The AURORAE (Air qUality foRecast fOR Arctic communitiEs) service.
Date: 2024-11-25
Institutions: Joint Research Centre
Subjects: Arctic, Northern Europe, air quality, atmospheric pollutant, data collection, environmental monitoring, pollution control, public health, quality of the environment, research report

ISSN 1831-9424 

The AURORAE (Air qUality foRecast fOR 
Arctic communitiEs) service  

Crotti, I., Cuzzucoli, A., De Marchi, D., Selmi, L., Eyraud, 
F., Ramalli, E., Pasini, A., Dobricic, S. 

2024 

 EUR 40121 

 
  
 
This document is a publication by the Joint Research Centre (JRC), the European Commission’s science and knowledge 
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Contact information  
Name: Ilaria Crotti 
Email: Ilaria.crotti@ec.europa.eu 

EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC139867 

EUR 40121 

PDF 

ISBN 978-92-68-22182-2 

ISSN 1831-9424 

doi:10.2760/5041509 

KJ-01-24-157-EN-N 

Luxembourg: Publications Office of the European Union, 2024 

© European Union, 2024 

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How to cite this report: European Commission, Joint Research Centre, Crotti, I., Cuzzucoli, A., De Marchi, D., Selmi, L., 
Eyraud, F., Ramalli, E., Pasini, A. and Dobricic, S., The AURORAE (Air qUality foRecast fOR Arctic communitiEs) service, 
Publications Office of the European Union, Luxembourg, 2024, https://data.europa.eu/doi/10.2760/5041509, JRC139867. 

 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

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

1 

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

2  Objective and scope ........................................................................................................................... 7 

3  Air pollution forecast models ............................................................................................................. 8 

3.1  CAMS models .............................................................................................................................. 8 

3.2  An Artificial Intelligence based approach for the Arctic ...........................................................10 

3.2.1  Design .............................................................................................................................10 

3.2.2  Training and validation ...................................................................................................12 

3.3  Model for AURORAE service .....................................................................................................13 

3.3.1  Architectures ..................................................................................................................14 

3.3.2  Data preparation ............................................................................................................16 

3.3.3  Model Analysis ...............................................................................................................16 

3.3.4  Model Tuning .................................................................................................................18 

3.3.5  Model training ................................................................................................................20 

4 

Iimplementation of the AURORAE service .......................................................................................21 

4.1  Data Gathering Module ............................................................................................................21 

4.1.1  EEA and FMI sub-module ...............................................................................................21 

4.1.2  CAMS PM10 forecast sub-module ...................................................................................22 

4.1.3  CAMS weather forecast sub-module .............................................................................23 

4.2  Forecasting Module ..................................................................................................................23 

4.2.1 

Forecasting sub-module .................................................................................................23 

4.2.2  Update sub-module .......................................................................................................24 

4.2.3  Migration issues .............................................................................................................24 

4.3  Online Platform Module ...........................................................................................................24 

4.4  Data flow ..................................................................................................................................25 

4.5  Technical requirements ............................................................................................................25 

5  The AURORAE Service Web Dashboard ...........................................................................................27 

5.1  Access to the dashboard ..........................................................................................................27 

5.2  Structure of the dashboard ......................................................................................................27 

5.3  Accessing and downloading data .............................................................................................28 

1 

6  Results ..............................................................................................................................................30 

7  Connection with INFRA Service ........................................................................................................32 

8  Stakeholders consultations and reccomendations ..........................................................................34 

9  Conclusions ......................................................................................................................................35 

References .............................................................................................................................................36 

List of abbreviations and definitions .....................................................................................................40 

List of figures .........................................................................................................................................42 

List of tables ..........................................................................................................................................43 

2 

Abstract 

Air pollution represents the first environmental health problem in EU. The EU Action Plan: "Towards 
a Zero Pollution for Air, Water and Soil” provides a vision for 2025, including 2030 targets such as 
improving air quality to reduce the number of premature deaths. In this technical report we present 
AURORAE (Air qUality foRecast fOR Arctic communitiEs), an online dashboard providing accessible air 
pollution PM10 forecasts tailored for European Arctic communities of countries in the north Europe 
(Norway, Sweden, Finland, Denmark and Iceland) using a novel approach that involves the use of 
Artificial Intelligence algorithms. Our methodology consists in providing a 2-days air quality forecast 
tailored for about 100 monitoring stations distributed in North Europe and European Arctic. The 
forecast at the selected monitoring stations is released daily on the AURORAE website 
(https://aurorae.azurewebsites.net/) along with near-real time concentrations. This online forecast 
service may empower local communities and support decisions regarding pollution reduction and 
prevention. It might further improve the use of local air pollution observations by providing locally 
more accurate forecasts and bringing feedback information to models on their agreement with 
observations. 

3 

Authors 

Crotti I.1, Cuzzucoli A.2, De Marchi D.1, Selmi L.2, Eyraud F.3, Ramalli E.1, Pasini A.2 and Dobricic S.1 

1European Commission – Joint research Centre (JRC), Ispra, Italy 

2National Research Council (CNR), Rome, Italy 

3Engineering Ingegneria Informatica S.p.A. (Italy) 

4 

1 

Introduction 

The Arctic is warming two to three times more than other regions (Masson-Delmotte et al., 2018). A 
coherent system of Earth Observation is needed to observe the current changes, predict future 
climate variations, and improve mitigation measures. In the Arctic the observational systems for the 
environment are often fragmented and inadequate to fulfil the needs of people living and working in 
the area. Arctic PASSION (https://arcticpassion.eu/) is a Horizon 2020 project and an innovative pan-
Arctic Observation and Monitoring action that aims at facing the challenges that people living and 
working in the Arctic are experiencing. In particular, the project endeavours integrating and 
improving existing Arctic observing system elements with new services and improving data 
interoperability. The JRC is participating in this project in the Work Package 4 ‘Implementing 
EuroGEO services to provide new information to support emergency preparedness, food security, 
responses to climate and socio-economic changes’ through the development of one of the eight 
‘Pilot Services’ called ‘Local Atmospheric Pollutant Forecast’ Service’ (PS5).  

For a long time, the atmosphere in the Arctic has been considered free from pollution until explorers 
in the ‘50s observed the phenomenon of Arctic Haze, which consists in the accumulation of 
pollutants in the lowest part of the troposphere during winter and early spring (Mitchell, 1957). This 
phenomenon is driven by low temperatures during the cold Arctic winter, trapping the atmospheric 
pollutants close to the Earth surface (Schmale et al., 2018). Particulate matter (PM) is an atmospheric 
pollutant which could be found in the Arctic and consists of a mixture of solids and liquid droplets 
containing several chemical species such as nitrate, sulphate, organic compounds, and many others. 
Nowadays residential combustion for heating, shipping, oil extraction and metal smelting represent 
the main local PM sources in the extreme North. Due to the small size of the particulate, with 10 µm 
of equivalent diameter or less, called PM10, the particles can enter in the lungs while breathing, and 
exposure to high concentrations might cause respiratory problems, cardiovascular diseases and 
cancer (Megido et al., 2017; Seaton et al., 1995). PM10 concentration limits established by the World 
Health Organization for PM10 are 15 µg/m3 mean annual concentration and 45 µg/m3 daily mean 
concentration (WHO, 2021), while the new European Union Diective on ambient air quality and 
cleaner air for Europe indicates 20 µg/m3 as the limit for mean annual concentration and 45  µg/m3 
as daily mean concentration for a maximum of 18 days per year (Parliament, 2024). Moreover, the 
EU Action Plan "Towards a Zero Pollution for Air, Water and Soil” provides a vision for 2025, including 
2030 targets such as improving air quality to reduce the number of premature deaths. Considering 
the adverse health effects caused by PM10, an accurate forecast of its concentrations is essential for 
pollution mitigation and emergency preparedness in Arctic villages and cities.  

The forecast of PM10 concentrations over Europe, including the Arctic, is performed by the 
Copernicus Atmosphere Monitoring Service (CAMS), based on an ensemble of 11 state-of-the-art 
numerical air quality models (Marécal et al., 2015). CAMS predictions, nonetheless, have 
uncertainties that mainly related to errors in the input data such as initial state estimation, model 
parametrization, emissions, and model algorithms (Bertrand et al., 2022). CAMS forecasted 
concentrations appear to agree less with in-situ measurements of PM10 in Northern Europe in 
comparison to other regions (Schulz et al., 2020). Recently several studies implemented artificial 
intelligence techniques to improve forecasting accuracy for air quality index or pollutant 
concentration (Méndez et al., 2023) also specifically for the Arctic (Fazzini et al., 2023). We address 
the dissagreement between CAMS forecasts and observations with the development of a PM10 
Transformer based Deep Learning forecast model. It combines in-situ measurements from about 100 
monitoring stations, nine 48 hours CAMS PM10 forecasts, and a 48 hours forecast of six 

5 

meteorological variables produced by Integrated Forecast System (IFS) of the European Centre for 
Medium-Range Weather Forecasts (ECMWF) for countries in the European Arctic (Denmark, Norway, 
Sweden, Finland and Iceland). Our novel approach delivers locally more accurate PM10 forecast 
tailored for northern communities. The PM10 forecasts produced by the Neural Network (NN) model 
are published with daily frequency for about 100 monitoring stations on the AURORAE dashboard, an 
easy-to-use interactive map where also near-real time concentrations are displayed. The forecasts 
can be easily downloaded along with PM10 near-real data for each station. 

In this technical report, we describe all the phases of the development of the ‘Local Atmospheric 
Pollutant Forecast’ Service: from scoping, the development of the Deep Learning (DL) approach, the 
dashboard structure and the validation of the forecast results. In addition, the two final chapters 
focus on data curation and the interaction with the stakeholders that helped to shape the final 
version of the online platform. 

6 

 
 
2  Objective and scope 

In the Arctic, global warming is creating advantageous conditions for new economic activities such as 
shipping and oil and gas extraction, extending local sources of atmospheric pollution (Arnold et al., 
2016). In addition, biomass burning from domestic heating and boreal wildfires represent relevant 
sources of PM10 affecting northern communities (Schmale et al., 2018). Considering the impact of 
atmospheric pollutants on public health, releasing reliable short-term forecasts is essential to 
support national governments and organizational stakeholders (Arctic Council, Arctic Science 
Ministerial, etc.) in aligning to the European Air Quality Directive 2008/50/EC, to plan policies and to 
limit the exposure of the population (Bertrand et al., 2022). The production of an accurate and 
accessible PM10 forecast in Northern European countries is therefore critical, especially to empower 
Arctic communities and support decisions on pollution reduction and prevention.  

The objective of the ‘Local Atmospheric Pollutant Forecast’ Service is to improve the available PM10 
CAMS forecasts and customize the prediction for municipalities in North Europe and the European 
Arctic. The Service also provides an easy-to use web interface that allows access to near-real time 
PM10 concentrations data and forecast for about a hundred air pollution monitoring stations. The 
dashboard is designed for a non-scientific audience and provides an interactive map where 
communities’ representatives, policymakers and citizens can easily visualize and download PM10 at 
the selected stations. The service, in addition, aims at providing added value to air pollution 
observations, improving data accessibility and interoperability. Our approach may strengthen the 
effectiveness of the air pollution monitoring stations network and provide additional societal 
benefits to people living in the Arctic. 

Figure 1. Schematic on the relationships among data from emissions and observations of atmospheric 
pollutants, model simulations to estimate the impact on air quality and climate and how this information is 
essential to manage emissions and define regulations

Source: AMAP, 2021 

7 

 
 
 
 
3  Air pollution forecast models  

The two main methodologies used to predict PM10 concentrations are numerical (deterministic) and 
statistical models. Numerical models, also known as Chemical Transport Models (CTMs), predict air 
pollutant concentrations, simulating considering the physical transformations and chemical reactions 
of the pollutants. Those models involve solving a set of differential equations describing chemical and 
physical processes happening in the atmosphere, which require a detailed knowledge of initial state 
conditions and external forcing such as pollutant sources, temporal variation of the emission 
quantity, chemical composition of emissions and, inflow of pollutants from remote regions and 
chemical and physical processes happening in the atmosphere (Hrust et al., 2009). However, due to 
the lack of extensive observations describing initial conditions in-situ measurements, data about 
accurate information on emissions rate and meteorological parameters, forecast by these models' 
predictions are often associated with uncertainties depending on the level of simplification (Qiu et 
al., 2023). Deterministic models often accurately estimate PM10 frequent events, but they seem to 
less accurately detect extreme peaks in pollution (Shahraiyni & Sodoudi, 2016), making them 
unsuitable for mitigation planning (Alam & McNabola, 2015). In the past 20 years, several CTMs have 
been developed to provide gas-phase species and PM forecast and such as EMEP (Simpson et al., 
2012), TM5  (Krol et al., 2005) and REMOTE (Langmann et al., 2008), while others such as CHIMERE 
(Menut et al., 2021) and LOTOS-EUROS (Schaap et al., 2008) are used by CAMS to perform the 
ensemble forecast (Marécal et al., 2015). 

Statistical models, on the other hand, have been successfully developed for the spatio-temporal 
prediction of air pollutants locally and at small spatial scales. Based on assumptions on statistical 
properties of model variables, they may implicitly detect the complex site-specific relationship 
between observed pollutant concentration and some explanatory variables avoiding the exact 
specification of emissions, meteorological conditions and external influences (Alam & McNabola, 
2015; Shahraiyni & Sodoudi, 2016). Explanatory variables may include arbitrary selected 
meteorological parameters which represent one of the main controls of PM10 concentrations. The 
transport and dispersion processes of pollutants are mainly governed by wind speed and direction, 
while solar radiation and air temperature are involved in the formation of secondary fraction (Grivas 
& Chaloulakou, 2006). In the Arctic, in particular, the planetary boundary layer height represents a 
relevant parameter, low temperatures and subsidence in high-pressure systems often cause 
inversions that suppress vertical mixing of the air and promote pollutant accumulation close to the 
ground (Mölders & Kramm, 2018). Moreover, information on sources such as vehicular/shipping 
traffic regime, land use patterns, oil/gas extraction activities and population density influence the 
spatial and temporal variations of PM10 as well (Law et al., 2017). The main drawback of statistical 
models is that they do not explicitly consider physical and chemical processes, consequently they 
could be applied for forecasting only to the area for which they are built for (Shahraiyni & Sodoudi, 
2016). 

3.1  CAMS models 

The regional air quality forecast for Europe is released daily by CAMS, an ensemble of 11 state-of-
the-art numerical atmospheric composition models developed by different institutions (Table 1). 
They are CTMs that simulate atmospheric chemistry and rely on the weather forecast, chemical 
conditions and wildfire emissions released by the Centre for ECMWF 
(https://www.ecmwf.int/en/forecasts), which affect the transport, mixing and removal of pollutants 
(Horálek et al., 2020) (Figure 2). They are characterized by different horizontal and vertical 

8 

 
 
resolutions (Table 1) and use different initial conditions (Collin, 2020). Each model performs daily 
analyses of pollutants near the surface by assimilating 1-day old observations. Observations (satellite 
and surface) are split into two sets used in the data assimilation process and for statistical 
assessments of forecasts and analyses. The forecasts and analyses from all 11 models are combined 
in calculating the median value of the model outputs, which provides the best 11 model ensemble 
estimate named ENSEMBLE. It provides a 4-day forecast at hourly resolution of the main atmospheric 
pollutants concentrations, including PM10, in the lowest layers of the atmosphere (east 
boundary=25.0° W, west=45.0° E, south=30.0° N, north=72.0°) with a horizontal coverage of 0.1°x0.1° 
(approximatively 10 to 20 km) and a vertical coverage of 5000 m (Collin, 2020). 

Table 1. Main characteristics of the air quality regional models used by CAMS (Collin, 2020; Marécal et al., 
2015). 

Model Institution  

Institution  

Horizontal 
Resolution  

Vertical Resolution  

Assimilated 
Measurements 

CHIMERE  

INERIS1  

0.1º x 0.1º 

8 levels, top at 500 
hPa 

O3 and PM10 from 
surface stations 

EMEP  

MET Norway2  

0.25º x 0.125º  

20 levels, top at 
100hPa 

EURAD-IM  

RIU UK3  

15 km, Lambert 
projection 

23 levels, top at 100 
hPa 

top at 100 hPa NO2 
columns from 
OMI/Aura remote 

O3, NO, NO2, SO2, 
CO, PM10, PM2.5 
from surface 
stations, NO2 from 
remote sensing 
column retrievals, 
CO profiles 

LOTOS-EUROS 

KNMI4 

0.25º x 0.125 º 

MATCH 

SMHI5 

0.2º x 0.2º 

MOCHAGE 

Météo France 

0.2º x 0.2 º 

34 levels, top at 3.5 
km  

km O3 from surface 
stations 

52 levels top at 300 
hPa 

O3, NO2, CO, PM10, 
PM2.5 from 

surface stations 

47 levels, top at 5 
hPa 

O3 from surface 
stations 

SILAM  

FMI6  

0.15º x 0.15º  

8 levels, top at 6.7 km   O3, NO2 and SO2 

GEMA-Q  

IEP-NRI7  

0.1º x 0.1º  

DEHM 

AARHUS 
UNIVERSITY 

18 km, polar 

stereographic 
projection 

9 

28 levels, top at 10 
hPa  

29 layers, top at 100 
hPa 

from surface 
stations 

O3, NO2, CO, SO2, 
PM10, PM2.5 from 
surface stations 

O3 and NO2 from 
surface stations, 
PM10 and PM2.5 
from global CAMS 
forecast 

 
 
MINNI  

ENEA8  

0.15° x 0.1° 
regular lat-lon 

14 layers, top at 7500 
m 

MONARCH 

BSC9 

0.15° x 0.1° 
rotated regular 
lat-lon 

24 layers, top at 50 
hPa 

Source: Collin, 2020 and Marécal et al., 2015 

NO2, O3, CO, SO2, 
PM2.5, PM10 from 
surface stations 

NO2, O3, CO, SO2, 
PM2.5, PM10 from 
surface stations 

3.2  An Artificial Intelligence based approach for the Arctic 

In the past years several models based on Artificial Intelligence (AI) algorithms have been developed 
to improve the quality of air forecast pollution at metropolitan and regional spatial scale (e.g. 
Cordova et al., 2021; Qiu et al., 2023; Zhang et al., 2024). Such data driven approaches often manage 
to tackle the limitations of both deterministic and classical statistical models at local dimension 
(Subramaniam et al., 2022). AI has shown to be a suitable tool to make informed decisions on air 
pollution mitigation measures to reduce public exposure risk due to its ability to handle complex and 
non-linear relationships that exist between air quality variables, which allow them to better capture 
the pollution episodes (Masood & Ahmad, 2021 and references therein). In the European Arctic, the 
air quality forecast performed by CAMS appears to provide results that agree less with in-situ 
measurements of PM10 and in Northern Europe with respect to other regions (Schulz et al., 2020) 
probably due to the limited amount of local air quality monitoring stations (about 20) involved in the 
assimilation process (CAMS European products support, personal communication, January 16, 2024).  

To overcome this flaw, Fazzini et al.(2023) developed a forecasting approach for the Arctic based on 
Artificial Neural Networks (ANNs) with the aim of improving PM10 concentrations prediction for 
geographic locations in the European Arctic (Figure 2). ANNs are inspired by biological neurons and 
the connections among artificial neurons, called perceptrons, are mathematically expressed as 
information processed through different functional layers. The data is acquired by the network 
through a learning process in the input layer; and then the signal is transmitted between neurons in 
the hidden layers, which perform the computation. The information can be amplified or decreased 
while transferred among perceptrons, depending on the weights that are continuously updated 
during the learning phase: the result is then released by the output layer (De Gennaro et al., 2013; 
Méndez et al., 2023). The basic structure of the multilayer perceptron can be improved in a 
multitude of ways, depending on the aim of the task, the quality of data and the computational 
effort. Thus, several variations of the multilayer perceptron can be involved in forecasting tasks 
dealing with multivariate time series, such as Recurrent Neural Networks (RNNs) or Echo State 
Networks (ESNs). Generically speaking, Neural Network modelling involves a four-stage process, 
consisting of input data selection, design, training and validation: here the details of the framework 
developed for the Arctic in the study of (Fazzini et al., 2023) is briefly explained for all the stages. 

3.2.1  Design 

The design phase involves the definition of input and output variables, the choice of an optimal ANN 
architecture and its main features(Subramaniam et al., 2022). The general architecture proposed in 
this study consist of an input layer, a memory layer, a dense layer and an output layer (Figure 2). 

10 

 
 
 
Figure 2. Left. Geographical localization of the air quality monitoring stations used for model training and 
testing (red dots) and for ANNs parameters’ tuning. The pink dashed region represents the CAMS models area 
and the green line the AMAP area. Right. ANNs scheme used for the study. 

Source: Fazzini et al., 2023 

—  Input layer: Recent studies proved that different ANNs architectures deal well with air 
quality forecast, however all of those approaches use only time series of pollutants 
registered by monitoring stations or air quality model predictions as input data 
(Biancofiore et al., 2017; Cordova et al., 2021; Jia et al., 2019). The conceptual novelty 
introduced by (Fazzini et al., 2023) consist in combining PM10 in situ observation with the 
24h CAMS forecast for the selected geographical points as input parameters. In particular, 
six air quality monitoring stations included in the Arctic Monitoring and Assessment 
Program (AMAP) area from the European Environmental Agency (EEA) and Finnish 
Meteorological Institute (FMI) networks were selected since they produced almost 
complete time series between the time period January 2020-December 2022 (Figure 2, 
red dots on the map), while two additional stations were selected to provide data for 
tuning the ANN models’ parameters (Figure 2, blue stars on the map). The input vector for 
the ANNs algorithms was built joining the PM10 observation registered by the 6 stations at 
00:00 UTC at the current day (t=0) and the 24h CAMS forecast (t=1) performed by 9 
models (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCHAGE, SILAM, GEMA-
Q, DEHM) initialized at t=0 (Figure 2). MINNI and MONARCH models were discarded since 
they do not perform forecast for the Nordic countries. Before feeding the ANNs with the 
input vector, missing data entries are filled in with linear interpolation and normalized to 
ensure consistent scaling across the features. 

—  Memory layer and dense layer: the memory layer plays an essential role in the network 
as it stores the information in the input layer. Five main architectures such as Echo State 
Networks (ESN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), 
Recurrent Neural Networks (RNN) and Multilayer Perceptron (MLP) were tested in order 
to explore their performances and select the one that better captures the dynamic of the 
Arctic PM10 time series. The hyperparameters, parameters that control the learning 
process, were determined using the data of two stations in the Arctic that were not 
involved in the validation processes (Figure 2, blue stars) to enable the selection of 
optimal values through an iterative procedure. Those two stations were disregarded 
during the validation to avoid overfitting. Further details on architectures and 
hyperparameters tuning can be found in Fazzini et al. (2023). 

11 

 
 
 
—  Output layer: the output layer consists of a single unit layer for the PM10 forecast 
concentration for each monitoring station at 00:00 UTC of the following day (t=1). 

3.2.2  Training and validation 

The input data used for the ANNs models training are PM10 measurements from 6 Arctic stations and 
CAMS forecast for the same geographical points between January 2020 and December 2022 at 00:00 
UTC. The time series were split into training set (730 steps) and test set (365 steps). Missing data 
were filled in with linear interpolation, but the data set was cropped when gaps were larger and it 
was not possible to apply the linear interpolation. Assuming that observations represent the true 
PM10 concentration, the forecast results were validated through the calculation of the Mean Square 
Error (MSE) of the ANNs forecast and the PM10 in situ measurements (Equation 1), then the MSE was 
compared with the MSE computed for all the CAMS models (Figure 3). The MSE is a unitless value. 
The ANNs model performances were also compared for the original dataset and for the filtered data 
set. For the filtered data set, the outliers were selected and removed when they exceeded a defined 
threshold, set on the lognormal distribution (further details in Fazzini et al., 2023). The validation 
results showed that all the ANNs architectures exceeds CAMS performances reducing the MSE 
between 25% to 40%, especially when the outliers were deleted from the datasets (Figure 3). All the 
ANNs architectures perform similarly in terms of MSE reduction, in particular the LSTM model 
exhibits the best performances in the scenario where all data are kept, however when outliers are 
removed both LSTM and ESN show outstanding performances and similar MSE values. 

Equation 1: Mean Square Error 

12 

 
 
 
 
 
Figure 3. MSE for 9 CAMS vs. observations (orange bars, left side of each panel), ANNs models vs. observations 
(coloured bars, right side of each panel), persistence (red bar, in the middle of each panel) and average (AVE) 
MSE for all ANNs models (green bar, right side of each panel). Each panel refers to a monitoring station: (a) 
Muonio, (b) Pyykösjärvi, (c) Oulu, (d) Kópavogur Dalsmári, (e) Grundartangi Gröf, (f) Tromso R. 

Source: Fazzini et al., 2023 

3.3  Model for AURORAE service 

Following the process behind the study by Fazzini et al.(2023), the forecast model employed in the 
AURORAE service is developed through a comparative analysis of state-of-the-art models for time 
series forecasting. The aim is to produce a prediction for PM10 concentration using historical data 
from monitoring stations and by taking advantage of air pollution forecasts released by CAMS 
numerical models. Nonetheless, there are several aspects in which our framework differs from the 
one in Fazzini et al. Firstly, the number of monitoring stations that provide PM10 timeseries is 
significantly higher (approximately 160 stations vs 6), scattered throughout Northern Europe. 
Secondly, data resolution is set to hourly concentration collected daily, which accounts for longer 
time series. Moreover, meteorological variables forecast (boundary layer height, temperature, wind 
speed components, total precipitation, mean sea level pressure) are added as input feature to 
improve forecasting accuracy, as previously argued. While these added values can increase the 
complexity of the task, they also account for a more high-performance model, better suited for the 
aim of the AURORAE service. The increased amount of data, together with an extension in 
dimensionality, represent strong limitations to the use of architectures such as RNNs as developed in 
Fazzini et al., since they might suffer from loss of information due to long-term dependencies. In 
recent years an increased interest in applications of Natural Language Processing (NLP) models in 
time series settings has risen; in fact, these architectures show remarkable performances in time 
series modelling, particularly due to their ability in handling long-range dependencies and sequential 

13 

 
 
 
data (Wen et al., 2023). Specifically, by taking inspiration from the Transformer model developed by 
Vaswani et al. (2017), DL architectures based on such backbone were conceived to approach time 
series modelling. When dealing with time series forecasting, several of these variations considered 
networks such as LSTM as baselines in their development and manage to outperform them in 
multiple domains (Wu et al., 2021; H. Zhou et al., 2021). 

3.3.1  Architectures 

Due to the promising results brought about such architectures and the intrinsic complexity of the 
setting, we analysed various Transformer based architectures in the fashion of the study of Fazzini et 
al. to find the most suitable model for our task.  

Transformer based architectures take advantage of three main ingredients :  

—  Encodings of the spatio-temporal features (geographical coordinates or timestamp) and 

the sequence position;  

—  Attention mechanisms, which can arise in different variations;  

—  Feed-forward networks.  

Such ingredients come together in an encoder-decoder architecture, where input data is embedded 
and encoded in a first phase, and then decoded to produce the output. In more detail, input data is 
first embedded in a higher dimensional vector space and equipped with the encodings to create an 
input vector that includes spatio-temporal and sequence related information. The new input vector is 
then passed on to the attention mechanism, which analyses the input sequence to track down 
dependencies and underlying patterns; the resulting vector is then passed on to a feed-forward 
network, ultimately giving the encoded input. The decoder, which is the block in the architecture in 
charge of producing the output (hence the forecast) takes in part of the input as the initial part of the 
message to decode, performs attention on such input, and subsequently the outcome is passed on to 
another attention block which performs on the output of the encoder together with the vector 
within the decoder. The resulting vector is then passed on to another feed-forward network to 
produce the decoded output. The blueprint of such architecture (which follows the blocks described 
by Vaswani et al.) is displayed in Figure 4. Such base model is the one defined as Vanilla Transformer. 
Informer, Autoformer, FEDformer and Crossformer show variations in the attention mechanisms, but 
they all keep the same encoder-decoder structure.  

14 

 
 
Figure 4. Schematic of the Original Vanilla Transformer Architecture 

In particular:  

Source: Vaswani et al., 2017 

—  Informer applies Probability Sparse Self-Attention, which involves a sparsity measure to 

identify relevant queries in the attention mechanism (Zhou et al., 2021); 

—  Autoformer acts on the decomposed timeseries with an Autocorrelation mechanism 

implied as Attention mechanism to find period-based dependencies and aggregates data 
from sub sequences in period-based decompositions (Wu et al., 2021); 

—  FEDformer inherits the idea of an autoregressive decomposed series generation and 
aggregation from Autoformer, but implies the representation of the timeseries in the 
frequency domain rather than in the time domain; the Attention mechanisms are thus 
superseded by Fequency Enhanced Attention blocks where queries, keys and values are 
extracted via Fourier Transforms (Zhou et al., 2022); 

—  Crossformer involves the segmentation of input sequence for the initial embedding 

(Dimension Segment-Wise embedding), a two-stage Cross Attention to capture cross-time 
and cross-dimension interactions and a Hierarchical Encoder-Decoder structure which 
accumulates information at different scales (Zhang & Yan, 2023). 

15 

 
 
 
 
3.3.2  Data preparation 

Due to the complexity of the task, tuning and training considering all available stations is 
computationally prohibitive, thus tuning, training and subsequent validation are executed on subsets 
of the given data. Firstly, from data collected from all stations we consider a selection following the 
below criteria:  

—  Data availability for every feature (PM10 concentration, CAMS PM10 forecast and CAMS 

meteorological variables’ forecast need to have compatible availability); 

—  Number and length of gaps (missing values); 

—  Heterogeneity in geographic selection (for training we should consider stations from 

distinct areas of Northern Europe). 

Considering the available dates for the collected datasets, the training set considers data from 10th 
June 2020 to 12th May 2024. From the 164 stations with data availability, we choose 113 the stations 
which have less than 10% missing values over the full time series, as training/fine-tuning set 
following the rule of thumb stating that, statistically speaking, we are likely to get a biased result 
when more than 10% of data is missing (Dong & Peng, 2013; Liu et al., 2020). The remaining stations 
are used as test set once the model is trained. Amongst the 113 remaining stations, we consider the 
subset consisting of stations with the longest gap of less than 4 days as the main training set, while 
the remaining stations will be used for fine-tuning. For model selection and hyperparameter tuning, 
we consider 5 stations which show the lowest number of missing values (around 0.1-0.2 %). All 
features are normalized to homogenize the diverse magnitudes and avoid biases in training. For each 
selected station, we subdivide the associated time series entries in three subsets: 70% of the dataset 
is considered for training, 10% for validation and 20% for test. We then concatenate the datasets to 
create unified training, validation and test datasets consisting of data from all of the chosen stations. 
For training and validation, data samples are shuffled to avoid overfitting. The usage of data is also 
taken into consideration, as we have several ways of displaying input data for auxiliary variables: 
both CAMS pollutant concentration models and meteorological variables have availability of 48 hours 
from the day they are collected, thus in order to predict PM10 concentration at day d we can either 
use data collected at day d-1 (shift=0) or collected at day d (shift=48). For the best performing model, 
we also include the cases where CAMS meteorological data are not involved as auxiliary variables, in 
order to evaluate the improvement of the forecast with the use of such indicators. 

3.3.3  Model Analysis 

Several trials have been carried out to define which model amongst the pinpointed state-of-the-art 
transformer-based architectures would perform best with given data (Table 2). To evaluate the best 
performance, every model has been set with standard hyperparameters, with exceptions made for 
the output length, which was set to 48 (hours), target length for the AURORAE forecast service.  

16 

 
 
 
 
Table 2. Best combination of paramters for each transformer model tested in this study. 

Hyperparameter 

Transformer 

Informer 

Autoformer 

FEDformer 

Crossformer 

Input length  

Token length  

Encoder layers  

Decoder layers  

Heads   

Embedding 
dimension  

Feedforward 
network 
dimension  

Dropout rate  

Learning rate  

Epochs  

Early stopping 
patience  

Batch size  

Activation   

Loss  

96  

48  

2  

1  

8  

96  

48  

4  

2  

8  

96  

48  

2  

1  

8  

96  

48  

2  

1  

8  

96  

6  

3  

1  

4  

512  

512  

512  

512  

256  

2048  

2048  

2048  

2048  

512  

0.05  

10−4 

10  

3  

32  

ReLu  

MSE  

0.05  

10−4 

10  

3  

32  

GeLu  

MSE  

Adam  

0.05  

10−4 

10  

3  

32  

GeLu  

MSE  

Adam  

0.05  

10−4 

10  

3  

32  

GeLu  

MSE  

Adam  

10−4 

0.2  

20  

3  

32  

GeLu  

MSE  

Adam  

Optimiser  

Adam  

Model specific 
parameters  

 - 

Factor=3  

Distil=True  

Source: JRC analysis 

Moving 
average=24  

Attention 
factor=3  

Attention 
factor=3  

Modes=64  

Factor=10  

Window 
size=2  

The evaluation dataset for model analysis consists in the time series from 5 stations characterized by 
the lowest percentage of missing values (less than 10%): FI_5_15986, FI_5_60955, SE_5_31026, 
SE_5_62474, FI_5_15584. The experiment for each model was carried out 5 times, and the average 
MSE and Mean Absolute Error (MAE) (Equation 2) calculated between each model forecast and the 
PM10 concentration measured by in situ stations are reported in Table 3. The MAE is a unitless value.  

Equation 2. Mean Absolute Error. 

17 

 
 
 
 
Table 3. MSE ad MAE calculated between model forecast and PM10 in situ observations. 

Model 

Shift 

Vanilla 
Transformer 

Informer 

Autoformer 

FEDformer 

Crossformer 

Vanilla 
Transformer 

Informer 

Autoformer 

FEDformer 

0  

0  

0  

0  

0  

48  

48  

48  

48  

Source: JRC analysis 

3.3.4  Model Tuning 

MSE 

1.213 

1.224 

1.735 

1.644 

1.141 

1.186 

1.226 

1.690 

1.605 

MAE 

0.431  

0.433  

0.7665  

0.754 

0.434  

0.428 

0.443 

0.750 

0.741 

Our experiments show that among all the teste architectures, the Crossformer is the most suitable 
model for our task (Table 3) as it minimizes both MSE and MAE. We carried out hyperparameter 
tuning for this architecture and enhanced the original Crossformer model including the geographical 
position of the station as further embedding information. Such implementation would allow for a 
better suited forecast considering the significantly large number of stations, as various pollutants 
behaviours coming from different areas might introduce noise to the prediction. This geometrical 
embedding, specifically developed for this study, consists of adding to the original Dimension 
Segment-Wise (DSW) embedding, a linear embedding of the three geographical coordinates 
(latitude, longitude, altitude), as embedded in a 3D (standardised) linear space (GDSW). We chose a 
separate subset of 5 stations (NO_5_28732, NO_5_63001, NO_5_60863, NO_5_43746, NO_5_65126) 
belonging to the selected 113 stations identified by the criteria to carry out hyperparameter tuning 
via Grid Search with the following parameter choices showed in Table 4. Our calculations show that 
defining the Geometric Dimension Segment-Wise (GDSW) embedding outperforms the original 
Dimension Segment-Wise (DSW) embedding on average over all of the trials. As a result, the best 
performing models, in terms of MSE and MAE, are reported in Table 5. 

We select as the preferred model the Crossformer architecture with the parameters described in the 
first line of Table 6 as such choices of factor, encoder layers and number of heads have better 
average score over all trials. Lastly, we verify the benefit from having included the CAMS 
meteorological parameters forecast as auxiliary variables within the development phase: we check 
again with the best performing model parameters but excluding the CAMS meteorological variables 
forecast, this including as input concentrations of PM10 from observations and CAMS predictions. 
Average scores for trials are summarised in Table 7. The results show that including the 
meteorological variables bring an average improvement over all the trials. 

18 

 
 
Table 4. Hyperparameter tuningchoces based on 5 selected stations. 

Parameter 

Choices 

Input Length  

96, 72  

Token Length  

6, 12, 24  

Data Shift  

0, 48  

Embedding  

DSW, GDSW  

Factor  

5, 6, 7, 8, 9, 10  

Encoder Layers  

Heads  

Embedding 
Dimension  

FeedForward 
Network 
Dimension  

2, 3  

3, 4  

128, 256  

256, 512  

Dropout Rate  

0, 0.1, 0.2  

Learning Rate  

10−2, 10−4 

Source: JRC analysis 

Table 5. MSE and MAE calculated for the Crossformer model for DSW and GDSW embedding configuration. 

Embedding 

DSW  

GDSW  

MSE 

1.484 

1.391  

Source: JRC analysis 

MAE 

0.555  

0.535  

19 

 
 
 
Table 6. Parameters such choices of factor, encoder layers and number of heads selected for the Crossformer 
architecture that show better average score over all trials. 

Parameters 

Choices test 1  Choices test 2 

Input Length  

Token Length  

Data shift  

96 

24 

48 

96 

24 

48 

GDSW 

GDSW 

Embed 

Factor 

Encod Layers  

Heads  

6 

3 

4 

Embed Dimension  256 

FFN Dimension 

512 

Drop 

Learn Rate 

MSE 

MAE 

0.2 

10−4 

1.31 

0.517 

Source: JRC analysis 

5 

2 

3 

256 

512 

0.2 

10−4 

1.347 

0.515 

Table 7. MSE and MAE calculated for trials with Crossformer model including and meteorological variables. 

MSE  MAE 

without meteo variables   1.179  0.425  

with meteo variables 

1.142  0.394 

Source: JRC analysis 

3.3.5  Model training 

The training for the chosen architecture for Crossformer model is run on a dataset consisting of 25 of 
the 113 selected stations time series. A fine-tuning of the remaining stations with a percentage of 
missing values below 10% are performed to reduce loss of information. Large gaps (>72h) are cut out 
to avoid adding bias due to linear interpolation. The training and the fine-tuning are run on one 
NVIDIA Quadro RTX 4000. 

20 

 
 
 
4 

Iimplementation of the AURORAE service  

The AURORAE service is designed to provide information to non-scientific users such as policymak-
ers, communities’ representatives, and citizens regarding near-real time PM10 concentrations regis-
tered by monitoring stations in Northern Europe (Denmark, Finland, Iceland, Sweden and Norway), 
and release the PM10 forecast based on Transformer architecture approach.  

The AURORAE service is composed by three main modules: 

—  Data gathering module;  

—  Forecasting Module; 

—  Online Platform Module (dashboard). 

The Data gathering module collects daily PM10 concentrations from EEA and FMI monitoring stations 
at 1-hour resolution, CAMS weather meteorological fields forecast, PM10 CAMS forecast for North 
Europe and treat the data to use them as input for the Forecasting module. The Forecasting module 
consist in a Crossformer model that provides a 48h PM10 forecast for each monitoring station. The 
near real time PM10 concentrations and the forecast can be visualized on the Online Platform Module 
(also called dashboard), an interactive map in which all monitoring stations are represented as dots 
and the near-real time data and the forecast are accessible as overlapping layers. The dashboard is 
the information access point for the users to visualized and downloaded the PM10 observation time 
series and forecast for each station. In addition, a daily air pollution report collecting the current 
PM10 concentration, the forecast for all monitoring stations and the pollution tendency is released on 
daily basis and can be downloadedfrom the AURORAE webpage.  

4.1  Data Gathering Module 

The Data Gathering Module consists in a series of Python codes that download, gather and process 
PM10 concentrations data provided by the EEA and FMI for monitoring stations in Norway, Finland, 
Sweden and Iceland as well as PM10 and meteorological variables forecast released by CAMS. Those 
data are treated to feed the model and provide a 48h PM10 forecast for the selected monitoring 
stations on daily basis. Every day at 08:00 UTC an automatic procedure is initiated and the codes that 
make up the Module are run sequentially. Each code is designed to be run multiple times in one day 
in case some technical disruptions may terminate the procedure. 

4.1.1  EEA and FMI sub-module 

The EEA sub-module consist in two Python scripts that download PM10 data concentrations regis-
tered by the EEA air quality monitoring stations (https://eeadmz1-downloads-
webapp.azurewebsites.net/) and that treat the downloaded data sets to create PM10 time series at 
hourly frequency since June 2020 and updated at daily level. An API request have been created fol-
lowing the EEA guidelines (https://eeadmz1-downloads-
webapp.azurewebsites.net/content/documentation/How_To_Downloads.pdf) to download PM10 up-
dated data at daily frequency for all the monitoring stations in Finland, Iceland, Norway and Sweden 
from the E1 (verified data until 31/12/2022) and E2a (data updated with daily frequency from 
01/01/2023) datasets. Each station time series is stored in a parquet file that is converted to a csv 
file. We merged E1a and E2a time series registered by a monitoring stations only if their data cover 
the time window form 01/01/2020 until today (2024) with a limited number of missing values (less 

21 

 
 
than 10%). The time and concentration data are extracted from each csv file and the complete time 
series for each station is updated on daily basis until 00:00 UTC of the previous day (d=-1). Since the 
raw time series are set on local time, the final data are converted in UTC time.  

The time series of PM10 concentrations measured by FMI monitoring stations are downloaded from 
the FMI data store website (https://en.ilmatieteenlaitos.fi/download-observations) for all the Finnish 
stations at daily frequency until 00:00 UTC of the previous day (d=-1). FMI supplies data through the 
Web Feature Service (WFS) (https://www.ogc.org/standard/wfs/), a generic standard interface for 
providing access to geospatial objects or objects with some kind of location or covered geospatial 
area. The access to the FMI data requires the installation of the Python library “fmiopendata”, a spe-
cific interface developed for FMI (https://github.com/pnuu/fmiopendata), and the creation of an ex-
plicit query in order to request the needed dataset. The query for PM10 in situ measurements have 
been defined following the instructions available in the Open Data WFS service and the stored que-
ries that provide surface observations (https://en.ilmatieteenlaitos.fi/open-data-manual-accessing-
data). The downloaded txt files are then modified by a second code in order to substitute all the val-
ues equal to 10000 with “NaN” and to generate a txt file for each station storing the whole time se-
ries from January 2020 in UTC time. A separate file is created to store a list of all the FMI air quality 
stations, their reference codes and the geographical coordinates 
(https://en.ilmatieteenlaitos.fi/observation-
stations?filterKey=groups&filterQuery=air%20quality%20%28FMI%29). The datasets produced for all 
the FMI and EEA stations serve as input files for the forecast model. 

4.1.2  CAMS PM10 forecast sub-module 

The PM10 concentration forecast released at daily frequency by 9 models of CAMS (CHIMERE, EMEP, 
EURAD-IM, LOTOS-EUROS, MATCH, MOCHAGE, SILAM, GEMA-Q, DEHM) are downloaded with daily 
frequency at 08:00 UTC for the current day (t=0) and for the following 48h (t=2) 
(https://ads.atmosphere.copernicus.eu/datasets/cams-europe-air-quality-forecasts?tab=download). 
We select the model base time at 00:00 UTC and the models’ level at 0 m above the surface. The 
CAMS models forecast downloaded for the AURORAE service holds the same features summarized in 
Section 3.1 and Table 2. The CAMS PM10 forecast sub-module is composed by two Python scripts that 
download the 48 hours forecast covering the European domain at daily frequency and extract the 
forecast for the geographical position of each station respectively. In particular, the forecast data are 
stored in NetCDF files and downloaded from the Copernicus Data Store(ACS) catalogue through the 
CDS Application Programming Interface (API), which requires the registration to the Copernicus web-
site and the installation of the CDS API client, a Python based library 
(https://ads.atmosphere.copernicus.eu/how-to-api). The API request code can be created at the 
CAMS European air quality forecasts webpage 
(https://ads.atmosphere.copernicus.eu/datasets/cams-europe-air-quality-forecasts?tab=download) 
after selecting the required fields. The 48 hours forecast is downloaded for each model at daily fre-
quency at 08:00 UTC as a NetCDF file and then the CAMS forecast data are extracted for all EEA and 
FMI monitoring stations selecting the PM10 values associated to the closest grid point on the models 
grid. For each monitoring station a txt file is created to store the CAMS models forecast since June 
2020 and is updated every day with the new 48 hours forecast (d=2) at hourly resolution. All the pro-
duced txt files are used as input for the forecast model. 

22 

 
 
4.1.3  CAMS weather forecast sub-module 

The weather forecast variables represent the third and last input data set for the Crossformer model. 
The forecast of weather variables is performed by Integrated Forecast System (IFS) of the ECWF 
(https://www.ecmwf.int/en/forecasts/documentation-and-support/changes-ecmwf-model) and is 
distributed in association with the CAMS global atmospheric composition forecast 
(https://ads.atmosphere.copernicus.eu/datasets/cams-global-atmospheric-composition-
forecasts?tab=download). The IFS employs a four-dimensional variational data assimilation method 
(4D-VAR) which combines the latest weather observations with a recent forecast to obtain the best 
possible estimate of the current state of the Earth system. The forecast has a global coverage with 
model base time at 00:00 UTC and 12:00 UTC, with a horizontal resolution of approximately 0.35ºx 
0.35º (40 km), an hourly resolution for single level parameters and 3-hours resolution for the multi-
ple levels parameters. The 48 hours IFS forecast with base time at 00:00 UTC is downloaded at at 
08:00 UTC on daily basis for seven meteorological parameters such as u and v wind components 
(m/s), Temperature at 2 m above the surface (K), boundary layer height (m), total precipitation (m), 
mean sea level pressure (Pa) at hourly frequency. All the meteorological variables data are down-
loaded as NetCDF files using the Copernicus CDS API in the same way it is described for the CAMS 
PM10 forecast sub-module. The time series of the IFS meteorological parameters are extracted for all 
EEA and FMI monitoring stations, selecting each variable values on the grid points closer to each sta-
tions geographical position. For every monitoring station and meteorological variable, a txt file is cre-
ated to store the IFS forecast since June 2020 and is updated every day with the new 48 hours fore-
cast (d=2) at hourly resolution. All the produced txt files are used as input for the forecast model. 

4.2  Forecasting Module 

The forecasting module consists of two mostly independent submodules: the main module performs 
the 48h forecast for PM10 concentration, while an independent submodule manages the update of 
weights for the model once enough new data has been collected to feed the architecture.  

4.2.1  Forecasting sub-module 

The main module is run every day to allow for new forecasts of particulate for each of the available 
stations. In fact, it operates sequentially with the following tasks:  

—  Find all available stations and checks that each of the variables has been downloaded 

correctly, thus creates a list of updated stations; 

—  Collects data from each of the variable's dataset to create input vector for the model for 

each station;  

—  Performs prediction for each feasible station;  

—  Releases a quality assessment of the prediction based on data availability for each station. 

In particular, the forecasting module will rely on a pre-trained model which was trained by using his-
torical data from the available stations. As described in the previous section, the chosen model is a 
variation of the original Crossformer architecture from Zhang & Yan (2023). 

23 

 
 
4.2.2  Update sub-module 

The pre-trained model only uses historical data, without taking into account newly downloaded data. 
As the model might benefit from being fed newer data, an update module has been considered to 
fine tune the pre-trained model with data which has not yet been seen by the model. To do so, we 
set a timeframe of 2160 hours (approx. 3 months) where new data is collected before feeding it to 
the network. The update submodule is then structured as follows:  

—  A daily check is run to verify whether the timeframe period has been reached; 

—  If enough time has passed since the latest update, the update module is run; otherwise, 

no additional task is run; 

—  When the update module is run, the pre-trained model is loaded; 

—  Once the pre-trained model is loaded, new batches of data collected during the 

timeframe are fed to the model; 

—  The updated model is then tested and if it outperforms the previously pre-trained model, 
the updated model is used as the standard model for forecast from the day after the 
tuning.  

The performance is measured through MSE and MAE averaged over all the available stations and 
over the timeframe. All model weights updated during fine tuning phase, also called checkpoints, will 
be stored.  

4.2.3  Migration issues 

Due to the server migration for data coming from EEA stations, some information about stations and 
data availability have changed since the preliminary training for the main model was completed. A 
quality assessment of the migrated data, as well as a comparison with the available stations, is being 
carried out to deliver a pre-trained model which is best adapted to the dynamic input data. 

4.3  Online Platform Module 

The AURORAE web application is built within a JupyterLab environment, a widely used platform for 
data science. It is developed using Jupyter Notebooks, which integrate code, text, and multimedia in 
a single document. To enhance the user experience, the open source Voilà tool 
(https://github.com/voila-dashboards/voila) is used to render these notebooks as interactive web 
applications, displaying outputs without exposing the underlying code. This approach enables the 
creation of sophisticated, data-driven applications where users can interact with data through intui-
tive graphical interfaces. The AURORAE dashboard is implemented in Python programming language, 
leveraging various libraries that facilitate page construction, information presentation, and user in-
teraction through graphical widgets. One of these libraries is VOIS, an open-source project developed 
by the JRC and hosted in the code.europa.eu repository (https://code.europa.eu/jrc-bdap/vois). VOIS 
simplifies the process of creating appealing visualizations within the Voilà environment, making it 
easier for developers to build engaging, interactive applications.   

The overall platform is hosted on Microsoft Azure web service instance. Currently, the employment 
of a commercial cloud solution to host the dashboard answers to the business needs to have an ac-

24 

 
 
cessible website without any restrictions. Instead, JRC, at this moment, does not fully support the de-
ployment of dashboard without an EU login access. This approach responded to the needs of creat-
ing a flexible website which runs with minimum costs. The Azure cloud's cost management service 
helps the users keep the costs under control with a detailed cost breakdown and analysis tools. The 
VMs used to host the dashboard have a cost of around 3.72€ per day. The app service plan selected 
for this amount offers 2 cores and 16 Giga Byte (GB) of RAM (Random Access Memory), but the 
"scale-up scale-out" Azure capability can be configured to allocate more computational resources to 
the VM serving the dashboard or allocate more VM, according to the incoming request to access the 
dashboard, in a flexibly way. 

4.4  Data flow 

The main objective of the AURORAE service is to gather and process data in diverse format (txt, 
parquet, NetCDF, etc) from different sources (EEA, FMI, CAMS) to feed the Crossformer model to 
forecast the PM10 concentrations which are updated at daily frequency and available to final user. 
The data are downloaded from external APIs at daily frequency and the forecast is performed on the 
JRC Big Data Analytics Platform (BDAP), a versatile multi-petabyte scale platform based on storage 
co-located with a high-throughput computing capacity (Soille et al., 2018). After the source data has 
been ingested and the forecast model has generated and computed the new forecast data, all 
information is uploaded to a GitHub repository. The dashboard uses the GitHub repository as data 
source to display on-the-fly the most recent data. The GitHub repository link will be available soon on 
the AURORAE website. 

Figure 5.Schematic of the data flow for the AURORAE service. 

Source: JRC analysis 

4.5  Technical requirements  

The AURORAE service and its modules are developed in Python programming language (version 
3.10.14) (https://www.python.org/downloads/release/python-3100/) and are hosted in the JEODPP 
platform. The Data Gathering Module has been built and tested via Spyder IDE (https://www.spyder-
ide.org/) and PyCharm Iintegrated Development Environment (IDE) 
(https://www.jetbrains.com/pycharm/), while the the Forecasting and the Online Platform Modules 
developing has been carried out via Jupyter (https://jupyter.org/) notebooks . The Crossformer 

25 

 
 
 
forecast model implemented in the dashboard employs the following Python packages: Numpy 
1.26.4(https://numpy.org/), Pandas 2.2.1 (https://pandas.pydata.org/), PyTorch 2.3.0 
(https://pytorch.org/), TensorFlowTM (https://www.tensorflow.org/).  

The online dashboard is developed in Python 3.10. The main libraries used are: the VOIS library 
(https://code.europa.eu/jrc-bdap/vois) for the creation of the GUI components (such as buttons, 
labels, etc..), Plotly (https://github.com/plotly/plotly.py) to display the data, ipyleaflet 
(https://github.com/jupyter-widgets/ipyleaflet) to display geo-data on an interactive map and voila 
to deploy the resulting Jupyter notebook into a web application. The web application is hosted within 
a web service provided by Microsoft Azure. The data are hosted on GitHub to make them accessible 
to everyone. 

26 

 
 
5  The AURORAE Service Web Dashboard 

The AURORAE service web dashboard is developed in the framework of the Horizon 2020 Arctic 
PASSION project, which aims at co-creating and implementing a coherent, integrated Arctic 
observing system: the ‘Pan-Arctic Observing System of Systems - pan-AOSS’, overcoming the flaws in 
the present observing systems by refining its operability, improving, and extending pan-Arctic 
scientific and community-based monitoring and the integration with Indigenous and Local 
knowledge, by streamlining the access and interoperability of Arctic Data systems and services 
(https://arcticpassion.eu/intro). Since the current observational systems for the Arctic environment 
are fragmented, incoherent, and not tuned to the users’ needs, one of the project goals consists in 
creating free services for non-specialists living and/or working in the Arctic. Those services envision 
to add value to the environmental data, address urgent users’ needs and empower the civil society, 
policymakers and private sector to observe, understand and predict future changes in the Arctic. 
Eight services are developed within Arctic PASSION, spanning various topics from permafrost thawing 
to fire risk management and lake ice forecast. All the services can be accessed from the Arctic 
Window page at the project website (https://arcticpassion.eu/arcticwindow/services).  

The AURORAE dashboard providing the ‘Local air pollution forecast service’ consists of a web 
interface to monitor the air quality of the North European countries on daily basis and to provide 48 
hours forecast of PM10 concentrations. At the moment there is no specific air quality forecast service 
designed for Northern communities. The forecast produced by CAMS in the European Arctic appears 
to provide results that agree less with in-situ measurements of PM10 and the CAMS Atmosphere Data 
Store supplies data in GRIB or NetCDF formats, accessible only by experts. The AURORAE service, on 
the other hand, releases a PM10 forecast tailored for the European Arctic and designed to be user-
friendly; the webpage can be navigated by a non-scientific public such as citizens and policymakers. 
In addition, the air pollution forecast information is conveyed with a plain design and a bulletin 
gathering the near-real time PM10 concentrations and the forecast for all the stations is released on a 
daily basis. The service aims at empowering Nordic and Arctic communities on relevant topics such 
air quality and its effects on public health, and to promptly take action in case of episodes of high 
level of air pollution. 

5.1  Access to the dashboard 

The AURORAE service dashboard is accessible without registration and without a EU Login Account at 
the following link: https://aurorae.azurewebsites.net. The website is expected to be fully operational 
in 2025. We aim at distributing a forecast service that is easily accessible by both expert and non-
expert users, with an open access website and documentation following the European Commission 
Guidelines for Horizon 2020 projects. 

5.2  Structure of the dashboard 

The landing page of the AURORAE dashboard consists in a web page divided in 3 main sections: a 
navigation panel, an interactive map and a time series window (Figure 6). The navigation panel is 
located on the left side of the webpage, the user can choose what data to visualize on the interactive 
maps selecting one of the 3 available buttons: “Measured”, “Forecast 1-day” and “Forecast 2-day”. 
Once the user clicks on the chosen button, the selected air pollution data are displayed on the main 
panel of the webpage consisting in the interactive map. The date to which the data refers to is 
displayed on the top of the page. The map is a geographic layer where all the air pollution monitoring 

27 

 
 
stations are drawn as coloured circles at their specific geographical location. The map allows to zoom 
in and out the view, and to facilitate the interpretation of the air quality status; each circle is 
coloured in green, orange or red depending on the PM10 concentration level at site according to the 
EEA health-based classification (https://www.eea.europa.eu/data-and-maps/explore-interactive-
maps/up-to-date-air-quality-data), while grey colour indicates the absence of data. The specific PM10 
concentration (24-hours average) at each station can be obtained by clicking on the chosen circle, 
the station information such as coordinates, station code and network are displayed on the left side 
of the map. Moreover, if the users want to search for a specific station, the lens icon on the 
navigation panel allows to look for a specific monitoring station by name or code. After selecting a 
station, either using the lens or clicking on the station circle on the map, the hourly resolution PM10 
concentration trend over time at that specific location is automatically displayed in the double 
window at the bottom of the page. The left panel shows the full time series recorded by the station, 
while the right panel displays the past week PM10 concentrations as a blue curve and the 48h and the 
forecast for the following 2 days at hurly resolution is shown as a red curve. 

Figure 6. The landing page of the AURORAE Service. 

Source : AURORAE website (https://aurorae.azurewebsites.net/) 

5.3  Accessing and downloading data 

The PM10 forecasted concentrations at monitoring stations locations provided by the AURORAE 
service is available also in format of downloadable txt files. For each station the PM10 concentrations 
48h forecast at hourly resolution can be obtained clicking on the icon when selecting a monitoring 
station from the map or using the searching lens. Each file is named after the EEA or FMI unique 
station code. In addition, a “Daily air quality bulletin” is released every day from the service and can 
be obtained clicking on the button “Download daily bulletin” at the top left of the page. The bulletin 
is a txt file composed by 11 columns where the first 7 columns hold the information regarding the 
monitoring stations, such as station code, country, network name and coordinates while the last the 
last 4 columns report the mean PM10 concentration registered by the station the previous day d-1 

28 

(until 00:00 UTC), the mean of the forecasted PM10 concentration for the following 24 hours (d) and 
for the following 48 hours (d+1). The last column is planned to evaluate the quality of the forecast. 
The validation method is not finalized yet and this report will be updated on that matter in next 
version. A guide that helps the users to navigate the website and download will be available clicking 
on the guide icon on the upper right side of the webpage.  

Figure 7: Example of the AURORAE daily air pollution bulletin file 

Source: JRC analysis 

29 

6  Results 

In section 2.1 “Model for AURORAE service”, we described the architecture and the related studies to 
define a suitable as well as effective model to deal with the available data. Such studies led to the 
best performing Crossformer model with hyperparameters defined by the values in Table 8. 

Table 8.Summary of the hypapramters of the Crossformer model employed in the AURORAE service. 

Parameter 

Choices 

Input Length 

Token Length 

Data Shift 

96 

24 

48 

Embedding 

GDSW 

Factor 

Encoder Layers 

Heads 

Embedding 
Dimension 

FeedForward 
Network Dimension 

Dropout Rate 

Learning Rate 

Source: JRC analysis 

6 

3 

4 

256 

512 

0.2 

10−4 

The preferred architecture trained on 5 stations from the selection (FI_5_15986, FI_5_60955, 
SE_5_31026, SE_5_62474, FI_5_15584) chosen by criteria presented in section 2.1.2 “Data 
Preparation” (data availability, missing values count, geographical heterogeneity) was evaluated on 
the test set described by the 20% of the available data (approximately a yearlong collection from 
each station). The metrics for this evaluation are described in Table 9. 

Table 9. MSE and MAE calculated for the Crossformer model applied in the AURORAE service. 

MSE 

1.099 

Improved 
Crossformer 

Source: JRC analysis 

MAE 

0.412 

We can compare the performance of our improved Crossformer model with the one from 
deterministic models provided by CAMS. For each of the selected CAMS models, the metrics 
evaluated on the same test set give the estimates summarized in Table 10. 

30 

Table 10. MSE and MAE calculated comparing our improved Crossformer model forecast with the one from 
deterministic models provided by CAMS. 

CAMS model 

CHIMERE 

DEHM 

EMEP 

EURADIM 

GEMA-Q 

LOTOS 

MATCH 

MOCAGE 

SILAM 

Source: JRC analysis 

MSE 

2.378 

1.793 

2.080 

2.520 

1.618 

2.166 

2.817 

2.458 

1.917 

MAE 

0.866 

0.740 

0.792 

0.925 

0.672 

0.826 

1.132 

0.934 

0.768 

The aforementioned evaluations result in a prediction improvement of approximately 32% on the 
MSE and of approximately 39% on the MAE comparing the improved Crossformer with the best 
performing CAMS model (GEMA-Q) (Figure 8). Ultimately, we developed a specified predictive model 
with Transformer based architecture which manages to efficiently assimilate heterogeneous data as 
well as produce a prediction which outperforms the state-of-the-art deterministic models. 

Figure 8. Comaprison of MSE calculated between the observations and the CAMS and the Crossformer models. 
The Crossformer outperforms all the CAMS models reducing the error by 32%. 

Source: JRC analysis 

31 

7  Connection with INFRA Service 

In the framework of the ArcticPASSION project, several Arctic services have been developed including 
the Integrated Fire Risk Management (INFRA) Service (https://arcticpassion.eu/wp4/ps4/). INFRA is a 
web mapping service for wildfires to make the access to relevant information and products easier 
from diverse sources. Focusing on a local scale, the service provides the possibility to generate and 
distribute messages on wildfires that are tailored to non-scientific end-users like municipalities, as 
well as Indigenous Peoples and local communities. The service currently covers test areas of about 
1500x 1500 km located in Sacha-Yakutia, Fennoscandia and Alaska. In the Arctic, wildfires represent a 
relevant source of PM10 emissions especially North America (Canada and Alaska) and Siberia; for this 
reason the developers of both services set up a connection between INFRA and AURORAE services 
with the aim of combining the information relative to wildfires with the data related to air pollution. 
Specifically, the AUROARE service is equipped with a Wildfire module that transfers the following 
datasets to the INFRA service on daily basis via an HTTPS/FTPS service:  

—  CAMS forecast of PM10 fraction emitted by wildfires. 

—  Flux of total PM10 emitted by wildfires. 

The CAMS forecast of PM10 fraction emitted by wildfires data are released at daily frequency by 9 
models of CAMS, as for the PM10 CAMS forecast, and are downloaded with daily frequency at 08:00 
UTC for the current day (t=0) and for the following 48h (t=2) 
(https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-
forecasts?tab=form) by the AURORAE automatic procedure. We select the model base time at 00:00 
UTC and the models’ level at 0 m above the surface. To download, treat the netCDF files and produce 
the text (.txt) files for each EEA and FMI in situ monitoring stations we developed a submodule (two 
Python scripts) like the one created for the CAMS PM10 forecast and described in section 4.1.2.  

The wildfire flux of total PM (kg m-2 s-1) is released at daily frequency at 07:00 UTC by the CAMS 
global biomass burning emissions based on fire radiative power (GFAS) 
(https://ads.atmosphere.copernicus.eu/datasets/cams-global-fire-emissions-gfas?tab=download) 
that assimilates fire radiative power (FRP) observations from satellite-based sensors to produce daily 
estimates of biomass burning emissions. CAMS GFAS data covers the period from 2003 to present, 
and includes: FRP, dry matter burnt and biomass burning emissions 
(https://confluence.ecmwf.int/display/CKB/CAMS+global+biomass+burning+emissions+based+on+fir
e+radiative+power+%28GFAS%29%3A+data+documentation).  

The features of the current version of CAMS GFAS (GFAS v1.2) are:  

—  Injection height daily data (Mean altitude of maximum injection and Altitude of plume 
top) as provided by the Plume Rise Model and IS4FIRES Flux of total PM10 emitted by 
wildfires. 

—  Pixel based quality control for the MODIS instruments on Aqua and Terra. 

—  Statistical regression of the output when assimilating only Aqua or Terra observations so 
as to preserve consistency with data obtained assimilating Aqua and Terra observations. 

The wildfire flux of total PM is downloaded everyday as a single netCDF file that covers the whole 
globe, the data are extracted selecting the variable ‘tpmfire’. The file is cropped on the areas of 

32 

 
 
Yakutia and Canada and Alaska, and two files are saved as a raster image (.tiff extension). Those 
images are displayed on the INFRA webpage. 

33 

 
 
8  Stakeholders consultations and reccomendations 

The AUROARE service is a website designed for Northern European and Arctic municipalities and 
citizens to provide an improved and accessible air quality forecast. The aim of the service is to 
empower people living and working in this areas on topics such as air pollution and its effects on 
human health. In addition, the service is also developed to help communities to monitor air pollution 
in their area, to plan mitigation actions and to foreseen protection actions in case of high pollution 
events.  

Considering that the service has been tailored for non-professional users, is necessary for the authors 
of this report to obtain feedbacks from potential users to improve the website and to provide a 
service designed for their specific needs. 

The AURORAE service has been presented at the following conferences: 

—  ArcticPASSION General Assembly 2023, 22-26 May 2023, Laveno (Italy) 

—  Arctic Frontiers 2024, 29 January-1 February 2024, Tromso (Norway) 

—  Arctic Science Summit Week 2024, 21- 29 March 2024, Edinburgh (UK) – online 

presentation 

—  ArcticPASSION General Assembly 2024, 11-13 June, Inari (Finland) 

—  EU Polar Science Week, 3-6 September, Copenhagen (Denmark) 

—  APECS and ArcticPASSION Webinar, 29 October 2024 – online presentation 

During the discussion with the public we gathered the following reccomendations that are planned 
to be implemented: 

—  Add on the website information on the reccomended actions that need to be taken by 

citizens in order to protect themselves by air pollution depending on the PM10 
concentrations; 

—  Make the website more interactive enabling users to add information regarding sources 
of atmospheric pollution in their area, report an extreme event of pollution in their 
municipality or provide any information considered to be relevant; 

—  Add in-situ stations located in Denmark in the website and provide the forecast at those 

geographical position. 

The AURORAE website will be presented at future conferences and distributed throught 
stakeholders’ networks such as Arctic Majors’ Forum and WWF Arctic. All the additional feedbacks 
will be considered and eventually implemented. 

34 

 
 
9  Conclusions 

The Arctic, once considered a pristine environment, has been experiencing atmospheric pollution 
since the 1950s, with the phenomenon of Arctic Haze. The recent increase in global warming has 
created opportunities for economic activities such as shipping and oil and gas extraction, leading to a 
rise in local sources of atmospheric pollution (Law et al., 2017). This pollution has significant 
implications for public health, making it essential to provide accurate and reliable short-term 
forecasts to support governments and stakeholders in developing policies to limit population 
exposure. The Copernicus Atmosphere Monitoring Service (CAMS) provides forecasts of PM10 
concentrations over Europe, including the Arctic, however these predictions are subject to 
uncertainties.  

To address these discrepancies and provide an accessible tool to Arctic cizens and communities we 
developed an Arctic Service, within the Horizon 2020 project ArcticPASSION, called AURORAE (Air 
qUality foRecast fOR Arctic communitiEs). The AUROARE service is an accessible, interactive and 
user-friendly platform that features 48 hours PM10 forecasts for around 100 monitoring stations and 
near-real-time concentration for Northen European countries. To provide an accurate PM10 forecast 
an ad-hoc Deep Learning model has been implemented. We tested several model architectures 
among the category of Transformers and an we slected the Crossformer model, which improves the 
MSE and MAE approximately by 32% and 39% respectively when compared with the best performing 
CAMS model (GEMAQ). This innovative approach combines in-situ measurements from about 100 
monitoring stations, 48 hours CAMS PM10 forecasts, and a 48 hours forecast of 6 meteorological 
variables produced by the ECMWF for countries in the European Arctic.  

The AURORAE service shows several key benefits, including: 

—  Improved accuracy: The service provides more accurate PM10 forecasts than CAMS, 

particularly in the Arctic region; 

—  Enhanced user experience: The service offers a user-friendly interface that allows non-

experts to access and understand air quality forecasts; 

—  Increased accessibility: The service provides near-real-time PM10 concentrations and 

forecasts for over 100 monitoring stations in Northern Europe and the Arctic. 

—  Support for decision-making: The service provides a daily air quality bulletin that 

summarizes the current air quality situation and forecast for all monitoring stations, 
enabling informed decision-making. 

The development of the AURORAE service has demonstrated the potential of Deep Learning models 
to improve air quality forecasting, particularly in regions with limited data coverage. The service has 
also highlighted the importance of stakeholder engagement and collaboration in the development of 
environmental services. Future developments for AURORAE include the integration with the INFRA 
(Integrated Fire Risk Management) service, in order to provide information on air prollution 
produced by Arctic wildfires, and enhance the user experience and accessibility of the website 
following the users’ feedbacks and reccomendations. 

35 

 
 
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39 

 
 
 
List of abbreviations and definitions  

Abbreviations 

Definitions 

4D-VAR 

AI 

AMAP 

API 

Four-Dimensional Variational Data Assimilation 

Artificial Intelligence  

Arctic Monitoring and Assessment Program 

Application Programming Interface 

AURORAE 

Air qUality foRecast fOR Arctic communitiEs 

CAMS 

CDS 

CTMs 

DL 

DL 

DSW 

ECMWF 

EEA 

EMEP 

ESN 

EU 

FMI 

GB 

GDSW 

GFAS 

Copernicus Atmopsheric Monitoring Service 

Copernic Data Store 

Chemical Transport Models 

Deep Learning 

Deep Learning 

Dimension Segment-Wise 

European Centre for Medium-Range Weather Forecasts 

European Environmental Agency 

European Monitoring and Evaluation Programme 

Echo State Networks 

European Uninion 

Finnish Meteorological Institute 

Giga Byte 

Geometric Dimension Segment-Wise 

Global Biomass Burning Emissions Based on Fire Radiative 
Power 

40 

 
 
Abbreviations 

Definitions 

GRU 

IDE 

IFS 

INFRA 

JRC 

LSTM 

MAE 

MLP 

MSE 

NLP 

NN 

PM 

PM10 

PS 

RAM 

RNN 

UTC 

Gated Recurrent Units 

Integrated Development Environment 

Integrated Forecast System 

Integrated Fire Risk Management 

Joint Research Centre 

Long Short-Term Memory 

Mean Absolute Error 

Multilayer Perceptron 

Mean Square Error 

Natural Language Processing 

Neural Network 

Particulate Matter 

Particulate Matter with diameter smaller than 10 µm 

Pilot Service 

Random Acccess Memory 

Recurrent Neural Networks 

Coordinated Universal Time 

41 

 
 
List of figures  

Figure 1. Schematic on the relationships among data from emissions and observations of 
atmospheric pollutants, model simulations to estimate the impact on air quality and climate and how 
this information is essential to manage emissions and define regulations ............................................ 7 

Figure 2. Left. Geographical localization of the air quality monitoring stations used for model training 
and testing (red dots) and for ANNs parameters’ tuning. The pink dashed region represents the CAMS 
models area and the green line the AMAP area. Right. ANNs scheme used for the study. ................. 11 

Figure 3. MSE for 9 CAMS vs. observations (orange bars, left side of each panel), ANNs models vs. 
observations (coloured bars, right side of each panel), persistence (red bar, in the middle of each 
panel) and average (AVE) MSE for all ANNs models (green bar, right side of each panel). Each panel 
refers to a monitoring station: (a) Muonio, (b) Pyykösjärvi, (c) Oulu, (d) Kópavogur Dalsmári, (e) 
Grundartangi Gröf, (f) Tromso R. .......................................................................................................... 13 

Figure 4. Schematic of the Original Vanilla Transformer Architecture ................................................. 15 

Figure 5.Schematic of the data flow for the AURORAE service. ........................................................... 25 

Figure 6. The landing page of the AURORAE Service. ........................................................................... 28 

Figure 7: Example of the AURORAE daily air pollution bulletin file ...................................................... 29 

Figure 8. Comaprison of MSE calculated between the observations and the CAMS and the 
Crossformer models. The Crossformer outperforms all the CAMS models reducing the error by 32%.
 ............................................................................................................................................................... 31 

42 

 
 
 
X
X
-
X
X
-
X
X
-
X
X
X
-
X
X
-
C

List of tables  

Table 1. Main characteristics of the air quality regional models used by CAMS (Collin, 2020; Marécal 
et al., 2015). ............................................................................................................................................. 9 

Table 2. Best combination of paramters for each transformer model tested in this study. ................ 17 

Table 3. MSE ad MAE calculated between model forecast and PM10 in situ observations. ................. 18 

Table 4. Hyperparameter tuningchoces based on 5 selected stations. ................................................ 19 

Table 5. MSE and MAE calculated for the Crossformer model for DSW and GDSW embedding 
configuration. ........................................................................................................................................ 19 

Table 6. Parameters such choices of factor, encoder layers and number of heads selected for the 
Crossformer architecture that show better average score over all trials. ............................................ 20 

Table 7. MSE and MAE calculated for trials with Crossformer model including and meteorological 
variables. ............................................................................................................................................... 20 

Table 9.Summary of the hypapramters of the Crossformer model employed in the AURORAE service.
 ............................................................................................................................................................... 30 

Table 10. MSE and MAE calculated for the Crossformer model applied in the AURORAE service. ..... 30 

Table 11. MSE and MAE calculated comparing our improved Crossformer model forecast with the 
one from deterministic models provided by CAMS. ............................................................................. 31 

43 

 
 
 
 
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