SCIENCE CHINA Earth Sciences. An emission source inversion model based on satellite data and its application in air quality forecasts

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1 SCIENCE CHINA Earth Sciences RESEARCH PAPER May 2010 Vol.53 No.5: doi: /s An emission source inversion model based on satellite data and its application in air quality forecasts CHENG XingHong 1,2*, XU XiangDe 1 & DING GuoAn 1 1 Chinese Academy of Meteorological Sciences, Beijing , China; 2 National Climate Center, Beijing , China Received January 12, 2009; accepted October 28, 2009; published online March 30, 2010 This paper aims at constructing an emission source inversion model using a variational processing method and adaptive nudging scheme for the Community Multiscale Air Quality Model (CMAQ) based on satellite data to investigate the applicability of high resolution OMI (Ozone Monitoring Instrument) column concentration data for air quality forecasts over the North China. The results show a reasonable consistency and good correlation between the spatial distributions of NO 2 from surface and OMI satellite measurements in both winter and summer. Such OMI products may be used to implement integrated variational analysis based on observation data on the ground. With linear and variational corrections made, the spatial distribution of OMI NO 2 clearly revealed more localized distributing characteristics of NO 2 concentration. With such information, emission sources in the southwest and southeast of North China are found to have greater impacts on air quality in Beijing. When the retrieved emission source inventory based on high-resolution OMI NO 2 data was used, the coupled Weather Research Forecasting CMAQ model (WRF-CMAQ) performed significantly better in forecasting NO 2 concentration level and its tendency as reflected by the more consistencies between the NO 2 concentrations from surface observation and model result. In conclusion, satellite data are particularly important for simulating NO 2 concentrations on urban and street-block scale. High-resolution OMI NO 2 data are applicable for inversing NO x emission source inventory, assessing the regional pollution status and pollution control strategy, and improving the model forecasting results on urban scale. OMI NO 2 product; variational processing method; emission source inversion model; CMAQ; air quality forecast Citation: Cheng X H, Xu X D, Ding G A. An emission source inversion model based on satellite data and its application in air quality forecasts. Sci China Earth Sci, 2010, 53: , doi: /s The mainstream of air quality modeling system nowadays is coupling an atmospheric dynamic model with a chemistry one. A typical example is MODEL-3 [1], a third-generation air quality forecasting and assessment system developed by the U.S. Environment Protection Agency (EPA). MODEL-3 contains three components: the meso-scale meteorological model, the pollution emission source model, and the CMAQ model with simulating capabilities from urban to regional and even continental scale. It is one of air quality modeling *Corresponding author ( cxingh@cma.gov.cn) systems widely used in scientific communities [2 8]. Tang et al. [9] pointed out that pollutant emission data was the important input for air quality models, and to some extent their uncertainty determines the accurate degree of model outputs. Therefore, a reliable representation of emission sources is vital for improving the new-generation air quality models, and this bottleneck problem should be appropriately addressed in model development. Xu et al. [10] proposed to retrieve pollutant emission sources by using an Adaptive Nudging Scheme in the CMAQ model. That means to add a nudging item into the air quality prediction equation to reduce the model errors. The modified Science China Press and Springer-Verlag Berlin Heidelberg 2010 earth.scichina.com

2 CHENG XingHong, et al. Sci China Earth Sci May (2010) Vol.53 No CMAQ model with this emission source inversion method simulated a nationwide NO 2 concentrations in January, April, August, and October in 2006, and the results were compared to the commonly used emission source inventory of Streets [11]. It turned out that with this adaptive nudging scheme, the model not only was able to simulate accurately the tendency of NO 2 concentrations in most of cities across China, but also significantly reduced the modeling errors. This NO 2 emission source inversion model is applicable to different regions, seasons, and weather processes. For more details about this nudging method, see ref. [12]. This adaptive nudging scheme uses the bias of the CMAQ outputs departing from the observations as a convergence criterion. Thereby, those observation data with lower spatial resolution are of limited use for NO 2 concentration forecasts. Due to the limited ground-based and aircraft NO x measurements, the NO x column concentrations from satellite with high geographical resolution have been widely used to fill the data gap since Martin et al. [13] suggested that applications of the satellite data in source inversing models allowed better capturing the emission sources and understanding of NO x chemical formation mechanisms. Currently, the satellite-derived NO x columns in the troposphere mainly come from three satellite instruments: (1) SCIAMACHY on the Envisat, (2) GOME spectrometer on the ERS-2, and (3) OMI radiometer on Aura/EOS. SCIAMACHY covers 960 km per scan with a horizontal resolution of 60 km 30 km (orbit path vertical orbit direction), accomplishing a global coverage in 6 days. The scan-width of GOME is also 960 km, with a horizontal resolution of 320 km 40 km and it takes 6 days for a global coverage. OMI scans 2600 km per orbit or a global coverage per day, with a horizontal resolution of 13 km 24 km. Overseas research findings suggest that the errors of individual satellite-derived NO x column density vary from 30% to 60% [14 16]. All these data need to be corrected with their estimated errors before they are used in an emission source retrieving model, especially in the case that the tropospheric NO x columns are affected by the atmospheric state such as the vertical profile of NO x. Currently, ground-based and aircraft observations are mainly used to correct the satellite-derived tropospheric NO x columns. Boersma et al. [17] compared the groundbased NO 2 concentration and its column concentrations from the ground to 1000-foot height observed by an aircraft with the OMI NO 2 column concentrations during the INTEX-B project conducted in New Mexico, USA, in March The two concentrations were significantly correlated, and errors were small. Boersma et al. [18] also found that both SCIAMACHY and OMI NO 2 column concentrations were lower than the surface observation. SCIAMACHY NO 2 column concentration was 40% higher than that of OMI NO 2 data observed in middle latitude region of the Northern Hemisphere, where the fossil fuel combustion was dominated. Wang et al. [19] analyzed the effects of reducing NO x emissions specified in relevant regulation for controlling vehicular emissions imposed by the Beijing municipal department. They also found that the OMI NO 2 products could provide higher spatial and temporal resolution information. Besides, they found that the NO 2 concentrations should be corrected with multiple algorithms and it can be applied to retrieve NO x emission sources. Jiang et al. [20] analyzed the variation of NO 2 using GOME data in Beijing. Li [21] compared the total NO 2 column concentration derived from ground-based DOAS observations with SCIAMACHY NO 2 data to establish the spatial and temporal distributions of NO 2. Zhang et al. [22] studied the inter-annual trends, characteristics of both temporal and spatial distributions of the tropospheric NO 2 column concentration in China with 10-year GOME and SCIAMACHY NO 2 data, and surface observations in Beijing, Shanghai, and other cities. Their results showed that the highest NO 2 values in the troposphere over China are located in Beijing, Tianjin, Hebei, the Yangtze River, the Pearl River, the Sichuan Basin, and populated metropolis. A comparison with near-surface NO 2 measurements suggested that satellite data gave excellent stability and uniformity when they were used to study long-term and large-scale variations of atmospheric trace gases and to monitor air quality in general. Using satellite data from global OMI NO 2 products, Zhang et al. [23] studied the monthly average NO 2 variation in Beijing, Huanghua Port, Sanya, Shenzhen, and Fushun. The results showed that the monthly vertical column concentration of tropospheric NO 2 in Beijing showed an increasing trend from 2004 to These studies mainly used GOME or SCIAMACHY data without making full use of OMI data with higher spatial and temporal resolutions. Besides, there are a few studies on the error analysis, data correction methodology, and emission source inversing model based on OMI NO 2 data. In this paper, a variational processing method and surface NO 2 measurements were adopted to correct the secondary OMI NO 2 products, leading to the improvements of both accuracy and reasonability of spatial distribution of NO 2. The variational processing method discussed in these studies demonstrated its objective rectification effectiveness, such as correction of aerosol distribution with surface PM 10 observations, studies on heat island effects, sand storm processes around Beijing and other regions with corrected satellite data [24 26]. In this paper, the secondary NO 2 column concentrations derived from satellite data were used to improve WRF- CMAQ outputs with assimilated NO x emission source, which were preprocessed with the adaptive nudging scheme. An analysis was made to assess the consistency between high-resolution OMI NO 2 column concentrations and surface measurements in North China. Moreover, the impact of NO 2 emission sources in North China on air quality in Beijing is also discussed. An experiment was conducted to correct OMI NO 2 data with a twice per day resolution and inverse the NO x emission sources in North China, and the

3 754 CHENG XingHong, et al. Sci China Earth Sci May (2010) Vol.53 No.5 CMAQ model was used to verify the forecast results with the retrieved sources. 1 OMI/Aura satellite products and data The main satellite system, Earth Observing System (EOS) launched by NASA, is used to monitor global change including three satellites, i.e., Terra, Aqua, and Aura. AURA is mainly used to atmospheric chemistry observations with four remote sensors, which are continuously upgraded: High Resolution Dynamics Limb Sounder (HIRDLS), Microwave Limb Sounder (MLS), Ozone Monitoring Instrument (OMI), and Tropospheric Emission Spectrometer (TES). OMI and other Aura/EOS instruments are mutually adjusted to detect backward scattered solar radiation in both visible and ultraviolet spectra using high-spectrum imaging scanner. This spectrometer has improved the accuracies of the total ozone observation, radiation, and wavelength rectifications. The OMI sensor observes along a polar-orbiting path, and provides a regional distribution dataset per day. An individual OMI NO 2 file corresponds to all OMI solar radiation measurements by Aura per orbital scan. In each process, 60 crossing paths are measured, which cover a strip of about 2600 km. An OMI NO 2 product file includes a slant NO 2 column amount (i.e., the total value measured along an optical path of the wave from the sun s rays to the atmosphere and reflection back to satellite), the NO 2 vertical column amount, and the estimated contribution of tropospheric NO 2 to total column. OMI NO 2 files also include other auxiliary data such as data quality flags, measurement precision, quality control information, and so on. The secondary OMI NO 2 products include four NO 2 vertical column amounts: (1) NO 2 column amount above the surface; (2) tropospheric NO 2 column amount (i.e., integration from the surface up to 150 hpa); (3) polluted NO 2 column amount (i.e., integration from the surface up to 250 hpa); (4) cloud-covered vertical NO 2 column amount. More detailed information on the methodologies and algorithm used to retrieve OMI NO 2 products are available in refs [27, 28]. This study used the polluted OMI NO 2 column density products, which are rectified with radiation and air quality factors. These products cover the entire country in China twice a day (around Beijing time 12:00 and 14:00). This paper uses the secondary OMI NO 2 product at around 12:00 that covers most parts of North China. The product has a horizontal resolution of 26 km 48 km. When the variational processing method is used to process the satellite data with surface observations, the variationally corrected products is interpolated in grids of 12 km 12 km to retrieve NO 2 emission sources in North China. 2 Comparative analyses of OMI NO 2 satellite and surface NO 2 measurement data The OMI NO 2 satellite and surface data for comparison covered the periods of January and August of The daily average surface data were from 1200 local standard time to the same time next day provided by the National Environmental Protection Bureau. They covered 10 cities in North China. Figure 1 showed the time series of the respective SO 2, NO 2, and PM 10 from the surface measurements, which demonstrate consistent higher values in January reflecting the heavy pollution during winter due to more extensive use of fuel for heating purpose in this part of region. The concentrations on January 3, 9, 10, 14, 15, 27 and 29, and August 18, 19, 22, 24, and 25 were selected for comparison with satellite data for their noticeably higher values. This paper analyzes the days with higher surface NO 2 concentration in January and August of 2006 in ten cities in North China by comparing surface NO 2 measurements with polluted OMI NO 2 column density (as shown in Figure 2). There were no available OMI NO 2 vertical column data in a station on January 29, two stations and one station on August 22 and 25 respectively due to the influence of cloud. As noted from Figure 2, regardless of winter or summer, the measured NO 2 concentrations in ten cities and the satellite-derived OMI NO 2 column density showed a general consistency in reflecting the spatial distribution. In January of 2006, on the days with higher surface NO 2 concentration in these cities, the NO 2 measurements and the OMI NO 2 columns showed a correlation coefficient of 0.43 (0.38 in August). It was also clear that in Beijing, Tianjin, Shijiazhuang, Yantai, and Hohhot, the measured NO 2 concentrations and the OMI NO 2 column density were relatively high, indicating local pollutions. In most these cities in January, the NO 2 concentration was above 80 µg/m 3, which exceeded the relevant air quality standard (category II); whereas in August the NO 2 concentration was below 80 µg/m 3. Besides, majority of satellite data on January 29 and in August have low values. This could be due to the better dispersion of pollutants by strong convection and horizontal advection during the noontime when the satellite was passing the North China region. Besides, stronger photochemical reactions of NO 2 and differences in meteorological conditions also played a role. However, OMI NO 2 column concentration was lower compared with surface NO 2 concentration from ground-based measurements due to lacking of data with higher temporal resolution that were able to reflect the lower concentrations before and after noontime. More in-depth analysis will be focused on this later. Boersma et al. [18] analyzed OMI NO 2 and real-world NO 2 column measurements obtained in August of 2006 and found that OMI NO 2 column concentration is lower. The authors also used GEOS-Chem model to analyze the cause of the lower OMI NO 2 and found that the main reason is the strong photolysis

4 CHENG XingHong, et al. Sci China Earth Sci May (2010) Vol.53 No Figure 1 Time series of regional average concentrations of three pollutants in North China. (a) January; (b) August. of NO 2 during noontime. Our results are thus similar to that of Boersma et al. [18]. 3 Variational correction of satellite-derived OMI NO 2 data over North China In this study, the linear relationship between surface NO 2 measurements and the OMI NO 2 column concentrations during the days with higher surface NO 2 concentration in January of 2006 is first used to correct the satellite data over North China on February 3 6, 2007 to obtain a new OMI NO 2 dataset with the same NO 2 concentration magnitude as surface measurements. Then the variational processing method is used with the NO 2 measurements from 23 stations on February 3 6 in North China to re-correct the new satellite dataset to improve the accuracy and reasonability of the satellite data for analyzing the effects of pollution sources in the surrounding regions on air quality in Beijing. A detailed description on the linear and variational processing methods can be found in ref. [29]. Eq. (1) gives the linear relationship between the two series of NO 2 concentrations on the days with higher surface NO 2 concentration in January of Y=0.14X+43.10, (1) where X is satellite-derived OMI NO 2 column concentration, and Y is the corrected OMI NO 2 dataset. Figure 3 shows the spatial distribution of satellite OMI NO 2 concentrations, the corrected values with the linear and variational processing methods, and surface NO 2 measurements on February 6, Overall, the distribution of linearly and variationally corrected OMI NO 2 concentrations is consistent with the surface measurements. Figure 3 also

5 756 CHENG XingHong, et al. Sci China Earth Sci May (2010) Vol.53 No.5 Figure 2 Comparisons of surface NO 2 measurements and satellite-derived OMI NO 2 column density (polluted products) on these days with higher surface NO 2 concentration in January and August of (a) January; (b) August. The date from left to right is Jan. 3, 9, 10, 14, 15, 27, 29 (a); Aug. 18, 19, 22, 24, 25 (b). shows the following spatial distribution characteristics of NO 2 : in these cities including Jinan, Shijiazhuang, Handan, Baoding, Tangshan, and Beijing, the OMI NO 2 concentrations and its corrected values were relatively higher indicating heavy pollution, while NO 2 concentrations in other cities were relatively lower values. The spatial distribution of OMI NO 2 values corrected with the variational approach showed that Tianjin and Hohhot are heavily polluted cities which were consistent with surface measurements. The above analysis suggested that the spatial distribution of the OMI NO 2 columns, which were variationally corrected with surface measurements, was reasonable, in particular, highvalue zones in Beijing and the surrounding regions (Shijiazhuang, Baoding, and Tianjin). This finding was consistent with the ref. [22]. 4 Application of OMI NO 2 data in emission source inversion model and air quality forecasts The above analysis showed that the OMI NO 2 data corrected with linear and variational processing methods were consistent with surface measurements in terms of spatial distribution. Therefore, we used the OMI NO 2 data with higher resolution corrected with linear and variational processing methods to further retrieve the original emission sources inventory with a 12 km mesh distance in North China. The assimilated emission sources and the WRF- CMAQ model were used to verify if they could better reflect the real-world measurements. As a relatively accurate emission inventory was essential for formulating a control strategy of pollution sources successful and keeping good air quality in Beijing, this paper highlights the impor-

6 CHENG XingHong, et al. Sci China Earth Sci May (2010) Vol.53 No Figure 3 Spatial distribution of satellite-derived OMI NO2 concentrations corrected with linear and variational processing methods compared with surface NO2 measurements on February 6, (a) OMI NO2 columns (10 14 molec/cm 2 ); (b) corrected OMI NO2 concentration values (µg/m 3 ); (c) NO2 surface measurements (µg/m 3 ).

7 758 CHENG XingHong, et al. Sci China Earth Sci May (2010) Vol.53 No.5 tance of high-resolution OMI NO 2 satellite data in clarifying NO 2 emission sources and improving air quality forecasts in North China. 4.1 WRF-CMAQ model parameterization and experiment design schemes Basic CMAQ and WRF model parameters (1) Grids. Dual one-way nested grids were adopted, with the first domain covering N, E, in which its center located at 34.0 N, E; grid number was and grid mesh was 36 km. The second domain covered N and E; grid number was and grid distance was 12 km. The dual grids were vertically divided into 13 irregular layers, higher resolutions were in the lower atmosphere, and the resolution decreased with height. Out of 13, nearly half were within 2 km above the ground. (2) Physical processes. For horizontal advection and vertical convection, the Piecewise Parabolic Method (PPM) was adopted. For vertical divergence, the Crank-Nicholson solution was applied. The dry- and wet-deposition processes were taken into account. (3) Chemical mechanism. The improved Carbon Bond Mechanism Version 4.0 (CBM-IV) mechanism (CB4-AE3- AQ) was selected, taking into account the liquid phases and aerosol chemistry. The chemical mechanism used the QSSA algorithm for solutions. (4) Meteorological field input. The hourly meteorological fields are from WRF model outputs, which include height, pressure, wind, temperature, moisture fields, cloud cover, precipitation, vertical dispersion coefficient etc. The horizontal grids of WRF were same to CMAQ and 28 irregular layers were set in the vertical direction. The model ceiling was about 17 km. The closer it was to the ground, the higher vertical resolution would be. The 1 1 NCEP data (6-hour interval) were used as a large-scale meteorological background and boundary conditions. For cumulus parameterization, the Kain-Fritsch scheme was adopted (new Eta); for the boundary layer parameterization, the Mellor-Yamada- Janjic (Eta) turbulent kinetic energy scheme was taken; and for the atmospheric radiation, RRTM long-wave and cloud (Dudhia) short-wave radiation scheme were used. (5) Source emission input. A list of original emission sources was released by the U.S. Environmental Protection Agency (EPA). Horizontal resolution was 36 km and vertical layers were set as 12. The emission sources inventory was from Streets for Asia [11]. The natural source was from Global Emission Inventory Activity (GEIA). Based on the original emission source, a list of the sources was retrieved using the adaptive nudging scheme. (6) Initial and boundary conditions. CMAQ s own vertical profile representing a clean atmosphere was used as initial and boundary concentration for the first day; the model output from the first day was used as initial concentration for the second day. (7) Periods of emission source inversion and verification were set as follows: 1) Emission source inversion: February 6, 2007 (winter), August 12, 2006 (summer); 2) Verification: February 3 5, 2007 and August 1 10, In analyzing the CMAQ model output for cities in North China, the grid-point values near cities would be used as the model outputs Experiment scheme design (1) Winter experiment. The OMI NO 2 satellite values from 340 grid points in North China on February 6, 2007 corrected with surface measurements were used to retrieve the inversed emission sources. Then, the assimilated emission sources and the WRF-CMAQ model were used to simulate the NO 2 concentrations for 20 stations on February 3 5. (2) Summer experiment. The OMI NO 2 values from 330 grid points in North China on August 12, 2006 corrected with surface measurements were used to retrieve the inversed emission sources. The assimilated emission sources and the WRF-CMAQ model were used to simulate the NO 2 concentrations for 35 stations in North China plus 5 stations in Beijing on August Description of observation data (1) Winter. The daily average SO 2 and NO 2 concentrations on February 3 6, 2007 were from 112 Environmental Monitoring Stations of the National Environmental Protection Bureau. (2) Summer. The daily average SO 2 and NO 2 concentrations on August 1 10, 2006 of the North China region were from 40 Environmental Monitoring Stations of the National Environmental Protection Bureau. 4.2 Analysis of experiment results Winter time Figure 4 compared the WRF-CMAQ model outputs using the assimilated emission sources based on the corrected high resolution OMI NO 2 data with the surface NO 2 concentration measurements at 20 stations in North China on February 3 5, As shown in Figure 4, when using the original emission sources,correlation between simulated and observed results at 20 monitoring stations in three days was relatively poor (R=0.25). When using assimilated emissions sources, the correlation was noticeably improved with R=0.59 on 99.9% confidence level (51 samples). The respective average deviations of using the two emission sources were 66.98% and 2.22%. The above analysis suggested that when using assimilated emission sources inventory based on high-resolution OMI NO 2 data, simulated NO 2 concentrations and real-world measurements are close and

8 CHENG XingHong, et al. Sci China Earth Sci May (2010) Vol.53 No Figure 4 A comparison between model outputs using the original and assimilated emission inventory based on high-resolution OMI NO 2 data respectively and surface NO 2 measurements at 20 stations in North China on February 3 5, (a) Correlation between simulated values and surface measurements using the original emission sources; (b) correlation between simulated values and surface measurements using the assimilated emission inventory; (c) comparison of simulated concentration using the two above inventory respectively and surface values. The date from left to right is Feb the spatial distributions of the two datasets are similar. The assimilated emission source based on corrected high-resolution OMI NO 2 satellite data and the WRF-CMAQ model can be used to improve the wintertime NO 2 concentration level and tendency forecasts significantly Summer time Figure 5 compared the WRF-CMAQ model outputs with surface NO 2 measurements at 35 stations in North China plus 5 stations in Beijing on August 1 10, The model inputs were the original emission inventory and assimilated emission source based on corrected high-resolution OMI NO 2 satellite data respectively. Noted from Figure 5(a) and (b) that when using initial and assimilated emission sources, correlation coefficients were 0.12 and 0.45 respectively while the average deviations were 71.29% and 44.16% respectively. It is clear that using the assimilated NO 2 emission sources based on OMI NO 2 datasets can improve, to some extent, the forecast results of NO 2. However, the simulated NO 2 values in most stations were found lower than the surface measurements. This may be because the satellite-derived OMI NO 2 concentrations were smaller than real-world measurements [18]. In addition, NO 2 data from only a few observing stations on the ground could be used to correct satellite, the accuracy of assimilated emission sources and simulated results was therefore limited. Further studies will be made in the near future. Figure 5(c) showed comparisons of observed NO 2 concentrations at five monitoring stations in Beijing with simulated results using original emission inventory and assimilated emission inventory respectively. It was clear that high-resolution OMI NO 2 satellite data were useful in retrieving NO x emission sources with high resolution and detailed information in Beijing, and improving forecast results of NO 2 concentration in urban and surrounding areas. With exception of underestimation at a few stations (e.g., Gucheng and Tiantan stations), simulated results were close to surface measurements. The 10-day average deviation at five stations was 17.0% and 7.43% for Aotizhongxin, Dongsi, and Nongzhanguan. This showed that the high-resolution OMI NO 2 satellite data could be useful to better retrieve, to some extent, the NO 2 emission sources in North China, especially in Beijing, and to improve NO 2 concentration forecasts on a street-block scale Spatial distribution of simulated NO 2 concentration in North China In above analysis, when using the assimilated emission sources based on high-resolution OMI NO 2 satellite data corrected by variational processing method, the CMAQ model outputs were closer to surface NO 2 measurements than simulated results using the original emission inventory. As shown in Figure 6, we intended to show the differences in the spatial distribution of NO 2 with two kinds of assimilated emission inventory: (1) one was directly retrieved based on surface NO 2 concentration measurements from

9 760 CHENG XingHong, et al. Sci China Earth Sci May (2010) Vol.53 No.5 Figure 5 A comparison between model outputs using the original and assimilated emission inventory based on high-resolution OMI NO 2 data respectively and surface NO 2 measurements for 35 stations in North China plus 5 stations in Beijing on August 1 10, (a) Correlation between simulated values using the original emission inventory and surface measurements for 35 stations in North China; (b) correlation between simulated values using the assimilated emission inventory and surface measurements for 35 stations in North China; (c) comparison of simulated concentration level using the two above inventory respectively and surface values for 5 stations in Beijing. The date from left to right is Aug limited stations; (2) another was retrieved based on highresolution OMI NO 2 satellite data corrected with linear and variational processing methods. The spatial distribution of simulated NO 2 concentrations in North China using the assimilated emission sources based on high resolution and corrected OMI NO 2 satellite data was more consistent with that of observations. It also provided more detailed and high-resolution information beyond observing sites. Particularly, the distribution of simulated higher value zones in Shijiazhuang, Baoding, and Beijing were more similar to that of observed values. On the contrary, when using the assimilated emission sources based on limited surface NO 2 measurements, outputs just represented NO 2 concentrations at stations, and NO 2 concentrations in other adjacent sites in the model domain showed almost same values to that at observing sites, which was not true actually. Besides, the spatial distribution of NO 2 concentrations simulated by CMAQ differed largely from the observed pattern. And it failed to capture the high-value zones around Baoding, giving well underestimated values than the true measurements. Again, the above analysis showed that using the assimilated emission inventory based on high resolution and corrected OMI NO 2 satellite data, the CMAQ can simulate more detailed and high-resolution NO 2 concentrations, which are more consistent with measured NO 2 concentrations. Satellite data are particularly important to fill the gaps due to limited surface measurements, and to simulate NO 2 concentrations on street-block scale. 5 Conclusions This paper gives a comparative analysis of consistency between high-resolution OMI NO 2 column concentrations derived from satellite data and surface NO 2 concentration measurements in both winter and summer in North China. The focus is to construct an emission source inversion model based on satellite data using variational processing method and adaptive nudging scheme to investigate the applicability of high resolution OMI column concentration data in air quality forecasts over North China. The conclusions are as follows: (1) The spatial distribution of high-resolution OMI NO 2 column concentration is consistent with surface NO 2 concentration in both winter and summer in North China, showing a significant correlation between the two datasets. Satellite-derived OMI NO 2 columns data corrected by linear and variational processing methods are applicable to a comprehensive analysis with surface observation in North China. After being linearly and variationally corrected, the spatial distribution of OMI NO 2 concentrations is in line with that of surface measurements, reflecting local polluted characteristics of NO 2 distribution, especially the high-value strip across southwest and southeast around Beijing (i.e., Shijiazhuang, Baoding, Tianjin).

10 CHENG XingHong, et al. Sci China Earth Sci May (2010) Vol.53 No Figure 6 A comparison of the spatial distributions of simulated NO2 concentrations using different assimilated sources in North China on August 7, 2006 from the WRF-CMAQ outputs. (a) Retrieved based on surface NO2 concentration measurements; (b) retrieved based on high resolution and corrected OMI NO2 satellite data; (c) observed NO2 concentrations.

11 762 CHENG XingHong, et al. Sci China Earth Sci May (2010) Vol.53 No.5 (2) By the assimilated emission sources based on corrected high-resolution OMI NO 2 data, more detailed and high-resolution distribution of NO 2 is simulated with CMAQ model, which is consistent with that of observed concentration. This is particularly useful to simulate NO 2 concentrations on urban and street-block scale, especially for the region where surface data is sparse. (3) Using the assimilated emission sources based on corrected high-resolution OMI NO 2 data, the WRF-CMAQ model significantly improved NO 2 concentration forecasts level in North China in both winter and summer. Therefore, the OMI NO 2 data are practically useful and preferable to better retrieve NO x emission sources in North China, and to improve NO 2 concentration forecasts on street-block scale. The surface data used in this study are with limited temporal and spatial resolutions. As such, we are not able to perform more detailed comparison analysis on the OMI NO 2 data. However, we focused on constructing an emission source inversion model based on satellite data using variational processing method and adaptive nudging scheme to investigate the applicability of high-resolution OMI column concentration data in air quality forecasts over North China. We will try to use more surface and ground-based data with better temporal and spatial resolution to compare with the OMI NO 2 satellite products. The findings from this study have certain scientific and referencing values on the utilization of satellite products for emission source inversing, regional pollution control, and air quality forecasts. Thanks are due to the anonymous reviewers. 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