megapoli scientific report 10-10 interactions between air quality...

MEGAPOLI Scientific Report 10-10

Interactions between Air Quality and Meteorology MEGAPOLI Deliverable D4.3 Alexander Baklanov, Alexander Mahura (Eds.)

Contributing Authors: Jaakko Kukkonen, Ulrik Korsholm, Alexander Baklanov, Matthias Beekmann, Alexander Mahura, Sandro Finardi, Ranjeet Sokhi, Georg Grell

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FP7 EC MEGAPOLI Project

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Colophon Serial title: MEGAPOLI Project Scientific Report 10-10

Title: Interactions between Air Quality and Meteorology Subtitle: MEGAPOLI Deliverable D4.3 Editor(s): Alexander Baklanov, Alexander Mahura Contributing Author(s): Jaakko Kukkonen Finish Meteorological Institute (FMI), Helsinki, Finland Matthias Beekmann Laboratoire Inter-universitaire des Systémes Atmosphériques, Centre National de Recherche Scientifique (CNRS- LISA), Paris, France Sandro Finardi ARIANET srl consulting, Italy Ranjeet Sokhi University of Hertfordshire, Centre for Atmospheric and Instrumentation Research (UH-CAIR), London, UK Georg Grell University of Colorado, Earth Systems Research Systems, Boluder, CO, US Alexander Baklanov, Ulrik Korsholm, Alexander Mahura Danish Meteorological Institute (DMI), Copenhagen, Denmark Responsible institution(s): Research Department, Danish Meteorological Institute, DMI Lyngbyvej 100, Copenhagen, DK-2100, Denmark Contact e-mail: [email protected] Language: English Keywords: Air pollution episodes, urban area, megacity, air quality monitoring, chemistry measurement campaign, meteorological conditions, emissions, aerosol feedbacks, Enviro-HIRLAM modelling Url: http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-10.pdf Digital ISBN: 978-87-993898-3-4 MEGAPOLI: MEGAPOLI-13-REP-2010-03 Website: www.megapoli.info Copyright: FP7 EC MEGAPOLI Project Part I – also partial contributions to COST-715, FUMAPEX, IGBP Reportings Part II – also contribution of Korsholm et al. (2010) to EGU-2010


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Content: Background on Deliverable .................................................................................................................5 Part I: Meteorological Conditions Favouring Development of Urban Air Pollution Episodes ..6

1 Introduction into the problem .......................................................................................................6 2 European city air pollution episodes............................................................................................8

2.1 FUMAPEX experience ..........................................................................................................8 2.2 Measured concentrations........................................................................................................8 2.3 Meteorological analyses.........................................................................................................9

3 London (United Kingdom)..........................................................................................................11 3.1 Air quality monitoring in the Greater London area .............................................................11 3.2 Air quality forecasting .........................................................................................................11

4 Paris (France) .............................................................................................................................12 4.1 AirParif monitoring network................................................................................................12 4.2 General air pollution situation..............................................................................................13 4.3 Specific scientific measurement campaigns ........................................................................13 4.4 Particulate matter pollution..................................................................................................16 4.5 MEGAPOLI campaign ........................................................................................................17 4.6 Summary outlook.................................................................................................................18

5 The Po Valley (Italy)...................................................................................................................18 5.1 Air quality conditions...........................................................................................................18 5.2 Meteorological classification of pollution episodes.............................................................20 5.3 Elevated ozone and PM10 pollution episodes in 2005..........................................................21

6 Moscow (Russia).........................................................................................................................23 7 Classification of air pollution episodes in European cities .........................................................25 8 Conclusions.................................................................................................................................26 References......................................................................................................................................28

Part II: Interactions between Air Quality and Meteorology/ Climate: Aerosol Feedbacks .....31

1 Overview of chemistry and aerosol feedbacks on meteorology .................................................31 2 Monthly averaged changes in surface temperature due to aerosol indirect effects of primary aerosol emissions in Western Europe ............................................................................................34

2.1 Introduction to the indirect aerosol effects study.................................................................34 2.2 Enviro-HIRLAM model description....................................................................................35 2.3 Experimental set-up .............................................................................................................36 2.4 Meteorological situation ......................................................................................................37 2.5 Results and discussion .........................................................................................................38 2.6 Comparison with observations.............................................................................................41

3 Summary and conclusions ..........................................................................................................43 References......................................................................................................................................45

Previous MEGAPOLI reports............................................................................................................47


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Background on Deliverable Within the frameworks of the MEGAPOLI WP4 Task 4.3 “Interactions between air quality and

meteorology/climate” (lead by DMI) the investigation of meteorological patterns favouring devel-

opment of urban air pollution episodes was carried out with a focus on interactions between city and

megacity air quality and meteorology which were described and quantified. Towards this aim, the

effect of elevated pollutant concentrations on the meteorology as well as the influence of meteoro-

logical patterns on urban air pollution episodes were both studied. In particular, the influence of air

pollution on cloud formation, precipitation and radiation is assessed through the application of

advanced modelling tools, such as the on-line coupled MEMO /MARS and the on-line coupled

Enviro-HIRLAM modelling systems.

The indicators relating meteorological patterns leading to urban air pollution episodes were devel-

oped The findings are planned to be used in MEGAPOLI WP8 for risk assessment closely related to

decision and policy making. In addition to the description of the feedback mechanisms directly

related to WP2, this task is assessing the indirect effect of urban air pollution on the regional cli-

mate which is addressed in WP6.


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Part I: Meteorological Conditions Favouring Development of Urban Air Pollution Episodes

Jaakko Kukkonen, Alexander Baklanov, Sandro Finardi, Matthias Beekmann, Alexander Mahura, Ranjeet Sokhi, Georg Grell

The first focus of the MEGAPOLI WP4 Task 4.3: “Interactions between air quality and meteorol-ogy/climate” (lead: DMI) is to describe and quantify the influence of meteorological patterns on urban air pollution especially high-level concentrations air pollution episodes in megacities. The findings of this Task will be further used in WP8 for risk assessment closely related to decision and policy making. This task also carries out analysis of meteorological patterns leading to urban air pollution episodes considered, for example, within the FP6 EC FUMAPEX project, by the development of suitable indicators linking particular meteorological conditions/ parameters to increased air pollution levels in the urban areas. These indicators constitute a useful tool for regulators in suggesting effective policies and mitigation measures. Finally, a combination of modelling and analysis of observations data can allow both the quality assurance of the new parameterisations as well as the verification of input emissions.

1 Introduction into the problem An air quality episode is defined as a situation, during which air pollutant concentrations exceed a specified threshold value. In the FUMAPEX project (Baklanov, 2005) the key pollutants are PM10, PM2.5, O3 and NO2, as these cause the worst air quality problems in European cities (Kukkonen et al., 2005; Sokhi et al., 2004). These pollutants are regulated by both the EU limit values and the national guidelines. The guidelines are applied as practical objectives in environmental policy, while the limit values are considered as strict limits. If the limit value is exceeded, the city or municipality has to take measures, in order to ensure that the limit value will not be exceeded again in the future. The EU Directives require practical measures to be taken, if the air quality limits are exceeded. The ability to reliably forecast air pollution episodes will be invaluable to minimise the health impact on the citizen, in particular, children and the elderly. If episodes can be forecasted reliably, local authorities can implement strategies and practical measures to reduce the impact on public health and the environment. Time warnings makes it possible to target local air quality management actions specifically to reduce population exposures and thus to minimise adverse health effects cost effectively. However, at present it can be noted that during episodes, when the pollutant concentra-tions are highest, the performance of dispersion models is commonly worst. The causes of air pollution episodes are complex and depend on various factors including emissions, meteorological parameters, topography, atmospheric chemical processes and solar radiation. The relative importance of such factors is dependent on the geographical region, its surrounding emis-sion source areas and the related climatic characteristics, as well as the season of the year (e.g. Sokhi et al., 2002 and 2003; Piringer and Kukkonen, 2002). For example, particulate matter epi-sodes in many cities have been experienced in winter and spring. Nitrogen dioxide episodes can occur both in winter and in summer, and ozone levels can be particularly high during summer periods. Episodes can also be influenced by the interaction of phenomena on the different meteoro-logical scales e.g. the mesoscale forces can perturb the synoptic conditions (Millan, 2002). Previously, selected historic episodes have been analysed e.g. in Oslo (Berge et al., 2002), Helsinki (Mäkelä et al., 1998, Berge et al., 2002, Karppinen et al., 2002, Pohjola et al., 2002, 2003 and


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2004, Rantamäki et al., 2004), London (Sokhi et al., 2002, 2003), various northern Italian cities (Finardi, 2002, 2004) and in Spain (e.g. Millan, 2002, Gangoiti et al., 2002). Sokhi et al. (2002, 2003) presented the analysis and evaluation of air pollution episodes in several European cities. Examples of episodes of PM10, NO2 and O3 were described and the causes were examined in relation to local emissions and meteorological conditions. For both particulate matter and nitrogen oxides, low lying inversions and, in some of the cases, local low wind speeds were in particular important as they tend to lead to high concentrations of air pollutants. Kukkonen et al. (2004ab) analysed selected PM10 episodes in four European cities in relation to prevailing meteorological conditions, local emissions, and regional and long range transported background concentrations. A particular aim of that study was to gain a better insight into the influence of various meteorological variables on the evolution of high pollutant concentrations in European cities. Inversions, which lead to stagnant air, are particularly important in relation to episodes and these are in many cases responsible for very high levels of pollution (e.g. Piringer & Kukkonen, 2002). In addition, regional and long-range transport of pollution can also lead to standards being exceeded, for example for fine particulate matter. Consequently, it is vital to understand the underlying proc-esses on local, regional and continental scales that lead to air pollution episodes. Available information concerning European peak pollution episodes has been reviewed within the COST-715 programme by Kukkonen (2001a). The report provides an overview of the main factors that lead to and influence air pollution episodes in 13 countries. Analysis showed the differences not only in national approaches, but also the common problems and challenges for improving the computational tools and the effectiveness of methodologies. This can be utilised as background information by users, such as national or local authorities that are concerned with this problem. Traditional models such as those based on Gaussian plume approaches cannot take into account the detailed meteorological processes that can lead to episodes, although the up-to-date generation of these models utilises atmospheric boundary layer scaling for deriving the input meteorological variables. Advances in meteorological models, based on more complete descriptions of atmospheric processes, offer the possibility of examining episodes in more details. Baklanov et al. (2002) have analysed the potential and shortcomings of numerical weather predic-tion modelling for providing meteorological data for urban air pollution forecasting. Clearly nu-merical weather forecasting models were originally designed for meteorological predictions in the synoptic and mesoscale, instead of local scale predictions within the lowest atmospheric layers. As an example, shortcomings in HIRLAM model predictions have been observed. For instance, tem-peratures near the ground tend to be too low during the day and too high during the night. Neunhäuserer et al. (2004), Pohjola et al. (2004) and Rantamäki et al. (2004ab) have also evalu-ated the performance of several numerical weather prediction and mesoscale meteorological models for forecasting urban air pollution episodes. Pohjola et al. (2004) investigated a severe air quality episode that occurred within the Helsinki metropolitan area during 27-29 Dec 1995. During this episode, both the inversion strengths and the temperature gradients predicted by the HIRLAM model were substantially weaker, or almost non-existent, compared both with the corresponding values extracted from a 330 m high mast and sounding data. These deviations of the HIRLAM forecasts and data are partly caused by deficien-cies in the treatment of humidity and the state of the ground surface, and partly by the finite compu-tational grid resolution. The finer resolution, non-hydrostatic MM5 model predicted the temperature profiles better than HIRLAM, although both models had problems especially in predicting the daytime temperature profiles. Rantamäki et al. (2004b) evaluated the performance of two versions of the Finnish variant of the HIRLAM model over the course of the above mentioned episode. Both model versions had difficul-ties in predicting strong surface-based inversions; the more recent version was slightly better, in comparison with the measured data.


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2 European city air pollution episodes

2.1 FUMAPEX experience The analysis of the air quality episodes in the European cities was done within the frameworks of the FUMAPEX project (http://fumapex.dmi.dk). Sokhi et al. (2002, 2003) and Kukkonen et al. (2004ab) analysed selected PM10 episodes in four European cities, aiming at a structured and homogeneous analysis in all the four cities. The considered episodes occurred during 4-10 January 2003 in Oslo (Norway), 3-14 April 2002 in Helsinki (Finland), 18-27 February 2003 in London (UK) and 14-19 December 1998 in Milan (Italy). For analysis the episodes with predominantly caused by various local emission sources were selected. These episodes can also be considered to be characteristic for each region, in terms of their frequency of occurrence. The evolution of the measured concentrations has been analysed, especially in terms of the meas-ured and pre-processed meteorological variables, and the predictions of several numerical weather prediction models and a meso-scale meteorological model. The episodes were also analysed by comparing the concentrations measured at various types of stations, and by comparing the measured PM10 concentrations with those of PM2.5and NO2. The cities considered are located in northern (Oslo and Helsinki), north-western (London) and southern (Milan) European geographic and climatic regions. The areas considered represent a maritime climate (London and Oslo), a partly maritime/ continental climate (Helsinki), and mainly continental climate (Milan). London and Milan are amongst the largest cities in Europe (the popula-tion is 7.5 million for London, and 3.5 million for the Milan urban area), while the metropolitan areas of Oslo and Helsinki are relatively smaller conurbations (the population is approximately 1 million inhabitants). The city of Oslo is located at the northern end of the Oslo fjord, surrounded on both sides by com-plex terrain. The topographical features of the area tend to worsen the dispersion conditions, captur-ing pollutants emitted within the urban airshed. The most important local sources of particulate matter in Oslo are domestic wood burning in stoves that are used for wintertime house heating, and traffic. The city of Helsinki and its surrounding area are situated in a relatively flat coastal area. The PM10 concentration in street level air is dominated by the combustion, non-combustion and resus-pended particulate emissions originating from vehicular traffic (e.g. Kukkonen et al., 2001b). London is placed in a relatively flat terrain with shallow hills to the west and south of the city. London is one of the most congested cities in Europe; more than half of NOx emissions are resulted from the road transport. Milan city and its surrounding urban area are located in the central part of the Po River basin, in a flat area. The atmospheric circulation of the Po Valley is characterised by the strong modification of synoptic flow due to the high mountains (Alps and Apennines) that surround the valley on three sides. Road traffic is mainly responsible for the PM10 emissions in the Milan Province.

2.2 Measured concentrations The evolution of measured hourly pollutant concentrations in the four cities are presented in Figure 2.1. The hourly PM10 concentrations at the urban stations exceeded values of 100 µg/m3 in London, 200 µg/m3 in Oslo and Helsinki, and 300 µg/m3 in Milan. The periods of highest concentrations, compared with the more commonly existing values in each city, occur in some cases in two periods lasting a couple or a few days (for instance in Oslo from 4 to 5 January and from 7 to 10 January 2003), or may extend for a more extensive period (for instance, in London from 18 to 27 February


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2003). Comparison of concentrations measured at various site categories makes it possible to evaluate the importance of local emissions. For instance, in Helsinki the regional background PM10 levels (measured at a station of Luukki) were substantially lower than the corresponding highest measured urban traffic-site concentrations at the stations of Töölö and Vallila; this indicates that local sources are mainly responsible for the formation of the highest concentrations. Based on the examination of the concentrations of PM10 and PM2.5 measured at various site catego-ries and the emission inventories in the cities, it is concluded that the selected episodes were caused mainly by local wood combustion in Oslo; mainly by suspended dust and local traffic emissions in Helsinki; by both urban traffic and long-range transport in London; and partly by local traffic and partly by long-range transport in Milan.

2.3 Meteorological analyses The synoptic scale meteorological analyses are based on the output computed by the national versions of the numerical weather prediction (NWP) model HIRLAM in the case of Norway and Finland, and on the ECMWF model in case of U.K. and Italy. The MM5 mesoscale meteorological model has also been used to predict the conditions in London. The synoptic analyses showed that all the episodes addressed were associated with the influence of areas of high pressure (Oslo, Helsinki, and London) or a high-pressure ridge (Milan).

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Figure 2.1: Pollutant concentrations relevant for the analysis of PM10 in the course of the selected episodes in (a) Oslo, (b) Helsinki (c) London and (d) Milan. Most of the stations represent urban traffic environments (Kirkeveien, Alna, Furuset, Løren, Manglerud, Töölö, Bloomsbury, Marylebone Road, Zavattari and Limito), but a few urban background (Iladalen and Juvara) and a rural background (Harwell) station are included.

The ticks on the horizontal axis indicate the beginning (i.e. the time 00.00 am) of the day marked Sokhi et al. (2002), Kukkonen et al. (2004ab).

Examples of the evolution of the vertical temperature profiles are presented in Figure 2.1. Strong


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ground-based or slightly elevated temperature inversions prevailed in the course of the episodes in Oslo, Helsinki and Milan, and there was a slight ground-based inversion also in London. A detailed examination of the meteorological conditions shows that the inversions in Oslo and Milan were mainly caused by advection, and that in Helsinki the inversion was a radiation inversion. For instance, in Helsinki the measured temperature profiles at midnight show that there were moderate or strong ground-based inversions on all days from 7 to 12 April 2002. The maximum ground-based inversion occurred on 11 April 2002; the measured temperature increased 8°C within the lowest 50 m of the atmosphere. The highest PM10 concentrations (during 3 April and from 8 to 13 April 2002) almost coincided with the occurrence of ground-based inversions. In Milan, the wind speed was low or it was calm during the whole period considered. Prevailing weak winds are one of the main features of the Po Valley climatology (Finardi, 2002; 2004). These conditions are mainly due to the blocking effect of the high mountains that surround the valley from three sides, and commonly do not allow synoptic flows to enter the lower atmospheric layers in the valley. The wind speed is therefore not an especially good predictor variable in terms of peak pollution episodes within this area. In Milan, an intense slightly elevated temperature inversion was formed on 13 December 1998 that reached its maximum depth and magnitude (with a temperature increase in height of about 15°C in the lowest 1500 m) on 15 December and prevailed until 19 December 1998. This period nearly coincided with the occurrence of the highest concentrations from 14 to 19 December 1998. The inversions were caused by the advection of warm air carried by the incoming high-pressure ridge.

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Figure 2.2: The temporal evolution of the vertical profiles of temperature in a) Oslo, b) Helsinki c) Milan and d) London during selected episodes of PM10. The times regarding Oslo, Helsinki and London refer to midnight (00.00 UTC), and the time regarding Milan noon (12.00 UTC). References: Sokhi et al. (2002),

Kukkonen et al. (2004ab).



3 London (United Kingdom)

3.1 Air quality monitoring in the Greater London area Within the Greater London area, the local authorities of the boroughs play a key role in monitoring, assessing and improving air quality. In this they follow the objectives laid down in the UK Air Quality Strategy for the main pollutants. Local authorities have been given a wide range of powers to execute this plan and achieve improvements in air quality. The London Air Quality Network (LAQN) was formed in 1993 to coordinate and improve air pollution monitoring in London. The LAQN is facilitated by the Association of London Government on behalf of the thirty-three London Boroughs and is operated and managed by the Environmental Research Group (ERG) at King’s College London. They also operate an internet site for disseminating the air pollution information and forecasts (http://www.erg.kcl.ac.uk/london/asp/home.asp). A more complete picture of air pollution in South East England can be obtained from the combined results of the LAQN, the Kent Air Quality Monitoring Network (KAQMN) and the Hertfordshire and Bedfordshire Air Pollution Monitoring Network (HBAPMN).

3.2 Air quality forecasting Air quality forecasts for the Greater London area are available from several sources, such as the U.K. Met Office’s urban air pollution model (NAME) and empirical approaches developed at the Kings College’s Environment Research Group. The local forecasts are made at ERG, King’s Col-lege London, who has developed comprehensive empirical methods, with an innovative approach for air pollution prediction. The statistical techniques include regression analysis and empirical relationships for NOX/NO2 chemistry and a new method for the prediction of PM10 concentrations. The methods are incorporated into an Air Quality Management system which allows concentration predictions to be made over large areas, like Greater London. It also allows detailed traffic scenar-ios to be modelled. The output information is being made available to the wider public via distinc-tive Internet pages for the Kent, Hertfordshire and Bedfordshire and London Networks. It has also an OMNI World Wide Web allowing users to access data as for their postcode or via a map of their area. The UK National Air Quality Information Archive publishes on its webpages (http://www.airquality.co.uk/archive/uk_forecasting/apfuk_home.php) a forecast provided by air quality experts from National Environmental Technology Centre (NETCEN). The forecast makes use of information from on-line measured pollutant concentrations from automatic monitoring networks, weather data from London Weather Centre and the UK Met Office plus ozone data from selected European monitoring sites. Several models are applied; A trajectory model, an urban scale box model and (from April 2000) the NAME model. Air pollution forecasts for are published daily. The authorities have the air pollution bulletin system, where the current air quality is classified into four Air Pollution Bands. These are intended as a tool to help the public assess the possible health impacts of pollution above certain thresholds. The Air Pollution Index has been introduced to give more specific information on pollution levels using an easily understandable index system. The index fits within the Air Pollution Bands. The thresholds between the Bands are based on U.K. Air Quality Standards for the eight major pollutants. Levels of pollution below these standards are considered acceptable as regards each pollutant's effects on health and the environment. There are three thresholds, the 'Standard Threshold', the 'Information' and the 'Alert' levels. Any concentration below the Standard threshold is described as 'Low air pollution'. A level between the Standard and Information thresholds would be described as 'Moder-ate', between the Information and Alert thresholds is 'High', and above the Alert threshold is 'Very High'. Two air quality episodes have been analysed for London. There were no specific measures under-taken in London in the event of these episodes, except to alert the public, especially the susceptible



groups such as children and elderly. A free-phone message system provides health information for the public during days when pollution levels are high or very high. No information about the effects of actions is available. Changes in the concentration of PM in London have also been linked to traffic. Annual mean PM10 from 1994 to 2004 at different types of locations throughout London were documented by (Fuller & Green, 2006). Fuller & Green (2006) found that secondary and natural sources of PM10 declined from 1997 to 2003, whereas primary sources increased from 1998 to 2003; the largest increases in primary PM were observed at roadside sites. It should also be noted that long-term transport of air masses, primarily from mainland Europe, can contribute significantly to PM concentrations, especially episodes of elevated concentrations (Charron et al., 2007).

4 Paris (France)

4.1 AirParif monitoring network Since 1994, the public authorities and general public are informed during an air pollution episode daily. The regional monitoring network of AIRPARIF (Surveillance de la qualité de l'air en Ile-de- France) - a registered association, carries out the measuring and control of air quality in Paris and its suburbs. AIRPARIF predicts, on a daily basis, the ATMO air quality index (very good, good, average, poor, very poor) as well as the maximum levels of O3 and NO2 for the same day and the following day, indicating the concentration ranges for both pollutants. Every day, this information is published on the French Minitel system and the Internet (http://www.airparif.asso.fr/).

Figure 4.1: Wind direction at the SIRTA/IPSL site at Ecole Polytechnique, 20 km south-west of downtown

Paris. Red bars: July 2005 – 2009, black bars: particular situation during the MEGAPOLI campaign in July 2009 (courtesy M. Haeffelin, SIRTA / IPSL).

Several statistical tools for forecasting pollutant concentrations are being used by AIRPARIF. The operational forecast is based on the implementation of complementary methods, like the CART method (Classification and Regression Tree). Recently, the deterministic POLLUX forecasting system has been developed by Institute Pierre Simon Laplace, based on a simplified CHIMERE

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photochemical model and using the pre-processed meteorological data of Météo-France. The SIMPAR modelling system (Système Informatique de Modélisation de la Pollution Atmosphérique à l’échelle Régionale), installed for AIRPARIF by the ARIA Technologies in 1997-1999, is used to evaluate the effect of emission reduction measures. Since 1994, the public authorities and general public are informed during an air pollution episode daily. This procedure was reinforced by a municipal decree on 1999, to take into account the provi-sions laid down in the French law on air quality. The warnings are based on air quality index having 10 ratings of air quality. The level of recommendation for measures is triggered whenever the guideline threshold level of one of pollutants NO2, O3 or SO2 is reached on 3 stations. The informa-tion and recommendation level corresponds to the public being informed of air pollution levels through the media. Recommendations to limit the effects on individuals whose health is in particu-lar at risk have been issued on public information bulletins since 1995. The level is triggered when the information threshold of one of the three pollutants is reached. The warning level corresponds to the warning threshold itself; the municipal authorities can take measures to restrict emissions released from industrial plants or to limit the use of motor vehicles and broadcast recommendations to restrict the effects on health for the entire population. Most of the time Paris benefits from relatively sustained winds from south-west to west, advecting relatively clean oceanic air masses to the region, and allowing for good dispersion of local pollution sources. Under anticyclonic conditions, sunny weather and weaker northerly to easterly winds allow for local pollution build-up, its photochemical processing, and for advection of continental air masses to the area (see example in Figure 4.1).

4.2 General air pollution situation In the Ile de France region, the Air Quality survey network AirParif (http://www.airparif.asso.fr) dispose of a dense routine measurement network with nearly 50 automatic sites located within the Paris agglomeration and neighbouring rural areas, giving hourly concentrations of target pollutants. These data are gathered since nearly 15 years and allow climatological analysis of the evolution of urban and peri-urban pollution. Yearly averages of primary pollutants such as benzene and NO, or SO2 (of dominant industrial origin) show a negative trend at urban background sites over the last ten years. Ozone shows an upward trend, probably due to decreasing titration with fresh NO emissions. NO2 and PM10 do not show a significant trend. Both are due to primary emissions and secondary chemical transformation.

From a regulative point of view, enhanced urban NO2 levels present the biggest problem. During the last few years, the annual average limit value that will be valid for 2010 (40 µg/m3) would have been violated over a large part of the Paris agglomeration. For ozone, the national ceiling value for health protection in 2010 requires not to exceed a daily maximum of the 8 h average of 120 µg/m3 more than 25 times per year averaged over three years. In light of the results for the last years, this limit is achievable, except for a year with exceptionally hot and anticyclonic meteorology like in summer 2003. The information threshold of 180 µg/m3 (hourly mean) for ozone is exceeded several times a year. For PM10, the objective of an annual average below 30 µg/m3 is violated in the central region of the agglomeration during some years (e.g. 2003 and 2007).

4.3 Specific scientific measurement campaigns In addition to the operational air quality survey, a large number of specific campaigns and scientific studies have been performed in the last ten years, in order to study the processes of air pollution build-up in the Paris agglomeration and its plume. Gas phase chemistry, a comprehensive experi-ment has been performed in particular during the ESQUIF campaign in summers 1998 and 1999. Particulate matter formation in the region has been recently addressed during the very comprehen-



sive MEGAPOLI experiment campaign in July 2009. Photo-oxidant pollution ESQUIF campaign and later studies The ESQUIF campaign (IPSL and LISA, Research Centre Jülich, Météo-France, AirParif, Labora-toire d’Aérologie) has allowed a detailed documentation of major gaseous pollutants (O3, NOx, NOy, VOC) and a first characterisation of particulate matter (chemical, size distribution) within the Paris agglomeration and its plume during 13 multiday IOP’s (Intensive Observation Periods) between 1998 and 2000 (Menut et al, 2002, Vautard et al., 2003). An integrated observation net-work including ground based in-situ and remote, and airborne measurements was set up. Major outcomes from this campaign are presented in the following paragraphs (Figure 4.2).

Figure 4.2: Trends of major atmospheric pollutants for urban background sites from the AirParif network; from top to down and right to left: benzene, ozone, nitrogen oxide, nitrogen dioxide, sulfur dioxide, black

smoke, total suspended matter (PM13), PM10. Emission uncertainty Airborne NOy, CO and VOC measurements from the ESQUIF campaign were used in combination with the air quality photochemical model CHIMERE in order to diagnose uncertainties in the current emission inventory from AirParif for the year 2000 (Vautard et al., 2003). There is reason-able consistency between simulated and measured concentrations. NO y simulations agree with measured concentrations to within 35%. There are significant underestimations and overestimations in some individual primary hydrocarbons. However, the total mass and reactivity of the measured



hydrocarbon mixture, which accounts for only about half of the total emitted mass, agree with modelled values to within an estimated uncertainty of 40%. These values give direct constraints for the emission uncertainty, given that uncertainty in model parameters (for example boundary layer height) is lower. This work was later on extended by inverse modelling work assessing the uncertainty in regional emission cadastres (Pison et al., 2006; Deguillaume et al., 2006). The latter authors applied a Bayesian Monte Carlo analysis using urban and surrounding air quality observations to correct average Paris agglomeration with respect to the initial inventory and to derive their uncertainty: for VOC emissions the result was 0 ± 20 % (1 sigma), and for VOC emissions +16 ± 30 %. Ozone plume characteristics Average photochemical ozone build-up in the Paris agglomeration plume during summers 1998 and 1999 was about 15 ppb (Deguillaume et al., 2006). During the ESQUIF IOP days, this value was often several tenths of ppb. During summers 1998 and 1999 (Figure 4.3), plumes are most often encountered in the north-eastern sector, consistent to the climatologically of predominantly south-westerly winds. The strongest plumes are encountered in the south-western sector, corresponding to anticyclonic situations with stagnant NW winds.

NumberNumber ofof plumesplumes

P(P(OOxx) (in ) (in ppbppb))

WindWind speed (msspeed (ms--11))

[[OOxx] (in ] (in ppbppb))

Figure 4.3: Number of ozone plumes during summers 1998 and 1999. An ozone plume is defined for days

with a photochemical OX (=O3+NO2) production of more than 10 ppb; from Derognat (2003). Major factors of photooxidant pollution and chemical regimes From an observational based approach, using ozone, NOy and VOC measurements in the plume, in conjunction with simulations, Sillman et al. (2003) derived either a VOC or NOx sensitive chemical regime oz ozone build-up, depending on meteorological conditions for particular days. Using Monte Carlo analysis with an observational constraint, Beekmann and Derognat (2003) showed that using the ESQUIF data set could reduce the uncertainty by a factor of 1.5 to 3 for different days. Extend-ing this type of analysis to summers 1998 and 1999, Deguillaume et al. (2008) showed that the photochemical ozone build-up in the plume was in general VOC sensitive, and that the result of an average VOC sensitive regime was robust with respect to model uncertainty. Derognat et al. 2003 showed that biogenic VOC emissions (mainly isoprene) significantly contribute to photochemical ozone build-up in the region, 9 ppb on the median for ESQUIF IOP days (a selection of more polluted days), and up to 40 ppb for an exceptional hot and polluted day. Using the CHIMERE adjoint for sensitivity analysis, Menut et al. (2003) tested the sensitivity of ozone build-up due to a large variety of parameters and found them mainly driven by traffic and solvent surface emissions and meteorological parameters such as temperature. On the average, only about a quarter of ozone



present in the Paris agglomeration plume is formed from local emissions, the major part is advected from outside. For polluted conditions (O3 > 90 ppb), the local fraction is more than 40% (Derognat, 2003). In extension of results of the ESQUIF campaign, the LISAIR campaign (LSCE /IPSL, AirParif) has allowed gathering a large amount of urban VOC measurements (C2 – C12) during May 2005 in conjunction with aerosol lidar measurements (boundary layer height evolution) in order to address the spatial and diurnal VOC variability for different urban sites.

4.4 Particulate matter pollution Besides from air quality network measurements, climatological information about aerosol loads over the Paris region is available from optical measurements. Aerosol optical density (AOD) is measured by a sunphotometer part of the AERONET network 20 km SW of Paris town centre. During clear sky days, AOD is mostly comprised between 0.1 and 0.4, with a median value of 0.17 (Chazette et al., 2004). The average single scattering ratio (at 532 nm) of 0.9 is typical for urban aerosol (Raut and Chazette, 2007). From comparisons of aerosol backscatter lidar measurements at the same site and model simulations, it has been inferred that secondary organic aerosol was proba-bly underestimated in the CHIMERE model simulations (Hodzic et al. 2004). Regional / continental origin of aerosols in the Paris region Hourly concentrations of inorganic salts (ions) and carbonaceous material in fine aerosols (aerody-namic diameter, A.D. < 2.5μm) have been determined from fast measurements performed for a 3-week period in spring 2007 at an urban site within Paris town (Sciar et al., 2009). The sum of these two chemical components (ions and carbonaceous aerosols) has shown to account for most of the fine aerosol mass (PM2.5) in this area. This time-resolved dataset allowed investigating the factors controlling the levels of PM2.5 and showed that polluted periods with PM2.5 >40μg/m3 (during periods I and III in Figure 4.4) were characterized by air masses of continental (European) origin and chemical composition made by 75% of ions. By contrast, clean marine air masses have shown the lowest PM2.5 concentrations (typically of about 10μg/m3, period II in Figure 4.4); carbonaceous aerosols contributing for most of this mass (typically 75%). The rather stable levels of carbonaceous aerosols observed during this study suggest that the region of Paris is a major contributor to this fraction.

Figure 4.4: Time resolved PM measurements in downtown Paris in May/June 2007.

By contrast, long-range transport from Europe is proposed as the main contributor for ions meas-ured in Paris during springtime. Further studies need to address, if these results valid on a larger climatological time scale.



4.5 MEGAPOLI campaign The campaign, performed in the framework of the EC FP7 MEGAPOLI project, aimed at better quantifying primary and secondary organic aerosol sources, and their relation to gaseous precursors, at the example of a big European Megacity, the Paris region. Indeed, these aerosol fractions make up an important contribution to urban fine particle matter, and their sources are among the most uncertain. The campaign design included three primary and four secondary fixed ground measure-ment sites, an aircraft and five mobile platforms (Figure 4.5). Fixed sites were distributed over urban and peri-urban areas. Mobile platforms allowed sampling the pollution plume and back-ground conditions. For many sites, a complete instrumentation was set-up comprising aerosol chemistry and physical properties, and gas chemistry. The summer part of the campaign took place in July 2009, the winter part took place from January 15 to February 15, 2010. More than 25 labora-tories participated (funded by EC, French national funding, or from own means). The campaign was clearly a success, with measurement coverage above 90%. At the moment, measurements are analyzed and quality checked by partners. Measurement quick-looks have al-ready been submitted to the campaign data base at LISA (http://megapoli.lisa.univ-paris12.fr/). First interesting results include:

Figure 4.5: MEGAPOLI campaign set-up.

• From airborne primary pollutant measurements, the pollution plume was still well defined at

more than one hundred kilometres downwind from the agglomeration. • Very preliminary attribution of organic aerosol (OA) from AMS mass spectrometer urban

and peri-urban measurements shows a large fraction of oxidised organic aerosol (OOA) of secondary origin, and a smaller fraction of unoxidised organic aerosol (HOA) of primary origin. At the urban site, about half of OA is water soluble, corresponding probably to clas-sical secondary organic aerosol, another half is water insoluble, corresponding probably to primary and chemically processed primary OA.

• Significant new particle formation events were observed in the area during the whole month of the campaign. These events were assisted by the relatively low particulate matter concen-tration levels and resulting low surface area during most of July 2009.

PARI

SIRTA

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Forest Créteil

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Livry-Gargan

Tour Eiffel, Ballon Air

3 primary sites => full in situ measurements / + meteo at SIRTA. 3 secondary sites => lidar and spectrospcopic measurments / or in some situ 3 mobile labs => in situ measurements, aerosol caracteristaion 1 mobile lab => lidar measurements 1 mobile lab => MAXDOAS 1 aircraft ATR-42 => full in situ measurements (SAFIRE, CNRS, MPI)



4.6 Summary outlook

In conclusion the Paris agglomeration is a major population hot-spot with intense pollutant emis-sions from traffic, the residential/tertiary sector and industry. Good dispersive conditions limit the impact on air quality of these emissions. With respect to valid air quality regulation (or valid in near future), NO2 (annual mean) is the most critical pollutant, followed by fine particles and ozone. Several intense studies have allowed to draw a rather coherent picture of photo-oxidant pollution in the agglomeration and its plume, i.e. to quantity precursor emissions (NOx, CO, and VOC), and their respective impact on photo-oxidant levels. For particulate matter, the observation, simulation and source apportionment of carbonaceous aerosol is still uncertain to highly uncertain. The recent MEGAPOLI campaign intends to close these knowledge gaps. Detailed data sets on aerosols and their radiative and hygroscopic properties from this campaign should also help to better quantity the aerosol impact on regional climate in the region.

5 The Po Valley (Italy)

5.1 Air quality conditions

The Po River Basin includes six administrative regions; its plains account for more than 2900 municipal units for a total population of about 20 million people and an average population density of 414 inhabitants/km2 with maximum value over 7000 inhabitants/km2 within Milan municipality. The major urban conglomeration of Milan its clearly identifiable in its central-northern part (Figure 5.1), while Turin urban area can be recognized towards its western edge while the largely urbanized zones of the Veneto plains and southern Emilia can be noticed near its eastern and southern limits. The core of Milan urban area, roughly coincident with its province, accounts for 3.7 millions inhabitants. Turin metropolitan area accounts for more than 1.5 million people, while many cities counting more than 100.000 inhabitants are scattered throughout the plains.

Figure 5.1: Population density of the Po Valley region. Urban agglomerations located within the Po Valley basin suffer poor air quality conditions due to the concentration of urban and industrial emissions and to the adverse meteorological conditions that often affect the whole region due to its peculiar topographic and geographic features. The atmospheric circulation of the Po Valley is characterized by the strong modification of synoptic flow due to the high mountains (Alps and Apennines chains) that surround the valley on three sides. The local atmospheric circulation features, dominated by calms and weak winds, flavor the devel-



opment of critical pollution episodes. The main cities of the Po Valley experience similar air quality problems with frequent exceedances of EC directives limit values for PM10 during the winter, and for ozone - during the summer. Non attainment conditions are recorded for yearly average values of PM10 and NO2.

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Figure 5.3: Ozone daily maximum of 8 hours mean concentrations recorded in Turin (TO), Milan (MI), Bologna (BO) and Venice (VE) urban background stations during year 2005.

The similarity among air quality conditions experienced within the Po Valley cities is shown in Figure 5.2, where PM10 concentrations measured within the major urban locations are compared for year 2005. High correlation among measurements and coincidence of most peak episodes is evident. Long lasting PM episodes have been recorded during January, February, March, October and December. A similar correlation is shown by ozone measurements (Figure 5.3). Photochemical smog episodes affected the whole Po River plains during May, June and July, when a large number of exceedances of the limit values for ozone daily maximum of the 8 hours running mean have been recorded. Larger differences can be observed for NO2 concentrations (Figure 5.4) for which local sources emissions are more influent. A good correlation is still present between Turin and Milan concentra-tions, while sensibly smaller concentrations were recorded in Venice and Modena, where stations are located in areas not directly exposed to traffic emissions.



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Figure 5.4: NO2 daily maximum hourly mean concentrations recorded in Turin (TO), Milan (MI), Modena (MO) and Venice (VE) urban background stations during year 2005.

5.2 Meteorological classification of pollution episodes A classification of European air quality episodes based on the definition of the influencing sources and meteorological forcing features has been proposed by Kukkonen et al. (2005) on the basis of an episodes inventory and survey compiled within COST-715 and the FUMAPEX project (Valkama & Kukkonen, 2004). The episodes have been broadly categorised into two classes according to the scale of the main source areas, i.e., those originating mainly from local emissions, and those con-nected with regionally and long-range transported (LRT) polluted air masses. The local-scale episodes can be further classified as those caused predominantly by either mobile or stationary sources. The larger-scale episodes can be schematically classified as either those involving photo-chemical pollution from both local, and regional and LRT sources, or those caused by LRT air masses. Photochemical episodes are especially prevalent in Southern European cities. These may characteristically involve recirculation of air masses, caused by meso-scale meteorological effects (such as the land-sea breeze) and orographic flows. Winter episodes have been shown to be mainly characterized by high pressure systems associated with the development of stable stratification and ground-based or elevated temperature inversions. A severe winter air pollution episode occurred in Milan during December 1998 has been analysed in Kukkonen et al. (2005) and compared with episodes occurred in other cities highlighting the role of elevated temperature inversion in driving air pollutant trapping within the shallow boundary layer. The more relevant winter air pollution episodes occurred during the period 1998-2003 inside Milan urban area have been analysed to characterize their meteorological features and to identify the key meteorological variables that need to be correctly forecasted to allow episodes prevention by Finardi and Pellegrini (2004). The study showed that one-two PM10 episodes occurred every winter in Milan during the analysed time window. These events are often characterized by simultaneous infringements of PM10 and NO2 limit values, and by similar meteorological conditions. All the episodes show the presence of anticyclonic structures and advection of warm air in the mid-troposphere (850-700 hPa) over the region (Figure 5.5). The advected warm air, superposed to cold air layers located near the surface, originates a stable thermal structure in the lower atmosphere. Inversions or very stable (nearly isothermal) vertical temperature profiles are observed in these layers (0-2000 m) during the episodes. Nearly constant surface high pressure is the more frequent condition, even if episodes have been observed with slowly varying pressure. Surface temperature is not directly correlated to the occurrence of episodes, even if cold episodes are those causing the highest concentration levels. Local temperature and humidity conditions seem to be relevant in determining different pollutant behaviors and possibly accumulation features. Wind speed is not a key parameter to identify episodes in Milan due to the high frequency of weak winds and calms that



affects the Po Valley. An analysis of weather types confirmed a clear prevalence of anticyclonic footprint during all the episodes evolution and allowed to identify typical circulation features over the area. The observed phenomena showed many common features and seem to be rather repetitive and predictable. Even if the Po Valley winter episodes can be classified as “driven by local sources emissions” following the previously introduced classification, this definition is not completely adequate for PM10 and pollutants characterised by a long lifetime in the atmosphere. Accumulation phenomena within the valley appear to be relevant for winter high pressure episodes and are confirmed by concentration growth at rural background stations located within the basin.

Figure 5.5: Sketch of geopotential patterns at 850 hPa representing weather types favouring winter air pollution episodes in Milan urban area.

5.3 Elevated ozone and PM10 pollution episodes in 2005 The analysis of PM10 and NO2 concentrations recorded over the Po Valley (see e.g. Figure 5.2-5.4) allowed to individuate the following 6 major peak concentrations periods occurred during winter of 2005: (i) 5-20 Jan; (ii) 8-13 Feb; (iii) 15-26 Mar; (iv) 13-17 Oct; (v) 24 Oct – 1 Nov, and (vi) 19-27 Dec. The meteorological conditions characterizing December 2005 episode are described by geopotential fields at 500 hPa and surface pressure fields provided by the NCEP Reanalyses for 19-22 Dec 2005 (Figure 5.6) and by the time evolution of vertical temperature profiles recorded by the Milan radio-sounding (Figure 5.7).

(a) (b)

Figure 5.6: Geopotential at 500 hPa and surface pressure provided by the NCEP Reanalyses For 19 Dec 2005 and (b) 22 Dec 2005.

A high pressure ridge enters the Po Valley area on December 19th causing a warming of the atmos-phere above 1000 metres, and a cooling of near surface layers giving rise to the development of inversion layers located within the first 500 m and at about 1500 m. This structure evolved into a nearly isothermal layer topper by a capping inversion that lasted until December 26th. The time variation of PM10 concentrations recorded within the Po Valley during the episode is shown in



Figure 5.8, where a growing tendency is recorded by all stations. The analysis of O3 concentrations recorded over the Po Valley (see e.g. Figure 5.3) allowed to individuate the following 3 major peak concentrations periods occurred during spring/ summer of 2005: (i) 25 May – 5 Jun; (ii) 17-30 June; and 12 Jul – 1 Aug.

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Figure 5.9: Geopotential at 500 hPa and surface pressure provided by the NCEP Reanalyses for 29 May 2005 and (b) 22 Jun 2005.



All the listed episodes are associated to persisting high pressure conditions due to the development of ridges of African origin causing hot and humid stagnating conditions within the Po Valley, as shown in Figure 5.9 for May and June episodes. The time variation of vertical temperature profiles recorded by Milan radiosounding during June 2005 episode (Figure 5.10) shows a progressive warming of the atmosphere, with surface temperatures over 30°C and a boundary layer depth between 800 and 1000 metres at 01:00 pm LST. These conditions were associated to weak surface winds and absence of significant precipitation.

Figure 5.10: Vertical temperature profiles recorded by the Milan radiosounding from 16 till 26 Jun 2005, at

12:00 UTC.

6 Moscow (Russia) An integrated air pollution index (API) calculated on the 5 major pollutants (CO, NO2, NO, O3, and formaldehyde) was 6.2 in 2008, 6.3 in 2007, 6.4 in 2006, 6.1 in 2005 and 6.2 in 2004, that charac-terizes the level of air pollution as a high degree. The slight increase in the level of air pollution in 2006 associated with the peculiarities of weather conditions that year. Air concentrations of ben-zopiren, NO2, phenol and formaldehyde in Moscow are enhanced. High levels of air pollution are observed near large highways and industrial zones, especially in eastern and south-eastern parts of the city. The highest air pollution levels are observed in areas of Kapotnya, Lyublino, and Maryino due to city-located the Moscow oil refinery. The minimum level of pollution is observed in the city districts Krylatsky and Silver Bor. Air pollution in Moscow is very inhomogeneous (Figure 6.1). Hotspots are the roads and surround-ing areas. In residential areas the pollutant concentrations are about 15-30% less than in the center of Moscow, and at 30-50% - than in the vicinity of highways (Table 6.1). The annual O3 concentration in 2008 exceeded the national threshold limit values (TLV, see Table 6.1) in 1.1 times (0.032 mg/m3), which remained at 2007 level. In the city average O3 concentra-tions varied from 0.023-0.027 mg/m3 (0.8-0.9 TLV) in the central part of the city and close to highways up to 0.036-0.038 mg/m3 (1.2-1.3 TLV) on periphery. The highest O3 concentrations were observed in May (1.5 TLV), June and July (1.3-1.4 TLV), the smallest - in the winter (0.5-0.6 TLV). Daily mean concentrations above the TLV observed from 30 to 70% of cases. Contamination of the air surface layer to a large extent depends on meteorological conditions. On average, the air pollution potential is low in the Moscow region (a good dispersion potential). In certain periods, when meteorological conditions trigger to accumulation of harmful substances in the surface layer, the pollution concentrations may be drastically increased – leading to high pollu-tion / smog episodes. Both summer and winter episodes with high concentrations are often in



Moscow: one the strongest examples of summer episodes was the Moscow smog in September 2002, caused by peat fires in Moscow region (Chubarova et al., 2009) and for winter episodes – February 2006 (Report, 2008). From 3 to 9 February 2006, in a combination of weak winds and locking inversion layer, the concentrations of pollutants have dramatically increased up to the maximum observed concentrations in 2006. Table 6.1: Annual average concentrations of the main pollutants (mg/m3) in years 2006 – 2008 for different

areas of the Moscow megacity and national threshold limit values (TLV) (based on Report, 2009).

Average for city Close to highways City centre Residence areas TLV 2006 2007 2008 2006 2007 2008 2006 2007 2008 2006 2007 2008CO 3.0 0.8 0.7 0.57 1 1 0.71 0.9 0.8 0.59 0.7 0.6 0.51NO2 0.04 0.046 0.042 0.036 0.05 0.051 0.044 0.05 0.044 0.035 0.043 0.039 0.031NO 0.06 0.048 0.046 0.038 0.067 0.057 0.053 0.055 0.054 0.035 0.039 0.041 0.026SO2 0.05 0.006 0.006 0.003 0.007 0.007 0.004 0.006 0.006 0.004 0.006 0.007 0.002PM10 (0.15) 0.033 0.035 0.037 0.045 0.048 0.046 0.035 0.035 0.039 0.031 0.032 0.038O3 0.03 0.028 0.032 0.032 0.031 0.039 0.037 0.025 0.031 0.03 0.026 0.028 0.032API ** - 6.4 6.3 6.2 7.3 7.1 7 6.3 6.5 6.3 6.1 6.1 6

* national threshold limit values for hourly average concentrations (TLV), ** integrated air pollution index (API) calculated on 5 major pollutants: CO, NO2, NO, O3, and formaldehyde.

Figure 6.1: Moscow NO2 air pollution in threshold limit values, TLV (0.04 mg/m3):

(a) from Moscow transport, (> 2.0 - deep blue, 1.0-2.0 - middle blue, 0.5-1.0 – light blue); and (b) from industrial and energy production sources (> 2.5 - deep red, < 0.5 - blue) (Atlas, 2000)

Elevated levels of air pollution led to the continuous growth of allergic and asthmatic diseases for children and high mortality among elderly population during the summer smog events. The Moscow urban agglomeration impacts on the surrounding areas: according to the official information atmospheric pollution extends to 70-100 km, thermal pollution and effects on the precipitation occurs at a distance of 90-100 km, and the degradation of forests - 30-40 km.



7 Classification of air pollution episodes in European cities Identification and classification of various types of air pollution episodes in cities located in differ-ent European climatic and geographic regions and identification the key meteorological parameters leading to air pollution episodes in various European climatological regions were done based on the FUMAPEX data and latest MEGAPOLI analysis (see the previous sections). Seven main target cities were chosen from the FP7 EC FUMAPEX project evaluations. These are: Helsinki (Finland), Paris (France), Turin (Italy), Bologna (Italy), Oslo (Norway), Castellon (Spain) and London (UK). For these cities a total of 23 episodes have been identified. Based on Valkama et al. (2003; 2004) it can be summarised: (i) the air pollution data received from FUMAPEX partners and latest MEGAPOLI megacities studies (Table 7.1); (ii) the meteorological observations, regard-ing synoptic or surface stations, and meteorological masts (Table 7.2); (iii) meteorological observa-tions, regarding for the sounding observations and atmospheric boundary layer (ABL) model data. An episode was defined as a situation, when air pollutant concentrations exceed a threshold value. The measured concentrations are compared with the currently applicable European Union limit values; the hourly limit value for nitrogen oxides (NO2) is 200 µg/m3, allowing 18 exceedings per calendar year; for ozone (O3) the hourly average limit is 180 µg/m³ and for carbon monoxide (CO) the eight-hourly gliding average limit value is 10 mg/m3. For suspended particles (PM10) the hourly limit value is 50 µg/m3 (allowing 35 exceedings per year) and 70 µg/m3 over 24 hours allowing an exceeding once in a month. The EU Directives require practical measures to be taken, if air quality limits are exceeded.

Table 7.1: Classification of European AQ episodes – local scale.

Pollutant Season Meteorological characteristics

Source and scale European region

Inversion-induced episodes PM10, PM2.5 Winter Inversion, stable

ABL Local traffic Northern, Central,

Southern NO2, CO Winter Inversion, low

winds Local traffic Northern, Central,

Southern “Spring dust” episodes

PM10 Spring, Autumn. Dry, melting snow

Suspended dust Northern, Central

Episodes caused partly by emissions from stationary sources

PM10, PM2.5 Winter, Spring Inversion, low winds

Local stationary sources

Northern, Eastern, E-Central

The work is based on datasets of concentration and meteorological data measured during air pollu-tion episodes in seven European cities. Over the whole of Europe, low wind speeds and stable atmospheric stratification are the key mete-orological factors leading to air pollution episodes. Particulate pollution is important in episodic conditions over most of the European continent. In Northern Europe ground-based inversions, stable atmospheric stratification and low wind speed are the key meteorological factors. The local topography has also a major effect to the formation of episodes. Particulate matter (e.g. PM10) and nitrogen oxides (NO2) are the most important pollutants. Episodes occur typically in winter (NO2) and spring (particles). In Northern and Central Europe, resuspension of particles, e.g. from street surfaces, is an important source of coarse particles. In Southern and Central Europe stable atmos-pheric stratification, low wind speeds, mesoscale circulation patterns, topography, solar radiation, etc. are the most important factors. Photochemical pollution episodes including ozone (O3), as well as particles, occur commonly during spring and summer.



Table 7.2: Classification of European AQ episodes – regional and LRT scale.

We classified the various episodes according to pollutant and prevailing meteorological conditions. The episodes can also be categorised according to the scale of the main source areas, i.e. those originating from local emissions and those from regional and long-distance sources. The so-called “spring dust episodes” refer to the episodes caused by particulate matter (PM) suspended from the road and street surfaces. These are characteristic especially for Northern parts of the Europe. The larger scale episodes can be schematically classified as those involving photochemical pollution from both local, and regional and LRT sources, and the episodes caused by long range transported air masses. The photochemical episodes are especially relevant in Southern European cities. These may characteristically involve recirculation of air masses, caused by meso-scale meteorological effects (such as land-sea breeze) and orographic flows. The classification of air quality episodes in European cities in terms defined above is presented in Tables 7.1 (local scale) and 7.2 (regional to long-range).

8 Conclusions Clearly the comparison of concentrations measured at urban sites and rural background makes it possible to evaluate the importance of local emissions in relation to long-range transport. Similarly the relative influence of local traffic in relation to other local sources can be evaluated by compar-ing the concentrations measured at urban traffic sites and those at urban background sites. The influence of various local source categories can also be assessed using the ratios of PM2.5 and PM10 measured at the same station e.g. this ratio is substantially different for pollution originated from local traffic and for that from wood combustion (Laupsa & Slørdal, 2002). The episodes examined by Sokhi et al. (2002) and Kukkonen et al. (2004ab) were caused mainly by local wood combustion in Oslo, mainly by suspended dust and local traffic emissions in Helsinki, by local traffic and long-range transport in London, and mainly by local traffic in Milan. All the episodes addressed were associated with the influence of areas of high pressure (Oslo, Helsinki and London) or a high-pressure ridge (Milan). High atmospheric pressure is commonly related with stable stratification. However it does not necessarily lead to extremely stable conditions or strong inversions near the ground level. Regard-ing episodes of PM10 and NO2, an elevated atmospheric pressure is commonly a necessary, but not a sufficient, condition for the occurrence of an episode. However there is one exception to the above mentioned rule. PM10 episodes can also be caused by the increased resuspension of particles from streets under the influence of strong winds. The radiation and advection inversions occur most frequently in the course of air pollution episodes. Clearly the influence of inversions on air quality crucially depends on their detailed vertical struc-ture and magnitude, and on their temporal evolution. In the cases examined by Sokhi et al. (2002)

Pollutant Season Meteorological characteristics

Source and scale European region

Summer and springtime photochemical pollution episodes

O3, photochemical pollutants

Summer, Spring High pressure, recirculation, thermal low pressure

Precursor, Regional

Southern, Central

Long-range transport PM and NO2 episodes

PM2.5 Winter, Spring Stable ABL LRT, regional Northern, Central, Southern



and Kukkonen et al. (2004ab), strong ground-based or slightly elevated temperature inversions prevailed in the course of the episodes in Oslo, Helsinki and Milan, and there was a slight ground-based inversion also in London. The inversions in Oslo and Milan were mainly caused by the advection of warmer air above a relatively colder surface, and that in Helsinki by radiation cooling of a snow-covered ground. It was also found out that a low wind speed is not necessarily a good indicator in terms of pollution episodes in all the European regions. In particular, in the Po Valley, wind speed is a poor indicator due to frequently occurring calm and low wind speed conditions (Finardi, 2004). In the cases studied by Sokhi et al. (2002) and Kukkonen et al. (2004ab) the best meteorological predictors for the elevated concentrations of PM10 were the temporal (daily) evolutions of tempera-ture inversions and atmospheric stability (which was measured in terms of the meteorologically pre-processed Monin-Obukhov length) and in some cases, wind speed. The hourly temporal variation of strong ground-based or slightly elevated temperature inversions was closely correlated with that of the highest PM10 concentrations. The measured temperature inversions can therefore be recom-mended as one predictor variable in statistical models, for forecasting in time the potential forma-tion of air pollution episodes. Episodes can also be influenced by the interaction of phenomena over different meteorological scales. For instance, in mountainous, coastal mid-latitude areas, meso-scale forces can perturb the synoptic conditions; this can either suppress or enhance ventilation conditions. For example, drain-age winds of cold surface air may be blocked upon reaching a relatively warmer sea. The tempera-ture contrast between the city air and the sea surface acts as a convective barrier that prevents the advection of air over the sea; and thus, confines the colder polluted air over a coastal city (Millan, 2002). In winter, the Western Mediterranean Basin is better ventilated due to the increased passage of travelling lows and their frontal systems. However, as soon as anticyclonic conditions develop, the pollutants can be trapped within industrialised valleys or in large, but confined airsheds (Millan, 2002). As the polluted air masses do not leave their airshed under these conditions, these autumn-winter episodes are amenable to short-term measures, if such measures are taken before the episode develops. Once the episode is in progress, however, short-term measures may be less effective (Commission Decision of 19 March 2004; 004/279/EC). Episodes can be broadly categorised according to the scale of the main source areas, i.e. those originating from local emissions and those from regional and long-distance sources. The local scale episodes can be further classified as those caused predominantly by mobile or stationary sources. The so-called ‘spring dust episodes’ refer to the episodes caused by particulate matter that is sus-pended from the road and street surfaces; these cases are characteristic especially of northern parts of Europe. The larger scale episodes can be schematically classified as those involving photochemical pollu-tion from both local, and regional and long range transport sources, and episodes caused by long-range transported air masses. The photochemical episodes are especially prevalent in southern European cities. These may characteristically involve re-circulation of air masses, caused by meso-scale meteorological effects (such as land-sea breeze) and orographic flows (e.g. Millan, 2002). Acknowledgements The research leading to these results has received funding from the European Union's Seventh Framework Programme FP/2007-2011 under grant agreement n°212520. Additionally, partial contributions from the COST-715, FUMAPEX, IGBP are acknowledged. Summaries of the MEGAPOLI, ESQUF, and LISAIR measurement campaigns are presented. AirParif (France), regional ARPAs (Italy), Moscow City Eco-Monitoring (Russia), and London (KCL, UK) air quality monitoring networks are acknowledged as well.



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Part II: Interactions between Air Quality and Meteorology/ Cli-mate: Aerosol Feedbacks

Ulrik Korsholm, Alexander Mahura, Alexander Baklanov, Georg Grell The second focus of the Task 4.3: “Interactions between air quality and meteorol-ogy/climate“ (lead: DMI) is to study and quantify the possible effects of elevated pollutant concen-trations in and from megacities on the meteorology. In particular, the influence of air pollution on cloud formation, precipitation and radiation should be assessed and indicators relating meteorologi-cal patterns to urban air pollution episodes will be developed through the application of advanced modelling tools, such as the coupled MEMO /MARS system and the online coupled environment model Enviro-HIRLAM. First results by AUTH of an on-line version of MEMO/MARS to quantify effects of the direct aerosol effect have been proven successful and presented in the MEGAPOLI Deliverable D4.1 Report (Moussiopoulos et al., 2010). For the assessment of the coupled model system performance, meteorological parameters were calculated for a synthetic test case with a flat topography assuming different types of aerosol composition. A comparison of coupled calculations reveals that the radiative forcing due to the direct effect has a substantial impact on certain meteorological variables and the development of a lower inversion layer. In this report (Chapter 2: Korsholm et al., 2010) and in the PhD thesis of Korsholm (2009) we are focusing on the indirect aerosol effects on meteor-ology and reporting the DMI team results using Enviro-HIRLAM model for the summer 2005 selected case study of Paris (see Korsholm, 2009) and other Western European cities (Chapter 2 of this Report), in particular in relation to the relative impact of urban effects vs. aerosol first and second indirect effects. Additionally, in order to investigate the formation of new particles and their growth to CCN size, FORTH has been using PMCAMx-UF (UltraFine) to simulate together with the mass/composition distribution, the aerosol number distribution starting at 1 nm (see the MEGA-POLI Deliverable D4.2 by Koraj & Pandis (2010)).

1 Overview of chemistry and aerosol feedbacks on meteorology Chemical species influencing weather and atmospheric processes include greenhouse gases which warm near-surface air and aerosols such as natural aerosols (e.g., sea salt, dust, volcanic aerosols) and primary and secondary particles of anthropogenic origin. Some aerosol particle components warm the air by absorbing solar radiation (e.g., black carbon (BC), iron, aluminium, polycyclic, and nitrated aromatic compounds) and thermal-IR (e.g., dust) , whereas others (water, sulphate, nitrate, most of organic compounds) cool the air by backscattering incident short-wave radiation to space. It is necessary to highlight the effects of aerosols and other chemical species on meteorological parameters, which have many different pathways (direct, semi-direct, and indirect effects, etc.) and they have to be prioritised and considered in on-line coupled modelling systems. Sensitivity studies are needed to understand the relative importance of different feedback mechanisms for different species and conditions relevant to air quality and climate interactions. A concerted action to mobi-lise and coordinate research in this area is needed. A particular area of development for on-line coupled models is in the area of parametrizations that allow for interactions of physics with chemistry. With the realization of the increasing importance of science questions related to global and regional climate change, two-way interactions between meteorology and chemistry are becoming a necessity in complex 3-D models. Such models are increasingly being used not only for meteorological predictions, but to better understand and simu-



late the wide range of processes and atmospheric feedbacks that influence climate. In contrast to global climate models, the flexible grid structure of high resolution nonhydrostatic models enables the simulation of climate processes at spatial and temporal scales compatible with measurements, providing a framework in which to test new parametrizations of climate processes that are either treated in a simple way in current global climate models or neglected entirely. The coordination of parametrizations that allow for interactions between aerosols, radiation, chem-istry, and clouds, as well as the coordination of new treatments for meteorological process modules (e.g., boundary layer, clouds) that are needed to improve predictions of atmospheric chemistry is an important consideration. Model development in atmospheric chemistry and weather prediction has so far developed separately from each other, leading, for example, to meteorological parametriza-tions that have no treatment of any chemical species. A well known example of linkage between chemistry and meteorology includes the treatment of cloud-aerosol interactions. Most current cloud physics schemes neglect the linkages of CCN to predicted aerosol distributions. Coupling the chemistry part with cloud physics involves modifying existing chemistry and cloud subroutines that normally do not interact with each other. This is a very complicated process that requires consider-able effort. One example not related to the aerosol direct or indirect effects is the current treatments of bound-ary layer mixing which greatly affects near-surface concentrations. Quite often chemical models (if running on-line) rely on eddy coefficients from the meteorological model for the vertical mixing of trace-gas and particulate scalars. This only works since the mixing is determined by eddy coeffi-cients that are calculated in the meteorological part and can then be used in the chemical part to mix the tracers. It cannot work if the meteorological parameterization uses a different method to mix tracers. It would be much more desirable if designers of the meteorological parametrizations would consider the mixing of a scalar in their scheme from the beginning. This would lead to more general and more accurate vertical mixing algorithms that handle both meteorological and chemical scalars. In addition it would provide meteorological modelers with an additional independent source for possible evaluation. Similar arguments can be made for the treatment of parameterized convection, where only very few convective parametrizations exist in meteorological models that allow for transport and modification of tracers. Implementation of the feedbacks into integrated NWP-ACTM models could be realized in different ways with varying complexity. The following variants serve as examples:

• Simplest off-line coupling: The chemical composition fields from atmospheric CTMs may be read by MetM/NWP at a limited time period and used as driver for aerosol forcing on meteorological processes.

• On-line access coupling: Driver and partial aerosol feedbacks, for ACTMs or for NWP (data exchange on each time step) with or without the following iterations with corrected fields.

• Fully on-line coupling/integration: ACTM and feedbacks included inside MetM for each time step.

The above examples represent the different levels of on-line integration that have been achieved within Europe. Many of the systems are currently either fully off-line or have some on-line capabili-ties. A few systems now exist that are fully on-line coupled (e.g., Enviro-HIRLAM, WRF-Chem and the UKCA systems). Historically Europe has not adopted a community approach to modelling and this has led to a large number of model development programmes, usually working independ-ently. However, a strategic framework will help to provide a common goal and direction to Euro-pean research in this field while having multiple models. There are a number of key elements that need to be part of the overall strategy framework (see also



Baklanov et al. 2010). These include: (i) Scientific questions to be addressed by on-line systems As stated earlier the major reason for developing on-line systems is to take account of feedback mechanisms for accurate modelling of NWP/MM-ACTM and quantifying direct and indirect effects of aerosols. Several questions can be identified in this regard:

• What are the effects of climate/meteorology on the abundance and properties (chemical, microphysical, and radiative) of aerosols on urban/regional scales?

• What are the effects of aerosols on urban/regional climate/meteorology and their relative importance (e.g., anthropogenic vs. natural)?

• How important the two-way/chain feedbacks among meteorology, climate, and air quality are in the estimated effects?

• What is the relative importance of aerosol direct and indirect effects in the estimates? • What are the key uncertainties associated with model predictions of those effects? • How can simulated feedbacks be verified with available datasets?

(ii) Processes/feedbacks to be considered A detailed treatment of the main processes is required in the models in order to answer the above questions. These processes include:

• Direct effect - decrease solar radiation and visibility: o Processes needed: radiation (such as scattering, absorption, and refraction); o Key variables: refractive indices, extinction coefficient, single scattering albedo (SSA),

asymmetry factor, aerosol optical depth (AOD), and visual range; o Key species: cooling: water, sulfate, nitrate, and most organic carbon (OC) warming:

black carbon (BC), OC, iron, aluminium, and polycyclic/nitrated aromatic compounds; • Semi-direct effect - affect PBL meteorology and photochemistry:

o Processes needed: planetary boundary layer (PBL)/land-surface (LS), photolysis, and other met-dependent processes;

o Key variables: temperature, pressure, relative humidity, water vapor mixing ratio, wind speed, wind direction, cloud fraction, stability, PBL height, photolysis rates, and the emission rates of met-dependent primary species (e.g., dust, sea-salt, biogenic aerosol, marine phytoplankton-produced aerosol);

• 1st indirect effect – affect cloud drop size, number, reflectivity, and optical depth via cloud condensation nuclei (CCN): o Processes needed: aerosol activation/resuspension, cloud microphysics, and hydrometeor

dynamics; o Key variables: interstitial/activated fraction, CCN size/composition., cloud drop

size/number/liquid water content (LWC), cloud optical depth (COD), and updraft veloc-ity;

• 2nd indirect effect - affect cloud LWC, lifetime, and precipitation: o Processes needed: in-/below-cloud scavenging and droplet sedimentation; o Key variables: scavenging efficiency, precipitation rate, and sedimentation rate;

• All aerosol effects: o Processes needed: aerosol thermodynamics/dynamics, aqueous-phase chemistry, gaseous

precursor emissions, primary aerosol emissions, and water uptake; o Key variables: aerosol mass, number, size, composition, hygroscopicity, and mixing

state.



2 Monthly averaged changes in surface temperature due to aerosol indirect effects of primary aerosol emissions in Western Europe Megacities emit aerosols and their pre-cursers and are transported downwind in urban plumes. Modifications of cloud properties due to anthropogenic aerosols may take place through modifica-tion of cloud reflectance and precipitation development, referred to as the first and second aerosol indirect effects respectively. These processes have received much attention on climatic time scales, since they represent one of the largest uncertainties in current climate models. There characteristic time scale, however, is the same as that of the clouds and significant differences may exist for various types of clouds. In this study we consider the monthly averaged effect of the first and second aerosol indirect effects. By comparing model runs with and without the indirect effects we found that a monthly averaged signal in surface temperature of about 0.5 °C exists. In particular the indirect effects led to stronger convection and heavier precipitation in some places and suppression of precipitation in other places. Changes in average cloud reflectivity and latent heat fluxes due to modification of cloud lifetime and precipitation led to changes in surface temperature. Comparison to temperature and dew point temperature measurement data showed that root mean square error and bias decreased near the surface, when averaged over all available measurement stations.

2.1 Introduction to the indirect aerosol effects study Megacities (cities with more than five million inhabitants) affect temperature structure on several scales. On the global to regional scale energy production and consumption, transportation and industrial activities account for the main greenhouse gas emissions affecting global and regional climate and on the local scale the urban micro climate is affected by heterogeneity effects (rough-ness, heat fluxes, and canalization), shadowing and sheltering effects of buildings and radiation trapping. The urban heat island (UHI) results from modifications in the surface energy balance. As rural vegetated areas are replaced by urban surfaces they dry out. Hereby, less incoming solar energy is consumed by evaporation of water located at the surface or in soils, plants, tress etc. Therefore, a larger fraction of the incoming solar energy is turned into heat. Urban materials accumulate larger amounts of solar energy than rural surfaces do and during night the accumulated energy is released, leading to reduction of the night time cooling. The UHI affects the urban microclimate but may also have effects on larger scales. If large scale forcing is weak thermal motion induced by the UHI may initiate convective plumes (Wong & Dirks, 1978; Masson et al., 2008; Hidalgo et al., 2008) affect-ing the horizontal and vertical regional temperature structure upon down wind transport of heat. Megacities are also characterized by large emissions of primary and secondary pollutants such as NOx, O3, volatile organic compounds as well as particles. Direct interaction between radiation and pollutants may cause strong local changes in temperature (Fan et al., 2008). Particles may be trans-ported downwind in the urban plume into cloudy environments where they activate and contribute to an increase in cloud droplet number concentration. Such an increase leads to enhanced cloud reflectance through the first aerosol indirect effect (Twomey, 1974) and modification of precipita-tion development through the second aerosol indirect effect (Albrecht, 1989). Clouds exert strong constraints on tropospheric temperature and changes in reflectance and lifetime may be an impor-tant contributor in shaping the down wind temperature structure. Modelling the effects of the aerosol indirect effects presents some difficulties due to the spatial and temporal scales which must be spanned. Aerosol and cloud microphysics take place on scales of the individual aerosols while cloud formation, transport and precipitation development have larger characteristic scales and require a good representation of the synoptic scales. Traditionally, models either contain a detailed description of chemistry, aerosols and cloud microphysics and a parameter-ized approach to dynamics or they contain detailed dynamics and highly parameterized microphys-ics.



In this study we have implemented representations of the first and second aerosol indirect effects in a short range weather model which contains a well tested parameterization of the autoconversion process (process by which cloud liquid water is transformed to rain drops). Quantification of the effect on thermal structure is not feasible in the present study due to a lack of suitable metrics for short lived species and because there is a strong dependency on the choice of auto conversion parameterization, i.e. many models should be included in such an exercise. The purpose of this work is to show the feasibility of the effects on the regional surface temperature structure on short time scales. This is done by comparing model simulations with and without representations of the first and second aerosol indirect effects. The simulations covered a full summer month and monthly averages were considered in order to exclude as much of the random signal as possible.

2.2 Enviro-HIRLAM model description Enviro-HIRLAM (Korsholm et al, 2008; Korsholm, 2009) is an extension of the HIRLAM (High Resolution Limited Area Model) short range numerical weather prediction model system (Unden et al., 2002) to include gas-phase chemistry, aerosols and aerosol cloud interactions. Dispersion of aerosols and trace gases are done using the same parameterizations and the same grid as for mete-orological variables. Prognostic variables comprise temperature, wind components, specific humid-ity, surface pressure, geopotential, trace gas mass concentrations and aerosol number and mass concentrations. The vertical coordinate is a hybrid between terrain following σ-coordinates and pressure levels while horizontal discretizations are done on an Arakawa-C grid. The dynamical core is based on the primitive equations and is solved numerically by using a semi-implicit semi-Lagrangian approach. For a more detailed description of HIRLAM the reader is referred to Unden et al., 2002. Wet deposition (in-cloud, below cloud and snow scavenging) of aerosols is based on a scavenging coefficient which is dependent on precipitation rates (Baklanov & Sorensen, 2001). For gases uptake in falling rain (in-cloud and below cloud) and dissolution in cloud water (in-cloud) is taken into account. The scavenging coefficient follows Seinfeld & Pandis (1994). Dry deposition of gases and aerosols follow the Wesely (1989) resistance approach. Cloud Radiative Properties Atmosphere radiation interactions are highly parameterized and follows a modified version of the scheme described in Sass et al. (1994). The short wave (SW) clear sky flux at the top of the atmos-phere is reduced by absorption due to the presence of stratospheric ozone, water vapour and due to Rayleigh scattering through vertical columns. Average CO2, O2 and aerosol absorption is also accounted for. If clouds are present the downward SW flux is reduced by cloud transmissivity and absorptivity. For a partly cloudy column the clear sky and cloudy fluxes are linearly combined. Cloud transmissivity and absorptivity depends on the cloud condensate content (CCC (kg m-3)) and cloud droplet effective radius (µm) (Wyser et al., 1999). For water clouds the effective radius is expressed as (Wyser et al., 1999):

Re3 = 3CCC/(4πρwaterkN),

where ρwater is the density of water (1000 kg m-3), N is the cloud droplet number concentration and k is a factor of proportionality between Re

3 and Rv

3 where Rv is the volume cloud droplet radius. Simultaneous measurements of Re and Rv shows that k is different for marine (k=0.81) and conti-nental (k=0.69) (Martin et al., 1994) conditions. The corresponding clean marine and continental values of N is 108 m-3 and 4·108 m-3 respectively. Hence, a CCC value of 1 g m-3 corresponds to Re = 9.6 µm over continental regions and Re = 14.4 µm over marine regions. Cloud transmissivity (Tr) is related to Re as: Tr = T1/(T1+M) where M (g m-2) is the vertical integral of CCC from a given level to the top of the atmosphere multiplied by the ratio of cloud cover and maximum cloud cover in the column and T1 = at(ct+cosθ) with θ the solar zenith angle, at = cta*Re+ctb and ct, cta, ctb are constants. Similarly, the absorptivity (A) is related to Re as: A = aa (ca1+ cosθ)log(1+ca2M), where aa



= caa*Re+cab, where ca1, ca2, caa and cab are constants. Hence, an increase in Re leads to a increase in Tr and A and a corresponding decrease in cloud reflectivity. Therefore, clouds over marine regions are less reflective than clouds over continental regions. The horizontal and vertical variations in Re is accounted for by weighing according to Re = fRe,cont+ Re,mar(1-f), where Re,cont and Re,mar are the effective radii for continental and maritime regions respectively and the weight f is given as f = fland (η- η0)(1- η0) for η > η0 where η is the vertical hybrid coordinate used in HIRLAM, η0 = 0.7 and fland is the fraction of land; f = 0 otherwise. A minimum effective radius of 4 µm is imposed. Cloud Microphysics Scheme The STRACO (Soft TRAnsition and COndensation) cloud scheme (Sass, 2002) represents convec-tive and stratiform cloud formation and contains a gradual transition between the two regimes. Subgrid scale variability of humidity is assumed to follow a predefined probability density function which differ in the two regimes and facilitates calculations of the cloud fraction (fc). Trace gases and aerosol species are convected as water vapour except for condensation and evaporation to and from the aerosols are not accounted for, hence, the aerosols are passive in this respect. Condensation, collection and autoconversion are assumed to have time scales faster than a model time step and is based on a bulk approach. In the version used here autoconversion in follows the scheme by Rasch & Kristjansson (1998), in which autoconversion depends on in-cloud specific cloud condensate (CCC/fc), air and water density and is proportional to N⅓ H(Re-R0), where H is the Heavy side step function and R0=5 µm is a cut-off below which droplets are considered too small to initiate rain, hence, for Re<R0 H is zero and autoconversion stops. Cloud droplet number concentration The primary aerosol sulfate content was given by the emission inventory and the mass concentra-tion was related to the number concentration of activated droplets (CDNC) by using the parameteri-zation by Boucher & Lohmann (1995), which distinguishes between marine and continental re-gions: CDNCmarine = 106 ·102.06+0.48log(ms) CDNCcontinental = 106 ·102.24+0.26log(ms) where ms is the sulfate mass concentration (µg m-3). The sulfate mass concentration is treated as a prognostic variable in the model and the appropriate CDNC value is added to the clean background cloud droplet number concentration. Hereby, autoconversion of cloud water into rain and the reflectivity of the clouds are affected.

2.3 Experimental set-up Enviro-HIRLAM model was run for a full summer month starting at May 30 2009 and ending at June 30 2009. This run is referred to as BASELINE. The first two days were discarded as spin-up and averages were taken over the period 1-30 June 2009. The forecasts were restarted every six hours at 00, 06, 12, 18 UTC and the forecast length was 24 hours. ECMWF (European Centre for Medium-Range Weather Forecasts) deterministic output was used for the boundary conditions which were updated every hour. The model domain covered western Europe in 0.15º x 0.15º hori-zontal resolution (154 x 148 grid points) using 40 levels in the vertical with the top level located near 10 hPa. The MEGAPOLI emission inventory (van der Gon et al., 2009) was used and sulfate emissions were extracted from PM10 according to the procedures used for generation of the inven-tory and interpolated to the model grid. No aerosol model of gas phase chemistry is included in these runs, which only considers the effect of primary aerosols. In a second run, termed 12IE, the same procedure was taken as in the BASELINE run except the model now included representations of the first and second aerosol indirect effects. Monthly aver-ages were taken by using the 00 and 03 hour forecasts from each restart in order to get full coverage of the month. Temperature spin-up in the model is typically a few hours and should not affect the



averaging. In order to test this hypothesis the surface temperature was averaged using the 00 UTC forecast every day through the month to get full coverage. There was only very slight differences of no relevance to the conclusions of this study. The averaging of accumulated fields such as precipita-tion is done by using the 24hour accumulated values each day, while daytime and night time aver-ages of activated aerosol number concentration are represented by 00 UTC and 12 UTC averages.

2.4 Meteorological situation Figure 2.1a displays the time averaged mean sea level pressure. The domain was effectively split in a western and an eastern part, dominated by high pressure ridges and low pressure troughs respec-tively. The western part was dominated by cool northerly winds (Figure 2.1b) and relatively little precipitation while the eastern domain was dominated by northward moving thermal lows and therefore stronger precipitation mainly of convective origin (Figure 2.1c).

(a) (b)

(c) (d)

Figure 2.1: (a) Mean sea level pressure (in hPa; at 0.5 hPa interval); (b) two-meter temperature (°C); (c) total precipitation (mm / 24 hours); and (d) number concentration (x 106 m-3) of activated anthropogenic

aerosols at approximately 850 hPa /plots show 12 UTC average over the forecast period 1-30 June 2009/.

The Alps experienced rain up to 18 mm over 24 hours on average while further east up to 13 mm per 24 hours was found. Due to the northerly winds rainout occur just before the Alps and lee side lows are generated. Figure 2.1d shows the number concentration of activated aerosols at about 850 hPa. Maximum values of 0.2x108 are found in the eastern part due to strong emissions of sulfate



from Belgrad, Budapest and Krakow. In the western part the effect of Paris and London are visible. The strong average precipitation in the eastern part renders the aerosol effects weak since it takes much larger concentrations to affect the strong precipitation events and due to wash out of the aerosols. Therefore, the following analysis focus on the western part of the domain where the largest impact of the aerosols is expected.

2.5 Results and discussion In the following all differences are calculated as BASELINE minus 12IE and the first and second aerosol indirect effects are referred to as the indirect effects. Figure 2.2a displays the monthly averaged surface temperature (Ts, °C). Average cooling and warming of up to 0.5ºC was found while the domain averages remained close to zero, i.e. there was no “climate effect” found on this timescale in this modelling domain. On individual days the maximum and minimum changes were up to 5ºC. Ts is controlled by the surface radiation balance and the sensible and latent heat fluxes. Incoming SW radiation below the tropopause is mainly affected by water vapour absorption and the presence of cloud layers that reflect in the short wave part of the spectrum (O3 and CO2 also have some influence, which remains fixed between the BASELINE and 12IE runs).

(a) (b)

(c) (d)

Figure 2.2: (a) Surface temperature (°C), (b) difference in surface temperature (BASELINE minus 12IE), (c) net daily accumulated short wave radiation at the surface (x 106 W m-2) and (d) cloud reflectivity (%)

/all plots displays averages over the period 1-30 Jun 2009/.



Cloud reflectivity is modified by the first aerosol indirect effect, while the second aerosol indirect effect affects the lifetime of the clouds through suppression of rain and thereby, has an average radiative impact. The monthly averaged 24 hour accumulated net SW radiation at the surface is displayed in Figure 2.2b while the average cloud reflectivity is shown in Figure 2.2c. Cloud reflectivity is a pseudo satellite image for the visible range calculated for direct comparison with satellite images. The calculation begins at the lowest model level where the a reflectivity value is calculated based on effective cloud droplet radius which in turn depends on cloud liquid water and cloud droplet num-ber concentration. This start value is then modified according to overlying clouds when looping through the layers (Tijm, 2009). Relative humidity only changes slightly and the changes in SW is therefore controlled by the modifications in cloud reflectivity. An increase in average cloud reflec-tivity leads to a decrease in direct SW at the surface and may be due to an increase in cloud lifetime, an increase in cloud water content or an increase in droplet number concentration. The indirect effects were postulated in the context of boundary layer clouds with a relatively low concentration of cloud droplets and their effects in relation to deep convective clouds is not fully understood. Increased cloud water path allows the cloud to grow to greater heights and thus induce stronger convective cells which at a later time deliver larger precipitation fluxes. However, convec-tive activity is also affected by Ts and thereby by increased reflectivity due to increased cloud lifetime. In particular evaporation of precipitation tends to stabilize the sub-cloud layer facilitating decoupling of the cloud layer and the surface. Furthermore, invigoration of convection may lead to increased entrainment of dry air and thereby to a decrease in cloud liquid water path (Feingold et al., 1996; Lu & Seinfeld, 2005). Hence, the aerosol indirect effects may act to increase or decrease convection and thereby precipitation, lifetime, liquid water path and Ts (Platnick et al., 2000; Coakley et al., 2002; Han et al., 2002; Andrae et al., 2004). The detailed mechanisms controlling the response is not fully understood, however, it seems from cloud resolving modelling studies that the rain rate, anthropogenic loading and the development of convective system have an influence.

(a) (b)

Figure 2.3: Accumulated precipitation (during 24 hour period) averaged over the forecast period: (a) convective precipitation (mm/24 hours); and (b) stratiform precipitation (mm/24 hours).

Consider North-Eastern France and Belgium (Figure 2.2b). This area is located downwind of Paris and was influenced by the megacity sulfate emissions which are mixed upwards during daytime. In this region convective precipitation has changed (Figure 2.3) and cloud reflectivity increased substantially (Figure 2.2c) due to the indirect effects. Figure 2.4 displays the difference in cloud top temperature, lifting condensation level and vertically integrated cloud water. The clouds reach higher levels and also extents further downwards as cloud water increase. These changes are consis-tent with more vigorous convection and longer cloud lifetime giving raise the observed decrease in



cloud reflectance (Figure 2.2c).

(a) (b)

(c)

Figure 2.4: (a) Difference (calculated as BASELINE minus 12IE) in cloud top temperature (°C), (b) differ-ence in lifting condensation level (m), (c) difference in vertical integrated cloud water content (kg m-2). All

plots are taken as averages over the period 1 June 2009 to 30 June 2009.

Figure 2.5 displays the sensible (Figure 2.5a) and latent heat (Figure 2.5c) fluxes along with the corresponding differences (Figure 2.5bd) between the runs. A negative flux is oriented from the surface to the atmosphere and hence leads to a cooling of the surface while a positive flux is di-rected into the surface. The latent fluxes are generally larger than the sensible ones in particular in the low pressure dominated region to the east of the domain. Urban areas stick out on the figures because urban areas are ascribed with desert properties in the model (dry and reflective) which thus affect the fluxes. In this region the precipitation changes induced by the aerosol effects led to a decrease in upward latent heat fluxes which, an average, led to warming. The changes in SW radiation and cloud reflectivity; however, was larger and the region experienced a net cooling. Next, consider the Ts decrease near and downwind of London and the temperature increase in mid-England originating near Birmingham (Figure 2.2b). In this region the change in cloud water path is modest (Figure 2.4c) and there is only little suppression of rain (Figure 2.3). Just north of London a



region of cooling was found. The upward sensible heat flux decreased (Figure 2.5) and thus acts to warm the surface while an increase in upward latent heat flux (of similar magnitude) acted to cool the surface. Since, the latent fluxes are generally larger than the sensible fluxes the net result be-comes a cooling of the surface. Just south of London an opposite situation exists. The aerosol effects induce a slight decrease in total precipitation which led to a decrease in the upward latent heat flux and thereby a slight net warming. By the same account the region in mid-England near Birmingham experienced a warming. The total precipitation decreased, the upward latent heat flux decreased accordingly while the upward sensible heat flux increased (similar in magnitude) and the net effect became a warming in this area.

(a) (b)

(c) (d)

Figure 2.5: (a) Daily accumulated sensible heat flux (x 103 W m-2 ), (b) difference (calculated as BASELINE minus 12IE) in accumulated sensible heat flux (x 106W m-2), and (c) daily accumulated latent heat flux (x 103

W m-2), and (d) difference in accumulated latent heat flux (x 106 W m-2) /all plots are averages over the period 1 June 2009 to 30 June 2009/.

2.6 Comparison with observations In order to complement the above findings a comparison between temperature, dew point tempera-ture and precipitation amount was made for the entire month. Figure 2.6 displays time series at 925 and 850 hPa of the observations, temperature and dew point temperature. Both BASLINE and 12IE seems to predict the parameters well at these levels, however, at the end of the period models and temperature observations seem to diverge generating increased bias and rmse. The error is evident at both levels and decrease upwards through the troposphere. Since, it is



present in both runs it is assumed to be independent of the aerosol effects under consideration here. The BASELINE and 12IE runs seems quite similar in the statistics for these levels and there is no general improvement or degradation of the results, however, at individual stations some spread may be found. It is likely that the degradation is connected to a general degradation in precipitation predictions for both runs at the end of the forecast period (Figure 2.8).

Figure 2.6: Time series of domain averaged temperature at 925 hPa (A) and 850 hPa (C) and dew point

temperature at 925 hPa (B) and 850 hPa (D) along with station averaged measurements.

(a) (b)

Figure 2.7: Domain averaged profiles of bias and rmse in temperature (a) and dew point temperature (b). Taking averages over all stations containing profiling data Figure 2.7 shows that there is slight improvements (decreased bias and rmse) in the statistical scores for temperature near the surface while the largest deviation in dew point is found near the 500 hPa level. Considering spatial statistics as a function of forecast time (Figure 2.8) at 925 and 850 hPa we



found that both dew point and temperature had slightly decreased bias (max increase of 0.12 K) and rmse (max decrease of about 0.28 K) during the forecast for the 12IE run. At the 925 hPa level dew point statistics remained constant between the two runs. Figure 2.9 displays the precipitation rmse, bias and time series. There seems to be no general increase or decrease in the average precipitation performance. However, both runs seems to a tendency of over-predicting precipitation, in particular in the second half of the forecast period.

Figure 2.8: Bias and root mean square error for temperature (A and C) and dew point temperature (B and D) at 925 (A and B) and 850 hPa (C and D) as a function of forecast length. All available stations and grid

points were included.

3 Summary and conclusions In this experiment runs with and without the first and second aerosol indirect effects were compared. Averages were taken over the month of June 2009 and the average effect investigated. On average northern France and Belgium experienced a 0.5°C cooling as did a region just north of London and southern England while mid-England and a region just south of London was dominated by an average heating with a maximum of 0.5 degrees near Birmingham. The cooling in northern France/Belgium resulted from larger reflection of incoming short wave radiation due to longer average cloud lifetime and more cloud droplets. The cooling north of Lon-don and the warming to the south of London were governed by changes in the latent heat fluxes due to changes of precipitation in the region. Similarly, the average warming in mid-England appeared because of decreased latent heat fluxes due to precipitation suppression. Comparison of temperature, dew point temperature and precipitation with measurements did not reveal any time averaged increase in precipitation performance. However, considering statistical



scores as a function of time revealed that temperature and dew point temperature retained slightly better scores during the forecast period. The aerosol indirect effects led to both heating and cooling of the surface. In particular, suppression of precipitation led to stronger convective cells and thereby more precipitation on average. If the effect of cloud reflectivity is not dominant the surface temperature may be modulated by latent heat fluxes due suppression or enhancement of precipitation. It should be noted that this experiment considered only primary sulfate emissions. A more detailed study including secondary aerosols is expected to result in a larger temperature response due to an increased loading of activated aerosols.

Figure 2.9: Domain averaged precipitation as a function of time for the BASELINE, and12IE runs along with observations (A). Horizontal distribution of rmse for temperature (B) and dew point temperature (C)

and the distribution of temperature bias (D) and dew point temperature bias (E).



Acknowledgements The research leading to these results has received funding from the European Union's Seventh Framework Programme FP/2007-2011 under grant agreement n°212520. Authors acknowledge the EDB Department of DMI for providing computing resources for the long-term Enviro-HIRLAM simulations.

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Previous MEGAPOLI reports Previous reports from the FP7 EC MEGAPOLI Project can be found at: http://www.megapoli.info/

Collins W.J. (2009): Global radiative forcing from megacity emissions of long-lived greenhouse gases. Deliverable 6.1, MEGAPOLI Scientific Report 09-01, 17p, MEGAPOLI-01-REP-2009-10, ISBN: 978-87-992924-1-7

http://megapoli.dmi.dk/publ/MEGAPOLI_sr09-01.pdf

Denier van der Gon, HAC, AJH Visschedijk, H. van der Brugh, R. Dröge, J. Kuenen (2009): A base year (2005) MEGAPOLI European gridded emission inventory (1st version). Deliverable 1.2, MEGAPOLI Scientific Report 09-02, 17p, MEGAPOLI-02-REP-2009-10, ISBN: 978-87-992924-2-4


Baklanov A., Mahura A. (Eds) (2009): First Year MEGAPOLI Dissemination Report. Deliverable 9.4.1, MEGAPOLI Scientific Report 09-03, 57p, MEGAPOLI-03-REP-2009-12, ISBN: 978-87-992924-3-1


Allen L., S Beevers, F Lindberg, Mario Iamarino, N Kitiwiroon, CSB Grimmond (2010): Global to City Scale Urban Anthropogenic Heat Flux: Model and Variability. Deliverable 1.4, MEGAPOLI Scientific Report 10-01, MEGAPOLI-04-REP-2010-03, 87p, ISBN: 978-87-992924-4-8 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-01.pdf

Pauli Sievinen, Antti Hellsten, Jaan Praks, Jarkko Koskinen, Jaakko Kukkonen (2010): Urban Morphological Database for Paris, France. Deliverable D2.1, MEGAPOLI Scientific Report 10-02, MEGAPOLI-05-REP-2010-03, 13p, ISBN: 978-87-992924-5-5 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-02.pdf

Moussiopoulos N., Douros J., Tsegas G. (Eds.) (2010): Evaluation of Zooming Approaches De-scribing Multiscale Physical Processes. Deliverable D4.1, MEGAPOLI Scientific Report 10-03, MEGAPOLI-06-REP-2010-01, 41p, ISBN: 978-87-992924-6-2 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-03.pdf

Mahura A., Baklanov A. (Eds.) (2010): Hierarchy of Urban Canopy Parameterisations for Different Scale Models. Deliverable D2.2, MEGAPOLI Scientific Report 10-04, MEGAPOLI-07-REP-2010-03, 49p, ISBN: 978-87-992924-7-9 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-04.pdf

Dhurata Koraj, Spyros N. Pandis (2010): Evaluation of Zooming Approaches Describing Multi-scale Chemical Transformations. Deliverable D4.2, MEGAPOLI Scientific Report 10-05, MEGAPOLI-08-REP-2010-01, 29p, ISBN: 978-87-992924-8-6 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-05.pdf

Igor Esau (2010): Urbanized Turbulence-Resolving Model and Evaluation for Paris. Deliverable D2.4.1, MEGAPOLI Scientific Report 10-06, MEGAPOLI-09-REP-2010-03, 20p, ISBN: 978-87-992924-9-3 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-06.pdf

Grimmond CSB., M. Blackett, M.J. Best, et al. (2010): Urban Energy Balance Models Comparison. Deliverable D2.3, MEGAPOLI Scientific Report 10-07, MEGAPOLI-10-REP-2010-03, 72p, ISBN: 978-87-993898-0-3 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-07.pdf



Gerd A. Folberth, Steve Rumbold, William J. Collins, Tim Butler (2010): Determination of Radia-tive Forcing from Megacity Emissions on the Global Scale. Deliverable D6.2, MEGAPOLI Scientific Report 10-08, MEGAPOLI-11-REP-2010-03, 19p, ISBN: 978-87-993898-1-0 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-08.pdf

Thomas Wagner, Steffen Beirle, Reza Shaiganfar (2010): Characterization of Megacity Impact on Regional and Global Scales Using Satellite Data. Deliverable D5.1, MEGAPOLI Scientific Report 10-09, MEGAPOLI-12-REP-2010-03, 25p, ISBN: 978-87-993898-2-7 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-09.pdf

Baklanov A., Mahura A. (Eds.) (2010): Interactions between Air Quality and Meteorology, Deliv-erable D4.3, MEGAPOLI Scientific Report 10-10, MEGAPOLI-13-REP-2010-03, 48p, ISBN: 978-87-993898-3-4 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-10.pdf



MEGAPOLI

Megacities: Emissions, urban, regional and Global Atmos-

pheric POLlution and climate effects, and Integrated tools for

assessment and mitigation

EC FP7 Collaborative Project

2008-2011

Theme 6: Environment (including climate change) Sub-Area: ENV-2007.1.1.2.1:

Megacities and regional hot-spots air quality and climate

MEGAPOLI Project web-site http://www.megapoli.info

MEGAPOLI Project Office Danish Meteorological Institute (DMI) Lyngbyvej 100, DK-2100 Copenhagen, Denmark

E-mail: [email protected] Phone: +45-3915-7441 Fax: +45-3915-7400

MEGAPOLI Project Partners

• DMI - Danish Meteorological Institute (Denmark) - Contact Persons: Prof. Alexander Baklanov (co-ordinator), Dr. Alexander Mahura (manager)

• FORTH - Foundation for Research and Technology, Hellas and University of Patras (Greece) - Prof. Spyros Pandis (vice-coordinator)

• MPIC - Max Planck Institute for Chemistry (Germany) - Dr. Mark Lawrence (vice-coordinator)

• ARIANET Consulting (Italy) – Dr. Sandro Finardi • AUTH - Aristotle University Thessaloniki (Greece)

- Prof. Nicolas Moussiopoulos • CNRS - Centre National de Recherche Scientifique

(incl. LISA, LaMP, LSCE, GAME, LGGE) (France) – Dr. Matthias Beekmann

• FMI - Finnish Meteorological Institute (Finland) – Prof. Jaakko Kukkonen

• JRC - Joint Research Center (Italy) – Dr. Stefano Galmarini

• ICTP - International Centre for Theoretical Physics (Italy) - Prof. Filippo Giorgi

• KCL - King's College London (UK) – Prof. Sue Grimmond

• NERSC - Nansen Environmental and Remote Sensing Center (Norway) – Dr. Igor Esau

• NILU - Norwegian Institute for Air Research (Norway) – Dr. Andreas Stohl

• PSI - Paul Scherrer Institute (Switzerland) – Prof. Urs Baltensperger

• TNO-Built Environment and Geosciences (The Netherlands) – Prof. Peter Builtjes

• MetO - UK MetOffice (UK) – Dr. Bill Collins • UHam - University of Hamburg (Germany) – Prof.

Heinke Schluenzen • UHel - University of Helsinki (Finland) – Prof.

Markku Kulmala • UH-CAIR - University of Hertfordshire, Centre for

Atmospheric and Instrumentation Research (UK) – Prof. Ranjeet Sokhi

• USTUTT - University of Stuttgart (Germany) – Prof. Rainer Friedrich

• WMO - World Meteorological Organization (Switzerland) – Dr. Liisa Jalkanen

• CUNI - Charles University Prague (Czech Repub-lic) – Dr. Tomas Halenka

• IfT - Institute of Tropospheric Research (Ger-many) – Prof. Alfred Wiedensohler

• UCam - Centre for Atmospheric Science, Univer-sity of Cambridge (UK) – Prof. John Pyle

Work Packages

WP1: Emissions (H. Denier van der Gon, P. Builtjes)

WP2: Megacity features (S. Grimmond, I. Esau)

WP3: Megacity plume case study (M. Beekmann, U. Baltensperger)

WP4: Megacity air quality (N. Moussiopoulos)

WP5: Regional and global atmospheric composition (J. Kukkonen, A. Stohl)

WP6: Regional and global climate impacts (W. Collins, F. Giorgii)

WP7: Integrated tools and implementation (R. Sokhi, H. Schlünzen)

WP8: Mitigation, policy options and impact assessment (R. Friedrich, D. van den Hout)

WP9: Dissemination and Coordination (A. Baklanov, M. Lawrence, S. Pandis)

megapoli scientific report 10-10 interactions between air quality...

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