarly, in the Hamburg urban area, shipping contributes a 
substantial percentage to NO2 and PM2.5 exposure. We employ the concept of an air pollution health impact 
assessment (HIA), which estimates mortality rates and disease incidence resulting from changes in air pollution 
concentrations. The traditional assumption is that concentration-response functions for PM2.5 are the same 
for long-range transported and locally produced particles but recent research indicates a potentially steeper 
concentration-response curve for locally emitted PM2.5. This distinction is particularly relevant in countries like 
Sweden, where in-transported PM2.5 plays a significant role.   

Our preliminary results show that when looking at the study area Skåne, Southern Sweden, we found that fine 
particulate air pollution (PM2.5) in total was estimated to be a contributing cause of 699 deaths annually. Of 
these, 7 (1%) were estimated to come from emissions from shipping. In comparison, the corresponding 
numbers from road traffic was 63 (9%), for small-scale residential heating was 49 (7%), and for PM2.5 from 
long-range transport was 580 (83%). Overall, the preliminary results emphasize the urgent need for 
international regulation of shipping emissions due to their substantial contribution to climate change, air 
pollution, and associated health burdens. The results furthermore provide insights into the specific impacts of 
shipping emissions in European coastal regions, highlighting the importance of addressing this issue at both 
global and local levels.  

Outlook 

In 2024 direct emission measurements will be carried out at a 60 kW demonstrator unit that is currently 
being constructed in the project. We aim to add additional emission instrumentation for this part of the 
project, including size distributions of ultrafine particles. Using the baseline emissions and different scenarios 
from the literature review the emission reduction potential of the new technology will be quantified for the 
key pollutants.  

Future health impact assessment analysis will focus on the port cities of Trelleborg and Hamburg urban area. 
We will furthermore investigate different assumptions regarding the concentration-response curve. We aim to 
investigate the health benefit of different scenarios of introduction of the novel SOFC technology. The 
reduction of exposure will be based on the direct emission measurements at the demonstrator.    

Acknowledgements: We acknowledge funding from EU (H2020-NAUTILUS, GA-861647).  

Patrik Nilsson, Stefan Diethelm, Florian Waeber and Zacharie Wuillemin are acknowledged for contributing to 
the experimental tests using SOFC relevant gas-mixtures.   

References  

Aakko-Saksa, P., and Lehtoranta, K. (2019). Ship emissions in the future: Review. VTT Technical Research Centre of Finland. VTT Research 

Report No. VTT-R-00335-19.  

Aako-Saksa, P. T., Lehtoranta, K., Kuittinen, N., Järvinen, A., Jalkanen, J.-P., Johnson, K., Jung, H., Ntziachristos, L., Gagne, S., Takahashi, C., 
Karjalainen, P., Rönkkö, T., & Timonen, H. (2023). Reduction in greenhouse gas and other emissions from ship engines: Current trends 
and future options. Progress in Energy and Combustion Science, 94, 101055.  

481 

 
 
Brandt, J., Silver, J. D., Christensen, J. H., Andersen, M. S., Bønløkke, J. H., Sigsgaard, T., ... & Frohn, L. M. (2013). Assessment of past, present 
and future health-cost externalities of air pollution in Europe and the contribution from international ship traffic using the EVA model 
system. Atmospheric Chemistry and Physics, 13(15), 7747-7764.  

Di Natale, F., & Caratonuto, C. (2015). Particulate matter in marine diesel engine exhausts: Emissions and control strategies. Transportation 

Research, Part D., 40, 166-191.  

Gandiglio, M., Lanzini, A., Santarelli, M., Acri, M., Hakala, T., & Rautanen, M. (2020). Results from an industrial size biogas-fed SOFC plant 

(the DEMOSOFC project). International Journal of Hydrogen Energy, 45(8), 5449-5464.  

Grigoriadis, A., Mamarikas, S., Ioannidis, I., Majamäki, E., Jalkanen, J. P., & Ntziachristos, L. (2021). Development of exhaust emission factors 

for vessels: A review and meta-analysis of available data. Atmospheric Environment: X, 12, 100142.  

Karl, M., Jonson, J. A., Uppstu, A., Aulinger, A., Prank, M., Sofiev, M., Jalkanen, J.-P., Johansson, L., Quante, M., & Matthias, V. (2019). Effects 
of ship emissions on air quality in the Baltic Sea region simulated with three different chemistry models. Atmospheric Chemistry and 
Physics, 19, 7019-7053.  

Maragkogianni, A., Papaefthimiou, S., & Zopounidis, C. (2016). Mitigating shipping emissions in European ports: Social and environmental 

benefits (pp. 1-9). Berlin: Springer.  

Nautilus Project: https://nautilus-project.eu/  

Pina, E. A., van Veldhuizen, B., & Marechal, F. (2023). A Comparative Techno-Economic Assessment of Alternative Fuels in SOFC Systems 

for Cruise Ships. ECS Transactions, 111(6), 2459.  

van Veldhuizen, B. N., van Biert, L., Amladi, A., Woudstra, T., Visser, K., & Aravind, P. V. (2023a). The effects of fuel type and cathode off-
gas recirculation on combined heat and power generation of marine SOFC systems. Energy Conversion and Management, 276, 116498. 
van Veldhuizen, B., van Biert, L., Aravind, P. V., & Visser, K. (2023b). Solid Oxide Fuel Cells for Marine Applications. International Journal of 
Energy Research, 2023.  

Viana, M., Rizza, V., Tobías, A., Carr, E., Corbett, J., Sofiev, M., ... & Fann, N. (2020). Estimated health impacts from maritime transport in the 

Mediterranean region and benefits from the use of cleaner fuels. Environment international, 138, 105670.  

482 

 
 
 
 
the  marine  environment 

2.13  SE.13. Marine processes   fate of pollution from shipping 
in 
studies  on 
ecotoxicology,  eutrophication  and  acidification,  energy 
pollution  including  underwater  noise  &  induced  mixing 
experimental  work,  modelling  studies  of  dispersion, 
transport, and chemical and biological processes in marine 
waters. 

impact 

483 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Ecotoxicological Effects of Exhaust Gas Cleaning System (EGCS) Discharge Water 
on Marine Copepods  

M. Picone1*, E. Giubilato1, K. Magnusson2, M. Granberg2, A. Volpi Ghirardini1, and A. Marcomini1  

1 

marco.picone@unive.it 
2 Swedish Environmental Research Institute (IVL), Kristineberg Marine Research Station, Kristineberg, 45178, Sweden 

Introduction 

Exhaust Gas Cleaning Systems (EGCS) operating in open-loop mode continuously release in marine waters acidic 
effluents (scrubber waters) containing metals, alkylated and non-alkylated polycyclic aromatic hydrocarbons 
(PAHs), other polycyclic aromatic compounds (PACs), and particulate matter (Lunde Hermansson et al. 2021). 
These  effluents  can  affect  the  planktonic  community  in  the  receiving  waters,  including  bacteria,  algae,  and 
invertebrates at the adult and larval stages (Ytreberg et al 2021).  

Copepods dominate the zooplankton biomass in marine and estuarine environments and occupy a crucial food 
resource in the aquatic food webs because they graze on the phytoplankton and serve as a food reservoir for 
consumers of higher trophic levels (Turner, 2004). Copepods primarily graze in surface waters, exposing them 
to the potentially toxic effects of scrubber water since organisms in the surface waters are exposed to less 
diluted EGCS discharges and hence to higher concentrations of the scrubber water than, e.g., bottom-living ones. 
Furthermore, since the productivity of aquatic ecosystems is often highly dependent on the lower trophic levels, 
according to bottom-up ecosystem regulations (Irigoien et al. 2004), adverse effects towards copepods might 
have detrimental effects also on the whole marine and coastal ecosystem.  

To assess the adverse effects of scrubber waters on copepods, we exposed laboratory-cultured specimens of 
the  calanoid  copepod  Acartia  tonsa  to  two  different  scrubber  waters:  1)  an  artificial  ECGS  discharge  water 
produced by the engine lab at the Chalmers University of Technology; 2) a real scrubber water collected on 
board the LEO C vessel in the Mediterranean Sea (36°26'21.86"N, 17°34'53.21"E) on the journey from Antwerp 
(Belgium) to Gebse (Turkey). 

Copepod culturing and toxicity tests 

Copepods used for testing derived from in-
Venice originated from adult copepods obtained from Guernsey Sea Farms (Port Vale, Guernsey, UK). Copepods 
(600-800  individuals)  were  cultured  in  2-
according to ISO 16778 (ISO, 2015). The cultures were kept at 20 ± 1 ◦C, under continuous aeration, 16-h light 
and 8-h dark photoperiod, and were fed ad libitum with a mixture of Tetraselmis suecica, Pavlova lutheri, and 
Tisochrysis lutea (Picone et al. 2021). The food was administered four times per day using a timer-controlled 
peristaltic pump. 

The toxicity tests included acute exposures, larval development tests, and long-term exposures aiming to detect 

water  and  were  performed  according  to  the  CRED  (Criteria  for  Reporting  and  Evaluating  ecotoxicity  Data) 
evaluation method for the reliability and relevance of toxicity data (Moermond et al. 2016).  

The acute test was performed according to the ISO 14669 standard method (ISO, 1999) using adult copepods 
and copepodite stage V (C-V) individuals exposed for 48-h to different scrubber water dilutions, covering the 
range of 0.01% to 40% scrubber water. The acute test was run in 4 replicates, using a sample volume of 20 
mL in 30 mL crystallising dishes. Copepod mortality was used as the endpoint.  

The larval development test was performed according to the ISO 16778 standard method (ISO, 2015), modified 
as reported by Picone et al. (2021). Freshly released eggs from the in-house culture were exposed for 5 days 
to  different  dilutions  of  test  solution  covering  the  range  of  0.01%  to  40%  scrubber  water.  The  larval 
development test was run in 6 replicates, using a sample volume of 30 mL in 100 mL glass beakers. On day 2, 
the test solution was refreshed by adding 30 mL of scrubber water dilution. The exposure ended on day 5, when 
approximately 40 % of the larvae in controls should have completed the metamorphosis to the copepodite-I 
stage  (C I).  Egg  hatching,  larval  mortality,  and  the  ratio  of  larvae  that  reached  the  C-I  stage  were  used  as 
endpoints.  

484 

 
 
 
The long-term exposure was performed according to the procedure outlined in Picone et al. (2022). The test 
started by adding 400 600 newly released eggs to each test solution (0.001%, 0.01%, 0.1% and 1% scrubber 
water) in 1L glass bottles (F0 exposure). The bottles were then kept at 20 ± 1 ◦C, with a 16-h light 8-h dark 
photoperiod for 21 days. The food was provided thrice weekly during the renewal of test solutions. On day 13, 
twelve mature females were collected from each test bottle and individually placed into 15 mL glass vials filled 
4 cells mL-1. Their egg production was 

checked daily for three days. 

In addition to the ship-derived and artificially produced scrubber water samples, ambient seawater collected in 
the North Adriatic Sea (NSW) was also tested to identify possible background effects in the receiving water 
offshore the Port of Venice. 

The  no-observed  effect  concentration  (NOEC)  and  the  lowest  observed  effect  concentration  (LOECs)  were 
estimated using the one-
and homoscedasticity were verified using the Kolmogorov-
concentrations 50 % (EC50s) were calculated using a statistical program for continuous response developed at 
the Technical University of Denmark (Christensen et al., 2009) by assuming a log-normal distribution of the 
observed effects. All statistical analyses were performed using the IBM SPSS statistical software V.2.  

post hoc 

Results and discussion 

The ship-derived scrubber water affected adult survivals starting from the 10% treatment (5d-LOEC = 10%; 
EC50 = 11%), while effects on hatching and larval survival in the larval development tests were significantly 
inhibited at 20%  scrubber water (Picone et  al., 2023). The larval development  to the C-I stage was a  more 
sensitive endpoint than hatching and mortality, being significantly inhibited at a lower concentration (5d-LOEC 
at 2% scrubber water) than hatching and early-life stages mortality (Picone et al., 2023). The reproduction after 
the long-term exposure was also affected by scrubber water, with egg production lower than the control (8.2 ± 
1.4 eggs female-1 d-1) in all the tested concentrations. However, a U-shaped trend was observed, with the lowest 
egg production obtained at 0.01% scrubber water (2.9 ± 0.7 eggs female-1 d-1) and then an increase at 0.1% 
and 1% scrubber water (4.2 ± 0.9 eggs female-1 d-1 and 6.2 ± 1.0 eggs female-1 d-1, respectively).  

The results obtained with the artificially produced scrubber water were similar but denoted a slightly higher 
toxicity of the ship-derived scrubber water than the artificial one (Table 1). The artificially produced scrubber 
water affected adult survival only at the 40% treatment (one-way ANOVA: F = 2.59, p = 0.031), which showed 
a  33%  mortality  compared  to  the  control.  Similarly,  in  the  larval  development  test,  the  hatching  rate  was 
significantly  inhibited  at  40%  scrubber  water  (one-way  ANOVA:  F  =  8.92,  p  <  0.001).  The  early-life  stage 
mortality was slightly more sensitive than hatching and adult mortality, providing a 5d-NOEC at 10% scrubber 
water and a 5d-EC10 at 9% scrubber water. The larval development from egg to the C-I stage was confirmed 
as the more sensitive endpoint and was also significantly inhibited at the 0.01% scrubber water concentration 
(Table 1). Egg production in the long-term exposure was significantly lower than the control (7.1 ± 1.5 eggs 
female-1 d-1) in all the tested concentrations. However, also in this case, it showed a U-shaped trend, with a 
higher daily egg release at 1% scrubber water (3.1 ± 0.8 eggs female-1 d-1), than at 0.01% and 0.1% scrubber 
water (2.3 ± 0.9 eggs female-1 d-1 and 2.2 ± 1.5 eggs female-1 d-1, respectively). 

Toxic effects in NSW were n
HSD test: p = 0.999; Picone et al., 2023). Similarly, in the larval development test, no differences were observed 

lower  in  NSW  (4.0  ±  1.5  eggs  female-1  d-1)  compared  to  the  control  (7.1  ±  1.5  eggs  female-1  d-1),  but  the 

485 

 
 
 
 
 
 
Table 1: Summary of the toxicity data for the scrubber water samples. Data for the ship-derived scrubber water 
were obtained from Picone et al. (2023). 

Toxicity test 

Endpoint 

Artificial scrubber water 

Ship-derived scrubber 
water 

NOEC 

LOEC 

EC50 

NOEC 

LOEC 

EC50 

Acute test 

Mortality 

20% 

40% 

Hatching 

Larval survival 

20% 

10% 

40% 

20% 

n.c. 

n.c. 

9% 

5% 

10% 

11% 

10% 

10% 

20% 

20% 

25% 

13% 

Larval development 

<0.01% 

0.01% 

0.009% 

1% 

2% 

1.5% 

Egg production 

parameters not calculable due to the U-shaped curve. Lowest egg 
production at 0.01% scrubber water 

Larval 
development 
test 

Long-term 
exposure 

Contaminant  concentrations  (metals  and  PAHs)  varied  relevantly  between  the  scrubber  water  samples, 
especially V, Cr, Fe, Cu, and PAHs (Table 2), confirming that different engines and fuels have a significant impact 
on the chemistry of the EGCS discharge water (Teuchies et al., 2020), which in turn might affect the plankton 
community  in  different  ways.  Metal  concentrations  in  the  undiluted  scrubber  waters  were  generally  below 
literature data on adverse effect levels for mortality and larval development. However, a few exceptions to this 
-1), which concentration 
trend were observed, including Cu in the 
-1) and the 5d-NOEC for the larval development ratio 
was higher than the 48h-LC20 
-1). Thus, the toxicity of both scrubber waters was likely due to the synergistic effects of the 

chemical mixture and physicochemical properties of the scrubber water.  

The different effects exerted by artificially produced and ship-derived scrubber water on A. tonsa in the acute 
and larval development test are probably due to the different chemical compositions of the two samples, with 
ship-derived scrubber water being more enriched in PAHs and artificially produced one in metals. In particular, 
both  trace  metals  and  PAH  are  known  to  affect  different  traits  in  copepods,  which  may  lead  to  retarded 
development, both through increasing costs for detoxification (Han et al., 2014; Lee et al. 2008) and limiting 
feeding and assimilation (Saiz et al., 2009; Xu et al., 2001).  

Different hypotheses were formulated about reduced egg production after exposure to low concentrations of 
scrubber  water,  including  impairment  of  gonad  development  and  maturation  and  oxidative  stress.  Indeed, 
contaminants in the scrubber water might have damaged the tissues of the gonads, impaired their maturation, 
or  disrupted  the  oogenesis  (Hook  and  Fisher  2001;  Jensen  et  al.,  2008).  Similarly,  reactive  oxygen  species 
generated by exposure to contaminants might have damaged the gonadal tissues and increased the costs for 
antioxidant defences, including antioxidant enzyme synthesis (Han et al., 2014).  

Due  to  the  complex  mixture  of  chemicals and  particles  in  scrubber water samples,  identifying  the  potential 
toxicity  pathways  is  very  complex.  Anyhow,  since  tests  with  single  substances  evidenced  a  significant  and 
positive  correlation  between  egg  production  and  feeding  (Figure  1;  Picone  et  al.,  unpublished  data),  we 
hypothesised  that  disruption  of  feeding  might  be  a  at  least  one  explanation  for  the  observed  effects.  The 
mechanisms underlying the inhibition of feeding need to be further studied. 

486 

 
 
 
 
 
 
 
 
Table 2: Summary of the chemical analyses in the undiluted scrubber water and NSW samples. Data for the 
ship-derived scrubber water and NSW were obtained from Picone et al. (2023). 

Contaminant  Unit  

Artificially produced scrubber 
water 

Ship-derived 
scrubber water 

Analysed sample 

V 

Cr 

Mn 

Fe 

Co 

Ni 

Cu 

Zn 

As 

Cd 

Hg 

Pb 

U 

-1 

-1 

-1 

-1 

-1 

-1 

-1 

-1 

-1 

-1 

-1 

-1 

-1 

PAHs 

ng L-1 

1.40 

63.00 

12.0 

611 

1.400 

74.0 

55.0 

66 

1.70 

0.057 

0.0004 

1.30 

3.3 

1,440 

348.28 

7.50 

3.1 

109 

0.474 

99.46 

5.3 

49 

2.02 

0.032 

NSW 

0.73 

0.16 

2.8 

4 

0.026 

0.35 

4.8 

49 

0.94 

0.007 

0.0018 

0.0003 

0.35 

6.9 

15,400 

0.65 

1.7 

38 

Figure 1: Scatterplot showing the relation between reproduction (using egg production as a proxy) and feeding 
(using pellet production as a proxy) in testing organic chemicals with A. tonsa. Colours identify different test 
substances; shapes indicate different test concentrations. The grey area identifies the 95% confidence limits 
for the regression line. 

487 

 
 
 
 
Conclusions  

The scrubbing process generates acidic effluents characterized by high concentrations of metals (V, Ni, Cr, Cu, 
and  Fe  in  particular),  PAHs,  alkylated  PAHs,  and  possibly  other  contaminants  not  measured  here,  with  the 
potential to exert adverse effects on the copepod assemblage of the receiving water.  

The data highlight that exposure to scrubber water significantly affected A. tonsa at low concentrations, mainly 
by  reducing  reproductive  success  and  retarding  larval  development.  These  adverse  effects  may  have 
detrimental consequences at the population level with possible impacts at higher levels of the aquatic food 
webs. The results of this work stress the relevance of long-term exposure to comprehensively assess the impact 
of scrubber water on the marine ecosystem and, in general, contribute to the evaluation of new technologies 
for emission control in maritime shipping. Nevertheless, for many contaminants of concern in scrubber water 
samples  (i.e.,  V  and  alkylated  PAHs),  there  is  a  need  to  increase  the  dataset  of  adverse  effects  levels  for 
individual chemicals since such information is not available for many relevant sublethal endpoints, including 
larval development and egg production. 

Acknowledgements  

under Grant Agreement No 874990 (EMERGE project). The authors are deeply grateful to Leonidas Ntziachristos 
(Aristotle University of Thessaloniki, Greece), Andreas Gondikas (Creative Nano, Greece), Jana Moldanova (IVL, 
Sweden) and the whole team planning and allowing the realisation of the field campaign on the Leo C and to 
Erik Ytreberg, Ida-Maja Hassellöv, Kent Salo for providing the artificial scrubber water using the engine lab at 
the Chalmers University of Technology.  Without their contribution, it would not have been possible to have any 
sample of scrubber water for the testing and evaluation of effects. The authors are also grateful to Anna Lunde 
Hermansson and all people that make feasible the on-board sampling of scrubber water, the storage of the 
samples, and finally their delivery in Venice, and to Mira Petrovic, Elisa Garcia, and Meritxell Gross for managing 
and analysing such an amount of samples during the Project. The authors also thank all partners in Task 2.3 of 
the EMERGE Project for the proficient discussions concerning the test species and methods used in the other 
project case studies. 

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Res, 181, 105761. 

Picone M., Russo M., Distefano G.G., Baccichet M., Marchetto D., Volpi Ghirardini A., Lunde Hermansson A., Petrovic M., Gros 
M., Garcia E., Giubilato E., Calgaro L., Magnusson K., Granberg M., Marcomini A. (2023). Impacts of exhaust gas cleaning 
systems (EGCS) discharge waters on planktonic biological indicators, Mar Pollut Bull, 190, 114846. 

Saiz  E.,  Movilla  J.,  Yebra  L.,  Barata  C.,  Calbet  A.  (2009).  Lethal  and  sublethal  effects  of  naphthalene  and  1,2-
dimethylnaphthalene on naupliar and adult stages of the marine cyclopoid copepod Oithona davisae, Environ Pollut, 157, 
1219 1226. 

Teuchies J., Cox T.J.S., Van Itterbeeck K., Meysman F.J.R., Blust R. (2020). The impact of scrubber discharge on the water 
quality in estuaries and ports, Environ Sci Eur, 32, 1 11. 

Turner J. (2004). The importance of small planktonic copepods and their roles in pelagic marine food webs, Zool Stud, 43, 
255-266. 

Xu Y., Wang W.X., Hsieh D.P.H. (2001). Influences of metal concentration in phytoplankton and seawater on metal assimilation 
and elimination in marine copepods, Environ Toxicol Chem, 20, 1067 1077. 

Ytreberg E., Karlberg M., Hassellöv I-M., Hedblom M., Nylund A.T., Salo K., Imber, H., Turner D., Tripp L., Yong J., Wulff, A. 
(2021). Effects of seawater scrubbing on a microplanktonic community during a summer-bloom in the Baltic Sea, Environ 
Pollut, 291,118251. 

489 

 
 
 
 
2.14  SE.14.  Holistic  assessment  of  shipping  impacts  on  the 
environment, shipping in the marine spatial planning. 

490 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Financial incentives for ship underwater noise mitigation  

A. T. Johansson*1, S. Sköld2, Carl Andersson1, Cecilia Andersson1, and H. Winnes2  

1 IVL Swedish Environmental Research Institute, Gothenburg, Sweden. torbjorn.johansson@ivl.se 
2 Swedish Maritime Administration, Norrköping, Sweden.  

Introduction 

When a ship arrives at a port, the ship owner is typically charged a port fee. In Sweden and Finland, ship owners 
are also levied with fairway dues. In Sweden, these dues are payable to the Swedish Maritime Administration 

financial incentive for ship owners to improve their environmental performance. Ports are free to structure their 
fees as they see fit and may choose to implement financial incentives for improved environmental performance. 
Port and fairway dues can be based on ship environmental indexes such as the Clean Shipping Index (CSI) or 
the Environmental Shipping Index (ESI). Here, we consider how ship underwater noise could be included in the 
Clean Shipping Index. 

The function of the Clean Shipping Index 

Ship owners who have regis
Index (CSI) system are eligible for a rebate on the Swedish national fairway dues. In the beginning of 2023, 
ce in categories (Clean Shipping Index, 2022). 

These are 

  Carbon dioxide emissions 
  Nitrous oxides emissions 
 
  Use of chemicals 
  Water and waste management 

Sulphur oxides and particulate matter emissions 

CSI scores the environmental performance on a scale from 0 to 150. Each category is assigned a maximum of 
30  points  (Figure  1).  A  ship  owner  wishing  to  certify  a  ship  answers  a  questionnaire  to  determine  the 
environmental performance. The CSI is structured in such a way that emission measurements are not required, 
placing emphasis on the low financial effort required of the ship owner to obtain the certification. However, a 
validation by a third party is sometimes required to obtain specific economic incentives, e.g., by the Swedish 
Maritime Administration. 

Figure 1: Category scoring by the Clean Shipping Index. 

491 

 
 
 
 
 
 
Introducing underwater noise into the Clean Shipping Index 

Introducing  an  underwater  noise  category  in  the  Clean  Shipping  Index  would  be  a  way  of  implementing  a 
financial incentive for the reduction of ship underwater noise. It is therefore of interest to investigate how this 

e.g. the Environmental Ship Index (ESI) and Green Marine. Each index has its own way of operating and scoring. 
While the focus here is on Clean Shipping Index, we will also attempt to draw general conclusions on introducing 
ship underwater noise into an environmental scoring system for ships. 

Basing an incentive on dedicated measurement of the underwater radiated noise of a ship is not appropriate. 
First,  Clean  Shipping  Index  does  not  require  measurement  of  any  other  environmental  category.  Second, 
performing an accurate measurement of ship underwater radiated noise requires specialised equipment and 
skills and is costly. There are few actors who offer this service to ship owners.  

This study has evaluated six different ways of developing an environmental score for the underwater noise 
radiated by a ship. These are 

Predicting the radiated noise of a ship based on its characteristics 
Rewarding speed reductions 
Rewarding technical measures for noise mitigation 

 
 
 
  Basing the score on silent notations from classification societies 
 
 

Performing a noise investigation (without direct measurement) 
Establishing bespoke measurement stations that measure the ship underwater noise with sufficient 
accuracy to establish a score 

We will now discuss each of these alternatives in turn. 

Predicting the radiated noise of a ship  

Ship noise levels in the environment can be modelled by combining data from measurements of ship source 
spectra  and  models  for  the  noise  propagation.  One  important  component  in  this  is  estimating  the  source 
spectrum  of  ships  without  requiring  an  acoustical  measurement  of  each  individual  ship,  i.e.,  using  some 
predictive model.  

Physically informed models, e.g. (Wittekind, 2014) and (Arveson and Vendittis, 2000), attempt to model the 
noise spectrum of a ship as accurately as possible, based on knowledge of the properties of the ship. These 
models  are  difficult  to  obtain  since  they  require  thorough  acoustical  measurements  of  ships  as  well  as  a 
detailed description of the ships in question. This makes these models able to predict how noise levels would 
change if the physical properties or operative conditions of a ship is changed, although only with certainty for 
the specific parameters and ships that were included in the study. If this behaviour, often derived from a small 
sample of ships, generalizes over an entire fleet, it can be used to predict how fleet-wide actions can impact 
the overall noise level. However, such a generalization is a source of uncertainty and possible errors. It can also 
be difficult to apply physical models in practice since they may rely on ship properties which are not easily 
available.  

Statistical ensemble models, e.g. (Wales and Heitmeyer, 2002) and (MacGillivray and de Jong, 2021), on the 
other hand are by design meant to represent an entire fleet and can as such accurately predict the average 
noise levels present due to a large number of ships. Most statistical models do not, however, attempt to describe 
anything but the current ensemble of ships. This means that they are not good predictors of the effects of any 
changes to ship design or operating conditions that are not represented in the current ensemble of ships. E.g., 
if a statistical model is derived from measurements of ships at cruise speed, it cannot be used to estimate the 
effect of operating the ships at speeds lower than their respective cruise speeds. 

Neither physically informed models or statistical ensemble models are currently not accurate enough to use for 
incentives. They can produce reasonably correct averages across a large fleet of ships of different types and 
characteristics  and  are  useful  for  noise  mapping.  They  can  also  inform  operative  measures  such  as  speed 
reduction by predicting the effect of a potential speed reduction, as long as the intended speed reduction is not 

492 

 
 
 
 
outside the range of the data that was used to develop the model(s). However, the predictions for individual 
vessels  may  be  quite  inaccurate.  Moreover,  the  models  do  not  consider  the  propeller  or  hull  design  or  any 
technical measures that may affect the underwater radiated noise performance. As such, they will not stimulate 
mitigation of underwater radiated noise. 

Rewarding speed reductions 

An incentive may reward vessel speed reductions. Large scale trials in Vancouver and off the coast of California 
(MacGillivray et al, 2019; ZoBell et al., 2022) found that on average, speed reductions led to underwater noise 

without the need for external verification. An incentive could then be based on giving rebates to those ships 
that have performed a speed reduction. Speed reductions are generally also beneficial for fuel efficiency and 
carbon footprint, thus may yield multiple benefits. It would not be sufficient to apply the same speed limit for 
all ship types, as some ships are designed to travel at slower speeds and may otherwise be granted the rebate 
despite not having changed their operations.  

It is not certain that a speed reduction will lead to a noise reduction; for some ships, there may be no noise 

from this cruise speed puts the ship  outside  its designed mode of operation which may  result in noise and 
efficiency drawbacks.  

A large enough speed reduction that the speed drops below the cavitation inception speed (CIS) will make the 
propeller significantly quieter than what it is above the CIS, which will probably have a large positive effect on 
the  overall  levels  of  underwater  radiated  noise.  However,  such large  speed reductions  may  not  be  feasible; 
transit times will increase significantly which will affect the financial situation of ship owners. During interviews 
and workshops, several ship owners have expressed that significant speed reductions are only feasible in small 
areas, so that the average transit times are not greatly affected.  

In Swedish and European waters, controllable pitch propellers (CPP) are common. This is not the case in North 
American  waters.  It  is  more  difficult  to  describe  the  noise  radiation  of  a  CPP  propeller  across  its  different 
operation  modes  because  not  only  the  speed  but  also  the  pitch  of  the  propeller  blades  is  variable.  The 
combination of speed and pitch is known as the combinator curve. This curve is typically optimized for fuel 
efficiency, and it is not clear how levels of radiated noise are affected by speed reductions for ships equipped 
with CPP propellers. 

Rewarding technical measures for noise mitigation 

Several  technical  noise  mitigation  measures  have  been  proposed  and  are  in  operation,  including  fins  and 
rudders, special propeller designs and resilient engine mounts, see e.g. (Vard Marine Inc., 2019). The port of 
Vancouver rewards a number of such measures with port fee rebates, and it would be straightforward to apply 
a  similar  rebate  system  in  Sweden.  To  secure  the  quality  of  such  a  system,  it  would  be  vital  to  obtain 
independent scientific results  on typical  noise  reductions  that result when using a specific technology.  Such 

obtained with a certain technology depend on ship characteristics, and it may be that certain technologies are 
only  applicable  to  ships  that  fulfil  certain  requirements.  According  to  interviews  with  technical  ship  design 
experts, there is also room for improvements on propeller design. It would be difficult to reward such efforts 
since it is difficult to define quantitative propeller requirements for low noise radiation. Nevertheless, the efforts 
of  the  port  of  Vancouver  stimulate  the adoption  of  technical  measures  that  can  be  expected  to  reduce  the 
radiated  noise  and  should  be  commended.  Basing  an  incentive  on  technical  measures  for  noise  mitigation 
rewards effort on the part of the ship owner, which in turn may drive a positive development in terms of ship 
underwater noise.  

493 

 
 
 
 
Using silent notations  

Currently,  six  ship  classification  societies  have  issued  guidelines  on  underwater  radiated  noise,  including 
measurement principles. These guidelines all prescribe one or two spectral limit curves. If the noise radiation of 
a ship under test is below the limit curve in all frequency bands, some form of silent notation is awarded. An 
incentive for underwater noise reduction could reward ships that have such silent notations. This would be a 
straightforward way to introduce an incentive and it would also be cost effective for the incentive organization. 
Classification societies perform independent investigations so there would be no need for external third-party 
verification. However, silent notations are not commonly issued. A representative of one of the classification 
societies stated that less than 100 ships had obtained their silent notation. The scarcity of silent notations is 
probably due to the lack of incentives or regulations that could motivate obtaining such a notation. Contracting 
a classification society to perform the detailed measurements that are required will cost at least a few hundred 
thousand SEK, equivalent to at least 20-
out of operation for the noise measurements, which may incur an even larger loss of income to the ship owner.  

For a level playing field, all silent notations should be given the same score within an incentive. It is however 
worth noting that classification societies have different limit curves for ship underwater noise. 

When a silent notation is issued, the classification society may report the noise levels of the vessels. If societies 

noise levels. If not, a simple pass/pass/failure is what is available, and only two different scores can be awarded.  

Performing a noise inquiry  

A  noise  inquiry  may  be  performed  to  determine  likely  noise  sources  onboard  a  ship  and  identify  relevant 
mitigations for reduced underwater radiated noise. This inquiry should be performed by an external party and 
may list recommended mitigations considering mitigation and cost efficiency as well as operational constraints. 
A lower level of incentive may be awarded at completion of such an inquiry. Upon fulfilment of one or several 
recommended mitigations, a higher level of incentive may be awarded.  An advantage of this approach is that 
measurements are not needed. A drawback is that it may not be clear from the inquiry what the dominant noise 
sources are.  

Establishing bespoke measurement stations  

There is really no alternative to direct measurement if one wants to know how much noise a certain vessel 
radiates.  Thus,  if  one  desires  to  base  an  incentive  on  the  actual  levels  of  radiated  noise,  these  must  be 
measured. Classification societies measure underwater noise for their silent notations, and we have seen that 
using  silent  notations  issued  by  classification  societies  is  an  alternative.  However,  these  notations  are  not 

radiation.  

Given that ships typically travel in specific fairways, underwater noise fingerprints may potentially be obtained 
by deploying autonomous measurement stations at certain points where many ships pass. Such measurements 
have been performed e.g., near the port of Vancouver. The data has been used to investigate average effects 
of speed reduction and to develop prediction models, but not to set incentives. An example of an opportunistic 
measurement station is given in Figure 2. It was placed at Böttö in the inlet to the port of Gothenburg, Sweden, 
and recorded underwater and airborne noise of passing vessels. Due to a limited number of hydrophones and 
insufficient knowledge of sound propagation conditions, the accuracy was however not sufficient for use in an 
incentive. 

494 

 
 
 
 
Figure  2:  A  ship  noise  measurement  station  deployed  at  the  island  of  Böttö,  Sweden,  adjacent  to  the  main 
fairway into the port of Gothenburg. The upper arrow indicates the position of a microphone, while the lower 
arrow indicates the position of a hydrophone at 13 m depth.  

Opportunistic measurements typically suffer from poor accuracy due to e.g., a lack of control of the studied 
ships and uncertain noise propagation conditions. The type of studies performed in Vancouver can still achieve 
their goals, but to set incentives on individual vessels the accuracy may need to be better. If the accuracy can 
indeed be improved, measurement stations could be placed at or near inlets to major ports and collect noise 
fingerprints on which an incentive could be based. 

Summary and conclusions 

Six different ways of designing a financial incentive for ship underwater noise reduction have been described, 
each having its benefits and drawbacks. Rewarding speed reductions or technical measures for noise mitigation 
is  feasible  but  the  scientific  basis  is  not  clear. An  incentive  may  be  based  on  a  silent  ship  notation  from  a 
classification society, but these are not commonly issued. A noise inquiry may be performed, but it may be 
difficult  to  identify  the  most  relevant  mitigations  without  underwater  noise  measurement.  Bespoke 
measurement stations at or near port inlets may be a cost-effective way to collect measurement data, but the 
accuracy of such opportunistic measurements would need to be improved if the data is to be used for a financial 
incentive. 

Acknowledgements  

This  work  was  funded  by  Formas,  a  Swedish  research  council  for  sustainable  development,  under  grant  no 
2021-02660. 

References 

Clean Shipping Index (2022). Methodology and Reporting Guidelines 2022. Clean Shipping Index, Gothenburg, Sweden. 
Arveson, P. T., Vendittis D. J. (2000). Radiated noise characteristics of a modern cargo ship. The Journal of the Acoustical 

Society of America, 107, 118-129. 

Wittekind, D. K. (2014). A Simple Model for the Underwater Noise Source Level of Ships. Journal of Ship Production and 

Design, 30, 7-14. doi:10.5957/jspd.30.1.120052 

495 

 
 
 
 
 
 
Wales, S. C., Heitmeyer, R. M. (2002). An ensemble source spectra model for merchant ship-radiated noise. The Journal of 

the Acoustical Society of America, 111, 1211-1231. doi:10.1121/1.1427355 

MacGillivray, A., & de Jong, C. (2021). A Reference Spectrum Model for Estimating Source Levels of Marine Shipping Based 
on Automated Identification System Data. Journal of Marine Science and Engineering, 9.  doi:10.3390/jmse9040369 

ZoBell V. M., Frasier K. E., Hildebrand J. A. (2022). Sound Levels of Participating Vessels during the 2021 Vessel Speed 

Reduction Program in the Santa Barbara Channel. Final Report. Marine Physical Laboratory Technical Memorandum 
659. 

MacGillivray, A. O., Zizheng L., Hannay D. E., Trounce K. B., Robinson O. M. (2019). Slowing Deep-Sea Commercial Vessels 

Reduces Underwater Radiated Noise. The Journal of the Acoustical Society of America 146,340 51. doi: 
10.1121/1.5116140. 

496 

 
 
 
3  Conference Proceedings   Short abstracts 

This  volume  of 
papers with short one-page abstracts for those, which have not been submitted as an extended abstract. 

-text  collection  of  the  conference 

Papers of this section are: 

structured based on the thematic sessions of the TAP&SE 2023, as defined in the final program of the 
conference, 

listed in the order that were presented during the conference, 

 

 

 

implementation dates of TAP&SE 2023. 

Table 1 lists the short abstracts by session/topic of TAP&SE 2023. 

Table 1. List of short abstracts presented in Volume 2 by session/topic of TAP&SE 2023.  

Corresponding author, Title 

Session/Paper 
ID 
TAP.02 

TAP.02.1 

TAP.02.2 

Wang, H. 

On-board  monitoring  and  diagnostics,  emission  tampering  and 
deterioration 
Pöhler, D. 

Identification of Manipulated & Defective Truck NOx Emission 
Reduction  Systems  with  Plume  Chasing  for  Authority 
Inspections 
Strong  impact  of  ambient  temperature  on  nitrogen  oxides 
emissions  from  heavy-duty  diesel  trucks  based  on  plume 
chasing tests 

TAP.03 

In-service conformity and new concepts for enhanced emission testing 

TAP.03.2 

Leblanc, M. 

Extended  evaluation  of  the  emissions  of  top-selling  Euro  6 
cars  depending  on  powertrain  and  exhaust  after-treatment 
technologies 

TAP.04 

TAP.04.1 

Imtiaz, H. 

Remote sensing of vehicle emissions 

TAP.04.3 

Schmidt, C. 

TAP.04.4 

Sjödin, Å. 

TAP.04.5 

Knoll, M. 

A  Gas  Schlieren  Imaging  Sensor  System  for  Locating  and 
Examining Automotive Exhaust Plumes for Remote Emission 
Sensing Applications 
Optimisation and Validation of Plume Chasing for Particle and 
NOx High Emitter Identification 
A  method  for  deriving  characteristic  emission  polygons  for 
engine families based on large datasets from remote sensing 
measurements 
Intercomparison  of  Remote  Emission  Sensing  Methods  and 
Validation with PEMS Measurements 

TAP.05 

TAP.05.4 

TAP.06 

TAP.06.1 
TAP.06.2 

TAP.07 

TAP.07.1 

Vehicle greenhouse gas emissions, energy consumption, vehicle and fuel 
life cycle analysis 
Mamarikas, S. 

Electric and conventional vehicles energy consumption under 
various traffic conditions 

Vehicle emission modelling and measurements and impact assessment 
of emission regulations 
Wen, Y. 
Sanna, S. 

Updated vehicle emission inventory in China. 
Long-term  trends  of  black  carbon  and  particle  number 
concentrations and vehicle emission factors in Stockholm 

Non-road emissions 

Tu, M. 

Train type effect on nanoparticles on an underground metro 
platform in Stockholm 

497 

 
 
 
 
TAP.07.2 

Juárez-Facio, A. 

TAP.07.5 

Vroom, Q. 

TAP.07.6 

Landl, L. 

Toxicity  of  particle  emissions  from  car  and  train  brake 
materials 
Brake  wear  ultrafine  particle  emissions  from  a  light  duty 
vehicle under real driving conditions 
A  Novel  Simulation  Approach  for  Non-Exhaust  Particle 
Emissions 

JS.08 

Air  pollutant  and  GHG  emissions,  water  contaminants,  ambient  and 
underwater noise and vessel-induced mixing 

JS.08.1 

Schade, J. 

JS.08.4 

JS.08.5 

JS.08.6 

JS.08.7 

JS.08.8 

JS.09 

JS.09.2 

JS.09.3 

JS.09.5 

JS.09.9 

JS.10 

JS.10.2 

JS.10.3 

JS.12.2 

JS.12.3 
JS.12.4 
JS.12.5 

SE.13 

Voniati, G. 

Moldanova, J. 

Grönholm, T. 

Hallquist, Å. 

Lunde 
Hermansson, A. 

Maragkidou, A. 

Russo, M. 

Dal Maso, M. 

Van Dinther, D. 

Beecken, J. 

Remote  detection  of  ship  exhaust  plumes  from  different 
marine  fuels  on  board  a  research  vessel  in  the  Baltic  Sea 
region using single-particle mass spectrometry 
Ammonia  as  a  Marine  Fuel  Towards  Decarbonization: 
Emission Control Challenges 
Characterisation  of  emissions  from  marine  vessel  with  E-
methanol fuel and NOx emission control 
Discharges from exhaust gas cleaning systems in the OSPAR 
marine area 
Characterisation of fresh and aged ship emissions in the Port 
of Marsei 
Distribution  of  PAHs  and  metals  between  exhaust  and 
scrubber water discharge from a large 2-stroke slow speed 
marine engine equipped with open loop scrubber 

Atmospheric processes and air quality impact studies: Modelling impacts 
of transport on air pollution, climate, health and ecosystems 
Jutterström, S. 

Impacts of shipping emissions on air pollution in 2040: effects 
of NECA and non-compliance 
Direct evidence of the substantial effect of SECA in the Baltic 
Sea 
Future impact of shipping emissions on air quality in Europe 
under climate change scenarios 
On the determination of ship exhaust aerosol volatility in the 
SCIPPER project 

Compliance  monitoring: 
technological  and 
experimental studies; theoretical impact studies 

legal 

frameworks; 

Comparison of particle emission factors from shipping using 
different instruments 
Performance  assessment  of  state-of-the-art  and  novel 
methods  for  remote  compliance  monitoring  of  sulphur 
emissions from shipping 
Remote monitoring of NOx from shipping- validation and long 
term results   

Malik  Kanchiralla, 
F. 
Fridell, E. 
Majamäki, E. 
Matthias, V. 

Potential impact on emissions of an introduction of hydrogen 
and fuel cell based propulsion in Nordic shipping 
Environmental and economic assessment of green and blue 
fuels for shipping 
Policy scenarios for analysing use of scrubbers in shipping 
Projections of shipping emissions in Europe in 2040 and 2050 
Climate friendly and pollution-free? Scenarios for air pollution 
from shipping in Europe in 2050 

Marine  processes  -  fate  of  pollution  from  shipping  in  the  marine 
environment:  impact  studies  on  ecotoxicology,  eutrophication  and 
acidification,  energy  pollution  including  underwater  noise  &  induced 

498 

JS.12 

Scenarios and policy options for sustainable transport 

JS.10.4 

Mellqvist, J. 

JS.12.1 

Parsmo, R. 

 
 
SE.13.7 

SE.13.8 

SE.14 

SE.14.1 

SE.14.3 

SE.14.4 

PS1 

PS.1.1 

PS.1.2 

PS.1.3 
PS.1.7 

PS.1.8 

mixing;  experimental  work,  modelling  studies  of  dispersion,  transport, 
and chemical and biological processes in marine waters 

SE.13.1 

Granberg, M. 

SE.13.3 

SE.13.4 

SE.13.5 

Mujingni, J. 

Nylund, A. 

Egardt, J. 

SE.13.6 

Chen, C. 

Bensow, R. 

Booge, D. 

A  multi  species  and  multi  system  evaluation  of  the 
ecotoxicological  effects  of  scrubber  water 
  a  synopsis  of 
results from the EMERGE project 
Sampling strategies and characterization of greywater from 
ships 
Turbulent  ship  wakes:  extent,  intensity,  and  interaction  with 
stratification 
Response  in  the  marine  diatom  Nitzschia  sp.,  following 
exposure to bilge water from different ships 
Scrubber water impairs fertilization and development in the 
green sea urchin (Strongylocentrotus droebachiensis) at very 
low concentrations 
The  possibilities  and  effects  of  ship  speed  reduction  on 
underwater noise   a case study 
Influence  of  scrubber  effluent  on  biogenic  trace  gas 
production 

SE.14.2 

Elliott, M. 

Holistic assessment of shipping impacts on the environment, shipping in 
marine spatial planning 
Guéret, S. 

Impact assessment of shipping activities: Applying the critical 
load concept to both the atmosphere and marine environment 
Managing  Marine  Resources  Sustainably:  a  transdisciplinary 
approach  to  the  causes,  consequences  and  responses  to 
environmental problems of shipping and navigation 
Impacts  of  expanding  commercial  anchoring  on  the  Pacific 
Coast of Canada 

Francis, F. 

Sakellariadou, F.  Reducing 

Poster session 1 

Kupper, M. 

Aletras, N. 

Mehlig, D. 
Svensson, N. 

Measurement  and  Analysis  of  Brake  and  Tyre  Particle 
Emissions for High-Load Driving Scenarios on a Test Bed 
Predictive  energy  management  for  a  plug-in  hybrid  electric 
truck 
The EV-olution of non-exhaust emissions  
The effect of a porous pavement on air quality in comparison 
to a dense pavement 

Suarez-Corujo, J.  CO2  emissions  targets,  revisiting  the  transition  from  2020 

PS.1.11 

Elihn, K. 

PS.1.14 

Rossi, T. 

NEDC to 2021 WLTP 
Development  of  a  mobile  ALI  exposure  system  for  toxicity 
testing of emissions from different transportation modes 
Fuel consumption, regulated and unregulated exhaust 
emission tests on three Euro 6d bi-fuel LPG passenger cars, 
fed by an innovative LPG/DME 80/20 (V/V) blend 

PS.1.16 

Gómez Vilchez, J.  Future SUV fleet and CO2 emissions projections in Australia 

PS.1.17 

PS.1.21 
PS.1.25 

Introna, M. 

Coulson, G. 
Rausch, J. 

PS.1.29 

Unterschütz, L. 

and the European Union 
Toxicity  of  real-world  road  tunnel  emissions  in  an  ALI 
exposure model. 
Non-Exhaust Emissions in Aotearoa New Zealand  
Characterization  &  quantification  of  traffic-derived  non-
exhaust particles (TWP/TRWP, brake & road wear) in airborne 
dust 
Data  protection  in  remote  sensing  through  profiling  high-
emitting vehicles 

499 

 
 
 
PS.1.32 

PS.1.37 

Priestley, M. 

Krasa, H. 

PS.1.54 

Qin, X. 

PS.1.55 

Molden, N. 

PS.1.56 

Thomas, D. 

Lubrication  oil  as  a  potential  source  of  traffic  originated 
secondary particulate mass 
A  simplified  in-Field  Calibration  method  for  Periodical 
Technical Inspections Particle Counters with atomized NaI 
Roadway diesel vehicle emission measurement by PEMS and 
its  dispersion  at  on-road  conditions  captured  by  plume 
chasing 
Comparative chemical composition of US and European tyres 
VOC profile, potential environmental impact, including 6PPD 
Incorporating  NOx  into  the  Periodic  Technical  Inspection 
vehicle emission test procedures 

PS.2 

PS.2.3 
PS.2.4 

PS.2.5 

PS.2.7 
PS.2.9 

Poster session 2 

Mao, W. 
Prignon, M. 

Beecken, J. 

Jalkanen, J. 
Kousias, N. 

PS.2.10 

Conde, V. 

PS.2.11 

PS.2.12 

PS.2.14 

PS.2.15 
PS.2.16 

PS.2.24 
PS.2.25 

Eger, P. 

Hundt, M. 

Lunde 
Hermansson, A. 
Salo, K. 
Malmgren, E. 

Argüello, G. 
Zheng, T. 

PS.2.26 

Abrantes, N. 

PS.2.27 

Lannes, M. 

PS.2.28 

Calgaro, L. 

PS.2.30 

Rapkos, N. 

PS.2.32 

PS.2.34 

Rathnamali, M. 

Winiwarter, W. 

PS.2.35 

Boikos, C. 

PS.2.36 

Hannah, L. 

PS.2.37 

Ramacher, M. 

from  Ships 

instrument  for  simultaneous  monitoring  of 

AIS data mining to identify tank cleaning operations at sea 
DOAS  applied  to  shipping  emission  monitoring:  compliance 
assessment and comparison to satellite measurements 
FUgitive  Methane  Emissions 
(FUMES): 
Characterizing  methane  emissions  from  LNG-fueled  ships 
using drones, helicopters, and on-board measurements 
Discharges from ships to the sea in European sea regions 
A Low-Cost Optoacoustic Sensor for Black Carbon monitoring 
of Ships 
Remote monitoring of sulphur emissions from shipping with a 
novel high sensitive laser system. 
Measurement  of  NOx  and  ultrafine  particles  from  inland 
shipping in Germany 
A  single 
greenhouse gases and air pollutants 
Monetary return on scrubber installations at the expense of 
environmental damage 
VOC emissions from ships 
The  actor  perspective  on  reaching  a  low  emitting  shipping 
sector   
Legal barriers to transporting CO2 streams by ships 
High-resolution  air  quality  mapping  via  massive  mobile 
monitoring and land use random forest models 
Is post-exposure feeding inhibition of Artemia sp. and Mytilus 
galloprovincialis impaired by exposure to scrubber-waters? 
Air quality assessment at the street level: sensitivity analysis 
of  a  road  traffic-emissions-CTM  model  chain  for  the  Paris 
region 
Environmental  fate  modelling  of  organic  pollutants  from 
land-based  and  shipping  emissions  in  the  Northern  Adriatic 
Sea coastal areas 
Deposition  quantification  of  gaseous  emissions  at  the  air-
water interface during a single vessel travel 
Variation in responses of microalgae, Nitzchia sp. exposed to 
grey water from ships. 
Holistic  environmental  impact  assessment  from  shipping:  A 
decision support tool for stakeholder engagement 
CFD dispersion modelling for the reproduction of real shipping 
emission conditions in a port area 
Cumulative  effects  of  commercial  anchoring  on  the  Pacific 
Coast of Canada: Ecological and Socio-Economic Effects 
The  impact  of  maritime  activities  on  air  quality  in  three 
European ports 

500 

 
 
PS.2.38 

Parsmo, R. 

PS.2.40 

PS.2.42 

Hassellöv, I. 

Hassellöv, I. 

PS.2.44 

Rutgersson, A. 

PS.2.46 

Gondikas, A. 

PS.2.51 

Yang, S. 

PS.2.52 

Santos, L. 

Assessing the impact of environmental policy instruments for 
ships in Europe with a modelling tool 
LNG  feeder  vessel  environmental  pressures,  partially 
decoupled from transport demand 
The  Poseidon  Principles:  Designing  and  implementing  a 
regulatory framework to create ecosystem sustainability  
ShipTRASE, Global shipping: Linking policy and economics to 
biogeochemical cycling and air-sea interaction 
Identification  and  quantification  of  contaminant  particles 
from  scrubber  water  effluents  in  a  high-intensity  shipping 
area 
Optimization  of  Drone-based  Sensor  Sniffing  System  for 
Monitoring Fuel Sulphur Content in Ocean-going Vessel Based 
on Field Measurements in Hong Kong Waters 
Changes in cloud activity of ship exhaust particles: Potential 
effects on Arctic mixed-phase clouds 

501 

 
 
 
 
 
3.1 TAP.02.  On-board  monitoring  and  diagnostics,  emission 

tampering and deterioration. 

502 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
503 

  Identification of Manipulated & Defective Truck NOx Emission Reduction Systems with Plume Chasing for Authority Inspections  D. Pöhler1,2, C. Schmidt1,2, S. Schmitt1, J.P. Lolinga3, T. Frateur3, N. E. Ligterink3, M. Vojtíšek4, J. Borken-Kleefeld5, Y. Bernard6, Å. Sjödin7  1 Airyx GmbH, Justus-von-Liebig-Straße 14, Eppelheim, 69214, Germany 2 Institute of Environmental Physics, University of Heidelberg, INF 229, Heidelberg, 69120, Germany 3TNO, Anna van Buerenplein 1, 2595, The Hague, Netherlands 4CVUT, Czech Technical University Prague, Technická 4, 166 07, Praha, Czech Republic  5Institute of Transport Planning and Road Traffic, TU Dresden, Hettnerstraße 1, 01069 Dresden, Germany 6ICCT International Council on Clean Transportation, Berlin, 10178, Germany 7IVL Swedish Environmental Research Institute, Goteborg, 411 33, Sweden   Keywords:   Plume Chasing; SCR manipulation, emulator, CARES, NOx emissions, high emitter, heavy duty. Presenting author email: denis.poehler@airyx.de  EURO V and VI trucks (heavy duty vehicles) achieve required low NOx (nitrogen oxides) emissions using Selective Catalytic Reduction (SCR) systems consuming AdBlue©. The operation and maintenance of the SCR system is costly. There are illegal manipulations of the emission system typical with emulators which turn down the SCR system while the engine electronics is not identifying the deactivation. Additionally, there are also defective systems which are not repaired or not properly maintained according to regulations. Manipulations and defects increase the emissions, harm environment and citizens and create an unfair advantage with respect to correctly operated and maintained vehicles. Thus, identification and punishment of illegal cost saving manipulations or avoided maintenance is important.   To detect these manipulations or defects in an inspection is difficult and time consuming. The Plume Chasing (PC) method is a remote measurement method to identify these high emitting vehicles. A system from Airyx was developed for this purpose with the support of the CARES project. It is designed to measure emissions from trucks in real traffic and identify the high emitters. It pre-select vehicles that should be investigated in further inspection, which significantly increase the effectiveness of authorities (Pöhler, 2021). The accuracy of this PC system for NOx emission measurements was demonstrated in several studies, e.g., Janssen and Hagberg (2020).  Figure 1. Rates of high and suspicious emitting trucks.  First authorities in Europe start to use Airyx PC as a standard tool for their daily inspection work. We here report on extensive PC studies in Denmark, Belgium, the Czech Republic and. Figure 1 shows the measured truck emission classification in Denmark (n=480) and the Czech Republic (n=986). While in Denmark less than 10% of trucks displayed suspicious or high emissions, these are about 40% in Czech Republic.   Several of the suspicious and high emitting trucks were stopped for detailed inspections by authorities (Figure 2). A reason for high emissions was found in all (0% “No Error”), confirming the effectiveness of the PC for high-emitter detection. Most trucks had defects, also manipulation was clearly proven on many vehicles. Some trucks had a cold SCR due to warm up problems (mainly EURO V, and suspicious emitters) and a few had missed emission software updates.    Figure 2. Results of inspection.  This work was supported by the EU H2020 CARES Project No. 814966.  Pöhler, D. (2021), Heavy Duty Vehicle (HDV) NOx emission measurement with mobile remote sensing (Plume Chasing) and subsequent inspection of high emitters, Final Report, Airyx GmbH, Germany.  
 
 
 
 
504 

  Strong impact of ambient temperature on nitrogen oxides emissions from heavy-duty diesel trucks based on plume chasing tests  Hui Wang 1, Shaojun Zhang 1, Xiaomeng Wu1, and Ye Wu 1  1 School of Environment, Tsinghua University, Beijing, 100084, China. Keywords:  heavy-duty diesel trucks, nitrogen oxides, ambient temperature     Presenting author email: hui-wang1992@tsinghua.edu.cn  Controlling the emissions from heavy-duty diesel trucks (HDDTs)  is one of the prioritized tasks to further improve air quality in China. Nitrogen oxides (NOX) emitted by HDDTs is a key precursor of ozone pollution and the precursor of ammonium salt for haze episodes. It is essential to accurately evaluate real-world emission levels. Previous studies have focused on the leading drivers, such as emission standards and driving speed affecting NOX emissions from HDDTs. However, few studies have explored the influence of environmental conditions, such as ambient temperature on NOX emissions, which is important for exactly evaluating seasonal emission differences for HDDTs. In this study, we conducted large-size plume chasing tests for on-road HDDTs across several seasons in China to explore the NOX emission trends caused by ambient temperature.  In this study, we dispatched the well-verified mobile platform (Wang, 2020) to conduct plume chasing tests for HDDTs across several major regions in China. This campaign measured more than two thousand HDDTs (gross vehicle weight over 4.5 tons) with NOX emissions between 2017 and 2020. Real-world NOX emissions trends for HDDTs affected by ambient temperature were thus analyzed. Generalized additive models (GAMs) with advantages in nonlinear fitting were used to predict the sensitivity of ambient temperature on NOX emissions for different emission standards and vehicle types based on the plume chasing test dataset (see Figure 1). HDDTs NOX emissions were found to be highly dependent on ambient temperature with low temperatures resulting in higher NOX emissions. For example, the low temperature (-3°C) would lead to a 137% increase in NOX emissions for China V HDDTs (gross vehicle weight between 4.5-31 tons) compared to under the high temperature (35°C). The ambient temperature corrections module that can characterize the seasonal and regional difference of NOX emission prediction for HDDTs was developed based on the large-size data. Figure 2 illustrates the NOX emission variation in wintertime and summertime compared to regional annual temperature in several regions of China in 2019 when considering the ambient temperature corrections module. Affected by the “low-temperature NOX emission penalty”, the seasonal variation between wintertime and summertime of NOX EFs for HDDTs is estimated to be 9%-33% for eight regions in China. This variation would be especially important for northern regions located in the high latitudes with greater seasonal temperature differences. This trend highlights the importance of accounting for the effect of ambient temperature in NOX emission modelling and prediction. The results revealed the high impact of ambient temperature dependency on NOx emissions from HDDTs and this is important for accurately assessing the spatial-temporal variation in on-road NOX emissions for HDDTs.   Figure 1. Generalized addition models (GAMs) of fuel-specific NOX emissions based on ambient temperature for China III to China V HDTs. (a) 4.5-31.0 tons HDDTs; (b) 31.1-49.0 tons HDDTs. The shaded zones represent the GAMs’ standard error for the prediction.  NorthNorthwestNortheastCentralEasternSouthwestSouth-0.20-0.15-0.10-0.050.000.050.100.150.20Rigion of China Wintertime    SummertimeSeasonal variation of NOX emissions compared to annual ambient temperature  Figure 2. The NOX emission variation for HDDTs in wintertime and summertime compared to regional annual temperature in major regions of China in 2019.  This work was supported by the Ministry of Science and Technology of China's International Science and Technology Cooperation Program (2018YFE0106800, MOST of China and European Union Horizon 2020 collaborative project).  Wang, H., Wu, Y., Zhang, K. M. et al. (2020) Evaluating mobile monitoring of on-road emission factors by comparing concurrent PEMS measurements. Science of the Total Environment, 2020, 736: 139507.   Ambient temperature（℃）NOXEFs（g/kg-fuel）Ambient temperature （℃）NOXEFs（g/kg-fuel）China IIIChina  IVChina V(a) 4.5-31.0 tons(b) 31.1-49.0 tons 
 
 
 
 
3.2 TAP.03.  In-service  conformity  and  new  concepts  for 

enhanced emission testing in PTI. 

505 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
506 

  Extended evaluation of the emissions of top-selling Euro 6 cars depending on powertrain and exhaust after-treatment technologies  M. Leblanc1, A. Albinet2 and S. Raux1  1 IFP Energies nouvelles, Rond-point de l’échangeur de Solaize, BP3, 69360 Solaize, France ; Institut Carnot IFPEN Transports Energie 2 INERIS - Parc technologique Alata, BP 2, 60550 Verneuil-en-Halatte, FRANCE Keywords: Euro 6, Euro 7, exhaust, Diesel, gasoline, regeneration, WLTC, RDE, NH3, N2O, PN, PAH Presenting author email: mickael.leblanc@ifpen.fr  While from 2035, all new cars sold on the European market should be zero-emission to ensure the transport sector to become carbon-neutral by 2050, vehicles equipped with a thermal engine still should represent up to 55 % of new registrations in 2030 (Ntziachristos, 2022). Of these, 61 % should be Diesel, 39 % gasoline, including 45 to 50 % hybrids (plug-in or not) for each. For this reason and to further reduce pollutant emissions from new motor vehicles sold meanwhile, the European Commission recently published Euro 7 standards proposal (European commission, 2022). The current Euro 6d standard considers distinct fuel-dependent emission limits while Euro 7 proposal finally introduces fuel neutral ones. Other improvements of the test procedures are proposed to better reflect the normal conditions of use. This includes Diesel particulate filter [DPF] active regenerations that still need to be taken into account to better evaluate the total exhaust emissions, as well as their possible impacts.  Objectives  This work provides a comprehensive evaluation of regulated and unregulated emissions of a Euro 6 car, among the most sold in France, equipped with different engine types and exhaust after-treatment systems [EATS].  Experimental setup  The RHAPSODIE project studied the exhaust emissions of three models of the same top-selling European sedan (Table 1), under various driving (WLTC, CADC and RDE) and starting (ambient/hot) conditions.  Table 1. Vehicle main characteristics.  A wide panel of online and laboratory protocols have been used to extensively analyse the regulated and unregulated pollutants present in the gaseous and particulate phases of the exhaust emissions, including ammonia [NH3], methane [CH4] and solid particle number [PN], above and below 23 nm, proposed to be limited for future Euro 7 cars. Other species were monitored, still not regulated for cars, including nitrous oxide [N2O], formaldehyde [CH2O], non-methane organic gases [NMOG], expected to be regulated only for heavy-duty vehicles, and polycyclic aromatic hydrocarbons and their nitrated and oxygenated derivatives [PAHs, nitro- and oxy-PAHs] quantified in both gaseous and particulate phases.  Results Thanks to the distance covered during the campaign (around 6,500 km), an extensive comparison of the emissions (regulated or not) of the two gasoline and Diesel versions of a same Euro 6 model car is proposed. The results (Figure 1) highlight that the two Euro 6d vehicles comply with their respective emission limits, with particularly low particle emissions. The results also allow to quantify the specific contribution of DPF regenerations to the gaseous and particulate emissions of the Diesel version. Finally, the results are compared to the proposed Euro 7 targets regarding WLTC and RDE-like trip, this latter being not strictly compliant with official Real Driving Emissions [RDE] conditions for Euro 6, particularly due to its lower distance and duration.     Figure 1. Emission factors of the vehicles under RDE-like (homothetic) trip  This study has been performed within the framework of the RHAPSODIE project funded by the French Environment and Energy Management Agency (Agreement 1766C0003) and supported by the French Ministry of Environment.  European Commission, Ntziachristos, L., Papadopoulos, G., Samos, Z. et al., Euro 7 impact assessment study, 2022, https://data.europa.eu/doi/10.2873/249061 European Commission, Proposal for a regulation of the European Parliament and of the council, 2022 https://ec.europa.eu/commission/presscorner/detail/en/ip_22_6495 Engine Standard EATS Gasoline 6b 3WC Gasoline Diesel 6d-TEMP 6d-TEMP 3WC+GPF DOC+SCR+SCRF  
 
 
 
 
3.3 TAP.04. Remote sensing of vehicle emissions. 

507 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
508 

  A Gas Schlieren Imaging Sensor System for Locating and Examining Automotive Exhaust Plumes for Remote Emission Sensing Applications  Hafiz Hashim Imtiaz1, Paul Schaffer1, Martin Kupper1 and Alexander Bergmann1  1Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, Graz, 8010, Austria Keywords: schlieren imaging, gas imaging, refractive index, vehicle exhaust, remote emission sensing Presenting author email: hafiz.imtiaz@tugraz.at  Synthetic Schlieren (or Background Oriented Schlieren) is an established method to visualize differences of the refractive index in a fluid and can thus be used to picture flows, (Sutherland, 1999). Changes of the refractive index can be caused e.g. by temperature, humidity or concentration changes. In this work, we present a Gas Schlieren Imaging Sensor System (GSIS) for imaging exhaust gas plumes for remote emission sensing (RES). The GSIS is a cost-effective way to detect plume sizes, spreading directions and velocity. Analysis is made possible by advanced image processing techniques and deep learning algorithms. The setup consists of a digital camera with a frame rate of 30 fps (target moving speed of vehicles ~ 50 km/h) and a uniformly structured (white and black checker pattern), illuminated background board. The principle is shown in fig. 1. With a disturbance between the pattern board and the camera, the light is deflected from the straight path. By comparison of two images, differences can be identified as movements of the gas - the resulting schlieren image.   Figure 1. Principle of synthetic schlieren.   Automated image processing steps are applied (contrasting, blurring, thresholding, erosion and dilation) to carve out the plume in schlieren image. The plume is then detected by a trained neural network. The plume size in mm is calculated from the image by considering the camera sensor size in mm and in pixels, distance to the gas plume and the focal length of the camera. The feasibility of the GSIS for imaging of exhaust plumes was proven by capturing the exhaust plume of bypassing cars with 30 km/h, see fig. 2a for an exemplary visualization. Data from vehicle measurements for an evaluation are gathered. Lab experiments were started to identify the actual influences of the exhaust (gas compounds, temperature, humidity) to the visibility. For first identification of influences on the GSIS, a defined gas mixture was led through a glass pipe (diameter 3 cm) in a tube furnace with 6 l/min and the outflow in air was captured by the GSIS, as shown in fig. 2b and 2c. Humidity, temperature, volume flow and concentrations of gas mixtures can be varied in a controlled manner. Preliminary results from the experiments can be seen in fig. 3. The relation between relative visibility of CO2, air, N2 and CO2-air mixtures at different temperatures is shown. The visibility is assessed by the number of pixels associated to the plume, normalized to the respective maximum acquired value. The identified dependencies suggest a dominant influence of CO2, what makes an application in RES under environmental conditions seem feasible. A direct relation between visibility and temperature can still be seen from air and N2 data, while CO2 and CO2-air mixture curves show opposite trends, here visibility decreased with the increase in temperature. For mixtures an overlay of both trends can be observed. A description of the observed behaviour via density changes of the gas components is pursued. The characterization of the GSIS in the lab is currently in progress. It aims to quantify the influences of the varied parameters on the visibility in the schlieren images and find limit values. A well-founded statement will be presented at the conference.  Figure 2. Photographs and the corresponding schlieren images. a) automotive exhaust, b) 20% CO2 in synthetic air 200 °C, c) 20% CO2 in synthetic air 25 °C.  Figure 3. Selected preliminary characterization results. Dots indicate measured values. Relative visibility is not comparable in-between datasets yet.  This work is funded by FFG Project 888105 “LASERS”  Sutherland, B.R., Dalziel, S.B., Hughes, G.O. and Linden, P.F. (1999) Journal of Fluid Mechanics, Cambridge University Press.  
 
 
 
 
509 

  Optimisation and Validation of Plume Chasing for Particle and NOx High Emitter Identification  C. Schmidt1,2, D. Pöhler1,2, S. Schmitt2, M. Knoll3, T. Frateur4, J. P. Lollinga5, N. E. Ligterink4, M. Vojtíšek6, N. J. Farren7, D. C. Carslaw7, Y. Bernard8, U. Platt1,2   1Institute of Environmental Physics, University of Heidelberg, Heidelberg, 69120, Germany 2Airyx GmbH, Eppelheim, 69214, Germany 3Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, Graz, 8010, Austria 4Department of Sustainable Transport and Logistics, TNO, The Hague, 2595 DA, NL 5Department of Environmental Modelling, Sensing & Analysis, TNO, Utrecht, 3584 CB, NL 6CVUT, Czech Technical University Prague, Praha, 166 07, Czech Republic  7Wolfson Atmospheric Chemistry Laboratories, University of York, YO10 5DD, United Kingdom 8ICCT International Council on Clean Transportation, Berlin, 10178, Germany Keywords:   air quality, plume chasing, remote sensing, particle emissions, CARES     Presenting author email: cschmidt@iup.uni-heidelberg.de  Besides Nitrogen Oxides (NOx), particle emissions from vehicles are a major cause of bad air quality in urban areas. Over their lifetime vehicles are often not properly maintained or tampered which leads to substantially higher emissions. In several studies it has been shown that those high emitters are responsible for the major share of emissions. It is therefore crucial to monitor and identify such high emitters to significantly improve the overall air quality. Plume Chasing (PC) is a remote emission sensing (RES) technique which is perfectly suitable for high emitter identification (e.g. Pöhler, 2021). Currently, it has mainly been applied for the detection of high NOx emitters. PC was further developed in the framework of the EU project CARES (City Air Remote Emission Sensing, https://cares-project.eu/). The project included test studies to optimize and validate the PC method in terms of hardware and data analysis algorithms. As one part of this study particle instruments were compared for their usage in PC and their applicability in high emitter identification.  The PC method uses a measurement vehicle equipped with different instruments to chase the emission plume of investigated vehicles. The sampled air from the emitted plume is analysed in real-time by instruments that allow fast (1 s time resolution) and simple measurements with high accuracy and wide measurement range. During a two weeks measurement campaign in Czech Republic (12.09.-24.09.22) 1,835 vehicles were measured with PC and several particle instruments were evaluated for their usage in PC. Several particle metrics exist, where especially particle number concentration (PN) and black carbon (BC) are of particular interest. PN is of increasing interest nowadays due to the introduced emission limits. BC is a selective marker for detecting diesel particulate filter (DPF) failures. In the campaign, two diffusion chargers (particle counter, TEN AEM; counter, AVL DiTest) for measuring PN and one photoacoustic instrument (Black Carbon Tracker (BCT), TUG) for measuring BC were compared. We used as reference instrument an SMPS (Electrostatic Classifier 3082 and TSI 3775 CPC) measuring 80 nm particles, which showed very good performance in detecting a DPF tampered vehicle in a validation study in the CARES project (Farren et al, 2022). All instruments were installed in a measurement vehicle from TNO (Utrecht, Netherlands).  The AVL DiTest counter showed a very good correlation (R2=0.94) with the SMPS (see Fig. 2). A good agreement could also be found for the BCT (R2=0.71). The performance of the TEN AEM particle counter could be improved compared to the validation study. Nevertheless, it still has a significantly higher, likely false positive, identification of high particle emitters compared to the SMPS. This evaluation shows the applicability of the individual instruments for their usage in PC and shows the great potential of high particle emitter identification with the PC method.    Figure 2. Particle number measured by AVL DiTest Counter versus SMPS (80nm) for 327 HDVs.  This work was supported by the EU H2020 CARES Project No. 814966.  Farren, N. et al (2022) D1.1 – Measurement technology intercomparison and evaluation, Public Report, CARES Project. Pöhler, D. (2021) Heavy Duty Vehicle (HDV) NOx emission measurement with mobile remote sensing  
 
 
 
 
510 

  A method for deriving characteristic emission polygons for engine families based on large datasets from remote sensing measurements  Å. Sjödin1, S. Hausberger2 , M. Jerksjö1 and Y. Cha1  1IVL Swedish Environmental Research Institute, Gothenburg, SE-400 14, Sweden 2 Institute for Thermodynamics & Sustainable Propulsion Systems, Graz University of Technology, Graz,  A-8010, Austria  Keywords: remote sensing, emission characteristics, light-duty vehicles, engine families     Presenting author email: ake.sjodin@ivl.se   Vehicle emission measurements are crucial for a comprehensive understanding of road traffic related air pollution and for effectively controlling the emissions through various policies - from the local to the international level. In the case of diesel vehicles, the strategies applied in Europe to control emissions of nitrogen oxides in the past have largely failed, probably best exemplified by the dieselgate scandal revealed in 2015 (Thompson et al., 2014).  The possibility of vehicle manufactures to cheat with the legislative emission tests, and the often-observed discrepancies between emissions measured in the laboratory over a standardized driving cycle and  emissions encountered in real-world driving, have substantially increased the interest during the last 5-10 years to apply various methods that measure emissions on the road in real drive operation, e.g., PEMS (Baldino et al., 2017) and remote sensing - RS (Davison et al., 2020).  Whereas PEMS can measure the emission performance of an individual vehicle in very large detail but at a rather high cost, and therefore relatively few vehicles are measured, remote sensing catches only an “emission snapshot” of each vehicle, but on the other hand can measure thousands of vehicles in one day, able to provide good statistics and fleet representativity.  We have developed a method that utilizes large datasets from remote sensing measurements to derive characteristic emission polygons for engine families, building on the engine family taxonomy developed in the H2020 uCARe project (https://www.project-ucare.eu/wp-content/uploads/2019/11/uCARe_D1.1_taxonomy.pdf).  The method also builds on a set of equations for estimates of VSP and of fuel flows developed from simulations with the TU Graz PHEM model and HBEFA data for vehicle and engine technologies, see Figure 1 (example for a Euro 6d-temp diesel car).  Figure 1. Fuel consumption vs VSP for an average Euro 6d-temp diesel car  according to PHEM simulations.  The PHEM and HBEFA derived equations are designed to be applied to convert remote sensing results from [g/kg fuel or CO2] into [g/s or g/km]. The derived remote sensing-based emission factors can then be plotted against vehicle speed over a large speed range. An example is given by Figure 2, in which average absolute NOX emissions (in g/s) by 5 km/h speed bins are plotted against vehicle speed for a very common engine family in Europe – the Volkswagen AG Euro 6b diesel engine with engine displacement 1598 cm3 and rated power 81 kW.   The method allows comparing the emission performance of different engine families that are the most common on the European market. Such comparisons will be presented based on the large datasets that are present in the older CONOX and the newer CARES RS database.  Figure 2. NOX (and CO2) emissions derived with the method as a function of speed, derived from remote sensing measurements on ≈1700 cars equipped with  the VAG 1598 cm3, 81 kW, Euro 6b diesel engine.  This work was supported by the H2020 projects uCARe (No. 815002) and CARES (No. 814966).  Thompson, G. J., Daniel, D. K., Carder, C., Marc C., Besch, M. C. and Thiruvengadam A. (2014) In-use emissions testing of light-duty diesel vehicles in the U.S. Final report prepared on behalf of International Council on Clean Transportation, May 15, 2014. Baldino, C., Tietge, U., Muncrief, R., Bernard, Y. and Mock, P. (2017) Road tested: Comparative overview of real-world versus type-approval NOX and CO2 emissions from diesel cars in Europe. ICCT White Paper, September 2017. Davison, J., Bernard, Y., Borken-Kleefeld, J., Farren, N. J., Hausberger, S., Sjödin Å., Tate, J. E., Vaughan, A.  
 
 
 
 
511 

  Intercomparison of Remote Emission Sensing Methods and Validation with PEMS Measurements  M. Knoll1, M. Penz1, T. Rossi2, H. Juchem3, C. Schmidt3,4, D. Pöhler3,4, S. Casadei2, Y. Bernard5, A. Bergmann1 and Å. Sjödin6  1Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, Graz, 8010, Austria 2 Innovhub SSI, Stazioni Sperimentali per l'Industria, Milan, 20133, Italy 3Institute of Environmental Physics, Heidelberg University, Heidelberg, 69120, Germany 4Airyx GmbH, Eppelheim, 69214, Germany 5ICCT International Council on Clean Transportation, Berlin, 10178, Germany 6IVL Swedish Environmental Research Institute, Goteborg, 411 33, Sweden  Keywords:   Remote emission sensing, point sampling, PEMS, validation study, CARES      Presenting author email: markus.knoll@tugraz.at  Emissions from internal combustion engine vehicles are currently not properly monitored during their lifetime. In specific, NOx and particulate matter (PM) emissions of a small share of poorly maintained or tampered vehicles are responsible for the vast amount of emissions (Park et al. 2011, Bainschab et al., 2020). Remote emission sensing (RES) is one potential method of screening in-use vehicles bringing along the potential capability of high emitter identification. Different RES concepts exist, such as open-path RES or extractive point sampling (PS).  So far, validation studies comparing these technologies are rare, especially under real-world conditions. As a part of the EU H2020 project CARES (City air remote emission sensing - https://cares-project.eu/) a detailed validation study was conducted during the city demonstration campaign in Milan (September-October 2021) which compares and evaluates the different methods.  A newly developed PS system was co-located alongside a commercial open-path RES system (EDAR, Hager Environmental & Atmospheric Technologies (HEAT)) during 8 days of the measurement campaign. Emission factors (EF) for several thousand vehicles could be derived using both systems. The PS system features the determination of black carbon (BC), particle number (PN) concentration and NOx EFs with high-grade equipment such as a newly developed black carbon tracker, a custom-developed diffusion charger (Schriefl et al., 2020) and an ICAD (Airyx GmbH). In addition to the co-location, validation measurements with a portable emission measurement system (PEMS) (AVL Move) were conducted to compare the performance of the RES methods. More than 100 test drives were performed with several test vehicles equipped with PEMS.   A good agreement was found for average NOx emissions of the overall measured fleet between PS and HEAT with mean values of 4.63 and 4.44 g/kg fuel (median: 1.11 and 1.04 g/kg fuel), respectively. In case of PM emissions, BC EFs from PS and PM EFs from HEAT were compared. Comparable average values (115.46 and 85.77 mg/kg fuel) were determined. In contrast, median values (16.25 and 0.46 mg/kg fuel) show a high deviation between the two systems with a factor of 35 between them. A more detailed view of individual measurements was performed with PEMS test drives. In case of NOx emissions, mean and median EFs determined by PS and HEAT agree very well with the PEMS result. Looking at the measurement precision, the PS system delivers a better performance for individual measurements compared to the HEAT system (Figure 1). For PN, a high correlation (R² = 0.8, excluding outliers) between PEMS and PS EFs was found (with median values of 0.09 and 0.10·1015 particles/kg fuel). The outcome of the investigations provides more insights into the capabilities of the different systems for real world emission screening and the potential identification of high emitter.   Figure 1. Boxplot of NOx EFs of the PEMS equipped test vehicles and the two RES systems (PS, HEAT).  This work is conducted in the framework of the H2020 project CARES with grant agreement No. 814966.  Park, S. S., Kozawa, K., Fruin, S., Mara, S., Hsu, Y.K.,  Jakober, C., Winer, A., and Herner, J. (2011) Emission Factors for High-Emitting Vehicles Based on On-Road Measurements of Individual Vehicle Exhaust with a Mobile Measurement Platform. Journal of the Air and Waste Management Association. Bainschab, M., Schriefl, M. A., and Bergmann, A. (2020) Particle number measurements within periodic technical inspections: A first quantitative assessment of the influence of size distributions and the fleet emission reduction. Atmospheric Environment: X. Schriefl, M.A., Nishida, R.T., Knoll, M., Boies, A.M., Bergmann, A. (2020) Characterization of particle number counters based on pulsed-mode diffusion charging. Aerosol Science and Technology.  
 
 
 
 
3.4 TAP.05.  Vehicle  greenhouse  gas  emissions,  energy 

consumption, vehicle and fuel life-cycle analysis. 

512 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
513 

  Electric and conventional vehicles energy consumption under various traffic conditions  S. Mamarikas1, S. Doulgeris1, Z. Samaras1 and L. Ntziachristos1  1Department of Mechanical Engineering, Aristotle University, P.O. Box 458, 54124 Thessaloniki, Greece   Keywords: Battery Electric Vehicles, energy consumption, driving cycles, average speed approach Presenting author email: smamarik@meng.auth.gr   As more Battery Electric Vehicles (BEVs) start to penetrate traffic streams (EEA 2020), a main research question is whether the traffic conditions that used to be considered as optimum, in energy consumption terms, for Internal Combustion Vehicles (ICEVs) continue to be so for BEVs. BEVs in principle seem to exhibit a different energy consumption pattern compared to ICEVs, while they are being driven under various traffic conditions. Main differentiation factors appear to be the regenerative braking and the efficiency characteristics of their powertrain (Gao et al., 2007, Huynh & Hsieh, 2018).  In this context, the target of the present paper is to evaluate the energy performance of BEVs under many traffic states and to identify those traffic patterns that result into optimum energy behaviour. The specific effect of congestion and traffic measures on the energy consumption of BEVs is also approached. The overall analysis is conducted comparatively to the according performance of ICEVs.  BEVs and ICEVs energy performance was revealed through a simulation process, in which validated instantaneous power-based models were used to characterize their consumption over more than 100 driving cycles. Then, consumption estimates of the current study became suitable for expressing the performance of powertrains at a macroscopic traffic level, with consideration of the average speed as the explanatory variable.    Figure 1. BEVs energy consumption as a function of average speed. The effect of braking regeneration on consumption is highlighted.   The impact of a range of traffic conditions on the energy consumption of BEVs is shown in Figure 1. Similar figures were reproduced for ICEVs. BEVs minimum consumption is identified at low-speed urban traffic, mainly because of regenerative braking and high part-load powertrain efficiency. This differs to ICEVs that exhibit their optimum fuel consumption at higher speeds, mostly experienced in suburban and rural environments.  Focusing more on traffic conditions, congestion mitigation at urban speeds brings minimal energy gains for BEVs as depicted in Figure 2 But, the gain becomes significant at higher speed regions, where the overall influence of regeneration is reduced.     Figure 2. BEVs energy consumption over various speeds for congested & normal, non-congested and steady-state traffic    Finally, traffic measures, such as Urban Traffic Control, roundabouts and other traffic calming interventions are beneficial for BEVs energy consumption, but the achieved relative gains are lower than ICEVs.   This research was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grants (Fellowship Number:1041). This work has been published in the Transportation Research Part D: Transport and Environment. doi.org/10.1016/j.trd.2022.103231  EEA, 2020. New registrations of electric vehicles in Europe. last accessed 27/1/2022 European Environmental Agency Data Gao, Y., Chu, L., & Ehsani, M. (2007). Design and control principles of hybrid braking system for EV, HEV and FCV. In 2007 IEEE Vehicle Power and Propulsion Conference (pp. 384-391). IEEE. Huynh, T. A., & Hsieh, M. F. (2018). Performance analysis of permanent magnet motors for electric vehicles (EV) traction considering driving cycles. Energies, 11(6), 1385.  
 
 
 
 
3.5 TAP.06.  Vehicle-emission  modelling  and  measurements 

and impact assessment of emission regulations. 

514 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
515 

  Updated vehicle emission inventory in China  Yifan Wen, Min Liu, Xiaomeng Wu, Shaojun Zhang, Ye Wu  School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China  Keywords: nitrogen oxide (NOX), emission factors, emission inventory, temperature impacts, localization.      Presenting author email: wenyf@mail.tsinghua.edu.cn    China's total vehicle population (excluding motorcycles) increased from 15 million in 2000 to 273 million in 2020, and have created substantial concerns regarding the adverse impacts on air quality, public health and energy systems. Vehicle emission model serves as an important tool to estimate emission factors (EF) of various vehicle categories and quantify on-road emissions. However, recent field measurements and modeling studies conducted in various countries have noted discrepancies in annual and seasonal trends between the real-world vehicle emissions and model estimates. Ambient conditions (e.g., temperatures) have recently been identified as an important source of the large variability in real-world emissions for both light-duty gasoline vehicles (LDGVs) and heavy-duty diesel trucks (HDDTs) vehicles (Grange et al., 2019). Also, several studies deduced that the real-world emissions from in-use HDDTs exceed emission limits due to the failures in aftertreatment and unideal driving conditions, thus, rendering the traditional supervision methods ineffective.   EMBEV model is the archetype of the on-road transportation chapter of China National Emission Inventory Guidebook. In the latest update of EMBEV model (V2.0), we introduced the start emission sub-model and modified temperature correction modules utilizing localized measurement data and longitudinal trip-chain profiles to improve the territorial and seasonal resolutions of EFs and inventory modelling in China (Wen et al., 2021). Typically, we collected a large sample of real-world plume chasing data from more than 6000 vehicles to better represent the emission levels of in-use HDDTs and cover a wide range of ambient and driving conditions.   Figure 1. NOX emission factors by emission standard and model year for LDGV and HDDT fleet  As shown in Fig 1, NOX EFs of LDGVs declined significantly with the updates of emission standards, while NOX EFs from China Ⅱ to China IV HDDTs were not significantly improved even with the widely application of selective catalytic reduction (SCR) systems since China IV. This phenomenon was primarily due to the unsatisfactory SCR working performance caused by low exhaust temperature, which is rather frequent for low-speed urban driving conditions. Moreover, we witnessed an obvious decreasing trend in NOX EFs of China V HDDTs with model year, reflecting the improved in-use compliance for HDDT fleet in China.   EFs of key fleets in China show significant regional and seasonal variations affected by ambient temperature (Fig 2). The temperature dependence of LDGVs is mainly dominated by cold start emissions, while HDDTs are mainly affected by aftertreatment performance. The annual average NOX EFs of LDGVs and HDDTs in Heilongjiang province are 1.3 and 1.9 times as large as those in Hainan province, respectively. In total, the ratio of vehicular NOX emissions in Jan and July, 2019 varies from 1.1 (Hainan) to 2.0 (Hebei) among various provinces in China.   Figure 2. Monthly fleet average NOX emission factors of key fleets in three typical provinces in China  Fig 3 shows the total vehicle NOX emissions in China estimated by the old and latest version of EMBEV model (i.e., V1.0 and V2.0) and COPERT model. The gaps between estimations of various models enlarge with the phase-in of Euro IV and V HDDTs, revealing the importance of real-world supervision for in-use vehicles and the timely update of vehicle emission models based on localized vehicle measurements.   Figure 3. Estimated total vehicle NOX emissions in China based on EMBEV and COPERT model  Grange, S., Farren, N., Vaughan, A., et al. (2019) Environmental science technology, 53, 6587-6596. Wen, Y., Zhang, S., He, L., et al. (2021) Atmospheric Environment, 245, 118040.  
 
 
 
 
516 

  Long-term trends of black carbon and particle number concentrations and                                            vehicle emission factors in Stockholm  P. Krecl1, C. Johansson2,3, M. Norman3, S. Silvergren3 and L. Burman3  1Graduate Program in Environmental Engineering, Federal University of Technology, Londrina, 86036-370, Brazil 2Department of Environmental Science, Stockholm University, Stockholm, 10691, Sweden                           3Stockholm Environment and Health Administration, Stockholm, 10420, Sweden Keywords: ultrafine particles, nanoparticles, on-road emissions, air pollution.      Presenting author email: patriciak@utfpr.edu.br  Given the detrimental effect on human health, the World Health Organization has recommended to systematically measure black carbon (BC) and ultrafine particle number (PN) concentrations (WHO, 2021). These concentrations are typically high in traffic environments (e.g., Krecl et al., 2017) and, thus, curbing vehicle emissions is of outmost importance to reduce population exposure.  We analyzed the trends of BC and PN concentrations and vehicle emission factors (EF) in Stockholm, Sweden. Hourly measurements were conducted at Hornsgatan (SHO, street canyon) and Torkel (STK, urban background) sites in the periods 2013-2019 (BC) and 2009-2019 (PN). Hourly meteorological variables were measured at Högdalen station. Pollutant concentrations were meteorologically normalized (rmweather package), and used to estimate the linear trends by the non-parametric Theil-Sen method (Krecl el al., 2021).  EF were determined based on the street increment of ambient concentrations (curbside minus urban background concentrations), traffic volume and dilution rate. The latter was calculated by inverse modeling with the Operational Street Pollution model (OSPM).   As expected, the street canyon was more polluted than the urban background site (mean values of BC and PN in 2019: 0.75 vs. 0.35 µg m-3 and 1.37x104 vs. 6.31x103 cm-3). A decreasing trend in BC and PN concentrations was found for all cases (Table 1). In 2013-2019, the reduction was higher for BC than for PN. The small PN reduction at STK suggests other sources than traffic contributing to PN at the urban background site.  Table 1. Trends in BC and PN concentrations (% yr-1) and 95% confidence intervals (in parentheses).  Table 2 displays mean EF for the fleet calculated with inverse modelling (real-word) and based on the HBEFA 4.2.2 database.   Table 2. Annual mean EF for the vehicle fleet.  EFBC [mg veh-1 km-1] EFPN [1012 veh-1 km-1] Year HBEFA Real-word aHBEFA bReal-world 2009 16.0 - 24.4 424.3 2010 14.6 - 22.4 418.8 2011 13.1 - 20.4 351.7 2012 11.0 - 17.2 336.6 2013 9.7 24.4 15.1 369.1 2014 8.3 24.4 13.0 379.1 2015 6.9 23.0 10.7 219.4 2016 5.7 14.7 8.7 222.8 2017 4.9 14.6 7.4 182.3 2018 4.1 15.8 6.2 182.3 2019 3.5 8.4 5.1 166.5 aNon-volatile particles, diameter dp≥23 nm. bdp≥4 nm.  There was a decreasing trend in EF due to the progressively stringent Euro emission standards, especially when diesel particulate filters (DPF) were fitted in Euro 5, 6 and VI vehicles. Real-world EFBC were a factor of 2.4-4.8 higher than the HBEFA-derived EFBC (Table 2). Direct comparison between real-world and HBEFA EFPN is difficult due to different lower cut-off sizes and measurement protocols. In 2013-2019, the reduction was similar for all EF (64-66%), except real-world EFPN (55%). This difference could be explained by DPF being less efficient for small dp (Krecl et al., 2017). Thus, EF from the HBEFA database should be revised for BC and PN since they are used to compile official national inventories for the road transport and assess their associated health impacts.  The BC monitoring was partly financed by the Swedish EPA. The work was funded by CNPq Brazil (grant 305145/2020-7) for PK, and the European Union's Horizon 2020 programme (nPETS, grant 954377) for her co-authors.   Krecl, P., Johansson, C., Targino, A.C., Burman, L. and Ström, J. (2017) Atmos. Env. 165, 155-168. Krecl, P., Harrison, R.M., Johansson, C., Targino, A.C., Beddows, D.C., Ellermann, T., Lara, C. and Ketzel, M. (2021) Env. Poll. 290, 118105. WHO (2021) WHO Global Air Quality Guidelines. Site BC [2013-2019] PN [2013-2019] PN [2009-2019] SHO -9.4  (-9.5, -9.3) -4.9 (-7.5, -4.0) -5.0 (-5.7, -5.2)     STK -7.2 (-8.1, -5.9) -0.5 (-1.0, 0.3) -0.8 (-1.0, -0.6)     Str. inc. -10.8  (-10.9, -10.7) -6.7 (-8.6, -5.8) -7.2 (-7.4, -6.4)  
 
 
 
 
3.6 TAP.07. Non-road emissions. 

517 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  Train type effect on nanoparticles on an underground metro platform in Stockholm  M. Tu1, L. Bard1, and U. Olofsson1  1Department of Machine Design, KTH Royal Institute of Technology, S-100 44 Stockholm, Sweden Keywords: nanoparticles, metro, emission, nPETS Presenting author email: minghuit@kth.se  Nanoparticles or ultrafine particles are airborne particles whose aerodynamic diameters are not larger than 100 nm. They have been frequently mentioned due to their adverse effects on human health (Donaldson et al., 2001). However, there are rare nanoparticle studies in multi-transport environments because of their complex environmental conditions.  As a part of the nPETS project, the present work focused on nanoparticle concentration and size distribution based on field measurement on the underground metro platform. The field measurement was performed on the Tekniska Högskolan underground metro platform in Stockholm, and the measurement period was between September 2021 and March to June 2022.  The TSI FMPS 3091 (TSI, 2023) was used during the measurement. Its measurement range is between 5.6 and 560 nm with 1Hz time resolution. The mean value and standard deviation region of the measurement results is shown in Figure 1.  Figure 1. Sub-fig a shows the particle number size distribution. The shaded area is the standard deviation region; sub-fig b shows the average particle size distribution during the day.   The distribution of the nanoparticle emission over the hour of the day has also been investigated, as shown in Figure 1. It indicates that the maximum concentration appeared between hours zero and four. As mentioned in previous work, a possible explanation can be the night maintenance work (Tu and Olofsson, 2021).  The other main finding is the effect of the C30 train fraction of the total hourly train frequency on particle emissions. Currently, three types of metro trains are operating in the Stockholm metro system: CX, C20 and C30 from old to new. The CX and C30 train fraction effects have been studied using a mixed linear model. As shown in Figure 2, with the increase in the train frequency, the PN0.1 level would decrease if having a higher CX fraction or lower C30 fraction.  Figure 2. Based on the mixed linear model results, sub-fig a and b show the variation of PN0.1 levels under different conditions of train frequency, CX train fraction, and C30 train fraction. The red points show the actual conditions that happened during the measurement.  The nPETS project is funded by the European Union’s Horizon 2020 programme under grant No 954377.  Donaldson, K., Stone, V., Clouter, A., Renwick, L., MacNee, W., 2001. Ultrafine particles. Occup. Environ. Med. 58, 211–216. https://doi.org/10.1136/oem.58.3.211 TSI, 2023. Fast Mobility Particle Sizer (FMPS) 3091 [WWW Document]. TSI.com. URL https://tsi.com/Products/Particle-Sizers/Fast-Particle-Sizer-Spectrometers/Fast-Mobility-Particle-Sizer-(FMPS)-3091 (accessed 2.22.23). Tu, M., Olofsson, U., 2021. PM levels on an underground metro platform: A study of the train, passenger flow, urban background, ventilation, and night maintenance effects. Atmospheric Environ. X 12, 100134. https://doi.org/10.1016/j.aeaoa.2021.100134   
 
 
 
 
519 

  Toxicity of particle emissions from car and train brake materials  A.T. Juárez-Facio1, M. Introna1, L. Bard2, M.H. Tu2, S.S. Steimer1, U. Olofsson2 and K. Elihn1  1Department of Environmental Science, Stockholm University, 10691 Stockholm, Sweden 2KTH Royal Institute of Technology, Stockholm, Sweden Keywords: Air-Liquid Interface exposure, airborne particles, brake wear, non-exhaust emissions.  Presenting author email: ana.juarez@aces.su.se  Road traffic is one of the main emitters of air pollution in cities. An important part of these emissions comes from non-exhaust sources like brakes, tires, road wear, and road dust resuspension (Amato, 2018).   Brake particles represent an important part of non-exhaust emissions. Their characteristics depend on the speed, load, temperature, and friction pair materials. The metallic components of the brakes may cause their toxic effects (Karlsson et al., 2005; Puisney et al., 2018). Trains are also known to emit airborne particles during braking. Studies have found high particle concentrations at underground stations (Tu et al., 2019) and inside train cabins (Cha et al., 2018).   This work aimed to evaluate the toxicity (in vitro) of fresh airborne particles from 4 car brake materials and 2 train brake materials using an alveolar cell line (A549) exposed at the air-liquid interface (ALI).   The particle generator (a tribometer using contact pressure 0.9 MPa, velocity 4 m/s) was connected to the ALI exposure system, as shown in Figure 1. Cells were exposed to airborne particles for 2h, and were then incubated for 24h before measuring the cell viability (Alamar BlueTM assay).               Figure 1. ALI system used to test the toxicity of airborne brake particles in the tribology laboratory.  Results  Brake particles decreased cell viability more than the gaseous emissions (control) in three of the car brakes (FM2, FM3, and FM4) and one of the rail brakes (C6), as shown in Figure 2. The brakes have different metal compositions: FM1 and FM2 have a low metal content and are Cu-free, FM3 is Cu-enriched, and FM4 is non asbestos organic (NAO) and Cu-free. In the train brakes, there is mainly Ca in C6, and Fe in C20. Moreover, the estimated exposure dose of the cells was around 4-6 µg/cm2. Further analyses will be done to better understand the toxicity of these materials by testing different particle fractions using submerged exposures.  Figure 2. Cell viability of A549 cells exposed to PM2,5 from different car and train brake materials for 2h and incubated for 24h. Results are expressed as mean ± SD of at least 3 independent experiments.  Acknowledgments This work was supported by the European Commission's Horizon 2020 research and innovation program: nPETS (grant agreement No 954377, https://www.npets-project.eu/), aimed at studying the sub-100 nm particles emitted from transport.  References Abbasi, S., Jansson, A., Sellgren, U., Olofsson, U., 2013. Particle Emissions From Rail Traffic: A Literature Review. Crit. Rev. Environ. Sci. Technol. 43, 2511–2544.  Amato, F., 2018. Non-Exhaust Emissions - 1st Edition URL https://www.elsevier.com/books/non-exhaust-emissions/amato/978-0-12-811770-5. Cha, Y., Tu, M., Elmgren, M., Silvergren, S., Olofsson, U., 2018. Factors affecting the exposure of passengers, service staff and train drivers inside trains to airborne particles. Environ. Res. 166, 16–24.  Karlsson, H.L., Nilsson, L., Möller, L., 2005. Subway particles are more genotoxic than street particles and induce oxidative stress in cultured human lung cells. Chem. Res. Toxicol. 18, 19–23.  Puisney, C., Baeza-Squiban, A., Boland, S., 2018. Mechanisms of Uptake and Translocation of Nanomaterials in the Lung. Adv. Exp. Med. Biol. 1048, 21–36.  Tu, M., Cha, Y., Wahlström, J., Olofsson, U., 2019. Towards a two-part train traffic emissions factor model for airborne wear particles. Transp. Res. Part Transp. Environ. 67, 67–76.   
 
 
 
 
520 

  Brake wear ultrafine particle emissions from a light duty vehicle under real driving conditions  G.Q. Vroom1, N.E. Ligterink1  1Department of Sustainable Transport and Logistics, TNO, The Hague, South Holland, 2595 DA, the Netherlands Keywords:   non-exhaust emissions, ultrafine particle emissions, brake wear, real-world conditions.      Presenting author email: quinn.vroom@tno.nl  Tailpipe emissions have been reducing significantly due to stringent particle number emission limits, while non-exhaust emissions such as brake wear have not yet been regulated. Currently, the majority of small air-borne road side particles, relevant for health, are from wear.  Laboratory setups, like in current Euro 7 proposal addresses brakes and tyres particle emissions, but  do not fully reflect the real world impact. The magnitude of the brake wear emissions depends on the temperature of braking system, and thus on cooling, and most work found in literature applies a methodology involving either laboratory testing (Chasapidis, 2018) with artificial cooling wind conditions or on-road testing with an (semi) enclosed setup (Hagen, 2019) pumping air through the system at a constant rate, thereby influencing the temperature behaviour of the system. At the same time health studies show that there are strong indicators that exposure to ultrafine particles, particles up to 100nm in size, causes negative effects on the cardiovascular system and the respiratory tract (Health Council, 2021). On road temperature and cooling need to be included.       Figure 1. Thermocouple inserted in the brake pad (left) and particle sampling point on the bottom of the calliper (right).   A passenger vehicle was equipped with a K-type thermocouple (brake pad, figure 1), a TEN AEM broadband particle counter (calibrated for 50-80nm particles) and OBD logging. This simple and fully open test setup allowed for real world measurements, without influencing the heat transfer of the braking system.  The test campaign involved multiple sessions in various conditions, ranging from slow urban to fast highway driving in the Netherlands. The time-based data showed indications of a rising trend in brake wear particle number concentration peaks with rising brake system temperature during similar braking events, see figure 2. Further analysis into this dependency was done by extracting the peaks in the calculated braking power, link them to the corresponding peaks in the particle number concentration and the brake system temperature. In four temperature bins, the particle number concentration was plotted against the braking power and a quadratic curve was fitted (in red), see figure 3.   Figure 2. Brake PN (red), brake temperature (blue), braking power (black) and vehicle speed (green)  plotted over time.   Figure 3. Brake PN vs. braking power for the lowest (left) and the highest (right) temperature bin.   The results show a clear increase in ultrafine particle concentration when the brake pad temperature increases and even a strong indication for a direct link between brake system temperature, applied braking power and ultrafine brake particle emissions, all under real driving conditions. However, for a statistically strong direct dependency further research is needed. Future steps involve measuring background particles and estimating total ultrafine particle number emissions.  This work was funded internally by TNO.  Health Council of the Netherlands (2021) Risks of ultrafine particles in the outside air, No. 2021/38, The Hague, September 15, 2021. Chasapidis, L. et al (2018) Study of brake wear particle emissions of a minivan on a chassis dynamometer, Emission Control Science and Technology, 4, 271-278. zum Hagen, F.H.F. et al (2019) On-road vehicle measurements of brake wear particle emissions, Atmospheric Environment, 217, 116943.  
 
 
 
 
521 

  A Novel Simulation Approach for Non-Exhaust Particle Emissions  L. Landl1, S. Hausberger1   1Institute for Sustainable Propulsion Systems, University of Technology, Graz, Styria, 8010, Austria Keywords: Non-Exhaust Emissions, PM, PN, Road Traffic, Simulation Presenting author email: landl@ivt.tugraz.at  Particle emissions from brake-, tyre- and road-wear have increased with traffic volumes while exhaust particle emissions have decreased considerably due to the introduction of strict exhaust standards requiring particle filters for most new vehicle categories.  Thus, the share of the "non-exhaust particles" (NEP) in traffic emissions is increasing sharply. In addition to the increasing share, the composition of NEP is relevant for the environmental impact. Tyre-wear produces e.g. large quantities of micro plastics, which are also mixed with additives from the tyre rubber. In the case of brake wear, ultra-fine particles are produced from binding agents, especially at high temperatures in the brake system, and abrasion of brake pads and discs generally occurs during every braking process. Due to the increasing importance of NEP emissions, a novel method was developed at TU Graz for data collection and simulation of emission factors of tire-, road- and brake wear PM and PN emissions. The new simulation method was developed in a Horizin 2020 project called uCARe (https://www.project-ucare.eu/) and further optimised in a project for BAFU (Hausberger, 2023). The method is integrated in the vehicle emission model PHEM from TU Graz, which is inter alia used to calculate all base emission factors for exhaust emissions for the HBEFA (www.hbefa.net). The model PHEM calculates the power at the wheels and at the mechanical brakes as well as corresponding rotational speeds for vehicles using the equations of longitudinal dynamics. The brake wear emissions are interpolated from characteristic polygons which define the emission flow over the product of mechanical brake power (P) and disc speed (Omega)0.5, as shown in Figure 1 for PN23.   Figure 1. Characteristic polygon for LDV brake wear PN23 emissions  The PM and PN emissions from tire-wear are interpolated from characteristic polygons defining the emission flow over transmitted power between wheel and road. Due to the limited data, the road wear emissions are calculated with a constant factor from tire wear results. All calculations run in 1 Hz resolution. The characteristic polygons are obtained from measured emission data by averaging the data for each section of the x-axis. This allows the use of a broad range of data sources, such as the WLTP brake test data and thus a systematic data collection and model update in future. The inclusion of more explaining parameters in the model, such as brake temperatures and tire slip is planned when sufficient test data is available. The equations of longitudinal dynamics are general valid and allow the simulations of any combination of vehicle, loading, speed trajectory and road gradients. Thus, the model PHEM offers a much higher resolution compared to other approaches.  Figure 2 shows e.g. results for motorway free-flow compared to dense urban driving, each for flat road and for +2% and -2% gradient respectively. Not surprising, brake wear emissions are almost zero on the free highway but dominate PM10 NEP emissions in dense urban traffic. Brake power and emissions are lower when driving uphill than when driving downhill. Thus, in dense urban traffic the total NEP emissions are lower in uphill than in downhill driving. On the motorway, more power is needed at the wheels when driving uphill than when driving downhill, so that the total NEP emissions there are higher for positive gradients.  Figure 2. Non-Exhaust emissions simulated for a passenger car for three different traffic situations  In the paper, we present the approach and the corresponding data base and vehicle tests performed. We show model results for pure electric, hybrid and conventional vehicles including also exhaust particle emissions for comparison.  This work was supported by H2020 and BAFU.  Hausberger S., Landl L.: Simulation of Non-Exhaust Particle Emissions; funded by BAFU; TU Graz 2023  
 
 
 
 
3.7 JS.08.  Air  pollutant  and  GHG  emissions,  water 
contaminants, ambient and underwater noise and vessel-
induced mixing. 

522 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  Remote detection of ship exhaust plumes from different marine fuels on board a research vessel in the Baltic Sea region using single-particle mass spectrometry  J. Schade1,2, J. Passig2,3, E.I. Rosewig2, H. Osterholz4, J. Hovorka5, D. Schulz-Bull4, R. Zimmermann2,3 and T.W. Adam1,3  1Faculty of Mechanical Engineering, Institute of Chemistry and Environmental Engineering, University of the Bundeswehr Munich, Neubiberg, 85577, Germany 2Joint Mass Spectrometry Center/Analytical Chemistry, University of Rostock, Rostock, 18059, Germany 3Joint Mass Spectrometry Center/Cooperation Group “Comprehensive Molecular Analytics” (CMA), Helmholtz Zentrum München, Neuherberg, 85764, Germany 4Leibniz-Institute for Baltic Sea Research Warnemünde (IOW), Rostock, 18119, Germany 5Faculty of Science, Institute for Environmental Studies, Charles University, Prague, 128 01, Czech Republic Keywords:   single-particle mass spectrometry, ship emissions, sulphur emission control area (SECA), ambient air measurement.      Presenting author email: julian.schade@unibw.de  Ship emissions are a major cause of global air pollution and have a significant impact on climate and human health. To reduce emissions and improve air quality, several sulphur emission control areas (SECAs) have been established worldwide to further improve air quality in densely populated coastal areas and preserve vulnerable ecosystems. Consequently, ships in SECAs are only authorized to operate on low-sulphur fuels or use exhaust after-treatment devices such as exhaust gas scrubbers. To comply with these regulations at sea, sophisticated measurement systems are needed. In this paper an approach to remotely detect and characterize ship exhaust plumes through on-board measurements from a research vessel in the Baltic Sea is presented.   The ship exhaust plumes are detected by particle number concentration and their size distribution through CPC and SMPS+CPC measurements and qualitatively analysed by on-line single-particle mass spectrometer (SPMS). Passig et al (2020) showed that the ionization method using KrF excimer laser with 248 nm wavelength, also used in this study, exhibits high sensitivity especially for iron but also for other transition metals. In particular, this high sensitivity of the measurement method to health-relevant metals, which are contained in PM from ships with exhaust gas scrubbers and thus serve to distinguish the fuels used, is exploited.  The method of SPMS and the device used here are described in more detail in the work of Li et al (2011) and Passig et al (2021). Essentially, single particles in a size range of 0.2 - 2.5 µm can be detected and analysed. In contrast to gas phase measurements, a measurement by SPMS can easily be performed from several kilometres distance in land-based measuring stations, but these measurements are strongly dependent on wind direction (Passig et al, 2021). To overcome the dependency on wind direction, the measurements for this study were performed on board the German research vessel Elisabeth Mann Borgese. The plumes of passing ships could be recorded and analysed from a distance of several kilometres. As an example, a single event is listed in Fig. 1, which suggests the use of HFO in conjunction with an exhaust gas scrubber. These measurements show that SPMS can be a powerful tool for ship exhaust monitoring.  Figure 1. (a) Total number concentration from CPC measurements showing single pollution events from ship exhaust plumes over time/a day. (b) Size distribution from SMPS+CPC in the marked time frame showing a single ship exhaust plume with an additional mode at around 100 nm at 14:00 h. (c) Average mass spectrum from the ship exhaust plume indicating the use of HFO due to signals of V/Fe/Ni in the positive mass spectrum as well as sulphate in the negative mass spectrum.  This work was funded by the DFG (German Research Foundation) – 471841824 (project “PlumeBaSe”), Czech Science Foundation (22-03426L), and by dtec.bw – Digitalization and Technology Research Center of the Bundeswehr (project LUKAS). dtec.bw is funded by the European Union – NextGenerationEU.   Passig, J., Schade, J., Rosewig, E.I., Irsig, R., Kröger-Badge, T., Czech, H., Sklorz, M., Streibel, T., Li, L., Li, X., Zhou, Z., Fallgren, H., Moldanova, J. and Zimmermann, R. (2020) Atmos. Chem. Phys. 20, 7139-7152.  Li, L., Huang, Z., Dong, J., Li, M., Gao, W., Nian, H., Fu, Z., Zhang, G., Bi, X., Cheng, P., Zhou, Z., (2011) Int. J. Mass Spectrom. 303, 118-124. Passig, J., Schade, J., Irsig, R., Li, L., Li, X., Zhou, Z., Adam, T. and Zimmermann, R. (2021) Atmos. Meas. Tech., 14, 4171-4185  
 
 
 
 
524 

  Ammonia as a Marine Fuel Towards Decarbonization: Emission Control Challenges  G. Voniati, A. Dimaratos, G. Koltsakis, L. Ntziachristos  Laboratory of Applied Thermodynamics, Department of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece  Keywords: shipping, decarbonization, GHG, ammonia, emission control, NOx, N2O grigoris@auth.gr (G. Koltsakis), adimaratos@auth.gr (A. Dimaratos)  Maritime transport emitted 1,056 million tons of Carbon Dioxide (CO2) in 2018 and is responsible for almost 3% of global Greenhouse Gas (GHG) emissions (IMO, 2020). In addition, it accounts for 24% of Nitrogen Oxides (NOx), 24% of Sulphur Oxides (SOx) and 9% of Particulate Matter (PM) emissions in the European Union (EU) (EMSA, 2021). According to the initial IMO strategy, GHG emissions shall be reduced by at least 50% by 2050 (compared to 2008), aiming at complete decarbonization of the maritime sector by 2100 (IMO, 2018). Latest meetings of the IMO’s Marine Environment Protection Committee (MEPC) have revised the targets, setting the complete decarbonization goal for 2050 (IMO 2023). Moreover, the global sulphur cap limits the fuel sulphur content to 0.5%, with a separate 0.1% limit in Sulphur Emission Control Areas (SECAs). In parallel, NOx emissions shall comply with Tier III requirements in Emission Control Areas (ECAs) and Tier II globally. Among alternative fuels with low or zero carbon content (LNG, LPG, methanol, biofuels and hydrogen), ammonia (NH3) is a promising solution to limit carbon emissions from the maritime sector due to several advantages such as high energy density and relatively easy storage (Rodriguez et al., 2022).   Ammonia combustion produces three main emission species: unburned NH3, NOx and Nitrous Oxide (N2O) (Rodriguez et al., 2022). The latter is a GHG with Global Warming Potential (GWP) almost 300 times higher compared to CO2 (Chai et al., 2021), therefore even small concentrations of N2O may counterbalance the benefit of CO2 reduction.  In the present study, commercial catalytic devices are examined, studying the potential reduction of NOx and NH3, as well as the formation of N2O in the catalytic system. However, the trial-and-error approach to design emission control systems is cost-prohibitive, particularly for a large 2-stroke engine. Therefore, it is crucial to develop accurate predictive models of aftertreatment systems that can guide optimal design in the early stages, which is the primary objective of this study. In this work, experiments are first conducted to derive the chemical kinetics information for two relevant catalyst technologies, which are then incorporated into physico-chemical models. The model is subsequently utilized to examine two potential scenarios regarding the proportion of NH3 and NOx emissions during ammonia combustion: (1) engine-out NH3 lower than NOx (NH3/NOx<1), requiring additional NH3 injection upstream of a Vanadium-based SCR (V-SCR) catalyst to achieve NOx levels below Tier III limits, and (2) excess of engine-out ammonia (NH3/NOx>1), where a platinum-based ammonia oxidation catalyst (AOC) is integrated into the aftertreatment system to address ammonia slip. Particular attention is given to the formation of N2O through catalytic reduction of NOx and oxidation of NH3. The outcomes of the simulations demonstrate that in both scenarios it is possible to optimize NOx conversion to meet the strictest IMO limits with minimal NH3 slip. Concerning N2O, it is produced in very low levels in the case of shortage of NH3 in the exhaust gas, while excess of NH3 tends to selectively promote the formation of N2O, particularly at higher NH3 concentrations. In such cases, the CO2 equivalent emissions are comparable to the levels of LNG engines (Pavlenko et al., 2020). Furthermore, N2O emissions from ammonia in-cylinder combustion are expected to increase the total N2O emissions. Thus, both sources need to be considered for the successful control of N2O emissions, eventually via a separate additional catalyst.   Acknowledgement This work has been funded by the ENGIMMONIA project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nr. 955413  Chai, W. S., Bao, Y., Jin, P., Tang, G., & Zhou, L. (2021). A review on ammonia, ammonia-hydrogen and ammonia-methane fuels. In Renewable and Sustainable Energy Reviews (Vol. 147). Elsevier Ltd.  EMSA. European Maritime Transport Environmental Report 2021. International Maritime Organization – IMO(MEPC72). Resolution MEPC.304(72), Initial IMO Strategy on Reduction of GHG Emissions from Ships, 2018. International Maritime Organization – IMO. Fourth IMO GHG study Full report, 2020. International Maritime Organization – IMO, Marine Environment Protection Committee (MEPC 80), 3-7 July 2023.  Pavlenko, N.; Comer, B.; Zhou, Y.; Clark, N.; Rutherford, D. The climate implications of using LNG as a marine fuel. International Council on Clean Transportation 2020.  Rodríguez, C. G., Lamas, M. I., Rodríguez, J. de D., & Abbas, A. (2022). Possibilities of Ammonia as Both Fuel and NOx Reductant in Marine Engines: A Numerical Study. Journal of Marine Science and Engineering, 10(1).  
 
 
 
 
525 

  Characterisation of emissions from marine vessel with E-methanol fuel and NOx emission control  J. Moldanova1, H. Timonen2, P. Simonen3, G. M. Lanzafame4, R. Verbeek5, H. Salberg1, L. Barreira2, K. Teinilä2, S. Saarikoski2, L. Markkula3, J. Kalliokoski3, B. D’Anna4, B. Temime-Roussel4, H. Hellen2, L. Ntziachristos3,6 1 IVL, Swedish Environmental Research Institute, Gothenburg, Sweden 2Atmospheric Composition Research, Finnish Meteorological Institute, Helsinki, 00101, Finland 3Aerosol Physics Laboratory, Physics Unit, Tampere University, Tampere, 33014, Finland 4 Aix Marseille University, CNRS, LCE, Marseille, France 5 Department of Sustainable Transport and Logistics, TNO, Den Haag, Netherlands 6 Laboratory of Applied Thermodynamics, Aristotle University of Thessaloniki, Thessaloniki, Greece Keywords:  Ship exhaust emissions, abatement measures, chemical composition  Presenting author email: jana.moldanova@ivl.se  During the last decade the International Maritime Organization’s (IMO) tightened the limits for emissions of SOx and NOx from the shipping sector, targeting reduction of contributions of shipping to air pollution with both NOx, SOx and particulate matter (PM). At the same time IMO has set a target to reduce the greenhouse gas emissions from shipping by 50% by 2050. Significant part of this decrease is expected to come from increasing energy efficiency of the shipping sector, however, to achieve such high reduction under simultaneous growth of the whole sector will require significant decarbonisation. One of the promising alternative fuels is synthetic methanol (e-methanol). To assess effectiveness and secondary impacts of the regulations, impacts of the shipping emissions on air quality under different regulatory scenarios need to be investigated and this requires good knowledge of impact of the deployed abatement measures on emissions of both the targeted pollutants and of other emitted species. In this paper, we present characterisation of emissions of gases and particles from a vessel equipped with a 4-stroke medium-speed marine engine equipped with SCR and operating on 2 different fuels, e-methanol and marine gasoil (MGO).  The measurements were performed during a one-week measurement campaign onboard a RoPax ferry, when emissions from the two investigated fuels and impact of selective catalytic reduction (SCR) system were characterized with an array of high-end instrumentation and onboard sensors under different operational conditions. The in-stack measurements were performed both in the engine room directly after the engine and close to the exhaust funnel outlet, downstream of the catalyst, and gave information about chemical and physical properties of the exhaust and its changes with the different fuels, with and without urea injection downstream of the catalyst and over the catalyst. The exhaust characterisation included both gaseous (SO2, NOx, NO, NO2, CO2, CO, O2, CH4, total HC, NMVOCs, PAHs) and particulate (PM, EC, OC, ions and elemental composition) pollutants measured both upstream and downstream of the catalyst. In addition, two mobile laboratories carried state-of-the-art instrumentation for online analyses of detailed chemical composition and physical properties of gases and particles in the exhaust downstream the catalyst, including size-resolved composition of PM, characterisation of organic matter, secondary PM formation potential determined by a Potential Aerosol Mass (PAM) chamber and measurements of BC.  The measurements showed that the SCR with urea injection reduced NOx emissions but also significantly reduced emissions of organic matter, both volatile and in particulate phase. Reduction of the organic matter over the catalyst was observed both with and without urea injection. Total hydrocarbon emissions were reduced with 80%, OC with 60% and different PAHs with 70% to 95%. Emissions of EC were not affected by the SCR but total PM emissions were reduced with 40%. Emissions from combustion of the methanol fuel (including 6-13% MGO), when compared to MGO, reduced emissions of NOx with ~30%, emissions of EC with ~70%, OC with ~40% while PAH emissions remained unchanged. Characterisation of organic species with several analytical methods gave new information on chemical composition and volatility profile of the exhaust.   Figure 1. Emissions of PM species (upper panel) and sum of VOCs and PAHs (lower panel) for measurements upstream (engine-out) and downstream (post-catalyst) the catalyst with and without urea injection for MGO and Methanol fuel. Mass closure = PM mass - Sum of PM species The SCIPPER project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nr.814893. Stena Line and engine crew of Stena Germanica are gratefully acknowledged for their great support of the measurement campaign. -2000200400600800Engine-out MGOEngine-out MeOHPost-catalyst MGO, urea onPost-catalyst MGO, urea offEF mg/kg fuelOMECSO4NH4+MetalsMass closure010203040Engine-out MGOEngine-out MeOHPost-catalyst MGO,…Post-catalyst MGO,…Post-catalyst MeOH,…EF mg/kg fuelsum VOCssum PAHs223 
 
 
 
 
526 

  Discharges from exhaust gas cleaning systems in the OSPAR marine area   T. Grönholm1, J.-P. Jalkanen1, J. Kukkonen1, and I.-M. Hassellöv2  1Finnish Meteorological Institute, Helsinki, FI-00560, Finland 2Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, SE-41296, Sweden Keywords: scrubber, shipping, discharge water, contaminant load.      Presenting author email: tiia.gronholm@fmi.fi  Since stricter global regulation of maximum allowed sulphur in marine fuels entered into force in 2020, an increasing number of ships are using scrubbers to enable continued use of heavy fuel oil. Scrubbers wash out sulphur oxides and other contaminants from the exhausts and produce large volumes of heavily polluted wash water. Depending on the system, the used scrubber water is discharged back into the ocean (open loop) or recirculated, treated (closed loop) and the sludge is disposed of in the port.    The Ship Traffic Emissions Assessment Model (STEAM v3.5, Jalkanen et al., 2021 and references therein) utilizing ship position data from Automatic Identification System was used to calculate the amount of discharged scrubber water from open and closed loop scrubbers. Combining the modelled water volume with the concentration results obtained from water sampling and laboratory analyses, it was possible to give the estimations for the mass fluxes of various pollutants to the sea.  In 2020, the total effluent release volume from scrubbers in all OSPAR areas was about 620 million tonnes.  Of this, more than 99% was from open loop scrubbers. About half of this discharge water was released in the North Sea and the English Channel.  The most (84%) of the scrubber effluent in the OSPAR domain was released inside the 200 nautical mile zone. Close to the coast, inside the 12 nautical mile zone about 20% of the total effluent volume was discharged. The volumes of produced water from the oil and gas extraction was estimated to be the individual largest industrial waste stream into the OSPAR area 300*106 m3 (e.g. Beyer et al., 2020).  Most contributing ship types were containerships, Roll-On/Roll-Off cargo ships, bulk cargo carriers, and crude oil tankers. About half of the effluent was released from vessels carrying an EU flag. Most of the scrubbers in the OSPAR area were of open loop type.   Contaminant loads from open loop scrubbers in the OSPAR area were dominated by vanadium (110 tonnes) and nickel (29 tonnes) which both may originate from heavy fuel oil. Nickel may also originate from piping material in the scrubber, which, together with marine growth protection systems, is the hypothesized primary source of zinc (69 tonnes) and copper (24 tonnes) in open loop discharge water.   The closed loop contaminant loads are in the order of 103-104 times lower than the open loop loads. Nickel constitutes the largest calculated closed loop load (190 kg), almost twice the load of vanadium (100 kg) and chromium (90 kg), while the estimated loads of copper and nickel are lower, 40 and 30 kg, respectively.  The estimated PAH loads in OSPAR region are dominated by phenanthrene (940 kg), followed by fluorene, pyrene acenaphthene, chrysene and fluoranthene (between 100-290 kg). The estimated total load of PAH:s is almost 2.5 times higher than the mass of the 16 high priority PAH pollutants (16 PAH, EPA), suggesting that for example alkylated PAHs should also be considered. The closed loop PAH load is in the order of 105-106 times lower compared to open loop PAH load, since closed loop scrubber technology are generally more efficient in removing PAHs (than metals) during the recirculating scrubber process. Figure 1. A map showing amount of discharged water from open loop scrubbers in Ospar regions (green polygons) in 2020.   This project has received funding from the European Union's Horizon2020 research and innovation programme under grant agreement #874990 (EMERGE project). This work reflects only the authors’ view and CINEA is not responsible for any use that may be made of the information it contains.  Beyer, J., Goksøyr, D., et al. (2020). Environmental effects of offshore produced water discharges: A review focused on the Norwegian continental shelf. Marine Environmental Research, 162: 105155. Jalkanen, J.-P., Johansson, L., et al. (2021). Modelling of discharges from Baltic Sea shipping. Ocean Science, 17(3), 699–728. https://doi.org/10.5194/os-17-699-2021.   
 
 
527 

  Characterisation of fresh and aged ship emissions in the Port of Marseille  Å. M. Hallquist1, G. M. Lanzafame2, B. Temime-Roussel2, P. Simonen3, M. Dal Maso3, H. Salberg1,  J. Kalliokoski3, J. Mellqvist4, V. Conde4, J. Keskinen3, L. Ntziachristos3,5 and B. D’Anna2  1IVL Swedish Environmental Research Institute, Gothenburg, Sweden 2Aix Marseille University, CNRS, LCE, Marseille, France  3Aerosol Physics Laboratory, Physics Unit, Tampere University, Tampere, Finland 4Department of Earth, Space and Environment, Chalmers University of Technology, Gothenburg, Sweden 5Aristotle University of Thessaloniki, Thessaloniki, Greece  Keywords: ship emissions, secondary particle mass, Go:PAM, OFR Presenting author email: asa.hallquist@ivl.se  Shipping contributes to emissions of gaseous and particulate pollutants that can affect climate and health. In the atmosphere the primary emissions can further oxidise leading to the formation of secondary pollution, e.g., secondary organic aerosol (SOA), which is very important on a regional and global scale (Hallquist et al., 2009).    As part of the EU H2020 project SCIPPER (Shipping contribution to Inland Pollution Push for the Enforcement of Regulations) detailed characterisation of the fresh and aged ship emissions was conducted in the Port of Marseille from 11 to 23 July in 2021. The measurements were done at the Phares at Balises (PHB) site [N43.3353, E5.3393] using the stationary ship plume measurement technique, including continuous extractive sampling of the ambient air (Jonsson et al., 2011). The experiments were set-up in order to enable simultaneous characterisation of the freshly emitted emissions and the oxidised emissions utilising an oxidation flow reactor (OFR), specially designed for these types of measurements, i.e., Go:PAM (Watne et al., 2018) (Fig. 1).     Figure 1. Schematic of the experimental set-up and instrumentation used.    Emission factors (EF) of individual ships were calculated using the carbon balance method, assuming complete combustion and an EFCO2 of 3.2 kg per kg fuel burnt (Jonsson et al., 2011). The identification of the plumes was done using AIS (automatic identification system) data, giving e.g., the identity, position and speed of the ship; and meteorological data (wind speed and wind direction) (Hallquist et al., 2023). In total 102 ship plumes have been identified and characterised.    Results will be presented with focus on the particle emissions (particle number, mass, size and chemical composition) (Fig. 2).   Figure 2. Example of a ship plume measured in Marseille harbour utilising Go:PAM. PMAGED and PMFRESH are the particle mass measured with and without oxidation of the emissions in the OFR.  This work was financed by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 814893.  Hallquist M. et al., (2009) The formation, properties and impact of secondary organic aerosol: current and emerging issues, Atmos. Chem. Phys., 9, 5155–5236. Hallquist Å. M., et al., (2023) Characterization of Ship Emissions 2010-2015: Impact of International Maritime Organization Regulations in a Sulfur Emission Control Area (SECA), Manuscript in preparation Jonsson, Å, M., Westerlund, J., Hallquist, M. (2011) Size-resolved particle emission factors for individual ships, Geophys Res. Lett, 38, L13809 Watne et al., (2018) Fresh and Oxidized Emissions from In-Use Transit Buses Running on Diesel, Biodiesel, and CNG, Environ. Sci. & Tech., 52 (14), 7720-7728    20151050PM (µg m-3)18:35:002021-07-1518:37:3018:40:00302520151050CO2 (ppm) 0.000.00 PM_AGED PM_FRESH CO2 
 
 
 
528 

  Distribution of PAHs and metals between exhaust and scrubber water discharge from a large 2-stroke slow speed marine engine equipped with open loop scrubber A. Lunde Hermansson1, J. Moldanová2, B. Strandberg3, E. García-Gómez4,5, M. Gros4,5, M. Petrović4,6, S. Rodríguez-Mozaz4,5, I.-M. Hassellöv1 and E. Ytreberg1   1 Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, SE-41296  2 IVL, Swedish Environmental Research Institute, P.O. Box 53021, 400 14 Gothenburg, Sweden 3 Division of Occupational and Environmental Medicine, Lund University, 22100 Lund, Sweden 4 Catalan Institute for Water Research (ICRA), C. Emili Grahit 101, 17003 Girona, Spain 5 Universitat de Girona (UdG), Girona, Spain 6 Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, Barcelona, Spain Keywords :   scrubber, pollution, emission factors, mass balance      Presenting author email: anna.lunde.hermansson@chalmers.se  To comply with IMO regulations, aiming at reducing negative impact of sulphur oxides (SOX) and particulate matter (PM) on air quality, shipowners are required to switch to compliant fuels, of lower sulphur content, or to install a scrubber, allowing for continued use of cheaper high sulphur heavy fuel oils (HSHFOs). The 2020 reduction of the maximum sulphur (S) content in marine fuels from 3.5% to 0.5% m/m S led to massive increase in number of scrubber installations.   Previous studies show that scrubber discharge water contains high concentrations of e.g. metals and polycyclic aromatic hydrocarbons (PAHs), proving that contaminants are being scavenged by the scrubber water and discharged to the marine environment. It is however unknown in what extent these contaminants are being scavenged in the scrubber and discharged to the marine environment and how much is emitted through the exhaust. Few studies have compiled emission factors from ships equipped with scrubbers to include both the atmospheric and marine perspective, but there is a lack in simultaneous measurements on-board ships.  The objective of this study was to assess emissions of contaminants to the marine environment, and to evaluate the mass balance between air emissions and water discharges downstream the scrubber.  During the fall of 2021, an on-board campaign was carried out, where sampling of metals and PAHs (including alkylated species) in exhaust gas and scrubber discharge water were done simultaneously together with characterisation of number of other pollutants, such as emissions of NOX, SOX, VOCs, PM and its composition. The container vessel, equipped with an open loop scrubber, was sailing through different marine domains, from the North Sea to the Eastern Mediterranean. To the knowledge of the authors, this is the first published mass balance assessment and the results from the analysis provide information on the distribution of contaminants between exhaust emissions and scrubber water discharge.      Metal and PAH content were characterized in the tested fuels (two batches of HSHFO and ultra-low S fuel oil, ULFSO), the exhaust and the scrubber discharge water. The exhaust was sampled before and after the scrubbing process, and in case of ULSFO, only after a deactivated scrubber. Exhaust PAH samples were pumped through combined samplers consisting of a particle filter and XA´D absorbent tube.  Metal concentrations of the exhaust were obtained from ED-XRF analysis on the PM filter samples. Scrubber discharge water was sampled on-board and the particulate and dissolved fractions  of metals and PAHs were differentiated in the lab.     Figure 1. Schematic over the sampling setup (red dots indicate sampling points) during the onboard campaign  Results suggest that the scrubber is scavenging a substantial fraction of PAHs (also alkylated and dibenzothiophenes (DBTs)). Air emissions were largely dominated by naphthalene and alkylated naphthalenes, In scrubber discharge water, naphthalene and phenanthrene represented the highest concentrations of the parent PAHs. The alkylated PAHs were also prevalent in scrubber water. Known tracers of HFO combustions, such as nickel and vanadium, are found in the exhaust gas before and after the scrubber as well as in elevated levels in the scrubber discharge water, indicating that the contaminants will be distributed between the different sources and that both the atmosphere and marine environment will act as primary recipients when a scrubber is used. Acknowledgement     This work was funded by the European Union’s Horizon 2020 research and innovation programme Evaluation, control and Mitigation of the EnviRonmental impacts of shippinG Emissions (EMERGE) [grant agreement No 874990].   
 
 
 
 
3.8 JS.09.  Atmospheric  processes  and  air  quality  impact 
studies:  Modelling  impacts  of  transport  on  air  pollution, 
climate, health and ecosystems. 

529 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
530 

  Impacts of shipping emissions on air quality in 2040: effects of nitrogen emission control areas and non-compliance S. Jutterström1, J. Moldanová1, E. Majamäki2, J.-P. Jalkanen2, J. Kuenen3, and V. Matthias4  1IVL Swedish Environmental Research Institute, Gothenburg, SE-400 14, Sweden 2 Finnish Meteorological Institute, Helsinki, FIN-00101, Finland 3 TNO, Netherlands Organization for Applied Scientific Research, Utrecht, 3584, The Netherlands 4 Helmholtz-Centre Hereon, Institute of Coastal Environmental Chemistry, Geesthacht, 21502, Germany  Keywords:  shipping, nitrogen oxide emissions, air pollution, future scenarios  Presenting author email: sara.jutterstrom@ivl.se  Shipping contributes significantly to the emissions of air pollutants such as SOx, NOx, and particulate matter (PM), especially in coastal areas, as well as to the emissions of the greenhouse gasses. As maritime transport is projected to continue to grow in the future, there are increasing efforts to reduce emissions. While emissions of SOx and associated PM sulphate are targeted by regulation of the fuel S content with immediate impact after implementation of the legislation, emissions of NOx are targeted by NOx Tiers in Nitrogen Emission Control Areas (NECAs) applying for new-build ships key laid after the date of implementation of the legislation, which leads to a slow, gradual reduction of emissions. Further, the International Maritime Organization (IMO) has set a target to reduce the greenhouse gas emissions from shipping by 50% by 2050 compared to emissions in 2008 where important tool is gradual increase of energy efficiency of the new-build vessels which also brings reduction of emissions of air pollutants. More stringent regulations can lead to for instance use of cleaner fuels, increased use of scrubbers, use of exhaust gas aftertreatment targeting NOx emissions, use of alternative fuels and improvements in fuel efficiency.   As a part of the SCIPPER project, the effects of shipping emissions on air quality in coastal regions in Europe were investigated for year 2015 and for three ship emission scenarios for year 2040 focusing on the effects of an implementation of a European NECA and a related possibility of partial failure of its implementation through partial non-compliance and the exhaust aftertreatment not operating at low engine loads. To highlight the impact of shipping emissions in the scenarios, these were compared to a scenario excluding the shipping emissions in the 2 investigated years. Three regions with high impact of shipping emissions were investigated in more detail.     The regional model simulations were done with a combination of the STEAM ship emission model with the EMEP-MSC-W regional chemistry transport model (open-source version rv3.34 on a 0.1°x 0.1° resolution). The land emissions were derived from the ECLIPSE v6b global emission scenarios with an MFR (Maximum Feasible Reduction) scenario selected.      In this contribution, the results from the year-2015 and the investigated year-2040 scenario simulations for concentration levels of NO2, O3 and PM2.5, as well as deposition of nitrogen and their shipping shares are compared and discussed (Figure 1).   The results clearly show that the implementation of a European-wide NECA will reduce NO2 and PM2.5 concentrations as well as nitrogen deposition. O3 concentrations are also expected to decrease in most areas. However, in regions with formerly very high NOx emissions ozone concentrations will increase because of reduced ozone titration through reactions with NO. The effects of non-compliance on air pollution from shipping are visible but they are much smaller than the gains through the NECA implementation. Figure 1. Annual mean NO2 concentrations in Europe in 2015 (top) and for a NECA shipping scenario in 2040 (bottom). Acknowledgments: This work was carried out in the SCIPPER project: Shipping Contributions to Inland Pollution Push for the Enforcement of Regulations, EU Horizon 2020 No. 814893.  
 
 
 
 
531 

  Direct evidence of the substantial effect of SECA in the Baltic Sea  A. Maragkidou1, T. Grönholm1, L.Rautiainen1, T.Mäkelä1, L. Laakso1 and J. Kukkonen1,2   1Finnish Meteorological Institute, Helsinki, Uusimaa, FI-00101, Finland 2 Centre for Atmospheric and Climate Physics Research, and Centre for Climate Change Research, University of Hertfordshire, College Lane, Hatfield, AL10 9AB, UK Keywords:   SO2, shipping, emissions, SECA.      Presenting author email: androniki.maragkidou@fmi.fi  From 1st of July 2010 and onwards, the International Maritime Organization (IMO) implemented a rule that required ships sailing in SECA regions (including the Baltic Sea) to use fuel with a sulfur content no higher than 1.0% This rule was further tightened in 2015, reducing the sulfur limit to 0.1%. Ships are therefore required either to use low-sulfur fuel or install scrubbers to remove sulfur from their exhaust.  The Air Quality Station of Utö is located on a small island in the Archipelago Sea of the Baltic Sea. The island has a long history of meteorological, marine, and air quality measurements. Air quality measurements have been carried out for SO2 since 1980 and for NO2 since 1986. Concentrations have been measured according to the EU standards using UV-fluorescence and chemiluminescence gas analyzers. The island's location makes it an optimal place for plume tracking, as the regional background levels of NOX and SO2 are very low, and it is near a busy sea route (Grönholm et al, 2021).   The results (Figure 1) evidenced a clear decrease of the measured concentrations of SO2 since 2015. This provides direct evidence that shipping in the nearby route has complied with the Annex VI of the IMO MARPOL Convention. An example of SO2 and NOx concentrations originating from shipping are presented in Figure 2, before and after the passing of a ship. Based on an analysis of the various timeseries of concentrations, and analyses of individual ship bypasses, we have compiled extensive evidence regarding the effect of the introduction of SECA in this region.    This study presents for the first time decadal long-term data (since the early 1980’s) from a remote station in the open sea in the Baltic. The measured data and their analysis provide conclusive evidence of the substantial effect of the introduction of SECA since 2015 regarding the concentrations of SO2.   In addition, compared to previous studies in the field, our work presents novel features in terms of analysis of shipping plumes of SO2, NOx and PM measured at a remote station close to a shipping lane. However, further analysis of plumes is still under progress and more results are expected.     Figure 1. Timeseries of the SO2 concentrations during 2006-2020 at Utö. The results highlight a substantial decrease of concentrations since 2015.    Figure 2. An example of our results concerning the measured concentrations of SO2 and NOx at Utö, before and after the passing of a ship called “FINNTIDE” on the 1st of May 2017.  This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 874990 (EMERGE project). This work reflects only the authors’ view and the Innovation and European Climate, Infrastructure and Environment Executive Agency (CINEA) is not responsible for any use that may be made of the information it contains.  Grönholm, T., Mäkelä, T., Hatakka, J., Jalkanen, J-P., Kuula, J., Laurila, T., Laakso, L. and Kukkonen, J. (2021) Environ. Sci. Technol. 55, 13677–13686.   
 
 
 
 
532 

  Future impact of shipping emissions on air quality in Europe under climate change scenarios A. Monteiro1, M. Russo1, D. Carvalho2 and J. Jukka-Peka3  1CESAM, Department of Environment and Planning, University of Aveiro, Portugal 2Department of Chemical Engineering, University of Elsewhere, City, Region, Postcode, Country 3Finnish Meteorological Institute, Helsinki, Finland Keywords:   shipping, atmospheric emissions, numerical modelling, air quality, climate change scenario.      Presenting author email: alexandra.monteiro@ua.pt    Ship engine combustion emits several atmospheric pollutants, such as particulate matter (PM), sulphur oxides (SOx), and nitrogen oxides (NOx), which can have adverse health effects and are significant contributors to decreased air quality (Viana et al, 2014; Aksoyoglu et al, 2016). Due to the distribution of maritime transport activity routes in the EU, a large portion of the population is exposed to shipping emissions throughout Europe (Sofiev el al, 2018). Therefore, in light of the European Commission long-term objective of “zero-waste, zero-emission” for maritime transport, the focus of this study is to quantify the impact of shipping emissions in 2050, considering both emissions projection for the shipping sector and climate change scenarios. The WRF-CHIMERE modelling system domain covers the entirety of Europe and main shipping routes throughout the continent.   To obtain future contributions of maritime transport to total pollutant concentrations, simulations were divided into a present and two future scenarios for 2050, one which only includes shipping projections, and another that include climate change (RCP8.5 worst-case scenario) and shipping emission projections.   The results obtained in terms of the impact of the emissions projection for NOx are (Figure 1) show that shipping has a large impact on coastal air quality and a non-negligible impact in areas < 50 km inland. The seasonal analysis indicates higher concentrations and differences during warm months.  The results indicate that the signal from climate change can be higher than the future impact of shipping emissions, mostly in the Mediterranean, but this effect is also noticeable in the North and Baltic seas (Figure 2). Climate change alone accounts for differences of over 25 µg.m-3 for NOx and Ozone (O3) concentrations throughout Europe, which is equivalent and even higher than the differences caused by the changes in shipping emissions.   Besides the shipping sector, climate change has significant impacts in large urban areas where NOx emissions are highest, registering differences of over 10 µg.m-3. SO2 concentrations are overall low at inland locations and northern Europe, with the only notable differences occurring in eastern Europe and the Mediterranean (mainly due to shipping and fossil fuelled energy production), with values below 10 µg.m-3.     Figure 1. Impact of shipping emissions projections (2050), per season, for NOx pollutant.    Figure 2. Impact of shipping emission projections and climate change scenario, per season, for NOx, O3 and SO2 pollutants.  This research has been supported by the EU H2020 project EMERGE, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement nº874990. Thanks are also due for the financial support to CESAM (UIDP/50017/2020+UIDB/50017/2020+LA/P/0094/2020).  Aksoyoglu, S., Baltensperger, U., Prévôt, A. (2016) Contribution of ship emissions to the concentration and deposition of air pollutants in Europe. Atmos. Chem. Phys. 16, 1895-1906. Sofiev, M., Winebrake, J., Johansson, L., Carr, E., Prank, M., Soares, J., Vira, J., Kouznetsov, R., Jalkanen, J.-P., Corbett, J. (2018) Cleaner fuels for ships provide public health benefits with climate tradeoffs. Nature Communications 9, 406 Viana, M., Hammingh, P., Colette, A., Querol, X., Degraeuwe, B., Vlieger, I., van Aardenn, J. (2014) Impact of maritime transport emissions on coastal air quality in Europe. Atmos. Environ. 90, 96-105.  
 
 
 
 
533 

  On the determination of ship exhaust aerosol volatility in the SCIPPER project  M. Dal Maso1, P. Simonen1, O. Kangasniemi, J. Kalliokoski., A. Wagner., L. Markkula1,  H. Timonen2, L. Barreira2, J. Moldanova3, B. D’Anna4, B. Temime-Roussel4, G. M. Lanzafame4, K. Teinilä2, S. Saarikoski2, H. Salberg3, J.-P. Jalkanen2, E. Majamäki2,  L. Ntziachristos1,5, J. Keskinen1  1Aerosol Physics Laboratory, Physics Unit, Tampere University, Tampere, 33720, Finland 2Atmospheric Composition Research, Finnish Meteorological Institute, Helsinki, 00101, Finland 3Swedish Environmental Research Institute, Gothenburg, Sweden 4Aix Marseille Univ, CNRS, LCE, Marseille, France 5Aristotle University of Thessaloniki, Thessaloniki, Greece Keywords:   Ship exhaust emissions, volatility, aerosol composition Presenting author email: miikka.dalmaso@tuni.fi  Particulate and gaseous emissions from ship engines have both climatic and health effects, which depend in various manners on the physical and chemical properties of the emitted aerosol. In order to understand, model, and finally efficiently regulate such emissions, these properties need to be accurately determined. One of the properties highlighted by research is particle volatility (Donahue et al., 2006) and its dependence on aerosol composition, on engine parameters, evolution during dilution and atmospheric aging.   Here, we report the results of a comprehensive set of measurements of ship exhaust with state-of-the-art instrumentation both on-board and from atmospheric conditions both from the shore and while chasing during the SCIPPER project. In the on-board and chasing measurements, the particle volatility was inspected with thermodenuder measurements, where the sample is led through heated tubing, and the fraction of mass remaining in particle phase was studied as a function of temperature. Another method was isothermal dilution, in which the sample was diluted and allowed to equilibrate for a longer time than in a thermodenuder.   The evaporation of individual chemical species was studied with a FIGAERO-CIMS (Lopez-Hilfiker et al., 2014) setup, where particles are first collected onto a filter, and then desorbed with heated nitrogen. The nitrogen temperature was ramped up to obtain thermograms of different particle-phase molecules. The evaporated compounds were then detected with chemical ionization mass spectrometry using iodide ions creating the necessary charges, in order to target semivolatile organic compounds. FIGAERO thermograms can be deconvoluted using positive matrix factorization to retrieve grouped volatility information (Buchholz et al., 2020)  In addition, the chemical composition of the particulate and gas phases was studied using an SP-AMS aerosol mass spectrometer as well as a time-of-flight proton transfer mass spectrometer for volatile organic compounds. Simulated aging of exhaust was performed by exposing the sample to high concentrations of oxidants in an Aerodyne Potential Aerosol Mass reactor (PAM). Even in the case of detailed characterization, determination of the volatility distribution of the primary aerosol is not straightforward (Riipinen et al., 2010). We applied a detailed microphysical multicomponent aerosol evaporation together with a genetic optimization algorithm (Tikkanen et al., 2019) to find the best estimate of volatility distributions.    Figure 1: Evaporation curve obtained by genetic optimization to thermodenuder measurements  The focus of our presentation is on the results of the volatility determination for ship emission, and the implications for the determination and modelling of both primary and secondary OA from shipping. We present a comparison of our results to existing literature and the STEAM emission model (Jalkanen et al., 2009), and also present the methodology the modification of existing emission data to include updated volatility information.  The SCIPPER project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nr.814893.  Buchholz, A., et al. Atmos. Chem. Phys., 20, 7693–7716 Donahue, N. M. et al (2006) Environ. Sci. Technol. 40, 2635-2643. Lopez-Hilfiker, et al. (2014) Atmos. Meas. Tech., 7, 983–1001. Jalkanen, J.-P. et al (2009) Atmos. Chem. Phys. 9, 9209-9223. Riipinen, I. et al (2010) Atmos. Environ. 44, 597-607. Tikkanen, O.P. et al (2019) Atmos. Chem. Phys. 19, 9333-9350.   
 
 
 
 
3.9 JS.10.  Compliance  monitoring  technological  and  legal 
frameworks;  experimental  studies;  theoretical  impact 
studies. 

534 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
535 

  Comparison of particle emission factors from shipping using different instruments  D. van Dinther1, A. Weigelt2, J. Beecken2,3, J. Mellqvist4, V. Conde Jacobo4, M. Blom1, and J. Duyzer1  1 Netherlands Organisation for Applied Scientific Research (TNO), Petten, the Netherlands  2 Federal Maritime and Hydrographic Agency (BSH), Hamburg, Germany 3now at Explicit ApS, Copenhagen, Denmark 4Chalmers University of Technology, Gothenburg, Sweden  Keywords:  emission factors, shipping, aerosol, particles, particle size distribution, emission factor.      Presenting author email: danielle.vandinther@tno.nl   Air quality in cities with large ports can significantly be impacted by shipping. For example, Visschedijk and Denier van der Gon (2022) showed that, in the Rotterdam area (the Netherlands), sea shipping is the largest source of ultra-fine particles (UFP) with a contribution of 56% to total UFP emissions. In the EU-project SCIPPER (Shipping Contributions to Inland Pollution Push for the Enforcement of Regulations; www-scipper-project.eu) a six week measurement campaign was undertaken at the river Elbe about 10 km downstream of the port of Hamburg (Germany) in 2020. Sea going vessels pass the measurement site at a distance of 400 to 800 m. Three different groups measured side-by side different gaseous compounds, aerosols and the meteorology conditions on-shore. The BSH and Chalmers University each measured the particle size distribution between 5.6 and 10 000 nm with combined EEPS and OPS systems. Next to their permanent installed aerosol- and trace gas monitoring system, BSH additionally operated a mobile system consisting of identical equipment. TNO used a LAS (type 3340A of TSI) to measure particles with sizes between 90 and 10 000 nm. Black carbon (BC) was measured by two groups using aethalometers (Magee AE33). However, the BC set-ups were different (dried vs non-dried and different size cut-offs). The emission factors (EF) of the different ships were calculated in #/kgfuel as well as mg/kgfuel with the simultaneously measured carbon dioxide concentrations. This provides a unique dataset to better understand comparability of particle emission factors measured by different devices. This comparability is important if, in the future, legislation to decrease aerosol emissions of shipping is applied and needs to be enforced. The standard BSH system operational on the site since 2020 and was used as the reference system. Note, this was an arbitrary choice as no true reference of particle emissions exist. Results showed the relatively large contribution of mainly UFP, with 90% of the total amount of particles being smaller than 80 nm and 75% of the total particle mass originate from particles smaller than 200 nm. A total of 1299   plumes where measured during the complete campaign. However, not all instruments were operated at the same times. Comparison of the EF were made for instances where all systems were operated and a plume was measured by all of them, which left around 60 plumes. Table 1 shows the regression statistics for EF of particle number (PN). The comparison showed very promising results when the same equipment was tested side-by-side. For the two BSH (EEPS/OPS) systems R2 is at least 0.79 over the entire size range. Results also showed that data treatment to retrieve EF influences the results. For example, the Chalmers system compared less well to the BSH standard system, though the same equipment was used. The EF of the TNO systems compared fairly well to BSH standard system for PN (R2 ≥0.68). Comparability of the different devices to measure EF of particle numbers, where in general somewhat better than those at of particle mass. For particle mass the TNO LAS system misses a too large large fraction of the smaller particles below 90 nm to give reliable EF. If only the EF of particles larger than 90 nm are used, the comparison of EF up to PM1 (particle diameter up to 1 µm) of the LAS system with the BSH standard system is fairly good with R2 values between 0.65 and 0.82. The difference in the experimental set-up for black carbon used by the different groups, proved to have a too large impact on the results to give comparable emission factors. In conclusion, the data agrees reasonably well between the used systems. Hence, in the future, it should be possible to use on-shore measurements to monitor aerosol emissions from shipping even when different equipment is used.   The SCIPPER project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nr.814893.  Visschedijk, A., Denier van der Gon, H. (2022). UFP emissie in de Rijnmond regio in 2019 (in Dutch). Utrecht: TNO-report R10616. Size range BSHMMS TNO Chalmers [nm] Slope Offset R2 Slope Offset R2 Slope Offset R2 <100 0.83 1.2x1015 0.79 NA NA NA 0.62 2.1x1015 0.49 90-300 0.86 3.4x1013 0.88 0.66 8.4x1013 0.69 0.99 1.7x1014 0.55 300-10000 1.1 -2.3x1011 0.98 1.3 1.7x1012 0.68 0.82 8.1x1011 0.83 Table 1: Regression statistics of EFPN of BSH mobile system (BSHMMS),TNO and Chalmers system against BSH standard system.  
 
 
 
 
536 

  Performance assessment of state-of-the-art and novel methods for remote compliance monitoring of sulphur emissions from shipping  J. Beecken1,4, A. Weigelt1, S. Griesel1, J. Mellqvist2, A.V. Conde Jacobo2, D. van Dinther3, J. Duyzer3, B. Knudsen4, J. Knudsen4, L. Ntziachristos5  1Federal Maritime and Hydrographic Agency (BSH), Hamburg, Germany 2Chalmers University of Technology, Gothenburg, Sweden 3Netherlands Organisation for Applied Scientific Research (TNO), Petten, the Netherlands 4Explicit ApS, Copenhagen, Denmark 5Aristotle University Thessaloniki, Thessaloniki, Greece  Keywords:   ship exhaust, sulphur emission, emission monitoring, remote measurements, fuel sulphur content, MARPOL Annex VI, compliance monitoring  Presenting author email: jbe@explicit.dk   Sulphur emissions to the air from sea-going and inland vessels were measured simultaneously by eight different, state-of-the-art and novel, monitoring systems during a six-week campaign at the Elbe River, at about 90 km upstream the river’s mouth to the North Sea and 10 km distance downstream of the port of Hamburg, Germany. The comparison was conducted as part of EU project SCIPPER: Shipping Contributions to Inland Pollution Push for the Enforcement of Regulations (www.scipper-project.eu/). Stationary, and airborne systems on unmanned aerial vehicles (UAV), were operated by four participating partners in a side-by-side measurement setup to observe the emissions from individual ships under real-world conditions. The measurement site is located in the Northern European SOx emission control area (SECA) where the allowed fuel sulphur content (FSC) is limited to 0.10 %Sm/m.   In total, 966 plumes that originated from 436 different vessels were analysed. At the same time, fuel samples, obtained from 34 different vessels, and bunker delivery notes from five additional vessels were used as references. The results are summarized in Figure 1.   Figure 1. Difference between reported FSC results and reference data from fuel analysis and bunker delivery notes for each remote sensing system.  The stations bsh.hor, bsh.ap, bsh.mms, cha.std, and tno.std are stationary sniffer systems that are operated from land-based sites near shipping lanes by BSH, Chalmers, and TNO, respectively. A novel stationary sniffer system with a more sensitive SO2 monitor, cha.las, was demonstrated for the first time. Airborne sniffers onboard UAV were applied by Explicit (exp.uas) and Chalmers (cha.uas), whereof the latter was an experimental prototype setup.  All systems except the UAV-borne exp.uas showed a significant negative bias of the remotely assessed FSC. Correlation plots indicate a possible dependence on relative humidity.  Total uncertainties were calculated for each system for a 95 % confidence interval as shown in Table 1  Table 1. Re-calculated uncertainties corresponding to a confidence level of 95 % for the systems. *experimental   It was found that the systems can detect non-compliances with a confidence of 95 % if the actual FSC of the combusted fuel of the monitored vessel exceeds 0.15 to 0.19 %Sm/m.   The SCIPPER project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nr.814893. system bias (%Sm/m) uncertainty (%Sm/m) total uncertainty (%Sm/m) bsh.hor -0.035 0.071 0.082 bsh.ap -0.040 0.064 0.080 bsh.mms -0.017 0.076 0.079 cha.std -0.067 0.049 0.092 cha.las -0.020 0.047 0.052 tno.std -0.059 0.060 0.092 exp.uas 0.000 0.067 0.067 cha.uas* -0.030 0.124 0.129  
 
 
 
 
537 

  Remote monitoring of NOx from shipping- validation and long-term results    J. Mellqvist1, J. Beecken2,4, A. Weigelt2, S. Griesel2, A.V. Conde Jacobo1, D. van Dinther3, J. Duyzer3, B. Knudsen4, J. Knudsen4, M. Irjala5, L. Ntziachristos6  1Chalmers University of Technology, Gothenburg, Sweden 2Federal Maritime and Hydrographic Agency (BSH), Hamburg, Germany 3Netherlands Organisation for Applied Scientific Research (TNO), Petten, the Netherlands 4Explicit ApS, Copenhagen, Denmark 5Aeromon Oy, Helsinki, Finland 6Aristotle University Thessaloniki, Thessaloniki, Greece  Keywords:   ship exhaust, NOx emission, emission monitoring, remote measurements, NECA    Presenting author email: johan.mellqvist@chalmers.se   A NOx emission control area (NECA) is established in Northern Europe since 2021, requiring ~75 % NOx reduction of new ships (Tier III) compared to the earlier requirement for ships built during 2011 and 2020 (Tier II). The emission value limit to be controlled is the brake specific emission (g∙NOx/kWh), corresponding to a weighted average of emissions at multiple engine loads, and this value is heavily weighted towards engine loads above 50 %. It is however not possible to take on board samples of NOx and use for legal proof, like for Fuel Sulfur content, since it is produced from the combustion air itself when running the engines.  Port state controls authorities instead inspect that the installed engine and the exhaust gas aftertreatment system is certified to meet the limits. In the EU project SCIPPER (www.scipper-project.eu/)  it has been investigated if remote measurements can be used to indicate whether ships follow the NECA limits.  Several sensor systems for remote sniffer measurements of fuel-mass specific emissions of NOx have been compared in side-by-side measurements in campaigns in Hamburg, Kiel and Marseille. The studied sensors include standard sniffers at fixed locations (BSH, Chalmers, TNO), mobile manned platforms (Chalmers), and compact mini-sniffers used on drones (Explicit and Aeromon). Several of the sensors have been operated over long periods on different sites. The standard sensor for CO2 is based on infrared absorption while for NOx it is based on chemiluminescence. In the drone-based system the NOx is measured by several electrochemical sensors while CO2 is measured by the same principle as for the standard sniffers. From the NOx-to-CO2 ratio in the flue gas the fuel-mass specific emission (g NOx/kgfuel) is obtained. The comparison in the different field campaigns shows that the fuel-mass specific emission of NOx (g/kgfuel) of by-passing ships had a measurement uncertainty (CI 95 %) of 8 - 17 g/kgfuel (17 % - 40 % relative uncertainty), varying for different campaigns and slightly for different sensors. These uncertainties were derived from the difference between more than 100 individual measurements and the ensemble average of all the individual side-by-side measurements.  The fuel-mass specific emission of NOx obtained by the remote measurements are converted to the brake specific emission by multiplying with the specific fuel oil consumption (gfuel/kWh). This value depends on ship and engine type and typically varies within 25 % from an average of 200 g/kWh, with generally increasing values at lower engine loads.  When taking this variability into account as a contribution to the total uncertainty, the latter ranges between 30 % and 47 % for the remotely measured brake specific emissions (g/kWh), for the different campaigns. The data in SCIPPER shows that the fuel-mass specific NOx emission, and the brake specific one, varies relatively little above 50 % engine load.  Since the emission value limit in the NOx technical code is heavily weighted towards higher engine loads this means that the remote measurements can be used to assess and control this value. For engine loads below 50 % more care must be taken when using such data. For Tier III ships, when doing type approval of engines, there already exists Not-To-Exceed limits (NTE) which are not allowed to be exceeded by more than 50 % at any engine load point. Here we show that the measurement errors are small enough to make it useful to implement NTE limits to facilitate compliance monitoring using remote measurements. In addition, long term NOx measurements by the individual groups participating in SCIPPER show that the NOx emissions of Tier III ships during 2021 were exceedingly high for more than 50 % of 65 Tier III vessel measurements at several locations. This should be studied further, since if continued it will hamper the success of NECA.    The SCIPPER project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nr.814893.  
 
 
 
 
3.10  JS.12.  Scenarios  and  policy  options  for  sustainable 

transport. 

538 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
539 

  Potential impact on emissions of an introduction of hydrogen and fuel cell-based propulsion in Nordic shipping R. Parsmo1,2, H. Lundström1, J. Hansson1,2, E. Fridell1,2, and K. Jivén1  1IVL Swedish Environmental Research Institute, Box 530 21, 400 14 Gothenburg, Sweden 2Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden Keywords: hydrogen, fuel cell, emissions, alternative marine fuels.      Presenting author email: rasmus.parsmo@ivl.se  One way of reducing harmful emissions from ships and at the same time reduce greenhouse gas (GHG) emissions is to utilize alternative fuels. This can be hydrogen or ammonia as a hydrogen carrier, methanol, or any biofuel or synthetic fuels with low GHG footprint.   The purpose of this study is to estimate the potential impact on selected emissions of an introduction of hydrogen and fuel cell-based propulsion in Nordic shipping. Besides direct and indirect GHG emissions (including carbon dioxide, CO2, methane, CH4, and nitrous oxide – N2O), also direct emissions of nitrogen oxides (NOx) and particles are included.    First, Nordic shipping and associated current CO2 emissions were investigated starting by identification of vessel movement patterns in the Nordic region using historical port call data for Nordic ports from Marine Traffic. This was followed by a mapping of the distance between Nordic ports using statistics from Sea routes. Vessels below 5 000 gross tonnage (GT) and fishing and service vessels were excluded from the data. Then, official data reported to European Union via the Monitoring, Reporting and Verification (MRV) system was used to compile average fuel consumption for each individual ship and CO2-emissions per nautical mile (THETIS-MRV, 2022). This resulted in estimations of the CO2 emissions that can be attributed to Nordic shipping on voyages to/from and between ports in the Nordic region. The direct emissions of CH4 and N2O are estimated based on estimated fuel used and emissions factors from Fourth IMO GHG study by IMO.   Two cases for the development of Nordic shipping by 2030 and 2050 was then outlined, a high a low growth case. Four factors were considered: 1) transport development based on DNV (2020), 2) ship size, 3) energy efficiency improvements, and 4) improved utilization.  A few possible deployment trajectories for hydrogen and fuel cells in Nordic shipping has been outlined. In the first scenario hydrogen and fuel cells are assumed to be implemented on all relevant vessels identified as Nordic shipping by this study. Relevant is here assumed to depend on the length of the routes for the vessels. Up to 600 NM are considered potentially possible for hydrogen and fuel cell propulsion as a rough estimate. In the second scenario, hydrogen and fuels cells are assumed to be implemented only on board all relevant RoPax vessels (ferries) in or between the Nordic countries (representing a considerable share of Nordic shipping).   The CO2 emission reduction potential in the two scenarios is presented in Table 1.   Table 1. Estimated potential emission reduction of CO2, in 2030 and 2050 from an implementation of hydrogen in fuel cells for (i) all vessels with voyages up to 600 NM (All ships) and (ii) for all ferries in and between the Nordic countries (Only Nordic ferries), for a high and low growth transport scenario respectively.  Growth Year Scenario CO2eq  WTW (Mtonnes) Low 2030 Only Nordic ferries           1.9  All ships           8.2  2050 Only Nordic ferries            2.6  All ships          10.2  High 2030 Only Nordic ferries           2.1  All ships           9.5  2050 Only Nordic ferries           3.7  All ships         17.1    The study finds that an introduction of hydrogen-based solutions may considerably reduce the emissions of GHG as well as other air pollution from Nordic shipping. Introducing hydrogen in other ship categories besides RoPax vessels such as cruise ship and cargo vessels will also reduce the GHG emissions but not to the same extent. A significant benefit on air quality can also be expected due to reductions also of other emissions. The quantitative impact is explored in more detail in Jivén et al (2023).  However, hydrogen-based solutions do not seem to be the lowest cost option for regional shipping, e.g., electrification has advantages on certain routes. Policies are crucial for the transition in the shipping sector and details in the policy design can be crucial for the prerequisites also for hydrogen solutions.  This work is supported by Swedish Transport Administration, Nordic Energy Research and other Nordic funders via the HOPE project.  THETIS-MRV. 2022. CO2 Emission Report. European Commission, EMSA.  DNV. 2020. Maritime Forcast to 2050 - Energy Transsition outlook 2020. Jivén K et al. 2023. Concept design and scenario and impact analysis in HOPE - Report of WP3 and WP5 in the HOPE project.       
 
 
 
 
540 

  Environmental and economic assessment of green and blue fuels for shipping Fayas Malik Kanchiralla1, Elin Malmgren1, Maria Grahn1 and Selma Brynolf1  1 Department of Mechanics and Maritime Sciences. Chalmers University of Technology, SE-412 96, Gothenburg, Sweden Keywords: alternate fuels, ship, green fuels, blue fuels, battery, and e-fuels.      Presenting author email: fayas.kanchiralla@chalmers.se   Transitioning to fuels with zero or very low greenhouse gas (GHG) emissions throughout their life cycle is necessary in the shipping sector to address the climate challenge. There are three types of energy sources that are considered to be potential climate-neutral solutions 1) biomass, 2) renewable electricity, and 3) fossil fuel with CCS. In this study, several energy carriers either based on renewable electricity or fossil fuels with CCS are assessed from a life cycle perspective. The purpose of this study is to compare the climate impact and cost assessment from cradle to grave for selected blue fuels, green fuels, and battery-electric for a case study vessel.   Prospective life cycle assessment is used to evaluate the climate impact, and life cycle costing is used to evaluate the cost considering a time horizon of 2030. Blue fuels considered are liquid hydrogen (blueLH2) and ammonia (blueNH3), which are assumed to be produced from natural gas with carbon capture and storage. The green fuels considered are electro-liquid hydrogen (greenLH2), electro-ammonia (greenNH3), and electro-methanol (greenMeOH) which are assumed to be produced from renewable electricity. In this particular investigation, the propulsion system is based on internal combustion engines, fuel cells for blue fuels and green fuels, and the utilization of renewable electricity as battery-electric. The results are compared with marine gas oil (MGO) used in an internal combustion engine (ICE).   Figure 1. GWP100 results of different fuels assessed from cradle to grave. ICE: internal combustion engine, SOFC-solid oxide fuel cell, PEMFC: proton exchange membrane fuel cell, BE: battery electric.   Figure 1 shows the global warming potential over 100 years for the assessed options. Compared to the marine gas oil used in shipping, all options could reduce GHG emissions by 2030. Blue fuels (from natural gas with carbon capture and storage) have a reduction potential of around 40% to 60%, whereas green fuels (from offshore wind electricity) have a reduction potential of around 80% to 95%, compared to MGOICE. Compared to engines, fuel cells have higher GHG reduction potential for the same energy carrier. The emissions of GHGs for engines is primarily related to the use of fossil fuels as pilot fuel in ICE and their higher consumption of fuel compared to fuel cells. The result shows that greenLH2PEMFC has the highest GHG reduction potential for the case vessel followed by greenNH3SOFC. The impact from ship structure is not significant compared to impacts from other phases.  Figure 2 shows the life cycle costs and carbon abatement costs of the assessed options. The life cycle cost of blue fuels in engines are lower compared to the cost of green fuels. Blue ammonia is found to have the lowest life cycle cost as the main cost of the options is related to fuel cost. Options based on blue fuels have 1.5 to 2 times the cost of marine gas oil option whereas options based on green fuels have 2 to 3 times the cost of marine gas oil option. Among green fuels, electro-ammonia, followed by electro-methanol, has the lowest cost when used in the ICE. Distribution cost is high for the hydrogen option and battery electric as the infrastructure for the bunkering is complex for these energy carriers. Despite lower fuel consumption compared to engines, fuel cell options tend to have relatively high costs for all ship types due to higher investment costs and the lower operation life of the fuel cell.   Figure 2. Life cycle cost results of different fuels assessed.  In summary, there is a potential to decarbonize the shipping industry by changing fuels. For the short term, blue fuels seem to be a solution that has only limited global warming reduction potential, however, they come with a lower cost. For the longer term, only the transition towards green fuels can meet the requirement of the GHG reduction targets as per the IMO’s GHG strategy.  
 
 
 
 
541 

  Policy scenarios for analysing use of scrubbers in shipping  Erik Fridell1, Rasmus Parsmo1, Elisa Majamäki2 and Jukka-Pekka Jalkanen2  1IVL Swedish Environmental Research Institute, Gothenburg, Sweden 2FMI, Helsinki, Finland Keywords:   scrubber, shipping, scenarios, emissions.      Presenting author email: erik.fridell@ivl.se  The future impact of shipping on the environment and climate is analysed in a set of scenarios, The aim is to describe that different policy measurs will lead to different outcomes in the future. Special focus is on the use of scrubbers and the effects on air quality and emission of scrubber water. The emissions of sulphur oxides and particles from shipping is regulated through the fuel sulphur content. However, it is also allowed to use high sulphur fuel if the emissions iof sulphur oxides is reduced by the use of scrubbers, in turn leading to emissions to sea of polluted scrubber water. In the EMERGE project we have constructed  eight scenarios where the development of the ship traffic is based on scenarios constructed by DNV (2020). The scenarios investigates the use of abatement equipement, different fuel mixes and different development of ship traffic. Following the assumptions made, the emissions to air and water are modelled with the STEAM-model, divided on different ship types and different sea areas. Scenario 1 (S1) is a high-pressure scenario. The maritime transport development is high, there are no further measures to reduce the use of fossil fuels in shipping other than those already decided today, there is high use of scrubbers. In this scenario there is thus a significant pressure on the environment from scrubber water. Scenario 2 (S2) is the same as Scenario 1 but instead of open-loop scrubbers the same ships are assumed to use closed-loop scrubbers. In Scenario 3 (S3) we use the same assumptions as in S1 and also that SECA and NECA are introduced in all European sea areas.  In Scenario 4 (S4) there is low development of ship traffic and more use of LNG for short-sea shipping but otherwise identical to S1.  Scenario 5 (S5) is a low-pressure scenario. There is low development of ship traffic, measures are assumed to be in place for shipping to reach the IMO objective of a 50% reduction in the emissions of green-house gases by 2050 (with 2008 as base year), there is no use of scrubbers.  For Scenario 6 (S6) we assume high development in ship traffic, measures in place to reach the IMO 50% goal, high use of scrubbers. When comparing with S1 this will show the effect of the IMO 50% goal. Scenario 7 (S7) is similar to S6 but with low use of scrubbers. Scenario 8 (S8) is an LNG scenario. It has the same assumptions as S7 but with an extensive use of LNG in Europe. The use of scrubbers is modelled with an economic model assuming that ships will install and use scrubbers when profitable, i.e. in principle when the investment cost is lower than the cost difference between high sulphur and low sulphur fuel. The costs for installing and using scrubbers are taken from EMERGE D1.1 (2020). The annual fuel use for individual ships is taken from the global STEAM model.  Figure 1 shows the cost-savings for individual ships in Europe vs annual fuel consumption; for ships above zero it is economically benefitial to use scrubbers. Figure 2 shows as an example the emission of scrubber water in Europe in 2050 in the scenarios, split on ship type.  Figure 1 Cost savings vs fuel use for all ships in Europe when using open-loop scrubbers rather than VLSFO.  Figure 2. The volumes of emitted scrubber water in Europe in the different scenarios split on ship type. Note that S2 assumes closed-loop scrubbers.  This presentation is based on results from the EU project EMERGE, (2020 – 2024; https://emerge-h2020.eu/). DNV, Energy Transition Outlook 2020. A global and regional forecast of the energy transition to 2050, DNV GL 2020,  Winnes et al., 2020. Summary and analysis of available abatement methods for SOX, NOX and PM, together with data on emissions, waste EMERGE D1.1  
 
 
 
 
542 

  Projections of shipping emissions in Europe in 2040 and 2050  E. Majamäki1, J.-P. Jalkanen1, V. Matthias2, J. Moldanova3, L. Johansson1  1 Finnish Meteorological Institute, Helsinki, 00560 Finland 2Helmholtz-Zentrum Hereon, Geesthacht, 21502 Germany  3IVL Swedish Environmental Research Institute, Gothenburg, 40014 Sweden Presenting author email: elisa.majamaki@fmi.fi  The International Maritime Organization (IMO) has set a target to reduce the greenhouse gas emissions from shipping by 50% by 2050 compared to emissions in 2008. To achieve this, shipping sector must find new energy sources to replace traditionally used fossil fuels. In addition, there are goals to reduce the impacts of shipping on air quality. One important tool for this is the implementation of emission control areas for sulphur (SECA) and nitrogen compounds (NECA). Estimations of the ship emissions in the future and the emission reduction potential of new regulations are needed for supporting policy makers in deciding the best pathways for reducing emissions from shipping. Aim of this study is to develop shipping emission inventories for Europe in 2040 and 2050 under different future scenarios.  In this study, several different projections of the future of shipping sector were considered. Emission scenarios for 2040 focus on different regulatory scenarios. Shipping emissions with and without an implementation of a new NECA within 200nm from the coastline is studied. In addition, scenarios of non-compliance and reduced efficiency of Selective Catalytic Reactors (SCRs) in low engine loads have been investigated. Scenarios for 2050 focus on impact of different energy sources on shipping emissions. Four different cases with different dominant fuels, such as traditional marine fuels, Liquified natural gas (LNG), ammonia and methanol, have been simulated.   Ship emission inventories have been produced using Ship Traffic Emission Assessment Model (STEAM) (Johansson et al. 2017). Shipping activity is based on Automatic Identification System (AIS) data from year 2015 and the development of the shipping fleet, transportation work and shipping fuel mix from 2015 to the target years 2040 and 2050 are based on projections by DNV-GL (2020). When defining the fuel type used by different vessels, it was assumed that short-sea shipping will move to alternative fuels, such as methanol and ammonia, first while ships that mainly sail on intercontinental routes will prefer more traditional fuels. Ship emission inventories for 2040 and 2050 were generated for European domain (lat; lon: 23.0 … 70.0; -30.0 … 38.0) with a resolution of 10km x 10km and an hourly temporal resolution.  Results for 2040 show that implementing a European wide NECA could reduce NOx emission by nearly 50% in comparison to a situation without implication of new NECAs in Europe by 2040. However, possible non-compliance or reduced efficiency of SCRs would weaken the benefits of the new NECA. Projections for 2050 show significant reduction e.g., in emissions of particulate matter and sulphur oxides regardless of the increase in ship traffic. However, results indicate that emissions of some currently unregulated pollutants, such as ammonia (NH3), methane (CH4) and nitrous oxide (N2O), can increase in the future if emissions of these pollutants remain uncontrolled. Figure 1 shows an example of the gridded results of the study: total emissions of NH3 in 2050 under a scenario with high fraction of energy (>40%) in shipping coming from combustion of ammonia.    Figure 1. Total predicted ammonia (NH3) emissions from shipping in Europe in 2050  This study has received funding from the European Union's Horizon 2020 Programme Research and Innovation action under grant agreement No 814893 (SCIPPER project).  Johansson, L., Jalkanen, J.-P. and Kukkonen, J. (2017) Global assessment of shipping emissions in 2015 on a high spatial and temporal resolution, Atmos. Environ., 167, 403–415 DNV-GL. (2020) Maritime forecast to 2050, Energy transition outlook 2020.     
 
 
 
 
543 

  Climate friendly and pollution-free? Scenarios for air pollution from shipping in Europe in 2050 Volker Matthias1, Lea Fink1, Achilleas Grigoriadis3, Josefine Hahn1, Jukka-Pekka Jalkanen2, Jereon Kuenen4, Elisa Majamäki2, Leonidas Ntziachristos3, Ronny Petrik1, Daniel Schwarzkopf1  1Helmholtz-Zentrum Hereon, Geesthacht, Germany 2Finnish Meteorological Institute, Helsinki, Finland 3Aristotle University of Thessaloniki, Thessaloniki, Greece 4TNO, Utrecht, The Netherlands Keywords: ship emissions, air quality, future scenarios, chemistry transport modelling Presenting author email: volker.matthias@hereon.de  Shipping contributes significantly to emissions of air pollutants and green house gases. The International Maritime Organisation (IMO) aims at reducing these emissions and adopted a strategy with the goal of a 50% reduction of greenhouse gas emissions in 2050 compared to 2008. This goal can only be reached through the introduction of new fuels. Among the most promising ones are liquefied natural gas (LNG) and ammonia (NH3). Possible effects on air quality through significantly modified emissions from ships were investigated through the construction of ship emissions scenarios for 2050 and subsequent chemistry transport model simulations for meteorological conditions in 2015. The scenarios were developed within the HORIZON 2020 project SCIPPER and the German SeAir project. They are based on projections of expected fuel use and transported cargo volumes given by the ship classification company DNV-GL in their report “Maritime Forecast 2050” (DNV-GL, 2020). We investigated a fuel use scenario with relatively high fraction of ammonia and LNG use and calculated spatially resolved emission maps for 2050 with the STEAM ship emission model (Johansson et al, 2017). Emission factors for new fuels were taken from recent literature and they were varied according to the installed engine technology (Majamäki et al., 2023). Impacts on air quality were simulated for entire Europe with the chemistry transport model CMAQ v5.3. Land based emissions included significant emission reductions until 2050, according to a current legislation scenario developed based on CAMS-REG inventory.  The STEAM results showed that NOx emissions per ton mile decrease drastically until 2050. However, the relative impact of shipping on atmospheric NO2 increases compared to 2015 because of lower land based emissions and increased ship traffic. Shipping emissions will still cause increased ozone concentrations with potential impacts in the order of 10% on annual average in the Mediterranean region. In a case with high ammonia emissions from shipping, HNO3 concentrations were drastically reduced because of ammonium nitrate particle formation close to the shipping lanes. The model simulations show ammonium concentrations of 0.4 – 0.6 µg/m³ and nitrate concentrations of 1-2 µg/m³ that can be attributed to shipping (Figure 1). The CMAQ results also show a considerable increase in ammonia concentrations over sea, indicating that only part of the emitted ammonia will be consumed in new particle formation processes.    Figure 1: Impact of shipping emissions on particulate ammonium (top) and nitrate (bottom) concentrations for the case of high ammonia emissions from shipping in 2050.   DNV-GL (2020). Maritime forecast to 2050 - Energy transition outlook 2020 Johansson, L., Jalkanen, J.-P. and Kukkonen, J. (2017). Atmos. Environ., 167, 403–415 Majamäki, E., Jalkanen, J.P., Matthias, V., Moldanova, J., Johansson, L. (2023). TAP and S&E Conference 2023   
 
 
 
 
3.11  SE.13  Marine  processes 

  fate  of  pollutions  from 
shipping  in  the  marine  environment:  impact  studies  on 
ecotoxicology,  eutrophication  and  acidification,  energy 
pollution  including  underwater  noise  &  induced  mixing, 
experimental  work,  modelling  studies  of  dispersion, 
transport, and chemical and biological processes in marine 
waters. 

544 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
545 

  A multi species and multi system evaluation of the ecotoxicological effects of scrubber water – a synopsis of results from the EMERGE project  M.E. Granberg1, K. Magnusson1, N. Abrantes2, M. Baccichet, L. Calgaro3, C.Y. Chen1, G. G. Distefano3,  S. Dupont4, E Garcia2, S Genitsaris5, A. Volpi Ghirardini5, E. Giubilato3, M. Gros6,7, I.-M. Hassellöv8,  A. Lunde Hermansson8, M.D. Hudson9, D. Marchetto3, Antonio Marcomini3, A.A. Monteiro3, M. Moustaka-Gouni5, L. Ntziachristos5, P. Osborne9, M. Petrovic6,10, M. Picone3, A. Ré11, M. Russo3, K. Salo8, N. Stefanidou5, I. Williams9, L. M. Zapata-Restrepo9 & E. Ytreberg8  1IVL Swedish Environmental Research Institute, Kristineberg Center, Fiskebäckskil, 45178, Sweden 2Dept. Of Biology & CESAM, University of Aveiro, Aveiro, 3810-193, Portuga.  3Environmental Sciences, Informatics and Statistics, University Ca’ Foscari Venice, 30172, Italy 4BIOENV, University of Gothenburg, Kristineberg Center, Fiskebäckskil, 45178, Sweden  5Biology, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece  6Catalan Institute for Water Research, Girona, 17003, Spain  7 Universitat de Girona (UdG), Girona, Spain  8Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, 412 96, Sweden. 9Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, United Kingdom 10Catalan Institute for Research and Advanced Studies (ICREA), Barcelona, Spain  11Dept. of Environmental planning & CESAM, University of Aveiro, Aveiro, 3810-193, Portugal Keywords:   Scrubber water, ecotoxicology, marine, effects  Presenting author email: maria.granberg@ivl.se  Allowing for installation of exhaust gas cleaning systems (scrubbers) as an alternative to using a cleaner fuel, to comply with the IMO 2020 Sulphur cap, has been done without appropriate consideration of negative impacts on marine life. The toxic and acidic cocktail of scrubber water may pose a serious threat to marine organisms and ecosystems, with consequences for commercial fish socks (Koski et al 2017, Thor et al 2021).   Ecotoxicological experiments were carried out on endemic species at five European research laboratories within the research project EMERGE (EC-Horizon 2020), aiming to investigate effects of scrubber water on marine life. Different life stages, species and microbial plankton communities were investigated. Scrubber water was obtained from open loop systems onboard operating ships (DANAOS Shipping co. LTD) and from a pilot system at Chalmers University of Technology, Sweden. A range of scrubber water concentrations (0.0001 to 40% v/v sea water) were tested. Protocols for collection, storage and handling of scrubber water were developed in order not to alter chemical characteristics. All laboratories used CRED methodology (Moermond et al, 2016).  Toxic effects were detected at much lower concentrations (0.0001-0.001%) than previously reported. Invertebrate fertilization and larval development were the most sensitive endpoints. These endpoints were not included in previous studies on scrubber water toxicity but are vital for species recruitment and thus for maintaining sustainable marine food webs. Microalgal growth rate of and invertebrate mortality are endpoints commonly used in standardized ecotoxicity tests; however, these parameters were found to be less sensitive. Marine microbial communities showed high resilience with effects detected at 10% scrubber water. Bacterial community change towards pollution resistant and PAH degrading strains was observed. We conclude that there is an apparent risk that scrubber water may have a serious impact on populations of key species of marine food webs. This risk must be considered in the continued debate about the future use of scrubbers in shipping.    Figure 1. Species, larvae and planktonic communities investigated.  A: Sea urchin (Paracentrotus lividus, Echinodermata) & pluteus larva (a), B: Copepod (Calanus sp., Crustacea) & nauplius larva (b), C: Blue mussel (Mytilus sp., Mollusca) & veliger larva (c), D: Polychaete (Sabellaria alveolate, Annelida) & trochophore larva (d), E: Microalgae, Phaeodactylum tricornutum (E1), Dunaliella tertiolecta (E2) and Pseudo-nitzschia sp. (E3), F: Phytoplankton community.   This work was supported by the EC Horizon 2020 EMERGE project (Grant Agreement No. 874990).  Koski, M., C. Stedmon, and S. Trapp (2017) Marine Environmental Research 129, 374-385. Moermond, C. T. A., R. Kase, M. Korkaric, and M. Ågerstrand (2016) Environmental toxicology and chemistry 35,1297-1309. Thor, P., M. E. Granberg, H. Winnes, and K. Magnusson (2021) Environmental Science & Technology 55, 5826-5835.  
 
 
 
 
546 

Sampling strategies and characterization of greywater from ships J.T.  Mujingni1, G.B.M Rathnamali2, I-M Hassellöv1, E. Ytreberg1 and K. Salo1 1Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, 412 96, Sweden 2Department of Marine Sciences, Gothenburg University, Gothenburg, 405 30, Sweden Keywords: greywater, characterization, sampling, ships, emissions. Presenting author email: mujingni@chalmers.se  Greywater is not regulated by the International Convention for the prevention of pollution from ships, the MARPOL Convention (IMO, 2019), as such ships are currently discharging this sanitary liquid stream into the sea in various forms; sometimes untreated, treated together with sewage before discharge, delivered to port reception facilities (PRF) or mixed and discharged with food waste. Recent studies have revealed that over 5.5 million m3 of grey water is discharged into the Baltic Sea annually but there is a lack of knowledge about the quantity of all the greywater sub-flows, their chemical composition as well as the generated and discharged volumes from various ship segments (Ytreberg et al., 2020).  The composition of greywater makes it hazardous when discharged untreated, hence, it presents a risk to the Baltic Sea environment due to lack of international and regional conventions governing its discharge. Ship-generated greywater originates from accommodation (sinks and showers), laundry facilities, and drains in the galley (kitchen area) and contains various contaminants (Folbert et al., 2022; US-EPA, 2011). However, there’s limited knowledge about the contributions from specific products (e.g., cleaning products) used on board and their chemical composition, as well as their individual contribution to the contaminant loads from various greywater sources. Moreover, there is, no standardized strategy to sample greywater from ships, given a variation in systems structure and configuration on different ship types. This makes it difficult to assess the magnitude and impact of greywater discharge on the marine environment, and for stakeholders to suggest relevant management strategies with respect to greywater discharges in the Baltic Region. This also illuminates the need for detailed prior planning and pre-sampling visits on board the ships. This paper presents a structured strategy for sampling, and characterization of greywater from a set of ships operating in the Baltic Region. This includes investigation of the presence and concentrations of nutrients, metals, TSS, pH, BOD7, Coliform Bacteria, TIC, TOC, PMOC, PFAS, Phthalates, pharmaceuticals and microplastics in greywater from the ships. A literature review was carried out to ascertain the pollutant types detected in greywater from prior research. Next, 9 shipping companies were invited to participate in the study, and 10 ships from 4 shipping lines expressed their intent to participate. Planned visits on board were organized, firstly, to map sampling points, and secondly, to carry out sampling of greywater on the ships. The sampling points were noted, and an assessment was made whether to sample from the holding tank or the discharge pipe. A consideration of the sampling of sub-flows from the accommodations, laundry and galley was also made. Sampling was done on high-flow days when the ships were at almost full occupancy. On each ship, a main sample was first collected using a 10L glass bottle which served as an initial sampling container where the sample was mixed to ensure homogeneity before distributing into smaller, pre-cleaned plastic and glass bottles according to the laboratory instructions. The 10L sampling bottles were prepared by treating with methanol and milli-Q water, while the smaller bottles were heated in an oven overnight at 400 degrees. Sampling bottles from ALS arrived already sterilized. Representative samples were obtained from the 10 ships and sent to the laboratories for analysis. The various parameters were analyzed by different laboratories according to their areas of expertise (ALS Scandinavia AB, ICRA and the University of Gothenburg).  The results will present a sampling strategy that is replicable in the industry, as well as a database containing the detectable concentrations of pollutants in the studied categories; ascertaining the need greywater environmental management strategies on board ships, mitigate their impact on the Baltic Sea. The novelty of this study lies in its sampling strategy and the provision of detailed primary ship-generated greywater characterization data within the Baltic Region, which is currently inexistent. It is also a potential contributor to the amendment of MARPOL Annex IV at IMO. This work is funded by the Swedish Transport Agency and the Swedish Agency for Marine and Water Management, as part of the project by “Gråvatten i Östersjön” HaV Dnr 865-22.  Folbert, M. E. F., Corbin, C., & Löhr, A. J. (2022). Sources and Leakages of Microplastics in Cruise Ship Wastewater. Frontiers in Marine Science, 9. https://doi.org/10.3389/fmars.2022.900047  US-EPA (2011). Graywater Discharges from Vessels. EPA -800-R-11-001, United States Environment Protection Agency, Office of Wastewater Management, Washington, DC 20460.  Ytreberg, E., Eriksson, M., Maljutenko, I., Jalkanen, J.-P., Johansson, L., Hassellöv, I.-M., & Granhag, L. (2020). Environmental impacts of grey water discharge from ships in the Baltic Sea. Marine Pollution Bulletin, 152, 110891. https://doi.org/10.1016/j.marpolbul.2020.110891   
 
 
547 

  Turbulent ship wakes: extent, intensity, and interaction with stratification Amanda T. Nylund1, Ida-Maja Hassellöv1, Anders Tengberg1, 2, Rickard Bensow1, Göran Broström2, Martin Hassellöv2, and Lars Arneborg3 1 Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96 Gothenburg, Sweden. 2Department of marine sciences, University of Gothenburg, 413 19 Gothenburg, Sweden. 3Department of Research and Development, Swedish Meteorological and Hydrological Institute (SMHI), Gothenburg, 426 71 Västra Frölunda, Sweden. Keywords:   Turbulent ship wake, stratification, contaminant dispersion, energy pollution.      Presenting author email: amanda.nylund@chalmers.se   Turbulent ship wakes are a form of ship-induced energy pollution that has previously been overlooked (Nylund et al., 2021). In intensively trafficked shipping lanes, ships pass up to every 10-15 min, resulting in a frequent input of intense turbulent mixing in the shipping lane surface layer. Frequent and intense turbulence has the potential to impact local hydrography, air-sea gas exchange, and mortality in plankton. In addition to the direct impact of the turbulence, understanding the development and intensity of the turbulent wake is also needed to correctly model and monitor the dispersion of contaminants in shipping lanes. Therefore, a characterisation of the turbulent wake development in natural conditions is important.    The turbulent wake and its impact occur on spatiotemporal scales that are challenging to observe and model and are therefore easily overlooked. There is currently a lack of suitable methodologies to capture the entire turbulent wake extent from the hull and propeller to the total wake length of several km (Nylund et al., 2021). Field observations do not allow for observations in the region closest to the ship, and, on the contrary, resolution requirements in the numerical models are too high to normally extend over the entire wake with sufficient resolution to capture the spatial scales of the wake.   In this work we have characterised the entire turbulent wake development, with respect to spatiotemporal scales and turbulence intensity, by combining in situ observations and high-resolution modelling. The interaction between turbulent wake development and stratification has been investigated. The presented work is based on a unique and extensive dataset of turbulent ship wakes, their spatiotemporal extent and input of turbulent energy.  Two field campaigns were conducted in shipping lanes on the Swedish west coast, where bottom-mounted Acoustic Doppler Current Profilers (ADCP) were used to observe the spatiotemporal extent and intensity of turbulent wakes. Turbulent kinetic energy dissipation rate (ε) was calculated from the observed current velocities and was used as an estimate of the turbulent intensity. Automatic Identification System (AIS) records were used to identify the wake-inducing ships and stratification was observed using conductivity, temperature, depth (CTD) measurements. Two ship-designs similar to ships present in the in situ dataset, were used in a high resolution Reynolds Average Navier Stokes (RANS) model, to compare the modelled wake distribution to the wake observed in the field. The model was run for 40 s (~300 m aft of the ship) in both stratified and non-stratified conditions.  The field observations showed turbulent wake durations of 5-10 min and depth extents of up to 30 m. However, the detected wake depths varied greatly between locations and ships, with similar ships inducing wakes of depths from 10-30 m. One explaining factor for this variance in wake depth, was the presence of stratification, as it was found to clearly limit the vertical extent of the wake and cause the wake to distribute horizontally along the stratification instead. However, vertical mixing across the stratification was also observed, in the field as well as in the model results.   The ship-induced ε observed in the wake were well above previously reported values shown to cause increased mortality in zooplankton. The modelled and observed ε were in the same order of magnitude in the small region of overlap, about 300 m after the propeller. This indicates that the model and observations are consistent with each other with respect to turbulent kinetic energy and its dissipation rate. Moreover, most of the turbulent kinetic energy dissipated within the first 40 s of the wake, hence relying only on ADCP observations would greatly underestimate the turbulent energy input. On the other hand, the deepest part of the wakes generally occurred after the first 40 s, hence the RANS modelling, performed up to 40 s, underestimates the vertical extent of the turbulent wake.   The results of this work show that stratification impacts the turbulent wake development and should be considered when sampling and modelling contaminant dispersion in shipping lanes. They also show that ship-induced turbulence is an overlooked source of vertical mixing across stratification in areas with intense ship traffic.    This work was funded by Chalmers Area of Advance Transport, Swedish Agency for Marine and Water Management (Dnr 796-20) and the European Union’s Horizon 2020 research and innovation programme Evaluation, control and Mitigation of the EnviRonmental impacts of shippinG Emissions (EMERGE) [grant agreement No 874990].   Nylund, A. T., Arneborg, L., Tengberg, A., Mallast, U. and Hassellöv, I. M. (2021) In situ observations of turbulent ship wakes and their spatiotemporal extent, Ocean Sci., 17, 1285-1302.  
 
 
 
548 

  Response in the marine diatom Nitzschia sp., following exposure to bilge water from different ships  J. Egardt1, I.-M. Hassellöv2, I. Dahllöf1  1Department of Biological & Environmental Sciences Gothenburg University, 405 30 Gothenburg, Sweden   2Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96 Gothenburg, Sweden   Keywords:   bilge water, toxicity, marine environment, Nitzschia       Presenting author email: jenny.egardt@bioenv.gu.se  The bilge is the lowest point in marine vessels, and the water collected here originates from several different on-board areas, primarily condense water from the engine room. Bilge water contains residues of fuel, hydraulic and lubricating oils, metals, detergents, degreasers etc. (US EPA, 2008, Tiselius & Magnusson, 2017).  Discharge of bilge water en route is allowed if compliant with IMO regulations. The main target is that oil content is below 15 ppm (MARPOL (73/78) Annex I) and to achieve that, prior to discharge the bilge water is treated in an oily water separator (OWS).   Reducing the total oil content below 15 ppm may, however, not make it environmentally safe to discharge. It has for example been shown in analysis of treated bilge water that although the total content of organic substances was below 15 ppm, individual PAH:s were above their predicted no effect levels (PNEC) (Isaksson & Möller, 2021). The same study showed that metals and surfactants remained after treatment in the OWS. When exposing the marine copepod Acartia tonsa to 1-10 % dilutions of treated bilge water, Tiselius & Magusson (2017) found significant negative effects already at 1-5 % of bilgewater content, and that effects differed markedly between bilge waters from different ships.   The amount of produced bilge water depends on a vessel’s engine capacity, and for passenger ships and RoPax vessels with engine powers ranging between 5287-62901 kW the daily production is between 70-10900 L (Det Norske Veritas, 2009, Magnusson et al. 2018). Considering the number of vessels of the global fleet trafficking our seas there is therefore potential for a negative impact on the marine environment.  In our study, we will show results from exposure of the diatom Nitzschia sp. to bilge water from six different vessels to assess toxicity to a common primary producer in the Baltic Sea, and compare difference in toxicity between bilge waters with known content. As effects of bilge water is not yet well studied and results from studies vary depending on the ship (Tiselius & Magusson, 2017), it has not been ascertained if it is specific compounds or the mixture itself that is driving the toxicity.  By comparing effects of bilge waters with known contents we can start narrowing down potential suspects that cause harm to marine organisms and aid in getting regulations in place to protect the marine environment.  Acknowledgment: Projects is funded by FRAM (Future Risk Assessment and Management) at Gothenburg University.   Det Norske Veritas. (2009). Study on Discharge Factors for Legal Operational Discharge to Sea from Vessels in Norwegian Waters. Report No. 2009-0284, 47pp. Isaksson, M., and Möller, M. (2021). Chemical pollutants released in treated bilge water. BSc thesis. Chalmers University of Technology. 124pp.  Magnusson, K., Jalkanen, J. P., Johansson, L., Smailys, V., Telemo, P., & Winnes, H. (2018). Mar Poll Bull, 126, 575-584. Tiselius, P., and Magnusson, K. (2017). Mar Poll Bull, 114(2), 860-866. US EPA (2008). Cruise Ship Discharge Assessment Report. Report no. 842-R-07-005, pp. 1-21            
 
 
 
 
549 

  Scrubber water impairs fertilization and development in the green sea urchin (Strongylocentrotus droebachiensis) at very low concentrations  C.Y. Chen1, K. Magnusson1, S. Dupont2, R. Pfeiffer1 & M.E. Granberg1  1IVL Swedish Environmental Institute, Kristineberg Center, Kristineberg 566, 45178 Fiskebäckskil, Sweden 2Department of Marine Ecology, Kristineberg Center, Kristineberg 566, 45178 Fiskebäckskil, Sweden Keywords: Scrubber effluent, sea urchin, larvae development, PAH Presenting author email: chiau.yu.chen@ivl.se    Fertilization and larval development of the green sea urchin (Strongylocentrotus droebachiensis) are susceptible to environmental stressors, especially during the planktonic (pelagic) larval stage, which in turn directly affects recruitment rates. Scrubber water generated from exhaust gas cleaning systems, intended to reduce sulphur emission from large commercial ships, have been shown to be one of these stressors. Scrubber water effluent is usually acidic and contains a complex mixture concentrated heavy metals and polycyclic aromatic hydrocarbons (PAHs) (Thor et al., 2021; Petrović et al., 2022). We investigated the effects of scrubber water (collected onboard a vessel in the English Channel on route from Belgium to Turkey) on the fertilization and larval development of the green sea urchin. Severe toxic effects were observed across the concentration gradient. Fertilization was affected at the lowest tested concentration of 0.0001% and a no-effect concentration could thus not be determined. Deformation of larvae severely increased across the concentration gradient, from 23% ± 10 (average ± SD) in the controls to 97% ± 6.6 (average ± SD) at a treatment concentration of 10% of scrubber water, as shown in Figures 1 and 2. Skeletal formation was absent at treatment concentrations from 5% and higher. Larvae exposed to 10% of scrubber water were not able to enter the pluteus stage. Growth in body length was decreased with increasing scrubber water concentration, with a total collapse at 10%. At Day 6 when larval feeding would normally begin, individuals in the 10% treatment exhibited negative growth. We hypothesize that lack of arm development due to inhibited skeletal formation hindered feeding activity. We conclude that scrubber water exposure already at very low concentrations may severely disrupt recruitment of green sea urchin larvae by affecting development of key life stages.   Figure 1. Increasing deformation of larvae across treatment concentrations. Skeletal formation was absent at 5% scrubber water and higher, and development is hampered at 10% scrubber water.  Figure 2. Percentage of deformed larvae across scrubber effluent concentration and time post-fertilization.  This work was supported by the EMERGE project funded under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 874990).  Petrović, M., Gros, M., García, E., Hassellöv, M., Gondikas, A. (2022). Report on measurements of dissolved and particulate contaminants in case study regions, Deliverable 2.2, EMERGE Project. Thor, P., Granberg, M. E., Winnes, H. and Magnusson. (2021). Environ. Sci. Technol. 55, 5826-5835.      
 
 
 
 
550 

  The possibilities and effects of ship speed reduction on underwater noise – a case study  R.E. Bensow1, D. Glebe2, M. Andersson3, L.-G. Malmberg4, E. Lalander3, D. Langlet,5 K. Larsson6, M. Svedendahl3, I.-M. Hassellöv1, E.-L. Sundblad7  1Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden 2Department of Process Modelling and Digitalisation, IVL Swedish Env. Research Inst., Gothenburg, Sweden  3Department of Defence Technology, Swedish Defence Research Agency, Stockholm, Sweden 4Department of Law, School of Business, Economics and Law, Gothenburg University, Sweden  5Department of Law, Uppsala University, Uppsala, Sweden 6Kalmar Maritime Academy, Linnaeus University, Kalmar, Sweden 7Swedish Institute for the Marine Environment, Gothenburg, Sweden Keywords: underwater noise, ship speed reduction, Automatic Identification System, regulation Presenting author email: rickard.bensow@chalmers.se  Underwater radiated noise (URN) by ships and its effect on the marine wildlife is receiving increased attention. Energy input, such as sound, to the ocean was acknowledged as a pollutant by the EU in the MSFD 2008 /56/EC. Recently, an assessment framework, including threshold values for URN, was adopted by EU, forming a basis for future national regulations (Borsani et al., 2022).  The current status of and the means to achieve good environmental status with respect to URN from shipping are however not clear. There are no technical solutions for all or any vessel that reduces URN without risk of increasing other shipping pressures, such as CO2-emissions, and modifying ship routes to protect certain areas may be difficult for business and judicial reasons. One possible option, though, may be to reduce the ship speed. In 2017, within the ECHO program, ships passing the Haro Strait outside Vancouver voluntarily reduced speed to 11 knots and the noise was measured before and after the trial period. Depending on ship type, measured noise levels were reduced by 3-12 dB by slowing down (MacGillivray et al., 2019).  Here, we investigate the possibilities to enforce a speed reduction in the southern Kattegat and the potential this may have in reducing ship URN. An area between Sweden and Denmark was selected, where there are major shipping lanes, ferry traffic, fishing areas, protected wildlife area, and important populations of cod, harbour porpoise and seabirds. The area is thus important for the protection of marine wildlife while including various types of ship traffic as well as some judicial complications as the economic zone is divided between the two states.  We have analysed the AIS data for ships passing the region for one year (2021) and classified these based on ship type, speed, and length. The traffic in this area is considerably different from the one studied in the ECHO program thus the experiences may not be directly transferable. A simulation model was set up, using the available AIS data and based on the JOMOPANS-ECHO source level (SL) model (MacGillivray and de Jong, 2021) to simulate the effect of speed reduction measured in the area. Different traffic regulation scenarios were assessed with respect to, e.g., number of ships affected by different general speed limitations, or noise reduction effect of limiting only certain types of traffic.  Further, we investigated the applicability of the JOMOPANS-ECHO SL-model, which is based on the data from the ECHO program, for the ships in our scenario. To do this, data from a hydrophone station outside Gothenburg, collected by Swedish Defence Research Agency, FOI, within the JOMOPANS project, were compared to the SL-model. This concerns, e.g., smaller tankers and RoRo vessels, rather common in our dataset and not present in the ECHO dataset.  Preliminary results indicate that a speed reduction to 11 knots is likely to influence URN levels in the area; preliminary results indicate a reduction by up to 5 dB. The effect is somewhat limited as some ships with higher design speed, like container ships, anyway slow down due to heavy traffic. With this strict speed limitation, almost all vessels in the area are affected; other, less restrictive, scenarios will also be presented at the conference. The simulations also indicate that single vessels have a minor impact on average equivalent sound levels but may be responsible for peak levels. The variations in sound level over time are in general minor, since the traffic in the area is heavy and loud single ship passages are averaged out some distance away from the routes.     Figure 1. The area studied; black lines mark shipping routes and the green area is a protected wildlife area  Borsani et al. (2022). Setting of EU Threshold Values for continuous underwater sound, TG Noise Recommendations (TG Noise Deliverable 4). Editorial coordination: Maud Casier, DG Environment, EC MacGillivray et al. (2019). Slowing deep-sea commercial vessels reduces underwater radiated noise J. of the Acoustical Society of America 146, 340 MacGillivray and de Jong (2021) Reference Spectrum Model for Estimating Source Levels of Marine Shipping Based on Automated Identification System Data. J. Mar. Sci. Eng., 9, 369  
 
 
 
 
551 

  Influence of scrubber effluent on biogenic trace gas production  D. Booge1, A. J. Paul1, J.F. Tavares2, S.C. Yang3, K. Salo4, K.R.M. Mackey2, S.G. John3 and C. Marandino1  1GEOMAR Helmholtz Centre for Ocean Research Kiel, 24105, Germany 2Department of Earth System Science, University of California, Irvine, 92697, United States 3Department of Earth Sciences, University of Southern California, Los Angeles, 90089, United States 4Maritime Environmental Sciences, Maritime Studies, Chalmers Univ. of Tech., Gothenburg, 41296, Sweden Keywords:   climate active trace gases, open-loop scrubber, closed-loop scrubber, incubation experiments      Presenting author email: dbooge@geomar.de  Transport of international goods by ships is the most carbon-efficient method and its use is projected to increase significantly in the future (Sardain et al. 2019). It is known, that emissions of exhaust gases and particles into the marine boundary layer from ships contribute significantly to the total emissions from the transportation sector (Dalsoren et al. 2009). Based upon IMO framework standards, shipbuilding and operational standards have been introduced to regulate the amount of certain emissions to the atmosphere. These regulations result in use of alternative fuels as well as in increased use of wet scrubbers. Ships can still run with cheaper high sulphur containing fuel and still fulfil atmospheric emission regulations by scrubbing the exhaust gas with seawater. Scrubber effluent is pumped over board, but the effects of this effluent on seawater could potentially cause problems for marine ecosystems (Endres et al. 2018, Ytreberg et al. 2019). Some research has been done to investigate the potential toxic effect of scrubber effluent on marine organisms, including phytoplankton (Ytreberg et al. 2019) or zooplankton communities (Thor et al. 2021). However, no research has been done to date to study the cycling of marine biogenic trace gases which are impacted by changes in biological activity. These climate-active trace gases reach the atmosphere where they are readily oxidized and form oxidation products leading to particles and clouds (e.g. Liss and Johnson 2014). Hence, it is criti-cal to understand how anthropogenic perturbations will affect climate-relevant trace gases formed in the ocean. In this study, we present incubation experiment results about the influence of scrubber effluent on the climate active biogenic trace gases isoprene and dimethyl sulphide (DMS). Baltic Sea water was spiked with different amounts of either open-loop or closed-loop scrubber, in addition to the control treatment with pure seawater. Besides trace gas concentrations we measured a variety of other parameters which could explain changes in trace gas production during the experiment. Our results clearly show that closed-loop scrubber effluent drastically influences biology and led to either reduced or no further trace gas production (Figure 1), dependent on the amount of scrubber addition, which was mainly due to incorporation of soot particles into phytoplankton cells and a consequent die off. However, open-loop scrubber effluent did not significantly influence the net trace gas production during the experiment (Figure 1). Scrubber effluent addition sometimes initially stunted phytoplankton growth but the system could recover after a few days with increasing cell  Figure 1. DMS concentrations in incubation treatments with different amounts of scrubber effluent compared to the control run (red) over a period of 7 days  counts and consequent trace gas production. Although the net trace gas production was not significantly influenced by open-loop scrubber addition, the initial trace gas concentration in the incubation bottles was drastically reduced. Addition of open-loop and closed-loop scrubber caused an initial decrease of DMS and isoprene concentrations within 24 hours of up to 90% and 64%, respectively, compared to the control treatment (Figure 1, T0). These results clearly show that scrubber effluent directly decreases the apparent trace gas concentration when entering the ocean. Climate relevant trace gases in the ocean will decrease through anthropogenic perturbation, as will atmospheric mixing ratios, likely leading to less aerosols and clouds and ultimately could facilitate climate change.  This work was conducted within the Belmont Ocean Sustainability joint research project ShipTRASE and was financed by the BMBF through grant 03F0843A.  Dalsoren, S. B., et al. (2009). Atmospheric Chemistry and Physics 9, 2171-2194. Endres, S., et al. (2018). Frontiers in Marine Science 5 Liss, P. S. and M. T. Johnson (2014). Ocean-atmosphere interactions of gases and particles. Sardain, A., et al. (2019). Nature Sustainability 2, 274-282. Thor, P., et al. (2021). Environmental Science & Technology 55, 5826-5835. Ytreberg, E., et al. (2019). Marine Pollution Bulletin 145, 316-324.  
 
 
 
 
3.12  SE.14.  Holistic  assessment  of  shipping  impacts  on  the 

environment, shipping in marine spatial planning. 

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  Impact assessment of shipping activities: Applying the critical load concept to both the atmosphere and marine environment S. Guéret1, W. Winiwarter1,2 A. Lunde Hermansson3, J. Borken-Kleefeld1,4, I.M. Hassellöv3 and E. Ytreberg3  1International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria 2Institute of Environmental Engineering, University of Zielona Góra, 65-417 Zielona Góra, Poland 3Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Horselgången 4, 41756 Göteborg, Sweden 4Technische Universität Dresden, Chair of Transportation Ecology, 01069 Dresden, Germany  Keywords:  Critical Load, Shipping emissions, Environmental Impact assessment, Scrubbers Presenting author email: gueret@iiasa.ac.at   The IMO global sulphur cap regulation allows the use of scrubbers for controlling sulphur dioxide emissions while keeping up the use of cheap heavy fuel oil with high sulphur contents. However, discharge water from scrubbers contains high concentrations of metals, polycyclic aromatic hydrocarbons (PAHs), and acidifying compounds, threatening marine habitats and biota as well as the buffering capacity of the ocean. Thus, there is a trade-off between pollution of the atmosphere and the marine environment as the total pollutant load with scrubbers may be higher compared to combustion of low sulphur fuels (e.g., MGO). As a widely used concept to devise emission control strategies and policies as a target for atmospheric depositions (De Vries et al., 2015), critical loads are relevant tools to develop impact indicators and assess this trade-off.  Here we propose the critical levels of metals and PAHs, as well as the calcite saturation state, as new marine indicators for marine biota toxicity and ocean acidification, respectively. These can be quantified in three steps: (i) the characterization of the area and the derivation of maximum permissible concentration/load per grid cell, (ii) determination of the loads here specifically from ship discharges (taken from the ship emission model STEAM – Jalkanen et al., 2021 – being distributed according to the ocean model Chemical Drift – Aghito et al., 2022) to estimate concentrations per grid cell, and (iii) estimation of potential exceedances of critical levels.  The new marine indicators are combined with an established air pollution assessment scheme, also applying the concept of critical loads, and here extended to the marine environment. We apply this new approach to assess the impact on water and air, and subsequent marine and terrestrial biota, based on a set of shipping scenarios covering open and closed loop scrubber systems as well as alternative fuels. In a first application to a shipping lane in the Baltic Sea, scrubbers are identified as contributing to local exceedance of benzo[a]pyrene critical levels (Figure 1) and increase of calcite-under-saturated areas. Our research serves as groundwork for cost-effectiveness analyses of the given scenarios, as well as the development of a decision-support tool allowing stakeholders to identify trade-offs or co-benefits connected to measures imposed on shipping (Winiwarter et al., this conference). Figure 1. Map of the Baltic Sea area showing bathymetry (depth) and the Karlshamn-Klaipeda study area (red dashed lines). Enlarged is shown the exceedance of critical levels of benzo[a]pyrene (μg/L) derived from monitoring data (ICES Dome Data Portal) and open-loop scrubber discharges data (L) based on ship activity data from the year of 2018 (Jalkanen et al (2021)).  This work was supported by EU H2020 project EMERGE, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 874990 (EMERGE project).  Aghito, M., Calgaro, L., Dagestad, K.-F., Ferrarin, C., Marcomini, A., Breivik, Ø., and Hole, L. R. (2022) ChemicalDrift 1.0: an open-source Lagrangian chemical fate and transport model for organic aquatic pollutants. Geosci. Model Dev. Discuss [preprint]. ICES Dome Data Portal: https://www.ices.dk/data/data-portals/Pages/DOME.aspx Jalkanen, J., Johansson, L., Wilewska-Bien, M., Granhag, L., Ytreberg, E., Eriksson, K., Yngsell, D., Hassellöv, I., Magnusson, K., Raudsepp, U., Maljutenko, I. , Styhre, L. , Winnes, H. , Moldanova, 004A. (2020). Modelling of discharges from Baltic Sea shipping. Ocean Science 17, 699-728.  De Vries, W., Hettelingh, J.-P., Posch, M. (2015). Critical Loads and Dynamic Risk Assessments: Nitrogen, Acidity and Metals in Terrestrial and Aquatic Ecosystems. Environmental Pollution Series. vol. 25. Springer, Dordrecht.  
 
 
 
 
554 

  Managing Marine Resources Sustainably: a transdisciplinary approach to the causes, consequences and responses to environmental problems of shipping and navigation  M. Elliott1,2, I.-M. Hassellöv3  1 International Estuarine & Coastal Specialists (IECS) Ltd., Leven, HU17 5LQ, UK 2 School of Environmental Sciences, University of Hull, Hull, HU6 7RX, UK  3Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, SE-41296, Sweden Keywords: DAPSI(W)R(M), shipping, environmental management, activity-footprints,   Presenting author email:  mike.elliott@hull.ac.uk  Ships compare to floating industries with a range of onboard systems giving rise to marine pollution (Jalkanen et al. 2021, Nylund et al. 2021). However, ship activities are not included in industrial regulatory frameworks e.g. the European Industrial Emission Directive (2010/75/EU), but rather that ship pollution prevention are often regulated through global conventions e.g. IMO MARPOL, and use of type approval of individual onboard systems. This implies a gap in relation to marine management which needs to consider the cumulative pressure from all ships operating in the marine area of interest. By merging concepts and approaches from the natural and social sciences, this paper considers all aspects of the causes of environmental changes due to shipping and navigation. It focuses on the DAPSI(W)R(M) problem-defining framework (Elliott et al. 2017), in which the Drivers (the basic needs for shipping), leading to Activities (ship movement patterns, including operation/functioning of onboard systems such as antifouling paint, scrubber wash water and ballast water) and their Pressures as mechanisms of effects (chemical, biological, physical and energy pollution). In turn, we identify the State Changes on the natural system and Impacts (on human welfare) which then require Responses (using management Measures). The latter will be presented using the so-called 10-tenets problem-solving framework, relating the solutions both inside and outside national waters. These encompass covering the ecological, technological, economic, societal, legislative, administrative, political, ethical/moral, cultural and communication aspects. This includes the identification of the activity-, pressures-, effects- and management response-footprints including the spatio-temporal characteristics. The spatio-temporal variations between activity-, pressures- and effects-footprints add complexity in the ability to adequately assess and eventually manage shipping environmental impacts; for example, the intermittent shipping activity sewage discharge may be considered a point source, but can result in a delayed organic-enrichment effect, not limited to the immediate point of discharge in the shipping lane. The effects of shipping will especially depend on the characteristics of the receiving environment; for example, the more organically-rich and hydrodynamically-enclosed Baltic Sea is more susceptible to eutrophication compared to the oligotrophic Mediterranean Sea or the open waters of the North Sea. Hence, the footprint analyses indicate both the problems and solutions especially given the challenge of ‘intermittent and mobile/moving activity-footprints’ typified by shipping. In this way, shipping and navigation are treated as creating both endogenic and exogenic pressures (pressures emanating inside and outside a particular management area) which then dictate where the management responses (the so-called Programmes of Measures) should lie. The analysis here has consequences for cumulative effects assessments and the bodies responsible for monitoring, assessment and management.    Jalkanen, J. P., Johansson, L., Wilewska-Bien, M., Granhag, L., Ytreberg, E., Eriksson, K. M., Yngsell, D., Hassellöv, I.-M., Magnusson, K., Raudsepp, U., Maljutenko, I., Winnes, H., & Moldanova, J. (2021). Modelling of discharges from Baltic Sea shipping. Ocean Sci., 17(3), 699-728.  https://doi.org/10.5194/os-17-699-2021  Nylund, A. T., Arneborg, L., Tengberg, A., Mallast, U., & Hassellöv, I. M. (2021). In situ observations of turbulent ship wakes and their spatiotemporal extent. Ocean Sci., 17(5), 1285-1302.  https://doi.org/10.5194/os-17-1285-2021 Elliott, M., Burdon, D., Atkins, J. P., Borja, A., Cormier, R., de Jonge, V. N., & Turner, R. K. (2017). “And DPSIR begat DAPSI(W)R(M)!” - A unifying framework for marine environmental management. Mar Poll Bull, 118(1), 27-40.  https://doi.org/https://doi.org/10.1016/j.marpolbul.2017.03.049         
 
 
 
 
555 

  Impacts of expanding commercial anchoring on the Pacific Coast of Canada  F.T. Francis1, K. Douglas2, L.C. Hannah1, S. Agbayani1, K. Berry1, S. Dudas1, R. Enkin2, D. Havens1, T. Norgard1, C. Robb1, E. Rubidge1, S. Verrin1, and C. Murray1  1Institute of Ocean Sciences, Fisheries and Oceans Canada, Sidney, British Columbia, V8L 5T5, Canada 2Geological Survey of Canada Pacific, Natural Resources Canada, Sidney, British Columbia, V8L 4B2, Canada Keywords:   anchorages, benthic impacts, scours, multiple stressors. Presenting author email: fiona.francis@dfo-mpo.gc.ca Vessels at anchor can have ecological and socio-economic effects in coastal areas, yet the impacts of large scale commercial anchoring are poorly understood. Anchorages are often sited in deep water soft sediment areas; understudied ecosystems with high diversity which play an important role in ecosystem function. First Nations and local communities have expressed concerns for anchorage stressor impacts in Pacific Canada (DFO, 2018). Building on a Pathways of Effects conceptual model for shipping (Hannah et al 2020) we identified three sub-activities of concern from anchoring, 1) discharges and emissions, 2) vessel movement and presence, and 3) anchor movement and presence. To date, few scientific studies have attempted to examine the effects and impacts of these anchoring activities, especially on the benthic ecosystem, and none have looked at these effects over a broad geographic scale.  In this study we provide novel baseline data to examine patterns of anchorage occupancy and anchoring events from usage data for 154 commercial anchorages along the entire Pacific Coast of Canada over a 15-year time period to identify how evenly anchorages are used and highlight areas where effects from both discharges and emissions and vessel movement are likely highest. We further examine a subset of these anchorages to determine how the potential effects from chain and anchor presence as well as patterns of repositioning, where ships            Figure 1. a) Total anchoring events across commercial anchorages in Pacific Canada have increased 60% over time  b) Anchoring events at an individual anchorage in relation to % of the year that anchorage is occupied  are required to retrieve and redeploy the anchor because of dragging, impact measures of seabed ruggedness (Sappington et al 1997) and numbers of presumed anchor scour marks identified from multibeam surveys to provide an estimate of benthic disturbance around anchorages along an entire coast.  Overall, anchoring events steadily increased over time from 2008-2022 (Fig. 1a) and were positively correlated with anchorage occupancy times (Fig. 1b) but            Figure 2. Anchorage occupancy in 2015 and 2020 in the southern region of Pacific Canada  were highly spatially variable (Fig. 2). Ships occupied individual anchorages from 0-89% of the year in some years, and this high spatial variation suggests the impacts of anchoring are not evenly distributed across the coast.  Anchorage repositioning events were infrequent and occurred in 1.85% of total anchoring events in the time series. A mixed-effects model showed that repositioning varied strongly between years but that there was a weak effect of both total number of anchoring events and region on repositioning. This suggests that factors other than usage, such as environmental variables like storms or currents may have a stronger effect on anchor dragging. We quantified 5,410 anchor scour marks at a subset of 117 anchorages and found that they were generally most prevalent in areas of high anchorage use but did not appear related to occurrence of repositioning events.    Figure 3. Multibeam images showing ruggedness within a 200m radius buffer around two commercial anchorages (A and B) compared to an adjacent control area (C).   a) b)  
 
 
 
 
556 

  Reducing ports’ contribution to climate change   F. Sakellariadou¹, I.-M. Hassellöv², C.F. Wooldridge³ and D. Kitsiou⁴ ¹Dept of Maritime Studies, University of Piraeus, Piraeus 18534, Greece. ²Dept of Mechanics and Maritime Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.  ³ Science Coordinator, EcoPorts EcoSLC.  ⁴Dept of Marine Sciences, University of the Aegean, Greece. Keywords:   first, second, third, fourth.      Presenting author email: fsakelar@unipi.gr  Seaports play a crucial role for maritime shipping and international trade, as the ports are vital hubs of the global supply chain.  Located in coastal areas, port activities will contribute to the cumulative environmental pressure from various anthropogenic activities, such as industry, tourism, fisheries, and human settlements. The resulting multiple environmental stressors will degrade the fragile coastal ecosystems and have several socioeconomic impacts.  Port activities give rise to pressures on all environmental compartments; air, water, and soil. The most commonly addressed pressures from port operations are Green House Gas (GHG) emissions contributing to global climate change (e.g. Winnes et al 2015; Styhre et al. 2017), and air pollutants (e.g. Tzannatos 2010) like sulphur and nitrogen oxides contributing to local and regional effects of acidification and eutrophication, as well as human health impacts. Ships are the single largest source of port-related air pollution.  Less attention has been paid to water pollution in port areas, although these pollutants will also contribute to degradation of coastal ecosystems through acidification, eutrophication and ecotoxicological effects. The exemption is dredging of contaminated port sediments, which is a well-recognized challenge in environmental management. The degradation of the marine environment will indirectly impact climate change as the capacity of the ocean to withstand, and hamper, climate change will decrease.  In some regions, like the Baltic Sea, shipping is responsible for a significant share of the contaminant loads to the marine environment. The IMO has set ambitious targets to achieve lower GHG emissions from shipping. However, to reduce contribution of shipping to climate change, both direct and indirect impacts need to be addressed.  Ports can play an important role in guiding shipping companies towards reduced contribution to climate change. Port authorities should facilitate increased supply of alternative fuels, extend shore-side power supply, and apply environmentally differentiated port fees for ships. Relevant Environmental Management System (EMS) Quality Standards require port authorities to set their own targets for GHG reduction, air emissions and to set low emission zones or berth standards. The sector recognizes that these issues require an integrated approach to monitoring, reporting and management due to cross-boundary considerations of multi-stakeholder involvement from quayside to the port area, and further to hinterland connections included in the logistics chain. The identification and application of related Environmental Performance Indicators (EPI) are vital to achieve sustainable development.  Another, strong, incentive for ports to reduce contribution to climate change is their own susceptibility to climate change related effects. Coastal areas are particularly vulnerable with respect to sea level rise and intense storms, while estuaries and coastal cities are at risk from flooding hazards. The severity of the potential climate change related impacts on ports creates an urgent need to strengthen the ports’ resilience and adaptation to projected future changes of e.g. sea level rise. To enable such a development, collaboration of a broad range of experts and stakeholders is required. Further, more systematic approaches and technological considerations are essential for  the construction of new resilient infrastructure, as well as use of soft and hard adaptation measures, to increase the resilience of existing infrastructure.  This study is part of the #2021-026-3-600 project sponsored by IUPAC Division VI.   Alamoush, A.S. et al. (2022) CLSCN, 3, 100021. Styhre, L. et al. (2017) Transp Res D Transp Environ. 54, 212-224. Tzannatos, E. (2010) Atmos Environ. 44(3), 400-407.  Winnes, H. et al. (2015) Res. Transp. Bus. Manag. 17, 73–82.   Projected climate change, potential impacts and associated risks strengthen the global response in the context of sustainable development. All industrial sectors, including shipping, are facing the sustainability challenge.      
 
 
 
 
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  Measurement and Analysis of Brake and Tyre Particle Emissions from Automotive Series Components for High-Load Driving Tests on a Wheel and Suspension Test Bed  M. Kupper1, L. Schubert2, M. Nachtnebel3, H. Schröttner3,4, M. Huber2, A. Bergmann1 and P. Fischer2  1 Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, Graz, 8010, Austria 2Institute of Automotive Engineering, Graz University of Technology, Graz, 8010, Austria 3Austrian Centre for Electron Microscopy, Graz, 8010, Austria 4Institute of Electron Microscopy and Nanoanalysis, Graz University of Technology, Graz, 8010, Austria Keywords: Tyre Wear, Brake Wear, Non-Exhaust Emissions, Measurement, Nanoanalysis. Presenting author email: martin.kupper@tugraz.at   The next challenge towards clean road and rail transport is non-exhaust emissions. Important advances regarding measurement systems including well defined characterization techniques as well as regulation will happen in the next few years (Grigoratos et al. 2023).  In this work, we present detailed results of particle emissions from a test bed for wheel suspension and brakes (see fig. 1), consisting of aerosol (size distribution, particle number (PN) and mass (PM)) and electron microscopy (EM) measurements under different load conditions.   Standard tires and brakes from series production (Continental ContiSportContact 5 255/45 R22 107Y XL tyres and SUV brake discs) were tested with a high-load driving cycle over a wide, well-defined range of operating parameters. The test cycle was repeated 1100 times (11 days, 100 cycles per day). The test cycle was carried out fully automated and is reproducible in terms of speed profile, force on wheel and tire, brake pressure as well as skew angle. The brake temperature was measured throughout.   Particle measurements were conducted by gravimetric measurements and with a TSI SMPS, a TSI APS and a GRIMM OPS at three sampling positions in the supply and exhaust air of the test bed, as well as directly inside the test bed (see fig. 1). A custom developed electrostatic sampler with four precipitator stages was used to collect electron microscopy (EM) samples, operated at Voltages from 800 V to 200 V in steps of 200V and 0 V as for the reference plate.    Figure 1. Left: CAD representation of the ventilation system of the test bed and the sampling positions. Right: Photography from the test bed.   In fig. 2 exemplary PM measurement results from 20 cycles from the GRIMM OPS are shown. The measurements were highly repeatable once the brake pad temperature levelled after the first two runs. Our results suggest, in agreement with previously published literature (Asbach et al. 2019), that particulate emissions are related to brake disc temperature and occur in significant amounts above a threshold temperature.   A bimodal particle size distribution was obtained with the SMPS, with peaks at 20 nm and 400 nm with the majority of PN below 50 nm. The OPS showed a trimodal distribution with peaks at 400 nm, 3 µm and 41 µm.  EM analysis of >1400 single particles from the electrostatic sampler matches these results. The measured PM values were between 300 and 400 µg/m³ with maximum values around 450 µg/m³ and PN concentrations between 2000 and 2500 #/cm³with the OPS. Particle emission can clearly be associated to brake events. EM characterization of filter samples revealed mostly particles of diameters between 100 nm - 4.5 µm (mean 0.82 µm), the largest size was 22.3 µm. Results from the electrostatic sampler show ultrafine particles, mainly from O, Fe, Si, Ba, Mg and S and also fractal particles with high C fractions, possibly from combustions at the brake disk surface.    Figure 2. Measurement data from GIMM OPS measurements of the test bed exhaust air.   Figure 3. Exemplary High-Resolution EM image   This work received no funding.  Asbach, Christof, et.al. 2019. “Entstehung Und Möglichkeiten Zur Messung von Fein- Und Ultrafeinstaub Beim Bremsen.” XXXVII. Internationales μ-Symposium 2018, 45–67. https://doi.org/10.1007/978-3-662-58024-0_4  Grigoratos, Theodoros, et al. 2023. “Characterization of Particle Number Setups for Measuring Brake Particle Emissions and Comparison with Exhaust  
 
 
 
 
559 

  Predictive energy management for a plug-in hybrid electric truck  Aletras1 N., Broekaert2 S., Bitsanis2 E., Fontaras2 G., Samaras1 Z., Ntziachristos1 L.*  1Mechanical Engineering Department, Aristotle University of Thessaloniki, GR 54124, Thessaloniki, Greece  2 European Commission, Joint Research Centre, Directorate for Energy, Transport and Climate, Via Enrico Fermi 2749, 21027 Ispra, Italy Keywords:   energy management, plug-in hybrid, heavy duty vehicles      Presenting author email: nealetra@auth.gr   This research work examines the potential fuel consumption (FC) benefits through predictive equivalent minimization strategy (ECMS) for hybrid heavy duty vehicles (HDVs).  Global warming is one of the major environmental problems and is directly related to carbon dioxide (CO2) emissions. In the transportation sector, HDVs are important contributors of CO2 emissions, indicating the importance of reducing emissions from those vehicles. Electrified vehicles have already emerged as one of the options for reducing CO2 emissions from transportation sector. Plug-in hybrid electric vehicles (PHEVs) have the potential to reduce CO2 emissions in the HDVs sector (Gao et al., 2017).   The FC – and thus CO2 emissions - of PHETs is strongly dependent on the performance of the on-board energy management system (EMS). EMS chooses the share between battery and engine power (Onori et al., 2016). One possible way to improve the performance of an EMS algorithm – in terms of FC – is to include power demand prediction (Tianheng et al., 2015). If the EMS algorithm utilizes both current and predicted future states of the vehicle – e.g., power demand for propulsion – then the algorithm can achieve solutions that are closer to the global optimum and lower FC (Tianheng et al., 2015). Recent technological developments in intelligent transportation systems (ITS) and 5G will enable prediction of vehicle speed and associated power demand (Zhang et al., 2019). Therefore, it is important to quantify the potential benefits in FC reduction - and corresponding reduction in CO2 emissions - by incorporating the predicted power demand into the EMS algorithm.  To quantify the potential FC benefits through predictive ECMS, a parallel P2 PHET model has been implemented. Figure 1 shows the layout of the PHET model which has been built in the AVL Cruise simulation platform. The applied EMS algorithm is a version of an ECMS adapted to the Vehicle Energy Consumption Calculation Tool’s (VECTO) equations for the cost function (Vecto User Manual). The PHET model has been validated with experimental data provided by the Joint Research Center (JRC).   After model validation, the cost function of the validated EMS algorithm has been extended with terms that take into consideration the predicted power demand. The extended and the validated versions of the EMS algorithm are compared over the different regulatory mission profiles for different HDV group categories.  The outcomes of the study could be used to quantify the FC benefits for hybrid HDVs due to future technologies - such as intelligent transportation systems and 5G.   Figure 1. Layout of the examined vehicle configuration. ICE: Internal Combustion Engine, EM: Electric Motor, B: Battery and GB: Gearbox, FT: Fuel tank. The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the 3rd Call for HFRI PhD Fellowships (Fellowship Number: 6653). Gao, Z., Lin, Z. & Franzese, O. (2017). Energy Consumption and Cost Savings of Truck Electrification for Heavy-Duty Vehicle Applications. Transportation Research Record: Journal of the Transportation Research Board, 2628(1), 99–109. https://doi.org/10.3141/2628-11 Onori, S., Serrao, L. & Rizzoni, G. (2016). Hybrid Electric Vehicles. Springer London. https://doi.org/10.1007/978-1-4471-6781-5 Tianheng, F., Lin, Y., Qing, G., Yanqing, H., Ting, Y. & Bin, Y. (2015). A Supervisory Control Strategy for Plug-In Hybrid Electric Vehicles Based on Energy Demand Prediction and Route Preview. IEEE Transactions on Vehicular Technology, 64(5), 1691–1700. https://doi.org/10.1109/TVT.2014.2336378 Vecto User Manual. Zhang, F., Hu, X., Langari, R. & Cao, D. (2019). Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook. Progress in Energy and Combustion Science, 73, 235–256. https://doi.org/10.1016/j.pecs.2019.04.002   
 
 
560 

  The EV-olution of non-exhaust emissions  D. Mehlig1, H. ApSimon1   1Centre for Environmental Policy, Imperial College London, London, SW7 1NE, UK Keywords:   Non-Exhaust Emissions, Electric Vehicle, PM2.5, Air Quality.      Presenting author email: d.mehlig18@imperial.ac.uk  Non-exhaust emissions (NEEs) from tyres, brakes, and the road surface, are now the major source of primary particulate matter from road transport in many countries and have grown in importance over the past decade due to the introduction of successful exhaust emission control technologies. NEEs are produced by all vehicles and so electric vehicles have fallen into the NEE spotlight with many asking will EVs increase or reduce these emissions? This study addresses this question using state-of-art modelling, the latest NEE emission factors, and a scenario of EV uptake.  There has been a recent increase in the number of studies modelling NEEs and estimating their emission factors. These models account for the EVs where there may be NEE increases due to heavier vehicles and decreases due to regenerative braking. These new emission factors need to be compared against one another to evaluate which is most appropriate today and in the future, where there will be an increasingly diverse fleet stock with various vehicle technologies affecting NEEs.  This study applies seven of these recent NEE emission factor sets with a fleet turnover and emission model (Mehlig, 2022; Mehlig et al. 2023), using the UK passenger car fleet as a case study, to simulate the emissions of NEEs today and how these emissions change over time as the fleet evolves through the adoption of EVs.     Figure 1. Car NEEs by source (given in colour) for each of the different seven NEE models from 2010 to 2050.   The results show, when using the standard emissions factors from the EMEP/EEA emission inventory guidebook (Ntziachristos and Boulter, 2016), that in the UK over the past decade for passenger cars exhaust PM2.5 has reduced by almost an order of magnitude, whereas NEEs have continued to increase in line with growth in vehicle kilometres. In the future, with the oldest most polluting vehicles leaving the fleet and clean ICEVs and zero-exhaust emission BEVs entering the fleet, exhaust PM2.5 will be far be outweighed by NEEs. The left-hand panel of Figure 1 shows for cars that by 2030 PM2.5 from NEEs will be 16 times greater than PM2.5 from exhaust, and by 2040 exhaust emissions will be negligible.    Figure 2. Total car NEEs from 2010 to 2050, where the blue line gives the mean emissions and coloured areas give 95% confidence intervals.   When using the latest NEE emission factors that account for the difference in emission rates depending on the vehicle technology and vehicle curb mass, emissions from tyre wear increase and brake wear decreases as more EVs enter the fleet. Despite these changes, Figure 2 shows that the overall amount of NEEs does not significantly change with the uptake of EVs. This result is consistent across each of the different NEE models. For models that are functions of vehicle curb mass there was added uncertainty in these results.    Overall, these results show the introduction of EVs alone will not substantially change the total amount of NEEs for passenger cars, instead changing the ratio between tyre wear and brake wear. This suggests that further technological solutions addressing vehicle emission components or polices aimed to reduce vehicle kilometres driven are still needed to mitigate NEEs.  This work was supported by the UK’s Department for Transport and the Natural Environment Research Council Grant NE/S013350/1.    Mehlig, D (2022) Electricity: powering vehicles & reducing pollution, Imperial College London.  Mehlig et al. (2023) The rapid uptake of electric vehicles: Vehicle and power station emissions, Manuscript submitted for publication. Ntziachristos, L, and P. Boulter (2016) "1.A.3.b.vi-vii Road tyre and brake wear 2016”, EMEP/EEA emission inventory guidebook.           
 
 
 
561 

  The effect of a porous pavement on air quality in comparison to a dense pavement  N. Svensson1, J. Lundberg2, M. Gustafsson1, S. Janhäll3 and S. Kulovuori4  1Swedish National Road and Transport Research Institute (VTI), Linköping, 58195, Sweden 2Transport and Roads, Lund University, Lund,22363, Sweden 3Research Institutes of Sweden (RISE), Borås, 50462, Sweden 4Automotive and Mechanical Engineering, Metropolia University of applied Sciences, Vantaa, 01600, Finland Keywords:   stone mastic asphalt, double layered porous asphalt concrete, PM10, road dust suspension      Presenting author email: nina.svensson@vti.se  Porous pavements are used because of their ability to remove water from the road surface, thus reducing the risk of aquaplaning, as well as reducing road traffic noise from the road/tyre interaction. Previous studies (e.g. Norman et al. 2017) have shown that porous pavements also can have positive effects on air quality. This study aims to investigate this effect and its causes further.  Air quality measurements (PM10 and NOx) were performed for 7 months (November to May) near two different asphalt pavements located along the same road link in Linköping, Sweden. Meteorological conditions were recorded and road moisture conditions were visually observed. The reference pavement was a dense SMA (Stone Mastic Asphalt) which is commonly used in Sweden. The porous pavement was a new DLPAC (Double Layered Porous Asphalt Concrete).  In addition, measurements of particle emissions behind the tyre of a moving light vehicle were performed with summer, studless and studded winter tyres. The car was driving back and forth along the road stretch for five laps for each tyre type.   The results from the air quality measurements showed that the average ratio of PM10/NOx was distinctly lower at the porous pavement during all meteorological conditions (Figure 1a), indicating that the porous pavement reduces traffic-induced suspension.   Figure 1: a) The ratio of PM10/NOx at the two surfaces for the whole measurement period and b) Normalised particle emissions measured behind the wheel of a moving vehicle from four different tyres.at the two road surfaces.    Two processes could be related to the lower suspension. Firstly, the visual observations of road moisture showed that the porous pavement was more often moist and less often wet or dry than the dense pavement (Figure 2). The longer the road stays moist after rain, the longer the dust suspension will be supressed. This indicates that the porous pavement’s void system retains moisture which contributes to its ability to retain road dust.  Figure 2: Visually observed road moisture conditions on the two surfaces. D: dense, P: porous.  Secondly, the mobile emission measurements made on dry road surface, showed that the emissions of road dust were lower on the porous pavement compared to the dense for all investigated tyres (Figure 1b). This is suggested to be an effect of dust being less available for suspension due to the air voids of the porous pavement. This is also mentioned as a possible explanation in Norman et al. (2017) and Rasmussen et al. (2023). Previous measurements of the dust load on the road surface (Lundberg et al, 2020) at the same site corroborate these findings.  The results from this study imply that porous pavements can reduce the PM10 suspension from the roads and therefore reduce the concentration of PM10.   This work was funded by The Swedish Transport Administration (2015/9482) and the foundation Fredrik Bachmans minnesfond (2019035).    Lundberg, J., Gustafsson, M., Janhäll, S., Eriksson, O., Blomqvist, G., Erlingsson, S. (2020) Temporal variation of road dust load and its size distribution – A comparative study of a porous and a dense pavement. Water, air and soil pollution 231:561  Norman, M., Elmgren, M. and Johansson, C. (2017) Emissions of PM10 and course particles from “silent” asphalt. Proceeding of abstracts 11th International Conference on Air Quality Science and Application Rasmussen, L.A., Lykkemark, J., Andersen, T.R., Vollertsen, J., 2023. Permeable pavements: A possible sink for tyre wear particles and other microplastics? Science of the Total Environment 869, 10.1016/j.scitotenv.2023.161770.  
 
 
 
 
562 

  CO2 emissions targets: revisiting the transition from  2020 NEDC to 2021 WLTP   J. Suarez1, D. Komnos2, M.A. Ktistakis2 and G. Fontaras1  1European Commission, Joint Research Centre (JRC), Ispra, 21027, Italy  2FINCONS Group, Vimercate, 20871, Italy Keywords: CO2 emissions, FC gap, WLTP, EU targets. Presenting author email: jaime.suarez-corujo@ec.europa.eu  Road transport forms the highest proportion of transport emissions, with passenger cars and vans contributing almost 15% of the European Union (EU) total emissions of carbon dioxide (CO2). Hence, they constitute one of the main focuses of the regulators. To progressively decarbonise the fleet, the EU establishes a general CO2 reference target for the whole fleet of vehicles. Annual specific targets for each manufacturer or pool of manufacturers are adjusted from the EU fleet-wide target based on the average mass of the manufacturers’ new registrations. Mandatory targets were first adopted in 2009, setting an EU fleet-wide target of 130 g/km from 2015 to 2019. In 2020 the EU fleet target was redefined to 95 g/km. The 2020 target compliance was determined after applying several flexible mechanisms, such as excluding a percentile of the highest emitting vehicles of the manufacturers and incentivising electrified vehicles. To monitor compliance with the targets, starting from 2011 the EC gathers annually the technical characteristics, including the CO2 emissions, of all new cars registered in the Member States. These official values are used to calculate the specific average CO2 emissions at a fleet and at a manufacturer level, which are assessed against the corresponding standards established by the EC. Up to 2020, target compliance was based on emissions obtained from the New European Driving Cycle (NEDC). The outdated conditions and flexibilities set out in the NEDC have been identified as one of the main causes of the gap between real-world and official CO2 emissions (and fuel consumption) – known as the fuel consumption gap. Starting in 2017, the EC introduced the Worldwide harmonised Light vehicles Procedure (WLTP), a more stringent testing procedure which is therefore expected to narrow down the gap. As of 2021, the WLTP totally replaced the NEDC, and compliance is assessed solely based on WLTP measurements. From 2021 to 2024, the 2020 NEDC-based EU fleet-wide target of 95 g/km is adjusted to a WLTP equivalent. This is done by renormalising each manufacturer’s target by a factor that reflects the respective CO2 average difference between the WLTP and NEDC emissions in 2020. 2020 and 2021 are two years of vital importance concerning the targets. This is not only because of the transition to the WLTP, but also due to the fact that new reduction targets of 15% and 37.5% will apply for the years 2025 and 2030, respectively. These reductions are calculated based on measured 2020 emissions, the 2021 masses, and the number of registrations. This study uses the final 2020 and 2021 datasets of all EU passenger car registrations published by the European Environment Agency to examine the factors determining target compliance. An in-depth analysis is performed on an EU fleet-wide and on a manufacturer level. 2020 and 2021 compliance are compared, and the reasons why in 2020 the pools, without utilising the provisions, are so far from their respective targets (Figure 1A), while in 2021 the flexible mechanisms are almost redundant (Figure 1B), is examined.     Figure 1. A: 2020 targets; B: 2021 targets; ND: niche derogation; dashed line: linear regression of 2021 targets   
 
 
 
 
563 

  Development of a mobile ALI exposure system for toxicity testing of emissions from different transportation modes  M. Introna1, A.T. Juárez-Facio1, S.S. Steimer1 and K. Elihn1  1Department of Environmental Science, Stockholm University, 10691 Stockholm, Sweden Keywords: ALI system, transportation modes, airborne particles, nanoparticle toxicity.  Presenting author email: Karine.Elihn@aces.su.se    Emissions from different transports cause several human health effects. An Air Liquid Interface (ALI) exposure system can examine the toxicity of airborne particles (Elihn et al., 2017), and be used to assess their effects on humans. ALI systems use human lung cells, and can be regarded as a model of the human lung. In our current project, we have developed the ALI system into a mobile unit, see Fig. 1, for outdoor usage to examine the toxicity of real-world particles, such as transportation emissions from cars, trains, ships, and aeroplanes.  Our ALI system has several advantages that enable toxicity testing of airborne nanoparticles and gases at outdoor locations:  1. The ALI system can test the toxicity of a flow of airborne nanoparticles directly from a particle source. Therefore, particles that deposit onto cells are unchanged and look exactly as in the air, i.e., they have the same size, shape, and chemical composition as particles inhaled by humans. 2. Our ALI system is designed to facilitate the deposition of nanoparticles. Nanoparticles can easily follow an airstream and may therefore be difficult to deposit in the cell chambers of an ALI system. An electrostatic field and charging of the particles enable efficient deposition of nanoparticles onto the cell cultures. 3) Our ALI system contains separate cell chambers. Therefore, different exposure scenarios can be examined at the same time, without the risk of cross-contamination between chambers. Simultaneously, we can separately study aerosols and gases (by using filters for particle removal). In this way, reference samples can be created at exactly the same conditions as exposed samples, with no need for separate gas exposure experiments (to see the effect of the flowing gas or gaseous air pollutants). 4) Our ALI system has a controlled temperature and humidity to resemble the conditions in a human lung, and to keep the cells alive in a flow of clean air (non-exposure conditions).  5) A Scanning mobility particle sizer (SMPS) is attached prior to and after our ALI system to enable online characterization of the particle size distribution and, subsequently, to determine the particle dose on cells. 6) Our mobile ALI unit makes it possible to perform exposure measurements at any out-of-the-lab location, e.g., in road tunnels and chassis dynos, which has not been possible before. This new design opens up the possibility to examine the toxicity of a great variety of aerosols/ emissions, and to judge their effects on human health.  7) A concentrator can be used prior to the ALI system to facilitate toxicity testing at outdoor locations with low particle concentrations. Alternatively, a longer exposure time may be used in case it is possible to keep the cell survival high at non-exposure conditions. Our ALI system can examine the toxicity of fresh unchanged airborne nanoparticles (and larger particles and gases) at any out-of-the-lab location. The parameters of the ALI system have been optimized for best cell survival (aerosol flow, temperature, humidity, voltage), and particle deposition and toxicity testing have been validated (Juárez-Facio et al., 2023). Two mobile systems have so far successfully tested the toxicity of emissions in a road tunnel (Introna et al., 2023), subway station, tribology laboratory, chassis dyno, harbour, and an outdoor background location. We expect mobile ALI systems to become a widely used tool for future assessments of aerosol´s effects on human health.           Figure 1. A mobile ALI system to examine the toxicity of airborne particles at outdoor locations.   This research was supported by the European Commission’s Horizon 2020 research and innovation programme: nPETS (grant agreement No 954377, https://www.npets-project. eu/) aimed at studying the sub-100 nm particles emitted from transport.  Latvala, S., Vare, D., Karlsson, H.L. and Elihn, K. (2017) J. Appl. Tox.  doi: 10.1002/jat.3510 Juarez, A., Introna, M., Steimer, S.S., and Elihn, K. (2023) Submit to Nanotoxicology Feb 2023. Introna, M., Juarez, A., Steimer, S.S., Karlsson, H.L., Vallabani, S., Tu, M.H., Olofsson, U. and Elihn, K. (2023) Submit to Int. J. Environ. Res. Public Health April 2023.  
 
 
 
 
564 

  Fuel consumption, regulated and unregulated exhaust emission tests on three Euro 6d bi-fuel LPG passenger cars, fed by an innovative LPG/DME 80/20 (V/V) blend T. Rossi*, S. Casadei, S. Lixi, M. Martini and D. Faedo  Innovhub - Stazioni Sperimentali per l’Industria, Sustainable Mobility Team, Via G. Galilei 1, 20097, San Donato Milanese, Milano, Italy Keywords:   exhaust emissions, alternative fuel, LPG/DME.      Presenting author email: tommaso.rossi@mi.camcom.it  The use of Dimethyl ether (DME) as a blend component in spark-ignition engines fuelled by liquefied petroleum gas (LPG) is a relatively new application compared to diesel engines [1]. DME can be produced by biomass and it has been considered by the EU as a part of its biofuel energy mix of 2030, and also by renewable feedstocks (e.g. landfill, municipal waste). In this respect, DME is also mentioned in the Renewable Energy Directive (RED) II [2].   In this research, in order to investigate a new potentially fully renewable fuel blend, DME was mixed with LPG and used to fuel LPG/gasoline bi-fuel passenger cars equipped with spark-ignition engines. Both DME and propane can be obtained from renewable sources, which makes this blend promising as an alternative sustainable fuel. Initially, several octane tests using CFR engines (both RON and MON) were carried out to find the suitable fuel blend that would allow to meet the European LPG specification (EN 589) and not to damage the engine components. The fuel blend chosen was made by 80% LPG (commercial propane) and 20% DME.  Three Euro 6d bi-fuel passenger cars of both small and medium segments (two PFI, one TGDI) were tested, both running on gasoline and on LPG/DME fuels, in order to verify if the innovative blend could affect the testing vehicles’ gaseous and particulate exhaust emissions.  The testing cars were driven in laboratory over the homologation WLTC (Worldwide harmonized Light duty driving Test Cycle) and over the hot-start CADC (Common Artemis Driving Cycle), as well as on road, following the EU RDE testing procedure.  On WLTC, all Euro 6 standards were well met both running on gasoline and on LPG/DME. Switching from gasoline to LPG/DME fuelling, the gaseous emission results point out an increase of CO and NOx (two PCs), whereas THC and NMHC were found to decrease (two PCs); the particulate emission results point out a reduction of solid particles (two PCs). Generally, due to the role of the three-way catalyst, much lower gaseous emissions were detected running the testing vehicles on the CADC: except for one PC, switching from gasoline to LPG/DME fuelling, the emission behaviour reflects the one of the testing PCs run on the WLTC.  Also PCs’ particulate unregulated emissions (solid and volatile ones) detected over both testing cycles basically reflect what was detected for the regulated ones.  All the regulated emissions were found to meet the latest Euro 7 standards proposed by the European Commission, except for PN10, which could not be measured. Interestingly, although NH3 emitted over the WLTC was found over its Euro 7 limit in PC 3 fuelled by gasoline, this emission was well lowered in the same PC fuelled by LPG/DME. With the LPG/DME fuelling, NH3 decreased for all testing PCs and cycles.  All emissions detected over the RDE tests were found to fully meet the RDE limits as well.  LPG/DME fuelling turned out to a systematic decrease of CO2 for all testing PCs and cycles, both in laboratory (Figure 1) and even on road.  Figure 1. CO2 equivalent emission over all cycles.  A final focus was dedicated to the energy consumption, a decreasing of which was found in two PCs and detected both in laboratory (Figure 2) and on road.  Figure 2. Energy consumption over WLTC.  All of these results identify the innovative LPG/DME 80/20 (V/V) blend not only as potentially convenient in terms of GHG emissions reduction, as long as both propane and DME are made from renewable sources, but even compliant with EN 589 and both Euro 6 and part of the Euro 7 exhaust emission standards.  This work was funded by Liquigas S.p.a.  [1] Arcoumanis C., Bae C., Crookes R., Kinoshita E. (2008). The potential of di-methyl ether (DME) as an alternative fuel for compression-ignition engines: A review. Fuel, 87(7), pp. 1014-1030. [2] Directive (EU) 2018/2001  020406080100120140160WLTCCADCRDECO2eq exhaust emissions (g/km)gasolineLPG/DME0.00.51.01.52.02.5Vehicle 1Vehicle 2Vehicle 3Energy consumption [MJ/km] -WLTCGasolineLPG/DME 
 
 
 
 
565 

  Future SUV fleet and CO2 emissions projections in Australia and the European Union  J.J. Gómez Vilchez1, R. Smit2, D. Komnos1 and G. Fontaras1* 1European Commission, Joint Research Centre (JRC), Ispra, Italy 2 Transport Energy/Emission Research (TER), Launceston, Tasmania, 7249, Australia Keywords: CO2 emissions, fleet modelling, simulation, sport utility vehicle.      Presenting author email: Jonatan.gomez-vilchez@ec.europa.eu  Background Two vehicle markets where heavier cars have gained importance over the past years are Australia and the European Union (EU). According to the European Environment Agency (EEA), “the increase in emissions between 2017 and 2019 was mostly due to the increase in the share of sport utility vehicles (SUVs) and other larger and heavier cars” (EEA, 2022) (p. 47). This abstract complements the work by Komnos et al (2023) by focusing on the car fleet in these two markets.   The specific goal of this study is to model the potential implications of possible future SUV fleets, in terms of energy demand and CO2 emissions. Electric SUVs are beyond the scope of this work (see (Gómez Vilchez, Pasqualino, & Hernandez, 2023)). Methodology A simulation model was built to calculate the car fleet, disaggregated into SUVs and nonSUVs, as influenced by the annual registration and deregistration rates. An overview of the model, following the system dynamics modelling approach ((Forrester, 1961), (Sterman, 2000)), is provided in Figure 1.    Figure 1. Fleet model. Rectangles and valves with pipes represent stock and flow variables, respectively.   The model is informed with data from ACEA (2022), EEA (2020) and TER. Two scenarios are reported: ‘SUV decline’ and ‘SUV growth’. The difference between them lies in the assumed future market share of SUV registrations, which is higher for the latter. The simulations run from 2005 to 2030, with a year resolution and a time step of 0.25 years. The solution is provided via Euler integration with Vensim. Results and conclusion Figure 2 shows the simulated evolution of SUV fleets in Australia and the EU in the two scenarios.     Figure 2. Simulated SUV fleet and CO2 emissions.   Figure 2 also shows the simulated level of CO2 emissions. The extended abstract reports the corresponding energy demand and driver costs resulting from fuel prices, comparing Australia and the EU.  We conclude that the SUV fleets in the two regions will become larger over time following the high share in SUV registrations in recent years. This will hamper decarbonisation, unless other measures are adopted.  Further research The work presented here is limited by the fact that the current version of the model captures the car fleet at a rather aggregated level. In future work, this limitation may be overcome by introducing one-year age cohorts and a more detailed categorisation of nonSUVs.  There is no funding for this work.  ACEA. (2022). New passenger cars by segment in the EU. European Automobile Manufacturers’ Association (ACEA). Various years. EEA. (2020). Monitoring of CO2 emissions from passenger cars – Regulation (EU) 2019/631. European Environment Agency (EEA). EEA. (2022). Decarbonising road transport — the role of vehicles, fuels and transport demand. Transport and environment report 2021. EEA Report No 02/2022. European Environment Agency (EEA).  Forrester, J. W. (1961). Industrial Dynamics. MIT Press. Gómez Vilchez, J. J., Pasqualino, R., & Hernandez, Y. (2023). The new electric SUV market under battery supply constraints: Might they increase CO2 emissions? Journal of Cleaner Production, 383, 135294.  Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston: McGraw-Hill/Irwin.  
 
 
 
 
566 

  Toxicity of real-world road tunnel emissions in an ALI exposure model  M. Introna1, A.T. Juárez-Facio1, S.S. Steimer1, M.H. Tu2, U. Olofsson2, H.L. Karlsson3, N.V.S. Vallabani3, and K. Elihn1.  1Department of Environmental Science, Stockholm University, 10691 Stockholm, Sweden 2KTH Royal Institute of Technology, Stockholm, Sweden 3Institute of Environmental Medicine, Karolinska Institute, SE-171 77, Stockholm, Sweden Keywords:  Air-Liquid Interface, real-world exposure, airborne particles, PM2.5, exhaust emissions, road tunnel.       Presenting author email: micol.introna@aces.su.se  Urban areas are a hotspot for air pollution because of the intensive presence of human activities and traffic. Since the late 90s, high pollution levels have proven to be responsible for reducing the quality of life, causing diseases and premature deaths. (Dockery et al., 1993).   Pollution can be composed of fine particle matter (PM2.5) generated by fuel combustion (Harrison, 2020). PM2.5 become harmful when inhaled because these particles are small enough to reach the deepest part of the lungs, where they can deposit and accumulate (Lv et al., 2021). Different studies investigated their toxicity in humans, but few studied their deposition in a real-world environment.   In this study, lung cells were exposed in real time to fresh fine particles emitted from vehicles driving in an urban road tunnel (Söderledstunneln) in the city centre of Stockholm. About 30.000 vehicles per direction use this tunnel weekly to cross the city.  A mobile air-liquid interface system (ALI) with human lung cells (A549) was used to mimic particle deposition in the lungs (Juárez-Facio et al. 2023). The ALI system was placed behind the wall next to the single-carriageway road of the tunnel (Fig.1). Cells were exposed for two hours during week days to road tunnel emissions, that were concentrated 5 times (Heikkilä et al., 2022). Afterwards, cells were investigated for cytotoxicity and inflammatory response (cytokine release) after 24h incubation.    Figure 1. Left: inside of the Söderledstunneln during a week day; right: ALI system behind the road tunnel wall.  Results   Cell viability was tested in human lung cells exposed to road tunnel particles. Cell viability was not affected in any of the groups (A: road tunnel air without particles, E: exposure to particle emissions, and LPS) compared to negative control. A biological increase of inflammatory markers IL-1β, IL-8 and IL-6 was observed in cells exposed to road tunnel emissions compared to filtered tunnel air and negative controls (Fig 2). No statistical significance was observed for any of the measured cytokine due to the variation of exposure dose between experiments in a real-world environment.     Figure 2. Cytokine release from cells exposed to road tunnel emissions, based on ≥ 4 experiments. LPS (1µg/mL) was our positive control. C: neg. control, A: air without particles, E: exposure to particle emissions.   Acknowledgments I acknowledge P. Heikkilä and J. Keskinen from Tampere University in Finland for borrowing the concentrator. This work was supported by the European Commission’s Horizon 2020 research and innovation programme: nPETS (grant agreement No 954377, https://www.npets-project.eu/) aimed at studying the sub-100 nm particles emitted from transport.  References Dockery, D. W., Pope, C. A., Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., Ferris Jr, B. G., & Speizer, F. E. 1993. An association between air pollution and mortality in six US cities. NEJM 329, 1753–1759. Harrison, R. M. 2020. Airborne particulate matter. Philos. Trans. Royal Soc., 378(2183), 20190319.  Heikkilä, P., Rostedt, A., Toivonen, J., & Keskinen, J. 2022. Elemental analysis of single ambient aerosol particles using laser-induced breakdown spectroscopy. Sci. Rep. 12.  Lv, H., Li, H., Qiu, Z., Zhang, F., & Song, J., 2021. Assessment of pedestrian exposure and deposition of PM10, PM2.5 and ultrafine particles at an urban roadside: A case study of Xi’an, China. Atmos. Pollut. Res., 12, 112–121. Juarez-Facio, A.T., Introna, M., Steimer, S.S., and Elihn, K. ,2023, Submit to Nanotoxicology Feb 2023.  
 
 
 
 
567 

  Non-Exhaust Emissions in Aotearoa New Zealand  G.F.J. Coulson1, P.K. Davy2, E.R. Somervell1, G. A. Olivares1, A. Semadeni-Davies1 and I. D. Longley1  1 National Institute of Water and Atmospheric Research, 41 Market Place, Auckland 1010, New Zealand 2GNS Science, PO Box 31312, Lower Hutt 5040, New Zealand  Keywords:   traffic emissions, non-exhaust, New Zealand      Presenting author email: guy.coulson@niwa.co.nz  As exhaust emissions from combustion engines fall and BEVs become more prevalent, emissions of particles from non-exhaust traffic sources (Non-Exhaust Emissions, NEE) are expected to become increasingly important.   Few studies have been carried out in Aotearoa New Zealand explicitly to isolate the non-exhaust traffic signal from the bulk PM.  EFs used in New Zealand are mostly derived from overseas studies. A road dust resuspension study carried out in Auckland by NIWA in 2008 was unable to conclusively quantify resuspended road dust but was able to conclude that it was consistent with then current literature (NIWA unpublished data). Source apportionment by Davy and Trompeter (2019) in Auckland indicates that NEE and exhaust emissions crossed over in 2016 with NEE becoming more important (Figure 1).    Figure 1. The contribution of fine and coarse fractions to road derived PM10 in Auckland - from Semadeni-Davies et al (2021)   A study is underway to develop New Zealand specific NEE EFs. The first part, a review by Semadeni-Davies et al (2021) concluded that there are numerous gaps in understanding NEEs that make it difficult to determine the most appropriate Emission Factors (EFs) for use in New Zealand – including that New Zealand’s vehicle fleet is significantly older than those in other countries. The review recommended a two-part approach to improving NEE EFs in NZ; 1) sensitivity analysis using current EFs to determine how choice of EF determines the outcome, and 2) reanalysis of existing air quality data to apportion airborne PM from traffic to vehicles. Based on the outcomes of these analyses, a decision could then be made to either retain the current EFs or undertake new monitoring and modelling to develop and test a new set of EFs for New Zealand. The second part is underway to carry out these recommendations. It will assess existing air quality data to derive New Zealand specific NEE EFs which will be used as part of a sensitivity study compared with existing EFs and establishing requirements for future measurement and modelling to reduce uncertainties in modelling impacts of air quality. Air quality modelling in New Zealand relies on a suite of homegrown and international models including emission, dispersion and meteorological, each bringing its own errors to the calculation. However, analysis of the sensitivity to errors of any of the models has never been conducted.   GNS Science holds an archive of filter-based, time-integrated particulate matter samples that have been analysed by ion beam analysis (IBA), X-ray fluorescence analysis (XRF) and light reflectance to provide elemental composition data (e.g., Trompetter et al 2014). PM samples have been collected from approximately 40 sites across New Zealand using National Environmental Standard (NES) compliant methods. In addition, several studies have targeted source specific particulate matter composition, these include motor vehicle tunnels in order to better understand emission source characteristics and composition (Ancelet et al 2011, Davy et al 2011).  This work was supported by Waka Kotahi, NIWA and GNS  Ancelet T, Davy PK, Trompetter WJ, Markwitz A & Weatherburn DC (2011).  Carbonaceous aerosols in an urban tunnel. Atmospheric Environment, 45, 4463-4469. Davy PK, Trompetter WJ & Markwitz A (2011).  Concentration, composition and sources of particulate matter in the Johnstone’s Hill Tunnel, Auckland. GNS Science consultancy report 2010/296. Davy PK & Trompetter WJ (2019).  Composition, sources and long-term trends for Auckland air particulate matter: Summary Report.  GNS Science Consultancy Report 2019/151. Institute of Geological and Nuclear Sciences. Semadeni-Davies, A.F., Coulson, G., Gadd, J., Somervell, E., Longley, I. and Olivares G (2021).  Non-exhaust emissions from vehicles: literature review and recommendations for developing new emission factors for New Zealand (Waka Kotahi NZ Transport Agency research report Trompetter WJ, Davy PK, Barry B & Kennedy J (2014).  Influence of filter thickness on PESA calibration.  Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms, 332, 445-448.  
 
 
 
 
568 

  Characterization & quantification of traffic-derived non-exhaust particles (TWP/TRWP, brake & road wear) in airborne dust  J. Rausch1, D. Jaramillo-Vogel1 and S. Perseguers2  1Particle Vision Ltd., Passage du Cardinal 13B, Fribourg, 1700, Switzerland 2 Gradiom Ltd., Avenue de Tivoli 4, 1700 Fribourg, Switzerland  Keywords:   non-exhaust, tire wear, brake wear, road wear, road marking (paint), SEM/EDX single particle analysis, source apportionment, machine-learning      Presenting author email: juanita.rausch@particle-vision.ch  The link between particle pollution and health issues has been widely demonstrated during the last decades.  This is the reason why the WHO set ambitious targets by drastically lowering the recommended limit values for particulate matter PM10 and PM2.5 in the newest Global air quality guidelines from September 2021. In view of these new recommendations, it can be expected that many measuring stations (and therefore, inhabited areas) are affected by PM10 and PM2.5 exceedances. Therefore, authorities have more than ever the need of seeking lowering PM values.  To achieve lower PM10 and PM2.5 concentrations we need first to obtain a better understanding on the fine dust constituents. This is crucial to implement targeted dust mitigation measures. Unfortunately, there is no one single method that can characterize the totality of PM10 and PM2.5. Owing to the intrinsic different characteristics of particles, and hence, stability and detectability with the various available methods, a combination of techniques needs to be applied. Here, a set of classical bulk analyses (e.g. TOT, IC) and a newly developed morpho-chemical single particle analysis method is applied to achieve an in-depth PM10 characterization and differentiation.   Due to the increasing share of traffic-derived non-exhaust particles in airborne dust a need for a reliable, and cost-effective method to quantify and differentiate particles like tire, brake, road and road marking wear becomes evident. However, the differentiation of such primary particles is not always straightforward based on bulk analysis. Here, we present a powerful approach to perform the differentiation of primary particles (e.g., traffic-derived non-exhaust). The method is based on single-particle analysis by Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX) coupled to a machine learning-based algorithm that classifies and quantifies the concentration of the different particle types (for methodological details see Rausch et al., 2022). The algorithm considers not only the elemental composition of single particles but also their size, morphology, and degree of heterogeneity (mixing), as they occur in real-world environmental samples.  Thanks to the detailed particle characterization, and the source-differentiated results (Figs. 1 & 2), the influence of specific human-induced activities (e.g., traffic, quarrying/mining, industry) on air quality can be monitored, allowing targeted measures to be taken by decision makers. In addition, a verification and quantification of the effectiveness of the implemented dust mitigation measures can be performed (e.g. determination of the contribution of non-exhaust particles emitted by combustion engine vehicles vs. electric vehicles).    Figure 1. Stacked plot showing the concentration and temporal variability over 1 year at an urban traffic site (Canton of Vaud, Switzerland) of the different constituents of PM10 (including non-exhaust particles).   Figure 2. Size-resolved mass concentration of the main primary particle types in PM40-2.5 and PM2.5 (annual mean of the above case study).  The Swiss Federal Office for the Environment (FOEN) and the Canton of Vaud (Switzerland) are thanked for funding the one-year measurement campaigns.  Rausch, J., Jaramillo-Vogel, D., Perseguers, S., Schnidrig, N., Grobéty, B., & Yajan, P. (2022). Automated identification and quantification of tire wear particles (TWP) in airborne dust: SEM/EDX single particle analysis coupled to a machine learning classifier. Science of The Total Environment, 803, 149832.  
 
 
 
 
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Data protection in remote sensing through profiling high-emitting vehicles  Lukas Unterschu¨tz, Pascal Kerschke, Jens Borken-Kleefeld  “Friedrich List” Faculty of Transportation and Traffic Sciences, Technische Universita¨t Dresden Dresden, Sachsen, 01069, Germany Keywords: Euro 5, Euro 6, CADC, PEMS, real world Presenting author email: lukas felix.unterschuetz@tu-dresden.de Remote sensing (RS) is a powerful method to quickly scan the exhaust emissions from thousands of vehicles passing by a monitoring site. However, proper analysis currently re- quires number plate photographing, storing and reading in order to match measured emissions with the technical spec- ifications of the vehicle retrieved from the registration data. This causes concerns for data protection and could be a road- block to a more widespread application. In particular, when searching for the potentially few high-emitting vehicles, the number plate processing could be considered unduly intrud- ing the privacy of many drivers with well functioning ex- haust emission controls. How can the emission standard of a passing vehicle be determined based on instantaneous emis- sions readings? Qiu and Borken-Kleefeld (2022) developed a scheme fo- cusing on NOx emissions from diesel cars to differentiate high-emitting from clean vehicles. Building on this idea, we here introduce a multivariate machine learning approach us- ing all the pollutants measured to detect high-emitting cars based only on their emission profile, in particular without recourse to technical data retrieved through number plate reading. For this, first, typical emission profiles are derived, differentiated by fuel type, emission standard and whether a car is high-emitting. This is done using data from chassis dy- namometer and PEMS tests. These profiles are then used to classify instantaneous RS observations into clean and high- emitting vehicles. In this work, we focus on petrol and diesel Euro 5 and    6 passenger cars and convert emissions from chassis and PEMS tests to gram pollutant per kilogram fuel, to be im- mediately compatible with RS results. To classify a vehicle as clean, emission data is taken from controlled experiments and compared to the applicable emission standard. If the measurements remain within a 20%-margin of the thresh- old for all relevant pollutants, a vehicle is classified as clean. This information is then used to derive a typical emission profile per Euro norm and fuel type. To check the feasibility of creating such profiles, the av- erage emission of clean and high-emitting Euro 6 petrol and diesel vehicles is displayed in Figure 1. The data is obtained from controlled experiments and observations comparable to RS measurements are selected, i.e., for positive engine loads as well as acceleration and speeds of more than 5 kilome- ters per hour. Each pollutant is displayed as the ratio of the  
 
 
 
 
570 

  Lubrication oil as a potential source of traffic originated secondary particulate mass  M. Priestley1,2, A. A. Hammoud2*, E. Tsiligiannis,  M. Hallquist2, Å. M. Hallquist1  1IVL Swedish Environmental Research Institute, Gothenburg, Sweden 2Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden  * Now at: LazeraH ab, Erik Dahlbergsgatan 11A, Gothenburg, Sweden   Keywords:   Lubrication oil, oxidation, aerosol, secondary particulate mass      Presenting author email: michael.priestley@ivl.se  It is well known that traffic is a large contributor to gaseous and particulate pollutants in the urban environment. In the atmosphere primary vehicle emissions can further oxidise forming secondary pollution. Previous work has shown that the formation of secondary particulate mass (PM) from atmospheric oxidation of exhaust can be significant and much larger than the primary emitted PM (Robinson et al. 2007, Watne et al., 2018) and based on our recent findings, attributed to an important non-fuel dependent source that we hypothesize can be lubrication oil (LO) (Le Breton et al. 2018).  LOs are a complex mixture of organic compounds that can evaporate and contribute to fugitive volatile organic compound (VOC) emissions. Once in the atmosphere these VOCs can oxidise to form oxygenated VOCs with reduced volatilities contributing to the formation of secondary organic aerosol (SOA). The extent to which these LOs can contribute to PM is currently not well known. Here, results from a laboratory study aiming to identify tracers of lubrication oil oxidation products and quantify their contribution to the formation of PM are presented. We atomized LOs used by CNG and RME-fuelled buses. The produced aerosol was heated to 200°C and passed through an oxidation flow reactor (Go:PAM) after which it was sampled by a Scanning Mobility Particle Sizer (SMPS), to measure particle size distributions and mass concentrations, and a Time-of-Flight Chemical Ionisation Mass Spectrometer (ToF-CIMS) with a Filter Inlet for Gas and AEROsols (FIGAERO) to measure gaseous and chemical composition. A measurement cycle included fresh sampling followed by OH/O3 oxidation to simulate their atmospheric ageing. These cycles were repeated with and without the addition of NOx. The FIGAERO-ToF-CIMS measured a large suite of gaseous and particle phase organics from both the fresh and aged emissions (e.g., Figure 1). Ageing the emissions produced a greater quantity of lower mass gas phase compounds and a large number of secondary particle phase compounds, of which the composition was different between the CNG and RME LOs. The impact of ageing and the presence of NOx on particle mass concentrations for the CNG and RME LO will also be presented.      Figure 1. Fresh and aged gas and particle phase mass spectra of (a) RME and (b) CNG lubrication oil (LO).   This work was financed by Formas (2020-1907).   Le Breton M., Psichoudaki M., Hallquist M., Watne Å. K., Lutz A., Hallquist Å. M. 2019 Application of a FIGAERO ToF CIMS for on-line characterization of real-world fresh and aged particle emissions from buses, AS&T, 53, 244 Robinson A. L. et al. 2007 Rethinking organic aerosols: Semivolatile emissions and photochemical aging, Science, 315, 1259 Watne Å. K., Psichoudaki M., Ljungström E., Le Breton M., Hallquist M., Jerksjö M., Fallgren H., Jutterström S. and Hallquist Å. M. 2018, Fresh and Oxidized Emissions from In-Use Transit Buses Running on Diesel, RME and CNG, ES&T 52, 7720    
 
 
 
 
571 

  A simplified in-Field Calibration method for Periodical Technical Inspections Particle Counters with atomized NaI  Helmut Krasa1, Martin Kupper1, Mario A. Schriefl1,2, Alexander Bergmann1  1Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, Graz, 8010, Austria, 2Emission Measurement Department, AVL DiTEST GmbH, Graz, 8010, Austria Keywords: Periodical Technical Inspections, Particle Number, Calibration, Natrium Iodide Presenting author email: helmut.krasa@tugraz.at  Starting with 2023, Germany included particle number (PN) measurement as part of periodical technical inspections (PTI) of registered vehicles. As PN instruments were previously limited to homologation measurements and laboratories, the number of distributed PN counters increased significantly and therewith the demand for regular calibrations. With an estimated number of 35 000 PN counters in Germany alone, a fast and high-throughput calibration procedure is required.   Calibration of PTI PN counters involves two measurements, (i) counting efficiency (CE) and (ii) linearity. The CE is defined as the ratio of the PN counted by the instrument to the PN counted by a traceable calibrated reference instrument. The CE decreases with smaller sizes and must be within a certain range for several sizes (e.g. 20 to 60 % at 23 nm (BMDV, 2021)). In addition, the CE is evaluated in the plateau region for larger particles. Furthermore, a linear response over a specified PN concentration range has to be fulfilled.   We present a mobile calibration method that allows for simultaneous calibration of the plateau efficiency parameter and linearity using atomized NaI.  Figure 1: Graph of fitted CE curves for different particle types of a PN counter curve and the required limits    In our proposed method, particles are generated with an atomizer containing a NaI salt solution. The atomizer requires pressurized air and is otherwise fully portable. It has been shown that NaI particles are fully counted down to less than 20 nm with butanol-based condensation particle counters (CPC) with d0.5 value calibrated to 23 nm from factory calibration (Krasa, 2023). A NaI concentration of 1.2 % was used in solution, resulting in a mean particle diameter of 46.8 nm and a geometric standard deviation of 1.7. The CE for NaI is in the plateau for the relevant size regime for 23 nm CPCs and particle concentrations for sizes below full detection are negligible. This allows the plateau efficiency to be assessed using a polydisperse aerosol. Typically, the plateau efficiency is evaluated with monodisperse aerosol, using a differential mobility analyzer, what is to be avoided for on-site calibrations. Using polydisperse aerosol also allows for simultaneous evaluation of the linearity, as polydisperse aerosol provides sufficiently high PN concentrations - two to three order of magnitudes higher compared to monodisperse distributions.  A TSI 3790 (CE0.5 = 23 nm) CPC was calibrated with a TSI 3775 (CE0.5 = 4 nm) as a reference using mono- and polydisperse aerosol for comparison. In the monodisperse case the CE was 1.085, evaluated for four sizes of 30, 50, 75 and 100 nm. In the polydisperse case, the concentration response was highly linear (R2 > 0.99) and the CE was 1.079. See figure 2 for the measurement data. The CE deviated less than 1% between the calibration methods, thus proving a precise calibration using polydisperse atomized NaI.    Figure 2. Linearity test with polydisperse NaI. The CE for monodisperse aerosol was 1.085 on average  Krasa H, Kupper, M. A. Schriefl & A. Bergmann (2023), Aerosol Science and Technology, Toward a simplified calibration method for 23 nm automotive particle counters using atomized inorganic salt particles DOI: 10.1080/02786826.2023.2174410. BMDV (2021). Anlage VIIIa Straßenverkehrs-Zulassungs-Ordnung (StVZO). VkBl. Nr. 8, 404  
 
 
 
 
572 

  Roadway diesel vehicle emission measurement by PEMS and its dispersion  at on-road conditions captured by OPCAS  Xiaoliang, Qin1; Anand, Abhishek1; Nirmal Kumar Gali1; Zhi, Ning1  1Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong Keywords: Mobile emission, Driving stye, Plume chasing, PEMS, Emission factors  Presenting author email: xqinaf@connect.ust.hk  Diesel commercial vehicles (DCVs) have long been a major source of particulate matter (PM), BC and nitrogen oxide (NOx) emissions. During the period from September 2013 to April 2014, Lau et al. (2015) collected a large dataset of on-road vehicle emission in Hong Kong by plume chasing methods. The results showed that individual diesel goods vehicles and buses exhibited varying emission factors (EFs), and high-emitting vehicles were not solely limited to older Euro emission standards. The study revealed that the high emitters were not all vehicles of older Euro emission standards. Additionally, there is clear evidence that the high-emitters of one pollutant may not be the high-emitter of another pollutant. Gini coefficients for fleet emissions have been established to give a quantitative measure of the emission disproportionality to prioritize the removal of high emitters. To tackle emissions from DCVs, the Hong Kong Government has been phasing out pre-Euro IV DCVs progressively since Feb 2014 with the completion date set to the end of 2019. Meanwhile, starting from 2014, franchised bus companies were reimbursed the full cost of retrofitting Euro II and Euro III franchised buses (FBs) with selective catalytic reduction devices (SCRs) to upgrade the NOx emission performance to Euro IV or above by the end of 2017. The DCV emission control programmes have been implemented for over four years and a significant reduction in primary emissions is expected.  Portable emissions measurement systems (PEMS) are widely recognized internationally as the most effective method for measuring vehicle emissions (Vlachos et al., 2014). However, PEMS still require modifications to the exhaust system and installation of equipment on the target vehicle. This study aims to compare on-road plume chasing and analysis system (OPCAS) with the PEMS under different driving conditions for applicability evaluation. A medium goods vehicle (MGV) and a heavy goods vehicle (HGV) were observed based on the real driving emissions regulation (RDE). The results demonstrate that plume chasing devices can be used as a screening tool to detect both low- and high-emitting vehicles during driving. The probability of producing high instantaneous NOx emissions increases with rapid changes in vehicle speed. When the ratio of acceleration to deceleration is greater than 0.6, the concentration of CO2 and NOx increases rapidly. The ratio of NO to NOx can be used as an indicator to define the plume. The measured emissions are in good correlation with the results from PEMS measurements. The study also establishes the limitations of plume pursuit in certain road layouts, traffic conditions, and speed variations. These findings are significant for designing high-emission vehicle regulation policies and provide a reference for future research on real-time monitoring of on-road emissions.    Figure 1. A schematic diagram of on-road experiments by using OPCAS and PEMS.  This research is funded by the Hong Kong Environment and Conservation Fund (ECF/21/2021).  Lau, C. F., Rakowska, A., Townsend, T., Brimblecombe, P., Chan, T. L., Yam, Y. S., ... & Ning, Z. (2015). Evaluation of diesel fleet emissions and control policies from plume chasing measurements of on-road vehicles. Atmospheric Environment, 122, 171-182.  Vlachos, T. G., Bonnel, P., Perujo, A., Weiss, M., Villafuerte, P. M., & Riccobono, F. (2014). In-use emissions testing with portable emissions measurement systems (PEMS) in the current and future European vehicle emissions legislation: overview, underlying principles and expected benefits. SAE International Journal of Commercial Vehicles, 7(2014-01-1549), 199-215.  
 
 
 
 
573 

  Comparative chemical composition of US and European tyres VOC profile, potential environmental impact, including 6PPD Nick Molden, Ime Usen  Emissions Analytics Ltd. Stokenchurch, Buckinghamshire, HP14 3PD, United Kingdom Presenting author email: Nick@emissionsanalytics.com  Tires are complex products, with sophisticated physical design and chemical composition.  They must deliver a range of often conflicting performances including slow wear (for maximum life), low rolling resistance (to reduce carbon dioxide emissions and fuel consumption), wet grip and low noise.  There exist regulatory and labelling requirements around these.  Where information is currently lacking is in the wear rates and chemical composition of the tires.  This is relevant to the amount material released into the environment, where it goes and what damage it does to human and animal health, and the environment more widely.  This presentation will focus on the chemical composition of hundreds of different models of tires sold, drawn from both the US and European markets.  Commonalities and differences will be analyzed.  A hypothesis will be considered that US tires contain different chemical components as the market focuses more on durability, compared to the European market that tends to value lower rolling resistance more highly.  It will draw on Emissions Analytics' tire material database, which typically identifies over 400 organic compounds in each tire, using its optimized process of thermal desorption and pyrolysis, coupled with two-dimensional gas chromatography and time-of-flight mass spectrometry.  Organic species that will be specifically analyzed are 6PPD and their substitutes.  These are preservatives that are added to tires to prevent ageing and cracking, especially under sunlight.  Recently, 6PPD has been linked to the death of a significant proportion of coho salmon and trout on the West Coast of the US.  The concentration of 6PPD will be shown to vary significantly between different models of tire.  The method will be shown to be the potential basis for enhanced product labelling.      
 
 
 
 
574 

  Incorporating NOx into the Periodic Technical Inspection vehicle emission test procedures D. Thomas1 and G.S. Sandhu2  13DATX Corporation, 3600 Genk, Belgium 23DATX Corporation, Buffalo, NY, 14228, USA Keywords:   Passenger vehicle, emissions, NOx, PTI.      Presenting author email: daisythomas@3datx.com  NOx pollution is one of the greatest air quality issues that urban areas face today, particularly within the European Union (EU), yet currently this pollutant is only controlled through the homologation process. There is no periodic technical inspection (PTI) process for NOx emissions within the EU, leaving a weakness in the legislation that is currently allowing gross polluters to negatively impact air quality (CITA, 2022). Work needs to be done to incorporate a simple, quick, inexpensive and representative test to accurately identify these high emitters within the on-road vehicle fleet.   In this project, vehicle and tailpipe emissions data was collected using the parSYNC® iPEMS and a HEM OBD data logger from over 600 light passenger vehicles simulating an enhanced PTI emissions test. The tested vehicles were a random sample of passenger cars passing through a Swedish PTI test centre, so are broadly representative of the fleet in Sweden. These vehicles ranged from model years 1979 to 2019, with vehicles covering the European emission standards Euro 1 to Euro 6, and including both gasoline and diesel fuel types of various engine powers and sizes.   The enhanced PTI emission test performed on each vehicle consisted of an idle test, one of two types of high idle test, and a dynamic acceleration test. The repeatability of all three test types was good. Options for the meaningful incorporation of the dynamic acceleration test are investigated, such as the use of VSP methods to characterise the dynamic properties of such tests. The NOx concentrations have strong correlations to the mass emissions for each test type, with the use of mean values being deemed more representative than the use of maximum values. The mean results across the tested fleet are calculated and used to define pass/fail thresholds for different vehicle types as suggested by CITA (Buekenhoudt, 2019). These are outlined in table 1.   Table 1. Suggested NOx (ppm) thresholds for each test type, for different vehicle types. Fuel Type Emission Standard Idle High Idle1 High Idle2 Acceleration Diesel Euro 4 298 228 240 445 Euro 5 306 312 234 400 Euro 6 226 186 228 362 Petrol Euro 4 130 392 NA 163 Euro 5 138 220 NA 387 Euro 6 74 138 NA 132 1 Mean NOx value from a ~5s hold at 2500 rpm 2 Mean NOx value from a free acceleration test   An investigation into the trends in emissions from the tested vehicle fleet is also performed using this dataset. The NOx concentrations resulting from the different test types (low idle, high idle, free acceleration and a range of dynamic acceleration tests) are compared against vehicle age, odometer reading, Euro standard, and other vehicle characteristics such as emissions abatement technology and fuel injection type. By comparing the average results from Euro 4, Euro 5, and Euro 6 vehicles as well as those Euro standards, Figure 1 shows that the NOx from EURO-6 vehicles reverse the increase seen in EURO-5, but still fail to deliver the level of reductions in real-world emissions as per the emission standards.  Figure 1. Mean NOx concentration values for different test types and NOx limits relative to Euro4 vehicles.  The ability of the above NOx test methods to identify high emitting vehicles from the test fleet is analysed, and indicates that a loaded test is required for NOx emitter detection, with a dynamic acceleration test performing well. The impact of engine and vehicle conditioning and ambient weather conditions on the results is investigated. Availability and assimilation into enhanced PTI of important ECU/OBD parameters is evaluated. Other challenges of doing such a program are also discussed.  Buekenhoudt, P.; Müller, G.; Mäurer, H.-J et al. SET II Project - Sustainable Emission Test for Diesel Vehicles Involving NOx Measurements; CITA: Brussels, Belgium, 2019.  CITA Monitoring of NOx Emissions as Part of the PTI; International Motor Vehicle Inspection Committee (CITA): Brussels, Belgium, 2022. 00.20.40.60.811.21.41.61.82456Change in NOx (cf. Euro-4)Euro Emissions StandardDiesel_NOx_Idle (ppm)Petrol_NOx_Idle (ppm)Diesel_HighIdle (ppm)Petrol_HighIdle (ppm)Diesel_NOx_Accel (ppm)Petrol_NOx_Accel (ppm)NOx_Standard_DieselNOx_Standard_Petrol 
 
 
 
 
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  AIS data mining to identify tank cleaning operations at sea    W. Mao1, S. Wang2 and I.-M. Hassellöv1  1Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, 41296, Sweden 2Department of Navigation, Dalian Maritime University, Dalian, Liaoning, 111404, China Keywords:   AIS, data missing, abnormal trajectories, tank cleaning.      Presenting author email: wengang.mao@chalmers.se  Following increased transport by chemical tankers of products classified as IMO MARPOL Annex II substances, such as biofuels, the Swedish Coast Guard are detecting an increasing number of chemical pollution events in Swedish waters and the Baltic Sea. The regulation of tank cleaning is complex and leaves room for different interpretation, yet also legal tank cleaning operations may pose a threat to the marine environment. To carry out environmental risk assessment there is a need to know when and where tank cleaning operations are carried out (in addition to what type of substance, cleaning agents and volume discharged). Currently, chemical tankers are not obliged to report such information. One possible way to get information about when and where tank cleaning operations are carried out is then to analyse the ship activities through their navigation data including ship position, speed, course, etc., collected and transmitted among ships and shore-based stations by the AIS (Automatic Identification System). The AIS is a mandatory instrument for ships sailing at sea to aid ship navigation safety (IMO 2015). It is also an effective means of maritime monitoring (Lensu and Goerlandt 2019, Yang et al. 2019).   In this study, the AIS information from chemical tankers sailing in Swedish waters and the Baltic Sea is used to investigate if there are some navigation patterns that can indicate a possible/suspected tank cleaning operation. In addition, detected pollution events confirmed by the Swedish Coast Guard and hypothesised to originate from tank cleaning operations, were matched to some of the patterns related to abnormal sailing trajectories during tank cleaning operations. Analysis of AIS data around those detected marine chemical pollution spots help to reveal certain sailing patterns for designing data mining techniques, for example, sharp turning, unnecessarily sailing to open sea (outside of Swedish Economic Zone), normal sailing speeds, ballast loading conditions, etc.  Three data mining techniques are implemented to fast search the big AIS database, find and analyse: 1) abnormal AIS signal missing that with a high probability is not caused by AIS transmission technical problems, 2) abnormal sailing trajectories (e.g., unnecessary sharp turning, unnecessary long-distance sailing, etc.) by the machine learning DBSCAN algorithm and direct filtering algorithms, 3) hotspot areas around the Swedish waters with large amount of abnormal AIS data transmission and sailing characteristics. By automatic identification of those abnormal sailing behaviours of chemical tankers by analysing both real-time and historical AIS data, the implemented data mining methods are expected to be able to provide data support for maritime regulatory authorities.   Based on the big data analysis of AIS signals collected around Swedish waters, this study shows that missing AIS data signals are more frequent for the investigated chemical tankers compared to cargo ships. Even though it is not possible to judge how many of those AIS signals missing scenarios are related to tank cleaning operations, serious AIS signal loss, i.e., more than 30-50 minutes data while the ship is sailing faster than 5 knots, may be interpreted as suspicion of tank cleaning. A follow up check of their trajectories before and after the AIS signal loss may give more confidence about the suspicion of tank cleaning operations. Furthermore, for chemical tankers granted prewash exemption, much fewer AIS missing data were observed (no serious AIS signal missing was observed).  In addition to those analysed sailing characteristics, further data mining analysis and discussions with domain experts on tank cleaning operations should be able to cluster more patterns to design machine learning algorithms to automatically searching potential tank cleaning operations based on the AIS data.    This work was supported by the Havs- och vattenmyndigheten, anslag 1:11.  IMO (2015). Resolution A.1106 (29) Revised guidelines for the onboard operational use of shipborne automatic identification systems (AIS). Assembly 19th session, adopted on 2 December 2015. Lensu, M. and Goerlandt, F. (2019). Big maritime data for the Baltic Sea with a focus on the winter navigation system. Marine Policy, 104, 53-65. Yang, D., Wu, L. X., Wang, S. A., Jia, H. Y. and Li, K. X. (2019). How big data enriches maritime research - a critical review of Automatic Identification System (AIS) data applications. Transport Reviews, 39(6), 755-773.     
 
 
 
 
577 

  DOAS applied to shipping emission monitoring: compliance assessment and comparison to satellite measurements  M. Prignon1, V. Conde1, T. J. Smyth2, A.-M. Sundström3, J. van Vliet4, and J. Mellqvist1  1Chalmers University of Technology, Department of Space, Earth and Environment, Göteborg, Sweden  2Plymouth Marine Laboratory, Plymouth, UK 3Space and Earth Observation Centre, Finnish Meteorological Institute, Helsinki, Finland 4Netherlands Human Environment and Transport Inspectorate, Den Haag, The Netherlands Keywords: shipping emission monitoring, remote sensing, DOAS, satellite observation.      Presenting author email: maxime.prignon@chalmers.se  The intensive activity of the shipping transport mode (80 % to 90 % of global merchandise trade volume), coupled with high specific emissions of nitrogen oxides (NOx), sulphur oxides (SOx) and particulate matter (PM) has significant impact on the environment and the human health. Today, NOx and SOx emissions are regulated under the International Maritime Organization (IMO) MARPOL Convention Annex VI (adopted in 1997 and entered into force on 19 May 2005). SOx emissions are regulated through the limitation of fuel sulphur content (FSC), which must be lower than 0.5% globally since 2020. FSC is further limited in emission control areas (ECAs). Since 2015, the European ECA (English Channel, North Sea and Baltic Sea) requires FSC to be lower than 0.1%. Compliance monitoring for FSC is usually done by the collection and analyse of fuel samples by competent authorities for ships at berth. The complexity and the cost of the method results in very few ships being formally controlled. Facing this, remote measurement techniques of individual exhaust ship plumes have been developed and applied for more than a decade. The state-of-the-art technique, the sniffer technique, usually implies a remote sample extraction of the ship exhaust plume followed by an analysis with analyser instruments (e.g., UV fluorescence-based, chemiluminescence-based, small electrochemical, or small-sized non-dispersion infrared instruments). FSC is then estimated by computing the ratio between the integrated SO2 and CO2 concentrations measured inside the ship plumes, with the assumption that most of the fuel carbon is converted to CO2 and that most of the fuel sulphur is converted to SO2. Well-known optical remote sensing techniques, as the ultraviolet/visible differential optical absorption spectroscopy (DOAS), have also been applied to ship emission monitoring in the past years. As sniffer measurements, the DOAS technique can be used from different platforms (e.g., ground-based, aircraft-based, ship-based, satellite-based) and is therefore well suited for the measurement of single ship plumes. In the framework of the SCIPPER European project (https://www.scipper-project.eu/, last access 23rd February 2023), we have performed DOAS observations, together with standard sniffer measurements, of single ship plumes during the two days of an observation campaign in the English Channel in May 2022. In between ship plume crossings, we also performed multi-axis DOAS observations (MAX-DOAS) to retrieve NO2 vertical columns with the objective of a comparison of the NO2 background abundance with the TROPOMI (Tropospheric Monitoring Instrument) space-based instrument. Our DOAS results show that almost all sampled plumes are detectable in the recorded NO2 spectra (33 over 34 sampled ships). For SO2, we could detect it in three plumes (e.g., ships #2 and #9 in Fig.1). When looking at the highly sensitive sniffer results, we see that 2 of these ships were actually running with FSCs above 0.1% (i.e., non-compliant). The sniffer system could detect one more ship that was about the legal limit (0.1%). All other ships sampled by the sniffer were complying to the FSC regulation (3 over 38 ships above 0.1%). These results suggest that the ship-based DOAS technique could be used to discriminate non-complying ships from complying one in a qualitative way, i.e., if SO2 can be detected from the measured spectra. In that way, the relatively simple DOAS method could be used as a first screening of passing ships, the flagged ships could be then reported to the competent authorities to proceed to a formal and legal control.  Figure 1. Example of DOAS results. On the vertical axis are the NO2 (blue) and SO2 (red) slant column densities measured while crossing the plumes (constant scale among the different ships). The horizontal axis is the time (varying from ship to ship).  The comparison between the ship-based and the TROPOMI -for which the plumes are detected following a deep-learning method- observations show encouraging results. Both instruments recorded higher background columns for the second day of observation, with a comparable absolute increase in comparison to the first day. The comparison of in-plume values is more challenging and is, at the moment, inconclusive. An effort is underway to determine a more relevant emission proxy from the DOAS and the satellite measurements.  
 
 
 
 
578 

  FUgitive Methane Emissions from Ships (FUMES): Characterizing methane emissions from LNG-fueled ships using drones, helicopters, and on-board measurements Bryan Comer1, Liudmila Osipova1, Elise Sturrup1, Jörg Beecken2, Bettina Knudsen2, Jon Knudsen2, Robin Vermeulen3, Pierre Paschinger3, Ann Delahaye3, and Ruud Verbeek3  1The International Council on Clean Transportation, Washington DC, USA 2Explicit ApS, Copenhagen, Denmark 3Netherlands Organisation for Applied Scientific Research (TNO), The Hague, the Netherlands  Keywords: methane emission, methane slip, Liquefied Natural Gas (LNG), marine fuel. Presenting author email: jbe@explicit.dk   The fleet of vessels which are propelled by Liquefied Natural Gas (LNG) is rapidly increasing, and as such the emissions of methane (CH4) in the maritime sector. Between 2012 and 2018, the use of LNG increased by 30 %, leading to an estimated increase of 150 % in methane emissions from international shipping over this period according to IMO’s Fourth Greenhouse Gas Study 2020. Methane contributes to global warming and its global warming potential is around 30 times stronger as compared to carbon dioxide (CO2) after a residence time of 100 years in the atmosphere and even around 82 times stronger after 20 years (IPCC, 2021).  Current estimates of CH4 emissions are based on limited on-board and laboratory measurements leaving a high uncertainty related to these findings. There are several sources for possible methane emissions on LNG propelled vessels, e.g. the methane slip in the marine engines, and fugitive emissions from the fuel and cargo tanks. These emissions are dependent on parameters such as engine type and engine load. But currently, there are not much data available on the real-sailing emissions of LNG propelled vessels.  Hence, the FUgitive Methane Emissions from Ships (FUMES) project, a collaboration between the International Council on Clean Transportation (ICCT), the emissions monitoring company Explicit ApS, and the Netherlands Organization for Applied Scientific Research (TNO), was established in 2022 to collect a comprehensive dataset of real-sailing emissions of methane in conjunction with nitrogen oxides (NOx).  One particular objective of the FUMES project is to gain a better understanding of the dependence of methane emissions on different engine types but also other engine factors such as age under consideration of different engine load conditions.  Data are collected by drone, helicopter, and on-board measurements. The drone and helicopter measurements are aiming to retrieve emissions data from high number of vessels, representing a broad spectrum of engine configurations while on-board measurements are focusing on emissions of specific engine types under different operational conditions.  The emission rates of fugitive CH4 from LNG carriers are assessed using the Drone Flux Measurement (DFM) method (Knudsen and De Rossi, 2022), while loading/unloading operations were ongoing at an LNG terminal, see Figure 1. Figure 1. Visualization of emission rates of fugitive CH4 of an LNG carrier at a terminal using the DFM method.   The data collection is now, February 2023, still ongoing until summer this year. But until now emissions of more than 30 different vessels were measured, some of them repeatedly, with abundant CH4 emissions. First observations show a large spread of methane slip, which relates the mass of uncombusted CH4 to the mass of the LNG supplied to the engine, from below 1 % up to double-digit percentages. Overall, the current results indicate a higher methane slip than estimated in ship emissions inventories or assumed in the regulations, e.g. FuelEU maritime.  IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2391 pp, https://doi.org/10.1017/9781009157896, 2021. Knudsen, J. and De Rossi, L.: The Plane Project: Mapping and quantification of GHGs from diffuse emission sources using drone technology and vertical measuring walls, The Danish Environmental Protection Agency, 2022.  
 
 
 
 
579 

  Discharges from ships to the sea in European sea regions  J.-P. Jalkanen1, E. Majamäki1, L. Johansson1, T. Grönholm1, A. Maragkidou1 and Jaakko Kukkonen  1Atmospheric Composition Research, P.O. Box 503, FI-00101, Helsinki, Finland  Keywords:   Ship emissions, discharges, SOx, scrubbers      Presenting author email: jukka-pekka.jalkanen@fmi.fi   The introduction of SOx Emission Control Areas (ECA) for marine fuel sulfur content has led to an increased use of alternative means to comply with the new regulation, which avoids the use of more expensive low sulfur liquid fuels. These include Exhaust Gas Cleaning Systems (EGCS) and a large-scale adoption of Liquid Natural Gas (LNG) as a fuel. However, both alternatives have their drawbacks. Use of LNG has increased the methane emissions from ships because unburnt methane escapes from combustion engines of ships. While cleaning the air with an EGCS, ships have created a new pollution stream to the sea, with increased concentrations of various Polyaromatic Hydrocarbons (PAH) and heavy metals.  In this paper, we present ship discharge modeling results for the EU domain. The work is based on global Automatic Identification System (AIS) data and was made with the Ship Traffic Emission Assessment Model (STEAM) of the FMI (Jalkanen et al., 2009;  Jalkanen et al., 2012; Johansson et al., 2013; Johansson et al,  2017; Jalkanen et al, 2021). During 2018, over 447 million tonnes of EGCS effluent was discharged to the sea from 287 vessels, and over 99.9% of that was from open loop systems. From that, over 28% was discharged to the Baltic Sea. Since the 0.1% sulfur limit became effective in SOx ECAs, also global sulfur cap of 0.5% on Jan 1st, 2020, increased the number of vessels using EGCS significantly. In 2021, over 4300 ships operated EGCS globally and estimated effluent releases exceeded 15 000 million tonnes. With the current high price premium of low sulfur fuels, it is expected that the number of ships with EGCS will increase significantly in the near future.     Figure 1. Discharge from Open Loop EGCS in the EU domain during 2018.  The STEAM emission and discharge data generated are used as input to atmospheric and ocean models. These enable large scale studies to understand the shipping contributions to impacts concerning humans and the marine environment.  This project has received funding from the European Union's Horizon2020 research and innovation programme under grant agreement #874990 (EMERGE project). This work reflects only the authors’ view and CINEA is not responsible for any use that may be made of the information it contains.   Jalkanen, J.-P., Johansson, L., Wilewska-Bien, M., Granhag, L., Ytreberg, E., Eriksson, K. M., … Moldanova, J. (2021). Ocean Science, 17(3), 699–728.  Jalkanen, J.-P., Johansson, L., Kukkonen, J., Brink, A., Kalli, J., and Stipa, T. (2012). Atmospheric Chemistry and Physics, 12(5), 2641–2659.  Jalkanen, J. P., Brink, A., Kalli, J., Pettersson, H., Kukkonen, J., and Stipa, T. (2009). Atmospheric Chemistry and Physics, 9(23), 9209–9223.  Johansson, L., Jalkanen, J.-P., and Kukkonen, J. (2017). Atmospheric Environment, 167, 403–415.  Johansson, L., Jalkanen, J.-P. P., Kalli, J. and Kukkonen, J. (2013). Atmospheric Chemistry and Physics, 13(22), 11375–11389.    
 
 
 
 
580 

  A Low-Cost Optoacoustic Sensor for Black Carbon monitoring of Ships  N. Kousias1, L. Haedrich², A. Stylogiannis2, I. Raptis1, V. Ntziachristos2, L. Ntziachristos1  1Department of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki, GR54124, Greece 2Chair of Biomedical Imaging, Technical University of Munich, Munich, Bavaria, D-81675, Germany Keywords: opto-acoustic, sensing, black carbon, ships, low-cost, monitoring Presenting author email: nkousias@meng.auth.gr  Black Carbon (BC) is a significant pollutant that has strong contribution to climate change (Bond et al. 2013) while it also has negative effects on human health (World Health Organization 2012). The shipping sector is a key emitter of BC particles due to their emissions in the artic. BC absorption is enhanced in the arctic, while it also accelerates snow melting due to deposition (Kühn et al. 2020). Monitoring of BC emissions from ships is considered by the IMO (5th meeting of the Sub-Committee on Pollution Prevention and Response (PPR 5)). Optoacoustics is a viable technology, but current systems are expensive for implementation on-board vessels where often multiple instruments, one for each exhaust line, are required. We have developed a sensor that is suitable for on-board monitoring and tested it on two ships under real conditions.  The main innovation of the sensor (Stylogiannis et al. 2021) is the geometry of the measuring cell, which is an ellipsoid. An ellipse has two focal spots. The sample flow and the light beam intersect at the first focal spot, where a sound source is created. The sound is then refocused to the second focal spot due to the ellipsoid geometry. A sensitive sound transducer (QTF) is positioned at the second focal spot where all the acoustic energy is refocused. The distance between the contaminating sample flow and the QTF is several centimetres, thus protecting the QTF from particle contamination. The chamber’s acoustic properties, in combination with the high sensitivity of the QTF, allow the use of a low-cost laser diode. The design is shown in Figure 1.   Figure 1. Design of the Optoacoustic sensor  The first implementation of the sensor was on a RoRo ferry that was transporting passengers and cargo in the Baltic. The ship was powered by four 4-stroke medium speed main engines equipped with SCR catalysts for NOx reduction. The fuel used during the campaign was MGO. Exhaust from one of the main engines of the ship was sampled with an eDiluter Pro (Dekati, eDiluter Pro). An aethalometer (ObservAir) is used as a reference.  The second implementation was on a large container ship, travelling from Netherlands to Turkey. The ship was equipped with one 2-stroke low-speed engine and an open-loop Scrubber for SOx reduction. The fuel used was HFO. In addition, measurements were performed both before (Upstream-US) and after (Downstream - DS) the scrubber. An eDiluter Pro was used for sampling and an optoacoustic instrument (AVL MSS) was used as a reference.  Figure 2: Comparison of the sensor’s signal with BC concentration for both ships   Figure 2 shows the average BC emission levels of the undiluted ship’s exhaust for both ships and for different conditions. The signal from our sensor agrees very well with the concentration that is measured from the reference instruments for both ships. We observe that the RoRo ferry has significantly higher concentration compared to the container ship. In the case of the container ship, the Scrubber does not have a significant effect on the emitted BC.  This work was supported by the European Union’s Horizon 2020 research and innovation programmes under grant agreement No 814893 (SCIPPER) and No 874990 (EMEGRE).  Bond, T. C. et al. 2013. Journal of Geophysical Research Atmospheres 118(11): 5380–5552. Kühn, Thomas et al. 2020. Atmospheric Chemistry and Physics 20(9): 5527–46. Stylogiannis, Antonios et al. 2021. Sensors (Switzerland) 21(4): 1–14. World Health Organization. 2012. Available at: http://hdl.handle.net/20.500.11822/8699 (Accessed: 8 March 2022)        MGO  -H O  -H O    alO se             O   e so      M                                  
 
 
 
 
581 

  Remote monitoring of sulphur emissions from shipping with a novel high sensitive laser system.  V. Conde Jacobo1, J. Mellqvist1, T. Smyth2, J. Beecken3,4, A. Weigelt3  1 Chalmers University of Technology, Gothenburg, Sweden 2 Plymouth Marine Laboratory, Plymouth, UK 3 Federal Maritime and Hydrographic Agency (BSH), Hamburg, Germany 4Explicit ApS, Copenhagen, Denmark  Keywords:   sulphur emission control area (SECA), fuel sulphur content, compliance monitoring, Laser spectroscopy.  Presenting author email: vladimir.conde@chalmers.se  Sulphur emissions have a major impact on nature, climate and human health.  Aiming to reduce their environmental impact; organizations like the International Marine Organization (IMO) in coordination with policy makers have agreed on regulations for reducing the global emissions of SO2 due to marine traffic.  The implementation of these policies have effectively lead to a new global cap of 0.5% mass Fuel Sulphur Content (FSC) on marine traffic, since 2020, from the previous global FSC limit of 3.5%. At sulphur Emission Control Areas (SECA), the FSC cap is even lower (0.1%).  In view of the current marine traffic emission policies, several remote measurement techniques have been developed and implemented. Stationary systems near shipping lanes are used to analyze the chemical composition of exhaust plume of by-passing ships. These systems, positioned up to a few kilometres downwind of the ship lanes, can run continuously and autonomously.   However, as the new regulations are pushing to lower sulphur emissions, they come with the drawback that the remote monitoring of the emissions compliance has become more challenging. This situation is particularly noticeable when SO2 measurements are carried out in SECA areas.    Figure 1. CO2 and SO2 measurements for a passing vessel by four different instruments in a Sulphur Emission Controlled Area.  a) CO2: Laser and cavity ring-down instruments. b) SO2: Laser and UV-fluorescence. c) FSC estimation with the Laser system.  In the frame of the EU project SCIPPER: Shipping Contributions to Inland Pollution Push for the Enforcement of Regulations (www.scipper-project.eu/), a novel gas sniffer technique based on laser spectroscopy was introduced. Several FSC measurement campaigns, using this technique, were carried out at different locations, including the English Channel (Figure 2), the Port of Marseille (France), the Elbe River (Germany), PeberHolm (Sweden-Denmark) and the port of Gothenburg.    Figure 2. Individual ships FSC measurements at the English Channel (May-2022) by two different systems: UV-Fluorescence with Cavity Ring-Down and the Laser System.  From field results and experiments, the performance and limitations of this system against other widely used methods for FSC monitoring will be presented and discussed in this study. Overall, an enhanced signal-to noise-ratio has been observed along the different measurement scenarios, including two different FSC limits of 0.1% and 0.5% respectively.  The SCIPPER project has received funding from the European Union’s Horizon 2020 research and innovation program under the grant agreement Nr.814893.   Beecken, J., Mellqvist, J., Salo, K., Ekholm, J., and Jalkanen, J.-P.: Airborne emission measurements of SO2, NOx and particles from individual ships using sniffer technique, Atmos. Meas. Tech., 7, 1957–1968, 2014. Mellqvist, J., Beecken, J., Conde, V., and Ekholm, J.: Surveillance of Sulphur Emissions from Ships in Danish Waters, https://doi.org/10.17196/DEPA.001, 2017.  
 
 
 
 
582 

  Measurement of NOx and ultrafine particles from inland shipping in Germany  P. Eger1, T. Mathes1, A. Zavarsky1 and L. Duester1  1Federal Institute of Hydrology, Qualitative Hydrology, Koblenz, Germany Presenting author email: eger@bafg.de  Inland shipping is a potential contributor to air pollution in cities along European rivers and canals, on which a large number of goods is transported. These waterways often run through densely populated areas, where the quantification of ship emissions is of major interest but the separation from other sources like road transport remains a difficult task. Literature studies often focus on sea-going ships in coastal and harbour areas, from which the findings cannot easily be transferred to inland vessels due to differences in ship dimensions, operating conditions and fuel composition (especially the sulfur content). With regard to inland vessels, there is a critical lack of representative measurements under real-world driving conditions. We show that this gap can be closed by the continuous monitoring of nitrogen oxides (NOx = NO + NO2), particulate matter (PM), particle number concentration (PNC), ultrafine particles (UFP) and black carbon (BC) along waterways with high traffic density. From the observations the additional contribution of inland vessels to local background concentrations can be calculated and specific emission factors can be derived for the use in emission inventories and transport models.     Figure 1. Measurement setup at (a) Rhine and (b) canal.    We present data from two long-term field studies at the river Rhine in Worms (see Fig. 1a) and at the Dortmund-Ems canal (Fig. 1b) in Germany, conducted as part of the RAUCH project. CO2, NO, NO2, BC and PM1 as well as particle number and size distribution from 5 nm to 10 μm were measured with high temporal resolution (1–3 s) for more than one year. A typical time series observed at the canal is presented in Fig. 2, with distinct spikes visible in all measured parameters when ships passed the station. Emission plumes were generally characterised by a large NO-to-NO2 ratio of about 10 and the dominance of UFP with observed geometric mean particle diameters varying from 20 to 100 nm.  Combining a peak analysis algorithm with automatic identification system (AIS) and technical ship data, the additional burden from individual ships was calculated for each analyte. From this, monthly average contributions for each location were derived, resulting in an NO2 increase of approximately 5–10 % due to inland vessels close to the shipping lanes at Rhine and canal. A similar contribution was observed for UFP and BC. Given their adverse health effects, this underlines the importance of considering them in ship-related studies and integrating them into existing air pollution monitoring networks.    Figure 2. Typical time series derived for the Rhine.   Average emission factors relative to the amount of fuel used were determined for both locations (Table 1), revealing a large ship-to-ship variability. The influence of different ship parameters, operating conditions and engine characteristics was examined, with the latter turning out to have the largest effect on NOx and particle emissions. These results stress the need to overcome limitations in data accessibility (engine-specific data is currently not sent via AIS) to improve the quantification of the contribution from inland shipping in emission inventories and modeling studies.  Table 1. Comparison of calculated emission factors (EF). EF (g kg-1)  Rhine  Canal  NOx  37 ± 16  46 ± 22  NO2  BC  PM1  2.6 ± 1.1 0.5 ± 0.3  1.7 ± 1.1  4.1 ± 2.0 0.9 ± 0.7  1.9 ± 1.5    The comparison of emission factors under real-world driving conditions with existing emission regulation limits by the EU revealed the potential to monitor the effect of fleet modernization measures like exhaust gas aftertreatment and diesel particulate filters.  
 
 
 
 
583 

  A single instrument for simultaneous monitoring of greenhouse gases and air pollutants  Jonas Bruckhuisen1, Marco Brunner1, Oleg Aseev1, Morten Hundt1  1MIRO Analytical AG, Widenholzstrasse 1, CH-8304 Wallisellen, Switzerland  Keywords:  GHG monitoring, multi-compound gas analyzer, air quality monitoring Presenting author email: morten.hundt@miro-analytical.com  Air pollution and greenhouse gas emissions are two closely linked problems. They can be attributed to a variety of sources, where transportation is always a main contributor. Monitoring several different air pollutants and GHG simultaneously with high selectivity and sensitivity helps to gain critical insights on the different sources as different sources contain specific rations of certain gases providing characteristic emission fingerprints. Additionally, plume-measurements to observe compliance of shipping companies with legislation regarding e.g. the fuel-sulfur content need multi-species measurements including CO2, NO, NO2, SO2 and NH3.  Solutions to monitor these air pollutants or GHG with high precision and temporal resolution were commonly offered as “one-species-one-instrument”, leading to large, immobile measurement setups with high energy consumption. We provide a new compact laser absorption spectrometer that combines several mid-IR lasers. Our solution allows simultaneous high-precision measurements of the greenhouse gases CO2, N2O, H2O and CH4, and the pollutants CO, NO, NO2, O3, SO2 and NH3 within a single instrument and is therefore well suited to detect the relations of the co-emitted pollutants and GHGs.  In our contribution, we will demonstrate examples of our instruments’ applications for mobile monitoring of 10 GHGs and air pollutants in urban areas and airborne measurements with airships (Tillmann, 2022; Schuldt, 2023). Furthermore, we will present the results of parallel monitoring with our instrument and standard conventional gas analysers used for GHG and air pollutant measurements. It demonstrates the ability of our instrument to serve as an all-in-one solution and to replace up to 7 standard gas analysers opening a wide range of new mobile multi-compound gas monitoring applications, for example, in airplanes, ships or cars.               References:  Tillmann, R., Gkatzelis, G. I., Rohrer, F., Winter, B., Wesolek, C., Schuldt, T., Lange, A. C., Franke, P., Friese, E., Decker, M., Wegener, R., Hundt, M., Aseev, O., and Kiendler-Scharr, A. (2022): Air quality observations onboard commercial and targeted Zeppelin flights in Germany – a platform for high-resolution trace-gas and aerosol measurements within the planetary boundary layer, Atmos. Meas. Tech., 15, 3827–3842, https://doi.org/10.5194/amt-15-3827-2022.  Schuldt, T., Gkatzelis, G. I., Wesolek, C., Rohrer, F., Winter, B., Kuhlbusch, T. A. J., Kiendler-Scharr, A., and Tillmann, R. (2023): Electrochemical sensors on board a Zeppelin NT: in-flight evaluation of low-cost trace gas measurements, Atmos. Meas. Tech., 16, 373–386, https://doi.org/10.5194/amt-16-373-2023.  
 
 
 
 
584 

  Monetary return on scrubber installations at the expense of environmental damage  A. Lunde Hermansson1, I.-M. Hassellöv1, J.-P. Jalkanen2, J. Hassellöv1 and E. Ytreberg1  1Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, SE-41296  2Finnish Meteorological Institute, Erik Palmenin aukio 1, 00101 Helsinki, Finland Keywords:   scrubbers, impact assessment, payback time, external costs.      Presenting author email: anna.lunde.hermansson@chalmers.se  Since the introduction of stricter regulations of sulphur content in marine fuels, more ships use exhaust gas cleaning systems (EGCS), often called scrubbers, as an alternative compliant method. In 2022, approximately 25% of the global marine bunker fuel demand was represented by heavy fuel oils (HFO) which can only be used on ships equipped with scrubbers. When the scrubber removes sulphur oxides (SOX) from the exhaust, other contaminants such as metals and polycyclic aromatic hydrocarbons (PAHs) are also scavenged by the scrubber water and discharged to the marine environment. There is an increasing concern regarding the contaminant loads from the scrubber fleet, and studies show that large quantities of metals (e.g. vanadium, chromium and nickel) and PAHs (e.g. anthracene and fluoranthene) are being discharged directly to the marine environment.   Figure 1. Assessment of Good Environmental Status in European waters aggregated over all descriptors.   The necessity of guidelines for scrubbers was acknowledged already at the forty-first session of the MEPC meeting in 1998. Since then, many States have commissioned research and literature reviews of the potential impact of scrubbers and stricter regulations and control are expected to be implemented. During the 78th Marine Environment Protection Committee (MEPC) meeting, new guidelines on how to assess risk and impact from scrubber water discharge were approved.  The impact assessment is described in section 7 of the guidelines where paragraph 7.4 stipulates criteria that, if fulfilled, motivates the adoption of restrictions or a ban on discharge of scrubber water. For example, if the environmental objectives in the area of concern are not met (e.g. Good Environmental Status (GES) according to the Marine Strategy Framework Directive (MSFD)), this could motivate stricter regulations or ban on scrubber water. From an EU perspective, most water bodies already fail to achieve GES (Figure 1) which motivate stricter regulations on scrubber-use within European waters. During the latest MEPC meeting (MEPC 79), questions were raised regarding the use of scrubbers as appropriate means of compliance (i.e., Equivalents)  and while several delegations expressed their support to restrict the use of scrubbers, other delegations raised their concern regarding the (economic) uncertainty for the industry, which has in good faith invested in EGCS technology in accordance with the provisions of MARPOL Annex VI.    The overall aim of this study was therefore to investigate all aspects of the discussion on the restriction of scrubbers by performing an impact assessment according to the MEPC guidelines (paragraph 7.4) and to evaluate the costs, both in terms of payback time for the ship owner and in terms of damage costs on the marine environment.  To accomplish this, results from EU Member States’ 2018 reporting on the extent to which they have achieved GES were collected from WISE-Marine (Water Information System for Europe). All descriptors were assessed but extra focus was directed towards Descriptor 8, Concentration of contaminants. The payback period of scrubber investments was calculated for all vessels known to be equipped with scrubbers from 2014 to 2022. The calculations were based on installation date, scrubber type and fuel consumption collected from the Ship Traffic Emission Assessment Model (STEAM) and high-resolution data covering fluctuations in fuel price of HFO, MGO and VLSFO was provided by Ship & Bunker. As a Baltic Sea case study, the monetary gain of the scrubber fleet, by not having to switch to more expensive fuels (MGO and VLSFO), was then compared to the damage costs for the marine environment, resulting from scrubber emissions of PAHs and metals.  The results show that most ship owners have already gotten return on their scrubber investments and that the comparative damage cost can be substantial. This study will thus provide important input to the discussion on restricting the use of scrubbers and prohibiting the discharge of scrubber water. The project has received funding from the Swedish Agency for Marine and Water management [grant agreement No 2911-22], The Swedish Transport Administration, [grant agreement TRV 2021/12071] and by the European Union’s Horizon 2020 research and innovation programme Evaluation, control and Mitigation of the EnviRonmental impacts of shippinG Emissions (EMERGE) [grant agreement No 874990].  
 
 
 
 
585 

  VOC emissions from Ships  K.Salo1, J. Mellqvist2 and A. V. Conde2  1Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, 412 96, Sweden 2Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, 412 96, Sweden Keywords:   shipping, hydrocarbons, VOC, UV-fluorescence, cross-sensitivity. Presenting author kent.salo@chalmers.se  Introduction Research studies using advanced instrumentation show that ships emit significant amounts of organic compounds and that these can dominate the total particle emissions in the exhaust (Zhang et al. 2020). The particulate emissions are presently not being removed by abatement technology and there is little discussion about their adverse impact on environment and health. This contrasts with the land-based sector in which all new diesel vehicles are obliged to utilize abatement technology which removes a considerable portion of these particles. Some studies propose that these particles mainly consist of volatile material originating from lubrication oil, and it is very difficult to measure these with simple instrumentation (Eichler et al. 2017). In this project several techniques to measure and quantify these emissions have been investigated, including a new unique way of combining two types of UV fluorescence measurements.  Results are presented from studies in a test engine and on a passenger ferry that was running different fuels, including methanol.   Methods    Three type of measurement techniques were compared, i.e.  FTIR and a temperature controlled multireflection cell measuring a wide range of hydrocarbons, UV-fluorescence measuring SO2 and a Flame Ionization Detector (FID) measuring the Total Hydrocarbons (THC).    Two fluorescence systems designed for SO2 monitoring have been used in parallel, one using a hydrocarbon denuder and one without. The latter one is cross-sensitive to VOCs and hence the difference between the two systems could, with appropriate calibration, be used for cost effective quantification of volatile hydrocarbons.  Results Several different VOC were identified with the FTIR, including methane, higher alkanes, alkenes and aldehydes. These data are presently being compared with the fluorescence system and the FID. In figure 1 is shown an example FTIR spectrum of exhaust gases sampled from the exhaust gas channel of the passenger ferry running on methanol showing a clear signal of formaldehyde.          Fig.1 Formaldehyde spectrum measured in the exhaust funnel when using methanol as a fuel on passenger ferry.  It was also clearly seen that the difference in signal from the two UV-fluorescence instruments were dependent on the concentrations of hydrocarbons in the exhaust gas. Implying that it is possible to use the cross-sensitivity for hydrocarbons as, at least, an indicator of hydrocarbon during exhaust characterisation.  In figure 2 a comparison is shown between the signal from the FID and the tandem fluorescence system during different engine loads is displayed.    Fig 2. THC measured with FID signal compared with the difference in signals from the tandem fluorescence system.  This work was funded by the Chalmers Area of Advance Transport and measurements on board the passenger ferry was carried out as part of the EU Horizon 2020 SCIPPER-project.   References Eichler, P et al. (2017) Environ. Sci. Technol. Lett. 2017, 4, 2, 54–58 Zhang, Y., et al. (2020) Atmos. Chem. Phys., 20, 1887–1900.      
 
 
 
 
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587 

  Transporting CO2 streams by ships: another threat for the marine environment? O. Bokareva1,  G. Argüello2  1 Faculty of Law, Lund University, Lund, 223 62, Sweden 2 Department of Law, Gothenburg University, Gothenburg, 405 30, Sweden Keywords: CO2 transport, ships, dumping, policy, legal and regulatory issues Presenting author email: gabriela.arguello@law.gu.se   The ocean is progressively transforming into the last frontier to pursue ambitious climate objectives. While climate action is often presented positively, it does not always lead to win-win scenarios. In fact, climate action can often cause social, environmental, and economic conflicts (Bogojević 2023). A case in point is carbon capture and storage (CCS). Over the years, CCS has been recognized as an integral part of the portfolio of measures to mitigate climate change (IPCC, 2022). At European Union (EU) level, CCS has a crucial role in climate policy, especially in decreasing CO2 emissions of energy-intensive industries (European Commission, 2021). Yet, important questions remain unanswered, including the liability for discharges in the marine environment occurring during the transportation of CO2 by ships and for long-term stewardship of sub-seabed storing sites. It must be noted that the transport of captured CO2 presents an important element in the whole chain and links the capture places with the storage sites. The new projects in Europe and worldwide indicate that ship transportation can become a viable solution to transport CO2 over longer distances and when pipeline transport is unavailable or not feasible ( Mittler 2023, Weber 2021). Notably, this type of transportation is relatively new and suitable ships need to be built to transport the CO2 in a safe and sustainable manner subject to the International Maritime Dangerous Goods Code (IMDG Code) and the International Gas Carrier Code (IGC Code). Several projects are underway and the new ships will be ready for service by 2024 (Northern Lights, 2022). Thus, it is evident that such emerging technologies require an adequate legal framework in light of the current legal regime or adoption of new suitable legislation at international, EU or national levels.  Against this background, the authors assess the barriers that CO2 transportation by ships faces and the available mechanisms to govern the environmental risks of this transportation. Considering that ship transport of CO2 will most likely have transboundary implications, we investigate existing legal limitations and risks related to the export of CO2 streams for disposal.  Particularly relevant is the 2009 amendment of Article 6 of the 1996 London Protocol to the 1972 London Convention on dumping, which allows the export of carbon dioxide streams for disposal. Despite the efforts to facilitate the deployment of CCS, the amendment is yet to enter into force. As an interim solution, in 2019, the Conference of the Parties (COP) to the London Protocol decided to allow “for the provisional application of the 2009 amendment (COP to the London Protocol 2019).”  Based on this decision, several contracting parties are planning to enter into arrangements to facilitate the export of CO2 in accordance with Article 6 of the London Protocol. Sweden, for example, intends to negotiate such an agreement with Norway, the United Kingdom and the Netherlands (Swedish Energy Agency 2022). This paper discusses whether this provisional application is a viable legal alternative in light of the Vienna Convention on the Law of Treaties. Furthermore, the authors analyze the operational liabilities that may occur in CCS operations from an international and EU law perspective considering for example, the Carriage of Hazardous and Noxious by Sea Convention (HNS Convention), its 2010 Protocol and Directive 2009/31/EC (CCS Directive).  References:  Bogojević, S. (2023). Legal Dilemmas of Climate Action. Journal of Environmental Law 35, 1-9. https://doi.org/ https://doi.org/10.1093/jel/eqad007 Council Directive 2009/31/EC of 23 April 2009 on the geological storage of CO2 2009 OJ L140/114 (CCS Directive) COP to the London Protocol. (2019). Resolution LP.5(14) on the provisional application of the 2009 amendment to Article 6 of the London Protocol.  European Commission. (2021). Sustainable Carbon Cycles (Communication from the Commission to the European Parliament and the Council Issue.  IPCC. (2022). Summary for Policymakers. In H.-O. Pörtner, D. C. Roberts, E. S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, & B. Rama (Eds.), Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 3-330). Cambridge University Press. https://doi.org/doi:10.1017/9781009325844.001  Northern Lights. (2022). Northern Lights awards ship management contract to “K” LINE.  Mittler, Cea. "The Carbon Voyage – Emissions Liability in Transporting Co2 by Sea for Ccs." NUS Centre for Maritime Law Working Paper 23/05 23, 5 (2023): 1-33. https://ssrn.com/abstract=4492441 or http://dx.doi.org/10.2139/ssrn.4492441. Swedish Energy Agency. (2022). National Centre for CCS. https://www.energimyndigheten.se/en/sustainability/carbon-capture-and-storage/national-centre-for-ccs/ Weber, V. (2021). Are we ready for the ship transport of CO2 for CCS? Crude  solutions from international and European law. RECIEL, Review of European, Comparative & International Environmental Law(30), 387-395. https://doi.org/10.1111/reel.12399    
 
 
 
 
588 

  High-resolution air quality mapping via massive mobile monitoring and land use random forest models  Tie Zheng1, Shaojun Zhang1,2, Yifan Wen1, Ye Wu1,2, Sheng Xiang1  1 School of Environment, Tsinghua University, Beijing, 100084, China 2 Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, China Keywords: Traffic-related air pollutants, Big data, Machine learning, Land use regression Presenting author email: tiezhengsjtu@gmail.com  Introducition. High-resolution air quality maps can provide important implications in the management and assessment of air pollutants. Now, the hyperlocal scale (< 100 m) and high-quality air quality maps were often obtained by massive mobile monitoring with fast response instruments (Apte et al., 2017). However, this method is costly and requires a large investment of manpower and time, thus, being difficult to scale larger areas. The costly measurement also limited the wider application of this method and the present studies were mainly conducted in developed and resourceful regions. This is a pioneer study in China that performed massive mobile monitoring using fast response instruments, producing hyperlocal air quality maps at 100 m. Over 4.6 million 1-s data of multiple traffic-related air pollutants were collected on major roadways in Shen Zhen in 2020~2021. Land use random forest (LURF) models were used to predict pollutant concentrations at locations without measurements. Then, the air quality of the full coverage road network in the interested area was revealed. This study provides important information on air quality maps in China and could address some of the major challenges in producing high-resolution air quality maps worldwide.   Method and Results. The measured air pollutants include particle number and mass concentration (PN and PM), black carbon (BC), etc. All involved roads were discretized into 100-m road segments. Then, data were cleaned and snapped to the nearest road segment. The data-snapping technology is similar to that of Messier et al., (2018) which we used “median of drive day median” as the core metric. The road segments with less than 5 visiting days were discarded in the analysis. In total, 227 land use variables were extracted in ArcGISpro 3.0, including the area of different land use types, road type and length, mean elevation, population, vegetation index, GDP, etc. Then, five-fold cross validation was used to assess the performance of the LURF model.  Fig. 1 shows the data-only map of PN0.001-1. Fig. 2 shows the results of model cross validation. Fig. 3 shows the predicted PN concentration at 100 m resolution in the interested area. Overall, the developed machine learning model can well predict the PN concentration at locations without measurements (R2 = 0.801) and the full coverage of road network PN concentration is presented (Fig. 3). The PN concentration is a lot higher on the highways and in the southwest part of the area, offering important implications for better management of air quality in the city. With the LURF model, the experimental investment could be greatly reduced to produce high-resolution air quality maps, making it easier to scale larger areas.  Figure 1. Data-only map of PN0.001-1 at 100 m resolution. The dashed line shows the selected area for PN prediction.  Figure 2. Cross validation results of the LURF model.  Figure 3. PN predictions at 100 m resolution. Apte et al., (2017). High-resolution air pollution mapping with Google Street View cars: exploiting big data. EST, 51(12), 6999-7008.  Messier et al., (2018). Mapping air pollution with Google Street View cars: Efficient approaches with mobile monitoring and land use regression. EST, 52(21), 12563-12572.  
 
 
 
 
589 

  Is post-exposure feeding inhibition of Artemia sp. and Mytilus galloprovincialis impaired by exposure to scrubber-waters?  A. Ré1, A.T. Rocha2, A. Monteiro1, M. Granberg3 and N. Abrantes2  1Department of Environment and Planning & Centre for Environmental and Marine Studies (CESAM), University of Aveiro, 3810-193 Aveiro, Portugal.  2Department of Biology & Centre for Environmental and Marine Studies (CESAM), University of Aveiro, 3810-193 Aveiro, Portugal. 3IVL Swedish Environmental Research Institute, Kristineberg Marine Research Station, Kristineberg 566, 45178 Fiskebäckskil, Sweden. Keywords:   Scrubber-water, post-exposure feeding inhibition, Saltwater ecosystems, Invertebrates      Presenting author email: njabrantes@ua.pt  In response to the sulphur regulation rules approved and adopted by the International Maritime Organization, many shipping companies have invested in scrubber-water systems instead of using fuels with a lower sulphur percentage, transferring atmospheric pollutants (SOx, NOx and COx, PAHs, metals and particles) to the seawater. This decision was made without any risk assessment on scrubber-water effects in marine and coastal ecosystems. Within the research project EMERGE (EC-Horizon 2020), the present work aimed to assess the ecotoxicological effects of scrubber-waters on the feeding behaviour of two seawater organisms. Post-exposure feeding inhibition bioassays were carried out with the filter crustacean, Artemia sp. and the mussel Mytilus galloprovincialis. Both species plays important roles in marine ecosystems: Artemia sp. cysts and nauplii provide food to many marine organisms being crucial to the success of the fish, crustaceans and cephalopod mollusks larval development and the mussel as a very powerful filter feeder, tend to accumulate for long periods waterborne contaminants of multiple types, being also used as human food. The feeding behaviour has been considered as a good indicator of toxic effects in suspension-feeding macroinvertebrates because it can be systematically linked to ecosystem functions, representing an ecologically relevant response. In this bioassay, three types of scrubber-water produced with Atlantic seawater were used: scrubber-water A (SWA) artificially produced, and Scrubber-water B and C (SWB, SWC) produced on board of two different ships. The scrubber-waters were tested at distinct concentrations: control; 0.001; 0.1; 1.0; and 10.0%. The three scrubber-waters were analysed, showing very low pH values and an enrichment of PAHs and alkylated-PAHs, as well as trace metals. Artemia sp. organisms showed low mortality when exposed to scrubber water dilutions below 50%. In the post-exposure feeding inhibition bioassay, artemia showed higher sensitivity to scrubber-water, with NOEC (No Observed Effects Concentration) values of 0.1% for SWA and 0.001% for SWB and SWC (Figure 1A). The mussel revealed a decrease in the amount of ingested algae with the increase of the percentage of scrubber-waters (Figure 2B). For the SWA and SWB the NOEC values were 0,1% and 1,0% for the SWA and SWB respectively. For SWC, the NOEC was <0.001%, but the proportionality ratio between scrubber-water percentage and feeding rate was not verified, with an increase in feeding for 0.1 and 1.0%. scrubber-water dilutions (Figure 1B). These responses seem to be related to the metals, PAHs and alkylated-PAHs concentrations of the scrubber-waters. The present results showed that scrubber-waters are highly toxic to the two tested species. Furthermore, the artemia and the mussel post-exposure feeding inhibition bioassay showed to be suitable to assess the risk of scrubber-waters on marine aquatic species.     Figure 1 – Post-exposure feeding inhibition results, A–Artemia sp. and B–Mytilus galloprovincialis (CN – control, SW(ABC) – scrubber-waters).  This work was supported by the EMERGE project -“Evaluation, control and Mitigation of the Environmental impacts of shipping Emissions”, with a postdoctoral fellowship BPD/UI88/2741/2021. 020004000600080001000012000140001600000,0010,110500,0010,110500,0010,11050Cells ingested*org-1*h-1Treatments (%)CNSWASWBSWC********0,00E+005,00E+041,00E+051,50E+052,00E+052,50E+053,00E+053,50E+054,00E+054,50E+055,00E+05CN0,0010,11,010,00,0010,11,010,00,0010,11,010,0Nº algae ingested algae*org-1*h-1Treatments (%)******CNSWASWBSWCB A  
 
 
 
 
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  Air quality assessment at the street level:                                                                              sensitivity analysis of a road traffic-emissions-CTM model chain for the Paris region Marjolaine Lannes1,2, Yelva Roustan1 and Nicolas Coulombel2 1 CEREA, École des Ponts, EDF R&D, Marne-la-Vallée, France 2 LVMT, École des Ponts, Université Gustave Eiffel, Marne-la-Vallée, France Keywords: street-scale air quality, vehicle emissions, agent-based simulation, sensitivity analysis      Presenting author email: marjolaine.lannes@enpc.fr Introduction The Paris region is a densely populated metropolitan area that regularly experiences overrunning regulatory thresholds for nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10). In order to assess the effect of transport policies on air quality and exposure, integrated mobility – emissions – air quality modelling chains have been developed (Gurram, Stuart, and Pinjari 2019; Vallamsundar et al. 2016). However, little is known regarding the uncertainties associated with these modelling chains. This study thus aims (a) to develop a street-level modelling chain for air quality assessment based on an agent-based mobility model coupled with an emissions model and an air quality model, and (b) to perform a sensitivity analysis on the calculation of air pollutants concentrations. The developed modelling chain will also allow to evaluate air pollution exposure for the simulated population in future works.  Methodology  Figure 1. An integrated modelling framework for ambient air pollution modelling   We develop a mobility-emissions–air quality modelling chain. Mobility and road traffic are simulated using the travel demand agent-based model, MATSim. We then use the outputs of the dynamic traffic assignment to estimate private car emissions at the street level and vehicle level, considering its fuel type and Euro standard. We complete these emissions with emissions inventories for other sectors. Road traffic emissions are assigned to streets in which air quality is simulated with the MUNICH street-network model (Kim et al. 2022). The urban background concentrations are simulated with the Polair3D Eulerian chemical transport model (CTM).   In order to study the modelling chain sensitivity, we compare concentrations when using a disaggregate vehicle fleet versus an average vehicle in the emissions model. Another sensitivity test compares the use of the background CTM model alone (Polair3D) and the combined CTM and street air quality model.  Results   Regarding the disaggregated vehicle fleet model, comparing the performance of ten AI models, the number of cars per household was predicted with 76.6% accuracy, 58.7% for fuel type, and 28.2% for emission standards. This model provides inputs for the agent-based mobility model to compute traffic-related daily emission profiles based on a synthetic population and synthetic vehicle fleet. The considered street network is created using OpenStreetMap’s road network. The emissions of passenger cars were then modelled based on HBEFA emission factors. The results showed that although petrol cars accounted for almost three-quarters of the car fleet, they were responsible for less than half of PM2.5 emissions.  Finally, these emissions will be used as inputs for the air quality models.   Conclusions and perspectives  This sensitivity analysis will highlight the concentration uncertainty resulting from averaging vehicular emissions, along with the uncertainty related to the use of background pollution concentrations instead of pollution levels in the streets. By combining individual travel patterns and street-level pollution concentrations from this modelling framework, future research will focus on the assessment of personal exposure to air pollution for the region's population.  This work is supported by Paris Region in the framework of DIM Qi2. Gurram, Sashikanth, Amy Lynette Stuart, and Abdul Rawoof Pinjari. 2019. ‘Agent-Based Modeling to Estimate Exposures to Urban Air Pollution from Transportation: Exposure Disparities and Impacts of High-Resolution Data’. Computers, Environment and Urban Systems 75: 22–34. Kim, Youngseob et al. 2022. ‘MUNICH v2.0: A Street-Network Model Coupled with SSH-Aerosol (v1.2) for Multi-Pollutant Modelling’. Geoscientific Model Development 15(19): 7371–96. Vallamsundar, Suriya et al. 2016. ‘A Comprehensive Modeling Framework for Transportation-Induced Population Exposure Assessment’. Transportation Research Part D: Transport and Environment 46: 94–113.   
 
 
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  Environmental fate modelling of organic pollutants from land-based and shipping emissions in the Northern Adriatic Sea coastal areas  Calgaro L.1, Giubilato E.1, Aghito M.2, Jalkanen J.P.3, Ferrarin C.4, Semenzin E.1, and Marcomini A.1  1 Dept. of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari Venice, Via Torino 155, 30172 Mestre, Venice (Italy) 2 Norwegian Meteorological Institute, Allégaten 70, 5007 Bergen, (Norway) 3 Finnish Meteorological Institute, Erik Palménin aukio 1, FI-00560 Helsinki, (Finland) 4 CNR-ISMAR, Arsenale Castello, 2737/F, 30122 Venice (Italy)  Keywords: water modelling, chemical fate, shipping emissions, exposure assessment       Presenting author email: loris.calgaro@unive.it  Coastal and marine ecosystems are threatened by diverse anthropogenic pollutants, both from land-based sources (e.g., industrial and agricultural activities) and direct discharges (e.g., shipping emissions). Recently, the adoption of the global limits on sulfur content for marine fuels increased the use of shipping emission abatement technologies like exhaust gas cleaning system (scrubbers) as an alternative to the use of cleaner but more expensive fuel types, whose acidic exhaust discharge water contain different chemical contaminants. Considering scrubber water and other shipping emissions to water, the presence of shipping lanes and ports may significantly contribute to the exposure of chemical pollutants in coastal areas, potentially increasing risks posed to aquatic ecosystems and human health.  In this context, fate and transport modelling offers an effective way to estimate the contribution of shipping emissions to the overall chemical exposure in coastal and marine waters.  In this work an exposure assessment for benzo(a)pyrene and fluoranthene accounting for both land-based sources and shipping emissions was carried out for the Northern Adriatic Sea. The modelling effort, realized within the H2020 “EMERGE” project, focused on the integration of high-resolution predictive models to help exploring the implications of different abatement emissions techniques in managing environmental risks under current and future scenarios.  In detail, shipping-related emissions of pollutants were simulated with the STEAM (Ship Traffic Emission Assessment Model) model (Maljutenko et al., 2021), based on Automatic Identification System data, while land-based emissions were quantified by combining daily river flow measurements with water concentrations from routinely monitoring of each tributary. The newly developed ChemicalDrift model, a chemical transport module included in the open-source Lagrangian framework OpenDrift, was applied for a baseline scenario using 2018 data. Forcing data for the case study area were obtained from the SHYFEM model (ocean currents, temperature, and salinity) (Ferrarin et al., 2019) and Copernicus Marine Services (mixed layer depth and winds). Preliminary results showed that shipping emissions can contribute significantly to the exposure to the selected contaminants, especially during the summer period when water flow from the tributaries reaches its minimum values and the number of cruise ships equipped with open-loop scrubbers increases.   This work has received funding from the European Union’s Horizon 2020 research and innovation program under Grant agreement No 874990 (EMERGE project).  Ferrarin C., Davolio, S., Bellafiore, D., Ghezzo, M., Maicu, F., Mc Kiver, W., Drofa, O., Umgiesser, G., et al. (2019) J. Oper. Oceanogr. 12, 86–103 Maljutenko, I., Hassellöv, I.M., Eriksson, M., Ytreberg, E., Yngsell, D., Johansson, L., Jalkanen, J.P., et al., (2021). Mar. Pollut. Bull., 173, 112985.      
 
 
 
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  Deposition quantification of gaseous emissions at the air-water interface during a single vessel travel  N. Rapkos and L. Ntziachristos  Department of Mechanical Engineering, Aristotle University, P.O. Box 458, 54124, Thessaloniki, Greece  Presenting author email: nikorapk@auth.gr    The objective of this study is to assess the quantification of gaseous pollutants that are emitted during a single vessel travel and deposited at the air-water interface. Gaseous emissions from ships are indirectly related to water quality degradation, as the dry and wet deposition of pollutants cause problems such as acidification and the deterioration of ecosystems (Doney et al., 2007, Grennfelt et al., 2020, Wu and Zhang, 2018).    The study utilizes AIS data for a small container ship that departs from the Piraeus port and is moving to Hellespont through the Aegean (Figure 1). Regarding the meteorological conditions, the wind is assumed as north with an intensity of 20 knots and considered constant for the total ship’s travel. The gaseous pollutants that were studied are sulphur dioxide (SO2) and nitrogen oxides (NOX).  The results of this study demonstrate the quantity that is deposited at the air-water interface during the total ship’s travel. The quantification considers, as a first step, the ship’s load factor estimation from the AIS data. Next, the load factor results are applied to the emission factor formulas that were introduced by Grigoriadis et. al (2021) for the emission rate calculation of SO2 and NOx. The deposited SO2 and NOx quantity is being predicted by the in-house quantification model of gaseous pollutants at the air-water interface which utilizes the emission rate data for the ship’s total travel.    Figure 1: Location points of the container ship during its travel in Aegean. The ship departs from the Piraeus port and attends Hellespont. Map source: Bing   Acknowledgements  The work has been supported by EU HORIZON 2020 Project EMERGE (Grant agreement ID: 874990). AIS data was provided by 'exactEarth Europe LTD'.  References  Doney, S. C., Mahowald, N., Lima, I., Feely, R. A., Mackenzie, F. T., Lamarque, J.-F., & Rasch, P. J. (2007). Impact of anthropogenic atmospheric nitrogen and sulfur deposition on ocean acidification and the inorganic carbon system. www.pnas.orgcgidoi10.1073pnas.0702218104  Grennfelt, P., Engleryd, A., Forsius, M., Hov, Ø., Rodhe, H., & Cowling, E. (2020). Acid rain and air pollution: 50 years of progress in environmental science and policy. In Ambio (Vol. 49, Issue 4, pp. 849–864). Springer. https://doi.org/10.1007/s13280-019-01244-4  Grigoriadis, A., Mamarikas, S., Ioannidis, I., Majamäki, E., Jalkanen, J.-P., & Ntziachristos, L. (2021). Development of exhaust emission factors for vessels: A review and meta-analysis of available data. Atmospheric Environment: X, 12, 100142. https://doi.org/10.1016/j.aeaoa.2021.100142  Wu, W., & Zhang, Y. (2018). Effects of particulate matter (PM2.5) and associated acidity on ecosystem functioning: response of leaf litter breakdown. Environmental Science and Pollution Research, 25(30), 30720–30727. https://doi.org/10.1007/s11356-018-2922-1   
 
 
 
 
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  Variation in responses of microalgae, Nitzchia sp. exposed to grey water from ships.  G. B. M. Rathnamali 1, J.T.  Mujingni 2, J. Egardt3, I. Dahllöf 3, I.-M. Hassellöv2, E. Ytreberg2, and K. Salo2   1Department of Marine Sciences, Gothenburg University, 405 30 Gothenburg, Sweden   2Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96 Gothenburg, Sweden  3Department of Biological & Environmental Sciences Gothenburg University, 405 30 Gothenburg, Sweden  Keywords:   grey water, toxicity, Nitzchia sp       Presenting author email: rathnamali.mekhala@gmail.com  Grey water originates from three primary sources onboard a ship, the accommodation area (showers, bath, and sinks), the galley (kitchen) area, and laundry practices. Thus, greywater contains different groups of pollutants such as bacteria, organic matter, metals, nutrients, pharmaceuticals, and microplastics (Ytreberg et al. 2020).  The average greywater generation from a ship differs from 130 L per person·day−1 (Furstenberg et al. 2009 cited in Holmberg, 2021) to 160 L per person·day−1 (Vicente-Cera et al. 2019a). But cruise ships (254 L per person·day−1) account for the highest greywater generation (EPA842- R-07-005, 2008) while oil and gas tanker ships represent the lowest greywater generation, 105 L per person·day−1 (Ytreberg et al., 2020). Counting the high generation loads and pollutants in greywater, there is an increased interest in assessments of the environmental impact of grey water from ships. Respectively, greywater is hypothesized to have ecotoxicological effects. Yet, there is a complete lack of toxicity studies on ship-generated grey water.  In this study, the marine diatom Nitzschia sp. will be measured for its growth as the response parameter and monitored with chlorophyll a fluorescence, and optical density. The unicellular algae, Nitzchia genus has been used extensively in ecotoxicological evaluations. Additionally, their ability to grow in milliliter volumes in test wells within short test durations makes them ideal test organisms for toxicity evaluations.   The Nitzschia sp culture for the study was obtained from the algae bank at the University of Gothenburg (Algbank - GUMACC | Department of Marine Sciences, University of Gothenburg). An onboard sampling of grey water was carried out on ships operating in the Baltic Sea during spring 2023. In total, 3 ferries, 3 cruise ships, and one coast guard ship were sampled. 10 L glass bottles rinsed with 70% ethanol and milli-q water were used for initial onboard sampling on the ships, and after mixing thoroughly, the subsamples were transferred to a set of bottles suited for the respective analysis of different pollutant groups, including nutrients, metals, pharmaceuticals, phthalates, and microplastics. One liter, precleaned bottles, were used to collect grey water samples for use in the toxicity tests.  For the toxicity tests, each grey water sample was prepared as follows: 10 mL volume of greywater was diluted with 10 mL of 20 psu (Practical Salinity Units) solution to test a 10psu, 50% diluted grey water from the different ships. Equal volumes of Nitzschia sp. culture and the nutrition media were added to each solution. Four replicates of each solution and positive control with the same physicochemical conditions except the grey water were arranged into five 48-well cell culture plates. The plates were run in a plate reader to record chlorophyll a fluorescence and optical density at specific time intervals of the algal growth phase. The software Statistical Package for the Social Sciences (SPSS 28.0) was used for the data analysis. The correlation of selected contaminant concentrations in the 7 samples and the growth rate of Nitzschia sp. in each sample will be discussed to evaluate the variations in toxicity between the different greywater samples.  This study will conclude the toxicity of ship-generated greywater according to the presence of different pollutants and their levels. Moreover, it will provide a comparison between different ships and their strategy for handling grey water. Acknowledgment: This study is part of the “Gråvatten I Östersjön” project, Ref. nr. TSA 2022-170, funded by the Swedish transportation agency.  Furstenberg S., Mohn H., Sverud T., 2009. Study on discharge factors for legal operational discharges  to sea from vessels in Norwegian waters, DNV report NO. 2009-0284, Hovik, Norway. Holmberg, J. (2021). (rep.). Study of grey water samples from cargo and passenger ships and an  assessment of the impacts of wastewater on the marine environment (Ser. ISSN 2670-2185).  https://dec.alaska.gov  Vicente-Cera, I., Moreno-Andrés, J., Amaya-Vías, D., Biel-Maeso, M., Pintado-Herrera, M., & Lara, Martín, P.  
 
 
 
 
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  Holistic environmental impact assessment from shipping: A decision support tool for stakeholder engagement  W. Winiwarter1,2 and S. Guéret1  1International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria 2Institute of Environmental Engineering, University of Zielona Góra, 65-417 Zielona Góra, Poland  Keywords:   Shipping emissions, Environmental Impact assessment, Scrubbers, Decision Support Tool Presenting author email: winiwarter@iiasa.ac.at   The shipping industry is a major cause of environmental harm, affecting both atmosphere and hydrosphere, and climate. The implementation of the Global Sulphur Cap in January 2020 focusses on removal of Sulphur Dioxide only, while the release of other compounds discharged into the air or sea and their environmental effects remains untouched. Environmental impact assessments are essential tools to evaluate the effects of operational shipping compared to other sources of pollution and to fill regulatory gaps. (Moldanová et al., 2022). Port authorities, policy makers, and other stakeholders, however, require more readily accessible tools to develop science-based decisions to uphold environmental quality relevant for their specific situation.   Here we present a decision support tool allowing stakeholders to evaluate the future impacts of scrubbers and other scenarios of future shipping to very different environmental aspects, which allows identification of co-benefits or trade-offs connected to measures imposed on shipping. The tool translates scientific insights into results useful for decision makers in practice and is being codesigned between scientists and stakeholders.  We use established impact assessment methodology in combination with newly developed features for the marine system to evaluate impacts leaning on the categories established as the U.N. Sustainable Development Goals: 1. Human Health (SDG3): using Years of Life Lost and ozone related health damage as parameters, impacts are taken from the GAINS model (Amann et al., 2011) 2. Life on land (SDG 15): ecosystem impacts of acidification and eutrophication are quantified via a critical loads approach, and photochemically caused damage is parameterized as phytotoxic ozone dose (POD), again from GAINS 3. Life below sea (SDG 14): differentiating between pelagic and benthic marine ecosystems, we take advantage of a newly developed impact assessment scheme presented by Guéret et al (this conference). In this scheme, critical loads of heavy metals and persistent organic pollutants – specifically, Polycyclic Aromatic Hydrocarbons (PAHs) –  as well as the calcite saturation state characterize ecosystem impacts.  We use results from the ship emission model STEAM (Jalkanen et al., 2021), the atmospheric model SILAM (Sofiev et al., 2015), and the ocean model Chemical Drift (Aghito et al., 2022) to quantify ship emissions and their discharges to air and water, respectively. Scenarios are taken from Fridell et al. (EMERGE D1/4) and reflect a general understanding of possible future shipping developments over Europe in 2030 and 2050.  The decision support tool allows users to understand the impact of different shipping scenarios to the respective local environment of any given grid cell (representing 20x20 km grids) in the European seas and adjacent shorelines. Adjustment of the scenarios allows to account for local conditions, to mimic change in ship movements as well as local restrictions regarding the release of certain compounds to air and sea and their respective altered impacts to the environmental endpoints.  This work was supported by EU H2020 project EMERGE, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 874990 (EMERGE project).  Aghito, M., Calgaro, L., Dagestad, K.-F., Ferrarin, C., Marcomini, A., Breivik, Ø., and Hole, L. R. (2022) ChemicalDrift 1.0: an open-source Lagrangian chemical fate and transport model for organic aquatic pollutants. Geosci. Model Dev. Discuss [preprint]. Amann, M., Bertok, I., Borken-Kleefeld, J., Cofala, J., Heyes, C., Höglund-Isaksson, L., Klimont, Z., Nguyen, B., Posch, M., Rafaj, P., Sandler, R., Schöpp, W., Wagner, F., Winiwarter, W. (2011). Cost-effective control of air quality and greenhouse gases in Europe: modeling and policy applications. Environmental Modelling and Software 26, 1489-1501. Jalkanen, J., Johansson, L., Wilewska-Bien, M., Granhag, L., Ytreberg, E., Eriksson, K., Yngsell, D., Hassellöv, I., Magnusson, K., Raudsepp, U., Maljutenko, I. , Styhre, L. , Winnes, H. , Moldanova, J. (2020). Modelling of discharges from Baltic Sea shipping. Ocean Science 17, 699-728.  Moldanová, J., Hassellöv, IM., Matthias, V. et al. (2022) Framework for the environmental impact assessment of operational shipping. Ambio 51, 754–769. Sofiev, M., Vira, J., Kouznetsov, R., Prank, M., Soares, J., Genikhovich, E. (2015). Construction of the SILAM Eulerian atmospheric dispersion model based on the advection algorithm of Michael Galperin. Geoscientific Model Development 8, 3497-3522.  
 
 
 
 
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  CFD dispersion modelling for the reproduction of real shipping emission conditions in a port area C. Boikos1, L. Ntziachristos1, *, S. Oppo2, A. Armengaud2   1 Mechanical Engineering Department, Aristotle University of Thessaloniki, GR 54124, Thessaloniki, Greece  2 AtmoSud, Air Quality Observatory in the Provence-Alpes-Côte d’Azur region, FR 13006, Marseille, France  Keywords: RANS-CFD, air quality modelling, port, shipping emissions.      Presenting author email: mpoikosc@auth.gr   This research work studies the impact of shipping emissions on the air quality of a port area using a CFD model for the dispersion of pollutants.   Transport sector, including shipping, is one of the major contributors regarding emissions of hazardous pollutants such as nitrogen oxides (NOx), carbon monoxide (CO), and particulate matter (PM10) (EEA, 2022). Recently, scientific community (Karl et al., 2019; Matthias et al., 2010) has been trying to quantify shipping emissions in coastal areas. In addition, the International Maritime Organization (IMO) has established new set of regulations to mitigate air pollution of shipping emission in the aforementioned areas.  Air quality models are often used to estimate concentration of pollutants in urban areas originated from various emission sources. These models can be classified in regional scale, local scale, and microscale ones, depending on their temporal and spatial resolution. Regional and local scale models are widely used for air quality studies. However, for city scale studies, microscale models can provide the spatial distribution of pollutant concentrations in higher resolution compared to local and regional scale ones.   For the purposes of this work, a Computational Fluid Dynamics (CFD) software was used for the modelling of plume dispersion. Port of Marseille was selected as case study, including detailed geometric representation of ships, their exhaust stacks as well as of the buildings in the surrounding area. No chemical reactions were included, meaning that pollutants were treated as inert gases. Pollutants concentration was quantified and depicted on a high-resolution grid (~3m) within urban canopy, providing detailed information about spatial distribution of pollutants, level of exposure on them, and possible hotspots in the city where pollutants can be trapped. Compared with in-port observations, predicted concentrations presented 33.9% average absolute deviation, which is considered within the acceptable range.  The main objective of this study is to provide a more holistic view of shipping emissions impact on the air pollution of port areas, giving useful information about plume evolution on them. A model like this can constitute an advisory tool for port authorities and policy makers, targeting to reduce the imprint of shipping emissions in urban environments.   Figure 1. Concentration field of a test case with three vessels in the port of Marseille, predicted with CFD model. The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the 3rd Call for HFRI PhD Fellowships (Fellowship Number: 6316).  European Environmental Agency. (2022). Air quality in Europe 2022. International Maritime Organization. (2016). Marpol Annex VI, Sulphur Oxides (SOx) and Particulate Matter (PM) – regulation 14. Resolution MEPC, 280 (70).  Karl, M., Bieser, J., Geyer, B., Matthias, V., Jalkanen, J. P., Johansson, L., & Fridell, E. (2019). Impact of a nitrogen emission control area (NECA) on the future air quality and nitrogen deposition to seawater in the Baltic Sea region. Atmospheric Chemistry and Physics, 19(3), 1721–1752. https://doi.org/10.5194/acp-19-1721-2019 Matthias, V., Bewersdorff, I., Aulinger, A., & Quante, M. (2010). The contribution of ship emissions to air pollution in the North Sea regions. Environmental Pollution, 158(6), 2241–2250. https://doi.org/10.1016/j.envpol.2010.02.013  
 
 
 
 
596 

  Cumulative effects of commercial anchoring on the Pacific coast of Canada: ecological and socio-economic effects L.C. Hannah1, F.T. Francis1, K. Douglas2, E. Rubidge1, T. Norgard1, S. Dudas1, K. Berry1, S. Agbayani1, C. Robb1, R. Enkin2, S. Verrin1, D. Havens1, and C.C. Murray1  1Institute of Ocean Sciences, Fisheries and Oceans Canada, Sidney, British Columbia, V8L 5T5, Canada 2Geological Survey of Canada Pacific, Natural Resources Canada, Sidney, British Columbia, V8L 4B2, Canada Keywords:   anchorages, anchoring, pathways of effects, multiple stressors, cumulative effects    Presenting author email: lucie.hannah@dfo-mpo.gc.ca  Commercial anchorages on Canada’s Pacific coast are located near the major Ports of Vancouver and Prince Rupert, and many are also in areas of natural beauty and within sight of local communities. Recent increases in anchorage use in these areas has heightened First Nations and local community concerns about potential ecological and socio-economic effects (Clear Seas, 2021) (Fig 1.). Few studies have examined the ecological effects of commercial anchoring together, and to our knowledge, none have considered socio-economic effects.   Figure 1. Artistic depiction of anchorage effects on environment and coastal communities.  To examine the cumulative effects of commercial anchoring, we developed an anchoring-specific Pathways of Effects (PoE) conceptual model that included both ecological and socio-economic components. To establish the structure of the PoE, we divided anchoring into three sub-activities: 1) discharges and emissions, 2) vessel movement and presence, and 3) anchor movement and presence, and identified the stressors associated with each. Using this structure, we next identified the effects and endpoints for the ecological and socio-economic components of the model. For the ecological components, we built upon previous work (Hannah et al 2020) that used literature review and expert elicitation to identify potential effects of stressors to marine organism and habitat endpoints. For the socio-economic component, we identified the effects and endpoints through the examination of official information submitted by Indigenous First Nations, coastal communities and special interest groups concerned about increases in commercial anchoring. These included documents relating to development projects (e.g., submissions in relation to proposed shipping terminal expansion and a pipeline expansion), as well as from local concern groups, and from submissions to Transport Canada from community members. Anchorage-related concerns were linked to the relevant stressor, effect and endpoint to create pathway linkages for the socio-economic component. PoE conceptual models are notoriously hard to represent visually given the numerous and complex linkages. To facilitate understanding and clarity we developed a new method to visually represent this information that is intuitive to understand and can be used for both ecological and socio-economic components (Fig 2).   Figure 2. Pathways of Effects conceptual model visualisation showing an overview of effects of commercial anchoring on endpoint categories  The visualisations of the diverse effects of commercial anchoring, combined with the database of supporting evidence for linkages, provides a way to understand the cumulative effects of commercial anchoring from different perspectives, as well as providing the structure and knowledge required for future assessments. Individual linkage chains identified in this work can help inform decision-makers, as each can be used as a potential management lever to help to mitigate or manage effects of commercial anchoring on the environment and local communities.  This work was supported by a CSRF grant from Fisheries and Oceans Canada.  Clear Seas (2021). Anchors Away: Understanding the issues about ships at anchor. Date modified: 2022-02-22. https://clearseas.org/en/blog/anchors-away-understanding-the-issues-about-ships-at-anchor/ Hannah, L., Thornborough, K., Murray, C., et. al. (2020) Pathways of Effects Conceptual Models for Marine Commercial Shipping in Canada:  
 
 
 
 
597 

  The impact of maritime activities on air quality in three European ports M. Karl1, M.O.P. Ramacher1, R. Badeke1, A. Aulinger1 1Department of Chemistry Transport Modelling, Institute of Coastal Environmental Chemistry, Helmholtz-Zentrum Hereon, Geesthacht, 21502, Germany Keywords:   shipping emissions, port cities, urban-scale air quality, EPISODE-CityChem.      Presenting author email: martin.ramacher@hereon.de  Ships are significant sources of air pollution, particularly in port cities and coastal areas. Emissions from ships include particulate matter, nitrogen oxides, and sulphur oxides, which can have negative impacts on human health and the environment. Accurate modelling of these emissions, their transport and transformation can help policymakers and regulators to develop effective strategies to reduce pollution and to protect public health. Additionally, modelling can also be used to evaluate the effectiveness of existing policies and regulations, and to identify areas where further action is needed. Consequently, in the EU Horizon 2020 project SCIPPER (Shipping Contributions to Inland Pollution Push for the Enforcement of Regulations) the impact of shipping emissions on air quality in the Mediterranean Sea and the North and Baltic Sea regions was investigated and compared. In this abstract, we present results that describe the air quality in three European port-cities: Hamburg (Germany), Kiel (Germany), and Marseille (France). The objective is to evaluate the local impacts of the emissions in order to estimate an average contribution of the port on the city’s air quality. For all city-scale simulations in this study, the urban-scale Chemistry Transport Model EPISODE-CityChem (Karl et al., 2019; Ramacher et al., 2019) was applied in a coupled setup with regional-scale simulations using the Community Multiscale Air Quality Modelling System (CMAQ). EPISODE-CityChem used the time-varying 3-D concentration field from CMAQ at the lateral and vertical boundaries as initial and boundary conditions. In EPISODE-CityChem, emission sources are defined as gridded area sources, linear sources and point sources. The emission inventories for the local shipping around Hamburg, Kiel and Marseille and in its ports consist of hourly emissions from ships on 250m × 250m grid resolution calculated with STEAM-3 (Johansson et al. 2017) provided by the Finnish Meteorological Institute (FMI). The STEAM-3 emissions are processed as input for EPISODE-CityChem, which calculates the vertical distribution of ship emissions individually at every x-y position of the surface grid and generates individual exponential Gauss distributions (Badeke et al., 2022) in dependence of the current wind speed and stability. Road traffic emissions from CAMS-REG-AP v5.1 gridded emission inventory for BAU (business as usual) in year 2020 were combined with lockdown correction factors for 2020 (Matthias et al. 2021) and downscaled to line sources for city-wide road traffic emissions applying the UrbEm framework (Ramacher et al., 2021).  The modelled simulations were all compared with measurements (SCIPPER field campaigns and data from local air quality networks) and the contribution of ships was assessed using the zero-out method. Our study shows a strong impact of maritime activities along the ship trajectories to and from the ports, in port areas, and even in the city centre. Indeed, ship manoeuvres in port areas but also their stay at the quay are connected with gaseous and particulate emissions, which affect the air quality in the port cities.   Table 1. Modelled shipping impact  The highest concentrations of PM2.5 and NO2 due to shipping activities were simulated in Hamburg, which is the 3rd most important port in Europe in terms of freight transport and the port is close to the city centre. In general, the simulated NO2 and PM2.5 concentrations in each port are correlated with the importance of the port. However, for SO2, the highest concentrations were modelled in the harbour of Marseille because it is a Mediterranean port, which is not in a sulphur emission control area (SECA), unlike other Northern European ports. Depending on the activities of the city, the contribution of the port to the degradation of the air quality varies. In Kiel, for example, ships impact NO2 concentrations by up to 73% in summer while in Marseille the impact is at most 31% (Table 1).   This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement Nr.814893.  Badeke, R., Matthias, V., Karl, M., and Grawe, D. (2022) Geosci. Model Dev. 15, 4077–4103. Johansson, L., Jalkanen, J.-P., Kukkonen, J. (2017) Atmospheric Environment 167, 403–1415. Karl, M., Walker, S.-E., Solberg, S. and Ramacher M.O.P. (2019) Geosci. Model Dev. 12, 3357–3399. Matthias, V., Quante, M., Arndt, J.A., Badeke, R., Fink, L., Petrik, R., Feldner, J., Schwarzkopf, D., Link, E.-M., Ramacher, M.O.P., and Wedemann, R. (2021) Atmos. Chem. Phys. 21, 13931–13971. Ramacher, M.O.P., Karl, M., Bieser, J., Jalkanen, J.-P., and Johansson, L. (2019) Atmos. Chem. Phys. 19 9153-9179. Ramacher, M.O.P., Kakouri, A., Speyer, O., Feldner, J., Karl, M., Timmermans, R., Denier van der Gon, H., Kuenen, J., Gerasopoulos, E., and Athanasopoulou, E. (2021) Atmosphere 12, 1404. City Modelled shipping impact  NO2 [µg/m3] SO2 [µg/m3] PM2.5 [µg/m3] Hamburg 10 (51%) 0.8 (14%) 0.6 (6%) Kiel 5 (73%) 0.2 (33%) 0.15 (2%) Marseille 5 (31%) 0.9 (34%) 0.4 (3%)  
 
 
 
 
598 

  Assessing the impact of environmental policy instruments for ships in Europe with a modelling tool  R. Parsmo1,2, J. Hansson1,2, E. Ytreberg2, E. Fridell1,2, and S. Brynolf 2  1IVL Swedish Environmental Research Institute, Box 530 21, 400 14 Gothenburg, Sweden 2Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden Keywords:   Environmental policy instruments, Cost of abatement, Abatement measures Presenting author email: rasmus.parsmo@ivl.se There are several policies in place, or being considered, to address environmental issues related to shipping at different levels: 1. Global policies include market-based incentives such as a proposed a CO2 levy, and regulations like the MARPOL (SECA and NECA) and AFS Conventions. 2. Regional policies include the inclusion of shipping in EU-ETS, and the proposed Fuel EU Maritime initiative. 3. National policies include the NOX fund and public procurement in Norway, as well as port- and fairway discounts adopted in Sweden. The total number of environmental policies being introduced or proposed is increasing, which makes it essential to systematically assess their impact on both the environment and the shipping industry. In this study, we propose and test a model (Figure 1) for evaluating the impact on seven different environmental policy instruments linked to shipping in Europe based on MRV data. Our model intends to assess policies from both the social planner and shipping industry perspectives by comparing their respective costs and benefits. The social planner perspective compares the damage cost (i.e., the cost pollution) with all other costs and benefits to society, while the industry perspective focuses on whether the policy is effective in encouraging change by considering only the costs and benefits relevant to the industry's decision-making. The model uses various types of input data related to: 1) individual ships (red) such as fuel consumption, engine size, and distance/time to the sea, 2) emission factors to air and water (yellow), and 3) the costs of abatement measures (blue). The model also includes scenario analysis (purple) to examine possible future outcomes. The results produced by the model depend on the policy (green) or measure (grey) being assessed. Some policies are of command-and-control type, and the industry must comply (such as requirements for reduced NOX emissions or a ban on scrubbers), while in other cases, the industry can choose not to implement measures (such as economic incentives like the EU ETS, where it is possible to buy emission allowances). The results can include potential absolute emission reductions, costs per emission, or absolute costs, depending on the policy instrument or abatement strategy's that is assessed.  For example, Figure 2 illustrates the annual abatement cost per ship if all ships in the EU were forced to use electro-methanol in 2030. In this case, the annual abatement costs have been applied to the theoretical European fleet in 2030, with each bar representing an individual ship. The scenario presented in Figure 2 can be analyzed in several ways for different purposes. For example, a decision-maker may want to determine whether the introduction of the EU ETS would prompt ships to install and use electro-methanol in 2030. If Figure 2 presents costs from the industry perspective, the result suggests that the CO2 price would need to be at least €350/tonneCO2 for any ship to be willing to invest in methanol propulsion, or approximately €500/tonnesCO2 to achieve a total emission reduction from shipping from this measure of 80 Mtonnes CO2. The current EU ETS price of around €90/tonnesCO2 and expected future levels indicate the need for additional policies. In addition, potential emissions reductions of NOX or SOX can be assessed in a similar way with the model.   Figure 1. Proposed model for assessing policy instruments for shipping. Figure 2. Example results: Comparing abatement cost of introducing electro-methanol with total annual abatement calculated the European ship fleet (2030).  This work was supported by the Swedish Transport Administration and the Swedish Foundation for Strategic Research (SSF)  
 
 
 
 
599 

  LNG feeder vessel environmental pressures, partially decoupled from transport demand  I.-M. Hassellöv1 and A. Hörteborn1,2  1 Dept of Mechanics and Maritime Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden  2 RISE Research Institutes of Sweden, SE-412 96 Gothenburg, Sweden Keywords: LNG, gas, energy system, shipping, marine environment, environmental assessment.      Presenting author email: ida-maja@chalmers.se  Liquified Natural Gas (LNG), is proposed to play a role in the transition to sustainable shipping. LNG can  reduce emissions of sulphur and nitrogen oxides, and particulate matter. However, LNG is a fossil fuel and will thereby contribute to global climate change. The ongoing discussion on the methane slip from ships operating on LNG is further challenging the assumptions of LNG as a sustainable solution (Grönholm et al. 2022).  In addition, LNG vessels give rise to other types of environmental pressures, such as leakage of biocides from antifouling paint, sewage, grey water and bilge water. Environmental assessments or life cycle assessment of LNG vessels should encompass all the different types of operational emissions and discharges. Finally, spending time at sea, implies an increased navigational risk and resulting maritime accidents, which should also be included in such analysis. Another aspect to consider in the environmental assessment of shipping is the goods that are transported (Liu et al. 2019). Here we show that LNG feeder vessels may spend as much as 25% of their time at sea just running the ship burning Boil Off Gas (BOG), to ensure the pressure in the tanks are not exceeded. This behaviour is not directly attributed to the shipment of gas from one port or ship, to another (Figure 1), which means, the economic incentives are currently allowing for roughly one third’s increase of the ships’ operational emissions and discharges and increased navigational risks.   Figure 1. Sailing paths of the LNG feeder between June 1st 2022 and Aug 31st 2022. The ship’s speed over ground is illustrated with three different colours. The inset shows a zoomed in view of the sailing path of the LNG feeder in the area between the Swedish Island Gotland and Swedish coast during the same period which corresponds to 130 hours spent on burning BOG. This behaviour can be put in the context of IMO GHG reduction strategy, to reduce the total annual GHG emissions from international shipping by at least 50 percent by 2050. Analogously, in the light of national and international work (e.g., the EU marine strategy framework directive, (2008/56/EC)), avoiding BOG at sea should be a low hanging fruit to reduce loads of hazardous substances and nutrients on the marine environment. Most coastal areas are heavily affected by anthropogenic activities and e.g., in the Baltic Sea shipping is recognized as a significant source of metals and PAHs (Ytreberg et al 2022).  Even if the LNG feeder vessels are currently few, this study highlights the importance to distinguish the economic and technological drivers of vessels activity patterns. During the fall 2022, LNG tanker vessels were reported to spend time at sea awaiting higher prices on the gas market. Identifying the vessel activity decoupled from transport demand, and including it in e.g. life cycle assessment, is the first step towards policy action. The possibility to reduce the environmental impact of vessels by up to 32% should be an attractive opportunity for future policy measures and investigation of technological solutions of the problem.    This work was supported by the Hugo Hammar Foundation (Grant no. HHS-297 and ML 112). The authors acknowledge the maintainers of the open-source GIS platform, QGIS.  Grönholm, T., Mäkelä, T., Hatakka, J., Jalkanen, J.-P., Kuula, J., Laurila, T., Laakso, L., & Kukkonen, J. (2021). ES&T, 55(20), 13677-13686.  Liu, H., Meng, Z.-H., Lv, Z.-F., Wang, X.-T., Deng, F.-Y., Liu, Y., Zhang, Y.-N., Shi, M.-S., Zhang, Q., & He, K.-B. (2019). Nature Sustainability, 2(11), 1027-1033.  Ytreberg, E., Hansson, K., Hermansson, A. L., Parsmo, R., Lagerström, M., Jalkanen, J.-P., & Hassellöv, I.-M. (2022). Mar Poll Bull, 182, 113904.      
 
 
 
 
600 

  The Poseidon Principles:  Designing and implementing a regulatory framework to create ecosystem sustainability  I.-M. Hassellöv1, L.Huemer2, and C. Martinson2  1 Dept of Mechanics and Maritime Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden. 2 Dept of Strategy and Entrepreneurship, BI Norwegian Business School, N-0442 Oslo, Norway. 3 Dept of Law, School of Business, Economics and Law, University of Gothenburg, SE-405 30, Gothenburg, Sweden. Keywords:   Poseidon Principles, ship emissions, shipping regulatory framework, decarbonization      Presenting author email: ida-maja@chalmers.se  The demand for regulation of international shipping has since long been high for many different reasons, equal treatment in competition being one of them. Climate sustainability has taken this demand to a higher level. Regulation of international shipping is however difficult because of the relative lack of regulatory power. Regulations are primarily made through consensus decisions within the International Maritime Organization, implying rather slow processes. The strive for global regulatory frameworks as base for sustainable development of the shipping industry is complemented by voluntary initiatives. For example, the Clean Shipping Index promotes sustainable technologies onboard ships through reduced fairway dues, yet the economic incentives of these instruments are rather limited compared to the shipping companies’ finances.  The initiative Poseidon Principles, launched in 2019, advances the idea of industry sectorial responsibility and enables stronger economic incentives by “integrating climate considerations into lending decisions to promote international shipping’s decarbonization” (Poseidon Principles, 2022). The Poseidon Principles are a framework for assessing and disclosing the climate alignment of ship finance portfolios. They create common global baselines that are consistent with and supportive of society’s goals to better enable financial institutions to align their portfolios with responsible climatic impacts.   Today 30 financial institutions are Signatories to the Poseidon Principles, and together they represent a bank loan portfolio covering over 70% of the global ship finance portfolio. With the combination of stakeholders from different branches, and the possibilities to involve also other bodies with regulatory interests, the Poseidon Principles show an interesting regulatory model where different kinds of analyses can indicate the potential.  This multidisciplinary study combines insights from cooperative strategy, maritime and transport law, and maritime environmental science. Based on a mixed methods design, we study the potential impact of the Poseidon Principles on sustainable value creation in the shipping industry. We demonstrate that regulatory frameworks represent resource bundles that extend (i) the resource-based view’s firm level focus on competitive advantage; (ii) the relational view’s emphasis on interorganizational competitive advantage, which, if based on rare and inimitable relationship, can lead to supranormal profits, and; (iii) recent research on resource bundles which maintain the conventional focus on rareness and imitability. A well-designed regulatory framework does not create a firm level competitive advantage as stressed by the resource-based view, or an interorganizational competitive advantage based on relational rents which result in supranormal profits if relationships are rare and inimitable. Regulatory frameworks provide ecosystem sustainability (primarily the economic ecosystem, but in this study extended to include natural marine ecosystem) when they provide match with other resources in the ecosystem, and when they increase the value of those resources. The conventional emphasis on rareness and inimitability are counterproductive with respect to ecosystem sustainability and what we label the creation of a planetary advantage. Today the Poseidon Principles are focused on decarbonization, but we foresee possible development towards integration of other types of environmental impacts of shipping. In some areas, typically estuarine or semi-enclosed environments with intensive shipping, like the Baltic Sea, shipping is a significant contributor to pollution of hazardous substances, e.g. close to 40% of the copper load and 8-9% of individual organic pollutants, to the Baltic Sea (Ytreberg et al. 2022). Integrating other pressures than CO2 emissions, stimulating internalization of the associated external costs, could realise the transformative ambition and assumed potential of the Poseidon Principles.   This work was supported by University of Gothenburg and Chalmers University of Technology’s Area of Advance Transport.  Poseidon Principles. (2022). A global framework for responsible ship finance. Version 4.1. 61pp.  Ytreberg, E., Hansson, K., Hermansson, A. L., Parsmo, R., Lagerström, M., Jalkanen, J.-P., & Hassellöv, I.-M. (2022) Mar Poll Bull, 182, 113904.        
 
 
 
 
601 

  ShipTRASE, Global shipping: Linking policy and economics to biogeochemical cycling and air-sea interaction  A. Rutgersson1, C. Marandino2, D. Booge2, S. Hajjaji3, R. Kumar1, N. Matz-Luck3, L. Recuero-Virto4, K Salo5, M. Sebe4, and F. Yao4  1Department of Earth Sciences, Uppsala University, Uppsala, 752 32, Sweden 2GEOMAR Helmholtz Centre for Ocean Research Kiel, 24105, Germany 3 The Walther Schücking Institute for International Law, University of Kiel, Kiel, 24118 Germany 4 Polytechnic School of Paris, and European Institute for Marine Studies, France 5 Chalmers University of Technology, Göteborg, Sweden Keywords:   scenarios, green shipping, scrubbers     Presenting author email: anna.rutgersson@met.uu.se  The shipping sector is presently going through a transitional phase with enhanced focus on green shipping, this includes implementation of control areas (ECAs) to reduce the shipping emissions of Nitrogen oxides, Sulphur oxides and Particulate matter, as well as targets for reduction of greenhouse gas (GHG) emissions. Switching to Liquefied Natural Gas (LNG) or installing exhaust gas cleaning systems (as scrubbers) have previously been identified as the most suitable solutions for shipping companies. However, some concerns have arised for the use of those technologies. Open-loop scrubbers release chemicals in seawater, and LNG releases unburned methane (so-called "methane slip").   There is a need for a holistic evaluation of new fuels and cleaning technologies. This includes understanding of climate and environmental impacts, economic implications, regulatory requirements and technical possibilities. Within the ShipTRASE project we evaluate short- and long-term possibilities for cleaning technologies and alternative fuels from a multidisciplinary perspective. Short-term perspective Regulations have led to increased use of low-sulphur fuels, but also allows abatement technologies such as scrubbers, which require a separate legal framework. In 2022, guidelines for risk and impact assessments of the discharge water from scrubbers was adopted. Although the guidelines have been stepwise improved, they are still only recommendatory in nature (Shi et al., 2023).   Since January 1, 2020, only 0.50% fuel oil sulphur content for non-emission control areas (ECAs) and 0.1% for ECAs and their corresponding SO2/CO2 are relevant. Although the scrubber’s air emissions are consistent with the limits set in the guidelines, there is a major uncertainty regarding national regulations on the scrubbers’ wash water (discharge) in the sea.  Results from incubation experiments show that the addition of scrubber effluent impacts the production of climate relevant biogenic trace gases (i.e. dimethyl sulfide and isoprene). The trace gas concentration is indirectly influenced as the toxicity of the scrubber effluent influences the growth of phytoplankton communities. Constituents of scrubber effluent directly react with dissolved trace gases leading to a significant decrease in the ocean concentration and therefore dampen the air-sea gas exchange.  Long-term perspective In a prospective approach to assess the development of shipping fuel for the Baltic Sea, we have defined scenarios considering legal (national and international regulations), environmental and economic aspects (investment costs and fuel prices) including multidisciplinary consultations with stakeholders. Figure 1 presents the share of fuel used in the Baltic Sea Region in 2050 in each scenario, and reveals the projected long-term decline of fossil fuels. The availability of alternative fuels is limited, consideration taken to LNG (concerns of methane slips), methanol (concerns with bunkering), hydrogen and ammonia. From the legal part, different scenarios will help to adopt the best legal and institutional model to achieve climate neutrality by 2050. The European Climate Law 2021 imposes a mandate to reach zero emission by 2050. However, it is not clear on what is the expected legal model that the EU, or its institutions, will employ to achieve that target regarding the shipping industry.   Figure 1. Share of fuel used in the Baltic Sea region in 2050 from Kumar et al. (2023).  This work was supported by the Belmont Forum with national funding agencies.  Kumar, R et al (2023). Assessment of Shipping fuel pathways in changing climates. Shi, Z, S. et al. (2023). Perspectives from ship stacks: air pollutant emissions and their impacts on the surface ocean and lower atmosphere. Elementa  
 
 
 
 
602 

  Identification and quantification of contaminant particles from scrubber water effluents in a high-intensity shipping area  A. Gondikas1,2, M. Hassellöv3, S. Chen4, I-M. Hassellöv5  1Creative nano, PC, Metamorfosi, 14451 Athens, Greece 2Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece 3Department of Marine Sciences, University of Gothenburg, BOX 461, SE-405 30 Gothenburg, Sweden 4State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai 200237, PR China 5Department of Mechanics and Maritime Science, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden  Keywords:   scrubber, washwater, particles, monitoring.      Presenting author email: a.gondikas@creativenano.gr  Shipping has been traditionally considered an environmentally friendly means of transportation of goods and people, a perspective that is challenged by scientific evidence on air quality impacts; as a result, the International Maritime Organization has put regulations in place to limit atmospheric pollution from shipping. Scrubbers are highly efficient in capturing sulphur species from ship exhaust gas and are currently the most popular sulphur abatement technology in large ships. In short, seawater is fed in the scrubber and brought in contact with the exhaust gas, allowing mass transfer of components from the gaseous into the water phase; the resulting washwater is returned to the sea with or without pre-treatment. In addition to sulphur, scrubbers are highly efficient in removing particulate matter from the exhaust gas, which is likely to contain hazardous elements, such as metallic and organic pollutants (Turner et al 2017) (Ytreberg et al 2022).  It is therefore imperative to determine the fate and transport of particles from scrubber effluents into the marine environment. The determination of physical and chemical properties of these particles is a crucial step, prior to testing toxicological effects on marine organisms and applying models to predict their transport in the ocean. These properties may then be used in monitoring efforts to identify pollution from ships operating with scrubbers. In this study, we apply automated microscopy and spectroscopy methods to characterize particle populations in seawater from marine areas with high shipping intensity.   International ports attract large numbers of cargo and passenger ships and are susceptible to shipping pollution. We therefore collected samples across the shipping lanes off of Piraeus port, which is the 5th largest port of the EU in terms of volume of good transferred (TEU). In addition, narrow crossings form a natural barrier for shipping routes, forcing ships to pass through small water bodies, making them susceptible to shipping pollution. We collected samples from the Öresund strait, which is one of the busiest crossings in the northern hemisphere. Samples were filtered and analysed with automated methods of scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS). The applied procedure allows for the determination of physical characteristics, such as size and shape and chemical characteristics, such as partial elemental composition. The automated methods tackle all issues related to operator bias in electron microscopy and produce large datasets that are representative of the particle populations in the sample. The analytical procedure is limited to the pore size and material of membranes used. Seawater passing through the filter was analyzed with single-particle inductively coupled plasma mass spectroscopy (spICPMS) for a selection of elements. This procedure allows counting particles containing the target element, thus shedding some light on the smaller particle population and is limited by the instrument detection limits for each element and the acquisition of partial composition data.  Due to the large number of particulate matter from natural and non-scrubber anthropogenic activities, combinations of properties of particle populations is necessary. The challenge increases with decreasing particles size, due to the overwhelming number of non-scrubber particles. Our study demonstrates the applicability of analytical techniques and remaining challenges in the detection of particles from scrubber washwater in the marine environment and highlights properties of particles that may be used for source allocation in monitoring efforts.     This work has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 874,990 (EMERGE project). This work reflects only the authors' view and CINEA is not responsible for any use that may be made of the information it contains.  Turner, D. R., Hassellöv, I.-M., Ytreberg, E., and Rutgersson, A. (2017) Elem Sci Anth, 5: 45  Ytreberg, E., Hansson, K., Hermassnon, A. L., Parsmo R., Lagerström M., Jalkanen, J.-K. and Hassellöv, I.-M.. (2022) Mar. Pollut. Bull. vol. 182, 113904.   
 
 
 
 
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  Optimization of Drone-based Sensor Sniffing System for Monitoring Fuel Sulphur Content in Ocean-going Vessel Based on Field Measurements in Hong Kong Waters  Shiyi Yang1, Ghadikolaei, Meisam Ahmadi1, Mengyuan Chu1, Xiaoliang Qin1, Zhi Ning1 1Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong Keywords: Sulfur dioxide; Ocean going vessels; Fuel sulfur content; Unmanned aerial vehicle; microsensor sniffing system Presenting author email: syangct@connect.ust.hk  Like most coastal cities with huge port activities, pollution from marine vessel emissions is one of the key contributors to the emission inventories in Hong Kong. The utilization of high fuel sulfur content (FSC) bunker oil makes ocean-going vessels (OGVs) as the primary anthropogenic source of sulfur dioxide (SO2) emissions. To regulate SO2 emissions from marine transportation, the Hong Kong government implemented regulations in 2019 that required OGVs operating in Hong Kong waters to adhere to an FSC limit of no more than 0.5% (HKEPD, 2018). Nevertheless, the need for an efficient tool for obtaining emission data from moving OGVs remains a major obstacle in enforcing air regulation policies for most emission control regions. Traditionally the fuel samples are collected from the bunker inside an OGV to measure its FSC. Though portable emission measurement systems (PEMs) have been developed and installed near the plume of a source to gather pollutant data under the actual operating conditions of OGVs, it can only be applied in individual vessel measurement and is not practical for regulating ship emissions on a large scale. In the last few years, microsensor technology integrated with unmanned aerial vehicles (UAV) have shown to exhibit potential ability in ship emission measurement, but there still lacks an optimum systematic operation protocol. From our previous study (Anand et al, 2020), a UAV-based microsensor sniffing system (MSS) has been developed and evaluated for measuring OGVs plume SO2, NO2, NO, CO2, and CO in real time. In this study, we further applied the UAV-based MSS in the practical field in Hong Kong waters and around Kwai Tsing Container Terminals (Figure 1). During the study period, 12 successful field trips were conducted to achieve 100 valid OGV samples (out of 151 measured) from April 2022 to December 2022. Out of the total 100 valid samples, 18 were collected using land-based measurement methods, while 82 were collected using sea-based measurement methods. According to the carbon balance method, the FSC value can be calculated by the ratio of SO2 and CO2 concentration excess in the plume compared with the background value. Furthermore, the crest, regression and area methods were applied in FSC calculations to further optimize the protocol such as sharp peak and wide plume area, respectively. Further the performance of UAV-MSS is evaluated by comparing the Bunker Delivery Note (BDN) in this study. Preliminary results indicated that the crest method exhibited the smallest absolute error (13.2%) and relative error (13.2%) compared to the area method and regression method. Moreover, the sea-based measurement method has the advantage of not being limited by LoRa link distance, making it easier to obtain multi-peak samples, which greatly reduces measurement uncertainty and results in less error (12.3%) than land-based measurement data. Additionally, correlation analysis of the 100 valid samples in this study with OGV characteristics revealed a significant relationship with the built year, while no clear relationship was found with the gross tonnage and area.   Figure 1. Operation process of UAV-based microsensor sniffing system This study will continue to optimize the systematic protocols, including sensor calibration methods, field operation procedures, and data analysis, to facilitate the efficient application of UAV-based MSS in real-world ship FSC monitoring. Additionally, based on the VIS database, the correlation between FSC values and characteristic factors of vessels will be analyzed to reveal potential patterns.  This research is funded by the Environmental Protection Department of Hong Kong (Reference: 17-04647), the Science and Technology Commission of Shanghai Municipality of China (Projects No. 17DZ1203101, 17DZ1203105).   References: HKEPD, 2018. Hong Kong air pollutant emission inventory - navigation. Retrieved May 9, 2020, from Environmental Protection Department. https://www.epd.gov.hk.  Anand, A., Wei, P., Gali, N. K., Sun, L., Yang, F., Westerdahl, D., ... & Ning, Z. (2020). Protocol development for real-time ship fuel sulfur content determination using drone based plume sniffing microsensor system. Science of The Total Environment, 744, 140885.   
 
 
 
 
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Changes in cloud activity of ship exhaust particles: Potential effects on Arctic mixed-phase clouds Luis Santos,1 Kent Salo,2 Hannah Frostenberg,3 Xiangrui Kong,1 Jun Noda,4 Thomas B. Kristensen,5 TakujiOhigashi,6 Annica Ekman,7 Luisa Ickes3 and Erik S. Thomson1 1Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden; 2Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden 3Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden 4School of Veterinary Medicine, Rakuno Gakuen University, Ebetsu, Japan 5FORCE Technology, Brøndby, Denmark 6UVSOR Synchrotron, Institute for Molecular Science, Okazaki, Japan 7Department of Meteorology, Stockholm University, Stockholm, Sweden Keywords:   Ships, aerosol, clouds, Arctic.     Presenting author email: luis.santos@cmb.gu.se Maritime shipping remains a large source of anthropogenic airborne pollutants, including exhaust particles that can act as cloud condensation nuclei (CCN). The International Maritime Organization (IMO) imposed global fuel sulfur content (FSC) limits on marine fuels in order to target ship exhaust sulfur oxides and particulate matter emissions, but has allowed competing pathways to regulatory compliance; i.e., low FSC fuels versus exhaustafter-treatment. Laboratory experiments revealed that these compliance measures have secondary effects on physicochemical properties of exhaust particles, affecting their CCN activity (Santos et al., 2022; 2023). We observe that combustion of low FSC fuels results in emissions of highly hydrophobic particles, causing significant reductions in CCN emissions, whereas wet scrubbing leads to an increase in CCN activity. One area of focus is the Arctic region, which has been shown to be particularly susceptible to the effects of climate warming. A steady decrease in observed sea ice cover amplifies the regional warming (Screen and Simmonds, 2010), but also opens the region to increased ship traffic which may result in further climate feedbacks (Stephenson et al., 2018). It is of particular interest to identify how increased ship exhaust particle emissions may affect cloud processes; for example, by facilitating liquid droplet formation and thus, potentially changing the radiative properties of the aerosol and clouds.   Here, we investigate how increased shipping activity potentially influences the properties of Arctic mixed-phase clouds. In our study the experimentally observed characteristics of marine particle emissions and their liquid droplet forming potential have been implemented in large eddy simulations. We use theMIMICA model (MISU/MIT Cloud-Aerosol model) (Savre et al., 2014) to simulate a stable stratiform mixed-phase cloud based on the Arctic Summer Cloud Ocean Study (ASCOS) (Tjernström et al., 2014). A range of input parameters for ship aerosol, including size distributions, number concentrations, vertical distributions and hygroscopicities, has been studied to assess the potential impact on cloud properties and regional climate. This work was supported by the Swedish Research Councils FORMAS and VR. Santos, L., Salo, K. and Thomson, E. (2022). Environ. Sci.: Processes Impacts, 24:1769-1781  Santos, L., Salo, K., Kong, X., Noda, J., Kristensen, T., Ohigashi, T. and Thomson, E. (2023). Environ. Sci.: Atmos., 3:182-195  Savre, J., Ekman, A. and Svensson, G. (2014). J. Adv. Model. Earth Syst., 6:630-649 Screen, J. and Simmonds, I. (2010). Nature, 464(7293):1334–1337 Stephenson, S., Wang, W., Zender, C., Wang, H., Davis, S. and Rasch, P. (2018). Geophys. Res. Lett., 45:9898–9908  Tjernström et al. (2014). Atmos. Chem. Phys., 14:2823-2869