Evaluation of high resolution simulated and OMI retrieved tropospheric NO2 column densities over Southeastern Europe

Atmospheric Research 122 (2013) 55–66

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Evaluation of high resolution simulated and OMI retrieved tropospheric NO2 column densities over Southeastern Europe I. Zyrichidou a,⁎, M.E. Koukouli a, D.S. Balis a, I. Kioutsioukis a, A. Poupkou a, E. Katragkou a, D. Melas a, K.F. Boersma b, c, M. van Roozendael d a b c d

Laboratory of Atmospheric Physics, Physics Department, Aristotle University of Thessaloniki, Thessaloniki, Greece Royal Netherlands Meteorological Service, De Bilt, The Netherlands Eindhoven University of Technology, Fluid Daynamics Lab, Eindhoven, The Netherlands Belgian Institute for Space Aeronomy, Brussels, Belgium

a r t i c l e

i n f o

Article history: Received 5 April 2012 Received in revised form 8 October 2012 Accepted 26 October 2012 Keywords: Tropospheric nitrogen dioxide Satellite measurements Model simulations

a b s t r a c t High resolution model estimates (10 × 10 km2) of tropospheric NO2 column amounts from the Comprehensive Air Quality Model (CAMx) for the Balkan Peninsula are compared with OMI/ Aura measurements (13 × 24 km2 at nadir) for the year April 2009 to March 2010. The Balkan area contributes significantly to the NO2 burden in European air and so numerous urban, industrial and rural regions are studied aiming to investigate the consistency of both satellite retrievals and model predictions at high spatial resolution. It has already been shown that OMI can detect the tropospheric column of NO2 over polluted Balkan cities due to its fine horizontal resolution and instrument sensitivity (Zyrichidou et al., 2009). In this study the improved OMI DOMINO v2.0 satellite retrievals showed that over South-Eastern Europe the monthly mean NO2 tropospheric column density fluctuated between 2.0 and 5.7 ± 1.1 × 10 15 molecules/cm 2 over urban areas, 1.6–5.0 ± 0.7 × 10 15 molecules/cm 2 over large industrial complexes and 1.1– 2.2 ± 0.4 × 10 15 molecules/cm 2 over rural areas for the year studied. The Comprehensive Air Quality Model with extensions (CAMx) version 4.40 is a publicly available open-source computer modeling system for the integrated assessment of gaseous and particulate air pollution. The anthropogenic emissions used in CAMx for the Greek domain being studied were compiled employing bottom-up approaches (road transport sector, off-road machinery, etc.) as well as other national registries and international databases. The rest of the Balkan domain has natural and anthropogenic emissions based on the TNO emission inventory of 2003. The high-resolution CAMx simulations reveal consistent spatial and temporal patterns with the OMI/Aura data. The annual spatial correlation coefficient between OMI and CAMx computed in this high spatial resolution analysis is of the order of 0.6, somewhat improved over those estimated in Zyrichidou et al. (2009) (R ≈ 0.5). However, in such a validation study it is important to take into account the averaging kernel (AK) information in order to achieve the creation of comparable data sets. Minor differences are found for area-averaged model columns with and without applying the kernel, which shows that the impact of limiting the effect of the a priori profile on the comparison is on average small. The main aim of the paper, which was to evaluate OMI retrieved and high resolution simulated tropospheric NO2 column densities over South-Eastern Europe and to assess the use of the averaging kernels, is achieved and the two data sources are being employed further in an inverse emission inventory creation study (Zyrichidou et al., in preparation). © 2012 Elsevier B.V. All rights reserved.

⁎ Corresponding author. E-mail addresses: [email protected] (I. Zyrichidou), [email protected] (K.F. Boersma), [email protected] (M. van Roozendael). 0169-8095/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.atmosres.2012.10.028

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1. Introduction Nitrogen dioxide is one of the most important air pollutants with impact on tropospheric chemical processes, playing a major role in the production of ground-level ozone (Knowlton et al., 2004) and on climate as an absorber of solar radiation (Solomon et al., 1999). Besides, nitrogen dioxide (NO2) is one of the main indicators of local pollution as it is emitted into the atmosphere from both anthropogenic and natural sources like fossil fuel and biofuel combustion, power plants, transport, industry, biomass burning, aircrafts, lightning and microbiological processes in soil (e.g. Jaeglé et al., 2005). Observations of the NO2 atmospheric amounts over broad spatial regions provide information for air quality monitoring and pollution management. Satellite retrievals of tropospheric NO2 columns broaden the existing ground based and aircraft observational databases with extensive spatial detail and improved temporal coverage. Since satellite data, beginning in 1995 with the Global Ozone Monitoring Experiment (GOME) spectrometer aboard the European Research Satellite (ERS-2) (Burrows et al., 1999), and then followed by the Scanning Imaging Absorption spectrometer for Atmospheric ChartograpHY (SCIAMACHY) (Bovensmann et al., 1999), have been validated in several air quality studies (e.g. Schaub et al., 2006; Ordonez et al., 2006; Ma et al., 2006; Blond et al., 2007) against ground-based and other satellite measurements, they can be used to increase the accuracy of global Chemistry Transport Models (CTMs). For instance, Velders et al. (2001) have shown that the global pattern of tropospheric NO2 columns agree well with the one calculated by MOZART (Model for Ozone And Related Chemical Tracers) and IMAGES (Intermediate Model for the Global Evolution of Species) models except for South America and Africa, areas in which biomass burning, savannah fires and soil emissions are the dominant NOx sources. Van Noije et al. (2006) have performed a multi-model intercomparison for NO2 on a global scale, based on GOME retrievals. They found that on average the models underestimate the retrievals in industrial regions and overestimate the retrievals in regions dominated by biomass burning. Furthermore, the spatial correlation between the individual models and retrievals was in the range of 0.81 to 0.93, after smoothing the data to a common coarse resolution of 5°× 5°. More recently, in 2004, the Ozone Monitoring Instrument (OMI) aboard the Earth Observing System (EOS) Aura was launched (Levelt et al., 2006) which has important advantages (see Section 2) compared to earlier instruments of GOME or SCIAMACHY. The OMI NO2 data (used also in this study) have been validated against surface, in-situ, aircraft and other satellite observations (Boersma et al., 2008a,b; Brinksma et al., 2008; Kramer et al., 2008; Lamsal et al., 2008a,b) and show good consistency with those observations. Huijnen et al. (2010) have presented a comparison of tropospheric NO2 from OMI measurements to the median of an ensemble of Regional Air Quality (RAQ) models (including the CAMx model studied in this work) for the period July 2008–June 2009 over Europe. Specific findings from this work are discussed below. Lamsal et al. (2010) developed a new data set using averaging kernels information from the OMI DOMINO tropospheric NO2 column products (version 1.0.2, collection 3) gridded to 0.1°×0.1° in order to achieve a more consistent comparison between OMI instrument

and GEOS–CHEM model. This product reduced to 5%–21% the annual mean bias over the southeast United States. In this paper we compare the tropospheric NO2 column data derived from the OMI satellite instrument within the auspices of the Derivation of OMI tropospheric NO2 (DOMINO) product (http://www.temis.nl/airpollution/no2.html) to the NO2 forecasts produced by the Comprehensive Air Quality Model with extensions (CAMx). Validation efforts for previous versions of the OMI NO2 dataset have shown that OMI is performing well and providing valuable data (Boersma et al., 2009b; Zhou et al., 2009; Hains et al., 2010). In our previous study (Zyrichidou et al., 2009), among others, we validated a CAMx model run with spatial resolution 50× 50 km for the period from 01-01-1996 to 31-12-2001 over Europe, with GOME (KNMI/BIRA algorithm) retrievals and we found a poor temporal correlation (0.5). The main aim of this work is to compare the latest version of OMI DOMINO v2.0 product with an updated emission inventory with finer resolution used for the operational application of CAMx model in Laboratory of Atmospheric Physics in AUTH (Aristotle University of Physics) spanning one year and to a greater extent, to investigate the main sources of the differences. The main differences between this study and the previous one (Zyrichidou et al., 2009) are summarized in Table 1 for quick reference. To the best of the authors' knowledge, no published research has yet been focused on the validation of the new DOMINO v2.0 algorithm over the Balkan domain. Thus, this study can also help better the understanding of the regional tropospheric NO2 distribution and the evaluation of the tropospheric NO2 CAMx simulations over this southeastern part of Europe. For the purpose of this study the OMI retrieval AK information is included in the data products of CAMx. Given that in literature comparison or validation studies of spaceborne trace gas columns with independently derived profiles should include averaging kernel (AK) information in order to distinguish between different error sources (Schaub et al., 2006; Huijnen et al., 2010), the introduction of AK in our study seems to be necessary. The manuscript is organized as follows: Section 2 gives a brief description of the OMI satellite instrument characteristics, the retrieval algorithm and the NO2 column density fields. The next section presents the photochemical model used. In Section 4 we introduce the use of averaging kernels in order to examine the effect of profile shape to satellite retrievals. The results of the annual and seasonal validation are discussed in Section 5. In the last part of this section a monthly statistical analysis of the tropospheric NO2 columns over representative Balkan locations is given. Finally in Section 6 the conclusions and outlook of this study are summarized. 2. OMI: ozone monitoring instrument OMI is flying onboard NASA's Aura satellite with a sun synchronous polar orbit that crosses the equator at approximately 13:30 local time and has an orbital period of around 100 minutes. OMI is a nadir viewing imaging spectrograph measuring direct and backscattered sunlight in the ultraviolet– visible (UV/VIS) range from 270 nm to 500 nm with a spectral resolution of about 0.5 nm and its design and performance are described in detail in Levelt et al. (2006). OMI uses two CCDs with 780 × 576 pixels each. The first dimension spans the

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Table 1 Differences between the two CAMx model runs. Features

CAMx (version 4.40)

MM5 CAMx (version 4.40 operational)

Spatial resolution Vertical profile Meteorological fields Anthropogenic emissions

50 × 50 km2 12 layers Regional Climate Model (forced by ECMWF) EMEP

Biogenic emissions Boundary conditions Project Time period

RegCM-CAMx interface in a 6-hour basis 1 ppb PROMOTE 01/1996–12/2001

10 × 10 km2 15 layers MM5 version 3.6 (forced by GFS/NCEP) TNO (for Greece detailed emission inventory according to Markakis et al., 2010a,b) BEM MOZART-IFS GEMS 04/2009–03/2010

spectral wavelengths, and the second dimension is re-binned to provide measurements at 60 positions across the orbital track every 2 seconds. OMI has a 114° field of view, which corresponds to an approximately 2600 km wide swath on the Earth's surface. Because of the wide swath of the 14–15 orbits per day, OMI achieves global coverage nearly in 1 day (except for tropics). However, during our study period, OMI has been affected by a number of so-called row anomalies that appear as signal suppressions in the level 1 radiance spectra for particular satellite viewing angles over the complete illuminated orbit, a fact that affects up to 40% of the coverage. Its pixels vary from 13× 24 km2 in nadir up to 26 × 135 km2 at the edges of swath (Marmer et al., 2009). Dobber et al. (2006) discuss the calibration of the instrument and the origin of the striping, or cross-track, bias. In Sections 2.1 and 2.2 we present the essential details of the algorithm, describe the methodology used to retrieve the tropospheric NO2 column and adduce the data extraction criteria. 2.1. The OMI NO2 DOMINO product The OMI tropospheric NO2 columns were retrieved at KNMI within the DOMINO project. The DOMINO v1.02 retrieval algorithm is described elaborately in Boersma et al. (2009a) and recent updates can be found in the DOMINO Product Specification Document (http://www.temis.nl/docs/OMI_NO2_ HE5_1.0.2.pdf). The new improved DOMINO retrieval algorithm v2.0 is described in Boersma et al. (2011) and in the DOMINO Product Specification Document (http://www.temis.nl/docs/ OMI_NO2_HE5_2.0_2011.pdf). The KNMI OMI datasets are publicly available from the Tropospheric Emission Monitoring Internet Service (TEMIS) at http://www.temis.nl/airpollution/ no2.html. In this study the NO2 tropospheric columns from the new DOMINO product, version 2.0, for a 12 month period are mainly used. The tropospheric NO2 DOMINO product, version 1.02, has been validated against surface, in-situ and aircraft observations, such as during the INTEX-B (Intercontinental Chemical Transport Experiment — Phase B) and DANDELIONS (Dutch Aerosol and Nitrogen Dioxide Experiments for Validation of OMI and SCIAMACHY) campaigns (Hains et al., 2010; Lamsal et al., 2010) and observations in Israel (Boersma et al., 2009b). The dominant source of error in the tropospheric NO2 retrieval over areas with enhanced NO2 is the AMF calculation (10–40%) (Boersma et al., 2004, 2007). These estimates are for polluted conditions like those measured during the DANDELIONS and INTEXB campaigns. In Herman

et al. (2009) the comparison of vertical column amounts of NO2 between the ground-based spectrometer system PANDORA and OMI at the Aristotle University (AUTH), Thessaloniki, Greece, shows good agreement over this urban polluted site of southeastern Europe. The version 2.0 of DOMINO product has not been extensively validated yet. According to Boersma et al. (2011), improvements were found in the calculation of the air mass factors (AMFs), through improved radiative transfer modeling, the use of high-resolution data on terrain height and surface albedo, and better a priori NO2 profiles (improving the sampling of the TM4 model). Over large polluted areas, version 2.0 tropospheric NO2 columns are generally reduced by 10–20% relative to previous versions. Locally, differences between v2.0 and v1.02 retrievals may be stronger as a result of the higher resolution terrain height and albedo maps, and de-striping corrections (Boersma et al., 2011). In Miyazaki et al. (2012), systematic differences were found between the CHASER global CTM and the DOMINO v1.02 and v2.0 satellite retrievals (global spatial correlation of 0.71–0.9% depending on season and retrieval) similar to that estimated using other global CTMs (van Noije et al., 2006; Huijnen et al., 2010). The model was generally negatively biased relatively to the OMI retrievals. 2.2. OMI Tropospheric NO2 column In order to retrieve valid tropospheric NO2 columns over the Balkans we used exclusivity criteria for some of the important parameters according to the DOMINO Product Specification Document. Clouds are a significant source of error for the retrieval of trace gases, as testified by the 30% uncertainty of the final product attributed to cloud fraction. In order to assure that the dominant part of the observed signal originates indeed from the cloud-free part of the pixel, only observations with cloud radiance fractions of less than 50% were included in the data set, which corresponds to a cloud fraction of less than about 20% (Van der A et al., 2008) and implies that the model is evaluated for (mostly) clear-sky conditions. During the winter months, satellite observations are heavily hindered by the presence of clouds, which results in a low amount of data points for those months. Of course, for the summer months, this amount is quite high, reaching the amount of days per month (i.e. max 31) considered for most 10 × 10 km pixels. In addition, only measurements that have tropospheric quality flag value equal to zero, which corresponds to meaningful tropospheric retrievals, were accepted (as it is indicated in

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the DOMINO Product Specification Document). During the analysis period several row anomalies occurred in OMI data. The affected rows have been removed from the dataset; see http://www.temis.nl and Boersma et al., 2009a. The non-affected cross-track scenes used in our analysis correspond to rows 9–24 of the OMI pixel. Finally, we discarded scenes with surface albedo values greater than 0.3 and tropospheric air mass factor (AMF) of less than 0.1. However, because of OMI's daily coverage a sufficient amount of data is available for a quantitative, statistical analysis on a monthly basis (minimum five measurements per month for each grid cell). We gridded satellite data to the resolution of the model grid, 0.1° × 0.1° (about 10 × 10 km for the area in question) in order to be able to compare equal measures. The monthly mean tropospheric NO2 fields derived from OMI span from April 2009 to March 2010. 3. CAMx: Model overview The simulated NO2 tropospheric vertical column densities (VCDs) are based on the results of the air quality forecast modeling system Fifth Generation Mesoscale Model version 3.7, MM5. The modeling system has been set up in the framework of the Global and Regional Earth-System Monitoring using Satellite and in-situ data project (GEMS project) (Hollingsworth et al., 2008) and was run from October 2007 to October 2010. The Comprehensive Air Quality Model with extensions (CAMx) version 4.40 is applied for four nested grids that share the same Lambert Conformal Conic coordinate system. The coarse grid covers Europe and has a spatial resolution equal to 30 km. The second grid focuses on the Balkan Peninsula with a 10 km spatial resolution. The third and the fourth domains of 2 km resolution extend over the two largest Greek urban agglomerations, Athens and Thessaloniki. The domains' vertical profile of the operational model run contains 15 layers of varying thickness, extending up to about 300 hPa, due to unsatisfying results above probably associated to problems in the coupling with MOZART-IFS (Integrating Forecasting System) (Zyryanov et al., 2011). Over the latitudes of the current study the average tropopause is close to 200 hPa and thus indeed a small part of the differences in tropospheric NO2 between model and OMI might be attributed due to this low upper boundary of the model setup, mainly because of missing emission of the model in this altitude range, and/or any transport of NO2 at this height which is not considered in the model. CAMx allows two-way grid nesting, which means that pollutant concentration information propagates into and out of all grid nests during model integration. The chemistry mechanism invoked is the Carbon Bond version 4 (CB4) (Gery et al., 1989). The meteorological fields are derived from the fifth generation NCAR/Penn State University Mesoscale Model MM5 version 3.7 (Dudhia et al., 2005). MM5 is applied for all four grids that are identical in projection and resolution with those of the photochemical model (although a bit more extended) to avoid interpolation errors. MM5 is forced by the global 12:00 UTC GFS (Global Forecasting System)/NCEP (National Centers for Environmental Prediction) forecast of 1° spatial resolution. The maximum dynamical and chemical timestep in CAMx is 5 minutes.

The emission inventory that was used in the MM5–CAMx (hereafter CAMx) simulations is a compilation process of several emission inventories. The most important anthropogenic emission sectors in Greece as well as in the two large cities of Athens, Greece and Thessaloniki, Greece were quantified employing real activity information as well as high resolution digital maps, utilizing mainly bottom-up emission estimation methodologies. These inventories (Markakis et al. 2010a,b) include gridded (10 km resolution) and hourly resolved emission rates for CO, NOx, SOx, NH3, NMVOCs and Particulate Matter (PM10 and PM2.5). The emissions for a number of anthropogenic sources such as road transport, non-road transport, domestic combustion, sea vessels (with the exception of cargo ships) are calculated based on detailed information gathered from official sources in Greece. The emissions for the cargo ships were extracted from the on-line EMEP database (Vestreng et al. 2006), available at http://www.ceip.at/ emission-data-webdab/emissions-used-in-emep-models. The 2003 gridded emissions of the EMEP database having 50 km grid spacing were reallocated to the 10 km resolution domain which was used in this study. Detailed information regarding the compilation of the aforementioned emission inventories are found in Markakis et al. (2010a,b) and references therein. To cover the remainder of the area of the Balkan domain, gridded annual emission data were obtained from emission inventory of TNO (Netherlands Organisation for Applied Scientific Research) (Visschedijk and Denier van der Gon, 2005; Visschedijk et al., 2007). The inventory was originally prepared for the Global and Regional Earth-System Monitoring using Satellite and in-situ data project (GEMS project) (Hollingsworth et al. 2008) for the reference year of 2003 with a grid spacing of 1/8 by 1/16 degrees. The TNO annual emission data were temporally disaggregated (seasonal, weekly and diurnal temporal profiles) according to Friedrich (1997). Biogenic NMVOC emissions are calculated from the Biogenic Emission Model (BEM) (Poupkou et al., 2010). Hourly isoprene, monoterpenes and biogenic other volatile organic compounds (OVOC) emissions are calculated over Europe, the Balkan Peninsula, Athens and Thessaloniki on the basis of: 1) the U.S. Geological Survey 1-km resolution land-use database, 2) a land-use specific, monthly isoprene, monoterpene and OVOC emission potentials and foliar biomass densities database and 3) temperature and solar radiation data provided by the mesoscale meteorological model MM5. The BEM grids have the same horizontal spatial configuration with those of the CAMx model grids. The global chemistry transport model MOZART-IFS (Flemming, 2008) provides the CAMx chemical boundary conditions since January 2009. It should be noted here that CAMx model runs with the absence of soil and biomass burning emissions. On the global scale soil NOx emission accounts for 22% of total NOx emissions (Martin et al., 2003; Jaeglé et al., 2005). Soil emissions appear to contribute a lot to the local NOx budget on a regional scale, such as over North-Central Montana where the local NOx budget is controlled exclusively by soil emissions (Bertram et al., 2005) and over northern mid-latitudes during summer where they account for almost half that of the fossil fuel combustion source (Jaeglé et al., 2005). In addition, fertilizer application during the cultivation period and precipitation events result in soil NOx emission flux enhancements (e.g. Akiyama et al., 2003; Jaeglé et al., 2004). On the other hand, fires, as one of the largest sources

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of biomass burning emissions, have a major impact on air quality and climate (Langmann et al., 2009). Biomass burning emissions account for less than 15% of global surface NOx emissions whereas fires over Africa account for half of the global biomass burning emissions (Jaeglé et al., 2005). The Balkans is not a region with high levels of biomass burning emissions suggesting that, except for the case of local fire episodes, they are not an important source of the possible discrepancies between simulations and observations. Although they should not be neglected in a model emission inventory, their estimation is beyond the scope of this paper. Within the European project GEMs, the CAMx air quality forecast has been operationally evaluated against surface measurements in Europe (rural stations of EMEP, urban stations of AIRBASE in Athens, Greece) and compared with the forecasts from other models (e.g. CHIMERE, EMEP, EURAD, etc.) and the European ensemble forecast (Kukkonen et al., 2012). 4. Application of averaging kernels (AK) The averaging kernels describe the sensitivity of the satellite observations to the trace gas profile and their use provides the link between modeled (used for the retrieval) and retrieved vertical column (Boersma et al., 2009a). The AK, a height-dependent vector that changes from one ground pixel to the next, is among others contingent on parameters like the presence of clouds, aerosols, the surface albedo of the scene, the viewing geometry and the a priori profile and terrain. The use of the retrieval averaging kernel in the comparisons has an impact when the model profile shape is different from the a priori profile used in the satellite retrieval (more details in the AK are given in Eskes and Boersma, 2003). The AK allows model-predicted profiles to be compared directly to satellite retrieved columns by removing the comparison's dependence on the a priori assumed profile shape (Boersma et al., 2005). The tropospheric NO2 averaging kernel, defined as AKtrop = AK(AMF/AMFtrop) (where AMFtrop is the tropospheric air mass factor) (Boersma et al., 2011), is applied to the CAMx profiles to compute augement NO2 tropospheric VCDs that can be directly compared to OMI. In particular, model simulations were multiplied with the AKtrop which was first interpolated to the CAMx 15 pressure layers. Then the produced gaseous profiles were integrated from the surface up to about 7 km and for the time period from 01/04/2009 to 31/03/2010. We compared 1-h (13:00–14:00 pm in order to make the data comparable to the OMI measurements) averaged NO2 concurrent predictions from CAMx with OMI tropospheric NO2 spatially distributed measurements over coincident 0.1° × 0.1° grid cells. The advantage of a comparison through the kernel is that the comparison is now independent of the a priori profile shape chosen in the retrieval (Eskes and Boersma, 2003). Hereafter, AK denotes the tropospheric averaging kernels (AKtrop) used in our statistical analysis. 5. Results 5.1. Annual spatial distribution of tropospheric NO2 columns In this section we discuss the main features of the yearly tropospheric NO2 concentrations over this part of South

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Eastern Europe, as shown in Fig. 1, where the two OMI DOMINO product estimates [upper plots] and the two model assessment (with and without AK) [lower plots] are presented. This 12-month analysis of OMI and CAMx tropospheric NO2 reveals a number of interesting features that merit further investigation. Higher concentrations are observed in DOMINO v1.02 than in DOMINO v2.0 over Balkans with a mean difference of about 8%. A similar result for Europe was found in the validation study shown by Miyazaki et al. (2012). The main patterns of the tropospheric NO2 amounts can be discerned in both satellite and model plots, such as the high NO2 amounts over the major cities of the region, the low values over the sea surfaces but also regions without a significant inhabitant concentration such as the Dalmatian coast and so on. Ship tracks South of Greece and Italy, as well as to the East of Italy, are also visible in both OMI and CAMx maps. Overall, the CAMx model shows similar spatial NO2 distribution patterns with the OMI instrument on a yearly basis. However, there exist areas where CAMx demonstrates a significant bias relative to the OMI datasets; CAMx shows relatively high tropospheric NO2 columns, of more than 15.0 × 10 15 molecules/cm 2, over the two Greek megacities of Athens and Thessaloniki. This is probably attributed to a specific parameterization concerning the vertical mixing used in this model and is discussed further below. Moreover, CAMx does not represent correctly the NO2 hotspots over Istanbul and the Asia Minor coastline, clearly observed by the satellite. OMI reports values of almost 4.0 × 10 15 molecules/cm 2 in the northern-most and easternmost part of the domain in a region which is highly affected by biomass burning [the regions surrounding the Black Sea] which is absent from the CAMx estimates for reasons discussed above. On the other hand, OMI underestimate the tropospheric NO2 columns over urban regions probably due to the limitation of OMI to resolve tropospheric NO2 near the surface (reflected in the averaging kernel) where we have the highest molecular density and therefore greatest contribution to the column. This underestimation may be also attributed to the impact of differences between the Planetary Boundary Layer (PBL) of TM4 and CAMx. However, a slight improvement appears when the retrieval averaging kernels are applied in CAMx. Specifically a drop in tropospheric NO2 concentrations is observed over the polluted hot spots probably due to the low resolution of the TM4 data used as a priori data in the retrievals and terrain discrepancies between TM4 and CAMx models. Although the results after the AK introduction imply a better agreement with the OMI ones, the differences are not large. A potential reason that CAMx is still biased high may be the fact that we did not add the ghostcolumn to our model columns. The spatial correlation of the annual maps shown in Fig. 1 is 0.48 between DOMINO v1.02 and CAMx without AK and 0.53 between DOMINO v2.0 and CAMx without AK. At the same time the spatial correlation coefficients between the two OMI products and CAMx with AK are slightly increased to 0.5 and 0.57 respectively. The values of the above correlation coefficients are a bit smaller than that between SCIAMACHY and CHIMERE (R = 0.73) calculated in the TEMIS Algorithm Document for Tropospheric NO2 for the global scale. Hereafter the reconstructed predicted tropospheric vertical column of NO2 are referred as CAMx.

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DOMINO V1.02

DOMINO v2.0

CAMx (without AK)

CAMx (with AK)

Fig. 1. Annually averaged OMI DOMINO v1.02 [upper left], OMI DOMINO v2.0 [upper right], CAMx without AK [lower left] and CAMx with AK [lower right] tropospheric NO2 columns for April 2009−March 2010. The CAMx simulations are sampled at 13:30 LT as the OMI overpass time. The concentration is given in 1015 molecules per square centimeter.

5.2. Seasonal spatial distribution of tropospheric NO2 Maps of mean seasonal tropospheric NO2 columns as observed by OMI and simulated by the CAMx model for the South Eastern domain are given in Fig. 2. The left plots in Fig. 2 depict the OMI mean tropospheric NO2 columns for the winter (DJF), spring (MAM), summer (JJA) and autumn (SON) season and the right ones illustrate the corresponding CAMx mean tropospheric NO2 columns. In the maps of Fig. 2 three overestimated regions by CAMx are visible during the whole year: Athens, Thessaloniki and Eskisehir (in the South–East of Istanbul). These columns are overestimated by approximately a factor of 3. In Huijnen et al. (2010) the OMI DOMINO v1.02 monthly mean tropospheric NO2 retrievals for 2008 do not exceed the 8.0, 6.0 and 5.0 × 10 15 molecules/cm 2 levels over Athens, Thessaloniki and Eskisehir respectively. In addition, in Zyrichidou et al., 2009, the GOME, SCIAMACHY and GOME-2 instruments retrieval over Athens and Thessaloniki levels are lower than 9.0 and 7.0 × 10 15 molecules/cm 2 respectively on a monthly mean basis for the time period 1996–2008. The results of these two studies suggest that the new emission inventory over the two Greek megacities overestimates the tropospheric NO2 levels. On the

other hand, simulated tropospheric NO2 columns over Istanbul are systematically low by more than 50%, throughout the year, compared to OMI retrievals. This result is in agreement with Zyrichidou et al.'s (2009) findings and is obviously not representative for this area and the economic growth in this region during the last ten years. In Markakis et al. (2012) the TNO inventory was found to indicate a possible underestimation of NO2 of more than 57% for Istanbul. A slighter undervaluation by CAMx is observed over the Asia Minor and Black Sea coastlines (apart from the winter season). In Huijnen et al. (2010) Istanbul appears more pronounced in the OMI DOMINO v1.02 data as well, indicating that the TNO emissions may be underestimated. Except for these systematic discrepancies, the CAMx model is generally well in line with the OMI instrument on a seasonal basis. Specifically, in autumn and spring months the urban and industrial polluted patterns are similarly distributed in both OMI and CAMx. Similar to the results in Huijnen et al. (2010), we found a significant underestimation in tropospheric NO2 simulated by the model in summer. This may be attributed either to the CAMx chemical mechanism or to anthropogenic emission inventories' underestimation during this season. It is true that many other factors can influence

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Fig. 2. Seasonally averaged OMI DOMINO v2.0 tropospheric NO2 columns [left] versus CAMx model estimates with AK [right]. Upper panels: winter months (December−January−February). Upper middle panels: spring months (March−April−May). Lower middle panels: summer months (June−July−August). Lower panels: autumn months (September−October−November). Values are in molecules per square centimeter.

the model estimated besides the problems in the emissions. However the fact that the model has a different performance in these three big cities (Athens, Thessaloniki, Istanbul) compared to the other spots studied is an indication for higher uncertainties of the emissions estimated for these cities. Concerning traffic and industrial emissions (not surface concentrations)

considered in the model, these are pretty constant between the morning and evening peak observed in the concentrations and thus the noon retrievals of OMI, at least for the major cities, could reflect the plateau of the daily plateau of the emissions. Though, the diurnal variability of NO2 emissions from other sources cannot be captured from the single noon overpass of

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OMI. As the OMI valid pixel sample is limited during the winter period mostly because of the cloud coverage, we do not proceed to any conclusion apart from a sign of CAMx overevaluation over the northern part of Balkan Peninsula and along the ship tracks in the eastern part of Mediterranean Sea. 5.3. Statistical analysis of the tropospheric NO2 columns over representative Balkan locations The Balkan Peninsula comprises of many anthropogenic NOx emission sources, such as megacities and industrial complexes contributing to the NO2 burden in European air. However, only few studies have in the past focused on this part of Europe (Konovalov et al., 2005; Ladstatter-Weißenmayer et al., 2007; Marmer et al., 2009; Zyrichidou et al., 2009). In previous studies, satellite instruments have indicated their ability to measure the increase of tropospheric NO2 concentration over regions dominated by industrialization, urbanization and traffic growth (e.g. van der A et al., 2006; Ghude et al., 2008; Sitnov, 2009). For the purposes of this study, 21 geolocations (Fig. 3) were selected in order to study South Eastern Europe as a mixture of equal number of urban, rural and industrial sites. This distinction helps to properly evaluate the spatial and temporal correlation analysis and the time series evolution of the monthly mean tropospheric NO2 columns over the Balkan domain. In this section the observed tropospheric NO2 columns are compared with the reconstructed CAMx model – simulated tropospheric NO2 columns for urban, rural and industrial regions to evaluate the NOx emission inventories of these three types of regions. We present time series of the monthly mean tropospheric NO2 columns in order to investigate the mean levels, the seasonal evolution and the consistency of

the observations and the simulations in more detail. The time series are shown in Fig. 4 and are grouped accordingly in urban [upper left], industrial [upper right] and rural [lower] sites. The OMI and CAMx values seem to be consistent with similar variability in all seasons. The CAMx seasonal behavior especially for urban and rural regions generally follows and agrees with OMI data giving maximum values during the winter months. OMI mean tropospheric NO2 values are about 2.9 ± 1.1, 1.5 ± 0.4 and 2.4 ± 0.7 × 10 15 molecules/cm 2 over urban, rural and industrial areas respectively, while the correspondent CAMx values are 4.2 ± 1.9, 1.3 ± 0.2 and 2.3 ± 1.0 × 10 15 molecules/cm 2. A prominent CAMx seasonality particularly over urban regions with remarkably high values during winter is mostly obvious in Fig. 4. There are two possible explanations for this CAMx overestimation during the cold wet season in this east part of Mediterranean — either the model deposition mechanism needs reconsideration or it could be attributed to over evaluated anthropogenic emissions during winter. The simulated regional mean tropospheric NO2 columns are slightly lower than the observed ones in industrial and rural regions from May to October. During the spring-summer season there is a relative offset of 1.0 × 10 15 molecules/cm 2 between CAMx and OMI over the industrial and unpolluted rural sites with CAMx underestimating the tropospheric NO2 levels. The underestimation during summer could be caused by missing emissions — soil or biomass burning. Generally, OMI and CAMx show a drop in NO2 concentrations during the summer season for all cases mainly related to the changing photochemistry when the AK are not applied (not shown here). As it is shown in Fig. 4 after AK's introduction the CAMx underestimation during summer does not exist over urban areas, though it is slightly increased over rural and industrial regions due to the changes of the profile shape. The NO2 levels are higher over urban sites reaching up to

Fig. 3. Map of the selected geolocations in Southeastern Europe.

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Fig. 4. Time series analysis of OMI and CAMx mean monthly NO2 tropospheric columns with AK and their error bars (the standard deviation, σ) for urban [upper left], industrial [upper right] and rural [lower] cases over southeastern Europe. The units for RMSE and MB are 1015 molecules/cm2.

5.7 and 6.8×1015 molecules/cm2 for OMI and CAMx respectively in the month of November. The rural sites have the smallest σ, since they represent the background values, whereas the urban sites have the biggest σ throughout the year due to the varied extent of pollution levels over the selected urban regions. As for the industrial regions, possible errors in model plume dispersion depending on predicted wind speed, wind direction, and turbulence may be involved. However, the root mean square errors (RMSE) between CAMx and OMI are less than 2.0×1015 molecules/cm2 in all cases. These RMSE values are quite consistent with those shown in the TEMIS Algorithm Document for Tropospheric NO2 (3.17×1015 molecules/cm2) where the global tropospheric NO2 columns from SCIAMACHY are compared to outputs from the CHIMERE model. Comparing the R values computed in this high spatial resolution analysis with those estimated in Zyrichidou et al. (2009), which was a low spatial resolution analysis, we infer that there is a general slight improvement. Although the resolution is higher and the emission inventory changed for some Greek locations, there are issues that still remain open and need to be reconsidered like the accuracy of this CAMx emission inventory and its filling with the missing emissions. The positive MB values over urban and industrial areas (see also Table 2) suggest an overestimation by

CAMx. At the same time, in Miyazaki et al. (2012) the CHASER model generally underestimates tropospheric NO2 in industrial areas over southwestern Europe and largely overestimates NO2 columns over Europe especially when compared to DOMINO v2.0. In Table 2, the spatial correlation, RMSE and MB for four indicative months are shown for the three types of regions.

Table 2 Comparison of monthly mean tropospheric NO2 columns between the CAMx simulations with AK and the satellite retrievals of OMI DOMINO v2.0. R is the correlation coefficient; RMSE is the Root Mean Square Error and MB the Mean Bias.

Urban

Rural

Industrial

R RMSE MB R RMSE MB R RMSE MB

JAN

APR

AUG

NOV

0.85 0.84 0.21 0.17 0.44 0.11 0.66 0.68 0.17

0.61 0.34 0.02 0.56 0.12 −0.03 0.68 0.21 0.02

0.40 0.48 0.02 0.73 0.23 −0.07 0.91 0.23 −0.04

0.87 0.68 0.09 0.81 0.28 −0.02 0.81 0.20 0.01

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Correlation coefficient monthly values span from 0.4 to 0.9 over urban, 0.2 to 0.8 over rural and 0.7 to 0.9 over industrial regions. The best spatial correlation is achieved on November in all cases while over urban sites January is the month with the worst correlation between CAMx and OMI. The model is negatively biased relative to the OMI retrievals over rural regions (with OMI values close to its detection limit) and the mean bias does not exceed 0.2 over urban sites in January whereas before kernels' application maximum mean bias (MB ≈ 2) was observed over urban areas in August. Finally, the monthly RMSE values range in acceptable levels (RMSE = 0.1–0.8) as mentioned before. The RMSE and MB values discussed above refer to the model results after the AK application. Table 3 demonstrates the corresponding values in case the AK information is not included in the CAMx profiles. Comparing the two tables we infer that the use of AK reduces the RMSE and MB values over all the three types of selected regions (urban, rural, and industrial). In order to give insight into how real surface layer NO2 is reflected in the OMI retrieval we used ground based measurements from the Greek Ministry of Environment and Climate Change (available at http://www.ypeka.gr/). In Fig. 5 we used coincident with the OMI overpass in situ hourly (between 13:00 and 14:00 LT) measurements of an urban station in Athens (latitude: 38.13 and longitude: 23.75) in order to show how real surface layer NO2 is reflected in the OMI retrieval. In Fig. 5 the monthly time series of both OMI tropospheric NO2 columns and ground based measurements over an urban station in Athens region is depicted and the temporal correlation coefficient is given (R = 0.6). The in situ measurements show moderate correlation with OMI tropospheric NO2 column retrievals and the timeseries shows a similar seasonality between the two data sets following a double peak during the winter–spring period. The discrepancies during summer may be attributed to missing biomass emission sources in CAMx model that are combined with fire episodes usually observed during this season. 6. Summary and conclusions In this study we presented a comparison of OMI/Aura DOMINO v2.0 tropospheric NO2 columns with high resolution (0.1° × 0.1°) simulated tropospheric NO2 from the CAMx

Table 3 Comparison of monthly mean tropospheric NO2 columns between the CAMx simulations without AK and the satellite retrievals of OMI DOMINO v2.0. R is the correlation coefficient; RMSE is the Root Mean Square Error and MB the Mean Bias.

Urban

Rural

Industrial

R RMSE MB R RMSE MB R RMSE MB

JAN

APR

AUG

NOV

0.76 1.82 0.17 0.19 0.51 0.25 0.67 0.86 0.57

0.59 1.15 0.84 0.28 0.21 −0.04 0.66 1.30 1.00

0.43 3.76 2.04 0.54 0.18 −0.11 0.91 1.87 1.02

0.87 2.03 0.75 0.69 0.60 −0.36 0.79 1.37 0.79

Fig. 5. Time series analysis of OMI mean monthly tropospheric NO2 columns (unit: 1015 molecules/cm2) and in situ (surface) NO2 measurements (unit: μg/m3) over an urban station in Athens region. R is the temporal correlation coefficient.

model for a period of one year (April 2009–March 2010). For the inter-comparison the information of the averaging kernels applied to the CAMx simulations in order to avoid the introduction of systematic errors in the retrieved columns. The averaging kernel (AK) vector describes the relation between the true vertical distribution of a species and the retrieved vertical column. The study focused first on the southeastern European domain and then on twenty-one locations of interest spread around the region. The main aim of the paper was to evaluate OMI retrieved and high resolution simulated tropospheric NO2 column densities over southeastern Europe, is achieved and the two data sources are being employed further in an inverse emission inventory creation study (Zyrichidou et al., in preparation). The major conclusions of this study may be summarized as follows: – Both OMI DOMINO retrievals compared with both CAMx products (with and without AK) show almost similar spatiotemporal patterns (R > 0.5) and the seasonality results are overall in good agreement. – The differences between DOMINO v1.02 and DOMINO v2.0 algorithm retrievals are of the order of 8% with DOMINO v1.02 overestimating the tropospheric NO2 columns over the Balkans compared to the new improved DOMINO v2.0 algorithm. – The DOMINO v2.0 dataset shows slightly improved correlations with both CAMx simulation approaches on annual basis compared to the DOMINO v1.02 (Zyrichidou et al., 2009). – The main patterns of the tropospheric NO2 amounts can be discerned in both satellite and model plots, such as the high NO2 amounts over the major cities of the region (e.g. Rome, Naples, Sofia, Bucharest, Budapest, Vienna) the low values over the sea surfaces but also regions without a significant inhabitant concentration such as the Dalmatian coast and so on. OMI measurements and CAMx

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–

–

–

–

–

–

simulations show similar pronounced tropospheric NO2 pattern over industrial sites like Ptolemaida, Maritsa and Craiova. Ship tracks South of Greece and Italy, as well as to the East of Italy, are also visible in both OMI and CAMx maps. According to our seasonal analysis and, as was also shown in Huijnen et al. (2010) and Zyrichidou et al. (2009), the emission inventory over Athens and Thessaloniki overestimates tropospheric NO2 columns by approximately a factor of 2 instead of 3 when using no AK information. The seasonal analysis showed that TNO emission inventory overevaluates NO2 columns over Eskisehir, Turkey, a known industrial region. On the contrary, TNO underestimates Istanbul of more than 50% throughout the year suggesting that it is based on unrealistic values of tropospheric NO2 for this ever-growing city. Overall, during the summer, the TNO database underestimates slightly NO2 columns over the whole domain. Both OMI and CAMx show a drop in NO2 concentrations during the summer season related to the changing photochemistry. CAMx shows a slight underestimation over the whole domain during this season except for urban regions when AK are used. CAMx and OMI DOMINO v2.0 are better correlated (R ≈ 0.6) over the urban sites. However, excluding Athens and Thessaloniki from our temporal distribution study, we found that the coefficient correlation is increased for all months and the mean bias becomes negative. This discrepancy has to do probably with a specific parameterization concerning the CAMx vertical mixing mechanism and needs further investigation. The AK application in CAMx profiles resulted to a slight improvement in the spatial correlation, reduced tropospheric NO2 columns over urban and polluted sites and finally to decreased RMSE and MB values between OMI and the model especially over polluted hot spots. From the above analysis, after including AK information that excludes the impact of profile shape on the tropospheric columns, it is inferred that there is still a combination of factors (concerning both OMI and CAMx tropospheric NO2 columns) which influence the shown results; namely, an update of NOx emissions over urban sites, especially Athens, Thessaloniki and Istanbul, should be performed, a reconsideration of NO2 boundary conditions and emissions' seasonal variability should be introduced and biomass burning and soil emissions adjustments in the model should be included.

Inverse modeling of OMI observations that may improve the model performance (mostly as far as the detection of NO2 emission sources and the calculation of missing emissions are concerned) regarding tropospheric NO2 is the obvious next step of research, once this assessment of both satellite and model behavior has been concluded. In Zyrichidou et al., in preparation a top-down emission inventory is calculated using OMI tropospheric NO2 columns and CAMx estimates. The main idea is to use the a priori information from the bottom up emission inventory used in the CAMx model, the tropospheric NO2 quantities estimated by the CAMx runs and the tropospheric NO2 columns deduced by the satellite observations to create an a posteriori NOx emission inventory. This new inventory, constrained in the top-down manner by the satellite

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estimates, will be used anew in the CAMx model to produce a new modeled NOx product. The results have identified biases in the original emission inventory for instance due to missing emission sources or over-estimation of the spread of emission sources and have improved the bottom-up emission inventory.

Acknowledgments The OMI/Aura satellite instrument tropospheric NO2 column data are available through ESA's TEMIS project (http://www. temis.nl). GEMS was funded by the European Commission under the EU Sixth Research Framework Programme, contract number SIP4-CT-2004-516099. References Akiyama, H., et al., 2003. Nitrous oxide, nitric oxide, and nitrogen dioxide fluxes from soils after manure and urea application. J. Environ. Qual. 32, 423–431. Bertram, T.H., et al., 2005. Satellite measurements of daily variations in soil NOx emissions. Geophys. Res. Lett. 32, L24812. http://dx.doi.org/ 10.1029/2005GL024640. Blond, N., et al., 2007. Intercomparison of SCIAMACHY nitrogen dioxide observations, in situ measurements and air quality modelling results over western Europe. J. Geophys. Res. 112, D10311. http://dx.doi.org/ 10.1029/2006JD007277. Boersma, K.F., Eskes, H.J., Brinksma, E., 2004. Error analysis for tropospheric NO2 retrieval from space. J. Geophys. Res. 109, D04311. http:// dx.doi.org/10.1029/2003JD003962. Boersma, K.F., Eskes, H.J., Meijer, E.W., Kelder, H.M., 2005. Estimates of lightning NOx production from GOME satellite observations. Atmos. Chem. Phys. 5, 2311–2331. http://dx.doi.org/10.5194/acp-5-2311-2005. Boersma, K.F., Eskes, H.J., Veefkind, J.P., et al., 2007. Near-real time retrieval of tropospheric NO2 from OMI. Atmos. Chem. Phys. 7, 2103–2118 (http://www.atmos-chem-phys.net/7/2103/2007/). Boersma, K.F., Jocob, D.J., Bucsela, E.J., Perring, A.E., Dirksen, R., van der A, R.J., Yantosca, R., Park, R.J., Wenig, M.O., Bertram, T.H., Cohen, R., 2008a. Validation of OMI tropospheric NO2 observations during INTEX-B and application to constrain NOx emissions over the eastern United States and Mexico. Atmos. Environ. 42 (16), e4480–e4497. Boersma, K.F., Jocob, D.J., Eskes, H.J., Pinder, R.W., Wang, J., van der A, R.J., 2008b. Intercomparison of SCIAMACHY and OMI tropospheric NO2 columns: observing the diurnal evolution of chemistry and emissions from space. J. Geophys. Res. 113, D16S26. Boersma, K.F., Dirksen, R.J., Veefkind, J.P., et al., 2009a. Dutch OMI NO2 (DOMINO) data product, HE5 data file user manual. Tech. rep., KNMI. Boersma, K.F., Jacob, D.J., Trainic, M., Rudich, Y., DeSmedt, I., Dirksen, R., Eskes, H.J., 2009b. Validation of urban NO2 concentrations and their diurnal and seasonal variations observed from space (SCIAMACHY and OMI sensors) using in situ measurements in Israeli cities. Atmos. Chem. Phys. 9, 3867–3879. Boersma, K.F., Eskes, H.J., Dirksen, R.J., van der A, R.J., et al., 2011. An improved tropospheric NO2 column retrieval for the Ozone Monitoring Instrument. Atmos. Meas. Technol. 4, 1905–1928. Bovensmann, H., Burrows, J.P., Buchwitz, M., Frerick, J., Noel, S., Rozanov, V.-V., Chance, K.V., Goede, A.P.H., 1999. SCIAMACHY: mission objectives and measurement modes. J. Atmos. Sci. 56, 127e150. Brinksma, E.J., Pinardi, G., Volten, H., Braak, R., Richter, A., Schonhardt, A., van Roozendael, A.M., Fayt, C., Hermans, C., Dirksen, R.J., Vlemmix, T., Berkhout, A.J.C., Swart, D.P.J., Oetjen, H., Wittrock, F., Wagner, T., Ibrahim, O.W., de Leeuw, G., Moerman, M., Curier, R.L., Celarier, E.A., Cede, A., Knap, W.H., Veefkind, J.P., Eskes, H.J., Allaart, M., Rothe, R., Piters, A.J.M., Levelt, P.F., 2008. The 2005 and 2006 DANDELIONS NO2 and aerosol intercomparison campaigns. J. Geophys. Res. 113, D16S46. Burrows, J.P., WebberBuchwitz, M., Rozanov, V., Ladstätter-Weißenmayer, A., Richter, A., DeBeek, R., Hoogen, R., Bramstedt, K., Eichmann, K., Eisinger, M., Perner, D., 1999. The global ozone monitoring experiment (GOME): mission concept and first scientific results. J. Atmos. Sci. 56, 151e175. Dobber, M.R., et al., 2006. Ozone Monitoring Instrument calibration. IEEE Trans. Geosci. Remote. Sens. 44 (5), 1209–1238. http://dx.doi.org/10.1109/ TGRS.2006.869987. Dudhia, J., Gill, D., Manning, K., Wang, W., Bruyere, C., 2005. PSU/NCAR Mesoscale Modeling System Tutorial Class Notes and Users' Guide (MM5

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