### EUROTRAC-2 (A EUREKA Environmental Project)



## Limitation of Oxidant Production



# Final Report

Editors Albrecht Neftel and Christoph Spirig Swiss Federal Research Station fo r Agroecology and Agriculture, Zurich, Switzerland

International Scientific Secretariat (ISS), GSF - National Research Center for Environment and Health Munich, Germany 2003

## Preface
Retrospectively, the suggestion to investigate the limitation of oxidant production as a EUROTRAC-2 subproject was a naïve act. To focus a whole subproject on this aspect was difficult for both group of affiliates: while pure scientists found it hard to accept such a projection on one focal point, actors related to agencies had to accept that their treatments of the ozone problems required more detailed investigations. The idea of such a project arose in 1997 during a meeting in Bern and LOOP took off, driven by the enthusiasm of a small group of atmospheric scientists. During the course of LOOP, many of the first principal investigators were forced or deliberately chose to leave the field. As a consequence, some of the ideas originally proposed were omitted but, more importantly, quite a few have been realised and produced valuable scientific results. Throughout the whole project, LOOP was characterised by an open spirit, discussions that were sometimes controversial but always fair, and often unconventional ways of acting. Having an idea is not enough; its successful realisation depends on close collaborators who fully support the idea. During the initial phase of the project, I was very lucky to have Thomas Staffelbach as the trouble-shooter. When he became aware that our former institute, the Institute for Environmental Protection and Agriculture located in Liebefeld – Bern would be incorporated into the new Swiss Federal Research Station for Agroecology and Agriculture (FAL) in Zurich, he decided to leave the field, and began actively to do something for air quality. He is now leading a small company specialising in information systems for public transportation. If you happen to enjoy a friendly announcement during a pleasant trip in a Swiss train, it was most probably done by his company. Luckily, Christoph Spirig stepped in as his successor and became the new trouble-shooter. Without his efficiency in compiling information, this document most probably would not exist. I am grateful for the LOOP coordination credit from the Commission of Technology and Innovation (KTI). Hans Richner, the Swiss representative on the EUROTRAC-2 IEC continuously supported the LOOP activities with great enthusiasm. Last but not least, I would like to thank my superiors at the FAL for letting me lead the LOOP project even though it was not directly related to farmers' needs.

Albrecht Neftel, LOOP Subproject coordinator

## Contents

1 Introduction ..................................................................................................... 1 1.1 EUROTRAC-2 and LOOP ............................................................................... 1 1.2 Goals of LOOP ................................................................................................. 1 1.3 LOOP activities................................................................................................. 2 1.4 LOOP investigators........................................................................................... 3

2 Scientific Background ..................................................................................... 5 2.1 Photo-oxidant production - NO X versus VOC sensitivity of P(O 3 ).................. 5 2.1.1 Ozone formation processes in the lower troposphere................................... 5 2.1.2 Chemical ageing ........................................................................................... 5 2.1.3 Expressions of P(O 3 )..................................................................................... 6 2.1.4 Approximation for ODD-H radicals ............................................................. 7 2.1.5 P(O 3 ) derived from Leighton ratio................................................................ 8 2.1.6 Approximate formulas for NO x /VOC dependence of P(O 3 ) ........................ 9 2.1.7 NO x and VOC sensitivity of P(O 3 ) ............................................................. 10 2.2 The dependence of ozone pr oduction on VOC reactivity and oxidation mechanisms..................................................................................... 13 2.2.1 Principles .................................................................................................... 13 2.2.2 Hydroperoxides........................................................................................... 14 2.2.3 Peroxyacetyl Nitrate (PAN)........................................................................ 15 2.2.4 Ozonolysis of carbon double bonds............................................................ 16 2.2.5 Radical measurements ................................................................................ 17 2.3 From the local ozone production to the ozone concentration......................... 19 2.3.1 Basic principles........................................................................................... 19 2.3.2 Indicators .................................................................................................... 20 2.3.3 Discussion of indicators.............................................................................. 22 2.4 Photo-oxidants and aerosols ........................................................................... 23 2.4.1 Inorganic aerosols ....................................................................................... 24 2.4.2 Secondary organic aerosols ........................................................................ 24 2.5 Performance of emission inventories – what can modellers expect? ............. 28 2.5.1 Introduction................................................................................................. 28 2.5.2 Steps of creating an emission inventories................................................... 28 2.5.3 Types of input into emission inventories.................................................... 29 2.5.4 Disaggregation of e mission inventories...................................................... 30 2.5.5 Methods for validation................................................................................ 31 2.5.6 Recommendations and conclusions............................................................ 32Contents

3 Field Campaigns ............................................................................................35 3.1 BOCCALINO..................................................................................................35 3.2 SLOPE.............................................................................................................36 3.3 BERLIOZ ........................................................................................................38 3.3.1 Scientific background..................................................................................39 3.3.2 Experimental ...............................................................................................42 3.3.3 Meteorological situation..............................................................................46 3.3.4 Data Quality and Inst rument Comparisons .................................................47 3.3.5 Observational results...................................................................................50 3.3.6 Model simulations.......................................................................................56 3.3.7 Conclusions .................................................................................................58 Acknowledgement...................................................................................................59 3.4 PIPAPO ...........................................................................................................60 3.4.1 The Milan basin as natural photoreactor .....................................................60 3.4.2 Intensive observation periods......................................................................61 3.4.3 QA/QC of PIPAPO. ....................................................................................62 3.4.4 Emissions ....................................................................................................63 3.4.5 Meteorology and photosmog ep isodes in the Milan area............................65 3.4.6 Investigation of P(O 3 ) based on observations .............................................67 3.4.7 Aerosols.......................................................................................................70 3.4.8 Model analysis.............................................................................................73 3.4.9 Summary of scientific finding s of the PIPAPO experiment .......................82 Acknowledgements .................................................................................................84 3.5 Atmospheric pollution over the Paris area: The ESQUIF project ...........85 3.5.1 General introduction to the campaign .........................................................85 3.5.2 Overview over results relevant for the LOOP project.................................86 3.5.3 Climatology of the chemical regime in the Paris - Ile de France region –a model simulation ...................................................88 Acknowledgements .................................................................................................92 3.6 ESCOMPTE....................................................................................................93 3.6.1 General framework of the ESCOMPTE programme ..................................93 3.6.2 The ESCOMPTE area and period ...............................................................93 3.6.3 Experimental set-up.....................................................................................93 3.6.4 The campaign progress................................................................................95 3.6.5 Specific results: photochemical ozone production and chemical regime ...96 Acknowledgements ...............................................................................................103

4 Integration - scaling up ...............................................................................105 4.1 LOOP interactions with environmental policy..............................................105Contents

4.2 The need for envir onmental agencies ........................................................... 105 4.2.1 Peak ozone ................................................................................................ 106 4.2.2 Ozone levels and cumulate d indices (AOT-values) ................................. 107 4.3 Influence of LOOP on environmental policy................................................ 107

5 Synthesis: LOOP Me ssages in a Nutshell ................................................. 109 5.1 Are the simulations of the efficiency of VOC versus NO x emission reduction on the ozone production on a sound base? ................................... 109 5.2 Do biogenic emissions influence the VOC – NO x sensitivity of the ozone production?......................................................................................... 109 5.3 Can indicators be used to char acterize chemical regimes of ozone production?......................................................................................... 109 5.4 How does the grid resolution influence the model results? .......................... 110 5.5 What is the influence of aerosols? ................................................................ 110 5.6 How do photochemical models rela te to the AOT concept ? ....................... 111 5.7 What kind of activities are still needed?....................................................... 111

6 References .................................................................................................... 113

## 1. Introduction

1.1 EUROTRAC-2 and LOOP The rationale for the EUROTRAC-2 project as a whole is “to support the further development of abatement strategies within Europe by providing an improvedscientific basis for the quantification of source-receptor relationships for photooxidants, aerosols and acidifying substances.”EUROTRAC-2 aims to provide an integrated scientific evaluation of photooxidants, aerosols and acidifying substances in the troposphere over Europe, using the extended knowledge base and improved methodologies gained in the first phase of EUROTRAC. In particular advanced techniques in linking numerical atmospheric models and observational data are used to provide an unprecedented capability for understanding the complex interactions between the sources, transformations and deposition which determine the concentrations of trace substances appropriate to environmental impact. The work thus provides scientific support for future measures needed to control photo-oxidants and the deposition of acidifying substances and nutrients. Photo-oxidants is a key issue in EUROTRAC-2. The subproject LOOP (Limitation of Oxidant Production) focuses on the question which species or group of species limit the formation of photo-oxidants – it is thus directly addressing the objectives of the EUROTRAC-2 project. LOOP proposed to investigate the temporal behaviour and the spatial extent of VOC and NO x sensitive areas of photo-oxidant production. Special emphasis is given to the relative roles of VOC and NO x in the photo-chemical production of ozone and other photo-oxidants in polluted areas. In order to assess effective and cost-efficient ozone management strategies it is important to know the spatial extent of the different ozone production regimes as well as their temporal behaviour. LOOP promised to provide improved experimental and modelling tools for this task.



### 1.2 Goals of LOOP

An improved understanding of the temporal evolution and the spatial extent of the different photo-oxidant production regimes in the planetary boundary layer in order to assess the effectiveness of reduction strategies towards ground level ozone.

An evaluation of the indicator concept to determine areas of NO x and VOC sensitive ozone production across Europe. The robustness of the indicator concept towards changes in chemical and meteorological parameters will be evaluated.Introduction 2



### 1.3 LOOP activities A major component of LOOP is the production of joint publications in peer reviewed journals. This guarantees the maintenance of a high scientific level as well as the distribution of the experience to a wide audience. In order to support joint publications and to facilitate the exchange of information between the LOOP community in general, a series of workshops was held (Table 1). LOOP established close links to EMEP, the Co-operative Programme for Monitoring and Evaluation of the Long-Range Transmission of Air Pollutants in Europe, in order also to ensure the links between science and environmental policy.

Table 1. Workshops within LOOP.

Workshop Location Date Focal point
Gerzensee March 20 th to 22 nd

, 1999 Limitation of ozone production based oninterpretation of field data (observationbased modelling) and on numerical models.
Varese December 13-16, 1999 Scientific results of the BERLIOZ and PIPAPO field campaigns. Joint workshop of the Lombardy Region, LOOP and the German TFS / LT3 (BERLIOZ). Gerzensee December 3-6, 2001 Joint LOOP / EMEP workshop. Exchange of information between the LOOP and EMEP communities

Activities within LOOP were centred on several field initiatives, each of them with its own characteristics and structure. SLOPE and BOCCALINO can be considered as “trigger initiatives” for LOOP.

Table 2. Field activities with links to LOOP. Project Name Duration Major focus
SLOPE Sept 1992 and June 1996 Budgets of OH radicals and ozone in the plume of Freiburg BOCCALINO July-August 1994 Peroxides as indicators for chemical regimes. BERLIOZ July-Aug 1998 Development of the city plume, radical chemistry PIPAPO May and June 1998 NO x / VOC limitation of Ozone production in the Po Valley, Italy Secondary aerosol formation ESQUIF July, August 1998; June and July 1999; August 2000; Documentation of phot ochemical oxidant formation in the Greater Paris area

ESCOMPTE June and July 2001 Build-up of an experimental data base allowing the thorough validation of air quality modelsLOOP Final Report 3


### 1.4 LOOP investigators

Table 3. Original list of LOOP principal investigators (PIs). Original P.I. Title of contribution
1 Johanna Altenstedt, Swedish Environment Research Institute, IVL, Sweden Using the IVL model as chemical reference in a highly variable chemical environment

2 Bertrand Calpini, Swiss Federal Institute of Technology, EPFL, Switzerland Characterisation of the PBL and the ozone development in the Milan - Ticino region. DIAL developments, field measurements and modelling case study analysis
3 Josef Dommen, Paul Scherrer Institute, Department of General Energy Technology, Switzerland HCHO/NO y as an indicator of the photo-chemical state of an air mass

4 Ugo Facchini, Istituto di Fisica Generale Applicata, Milan, Italy Ozone and Radon measurements in the Milan basin 5 Jens Hjorth, JRC- Ispra, Italy VOC and peroxyradical fi eld measurements to understand photo-chemical ozone formation
6 Bruno Neininger MetAir AG, Applied Atmospheric Physics, Switzerland Aircraft measurements between Milan and the Alps

7 Nicola Quaranta Regione Lombardia, Settore Ambiente ed Energia, Italy Chemical-physical model approach to determine the different photo-chemical regimes in the Milan aerologic basin
8 Jana Slemr, Frauenhofer-Institut für Atmosphärische Umweltforschung, IFU, Germany Urban emissions and their impact on surroundings

9 Johannes Staehelin, Atmospheric Science, Swiss Federal Institute of Technology, Switzerland Surface measurements of NO x , NOy, J(NO 2 ), O 3 and volatile hydrocarbons (C5-C11) and trajectory calculations
10 Thomas Staffelbach, Institute for Environmental Protection and Agriculture, Switzerland Limitation of Oxidant Production in the Milan basin

11 Gérard Toupance, LISA, Université Paris, France ESCOMPTE

12 Bernhard Vogel, IMK, University Karlsruhe, Germany Numerical Simulation of the Temporal and Spatial Distribution of Indicator Species
13 Gerard Ancellet and Pascal Perros, Université Paris, France Aircraft studies of the ozone 3 dimensional field and of the nitrogen oxides partitioning during the Milan experiment
14 Andreas Volz-Thomas, Forschungszentrum Jülich, Germany Investigation of Phot o-oxidant Precursor Relations and the in situ Formation Rate from the TOR-Schauinsland Data
15 Wilfried Winiwarter, Austrian Research Centre Seibersdorf, Austria Development of a High Performance Emission Inventory for Assessing Urban Plumes
16 Ulrich Joss, Meteotest, Bern, Switzerland Photochemical regimes simulated with BerphomodIntroduction 4

17 John Burrows, University of Bremen, Germany RO x measurements

18 Ulrich Vogt, University of Stuttgart, Germany Balloon soundings

19 Theo Brauers, Forschungszentrum Jülich, Germany Measurements of the OH and HO 2 radical concentrations in polluted air masses
20 Helga Kromp-Kolb, University of Vienna, Austria Vertical Ozone transports in the Alps II (VOTALP II)
21 Ulrich Platt, University of Heidelberg Measurements of HONO, NO2 and HCHO by DOAS and investigation of the HONO formation mechanisms
22 Matthias Beekmann, Service d’Aéronomie/ IPSL / France Chemical regime within the ESQUIF and ESCOMPTE campaigns

## 2. Scientific Background

2.1 Photo-oxidant production - NO X 

### versus VOC sensitivity of P(O 3 

### )

Albrecht Neftel Swiss Federal Research Station for Agroecology and Agriculture, Zurich, Switzerland

Lawrence I. Kleinman Brookhaven Natl. Lab., Environmental Sciences Division, Upton, NY, USA



### 2.1.1 Ozone formation processes in the lower troposphere Ozone is formed from the photochemical oxidation of volatile organic compounds (VOCs) in the presence of oxides of nitrogen (NO x = NO + NO 2 ). The oxidation processes that lead to ozone production are initiated by photolysis of various molecules. Figure 1 shows a simplified representation of the complex pathway involved in producing ozone leaving out organic peroxy radicals.

THE TROPOSPHERIC OZONE CYCLE

P(O 3 ) = k[HO 2 ][NO] NO

HO 2 OH

HO 2 + NO OH + NO 2 OH + CO H + CO 2 H + O 2 HO 2 CO

CO 2

P(O 3 ) = k[OH][CO] O 3

hv NO 2 + hv NO + O O + O 2 O 3 NO 2

Figure 1. Scheme of ozone production with CO as a surrogate for VOCs.



### 2.1.2 Chemi cal ageing Determining the relation between O 3 and its precursors has been the object of more than 4 decades of research ( NARSTO , 2000; NRC , 1991; Sillman et al. ,Scientific Background 6

1990). It has been found that the production of ozone P(O 3 ) depends on NO x and VOC emissions in a complicated and non-linear way. Sillman et al. (1990) presented approximations of P(O 3 ) as function of NO x and VOCs. Figure 2 shows ozone production as function of NO x for various levels of VOCs

. Either of the two precursors may limit P(O 3 ). For a given level of VOC, the rate of ozone production will increase with increasing NO x so long as NO x is low. Eventually the rate of ozone production reaches a maximum value as the chemistry undergoes a transition from low-NO x to high-NO x conditions. At higher NO x concentrations the rate of ozone production decreases with increasing NO x but would increase with increasing VOC. The transition from low-NO x to high-NO x chemistry is associated with a specific ratio of VOC to NO x . The exact transition depends on the speciation and reactivity of the VOC mix and not just on the absolute concentration of VOC.

0 1 2 3 4 5 6 7

0 5 10 15 20 NOx (ppb) VOC=20ppbC

VOC=50ppbC

VOC=100ppbClowNOx

transition

high-NOx

Figure 2. Net rates of ozone production (ppb per hour) as a function of NO x (ppb), for VOC = 20 ppbC (solid line), 50 ppbC (long dashed line) and 100 ppbC (short dashed line), based on calculations shown in Milford et al . (1994).



### 2.1.3 Expressions of P(O 3 

### ) Ozone production rates cannot be directly observed but instead has to be deduced in a more or less indirect way from observed concentrations. The least indirect way of obtaining P(O 3 ) is from measurements of peroxy radicals and NO. The relevant chemistry is:
HO 2 + NO  OH + NO 2 (R 1)
RO 2 + NO  RO + NO 2 (R 2) 1 2

3LOOP Final Report 7

NO 2 + h ν  NO + O (R 3)
O + O 2  O 3 (R 4)
NO + O 3  NO 2 + O 2 (R 5) Since O 3 is formed with nearly 100% yield from NO 2 : P(O 3 ) = (k 1 [HO 2 ] + k 2 [RO 2 ]) [NO] (1) The difficulty in using (1) is that peroxy radicals are difficult to measure and k 2 (as the average rate constant of all species encompassed by the term ‘RO 2 ’) is hard to determine. Therefore alternate methods of determining P(O 3 ) are needed.


### 2.1.4 Approximation for ODD-H radicals Peroxy and OH radicals react rapidly (characteristic lifetimes of 100 and 1 s, respectively) so it is expected that they are in a steady state equilibrium with other longer lived atmospheric trace gases. Only a few field measurements exist on ground stations. Routine and/or operational measurements are not existent. As a consequence indirect methods have been developed to characterise ODD-H levels and P(O 3 ) and their dependence on NO x and VOC concentrations. If all of the stable reactants are measured then HO 2 , RO 2 , and OH can be calculated by solving a set of steady state equations.

0 ] [S k [OH] P dt d[OH]

i i i OH   

##  ( 2)

0 ] [HO 2k ] [S k ] [HO P dt ] d[HO 2 2 peroxid j j j 2 HO 2

2   

##  ( 3)

0 ] [RO 2k ] [S k ] [RO P dt ] d[RO 2 2 o_peroxid j' j' j' 2 RO 2

2   

##  ( 4) where P OH , P HO2 and P RO2 are the production rates of OH, HO 2 and RO 2 , respectively. S i , S j , and S j’ denote (radical or non-radical) species that act as reaction partners in sink reactions of OH, HO 2 and RO 2 , respectively. The radical production rates are determined from photolysis rate constants and the concentrations of radical precursors. A solution of (2-4) can be obtained by solving these equations algebraically or by integrating a coupled set of ordinary differential equations (e.g., for d[OH]/dt, d[HO 2 ]/dt, d[RO 2 (i)]/dt) to steady state. In either case concentrations of stable compounds are fixed at their observed concentration. NO 2 has a short lifetime and in both approaches it can either be constrained or calculated. The treatment of compounds such as PAN with an intermediate lifetime can be problematic. If PAN is measured then it can be included as a reactant on the right hand side of (2-4). If PAN is not measured, a steady state assumption can be made as best approximation. The accuracy of (2-4) will depend on the completeness and accuracy of the input variables, namely measured concentrations and kinetic parameters. A particular concern is the accurate quantification of HONO. Another concern is that observations of VOCs and their oxidation products be sufficiently complete, soScientific Background 8

as to give a realistic value of OH-reactivity and radical production. Ultimately the accuracy of (2-4), and indeed of any mechanism describing "fast" photochemistry, will have to be assessed by comparing measured and calculated values of free radicals. A large influence has the NO concentrations. NO 2 is difficult to measure. Routine network data with conventional monitors that convert NO 2 to NO using molybdenum converters systematically overestimate NO 2 values. Such a set of measurement is not in a quasi equilibrium state and will lead to erroneous conclusions. This can easily be checked when the set of measurement is used as initial values in a box model. In case the NO / NO 2 ratio shows a very fast change the set of measurement was not in equilibrium, either due to fresh emissions or due to an overestimation of NO 2 . 

### 2.1.5 P( O 3 

### ) derived from Leighton ratio P(O 3 ) can alternatively be calculated by the deviation from the Leighton ratio, also named Photostationary State relation (PSS):

##    3 5 2 2 3 ) ( O NO k NO j O P NO    ( 5) The PSS relations come from setting d[NO]/dt or d[NO 2 ]/dt = 0. We can expand the set of processes to include sink reactions for NO and NO 2 . If we do this we also have to include an emission term, else [NO] and [NO 2 ] will decrease. Suppose organic NO + RO 2 has an organic nitrate yield of δ , i.e., k 1 [HO 2 ] [NO] + k 2 [RO 2 ][NO] = P(O 3 ) + δ organic nitrate ( 6) then: d[NO]/dt = 0 = j NO2 [NO 2 ] – k 5 [NO][O 3 ] – P(O 3 ) + E(NO) - δ P(O 3 ) ( 7) yielding P(O 3 ) = j NO2 [NO 2 ] – k 5 [NO][O 3 ] + E(NO) - δ P(O 3 ) ( 8) δ has a value of about 0.03 for a typical mixture of VOCs, so it will be ignored. the NO 2 equation gives us: d[NO 2 ]/dt = 0 = - j NO2 [NO 2 ] + k 5 [NO][O 3 ] – k 6 [OH][NO 2 ] + P(O 3 ) ( 9) yielding P(O 3 ) = j NO2 [NO 2 ] – k[NO][O 3 ] + k[OH][NO 2 ] (10) In order for both equations to be correct E(NO) = k 6 [OH][NO 2 ], which is the conservation equation for NO x . The NO 2 sink term can be written in terms of an O 3 production efficiency per NO x as k 6 [OH][NO 2 ] = P(O 3 )/OPE (11) so if OPE is small (i.e., 2), then ignoring it can lead to a 100% under-estimate of P(O 3 ), i.e., P(O 3 ) with sinks = P(O 3 ) without sinks / (1 - 1/OPE). (12) One could add dilution terms to these equations. Dilution does not interchange NO and NO 2 but it's rate can be greater than chemical loss.LOOP Final Report 9

Suppose there are no NO x emissions and the air parcel is not in steady state because NO x is getting lost in the form of NO 2 . Then

d[NO x ]/dt = d[NO]/dt + d[NO 2 ]/dt = -k 6 [OH][NO 2 ] ( 13) and d[NO 2 ]/dt > d[NO x ]/dt (i.e.,, d[NO 2 ]/dt is less negative than d[NO x ]/dt) d[NO x ]/dt = -k 6 [OH][NO 2 ] < d[NO 2 ]/dt ( 14) d[NO 2 ]/dt = - j[NO 2 ] + k 5 [NO][O 3 ] + P(O 3 ) – k 6 [OH][NO 2 ] ( 15) -k 6 [OH][NO 2 ] < - j[NO 2 ] + k 5 [NO][O 3 ] + P(O 3 ) – k 6 [OH][NO 2 ] ( 16) 0 < - j[NO 2 ] + k 5 [NO][O 3 ] + P(O 3 ) ( 17) P(O 3 ) > j[NO 2 ] – k 5 [NO][O 3 ] ( 18)

But since d[NO x ]/dt is not too much smaller than d[NO 2 ]/dt , the last Eq is almost the usual result.


### 2.1.6 Approximate formulas for NO x 

### /VOC dependence of P(O 3 

### ) Sometimes it is useful to have an approximate formula for P(O 3 ), especially if it can be evaluated from a limited set of measurements or if it has a functional form that can give insights into the relation between P(O 3 ) and O 3 precursors. Such formulas can be derived from a simplified set of photochemical equations organized as a chain reaction (Kleinman, 1994; Kleinman et al. , 1997, Kleinman, 2000; Kleinman et al. , 2000):

Initiation; Radical Production
O 3 + h ν  O( 1 D) (R 6) O( 1 D) + H 2 O  2 OH (R 7) HCHO + h ν  2 HO 2 (R 8) Chain Propagation (schematic)
OH + VOC i  HO 2 (or RO 2 ) + Carbonyl (R 9)
HO 2 (or RO 2 ) + NO  OH (or Carbonyl + HO 2 ) + NO 2 (R 1 and R 2)
NO 2 + h ν (+O 2 )  NO + O 3 (R 3) Chain Termination
L N (NO x + Radical)
OH + NO 2  HNO 3 (R 10)
L R (radical + radical)
HO 2 + HO 2  H 2 O 2 (R 11)
RO 2 + HO 2  ROOH (R 12)Scientific Background 10

Sources and sinks of free radicals must be in balance, yielding an odd-hydrogen budget equation: Q = L N + L R ( 19) where Q is the primary production rate of radicals and the removal terms L N and L R are approximated as above. At low NO x concentration, almost all radicals are removed by peroxide formation. The radical budget equation becomes Q = L R , which can be solved for [HO 2 ]+[RO 2 ], which gives P(O 3 ) from (1). The low NO x formula for P(O 3 ) is P(O 3 ) = k 1 /(2 k eff ) 1/2 Q 1/2 [NO] ( 20) where k eff is an effective rate constant for peroxide formation that depends on the individual rate constants for HO 2 +HO 2 and RO 2 +HO 2 as well as the fraction [RO 2 ]/[HO 2 ]. A high NO x formula is derived in the limit that Q = L N with the additional assumption that propagation >> termination. The latter approximation gives usk OH+VOCi Y i [OH] [VOC i ] = k 1 ([HO 2 ] + [RO 2 ]) [NO] = P(O 3 ) ( 21) where the summation is over all VOCs and Y i is a stoichiometric factor for the number of peroxy radicals produced from OH+VOC (for an individual VOC, Y i is usually equal to 1 or 2) (Sillman, 1995a; Tonnesen and Dennis, 2000). [OH] is derived from the budget equation Q = k OH+NO2 [OH] [NO 2 ]. ( 22) Combining (21) and (22), we obtain P(O 3 ) = Q k OH+VOCi Y i [VOC i ]/ (k OH+NO2 [NO 2 ]) ( 23) Equation (23) shows that in the high NO x limit, P(O 3 ) depends on a VOC to NO 2 reactivity ratio.



### 2.1.7 NO x 

### and VOC sensitivity of P(O 3 

### ) In a similar fashion to (23) we can derive a formula that is valid for intermediate NO x conditions where both L N and L R pathways are important: P(O 3 ) = k 1 /(2 k eff ) 1/2 (Q- L N ) 1/2 [NO] ( 24) Equation (24) can be used to evaluate P(O 3 ) based on the formula given below for L N /Q. The resulting expression is somewhat complicated and does not clearly show how P(O 3 ) depends on NO x and VOCs. Instead, the utility of (24) is that relative sensitivities of P(O 3 ) to NO and VOCs can be determined by differentiation with respect to [NO] or [VOC] (Kleinman et al. , 1997). The derivation makes use of the approximations:
L N = k OH+NO2 [OH] [NO 2 ] ( 25) P(O 3 ) = k OH+VOCi Y i [OH] [VOC i ] ( 26) Relative sensitivities are given by dlnP(O 3 )/dln[NO] = (1 - 3/2 L N /Q)/(1 - 1/2 L N /Q) ( 27)LOOP Final Report 11

dlnP(O 3 )/dln[VOC] = (1/2 L N /Q)/(1 - 1/2 L N /Q) ( 28) The sensitivities are given in terms of a single independent variable, L N /Q; the fraction of radicals which are removed by reactions involving NO x . The utility of (27) and (28) is that they show very clearly the functional form of the solutions to a complex photochemical process and also provide a theoretical justification for one combination of indicator species (Sillman, 1995a 

### )

. An explicit equation for L N /Q has been derived using the same type of approximations that were used to determine relative sensitivities (Kleinman et al. , 2001). The derivation depends on radical balance as given in (19), with L R expressed in terms of peroxide formation and L N expressed in terms of HNO 3 formation (25). It is also assumed that chain propagation is more likely than termination. This is expressed in (21) and in a slightly different form below:  k OH+VOCi [OH] [VOC i ] = k 1 [HO 2 ] [NO] ( 29) The desired equations are:
L N /Q = - /2 + ( 2 + 4 ) 1/2 /2 ( 30) where

= (k OH+NO2 [NO 2 ] k 1  [NO]/(  k OH+VOCi [VOC i ])) 2 (1/(2 Q k eff )) ( 31) and
 = [HO 2 ]/[HO 2 + RO 2 ] ( 32)

Figure 3. Dependence of L N /Q as function of Log( α ) and NO x (Kleinman et al. , 2001).

The utility of (30-32), is that L N /Q can be shown to be a monotonic increasing function of [NO x ] 4 /(Q*[VOC] 2 ), which allows qualitative arguments to be made about the effects of chemistry and dilution on NO x and VOC sensitivity. ThereScientific Background 12

are interesting city to city differences in the dependence of L N /Q on NO x and VOCs which are helpful in understanding the conditions under which O 3 is formed. These approximate formulas are tested by comparison with a CSS calculation and in general are reasonably accurate. This is more a test of internal consistency than a demonstration of absolute accuracy. For example, PAN is problematic because it is ignored in (25), but as long as PAN is ignored in the CSS calculation (i.e.,, assumed to be in steady state), no errors are detected in the approximate formulas. Some of the formulas can (and should) be revised to include PAN. Formulas such as (30-32) for L N /Q depend on most of the substances measured. With only a little extra effort, a CSS calculation could be done, in which no approximations need be made. Therefore the usefulness of some of the approximate formulas is for the insight that they provide, rather than for specific quantitative results. Figure 4 shows an example of a L N /Q analysis from the original paper by Kleinman et al . (1997). It is important to note that relative changes in [NO x ] and [VOC] always result in smaller relative changes of P(O 3 ).

Figure 4. Relative sensitivities of local ozone production as determined from measurements.LOOP Final Report 13


### 2.2 The dependence of ozone production on VOC reactivity and oxidation mechanisms Jens Hjorth Institute for Environment and Sustainability (IES), European Commission Joint Research Centre, Ispra, Italy



### 2.2.1 Principles Nearly all VOCs can contribute to photo-oxidant production, but the impact of the individual species on ozone formation depends on their reactivity (particularly the rate coefficient for their reaction with OH radicals) and their atmospheric degradation mechanism. The contribution to ozone formation of a VOC emitted in a certain quantity at a certain place depends significantly on the ambient conditions (meteorology and concentrations of trace gases, particularly NO x ), as well as on the time scale over which the build up of ozone is followed. The question of interest from an environmental point of view is, how much a certain reduction of the emission of individual VOCs will influence ozone levels on relevant scales. This kind of information is, obviously, one of the prerequisites for developing the most cost-efficient VOC-reduction policy, but it is not a trivial problem to make a definition of the 'ozone production efficiency' of VOCs. The simplest approach is to use the rate coefficient of the first step in the OH-initiated degradation mechanism
O H ROO OH RH 2 O 2 

  (R 9) as an indicator, because the peroxy radicals produced subsequently can contribute to the oxidation of NO and thus to the production of ozone:

2 NO RO NO ROO 

   (R 2)

O R' HO O RO 2 2 

  (R 13)

OH NO NO HO 2 2 

 (R 1)

3 O 2 O h NO 2   (R 3) However, this approach does not take into account the successive steps in the degradation mechanism where other peroxy radicals are formed and it does not take into account that for the larger alkyl peroxy radicals the formation of organic nitrates becomes a significant pathway for the reaction with NO

2 RONO NO ROO 

  (R 14) This pathway does not lead to formation of ozone but to radical loss. The most important recent approaches to a quantification of the relative capability of individual VOCs to form ozone are both based on simulations by photochemical models. One example is the 'Maximum Incremental Reactivity' (MIR) scale (Carter, 1995; Carter et al. , 1995), another is the 'Photochemical Ozone Creation Potentials' (POCP) (Derwent et al. , 1998). The MIR scale isScientific Background 14

based on an urban air quality model developed on the basis of smog chamber experiments. The POCP scale is based on a trajectory model at regional, European scale and uses the Master Chemical Mechanism. In both cases the increase of ozone due to an increase in the emission of a certain VOC is estimated under conditions with relatively high NO x levels, where ozone levels are most sensitive to VOC emissions. Some important mechanistic aspects that influence the ozone forming potential of a VOC are:

 The number of NO to NO 2 conversions caused immediately by the initial attack of OH on the compound.

 The reactivity of the primary degradation products with respect to the OH radical.

 Formation of reaction products that by their photolysis can provide additional significant radical sources ( e.g., formaldehyde, glyoxal).

 The degree of radical (and NO x ) sequestering/removal through formation of organic nitrates or PAN-type compounds.

 There is a relation between NO x vs. VOC limitation of ozone production and the ozone forming potential of individual VOCs: In VOC limited situations, an increase in the concentration of peroxy radicals will efficiently enhance the formation rate of ozone since the reaction with NO is important because of the (relatively) high concentrations of NO. Thus the capability of a VOC to produce ozone by its atmospheric degradation will depend strongly on the number of peroxy radicals that it forms. NO x -limited situations are characterised by low levels of NO; thus the peroxy radical reactions competing with the one with NO become important and the number of peroxy radicals formed by the oxidation of a certain VOC species has less impact on the formation of ozone. On the other hand, the amount of NO x that is sequestered through formation of nitrates or PAN-type compounds becomes more important for ozone production when it is limited by the availability of NO x . One of the approaches that have been used within the present project, is to estimate radical concentrations and ozone production rates have been based on the measured concentrations of the stable species and assuming a steady state concentration for the (short-lived) radicals, as described in section 2.1.4. In this case, only the instant radical and ozone production is calculated and thus the effect of reaction products and NO x -removal are not relevant. Only the consequences of reaction mechanisms for the immediate production of radicals and ozone must be taken into account


### 2.2.2 Hydroperoxides Hydroperoxides are formed in the atmosphere by reactions between peroxy radicals:

2 2 2 O H M, 2 2 2O O H HO HO 2 



 (R 11)LOOP Final Report 15

2 2 2 2O ROOH HO RO 

  (R 12) These reactions of the peroxy radicals are, obviously, competing with their reaction with NO (eventually leading to formation of ozone). At very low NO concentrations the photochemical formation of ozone becomes slower than the destruction of ozone by its photolysis and its reactions with HO 2 and OH:

2 3 2 2O OH O HO 

 (R 15)

2 2 3 O HO O HO 

 (R 16) In this case, tropospheric photochemistry becomes a sink for ozone. Hydroperoxides are destroyed by the gas phase reaction with OH radicals and by photolysis, the latter leading to formation of OH radicals. They are also taken up by liquid water (clouds, fog); the uptake of H 2 O 2 is particularly favoured and it is an important oxidising species in the tropospheric aqueous phase. Since the concentration of hydrogen peroxide in the troposphere appears to be largely determined by photochemical reactions and since the formation of this species, as explained above, is favoured under conditions where ozone production is NO x -limited, it is intuitively understandable, that [H 2 O 2 ], ratioed to the concentrations of other species is one of the parameters that has been used for the calculation of the indicators for NO x - or VOC-sensitive conditions, discussed in section 2.3.2.


### 2.2.3 Peroxyacetyl nitrate (PAN) Several reactions of NO or NO 2 with radicals lead to the formation of stable, non-radical species, that thus represent a sink for radicals and NO x . However, to some extent all of these species can regenerate NO x and radicals by their subsequent degradation in the atmosphere. This is the case for the organic nitrates mentioned earlier, which can form NO x by their photolytical degradation. It is also the case for nitric acid, e.g., formed by the reaction of NO 2 with OH

3 M HNO OH NO   (R 10) Nitric acid may subsequently be photolysed to form OH radicals and NO 2 , however it is a highly soluble strong acid and it has a high dry deposition velocity, thus it is likely to be removed by wet or dry deposition processes. The most important among the NO x and radical 'reservoir' species appear to be the peroxyacetyl nitrates, among which peroxyacetyl nitrate (PAN) is the most abundant. These compounds, with the general structure RC(O)OONO 2, are formed by the degradation of VOC in the presence of NO 2 , e.g., by the OHreaction with aldehydes:
O H CO CH OH CHO CH 2 3 3    (R 17)

    C(O)OO CH O CO CH 3 2 3 (R 18)

2 3 M 2 3 C(O)OONO CH NO C(O)OO CH

   (R 19) The peroxyacyl nitrates have been found to be much more abundant in the atmosphere than the simple alkyl nitrates. While pernitric acid and simpleScientific Background 16

alkylperoxynitrates are too unstable to be relevant at standard conditions (but stable at lower temperatures) PAN-type compounds are reasonably stable also at typical ground level temperatures: at room temperature the lifetime of PAN is appr. 1 hour and it increases to more than 1 year at upper troposphere temperatures. PAN is a strong eye irritant and has been found to be phytotoxic. The formation of nitric acid and the formation of PAN are competing atmospheric sinks for NO x . A model study by Meng et al. (1997) suggests thatthe relative importance of PAN-formation decreases with decreasing VOClevels. The implication of this hypothesis is that a reduction of VOC can lead to formation of more HNO 3 and consequently, if enough ammonia is available, of more ammonium nitrate, meaning more particulate matter. This is an aspect that may need to be taken into account when evaluating the costs and benefits of NO x or VOC reductions.


### 2.2.4 Ozonolysis of carbon double bonds Ozonolysis of olefinic VOC, c.f. (Kroll et al., 2001; Paulson and Orlando, 1996; Siese et al. , 2001) constitute a source of OH and peroxy radicals. The reaction pathways and the yields of ODD-H radical formation is not yet “common knowledge” and is very differently treated in models. The formation of OH radicals by photolysis of alkenes has been observed byseveral research groups by different techniques and it seems now to be a wellestablished fact that OH radicals are formed in yields that range from appr. 0.1 in the case of ethene to about unity for highly branched alkenes (Finlayson-Pitts and Pitts, 2000). The reactions leading to their generation must follow the initial formation of a Crigee intermediate. In the case of substituted alkenes, the most likely mechanism goes via the formation of an excited unsaturated hydroperoxide and leads eventually to the formation of an OH and a RO 2 radical, as shown in the example in Figure 5 (Gutbrod et al. , 1996). It should be noticed, that any reaction of a Criegee intermediate leading to the formation of an OH radical must necessarily also lead to the formation of a second radical species which normally, upon reaction with O 2 , will form a peroxy radical.

Figure 5. Mechanism for the formation of OH and peroxy radicals by the ozonolysis of a substituted alkene (Gutbrod et al. , 1996). + O 3 O + O O

O OH O

+ O 2 + OH

O O

O

O *

O O * *LOOP Final Report 17

Paulson and Orlando (1996) estimated that the ozonolysis of alkenes constitutes the most important radical formation pathway in the urban air masses of Los Angeles.



### 2.2.5 Radical measurements OH, HO 2 and organic peroxy radicals have a key role in tropospheric photochemistry and their measurement provide a critical test of the performance of atmospheric chemical models. These measurements are difficult because of the low concentrations of these species (OH peak concentrations appear to be about 10 7 molecules/cm 3 while peak concentrations of the sum of peroxy radicals, HO 2 and RO 2 have been found to be about 10 9 molecules/cm 3 ) and require advanced measurement techniques. OH radicals have been measured by several methods, often based on optical techniques. Long path absorption spectroscopy has been applied with a DOAS (Differential Optical Absorption Spectroscopy) technique, using the absorption lines of OH around 308 nm (e.g., Dorn et al. 1995). Another important optical method used for measurements of OH is based on Laser Induced Fluorescence (LIF) measurements (e.g., Holland et al. , 1995), particularly the FAGE (Fluorescence Assay with Gas Expansion) technique (e.g., Hard et al. , 1995; Creasey et al. , 1997). An interesting mass spectrometric technique for determination of OH radicals was developed by Eisele and Tanner (1991). The principle of this method is that the OH radicals in the sampled air react with 34 SO 2 to form isotopically labelled sulphuric acid, which subsequently is determined by chemical ionisation mass spectrometry. Different methods have been applied also for the measurement of peroxy radicals: HO 2 has been measured by converting it to OH which then was detected by LIF (e.g., Hard et al. , 1995; Creasey et al. , 1997). Another, but. very different, spectroscopical method for measurement of peroxy radicals is MIESR (Matrix Isolation - Electron Spin Resonance) where radicals in air are sampled in a matrix at low temperature and subsequently analysed by electron spin resonance (ESR) spectroscopy (Mihelcic et al. , 1993). This method allows to distinguish between HO 2 and organic peroxy radicals (RO 2 ). A popular and relatively inexpensive method for measurement of the sum of HO 2 and RO 2 is the chemical amplifier technique, often named PERCA (PEroxy Radical Chemical Amplifier). The principle of this method is that NO and CO are added to the sampled air, whereby the OH and HO 2 radicals take part in a chain reaction that for each radical generates several molecules of NO 2 , which eventually is measured (e.g., Hastie et al. , 1991; Clemitshaw et al. , 1997). Organic RO 2 are to a large extent converted to HO 2 by their reaction with NO in air and the PERCA does thus also measure these radicals. OH radicals are measured by the instrument also, but its concentration can be considered to be negligible compared to that of the peroxy radicals. The principle of 'chemical amplification' is also used in a recently developed instrument, where SO 2 and NO are added to the sampled air, which leads to aScientific Background 18

chain reaction, where each HO 2 or RO 2 radical produce a certain number of sulphuric acid molecules that finally is measured by chemical ionisation mass spectrometry. By adding different mixtures of nitrogen and oxygen to the sampled air, this technique allows to switch between measurement of HO 2 only and of the sum of all peroxy radicals (Hanke et al. , 2002). Calibration of radical measurements is a complicated issue. The technique that probably is most frequently applied is based on the photolysis of water in air to create OH and HO 2 radicals (Schultz et al. , 1995).
H 2 O + h  H + OH (R 20)
H + O 2  HO 2 (R 21) A more detailed discussion of radical measurement techniques can be found in the book by Finlayson-Pitts and Pitts, (2000).LOOP Final Report 19


### 2.3 From the local ozone production to the ozone concentration Christoph Spirig, Albrecht Neftel Swiss Federal Research Station for Agroecology and Agriculture, Zurich, Switzerland

Sanford Sillman Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, Michigan, USA



### 2.3.1 Basic principles The ozone concentration at a given point in time and space is the result of the chemical and meteorological processes integrated over the lifetime of ozone. This lifetime is in the order of days and is determined mainly by deposition processes and chemical loss processes.

##  

## 



0 3 3 dt dt O d O ( 33)
The change of ozone concentration in an air parcel is given by:

##  d O

dt PO LO O

3 33 3     ( ) ( 34)
P(O 3 ) : Chemical production L(O 3 ): Chemical losses and deposition  (O 3 ): Changes due to advection and mixing

P(O 3 ) and the NO x /VOC sensitivity derived from field measurements characterise the local P(O 3 ). As stated above the ozone concentration itself is only marginally related to the local production at the same place. The integration back in time has to be done with numerical models in order to get a source - receptor relationship. A general feature of the chemical aging of an air mass with a high load of pollutants ( e.g., taken up over a large urban area) is demonstrated within Figure 2. Air with fresh emissions very often starts in the high-NO x region (label 1). As the air mass travels downwind from an urban area to a region with lower emissions, dilution and chemical reactions reduce the precursor concentrations. NO x concentrations typically decrease more rapidly than VOC as the air mass ages, because NO x has a relatively short photochemical lifetime. Thus, the ratio of VOC to NO x increases as air masses age, and the air mass undergoes a transition from high-NO x to low-NO x chemistry (label 2). The process of transition is hastened if the downwind region includes high emission rates of biogenic VOC, which also shift the VOC-NO x ratio in the direction of low-NO x chemistry (label 3). Individual situations vary greatly and there are manyScientific Background 20

exceptions to this rule, but it frequently happens that air undergoes a transition from high-NO x to low-NO x chemistry as fresh emissions from an urban plume move downwind. Frequently an aged urban air mass will be recharged with emissions causing a flipping back from NO x sensitive to VOC sensitive ozone production as symbolised in Figure 6. This typical pattern of the VOC / NO x sensitivity of the ozone production of an urban air mass transported into a region with lower emission densities is symbolized in the LOOP logo.



### Downtown Downwind:
Transition from VOC sensitivity (red) to NOx sensitivity (yellow) 

### Wind direction

Figure 6. Ozone production in city plumes and the LOOP logo.

Box models are sufficient to describe and analyse the chemical production for an ideally mixed case, typically a smog chamber. Sensitivity studies with such models show the basic features of the non-linear dependence of P(O 3 ) on NO x and VOC concentrations. 1-D Lagrangian models are best suited for sensitivity studies in isolated air masses. 3-D models are needed to describe the spatial and temporal evolution of ozone and other photo-oxidants. The resolution of the chosen model must be adapted to the target that has to be investigated. The occurrence of a city plume is likely to occur on a relative small scale a few tens of kilometres, whereas the inflow conditions will depend on larger scale. Ideally grid nested models should be used that are capable of linking scales from thousands of kilometres down to 1 km. Observation based answers to emission control questions ideally should rely on simple approaches that can be based on routine data. A seminal contribution to this topic has been the idea to use indicators to distinguish between VOC and NO x sensitivity of the integrated O 3 production (Milford et al. , 1994; Sillman, 1995a).



### 2.3.2 Indicators Sillman (1995a) suggested that NO x -VOC sensitivity could be evaluated from measurements by using a series of "indicator ratios" which were linked to NO x VOC sensitivity in 3-D models. The species involved shared two characteristics: they were relatively long-lived, so that their concentrations provided evidence concerning the photochemical history of the air mass. The species were alsoLOOP Final Report 21

directly related to the chemistry of OH radicals that drives the separation into low-NO x and high-NO x regimes. In particular, they are related to the L n /Q formula discussed above. In a ratio such as O 3 /NO y , the NO y is related to the rate of removal of NO x (L n ) over the history of the air mass, while O 3 is related to the source of radicals (Q). These ratios thus provide an analogue to the formula for instantaneous ozone chemistry discussed above, but for ozone concentrations. The NO x -VOC indicators proposed by Sillman (1995a) included the following: O 3 /NO y (where NO y represents total reactive nitrogen); O 3 /NO z (where NO z represents summed NO x reaction products, or NO y -NO x ); O 3 /HNO 3 ; H 2 O 2 /HNO 3 ; H 2 O 2 /NO z ; and the equivalent ratios with summed H 2 O 2 and organic peroxides Figure 7 shows how the indicator ratios are related to O 3 -NO x -VOC sensitivity in 3-D models.

-60 -40 -20 0

20 40 60

0 5 10 15 20 O3/NOyO3 reduction (ppb)

VOC controls NOx controls

Figure 7. Predicted reduction in peak O 3 (ppb) resulting from a 35% reduction in the emission rate for anthropogenic ROG (crosses) and from a 35% reduction in the emission rate for NO x (circles), plotted against predicted O 3 /NO y concurrent with the ozone peak, in simulations for Chicago/Lake Michigan. From Sillman (1995a)

.
The figure (representing a model for the Chicago area in the U.S.) shows the predicted change in O 3 resulting from a 35% decrease in region-wide VOC emissions and from a 35% decrease in NO x -emissions. Each point represents a different model location. In some of these locations, reduced VOC results in reduced O 3 , but reduced NO x results in either unchanged or increased O 3 (highNO x chemistry). In other locations, reduced VOC has little impact on O 3 , but reduced NO x results in lower O 3 (low-NO x region). The locations with predicted high-NO x chemistry almost always have predicted low values for the "indicator" ratio O 3 /NO y , while the locations with predicted low-NO x chemistry have predicted high values for the same ratio. Thus, measured values of the indicator ratios might be used as an indirect test of the accuracy of the model predictions concerning O 3 -NO x -VOC sensitivity.Scientific Background 22

Previously Sillman (1995a) associated NO x -sensitive and VOC-sensitive conditions with high and low values of indicator ratios (O 3 /NO y , etc.) and identified transition values that separated NO x -sensitive and VOC-sensitive locations (O 3 /NO y =6-8, O 3 /NO z =8-10, O 3 /HNO 3 =12-15). A careful examination of Figure 8 shows that the NO x -VOC transition does not correspond exactly to these transition values. It can also be shown that the transition values of indicator ratios decrease with increasing O 3 . The change in indicator transition values is especially noticeable for O 3 less than 80 ppb (with NO x -VOC transitions at O 3 /NO y =11-15, O 3 /NO z =15-20). For O 3 greater than 100 ppb the NO x -VOC transition corresponds to the ratio values previously identified by Sillman (1995a). By contrast, NO x -VOC transition for ratios involving peroxides (H 2 O 2 /HNO 3 =0.2-0.3; total peroxides/HNO 3 =0.3-0.45) do not appear to vary for the range of clean and polluted conditions shown here.



### 2.3.3 Discussion of indicators The NO x -VOC transition values for some indicator ratios can vary significantly. However, this does not appear to be just a random variation among different models or locations. Rather, it is part of a systematic variation from relatively clean to highly polluted conditions, which is imperfectly captured by the indicator ratios. This variation can be explained in terms of the radical chemistry that drives the split into NO x -sensitive and VOC-sensitive conditions. As was done in Sillman et al. (1998), the indicator results for individual simulations are summarised by recording the 5th, 50th and 95th percentile indicator values for NO x -sensitive and VOC-sensitive locations. A NO x -VOC indicator is successful if the 95th percentile value for VOC-sensitive locations is equal to or less than the 5th percentile value for NO x -sensitive locations, and if the transition values remain similar in models for different locations and assumptions. These 95th percentile and 5th percentile values identify the transition from NO x -sensitive to VOC-sensitive conditions.LOOP Final Report 23



### 2.4 Photo-oxidants and aerosols
Jean-Philippe Putaud European Commission Joint Research Centre, Ispra, Italy Christoph Spirig Swiss Federal Research Station for Agroecology and Agriculture, Zurich, Switzerland

A very large part (generally >40%, often more) of the fine aerosol mass consists of secondary components such as non sea salt sulphate (nssSO 4 2- ), nitrate (NO 3 - ) and a fraction of the particulate organic matter (POM). These components result from the oxidation of SO 2 , NO 2 and certain VOCs by OH, O 3 , peroxides, etc.... Some of these reactions represent a sink of photo-oxidants. The formation of particulate NO 3 - and the formation of secondary POM also represent a sink of NO x and VOCs, which are precursors of ozone. The net production of ozone and the formation of secondary aerosol are therefore intimately coupled (Figure 8). This leads to difficulties in conceiving efficient abatement strategies for both ozone and particulate matter (PM).

OH

HO 2 HNO 3

NH 3 POM NH 4 NO 3 h  NO 2

O 3

NO condensable products VOCs

RCOO 2 Other volatile products

PAN

Figure 8. Chemical coupling in the production of oz one and secondary aerosol. Some termination reactions in the ozone cycle lead to the production of particulate species.

For instance, in a VOC rich area where the net production of ozone is limited by the availability of NO x , a reduction in NO x concentrations (to reduce O 3 level) would leave more OH radicals available for the oxidation of VOCs, which might lead to an increase in secondary POM concentration. In contrast, in a NO x rich area, the reduction in VOC would be efficient to decrease ozone levels, butScientific Background 24

would favour the production of nitric acid (HNO 3 ) and then the formation of NH 4 NO 3 when enough ammonia (NH 3 ) is available.


### 2.4.1 Inorga nic aerosols The inorganic fraction of the aerosol consists mainly of SO 4 2- , NO 3 - , NH 4 + , and often minor amounts of other ions and elements. These latter are primary aerosol components (sea spray, soil dust, etc...) whereas NssSO 4 2- , NO 3 - , and NH 4 + are secondary components, the precursors of which are the gaseous species SO 2 , NO 2 and NH 3 , respectively. NssSO 4 2- can be produced by the gas phase oxidation of SO 2 : SO 2 + OH + (O 2 , H 2 O)  H 2 SO 4 + HO 2 (R 22) The lifetime of SO 2 relative to this reaction is > a few days at typical atmospheric levels of OH. Sulphuric acid (H 2 SO 4 ) reacts with NH 3 to produce condensable NH 4 HSO 4 and (NH 4 ) 2 SO 4 when enough NH 3 is available. This mechanism can lead to the formation of new particles (nucleation process). However, a large fraction of nssSO 4 2- is formed by oxidation of SO 2 in cloud droplets or at the surface of pre-existing particles: (SO 2 , H 2 O) + ROOH  SO 4 2- +2 H + + ROH (R 23) (R = H or alkyl group) (SO 2 , H 2 O) + O 3  SO 4 2- + 2 H + + O 2 (R 24) These processes are generally more efficient than the gas phase oxidation. They lead to the growth of particles (when clouds evaporate in the case of cloud droplets) but not to the formation of new particles. HNO 3 is formed according to the reaction:
NO 2 + OH + (M)  HNO 3 + (M) (R 10) The lifetime of NO 2 with respect to this reaction is about one day. HNO 3 and NH 3 are in equilibrium with the particulate compounds NH 4 NO 3 : HNO 3 + NH 3 NH 4 NO 3 (R 25) The equilibrium constant depends on the temperature, the state of NH 4 NO 3 (solid/liquid) and its concentration in solution when NO 3 - and NH 4 + are mixed with SO 4 2- in the aqueous phase (Seinfeld and Pandis, 1998). NH 4 NO 3 cannot exist in the particulate phase if the amount alkaline species is not sufficient to neutralise at least the first acidity of H 2 SO 4 . 

### 2.4.2 Secondary organic aerosols Particle measurements in both urban and rural areas have shown that a major fraction of particulate mass consists of organic matter. Both primary and secondary sources contribute to organic matter in aerosols. Numerous smog chamber studies have proven the potential of certain VOC to form particles (Forstner et al. , 1997; Grosjean and Seinfeld, 1989; Hoffmann et al. , 1997; Koch et al. , 2000; Wang et al. , 1992).LOOP Final Report 25

While the formation of new particles is still poorly understood, there is a framework of theory available for the transfer of gaseous organic compounds onto existing particles. In urban areas, this is expected to be the major pathway in terms of mass transfer from gaseous to particulate phase, as high aerosol surface areas suppress the formation of new particles (Aalto et al. , 2001). Once VOC are released into the atmosphere, they react with oxidizing agents like OH, O 3 or NO 3 . Some of the products formed in these reactions exhibit vapour pressures low enough so that they readily condense onto existing particles. Among the VOC found to be potentially important for secondary organic aerosol formation are compounds from both anthropogenic and biogenic origin: aromatics, alkanes, alkenes, and carbonyls from anthropogenic sources, while biogenic VOCs include organics such as mono- and sesquiterpenes as well as a series of oxygen-containing compounds. As the volatility of oxidation products is one of the most determining factors, a rule of thumb says that only hydrocarbons with more than six carbon atoms contribute to secondary organic aerosol under atmospheric conditions. Figure 9 shows the aerosol forming potential of selected VOCs, determined as the sum of their condensable oxidation products (Seinfeld and Pandis, 1998).

0 200 400 600 800 1000CyclohexanC11CycloparaffineIsoprenBenzolm/o-Xylen1,3,5-TrimethylbenzolNitrophenole(gm-3ppm-1)

Figure 9. Secondary organic aerosol yields of selected VOCs (Seinfeld and Pandis, 1998).
The aerosol yield (Y) is a measure for the particle forming potential of a certain compound:
     
 



 

3

3 ] [ ) (
gm gm VOC Aerosol M Y   ( 35)

with DM: Organic aerosol mass produced DVOC amount of VOC reacted As different experiments showed a big variety of aerosol yields for a certain compound it has been recognized that these yields cannot be constant, but rather depend on the properties of existing aerosol. Oxidation products partition into theScientific Background 26

organic fraction of the aerosol before they reach saturation vapour pressure. As a result, aerosol yields depend strongly on the organic aerosol mass. Partitioning models considering this dependency have been described by Pankow (1994) and Odum et al. (1996). In this framework the partitioning of each oxidation product is described by a partitioning coefficient (K i ) as

0

, i L O o i i i p MW RT M G A K    ( 36)

A i concentration of product i in the absorbing organic aerosol phase
G i gas phase concentration of product i Mo absorbing organic aerosol mass concentr ation with mean molecular weight MWO, R universal gas constant T temperature  activity coefficient of product i in the organic phase

p L,i vapour pressure of product i as a pure liquid
Partitioning coefficients therefore strongly depend on the liquid vapour pressure of the compound and on the organic aerosol mass. Experimentally found aerosol yields from various VOCs could be reasonably described with a model assuming two oxidation products:

     

    



o o o M K

K M K

K M VOC A A Y

2 2 2

1 1 1 2 1 1 1 ] [   ( 37)

where 1 and 2 are the mass-based stoichiometric coefficients for the reaction generating products 1 and 2 from the parent VOC. Figure 10 illustrates the dependency of aerosol yields on organic aerosol mass in the example of some aromatic compounds (Odum et al. , 1997).

0 40 80 120 160 200  M om  g m -3 ) 0 2 4 6 8 10 12Yield (%) xylenes toluene ethyltoluene ethylbenzene

 1 =0.038,  2 =0.167 K 1 =0.042 , K 2 =0.0014  1 =0.071,  2 =0.138 K 1 =0.053 , K 2 =0.0019

Figure 10. Secondary organic aerosol yields of aromatics as a function of organic aerosol mass concentration. Curves are fits to data using the two-product model of equation (37) (Odum et al. , 1997).LOOP Final Report 27

In the context of a project devoted to photo-oxidant production it should be noted that most VOCs of importance for secondary aerosol production exhibit higher aerosol yields through reaction with O 3 as compared to their reaction with OH. It is therefore expected that regimes with high concentrations of photo-oxidants and VOCs effectively produce secondary organic aerosols.Scientific Background 28



### 2.5 Performance of emission inventories – what can modellers expect?

Wilfried Winiwarter ARC Seibersdorf Research, Seibersdorf, Austria



### 2.5.1 Introduction Emission inventories traditionally have been created from a regulatory perspective. Available information was centrally collected, and attributed to certain grid cells or polygon areas. Requirements for emission inventories quickly increased. Some of these requirements could be settled by installing additional measurements. Especially as long as point source emissions accounted for the major part of total emissions, this approach proved very sensible. Later, the success of air pollution regulation decreased the contribution of large point sources. Minor sources relatively gained in importance. Small industry, traffic (despite the immense success of emission reductions by introducing catalytic converters in cars) and small-scale combustion processes as in domestic heating started to dominate the field. This small sketch shows that the technique in emission assessment has always lagged behind the actual situation. As soon as novel approaches have been implemented (in terms of additional measurements, in terms of improving emission factors), the emission situation itself also had changed due to newrequirements and technologies. Moreover, it proved very difficult to retrospectively apply techniques that were developed for a certain situation. Still the regulatory approach made it possible to develop inventories, such that data were accessible as soon as atmospheric modellers required them. However, the focus on data quality was not fully identical. While a regulatory approach aimed at full assessment (“completeness”) of all sources, and on the possibility of assessing prescribed changes of emissions (“comparability”), an approach towards the “true value” (“accuracy”) only very recently has become an explicit aim of the regulatory type inventory (Lim et al. , 1999). Consequently, emission data were never fully satisfactory for input to atmospheric modelling. Nevertheless they are still used because creating new emission inventories from scratch is a huge effort. In the following section, the possibilities and the limitations of emission inventories are discussed in some more detail, in order to limit frustrations and false expectations of atmospheric modellers. Potentials for improvements are also suggested.


### 2.5.2 Steps of creating an emission inventories When creating an emission inventory, efforts are concentrated in obtaining as much information as possible within a limited time frame or project volume. The amount of work required is normally not defined, but depends – in addition to local circumstances of data availability – on the specific interrelation between aLOOP Final Report 29

contractor and a client. Only in few cases the exact extent of the emission inventory or the detailed parameters to be assessed are defined independently, and thus a quality criterion is defined beforehand. Such examples are the Austrian standard on emission inventories (ON, 2000), or the international activities on the European scale (CORINAIR: EEA, 2001) and on the global scale (UNFCCC / IPCC: Penman et al. , 2000). The latter examples are on a country scale only, however.


### 2.5.3 Types of input into emission inventories Emission inventories typically comprise information from a multitude of data sources. Best in quality are data based on operational emission measurements available from very large sources. Continuous measurements, as required in the regulatory process, provide a good temporal resolution, while the place of emissions likewise is well known. The only difficulty may lie in the accessibility of data: point source emission information may be considered confidential and thus not freely enter emission inventories. Next in quality are existing emission measurements for a specific emission source. Again, for certain industrial installations data from prescribed measurement programs may be obtained. Measurements usually tend to represent stable operation conditions of the respective installation. It is however well possible that emissions from instable conditions dominate. Additionally, the relation of emission measurements to the activity rates of parameters that are publicly available may be difficult. Such activity rates are required for the period of measurement in order to derive an emission factor, but then also for the time period that the emission inventory is valid for (usually a year). Emissions in this case, as generally, are calculated as the product of emission factor times activity rate (E = EF x A). Activity rates are usually statistical data or other forms of accessible quantitative information, for bottom-up inventories this may also include the results of specific counts as well as evaluation of questionnaires. In a still more general way, emission factors available from literature are applied to activity rates describing a specific emission source. It is evident that the appropriateness of an emission factor to a certain activity, as wells as the precision of this emission factor can be very different from case to case. The same is true for applicability and magnitude of activity rates. Consequently the quality of the resulting emission data depends on very subtle differences and variations of input choices. Many emission inventories, which are not required to undergo a standardized protocol of quality control, may consequently not fully reflect reality. There are too many possibilities to choose input data incorrectly. Still, such an inventory can provide very valuable information to establish a useful air quality program. The main requirement for an inventory to be valuable in this direction is consistency. Consistency is however not sufficient to provide externally useful emission data. Even standardized protocols do not guarantee a general validity of data, but at least they provide another important aspect, comparability.Scientific Background 30

Comparable emission inventories allow for judging the same mistakes between different inventory regions or regimes. Emission inventories for use in air quality modelling however require high precision information, which only can be achieved when emissions are validated (see section 2.5.5 “methods for validation”).


### 2.5.4 Disaggregat ion of emission inventories Certain input data to be used for the inventory may be available at a scale that is larger than the inventory scale itself. This may be in terms of spatial resolution as well as in temporal resolution. Examples are statistical information which may be available for an administrative unit rather than for the grid chosen to represent the inventory, or for a year rather than for the hourly information required by air quality models. The general way to resolve this kind of top-down information onto smaller units is to apply surrogate statistics. This information is available at the desired scale, and sufficiently related to the process or activity to be investigated. The most characteristic example for anthropogenic emissions is the population density, which is widely available at a very high resolution, much higher than common statistical information. Emissions calculated for a certain type of anthropogenic activity may consequently be distributed in space according to population. Similarly, parameters to adequately describe temporal behaviour may be derived. Temporal behaviour can mostly be described using three sets of cycles: a seasonal cycle, a weekly cycle and a diurnal cycle. It is interesting to note that these three cycles are applicable to anthropogenic emissions, while the weekly cycle is not relevant for natural emissions. Clearly, each step of disaggregation adds more uncertainty to the emission figures presented. Surrogate statistics are per se not activity statistics, and their applicability is limited. Even if such a disaggregation always may be considered an improvement over just applying an average value (which is the alternative), it may be misunderstood as the representation of a real value. This becomes extremely evident when the third application of disaggregation commonly used is discussed, the application of split data to total non-methane volatile organic compound (NMVOC) emissions. Generally, total NMVOC is measured using an apparatus consisting of a Flame Ionization Detector (FID) and a GC column which is flushed backwards after the methane peak passed. Consequently no information on the actual NMVOC species is available for the vast majority of emission measurements. Species information, which is required as an input to air chemistry, is available for a small set of data and for a specific area or region. A widely used set has been published for UK (Derwent et al. , 1996). Now there is no indication that solvents, processes, fuels, or even general temperature conditions in, say, Italy, are sufficiently similar to warrant using the same split. Even worse, there are data on the composition of a refinery plume indicating drastic variations within days in the plume of a single refinery (Sexton and Westberg, 1983).LOOP Final Report 31

Again, this indicates that, while performing a split into VOC species is easily possible, resulting numbers need to be treated with great care. Not many sensitivity studies are available that would allow to assess the results in terms of ozone chemistry when using alternate speciation sets. Instead, VOC splits are used as a fixed input parameter. These splits however rather represent an expert’s guess (or a model in terms of an expert system), and their ease in computation must not misinterpreted as a real result. Still, also in this case, a guessed VOC split will allow a far better representation than if no split at all were provided.



### 2.5.5 Methods for validation In the development of emission inventories, only very recently quality considerations also have gained in weight. A number of approaches is available to assess and validate data quality of emission inventories. Not only the absolute magnitude of emissions may be validated, but methods have been developed that also allow to assess and compare the spatial variability of data sets. A detailed assessment of quality requirements in terms of urban emissions inventories has been developed by Sturm et al. (1999) in the framework of the EUROTRAC SATURN and GENEMIS subprojects with contributions for six different European cities. While developing the spreadsheets as shown in Figure 11, information was also spread to the LOOP inventory community. The availability of all specific information required for the quality sheets was discussed and confirmed by all participating institutions. However, no completely filled sheet ever appeared.

Figure 11. Quality assessment sheets for emission inventories. Element Parameters Emissions choice of indicator value of indicator (activity) emission factor model structure surrogate for spatial disaggregation surrogate for temporal disaggregation choice of VOC-split
Public power plants District heating plants Petroleum refining plants Solid fuel transform. plants

...... Completeness analysis emission covered ? (Y/N) (If emission not covered: Comment why considered irrelevant )

Public power plants District heating plants Petroleum refining plants Solid fuel transform. plants

...... Uncertainty applicability of indicator uncertainty of indicator (activity) uncertainty of emission factor representativity of emission factor model structure uncertainty of spatial disaggregation uncertainty of temporal disaggregation Public power plants District heating plants Petroleum refining plants Solid fuel transform. plants

......

Element Approaches Retrospective analysis relative contribution of source groups sensitivity analysis (optional) overall uncertainties

Validation (at least one approach is required) Alternative emission assessment Emission trends vs. ambient air concentration trends Flux estimations from downwind measurements Atmospheric models Fingerprints and receptor modellingScientific Background 32



### 2.5.6 Recommendations and conclusions In a purely scientific context, the above-mentioned shortcomings of inventories call for an improvement of procedures. Especially, this needs to facilitate well documented updates, as soon as new information becomes available. However, emission data used for policy purposes can not easily be altered or adapted. They represent a precondition for negotiations, and even small changes may undermine the confidence in the inventory (also as the term "inventory" implies an actual counting). Also, the robustness of information and the reliability of the measures set upon may be more important to negotiation partners than the closeness to the real world - see the diverging concepts of "truth" set forth by different professions as discussed by Pulles (1999). Consequently, environment agencies are extremely reluctant to update their estimations. Moreover, an adaptation would require to also adjust a chain of other data (e.g., emissions form previous years), often causing efforts beyond the resources available. It poses a considerable challenge to atmospheric modellers to apply good emission data under these preconditions. The following procedures should however help doing so:

 Select an emission database that fulfils as much of the requirements shown in Figure 11. Especially, validation of the inventory and verification through field data are extremely beneficial to the inventory. It cannot be expected that a full verification of the inventory is available, but quality assurance must be considered from the beginning.

 Choose a local emissions expert. Many of the questions arising from applying an inventory can only be solved by the persons familiar with the instances in the respective area in their daily lives. Also, the availability of certain data is best assessed by people who are aware of local habits.

 Assess the confidence range of emission information. This may require an outside expert, who again may need to contact the local emissions expert. Uncertainty or confidence ranges are often difficult to obtain. One reason is that the input data are of unknown quality, another is that the applicability of a certain emission calculation method, an emission factor or of a surrogate statistic is simply not known. Still, there always remain possibilities to estimate this. In contrast to models, emission inventories should be seen as open systems, where the influence of the respective data sources also needs to be considered.

 Do a sensitivity analysis of model results. Those input parameters which severely affect the outcome of the model should be flagged and checked with double care.

 Mark these inconsistencies and refer them back to the compiler of the inventory, and/or the local expert. In many cases there may be simple explanations, but experience shows that too often large discrepancies remain. This discussion should however bear in mind that the inventory relies on a substantial amount of external data, which do not allow further checks.LOOP Final Report 33

 An adaptation of the emission information according to model results should be avoided as much as possible. Only when considerable further evidence is available ( e.g., atmospheric measurements), the changes may be performed but need to be very well documented. Emission data should never be used to tune a model in order to fit the atmospheric concentration (unless a validated model is used for inverse modelling) While emission inventories are far from producing optimised results to atmospheric modellers, they still remain to be a very important source of information. As all purpose in atmospheric modelling is to improve the air quality, emission inventories are indispensable. They describe the main anthropogenic influence to the atmosphere, and consequently the only control to change air quality. Consequently, emission data are required for any attempt to design a reduction measure. Using the approach described above, the reliability of the model can be assessed in relation to the confidence in the inventory. This is what ultimately is needed when applying results of models to improve the air quality.

## 3. Field Campaigns

Activities within LOOP were centred on several field initiatives, each of them with its own characteristics and structure. BOCCALINO and SLOPE can be considered as “trigger initiatives” for LOOP. They are shortly described below before the actual LOOP projects are presented in more detail.



### 3.1 BOCCALINO

Albrecht Neftel Swiss Federal Research Station for Agroecology and Agriculture, Zurich, Switzerland

The BOCCALINO (= B iogenic O rganic C arbon C ompounds a s L eading I ndicators of N on – H 2 O 2 Per o xides) study was performed in July and August 1994 in the southern part of Switzerland. The region is adjacent to the Po Valley and exhibits the highest ozone concentrations in Switzerland. The main results are summarised below:

Elevated O 3 concentrations are generally influenced by the “Milan urban plume” and often are accompanied by elevated SO 2 concentrations

An estimation of the isoprene emissions shows roughly a five times higher emission rate as predicted by the inventories used at that time. As consequence, isoprene dominates the VOC reactivity towards OH at the ground

The dominant peroxide is H 2 O 2 . It accounts for 64% of the total mean peroxide concentrations. HMHP concentrations were below 0.3 ppbv.

Maximum concentrations of 10 ppbv were measured for HCHO. It was estimated that isoprene oxidation accounts for 30% to 70% of the HCHO production.

HONO concentrations were around 0.2 ppbv in the afternoon and cannot be explained by homogeneous gas phase photochemistry. The measured HONO concentration would account for about 30% of the radical production in air close to the ground.

Comparison of radical production and NO x emissions show that the air masses are in the low NO x chemical regime. Those calculations are based on data that are not influenced by the plume.

Indicators for NO x or VOC sensitivity calculated from the measurements from August 1994 show that reduction of NO x emissions would be most efficient in reducing O 3 in the Ticino region. Detailed results are described in Prévôt et al. (1997), Staffelbach and Neftel (1996a,b), Staffelbach and Neftel (1997) and Staffelbach et al. (1997a,b).Field Campaigns 36



### 3.2 SLOPE

Andreas Volz-Thomas FZJ Research Center Jülich, Institut für Chemie und Dynamik der Geosphäre; IUP, Germany

SLOPE ( S chauins l and O zone P recursor E xperiment) was conducted in 1996 and aimed at studying the chemical and physical development of the plume of the city of Freiburg during transport to the TOR station Schauinsland. The experiment was designed in a semi-Lagrangian fashion, by using the valley that descends from Schauinsland towards Freiburg as a flow reactor. A large suite of chemical species and meteorological parameters was measured at Schauinsland and at the entrance to the valley. Transport path and time, as well as the mixing with air from aloft were constrained by a dispersion experiment with SF6. Several tethered balloons, LIDAR and SODAR systems were distributed in the valley in order to provide information on the vertical structure of and a small research aircraft provided detailed information on VOCs and NO x within the valley and on the residual layer above. The setting of SLOPE was based on the experience gained in an earlier experiment with much simpler logistics that was conducted in 1992 as part of TOR and TRACT. The major scientific findings of SLOPE are:

Given the large prevailing NO 2 mixing ratios (between 3 and 15 ppb), a relatively large OH concentration of (7  2)x10 6 cm -3 was estimated from the degradation of reactive hydrocarbons in the plume of Freiburg during transport to Schauinsland. The thus derived OH concentration was almost a factor of two larger than what was calculated with a chemical box model (EURO-RADM) constraint by the measured trace gas concentrations and photolysis rates. The conclusion was that current model results on the basis of RADM2, which is quite similar to EURO-RADM, would tend to under predict OH radical concentrations and, hence, the ozone formation rate in polluted air masses.

It was possible to close the radical budget within the experimental uncertainties, when an upper limit was adopted for the radical production from photolysis of nitrous acid, in addition to that from photolysis of ozone and carbonyl compounds as well as ozonolysis of unsaturated VOCs.

From the ground based VOC measurements (C 2 -C 15 ), it was found that the total VOC concentration was dominated by anthropogenic hydrocarbons (alkanes and aromatics). The contribution of biogenic VOCs to the total VOC reactivity (  VOC i ]·k OHi ), however was already about 30% in the polluted air masses at the entrance to the valley and about 70% at Schauinsland.LOOP Final Report 37

The results of SLOPE basically confirmed the conclusions from an earlier experiment made during the TRACT campaign in 1992 at the same location. Detailed findings are described in Fiedler et al. (2000), Kalthoff et al. (2000), Kramp and Volz-Thomas (1997), Pätz et al. (2000), Volz-Thomas and Kolaghar (2000), Volz-Thomas et al. ((2000).Field Campaigns 38



### 3.3 BERLIOZ

Andreas Volz-Thomas FZJ Research Center Jülich, Institut für Chemie und Dynamik der Geosphäre; IUP, Germany

The Berlin Ozone Experiment (BERLIOZ) was conducted as part of the German Tropospheric Research Focus (TFS) between July 5 and August 7, 1998 near the city of Berlin. The overall aim was to analyse the transport and chemical processes responsible for photo-oxidant formation and the contribution of groups of precursor substances to the ozone formation in the Berlin area. For this purpose, speciated hydrocarbons and carbonyl compounds, O 3 , PAN, NO, NO 2 , HNO 2 , HNO 3 , and NO 3 radicals (e.g., J NO 2 and J O 1 D) were measured at different ground based sites by in situ techniques, along with measurements of meteorological parameters and photolysis frequencies. A map of the area around Berlin with the BERLIOZ sites is shown in Figure 12. The sites had been lined up along an axis extending from ca. 70 km south-east to 80 km north-west of Berlin, according to the predicted main wind direction during episodes with large photochemical activity. In addition to the surface sites, which were operational during the whole BERLIOZ period, 6 ozone LIDAR systems, 5 tethered balloons and 6 aircraft were deployed during intensive operational periods (IOPs). Some of the ground based measurements, i.e., carbonyl compounds, carboxylic acids, and the measurements of peroxy radicals by MIESR (see below) were also confined to the IOPs. The Photochemistry Experiment in BERLIOZ (PHOEBE), which was conducted at Pabstthum, about 50 km NW of Berlin, aimed at a quantitative understanding of the fast radical chemistry in rural and suburban air by simultaneous measurements of the major free radicals (OH, HO 2 , RO 2 , NO 3 ) and the chemical compounds and physical parameters that control the radical concentrations (NO, NO 2 , NO y , non-soluble NO y , PAN, hydrocarbons and oxygenated organic compounds, peroxides, ozone, CO, photolysis frequencies and meteorological parameters). Interpretation of the measurements involved models of different complexity. Specific questions addressed by PHOEBE were:

How do the concentrations of free radicals depend on the primary production, i.e., J O 1 D , and on the concentrations of, e.g., NO x , VOC?

What is the influence of other sources (e.g., HONO photolysis, ozonolysis) on the radical concentrations?

How well do state of the art photochemical models reproduce the measured radical concentrations?

Is the photo-stationary state (PSS) approach (see section 2.1.5) a suitable tool to determine the local ozone production rate P O 3 ?LOOP Final Report 39

What are the main sources of free radicals at night and how large is the influence of NO 3 radicals on the oxidation capacity of the atmosphere?

Figure 12. Map of the area around Berlin with the surface sites of BERLIOZ indicated by the red dots and the airfield Schönhagen by the yellow dot. The red lines denote motorways and major roads.

The methods deployed in BERLIOZ and the results are discussed in a special section in J. Atmos. Chem . 42, 2002, (Corsmeier et al. , 2002; Junkermann et al. , 2002; Moortgat et al. , 2002; Platt et al. , 2002; Winkler et al. , 2002). The scientific results from PHOEBE are published as a separate special section in J. Geophys. Res . and are summarised in the following.


### 3.3.1 Scientific background The transition region between urban and rural areas is important in understanding the fast photo-chemistry that leads to the build up of high concentrations of ozone and other photo-oxidants during so-called summer smog episodes. As is schematically outlined in reactions R26 to R30, hydrocarbon oxidation by hydroxyl radicals (OH) leads to formation of peroxy radicals (RO 2 and HO 2 ), which oxidise NO to NO 2 , thereby regenerating the OH. Photolysis of the NO 2 then generates the oxygen atoms required to produce ozone (Chameides and Walker, 1973; Crutzen, 1979). In this simplified view, the net effect of the radical chain in the presence of sufficient NO is that hydrocarbons are oxidised toField Campaigns 40

carbonyl compounds (CARB, i.e., aldehydes and ketones) and O 3 , c.f. (Chameides et al. , 1992).

R-H + OH + O 2 (+ M)  RO 2 + H 2 O (+ M) (R 26)
RO 2 + NO  RO + NO 2 (R 27)
RO + O 2  HO 2 + CARB (R 28)
HO 2 + NO  OH + NO 2 (R 29) (2x)NO 2 + h  + O 2 (+ M)  NO + O 3 (+ M)  < 420nm (R 30)
net: R-H + 3 O 2 + h   CARB + 2 O 3 + H 2 O

Additional HO 2 is produced from the oxidation of CO:
CO + OH + O 2 (+M)  CO 2 + HO 2 (+M) (R 31) Production of OH radicals in the troposphere occurs mainly via photolysis of O 3 (R32 and R33) ( Levy , 1971)
O 3 + h   O 1 D + O 2  < 340nm (R 32)
O 1 D + H 2 O  2 OH (R 33) Another potentially important source for OH, particularly in polluted air, is the photolysis of HONO which is formed either in the tail pipe of motor vehicles (Kessler and Platt, 1984) or upon emission in the atmosphere via heterogeneous reactions (Stockwell and Calvert, 1983). HONO + h   OH + NO  < 405nm (R 34) Since R34 occurs at much longer wavelengths than R33, it can produce OH during the early morning, when R33 is still rather ineffective. Thereby, HONO photolysis is thought to provide an important trigger for photochemical smog formation in the urban and sub-urban environment (Calvert et al. , 1994; Harris et al., 1982; Harrison et al ., 1996; Jenkin et al ., 1988; Platt, 1986; Volz-Thomas and Kolahgar, 2000). Other sources of OH and peroxy radicals include the ozonolysis of olefinic VOC, c.f. (Kroll et al ., 2001; Paulson and Orlando, 1996; Siese et al ., 2001), and the reaction of NO 3 radicals with organic compounds at night (Carslaw et al ., 1997; Mihelcic et al ., 1993; Platt and Volz-Thomas, 2000; Salisbury et al ., 2001; Wayne et al ., 1991). During daytime, the concentration of OH and the production rate of ozone both depend on the UV-flux (i.e., on the photolysis frequencies J O 1 D , J NO 2 , J HONO, J HCHO,... ) and on the concentrations of H 2 O, O 3 , CO, NO x and VOCs. The role of NO x in the budgets of HO x (OH and HO 2 ) and ozone is ambivalent: At low NO x concentrations, where recombination reactions of the peroxy radicals (R35 + R36) constitute the major radical loss, NO controls the recycling of HO x and is the rate limiting factor in ozone production (Crutzen, 1979).
HO 2 + HO 2 + M  H 2 O 2 + M (R 35)
HO 2 + RO 2 + M  ROOH + M (R 36)LOOP Final Report 41

At high NO x levels, radical losses by recombination of peroxy radicals become insignificant and HO 2 is almost completely recycled to OH in reaction R29. Due to photolysis of the carbonyl compounds formed in reaction R28, more radicals can be produced from the hydrocarbon oxidation mechanism than are initially consumed in R26, depending on the VOC to NO x ratio and VOC reactivity (Jeffries and Tonnesen, 1994). CARB + h   HO 2 + ....... (R 37) This effect is counteracted by the reaction of OH with NO 2 , which becomes the predominant sink of HO x in the high-NO x regime and thus effectively limits the production of RO 2 and HO 2 . NO 2 + OH + M  HNO 3 + M (R 38) The net effect of NO x on the radical balance and ozone formation depends on the NO/NO 2 ratio, which is controlled by the photolysis rate of NO 2 (R30) and by the concentrations of peroxy radicals and O 3 via reactions (R27, R29, and R39).
NO 2 + O 3  NO + O 2 (R 39) The radical chemistry in the high-NO x regime is further complicated by the photolysis of HONO (R34). Another complication in the understanding of the radical budget is the formation of alkyl nitrates and peroxyacyl nitrates, e.g., PAN. Alkyl nitrates are formed as by-products in R27. The branching ratios tend to increase with the size of the organic rest and are higher for secondary and tertiary RO 2 (Atkinson et al. , 1982; Atkinson et al. , 1984; Atkinson et al. , 1987; Atkinson et al. , 1983; Becker and Wirtz, 1989). The net effect is a decrease in the local ozone formation rate. Similarly, the production of peroxyacteyl nitrate (PAN) and its homologues by reaction of acylperoxy radicals with NO 2 (R40) removes peroxy radicals and NO x from the active pool. Thermal decomposition of the PAN compounds establishes equilibrium, but the net effect on the local radical balance depends on temperature and history of the air mass. R-C(O)-OO + NO 2 + M R-C(O)-OO-NO 2 + M) (R 40) Because of their short chemical lifetimes, free radicals (OH, HO 2 and RO 2 ) are largely de-coupled from transport processes and can thus be investigated with zero dimensional chemistry models, provided that the chemical and physical parameters which control the local radical concentration, e.g., UV radiation, NO x and VOC, are quantified. This is strictly the case only if the radicals and other parameters are measured with a time resolution given by the atmospheric lifetimes of the radicals, e.g., between less than one second for OH and a few seconds to minutes for the peroxy radicals. Until now this is an unsolved challenge for many atmospheric trace gases, in particular for hydrocarbons which in urban air can constitute of more than 100 different compounds (Ciccioli et al. , 1992). The interpretation of the radical measurements thus relies on assumptions about stationarity and homogeneity of the investigated air masses. These conditions are best achieved if the measurements are made at sufficient distance from sources or sinks and if the different measurements are made as close together as possible.Field Campaigns 42

The fast photochemistry of the troposphere has been investigated by measurements of the concentrations of the free radicals (OH, HO 2 , RO 2 ) and of those chemical and physical parameters that are thought to control the radical concentrations. Besides airborne missions in the free troposphere (Brune et al. , 1998; Mauldin III et al. , 1998; Wennberg et al. , 1998), a strong emphasis has been on ground based studies, mostly in the planetary boundary-layer (PBL), c.f., (Armerding et al. , 1997; Brandenburger et al. , 1998; Brauers et al. , 2001; Cantrell et al. , 1997; Cantrell et al. , 1996; Carpenter et al. , 1997; Carroll et al. , 2001; Carslaw et al. , 1999; Ehhalt, 1999; Eisele et al. , 1994; Eisele et al. , 1997; Eisele et al. , 1996; Hauglustaine et al. , 1999; Holland et al. , 1998; Jefferson et al. , 1998; Kanaya et al. , 2000; Kanaya et al. , 1999; McKeen et al. , 1997; Mount and Williams, 1997; Penkett et al. , 1997; Perner et al. , 1987; Platt et al. , 1988; Poppe et al. , 1994; Savage et al. , 2001; Volz-Thomas and Kolahgar, 2000). These field experiments qualitatively confirmed fundamental aspects of the free radical chemistry, such as the principal dependencies of OH on the ozone photolysis frequency J O 1 D and the concentration of NO x . There is a general tendency of photochemical models to overestimate OH and peroxy radicals. Exceptions are, e.g., the ALBATROSS, POPCORN, and SCATE campaigns, and the MLOPEX-2 campaign in spring, where modelled and measured OH were found to agree within the experimental uncertainties for clean air conditions (Brauers et al. , 2001; Ehhalt, 1999; Eisele et al. , 1996; Jefferson et al. , 1998), and some campaigns conducted in forested areas (e.g., AEROBIC, PROPHET, and SLOPE) where OH was found to be significantly underestimated by the models (Carslaw et al. , 2001; Creasey et al. , 2001; Tan et al. , 2001; Volz-Thomas and Kolahgar, 2000). It is also noted that in some of the earlier studies, the comparison was somewhat limited by the lack of measurements for several trace gases that are of importance to OH formation or loss, e.g., part of the VOC. Very little information exists so far on the behaviour of OH radicals in highly polluted atmospheres, except of the LAFRE campaign (George et al. , 1999).


### 3.3.2 Experimental The PHOEBE site (12°56'25" E, 52°51'15" N, 50 m above sea level) was established about 400 m east of the small village Pabstthum at the western border of a flat grassland of 7.5 x 4.5 km2 extension (Figure 13). Selection criteria were the distance from Berlin in order to obtain a good representation of the plume development in conjunction with the other BERLIOZ sites (see Figure 12), as well as the absence of local anthropogenic sources, reasonably flat terrain with unrestricted flow from the direction of Berlin, and logistical requirements such as electrical power and road access for supplying liquid nitrogen and gases. The area around Pabstthum, which itself consists of only 5 residences, is extremely sparsely populated. Local emissions from traffic, industry and residences in the vicinity of Pabstthum are negligible, except for the motorway A24 in the west about 10 km away. The nearest villages Wall and Radensleben located about 5 km and 7 km to the south east and north west, respectively, have less than 500 inhabitants. The closest city is Neuruppin, 12 km in the north west,LOOP Final Report 43

with 38000 inhabitants. Berlin (3.4 million inhabitants) is located in south easterly direction (140°) at a distance of about 50 km (city centre). A mixed forest dominated by Scots pine ( pinus sylvestris ) extends from the station in westerly and northerly direction. About fifty meters in westerly direction of the station is a line of Holm oaks ( quercus ilex ) along the trail to the site. The grass land is used for cattle farming, except for an area of about 100 m in westerly direction which was kept clear during the campaign.

Figure 13. Location of the PHOEBE observational site at Pabstthum. Forested areas are shown in green colour.

The instrumentation deployed at Pabstthum is summarised in Table 4 together with information on the time resolution of the measurements and the responsible institutions. More detailed information is found in the special section PHOEBE. In order to keep the influence of local surface emissions and deposition at a minimum, the instruments were placed as close together as possible at a height of approximately 10m (Figure 14). The instruments for odd nitrogen compounds, O 3 , CO, PAN, HCHO and speciated hydrocarbons were housed inside an air conditioned mobile laboratory. The inlets were mounted 10-11 m above ground on a pneumatic mast which also carried a photometer for the measurement of J NO2 . The gold converter for NO y was mounted directly at the inlet. Details are given in Konrad et al. (2003) and Volz-Thomas et al. (2002). Air temperature, wind direction and wind speed were also measured at 11 m height on the same mast. The cryogenic sampler of the MIESR (Matrix Isolation ESR-spectroscopy) was also operated on a pneumatic mast, 11 m above ground, about 10 m north east of the mobile laboratory. The samples were collected over 30 min intervals and analysed after the campaign at the laboratory in Jülich (Mihelcic et al. , 2003).Field Campaigns 44

The LIF (Laser Induced Resonance Fluorescence) instrument for the measurement of OH and HO 2 radicals was mounted inside a laboratory container which was placed on a 5 m high scaffolding platform. The inlet of the LIF instrument was 10 m above ground. Measurements of photolysis frequencies were made on the LIF container using a scanning spectral radiometer and two filter radiometers (J NO 2 and J O 1 D ). Another container on the platform housed the sampling devices for the measurement of peroxides, carbonyl compounds and carboxylic acids (Großmann et al. , 2003) and a chemical amplifier for the measurement of peroxy radicals, i.e., the sum of HO 2 and RO 2 (Volz-Thomas et al. , 2002).

Figure 14. The PHOEBE site viewed from east.

A DOAS (Differential Optical Absorption Spectroscopy) system for the measurement of NO 2 , NO 3 , O 3 , HCHO and SO 2 was also mounted on the platform. It had a folded light path of a total length of more than 2 km (Alicke et al. , 2003). A second DOAS system for the measurement of NO 3 , NO 2 , HONO, HCHO, SO 2 , O 3 , and aromatic VOC pointed in easterly direction from a height of 1.5 m at the station to a retro-reflector mounted 36 m above the ground at a distance of 6.3 km ( Geyer et al. , 2003). Vertical soundings of O 3 , NO x , and meteorological parameters were conducted with a tethered balloon. A second tethered balloon carried four automatic sampling units for hydrocarbons (Glaser et al. , 2003).LOOP Final Report 45

Table 4. Instrumentation deployed at Pabstthum. Parameter Instrument Timeres. Inst. Ref. Odd nitrogen compounds NO Chemiluminescence (770 Alppt, EcoPhysics) 1 min FZJ A NO x CLD 770 Alppt + PLC 760 (EcoPhysics) 1 min FZJ A NO 2 Differential optical absorption spectroscopy (DOAS 1 and 2) 30 min IUP F NO 2 * Matrix isolation and ESR-spectroscopy (MIESR) 30 min FZJ C NO y CLD 770 ALppt + Au-converter 1 min FZJ A NOy-HNO 3 CLD 770 ALppt + Au-converter + Denuder 1 min FZJ A HNO 2 DOAS 1 30 min IUP F PAN GC-ECD (Meteorologieconsult) 6 min FZJ A Other O 3 DOAS 30 min IUP F O 3 UV-Absorption (TE-49) 1 min FZJ A H 2 O 2 , ROOH Enzyme catalysed fluorescence 23 min MPI E CO IR-Absorption (TE-48) 1 min FZJ A CN Condensation Nuclei Counter (TSI) 1 min FZJ A SO 2 DOAS 30 min IUP F Organic compounds NMHC: C 5 – C 10 In situ -GC (Airmotec HC1010) 20 min FZJ B NMHC: C 2 – C 10 In situ -GC (HPGC/Cryogenic sampling) 85 min FZJ B H 2 CO Fluorescence (AeroLaser, AL4001) 1 min FZJ A H 2 CO DOAS 30 min IUP F Carbonyl comp. * Derivatisation, GC/ECD 1 h TUD E Carboxylic acids * Capillary Electrophoresis-LIF 1 h TUD E Aromatics, Phenols DOAS 30 min IUP F Free Radicals OH Laser induced fluorescence (LIF) 90 s FZJ D HO 2 LIF 90 s FZJ D HO 2 * MIESR 30 min FZJ C RO 2 * MIESR 30 min FZJ C ROx (HO 2 +RO 2 ) Chemical Amplifier (CA) 72 s MPI-C A NO 3 DOAS 30 min IUP G NO 3 * MIESR 30 min FZJ C Meteorology and Radiation T, rH, P, wind vector Psychrometer, wind vane, cup anemometer (Thiess) 1 min FZJ A Global radiation Pyranometer (Kipp&Zonen) 1 min FZJ A JO 1 D, JNO 2 2  sr Filter radiometers 10 s FZJ D JNO 3 2  sr Filter radiometer 10 s IUP G Ji,j (actinic flux) 2  sr spectral radiometer (Bentham) 90 s FZJ D Vertical Profiles (tethered balloon) O 3 Chemiluminescence (GFAS, OS-B-2) 10 s IVD H

NO 2 and NO x Luminol chemiluminescence (Scintrex, LMA-4) 10 s IVD H NMHC Adsorption tubes, GC/FID 3 h IVD H T, rH, P, wind vector Meteorological Sonde (Atmos. Instr. Res. Inc.) 10 s IVD H A: (Volz-Thomas et al. , 2002), B: (Konrad et al. , 2003), C: (Mihelcic et al. , 2003), D: (Holland et al. , 2003), E: (Großmann et al. , 2003), F: (Alicke et al. , 2003), G: (Geyer et al. , 1999; 2003), H: (Glase r et al. , 2003). FZJ:Research Center Jülich, Institut für Chemie und Dynamik der Geosphäre; IUP: University of Heidelberg, Institut für Umweltphysik; IVD: University of Stuttgart, Institu t für Dampfkesselwesen; MPI-C: Max Planck Institute for Chemistry, Mainz; TUD: Technical Univer sity of Darmstadt, Institut für ChemieField Campaigns 46

The site was in operation from July 13 th to August 8 th . Most of the continuous in situ measurements, including DOAS, LIF and the GCs, were conducted over the entire period and were interrupted only for calibration and maintenance, or during rain fall. The vertical soundings and some other experiments (i.e., MIESR, peroxides, carbonyl compounds and carboxylic acids) were concentrated on the intensive observational periods (IOPs) on July 20 th - 21 st and August 4 th - 5 th

. 

### 3.3.3 Meteorologi cal situation The meteorological situation during BERLIOZ was dominated by a westerly monsoon regime, which lasted unusually long. Winds between 180° and 360° prevailed more than 80% of the time. The city plume of Berlin with was only observed on a few occasions, i.e., on July 20, 21and 23. On July 20, surface wind at Pabstthum (10 m above ground) was calm during the night, increased after sunrise, and turned to south-easterly directions between 5 and 5:30 UTC. During the day the wind direction remained at about 160 degrees until 18 UTC allowing the advection of air from the south-western part of the greater Berlin area. Local profiles of potential temperature at Pabstthum ( Glaser et al. , 2003) and the analysis of aircraft data (Corsmeier et al. , 2002) for the entire BERLIOZ area showed the vertical development of the mixing layer from < 100 m in the early morning to 900 m at 12 UTC. South easterly winds directions prevailed in the boundary layer with a sharp transition to south westerly winds aloft. In the late afternoon, the vertical extent of the southerly wind regime decreased again to approx. 1 km around 18 UTC. In the evening, the westerly wind regime proceeded downwards by the combined action of the diurnal change in the heat balance and the shear induced turbulence at the top of the PBL. Due to the latter, transport conditions were not as well defined as intended for the BERLIOZ experiment.Advection of warm air ahead of a low pressure system that moved slowly northeast ward from Scotland lead to temperatures well above 30 0 C in Berlin on the 21 st . Starting sunny in the morning, clouds developed during the day while a frontal system that had originated in the morning west of Berlin moved eastward creating some precipitation in isolated showers and thunderstorms. Ahead of the convergence the wind direction turned towards south-east. During the day the winds moved on to westerly directions just behind the frontal system bringing in slightly dryer and colder air masses than before. The wind speed was generally lower than on 20 th and decreased further except for some isolated gusts in the greater Berlin area. At Pabstthum, wind from south east was observed shortly after 6 UTC for about two hours. After 8 UTC, the wind turned to south west and west throughout the whole boundary layer. Wind speed at the surface was low with 2-4 m/s. In contrast wind speeds at 200 m above ground were much higher reaching more than 10 m/s. A mixing layer depth of only 200 m was observed in the morning increasing to about 500 m at 12 UTC (Glaser et al. , 2003)

. Cloudiness increased after 11 UTC diminishing gradually the insolation.LOOP Final Report 47


### 3.3.4 Data quality and instrument comparisons Quality assurance was a major focus in TFS. Photolysis frequency instruments, which participated in BERLIOZ, were compared in advance against reference techniques during the field experiment JCOM97 in summer 1997 at Jülich (Kraus et al. , 2000). Most other in situ instruments (VOC, NO x , CO, O 3 , PAN, HCHO) were brought together one week before the campaign at the airfield Schönhagen, where the measurements of all participants were formally calibrated against certified standards and compared to reference instruments in ambient air (Kanter et al. , 2002; Volz-Thomas et al. , 2002). Besides putting all the different measurements deployed in BERLIOZ on the same level of known data quality, these activities also helped to improve the data quality in the other European experiments in which several groups participated. This concerns in particular the instrumentation aboard the MetAir aircraft, which participated in most of the LOOP campaigns (SLOPE, PIPAPO, BERLIOZ and ESQUIF), was successfully quality assured during the TFS QA activities. Additional instrument comparisons were made at Pabstthum where most of the free radicals (HO 2 , HO 2 +RO 2 , NO 3 ) and longer lived compounds (O 3 , NO x , HCHO) were measured with more than one technique (Geyer et al. , 1999; Platt et al. , 2002; Volz-Thomas et al. , 2002).



### 3.3.4.1 Radical measurementsHydroxyl and hydroperoxy radicals were measured at Pabstthum by laserinduced fluorescence (LIF) (Holland et al. , 2003). The 2 -calibration error was estimated to 20% for both OH and HO 2 . The LIF instrument was previously found to be in good agreement for OH with an absolute laser-absorption spectrometer (DOAS) (Hofzumahaus et al. , 1998). On 20-21 July, HO 2 was also measured by MIESR, an absolute technique with an estimated accuracy of 5% (Mihelcic et al. , 2003). The correlation of the temporally overlapping measurements was linear (R 2 =0.88) and showed a good agreement with a slope of 1.03, well within the combined uncertainty of both instruments (Platt et al. , 2002). The MIESR also provided measurements of the organic peroxy-radicals (CH 3 COO 2 , and the sum of all other RO 2 ) and of NO 3 (Mihelcic et al. , 2003). In addition, the sum of all peroxy-radicals (HO 2 + RO 2 ) was measured with a chemical amplifier with a calibration uncertainty of 20% (Volz-Thomas et al. , 2002). The comparison of both techniques showed good agreement, the chemical amplifier data being about 7% lower than the MIESR measurements, again well within the estimated calibration errors. Simultaneous NO 3 measurements were achieved in the night of August 4 to 5 byMIESR and a DOAS instrument, which was operated with a single-folded lightpath over a distance of 6.3 km. Despite of relatively low concentrations, the comparison of the DOAS and MIESR data sets showed a tight linear correlation (R 2 =0.98). The systematic deviation of 17% was within the combined systematic errors of both measurement techniques (Geyer et al. , 1999).Field Campaigns 48


### 3.3.4.2 Photolysi s frequencies A scanning spectro-radiometer was deployed at Pabstthum to measure absolute spectra of the solar actinic flux from which photolysis frequencies of different species (O 3 , NO 2 , HCHO, H 2 O 2 , HONO, CH 3 OOH, CH 3 CHO) were derived. A filter radiometer was also deployed for J O 1 D and was calibrated against the spectro-radiometer measurements. For J NO 2 , two independently calibrated filter radiometers and a spectro-radiometer measured simultaneously at Pabstthum. The agreement was on average within 2.5% for solar zenith angles less than 70° (Holland et al. , 2003).



### 3.3.4.3 NO x



### , O 3 

### , CO, PAN As outlined in Kanter et al. (2002), the O 3 monitor deployed at Pabstthum agreed within 5% with a reference instrument that was calibrated against the NIST traceable primary standard of the Eidgenössische Materialprüfungs- und Forschungsanstalt (EMPA, Dübendorf, CH). For quality assurance of CO, two canister samples were collected at Pabstthum and measured against certified standards traceable to NOAA CMDL, Boulder (3-8% disagreement with the reference). The dynamic PAN calibration systems (Pätz et al. , 2002) used by the different investigators agreed within 10% (Kanter et al. , 2002). For NO 2 , NO y and PAN, the instruments deployed at Pabstthum served as the reference in BERLIOZ. The NO standard used at Pabstthum for calibration of all four CLDs agreed within 1% with the BERLIOZ reference standard (NIST traceable via EMPA). The data quality of the NO 2 measurements was investigated by comparison of the simultaneous measurements made at Pabstthum by CLD/PLC, DOAS, MIESR, and with the balloon borne LMA-4 instrument. Although the comparison was not organised in a strictly blind fashion, the different instruments had been calibrated independently and the data were not harmonised before the comparison. As shown in Figure 15, the deviation between the four techniques was, on average, less than 5% with slightly higher values determined by the spectroscopic methods (Volz-Thomas et al. , 2003). The agreement for NO x (NO and NO 2 ), between the balloon borne LMA4 instrument employing conversion of NO to NO 2 on Cr 2 O 6 and the ground based CLD/PLC instrument was similarly good as for the NO 2 measurements. A direct comparison of the NO concentrations was not possible as the LMA4 on the tethered balloon measured NO x and NO 2 sequentially and thus at different heights.LOOP Final Report 49

0 5 10 15 20 0 5 10 15 20

0 5 10 15 20 0 5 10 15 20

0 5 10 15 20 25

0 5 10 15 20 25 NO 2 CLD-PLC [ppb] Ao = 0.18 ppb m = 1.05 R = 0.996NO2[ppb] MIESR

NO 2 CLD-PLC [ppb] Ao = 0.53 ppb m = 1.0 R = 0.975NO2[ppb] LMA-4

0 5 10 15 20 0 5 10 15 20

NO 2 CLD-PLC [ppb] Ao = 0.06 ppb m = 1.01 R = 0.990NO2[ppb] DOAS

Ao = 0.38 ppb m = 0.94 R = 0.976NOxLMA-4 [ppb]

NO
x CLD/PLC [ppb]

Figure 15. Correlation between the four NO2 measurements deployed at Pabstthum (CLD/PLC, DOAS, MIESR, LMA-4) and for NOx between CLD/PLC and LMA-4.



### 3.3.4.4 VOC Hydrocarbons were determined at Pabstthum by in situ gas chromatography with two different systems similar to those deployed during the SLOPE campaign (see above), namely an Airmotec HC1010 and a custom made system built around a HP5890 (referred to as HP-GC). The HC1010 is appropriate for measuring C 5 – C 10 HC with a time resolution of 20 min, whereas the HP-GC is suitable for C 2 – C 10 HC with a 80-90 min measuring cycle. The HP-GC had been elected as the reference instrument for the overall quality assurance in BERLIOZ ( Volz-Thomas et al. , 2002). A comparison of the two GCs and tests performed with the inlet system are described in Konrad et al. (2003) and Konrad and Volz-Thomas (2000). The characterisation and harmonisation of the different in situ GC systems (10 different laboratories with almost 20 instruments) deployed in BERLIOZ was a major task of the quality assurance activities. Because of the many new systems deployed and the involvement of groups with varying experience in field measurements, the activities involved several comparisons of standard mixtures before a final assessment of instrument performance was made by a comparison in ambient air at Schönhagen (Volz-Thomas et al. , 2002). A DOAS system was also set up to measure several aromatic hydrocarbons, as well as phenols and cresols. While some of these compounds were indeed found at significant levels at Eichstädt, the concentrations at Pabstthum remained always below the detection limit.Field Campaigns 50


### 3.3.4.5 Formaldehyde Several commercial monitors (AeroLaser) that utilise fluorescence for detection of HCHO following the Hantzsch reaction were deployed at the different BERLIOZ sites. Calibration of the monitors was based on a common liquid standard provided by the University of Wuppertal. The comparison at Schönhagen (Kanter et al. , 2002) was formally satisfying, although somewhat hampered by very low concentrations. At Pabstthum, HCHO was also measured by DOAS (Alicke et al. , 2003). The mixing ratios measured by DOAS (max. 7.7 ppb) were systematically larger by a factor 1.3 on average and by a factor 1.7 during high photochemical activity (Großmann et al. , 2003). An even larger discrepancy between Hantzsch and DOAS was observed at the BERLIOZ sites Eichstädt and Blossin. Großmann et al. ( 2003) discuss the possible reasons for the discrepancy in the light of results from other comparisons and conclude that none of the two measurements at Pabstthum can be proven wrong on the basis of the existing information and that the unresolved discrepancy of about a factor of two must be included in the uncertainty of (HCHO) for model simulations of the radical budgets (see below). Further comparison of the different methods for the measurement of HCHO under atmospheric conditions certainly remains an important task.



### 3.3.5 Observational results

3.3.5.1 Radicals The variability of OH was mainly controlled by changes in the solar UV responsible for the photochemical formation of OH (Holland et al. , 2003). Daily maxima of OH were usually observed at midday and showed values in the range of (4-8) x 10 6 cm -3 . The shape of the diurnal OH profiles followed closely the variations of J O 1 D with a linear correlation-coefficient of R~ 0.9. During the early morning and late evening hours, however, OH concentrations as high as 1.5 x 10 6

cm -3 were measured even when J O 1 D was essentially zero, due to the radical production by photolysis of HONO and HCHO. These precursors were found to be efficient photochemical sources of OH under polluted conditions like in the morning of 20 and 21 July (Alicke et al. , 2003). At sunset the observed OH may be explained as a result of reactions of O 3 and NO 3 radicals with alkenes. (Geyer et al. , 1999; Geye r et al. , 2003) NO x was found to be another important parameter that controls OH. The J O 1 D

normalised OH data exhibited a non-linear NO x dependence with a maximum around 3 ppb NO x (Holland et al. , 2003). Towards lower NO x levels the OH concentrations decline due to the decreasing conversion of HO 2 into OH by reaction with NO (R29), whereas towards higher NO x levels the OH concentrations decrease due to increasing depletion of OH by reaction with NO 2 (R38). In comparison to an earlier field campaign (POPCORN), which took place in a clean rural environment (Plass-Dülmer et al. , 1998), the OH data from BERLIOZ are about a factor of 2 lower for the same values of J O 1 D and NO x (Holland et al. , 2003).LOOP Final Report 51

At night, OH concentrations were usually below the detection limit of the LIFwhen short integration times (90 s) were applied. Averaging the data over timeintervals of 20 minutes yielded significant OH concentrations of (1.9 ± 0.8) x 10 5

cm -3 in the night of July 20-21, which could be explained by the nocturnal production of OH from the reactions of biogenic monoterpenes with O 3 and NO 3 radicals (Geyer et al. , 2003). Averaging all nocturnal OH data measured at Pabstthum between 21:00 UT and 3:00 UT, yielded an experimental upper limit of 5 x 10 4 cm -3 for nocturnal OH (Holland et al. , 2003). The highest HO 2 concentration (8 x 10 8 cm -3 , i.e., 32 ppt) was observed at noon on July 21, a day which was characterised by exceptionally high VOC mixing ratios (Konrad et al. , 2003). On the other days, the daily maxima were in the range of (2-4) x 10 8 cm -3 (8-16 ppt). HO 2 exhibited a much less pronounced dependence on the solar UV then OH and was strongly influenced by NO which efficiently converts the peroxy radicals into OH by reaction R29. In the morning hours (4:00 - 10:00 UT) when NO usually exhibited a morning peak, the HO 2 concentration was typically close to or below the detection limit. After sunset HO 2 usually remained at significant levels above the detection limit (1x10 7 cm -3 ) and had an average night-time value of 3 x 10 7 cm -3 (~ 1 ppt). On some occasions HO 2 even increased after sunset as a result of HO 2 production by the reaction of biogenic VOC's with NO 3 and O 3 (Geyer et al. , 2003). The highest nocturnal HO 2 value (1 x 10 8 cm -3 , i.e., 4 ppt) was observed in the night from 20 - 21 July when very high mixing ratios of organic compounds were encountered (e.g., up to ~ 0.5 ppb -pinene). From midnight to sunrise the HO 2 concentrations usually decayed and reached values near the detection limit in the early morning as mentioned above. The diurnal profiles of the organic peroxy radicals (RO 2 ) showed a great similarity to HO 2 . RO 2 radicals were often close to the detection limit in the morning due to elevated NO mixing ratios. The daily maxima of RO 2 occurred typically 1 to 2 hours after local noon and reached the highest value, about 20 ppt (i.e., ~55 ppt of HO 2 + RO 2 ), on 21 July 1998 (Mihelcic et al. , 2003; VolzThomas et al. , 2003). The concentration of CH 3 COO 2 never exceeded the limit of detection (2 ppt) of the MIESR. The sum of other organic peroxy radicals during daytime was about the same as the concentration of HO 2 . At night RO 2 concentrations were much higher than HO 2 . During many nights a significant increase of HO 2 + RO 2 was observed after sunset as a result of the reaction of NO 3 radicals with biogenic monoterpenes (Geyer et al. , 2003). Night-time maxima of NO 3 were usually of the order of 10 ppt ( Geyer et al. , 2003). In one night the NO 3 mixing ratio reached 70 ppt due to an exceptional high production rate of nitrate radicals by the reaction of NO 2 with O 3 . Similar time series of NO 3 were measured at the BERLIOZ sites Lindenberg, Blossin, and Eichstädt, where other DOAS instruments were operated. A correlation of the night-time levels of NO 3 was observed among all four measurement sites (Platt et al. , 2002). At Pabsttum the average night-time profile of NO 3 started to rise above the detection limit around sunset and reached a maximum between 20:00 and 22:00Field Campaigns 52

UT, followed by a continuous decay reaching levels below the detection limit at sunrise (Geyer et al. , 2003). This time profile was found to have a positive, linear correlation with the concentration of HO 2 + RO 2 which also exhibited a nighttime maximum before midnight. For example, in the night of 4-5 August the correlation coefficient was R 2 = 0.83. The observed correlation can be described generally by a chemistry model showing that the night-time HO 2 and RO 2 radicals were largely formed by the reaction of NO 3 with biogenic VOC's at Pabstthum ( Geyer et al. , 2003).



### 3.3.5.2 VOC More than 60 hydrocarbons (NMHC) in the range of C 2 – C 10 were identified and quantified with the two GC systems deployed at Pabstthum (Konrad et al. , 2003). The data sets from both GC systems were combined to join the high time resolution of the HC1010 with the high precision and the larger number of measurable compounds on the HP-GC. This was achieved by interpolation between two data points of the HP-GC with the pattern given by the HC1010. For compounds that could not be reliably measured with the HC1010, patterns of compounds with similar reactivity were used. Air masses with the lowest photochemical age as estimated from the toluene/benzene ratio and the highest hydrocarbon mixing ratios were observed on July 20 th and 21 st when air was advected from the direction of Berlin. Alkanes were the most abundant hydrocarbons (~ 60%) on a molecular basis, followed by alkenes and aromatics. The reactivity of the hydrocarbons towards OH, however, was dominated by the alkenes (> 60%), with the biogenic hydrocarbons isoprene and -pinene constituting the major fraction even during the time when the site was under the influence of air from the greater Berlin area.


### 3.3.5.3 Oxygenated VOC Up to 30 different aldehydes and ketones ( C 2 ) were identified and quantified at Pabstthum during the IOPs using a novel analytical technique (derivatisation with PFBHA on cartridges, followed by GC-ECD analysis in the laboratory, (Schlomski et al. , 1997). HCHO was measured continuously by both, DOAS and a commercial Hantzsch monitor (see above). Carboxylic acids (C 1 – C 9 ) were sampled with a new optimised scrubber system. The samples were analysed by sequential reaction with dicyclohexylcarbodiimide and 4-aminofluoresceine, followed by capillary zone electrophoresis (CEP) and detection by LIF (Kibler et al. , 1999). These measurements were made during the IOPs only. Großmann et al. (2003) discuss the diurnal variation of the oxygenated compounds and the correlation between primary and the secondary species that were observed at Pabstthum. The C 2 -C 10 aldehydes, which originate primarily from anthropogenic hydrocarbon degradation processes, showed maximum mixing ratios from 0.6 ppb (C 2 ) to 0.1 ppb (C 5 ). The C 6 , C 9 and C 10 alkanals exhibited strong fluctuations, thus giving evidence of biogenic emissions. The primary unsaturated carbonyl compounds (methylvinylketone and methacrolein)LOOP Final Report 53

and the secondary oxidation products of isoprene (hydroxyacetone and glycolaldehyde) showed excellent correlation. Also, diurnal profiles of glyoxal, methylglyoxal, biacetyl, benzaldehyde and pinonaldehyde were obtained. Formic and acetic acid varied between 0.6 and 2.6 ppb. The mixing ratio of the other acids decreased from 0.1-0.2 ppb for C 3 to a few ppt for the C 9 - monocarboxylic acids. The measurements highlight the importance of carbonyl compounds for accurately determining the total VOC reactivity. In some cases, important information on the chemical mechanism is derived from the observed correlation between precursors and oxidation products.



### 3.3.5.4 PAN The PAN concentrations measured at Pabstthum were similar as on the other BERLIOZ sites, with maximum mixing ratios around 1 ppb observed on the afternoon of July 20 and 21. Because of the high temperatures on these days, PAN was an important source of peroxy radicals (Konrad et al. , 2003) despite the low concentrations. On the other days with westerly winds, PAN usually remained around a few hundred ppt (Volz-Thomas et al. , 2003).



### 3.3.5.5 Peroxides The hydroperoxides show pronounced diurnal variations with peak mixing ratios in the early afternoon. The maximum mixing ratios were observed on July 21 st (1.4 ppb H 2 O 2 , 0.64 ppb methylhydroperoxide, and 0.22 ppb hydroxymethylhydroperoxide). The increase of H 2 O 2 in the morning originated mainly from vertical transport from the residual layer and changes in advection. A secondary maximum sometimes observed in the late afternoon indicates the formation from ozonolysis of biogenic alkenes. The H 2 O 2 /HNO 3 ratio is used as indicator for distinguishing between NO x and VOC limitation of photochemical ozone production at Pabstthum (Großmann et al. , 2003).


### 3.3.5.6 Odd nitrogen compounds and photo stationary state (PSS) The ozone production rates P(O 3 ) as calculated from PSS and from the radical concentrations provided by the CA are shown in Figure 16. P(O 3 ) from PSS reached daily maxima of approximately 30 ppb/h, except on July 20, 21, and 23 when air was advected from the greater Berlin area. Then the maximum production rates are 80-120 ppb/h. The ozone production calculated from ROx and NO show a similar pattern with the highest values on July 20, 21, 23. The absolute values, however, are on average by a factor of 3-5 lower. On the days with the highest production rates, the ozone concentration also exhibited significantly larger daily maxima than the 50 ppb observed during the rest of the campaign. Figure 16 also compares the radical measurements made by MIESR and by CA with those calculated from PSS. Clearly, PSS overestimates the peroxy radical concentrations by a factor of 4.Field Campaigns 54

0 50 100

0 20 40 P(O 3 ) [ppb/h] PSS CA MIESR

0 10 20 30

NO
x RO 2 +HO 2 [ppt] PSS :4 CA MIESR

0 2 4 6 8 JNO 2 *1000

0 50 PAN Ozone

0.0 0.4 0.8 1.2

0 10 20 30
rh [%] T [°C]

0 50

0 90 180 270 wind direction [deg]

06 08 10 12 14 16 0 2 4 6 wind speed [m/s]

UT 21.7.1998 0 50 100

0 20 40 PSS CA MIESR

0 10 20 30 NO
x [ppb] RO 2 +HO 2 [ppt] PSS :4 CA MIESR

0 2 4 6 8

JNO 2 *1000 [s -1 ]

0 50 PAN [ppb] Ozone [ppb]

0.0 0.4 0.8 1.2

0 10 20 30
rh [%] T [°C]

0 50

0 90 180 270 wind direction [deg]

06 08 10 12 14 16 0 2 4 6
wind speed [m/s]

UT 20.7.1998
Figure 16. Comparison of P(O 3 ) and total peroxy radical concentrations as determined by PSS, MIESR and Chemical Amplifier (from Volz-Thomas et al. (2003)).

In several of the published data sets data quality issues were discussed as potential causes for the large values of PSS derived P(O 3 ) or RO 2 , particularly concerning the accuracy of the NO 2 measurements (e.g., Davis et al. , 1993). We also note that in many of the published data sets, J NO 2 was inferred from model calculations with adjustments for cloudiness made by flat plate radiometers. As noted above, the contributions from imprecision can always be sufficiently reduced by selecting data well above the instrumental detection limits and for relatively homogeneous atmospheric conditions as well as by averaging over long enough time periods. The contributions from accuracy, however, remain. At Pabstthum, the accuracy of the NO 2 measurements is confirmed by the comparison of four independent methods. In fact, when taking the spectroscopic NO 2 measurements as the true values and correcting the CLD/PLC measurements accordingly, the deviation between P(O 3 ) from (2) and (3) would become slightly larger. For NO, a direct independent method was not available at Pabstthum. However, problems with the NO measurement are difficult to conceive because the NO 2 measurement by CLD/PLC is based on theLOOP Final Report 55

measurement of NO which should thus have a similar or better accuracy than NO 2 . The NO calibration agreed with the certified standard within 1%. Finally, NO x measured by the balloon borne LMA4 instrument employing conversion of NO to NO 2 on Cr 2 O 6 (fourth panel of Figure 15 agreed with the CLD/PLC data as well as the NO 2 measurements. A direct comparison of the NO concentrations was not possible as the LMA4 on the tethered balloon measured NO x and NO 2 sequentially and thus at different heights. The ozone monitor agreed with the primary ozone standard within < 5% and the two independently calibrated J NO 2 instruments agreed within < 3%. Therefore, the uncertainty in the rate coefficient (8%, Atkinson et al. (1997)) represents the largest uncertainty.

01234 0 30 60 90 01234

0 30 60 90

0.0 0.1 0.2 0.3 0.4 0.5 0 5 10 15 20 25 30

BERLIOZ 1998PO3[ppb/h]

NO [ppb] PSS, JNO 2 >0.006 s -1 CA O
x -Budget aircraft MIESR

PSS, JNO 2 >0.006 s -1 MIESR Schauinsland 1994PO3[ppb/h]

PO3_vs_NOx_mal_J: PabSchauza Izana 1993PO3[ppb/h] PSS CA MIESR

Figure 17. Comparison of P(O 3 ) determined by PSS and from RO 2 measurements at Izana, Schauinsland and Pabstthum. The values of P(O 3 ) calculated from the radical measurements in BERLIOZ are in excellent agreement with the ozone budget obtained from airborne measurements of the ozone fluxes (lower panel).Field Campaigns 56

The result from BERLIOZ is similar to observations made at Schauinsland (Figure 17), whereas in clean air at Izana good agreement between PSS and radical measurements was observed. It was thus concluded that the PSS approach severely overestimates the local ozone formation rate in the continental boundary layer (Volz-Thomas et al. , 2003). In none of the radical measurements made by MIESR, values of P(O 3 ) greater than 10 ppb/h were found (Mihelcic et al. , 2003). Faster rate coefficients for the reaction of RO 2 with NO, as discussed by, e.g., Frost et al. (1998), are not considered a likely explanation, because the discrepancy exists also at high NO concentrations where the peroxy radical concentrations are at or below the detection limit of the MIESR. Moreover, the budget of ozone in the plume as derived from the aircraft measurements in BERLIOZ (Corsmeier et al. , 2002) does not allow for much larger formation rates than those calculated from the measured RO 2 concentrations and the accepted rate coefficients. Therefore, an unknown oxidation process must exist in the atmosphere for NO. While similar suggestions have been made before (Carpenter et al., 1998; Parrish et al., 1986; Volz-Thomas et al., 1997), the high data quality achieved in PHOEBE precludes the possibility of analytical errors being responsible for the observed discrepancy. Furthermore, the comparison with the ozone budget gives evidence that the unknown process does not lead to a net formation of O 3 . The MIESR data from BERLIOZ and several other campaigns conducted at Schauinsland in Southern Germany and at Tenerife suggest that ozone formation rates do not exceed values of 10 ppb/h (Mihelcic et al. , 2003).



### 3.3.6 Model simulations The measurements were used to calculate the radical concentrations with photochemical box models of different complexity, i.e., the Master Chemical Mechanism (MCM, (Jenkin et al. , 1997)) and the condensed mechanism RACM (Stockwell et al. , 1997). As is seen in Figure 18, both models predict the concentrations of OH, HO 2 and RO 2 quite well in the high NO x regime (Konrad et al. , 2003; Mihelcic et al. , 2003). An important conclusion was that the photolysis of HONO accounted for most of the HOx production in these air masses during early morning (Alicke et al. , 2003). On the other hand, both models significantly overestimated the OH concentrations at low NO x (< 5 ppb). The disagreement was up to 60% for the MCM and 80% for RACM. The agreement for HO 2 and RO 2 was better. Ozonolysis of mostly biogenic olefins was an important source of RO 2 and HO 2 in the late afternoon. Qualitatively, the results from Pabstthum confirmed earlier exercises, in which constrained models were also found to over predict OH concentrations (e.g., Carslaw et al. , 1999; Eisele et al. , 1994, 1996; George et al. , 1999; Hauglustaine et al., 1999; McKeen et al. , 1997; Poppe et al. , 1994; Perner et al. , 1987; Platt et al. , 1988; Savage et al. , 2001). The sensitivity studies conducted in PHOEBE demonstrated that missing hydrocarbon reactivity, as argued in several earlier studies, was not the sole reason for the overestimation of OH. This conclusion was possible from the specific RO 2 measurements made in PHOEBE (MihelcicLOOP Final Report 57

et al. , 2003). Increasing the VOC reactivity lead to a pronounced over prediction of the RO 2 concentration by the model. The concentrations of all radicals was brought into reasonable agreement with the measurements by introducing a sink term in the models, corresponding to a first order loss of approximately 1 s -1 for OH or 0.02 s -1 for HO 2 , and by increasing the total biogenic VOC reactivity by approximately 20% (Konrad et al. , 2003).

0 20 40 60 80

0 2 4 6

0 5 10 15

0 200 400 600

06 09 12 15 18 21 0 10 20RO2[ppt]HO2[108cm-3]OH [106cm-3]HO2/OHNOx[ppb]

UT 20.7.98 J(O 1 D)*10 6 [s -1 ]

Figure 18. Diurnal variation of simulated and measured concentrations of free radicals (OH, HO 2 and the sum of all organic peroxy radicals RO 2 ) on July 20th at Pabstthum (open circles: LIF (OH and HO 2 ); squares: MIESR (HO 2 and RO 2 ); solid lines: RACM simulation; broken lines: RACM with 500 ppt of limonene added. The dotted lines: MCM simulation. The lowest panel shows the NOx mixing ratio, condensation nuclei (CN), and the photolysis frequency J(O 1 D). (from Konrad et al. (2003)).



### 3.3.6.1 Night ti me chemistry The simultaneous observations of NO 3 , RO 2 , HO 2 , and OH during several nights were interpreted with a chemical box model by Geyer et al. (2003). For most of the nights the model results and ambient observations of RO 2 and HO 2 are inField Campaigns 58

good agreement, whereas the modelled OH concentrations are just within the upper limit of the measurements (5 x 10 4 cm -3 ). In one night (20-21 July), when very high mixing ratios of VOCs were encountered, the model yielded a maximum nocturnal OH level of (4.1 ± 0.7) x 10 5 cm -3 , significantly higher than the measured value of (1.60 ± 0.85) x 10 5 cm -3 . The overestimation by the model could point to a missing nocturnal sink of OH. Reactions of NO 3 with VOCs, mainly with biogenic monoterpenes, were shown to constitute a considerable source of organic peroxy radicals at night. Generally a positive linear correlation of the mixing ratios of RO 2 and NO 3 was observed and modelled during the campaign. The model analysis shows that this dependence can be attributed to the role of NO as a sink for both radicals. In addition, the production rate of RO 2 increased with increasing NO 3 concentration during nights, when VOCs were of minor importance for the NO 3 removal. While Carslaw et al. (1997) also found a positive correlation of RO 2 and NO 3 at the eastern coast of England, Mihelcic et al. (1993) observed a negative correlation for a clean, forested site where the NO 3 losses were dominated by reactions with VOCs. The contribution of OH to the nocturnal oxidation of VOCs, CO, and CH 4 was modelled to be 15% of that of NO 3 for the situation at Pabstthum. This corresponds to a contribution of 2.6% by night time OH to the 24h integral of the total atmospheric oxidation capacity. Since the model tends to overestimate the measured OH levels at night, the contribution is even smaller and therefore negligible during BERLIOZ.



### 3.3.7 Conclusions The measurements made during BERLIOZ at Pabstthum comprise the most complete data set collected so far in the PBL on the chemistry of free radicals. Most radicals, i.e., HO 2 , RO 2 and NO 3 , were measured with two different analytical techniques. The good agreement gives high confidence in the accuracy of the data. The situation is similar for the stable compounds, e.g., excellent agreement (<5%) was observed between four different methods for the measurement of NO 2 , more than 50 hydrocarbons and 30 carbonyl compounds were identified and quantified, and quantitative experimental information was obtained on the relevant photolysis rates that control radical production. For the concentration of HCHO, significant differences of up to a factor of two were observed between DOAS and the in situ measurement, which could not be resolved. Although the city plume was observed only for a few hours, the measurements covered the entire range of chemical conditions from continental background (< 1 ppb NO x ) to suburban air with up to 30 ppb of NO x . The interpretation of the data set in the accompanying papers leads to important conclusions regarding our understanding of the radical chemistry:

The NO/NO 2 ratio is 25% lower than predicted by photochemical models and by the PSS approach.LOOP Final Report 59

The photochemical ozone production rate does not seem to exceed values of 10 ppb/h. The production rates derived from PSS are much larger and must be regarded with great caution.

The large overestimation of OH by the models at low NO x mixing ratios suggest a yet unidentified loss process for HO x radicals. The RO 2 data show that missing VOC reactivity is not the sole reason.

Night time chemistry initiated by NO 3 radicals and ozonolysis plays an important role in the degradation of many VOCs and is an important source for peroxy radicals at night.

HONO is an important source for OH in polluted air in the morning.


### Acknowledgement The financial support by the German Ministry for Research and Education (BMBF) as part of the Tropospheric Research Focus (TFS, LT 3) is gratefully acknowledged.Field Campaigns 60



### 3.4 PIPAPO Christoph Spirig, Albrecht Neftel Swiss Federal Research Station for Agroecology and Agriculture, Zurich,. Switzerland

Josef Dommen Paul Scherrer Institute, Laboratory of Atmospheric Chemistry, Villigen, Switzerland
Rita Van Dingenen, Jean-Philippe Putaud European Commission Joint Research Centre, Ispra, Italy



### 3.4.1 The Milan basin as natural photoreactor The PIPAPO (Pianura Padana Produzione di Ozono) campaign took place May to mid June 1998 in the northern part of the Po Valley (information about air quality in the Lombardy region on the web: http://www.ambiente.regione.lombardia.it This region offers many interesting aspects with respect to air pollution problems and reduction strategies:

Frequent episodes with very high photo-oxidant levels.

Re-circulation of air masses over the Po Valley due to the topographic situation.

Presumably rapid changeover from VOC to NO x limited ozone production.

Large production of secondary aerosol with a major influence from biogenic VOC and NH 3 emission due to intensive farming and high emissions of isoprene and terpenes in the pre-Alps. The PIPAPO field campaign was basically set up as a Lagrangian experiment with several stations within the so-called Milan plume, i.e., downwind of Milan. During two intensive observation periods (IOP’s, May 12 and 13, June 1 to 10, 1998), surface station and aircraft measurements were performed. Observational stations at Bresso, Seregno and Verzago were established downwind of Milan, hypothetically located in different chemical regimes according to earlier investigations with a one-dimensional Lagrangian model (Staffelbach et al ., 1997b) (Figure 19). Aerosol issues, especially concentration of fine particles (PM 2.5 ), gained increasing attention from environmental agencies, because air quality guidelines for PM 2.5 and/or PM 10 are now generally being established. As for ozone, development of reduction strategies to meet the newly established guidelines needs a quantitative source – receptor relationship. Reduction strategies for aerosols might interfere with ozone reduction strategies (Meng et al. , 1997). It was therefore a logical step to start a close cooperation with the Eurotrac-2 subproject AEROSOL. In fact it was the steering committee of the AEROSOL subproject that offered to actively participate in the PIPAPO field experiment.LOOP Final Report 61

Milano Como

Bergamo

Verzago

Seregno

Bresso

680 700 720 740 760 780 x [km] 0 20 40 60 80 100 120 140y [km]

Figure 19. PIPAPO – an experiment with a Lagrangian approach. Colours of the LOOP logo (see section 2.3) indicate different ozone production regimes of the Milan Plume as found in 1-D Lagrangian model

runs.



### 3.4.2 Intensive observation periods The PIPAPO experiment was organised as a pure bottom-up project without central funding. A close co-operation with local authorities was established from the beginning. During two intensive observation periods (IOP’s) (May 12 th and 13 th , June 1 st to June 10 th ), measurements at the ground stations were intensified and air plane measurements completed the field activity. Meteorological criteria for an IOP were i) a high pressure situation lasting for several consecutive days, ii) high insolation producing strong thermally driven circulations and intensive photochemical activity, iii) weak synoptic winds aloft and southerly winds near the surface, and iv) absence of precipitation. BecauseField Campaigns 62

ozone episodes often occur in spring and early summer, this time of the year was selected over the better-known mid-summer episodes. The first IOP lasted from May 12 to 13, 1998, and was embedded in a period of exceptionally warm, dry and sunny weather. The Po Basin was free of clouds on both days, while in the afternoon of the second day convective clouds developed in the mountains east of the Lake of Como, which led to thunderstorms that terminated the IOP. A heat low over the Alps was well established on both days. The pressure gradient induced a weak south-to-north flow over the study area, and the ideal flow configuration for PIPAPO was realized on May 13. High insolation and high temperatures were very favourable for photochemistry in the planetary boundary layer (PBL), and the winds supported the transport of polluted air from the basin towards the Alps. Ozone concentrations reached values of 190 ppb during this IOP. The second IOP took place from June 1 to 10, 1998. A consistent southwesterly flow across the Alps existed for the whole period, while the surface charts exhibit various frontal systems, which remained north of the Alpine crest. Winds aloft were mostly southwesterly, while surface winds showed variable directions. Very frequently thunderstorms and scattered intermittent heavy showers developed during the second IOP. In contrast to the first IOP, Milan always reported moderate cloudiness around noon. The ideal flow situation for the second IOP lasted from June 2 to 4, before a cold front approached the area. In this period, maximum ozone concentrations increased from day to day, reaching peak values of 140 ppb on June 4. May 12th and 13th have been chosen as the ideal days for the application of the Eulerian models.



### 3.4.3 QA/QC of PIPAPO The quality assurance, quality control (QA/QC) within PIPAPO is based primarily on the conscientiousness of the individual contributors. The data have been compiled on an MS ACCESS database, including tools for extracting and visualizing of data 1 . Figure 20 shows an example for the representation of the flight data. The user can e.g. scan the flight for any measured species, and then the height above ground, the geographical location and the concentration are shown.

1 The CD can be ordered at FAL, P.O. Box, CH 8046 ZürichLOOP Final Report 63

Figure 20. Visualization tool for the PIPAPO database.



### 3.4.4 Emissions
3.4.4.1 Development of the emi ssion inventory for the LOOP domain An emission inventory was developed for the Lombardy region and Southern Switzerland. The resolution is 3 x 3 km in space and 1 hour in time. It contains anthropogenic and biogenic area emissions and a few large point sources. The available information on emissions in Italy and Switzerland was different and had to be combined. In the Italian part of the domain, a bottom-up approach was used to derive the emission inventory from statistical data. Emissions of SO 2 , NO x , CO and volatile organic compounds were considered. For the VOCs eleven different speciation profiles (eight anthropogenic and three biogenic) were used. Statistical data were available on a municipality level. The emissions calculated for a municipality were then allocated to each grid cell using land-use data. For the Swiss part of the region an emission inventory of high spatial (5x5 km 2 ) and temporal (1 hour) resolution with base year 1995 existed. It was an updated version of the emission inventory developed during the Swiss Pollumet project (BUWAL, 1996). The inventory is divided into the two categories traffic and industry, and includes emissions of SO 2 , nitrogen oxides (NO x ), CO and volatile organic compounds (VOC). This inventory was spatially re-sampled to match the 3x3 km 2 grid resolution. The VOC-species profile was also adapted to the profiles used for the Italian part of the domain. A detailed description of theField Campaigns 64

preparation of the emission inventory is given in Maffeis et al. (1999) and Dommen et al . (2003). Finally, the emission inventory was provided to the modellers with different lumping schemes of the VOCs. Figure 21 shows the daily average emissions for NO x . The Milan area with its high emissions is clearly seen. There are many peaks scattered throughout the domain, which are from smaller cities and power plants. Highways and main roads are also clearly seen as line emission sources.

8.5 9 9.5 10 Longitude 45.5

46 46.5Latitude V

M NOx
0 to 1 1 to 10 10 to 100 100 to 500 500 to 1000 1000 to 2000 2000 to 4000

Figure 21. Average-day NO x emissions (kg/day) in LOOP domain for all source categories. V = Verzago, M = Milano.
Figure 22 shows the magnitude of the domain total hourly NO x emissions. NO x emissions from traffic and stationary sources are roughly equal. Point sources add about 10% to the total NO x emissions.

- 2 4 6 8 10 12 14 16

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Human activities
Linear traffic
Urban traffic

Figure 22. Diurnal variation of domain total NOx emissions (without point sources) in tons of NO 2 -eq /hour (from Dommen et al . (2003)).LOOP Final Report 65

Besides the advantages of this bottom-up methodology, there are also limitations to this approach as mentioned above (section 2.5). This inventory used a statistical method based on industrial plants declarations in which CO emissions are underestimated. Another weakness is that more point sources, beyond the seven considered here, need to be known in more detail. The use of UK annual detailed emission profiles (Derwent et al. , 1996) to speciate VOCs is also a rough approximation; in fact, no alternative information is at disposal on a local base. This could cause an inadequate VOC speciation with consequence on the reactivity of the emitted pollutants, playing an important role in photochemical modelling. Finally, it should be underlined that both national inventories are based on indicators and emission factors referenced to the year 1995. An update of this inventory should include:

newly realized important infrastructure (e.g., Malpensa airport and connected to this additional transportation);

the strong car fleet renewal over the last years;

the change in industrial processes and their decentralisation out of the main cities.


### 3.4.4.2 Comparison of the emission inventory with measurements A comparison of emission ratios of CO/NO x and CO to various hydrocarbon classes have been performed between the emission inventory and measurements in rural and urban environment (Dommen et al ., 2003). Measurements yield CO/NO x ratios between 11 and 27. The higher values are related to situations of dense traffic in the centre of a city with traffic jams. The inventory-based ratios are 5.5-7 for the urban area and 2-3 elsewhere. The measurements do not show a pronounced difference of the CO/NO x ratio between urban and non-urban regions as given by the inventory. Model studies of the urban plume show a clear underestimation of the modelled CO mixing ratio compared to measurements (Martilli et al ., 2002; Bärtsch-Ritter et al ., 2003b). From these model calculations it was concluded, that CO emissions are underestimated by about a factor of 1.8 (Bärtsch-Ritter et al ., 2003b). Ratios of some classes of hydrocarbons to CO were also inter-compared between emission inventory and measurements. The emission inventory underestimates CO/VOC-class ratios by factors of 1.3 - 8. Bärtsch-Ritter et al . (2003b) evaluated the influence of such discrepancies on the photo-oxidant formation (see section 3.4.8).


### 3.4.5 Meteorology and photosmog episodes in the Milan area Occurrences of elevated ozone concentration in the region between Milan and the Alps are strongly related to the meteorological conditions. As an example, Figure 23 shows the variability of late afternoon concentrations in Verzago as a function of the prevailing meteorological conditions. Daily average global radiation and daily temperature maximums were taken from representative ground stations.Field Campaigns 66

The prevailing wind conditions were derived from wind profiler measurements at Seregno, which is located between Milan and Verzago. The best correlation is found between temperature and ozone concentrations at Verzago in the late afternoon (17.30 Central European Daylight Time, = LST + 1h = UTC + 2h) during the PIPAPO campaign. Ozone increases from around 45-70 ppb at 20°C to 80-145 ppb at 30°C. The main reasons for this relationship are higher biogenic emissions, higher chemical reaction rates, and usually higher global radiation at higher temperatures, yielding higher ozone production. However, taking only temperature into account, a great part of the variability in the ozone concentration remains unexplained. Ozone does not exhibit a consistent dependence on global radiation. Model simulations show that ozone concentrations have a large temperature dependence (Bärtsch-Ritter et al ., 2003a). On days with low radiation (lower than 2/3 of typical clear days), ozone concentrations do not exceed 80 ppb (crosses in Figure 23). Assuming the wind profiler data to be representative for the region around Milan, we can deduce the influence of the air mass origin on the ozone concentrations in Verzago. The highest concentrations in a particular temperature range are found for the cases when southerly winds prevail, transporting the Milan plume to Verzago (large circles in Figure 23). In the case of strong winds (several hundred kilometres transport distance within 10 hours) and no development of thermally induced winds towards the Alps, low ozone concentrations are found in the according temperature range (squares in Figure 23). On days with thermally induced winds not originating from Milan (usually southwesterly or southeasterly wind directions), the ozone concentrations were close to the linear correlation fit (diamonds in Figure 23). Small circles denote days with incomplete or missing data from the wind profiler.

15 20 25 30 35 0 50 100 150

temperature (°C) O z o n e ( p p b ) i n V e r z a g o a t 1 7 : 3 0

A i r f r o m

M

i l a n o

S t r o n g

w i n d

s ,

n o

t h

e r m

a ll y

i n d

u c e d

w i n d

s

Low radiation

Figure 23. Variability of ozone levels at Verzago as function of meteorological parameters.LOOP Final Report 67

In summary, the ozone concentrations in Verzago in the late afternoon typically increase by around 6 ppb ozone per 1 °C daily maximum temperature. On days with low global radiation ozone concentrations do not exceed 80 ppb. When the air originates from Milan, we find around 20-30 ppb higher ozone concentrations than the concentration typical for the prevailing temperature. This is similar to what was found in cross-sections of the plumes, where the maximum ozone concentrations are often 20-40 ppb higher than in the adjacent air masses (Dommen et al ., 2002). For conditions with strong winds or no development of southerly thermally induced winds, ozone concentrations are 20-30 ppb lower than typical for the prevailing temperature.


### 3.4.6 Investigation of P(O 3 

### ) based on observations

3.4.6.1 PSS versus O DD-H approximation For the comprehensive data sets at the site of Verzago, radical concentrations and local ozone productions (P(O 3 )) were determined by steady state calculations. Peroxy radical levels as determined by steady state calculations were usually lower than concentrations measured with a chemical amplifier at this site (Spirig et al ., 2002). Part of this difference can be explained by the negligence of O 3 -alkene reactions as a radical source (see section 2.2.4). Based on VOCmeasurements at Verzago, we estimated the importance of this radical source during the passage of the plume at the station on May 13 1998. This is considered to be an extreme case and likely to represent an upper limit of VOC levels for this location. Ozonolysis of alkenes contributed 25% to odd-H production in this case, a smaller percentage than Paulson and Orlando (1996) found for an urban ground station in the Los Angeles area. Calculations of P(O 3 ) by using either equation (5) or (1) frequently results in significant discrepancies. For sunny conditions with well mixed conditions, P(O 3 ) calculated with the Leighton ratio (5) is higher than with the formation over R1-R4.

local time -20 -10

0 10 20 30 40 50P(O3) [ppb/h] Equation (1) PSS

9:00 11:00 13:00 15:00 17:00 19:00

Figure 24. Comparison of P(O 3 ) calculated by the Leighton ratio (PSS) and (1) for May 13th 1998 at Verzago. (Data from the PIPAPO field experiment).Field Campaigns 68

Figure 24 shows a comparison of P(O 3 ) calculated by equation (1) and the PSS based on measurements of the PIPAPO campaign in Verzago (May 13 th ). There is a clear tendency - during the afternoon, when the boundary layer height is constant - that P(O 3 ) derived from PSS is higher than calculated via the radical steady state approximation.


### 3.4.6.2 Investigation of NO x



### / VOC sensitivity of P(O 3 

### ) For datasets gathered during the two IOP’s, the relative change of the ozone production rate due to a small change (5%) in the concentrations of NO x and VOC (including CO, biogenics and CH4) was calculated. In Figure 25, these relative ozone production sensitivities (dlnP(O 3 )/dln [NO x ] and dlnP(O 3 )/dln[VOC]) are plotted against the parameter L N /Q.

Figure 25. Relative sensitivities of local ozone production as determined from measurements between 1000 and 1800 LT at Bresso and Verzago.

The L N /Q values calculated from observations (circles and squares) follow the theoretical pattern (lines) quite well, except at higher L N /Q values, where the “observed” relative sensitivity of VOC-changes is somewhat lower than in theory. The transition between NO x and VOC sensitive O 3 production occurs almost exactly at the theoretical L N /Qvalue of 0.5. Calculated L N /Q values for the sites of Bresso and Verzago reveal that ozone production at the urban site of Bresso is VOC sensitive, while Verzago exhibited both VOC and NO x sensitive conditions. The VOC sensitivity at Bresso is not surprising due to the various strong emission sources nearby. The situation in Verzago is more complex. Ozone production and its sensitivity are influenced by the diurnal variation of radiation, vertical mixing and horizontal transport processes. The results of the L N /Q-approach for Verzago and Bresso agree with analyses of the ozone production regimes by other methods. Empirical ozone isopleths (Thielmann et al ., 2001) and measurements of indicator species (Thielmann et al ., 2002) both suggest VOC-sensitive ozone production at Bresso and mainly 0 0.2 0.4 0.6 0.8 1 L N /Q -1 -0.5

0 0.5

1dlnPO3/dln[X]

dlnPO3/dln[HC] dlnPO3/dln[NOx]

NO x sensitive VOCsensitiveLOOP Final Report 69

NO x sensitive ozone production at Verzago (Figure 26). Cases of VOC-sensitive ozone production at Verzago occurred during situations with direct advection of air masses from Milan.

Figure 26. Ozone isopleths from measurements at Bresso and Verzago (Thielmann et al ., 2001): VOC (as propene-equivalents) versus NO x and NO z concentrations.



### 3.4.6.3 Measurements of indicator species May 12 and May 13 1998 were ideal days to compare ozone sensitivities at the site of Verzago: Although temperature and radiation conditions were similar, two distinct situations were observed. On May 12, no direct advection of air masses from Milan occurred, whereas on May 13, a distinct plume arrived at theVerzago site. The analyses above and 3-D model studies (see 0) suggest VOCsensitive ozone concentrations within the Milan plume and NO x sensitivity in areas not affected by the plume. The indicator values registered at Verzago on these two days show different levels during the passage of the plume (grey area in Figure 27) and the afternoon on May 12. The transition values of the indicators deviated from these specific events are in agreement with the values proposed by Sillman (1995a), i.e., 6-8 for O 3 /NO y , and 0.2 –0.3 for H 2 O 2 /HNO 3 .

12:00 15:00 18:00 May 12 100 120 140 160 180 200O3[ppb]

2

4 6 8O3/NOy

0.2 0.4 0.6H2O2/HNO3

12:00 15:00 18:00 May 13 10 20 30 40 50NOy[ppb]

0.2 0.4 0.6H2O2/HNO3 12:00 15:00 18:00

2 4 6 8O3/NOy 12:00 15:00 18:00

Ozone NOy

Figure 27. Measurements of indicators at the site of Verzago during May 12 and May 13 1998. The grey area indicates the passage of the VOC-sensitive Milan plume.Field Campaigns 70



### 3.4.6.4 Results from aircraft observations Measurements of VOC, NO x , peroxides and O 3 aboard an aircraft were also used to characterize ozone production rates in the Milan area (Dommen et al ., 2002). These analyses focused on heavily polluted air masses, i.e. the so-called ‘Milan plume’. Steady state calculations (similar to those described in section 2.1.4) showed that the core of the Milan plume is VOC sensitive. The plume showed a downwind extension of about 40 km and a width of 10 to 20 km. Most of the measurements in the Milan plume fulfil the high NO x formula for the ozone production rate, as given by eq. 23 (see Figure 28). Peroxide and ozone productions rates were calculated from the measurements and also deduced from a local steady state photochemical analysis. The intercomparison of these two approaches yielded reasonable agreement for peroxide but lower ozone production rates for the steady state analysis.

0 5 10 15 20 25k i [VOC i ]Q)/(k N [NO 2 ]) 0 4 8 12 16P(O3) (ppb/h)

Figure 28. Calculated values of P(O 3 ) from the high NO x formula eq. 23. Circles are values with L N /Q < 0.7.



### 3.4.7 Aerosols

3.4.7.1 Number si ze distributions Aerosol number size distributions in Bresso (urban background) and Verzago (rural, regional background) have quite different shapes. Figure 29 shows 6- hourly averages (over the whole PIPAPO measurement period) of the size distributions for both sites. The total number concentration in Bresso is always higher than in Verzago. Bresso size distributions are clearly bi-modal with a very pronounced peak in the Aitken mode (around 20 - 25 nm diameter size). In Verzago, the modes appear to be merged into a single broader mode, with a strongly reduced number concentration compared to Bresso. The high number concentration of small particles in Bresso is obviously due to traffic emissions, and most pronounced during morning rush hours (06:00 - 12:00).LOOP Final Report 71

0000-0600

0 5000 10000 15000 20000 25000 30000 35000 40000

1 10 100 1000 10000 Dp (nm)dN/dlog(Dp)(#/cm3) Bresso Verzago

0060-1200

0 5000 10000 15000 20000 25000 30000 35000 40000

1 10 100 1000 10000 Dp (nm)dN/dlog(Dp)(#/cm3) Bresso Verzago

1200-1800

0 5000 10000 15000 20000 25000 30000 35000 40000

1 10 100 1000 10000 Dp (nm)dN/dlog(Dp) (#/cm3) Bresso Verzago

1800-2400

0 5000 10000 15000 20000 25000 30000 35000 40000

1 10 100 1000 10000 Dp (nm)dN/dlog(Dp) (#/cm3) Bresso Verzago

Figure 29. Averaged size distributions for different periods of the day in Bresso (urban background) and Verzago (rural, regional background).

Apart from the lower number concentration, Verzago also shows larger mode diameters, indicating that a growth process has taken place during air mass transport. We investigated with a sectional aerosol box model in how far the coagulation process can explain the observed changes in number and mode diameter. It appears that coagulation only can not explain the particle number reduction in a realistic time period (3 - 6 hours), and that the observed mode diameters in Verzago are larger than the modelled one, in particular for the Aitken mode. This provides some evidence that other growth processes (condensation of secondary aerosol) has contributed to the Verzago aerosol volume. Further evidence for secondary aerosol formation is given by the volatile aerosol fraction, which was obtained by measuring the residual size distribution after heating the aerosol up to 280°C. At this temperature, sulphuric acid, ammonium sulphate, ammonium nitrate and semi-volatile organic compounds are evaporated, which are making up most of the secondary aerosol. The refractory aerosol can be attributed to primary emitted black carbon, low-volatility organic compounds and dust. Figure 30 shows the average diurnal trend of the volatile aerosol fraction at both sites. It appears that most of the aerosol mass (50 - 80%) consists of semi-volatile (secondary) compounds for both sites, but Verzago has a systematic higher fraction than Bresso. Note that both sites show a significant increase in non-volatile compounds during the morning rush hour, consistent with traffic-related emissions of black carbon and primary organic compounds.Field Campaigns 72

Fraction volatile volume (280°C), average diurnal trend

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 Verzago Bresso

Figure 30. Average diurnal trend of the volatile aerosol volume fraction in Bresso and Verzago (sub-μm particles only).



### 3.4.7.2 Chemical composition The comparison of the fine aerosol chemical composition at Verzago (near-city site) and Bresso (urban site) shows that the concentration of ammonium nitrate normalized to elemental carbon (NH 4 NO 3 /EC) was on average 10% larger at the near-city site compared to the urban site. Although it is difficult to determine whether this difference was significant or not, it might be explained by the conversion of NO 2 to HNO 3 (and then to NH 4 NO 3 -, NH 3 being present in excess in the PiPapO area) during the transport of the photo-chemical smog plume. Thenormalized concentration of POM (POM/EC) was in turn 70% larger at the nearcity site compared to the urban site. The contribution of biogenic sources to the POM collected at the near-city site could not be investigated with the analytical methods we applied. Another source for the POM observed at this site could be the oxidation in condensable species of anthropogenic VOCs emitted upwind, in urban areas. To examine this hypothesis, we looked for changes in ratios between reactive species and stable tracers of pollution such as CO and EC. Variations in such normalized concentrations are independent of dilution and reflect mainly chemical processes. We observed for instance that the toluene/CO ratio was 2  lower at the near-city site compared to the urban site (Thielmann et al ., 2002). The oxidation of toluene can lead to condensable species, which would contribute to POM concentration (Pandis et al ., 1992), and to ozone formation. We actually observed a correlation between afternoon average O 3 /EC and POM/EC ratios (R3=0.53, n=18, Figure 31). These observations indicate that the high POM concentrations observed at the near-city site were at least partly due to the gas-particle conversion of VOCs’ oxidation products.LOOP Final Report 73

R 2 = 0.53

0 2 4 6 8 10 12 14 16 18

0 50 100 150 200 250 300 Normalized ozone concentration (μg m -3 O 3 / μg m -3 EC)Normalized POM concentration (μg m-3OC / μg m-3EC)

Figure 31. Correlation between POM/EC and O 3 /EC afternoon concentrations at the near-city site of Verzago during PiPaPO (Putaud et al ., 2002).



### 3.4.8 Model analysis Environmental policy to manage regional ozone problems has strongly relied on the use of photochemical air quality models (PAQM). These models contain representations of dynamics, emission rates and photochemistry. The differences between the models lie in the numerical routines, detail in the description of the physical and chemical processes, and how they use or develop meteorological fields. There is great concern about the uncertainties of such model predictions for ozone management strategies. Uncertainties may be due to model formulation, model inputs, or lack of a comprehensive set of measurements for model evaluation. There is no direct way to test whether a models prediction is accurate. It was the central theme of the LOOP project to determine the uncertain nature of model predictions concerning the response of ozone to reductions in NO x and VOC emissions and to improve the basis on how to evaluate a model. Sillman et al . (1995b) showed that 25% changes in anthropogenic emissions, wind speeds and mixing heights could alter the size of the reductions in peak ozone resulting from reduced VOC or NO x by a factor of two to ten. The biggest concern associated with model performance is the possibility of bias in predictions for the impact of NO x versus VOC. Emissions uncertainties are one of the limiting factors to accurately predict the dynamics of ozone over urban and regional scales, as well as the impact of control strategies. Evaluation of models with respect to meteorological parameters and comparison with ambient measurements other than ozone are also crucial. In recent years, different diagnostic tests have been proposed which should help in judging a model's predictive behaviour. Russell and Dennis (2000) summarized these in the following way:Field Campaigns 74

a) comparisons of predicted and measured outcome variables across many species, including predicted and observed correlational structure among/between species b) comparisons of in situ indicators or ratios of species that probe or illuminate chemical processes c) indicator tests that probe the dynamic response surface of model sensitivity, such as the NO x -VOC sensitivity measures d) model sensitivity and uncertainty analyses.



### 3.4.8.1 Photochemical model simulations - What has been done? On May 13 thermally induced southerly winds developed transporting the urban plume of Milan to the north towards the Alps. The highest peak ozone values of the observation period were reached with 190 ppb, measured at the ground station Verzago 30 km north of Milan. This situation was chosen as the test case for the model community in the project. Table 5 gives some parameters of the different model systems, which were used to simulate May 13. The European Air Pollution Dispersion Model (EURAD) group of the Institute for Geophysics and Meteorology, University of Cologne, simulated the same period on a larger European scale to provide boundary conditions for the smaller regional models. However, some model groups used other approaches.

Table 5. Photochemical models which simulated the situation in the Po Valley on May 13, 1998.
Model name Meteorological module Chemical module Vertical layers (lowest layer) boundary conditions; nudging Vertical layers Chemical Mechanism Species boundary conditions Ref.

KAMM/DRAIS 25 (20 m) Const. Geostrophical wind RADM2 Derived from averaged emissions (Hammer et al ., 2002)

TVM 24 (25 m) Vertical sounding Linate airport 15 LCC 1-D model calc. over 8 days with av. emissions south of Milan (Martilli et al ., 2002)

TVM 25 (25 m) ECMWF 15 LCC EURAD aircraft data (Dosio et al ., 2002)

UAM-V variable SM; Ground stations, windprofiler 8 CBM-IV EURAD (BärtschRitter et al ., 2003b) MM5-TAPOM 31 (30 m) ECMWF 16 RACM Measurem ents (Thunis, 2001)LOOP Final Report 75

The following gives a short summary of the work done by the various groups. A comparison and discussion on some aspects is then given below.

KAMM/DRAIS (Hammer et al ., 2002). The work deals with the usefulness of the indicator H 2 O 2 /HNO 3 to separate NO x -sensitive and VOC-sensitive regions. They performed model simulations for two regions in Germany (Baden-Württemberg, Berlin) and the PIPAPO domain and developed a new procedure to derive the transition value.

TVM (Martilli et al ., 2002) A simulation for May 13, 1998 was performed. Model results and measurements were compared for several monitoring stations. The sensitivity of the ozone formation toward the reduction of VOCs and/or NO x is investigated and transition values for indicator species were determined.

TVM (Dosio et al ., 2002) A simulation for May 13, 1998 was performed. Model results and measurements were compared for several monitoring stations and aircraft measurements. A focus is put on the complex circulation patterns of air masses in this region and their influence on air pollution.

UAM-V (Bärtsch-Ritter et al ., 2003b) A simulation for May 13, 1998 was performed. Model results and measurements were compared for several monitoring stations and aircraft measurements. The influence of the emission inventory on ozone production and ozone formation sensitivity is studied.

UAM-V (Bärtsch-Ritter et al ., 2003a) A sensitivity study of the base case simulation of May 13, 1998 was performed. The influence of the meteorological parameters temperature, humidity, mixing height and wind speed on NO 2 and photo-oxidant mixing ratios as well as on the ozone formation sensitivity was investigated. Furthermore, the effect of the spatial resolution of the emission inventory on ozone concentrations and the limitation question was addressed.

MM5-TAPOM (Thunis, 2001) A simulation for May 13, 1998 was performed. The influence of the grid size and two different emission inventories on ozone mixing ratios and ozone formation sensitivity was studied.



### 3.4.8.2 Photochemical model simulations - Evaluation with respect to:
a) Meteorology There are two pollutant transport terms, an advection term, in which pollutants are carried along with the time-averaged mean wind flow, and a dispersion term representing transport due to turbulence. The advection term determines the amount of pollutants captured over an emitting region and the distance an urbanField Campaigns 76

plume is transported. The turbulent mixing and the so-called mixing depth are critical to the vertical transport of pollutants, the horizontal plume dispersion and dry deposition in the PBL. Dosio et al . (2002) compared various ground stations and aircraft measurements with modelled wind velocities and direction as well as temperature. The stations were grouped into two classes (stations in Po Basin, stations in pre-alpine and alpine areas) and a statistical analysis was performed. The bias and the root mean square error were determined as functions of time. For the stations in the Po Basin good agreement was found for the wind velocity and direction. Martilli et al . (2002) and Bärtsch-Ritter et al . (2003b) compared the arrival time of the high ozone urban plume at Verzago with the model result. Both models showed such an urban plume transit near Verzago by about one hour delay. Thus, wind direction seems to be well captured by the models and at measured wind velocities of 2-3 m/s in this region the delay is still within an uncertainty of 20%, which can be rated as good. The development of the mixing height, which strongly influences the dilution of the emissions, was compared with measured 222 Rn concentrations. On a time scale of days a constant ground emission rate of 222 Rn can be assumed. Figure 14 shows a comparison of the measured 222 Rn activity and the mixing ratio of an inert tracer, which is emitted at a constant rate. This indicates, that the UAM-V model simulates the increase of the mixing height quite well for that day.

Figure 32. A simulation of 222 Rn (Bq m -3 ) on May 13 with measurements at the ground station Brera (crosses). (from Bärtsch-Ritter (2002). A constant surface emission of 72 Bq m -2 h -1 was assumed for the simulations.
b) Emissions A comparison between the emission inventory and measurements yielded some discrepancies, as for instance the CO/NO x emission ratio in the urban and nonurban areas (see above). From a comparison between measurements and model calculated CO mixing ratios in the urban plume it was concluded, that the CO emission rate of the inventory must be underestimated by at least a factor of 1.8 Time ( CET )LOOP Final Report 77

(Bärtsch-Ritter et al ., 2003b). These authors then modified the emission inventory based on observations described by Dommen et al . (2003). A simulation with this modified emission inventory predicted similar peak ozone values in the urban plume and only small differences in the ozone distribution in the domain. However, better agreement was obtained for hydrogen peroxide, which is one of the test species for diagnostic evaluations of model performance (see paragraph c). Thus, there is evidence, that a discrepancy between the emission inventory and real emissions might be the reason for the incompatible behaviour of H 2 O 2 between model and measurements. Sensitivity analyses of meteorological parameters showed, that their influence on peroxide formation is lower (Bärtsch-Ritter et al ., 2003a).
c) Chemistry Martilli et al . (2002) and Bärtsch-Ritter et al . (2003b) compared many species between model and measurements. Both models predict an urban ozone plume of similar magnitude near the Verzago station, where it was observed. However, other species did not show such a good agreement. This is a good example, which shows that a comparison of ozone alone may be misleading. Martilli et al

. (2002) obtained agreements with measurements for O 3 , NO y and VOCs, an underestimation of NO, NO 2 , CO and an overestimation of PAN, HNO 3 and H 2 O 2 . Bärtsch-Ritter et al . (2003b) obtained agreements with measurements for O 3 , NO y , HCHO and PAN, CO was strongly underestimated. Two features, which are good indicators of the photochemical processes in an urban plume were not modelled correctly; hydrogen peroxide concentrations increased in the plume concurrent with the ozone while observations showed a decrease and NO 2 did not increase during the plume event in Verzago as observed. These discrepancies might indicate a bias of the models toward NO x sensitivity. One of the reasons for these discrepancies could be the differences observed between emission inventory and measurements (see 3.4.4.2). Therefore, these authors performed simulations with an emission inventory modified according to observations. These simulations showed only small differences in the ozone distribution in the domain and predicted similar peak ozone values in the urban plume, but no elevated peroxide mixing ratios anymore (see Figure 33). The response on the VOC-NO x sensitivity using either the original or the modified emission inventory shows only marginal differences in the NO x sensitive areas outside the urban plume, whereas the ROG sensitivity of the plume is enhanced there using the modified emissions.Field Campaigns 78

Figure 33. H 2 O 2 mixing ratio simulated with the UAM-V. The circle points to the position of the urban plume. Simulation with the original emission inventory (a) and with the modified emission inventory (b), (from Dommen and Ritter (2002)).



### 3.4.8.3 Comparison of photochemical model simulations Figure 34 shows the spatial distribution of the ozone mixing ratio at 4 p.m. (LT) on May 13 simulated by three models, TVM, UAM-V and TAPOM. The first two models used a grid size of 3x3 km 2 compared to 4x4 and 10x10 km 2 of the last one. Predicted and observed ozone values are high in the Po Basin with over 100 ppb. The TVM, UAM-V models simulate an ozone peak of more than 180 ppb about 30 km north of Milan close to Verzago, where a peak ozone value of 195 ppb was observed on that day. The TAPOM model predicts an ozone plume

a)

b)LOOP Final Report 79

of 170 ppb only 15 km to the west/north-west of Milan. This model has problems to represent the wind field correctly.

Figure 34. Ozone mixing ratio for May 13, 1998, 4 p. m. simulated with the TVM (a) (Martilli et al ., 2002), UAM-V (b) (Bärtsch-Ritter et al ., 2003b), TAPOM (T hunis, 2001) for (c) a 10x10 km 2 and (d) 4x4 km 2 grid size. TVM and UAM-V have a grid size of 3x3 km 2

.
Reduction scenarios were calculated with all three models, whereby 35% of NO x or VOC emissions were reduced. Figure 35 presents the difference of the ozone mixing ratios (  O 3 ) between those simulations, i.e., O 3 (VOC-reduction) minus O 3 (NO x -reduction). Thus, a positive value of  O 3 represents a NO x -sensitive chemistry and a negative value a VOC-sensitive chemistry. The TVM calculates a small VOC-sensitive region of about 15 km width north of Milan while the UAM-V predicts a large area north of Milan from Lake Maggiore to the right domain boundary as being VOC-sensitive. The TVM and TAPOM did not consider point emission sources. In the UAM-V simulations point sources have a pronounced effect on the extension of this VOC-sensitive area. The TAPOM simulation shows no VOC-sensitive region in the domain for 10x10 km 2 grid resolution. It has a VOC sensitive region in the case of a 4x4 km 2 grid size, whereby for this calculation VOCs and NO x where reduced by 40% each. There are various facts which could explain the weaker VOC sensitivity of the TAPOM a) b)

c) d)Field Campaigns 80

e.g., a) the emissions downwind of Milan are important, which might be smaller along the urban plume trajectory to the north-west of this model; b) the lower grid resolution; c) since the model domain of TAPOM is larger than that of the other two models, emissions are also reduced on a larger scale, while boundary conditions were kept constant in the TVM and UAM-V.

Figure 35. VOC control minus NO x control ozone mixing ratio for May 13, 1998, 4 p.m. simulated by TVM (a) (Martilli et al ., 2002), UAM-V (b) (Bärtsch-Ritter et al ., 2003b) and TAPOM (T hunis, 2001) for (c) a 10x10 km 2 and (d) 4x4 km 2 grid size. TVM and UAM-V have a grid size of 3x3 km 2 . (a)-(c) calculated a 35% reduction, (d) a 40% reduction of NO x or VOCs.



### 3.4.8.4 Sensitivity analyses of photochemical model simulations A sensitivity analysis by Bärtsch-Ritter et al . (2003a) assessed how changing various parameters would impact predictions of ozone mixing ratios and the VOC-NO x sensitivity of the ozone production. Sensitivity analysis aids in the development of uncertainty estimates about a models predictions due to uncertainties in the inputs. It may also help to generalize the model results to a certain degree. a) b)

c ) d )LOOP Final Report 81

 The following parameters or combinations of them were varied: mixing height, temperature, humidity, temperature and humidity, wind speed. The following trends were obtained:

 The VOC-sensitive areas increase with higher temperatures, mostly because of lower PAN formation at higher temperatures.

 Increased humidity leads to increase of the NO x sensitive areas.

 An increase of humidity and temperature also leads to more NO x sensitive conditions.

 With increasing wind speed, emissions get more evenly distributed in the whole modelling area and the situation also becomes more influenced by the boundary conditions. VOC sensitive areas increase with elevated wind speeds, while simulations without point sources tend to increase NO x sensitive areas. In summary, for each of the meteorological variations performed in this sensitivity study, the modelled ozone plume remains VOC sensitive. The variations were fairly large and should well cover uncertainties of these meteorological parameters.


### 3.4.8.5 Influence of the spat ial resolution of the emission inventory Spatial resolution in air pollution and meteorology modelling has always been a crucial issue. Ideally, simulations should use a resolution as fine as possible and a domain as large as possible to capture all processes. Unfortunately, this is prevented by CPU limitations. Therefore a compromise must be found between the resolution and the extent of the domain we want to simulate. The key issue consists thus in identifying the size of the relevant processes to determine the optimal scale and resolution. Thunis (2001) and Bärtsch-Ritter et al . (2003a) investigated which resolution is necessary to simulate the relevant processes in air quality modelling around cities. It might well happen that a coarse resolution leads to the prediction of a different emission reduction strategy. This could have fatal implications on ozone abatement strategies. Peak ozone values were similar for a resolution up to 10 x 10 km 2 . At lower resolution peak ozone decreased by as much as 20% at a resolution of 50 x 50 km 2 while it increased in the urban areas and the Po Basin area of the model domain. When the grid size is increased the ozone reducing effect in VOC sensitive areas strongly decreases. The TAPOM model has already no VOC sensitive area at a grid resolution of 10x10 km 2 (Figure 35c). The UAM-V shows only a slight increase of the number of NO x sensitive grid cells up to 27x27 km 2 . At 54x54 km 2 only a small area remains VOC sensitive (Figure 36a). Without point emission sources no VOC sensitive area is predicted for a grid size of 54x54 km 2 (Figure 36b).Field Campaigns 82

Figure 36. VOC control minus NO x control ozone mixing ratio for May 13, 1998, 4 p.m. simulated by UAM-V (Bärtsch-Ritter et al ., 2003b) with a grid size of 54x54 km 2 ; (a) with (b) without point emission

sources.



### 3.4.9 Summary of scien tific findings of th e PIPAPO experiment The main goal of the PIPAPO field campaign was to describe the spatial and temporal dynamics of the NO x /VOC sensitivity in the plume of the Milan metropolitan area. This can only be reached with models that have been proven not only to reproduce well the ozone distribution in space and time, but also the correct sensitivity to changed emission rates. However, a comparison with experimental data can be based on the base case model run only. Sensitivity runs are thus simulating situations that do not correspond to the real situation, and can a)

b)LOOP Final Report 83

therefore not be validated with measurements. This situation is a dilemma, as a validation of sensitivity runs with field data cannot be performed. Reasons for deviations between modelled and measured values must be assessed, and it must be evaluated whether the causes for the deviations are affecting the sensitivity. GCMs need not only correctly predict ozone concentrations but also other secondary pollutants, such as PAN, peroxides, and HNO 3 .


### 3.4.9.1 I ndicators The comparison of measured indicator values with ozone concentration sensitivities at Verzago (see section 3.4.6.3) suggests that the transition values were in agreement with those defined by Sillman (1995a). However, the data base is small and should not be generalized. Analyses with 3D-models revealed that the transition values for the indicators are very sensitive to the chosen boundary conditions, especially for the small domain. It could well be that transition values in the real world are close to those suggested by Sillman 1995. In the simulations however, the transition values show a larger scatter due to systematic differences between individual models (e.g., how inflow peroxide concentrations are determined etc). Recently, a suggestion for adapting the original indicator concept has been made (Sillman and He, 2002). It is recommended to use the changes of indicator values during a given transport time rather than absolute values.



### 3.4.9.2 Influen ce of aerosols Photochemical smog episodes are accompanied by high aerosol loads, with an important contribution from secondary aerosols. The high radical level associated with photochemical smog favours the formation of secondary aerosols, both inorganic and organic. The aerosol measurements have confirmed the large contribution from secondary aerosols (see section 3.4.7). However, a simple description of the aerosol in a kind of a flow reactor downwind of Milan cannot be expected due to the patchy emissions. Fresh emissions are always mixed into chemically aged air masses. The NH 3 emissions are one of the driving forces for the inorganic aerosol formation. However, an adequate spatially and temporally resolved emission inventory was not available, and atmospheric NH 3 concentration measurements to validate the plausibility of such an inventory do not exist. Aerosol formation in the model world has a very minor influence onozone formation. The two most important processes that couple the photooxidant and aerosol formation are not yet in the models: there is no feedback of the aerosol levels on the radiation properties, and there is no reaction of radicals with aerosol surface or within water droplets. E.g., the plume that arrived in Verzago in the early afternoon caused a 10% drop of the J NO2 values at the surface.



### 3.4.9.3 Role of vertical mixing The representation of the vertical mixing in the models has an important influence on the VOC/NO x sensitivity. In the analysis of Spirig et al . (2002), model calculations predicted a NO x sensitive O 3 production in the residual layerField Campaigns 84

earlier in the day. LIDAR measurements on the ARAT aircraft confirm that there was an increase in O 3 concentration above the mixing layer. The analysis of the flight in the morning of June 2 (0930 LST to 1030 LST) indicates a mean net ozone production decreasing from 10 to 2 ppb/h, going from 700 m asl to 1200 m asl. However, these increases are not significant due to the large scatter of the data. We cannot determine whether the ozone production in the residual layer is due to remaining precursors or due to fresh emissions, because we have no direct information on the vertical profiles of the parameters driving the ozone formation, i.e., NO and the ODD-H levels. But it is evident that ozone produced in the residual layer earlier in the day would influence the sensitivity of the O 3 concentration at the surface in the afternoon, most likely in an accentuation of NO x sensitivity. The sensitivity analysis by Bärtsch-Ritter et al . (2003a) showed a lower ozone peak in the urban plume and a smaller spatial extension of this plume as a result of higher mixing layers. Increasing the mixing layer increased the number of NO x sensitive grid cells.



### 3.4.9.4 Scale c onsiderations The PIPAPO field campaign showed that the relevant spatial scale for the ozone production in the Milan plume is 10 to 30 km. Peak values of ozone, secondary aerosols, and other photo-oxidants are reached within a few 10 km from the major emission sources. It is on this scale that reduction strategies have to be assessed. PIPAPO looked at two periods of only a few days. Extrapolation of the analysis to the seasonal scale relevant for reduction strategies and application was not in the scope of the PIPAPO activity. Nevertheless, we are convinced that the model approaches used in the PIPAPO studies can be extended to longer time scales to get the integrated NO x /VOC sensitivity for the whole summer. However, it is essential to use a sufficient spatial resolution in the models (see section 3.4.8.5) for this task. The analysis also shows that the measuring network in the Po Basin should be complemented by indicator species in order to get an independent approach of the spatial and temporal variability of the ozone production sensitivity based on field measurements. The situation in the Milan area calls for a second project focused on the interaction of secondary aerosol formation and photo-oxidant production. Because the highest aerosol levels are found during the stagnant winter conditions, the measurements should also include winter campaigns.


### Acknowledgements Financial support through the commission for technology and industry (KTI) (Eurotrac-2 project LOOP), and the Swiss Agency for the Environment, Forests and Landscape (SAEFL) (Project “Biogene Aerosole”) is greatly acknowledged. Various institutions of the Lombardy region helped to organize the campaign. We especially thank Dr. Matteo Tamponi and Dr. Guido Lanzani for their enthusiastic support before and during the PIPAPO field campaign. Thomas Staffelbach built up the PIPAPO database and Marx Stampfli ( www.mathematics.ch ) developed the data visualization tool.LOOP Final Report 85



### 3.5 Atmospheric pollution over the Paris area: the ESQUIF project

Matthias Beekmann Institut Perre et Simon Laplace, Service d’Aeronomie, Paris, France

Laurent Menut Laboratoire Interuniversitaires des Sciences Atmospheriques (LISA), France



### 3.5.1 General introduction to the campaign ESQUIF (“Étude et Simulation de la QUalité de l'air en Ile de France”, a French acronym for “Air quality study and modelling over the Paris area”) is the first comprehensive project dedicated to the study of air pollution in the French Ile de France region, with Paris in its centre (Menut et al. , 2000). The project has been initiated jointly by the Institut Pierre Simon Laplace (IPSL) and the Laboratoire Inter-universitaire des Systèmes Atmosphériques (LISA), and the partnership was extended to the Laboratoire d'Aérologie, the Forschung Zentrum of Jülich, AIRPARIF (the local air-quality monitoring network) and Météo-France. The project was carried out over three years (1998-2000). The project was coordinated by Robert Vautard (Laboratoire de Meteorologie Dynamique, IPSL). Besides the chemical and physical understanding of pollution episodes, ESQUIF also aims at the validation of existing models dedicated to pollution analysis, scenarios and forecast, by establishing a comprehensive and thorough database. The particular interest of the Paris area stems from its geographical situation: It is a large city located far (by about 200 km) from other densely populated areas. It is therefore in principle easier than for other European cities to distinguish the air masses charged with locally emitted pollutants from those charged with pollutants emitted in remote areas. Another geographical interest of the Paris area is its low topography, which in the absence of local forcing makes the accumulation / dispersion of pollutants more complex. More specifically, the ESQUIF project focuses on several research issues, the most important ones being listed here:

The thermodynamics of the boundary layer in and around a big city: The main questions are (a) the role of the vertical structure of the boundary layer and the turbulent mixing processes in the occurrence of pollution episodes, and (b) whether models are able to simulate the air flow, its vertical structure and the surface fluxes encountered during low dispersion episodes, and, if not, how to improve the various parameterisations used within the models.

The photochemistry of pollutants: the focus here is essentially on the relationship between local photochemical ozone build-up and anthropogenic and biogenic emissions; this question includes the investigation of the chemical regime in the Paris area (VOC or NO x limited) and is thus stronglyField Campaigns 86

linked to the LOOP question. This question also includes the evaluation of existing emission inventories using measurements acquired during the campaign.

The transport of pollutants at the interface of the urban/regional and the continental scale: both the inflow of pollutants into the Ile de France region and the fate of the plume outside of the region have been evaluated.

The physical and chemical evolution of urban and suburban aerosols and their impact on photolysis frequencies. The measurement campaign started during summer 1998, and lasted until the winter 1999-2000: eleven episodes of 1-5 days duration, denoted hereafter the ESQUIF IOPs (Intensive Observation Periods) and representative for different weather situations implying enhanced photochemical activity have been documented by measurements and simulations. Following the ESQUIF objectives, the campaign consisted both in aircraft and ground-based measurements. The latter included routine measurements by the local air quality network AIRPARIF and dedicated measurements by a equipped van, by lidar systems or by a DOAS system. For airborne measurements, various flight plans were defined: the flights aiming at documenting the spatial ozone distribution in loops around the city at various distances from the centre, the “city plume”' flights aiming at following the urban plume, and the “chemical regimes'' flights consisting in iso-latitude regular legs over the Paris area. The large body of experimental data gathered during these flights includes measurements of gaseous pollutants (O 3 , NO, NO 2 , PAN, HONO, NOy, SO 2 , CO, VOC’s and HCHO), aerosol parameters (chemical composition, size distribution, optical parameter as extinction coefficient), radiative parameters like the NO 2 photolysis frequency and meteorological parameters (winds, humidity, boundary layer height etc.). The ESQUIF campaign also allowed a detailed evaluation of chemistry-transport and meteorology-chemistry models which were in part developed in parallel to the project (AZUR (LISA), CHIMERE (IPSL), MESO-NH Chimie (Laboratoire d’Aerologie), MOCAGE (Meteo France). More detailed information about the outset and major results of the campaign can be found on the ESQUIF website ( http://esquif.lmd.polytechnique.fr ) and in peer reviewed articles (see publication list).



### 3.5.2 Overview of results re levant for the LOOP project In the following, some results that are especially relevant for the LOOP project will be highlighted. The sensitivity of local ozone build-up to VOC and NO x emissions was evaluated in several model studies: For instance, simulations with the AZUR model (LISA), driven by ECMWF meteorological data, indicate a clearly VOC limited chemical regime in the pollution plume downwind of Paris for August 8 and 9 during IOP 2. In this study (and in the following text), the chemical regime is always determined by the difference in ozone levels within the urban plumeLOOP Final Report 87

simulated for 30% reduced VOC and NO x emissions. This result is confirmed by corresponding simulations with another chemistry-transport model, CHIMERE (IPSL), but using, among others, a different chemical mechanism (MELCHIOR instead of MOCA) (Derognat et al. , 2002). For another IOP (July 16-17/ 7 1999), simulations with the base case version of CHIMERE also yield a VOC limited regime, but a NO x limited regime for July 18. For this period , additional numerical experiments were performed in order to evaluate the degree of confidence that can be put into chemical regime simulations (Sillman et al. , 2002). When simultaneously increasing VOC emissions by 40% and decreasing NO x emissions by 30%, a NO x limited chemical regime is simulated for July 17, instead of the VOC limited regime for the base case simulations (Sillman et al. , 2002). Moreover, O 3 / NO y scatter plots show a better agreement between simulations and measurements for the modified case. This shows the potential of uncertainty in factors like emissions, and in particular in the VOC / NO x emission ratio, to make simulations of the chemical regime uncertain for specific days. The question now is whether such large changes in the emission ratio (by a factor of two) as applied in this study, are compatible with uncertainty in emission data bases. A comparison between simulated and measured VOC and NO y species for the whole set of afternoon flights performed during ESQUIF allows to give uncertainty bounds on emissions (Vautard et al. , 2002). For these simulations, the CHIMERE model was modified in order to take into account transport and chemical loss for each of the more than 30 VOC species measured on the DIMONA and AZTEC aircraft and specified in the emission profiles provided by IER (R. Friedrichs), University of Stuttgart. This study does not make evident any significant bias in the NO x and VOC emission data base used, but fixes their uncertainty bounds (90% confidence level) at respectively 35 and 40% (Vautard et al. , 2002). However, only a third to a half of individual VOC’s specified in the emission data base were measured. Thus, the uncertainty in the total VOC / NO x emission ratio may be somewhat larger than indicated in the present study, and an increase by a factor of two, as applied in the Sillman et al. study, still could be realistic. A more formal uncertainty analysis was performed with the CHIMERE model using Bayesian Monte Carlo analysis and taking into account uncertainty in model input parameters as emissions, meteorological data and chemical rates coefficients (Beekmann and Derognat, 2002). This type of analysis is able to take into account the measurement constraint, i.e., only Monte Carlo simulations comparing sufficiently well with observations are retained as realistic. It allows to make probabilistic statements about chemical regimes. For July 17, the chemical regimes are found to be NO x limited with 60% probability, again showing as in the Sillman (2002) study that a NO x limited regime better fits with measurements than a VOC limited one for this day. Certainly, such probabilistic statements are to some extent uncertain themselves due to the fact that the initial uncertainties in input parameters cannot be determined precisely. Nevertheless, sensitivity tests, with alternative input parameter uncertainties, prove them to be qualitatively correct. The model sensitivity of ozone build-up for another CTM (similar to CHIMERE and AZUR) with respect to emissions, rate constants and meteorological parameters was determined using the adjoint technique (Menut, 2002). ThisField Campaigns 88

technique allows the evaluation of a whole set of temporally resolved sensitivities for infinitesimally small changes of different parameters in only one simulation. For example, the ozone level at the suburban site Rambouillet at 15 UT (near the centre and time of the urban plume ozone maximum) is positively sensitive to NO emissions occurring between 10 and 15 UT, but negatively sensitive to those occurring before 10 UT (yielding a very small integrated sensitivity). The sensitivity to reactive VOC emissions like alkenes and aromatic compounds peaks at 6 UT and 10 UT, indicating a several hours time scale of ozone build-up and transport between the urban emissions regions and the suburban site. The impact of biogenic VOC (isoprene and terpene) emissions on ozone levels in the urban plume was assessed in another specific study (Derognat et al. , 2002). Biogenic VOC emission data bases for the continental scale were elaborated using the methodology and emission factors outlined in Simpson et al. (1999). For the Ile de France region, additional information from the high resolution USGS data base (1 km) and form species distributions given by the Office National des Forets was taken into account, and could be evaluated with isoprene measurements taken with online gas-chromatography on board of the METAIR/ DIMONA aircraft. The maximal impact of biogenic VOC emissions on ozone peaks in the urban plume varied between 3 and 40 ppb, with a median of 9 ppb, for all simulated intensive days during the ESQUIF campaign. The most important determining factor was temperature (higher T increasing biogenic emissions) and local NO x levels (higher NO x increasing ozone build-up from BVOCs). A bit more than a half of the effect was due to BVOC emissions outside of the Ile de France region, enhancing ozone advected to the Ile de France area. For the IOP2 period (August 6-11, 1998), it also could be shown that biogenic emissions rendered the chemical regime less VOC limited, but without changing it to NO x limited. Indeed, the mean difference between afternoon ozone levels for 35% NO x emission and VOC emission reductions averaged over the plume decreased from 25 ppb to 15 ppb. More detailed results concerning chemical regime simulations for the whole summers 1998 and 1999 are presented in the next section.


### 3.5.3 Climatology of the chemical regime in the Paris - Ile de France region – a model simulation In order to assess the climatological occurrence of VOC or NO x limited chemical regimes in the ozone plume of the Paris agglomeration, we performed a climatological simulation during two summers (respectively from May to September 1998 and 1999) using the CHIMERE regional model with reference simulations and with either anthropogenic NOx and VOC emissions reduced by 35%. CHIMERE regional (Vautard et al. , 2001) is a chemistry and transport model covering a domain of 150x150 km around Paris and driven by ECMWF meteorological data. It is imbedded in the CHIMERE continental model covering Western Europe (Schmidt et al. , 2001, http://euler.polytechnique.fr/chimere ). The study is part of Claude Derognat's thesis work (Derognat, 2002).LOOP Final Report 89

Results of simulations are displayed as relative changes in O X (O 3 +NO 2 ) for two emission scenarios, NO x Ile de France emissions –35% and VOC Ile de France emissions –35%: 

## ) Ox

- )/(Ox Ox (Ox Eff noemis ref 35% E 35% E E E VOC NOx VOC
NOx  



## where O X noemis designs a simulation without Ile de France emissions allowing to determine O x produced from Ile de France emissions (local O X build-up). For each day at 15 UT, O X is averaged for different distances from the Paris centre (6-20 km, 20-40 km, 40-60 km) for points with large local O X build-up. Positive values of the term above correspond to a VOC limited chemical regime. Figure 37 shows that independently of the distance, the chemical regime is either VOC limited, or intermediate (i.e., when differences are below about 10%) (Derognat, 2002). Days with frankly NO x limited regimes apparently do not occur in the simulations. Interestingly, for days with local O X build-up below 20 ppb (grey bars), the chemical regime becomes more VOC limited for larger distances from the agglomeration. This feature differs from the general view, that the chemical regime should become more and more NO x limited with larger distances from the sources. On the contrary, in particular for days when cloud cover reduces actinic fluxes and radical production by photolysis, and when photochemical ozone production is not very strong, positive feedbacks apparently strengthen the VOC limited regime over time. Nevertheless, it is expected that at still larger distances, but which are outside the model domain, the chemical regime finally switch to NO x limited. We next question, weather CHIMERE is able to correctly simulate the chemical regime. For this purpose, we compare for four different flights performed during the ESQUIF campaign simulated and measured O 3 / NO z scatter plots (Figure 38). For two of the days the simulated O 3 / NO z slope is underestimated by CHIMERE, while it is overestimated for another day. This does not allow to conclude on any bias in the simulated chemical regimes. Nevertheless, as also was shown in the previous section by means of Monte Carlo analysis, still a significant uncertainty in the chemical regime subsists, especially for intermediate regimes (Beekmann and Derognat, 2002). This has also been shown by an analysis for a particular day of the ESQUIF campaign, July 17, 1999 (Sillman et al. , 2002). We next seek for reasons for the day-to-day variability in the chemical regime. A linear correlation analysis shows that among many parameters tested, the ozone concentrations advected into the model domain and the inverse of wind speed are best correlated with the chemical regime. Figure 39 shows the chemical regime as defined above, but at a distance comprised between 40 and 60 km from town centre, as a function of these two parameters.Field Campaigns 90

Figure 37. Predicted reduction in O x (in %) resulting from a 35% reduction in the emissions rate for anthropogenic VOC minus reduction in O x (in %) resulting from a 35% reduction in the emission rate for anthropogenic NO x , averaged for different distances from the Paris centre (6-20 km (a), 20-40 km (b), 40- 60 km (c)) for points with large local O x build-up and plotted for each day of spring-summer (MayAugust) 1998 and 1999. a)

b)

c)LOOP Final Report 91

Figure 38. Ozone mixing ratios plotted over the NO z (NO y – NO x ), for measurement collected by the DIMONA plane (crosses) during IOPs of the ESQUIF campaign (Menut et al. , 2000) or predicted (triangles) by the CHIMERE model and interpolated over the flights.

Figure 39. Predicted reduction in O x (in %) resulting from a 35% reduction in the emission rate for anthropogenic VOC minus reduction in O x (in %) resulting from a 35% reduction in the emission rate for anthropogenic NO x , as a function of ozone concentrations advected from the large scale and the inverse of wind speed (averaged values for a distance comprised between 40 and 60 km from the Paris centre and for points grid with large local O x build-up) and plotted only for the clear sky days of spring-summer (May-August) 1998 and 1999.Field Campaigns 92

As expected, the chemical regime has a tendency to become more NO x limited for advected high ozone concentrations and for strong wind speeds. Indeed, on one hand, photolysis of imported ozone is an important radical source favouring the occurrence of NO x limited regimes. On the other hand, the inverse of wind speed is well correlated with NO y , which has been used as an indicator of the chemical regime (low NO Y = strong wind = NO x limited). Both parameters alone explain already about 50-60% of the variability in the chemical regime. Other parameters (direction of the plume, temperature triggering or not biogenic emissions, humidity, etc. may account for the rest of the variability.


### Acknowledgements The ESQUIF project has been supported by the Ministère de l'Aménagement duTerritoire et de l'Environnement, the Conseil Régional Ile de France and ElfAquitaine.LOOP Final Report 93



### 3.6 ESCOMPTE

B. Cros Laboratoire d’Aerologie, Toulouse, France

P. Durand METEO-FRANCE, Toulouse, France S. Pinceloup, G. Laverdet, G. Le Bras CNRS-LCSR, Orléans, France
P. Perros, Isabelle Coll LISA, University of Paris 12, Creteil, France



### 3.6.1 General framework of the ESCOMPTE programme In order to help in designing ozone abatement policy, development of combined photochemical and dynamical models is essential to better understand and forecast air pollution events over urbanized and rural areas. There is presently an important effort in developing and improving meso-scale chemistry-transport models, the more advanced ones involving an aerosol scheme. Preliminary work confirms the important contribution of the regional scale processes to the pollutant concentrations observed at the local scale. This is particularly true for ozone which concentration depends on reservoir species likely to be transported over long distances. Thus, comparisons of model output against field measurements require a detailed 4-D data base of primary pollutants emission together with the dynamics and chemical composition of the atmosphere. The ESCOMPTE field campaign (Cros, 2002) has been designed to provide us with this database for some pollution events. The main characteristics of the ESCOMPTE program can be found at the URL http://medias.obsmip.fr/escompte

.


### 3.6.2 The ESCOMPT E area and period An area around Marseille city and Berre pond, in the south-eastern part of France, has been selected to host the ESCOMPTE field campaign. This site of 120kmX120km fulfils most of the conditions required for the project: strong pollutant sources (urban and industrial) within the experimental area and high occurrence of photochemical pollution events ; existence of a dense, operational pollution monitoring network; a channelling of pollutant plumes by sea-breeze circulations and terrain features. The experiment was conducted between June 4th and July 16th, 2001.


### 3.6.3 Experimental set-up The ground-based platforms, specifically deployed for the experiment, are represented on Figure 40. They involve 20 stations, equipped for gas (ozone,Field Campaigns 94

nitrogen oxides, VOCs, ...) and/or particles measurements; among them, two were installed on ships, and two mobile stations (cars, not indicated on the Figure 40) were placed according to the plume locations. The surface energy budget was measured on 9 sites to cover the landscape variety in the area ; among them, four were in the urbanized area of Marseille (Mestayer and Durand, 2002); at some sites, fluxes of trace gases (ozone, NO x ) were also measured for emission/deposition velocities computation.

Figure 40. Ground based measurement platforms of the ESCOMPTE campaign.
The meteorological basic parameters (wind, temperature, moisture and radiation) were measured on the 9 abovementioned and on 5 complementary sites. Wind profile was continuously measured on 12 sites by 7 sodars, 4 UHF and 4 VHF radars. A scanning Doppler Lidar measured the 3-D wind field over the Marseille agglomeration. 3 upward pointing, and 2 scanning ozone lidars were set-up on a SW-NE axis (main breeze axis) through the domain. 4 radiosonde systems, 2 of them capable of ozone profiling, were activated during the Intense Observing Periods (IOPs). During these IOPs, the pollutants plumes were tracked by 33 constant-volume balloons, launched in the boundary layer from the emission areas (Marseille city or Berre pond); they were equipped with radiosondes, some of them with an ozone probe. 7 aircraft were flown during pollution episodes; 4 (DO 128 from IMK, FRG ; Fokker 27 from INSU, France ; Merlin 4 and Piper Ozone Lidar profiler Radar (VHF or UHF) wind profiler Sodar (wind profiler)
Ozone scanning Lidar Surface chemistry (gaz or particles) Radiosonde station Surface meteo. Station Wind scanning Lidar Surface flux and meteo. station ESCOMPTE : surface

- based p latformsLOOP Final Report 95

Aztec 23 from Météo-France) were able to in situ document dynamics and chemistry ; a ULM from IFU (FRG) measured ozone, aerosol and UV radiation ; the Falcon 20 from DLR (FRG) embarked the Doppler lidar WIND ; and a Piper Aztec 28 embarked an IR camera for surface temperature characterization over the Marseille area.



### 3.6.4 The cam paign progressThe first week was devoted to a QC/QA operation. This was completed by wingto-wing flights performed at the end of the campaign. 5 IOPs for a total of 15 days were documented, two of them corresponding to breeze circulation with high pollution levels, and two characterized by a “plume regime”, with a channelling of the pollutants towards the eastern part of the domain.
Side-projects: At the same place and during the same period two other scientific experiments were associated to ESCOMPTE. These side-programs UBL (Urban Boundary Layer) and GPS/H2O (Atmospheric water vapour tomography by GPS) benefited of the ESCOMPTE experimental environment but also usefully complemented some measurements of the main program.
On-going studies : ESCOMPTE has to bring inputs on the following questions:

what are the respective role of dynamics and chemistry on the pollution events?

how to take into account urban emissions in regional or global models?

can an operational model of pollution events be developed ?

which strategy has to be set up in order to reduce photo-oxidant and particle concentrations?
ESCOMPTE data base and data policy: From the ESCOMPTE program it is expected to put together a comprehensive and coherent database, allowing for testing and evaluating regional pollutionmodels. The so-called ESCOMPTE database (EDB) is managed by MédiasFrance. It will involve the data collected during the 2001 campaign, the hourly emissions data for the ESCOMPTE IOPs and a background data set (meteorological data, data from monitoring networks on air quality, meteorological and chemical large-scale environment, satellite data, orography, surface coverage, etc.). The EDB access is restricted to the ESCOMPTE community until Apr. 2003. The data of the IOP3 (July 2nd – July 4th) will be hidden, even for the ESCOMPTE community, until Oct. 2003, in order to contribute to a model evaluation exercise.Field Campaigns 96


### 3.6.5 Specific results: photochemical ozone production and chemical regime In this section, first “chemical” results from the ESCOMPTE campaign directly addressing LOOP objectives are presented. Photochemical ozone production rates are estimated from several techniques (radical measurements, PSS method, simulations). The local and integrated chemical regime is determined. Results from ESCOMPTE are at this stage more preliminary than those from other campaigns, but nevertheless they illustrate the potential of results that can be expected.


### 3.6.5.1 Measurements of pero xy radicals and related species at a rural site duri ng the ESCOMPTE Campaign Concentrations of peroxy radicals, RO 2 , NO x and ozone were continuously measured during the ESCOMPTE campaign at the rural site of Dupail. The site is located 35 km North-East of Marseille. Complementary measurements of aldehydes, peroxides, hydrocarbons were made by other groups. The aim of these measurements, especially those of peroxy radicals, was to contribute to better understand the photochemistry of ozone and to assess the contribution of local photochemical ozone production to the ozone budget. Peroxy radical concentrations were measured using the chemical amplifier technique. The influence of relative humidity on the chain length determined in the laboratory was taken into account. Daily profiles of RO 2 , NO x and ozone Daily profiles of the above species were recorded every day during the period 14 th June – 11 th July 2001, including the 3 IOP (14 - 15 th June, 21 – 26 th June, 2 nd – 3 rd July) periods. (Figure 41).

The RO 2 concentration peak occurs at around 14h (local time) with maxima of 80 pptv. The ozone peak occurs at 15h –16h.

The NO x levels were most of the time lower than a few ppbv, ie characteristic of a rural site.

Figure 41. Averaged profiles over the whole campaign, a) O 3 and NO x , b) RO 2 .LOOP Final Report 97

Very different RO 2 and ozone profiles were observed under different meteorological conditions: Case a : 18/06/01 Case b : 25/06/01 Wind: North, North-East (mistral) Wind: South, South-East (breeze) Mean wind speed : 20 km/h Mean wind speed : 3,5 km/h Maximum wind speed : 30 km/h Maximum wind speed : 11 km/h T mean = 15,3°C T max = 20°C T mean = 22,2°C T max = 31°C

Figure 42. RO 2 and ozone profiles under different meteorological conditions, a) in presence of mistral, b) in presence of breeze. Ozone concentration is almost The ozone profile is more usual constant throughout the day with daytime maximum (» 40 ppbv). There is no decrease High levels of ozone were observed of ozone during the night (loss by deposition is compensated by transportation over the site).

The concentration of RO 2 are low The concentrations of RO 2 are high with a maximum of 40 pptv with a maximum of 180 pptv.
Ozone production rate The ozone production rate, PO 3 , has been calculated using the radical budget method from the simultaneous measurements of NO and peroxy radicals:
HO 2 + NO  OH + NO 2 (R 1)
HO 2 + NO  RO + NO 2 (R 2)
NO 2 + h   NO + O (R 3)
O + O 2 + M  O 3 + M (R 4)

P(O 3 ) = d[O 3 ]/dt = k 1 [HO 2 ] [NO] + k 2 [RO 2 ] [NO] (with k 1  k 2  8.10 -12 cm 3 molecule -1 s -1 ). 0 30 60 90 120 150 180

00:00 06:00 12:00 18:00 00:00[RO2] (pptv)

0 20 40 60 80 100 120 140[O3] (ppbv)

0 30 60 90 120 150 180

00:00 06:00 12:00 18:00 00:00[RO2] (pptv)

0 20 40 60 80 100 120 140[O3] (ppbv)Field Campaigns 98

Figure 43 shows the calculated P(O 3 ) for the IOP of 21-26 June, where two different sets of values have been obtained :

IOP 2a (21-23 June): P(O 3 ) : up to 40 ppbv h -1 corresponding to relatively clean air masses advected over the site from NW wind.

IOP 2b (24-26 June): P(O 3 ) : up to 100 ppbv h-1 corresponding to polluted air masses originating the Marseille and Etang de Berre areas.

0 50 100 150 200 250

21/06 22/06 23/06 24/06 25/06 26/06 27/06PO3(ppbv h-1)

Figure 43. P(O 3 ) from the radical budget method.

However, the obtained P(O 3 ) values appear to be high compared to those derived from the radical budget method at similar sites (e.g., Volz Thomas et al. (2003) and Spirig et al. (2002)). A possible overestimation of our P(O 3 ) values can not be totally excluded. It may result from the overestimation of the measured NO concentrations, these concentrations being always close to the detection limit. Similar high values of P(O 3 ) have been calculated by the photostationary state method over the same area and during the same period, from aircraft measurements (Perros and Pleuvy, pers. communication 2002).
Sensitivity of ozone production to NO x and VOCs The sensitivities of P(O 3 ) to NO x and VOCs has been calculated by applying the Steady State Approximation to HO x and RO 2 radicals, and using a 0-D model (Spirig et al. , 2002). The model used input data concentrations of species such as VOCs measured at the site by Blake et al. (pers. communication, 2002).- dln P(O 3 ) / dln [X] , when X are NO x or VOCs are plotted in Figure 44 as a function of L N /Q where Q is the formation rate of HO x and RO 2 radicals and L N , the loss rate of the radicals by reaction with NO x (Kleinman et al. , 1997). The results of Figure 44 shows that during the IOP considered (21-26 June), the ozone production rate is sensitive to NO x in most of the cases. This behaviour is characteristic of a rural site as Dupail.LOOP Final Report 99

-1,0 -0,8 -0,6 -0,4 -0,2 0,0 0,2 0,4 0,6 0,8 1,0

0 0,2 0,4 0,6 0,8 1 Ln/QdlnPO3/dln[X]

dlnPO3/dlnNOx SSA POI2a dlnPO3/dlnCOV SSA POI2a dlnPO3/dlnNOx SSA POI2b dlnPO3/dlnCOV SSA POI2b Sensible aux NO x Sensible aux COV

Figure 44. Sensitivity of P(O 3 ) to NO x and VOCs.

Conclusion

Concentrations of O 3 , NO x and RO 2 have been simultaneously and continuously measured over a long period at a rural site.

P(O 3 ) calculated from the radical budget indicates a significant local photochemical production of ozone, although P(O 3 ) appears to be too high.

P(O 3 ) have been found to be mostly sensitive to NO x at the rural site of Dupail.


### 3.6.5.2 Estimate of local ozone production rates and its chemical regime from airborne measurements During the ESCOMPTE campaign, airborne measurements on several aircrafts have measured ozone and related trace gas species. In order to quantify the ozone production in the ESCOMPTE domain we calculated the ozone production and the ozone production efficiency from measurements performed on the INSU/ARAT aircraft. We also determined the chemical regime governing the ozone production. The ozone production P(O 3 ) was determined for each flight during each IOP using the photostationary steady state relation (see section 2.1.5): P(O 3 )=J(NO 2 )·[NO 2 ] - k 3 · [NO] · [O 3 ] (5) All the parameters of this relation were continuously measured aboard the aircraft.Field Campaigns 100

The aircraft flew around noon in the boundary layer (altitude 750 meters above sea level). The trajectory of the aircraft is presented on the following maps by the signs. P(O 3 ) was calculated for each point of the trajectory and then interpolated for the whole domain covered by the flight. in Figure 45 we present the spatial distribution of the ozone production velocity for the IOP2b (June 24 and 25 th ) at

noon.

June 24th June 25th Figure 45. Spatial distribution of P(O 3 ) (ppb h -1 ) observed on June 24 and 25th at noon.

The ozone production velocity was generally high (> 10 ppbv/h) and even exceeded 50 ppbv/h over some parts of the domain. It is known that the stationary state method has a tendency to overestimate ozone production rates. Nevertheless, similarly high values were also calculated during the same period using NO and radical measurements at Dupail (see section 3.6.5.1). Ozone production efficiency (OPE) gives the number of ozone molecules produced in the plume per molecule of NO emitted. This number is calculated from the slope of the regression between [O 3 ] and [NO z ] when d(O 3 )/dt >0 (Trainer et al. , 1993). We calculated OPE for each flight performed at noon during the IOP 2b (Table 6).

Table 6. Ozone production efficiencies for different dates and locations from ARAT data during IOP2b. Day June 24 th June 25th June 26th Origin of the plume Marseille (urban) Berre (Industrial) Berre (Industrial) Fos (Industrial) Marseille (urban) Berre (Industrial)
OPE 15 16 7 3 6 8

R 2 0.93 0.86 0.76 0.58 0.66 0.86

The observed regressions observed were generally good, allowing thus a proper determination of the slopes. We can notice that OPE is strongly variable depending on the location and the day. At the beginning of the period the values were high as well for an urban plume (Marseille) as for an industrial plume (Berre). At the end of the IOP, OPE decreased and some difference can be noticed between urban and industrial plumes (June 26 th ) or between industrial plumes from different origin (June 25 th ). Research is currently performed in our 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 6.0 43.0 43.2 43.4 43.6 43.8 44.0

4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 6.0 43.0 43.2 43.4 43.6 43.8 44.0



## 10 20 40 50

10 20 40 50LOOP Final Report 101

laboratory in order to understand these differences. These rather large values for OPE’s (with the exception of the Fos plume on June 25 th ) also indicate an intermediate or NO x limited chemical regime. The study of the variation of ozone production velocity as a function of NO x concentration allowed us to determine also the local ozone production chemical regime. If P(O 3 ) increase as a function of NO x then the chemical system is NO x limited. If P(O 3 ) decrease with an increase of NO x then the chemical system is VOC limited. At the beginning of the IOP (June 24 th ) P(O 3 ) and [NO x ] were strongly correlated. P(O 3 ) increase and reach high values (50 ppb/h) The NO x concentrations were moderate and remained lower than 3 ppb. For this day, the local chemical regime is NO x limited for the whole domain. For the second day we can see a similar variation except for the highest NO x levels above 7 ppb where PO 3 saturates (Figure 46). The chemical regime is thus NO x limited below 7 ppb, and intermediate or VOC limited above. The last day of the IOP (June 26 th ) NO x concentrations reached again levels above 10 ppb, but this time no saturation occurred even for these large values and thus the chemical regime witch was NO x limited. As a conclusion, at least for NO x values below 3ppb, the chemical regime was locally NO x limited for all three days.

y = 0,0224x + 9,0684 R

2 = 0,9464

y = 0,0022x + 151,2 R

2 = 0,4232

0 20 40 60 80 100 120 140 160 180 200

0 2000 4000 6000 8000 10000 12000
Figure 46. Variation of P(O 3 ) (ppb/h) as a function of NO x (ppt) for June 25th at noon for all flight data.



### 3.6.5.3 Simulation of local ozone production rates during ESCOMPTE 3-D Eulerian modelling of the IOP2B episode has been conducted at LISA with the A 3 UR model (EDF-IFP-LISA) using the chemical scheme Moca, in order to better understand the chemical ways and primary pollutants responsible for such ozone production. Meteorological fields were calculated with RAMS at LaMP (S. Cautenet / M.Taghavi) using ECMWF initialisation and nudging every 6 hours, soil and vegetation model, and two nested grids (grid 1: (28 x 28 cells) Dx=Dy=15km, grid 2: (61 x 61 cells) Dx=Dy=3km with 35 levels in the vertical dimension). Anthropogenic and biogenic emissions have been supplied withinField Campaigns 102

the ESCOMPTE frame. It is, however, quite important to keep in mind that this inventory is only a preliminary one - it thus has consequences on all simulation interpretations. The domain of A 3 UR calculations is a 162 km large grid with a horizontal resolution of 3km and 15 vertical levels of increasing depth from 0 to 3000m. As can be seen in Figure 47, A 3 UR (in red) correctly reproduces the diurnal ozone maxima and fits its temporal evolution well (in black).

Figure 47. Ozone time series at three locations in the ESCOMPTE domain. AZUR is the CTM of the A 3 UR system. Results are shown for June 24 th to 26 th

. Instantaneous O 3 production rates P(O 3 ) have been obtained by explicit calculation during Azur runs. Hourly values have been extracted, and the diurnal maxima are presented here for 12 UTC.

Figure 48. Instantaneous O 3 production rates calculated with AZUR between 0-50 m height for June 25, 12 UTC in ppb/h.

The most noticeable feature is the high values of ozone production calculated by the model. Indeed, the whole industrialized, heavy-traffic and urban zones are subject to fast ozone production of 60 to 70 ppbv/hour as a mean, during 3 hours at the end of the morning. At 12 UTC, we still observe very intense ozone production in this area, reaching 100-120ppbv/h in the heart of the industrial BERRE AIX CENTRE CADARACHELOOP Final Report 103

zone. The weak dispersion of the plume accounts for a large proportion of these values. Nevertheless, the intense local peaks could be due to a bad resolution in the temporary emission inventory, leading to locally unrealistic emissions, and has to be tested again with the final version of the inventory. Although no explicit comparison to estimated P(O 3 ) values from observations has yet been made, the range of simulation and observation based values seems to fit rather well for noon. After 15 UTC, simulated P(O 3 ) strongly decreases, due to a combination of lower emissions, maximum boundary layer depth and decreasing photolysis rates.


### 3.6.5.4 Conclusion for sp ecific ESCOMPTE results The most striking feature of these three contributions are the large photochemical production rates (up to 50-100 ppb/h), estimated independently by three different methods. Clearly, each of these methods has its own uncertainty, which has further to be elucidated in future. It also appears a tendency for a more NO x limited chemical regime (both locally and when integrated) than generally expected for urban plumes. Reasons for this may be the large biogenic VOC emissions in the area, a possibly higher VOC / NO x emission ratio of industrial emissions or the specific meteorology of the region and have to be explored in future research.


### Acknowledgements The ESCOMPTE program is a French initiative. It is supported by a large number of European, U.S. and Canadian organisations, among them the contributions of the Agence pour l’Environnement et la Maîtrise de l’Energie, the French Ministry of Environment, Météo-France and the Centre National de la Recherche Scientifique are of considerable importance.

## 4. Integration - scaling up

Albrecht Neftel Swiss Federal Institute for Agroecology and Agriculture, Zürich, Switzerland

Wilfried Winiwarter ARC Seibersdorf Research, Vienna, Austria



### 4.1 LOOP interactions with environmental policy There are close links between the scientific community and the policy application, the links are often closer than scientists believe. In atmospheric sciences, this starts at the level of input data for air quality models. Emission inventories generally are policy driven instruments. They first have been established in order to allow environmental policy to start. Only at a later stage more scientific approaches (validation, uncertainty assessment) of inventories started. Consequently, emission inventories (even if compiled at high standards) basically are policy driven. They are constructed in part reflecting the main interests of the stakeholders involved. A general agreement between the different stakeholders on the validity of the results is more important than the agreement towards actually measurable values. Furthermore, discrepancies exist between emission inventories and real emissions, part of which may be attributed to the above mechanism, the other part to more fundamental difficulties in a correct assessment of emissions (see section 2.5 for details). An additional link is evident in the role of policy makers as users of the atmospheric scientists’ results. In order to be able to deliver what may be needed, the scientist must know the needs of the users.


### 4.2 The need for environmental agencies Environmental agencies require solutions for the problems they are facing. Environmental problems exist at various scales; but the responsibility of agencies is directed towards different scales as well, according to the generally accepted principle of subsidiarity (local agencies vs. international agencies). Consequently, agencies need the ability to distinguish which kind of problems they may tackle, and which problems go beyond their scope. This clearly makes it necessary to assess the range of effects a certain pollution might have, and to identify sources of a certain pollution. In terms of ozone, problem areas have been identified due to the effects. Thereare short time health effects, coinciding with high peak concentrations, and longterm effects on both plants and humans that depend on the exposure level over aIntegration – scaling up 106

longer period. Consequently, threshold values have been identified for both peak ozone and long-term exposures. A one-hour mean value of 180 μg/m3 was set as an information threshold by the EU directive 92/72/EEC (now replaced by directive 2002/3/EC), and target values for an 8-hour mean of 120 μg/m3 (with certain tolerance conditions) according to WHO recommendations, also in directive 2002/3/EC. In current practice, long-term exposure is measured as the integral above a certain threshold, currently set at 80 μg/m3 (AOT40 = accumulation over threshold 40 ppb = 80 μg/m3). Critical levels are defined as the cumulative exposure above threshold below which no damages occur. In the example of crops, an AOT40 values below 3,000 ppb·h accumulated over three months during the vegetation period is considered cause no damage, whilst an AOT40 of 10,000 ppb·h accumulated over six months is the corresponding critical level for forest trees. Only hours with global radiation above 50 Wm -2 are considered. This approach is defined as “level I”. Still under scientific discussion are “level II”-approachesthat would address the actual fluxes to plants and thresholds towards the longterm health effects of humans. From a scientific point of view such approaches would clearly be preferable. Peak ozone is strongly linked with patterns of hot and sunny weather coupled with low wind speeds, so-called ozone episodes, in air masses with a high density of precursor emissions (i.e., usually urban or industrial plumes upon a continental background of pre-existing ozone). In contrast, AOT values are dominated by ozone levels under moderate ozone production conditions, as a consequence of their cumulative nature.



### 4.2.1 Peak ozone The experiments within LOOP were designed to investigate the changeover from VOC to NO x sensitive ozone productions that occur mainly in urban plumes. Spatial scales were chosen accordingly and ranged up to a few tens of kilometres. The existence of urban plumes is basically well known, and LOOP helped to shed light on the chemical processes and the status of the chemical regimes within the plume. Consequently, LOOP solutions support local and regional scale questions. Results may directly flow into abatement plans of local agencies. Modelling at different scales indicates that a critical size of modelling grids exists in order to capture VOC sensitive cores of plumes. E.g., in order to resolve properly the VOC/NO x sensitivity of the Milan plume, a grid resolution of 4 to 10 km (depending on the model, see section 3.4.8.5) is required. Results change dramatically when grid sizes above the critical size are used. In general, VOC sensitive areas during ozone episodes decrease with increasing grid size. It is interesting to note that in most cases (the Milan area is an exception) no further difference seems to exist at grid sizes below 10x10 km 2 . A possible reason for this is that the resolution of the emission inventory starts to match the resolution of all other elements of the model. In that case, further refinement of the inventory would not lead to any better results, unless the other elements (temporal resolution, meteorological information) also were refined. As soon asLOOP Final Report 107

the urban plume is completely covered in one grid cell of the model, and is not mixed over a much larger area any more, the chemical regime is resolved adequately. Further advancements could only be made if emissions and meteorology were all also known on a finer scale.



### 4.2.2 Ozone levels and cumu lated indices (AOT-values) Ozone formed in plumes, as described above, will be distributed over considerable distances and will contribute to the ozone burden further downwind. Generally spoken, the ozone burden in the European PBL is always a mixture of ozone transported from the free troposphere, ozone advected from other continents and ozone produced within the European PBL. The first two contributions yield ozone concentrations around 40 ppb over large areas. As a consequence of the step function included in the AOT40 calculation, AOT40 is extremely sensitive to small changes in ozone concentrations around 40 ppb. Investigations within LOOP focused on areas up to hundred km. AOT 40 values within this scale are at least as strongly determined by inflow conditions as by the production within the domain. As mentioned above, the level II approach requires the calculation of ozone deposition fluxes. This is a difficult task, as these fluxes will strongly depend on morning hour concentrations and mixing heights, which are very difficult to calculate. A transfer of results from event based samples to the regional and long term situation requires clustering of all important weather patterns, and transferring results for each cluster to a long term average according to its statistical probability or observed occurrence to a long-term situation. Results for each cluster (=weather pattern) need to be available. As ozone chemistry is considered to be clearly non-linear, it is not possible to extrapolate from episode-focused field experiments (=one cluster) to the conditions outside of episodes. Only if further research allows to assign chemical regimes to the specific weather patterns, such an extrapolation (along the respective chemical regimes) is possible. Currently that has not been achieved, though. Consequently, no definite conclusions from the LOOP campaign can be drawn in terms of ozone levels.


### 4.3 Influence of LOOP on environmental policy LOOP has been devised bottom-up, in order to answer questions posed by scientists. Such project structures may influence and shape policy decisions, but are normally not directly used. Instead, results initiate further activities. The success in influencing environmental policy should thus be measured in terms of footprints. The distinctive LOOP - footprints are:

Better understanding of ozone formation on the local scale for each LOOP site may be used by the respective local organizations. As, in some cases, the LOOP contractors are directly responsible for atmospheric quality, a transferIntegration – scaling up 108

to these local agencies is assured. Similar arguments also apply for cases of transboundary pollutant transport at the same scales.

LOOP provided a seed for the City Delta project, which runs under the EU CAFE (clean air for Europe) programme. City Delta concentrates in part on the same sites of Europe, and some of the scientists involved are the same. City Delta is a modelling study focussing on different cities, different photochemical models and runs on different spatial scales. An extension to modelling efforts within LOOP, it applies more different emission reduction scenarios and it has an emphasis on particulate matter. As it is closely linked (from the side of the contractor) with European environmental policy, its outcome will flow into European policy more directly.

Joint meetings with the EMEP community have triggered intensified considerations of small-scale processes in continental-scale models. These should certainly be considered in the further development of the EMEP model, which is the basis of European ozone related policy, as it has been used in the full scenario analysis for deriving the Gothenborg protocol as well as EU's national emission ceilings.

## 5. Synthesis: LOOP Messages in a Nutshell

Christoph Spirig, Albrecht Neftel Swiss Federal Research Station for Agroecology and Agriculture, Zurich, Switzerland

Josef Dommen Paul Scherrer Institute, Laboratory of Atmospheric Chemistry, Villigen, Switzerland



### 5.1 Are the simulations of the efficiency of VOC versus NO x 

### emission reduction on the ozone production on a sound base? The LOOP community attending the 2 nd Gerzensee workshop judged that errors in the state of the art chemical schemes in CTMs are smaller compared to uncertainties in emission inventories and transport schemes (horizontal and vertical). Model results need careful evaluation. Such testing needs consideration of more parameters than ozone concentration alone (for example peroxides, NO y , formaldehyde etc.). Chemical schemes must be evaluated with measurements of as many species as possible, including radicals. If no radical measurements are available they should be assessed using observation based modelling.


### 5.2 Do biogenic emissions influence the VOC – NO x 

### sensitivity of the ozone production? Biogenic emissions depend on environmental conditions, mainly temperature, radiation and water supply, and on the phenological state of the plants. Conditions that are favourable for ozone formation also have the potential of high biogenic VOC and NO emissions. Biogenic VOC emissions are considered to have more influence on ozone formation than biogenic NO emissions. VOC emissions show a strong seasonal pattern with highest values in July and August. The contribution of biogenic VOCs to the total OH reactivity typically increases from values below 20% in urban areas to over 60% a few hours downwind. As a consequence, the impact of biogenic emissions on ozone formation also increases during this transport to remote areas. The biogenic emissions shift the VOC/NO x concentration ratio toward higher values and therefore influence the chemical regime toward NO x sensitivity.



### 5.3 Can indicators be used to characterize chemical regimes of ozone production? Correlation plots of O 3 vs NO y , H 2 O 2 vs HNO 3 , etc. are valuable diagnostic tools to analyse the VOC/NO x sensitivity of the ozone chemistry. Such ratios have been proposed as indicators to distinguish VOC and NO x sensitivity of theSynthesis: LOOP Messages in a Nutshell 110

integrated ozone production, as their values show characteristic different ranges for VOC and NO x sensitive regimes. Indicator values may help to elucidate reasons for model/measurement discrepancies. However, both measurement and interpretation of indicator species and their ratios is difficult, and models should not be tuned based on a model/measurement discrepancy of indicators alone. The absolute values of the indicator ratios are influenced by boundary conditions. Especially for smaller domains, the concentrations at the inflow will largely determine the level of species with a lifetime of days and more. However it is still very useful to compare the spatial patterns and temporal changes of the indicator ratios within the model with measurements. It was recently suggested to use the changes of indicator values during a given transport time rather than absolute values. This requires measurements of the indicator species to be made in a Lagrangian experiment, i.e., by aircraft.


### 5.4 How does the grid resolution influence the model results? There are many urban plumes observed in Europe, which are not captured by the coarse grids of many continental scale models. Model calculations from fine to coarse grids in the Milan area suggest that the averaged ozone concentrations over a fine grid is not much different compared to the corresponding mean ozone concentration in a coarse grid, but peak values often increase with increasing resolution. It seems likely that a coarse grid model may lose some of or all of the VOC sensitive part of an urban plume. This may lead to wrong predictions regarding emission reductions. On the other hand, big point sources of NO x create erroneously large areas of VOC limitation in coarse grid models. For the convention on Long Range Transboundary Air Pollution (LRTAP) of the United Nations Economic Commission for Europe (UN-ECE), source-receptor matrices between individual countries need to be calculated. For this purpose, the EMEP model activities were set up. There is no quantification of how much regional plumes contribute to ozone sensitivity of the EMEP model. Furthermore, there is not enough experimental data (covering the long-term EMEP scale) to evaluate the model thoroughly because of the lack of ozone stations in certain regions of Europe and the lack of long term monitoring of precursors, meteorological data and indicator species.



### 5.5 What is the influence of aerosols? Although published results are not conclusive up to now, they indicate that the general picture of ozone production on regional scales should not be greatly affected by the amount of the aerosol load. On the other hand, the PIPAPO campaign showed that J NO2 values were reduced by more than 10% in the Milan plume by the presence of aerosols, leading to a substantial reduction in local ozone formation rates. Other effects of aerosols could be due to heterogeneous reactions. Night-time loss of NO 2 via formation of N 2 O 5 and its adsorption on aerosols is an important process in the NO 2 budget and should be considered inLOOP Final Report 111

models. High NO 2 concentrations with unexplained NO 2 /NO ratios were observed in Paris in winter, which might be due to heterogeneous processes and reactions on aerosols have been suggested as a possible loss process for radicals in order to explain the lower OH concentrations than those predicted by photochemical models constrained by observations. In general, the influence of heterogeneous processes on photochemistry is still uncertain and there is not yet a sound basis for including it in models.


### 5.6 How do photochemical models relate to the AOT concept ? Whereas the AOT value (accumulated ozone dose over a threshold) has initially been a useful parameter to link ozone concentrations to its environmental effects, it is unfavourable for use in the interpretation of measurements and model simulations. The concept of the AOT value involves a step function from zero to unity at the threshold level. The inherent uncertainty of both measured and modelled concentration data therefore leads to a much larger uncertainty in AOT values. The flux approach (as proposed at the UN/ECE-LRTAP workshop in Switzerland, 1999) may prove more robust than AOT40, at least if no threshold is introduced or if this threshold is very low. This so-called “level-II” approach is under development under the LRTAP Convention’s Working Group on Effects. However the modelling of ozone deposition fluxes is a difficult task, as these fluxes will strongly depend on morning hour concentrations and mixing heights. Both stability and ozone concentrations near ground are difficult to calculate during night and morning hours.



### 5.7 What kind of activities are still needed?

Improving emission inventories is a must for significantly improving the diagnostic and prognostic capability of photochemical models. The present VOC measuring network has been very valuable to evaluate emission inventories. Further evaluation of emission inventories is necessary.

For a reliable simulation of the aerosol levels, spatially high resolved NH 3 inventories are necessary.

Investigations on the role of biogenic emissions, both VOCs and NO x should continue. Approaches for obtaining regionally integrated fluxes of isoprene and its oxidation products, terpenes and carbonyl compounds have to be implemented.

More local closures including radical measurements would be useful in order to investigate the discrepancies between measured and modelled radical concentrations.

Models should be tested on real situations, where reductions of VOC’s and/or NO x occur, as for example weekday vs. weekend, holiday effects, emission reductions over longer time scales, different reduction achievements in different European regions. In the Milan area, the month ofSynthesis: LOOP Messages in a Nutshell 112

August offers an interesting possibility. Due to general holidays (“Ferragosto”) during that month, human activities and emissions of NO x and VOC are considerably changed as first investigations have shown.

In order to meet EMEP requirements, a change in focus from episodic to long-term campaigns is needed. Therefore, more monitoring stations with a comprehensive set of measured species (allowing a determination of ODD-H radicals and a characterisation of the ozone production) are required for urban and rural situations. Vertical information on species composition and also mixing heights are of great importance for model evaluation. At least 10 specialised monitoring stations (super-sites) across Europe should be installed. They should not only be geographically distributed, but also consider different VOC/NO x sensitive areas according to present EMEP model calculations. Besides O 3 , NO x , and SO 2 (frequently measured species in the current monitoring network), measurements of additional species and meteorological data are needed to evaluate emission inventories, transport, and chemistry: Urban station Rural station Basic equipment O 3 , NO x , SO 2 O 3 NO x , SO 2 Emission evaluation Speciated VOCs, aldehydes, CO Speciated VOCs, aldehydes, CO Chemistry evaluation NO y , HCHO, J NO2 NO y , HCHO, H 2 O 2 , ROOH, J NO2

A complement to super-sites would be a concept with mainly airborne measurements. However, aircraft measurements should be performed for at least 2 months (or at regular intervals, e.g., biweekly) in order to be more valuable than year-round measurements at surface super sites.

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