Advantages of city-scale emission inventory for urban air quality research and policy

Advantages of city-scale emission inventory for urban air quality research and policy: the case of Nanjing, a typical industrial city in the Yangtze River Delta, China Y. Zhao, L. Qiu, R. Xu, F. Xie, Q. Zhang, Y. Yu, C. P. Nielsen, H. Qin, H. Wang, X. Wu, W. Li, and J. Zhang State Key Laboratory of Pollution Control & Resource Reuse and School of the Environment, Nanjing University, 163 Xianlin Ave., Nanjing, Jiangsu 210023, China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Jiangsu 210044, China Nanjing Academy of Environmental Protection Science, 175 Huju Rd., Nanjing, Jiangsu 210013, China Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China


Introduction
Emission inventories are crucial for atmospheric science research, particularly chemical transport modeling (CTM), and for air quality policymaking that seeks to identify 10 and control pollution sources. Given China's important role in the origins and transport of air pollutants in East Asia and beyond, a number of emission inventories at national scale have been established in recent years. These include the Transport and Chemical Evolution over the Pacific Mission (TRACE-P, Streets et al., 2003), the Intercontinental Chemical Transport Experiment-Phase B (INTEX-B, ), the Regional 15 Emission inventory in ASia (REAS, Ohara et al., 2007;Kurokawa et al., 2013), and the Multi-resolution Emission Inventory for China (MEIC, http://www.meicmodel.org/). Based on "bottom-up" principles and frameworks similar to those described in Streets et al. (2003), more detailed source categories and expanded domestic information on emission factors and activity levels have been integrated into most recent work, yield- 20 ing improved inter-annual trends in national estimates of anthropogenic air pollutant emissions (e.g., MEIC; Zhao et al., 2012aZhao et al., , b, 2013. Aside from the national-level work, regional emission inventories have also been established with improved understanding of local conditions for key areas with high densities of population, industry, and energy consumption, e.g., the Jing-Jin-Ji region including Beijing and Tianjin (JJJ; S. Wang Without integrating more detailed local information, some cities simply downscale a national emission inventory into high-resolution emission inputs for a CTM or develop local inventories based on the same source data and methods used for national ones. Despite improvements from some information (e.g., the precise location of large point sources), the quality and reliability of those inventories have not been well evaluated 15 using, for example, integrated observational data as top-down constraints. Thus the emission estimates introduce large uncertainties into the city-scale air quality simulations.
It should be noted that such improvements in emission inventories for a few megacities, including Beijing and Shanghai, have been driven by air quality planning for major 20 events like the 2008 Summer Olympic Games and 2010 World Expo. During recent years, however, satellite observations have detected that the most significant growth in air pollution (indicated for example by vertical column densities of tropospheric NO 2 ) across the country is occurring not within such megacities but in the less-developed regions around them, due to faster growth in the economies and emission sources 25 in those areas (Zhang et al., 2012a). This finding highlights the importance of developing and assessing air pollutant emission inventories for regions other than China's much-studied megacities. Introduction

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Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | with gas stations for VOC estimation only), agriculture (AGR, including livestock farming and fertilizer use for NH 3 estimation only), and fugitive dust (FUD, including that from construction sites and roads). IND is further divided into cement plants (CEM), iron and steel plants (ISP), refineries and chemical plants (RCP), solvent use (SOL; although some solvent is not used in industry, we include it in this category for classification 5 simplicity) and other industry plants (OIN).
To improve the accuracy and reliability of the city-level emission inventory, new data are collected from various sources and modified methods are applied compared to previous studies, as briefly summarized below.
With sufficient information related to emission estimation now available, more 10 sources can be characterized as point sources in the current inventory. These include power plants (total number in 2012, similarly for subsequent categories: 18), cement plants (23), iron and steel plants (2), chemical plants (173), non-ferrous metal smelters (14), lime plants (9), brick plants (31) where i , j and m represent the species, individual plant, and fuel/technology type, respectively; A is the activity level data; EF is the uncontrolled emission factor; and η 20 is removal efficiency of air pollutant control device (APCD). For all the point sources, information from the Environmental Statistics database and Pollution Source Census (internal data of NJEPB) is collected and compiled to obtain the activity levels (energy consumption or industrial production) and parameters related to emission factors, plant by plant. Moreover, we conduct onsite surveys individually for 25 all of the CPP, CEM, ISP and RCP sources (labeled "key sources" in this paper), to get further information that is crucial for emission estimation but not covered by the 18698 Introduction

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Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | official census or statistics (see details in Sect. 2.2). In 2012, all of the point sources and all of the key sources in Nanjing accounted for 97 and 96 % of the city's total coal consumption, respectively, reflecting the highly centralized energy use of Nanjing. Besides annual levels, monthly energy consumption, industrial production, and fluegas concentrations from CEMS are obtained whenever possible through our plant-by- 5 plant onsite investigations. The monthly distribution of emissions from key sources is then revised based on these data.
Emissions from on-road transportation are calculated using COPERT 4 (version 9.0) (EEA, 2012). The parameters required by the model, including vehicle population by type, fleet composition by control stage (Stage I-IV, equivalent to Euro I-IV), and annual average kilometers traveled (VKT), are taken from investigations by NJEPB. The detailed information for 2012 is summarized in Tables S1 and S2 in the Supplement. The traffic flows for the main roads in the city are compiled from the real-time observations of the Intelligent Traffic Violation Monitoring Systems (internal data of NJEPB). The spatial and diurnal distribution of emissions can then be quantified by combining 15 the information of traffic flow and the road network.
For area sources including other small industry, solvent use, residential combustion, agricultural activity, and non-road transportation, emissions are estimated following previous work (Zhao et al., 2012a(Zhao et al., , b, 2013, with up-to-date emission factors from domestic measurements and city-scale activity levels. The energy consumption data 20 are taken from the Environmental Statistics (internal data of NJEPB) and the agricultural and industrial outputs are mainly from the Nanjing Statistical Yearbook (NJNBS, 2013). Fire counts and intensity observed from MODIS (Moderate Resolution Imaging Spectroradiometer, https://earthdata.nasa.gov/data/near-real-time-data/firms) are used to determine the spatial and temporal distribution of emissions from biomass 25 open burning. Regarding fugitive dust, information about individual construction sites in the city is obtained from NJEPB to improve the estimation of emission levels and spatial and temporal distributions. This includes location, period of operations, construction area, and amount of earthworks). The largest 221 construction sites in Nanjing in 2012 Introduction (accounting for 45 % of the total construction area) are shown in Fig. S1c in the Supplement.

Emission factors
As mentioned previously, parameters related to emission factors for key sources (CPP, CEM, ISP, and RCP) are obtained through onsite surveys for each plant. The param-5 eters for CPP include boiler type, combustion technology, fuel quality (sulfur, ash, and volatile matter contents), types and pollutant removal efficiencies of APCDs (flue gas desulphurization (FGD), selective catalytic reduction (SCR)/selective non-catalytic reduction (SNCR), dust collection). Emission factors can then be determined or calculated based on the method described by Zhao et al. (2010). For cement production, the 10 kiln type, PM removal efficiency of dust collectors, and fuel quality are investigated and emission factors are calculated following Lei et al. (2011) and Zhao et al. (2012a. For ISP, key parameters in four main processes (coking, sintering, pig iron production, and steelmaking) are obtained, including the SO 2 removal rate of FGD for sintering, PM removal rate of dust collectors, and the gas release ratios of coke ovens, blast furnaces, 15 and basic oxygen furnaces. Emission factors for each process are then calculated following Zhao et al. (2012a. For the chemical industry, the surveyed parameters include the types and amounts of raw materials and products, the types and volumes of tanks, and technologies applied for VOC control. In particular, the emission factors for refineries are determined by industrial process, including production, storage, loading, 20 and unloading (Wei et al., 2008;USEPA, 2002;EEA, 2013). Inter-annual variations in these parameters for major sources are tracked in the survey so that changing emission factors over time (2010)(2011)(2012) can be determined. the database by Zhao et al. (2011, after incorporating the most recent results from domestic measurements.  Fan et al. (2007) and Huang et al. (2006), with some adjustments of road types for Nanjing. Emission factors of construction dust recommended by USEPA (2002) are used in this work, i.e., 0.026, 0.106, and 0.191 (kg m −2 ) month −1 for PM 2.5 , PM 10 , and TSP, respectively. The mass fractions of BC and OC in construction PM 2.5 are assumed to be 2.4 and 3.4 %, respectively, from measurements by Zhao et al. (2009).
For gasoline stations, Nanjing completed installation of vapor recovery systems at all stations at the end of 2012. VOC emission factors for gas storage, loading, unloading and sales are determined at 0.03, 0.87, 0.10 and 2.44 g kg −1 , respectively (Wei et al., 2008;Fu et al., 2013). Solvents include paints for buildings and furniture, ink, fabric coating adhesives, and pesticides. VOC emission factors for decorative adhesives, in-15 terior wall paints, and wood paints are taken from Fu et al. (2013), while those for other solvent use come mainly from Wei et al. (2008). Emission factors for non-road transportation are mainly from Zhang et al. (2010) and Ye et al. (2014). Emission factors for household biofuel use are estimated based on results of various domestic measurements as summarized in

Inter-annual variability and sector distribution of emissions
The annual emissions of various air pollutants and CO 2 from anthropogenic sources in Nanjing are shown in Fig. 1a for 2010-2012. In 2010, the total emissions of SO 2 , NO x , CO, VOCs, NH 3 , PM 2.5 , PM 10 , TSP, CO 2 , BC and OC are estimated at 165,216,5 774,224,21,71,94,158,79 976,6.2,and 6.7 Gigagrams (Gg), respectively. Note the numbers here for PM emissions do not include fugitive dust from construction and transportation, to facilitate comparison with inventories that omit the source. Despite large growth in coal consumption from 2010 to 2012, the emissions of SO 2 and NO x in 2012 are estimated to be smaller than those in 2010, implying the effectiveness of emission control measures for the city in recent years. These measures include mainly the increased use of flue gas desulfurization (FGD) and selective catalytic reduction (SCR) systems in the power generation sector (see the detailed information in Table 1). The slight increase in SO 2 emissions between 2011 and 2012 resulted mainly from the growth in coal consumption in industries other than power generation, where FGD 15 systems have not been widely deployed. PM emissions are estimated to be quite stable for the three years, with small increases in PM 2.5 and PM 10 . Rising mass fractions of PM 2.5 to TSP (from 45 to 48 %) indicate the difficulty in controlling emissions of finer primary particles compared to coarser ones. For VOCs and NH 3 , which have not been well regulated in national action plans for air pollution prevention and control (Zhao 20 et al., 2014), the inter-annual variabilities of emissions are small and driven mainly by relative stability in chemical and agricultural production, respectively. While CO 2 continues to rise, no growth is estimated for CO from 2011 to 2012, implying improved overall combustion efficiency in the city. Figure S2 in the Supplement shows the sector contributions to total emissions by 25 year and species, as well as the shares of coal consumption by sector for comparison. respectively. NO x emissions come mainly from power plants (45 %) and on-road transportation (20 %) throughout the time period. The shares of SO 2 and NO x emissions from the power sector are clearly smaller than its shares of coal consumption (57-64 %) or CO 2 emissions (48-57 %), due largely to relatively stringent emission controls in the sector. Fugitive dust, particularly that of road origin, is identified as the dominant 5 anthropogenic source of PM emissions. The fugitive dust shares of TSP are estimated to range 64-70 % during the research period, while smaller fractions are found for finer particles and carbonaceous aerosols. Apart from fugitive dust, iron and steel production plays a significant role in PM emissions in Nanjing, with its shares of TSP, PM 10 , and PM 2.5 calculated at 15-16, 20-23, and 35-41 %, respectively. This results mainly 10 from the large coal use by the sector, and relatively poor PM control measures of certain plants compared to other major coal-consuming sources, e.g., power plants. Iron and steel production is also identified as the biggest contributor of CO emissions for the city with its share reaching 60 % in 2012, even though emission factors for the sector in Nanjing (based on field investigations) are smaller than the national average (Zhao 15 et al., 2012a). This is partly attributed to relatively little inefficient coal combustion at other sources in the city (e.g., in small industry and residential use), resulting in much lower fractions of CO emissions from those sources than the national averages. VOCs come mainly from chemical production (52 %) and solvent use (29-30 %). With vapor recovery systems increasingly applied, VOC emissions from gas stations decline 20 during the research period. Despite an increase in vehicle population, the fractions of on-road transportation emissions for most species decrease from 2010 to 2012, attributed mainly to implementation of increasingly strict vehicle emission standards. From effective prohibition of burning of agricultural wastes, the emission contributions of this source, mainly of particles, carbonaceous aerosols, and CO, are also consider-Introduction

Spatial and temporal distribution
For simulation of atmospheric transport and chemistry, the emission inventories are allocated into a 3 km × 3 km grid system. For sources lacking specific location information, their emissions are assumed to be correlated with population density, with the exception of NH 3 , which is allocated based on the density of agricultural GDP. Shown 5 in Fig. 2 are the spatial distributions of SO 2 , NO x , PM 2.5 (exluding fugitive dust from construction and roads) and VOC emissions for Nanjing in 2012, and the locations of the ten largest point sources of each species. Relatively high emission densities are found in the urban area, particularly around certain large power generation and industrial sources. As illustrated Fig. 3, the fractions of emissions from point sources for all concerned species are estimated to exceed 50 %, as are those from the collective four key source types, with the exception of BC, at 38 %. Monthly distributions of SO 2 emissions by sector and that of total emissions of all species for 2012 are respectively shown in Fig. S3a and b in the Supplement. Note again that fugitive dust from construction sites and roads is excluded. The results of 15 MEIC are also provided in Fig. S3a for comparison. It can be seen that the temporal distributions of the two studies are similar except for residential emissions, which are smaller overall in this work compared to MEIC. As indicated by MODIS fire counts, over 90 % of biomass open burning occurred in May-July, leading to much higher OC emissions in those three months compared to any other time of the year. For other 20 species, the temporal distributions of emissions correlate closely with those of activity levels, with a drop in February attributed mainly to reduced energy supply and industrial production during the Spring Festival. Pronounced diurnal variations of on-road transportation emissions are illustrated in Fig. S4 in the Supplement, with two peaks at the rush hours. The daily shares of CO and VOC emissions in the morning rush hour 25 (16 %) are slightly higher than those of NO x (14 %) and PM 2.5 (15 %). Based on the assumptions of COPERT, the cold start of most vehicles occurs in the morning, leading to larger CO and VOC emission factors during this time compared to those during Introduction

Comparisons with other studies in emission estimates
Figure 1b compares our estimates of Nanjing emissions with those from other inventories ; MEIC) for a common year, 2010. In the other studies, national 5 or regional average levels for some parameters related to emissions, e.g., the penetrations and pollutant removal rates of emission control devices, are applied. These values can vary considerably from those based on plant-by-plant field investigations, leading to clear differences in emission estimates compared to the current work. Our estimate of SO 2 emissions for Nanjing is 25 and 22 % higher than those of Fu 10 et al. (2013) and MEIC, respectively, even though the plant-by-plant survey indicates an FGD penetration rate of 92 % of installed power generating capacity, higher than the provincial average of 85 % used in Fu et al. (2013). The higher estimate results because: (1) the total coal consumption from the Environmental Statistics applied in this work is 14 % larger than that provided by the Nanjing Almanac used in other studies 15 (NJCLCC, 2011; see Sect. 4.6 for more discussion), and (2) a relatively lower removal efficiency of FGD is obtained from the onsite survey for 2010. Similar NO x emission levels are found between current work and MEIC, while lower emissions were provided by Fu et al. (2013). According to filed survey, the penetration rate of SCR/SNCR increased from 44 to 67 %, and the NO x removal efficiency increased from 18 to 77 % during 20 2010-2012 (Table 1). The penetration rate is much larger compared to the provincial average of 22 % applied in MEIC and Fu et al. (2013), partly offsetting a discrepancy in estimated emissions caused by larger activity levels used in current city-scale inventory.
Our estimates for PM 2.5 , PM 10 , and BC emissions (without fugitive dust emissions) 25 are larger than those of Fu et al. (2013) or MEIC in 2010. This results mainly from larger emissions from industry (particularly iron and steel production), as the survey revealed that relatively old and inefficient wet dust collectors were still used at some 18705 Introduction plants. OC emissions, however, are estimated to be lower than MEIC indicates, due mainly to very little coal or biomass burning in the city-level statistics. VOC emissions estimated in this work in 2010 are 34 % larger than Fu et al. (2013) and 36 % larger than MEIC. In particular, emissions from refineries and chemical plants, calculated using detailed information on each plant's inputs of raw materials 5 and the product types and amounts, are 116 % higher than those in regional inventories . Thus the fraction of total VOC emissions attributed to industrial processes is estimated at 48 % by us, larger than the YRD average level of 34 % . Given Nanjing is a city with large petroleum refining and chemical industries, and that much higher production of crude oil, gasoline, diesel and liquefied petroleum gas is reported than in other YRD cities in 2010 (NJNBS, 2013), the higher VOCs emissions indicated by the plant-based inventory is believed to better reflect the city's true industrial structure.
For CO, our estimates are 12 % higher for industry than those of MEIC, but 26 and 37 % lower respectively for residential and transportation sectors, resulting in 2 % lower 15 emissions for anthropogenic sources as a whole. The discrepancy in sector contributions is caused mainly by the high percentage of centralized coal combustion in the city: power, iron and steel, cement, and chemical plants consumed over 95 % of the city's coal, based on our field survey. Our CO 2 emission estimate is 22 % higher than that of MEIC, resulting mainly from the difference in coal consumption reported by the 20 Environmental Statistics database and the city almanac.

Assessment of the city-scale emission inventory
The current inventory is assessed to gauge improvements of emission estimates using a city-scale framework. The inter-annual variability, spatial distributions, and correlations of a number of species of the inventory are evaluated by comparison to available ACPD 15,2015 Advantages of city-scale emission inventory for urban air quality research and policy

Evaluation of inter-annual trends and spatial distribution of NO x emissions with satellite observations
The inter-annual trend in NO x emissions estimated bottom-up is compared with that of NO 2 vertical column densities (VCDs) based on satellite observations. The VCDs of tropospheric NO 2 are retrieved from the Ozone Monitoring Instrument (OMI) by 5 the Royal Netherlands Meteorological Institute (Boersma et al., 2007(Boersma et al., , 2011, using monthly data with spatial resolution of 0.125 To further assess possible improvement of emission estimates by the current citylevel inventory, the spatial distribution of monthly means of OMI NO 2 VCD in summer (June-August) 2010 over Nanjing is compared with that of two emission studies: (1) city-level emissions at spatial resolution of 3 km × 3 km by the current work, and (2) MEIC emissions developed at the provincial level with a resolution of 5 km × 5 km. 5 For the purpose of visualization and further analysis, the emissions are reallocated to a 0.125 • × 0.125 • grid system from the original spatial distributions, consistent with the resolution of retrieved OMI NO 2 VCD. We assume that the NO 2 VCD from satellite observations reflect the anthropogenic NO x emissions of the city for the following reasons. NO x emissions in East China are predominantly anthropogenic (Mijling et al.,10 2013); lightning and soil sources as a share of total emissions are estimated to peak in July, when they account for 9 and 12 %, respectively (Lin et al., 2012). NO x emissions in Nanjing are clearly larger than in surrounding areas (Huang et al., 2011), and the NO 2 VCD over the city is believed to be most influenced by local emissions.
As shown in Fig. 5, a similar spatial pattern of NO x is captured by the gridded emis- 15 sions and satellite observations, and relatively higher pollution in the urban area in the center of the city is indicated, attributed mainly to the combined effects of intensive transportation and large point sources. The emission inventories, however, underestimate the high-pollution areas compared to OMI observations, particularly MEIC. To further gauge improvement in spatial distribution by the city-scale emissions, correla-20 tions between the gridded emissions and the VCD are analyzed. As shown in Fig. 6a, the correlation coefficients (R) between the emissions and the VCDs are calculated at 0.450 and 0.408 for this work and MEIC, respectively, indicating better agreement by the city-scale inventory. Moreover, a sensitivity test on the correlation coefficients is conducted through step-wise exclusion of the grid cells with the largest emissions. 25 Along with the increase in excluded grid cells, the R for this city-scale emission inventory remains above 0.43, while those of MEIC sharply decrease (Fig. 6b). In order to estimate emissions of the whole country, the MEIC is based mainly on energy and economic statistics at the provincial level, though it includes a limited number of major point ACPD 15,2015 Advantages of city-scale emission inventory for urban air quality research and policy sion levels and spatial distributions should thus be expected, particularly for small or medium-size emission sources. Once the grid cells dominated by major power plants are excluded from the two inventories, as shown in Fig. 6c, the current city-scale emissions still correlate well with satellite observations (R = 0.436) while MEIC shows little correlation (R = 0.085). The results reflect that inventories compiled at the provincial or regional level better estimate emissions of large sources than small or medium-sized ones, due to relative availability of information on power plants but much poorer nationwide data availability for other industrial plants. When focusing on smaller regions like cities, however, detailed information on more emission sources from onsite survey becomes crucial for improving emission estimates. 15 It should be noted that high NO 2 VCDs are found over the Yangtze River by OMI (roughly following the dark red zone in Fig. S5 in the Supplement) while current emission inventories cannot capture this. Possible underestimation of emissions from ships is indicated. Due to data limits, only ships arriving or leaving the port of Nanjing are taken into account in the current city-scale inventory, while those passing through Nan-20 jing are omitted. Further investigation of the vessel flow along the Yangtze River is thus necessary to improve the estimation of ship emissions, which may be particularly influential at small spatial scales.

Spatial correlations between pollutant emissions and ambient
concentrations from ground observations 25 Ambient concentrations for selected pollutants from ground observations are used to test the city-scale emission inventory. in Nanjing, mapped in Fig. S1d. The SO 2 , NO 2 , CO, and PM 2.5 concentrations were measured by Ecotech EC9850B, Ecotech EC9841B, Ecotech EC9830B and Met One 1020 analyzers, respectively. The emissions of specific pollutants around each site with a grid cell size of 0.04 • × 0.04 • are calculated from the 3 km × 3 km gridded inventories, and correlations with annual mean concentrations of corresponding species are ana-5 lyzed. Since none of the city's key sources (CPP, CEM, ISP or RCP) are located in those grid cells, the effects of individual big sources on the correlation between emissions and observation are assumed to be limited. As shown in Fig. 7a, modest agreement is found in spatial patterns between the observed concentrations and the emissions for SO 2 and NO x (NO 2 ), with the R calculated at 0.58 and 0.46, respectively. SO 2 and NO x have average atmospheric lifetimes of several days and one day, respectively, thus the ambient concentrations are expected to partly reflect emission intensities nearby and the correlation analysis adds support for the reliability of the city-scale emission inventory. As shown in Fig. 7b, the correlation coefficient for CO is calculated at 0.61, and it reaches 0.86 when the observation 15 of the Caochangmen site is excluded, where extremely high emissions are calculated but low ambient levels were observed (to be further discussed in Sect. 4.4). Even with a longer lifetime (weeks to months) than SO 2 or NO x , CO in the atmosphere over Nanjing results mainly from primary emissions from incomplete combustion, implying reasonable agreement between emissions and concentrations. However, emissions from 20 small coal combustion sources still cannot be fully tracked or precisely quantified, and this evidence is thus tentative.

Evaluation of emissions against top-down constraints from observations
For certain pairs of pollutants that come from common sources and thus share emission characteristics, or weakly reactive species that are relatively stable in the at- 25 mosphere, correlations of ambient concentrations can provide useful "top-down" constraints on "bottom-up" estimates of primary emissions. In this work, the correlations of three pairs of species in the atmosphere -BC and CO, OC and EC, and CO 2 and CO 18710 Introduction -are analyzed based on daily mean concentrations from ground observations in 2012.
Combining the mass or molar ratios of emissions for corresponding species allows further evaluation of the city-scale inventory.

BC and CO
BC and CO both result from incomplete combustion of solid fuels and certain indus-5 trial processes such as coking. With relatively long atmospheric lifetime, CO is usually recognized as a tracer of pollution transport. Combined with BC levels, it can also be used to test emission inventories of the two species (most at regional or national scale), which is particularly useful for BC given its relatively large emission uncertainties (Kondo et al., 2011;Wang et al., 2011;Zhao et al., 2011Zhao et al., , 2012a. We follow the method presented in Wang et al. (2011) but focus on evaluating the city-level, top-down emission ratio of BC to CO based on observations at Caochangmen in Nanjing (point A in Fig. S1d in the Supplement). We choose this site for emission evaluation for two main reasons. First, it is an urban site and thus assumed to be more representative for the city emissions, compared to suburban/rural sites that are more influenced by 15 emissions from broader areas. Second, Caochangmen is the biggest and the most comprehensive state-operated station in the city. Among all the 9 state-operated sites in Nanjing, it is one and only station that conducts observation not only for the six criterion pollutants (i.e., SO 2 , NO 2 , CO, O 3 , PM 10 , and PM 2.5 ) but also for certain species including BC used here and CO 2 used later. Daily means of BC and CO concentrations 20 are calculated based on the hourly data from continuous observations using Magee AE 31 and Ecotech EC9830B analyzers, respectively, and the correlation between the two species are then evaluated and used to check the bottom-up emission inventories.
Since ambient levels of BC and CO depend not only on emissions but also on atmospheric processes (e.g., wet and dry depositions of BC, chemical reactions of CO with 25 OH, and mixing of both BC and CO) that exert different influences on the two species (Wang et al., 2011) above-mentioned atmospheric processes, as indicated in Eq. (2): F wet indicates the wet deposition screening. Based on precipitation data from the Weather Underground weather site (http://www.wunderground.com/history/), the data in precipitation days were excluded to eliminate the effects of wet deposition. F dry , F chem 5 and F mixing indicate the screening of dry deposition of BC, chemical reactions of CO with OH, and mixing of both BC and CO, respectively. Following the methods by Wang et al. (2011), F mixing is set at 1 and F chem+dry is calculated at 0.88 based on the lifetime of BC and CO in the atmosphere. As shown in Fig. 8, the annual ratio of BC to CO from observations is estimated at 0.0071 µg m −3 ppbv −1 by linear regression with the reduced major axis method (Hirsch and Gilroy, 1984), and it is 0.0073 µg m −3 ppbv −1 if the days of wet deposition are excluded. Once influence from other atmospheric processes are further eliminated, BC/CO| E,top-down rises to 0.0084, lower than the ratio from the city-scale bottom-up emission inventory at 0.0097, or that from the MEIC national inventory at 0.0095.

15
It should be noted that the downtown observation site is influenced heavily by local transportation, particularly gasoline vehicles that have relatively high CO but low BC emissions. Therefore, the top-down ratio of BC to CO observed at the site is expected to be somewhat lower than that of emissions over the entire city. The comparison of top-down and bottom-up results is thus roughly consistent with the city-scale emission 20 inventory, although possible overestimation of BC, or underestimation of CO emissions are indicated. Aside from mean annual levels, comparisons are also conducted for seasonal BC to CO ratios, as summarized in Table 2. The highest BC/CO| E,top-down is found in summer while the lowest is in winter. Such seasonal variation, however, is not indicated in the 25 current bottom-up emission inventory, for the following possible reasons. First, as described in Sect. 2, the temporal distribution of emissions is based on investigation of large and medium enterprises. However, the species of concern here, especially BC, come largely from small industrial and residential sources, for which temporal information is still lacking. For transportation, the increased cold start of vehicles in winter also leads to higher CO emissions that cannot be fully captured by COPERT (Cai and Xie, 2010;Xiao et al., 2004) and could thereby lead to an overestimate of BC/CO from the bottom-up method. Second, although Caochangmen is located in urban Nanjing, 5 it would inevitably be influenced by emissions from wider regions outside the city that are not quantified in the city-scale inventory. For example, biomass burning, which has a higher BC to CO ratio than exists in the ambient atmosphere, occurs more frequently in less-developed areas such as northern Jiangsu and Anhui provinces than in Nanjing. According to MODIS, 79 % of agricultural fire points in Jiangsu 2012 were found in 10 summer, elevating the ambient BC/CO in this season. Third, uncertainty exists about the estimation of F chem . In winter, the lowest OH densities in the boundary layer resulting from the weakest radiation lead to the smallest CO sink, and the opposite is true in summer (Seiler et al., 1984;Huang et al., 2013). The elevated F chem in summer should thus lead to reduced BC/CO| E,top-down . In this work, however, the seasonal 15 difference in F chem cannot be precisely quantified precisely based on existing studies, and the same F chem has to be used for all seasons, leading to possible overestimation of BC/CO| E,top-down for summer and underestimation for winter.

OC and EC
EC and primary OC result from incomplete combustion, and the ratio of OC to EC con-20 centrations is used to evaluate carbonaceous aerosol emissions and the formation of secondary organic aerosols (SOA bottom-up are clearly lower than the top-down (OC/EC) pri from observations. With few emission sources nearby, the observation site is thought to be less influenced by local sources (e.g., on-road transportation that has a relatively low emission OC to BC ratio) than the regional transport of pollutants (Li et al., 2015). Thus some sources with high OC to BC ratios that are uncommon in Nanjing but more dispersed out- ). The uncertainty of OC quantification from this sampling approach can reach 100 % in some cities in China (Hu et al., 2008). Finally, uncertainty also exists in the (OC/EC) pri determination 20 by Li et al. (2015), as the sample size from off-line measurements was small. To better evaluate city-level OC and BC emissions, therefore, more observational research with improved (e.g., long-term, continuous) measurements is strongly recommended at sites where local sources dominate.

25
CO 2 is a well-known greenhouse gas, with the main anthropogenic sources fossil energy combustion and industrial processes. The ratios of CO 2 to CO emissions differ between source types, reflecting varying combustion efficiencies.  Zhao et al., 2012a). They are also higher than the mixing ratios observed in rural 5 Beijing in 2008 (26.8, Y. Wang et al., 2010) or at Hatetuma Island (HAT), a remote site located off the coast of continental East Asia and influenced by air masses transported from East Asian countries from late fall to early spring (34. 5, Tohjima et al., 2014). Given the large discrepancy, data from measurements in urban Nanjing (Caochangmen, Point A in Fig. S1d) are further analyzed to test the emissions. Daily mean concentrations of CO 2 and CO are derived from hourly observations using Thermo 410i and Ecotech EC9830B analyzers, respectively, for all of 2012. To exclude the effects of biogenic emissions that prevail in warm seasons, data for the winter months (January, February, and December) are used. The prevailing wind directions over Nanjing in winter are east and northeast, and large point sources, accounting for 64 % of the city's 15 CO 2 emissions, are located to the east and northeast of Caochengmen, supporting use of the observational data for the current purpose. The average observed concentrations of CO 2 and CO in winter were 421 ppmv and 608 ppbv, respectively. Based on the cumulative probability distribution of daily CO concentrations (as shown in Fig. S6 in the Supplement), the whole dataset is divided into 20 three subsets: (1) below the 30th percentile (with average CO and CO 2 concentrations at 350 ppbv and 410 ppmv, respectively), (2) between the 30th and 95th percentile (677 ppbv for CO and 424 ppmv for CO 2 ), and (3) above the 95th percentile (above 1200 ppbv for CO and 448 ppmv for CO 2 ). We consider that subset (1) represents air masses from relatively clean areas outside Nanjing, and subset (2) a well-mixed blend 25 of sources of CO 2 and CO over Nanjing. Subset (3) is assumed to indicate extremely serious regional pollution episodes, in which pollutants were trapped in a shallow inversion layer (Y. Wang et al., 2010). Subset (2) is believed to best reflect the typical effects of local emissions and is used for bottom-up emission comparisons. 15,2015 Advantages of city-scale emission inventory for urban air quality research and policy  Fig. 9 is the CO 2 -CO correlation estimated with the reduced major axis method based on surface observations and the CO 2 to CO ratios from bottom-up emission inventories for Nanjing. Our estimate of the CO 2 to CO ratio (76.1) is closer to observations (86.9) than MEIC (52.8), implying improvement in the current city-scale inventory. The observed CO 2 to CO ratio, however, should theoretically be lower than 5 that from emissions for the following three reasons. First, compared to CO, the observation of CO 2 at an urban site would be more influenced by sources within a broader region than the city, as CO 2 has a longer lifetime in the atmosphere. Thus it is not fully representative of the very centralized and large CO 2 emissions inside the city, particularly those from large point sources (e.g., 17 power plants and 2 iron and steel plants, 10 which are estimated to account for 78 % of total CO 2 emissions in Nanjing), and the CO 2 to CO ratio from observations should be reduced. Second, the current emission inventory includes only the primary CO emissions while there may be a fair amount of secondary CO from the oxidation of NMVOC. Duncan et al. (2007) estimated that CO from NMVOC oxidation equaled nearly 50 % of global primary CO emissions. Given 15 the intensive refineries and chemical plants and thereby elevated NMVOC emissions in Nanjing, considerable secondary CO from NMVOC oxidation can be expected, leading to a lower ratio of CO 2 to CO from observations than that from primary emissions. Third, as discussed previously, the Caochangmen site is influenced heavily by local transportation that exhibits a lower CO 2 to CO emission ratio than industry. We believe 20 that the higher CO 2 to CO ratio from observations than bottom-up emissions reflect the uncertainties from both approaches. On one hand, emissions from certain species and sectors need to be further improved, e.g., CO from vehicles might be underestimated by the current work, since relatively poor management of vehicle emissions in China cannot be tracked by COPERT. On the other hand, we speculate that possible bias 25 also exists in observations, with more discussion to follow in Sect. 4.4.

Illustrated in
The larger molar ratios of CO 2 to CO in Nanjing than in Beijing, both from observations and emissions, are attributed mainly to the structure of emission sources. Nanjing is a city with intensive heavy industry, and over 90 % of coal was consumed by ACPD 15,2015 Advantages of city-scale emission inventory for urban air quality research and policy  , but the value is much smaller in Nanjing (10 %), elevating the molar ratio of CO 2 to CO in the city.

Evaluation of local ground observations based on the city-scale emission inventory
While ground observations can be used as top-down constraints on emissions, we 15 suggest that a high-resolution emission inventory can also be used to evaluate observational data. Although detailed local information demonstrably improves emission estimation, inconsistencies still exist between the city-scale emission inventory and ground observations of CO at Caochangmen. These inconsistencies include: (1) the significant increase in correlation coefficients between CO emissions and ambient concentrations 20 at state-operated monitoring sites when the data at Caochangmen are excluded (from 0.61 to 0.86), as described in Sect. 4.2 and Fig. 7b, and (2) the higher CO 2 / CO from observations at Caochangmen than that from city-scale emissions, which contradicts expectations based on atmospheric chemistry principles, as described in Sect. 4.3 and shown in Fig. 9. This suggests the possibility of instrumental or other error reflected in Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | an urban site, 3.5 km from Caochangmen. Frequency histograms of hourly CO concentrations at the two sites for 2012 and 2014 are shown in Fig. S7. It can be seen that CO levels at Caochangmen were significantly lower than those at Shanxilu in 2012 ( Fig. S7a) but the CO levels were quite similar at the two sites in 2014 (Fig. S7b). Moreover, a clear difference (∼ 30 %) in CO levels between 2012 and 2014 were found at 5 Caochangmen ( Fig. S7c) but not at Shanxilu (Fig. S7d). Given the very close distance and similar characteristics of the two sites, we tentatively assume that there should not be a significant difference in CO levels between them. Thus we conduct a sensitivity test by increasing the CO concentrations at Caochangmen by 30 % in 2012, and repeat the assessment of the city-scale emission inventory with the revised CO dataset.

10
The correlation coefficient between CO emissions and ambient concentrations at the 9 state-operated sites would be increased substantially, from 0.62 to 0.83. The ratio of CO 2 to CO in winter from the revised observational data would decrease from to 86.9 to 66.8, close to and lower than the ratio from the bottom-up city-scale inventory (76.1), consistent with the expectation that observed CO 2 / CO should be smaller than 15 emissions. Such data revision is clearly speculative, but encourages further analysis when observational data for a longer period become available at both sites. The cityscale emission inventory may thus provide a basis to raise questions about the quality of local ground observations, which should not be taken for granted. 20 To further explore the effects of methods and data employed in emission estimation at city and national levels, we conduct comparisons of emission levels and spatial distributions between the current inventory and MEIC for given pollutants from typical sources, including SO 2 from power generation, NO x from transportation, and PM 2.5 from industry, for 2010 in Nanjing. Our estimates are reallocated to a resolution of 5 km × 5 km, the same as MEIC, so that spatial correlations between the two inventories can be quantified.

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Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | As shown in Fig. 10a, relatively good correlation in the spatial distributions of SO 2 emissions from power generation is found for the two inventories, with the R estimated at 0.74. The result indicates consistency between the emission estimates of the two studies for large point sources, as might be expected given their shared reliance on relatively transparent, publicly available information on power plants nationwide. Lack-5 ing detailed field investigation of individual sources, however, national inventory studies have to rely on standard information for which routine updates or revisions are not guaranteed, and the latest changes in individual plants, including the closure of small power units or relocation of some power plants, cannot be tracked fully or on a timely basis. This is reflected by a number of data points in Fig. 10a with positive emission values on one axis but zero on the other. Regarding the total emission levels, MEIC is 35 % lower than our estimate, attributed mainly to the different SO 2 removal efficiencies of FGD applied in the two studies. Based on the field measurement data that were reported by individual plants and verified by the local environmental protection bureau, the average removal efficiency of FGD for power plants in Nanjing in 2010 is estimated at 15 66 %, lower than the values commonly applied by researchers in national emission assessments (e.g., above 70 %, . The discrepancy reveals the value of site-specific investigation of key parameters influencing emission estimates, including the SO 2 -removal rate of FGD. For NO x from transportation, the spatial R is calculated at 0.652 between the two es-20 timates, and the value would rise to 0.75 if the two grid cells with the largest emissions in the city-scale inventory were excluded, as shown in Fig. 10b. Similar to the power sector, the general spatial pattern of emissions from transportation for the two inventories is largely consistent. The emissions in MEIC, however, are much more concentrated in downtown urban regions compared to our estimate, resulting from differences 25 in spatial densities of population vs. transportation flows based on road networks. The former is commonly applied in spatial distribution of national emission inventories while the latter, when available through field investigation or real-time recording, are used in city-scale ones like ours. The total NO x emissions from transportation estimated by MEIC are 27 % lower than those by the city-scale inventory, suggesting introduction of considerable uncertainty when emissions estimated at the national level are downscaled to the city level based on proxies like population or economic activity. In contrast to the above two cases, little correlation is found between the two estimates in the spatial distribution of PM 2.5 emissions from industrial sources (Fig. 10c).
Shown in the maps of Fig. 10c are not only the PM 2.5 emissions but also the locations of the 20 largest emitting industrial enterprises. A clear discrepancy is observed between the distribution of those sources and emissions from MEIC, while much stronger consistency is found in the current work. Without sufficient information on individual sources, inventories developed at the national level tend to allocate large fractions of emissions into urban regions with relatively high densities of population and/or economic activity, assuming good spatial correlation between emissions and those proxies. Such correlation, however, likely weakens as pollution control in urban regions is implemented because it includes significant relocation of emission sources to suburban or rural areas (a primary element of urban pollution control policy in China). The total 15 PM 2.5 emissions from industrial sources estimated by MEIC are 50 % lower than our estimate, moreover, because: (1) a national emission inventory based on the sectoraverage levels of controls and emission factors cannot capture atypical, extremely large sources (super emitters), and (2) coal consumption from the official statistics used by MEIC is much lower than the aggregate of individual sources evaluated in the field sur-20 vey (3.0 vs. 5.0 million metric tons (Mt) for Nanjing, 2010). Comparisons and correlation analyses between inventories developed at different spatial scales, therefore, show the advantages of thorough investigation of individual emission sources, particularly for cities with many large industrial enterprises like Nanjing. 25 If probability distributions for each parameter of an emission inventory can be determined in advance, the uncertainties of the inventory can be quantified using Monte-Carlo simulation, as demonstrated and described in our previous studies (Zhao et al., 18720 Introduction   , 2013). Targeting emissions at the city scale, the current work does not repeat the methods applied in those studies, as most information on the variation of emission factors obtained here is city-or device-dependent and cannot be simply applied generally. Instead, we try to identify some common sources of uncertainty in the development of city-level emission inventories, including: (1) the inconsistencies of activity-level data 5 from various sources, and (2) the downscaling of activity data or emissions due to lack of city-level information.

Uncertainty assessment of city-scale emission inventory
There has been continuing concern about the accuracy and reliability of China's energy statistics for more than a decade (Sinton, 2001). Statistics from various sources report divergent energy consumption levels for the country, and the choice of activity-10 level data for emission inventories continues to be debated. For example, China's total energy use from national statistics has been inconsistent with that aggregated from provincial statistics, driving considerable differences in national emission estimates (Akimoto et al., 2006;Guan et al., 2012;. While Akimoto et al. (2006) concluded that an emission inventory based on province-by-province statistics were 15 in better agreement with satellite observations, Guan et al. (2012) indicated that overreporting in provincial energy statistics could be a factor. At the city level, however, there are far fewer evaluations of the accuracy of energy statistics. We find a clear discrepancy in energy consumption data in statistical sources for Nanjing: the total coal consumption in 2010 was reported at 27.9 Mt in the Nanjing Almanac (NJCLCC, 20 2011), while the value from the Environmental Statistics was 14 % higher, 31.9 Mt. The disparity results mainly from differences in data collection for small emission sources (enterprises). While data reporting systems and resulting data quality have been gradually improved for large-and medium-scale enterprises, many small-scale ones still do not maintain well-documented records on energy consumption, and the energy use 25 of those enterprises is poorly captured by the city almanac (personal communications with officials from Nanjing Municipal Commission of Economy and Information Technology, 2014). Aimed at pollution control, the environmental statistical system obtains and verifies energy data for each enterprise through field surveys, and we thus believe that these energy consumption data are more complete and reliable for emission inventory development. The uncertainty from such varied statistical sources could be reduced as retirement of small boilers and/or closure of small enterprises increases. Although the Nanjing Almanac stopped reporting coal consumption for the city after 2010, the Environmental Statistics indicates that the combined share of coal consumption by large-5 and medium-sized sources increased from 84 % in 2010 to 91 % in 2012, attributed to closure of small enterprises reporting highly uncertain energy data during the period. Besides problems in the energy data, uncertainty in the city-scale emission inventory can also result from lack of information on certain industrial sectors in the city statistics. If field surveys of individual sources cannot be conducted due to labor or time constraints, emissions have to be estimated by downscaling national or provincial estimates. To evaluate the resulting uncertainty, air pollutant emissions from non-ferrous metal smelting and the production of brick and lime in Nanjing 2012 are recalculated by the downscaling provincial estimates method (method B). In this method, emissions in Jiangsu province are first calculated based on the provincial statistics and provincial 15 average levels of emission control. Emissions in Nanjing are then obtained according to Nanjing's fraction of certain proxy (industrial GDP in this case) out of the whole province. The results are compared with those based on detailed source investigations (method A). Shown in Table 3 are the product output (activity level) and pollutant emissions estimated by methods A and B. The activity levels estimated from provincial-level 20 information are much higher than the actual industrial production aggregated from individual plants, suggesting downscaling produces emission overestimates. For example, gaseous pollutant emissions calculated with method B are 2, 10, and 30 times larger than those produced by method A for brick, lime, and copper production, respectively. For PM emissions, the discrepancies in emissions between the two methods 25 are smaller, attributed partly to the compensating effects of divergent removal efficiencies of dust collectors applied in the two methods, obtained either from plant-by-plant surveys (method A) or from national or provincial average levels (method B). The differences are believed to reflect disparities in the considerable fractions of total emis-

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Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | sions from OIN (i.e., industrial sources excluding iron and steel, cement, and chemical plants, for which information is relatively clear, as defined in Sect. 2.1), specifically 6, 22, 15, and 24 % of SO 2 , NO x , PM 2.5 , and CO for lime production, as shown in Table 3. The results suggest relatively large uncertainties in city-level emission estimates lacking sufficient individual source information. In this case, moreover, the overestimates in 5 Nanjing's emissions from downscaling provincial emissions would inevitably lead to underestimates for other cities within the province, weakening understanding of emission sources and the quantitative basis of regional control policies.

The effects of pollution control policies on emission abatement and air
10 quality Substantial efforts have been undertaken in specific Chinese sectors to achieve national targets in both energy conservation and emission reduction . Under the air pollution control measures, clear benefits in emission abatement, particularly in the power and transportation sectors, are found for Nanjing from 2010 to 2012, 15 a relatively short period. In the power sector, electricity generation increased by 58 % during 2010-2012 while that specifically from coal-fired plants grew by 47 %, reflecting the switch of coal to gas combustion and other diversification of power generation in the city. Meanwhile, coal consumption of the power sector increased by only 25 %, much slower than the resulting electricity generation, reflecting improved energy efficiency 20 due to replacement of small and old power units with larger and more energy-efficient ones. As shown in Fig. S8a in the Supplement, the capacity share of large units (above 300 MW) increased from 72 % in 2010 to 78 % in 2012 while that of small ones (below 100 MW) decreased from 20 to 16 %. The penetration of large units raised as well the use of APCD for SO 2 , NO x , and PM, leading to significant reduction of emission fac-Introduction

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Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | seen for vehicles. With implementation of staged emission standards for new vehicles from 2010 to 2012, the emission factors of SO 2 , NO x , CO, and VOCs are estimated to have declined by 66, 33, 34, and 37 % for gasoline vehicles (Fig. S8b), those of NO x , CO, and VOCs by 12, 13, and 24 % for diesel vehicles (Fig. S8c), and those of CO and VOCs by 25 and 34 % for motorcycles (Fig. S8d), respectively. The SO 2 , 5 CO, and VOCs emissions from on-road transportation decreased by 39, 11, and 27 %, respectively, while the vehicle population increased by 27 % in the city over the three years.
The benefits of emission control on air quality can be partly confirmed by comparisons of changes in emissions and ambient concentrations. During 16-24 August 2013 when the 2nd Asian Youth Games (AYG) were held in Nanjing, a series of extra emission controls measures were undertaken by the government to improve air quality for the Games. Those measures included increasing use of low-sulfur coal at power plants, closing small factories with relatively large pollutant emissions, stopping construction in some regions, and restricting traffic. Taking these extra measures into account, the 15 emissions of SO 2 , NO x , PM 2.5 , PM 10 , and CO over 16-24 August 2013 are estimated to have declined 23, 31, 21, 14, and 33 %, respectively, compared to those in the same period in 2012, based on the monthly distributions described in Sect. 3.2 (see Table S4 in the Supplement). Correspondingly, the daily average concentrations for those pollutants in Nanjing during the AYG period were found to decline by 22, 27, 10, 5, and 20 22 %, respectively, compared to the same period in 2012 (Yu et al., 2014). Although changes in other factors including meteorological conditions also influenced air quality, the consistency between the reduced emissions and concentrations suggests that local emission abatement played a primary role in the air quality improvement. inate the levels and spatial distribution of emissions of the city. As shown in Fig. 3, the areas with high emission densities in Nanjing are in good agreement with geographical locations of point sources for all pollutants. The ten largest point sources of SO 2 emissions are estimated to account for 54 % of total emissions in the city (Fig. 2a), and the analogous number for NO x is 43 % (Fig. 2b). For PM 2.5 , as shown in Fig. 2c, 5 the ten largest sources are estimated to be responsible for 75 % of total primary emissions in Nanjing (excluding construction and road dust). In particular, extremely high emissions are found for iron and steel plants, resulting mainly from the high production of steel and reliance on wet scrubbers with relatively low removal efficiencies (annual average of 85 %) in the exhaust streams of basic oxygen furnaces. Similarly, the ten largest refineries and chemical plants shown in Fig. 2d are responsible for 52 % of VOC emissions in Nanjing. The dominant roles of these big sources on emission levels and spatial distributions indicate that careful investigation and analysis of source-specific parameters relevant to emissions from these super emitters (e.g., removal efficiency of APCDs) are particularly crucial to the reliability of city-scale emission inventories. 15 Although large point sources dominate emissions at the city level, the contributions from scattered small sources cannot be overlooked. As shown in Fig. 3, the fractions of air pollutant emissions from power, cement, iron and steel, and chemical plants to the city's total emissions are estimated to range from 38 to 88 %, significantly lower than that of coal consumption (96 %). Despite the tiny share of coal use, decentral-20 ized small coal combustion sources have a relatively high proportion of emissions, resulting from poorer emission control technologies and management than big enterprises. Regarding emission abatement and air quality improvement, it is imperative to expand pollution control from large sources to small-and medium-sized enterprises, as the potential for further reductions from the major sources are diminishing due to near-saturation of APCDs. As for improvement of emission inventories, more varied and uncertain emission factors for small boilers and kilns result from much greater diversities of manufacturing technologies. This necessitates more field measurements in the future to inform the application of emission factors in inventories and to better understand the emission characteristics of small sources.

Conclusions
With updated methods and substantial new data on local emission sources, a cityscale emission inventory of air pollutants and CO 2 is developed for Nanjing for three

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Limitations remain in the current work. First, some available and potentially valuable information cannot yet be fully exploited to improve emission estimates. CEMS data help to determine the time distribution of emissions, for example, but are currently less useful for estimating absolute emission levels, due to incompleteness and systematic errors in relevant parameters (e.g., flue gas flow rate). Second, since emission factors for some sources are still based on provincial or national assessments due to lack of local information, the uncertainties of the city-scale emission inventory have not yet been systematically quantified. In particular, the degree of uncertainty in the city-scale inventory compared to that of national ones remains unknown. Finally, it is currently difficult to assess emissions of some species believed to have high emission uncertainty, e.g., VOC and NH 3 , due to lack of sufficient instrumental observations. More field measurements of both emissions and ambient levels of these species are thus 5 recommended in the future.
The Supplement related to this article is available online at doi:10.5194/acpd-15-18691-2015-supplement.
of Science and Technology of China (2011BAK21B00), and Collaborative Innovation Center for Regional Environmental Quality. We would like to thank Hongxin Bao and Danning Zhang from NJEPB for technical support of this work, and TEMIS for free use of their monitoring data. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the sponsors.

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Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ner, D.: An improved tropospheric NO 2 column retrieval algorithm for the Ozone Monitoring Instrument, Atmos. Meas. Tech., 4, 1905-1928, doi:10.5194/amt-4-1905  ACPD 15,2015 Advantages of city-scale emission inventory for urban air quality research and policy  Gon, H. A. C., Kuenen, J. J. P., Segers, A. J., Honoré, C., Perrussel, O., Builtjes, P. J. H., and Schaap, M.: Quantification of the urban air pollution increment and its dependency on the use of down-scaled and bottom-up city emission inventories, Urban Climate, 6, 44-62, 2013. Tohjima, Y., Kubo, M., Minejima, C., Mukai, H., Tanimoto, H., Ganshin, A., Maksyutov, S., Kat-5 sumata, K., Machida, T., and Kita, K.: Temporal changes in the emissions of CH 4 and CO from China estimated from CH 4 /CO 2 and CO/CO 2 correlations observed at Hateruma Island, Atmos. Chem. Phys., 14, 1663-1677, doi:10.5194/acp-14-1663 15,2015 Advantages of city-scale emission inventory for urban air quality research and policy     ACPD 15,2015 Advantages of city-scale emission inventory for urban air quality research and policy   15,2015 Advantages of city-scale emission inventory for urban air quality research and policy ± NOx (10^15mol/cm^2) 9.9 9.9 -11.0 11.0 -12. ACPD 15,2015 Advantages of city-scale emission inventory for urban air quality research and policy  Figure 6. Spatial correlation between NO x emissions from city-and national-scale inventories and NO 2 vertical column density (VCD) from OMI, in Nanjing, 2010 for (a) all grids, (b) step-wise exclusion of grid cells with largest emissions and (c) grid cells without power plant emissions. 15,2015 Advantages of city-scale emission inventory for urban air quality research and policy   15,2015 Advantages of city-scale emission inventory for urban air quality research and policy Slope=0.00709

ACPD
Excluding the influence of wet deposition of BC The origional observation Excluding the influence of all the atmospheric processes ACPD 15,2015 Advantages of city-scale emission inventory for urban air quality research and policy