Development of a high-resolution emission inventory and its evaluation through air quality modeling for Jiangsu Province, China

1. State Key Laboratory of Pollution Control & Resource Reuse and School of the Environment, Nanjing University, 163 Xianlin Ave., Nanjing, Jiangsu 210023, China 2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Jiangsu 210044, China 3. Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China 4. Jiangsu Provincial Academy of Environmental Science, 176 North Jiangdong Rd., Nanjing, Jiangsu 210036, China

to downscaled national inventory that generally applied regional average levels of emission factors due to unavailability of data (Zhao et al., 2015).The benefits of improved emission estimation, spatial and temporal distribution, or chemical speciation of pollutants, however, have not been sufficiently confirmed with CTM.Recently, Yin et al. (2015) conducted CMAQ simulation on O 3 using updated VOC emission inventory for PRD, implying that the reduced uncertainties of total emission level and spatial distribution could improve the model performance compared with ground observation.
We select Jiangsu, a typical province with well-developed industry in eastern China, to develop and evaluate the high-resolution emission inventory.The geographic location and cities of the province are illustrated in Figure S1 in the supplement.With a total area of 107 200 km 2 and population of 79.2 million in 2012, Jiangsu was the first ranked province in gross domestic product (GDP) per capita in China (NBSC, 2013a;JSNBS, 2013).It accounted for 8.0%, 7.6%, 8.9%, and 10.2% of the country's power generation, cement, pig iron, and crude steel production in 2012, respectively (NBSC, 2013b).Intensive energy consumption and industry resulted in heavy air pollution: all the 13 cities had their annual average concentrations of PM 2.5 exceeding the NAAQS in 2012, with the highest reaching 74 μg/m 3 in the capital city, Nanjing.Clear uncertainties exist in current multi-scale emission inventories.Zhao et al. (2015), for example, estimated Nanjing's SO 2 and PM 2.5 emissions at 165 and 71 Gg in 2012, respectively, while the results by Fu et al. (2013) were 131.8 and 35.3 Gg, implying the necessity of improvement and assessment of regional emission inventory, for both scientific and policy implication.In this work, a comprehensive emission inventory for Jiangsu with high temporal and spatial resolutions was first established with the best available data of local emission sources incorporated.This provincial emission inventory was then compared with other inventories and satellite observation to test its improvement on emission estimation and spatial distribution.CMAQ was further applied to indicate the advantage of the provincial inventory prior to downscaled national and regional ones.In particular, the impacts of spatial distribution of emissions on model performance were analyzed for period with unfavorable meteorological condition.Finally, the improved inventory was applied for sensitivity analysis on regional PM 2.5 and O 3 formation.

Methodology of provincial emission inventory development
The emissions of gaseous pollutants (SO 2 , NOx, CO, NH 3 and VOCs), greenhouse gas CO 2 , particulate matter (total suspended particulates (TSP), PM 10 and PM 2.5 ) and its chemical compositions (black carbon, BC and organic carbon, OC) of anthropogenic origin in Jiangsu were estimated with a bottom-up method.Emission sources were classified into seven main categories, including power plants, industry, solvent usage, transportation, residential & commercial, agriculture and others.Industry was subdivided into iron & steel, cement, and other industry including nonferrous metal smelting, brick and lime kilns, chemical industry and other industry boilers.Residential & commercial sector included household combustion of fossil fuel and biofuel.Agriculture included livestock and fertilizer usage.Open biomass burning, cooking, and waste (water) disposal, were considered as other sources.The detailed categories were summarized in Table S1 in the supplement.For each category, point, mobile and area sources were defined depending on the detailed levels of information and the emission characteristics, For point sources, information on emission factor and activity level was investigated and compiled for individual plants, and the annual emissions of atmospheric pollutants were calculated using Eq.(1), as described in Zhao et al (2015) : (1) where i, j and m represented the pollutant species, individual plant, and fuel/technology type, respectively; AL was the activity level data; EF was the uncontrolled emission factor; and η was removal efficiency of air pollutant control device.
Regarded as mobile sources, the emissions of on-road transportation were calculated by the CORPERT model (EEA, 2012) and then spatially allocated based on the road net information of the province.Area sources include non-road transportation, solvent use, residential & commercial sector, agriculture, and small industry plants without detailed information collected.The emissions from non-road transportation and agriculture were estimated following the methods by Zhang et al. (2010) and Dong et al. (2009), respectively.

Activity level
Most of coals in Jiangsu were used by power and industry sectors, and household In particular, the kilns for combustion and factories for calcination were separately investigated for cement production, and 25% of cement plants contained the both processes.For power, cement, and iron & steel sectors, the aggregated activity levels compiled plant by plant, i.e., the coal consumption of power generation, and the production of cement, clinker, coke, pig iron, and crude steel, were estimated at 108%, 95%, 120%, 109%, 104%, and 98% of the provincial statistics, respectively (JSNBS, 2013).
The comparison indicates, on one hand, that larger activity levels would be obtained based on detailed investigation of individual emission sources than official statistics for power and most processes of iron & steel sectors.On the other hand, almost completed investigation on point sources was conducted for those sectors, as very small fractions of activities (5% for cement and 2% for steel production) had to be estimated as area sources.For other industrial sectors, smaller fractions of point sources were obtained, e.g., 32% and 36% for ammonia and sulfuric acid production, respectively.
For on-road transportation, the input parameters of COPERT 4 include regional was obtained from the survey by local government (internal data, Zhao et al., 2015).Vehicle speed by road type (i.e., freeway, arterial and residential road) and VKT by vehicle type were determined according to previous studies (Cai and Xie, 2007;Wang et al., 2008) and the guidebook of emission inventory development for Chinese cities (He, 2015).For area sources, the coal consumption of residential & commercial activities was directly taken from National Energy Statistic Yearbook (NBSC, 2013c), while that of small industrial plants were calculated as the coal consumption of total industry from the provincial energy balance (NBSC, 2013c) minus the coal consumption of industrial point sources.The original data on the activity levels of agriculture, solvent use, non-road transportation and open biomass burning were obtained from the provincial or city statistical yearbooks (JSBNS, 2013).

Emission factor
Following previous studies (Zhao et al., 2008;2010;2011;2012a;2012b;Y. Zhao et al., 2013), an emission factor database for Jiangsu was established with detailed information and available results of emission measurements on local sources incorporated.For power sector, parameters relevant to emission factors were obtained at individual plant level including installed capacity, fuel type and quality (e.g., sulfur and ash content), combustion technology, and the type and removal efficiencies of APCDs.In particular, the information of APCD installation obtained from provincial environmental statistics and on-site survey was further corrected according to the official documents on APCD projects of power plants published by (coking, sintering, pig iron production, and steel making) were estimated combining the unabated emission factors from previous database (Zhao et al., 2011;Y. Zhao et al., 2013) and the investigated information on penetrations and removal efficiencies of APCDs at plant level.
Provided in Table S2, 64.3% of Jiangsu's iron & steel plants installed FGD in 2012 and the average SO 2 removal efficiency was estimated at 78.0%.Dust collectors were installed at almost all the furnaces for pig iron production and steel making, with the averages of PM removal efficiency estimated at 96% and 94%, respectively.For cement production, emission factors were calculated for the two main processes, coal combustion and calcination, following Lei et al. (2011).With dust collectors installed at 99% of plants, the average of overall removal efficiency on TSP was estimated at 97.3% according to our plant-by-plant investigation (Table S2).
For area sources, emission factors for non-road transportation were obtained from Zhang et al. (2010), Ye et al. (2014) and Fu et al. (2012).Emission factors for household fossil fuel and biofuel combustion were from the summary of field measurements in Y. Zhao et al. (2013).For agricultural activities including livestock and fertilizer use, emission factors were obtained from Dong et al. (2009) and Yin et al. (2010).Emission factors of VOCs were mainly from Wei et al. (2009) with update for typical sources based on limited local measurements and survey (Bo et al., 2008;EEA, 2013;Xia et al., 2014).

Temporal and spatial distributions
The monthly variations of emissions from power plants and industrial sources were assumed to be dominated by with the variations of electricity generation and typical industrial production, respectively, and those data were obtained from National Bureau of Statistics of China (http://data.stats.gov.cn/).As the real-time monitoring on urban traffic was unavailable for the whole province, the temporal distribution of emissions from on-road vehicles in other cities was considered to the same as Nanjing (Zhao et al., 2015).For other sources, the temporal distributions in Shanghai investigated by Li et al. (2011).
Different parameters were used to conduct the spatial allocation of emissions by sector.
Latitude and longitude of each point source collected from PSC were checked and revised according to Google Earth to avoid the unexpected errors in the existing database.

Configuration of air quality modeling
The Models-3/Community Multi-scale Air Quality (CMAQ) version 4. The third (D3, 124×70 cells) covered the mega city Shanghai and six most developed cities in southern Jiangsu including Nanjing, Changzhou, Zhenjiang, Wuxi, Suzhou and Nantong.
The simulation period was selected from October 1 to 31, 2012, with the first five days chosen as spin-up period to provide initial conditions for later simulations.three hours interval at meteorological stations were downloaded from National Climatic Data Center (NCDC), including 43 stations in the second modeling domain D2 and 7 stations in the innermost domain D3 (as labeled in Figure 1).The statistics of those indicators for wind speed and direction at 10 m (WS10 and WD10), temperature at 2 m (T2) and relative humidity at 2 m (RH2) for October 2012 in D2 and D3 were summarized in Table S3.
Discrepancies between WRF simulations and ground observations were within acceptable range (Emory et al., 2001) and comparable to the results of other studies (Wang et al., 2014).
Better agreements were found for simulations of T2 and RH2 than WS10 and WD10.In spite of moderate overestimation by 0.3% and 3.5% in T2 and RH2, the IOA of those two variables were 0.97 and 0.90, indicating the high consistency with observationa.Slightly higher than observation in D2 and D3, simulated WS10 might enhance the diffusion process of pollutants in atmosphere eventually and thus lead to underestimation in pollutant concentrations.For WD10, the bias between simulations and observations was 3.6 degree in D3 within the benchmark range (Emory et al., 2001).
The carbon bond gas-phase mechanism (CB05) and AERO5 aerosol module were adopted in all the CMAQ modules.The initial and boundary conditions for first modeling domain was the default clean profile, while for nested domain they were extracted from the CCTM outputs of its mother domain.Anthropogenic emissions used for domains D1 and D2 were obtained from the downscaled MEIC with an original spatial resolution of 0.25°×0.25°.
For Jiangsu domain in D3, three inventories, i.e., downscaled MEIC, the regional inventory of YRD by Fu et al. (2013), and the provincial inventory developed in this work, were used to test the modeling performance and potential improvement in emission estimation.In addition, biogenic emission inventory were from the Model Emissions of Gases and Aerosols from Nature developed under the Monitoring Atmospheric Composition and Climate project (MEGAN-MACC, Sindelarova et al., 2014), and the emission inventories of Cl, HCl and lightning NO X were from the Global Emissions Initiative (GEIA, Price et al., 1997).The vertical distribution of emissions was determined by source following L. Wang et al. (2010).

Emission estimation and sector contribution
The total annual emissions of SO 2 , NOx, CO, TSP, PM 10 , PM 2.5 , BC, OC, CO 2 , NH 3 and VOCs were calculated at 1142, 1642, 7680, 2606, 1394, 941, 57, 138, 860458, 1100 and 1747 Gg for Jiangsu in 2012, respectively.The emissions by city were provided in Table 1.In general, higher emissions were found in cities in southern Jiangsu with large population and intensive economy and industry than those in northern Jiangsu.Taking 52% of the provincial industrial GDP, Suzhou, Nanjing, and Wuxi were estimated to collectively account for 41%, 41%, 35%, 31%, 43% and 39% of the total emissions of SO 2 , NOx, CO, PM 2.5 , CO 2 and VOCs, respectively.Xuzhou, different from other cities in northern Jiangsu, had a relative high emissions of pollutants due to its well development of large-scale industry.Because of the active agricultural development, NH 3 emissions in Huai'an and Nantong were estimated at 195.9 and 187.1 Gg, significantly higher than other cities.
Shown in Figure 2 is the detailed sector contribution of pollutants from point, mobile (on-road transportation) and area sources.The point sources including power and industrial plants contributed 84%, 71%, 55%, 83%, 75%, 64%, 41%, 31%, 83%, 2% and 36%, to the total emissions of SO 2 , NOx, CO, TSP, PM 10 , PM 2.5 , BC, OC, CO 2 , NH 3 and VOCs, respectively.The emission fractions of point sources were notably larger than those in other regional inventories (Fu, 2009;Tang et al., 2012;B. Zhao et al., 2012), resulting mainly from the compiling and application of information on individual power and industrial plants from varied data sources.Defined as area source, open biomass burning contributed 12%, 19%, 23%, 11% and 41% to the total CO, PM 10 , PM 2.5 , BC and OC, respectively.
The dominant contributors to SO 2 were power plant, iron & steel and other industry, with the emission fractions estimated at 38%, 10% and 45%, respectively.Although the coal consumption in power sector was 3.5 times larger than that in other industry sector (cement and iron & steel production excluded, JSNBS, 2013), smaller contribution to SO 2 emissions were found for coal-fired power plants, implying the benefits of strict control on SO 2 emissions from power sector.As shown in Table S2, the application rate and average SO 2 removal efficiency of FGD in power sector were significantly higher than those in other  S2), while Tian et al. (2013) estimated the values for SCR and SNCR at 70% and 25%, respectively, indicating the differences in assessment of emission controls for power sector between the provincial and national emission inventories with varied data sources.Transportation (including on-road and non-road) was estimated to be the second largest sector for NO X emissions, with the share to the total emissions calculated at 24%.Without specific control measures, cement and other industry were estimated to account for 7% and 17% of total NOx emissions.
CO was mainly generated from the manufacturing processes in iron & steel plants.The production of pig iron and crude steel in Jiangsu accounted for 9% and 10% to the national total in 2012, respectively (NBS, 2013), and was higher than other provinces in China except Hebei.Due to the intensive iron & steel industry, the contribution of the sector to the provincial total CO emissions was estimated at 35%.Residential biofuel combustion, open biomass burning and on-road transportation were also large contributors to CO with the emission fractions calculated as 24%, 12% and 11% respectively.
For particles, iron & steel and cement production were estimated to be the largest sources, contributing 24% and 27% to the total emissions of PM 10 , and 27% and 19% to PM 2.5 , respectively.Even with the largest coal consumption among all the sectors, the emissions from power plants were relatively small (6% and 4% to PM 10 and PM 2.5 emissions, respectively), resulting mainly from the relatively high penetrations and removal efficiencies of dust collectors.Great differences existed in the sector distribution of BC and OC emissions.
Iron & steel was estimated to be the largest source of BC, while open biomass burning and biofuel burning in residential stoves dominated OC, with the shares estimated at 41% and 29%, respectively.Moreover, as BC exhausted from the diesel engines was demonstrated to be higher than OC in previous situ measurements (He et al., 2015), BC emissions from non-road transportation (agricultural machines, rural vehicles, ships and construction machines) was estimated more than twice larger than OC.
For VOCs, solvent use and other industry including oil refinery, chemical industry and combustion were identified as the largest sources contributing 30% and 29% to total emissions, respectively.In particular, oil refinery and chemical engineering collectively accounted for 74% of the emissions of other industry.Due to lack of investigation on chemical industry plants, the fraction of area sources to the emissions of other industry reached 35%.Transportation and residential cooking are estimated to contribute 12% and 4% to total VOCs emissions, respectively.Livestock and fertilizer use were the two dominating sources of NH 3 , with the shares to total emissions estimated at 47% and 45%, respectively.
For industry, ammonia production was the main source accounting for half of NH 3 emissions.
The spatial distribution of SO 2 , NOx, CO, PM 2.5 , VOCs and NH 3 emissions at a resolution of 3×3km were illustrated in Figure S3 in the supplement.Outstandingly high emissions of SO 2 , NOx, PM 2.5 and VOC indicated the existence of large industrial plants, particularly in Suzhou, Nanjing and Wuxi along with the Yangtze River.For CO and NOx, large emissions were distributed along the road net in the province, reflecting the important contribution of on-road transportation.Unlike other pollutants, high NH 3 emissions were more evenly distributed in rural areas as dominated by agricultural activities.

Comparisons with other studies
Figure 3 compares the emission estimations for Jiangsu between our provincial inventory and previous studies including two regional inventories (Fu et al., 2013;Li et al., 2011)  While provincial economy and energy data were generally applied in all the national/regional inventories, information of individual large emitters were incorporated as well in MEIC, Fu et and Li et al. (2011).For MEIC, large fraction of emissions from power generation sector was calculated plant by plant with relatively good data availability, while emissions from other industrial sectors were basically calculated at regional average and spatially allocated as area sources.The results in Fu et al. (2013) were generally smaller than those in other two inventories for 2010.
Attributed mainly to the improved use of FGD, the total SO 2 emissions were estimated to decline from 2010 to 2012 for the whole country (Xia et al., 2016) and typical city in Jiangsu (Zhao et al., 2015).It was reasonable to some extent that the SO 2 emissions in Jiangsu estimated in this work for 2012 was less than the 2010 results by Li et al. (2011) and MEIC.
Our estimation was 15% lower than the result for Jiangsu extracted from the national inventory by Xia et al. (2016), due mainly to the discrepancies in the penetration and SO 2 removal efficiency of FGD applied in the two inventories.Such information was obtained at provincial or national average level by Xia et al. (2016), in contrast to the provincial inventory based on investigation at plant level.For example, Xia et al. (2016) assumed that the penetration rates of wet and dry FGD technologies in coal-fired power sector were 83% and 5% in 2012, with the removal efficiencies estimated at 80% and 40%, respectively, and that there was not any SO 2 control in the remaining 11% of installed capacity at all.According to our plant-based investigation, the controls in Jiangsu were clearly enhanced, as shown in Table S2.As a result, SO 2 emissions from power sector was calculated at 430.0 Gg for Jiangsu 2012 in this work, 42% lower than those in Xia et al. (2016).The result for 2012 in our provincial inventory, however, is very close to the estimation by MEIC for 2010 (437.4 Gg), even though the coal consumption of power generation increased 29% for the period 2010-2012(JSNBS, 2013)).Besides the uncertainty in emission estimation from varied data sources of the two inventories, the improved use of FGD in the sector could be an important reason for the restrained emissions.Similar fact was found for Nanjing, the capital city of Jiangsu, that the SO 2 emissions of power generation calculated at city level kept stable along with a 25% growth of coal consumption from 2010 to 2012 (Zhao et al., 2015).
NOx emissions in our provincial inventory was slightly higher than those of Li et al.  2016).For on-road transportation, the emission factors were estimated using CORPERT in this work, while they were obtained from limited domestic measurements in Xia et al. (2016).That was also the most important reason for the discrepancies in CO emission estimation between the two studies.For 2010, the NO X emissions estimated by Fu et al. (2013) was 18% and 36% lower than those by Li et al. (2011) and MEIC, resulting mainly from the higher application rate and removal efficiency of SCR/SNCR technologies for power sector used in Fu et al. (2013).
The PM 2.5 and PM 10 emissions in the provincial inventory were estimated to be 6% and 23% higher than those of Xia et al. (2016), and the sector contributions were notably different in the two inventories.For example, industry was estimated to contribute 77% and 80% of PM 2.5 and PM 10 in the provincial inventory, much larger than the fractions at 45% and 52% by Xia et al. (2016), respectively.In this work, the PM 2.5 and PM 10 emissions from cement production were calculated at 181 and 384 Gg, i.e., 2.5 and 2.0 times to those in Xia et al. (2016), and the analogue numbers for iron & steel production were 134 and 263 Gg, and 1.8 and 1.7 times, respectively.The discrepancies resulted mainly from the inconsistent penetration rates and removal efficiencies of dust collectors determined at national level and from on-site survey at provincial level.Taking cement as an example, all the plants were assumed to be installed with dust collectors, and the national average removal efficiency at 99.3% was applied in Xia et al. (2016), clearly larger than that from plant-by-plant survey as shown in Table S2.Note that the particle emissions in the provincial inventory were estimated higher than those in national ones including MEIC and Xia et al. (2016), while the gaseous pollutant emissions were lower except for NH 3 and CO 2 .It thus implied that the control of SO 2 and NOx in Jiangsu were stronger than the national average level but weaker for particles.
Compared to the emissions for 2010 estimated by other studies, the PM 2.5 and PM 10 in our provincial inventory were 58% and 56% larger than  , 2013), thus the growth of activity levels were expected to result in enhanced emissions, as very little progress was achieved for NH 3 control for these years.

Analysis of spatial distribution of emissions from given sectors
To further explore the discrepancies in emission estimation and spatial distribution from varied data and emission allocation methods, comparisons between MEIC and our provincial inventory were conducted for pollutants from typical sources, including SO 2 from power plants, NO X from transportation, and PM 2.5 from industry.The estimates in this work were reallocated into the 0.25°×0.25°grids, consistent with the spatial resolution of MEIC, and the correlation coefficients for emissions in all the grids can be calculated, as shown in Figure 4.
Due mainly to the relatively transparent and easily available information of power plants, good consistency was found for SO 2 emissions from power sector in the two inventories, with the correlation coefficient calculated at 0.7 (Figure 4a).Even though the fundamental information of power plants in China is more accessible than other industry sources, mismatches still exist in different data sources.For example, some emission hotspots in our provincial inventory were not totally identical with those in MEIC in Suzhou, Nantong and Nanjing.In contrast to plant-by-plant investigation, the data from existing statistics at national level could not fully track the actual changes in the emitters, e.g., operation of new-built units, shutting down the small ones, or relocation of individual plants.In MEIC, moreover, the SO 2 emissions in several grids were estimated extremely small (less than 1 Mg), indicating that part of emissions from power sector was still allocated as area sources based on density of GDP or population.In contrast, all the plants were identified as point sources in the provincial inventory, based on the thorough investigation on individual sources.For NO X from transportation, the correlation coefficient was calculated at 0.8, indicating an even better consistency than SO 2 from power plants between the two inventories (Figure 4b).Although the difference in total emissions was small between our provincial inventory (682 Gg) and MEIC (722 Gg), the estimation of MEIC was notably higher than our result for northern Jiangsu including Yancheng, Huai'an and Suqian, implying the impacts from different ways for emission allocation.In this work, emissions from on-road vehicles were calculated and allocated based on road net that incorporates the information of transportation flow by road grade for each city.For non-road sources, large fraction of emissions was allocated based on the GDP density incorporated with land-use type.In national emission inventories, however, the emissions were first calculated at provincial level, and then downscaled at certain horizontal resolution.Despite of the discrepancies, it could be indicated by the relatively high spatial correlation between the two inventories that using GDP as proxy for emission allocation would be acceptable when detailed information on road net and transportation flow was unavailable, since vehicles were largely concentrated in downtown with the intensive economic activity.
For PM 2.5 from industry, the correlation coefficient was calculated at 0.335, significantly lower than those mentioned above, indicating larger discrepancy in spatial distribution of industrial emissions between provincial and national inventories compared to power and transportation sectors.As shown in Figure 4c, the emission hotspots in the provincial inventory are highly consistent with the locations of large industrial PM 2.5 emitters (more than 10 Gg) estimated in this work, while the emission in MEIC were more distributed in developed cities (e.g., Suzhou) with high density of population or economy.Along with fast urbanization, super industrial sources have gradually been relocated to the rural or suburban areas, and the spatial correlation between industrial emissions and population could thus be weakened.In our provincial inventory, most industrial enterprises were identified as point sources, with the key parameters including geographic location, activity level and removal efficiency of dust collector investigated and corrected at plant level.In MEIC, the emissions were calculated using parameters at regional average level and allocated as area sources according to densities of population and/or economic activity.Without detailed information for individual sources, it might lead to errors in emission estimation and spatial distribution at (roughly 90%) assumed in national inventories.As a result, the PM 2.5 emissions from industry were calculated at 570 Gg in our provincial inventory, 78% higher than those of MEIC.

Evaluation of spatial distribution of NO X emissions with satellite observation
Troposphere NO 2 vertical column density (VCD) retrieved from Ozone Monitoring Instrument (OMI) by the Royal Netherlands Meteorological Institute (Boersma et al., 2011) was employed to test the spatial distribution of NOx emissions in MEIC and this work.NO 2 VCDs in summer were used due to the short lifetime of NO 2 in atmosphere at high temperature and the difficulty in accumulation for primary emissions with strong air convection.In addition, the summer prevailing wind for Jiangsu was generally from southeast where Shanghai and Zhejiang Province are located (see Figure S1 for the locations of the three regions).Total NO X emissions of Jiangsu were estimated to be 65% and 282% larger than those of Shanghai and Zhejiang Shanghai in MEIC, and local sources were expected to dominate the pollution for the province (Cheng et al., 2011).As Mijling et al. (2013) illustrated satellite observations could be used to evaluate the primary emissions for regions where NO 2 VCDs were mainly affected by local emissions, it was thus feasible to apply the OMI NO 2 VCDs in Jiangsu to assess its NOx emissions.
NO 2 VCDs in July 2010 and 2012 with original spatial resolution of 0.125°×0.125°were used for comparisons with the emissions in MEIC and our provincial inventory, respectively.
To be consistent with MEIC, the emissions in our provincial inventory and the NO 2 VCDs from OMI were first upscaled to 0.25°×0.25°for the purpose of visualization and correlation analysis.As can be seen in Figure 5a  without stringent controls as big power plants.Shown in Figure 5c and 5d are the spatial distributions of Jiangsu's NO X emissions in MEIC and our provincial inventory, respectively， and the emission hotspots were generally consistent between the two inventories.Figure 5e and 5f shows the linear regression results between NO 2 VCDs and NOx emissions in MEIC and the provincial inventory, respectively.The correlation coefficients between VCDs and emissions were separately provided for all the grids and grids in different emission intervals, i.e., top 50%, 50%-75%, and last 25%.
The correlation coefficient between NO 2 VCDs and NOx emissions from the provincial inventory was 0.534, close to that between NO 2 VCDs and MEIC at 0.531.The result indicated that there was no significant difference in spatial distribution of emissions between the national and provincial inventories at the relatively low horizontal resolution.However, great discrepancies existed when the correlation analysis was conducted for grids in different emission intervals.As shown in Figure 5e, the correlation coefficients between VCDs and MEIC emissions were calculated at 0.24 and 0.34, respectively, for the top 50% (20 grids with emissions ranged 32-121 Gg) and last 25% of gridded emissions (161 grids with emissions ranged 0-12 Gg) .For our provincial emission inventory, the correlation coefficients were estimated slightly higher at 0.28 and 0.38, respectively, for the top 50% (18 grids with emissions ranged from 30-75 Gg) and last 25% of gridded emissions (176 grids with emissions ranged 0-12 Gg).Moreover, the coefficient between NO 2 VCDs and gridded emissions for the 50% -75% interval in provincial inventory was 0.26, while negative value (-0.07) was calculated for MEIC, indicating that the emission estimation for areas with small and medium plants in the provincial inventory was more consistent with satellite observation.
To better quantify the emissions at local scale, the results revealed the practical significance of careful investigation on individual small industrial plants that were usually identified as area sources due to lack of detailed information in national or regional inventories.

Evaluation of multi-scale inventories with CMAQ
As mentioned in Section 2.5, anthropogenic emission inventories at provincial, regional and national scales were applied respectively to explore the impacts of emission input on the performance of city-scale air quality simulation using CMAQ.With the original horizontal secondary formation process (PM 2.5 and O 3 in this case) were clearly under predicted by CMAQ, no matter which inventory was applied.Lack of dust emissions in inventories might be one reason for underestimation of PM 2.5 .Moreover, as the significant composition of PM 2.5 in eastern China (Yang et al., 2011), secondary organic and inorganic aerosols might be under predicted attribute to the weakness of chemical mechanisms in the version of CMAQ including the transformation of sulfate and the formation of secondary organic aerosols (Wang et al., 2009).For O 3 simulation, better performance was found at suburban sites than urban sites, and the lower simulated concentrations than observation could possibly come from the underestimation in precursor VOCs emissions.For example, the NMB was estimated at -26% for PKS, where many chemical industrial plants were located nearby.In addition, the uncertainty of NO X emission estimation might also contribute to the discrepancy.As indicated by the data from available continuous emission monitoring systems on Jiangsu's power plants, the NO X emission factors of power sector applied in current inventory might be overestimated for 2012 (unpublished).
The total emissions of SO 2 and NOx in Jiangsu estimated by Fu et al. (2013) was 1126 and 1257 Gg, i.e., 9% and 22% lower than the results of our provincial inventory, respectively.Using the regional inventory by Fu et al. (2013), much higher concentrations of SO 2 and NO 2 were simulated than observation at the monitoring sites, with the NMBs calculated at 74% and 30%, respectively.Even with larger emissions, in contrast, the NMBs for simulation with our provincial inventory were -10% and -14%, indicating lower simulated concentrations than observation.This result implies the possible impacts of spatial distributions of emissions on air quality modeling.In regional inventory, densities of population and economic activities were generally applied to allocate large fraction of emissions, leading to particularly high emissions in urban areas, as the economy and population was generally centralized in downtown.Given all the monitoring sites in Nanjing are located in urban or suburban areas, air quality simulation using regional emission inventory was thus liable to over predict the ground concentrations at those sites.
Spatial distributions of the monthly mean for simulated concentrations using national, regional and provincial inventories were plotted for SO 2 , NO 2 , PM 2.5 and O 3 in Figure 6, and the differences between simulations with varied emissions were shown in Figure 7.As the MEIC emissions were greatly averaged when they were directly downscaled from 0.25°×0.25°to 3×3 km, the simulated high concentrations using MEIC were broadly distributed in the modeling domain and commonly located in downtown (as indicated in Figure S1), with the large emitters hardly identified (Figure 6a).For results using the regional and provincial inventories, there were several grids with notably outstanding simulated concentrations indicating the existence of large emitters (Figure 6b and 6c), and differences with the simulation using MEIC were induced (Figure 7a and 7b).As shown in Figure 7c, moreover, clear differences were also detected between simulations using regional and provincial inventories, implying the discrepancy in allocations of high emissions between the two inventories.With the locations of large power, iron & steel, and cement plants incorporated, the YRD regional emission inventory by Fu et al. (2013) allocated a large fraction of emissions from industries as area sources.In contrast, the emissions from most power and industrial plants were calculated based on source-specific information and were precisely allocated in the provincial inventory, avoiding particularly the emission overestimation in downtown.In addition, the simulated NO 2 and O 3 concentrations for regions outside Jiangsu (i.e., Shanghai and part of Zhejiang and Anhui) using the provincial inventory were 22% lower and 40% higher in average than those using the regional one, respectively (Figure 7c), although same emissions (Fu et al., 2013) were used outside Jiangsu for the two inventories.The result indicated that both local and regional emissions were important for the simulations of the secondary pollutant like O 3 .Total VOCs emissions for Jiangsu were estimated at 1740 Gg in MEIC, slight higher than those in the regional (1659 Gg) and provincial inventory (1617 Gg), while the simulated monthly mean O 3 concentrations within Jiangsu using MEIC were notably lower than those using the latter two emissions.
Categorized by CB05, differences in chemical compositions of VOCs could be found in the three inventories.For example, the emissions of ethene (ETH) and ethanol (ETHA) with relatively high ozone formation potential in the provincial inventory were 44% and 209% higher than those in MEIC, respectively.Therefore, the total emission amount, spatial distribution of emissions, and the chemical compositions of precursors are all crucial to the accuracy of ozone simulations, and further analysis on those factors are suggested.

Improved SO 2 simulation under special meteorological condition
To further examine the simulated concentration response to varied emission inputs at local scale, the simulated SO 2 concentrations using national, regional and provincial inventories were compared with observation at three monitoring sites in downtown Nanjing (XWH, RJL and ZHM) for 6 th -14 th October 2012, as illustrated in Figure 8.The simulated concentrations using our provincial inventory were the most consistent with observation, while apparent overestimation was found for the simulations using national or regional inventories.At 8 pm October 9 (local time), in particular, the SO 2 concentrations were observed at 33, 12, and 14 μg/m 3 at XWH, RJL and ZHM sites, respectively, while the simulated concentrations were respectively simulated at 205, 246 and 228 μg/m 3 using MEIC, i.e., 5-19 times higher than the observation.The analogue numbers with regional inventory by Fu et al. (2013) even reached 550, 477 and 476μg/m 3 , i.e., 15-38 times higher than observation.Although concentrations remained over predicted, better performance was achieved when the improved provincial inventory was used, implying its advantage prior to national or regional ones in the high-resolution air quality modeling.The discrepancies in emissions and the simulated meteorological condition including wind velocity and height of planetary boundary layer (PBL) were inspected to understand the very high concentrations from simulation.
Figure S5 in the supplement shows the simulated wind fileds from 2 pm on 9 th to 5am on 10 th October.From 2 pm on 9 th October 9, WS10 in downtown Nanjing started to decline gradually and reached the minimum of 0.22 m/s at 8 pm , simply not beneficial for the horizontal convection of atmosphere.In addition, the monthly average of PBL height at XWH was simulated at 485 m at day and 140 m at night in October.From 5pm on 9 th to 10am on 10 th , however, the average PBL height decreased to 39m, with the minimum simulated at 32 m at 11pm on 9 th , seriously restricting the vertical diffusion of pollutants.Under the meteorological condition that horizontal and vertical movement of atmosphere were limited, primary pollutants from large emitters would be easily accumulated over time, possibly leading to high concentrations for areas close to the emission sources.In this case, therefore, the simulated SO 2 concentrations would be largely influenced by the emissions from local and nearby sources, as discussed below.The total SO 2 emissions in Nanjing were estimated at 141 Gg in the provincial inventory, 2% and 7% higher than those of national and regional ones respectively.Without big difference in total amount, large discrepancies in spatial distribution existed in those inventories, particularly at high horizontal resolution as shown in Figure 9. Downscaled from 0.25°×0.25°to 3×3 km, grids with similar emissions were clustered for MEIC and spatial variations in emissions could hardly be detected other than the hotspot in downtown (Figure 9c).Notably lower emissions in downtown Nanjing were found in our provincial inventory than the regional one (Figure 9a and 9b).In addition, the grid with maximum SO 2 emissions (15.7 Gg) in the provincial inventory was in the northwestern of Nanjing where a super power plant was located, labeled as the black star (point A) in Figure 9.As a comparison, the grid with the maximum SO 2 emissions in the regional inventory labeled as the black triangle (point B) in Figure 9 was adjacent to the location of A, and its emissions were calculated to be only 28% of the result in the provincial one.Given no other super emitters located nearby, we expected that the discrepancy resulted mainly from the varied emission estimation and positioning for the same power plant in the two inventories.According to on-site survey, only one unit out of two for the plant was installed with FGD, and the SO 2 emissions of the plant was estimated at 13.6 Gg, accounting for 87% of the total emissions in the grid.In contrast, a higher FGD installation rate at 85% was uniformly assumed for the power sector in the regional inventory by Fu et al. (2013), leading to possible underestimation in emissions for the plant.The comparison implied that detailed information compiled from individual plants was crucial for estimation and spatial distribution of pollutant emissions at local scales.SO 2 emissions at given monitoring sites were extracted from the gridded national, regional and provincial inventories and summarized in Table 3.As most large SO 2 emitters were located in suburban or rural areas, relatively small emissions were found in the provincial inventory for downtown Nanjing where the monitoring sites were located.As large fractions of emissions were allocated by the density of economy and population, however, the SO 2 emissions in the regional emission inventory were estimated at 1791, 1721, 1918, and 1635 Mg at XWH, RJL, ZHM and CCM sites, which were 4-5 times higher than those of our provincial inventory.In MEIC, the emissions at XWH, RJL, ZHM and MGQ sites were identically estimated at 1298 Mg from the downscaling approach, and they were also much larger than those in the provincial inventory.Given the unfavorable condition of pollutant transport for 9 th -10 th October, the overestimation in local emissions around the downtown monitoring sites in the national and regional inventories thus lead to terribly high simulated concentrations, while the results using the provincial one were much more reasonable.The comparison confirmed the benefits of precise quantification of emissions on local air quality modeling.
Despite of significant improvement, overestimation in SO 2 concentrations still existed in the simulation with our provincial inventory, attributed possibly to the error of meteorology modeling.Here we selected XWH site as an example to conduct the back trajectory analysis using HYSPLIT model (http://ready.arl.noaa.gov/HYSPLIT.php).Shown in Figure S6 in the supplement, the air mass reaching the site at 50 m altitude came mainly from northeast at 11pm on 9 th October.However, it was inconsistent with WRF modeling results, which indicated the dominating wind was from northwest (150°-170°) at that time.As mentioned above, a big power plant was located northwest to XWH (Figure 9a), and the site might partly be influenced by the large emissions from the plant and enhanced concentrations would then be obtained when northwest wind was simulated.

SENSITIVITY ANALYSIS OF PM 2.AND OZONE FORMATION IN NANJING
Using the improved provincial inventory, the sensitivity of PM 2.5 and O 3 concentrations to emissions were further analyzed through the Brute-Force method (BFM, Dunker et al., 1996).For PM 2.5 , four simulation scenarios were designed: Scenario B (the base case) in which the emissions from all types of sources are included; and Scenarios S1, S2, and S3 in which the pollutant emissions of power, iron & steel and cement plants in D3 were zero out, respectively.The changes in simulated PM 2.5 ground concentrations in S1, S2, and S3 compared to those in base case for October 2012 are illustrated in Figure S7 in the supplement.
The average concentration increments in urban area of Nanjing caused by power, iron &steel, and cement plants were calculated respectively at 3, 11 and 7 μg/m 3 , accounting for 6%, 26% and 16% of the monthly mean PM 2.5 concentrations, and the maximum increments within the domain reached 10, 72, and 25 μg/m 3 , respectively.Given the tiny emission fraction of power sector for primary PM 2.5 (4% in Jiangsu Province) and the small share in the ground layers (15% for 1 st plus 2 nd vertical layers), its contribution to PM 2.5 ground concentration was notably lower than those of iron & steel and cement.Summarized in Table 4 are the contributions of power, iron & steel, cement sectors to monthly mean PM 2.5 at the nine monitoring sites in Nanjing, October 2012.The contributions of the three sectors to average PM 2.5 concentrations at all the sites were estimated at 8%, 13% and 9%, respectively.Since all the sites are located in the urban or suburban areas, the estimated PM 2.5 contributions at individual site varied slightly to each other.Besides monthly mean, the hourly maximum and minimum contributions are provided as well in Table 4.The largest hourly contributions from power, iron & steel and cement plants to PM 2.5 concentrations were 65% at PKS, 89% at MGQ and 58% at both CCM and OCS, respectively.The contributions became negative at 2 pm on 26 th October with average PM 2.5 concentration of all the sites observed as 164 μg/m 3 and simulated as 151 μg/m 3 under the base case, i.e., increased particle concentrations were simulated at the moment when emissions from given sector was turned off.The result indicated, on one hand, the relatively high uncertainty of simulation for heavy PM pollution episode dominated by regional transport.On the other hand, as the simulated increments were mostly from the elevated sulfate (SO 4 2- ), nitrate (NO 3 -) and ammonium (NH 4 + ), the negative contributions might also be caused by the complex chemical mechanisms of SO 2 and NOx reactions with NH 3 under the NH 3 -rich condition in YRD (Wang et al., 2011).Intensive real-time observation on chemical composition of PM 2.5 is thus recommended to better capture and analyze the process.
To explore the sensitivity of O 3 formation to its precursor emissions, two scenarios were set besides the base case: the VOC-abatement scenario with 50% reduction of all anthropogenic VOCs emissions in D3 (Scenario P1), and the NOx-abatement scenario with 50% reduction of NOx in D3 (Scenario P2).Shown in Figure S8 in the supplement were the average O 3 concentration changes from October 6 th to October 15 th .The simulated O 3 average concentration from 11am to 5pm declined significantly under Scenario P1, with the maximum reduction at 54 μg/m 3 (Figure S8a) within D3, and changes in the downwind region were greater than the upwind.In contrast, the concentrations were generally enhanced under P2 with the maximum increment at 19 μg/m 3 .Similar variation pattern was found for 1-hour maximum O 3 concentration in Figure S8b and monthly mean concentration in Figure S8c.scenario at urban monitoring sites were notably greater than those at suburban sites.

CONCLUSIONS
The bottom-up approach was applied to develop a high-resolution emission inventory for Jiangsu, with substantial detailed information on local sources incorporated.Key parameters relevant to emission estimation were examined and revised plant by plant including geographic position, energy consumption and removal efficiencies of APCDs from various data sources and on-site survey on large emitters.Compared to previous studies, the emission fractions of point sources were significantly enhanced, except for NH 3 and OC, which are mainly from agriculture activities and biomass open burning, respectively.As lower removal efficiencies of dust collectors were obtained from local investigation, larger primary PM emissions were estimated in our provincial inventory than other national or regional ones.from OMI than that of MEIC, particularly for the emissions from small and medium industrial plants.WRF-CMAQ air quality modeling system was set up to evaluate the reliability and improvement of the provincial emission inventory by comparing the simulation performance with that using a national (MEIC) and regional one.Among the three inventories, the best agreement was found between the observation and simulation with the provincial one for all the concerned species at the nine monitoring sites in Nanjing, while underestimation existed particularly for PM 2.5 and O 3 that were strongly influenced by secondary formation.Under the unfavorable meteorology of pollutant transport, extremely high SO 2 concentrations were simulated using the regional and national inventories, while the results using provincial one were much closer to the observation.The results indicated the advantage of improved estimation and spatial distribution of emissions on air quality modeling at regional or local scales.The improved provincial inventory was further applied for the sensitivity analysis on PM 2.5 and O 3 formation using BFM simulation, and provided the preliminary results for the policy making of regional haze and photochemical pollution control in southern Jiangsu.
Limitations remained in the current inventory.Attributed to unavailability of detailed information, the weekly and hourly variations of emissions could not be fully tracked for each city, and the vertical distribution of emissions by sector, depending mainly on the stack height, temperature and flow of flue gas, could not be accurately determined.Instead, empirical data from previous work (Li et al., 2011;L. Wang et al., 2010;2014) had to be applied, which might be inconsistent with the reality.In addition, some sources were not included in the current inventory, e.g., fugitive dust emissions from construction sites and road transportation, resulting from lack of reliable data and thereby potentially large uncertainties in the emission estimation at provincial level.Finally, the effects of source profiles on air quality modeling, e.g., the speciation of primary PM 2.5 and VOC, were not evaluated.As they are important on the formation of O 3 and secondary particles, more investigations on typical sources and evaluation through chemistry transport modeling are suggested in the future.
Regional (Fu et          ) Observation National (MEIC) Regional (Fu et al.,2013) Provincial (this work) FigureS2in the supplement.In particular, the kilns for combustion and factories for meteorological information, vehicle population by type, fleet composition by control stage (China I-IV, equivalent to Euro I-IV), average vehicle speeds, and annual average kilometers traveled (VKT).Monthly mean temperature and relative humidity were obtained from the China Meteorological Data Sharing Service System (http://www.escience.gov.cn).Populations of different vehicle types were derived from statistical yearbooks by city and then adjusted in accordance with the model requirement.The fleet composition by control stage Atmos.Chem.Phys.Discuss., doi:10.5194/acp-2016-567,2016 Manuscript under review for journal Atmos.Chem.Phys.Published: 25 July 2016 c Author(s) 2016.CC-BY 3.0 License.
7.1 was applied to evaluate the emission inventory for Jiangsu.As shown in Figure 1, three one-way nested domain modeling was conducted, and the spatial resolutions were set at 27, 9 and 3 km respectively in Lambert Conformal Conic projection, centered at (110°E, 34°N) with two true latitudes 25 o N and 40 o N. The mother domain (D1, 180×130 cells) covered most part of China, Japan and the whole Korea and part of other country.Jiangsu, Zhejiang, Shanghai, Anhui and parts of other provinces were at the second modeling region (D2, 118×97 cells).
Atmos.Chem.Phys.Discuss., doi:10.5194/acp-2016-567,2016   Manuscript under review for journal Atmos.Chem.Phys.Published: 25 July 2016 c Author(s) 2016.CC-BY 3.0 License.industry, suggesting the improvement in SO 2 abatement for industrial coal combustion other than power plants would be an effective measure to further reduce the emissions.Power sector was the largest source for NO X , contributing 41% to the total emissions, while the share of coal consumption of the sector reached 65%(JSNBS, 2013).It thus implied the tightened controls from implementation of new emission standard (GB13223-2011) and improved use of SCR/SNCR on power plants since 2011 compared to other sectors.Compiled from unit level, the average NOx removal efficiency of SCR/SNCR was calculated at 37% for Jiangsu's power plants in 2012 (Table Atmos.Chem.Phys.Discuss., doi:10.5194/acp-2016-567,2016   Manuscript under review for journal Atmos.Chem.Phys.Published: 25 July 2016 c Author(s) 2016.CC-BY 3.0 License.
Atmos.Chem.Phys.Discuss., doi:10.5194/acp-2016-567,2016   Manuscript under review for journal Atmos.Chem.Phys.Published: 25 July 2016 c Author(s) 2016.CC-BY 3.0 License.regional or local scale.According to the survey at plant level, for example, only 20% of the lime factories were installed dust collectors in Jiangsu 2012, much lower than the value and 5b, clear reduction in summer NO 2 VCDs was found in southern Jiangsu from 2010 to 2012, indicating the benefits of efforts on NO X abatement since 2011.The NO 2 VCDs in the area along the Yangtze River were notably higher than that in other regions, attributed possibly to the substantial emissions from vessels and small captive power plants of the chemical and refinery industrial parks along the river Atmos.Chem.Phys.Discuss., doi:10.5194/acp-2016-567,2016 Manuscript under review for journal Atmos.Chem.Phys.Published: 25 July 2016 c Author(s) 2016.CC-BY 3.0 License.
Atmos.Chem.Phys.Discuss., doi:10.5194/acp-2016-567,2016 Manuscript under review for journal Atmos.Chem.Phys.Published: 25 July 2016 c Author(s) 2016.CC-BY 3.0 License.The 1-hour maximum O 3 concentrations in most downwind area of Shanghai and southern Jiangsu decreased 10-20 μg/m 3 with the reduction in VOCs emissions, and the concentrations would generally increase 10-30 μg/m 3 with the NO X reduction.The similar patterns of O 3 concentration variation in urban and downwind areas in D3 under P1 or P2 scenario indicated that the O 3 formation was VOCs-limited in all those areas in southern Jiangsu.Therefore, VOC emission abatement could be effective for O 3 pollution control in southern Jiangsu, while NO X abatement might aggravate the pollution in autumn.The temporal changes in the simulated O 3 concentrations between the P1/P2 and base scenarios at urban (XWH, SXL, RJL, MGQ, ZHM and CCM) and suburban sites (XLS, OCS and PKS) in Nanjing were illustrated for October 6 th -16 th in Figure 10.Simulated O 3 concentrations at urban and suburban sites were generally decreased once the VOC emissions declined and the maximum hourly reduction reached 77.3 and 49.6 μg/m 3 , respectively.In contrast, concentrations were elevated with the NOx emission reduction and the maximum growth were 78.7 and 15.4 μg/m 3 , respectively.Under VOCs-limited regime, in general, the O 3 concentration would be little sensitive to the changes in NOx unless it was rich enough to turn to the negative correlation with O 3 .Therefore, due to the intensive NOx emissions from on-road transportation in downtown Nanjing, the variations of O 3 concentrations in P2 Atmos.Chem.Phys.Discuss., doi:10.5194/acp-2016-567,2016 Manuscript under review for journal Atmos.Chem.Phys.Published: 25 July 2016 c Author(s) 2016.CC-BY 3.0 License.Moreover, clear discrepancy existed in spatial distribution of industrial PM 2.5 emissions between this work and the national inventory MEIC, indicating the uncertainty of emission downscaling from coarse horizontal resolution.The spatial distribution of NO X emissions in the provincial inventory was more consistent with summer tropospheric NO 2 VCDs observed FIGURE CAPTIONS Figure 1.Modeling domain and locations of 43 meteorological and 9 air quality monitoring sites.

Figure 2 .
Figure 2. Source contributions to total estimated emissions by species in Jiangsu 2012.Colors indicate the sectors and the shade patterns indicate the source type (point, mobile and area).

Figure 3 .
Figure 3.Comparison between the emissions estimated in this work and other studies for Jiangsu.A and B indicate the emissions without and with open biomass burning, respectively.

Figure 4 .
Figure 4. Spatial distributions (a-c) and linear regression (d) of certain pollutant emissions from typical sources estimated in our provincial inventory and MEIC.(a) SO 2 from power plant; (b) NOx from transportation; (c) PM 2.5 from industry.The black points indicate the locations of plants with PM 2.5 emissions larger than 10 Gg estimated in this work.

Figure 5 .
Figure 5. Spatial distributions of NO 2 VCDs observed by OMI in Jiangsu in 2010 (a) and 2012 (b), and those of Jiangsu's NOx emissions from MEIC (c) and our provincial inventory (d) at the resolution of 0.25 °×0.25 °.Linear regressions of gridded VCDs and emissions are illustrated for MEIC (e) and our provincial inventory (f).

Figure 6 .
Figure 6.Spatial distributions of the monthly means of simulated SO 2 , NO 2 , PM 2.5 and O 3 concentrations using the national, regional and provincial emission inventories for October 2012.

Figure 7 .
Figure 7.The differences in the monthly means of simulated SO 2 , NO 2 , PM 2.5 and O 3 concentrations using different emission inventories: (a) Provincial-national; (b) Regional-national; and (c) provincial-regional.The black star A and triangle B referred to the locations of grids with maximum SO 2 emissions in provincial and regional inventory.

Figure
Figure 8.The observed and simulated hourly SO 2 concentrations at 3-hour interval using the national, regional, and provincial inventories at XWH (a), RJL (b), and ZHM (c) from October 6 th to 13 th , 2012.

Figure 9 .
Figure 9. Spatial distributions of the estimated SO 2 emissions in Nanjing at the resolution of 3×3km in the provincial (a), regional (b) and national emission inventory (c).The black dots indicate the locations of given air quality monitoring sites.The black star (point A) indicates the location of the power plant with the largest SO 2 emissions estimated in the provincial inventory.The black triangle (point B) indicates the speculated position of the same power plant in the regional inventory.

Figure 10 .
Figure 10.The changes in simulated O 3 concentrations at urban (XWH, SXL, RJL, MGQ, ZHM, and CCM) and suburban air quality monitoring sites (XLS, OSC, and PKS) in Nanjing under P1 (a) and P2 (b) scenarios compared to the base case for 6 th -16 th October 2012.

Table 2 . Model performance statistics for concentrations of given species from observation and CMAQ simulation using the national, regional and provincial inventories at the nine air quality monitoring sites in Nanjing for October 2012.
Note: NMB and NME were calculated using following equations (P and O indicate the results from modeling prediction and observation, respectively): %

Table 4 . The monthly mean contributions of power, iron & steel and cement plants to the PM 2.5 concentrations at the air quality monitoring sites in Nanjing in October 2012.
Note: Max., min., ave.and contri.indicate maximum, minimum, average and contribution, respectively.