Air quality over Europe : modeling gaseous and particulate pollutants and the e ff ect of precursor emissions

Introduction Conclusions References


Introduction
Air quality is a focus of attention, because of its important role in many areas, including human health, atmospheric reactions, acid deposition and the earth's radiation budget (e.g.Seinfeld and Pandis, 2006;Peng et al., 2005).Although air quality management strategies are applied during the last years in order to reduce atmospheric pollutant Figures concentrations, ozone and particulate matter pollution are still an issue.For this reason simulating and forecasting gaseous and particle concentrations as accurately as possible is fundamental in air quality planning for more effective adaptation and implementation guidelines.Air pollution is not a local issue since the pollutants released in one country can be transported in the atmosphere, affecting air quality in the nearby countries.As such several research groups have started simulations of the gaseous and particulate matter concentrations over the whole Europe.However, there are a limited number of such studies.In order to explain the European trends in ozone since 1990, Jonson et al. (2006) have used the EMEP regional photochemistry model for the years 1990 and 1995-2002.The increase in winter ozone partially, and the decrease in the magnitude of high ozone episodes, is attributed to the decrease in ozone precursor emissions while emission reductions have resulted in a marked decrease in summer ozone in major parts of Europe.A modeling set up for the whole Europe has been performed by Pay et al. (2010) suggesting satisfactory performance for ozone but poor performance for particles.Largely, this is caused by the inability of the models to correctly capture the concentrations of organic matter (e.g.Chen and Griffin, 2005).Applying CAMx modeling system over Europe Nopmongcol et al. (2012) found an underestimation trend for all pollutants examined (i.e.O 3 , NO x , NO 2 , CO, PM 10 ) except for SO 2 .Appel et al. (2012) using CMAQ modeling system found that the model overestimates winter daytime ozone mixing ratios in Europe by an average of 8.4 % while in the summer slightly underestimated by 1.6 %.PM 2.5 is underestimated throughout the entire year mentioned that it is not clear what is driving the bias, since speciated PM 2.5 data are not readily available for EU.Langmann et al. ( 2008) using the regional scale atmospheric climate chemistry/ aerosol model REMOTE, found that the deviation between modeled and measured organic carbon concentrations can be mainly explained by missing formation of secondary organic aerosols (SOA) and deficiencies in emission data.As such authors suggest that an updated emission inventories need to take into account the changing heating practices in Europe.The need for a more detailed treatment of Figures the formation of SOA has been also pointed out by Sartelet et al. (2007) while simulated aerosols and gas-phase species over Europe with the POLYPHEMUS system.Although they found out that hourly ozone, sulfate and ammonium simulation was good; SO 2 and nitrate concentrations tend to be overestimated.While modeling carbonaceous aerosol over Europe using EMEP modeling system Simpson et al. (2007) found the contribution of biogenic secondary organic aerosol far exceeds that of anthropogenic one.This modeling work confirms the difficulties of modeling SOA in Europe where a severe underprediction of the SOA components was found.The evaluation of the aerosol components in the CALIOPE air quality modeling system over Europe (Basart et al., 2012) also highlights underestimations in the fine fraction of carbonaceous matter (EC and OC) and secondary inorganic aerosols (i.e.nitrate, sulphate and ammonium).
The objective of this study is to simulate gaseous (i.e.O 3 , NO 2 , SO 2 ) and particle (i.e.PM 2.5 ) concentrations over Europe assessing their magnitude of disparity for each country and the effect of precursor emissions.The current analysis provides an opportunity to compare the modeling results with the results obtained by other regional air quality models commonly used in Europe and suggests possible uncertainties in precursor emissions for European countries.

Modeling setup
Meteorological fields are derived using the Penn State/NCAR Mesoscale Model (MM5) (Grell et al., 1994).MM5 is a limited-area, nonhydrostatic, terrain-following sigmacoordinate model designed to simulate or predict mesoscale atmospheric circulation.Since most meteorological models, such as MM5, are not built for air quality modeling purposes, to address issues related to data format translation, conversion of units of meteorological data on different grid and layer structures is needed.Meteorology Chemistry Interface Processor (MCIP) (Byun et al., 1999) is used to provide the meteorological data from the MM5 outputs needed for the emissions and air quality models.Gridded yearly averaged anthropogenic emissions for the year 2006 over Europe are provided by TNO in a 0.1 × 0.1 degrees resolution (http://www.tno.nl) in the frame of the AQMEII exercise (http://aqmeii.jrc.ec.europa.eu/).The available data include annual total emissions of CH 4 , CO, NH 3 , NMVOC, NO x , PM 10 , PM 2.5 and SO 2 for both area and point sources in ten (10) Standardized Nomenclature for Air Pollutants (SNAP) categories (i.e.power generation, residential-commercial and other combustion, industrial combustion, industrial processes, extraction distribution of fossil fuels, solvent use, road transport, other mobile sources, waste treatment and disposal, agriculture) (Table 1).According to this emission inventory UK, Spain, Germany, Ukraine, France and Italy have the highest NO x emissions while Ukraine, Spain and Poland have the highest SO 2 emissions (only a part of the Russian Federation and Turkey belongs to the domain examined).In general, road transport and energy sector-utilities-refineries are the major sources for NO x emissions while SO 2 emissions originate mainly from the energy sector-utilities-refineries.Emissions are processed by the Sparse Matrix Operator Kernel Emissions (SMOKE v2.6) modeling system (http://www.smoke-model.org/index.cfm) to convert their resolution to the resolution needed by the air quality model using monthly, weekly and hourly time profiles provided by TNO (TNO, 2011).However, TNO has reported that the temporal profiles are a generalization, not regularly updated and not country specific and could affect emissions over time for air quality modeling.The Biogenic Emission Inventory System, version 3 (BEIS3) is used for processing biogenic source emissions.Gridded land use data in 1 km resolution provided by USGS (http://edc2.usgs.gov/glcc/glcc.php),the default summer and winter emission factors and meteorological fields are used to create hourly model-ready biogenic emissions estimates.
The Community Multiscale Air Quality (CMAQ) v4.7 Modeling System with the Carbon Bond mechanism (CB05) is used for the regional air quality modeling (Byun et al., Figures Back Close Full  January, 2006) andsummer (i.e. July, 2006) months.CMAQ is a multipollutant, multiscale air quality model for simulating all atmospheric and land processes that affect transport, transformation, and deposition of atmospheric pollutants on both regional and urban scales.The modeling domain covers almost entire Europe with 177 × 217 grid cells of 35 km × 35 km spatial resolution and 14 vertical layers (Fig. 1).Although a finer domain could affect modeling results studies found that it does not always enhance model performance (e.g.Queen et al., 2008).The default boundary and initial conditions for gaseous and particulate species have been used.Boundary conditions have a very minor impact on pollutants concentrations since European land is far away from the domain borders expect the eastern border.However, due to the prevailing wind direction over Europe it does not affect pollutant concentrations.Moreover, a spin up time of 10 days was used to wash out errors in the initial conditions.In the version used, several new pathways for secondary organic aerosol (SOA) formation have been implemented (Edney et al., 2007;Carlton et al., 2008).The CB05 is a condensed mechanism of atmospheric oxidant chemistry that provides a basis for computer modeling studies of ozone, particulate matter (PM), visibility, acid deposition and air toxics issues (Yarwood et al., 2005).The core CB05 mechanism has 51 species and 156 reactions.The CB05 has been evaluated against smog chamber data (Jeffries et al., 2002;Carter, 2000) and the results are discussed in detail by Yarwood et al. (2005).
Since an extensive evaluation and discussion of meteorology used has been presented by Vautard et al., (2012), here, we focus on gaseous and particulate pollutant concentrations.Briefly, Vautard et al., (2012) found that the seasonal cycle of the 10 m wind speed is well reproduced although it is overestimated over Europe.The spatial distribution of surface wind speed is fairly well simulated.Wind speed is well simulated along the vertical profile but markedly overestimated at lower altitudes over Europe.It was also found that the PBL height at noon is simulated quite well.However, at 18 UTC and particularly in the summer months, the modelled PBL height is much lower than the observed.Biases of monthly means of 2 m temperature are generally small.The Figures

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Full diurnal cycle of 2 m temperature is also fairly well reproduced while the typical vertical temperature profile bias is between ± 1 K. On average the temperature is slightly underestimated while relative humidity above the surface is overestimated.

Model evaluation
Comparison between predicted and observed gas and particle concentrations is performed for January and July, 2006 using observation data from AirBase, the European air quality database (http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-2).AirBase is the air quality information system maintained by the European Environment Agency (EEA) through the European topic centre on Air and Climate Change.It contains air quality data delivered annually establishing a reciprocal exchange of information and data from networks and individual stations measuring ambient air pollution within the Member States.Model evaluation is conducted, here, for species with sufficient monitoring data all over Europe such as sulfur dioxide (data from 35 countries, in our domain there are 1928 stations for winter and 1883 for summer months), nitrogen dioxide (data from 35 countries, in our domain there are 2591 stations for winter and 2508 for summer months), ozone (data from 35 countries, in our domain there are 1954 stations for winter and 1977 for summer months) and particulate matter < 2.5 µm (data from 30 countries, in our domain there are 266 stations for winter and 267 for summer months) (Fig. 2).Unfortunately comparison with observed PM 2.5 components could not be performed since speciated PM 2.5 data are not readily available for EU; this has also recently been pointed out by other researchers (Appel et al., 2012).

Effect of precursor emissions
Emissions of air pollutants originated from a variety of small and large individual sources (e.g.power plants, industries, motor vehicles) varying temporally and spatially.Therefore, emission inventories are subject to significant uncertainties given that Introduction

Conclusions References
Tables Figures

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Full they are based on data sets of limited spatiotemporal coverage while countries do not always estimate emissions in a uniform and transparent manner.Assessing such uncertainties is an essential step towards the better computation of air pollutants concentrations.In an effort to do so, here, we assess the effect of precursor emissions on air quality applying scaling factors for January and July, 2006 on NO x , SO 2 and PM 2.5 emissions based on the ratio Observed average Predicted average for each country.This is not just a sensitivity analysis assessing the effect of each precursor separately on air pollution since we examine the effect of the simultaneous emission change for multiple precursors on air quality.The selection of the above precursor emissions is due to the significant number of monitoring data throughout the modeling domain.Although other precursor emissions (e.g.VOCs, NH 3 ) affect air pollutant concentrations the limited number of their monitoring data throughout the modeling domain does not allow us to include them in the presented analysis.

Air quality
High ozone concentrations are illustrated in southern Europe where meteorological conditions enhance ozone production (Fig. 3).The daily average maximum 8 h ozone (Max8hrO 3 ) concentration during July is simulated up to 75 ppbV while a big part of the domain faces concentrations higher than 50 ppbV.Higher NO 2 concentrations are simulated over western Europe (i.e.Belgium, the Netherlands, Germany and northern  Europe and in a few eastern countries due to the high NO 2 concentrations (Fig. 4).
During summer NO 3 is dominant only in regions with elevated NH 3 emissions (i.e. the Netherlands and northern Italy).For the rest of the domain SO 4 is dominant.Low OC concentrations are simulated in general.Representation of secondary organic aerosol (SOA) formation is uncertain, and low OC has been noted in the CMAQ approaches (Foley et al., 2010).NH 4 follows SO 4 and NO 3 spatial distribution plots for both seasons since atmospheric SO 2 is oxidized to sulfuric acid which reacts with ammonia to form ammonium sulfate while gas-phase NO x , oxidizes to nitric acid which reacts with ammonia to form ammonium nitrate.Spatial distribution plots presented here for gaseous pollutants and PM 2.5 are similar with those presented by another study (Pay et al., 2010).Using the WRF-ARW meteorological model, the HERMES-EMEP emission processing model, a mineral dust dynamic model (BSC-DREAM8b) and CMAQ chemical transport model they provide annual simulations for 2004 over Europe.
Model performance for ozone shows a mixed trend: Max8hrO 3 is overpredicted for low concentrations (about 50 ppbV) while it is underpredicted for the higher ones.This trend is in agreement with the CMAQ application performed by Appel et al. (2012) for Europe where daytime ozone mixing ratio is overestimated in winter and underestimated in summer months.The overprediction tendency for the lower concentrations gives a much higher mean concentration during winter ( Europe where higher underestimation in PM 2.5 concentrations is noted.

Effect of precursor emissions on air quality
The difference between observed and predicted concentrations for each country based on the ratio Observed average Predicted average is presented in Table 3.This ratio for Max8hrO 3 is less than 1.0 during January, 2006 in all countries due to the underprediction of low ozone concentration observed in winter.During July, 2006 the average observed concentrations are closer to the average predicted ones for all countries (this ratio is 1.0 ± 0.1 for the majority of the countries).Beside the general overprediction trend for SO 2 concentrations regionally over Europe, the ratio is greater than 1.0 in few countries for both seasons denoting that the average observed concentrations are higher than the average predicted ones for those countries.The ratio for NO 2 and PM 2.5 concentrations is almost in all countries greater than 1.0.There are numerous reasons why a bias may exist.This Figures

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Full could be related to inaccuracies in emission inventories; a discrepancy with the meteorological data and the source locations; topographic effects that are not accounted for in the model; or the model itself may have built in biases.To explore this, here, we examine modeling results using modified NO x , SO 2 and PM 2.5 emissions based on the ratio Observed average Predicted average for each country.
The modified emissions improve model's performance for all examined pollutants (Table 2).Better closure is noticed for SO 2 predictions.Mean simulated SO 2 concentrations using scaled emissions is similar to the mean observed concentrations while both simulated and observed values are similarly spread out around means for both seasons (i.e.similar standard deviation and mean absolutely values).Moreover all statistical parameters examined here (i.e.root mean square, index of agreement and mean absolute error) are improved suggesting better model performance using scaled emissions.Statistical analysis suggests that while NO 2 concentrations are better simulated using scaled emissions improvement is minor compared to SO 2 performance.This is attributed to the fact that scaling factors are not applied directly on NO 2 but on NO x .As such NO 2 to NO x ratio probably includes additional uncertainties that need to be investigated in the future.Standard and mean absolute deviations suggest that simulated and observed values are similarly spread out around means for both seasons using scaled emissions.An improved performance is also noted for PM 2.5 for both seasons.Although higher PM 2.5 concentrations are simulated using scaled emissions and values are spread out far away of the mean value compared to the simulations with original emissions (higher standard deviation and mean absolute error) model still underpredicts PM 2.5 concentrations.This is probably related to the uncertain representation of secondary organic aerosol formation (e.g.Chen et al., 2005;Kroll et al., 2006).Better performance is also noted for Max8hrO 3 concentrations mainly during winter; however, the model seems to overpredict Max8hrO 3 concentrations.
The modified emissions used here although improve model's performance for all examined pollutants could not actually address the real issues in the emissions as the simplified method used takes into account chemistry and transport (e.g.small countries Introduction

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Full is affected by transported pollutants from neighbor countries).However, this part of our work could suggest possible uncertainties in precursor emissions for European countries although a more robust method to improve the emissions (e.g.inverse modeling on the precursor emissions) is needed for real improvements in the temporal and/or spatial allocation of the emissions.Assessing (i) the effect of other precursor emissions, (ii) the effect of a finer resolution domain, (iii) the effect of other chemical mechanism or even a different air quality model will provide more information for the role of other sources of uncertainty.

Conclusions
Application of CMAQ modeling system over Europe for January and July, 2006 using the TNO gridded anthropogenic emissions database for the year 2006 shows an overprediction trend for low ozone concentrations (less than 50 ppbV) while it is underpredicted for the higher ones, although spatial distributions are reasonably estimated (e.g. higher ozone concentrations in southern Europe).Simulated concentrations for NO 2, SO 2 and PM 2.5 suggest a consistent bias: SO 2 is overpredicted while NO 2 and PM 2.5 are underpredicted.Speciated PM 2.5 components give low OC concentrations as a result of the uncertain representation of SOA formation.Assessing the difference between observed and predicted concentrations for each country and scaling the emissions based on that seems to statistically enhance model performance (i.e.root mean square, index of agreement and mean absolute error are improved).Although a number of reasons could affect model performance (e.g.meteorological data, topographic effects or the model itself), results from the current study could suggest possible uncertainties in precursor emissions for the European countries.Introduction

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Full    Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | of parameters, extraction of data for appropriate window domains, and reconstruction Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2006) for winter (i.e.
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | France), northern Italy and UK for both seasons.Belgium and the Netherlands have elevated NO 2 concentrations since their small area results in a high emission rate per acre, however, they are not ranked as one of the countries with high NO x emission rates.Road transport and industry are responsible for the elevated NO x emissions at northertn Italy while road and non-road transport energy sector and industry are responsible for the high NO x emission in the UK.NO 2 concentrations are higher during Introduction Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | is included: comparisons of organic carbon predictions with measurements, Environ.Sci.Technol., 42, 8798-8802, 2008.Carter, W. P. L.: Programs and Files Implementing the SAPRC-99 Mechanism and its Associated Emissions Processing Procedures for Models-3 and Other Regional Models, Report to the US EPA, available at: www.cert.ucr.edu/∼ carter/pubs/s99mod3.pdf (last access: Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Fig. 1 .
Fig. 1.Modeling domain and the regional European grouping used by the United Nations Statistics Department.

Fig. 2 .
Fig. 2. Location of O 3 , NO 2 , SO 2 and PM 2.5 monitoring stations (red color for monitoring stations active in both months, blue color for monitoring stations active only in January, green color for monitoring stations active only in July).
January compared to July for two reasons: energy sector and industry emit more NO x during winter and NO 2 photolysis is unfavorable during winter.Elevated SO 2 concentrations are simulated over eastern Europe with higher concentrations over Poland and the North Balkan Peninsula.Since power generation and industry are mainly responsible for SO 2 emissions, SO 2 concentrations are very locally depended showing higher values during winter.Elevated PM 2.5 levels are simulated over eastern and western Europe (i.e. up to 30 µg m −3 during winter).NO 3 is dominant during winter in western

Table 2 )
Appel et al. (2012) during summer where higher ozone values are simulated.Both observed and predicted ozone concentrations are similarly spread out around mean values during winter Figures (similar simulated standard and mean absolute deviations).During summer simulated concentrations are closer to the mean simulated concentration compared to observation data; this is related to the overprediction of the lower concentrations.At regional scale according to the grouping used by the United Nations Statistics Department for northern, western, eastern and southern Europe (http://unstats.un.org/unsd/methods/ m49/m49regin.htm#europe;Fig.1)modeloverestimatesozoneconcentrations in all regions during winter while during summer model overestimates ozone concentrations in northern and southern Europe and underestimates them in eastern and western Europe where higher concentrations have been recorded (Table2).A consistent bias for NO 2 , SO 2 and PM 2.5 estimations is noted: NO 2 and PM 2.5 are consistently underpredicted while SO 2 is consistently overpredicted for both seasons.The same biases have been also noticed by another previous study(Pay et al., 2010).The consistent underprediction trend for PM 2.5 has been found also byAppel et al. (2012)using CMAQ modeling system for Europe.At regional scale model underestimates NO 2 concentrations more in southern Europe and overestimates SO 2 concentrations more in eastern Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |