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Atmospheric Environment 98 (2014) 134e145

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Atmospheric Environment

journal homepage: www.elsevier .com/locate/atmosenv

Inter-comparison between HERMESv2.0 and TNO-MACC-II emissiondata using the CALIOPE air quality system (Spain)

Marc Guevara a, *, María Teresa Pay a, Francesc Martínez a, Albert Soret a,Hugo Denier van der Gon b, Jos�e M. Baldasano a, c

a Barcelona Supercomputing Center e Centro Nacional de Supercomputaci�on (BSC-CNS), Earth Sciences Department, Jordi Girona 29, Edificio Nexus II,08034 Barcelona, Spainb TNO Department Climate Air, and Sustainability, Utrecht, The Netherlandsc Environmental Modelling Laboratory, Technical University of Catalonia, Avda. Diagonal 647, Edificio H, Oficina 10.23, 08028 Barcelona, Spain

h i g h l i g h t s

� The performance of two emission datasets was evaluated by means of air quality.� The datasets are based on a bottom-up and downscaling approach, respectively.� NO2, SO2, O3 and PM10 were modelled over Spain using both emission datasets.� Model performance improves in urban areas when using the bottom-up dataset.� Results with the downscaled emissions show consistence at background stations.

a r t i c l e i n f o

Article history:Received 8 April 2014Received in revised form17 July 2014Accepted 27 August 2014Available online 27 August 2014

Keywords:Air qualityEmissionsBottom-up emission modelDownscaled methodologyUrban scale

* Corresponding author.E-mail address: marc.guevara@bsc.es (M. Guevara

http://dx.doi.org/10.1016/j.atmosenv.2014.08.0671352-2310/© 2014 Elsevier Ltd. All rights reserved.

a b s t r a c t

This work examines and compares the performance of two emission datasets on modelling air qualityconcentrations for Spain: (i) the High-Elective Resolution Modelling Emissions System (HERMESv2.0)and (ii) the TNO-MACC-II emission inventory. For this purpose, the air quality system CALIOPE-AQFS(WRF-ARW/CMAQ/BSC-DREAM8b) was run over Spain for February and June 2009 using the twoemission datasets (4 km � 4 km and 1 h). Nitrogen dioxide (NO2), sulphur dioxide (SO2), Ozone (O3) andparticular matter (PM10) modelled concentrations were compared with measurements at different typeof air quality stations (i.e. rural background, urban, suburban industrial). A preliminary emission com-parison showed significant discrepancies between the two datasets, highlighting an overestimation ofindustrial emissions in urban areas when using TNO-MACC-II. However, simulations showed similarperformances of both emission datasets in terms of air quality. Modelled NO2 concentrations weresimilar between both datasets at the background stations, although TNO-MACC-II presented lower un-derestimations due to differences in industrial, other mobile sources and residential emissions. AtMadrid urban stations NO2 was significantly underestimated in both cases despite the fact that HER-MESv2.0 estimates traffic emissions using a more local information and detailed methodology. This NO2

underestimation problem was not found in Barcelona due to the influence of international shippingemissions located in the coastline. An inadequate characterization of some TNO-MACC-II's point sourcesled to high SO2 biases at industrial stations, especially in northwest Spain where large facilities aregrouped. In general, surface O3 was overestimated regardless of the emission dataset used, depicting theproblematic of CMAQ on overestimating low ozone at night. On the other hand, modelled PM10 con-centrations were less underestimated in urban areas when applying HERMESv2.0 due to the inclusion ofroad dust resuspension, whereas the underestimation at suburban industrial stations indicated de-ficiencies in fugitive emission sources characterization (agricultural operations, windblown dustemissions).

© 2014 Elsevier Ltd. All rights reserved.

).

M. Guevara et al. / Atmospheric Environment 98 (2014) 134e145 135

1. Introduction

According to the European regulations (EC, 2008), local toregional air quality modelling systems are useful tools to assess thedynamics of air pollutants, to forecast the air quality, to developemission abatement plans and alert the population when health-related issues occur (EEA, 2011). Emission datasets play a key rolein modelling air quality as they provide crucial model input, next toe.g. meteorological fields and boundary conditions, and can be oneof the main sources of uncertainty in the modelling results (e.g.Menut and Bessagnet, 2010).

For global and regional applications, gridded emission in-ventories (e.g. EMEP; Mareckova et al., 2012) based on top-downapproaches (i.e. based on aggregated activity data and emissionfactors) and downscaling methodologies applied to national re-ported emission inventories are generally used as model input forthe assessment of air quality. However, in regard to high-resolutionair quality modelling, the use of local information combined withbottom-up approaches (i.e. based on specific activity data andemission factors) is preferable to more accurately characterise thelocal emission sources and obtain more realistic results (e.g.Kannari et al., 2007). Unfortunately, the development of (local) highresolution bottom-up emissions requires a huge investment of timeand resources, as well as having access to local and detailed infor-mation, which may not always be available to the model developer.Moreover, it cannot be automatically assumed that a bottom-upemission dataset is better than a top-down or a downscaled one.More complex models have the potential to provide more accuratepredictions, but they also requiremore detailed input data that maycontain simple assumptions and therefore offset the potential ac-curacy gains.

Comparisons between air quality model simulations usingmultiple emission datasets and observational data may help tovalidate emission estimates, confirm distribution patterns andidentify gaps in emission datasets (Lamarque et al., 2010; Deniervan der Gon et al., 2011). Timmermans et al. (2013) compared thesimulated average concentrations of PM and NO2 over the Parisregion using, on the one hand, the TNO-MACC-I downscaledemission inventory and, on the other hand, the EU FP7 MEGAPOLIbottom-up emission inventory, which included refined localemission data over the megacity of Paris. Results showed thatmodelled concentrations were more consistent with observationaldata when using the local bottom-up inventory. In the same di-rection, Amnuaylojaroen et al. (2014) applied different anthropo-genic emission inventories (RETRO, INTEX-B, MACCity, SEAC4RS) inthe WRF-Chem to examine the differences in predicted CO and O3surface mixing ratios for Southeast Asia. The simulations showedthat none of the emission datasets were better than the others andany of them could be used for air quality simulations.

The main goal of the present paper is to assess and contrast theperformance of two emission datasets on modelling air qualityconcentrations for Spain: (i) the High-Elective Resolution Model-ling Emissions System (HERMESv2.0) (Guevara et al., 2013), a highresolution emission model developed in the Barcelona Super-computing Center e Centro Nacional de Supercomputaci�on (BSC e

CNS) that estimates atmospheric emissions for Spain using mainlybottom-up approaches and with a temporal and spatial resolutionof 1 h and up to 1 km2 and (ii) the TNO-MACC-II emission inventory(Pouliot et al., 2012; Kuenen et al., 2014), a consistent high-resolution European emission inventory setup applying a down-scaling methodology to the national official reported emissions toEMEP and that is widely used for the scientific community, as forexample in the EC JRC/US EPA AQMEII model inter-comparison(Solazzo et al., 2012). For this purpose, the air quality systemCALIOPE-AQFS (http://www.bsc.es/caliope) was run over Spain for

February and June 2009 using the two emission datasets. Theconcentration results obtained running the four simulations (onefor each emission input data and period of time) were evaluatedagainst observational data. The analysed pollutants are nitrogendioxide (NO2); sulphur dioxide (SO2); ozone (O3) and particularmatter with a diameter less than 10 mm (PM10). The analysisfocusses on three types of stations so multiple environments arecovered in the study: (i) rural (background) EMEP stations, (ii) ur-ban (background and traffic) stations located in Barcelona andMadrid greater areas and (iii) suburban (industrial) stations locatednear large point sources.

Section 2 describes the model setup and the observationaldataset used. Section 3 performs an emission comparison and an-alyses the modelled concentrations against available observationaldata. Finally, Section 4 summarizes and discusses the results.

2. Methodology

The CALIOPE-AQFS system (WRF-ARW/CMAQ/BSC-DREAM8b)is a state-of-the-art modelling framework implemented in theMareNostrum3 supercomputer and that integrates the WeatherResearch and Forecasting e Advanced Research Weather meteo-rological model (WRF-ARW) (Skamarock and Klemp, 2008), theCommunity Multiscale Air Quality Modeling System (CMAQ) (Byunand Schere, 2006) and the mineral Dust REgional AtmosphericModel (BSC-DREAM8b) (Basart et al., 2012). The systemworks witha temporal resolution of 1 h and with a horizontal resolution thatvaries according to the working domain, from 12 km � 12 km forEurope and nested domains of 4 km � 4 km for the IberianPeninsula and 1 km � 1 km for the Madrid and Barcelona greaterurban areas. The air quality results are continuously evaluated witha near real time system based on measurements from the Spanishair quality network, and the performance of the system has beenpreviously tested in different evaluation, air quality managementand epidemiological studies (e.g. Baldasano et al., 2011, 2014).

2.1. Modelling setup

The CALIOPE-AQFS modelling framework using the HER-MESv2.0 and TNO-MACC-II gridded emission datasets as input wassetup for performing the simulations for February and June 2009over the Iberian Peninsula domain (IP-4km). To provide adequateboundary and initial conditions to the IP-4km domain, the CALIOPEsystem was initially run on a regional scale to model the Europeandomain (EU-12km). Then, a one way-nesting was performed fromthe EU-12km domain to IP-4km in order to retrieve the meteoro-logical and chemical boundary conditions. The study domain,located in the region of Spain (SW of Europe), is 1596 km*1596 kmand centred at �3.164 Lon and 39.971 Lat, with a 4 km � 4 kmhorizontal resolution and uses a Lambert Conformal projection(Fig. 1). It should be noted that the boundary conditions for the IP-4km domain were obtained based on the HERMESv2.0 emissiondataset for both runs, so that the observed impact is the impact ofdifferent emissions within the high resolution domain only. Theconfigurations and parameterizations of the meteorological (WRF-ARWv3.2.1) and chemical transport (CMAQ5.0.1) models used aresummarized in the Supplementary material (Table S1).

2.1.1. Emission modelling (HERMESv2.0 and TNO-MACC-II)HERMESv2.0 is a high resolution emission model that estimates

atmospheric emissions for Europe and Spain with a temporal andspatial resolution of 1 h and up to 1 km � 1 km, according to theSelected Nomenclature for Air Pollution (SNAP) and taking the year2009 as the reference period. The model estimates emissions fornitrogen oxides (NOx), non-methane volatile organic compounds

Fig. 1. Framework of the modelling setup and model domains used to simulate air quality dynamics.

M. Guevara et al. / Atmospheric Environment 98 (2014) 134e145136

(NMVOCs), sulphur dioxide (SO2), carbonmonoxide (CO), ammonia(NH3), total suspended particles (TSP) and PM10 and PM2.5 fractions.The final output obtained consists of hourly, gridded and speciatedemissions according to the CB05 chemical mechanism. For Europe,HERMESv2.0 performs a SNAP sector-dependent spatial, temporaland speciation treatment of the original annual EMEP griddedemissions (Guevara et al., 2012; Ferreira et al., 2013). For Spain, themodel uses a bottom-up approach for themost significant pollutantsources. Emissions from point source sectors (e.g. power plants,industries) are estimated according to a facility database (1796stacks included) that compiles specific information per stackincluding, among others, geographical location and activity/emis-sion factors. Emissions from road transport are estimatedcombining the Tier 3 method described in the EMEP/EEA airpollutant emission inventory guidebook (fully incorporated inversion 5.1 of the COPERT 4 software) with a digitized trafficnetwork (over 111,000 km) that contains specific information byroad stretch for daily average traffic, mean speed circulation, tem-poral profiles and vehicular park profiles. Concerning other mobilesources, maritime and air traffic emissions are estimated by portsand airports considering specific information by each type of vesseland aircraft categories (e.g. number of operations, emission fac-tors), while tractors and harvesters are estimated considering thetotal fleet number compiled at the NUTS 3 level and agriculturalland uses. For the rest of pollutant sources a combination of top-down approaches (i.e. residential/commercial combustion; energy

consumption statistics combined with a population map) anddownscaling methodologies (i.e. use of solvents, extraction anddistribution of fossil fuels, agriculture; specific spatial proxies andtemporal profiles assigned to the Spanish National Emission In-ventory by categories at third level of SNAP) is adopted. Deeperspecifications on the methodologies and data used in the model, aswell as the emission results obtained are presented and analysed inGuevara et al. (2013).

The TNO-MACC-II emission inventory consists of a griddedannual anthropogenic emission database for the years 2003e2009across the European region and with a horizontal resolution of 1/8� � 1/16� (~7 km� 7 km) (Pouliot et al., 2012; Kuenen et al., 2014).It is an emission dataset developed in the framework of the Euro-pean Integrated Project MACC II (http://www.gmes-atmosphere.eu). As in HERMESv2.0, the dataset reports emissions for primary airpollutants and according to the SNAP sector nomenclature. How-ever, the TNO-MACC-II is not a bottom-up emission inventory but issetup using official reported emissions at the source sector level tothe extent possible without reducing the overall quality of the in-ventory. Emissions are downloaded from the European Environ-ment Agency (www.eea.europa.eu/data-and-maps/data). However,the reported emissions by an individual country may contain gapsand errors, therefore various consistency checks are made asdescribed in detail by Denier van der Gon et al. (2010). If necessary,gaps and unreliable data are replaced by emissions estimated fromthe IIASA-GAINS (International Institute for Applied Systems

M. Guevara et al. / Atmospheric Environment 98 (2014) 134e145 137

Analysis e Greenhouse Gas and Air Pollution Interactions andSynergies) model (http://gains.iiasa.ac.at/) or TNO's own defaultemission database. For Spain, TNO-MACC-II uses the emissionsreported by the Spanish National Emission Inventory (INESP) toEMEP. Unlike HERMESv2.0, INESP estimates emissions using ac-tivity data compiled with a lower level of detail (e.g. national sta-tistics of fuel consumed by industries) and lower tieredmethodologies (Tier 2 methodologies) (MAGRAMA, 2013). Toperform a distribution of the national reported emission in-ventories across the European TNO-MACC-II working grid, emis-sions are first split by TNO in ~200 different source categories, andthen linked to specific distribution patterns (e.g. point source data,transport routes).

Since the working domain and resolution of CALIOPE-AQFS andTNO-MACC-II are not the same, and the CMAQ model requiresemission input data in the form of hourly and chemical species-based emissions, the TNO-MACC-II emission data was processed.In this sense, a SNAP sector-dependent spatial, temporal andspeciation treatment was performed, following the criteria appliedin Guevara et al. (2012) and Ferreira et al. (2013). All the datasetsused for performing this pre-processing are summarized in theSupplementary material (Figs. S1eS3 and Tables S2eS7).

The Model of Emissions of Gases and Aerosols from Nature(MEGAN) (Guenther et al., 2006) was used for the estimation ofbiogenic emissions in all the simulations.

2.2. Air quality network

In order to evaluate the performance of the two emissiondatasets, HERMESv2.0 and TNO-MACC-II, by means of air qualitywith the CALIOPE-AQFS system, comparison with measurementsfrom AirBasev7 network of air quality monitoring stations wasperformed (EEA, 2013). For this work, all the stations with a tem-poral coverage below 90% of the entire periods (i.e. February andJune) were discarded. A total of 59 air quality monitoring stationswere used: (i) 13 EMEP rural background stations, (ii) 18 Madridurban traffic and background stations, (iii) 9 Barcelona urban trafficand background stations and (iv) 19 suburban industrial stations.The location and characteristics of the selected stations are sum-marized in the Supplementary material (Fig. S14 and Table S10).

Table 1Annual emissions [kt$year�1] per SNAP sector for Spain 2009 estimated by HERMESv2.0

SNAP Hv2.0 TNO Diff Hv2.0

NOx [kt·year¡1] NMV01 e Energy industries 135.0 191.8 �56.8 (�42.0%) 5.502 e Residential combustion 33.7 48.9 �15.2 (�45.0%) 40.00304 e Industry 156.0 186.2 �30.2 (�19.4%) 21.805 e Extr. Distrib. fossil fuels 0.0 3.1 �3.1 e 25.006 e Solvent use 0.0 0.0 0.0 e 235.507 e Road transport 344.8 395.7 �51.0 (�14.8%) 125.208 e Non-road transport 160.2 194.8 �34.6 (�21.6%) 23.909 e Waste management 4.4 0.2 4.2 (94.6%) 0.110 e Agriculture 18.9 3.5 15.4 (81.5%) 268.8Total 853.0 1024.2 ¡171.2 (¡20.1%) 745.8

CO [kt$year�1] NH3 [01 e Energy industries 17.9 19.7 �1.8 (�10.1%) 0.102 e Residential combustion 484.0 508.0 �24.0 (�5.0%) 0.30304 e Industry 372.1 490.8 �118.8 (�31.9%) 2.805 e Extr. Distrib. fossil fuels 0.0 0.8 �0.8 e 0.006 e Solvent use 0.0 0.0 0.0 e 0.007 e Road transport 623.0 311.4 311.6 (50.0%) 5.208 e Non-road transport 81.4 43.7 37.7 (46.3%) 0.009 e Waste management 0.9 2.2 �1.3 (�149.1%) 0.010 e Agriculture 339.7 0.2 339.5 (99.9%) 331.1Total 1918.9 1376.8 542.1 (28.3%) 339.5

3. Results and discussion

3.1. Emissions

The total annual emissions for Spain 2009 estimated by HER-MESv2.0 and TNO-MACC-II are summarized in Table 1. Significantdiscrepancies between the two emission datasets (HERMESv2.0 e

TNO-MACC-II) are detected for CO (542.1 kt$year�1, þ28%), NOx

(�171.2 kt$year�1, �20%) and SOx, (160.4 kt$year�1, �61%). For CO,differences are mainly due to the road transport (SNAP07) andagricultural sectors (SNAP10) for which HERMESv2.0 reports higheremissions. For NOx, total differences come mostly from the energycombustion (SNAP01) and road transport sectors, while for SOx

differences are mainly due to point source emissions from energycombustion and industrial sector (SNAP0304). In the case of roadtransport, a key sector considering its contribution to total emis-sions, differences between the two datasets are mainly due to thedifferent estimation methodology used in each case. As mentionedbefore, HERMESv2.0 estimates traffic emissions according to theTier 3 method described in the EMEP/EEA inventory guidebook,which propose speed-dependency expressions per technology andpollutant to estimate the emission factors (EF). On the other hand,emissions used by TNO-MACC-II are estimated based on the Tier 2method, in which case EF are constant and derived from Tier 3methodology using typical values for driving speeds. This fact en-tails significant differences between the EF used, especially whencomparing highway driving conditions (e.g. Tier 2 proposes aconstant EF for CO of 0.624 g km�1 for Gasoline passenger cars EuroIV while Tier 3 propose, considering a typical highway drivingspeed of 120 km h�1, .a value of 1.095 g km�1). In the case ofagriculture emissions (SNAP10) it is important to highlight thatTNO-MACC-II excludes all the pollutants except for NH3 and PM10due to inconsistencies found in the national reported emissions(Kuenen et al., 2014). A further analysis of the differences betweenthe emissions in terms of spatial and temporal allocation is avail-able in the Supplementary material (Figs. S4eS9).

Considering that modelled concentrations in urban areas are animportant topic in this work, a comparison of the emissions re-ported in theMadrid greater area (themost populated Spanish city)was also performed. The analysis focussed on a square area centredin the city, considering SOx, NOx and PM10 emissions from industrial

(Hv2.0) and TNO-MACC-II (TNO) and the differences between them (Diff).

TNO Diff Hv2.0 TNO Diff

OC [kt·year¡1] SOx [kt·year¡1]15.0 �9.5 (�172.2%) 138.5 175.4 �36.9 (�26.7%)41.5 �1.5 (�3.7%) 13.7 14.4 �0.6 (�4.6%)72.5 �50.7 (�232.2%) 87.1 209.6 �122.5 (�140.6%)27.5 �2.5 (�10.1%) 0.0 4.9 �4.9 e

399.0 �163.5 (�69.4%) 0.0 0.0 0.0 e

52.6 72.5 (57.9%) 4.0 0.4 3.6 (89.1%)12.9 11.0 (46.1%) 12.4 18.0 �5.5 (�44.7%)24.5 �24.3 e 4.6 0.2 4.4 (95.5%)23.0 245.8 (91.4%) 3.6 1.5 2.1 (57.9%)

668.5 77.3 (10.4%) 264.0 424.4 ¡160.4 (¡60.8%)kt·year�1] PM10 [kt·year�1]

0.0 0.1 (85.0%) 6.2 8.1 �2.0 (�31.7%)0.0 0.3 e 32.3 25.2 7.1 (22.1%)9.3 �6.6 (�239.4%) 14.9 20.3 �5.3 (�35.7%)0.0 0.0 e 0.0 1.3 �1.3 (0.0%)0.0 0.0 e 0.0 0.0 0.0 (0.0%)4.4 0.8 (16.2%) 49.6 24.2 25.4 (51.3%)0.0 0.0 (�26.0%) 14.3 7.5 6.8 (47.6%)9.7 �9.7 e 0.0 0.0 0.0 (57.3%)

332.3 �1.2 (�0.4%) 20.1 38.3 �18.2 (�90.8%)355.8 ¡16.3 (¡4.8%) 137.4 124.8 12.6 (9.2%)

M. Guevara et al. / Atmospheric Environment 98 (2014) 134e145138

(SNAP0304) and road transport (SNAP07) sources reported byHERMESv2.0, TNO-MACC-II and the official city inventory esti-mated by the local government of Madrid (AM, 2013) (Fig. 2). Asresults show, high discrepancies were found for the industrialsector (Fig. 2b). Emissions reported by TNO-MACC-II are found to beup to a factor 100 higher than those reported by the other emissiondatasets (i.e. PM10). Sincemost of the industrial facilities consideredin HERMESv2.0 are located outside the analysed domain (black dotsin Fig. 2a), total SNAP0304 emissions are almost zero. Consideringthat the area of study of AM (2013) is bigger than the squaredomain analysed (Fig. 2a) HERMESv2.0 industrial emissions are notnecessarily in disagreement with those reported by AM (2013). Forthe road transport sector, HERMESv2.0 presents higher values thanthose reported by TNO-MACC-II in all cases (Fig. 2c). Differences inPM10 are mostly due to the contribution of road dust resuspensionin HERMESv2.0, which is not included in TNO-MACC-II and ac-counts for ~45% of total PM10 emissions. On the other hand, thediscrepancies observed for SOx indicate that HERMESv2.0 emissionfactors should possibly be revised.

This emission analysis points out that industrial area emissionsreported by TNO-MACC-II are significantly overestimated in theMadrid urban area. Although TNO-MACC-II uses a point sourcedatabase (partly based on E-PRTR), still not all industrial emissionscan be allocated to known point sources and the “left over” emis-sions are by default distributed by population density (i.e. urbanand rural), implying that a too high fraction ends up in the mostdensely populated cities. In the case of Spain, “left over” industrialemissions account for: ~61% of NOx (113.2 kt$year�1); ~46% of SOx(105.7 kt$year�1); ~74% of NMVOCs (53.4 kt$year�1); ~23% of CO(114.3 kt$year�1) and ~47% of PM10 (9.5 kt$year�1). Consideringthese significant shares, the same emission analysis was carried outin other important Spanish urban areas (i.e. Barcelona, Sevilla andM�alaga). Results show that in all cases the previous pattern isrepeated, and TNO-MACC-II reports a higher amount of industrialemissions than HERMESv2.0 in urban centres (Figs. S11eS13 andTable S9).

Fig. 2. (a) Domains used for the emission analysis in the Madrid greater area and HERMEStransport emissions [t$year�1] reported by HERMESv2.0, TNO-MACC-II and the Madrid city

3.2. Air quality concentrations

Next subsections present the evaluation of the air qualityresults obtained with each emission dataset (i.e. HERMESv2.0and TNO-MACC-II) over one winter and summer month (i.e.February and June). Classical statistics such as mean bias (MB),correlation coefficient (r) and root mean squared error (RMSE)are calculated on an hourly basis for NO2, SO2, O3, and PM10.Moreover, modelled and observed time series are presented todescribe the variability for each pollutant at different types ofstations (variability in terms of spatial distribution is presentedin the Supplementary material. Fig. S15). Note that due to a lackof measurements, not all the pollutants could be evaluated foreach type of station. It is also important to highlight that in allcases the modelled outputs are presented without any correctionfactor or post-processing.

3.2.1. Nitrogen dioxideThe NO2 concentrations were evaluated at the EMEP rural

background stations (13 stations), as well as at Barcelona (9 sta-tions) and Madrid (18 stations) urban stations.

Modelled concentrations using both emission datasets showsimilar performance at EMEP stations, although results driven byTNO-MACC-II present reduced mean bias values, especially duringFebruary (HERMESv2.0: �1.7; TNO-MACC-II: �0.5) when observedpeaks are better characterized (Fig. 3a and b and Table 2). EMEPstations are located such that significant local influences (e.g.emissions sources, topographic features) are minimized (Tørsethet al., 2012). Hence, concentrations measured at these stations area consequence of different local and regional activities (e.g. agri-cultural, natural and other local sources) as well as long-rangetransport effects. Considering this fact, the differences in the NO2concentrations driven by each emission dataset at these stations inFebruary can be related to a combination of multiple factors,including: the “left over” TNO-MACC-II industrial emissionsdistributed according to total (i.e. rural and urban) population

v2.0 industrial sources (black dots) and SOx, NOx and PM10 (b) industrial and (c) roadcouncil (AM, 2013).

Fig. 3. Measured (dots) and modelled (solid lines) time series of hourly mean concentrations for NO2 at EMEP stations during (a) February and (b) June. Measured (dots) andmodelled (solid lines) time series of daily mean maximum concentrations and scatter plots of the NO2 [mg m�3] pair measurement model in hourly basis at (c)/(d) Madrid and (e)/(f)Barcelona urban stations.

M. Guevara et al. / Atmospheric Environment 98 (2014) 134e145 139

Table 2Monthly statistics obtained with HERMESv2.0 (Hv2) and TNO-MACC-II (TNO) on an hourly basis for February (Feb) and June (Jun) 2009. Statistics are calculated according tofour categories: EMEP rural stations (EMEP-RU), Madrid urban stations (MAD-UR), Barcelona urban stations (BCN-UR) and suburban (industrial) stations (SUB-IND). Thecalculated statistics are measured mean (MeMn, mg m�3), modelled mean (MoMn, mg m�3), correlation coefficient (r), mean bias (MB, mg m�3) and root mean square error(RMSE, mg m�3).

Period Pollutant Category MeMn [mg m�3] MoMn [mg m�3] MB [mg m�3] r RMSE [mg m�3]

Hv2 TNO Hv2 TNO Hv2 TNO Hv2 TNO

Feb NO2 Hourly EMEP-RU 4.2 2.5 3.8 �1.7 �0.5 0.43 0.47 5.0 5.0MAD-UR 64.0 45.5 41.3 �18.5 �22.8 0.50 0.49 38.2 37.9BCN-UR 52.2 47.7 44.1 �4.5 �8.1 0.53 0.50 28.7 27.1

SO2 Hourly EMEP-RU 0.5 1.4 2.2 0.9 1.7 0.27 0.36 1.9 3.3SUB-IND 8.7 5.1 6.7 �3.6 �2.1 0.19 0.14 13.7 14.7

PM10 Hourly MAD-UR 23.2 13.8 10.3 �9.4 �12.8 0.51 0.55 18.5 19.9SUB-IND 28.1 8.1 7.7 �19.9 �20.3 0.33 0.36 29.8 29.9

Jun NO2 Hourly EMEP-RU 2.7 2.0 2.3 �0.7 �0.4 0.47 0.49 3.4 4.1MAD-UR 46.0 25.4 23.7 �20.7 �22.3 0.43 0.31 36.1 38.2BCN-UR 41.5 44.2 43.2 2.7 1.7 0.33 0.29 32.0 30.1

SO2 Hourly EMEP-RU 0.4 1.3 1.5 0.8 1.1 0.14 0.23 2.0 2.0SUB-IND 8.7 7.0 14.6 �1.7 5.8 0.19 0.24 16.3 37.1

O3 Hourly EMEP-RU 76.9 88.5 87.2 11.6 10.3 0.58 0.56 23.6 23.3MAD-UR 60.6 76.8 68.3 16.1 7.6 0.60 0.57 30.3 27.7BCN-UR 52.5 65.3 57.3 12.8 4.8 0.52 0.48 31.3 29.4SUB-IND 59.4 79.3 77.3 19.9 17.9 0.53 0.53 30.7 29.9

PM10 Hourly MAD-UR 25.5 21.6 18.7 �3.9 �6.7 0.37 0.34 22.3 23.1SUB-IND 30.0 15.1 15.2 �14.9 �14.8 0.48 0.49 23.7 23.7

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(differences between HERMESv2.0 and TNO-MACC for SNAP0304account for �3907.9 t$month�1); the differences between othermobile sources emissions, which include agricultural machinery(�8606.8 t$month�1) and the differences between residentialcombustion emissions (�1855.4 t$month�1). In June, differencesbetween modelled concentrations are minimized (HERMESv2.0:2.0 mg m�3; TNO-MACC-II: 2.3 mg m�3) basically because of tworeasons: differences between industrial and residential combustionemissions are lower (�883.2 and �43.7 t$month�1, respectively);HERMESv2.0 reports higher other mobile source emissions (dif-ferences between the two datasets accountfor þ7214.6 t$month�1). The significant change in the differencesbetween other mobile source emissions from one month to theother (�8606.8 and þ7214.6 t$month�1) is due to the fact thatHERMESv2.0 allocates 17% of total agricultural machinery emis-sions during June and only 3% during February, while TNO-MACC-IIdistributes total SNAP08 emissions using a constant monthly pro-file (Fig. S8).

In the case of Madrid urban area, both HERMESv2.0 and TNO-MACC-II systematically underestimate observations. CALIOPE-AQFS system is not capable of reproducing the urban concentra-tions regardless of the emission dataset used, involving high meanbiases values in both cases especially during June (between �18.5and �22.3 mg m�3) (Table 2). The fact that TNO-MACC-II reportshigher NOx emissions compared to HERMESv2.0 (HERMESv2.0:9535.3 t$year�1; TNO-MACC-II: 12602.6 t$year�1; mainly due to theoverestimation of industrial emissions as seen in Section 3.1) doesnot seem to have an impact on NO2 concentrations. If only groundemissions are considered (e.g. only 21% of TNO-MACC-II's industrialemissions are released to the lowest layer) then HERMESv2.0 re-ports a higher amount of NOx (HERMESv2.0: 9474.3 t$year�1; TNO-MACC-II: 9284.2 t$year�1). On the other hand, high concentrationpeaks are better reproduced when using HERMESv2.0 (Fig. 3c)leading to higher mean correlation coefficients (HERMESv2.0: 0.43;TNO-MACC-II: 0.31, in June). This fact is to a large extent due to thehigher road transport emissions reported (see Section 3.1) and tothe local and specific information used in HERMESv2.0 for theestimation of this pollutant source (see Section 2.1.1). In the case ofMadrid, road transport information is obtained from over 3198observation stations and statistics based on real circulation data(AM, 2009). However, and despite the specificity of the information

used, hourly NO2 concentrations driven by HERMESv2.0 are notmuch better than those driven by TNO-MACC-II (Fig. 3d). As shownin Kouridis et al. (2010) the uncertainty in the calculation of totalemissions can depend mostly on the inherent uncertainty ofCOPERT IV (emission factors) rather than on the uncertainty of thedata provided by the inventory compiler.

Regarding Barcelona urban stations, modelled and observedtime series of NO2 concentrations show a consistent picture forboth emission datasets, with low mean biases and high correlationfactors (Table 2). As in the case of Madrid, major sources of NOx areroad transport (HERMESv2.0: 5241.6 t$year�1; 63%; TNO-MACC-II:6172.2 t$year�1; 50%), followed by residential combustion in thecase of HERMESv2.0 (1242.6 t$year�1; 15%) and industrial com-bustion in the case of TNO-MACC-II (3508.0 t$year�1; 29%). UnlikeMadrid, if only ground emissions are considered (i.e. lowest layer)TNO-MACC-II still presents a slightly higher amount of total NOx

(HERMESv2.0: 7414.8 t$year�1; TNO-MACC-II: 8719.3 t$year�1).The fact that the underestimations of NO2 concentration in Barce-lona urban area are lower compared to Madrid is mainly due to theinfluence of the international shipping emissions from Barcelona'sharbour. HERMESv2.0 and TNO-MACC-II report a total of~3100 t$year�1 and ~1800 t$year�1 maritime emissions near thecoastline of Barcelona. As deeply discussed in the Supplementarymaterial (Fig. S10 and Table S8) both datasets combine indifferent ways the international EMEP shipping emissions with theWang et al. (2008) shipping routes to allocate these emissions.Although the Wang et al. (2008) proxy is a state-of-the-art widelyused database, uncertainties in the reported routes near coastlinesare observed (Eyring et al., 2010). In this sense, and considering themodelled NO2 concentrations, shipping emissions at both datasetscould be overestimated in the coastline of Barcelona. This is in linewith the overestimated NO2 maximum daily concentrations ob-tained with the model, especially when using HERMESv2.0 (Fig. 3eand f).

It is important to note that both at the Madrid or Barcelonastations, the temporal variability of NO2 concentrations is betterreproduced in the winter month (February) than in the summermonth (June). In the case of Barcelona, mean correlation co-efficients decrease from 0.53 to 0.33 (HERMESv2.0) and 0.50 to 0.29(TNO-MACC-II) (Table 2). The comparison of model calculated andobserved temperature at 2m andwind speed at 10m at twoMETAR

Fig. 4. Scatter plots of the SO2 [mg m�3] pair measurement model in hourly basis at the suburban industrial stations using HERMESv2.0 and TNO-MACC-II for (a) February and (b)June of 2009. (c) Observed (dots) and modelled (solid lines) time series of hourly concentrations [mg m�3] for SO2 at Matadero station (Asturias, N Spain) and (d) total annual SOx

[t$year�1] emissions released from two facilities located in the same area.

M. Guevara et al. / Atmospheric Environment 98 (2014) 134e145 141

stations located at the Madrid and Barcelona's airports indicatesthat this fact can be partly attributable to the meteorological modelperformance. In the case of Barcelona, the underestimation oftemperature increases from �0.8 �C (February) to �2.4 �C (June)(Table S11).

3.2.2. Sulphur dioxideFor this pollutant, and considering that SOx emissions are

mainly emitted by point sources (refineries and coal-fired powerplants), the analysis was focussed on suburban (industrial) stations

Fig. 5. Modelled versus observed O3 concentrations [mg m�3] driven by (a) HERMESv2.0 (b) and TNO-MACC-II for June of 2009. (c) Temporal correlation and (d) RMSE betweensimulated and observed values, using HERMESv2.0 and TNO-MACC-II.

M. Guevara et al. / Atmospheric Environment 98 (2014) 134e145142

(19 stations), although EMEP rural stations were also considered (7stations).

For the latter case, model biases and RMSE are higher whenapplying the TNO-MACC-II dataset, especially during February(Table 2). This overestimation, as pointed out in the case of NO2 (seeSection 3.1), is related to the “left over” industrial emissions and thelarge differences (HERMESv2.0 e TNO-MACC-II) observed betweentotal amounts of SOx reported for this sector (�11612.1 t$month�1

in February; �8520.0 t month�1 in June).Fig. 4a and b show scatter plots of the SO2 modelled and

observed concentrations on an hourly basis for industrial stations(February and June) as a function of the emission dataset. Theobserved concentrations are largely overestimated when using theTNO-MACC-II emission inventory, especially during June. Statisticsalso show that mean correlation coefficients obtained are ratherlow in both cases (Table 2). The frequently episodic character ofhigh SO2 events and their dependency on the accuracy of meteo-rological patterns may be pointed out as the main causes. The SO2

underestimations when applying HERMESv2.0 could be related tothe fixed vertical profiles used in the model in order to allocate

point source emissions. As Bieser et al. (2011) showed the use ofvertical profiles based on stack parameters and meteorologicalconditions (i.e. CMAQ plume rise module) can lead to higher SO2concentrations in the surface layer. On the other hand, the highoverestimation of the model when using the TNO-MACC-II datasetis to a large extent attributable to the poor characterization of someSpanish industrial point sources, which are based on the EuropeanEmissions and Transfer Register of Pollutants (E-PRTR) database(http://prtr.ec.europa.eu/). Although E-PRTR is an official, completeand widely used industrial emission inventory, the accuracy of itsinformation (i.e. emissions released and geographical location) isnot always reliable, as shown by Dios et al. (2012).

Fig. 4c illustrates the impact of E-PRTR inaccuracy with a timeseries of SO2 at the suburban industrial station of Matadero(Asturias, NW of Spain) for June 2009. Inspection of the time seriesreveals that the simulation driven by TNO-MACC-II show extremelyhigh peaks throughout themonth, reaching values up to 500 mg/m3.In order to understand these high differences between SO2

modelled and observed concentrations, a comparison between theemissions reported by HERMESv2.0 and TNO-MACC-II from the

Fig. 6. Observed (dots) and modelled (solid lines) time series of hourly mean concentrations [mg m�3] for PM10 at the: (a) Madrid urban stations and (b) suburban (industrial)stations.

M. Guevara et al. / Atmospheric Environment 98 (2014) 134e145 143

facilities located in the Asturias industrialized region was per-formed. Results shown two facilities (“Arcelor Mittal Espa~na S.A.”and “Asturiana de Zinc S.A.”) in which TNO-MACC-II reports anamount of SOx emissions 10 times higher than the ones reported byHERMESv2.0 (Fig. 4d). An example of the inaccuracy of the E-PRTRdatabase in terms of geographical location is provided in theSupplementary material (Fig. S12).

3.2.3. OzoneThe evaluation of this pollutant was performed at EMEP rural

stations (13 stations), suburban (industrial) stations (12) as well asMadrid (18 stations) and Barcelona (9 stations) urban stations.

Fig. 5aed summarise this evaluation in a statistical sense. Themodelled concentrations remain overestimated regardless of theemission dataset used (Fig. 5a and b). This is an already knowncharacteristic of the CMAQ model, attributed to a problem inmodelling O3 titration by NO (Mao et al., 2010) and over predictinglow ozone at night (Fig. S16). This overestimation is especiallysignificant at suburban (industrial) stations located near large pointsources (Table 2). On the other hand, rural EMEP stations are theones in which the discrepancy between modelled and observedconcentrations is the lowest. While concentrations driven byHERMESv2.0 present overestimations at almost all the stations, inthe case of TNO-MACC-II there are some stations (especially urbanstations) at which the model underestimates observedconcentrations.

As seen in Fig. 5c, the O3 temporal variability is quite wellreproduced (correlation coefficients are higher than 0.4 in almostall the stations). Urban stations are the ones that present thehighest correlation coefficient regardless the emission dataset used(r ~ 0.7). Generally speaking, the differences between HERMESv2.0and TNO-MACC-II for correlation coefficient are kept to minimum.In the case of RMSE (Fig. 5d) it is shown that at large part of urban

stations simulation driven by HERMESv2.0 presents higher values.This result is in accordance with the higher NO2 concentrationsmodelled at urban environments when applying HERMESv2.0 (seeSection 3.2.1). On the other hand, RMSE values are kept to lowvalues in the rural EMEP stations. A deep analysis of the CALIOPE-AQFS behaviour in rural (NO2-limited regime) and urban (non-limited-NO2 regime) environments when it comes to reproduce theO3 variability is presented in the Supplementary material (Fig. S17),showing that O3 model performance is more related to the NO2regimen (e.g. rural background versus traffic environments) than tothe emission dataset used.

3.2.4. Particulate matter (PM10)Due to the scarcity of observations, the evaluation of modelled

PM10 concentrationwas focussed at 3 Madrid urban stations and 14suburban industrial stations.

Fig. 6 illustrates time series of mean modelled and observedPM10 concentration at each type of station for June. For the Madridstations (Fig. 6a), simulations with the HERMESv2.0 emissionmodel give a slightly better accordance with observations thanTNO-MACC-II, reducing the mean bias (~3 mg m�3) and increasingthe correlation factor (0.03) (Table 2). This fact is to a large extentattributed to the inclusion of resuspension by road transport in themodel (Pay et al., 2011) which is not present in TNO-MACC-II. Theincrease of modelled concentrations shown between the 13th and20th of June 2009 correspond to an episode of Sahara desert dustintrusion modelled by the BSC-DREAM8b which affected southern,central and eastern Spain. On the other hand, results obtained atsuburban industrial stations (Fig. 6b) show that modelled concen-trations are persistently underestimated throughout both periodsanalysed and regardless of the emission dataset used. Although thegeneral dynamics are well captured (with correlation factors thatreach up to 0.49 for TNO-MACC-II and 0.48 for HERMESv2.0 for June

M. Guevara et al. / Atmospheric Environment 98 (2014) 134e145144

2009) MB amounts to ~�14.8 mg m�3 with a RMSE value of~�23.7 mg m�3 regardless the emission dataset used. This under-estimation is a common feature of most of the current regionalmodels (Solazzo et al., 2012) and to a large extent can be attributedto deficiencies in particulate matter source characterization,including semi-natural sources. In the case of Spain, characterisedby large agricultural areas and some semiarid regions, integrationof fugitive dust emissions caused by wind erosion (i.e. windblownemissions) and agricultural activities (e.g. tillage) would increasemodelled PM10 concentrations (Schaap et al., 2009).

4. Conclusions

This work analyses the impact of two emission datasets, theHERMESv2.0 emission model and the TNO-MACC-II emission in-ventory, on modelling air quality concentrations within the airquality system CALIOPE-AQFS for Spain. In order to perform thistask, the concentration results driven by each one of the emissiondatasets have been analysed and contrasted against availableobservational data for February and June 2009.

A preliminary emission comparison showed high discrepanciesbetween the two emission datasets in terms of total amounts andspatial/temporal allocations. For several pollutants the HER-MESv2.0 bottom-up model differs significantly from the Spanishofficial reported emission totals as used in the TNO-MACC-II in-ventory; CO (þ28%); SOx (�61%), NOx (�20%); PM10 (þ9%). In mostcases, these differences were attributed to the different informationand approaches used for the emission estimations (e.g. roadtransport sector, industrial combustion, inclusion of resuspensionemissions). This fact highlights the need of initiatives to secure theconsistency of detailed bottom-up emission information with thatcompiled at national and European scale, in line with the conclu-sions of Timmermans et al. (2013). When zooming in on urbanareas, significant inconsistencies were found for the TNO-MACC-IIemission dataset (i.e. overestimation of industrial emissions),stressing the point that the use of population density as a proxydata for the allocation of the “left over” industrial emissions (i.e.emissions that cannot be allocated to known point sources) shouldbe revised.

Despite all these discrepancies, air quality simulations driven byeach emission dataset generally showed similar performances.Modelled NO2 concentrations presented similar results at EMEPrural stations, although the use of TNO-MACC-II lead to lower un-derestimations due to the higher industrial, other mobile sourcesand residential emissions reported. The use by HERMESv2.0 of alocal point source inventory presented a clear advantage in themodelling of SO2 concentrations at suburban industrial stations;the inaccuracy of the point source dataset used by TNO-MACC-II(based on the E-PRTR database) led to large SO2 overestimationsand mean biases. However, in other cases the more detailed in-formation and emission estimation methodologies used by HER-MESv2.0 did not seem to have a clear impact on the air qualityresults; in Madrid urban stations NO2 concentrations were sys-tematically underestimated despite of the fact that HERMESv2.0works with a more tiered methodology of the COPERT approachreported by the EMEP/EEA guidelines. This fact underlines the needfor new experimental data on vehicle emissions, as well as forrevising and updating current COPERT emission factors so morereliable results can be obtained (Smit and Bluett, 2011; Kousoulidouet al., 2013). In this sense, it is important to highlight the creation ofinitiatives and programs such as the European Research for MobileEmission Sources (ERMES) (http://www.ermes-group.eu/web/),which aims to give support on cooperative research in the field oftransport emission modelling. On the other hand, the systemati-cally underestimation of O3 concentrations regardless the emission

dataset used depicted the difficulty of achieving a good perfor-mance of this pollutant, especially in rural (NO2-limited regime)environments, where the chemical transport model plays a moredeterminant role than the characterization of emission sources (i.e.CMAQ's overestimation of low O3 at night). Finally, the PM10 eval-uation at urban areas highlighted the importance of including roaddust resuspension emissions, whereas the significant underesti-mation observed at suburban industrial stations indicated de-ficiencies in fugitive emission sources characterization.

The present inter-comparison also allowed the identification ofHERMESv2.0 and TNO-MACC-II gaps that need further revision. Forthe first dataset, these include possible model improvementsrelated to the implementation of PM10 fugitive dust emissions(i.e. wind-blown dust and agricultural activities) as well as theintegration of plume rise calculations (i.e. CMAQ plume-rise mod-ule) to vertically allocate point source emissions. Future works willbe related to the implementation and evaluation of these potentialimprovements. As for the TNO-MACC-II emission dataset, the pre-sent work highlighted the need for a revision of the spatial proxiesused for the allocation of industrial emissions (e.g. industrial landuses instead of population maps), as well as of the point sourcedatabase used for the estimation of emissions from large facilities.

Acknowledgements

The authors wish to thank S. Basart for providing the BSC-DREAM8b outputs, as well as the Severo Ochoa Program awardedby the Spanish Government (SEV-2011-00067), the Beatriu Pin�osprogramme for the post-doctoral grant held by M.T. Pay (2011 BP-A00427) and the EU FP7 projects MACC (grant agreement no.:218793) and MACC-II (grant agreement no.: 283576) for financialsupport. Authors also want to thank the two anonymous reviewerswhose comments helped to improve this paper substantially. Allsimulations were performed on the MareNostrum3 supercomputerhosted by the Barcelona Supercomputing Center.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.atmosenv.2014.08.067.

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