TY - JOUR
AU - He, Kebin
AB - Abstract The development of reliable anthropogenic emission inventories is essential for both understanding the sources of air pollution and designing effective air-pollution-control measures in China. However, it is challenging to quantify emissions in China accurately, given the variety of contributing sources, the complexity of the technology mix and the lack of reliable measurements. Over the last two decades, tremendous efforts have been made to improve the accuracy of emission inventories, and significant improvements have been realized. More reliable statistics and survey-based data have been used to reduce the uncertainties in activity rates and technology distributions. Local emission factors and source profiles covering various sources have been measured and reported. Based on these local databases, improved emission inventory models have been developed for power plants, large industrial plants and the residential, transportation and agricultural sectors. In this paper, we review the progress that has been made in developing inventories of anthropogenic emissions in China. We first highlight the major updates that have been made to emission inventory models and the underlying data by source category. We then summarize the sector-based estimates of emissions of different species contained in current inventories. The progress that has been made in the development of model-ready emissions is also presented. Finally, we suggest future directions for further improving the accuracy of emission inventories in China. emission inventory, atmospheric chemistry, China, chemical transport model, air pollution INTRODUCTION AND BACKGROUND Understanding emissions in China is essential for studies of atmospheric chemistry and climate. Specifically, anthropogenic emissions from China cause severe haze events, which lead to adverse health impacts and reductions in visibility. Quantifying these emissions accurately is challenging, due to the variety of contributing sources, the complexity of the technology mix and the lack of reliable local measurements within China. Developing a reliable emission inventory with high accuracy is of great importance for designing air-pollution-control measures. One of the main goals of developing emission inventories is to provide gridded emissions for use as inputs in atmospheric and climate models. Emission inventories have been developed in supporting of scientific research projects in which modelling activities are always accompanied by top-down validations. Examples include the Transport and Chemical Evolution over the Pacific (TRACE-P) [1], Intercontinental Chemical Transport Experiment-Phase B (INTEX-B) [2], MIX for MICS-Asia (Model Inter-Comparison Study for Asia) [3], Hemispheric Transport of Air Pollution (HTAP) [4] and Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants (ECLIPSE) emission inventories [5]. Emissions have also been estimated to support policy-making by stakeholders. Examples include the Greenhouse gas-Air pollution Interactions and Synergies (GAINS) inventory model and inventories used in policy evaluations [6–8]. Within the last two decades, tremendous efforts have been made to develop reliable emission inventories in China, and significant improvements have been made. Early emission inventories over China were conducted mainly using ‘bottom-up’ methodologies, which employ activity rates and emission factors (EFs); the values of these parameters were drawn from those determined for Western countries, due to the lack of local data [1,9–12]. To support the INTEX-B (Intercontinental Chemical Transport Experiment-Phase B) mission, Zhang et al. [2] used an improved, detailed technology-based approach to estimate emissions in China. Using a consistent inventory framework, Ohara et al. [12] developed the first emission inventory covering China (the Regional Emission inventory in Asia, REAS) that includes both the historical period and projections; this inventory was updated to REAS v2 by Kurokawa et al. [13]. Increasing numbers of emission inventories have been compiled by parameterizing up-to-date technology distributions, datasets containing local measurements and improved methodologies for specific source categories (e.g. [14–20]) or specified regions (e.g. [21–24]). Reliable data are crucial in improving the accuracy of emission estimates. The government of China releases an annual statistical yearbook on energy consumption and product yields covering diverse source categories, and these data constitute the basic database of activity rates used in compiling inventories. Large emission gaps are found when different official statistics are used [25,26]. Quantifying and further reducing the uncertainties due to statistical data are presently focuses of study. Given the increasing numbers of detailed surveys and data-collection activities that are being conducted by the Ministry of Environmental Protection (MEP) of China, industrial associations and research groups, emission estimates at the level of individual units or production lines are becoming feasible. Large numbers of real-world EFs have been measured in China. Chinese researchers have produced large quantities of local measurements for stationary, mobile and fugitive sources over the past two decades. These data significantly improve our understanding of emission characteristics in China. Surveys of technological evolution and the control measures that have been implemented are important in determining net EFs. Although these surveys are being conducted in increasing numbers, they are limited to specific sources, such as power plants, large industrial plants and vehicles. The evolution of technology-based EFs calculated in inventory models provides a basis for analyses of historical trends and projections of future emissions. Tsinghua University has developed a uniform emission model framework, the Multi-resolution Emission Inventory for China (MEIC), to estimate anthropogenic emissions over China. The MEIC model is based on a series of improved emission inventory models including unit-based emission inventories for power plants [19] and cement plants [15]; a high-resolution county-level vehicle emission inventory [16]; a residential combustion emission inventory based on national-wide survey data [27]; and an explicit profile-based non-methane volatile organic compound (NMVOC) speciation framework [28]. MEIC provides the community a publically accessible emission dataset over China with regular updates (http://www.meicmodel.org). Nine chemical species, including both gaseous and aerosol species, are included in this review, as they are always included as inputs to chemical transport models: SO2 (sulphur dioxide), NOx (nitrogen oxides), CO (carbon monoxide), NMVOCs, NH3 (ammonia), PM10 (particulate matter with diameter less than or equal to 10 μm), PM2.5 (particulate matter with diameter less than or equal to 2.5 μm), BC (black carbon) and OC (organic carbon). To fill the gap between the inventoried and modelled species, explicit speciation of the NMVOCs and PM (particulate matter) is required. In this article, we review the considerable progress that has been made in the methods and data used in the estimation of emissions and the results of these estimates (second and third sections) and model-ready emissions processing (fourth section) in China. Main uncertainties in current inventories are discussed in the fifth section. Outlook for developing improved emission inventories over China are provided in the sixth section. EMISSIONS BY SECTORS In this section, we provide an overview of the development of emission inventories by sector and subsector that focuses on (i) the history of emission estimates, the challenges, and up-to-date methods used in estimating emissions; (ii) key data sources of activity rates, locally measured EFs and other significantly improved parameters; and (iii) emission estimates and trends for specific sectors. Sectoral emissions by pollutant for 2010 are summarized in Table 1 (derived from version 1.2 of MEIC). Table 1. Emission estimates by sectors in China, 2010 (derived from MEIC v1.2, www.meicmodel.org) (unit: Gg/year for the sectoral emissions, Tg/year for the total emissions). Species . Power . Industry . Residential . Transportation . Solvent use . Agriculture . Total . SO2 7779.06 16 372.99 4153.77 222.98 28.53 NOx 9265.93 9344.26 1726.82 6982.79 27.32 CO 3632.28 74 739.76 71 281.77 20 323.67 169.98 NMVOCs 66.39 7878.63 5014.79 2351.98 7151.05 22.46 NH3 0.00 273.57 427.65 25.42 9701.62 10.43 PM10 978.19 6060.13 8068.53 497.78 15.60 PM2.5 847.27 5805.25 4452.15 493.31 11.60 BC 1.75 584.35 848.19 273.13 1.71 OC 0.02 578.64 2481.50 99.61 3.16 Species . Power . Industry . Residential . Transportation . Solvent use . Agriculture . Total . SO2 7779.06 16 372.99 4153.77 222.98 28.53 NOx 9265.93 9344.26 1726.82 6982.79 27.32 CO 3632.28 74 739.76 71 281.77 20 323.67 169.98 NMVOCs 66.39 7878.63 5014.79 2351.98 7151.05 22.46 NH3 0.00 273.57 427.65 25.42 9701.62 10.43 PM10 978.19 6060.13 8068.53 497.78 15.60 PM2.5 847.27 5805.25 4452.15 493.31 11.60 BC 1.75 584.35 848.19 273.13 1.71 OC 0.02 578.64 2481.50 99.61 3.16 Open in new tab Table 1. Emission estimates by sectors in China, 2010 (derived from MEIC v1.2, www.meicmodel.org) (unit: Gg/year for the sectoral emissions, Tg/year for the total emissions). Species . Power . Industry . Residential . Transportation . Solvent use . Agriculture . Total . SO2 7779.06 16 372.99 4153.77 222.98 28.53 NOx 9265.93 9344.26 1726.82 6982.79 27.32 CO 3632.28 74 739.76 71 281.77 20 323.67 169.98 NMVOCs 66.39 7878.63 5014.79 2351.98 7151.05 22.46 NH3 0.00 273.57 427.65 25.42 9701.62 10.43 PM10 978.19 6060.13 8068.53 497.78 15.60 PM2.5 847.27 5805.25 4452.15 493.31 11.60 BC 1.75 584.35 848.19 273.13 1.71 OC 0.02 578.64 2481.50 99.61 3.16 Species . Power . Industry . Residential . Transportation . Solvent use . Agriculture . Total . SO2 7779.06 16 372.99 4153.77 222.98 28.53 NOx 9265.93 9344.26 1726.82 6982.79 27.32 CO 3632.28 74 739.76 71 281.77 20 323.67 169.98 NMVOCs 66.39 7878.63 5014.79 2351.98 7151.05 22.46 NH3 0.00 273.57 427.65 25.42 9701.62 10.43 PM10 978.19 6060.13 8068.53 497.78 15.60 PM2.5 847.27 5805.25 4452.15 493.31 11.60 BC 1.75 584.35 848.19 273.13 1.71 OC 0.02 578.64 2481.50 99.61 3.16 Open in new tab Power plants Power plants are major contributors to the total emissions of air pollutants in China, and they have been widely considered to be a separate sector in many emission inventories. As the largest consumer of coal in China, the power plant sector has reported its total fuel consumption in each province in the official statistical yearbooks for over two decades (China Energy Statistical Yearbook, National Bureau of Statistics (NBS), 1992–2016). Early studies employed the reported annual fuel consumption values and fixed EFs to calculate the yearly emissions; however, this procedure does not accurately represent the rapid changes in emission rates driven by technological improvements [9,12,29,30]. Recent studies have adopted dynamic EFs derived from technology-based methodologies and locally measured EFs to improve the accuracy of both the magnitudes of power plant emissions and the trends in these emissions [1,2,31–35]. In addition to accurate estimates of total emissions, the spatial allocation of emissions is also important in power plant inventories for model applications and analyses. Ideally, emissions are estimated for individual power plants and allocated to grid cells using their exact geographical coordinates. However, due to the lack of detailed information (i.e. location, fuel consumption and emissions) on the power plants in China, many bottom-up inventories rely on a downscaling approach to estimate the spatial distribution of emissions. This approach ignores the differences in technology used at power plants; thus, different inventories indicate different unit-based emission rates. The early inventories only included information on large electricity-generating units and treat the remaining small units as area sources [1,12]. The emissions from large units were derived by downscaling provincial total emissions based on unit size [2,36]. Subsequent studies used a global power plant database, CARbon Monitoring for Action (CARMA) [37], which provides more extensive information (including the magnitudes of CO2 emissions and locations) for individual power plants including small units, and breaks down the total emissions to the level of individual power plants [13,38,39]. The calculation of power plant emissions based on their unit-level coal consumption, instead of the downscaling approach, was initially performed by Zhao et al. [40] for 2000 and 2005 and allocated to the corresponding geographic coordinates. Similar unit-based power plant emission inventories were subsequently constructed for other years, including the period of 2005–07 (including only NOx; [41]), the year 2010 (including only NOx; [42]) and the year 2011 [43]. More recently, a unit-based power plant database that covers the period of 1990–2010, the China coal-fired Power plant Emissions Database (CPED), was developed; this database includes time-dependent information on the technologies, fuel consumption, EFs and locations of individual units [19]. This inventory was the first long-term, unit-based inventory to be constructed, and it permits improved estimates of the spatial distribution of emissions and their trends. EFs change over time with the operation of new combustion or emission control technologies required by new emission standards and the changes in fuel property. The sulphur content of coal varies widely among power plants, and the national average value from 1990 to 2010 is 1.07–0.95% [19]. The sulphur retention ratios range from 0.10 [40] to 0.15 [2,19,44]. Flue-gas desulphurization (FGD) systems have been gradually installed to remove SO2 emissions since 2005; the penetration of this technology has increased from 12% in 2005 to 86% in 2010. Because the operating conditions of installed FGD facilities improved after 2008 [45–47], the SO2 removal efficiency improved accordingly, and a coal consumption weighted mean of 78% was reached for all FGD facilities in 2010 [19]. SO2 emissions can also be removed from wet scrubbers as a co-benefit of PM removal and the corresponding suggested removal efficiency is 20% [48,49]. Table 2 summarizes the measured NOx EFs for coal-fired power plants in China. The NOx EFs are determined by boiler size, combustion technology and coal type. Low-NOx burners (LNBs) were the only technology that was widely used to control NOx emissions before 2010. Most power plants were required to be equipped with LNBs to meet emission standards [50]. Selective catalytic reduction (SCR) and selective non-catalytic reduction (SNCR) systems were subsequently employed, and their penetration increased from 13% in 2010 to nearly 90% in 2015. Table 2. Summary of NOx emission factors for different types of coal-fired power plants. Unit size . Combustion technology . Bituminous coal, g/kga . Anthracite coal, g/kga . Large (≥300 MW) Advanced LNBb 2.881, 3.052, 3.283, 3.554, 4.135, 4.176, 4.647 6.147, 6.584, 6.998 Traditional LNB 4.409, 4.9810, 5.2311, 5.0612, 5.658, 7.784 4.6111, 4.9912, 7.777, 7.948, 8.0510, 8.739 Medium (≥100 MW and <300 MW) Traditional LNB 4.3410, 5.5211, 6.9713 7.0711, 7.5610 Non-LNB 5.4614, 8.1211 8.2510, 12.1111 Small (<100 MW) Non-LNB 6.5515, 6.8811 10.0115, 11.5011 Unit size . Combustion technology . Bituminous coal, g/kga . Anthracite coal, g/kga . Large (≥300 MW) Advanced LNBb 2.881, 3.052, 3.283, 3.554, 4.135, 4.176, 4.647 6.147, 6.584, 6.998 Traditional LNB 4.409, 4.9810, 5.2311, 5.0612, 5.658, 7.784 4.6111, 4.9912, 7.777, 7.948, 8.0510, 8.739 Medium (≥100 MW and <300 MW) Traditional LNB 4.3410, 5.5211, 6.9713 7.0711, 7.5610 Non-LNB 5.4614, 8.1211 8.2510, 12.1111 Small (<100 MW) Non-LNB 6.5515, 6.8811 10.0115, 11.5011 aSample weighted mean. bLNB: Low-NOx burners. Data sources: 1Qian [53]. 2Cao and Liu [54]. 3Zhu [55]. 4Wang et al. [56]. 5Yi et al. [57]. 6Zhu et al. [58]. 7Xie et al. [59]. 8Wang et al. [60]. 9Bi and Chen [61]. 10Tian [62]. 11Zhu [63]. 12Zhu et al. [64]. 13Feng and Yan [65]. 14Zhao et al. [66]. 15Zhao et al. [14]. Open in new tab Table 2. Summary of NOx emission factors for different types of coal-fired power plants. Unit size . Combustion technology . Bituminous coal, g/kga . Anthracite coal, g/kga . Large (≥300 MW) Advanced LNBb 2.881, 3.052, 3.283, 3.554, 4.135, 4.176, 4.647 6.147, 6.584, 6.998 Traditional LNB 4.409, 4.9810, 5.2311, 5.0612, 5.658, 7.784 4.6111, 4.9912, 7.777, 7.948, 8.0510, 8.739 Medium (≥100 MW and <300 MW) Traditional LNB 4.3410, 5.5211, 6.9713 7.0711, 7.5610 Non-LNB 5.4614, 8.1211 8.2510, 12.1111 Small (<100 MW) Non-LNB 6.5515, 6.8811 10.0115, 11.5011 Unit size . Combustion technology . Bituminous coal, g/kga . Anthracite coal, g/kga . Large (≥300 MW) Advanced LNBb 2.881, 3.052, 3.283, 3.554, 4.135, 4.176, 4.647 6.147, 6.584, 6.998 Traditional LNB 4.409, 4.9810, 5.2311, 5.0612, 5.658, 7.784 4.6111, 4.9912, 7.777, 7.948, 8.0510, 8.739 Medium (≥100 MW and <300 MW) Traditional LNB 4.3410, 5.5211, 6.9713 7.0711, 7.5610 Non-LNB 5.4614, 8.1211 8.2510, 12.1111 Small (<100 MW) Non-LNB 6.5515, 6.8811 10.0115, 11.5011 aSample weighted mean. bLNB: Low-NOx burners. Data sources: 1Qian [53]. 2Cao and Liu [54]. 3Zhu [55]. 4Wang et al. [56]. 5Yi et al. [57]. 6Zhu et al. [58]. 7Xie et al. [59]. 8Wang et al. [60]. 9Bi and Chen [61]. 10Tian [62]. 11Zhu [63]. 12Zhu et al. [64]. 13Feng and Yan [65]. 14Zhao et al. [66]. 15Zhao et al. [14]. Open in new tab PM emission rates are related to boiler types. Different types of boilers (pulverized coal boilers, circulating fluidized beds and grate furnaces) retain different fractions of ash (20%, 44% and 85%, respectively), and the corresponding PM size distributions are listed in Table 3 [51,52]. Cyclones, wet scrubbers, electrostatic precipitators and bag filters are widely used in power plants to remove PM, and the removal efficiencies of these technologies have been summarized by Lei et al. [34]. In addition, wet FGD systems can remove PM as a co-benefit of SO2 removal. Table 3. Summary of the mass fractions of particulate matter of different size fractions to the total particulate matter in fly ash for different types of boilers. . Boiler type . Size fraction . Pulverized boilers . Circulating fluidized beds . Grate furnaces . PM>10 0.561, 0.612, 0.823, 0.824 0.715 0.724, 0.6385 PM2.5–10 0.281, 0.372, 0.153, 0.134 0.225 0.184, 0.235 PM2.5 0.161, 0.022, 0.033, 0.054 0.075 0.104, 0.145 . Boiler type . Size fraction . Pulverized boilers . Circulating fluidized beds . Grate furnaces . PM>10 0.561, 0.612, 0.823, 0.824 0.715 0.724, 0.6385 PM2.5–10 0.281, 0.372, 0.153, 0.134 0.225 0.184, 0.235 PM2.5 0.161, 0.022, 0.033, 0.054 0.075 0.104, 0.145 Data sources: 1Huang et al. [67]. 2Liu et al. [68]. 3Yi et al. [57]. 4Zhao et al. [14]; in-field measurements. 5Klimont et al. [51]; literature review. Open in new tab Table 3. Summary of the mass fractions of particulate matter of different size fractions to the total particulate matter in fly ash for different types of boilers. . Boiler type . Size fraction . Pulverized boilers . Circulating fluidized beds . Grate furnaces . PM>10 0.561, 0.612, 0.823, 0.824 0.715 0.724, 0.6385 PM2.5–10 0.281, 0.372, 0.153, 0.134 0.225 0.184, 0.235 PM2.5 0.161, 0.022, 0.033, 0.054 0.075 0.104, 0.145 . Boiler type . Size fraction . Pulverized boilers . Circulating fluidized beds . Grate furnaces . PM>10 0.561, 0.612, 0.823, 0.824 0.715 0.724, 0.6385 PM2.5–10 0.281, 0.372, 0.153, 0.134 0.225 0.184, 0.235 PM2.5 0.161, 0.022, 0.033, 0.054 0.075 0.104, 0.145 Data sources: 1Huang et al. [67]. 2Liu et al. [68]. 3Yi et al. [57]. 4Zhao et al. [14]; in-field measurements. 5Klimont et al. [51]; literature review. Open in new tab Driven by the ever-increasing demand for electricity, power plant emissions have increased sharply since 1990. However, emissions have grown at significantly lower rates than electricity generation, given the technological changes that have occurred in the power sector. Figure 1 compares multi-year estimates for power plant emissions derived from bottom-up inventories that are available for multiple species and are widely used in the research community. Nearly all of the inventories reflect consistent trends for SO2 emissions, which peaked in approximately 2006 owing to the installation of FGD systems. The SO2 emission trends after 2005 in the Emission Database for Global Atmospheric Research (EDGAR) 4.2 differ from those of the other inventories, most likely due to a lack of information on the progress that has been made in controlling SO2 emissions in power plants in China. In addition, the official estimates of SO2 emissions published by the MEP (China Statistical Yearbook, NBS, 1997–2011) are generally lower than those reached by other studies. The NOx emission trends display good consistency among all of the inventories. More recent NOx estimates [19] are lower than the previous values [13,32], due to the smaller EFs adopted. The PM emission trends generally agree well with each other, except for REAS v2, which makes different assumptions regarding the penetration of PM removal devices. Figure 1. Open in new tabDownload slide Comparisons of SO2, NOx and PM2.5 emissions from China's coal-fired power plants during 1990 and 2010. Industry Industry is the largest contributor of emissions of SO2 (57%), NOx (34%), CO (44%), NMVOCs (35%) and PM2.5 (50%), and contributes over 34% of the total emissions of PM10 and BC in China (see Table 1 for 2010). These pollutants are emitted both from stationary industrial factories and by industrial processes. Cement plants, iron and steel plants and industrial boilers are identified as major contributors of SO2, NOx and PM. In recent years, unit-based emission inventories have been developed for cement plants [70] and iron and steel plants [71,72] based on detailed (factory-level) local information, which has substantially improved the development of emission estimates. The petroleum industry is the largest contributor to industrial emissions of NMVOCs, but these emissions are far from accurately quantified. We illustrate the emission estimates for the above subsectors separately below. Cement China is the largest producer and consumer of cement in the world. In 2014, cement production in China was as high as 2.49 billion metric tons, and this amount accounted for ∼60% of the world's production [73]. The cement industry has been identified as one of the major contributors to the total national emissions of air pollutants in China. Several early studies treated the cement industry as a part of the industrial sector and estimated the corresponding emissions based on total coal consumption [1,12]. In addition, other studies applied uniform EFs to the reported cement production figures [74], and this practice did not reflect changes in technology and equipment replacement over time. Recent studies have developed dynamic EFs using technology-based methodologies [13,15,70,75,76] and local measurements have been incorporated, thus improving the accuracy of estimated emissions in China (e.g. [70,75,76]). With the improvement of total estimated emission accuracy in the cement industry, the resolution of spatial distribution also has been improved gradually. Early studies adopted various spatial proxies to allocate total emissions to grid cells, due to the lack of detailed information for each cement plant (e.g. [1,75,77]). Recent studies allocate country and regional emissions using surrogate data developed based on the locations and annual capacities of the plants in each country and region (e.g. [13,15]). More recently, detailed plant-based information on large cement plants was collected and used to calculate plant-level emissions for the period of 1980–2012, and the remaining emissions were all treated as regional area sources in each province. This procedure significantly improves the accuracy of the inferred spatial distribution [70]. EFs reflect changes in technology over time. In general, two major types of kilns—shaft kilns and rotary kilns—are used in China. Due to the large emissions from shaft kilns, the use of precalciner kilns (the most advanced rotary kilns) has been promoted since the end of the 1990s. Therefore, equipment updates in cement plants in China have caused significant changes in the net EF. The burning of fuel in the cement industry is usually identified as the sole source of SO2 and NOx emissions [15]. SO2 is primarily produced by the oxidation of sulphur in fuels such as coal. In precalciner kilns, approximately 70% of SO2 is absorbed by reaction with calcium oxide or calcium carbide [78], while much less is absorbed in other rotary kilns and in shaft kilns. Recent studies adopt the assumption that 80% of SO2 is absorbed for precalciner kilns, whereas this proportion is 30% for other kiln types. In general, SO2 emissions are estimated using a mass balance approach and are based on the average sulphur content of coal in each province [15,70]. The generation of NOx is highly influenced by kiln temperature and oxygen availability. Compared to shaft kilns, rotary kilns produce much more NOx because of their higher operation temperatures and stable ventilation [15]. Table 4 summarizes the measured NOx EFs in cement plants in China published in various studies. As awareness of the increasing NOx emissions from the cement industry in China has grown, increasing numbers of SCR and SNCR systems have been requested for installation in the cement industry [70]. De-NOx systems are reported to have been subsequently installed in precalciner kilns, and their penetration rate had reached nearly 92% as of 2015 (China's Ministry Environmental Protection). Table 4. Summary of NOx emission factors for different kiln types in cement industry. Cement kiln types . NOx emission factors (g/kg-clinker) a,b . Precalciner kilns 1.1681, 1.5352, 1.5843,4,c, 1.4945, 1.6936, 1.7463,d, 1.8347, 2.0168, 2.1469 Shaft kilns 0.2023,e, 0.2433,f, 0.48210 Other rotary kilns 1.6091, 2.4488 Cement kiln types . NOx emission factors (g/kg-clinker) a,b . Precalciner kilns 1.1681, 1.5352, 1.5843,4,c, 1.4945, 1.6936, 1.7463,d, 1.8347, 2.0168, 2.1469 Shaft kilns 0.2023,e, 0.2433,f, 0.48210 Other rotary kilns 1.6091, 2.4488 aSample weighted mean. bAssumptions introduced when transferring measurements to emission factor unit in g/kg-clinker: flue-gas volume is 2.47 m3/kg-clinker (Wu [79]); average clinker to cement ratio is 0.72 (Lei et al. [15]); average coal consumption is 183g-coal/kg-clinker for precalciner and other rotary kilns, and 177g-coal/kg-clinker for shaft kilns (Zhang et al. [75]). cFor precalciner kiln capacity ≥4000 t/d. dFor precalciner kiln capacity <4000 t/d. eFor shaft kiln capacity <10 0000 t/yr. fFor shaft kiln capacity ≥10 0000 t/yr. Data sources: 1Yuan et al. [80]. 2Ren et al. [81]. 3Handbook of Industrial Pollution Emission Factors. 4Guo et al. [82]. 5Liu et al. [83]. 6Chen et al. [84]. 7Wu et al. [79]. 8Su et al. [85]. 9Ding et al. [86]. 10Li [87]. Open in new tab Table 4. Summary of NOx emission factors for different kiln types in cement industry. Cement kiln types . NOx emission factors (g/kg-clinker) a,b . Precalciner kilns 1.1681, 1.5352, 1.5843,4,c, 1.4945, 1.6936, 1.7463,d, 1.8347, 2.0168, 2.1469 Shaft kilns 0.2023,e, 0.2433,f, 0.48210 Other rotary kilns 1.6091, 2.4488 Cement kiln types . NOx emission factors (g/kg-clinker) a,b . Precalciner kilns 1.1681, 1.5352, 1.5843,4,c, 1.4945, 1.6936, 1.7463,d, 1.8347, 2.0168, 2.1469 Shaft kilns 0.2023,e, 0.2433,f, 0.48210 Other rotary kilns 1.6091, 2.4488 aSample weighted mean. bAssumptions introduced when transferring measurements to emission factor unit in g/kg-clinker: flue-gas volume is 2.47 m3/kg-clinker (Wu [79]); average clinker to cement ratio is 0.72 (Lei et al. [15]); average coal consumption is 183g-coal/kg-clinker for precalciner and other rotary kilns, and 177g-coal/kg-clinker for shaft kilns (Zhang et al. [75]). cFor precalciner kiln capacity ≥4000 t/d. dFor precalciner kiln capacity <4000 t/d. eFor shaft kiln capacity <10 0000 t/yr. fFor shaft kiln capacity ≥10 0000 t/yr. Data sources: 1Yuan et al. [80]. 2Ren et al. [81]. 3Handbook of Industrial Pollution Emission Factors. 4Guo et al. [82]. 5Liu et al. [83]. 6Chen et al. [84]. 7Wu et al. [79]. 8Su et al. [85]. 9Ding et al. [86]. 10Li [87]. Open in new tab Besides kilns, PM is emitted from several other emission sources, including quarrying and crushing, grinding and blending, packaging and loading, and the storage of raw material [15]. The PM EF depends on both the characteristics of unabated emissions from the overall production process and the removal efficiencies of PM emission control devices. As shown in Table 5, the three types of kilns emit PM with different size fraction distributions. PM control devices can reduce PM emissions by 10–99.9%, depending on the type of control technology employed and the size distribution of PM in the raw flue gas [76]. The removal efficiencies of various PM emission control devices are summarized by Lei et al. [15]. Table 5. Summary of the mass fractions of particulate matter of different size fractions to the total particulate matter in fly ash for different types of kilns. . Kiln types . Size fraction . Precalciner kilns . Shaft kilns . Other rotary kilns . PM>10 0.181 0.141 0.111 PM2.5–10 0.241 0.221 0.201 PM2.5 0.581 0.641 0.691 . Kiln types . Size fraction . Precalciner kilns . Shaft kilns . Other rotary kilns . PM>10 0.181 0.141 0.111 PM2.5–10 0.241 0.221 0.201 PM2.5 0.581 0.641 0.691 Data sources: 1Lei et al. [76]. Open in new tab Table 5. Summary of the mass fractions of particulate matter of different size fractions to the total particulate matter in fly ash for different types of kilns. . Kiln types . Size fraction . Precalciner kilns . Shaft kilns . Other rotary kilns . PM>10 0.181 0.141 0.111 PM2.5–10 0.241 0.221 0.201 PM2.5 0.581 0.641 0.691 . Kiln types . Size fraction . Precalciner kilns . Shaft kilns . Other rotary kilns . PM>10 0.181 0.141 0.111 PM2.5–10 0.241 0.221 0.201 PM2.5 0.581 0.641 0.691 Data sources: 1Lei et al. [76]. Open in new tab Large increases in cement production in China since 1980 have resulted in dramatic increases in emissions of air pollutants. Figure 2 compares multi-year estimates for cement plant emissions from bottom-up inventories that have been published by different researchers. The inventories developed by Lei et al. [15] and Hua et al. [70] provided comparatively consistent trends for SO2, NOx and PM emissions. These two studies show that SO2 emissions increased rapidly from 1990 to 2003 and fell significantly after 2007. However, the peak SO2 emissions estimated by Lei et al. [15] and Hua et al. [70] occurred in 2007 and 2003, respectively. A detailed comparison of the methods and data used in these two studies indicates that the differences in the trends in SO2 emissions from 2003 to 2007 can be attributed to the derived province-level coal consumption. NOx emissions show significant increases since 1980; emissions of this pollutant increased much faster than any other pollutant, due to the rapid expansion of precalciner kilns in China. These two studies display similar trends in PM emissions from 1990 to 2008. The emissions of PM rose rapidly from 1990 to 1995, and PM emissions decreased gradually, due to the replacement of shaft kilns by precalciner kilns and the application of high-performance PM removal technology, especially after 2004. Figure 2. Open in new tabDownload slide Comparisons of SO2, NOx and TSP emissions from China's cement industry during 1980 and 2012. Iron and steel China has been the largest producer and consumer of iron and steel in the world since 1996, to meet the rapidly growing demand of infrastructure construction. As an energy-intensive and pollution-intensive sector, it is estimated that the production of iron and steel contributed approximately 27% of the dust emissions, 20% of the SO2 emissions and 8% of the NOx emissions from all of the key manufacturing industries in China in 2013 [71]. The primary emissions characteristics of iron and steel production have been estimated by recent studies [71,72,91]. Wang et al. [71] proposed a unit-based bottom-up methodology that employs detailed information on 300 integrated steel plants in China, including steel production, capacity, geographical location and installation status of FGD systems. To accurately estimate the PM emissions, Wang et al. [71] developed an emission inventory model for the iron and steel industry that provided information on each emission node within the production process, including both stationary and fugitive sources. Activity rates, including the product yields for each process, are accessible from the official statistics (China Steel Yearbook, Steel Statistical Yearbook), the reports of the China Iron and Steel Industry Association and local literature. The EFs of iron and steel production processes used in current emission inventories are mainly based on the Manual of Emission Coefficients, which was produced using industry census data from China and was released by the MEP in 2011. Western data and reports are available in corresponding publications, specifically the US EPA (Environmental Protection Agency)’s Compilation of Air Pollution Emission Factors (AP-42), the European Environment Agency (EEA) guidebook, the Best Available Techniques Reference Document for Iron and Steel Production released by the European Commission (EU-BAT) and the National Atmospheric Emissions Inventory for the UK (NAEI). The detailed EFs for different source categories can be found in the inventory papers [71,72]. End-of-pipe technologies for the control of SO2 and PM with high removal efficiencies have been increasingly installed in iron and steel plants. By 2012, the proportion of wet FGD systems used in sintering reached 73.1% [91]. According to Wang et al. [72], the EFs for PM2.5 decreased by 21.2% from 2006 to 2012, due to the implementation of emission control policies. The gas-recycling ratio in iron and steel plants reached values exceeding 96% in 2013 (as estimated by Wang et al. [71]). The government of China has released emission standards for the iron and steel industry (GB 28662–2012, GB 28663–2012, GB 28664–2012) and has provided instructions to adjust the industrial structure, which has also affected the net EFs. Driven by the huge growth in production, the emissions of SO2, NOx and PM2.5 due to the production of iron and steel increased from 1.22 to 2.31 Tg (+89%), 0.33 to 0.69 Tg (+97%) and 0.89 to 1.71 Tg (+92%), respectively, between 2005 and 2011 [71]. The emissions of PM began to decrease in 2012, due to the increasing control level. Sintering is the process that produces the largest amounts of all of these pollutants. Industrial boilers Industrial boilers are important sources of emissions in China for NOx, SO2 and PM. In 2010, they released approximately 5.2 Tg of NOx (20% of total emissions), 11.0 Tg of SO2 (40%) and 0.96 Tg of PM2.5 (8%) (estimated by MEIC v1.2). Of the industrial boilers in China, 85% are coal-fuelled. Approximately 50% of coal is burned in boilers with small capacities of ≤35 t/h that feature low combustion efficiencies and inefficient particulate emission control measures and desulphurization devices. Most industrial boilers in China are grate boilers, and small coal-fired boilers are especially prevalent. Cyclone or wet dust collectors are usually installed for PM abatement. The EFs of coal combustion in industrial boilers have been reported by several native studies and are summarized in Table 6 [92–95]. PM components, including BC (EC in the original paper) and OC, have also been measured by Wang et al. [95] and Zhang et al. [93]. The measured EFs reported by different studies show very large differences, indicating substantial underlying uncertainties. Other key parameters that introduce uncertainties in emission estimates, including activity rates, fuel quality and technology distributions, are rarely investigated for industrial boilers in China. The development of a reliable local-scale database of industrial boilers in China is quite urgent. Table 6. Summary of emission factors for industrial boilersa. Fuel type . SO2 . NOx . CO . PM10 . PM2.5 . BC . OC . Briquette 19.91, 9.952,b 1.632,b 141, 112,b 2.511, 0.243 0.791, 0.2192,b, 0.223 0.01062,b 0.01962,b Raw coal/bituminous 5.82, 5.652, 1.352, 2.92, 46.71 1.712, 2.132, 2.382, 2.472 1.432, 2.872, 9.052, 0.672, 151 1.681, 0.13–0.653, 0.0164, 0.14, 0.0454 0.2092, 0.4862, 0.0592, 0.0462, 0.871, 0.08–0.493, 1.143,c 0.0372, 0.0182, 0.00132, 0.00142, 0.0000624, 0.00284, 0.00074 0.0292, 0.0262, 0.0032, 0.0062, 0.00034, 0.01714, 0.00194 Fuel type . SO2 . NOx . CO . PM10 . PM2.5 . BC . OC . Briquette 19.91, 9.952,b 1.632,b 141, 112,b 2.511, 0.243 0.791, 0.2192,b, 0.223 0.01062,b 0.01962,b Raw coal/bituminous 5.82, 5.652, 1.352, 2.92, 46.71 1.712, 2.132, 2.382, 2.472 1.432, 2.872, 9.052, 0.672, 151 1.681, 0.13–0.653, 0.0164, 0.14, 0.0454 0.2092, 0.4862, 0.0592, 0.0462, 0.871, 0.08–0.493, 1.143,c 0.0372, 0.0182, 0.00132, 0.00142, 0.0000624, 0.00284, 0.00074 0.0292, 0.0262, 0.0032, 0.0062, 0.00034, 0.01714, 0.00194 aFuels are burned in grate boiler if not specified, unit: g/kg-fuel. bNo control facilities. cCirculating fluidized beds. Data sources: 1Ge et al. [92]. 2Wang et al. [95]. 3Li et al. [94]. 4Zhang et al. [93]. Open in new tab Table 6. Summary of emission factors for industrial boilersa. Fuel type . SO2 . NOx . CO . PM10 . PM2.5 . BC . OC . Briquette 19.91, 9.952,b 1.632,b 141, 112,b 2.511, 0.243 0.791, 0.2192,b, 0.223 0.01062,b 0.01962,b Raw coal/bituminous 5.82, 5.652, 1.352, 2.92, 46.71 1.712, 2.132, 2.382, 2.472 1.432, 2.872, 9.052, 0.672, 151 1.681, 0.13–0.653, 0.0164, 0.14, 0.0454 0.2092, 0.4862, 0.0592, 0.0462, 0.871, 0.08–0.493, 1.143,c 0.0372, 0.0182, 0.00132, 0.00142, 0.0000624, 0.00284, 0.00074 0.0292, 0.0262, 0.0032, 0.0062, 0.00034, 0.01714, 0.00194 Fuel type . SO2 . NOx . CO . PM10 . PM2.5 . BC . OC . Briquette 19.91, 9.952,b 1.632,b 141, 112,b 2.511, 0.243 0.791, 0.2192,b, 0.223 0.01062,b 0.01962,b Raw coal/bituminous 5.82, 5.652, 1.352, 2.92, 46.71 1.712, 2.132, 2.382, 2.472 1.432, 2.872, 9.052, 0.672, 151 1.681, 0.13–0.653, 0.0164, 0.14, 0.0454 0.2092, 0.4862, 0.0592, 0.0462, 0.871, 0.08–0.493, 1.143,c 0.0372, 0.0182, 0.00132, 0.00142, 0.0000624, 0.00284, 0.00074 0.0292, 0.0262, 0.0032, 0.0062, 0.00034, 0.01714, 0.00194 aFuels are burned in grate boiler if not specified, unit: g/kg-fuel. bNo control facilities. cCirculating fluidized beds. Data sources: 1Ge et al. [92]. 2Wang et al. [95]. 3Li et al. [94]. 4Zhang et al. [93]. Open in new tab To reduce the emissions, MEP of China released the emission standards for industrial boilers in 1983, and updated in 1991, 1999 and 2014 (GB 13271–2014). In the up-to-date emission standards, emission limits for SO2 and NOx are included that are separate from those of PM. However, little information on the effects of these environmental standards at the national level is available. Effective control measures have produced significant reductions in the emissions from industrial boilers in the megacities, such as Beijing and Shanghai. In Beijing, small scattered boilers have been shut down, highly efficient units have been constructed and the energy sources used have shifted from coal to cleaner fuels since 1998. As a result, reductions in the emissions from industrial boilers of 136 Gg of SO2, 48.7 Gg of NOx, 24 Gg of PM10 and 14.3 Gg of PM2.5 were achieved between 1998 and 2013 [96]. Similar energy-saving measures have been carried out in Shanghai [97]. Other pioneering provinces, including Shandong, Heibei and Guangdong, have also released regional emission standards for industrial boilers, and the effects of implementing these standards have yet to be investigated and evaluated. Petroleum industry Petroleum-related industries are the largest contributors to industrial NMVOCs. These emissions are estimated based on the ‘EF method’ and are mainly produced by the exploitation, storage and transport of oil and gas, oil refineries, gas stations, the chemical industry and carbon black production. Qiu et al. [98] established a new classification of industrial sectors using a source-tracing method, which covers the emission sources in entire industries by tracing the material flow of NMVOCs in each industrial process. The industrial sources are grouped into four categories, which are the production of NMVOCs, the storage and transport of NMVOCs, industrial processes that use NMVOCs as raw materials and processes that use NMVOCs-containing products. This classification system was followed by Wu et al. [99] to characterize industrial emissions in China. Local measurements of EFs for processes used in the petroleum industry are quite limited. Wei et al. [100] and Bo et al. [101] summarized the EFs of NMVOCs for the exploitation and distribution of oil and gas and oil refineries derived from reports from Western countries, including the US AP-42 database, the EEA guidebook and local emission standards. The EFs of rubber products were measured by Wu et al. [102]. A national standard (GB 11085–89) has been released for the storage and distribution of fossil fuels that provides a reference that can be used to determine the corresponding EFs. Residential The residential consumption of fossil fuels (i.e. coal, oil and gas) for energy and biofuels (i.e. wood and crop residue) for cooking and heating is associated with large emissions of air pollutants in China, due to its relatively low combustion efficiency and the lack of controls. In China, the residential sector is a major contributor to emissions of anthropogenic pollutants including PM2.5, BC, OC and NMVOCs (this sector accounts for 36–82% of the total emissions of these pollutants, according to MEIC), despite its small proportion of total energy consumption (<10%). The residential sector has been identified as a major source of uncertainty in current inventories of anthropogenic emissions in China because of the lack of reliable data and locally measured EFs [1,2,36,103]. Residential emissions in China have been estimated in many global, regional and national bottom-up emission inventories compiled using activity rates and EFs (e.g. EDGAR, [1,2,12]). These inventories typically employ activity rates (i.e. energy consumption) obtained from official sources of energy statistics, such as the China Energy Statistical Yearbook (CESY) or the International Energy Agency (IEA). Early inventories [1,2] estimated residential emissions by applying uniform EFs for a given fuel to entire sectors, due to the lack of more detailed information. However, these inventories ignore the large variations in EFs that may occur among different fuel sub-types (e.g. bituminous coal vs. anthracite coal), fuel combustion types (e.g. raw coal vs. briquette) and combustion devices (e.g. boilers, traditional stoves vs. improved stoves). In recent inventories, technology-based approaches have been adopted to better represent the dynamic changes in residential emissions in China (e.g. MEIC [34]). Lei et al. [34] considered dynamic changes in the share of briquettes in residential coal consumption and estimated that, because of the increased share of briquettes, the average net EFs for BC and OC in residential coal stoves decreased by 34% and 10%, respectively, from 1990 to 2005. In the MEIC inventory, the technology distribution of different coal combustion devices (i.e. boilers and stoves) in the urban residential sector was estimated using a bottom-up demand-side energy model. Although several residential inventories have been developed for China, quantifying emissions from the residential sector remains a challenge because of the wide variety of fuel-use patterns and emission characteristics and the lack of relevant data. The estimated emissions from residential sector are much more uncertain than those from other anthropogenic sectors. Energy consumption in the residential sector is highly uncertain compared to that in other sectors. Lu et al. [36] assigned uncertainties (95% confidence intervals, CIs) of 33% and 80% to residential coal and biofuel use in China, respectively. Inconsistencies between provincial and national energy statistics have been reported by previous studies [25,26]. Zhang et al. [2] argued that coal briquettes are widely used in the residential sector; however, only a small proportion (<10%) are reported in the official energy statistics. Alternative approaches have been developed to better understand and represent real-world fuel consumption in the residential sector. A series of surveys to assess residential energy consumption in China was carried out at the national [104,105] and regional scales [106–111]. Those survey data have provided useful information on residential fuel consumption and choices and have helped to identify their relationships with natural and socio-economic factors (e.g. temperature, household income, energy prices, fuel access, electrification and level of education). Several of these surveys [104,107,108,110,111] revealed large amounts of residential coal consumption that are missing from the current statistical system, and the emission inventories for some regions were subsequently revised based on the survey data [111]. Zhu et al. [112] developed regression models using climate and socio-economic parameters, including heating days and heating degree days (HDDs), to project the spatial and temporal trends in residential fuel consumption and air pollutant emissions over China. Many laboratory and field measurements in China have been performed to assess local residential EFs. These EFs have been found to vary greatly depending on the type of fuel and the stove used, fuel quality and combustion conditions [113–123]. Based on measurements of 28 fuel/stove combinations in China, Zhang et al. [113] generated a database of EFs from household stoves containing species such as CO2, CO, CH4, TNMHCs (Total Non-Methane Hydrocarbons), SO2, NOx and TSPs (Total Suspended Particles) and found that the EFs associated with solid fuels were substantially greater than those of liquid and gaseous fuels. Tsai et al. [114] further measured the EFs of speciated NMHCs (Non-methane Hydrocarbons) for 16 fuel/stove combinations. Chen et al. [115] reported the EFs of particles and their carbonaceous fractions (i.e. BC and OC) for five coal briquettes burned on a residential coal stove based on measurements; they also reported the EFs for residential burning of coal chunks [116]. Li et al. [118] conducted field measurements to assess the emissions of carbonaceous aerosols from household biofuel combustion. Shen et al. [119–123] measured EFs of PM, OC and EC from the residential combustion of solid fuels under field and laboratory conditions. Most of the EFs measured in the field were higher than the corresponding EFs measured under laboratory conditions. Table 7 summarizes the measured EFs for residential fuel combustion in China, based on a literature review. As shown in Table 7, the EFs for coal briquettes are generally lower than those for raw coal. The BC EFs of bituminous raw coal are 50–200 times higher than the other EFs. Table 7. Emission factors for residential fuel combustion in China (unit: g/kg). . Raw coal . Coal briquette . . . Species . Bituminous . Anthracite . Bituminous . Anthracite . Wood . Crop residues . SO2 0.15–20.4a 0.08–9.9a,c 0.002–0.03a 0.015–0.21a NOx 0.15–3.9a 0.07–1.1a,c 0.5–2a,j 0.23–1.7a,j CO 70.7–288a,k 19.1–68.4a,k 19.7–102.7a,i,j,m 29.3–215.4a,i,j,k,m NMVOC 0.17–6.5a,b 0.0003–1a,b 0.08–5a,b,j 0.18–26.6a,b,j PM2.5 0.13–46.6a,e,f,g,k 0.62–1.54e,f,g 0.03–12.9a,c,d,f,g 0.17–2.2d,g,k 0.7–8.3a,i,l,n,o 1.7–18a,i,k BC 0.006–28.5e,f,g,k 0.002–0.035e,f,g 0.006–0.5d,f,g,h,k 0.001–0.012d,g,h,k 0.06–2.5i,l,n,o 0.1–2.6i,k OC 0.1–17.0e,f,g,k 0.03–0.47e,f,g 0.007–10.1d,f,g,h,k 0.017–0.36d,g,h,k 0.11–4.3i,l,n,o 0.35–3.6i,k . Raw coal . Coal briquette . . . Species . Bituminous . Anthracite . Bituminous . Anthracite . Wood . Crop residues . SO2 0.15–20.4a 0.08–9.9a,c 0.002–0.03a 0.015–0.21a NOx 0.15–3.9a 0.07–1.1a,c 0.5–2a,j 0.23–1.7a,j CO 70.7–288a,k 19.1–68.4a,k 19.7–102.7a,i,j,m 29.3–215.4a,i,j,k,m NMVOC 0.17–6.5a,b 0.0003–1a,b 0.08–5a,b,j 0.18–26.6a,b,j PM2.5 0.13–46.6a,e,f,g,k 0.62–1.54e,f,g 0.03–12.9a,c,d,f,g 0.17–2.2d,g,k 0.7–8.3a,i,l,n,o 1.7–18a,i,k BC 0.006–28.5e,f,g,k 0.002–0.035e,f,g 0.006–0.5d,f,g,h,k 0.001–0.012d,g,h,k 0.06–2.5i,l,n,o 0.1–2.6i,k OC 0.1–17.0e,f,g,k 0.03–0.47e,f,g 0.007–10.1d,f,g,h,k 0.017–0.36d,g,h,k 0.11–4.3i,l,n,o 0.35–3.6i,k *Data sources: aZhang et al. [113]. bTsai et al. [114]. cGe et al. [124]. dChen et al. [115]. eChen et al. [116]. fZhang et al. [93]. gZhi et al. [117]. hChen et al. [125]. iLi et al. [118]. jWang et al. [126]. kShen et al. [119]. lShen et al. [120]. mWei et al. [127]. nShen et al. [121]. oShen et al. [122]. Open in new tab Table 7. Emission factors for residential fuel combustion in China (unit: g/kg). . Raw coal . Coal briquette . . . Species . Bituminous . Anthracite . Bituminous . Anthracite . Wood . Crop residues . SO2 0.15–20.4a 0.08–9.9a,c 0.002–0.03a 0.015–0.21a NOx 0.15–3.9a 0.07–1.1a,c 0.5–2a,j 0.23–1.7a,j CO 70.7–288a,k 19.1–68.4a,k 19.7–102.7a,i,j,m 29.3–215.4a,i,j,k,m NMVOC 0.17–6.5a,b 0.0003–1a,b 0.08–5a,b,j 0.18–26.6a,b,j PM2.5 0.13–46.6a,e,f,g,k 0.62–1.54e,f,g 0.03–12.9a,c,d,f,g 0.17–2.2d,g,k 0.7–8.3a,i,l,n,o 1.7–18a,i,k BC 0.006–28.5e,f,g,k 0.002–0.035e,f,g 0.006–0.5d,f,g,h,k 0.001–0.012d,g,h,k 0.06–2.5i,l,n,o 0.1–2.6i,k OC 0.1–17.0e,f,g,k 0.03–0.47e,f,g 0.007–10.1d,f,g,h,k 0.017–0.36d,g,h,k 0.11–4.3i,l,n,o 0.35–3.6i,k . Raw coal . Coal briquette . . . Species . Bituminous . Anthracite . Bituminous . Anthracite . Wood . Crop residues . SO2 0.15–20.4a 0.08–9.9a,c 0.002–0.03a 0.015–0.21a NOx 0.15–3.9a 0.07–1.1a,c 0.5–2a,j 0.23–1.7a,j CO 70.7–288a,k 19.1–68.4a,k 19.7–102.7a,i,j,m 29.3–215.4a,i,j,k,m NMVOC 0.17–6.5a,b 0.0003–1a,b 0.08–5a,b,j 0.18–26.6a,b,j PM2.5 0.13–46.6a,e,f,g,k 0.62–1.54e,f,g 0.03–12.9a,c,d,f,g 0.17–2.2d,g,k 0.7–8.3a,i,l,n,o 1.7–18a,i,k BC 0.006–28.5e,f,g,k 0.002–0.035e,f,g 0.006–0.5d,f,g,h,k 0.001–0.012d,g,h,k 0.06–2.5i,l,n,o 0.1–2.6i,k OC 0.1–17.0e,f,g,k 0.03–0.47e,f,g 0.007–10.1d,f,g,h,k 0.017–0.36d,g,h,k 0.11–4.3i,l,n,o 0.35–3.6i,k *Data sources: aZhang et al. [113]. bTsai et al. [114]. cGe et al. [124]. dChen et al. [115]. eChen et al. [116]. fZhang et al. [93]. gZhi et al. [117]. hChen et al. [125]. iLi et al. [118]. jWang et al. [126]. kShen et al. [119]. lShen et al. [120]. mWei et al. [127]. nShen et al. [121]. oShen et al. [122]. Open in new tab A comparison of estimates of residential emissions in China between 2000 and 2010 is shown in Table 8. Large discrepancies are associated with the SO2 emissions in 2000 and the NOx emissions in 2010. Ohara et al. [12] estimated that the year-2000 residential SO2 emissions in China were 2.8 Tg; this value is 44% higher than that obtained by Lu et al. [36]. As estimated by Zhao et al. [6], the year-2010 residential NOx emissions in China were 2.6 Tg; this value is 149% higher than that published by MEIC. Although large discrepancies are not observed in the emissions of BC and OC in individual years, different trends are reported by Lu et al. [36] and MEIC. According to Lu et al. [36], these quantities increased by ∼47% from 2000 to 2010; however, the corresponding estimates published by MEIC reflect increases of only 13–18%. Note that an uncertainty analysis suggests that the largest uncertainties in the residential sector are associated with the emissions of BC and OC [103]. Table 8. Estimates of China's residential emissions (unit: Gg/year). Inventories . Year . SO2 . NOx . CO . NMVOC . PM2.5 . BC . OC . Streets et al. [1] 2000 2523 702 43 867 5604 782 2561 Ohara et al. [12] 2000 2801 974 58 308 938 2497 Zhang et al. [2] 2001 2599 997 48 254 5996 3853 868 2254 2006 2838 1166 55 883 7601 4461 1002 2606 Lu et al. [36] 2000 1947 639 1893 2004 2048 826 2519 2008 2365 888 2670 2010 2931 936 2790 Zhao et al. [6] 2010 2888 2604 63 765 4429 809 2228 MEIC 2000 2329 741 59 820 4733 3750 721 2198 2005 2926 988 74 799 6071 4725 869 2775 2010 3470 1044 70 970 5704 4326 848 2481 Inventories . Year . SO2 . NOx . CO . NMVOC . PM2.5 . BC . OC . Streets et al. [1] 2000 2523 702 43 867 5604 782 2561 Ohara et al. [12] 2000 2801 974 58 308 938 2497 Zhang et al. [2] 2001 2599 997 48 254 5996 3853 868 2254 2006 2838 1166 55 883 7601 4461 1002 2606 Lu et al. [36] 2000 1947 639 1893 2004 2048 826 2519 2008 2365 888 2670 2010 2931 936 2790 Zhao et al. [6] 2010 2888 2604 63 765 4429 809 2228 MEIC 2000 2329 741 59 820 4733 3750 721 2198 2005 2926 988 74 799 6071 4725 869 2775 2010 3470 1044 70 970 5704 4326 848 2481 Open in new tab Table 8. Estimates of China's residential emissions (unit: Gg/year). Inventories . Year . SO2 . NOx . CO . NMVOC . PM2.5 . BC . OC . Streets et al. [1] 2000 2523 702 43 867 5604 782 2561 Ohara et al. [12] 2000 2801 974 58 308 938 2497 Zhang et al. [2] 2001 2599 997 48 254 5996 3853 868 2254 2006 2838 1166 55 883 7601 4461 1002 2606 Lu et al. [36] 2000 1947 639 1893 2004 2048 826 2519 2008 2365 888 2670 2010 2931 936 2790 Zhao et al. [6] 2010 2888 2604 63 765 4429 809 2228 MEIC 2000 2329 741 59 820 4733 3750 721 2198 2005 2926 988 74 799 6071 4725 869 2775 2010 3470 1044 70 970 5704 4326 848 2481 Inventories . Year . SO2 . NOx . CO . NMVOC . PM2.5 . BC . OC . Streets et al. [1] 2000 2523 702 43 867 5604 782 2561 Ohara et al. [12] 2000 2801 974 58 308 938 2497 Zhang et al. [2] 2001 2599 997 48 254 5996 3853 868 2254 2006 2838 1166 55 883 7601 4461 1002 2606 Lu et al. [36] 2000 1947 639 1893 2004 2048 826 2519 2008 2365 888 2670 2010 2931 936 2790 Zhao et al. [6] 2010 2888 2604 63 765 4429 809 2228 MEIC 2000 2329 741 59 820 4733 3750 721 2198 2005 2926 988 74 799 6071 4725 869 2775 2010 3470 1044 70 970 5704 4326 848 2481 Open in new tab Transportation On-road vehicles The air pollutants from on-road vehicles are divided into two subsectors: tailpipe exhaust and evaporative emissions. These types of emissions are discussed separately below. Tailpipe exhaust emissions. China accounts for 14% of the world's automobiles. Reducing vehicle emissions during rapid urbanization is a major challenge in China [128]. The on-road transportation sector is the largest contributor to PM2.5 pollution in major Chinese cities, including Shanghai, Beijing, Guangzhou and Nanjing [129–132], and this pollution has detrimental impacts on human health [133–135]. Early studies of the emission estimation from on-road vehicles in China were based on data from Western countries [9,136]. During the last two decades, dozens of papers have been published that present vehicle emission inventories based on local statistics and native investigations [16,137–147]. Figure 3 presents a detailed summary of estimates of vehicular emissions. Although significant differences are seen among the different inventories, clear trends in emission reductions that were driven by progressive vehicle environmental standards are observed. The total emissions of NMHCs from vehicles peaked in approximately 2007, the emissions of CO and PM peaked in around 2010 and the total emissions of NOx from vehicles peaked in approximately 2013 and have decreased since that time. Figure 3. Open in new tabDownload slide Summary of vehicular emission estimates in China. Local databases of EFs have been developed to reflect the real-world emission characteristics of vehicles in China. The EFs used in early studies were derived directly from international models, due to the lack of sufficient local emission testing data and comprehensive localized models. The international models from which these EFs were taken include the Mobile Source Emissions Factor (MOBILE) and Motor Vehicle Emission Simulator (MOVES) models developed by the US EPA, the International Vehicle Emissions (IVE) model developed at the University of California at Riverside and the COmputer Programme to calculate Emissions from Road Transport (COPERT). EFs from international models developed based on testing results in US or European countries do not accurately reflect the emission characteristics of vehicles in China, due to differences in vehicle configurations, ambient conditions and driving cycles. Emphasis has been placed on the localization of international models. Hao et al. [148] tested 171 vehicles on chassis dynamometers, and the results were used to localize the MOBILE model. Fu et al. [149] and Liu et al. [150] measured the emission rates of 12 and 75 vehicles, respectively, using portable emission measurement systems (PEMS); the testing results were used to localize the MOBILE and IVE models, respectively. Zhang et al. [151] developed an updated EF model for the Beijing vehicle fleet (EMBEV) that was based on large amounts of testing data produced by a comprehensive vehicle emission testing programme. The failure to represent real-life driving cycles in the laboratory, which significantly lowered the accuracy of emission estimates, revealed the presence of ‘lab-to-road’ issues that arise from erroneous driving cycles or the disabling of pollution control devices [152]. The importance of real-world emission testing has increased in recent years. Investigations based on the PEMS testing programmes led by the Tsinghua University, Beijing Institute of Technology (BIT) and Vehicle Emission Control Centre (VECC) teams, as well as other researchers, have been launched, and the results of these investigations represent a comprehensive database of real-world EFs [150,151,153–161]. The accuracy of activity rates is another key factor that affects the quality of vehicular emission inventories. Activity levels are primarily calculated using the number of vehicles registered and vehicle kilometres travelled (VKT) data. The vehicle registration data can be obtained from public statistics and contain relatively low uncertainties. To provide reliable activity level data, representative and accurate VKT data are of great importance. Approximately 80–2000 samples have been investigated in China [144,162,163]. Liu et al. [164] derived vehicle activity data from the trajectories of more than 70 000 cars and the annual mileages of 2 million trucks in China; this study represents an important step towards improved emission estimates. In addition to the emission estimates, the gridding of emissions significantly affects the accuracy of emission inventories for the transportation sector. Most existing studies employ parameters such as GDP (Gross Domestic Product), road length, population, city lights and vehicle stocks as spatial proxies to allocate emissions to individual grid cells [38,137]. Zheng et al. [16] developed emissions using county-level geographical parameters; these county-level emission estimates were then allocated to grid cells using the China Digital Road Network Map (CDRM). Integrating traffic flow data into the development of high-resolution vehicle emission inventories has been investigated by several studies [139,147,163,165]. In addition, vehicle registration data can be employed in estimating emissions from passenger cars; however, these data cannot be used for freight trucks that often travel long distances between cities and provinces. Yang et al. [144] developed a road emission intensity-based (REIB) approach to better describe the spatial distribution of truck emissions in China. The development of future vehicle emission inventories requires additional real-world emission testing data collected under different ambient conditions (e.g. high altitude) and driving conditions (e.g. cold start). The MEP should utilize the second national pollution census to launch more testing programmes to enhance the EF database. Detailed traffic flow data are needed to improve the accuracy of vehicle activity rates and the spatial distribution of emissions. Evaporative emissions. Emissions of NMVOCs from vehicles, including tailpipe exhaust and evaporative emissions, have become a major and growing source of NMVOC emissions in China [21,131,170–172]. Vehicular evaporation has joined tailpipe exhaust as a dominant pathway of vehicular NMVOC emissions, due to the limited controls on evaporation losses [173,174]. Previous studies used the ratios of toluene and benzene concentrations (T/B) to evaluate the contributions from vehicle sources to ambient NMVOCs. The T/B ratios of tailpipe exhaust are typically 2.0; higher ratios are observed in evaporative emissions in Asia, due to the elevated toluene concentrations in gasoline. The T/B ratios in most urbanized areas are high, e.g. 37 in Hong Kong, 10 in Manila and Bangkok and 6 in Seoul, indicating contributions from evaporative emissions [175–178]. Evaporative sources include the venting of canisters and fuel permeation/leakage. Based on certified test procedures, evaporative emissions can be divided into those from refuelling, hot soak, diurnal and running loss, listed in order of decreasing amounts emitted. Speciation profiles have been constructed using headspace vapour [179,180], liquid fuel [180,181], tunnel tests [182] and Sealed Housing for Evaporative Determination (SHED) tests [183]. Liu et al. [164] detected 93 species of NMVOCs and determined their individual shares in emissions produced by different processes, mechanisms and vehicle control technologies. Furthermore, a comprehensive vehicle activity-based speciation profile was developed that includes the 35 major NMVOC species that account for 90.6∼98.6% of total detected organics. Studies of such EFs are highly complex, considering the evaporative nature of fugitive emissions; the methods used to determine these EFs include SHED tests and tunnel tests. The former type of test is more accurate and widely used. Pang et al. [184] reported evaporative EFs for 49 in-use vehicles from fleets ranging from 1999 to 2003 in the USA. Mellios et al. [185] tested four vehicles in Europe to validate existing evaporative emission results. Liu et al. [174] evaluated emission rates in China using 30 crossover tests performed on five tested vehicles using non-ethanol gasoline and 41 crossover tests performed on five vehicles using four concentrations of ethanol-blended gasoline (0–10%). Four types of local EFs—diurnal, hot soak, permeation and refuelling—were provided. The evaporative emission rates from Euro4 vehicles (which have diurnal evaporative emission rates of 2–8 g/day) are dozens of times those of vehicles in the USA (0.3 g/day), reflecting a substantial gap between these two sets of regulations. An ethanol concentration of 10% can lead to increases in evaporative emissions of 20–40%. Models of evaporative emissions are either based on the Wade-Reddy equation, which considers changes in various factors (including gasoline volatility, ethanol content, canister size, canister load, canister purging, vehicle, fuel system design, fuel tank fill level, parking and driving patterns, absolute ambient temperature, temperature variations and ambient pressure), or are determined directly from experimental results. The former approach has been used in several well-known models, such as MOVES, MOBILE and IVE [186]. Studies that employ these models include Huang et al. [187] (IVE model), Dong et al. [188] (Wade-Reddy equation with consideration of the impact of parking activities) and Yang et al. [189] (Wade-Reddy equation). The latter approach has been used in the studies by Mellios and Samars (simulation of final diurnal emissions) [185,190], Yamada (24-hour diurnal and hot soak emissions) [173] and Liu (72 hour diurnal and hot soak, refuelling and permeation emissions) [164,174]. In 2015, the evaporative emissions of volatile organic compounds (VOCs) in China ranged from 185 Gg in 2010 to 264 Gg. These emissions correspond to approximately 0.12–0.21 g/km, excluding running losses and motorcycles [164,189], and are greater than the Euro3 tailpipe emission rate (0.19 g/km) [174]. Liu et al. [164] estimated total running losses of 1146 ± 768 Gg in China. Evaporative emissions from vehicles (including motorcycles) are responsible for 39.2% of the total vehicular emissions of NMVOCs [164]. The largest uncertainty is due to the lack of direct evidence for the magnitude of running losses in China. Other major uncertainties in current evaporative inventories of China are due to the lack of consideration of driving and canister conditions before parking, which lead to differences between actual EFs and those measured in the laboratory. Off-road engines Off-road equipment is usually diesel-fuelled in China. Unlike on-road vehicles, little attention has been devoted to this subsector. Recent studies indicate that the emissions of NOx and PM from off-road engines are comparable or even higher than those of on-road vehicles [191,192]. Wang et al. [192] provided an overview of the methodology, EFs and emission estimates for five major contributing sources: agricultural equipment, industrial equipment, shipping, locomotives and commercial airplanes. Shipping emissions are illustrated separately in the following subsection, followed by a summary of other off-road sources. Shipping. Increased connectivity and the international marine trade have stimulated inland development. In 2015, 41% of the cargo loading and 60% of the unloading of the world marine trade occurred in Asia [193]. China is one of the largest shipping countries and has more than 18 000 km of coastline and over 50 ports. These ports are primarily located within five port clusters, namely the Pearl River Delta (PRD), the Yangtze River Delta (YRD), the Bohai Rim Area (BRA), the south-east coast and the south-west coast. Seven of the top 10 container ports in the world are located in China. Specifically, these ports are those of Shanghai, Shenzhen, Ningbo, Hong Kong, Qingdao, Guangzhou and Tianjin (http://www.worldshipping.org/). The establishment of port-level and regional shipping emission inventories is an urgent priority. The fuel-based method [194–198] applies to calculations that apply to large scales and overlong time scales, whereas the trade-based method [199] requires less data but produces results with greater uncertainty. Both of those methods are categorized as top-down approaches, and their results are generally of lower accuracy compared to those of bottom-up ones, which derive their estimates from the characteristics and movements of ships [200]. The vessel-visa-based method is a bottom-up approach that is based on ship visa registration data; it was used predominantly in earlier studies of port-scale or port-cluster-scale emissions [194–197]. The automatic identification system (AIS)-based method is the most advanced; it is based on the movements of individual vessels. The AIS-based method has been used in recent studies to obtain emission inventories with high spatial resolutions. The national-level ship emission inventory has been continuously developed in the past two years. Liu et al. [201] obtained the first estimates of emissions from ocean-going vessels (OGVs) in East Asia using an advanced method based on detailed dynamic AIS data; the climate impacts of the associated radiative forcing and the health impacts and premature deaths caused by ship emissions were also assessed. Chen et al. [202] developed a comprehensive national-scale ship emission inventory in China for 2014 based on AIS data, including OGVs, coastal vessels (CVs) and river vessels (RVs); moreover, the emission characteristics were discussed from various perspectives, such as vessel type, operating mode, discharge equipment, monthly variations and the spatial distributions. Fu et al. [203] compiled national- to port-level emission inventories for China, hotspot regions and individual ports and compared ship emissions with on-road mobile source emissions. Estimates of the shipping emissions from the abovementioned sources for various regions are presented in Table 9. The total shipping emissions of SO2, NOx and PM determined using bottom-up inventory methods are 1300, 1910 and 164 Gg/yr in the year 2013 [203] and 1193.7, 2208.4 and 347.2 Gg/yr in the year 2014 [202]. The largest body of contributions regarding shipping emissions addresses ships in the YRD [204]. This region includes emissions from the ports of Ningbo-Zhoushan and Shanghai, as well as 13 other ports. The largest sources are container ships. Of the other types of ships, the dominant emitters depend on the location. Non-container cargo ships contribute 32–36% of emissions in the YRD [204], whereas bulk carriers contribute approximately 17.5% of emissions in the Jing-Jin-Ji (JJJ) region [202] and 14–20% of emissions in the PRD [205,206]. Variations in emission amounts are difficult to explain, given the variability in domain size, the raw data and methods used, and the target year. Table 9. Shipping emissions in China (unit: Gg/year). . . Air pollutants . . . . Region . Port . SOx . PM10 . PM2.5 . NOx . CO2 . CO . VOC . HC . Base year . Method . Reference . East Asia/ / 1850 240 2800 126 000 108 100 2013 AIS Liu et al. [201] China / 1194 181 167 2208 78 430 242 112 2014 AIS Chen et al. [202] China / 1300 1910 86 300 74 69 2013 AIS Fu et al. [203] YRD Regional 380 710 2010 AIS Fan et al. [204] Shanghai 82 12.3 11.4 152 7.1 2010 AIS Fan et al. [204] Shanghai 35.4 4.6 3.7 57 289 4.9 2.1 2010 Fuel-based, vessel-visa data Fu et al. [197] Shanghai 51 7 58 3013 4.6 2003 Fuel-based, vessel-visa based Yang et al. [194] Ningbo-Zhoushan 21.6 2.6 2.4 35 3 1.5 2010 AIS Yin et al. [219] Yangshan 5.6 0.22 0.86 10.8 578 1.1 2009 AIS Song et al. [220] Estuary of the Yangtze River 147 245 4054 57 9 2010 AIS Yao et al. [221] GPRD Regional 104 150 2015 AIS Mao et al. [206] PRD Regional 62 7.2 6.6 104 10.6 4.2 2013 AIS Li et al. [205] Regional 63 2.3 92 10.5 0.9 2006 Fuel-based Zhang et al. [196] Shenzhen 1.6 0.16 0.15 6.7 1.6 0.7 0.08 2003 Trade-based Li et al. [199] Shenzhen 13.6 2.2 1.7 23.3 2.2 1.1 2010 AIS Yang et al. [222] Hong Kong 12.4 1.4 14.5 1.4 0.6 2007 AIS Ng et al. [223] Hong Kong 8.2 1 17.1 2007 AIS Yau et al. [224] Guangdong Province 146 7.9 7.2 231 30 9.3 2010 Fuel-based. vessel-visa based Ye et al. [198] BRA Regional 121 174 7209 14.4 6.1 2014 AIS Xing et al. [225] Regional 231 21.5 19.7 306 13 344 23.8 9.9 2013 AIS Song et al. [226] Tianjin 29.3 4 3.7 41.3 3.6 1.7 2014 AIS Chen et al. [227] Tianjin 0.2 4.3 0.7 0.2 2006 Fuel-based Jin et al. [195] Tianjin 61 5.4 4.9 73 3471 5.7 2.4 2013 AIS Xing et al. [225] Qingdao 33.2 4.5 4.2 42.9 3.7 1.9 2014 AIS Chen et al. [228] Qingdao 2004 Vessel-visa based Liu et al. [229] Tangshan 80 7.6 7 112 4620 8.7 3.6 2013 AIS Xing et al. [225] Qinhuangdao 64 6 5.5 86 3727 6.7 2.8 2013 AIS Xing et al. [225] Huanghua 26.3 2.5 2.3 34.6 1526 2.7 1.1 2013 AIS Xing et al. [225] Dalian 49.4 5.8 52 2885 4.7 2 2012 AIS Tan et al. [230] Taiwan Kaohsiung 0.64 0.053 0.049 0.68 34.8 0.053 0.019 2012 AIS Cullinane et al. [231] Keelung 0.19 0.016 0.014 0.2 10.19 0.016 0.006 2012 AIS Cullinane et al. [231] Taichung 0.27 0.022 0.02 0.28 14.4 0.022 0.008 2012 AIS Cullinane et al. [231] . . Air pollutants . . . . Region . Port . SOx . PM10 . PM2.5 . NOx . CO2 . CO . VOC . HC . Base year . Method . Reference . East Asia/ / 1850 240 2800 126 000 108 100 2013 AIS Liu et al. [201] China / 1194 181 167 2208 78 430 242 112 2014 AIS Chen et al. [202] China / 1300 1910 86 300 74 69 2013 AIS Fu et al. [203] YRD Regional 380 710 2010 AIS Fan et al. [204] Shanghai 82 12.3 11.4 152 7.1 2010 AIS Fan et al. [204] Shanghai 35.4 4.6 3.7 57 289 4.9 2.1 2010 Fuel-based, vessel-visa data Fu et al. [197] Shanghai 51 7 58 3013 4.6 2003 Fuel-based, vessel-visa based Yang et al. [194] Ningbo-Zhoushan 21.6 2.6 2.4 35 3 1.5 2010 AIS Yin et al. [219] Yangshan 5.6 0.22 0.86 10.8 578 1.1 2009 AIS Song et al. [220] Estuary of the Yangtze River 147 245 4054 57 9 2010 AIS Yao et al. [221] GPRD Regional 104 150 2015 AIS Mao et al. [206] PRD Regional 62 7.2 6.6 104 10.6 4.2 2013 AIS Li et al. [205] Regional 63 2.3 92 10.5 0.9 2006 Fuel-based Zhang et al. [196] Shenzhen 1.6 0.16 0.15 6.7 1.6 0.7 0.08 2003 Trade-based Li et al. [199] Shenzhen 13.6 2.2 1.7 23.3 2.2 1.1 2010 AIS Yang et al. [222] Hong Kong 12.4 1.4 14.5 1.4 0.6 2007 AIS Ng et al. [223] Hong Kong 8.2 1 17.1 2007 AIS Yau et al. [224] Guangdong Province 146 7.9 7.2 231 30 9.3 2010 Fuel-based. vessel-visa based Ye et al. [198] BRA Regional 121 174 7209 14.4 6.1 2014 AIS Xing et al. [225] Regional 231 21.5 19.7 306 13 344 23.8 9.9 2013 AIS Song et al. [226] Tianjin 29.3 4 3.7 41.3 3.6 1.7 2014 AIS Chen et al. [227] Tianjin 0.2 4.3 0.7 0.2 2006 Fuel-based Jin et al. [195] Tianjin 61 5.4 4.9 73 3471 5.7 2.4 2013 AIS Xing et al. [225] Qingdao 33.2 4.5 4.2 42.9 3.7 1.9 2014 AIS Chen et al. [228] Qingdao 2004 Vessel-visa based Liu et al. [229] Tangshan 80 7.6 7 112 4620 8.7 3.6 2013 AIS Xing et al. [225] Qinhuangdao 64 6 5.5 86 3727 6.7 2.8 2013 AIS Xing et al. [225] Huanghua 26.3 2.5 2.3 34.6 1526 2.7 1.1 2013 AIS Xing et al. [225] Dalian 49.4 5.8 52 2885 4.7 2 2012 AIS Tan et al. [230] Taiwan Kaohsiung 0.64 0.053 0.049 0.68 34.8 0.053 0.019 2012 AIS Cullinane et al. [231] Keelung 0.19 0.016 0.014 0.2 10.19 0.016 0.006 2012 AIS Cullinane et al. [231] Taichung 0.27 0.022 0.02 0.28 14.4 0.022 0.008 2012 AIS Cullinane et al. [231] Open in new tab Table 9. Shipping emissions in China (unit: Gg/year). . . Air pollutants . . . . Region . Port . SOx . PM10 . PM2.5 . NOx . CO2 . CO . VOC . HC . Base year . Method . Reference . East Asia/ / 1850 240 2800 126 000 108 100 2013 AIS Liu et al. [201] China / 1194 181 167 2208 78 430 242 112 2014 AIS Chen et al. [202] China / 1300 1910 86 300 74 69 2013 AIS Fu et al. [203] YRD Regional 380 710 2010 AIS Fan et al. [204] Shanghai 82 12.3 11.4 152 7.1 2010 AIS Fan et al. [204] Shanghai 35.4 4.6 3.7 57 289 4.9 2.1 2010 Fuel-based, vessel-visa data Fu et al. [197] Shanghai 51 7 58 3013 4.6 2003 Fuel-based, vessel-visa based Yang et al. [194] Ningbo-Zhoushan 21.6 2.6 2.4 35 3 1.5 2010 AIS Yin et al. [219] Yangshan 5.6 0.22 0.86 10.8 578 1.1 2009 AIS Song et al. [220] Estuary of the Yangtze River 147 245 4054 57 9 2010 AIS Yao et al. [221] GPRD Regional 104 150 2015 AIS Mao et al. [206] PRD Regional 62 7.2 6.6 104 10.6 4.2 2013 AIS Li et al. [205] Regional 63 2.3 92 10.5 0.9 2006 Fuel-based Zhang et al. [196] Shenzhen 1.6 0.16 0.15 6.7 1.6 0.7 0.08 2003 Trade-based Li et al. [199] Shenzhen 13.6 2.2 1.7 23.3 2.2 1.1 2010 AIS Yang et al. [222] Hong Kong 12.4 1.4 14.5 1.4 0.6 2007 AIS Ng et al. [223] Hong Kong 8.2 1 17.1 2007 AIS Yau et al. [224] Guangdong Province 146 7.9 7.2 231 30 9.3 2010 Fuel-based. vessel-visa based Ye et al. [198] BRA Regional 121 174 7209 14.4 6.1 2014 AIS Xing et al. [225] Regional 231 21.5 19.7 306 13 344 23.8 9.9 2013 AIS Song et al. [226] Tianjin 29.3 4 3.7 41.3 3.6 1.7 2014 AIS Chen et al. [227] Tianjin 0.2 4.3 0.7 0.2 2006 Fuel-based Jin et al. [195] Tianjin 61 5.4 4.9 73 3471 5.7 2.4 2013 AIS Xing et al. [225] Qingdao 33.2 4.5 4.2 42.9 3.7 1.9 2014 AIS Chen et al. [228] Qingdao 2004 Vessel-visa based Liu et al. [229] Tangshan 80 7.6 7 112 4620 8.7 3.6 2013 AIS Xing et al. [225] Qinhuangdao 64 6 5.5 86 3727 6.7 2.8 2013 AIS Xing et al. [225] Huanghua 26.3 2.5 2.3 34.6 1526 2.7 1.1 2013 AIS Xing et al. [225] Dalian 49.4 5.8 52 2885 4.7 2 2012 AIS Tan et al. [230] Taiwan Kaohsiung 0.64 0.053 0.049 0.68 34.8 0.053 0.019 2012 AIS Cullinane et al. [231] Keelung 0.19 0.016 0.014 0.2 10.19 0.016 0.006 2012 AIS Cullinane et al. [231] Taichung 0.27 0.022 0.02 0.28 14.4 0.022 0.008 2012 AIS Cullinane et al. [231] . . Air pollutants . . . . Region . Port . SOx . PM10 . PM2.5 . NOx . CO2 . CO . VOC . HC . Base year . Method . Reference . East Asia/ / 1850 240 2800 126 000 108 100 2013 AIS Liu et al. [201] China / 1194 181 167 2208 78 430 242 112 2014 AIS Chen et al. [202] China / 1300 1910 86 300 74 69 2013 AIS Fu et al. [203] YRD Regional 380 710 2010 AIS Fan et al. [204] Shanghai 82 12.3 11.4 152 7.1 2010 AIS Fan et al. [204] Shanghai 35.4 4.6 3.7 57 289 4.9 2.1 2010 Fuel-based, vessel-visa data Fu et al. [197] Shanghai 51 7 58 3013 4.6 2003 Fuel-based, vessel-visa based Yang et al. [194] Ningbo-Zhoushan 21.6 2.6 2.4 35 3 1.5 2010 AIS Yin et al. [219] Yangshan 5.6 0.22 0.86 10.8 578 1.1 2009 AIS Song et al. [220] Estuary of the Yangtze River 147 245 4054 57 9 2010 AIS Yao et al. [221] GPRD Regional 104 150 2015 AIS Mao et al. [206] PRD Regional 62 7.2 6.6 104 10.6 4.2 2013 AIS Li et al. [205] Regional 63 2.3 92 10.5 0.9 2006 Fuel-based Zhang et al. [196] Shenzhen 1.6 0.16 0.15 6.7 1.6 0.7 0.08 2003 Trade-based Li et al. [199] Shenzhen 13.6 2.2 1.7 23.3 2.2 1.1 2010 AIS Yang et al. [222] Hong Kong 12.4 1.4 14.5 1.4 0.6 2007 AIS Ng et al. [223] Hong Kong 8.2 1 17.1 2007 AIS Yau et al. [224] Guangdong Province 146 7.9 7.2 231 30 9.3 2010 Fuel-based. vessel-visa based Ye et al. [198] BRA Regional 121 174 7209 14.4 6.1 2014 AIS Xing et al. [225] Regional 231 21.5 19.7 306 13 344 23.8 9.9 2013 AIS Song et al. [226] Tianjin 29.3 4 3.7 41.3 3.6 1.7 2014 AIS Chen et al. [227] Tianjin 0.2 4.3 0.7 0.2 2006 Fuel-based Jin et al. [195] Tianjin 61 5.4 4.9 73 3471 5.7 2.4 2013 AIS Xing et al. [225] Qingdao 33.2 4.5 4.2 42.9 3.7 1.9 2014 AIS Chen et al. [228] Qingdao 2004 Vessel-visa based Liu et al. [229] Tangshan 80 7.6 7 112 4620 8.7 3.6 2013 AIS Xing et al. [225] Qinhuangdao 64 6 5.5 86 3727 6.7 2.8 2013 AIS Xing et al. [225] Huanghua 26.3 2.5 2.3 34.6 1526 2.7 1.1 2013 AIS Xing et al. [225] Dalian 49.4 5.8 52 2885 4.7 2 2012 AIS Tan et al. [230] Taiwan Kaohsiung 0.64 0.053 0.049 0.68 34.8 0.053 0.019 2012 AIS Cullinane et al. [231] Keelung 0.19 0.016 0.014 0.2 10.19 0.016 0.006 2012 AIS Cullinane et al. [231] Taichung 0.27 0.022 0.02 0.28 14.4 0.022 0.008 2012 AIS Cullinane et al. [231] Open in new tab The biggest challenge in the future development of emission inventories is the unreliability of EF data. Current emission inventories in China usually adopt ship EFs from mainstream reports, including those of the International Maritime Organization [207,208], Entec [209], Energy and Environmental Analysis Inc. [210], the EPA [211], ICF International [212] and EDGAR. Despite the consistency of the SO2 (9.6–10.6 g/kWh), PM and NOx EFs are of large uncertainties (1.2–1.5 g/kWh for PM and the emission regulation threshold is used for NOx). Measurements of in-use ships in China improved the local EF database in several studies [213–216]. The measured EFs in China are insufficient to build an emission inventory because (i) the sample size is suboptimal (seven ships were tested in Fu et al. [213] and Peng et al. [215], three ships were tested in Zhang et al. [216] and one ship was tested in Lou et al. [214]) and (ii) the engine sizes on the measured vessels are much smaller than the fleet average of OGVs; the researchers tested ships with main engines ranging from 76 to 2648 kW [213–216]—much lower than the fleet average of cargo vessels for 2001 (4975 kW) determined from the Lloyd's Maritime Information System [217]. Difficulties encountered in the systematic establishment of EFs include on-board monitoring of international ships and measurement authorization. Inconsistent results were seen in determining the total emissions of PM (0.72∼9.4 g/kg with fuel sulphur content of 0.05% [216], 1.5–3.2 g/kg with 0.2% sulphur [215]), NOx (35.7∼115 g/kg [216], 64.1∼83.9 g/kg [215]) and CO (6.93∼30.3 g/kg [216], 30.7∼51.7 g/kg [215]); the total emissions of HC were 1.4–4.4 g/kg with a fuel sulphur content of 0.2% [215]. AIS data display large uncertainties, especially in Asia. AIS data include satellite-based and territory-based signals. Low earth orbiting satellites record information on ships on the high seas; however, the coverage is lower in Asia than in North America or Europe [208]. The signal intervals are also longer in Asia than elsewhere, occasionally exceeding multiple days. Territory-based stations are distributed near shore and generally display satisfactory signals for offshore areas; however, difficulties in data access and coverage evaluations occur in Asia. Fortunately, AIS data coverage in Europe has been observed to be increasing [218], emphasizing the importance of updating shipping emission inventories using the latest AIS data. The statistical database of ships is also a limiting factor. In the series of studies by Liu, the number of documented ships with detailed statistical information increased from 65 903 to 71 058; however, missing information still caused considerable uncertainties: (i) 8% of the ship records could not be matched, given the absence of a Ship Identification Number; (ii) inconsistent properties were noted for 30% of the ships, such as the vessel type, rated engine speed, rated engine power, length, width, height, design maximum speed, dead weight tonnage (dwt), maximum draught and build year; a gradient-boosting regression tree approach (GBRT) was adopted in the studies by Liu [201] to solve this problem; (iii) many smaller vessels and fishing vessels were not entered in either Lloyd's Register or the China Classification Society database. Thus, although uncertainties are known to exist at the fleet aggregate level, the magnitudes of these uncertainties are unknown. A cross-study comparison of shipping emissions with port function, the urban population and economic development would be invaluable and would reveal the impacts of various factors and their relationships with the differentiation of regional shipping emissions. In addition, shipping emission inventories should be used broadly in impact evaluations of air quality, health and climate. A thorough evaluation is necessary to guide the development of future policies and regulations. Other off-road sources. The emissions from agricultural equipment, construction machinery, locomotives and off-road vehicles are mainly estimated based on fuel consumption and the corresponding EFs. The populations of agricultural equipment and construction machinery, the quantities of freight transported by locomotives and the sales of three-wheelers and low-speed trucks can be obtained from the Chinese statistical yearbooks. Several studies report the activity rates for various sources: Fan et al. [232] for different types of agricultural equipment, Fu et al. [233] and Ge et al. [234] for harvesters and agricultural tractors and Li et al. [235] for typical construction equipment. Fuel consumption is then calculated based on the average fuel consumption rate for each activity. Wang et al. [192] estimated that, in 2012, agricultural equipment, industrial equipment and locomotives consumed 37.1, 5.2 and 3.7 Tg of diesel fuel, respectively. These EFs may vary with working conditions, the engine technologies deployed and the emission standards that are in force; thus, in-field measurements of EFs are needed in China. However, most of the EFs used are extracted from existing EF models, such as NONROAD, which was developed by the US EPA. Local studies that characterize the emissions from off-road engines are rare. Representative EFs that are currently used for inventory development can be found in Wang et al. [192]. Off-road emissions in China are far from accurately quantified and validated. Agricultural machinery (harvesters, agricultural tractors, etc.) makes the largest contributions to NOx emissions, followed by vessels (see the above subsection) and industrial equipment; specifically, agricultural machinery emits 1744 Gg of NOx and 147 Gg of PM annually [192]. Very large gaps exist in estimates of emissions from off-road sources in China. Real-world characterizations of both activity rates and EFs are urgently needed. Solvent use Solvent use contributes more than 20% of the total emissions of NMVOCs in China [10,28,100]. Emissions due to solvent use are estimated based on the ‘EF’ method, which relies on the activity rates and EFs for each source category. The major sources include the industrial use of paint for vehicles and architectural walls, industrial adhesives, printing, degreasing, pesticide use, pharmaceutical production and dry cleaning. Activity data for solvent use mainly indicate paint consumption, production output, vehicle number and population, which can be obtained from the statistical yearbooks or the reports of the NBS and domestic industrial associations [2,10,100–102,169,236–237]. Current studies that employ EFs refer mainly to Klimont et al. [10], Wei et al. [100] or Bo et al. [101]. Klimont et al. [10] developed the first long-term inventory of NMVOC emissions over China using properly revised European EFs, due to the lack of local information. Wei et al. [100] provided summary values of local EFs associated with paint use in vehicle manufacturing, printing and decorative paint use. For sources in which the solvent content is limited by nationwide regulations or standards in China, the limiting value is used (see below). Other EFs are based on data from Western countries obtained from the European Monitoring and Evaluation Program/European Environment Agency (EMEP/EEA) guidebook. Bo et al. [101] estimated emissions using EFs derived from the US AP-42 database. Wu et al. [102] updated the EF database with locally measured EFs for solvent use in pharmaceutical production, vehicle paint use, the manufacturing of cans and enamelled wire, and household appliances. These EFs, which include up-to-date local EF data in China, are summarized in Wu et al. [102]. To suppress the dramatic growth in emissions of NMVOCs, the government of China has imposed limits on solvent contents, thus providing a reference in the determination of EFs. Up to 2015, the nationally regulated sources included wood paint by GB 18581–2009, interior wall paint by GB 18582–2008, water-based solvents for architecture and industrial paints by HJ 2537–2014, indoor adhesives by GB 18583–2008, vehicle paint by GB 24409–2009, wood production by HJ 571–2010, leather products by HJ 507–2009, textile products by HJ 2546–2016, solvent-based wood furniture finishes by HJ/T 414–2007, printing ink by HJ 2542–2016, HJ/T 371–2007 and HJ 567–2010, and household detergents by HJ 458–2009. More local surveys and independent validations are needed to determine the effects of applying this legislation nationwide and to better quantify the emissions of NMVOCs associated with solvent use. Agriculture China may be the largest emitter of atmospheric ammonia in the world. However, the annual emissions reported by researchers vary from 9.8 to 17.2 Tg [1,13,238,239], implying that these estimates contain large uncertainties. Because of the very large demand for agricultural products, the application of synthetic nitrogen fertilizer and the management of livestock manure dominate ammonia emissions in China [240]. Accurate estimation of the emissions nationwide is difficult because farming practices (e.g. fertilizer application methods, animal housing conditions and animal manure management) and environmental conditions that determine ammonia volatilization (e.g. surface temperature, soil acidity, soil water content and wind speed) are regionally diverse. A mathematical model that is applicable to all of China, together with local EFs, is needed for realistic estimates. Huang et al. [238] attempted to develop a simple multi-parameter model and Huo et al. [241] derived ammonia EFs for urea, ammonium sulphate and compound fertilizer based on a field experiment performed on croplands planted with winter wheat in northern China. Moreover, satellite retrievals [242,243] and model inversions [244] have also been used to constrain ammonia emissions. Note that the Infrared Atmospheric Sounding Interferometer aboard the European MetOp satellite and the Atmospheric Infrared Sounder aboard NASA’s Aqua satellite are capable of providing more detailed spatial patterns and temporal trends than ground measurements. To better estimate the ammonia emissions in China, more in-field measurements should be conducted to reflect real-world conditions of fertilization and animal manure management in China. In-field crop residue burning In-field crop residue burning (also referred as agricultural fire) is the predominant type of open biomass burning that is conducted in China, and it occurs mainly during the harvest season. Emission estimates for this subsector obtained using various methods have frequently been reported. These emissions can be calculated as the product of crop yields, the residue-to-production ratio, the dry matter-to-crop residue ratio, the percentage of dry matter burned in the fields and EFs [245]. This method requires multiple parameters, and the results incorporate significant uncertainties. Although MODIS (Moderate Resolution Imaging Spectroradiometer) sensors aboard polar-orbiting satellites can capture the spatial patterns of agricultural fires well, it has been found that many fire events are likely not identified in China because the resolution of these data is still too coarse to pinpoint the small fires that commonly occur on croplands in China [246]. Randerson et al. [247] proposed a method to estimate the areas burned by small fires using the active fire data. Wooster et al. [248,249] found a strong linear relationship between time-integrated fire radiative power (FRP) and the amount of biomass combusted; thus, in recent years, FRP has been suggested as a new tool for estimating the emissions from vegetation fires [250,251]. This method is almost independent of the characteristics of local biomes and may produce realistic estimates. Liu et al. [252] used an FRP-based method to estimate the emissions from crop residue burning in northern China. In addition, geostationary satellites (e.g. Himawari-8) can assist in better characterizing the FRP diurnal cycles of agricultural fire events in China [253]. EVOLUTION OF EMISSIONS IN CHINA Using the updated methods and input data for each sector described in the previous sections, significant improvements in total emission estimates have been made during the last several decades. As is reasonable, emission estimates differ among inventories, due to differences in the compilation methods and data used [254]. Recognizing these differences, we focus here on reviewing the best available knowledge of the emission characteristics of pollutants, as well as their sectoral distributions and historical trends between 2000 and 2015 in China; we also examine future projections of emissions to 2030. The sectoral distributions of the emissions of each pollutant are shown in Table 1, and the changes in emissions from 2000 to 2015 derived from various studies are shown and compared in Fig. 4. The year 2010 is chosen for use as a reference year in the following analyses. Figure 4. Open in new tabDownload slide Evolution of emissions from 2000 to 2015 in China (Unit: Tg). References marked with * are emission estimates including agricultural waste field burning. ‘Xia et al. [255], PRI’ and ‘Xia et al. [255], STD’ are two cases of emission estimates regarding to the pollutant control devices and further environmental standards, respectively (Xia et al. [255]). SO2 SO2 can cause adverse effects on air quality, human health and ecosystems. SO2 emissions in China are estimated to have been 18–28, 29–35, 24–31 and 22–29 Tg in 2000, 2005, 2010 and 2014, respectively (derived from literature in Fig. 4). From 2000 to 2006, SO2 emissions in China increased by 53–65% at an annual growth rate of 7.3–8.7% [13,36,255] (EDGAR v4.2 (available at http://edgar.jrc.ec.europa.eu/), MEIC v1.2 (available at www.meicmodel.org)). The growth of emissions began to slow in approximately 2005; emissions then decreased after 2006, due to the nationwide use of FGD systems in power plants [36,47]. The annual growth rate of SO2 emissions for the period of 2006–10 was –4.6%, according to the MEIC dataset; this value reflects the efficacy of the control measures implemented during the 11th Five-Year Plan (FYP). During the 12th FYP, the 8% emission reduction target was achieved (a 14% reduction was realized, according to MEIC). The SO2 emission trends are in good agreement with satellite and ground-based observations [36,44,256–259]. SO2 emissions are dominated by the power and industrial sectors. In 2010, power plants and industry contributed approximately 8 Tg (27%) and 16 Tg (58%) to the total emissions, respectively. The fractional contribution of power plants decreased from 49% in 2006 to 27% in 2010. The emissions from power plants decreased by approximately 9 Tg during the same period, due to the installation of FGD systems, the construction of large units and the decommissioning of small units; these steps were taken to achieve the planned 10% reduction in emissions during the 11th FYP period [19]. On the other hand, control measures are still lacking in industrial sources. The industrial combustion sector became the largest contributor (more than 50%) to the total emissions after 2008. The sulphur content of fuel, fuel use, the degree of sulphur retention in hard coal and the actual removal efficiency of FGD systems are the main factors that contribute to the uncertainties in SO2 emissions [36]. Liu et al. [19] developed a unit-based coal-fired power plant emission inventory based on the CPED; this inventory reduced the uncertainty range in SO2 emissions to –22 to 23%. Due to the large contributions from industrial coal combustion, SO2 emissions are quite sensitive to uncertainties in energy statistics [26]. Energy-saving measures are increasingly important for achieving further SO2 emission reductions by 2030. Under the current emission control strategy, SO2 emissions will increase by 26% or 37% [7,8]. The enforcement of energy-saving measures and progressive end-of-pipe control measures are projected to lead to reductions in SO2 emissions of 36% and 26%, respectively, compared with a baseline scenario. The reduction potential associated with the installation of end-of-pipe control technologies will decrease, highlighting the importance of energy-saving measures in achieving further reductions in SO2 emissions. NOx NOx (NO+NO2) plays a key role in the formation of ozone and secondary aerosols. The emissions of NOx in 2010 are generally consistently estimated to have been 26–29 Tg [7,8,69,255] (MEICv1.2). Power plants, industry and transportation are the major contributors to the total emissions, and these sectors have shares of 28–34%, 34% and 25%, respectively [69,260] (MEICv1.2). The power and transportation sectors contributed 9 and 7 Tg to the total emissions in 2010, respectively (MEICv1.2). The industrial sector has become the main contributor since 2010. The sector distributions remain relatively stable, although the industrial sector displays increases, whereas the power sector displays decreases [3]. Driven by the rapid economic development and the lack of relevant emission controls, NOx emissions increased during both the 10th FYP and the 11th FYP [12,13,32,69] (EDGAR v4.2, MEICv1.2). The rate of growth in NOx emissions was 10.3% from 2000 to 2005 and 5.7% from 2005 to 2010 (MEIC v1.2). The rapid increases in NOx over China during this period are confirmed by satellite-based observations [258,261–266]. During the 12th FYP, the government of China set a target of reducing NOx emissions in 2015 by 10% compared to 2010. To achieve this goal, end-of-pipe pollutant abatement strategies were carried out nationwide for the power, industry and transportation sectors, and these strategies tended to be effective in controlling NOx emissions [7,69]. From 2011 to 2015, a decrease in emissions of 21% is estimated to have occurred, consistent with the changes in NO2 columns measured by satellites [267]. The power sector was the primary contributor to these emission reductions; in this sector, a 56% reduction is estimated to have been realized in this period. This reduction is associated with the increase in the penetration of SCR systems from 18% to 86%. The release of emission standards for vehicles also had a significant effect in terms of limiting emissions, especially in the urbanized regions, such as Beijing and Shanghai [267]. It is predicted that dramatic reductions in emissions of NOx can be achieved if end-of-pipe facilities are installed and stringent vehicle standards are applied in 2030. Zhao et al. [7] predict that the NOx emissions will decrease by 20% from 2010 to 2030 in a best-guess scenario, and they will be further reduced 24% if the issued and proposed emission standards are fully achieved. Six scenarios that combine two energy scenarios and three sets of end-of-pipe pollution control measures were designed by Zhao et al. [69] to predict the trends in NOx emissions from 2010 to 2030. By 2030, NOx emissions are projected to increase by 36% in the baseline case. In the most stringent control scenario, in which SCR/SNCR systems installed and stringent vehicle standards are applied, emissions would decrease by 61% compared to the 2010 level. This reduction was updated by Wang et al. [8] to 72% using the same prediction framework. CO CO is emitted by incomplete combustion and is a precursor of ozone formation. Streets et al. [268] and Zhang et al. [2] carried out two pioneering investigations that improved the accuracy of estimates of CO emissions in China. With the exception of EDGAR, emission estimates are generally consistent among the inventories shown in Fig. 4. EDGAR estimates much lower CO emissions because it underestimates fuel consumption by the residential sector [254]. According to the MEIC dataset, emissions of CO increased from 135 to 177 Tg (+31%) for the period of 2000 to 2005 and then decreased from 182 to 170 Tg (–6%) from 2006 to 2010. The decrease in CO emissions during 11th FYP is attributable to improvements in combustion efficiency, the recycling of industrial coal gases and strengthened vehicle emission standards [3]. The annual growth rate associated with the downward trends in CO emissions was estimated to be –1.2% per year, which agrees well with multiple satellite datasets [269–271]. From 2011 to 2015, emissions of CO continue to decrease by 4% and are reduced by as much as 162 Tg in 2015 (MEIC v1.2). The sectoral distributions of CO emissions are relatively stable from 2000 to 2015. The industrial and residential sectors are the main contributors and are estimated to have emitted 75 Tg (44%) and 71 Tg (42%) of CO in 2010, respectively (MEICv1.2). Industrial emissions of CO increased by more than 50% from 2000 to 2008, due to the production of steel, coke, cement, bricks and other materials. Since 2008, CO emissions from industrial sources have decreased, mainly due to the recycling of coal gas in plants and the substitution of shaft kilns in cement production. Residential emissions began to decrease after 2007 because of the reduced consumption of coal and biofuels in households [13]. The transportation sector contributed 12% of the total emissions in 2010. Due to the implementation of stringent vehicle emissions standards, the share of transportation has decreased since 2006. Further reductions in CO emissions could be achieved through improving combustion technologies in the future. CO emissions from cement production, which contribute approximately 8% of the total, are estimated to decrease by between 32% and 63% in 2020 compared to 2010, due to the continuing promotion of rotary kilns and the closure of shaft kilns [15]. NMVOCs NMVOCs are crucial precursors in the formation of tropospheric ozone and secondary organic aerosols. Given the important role of NMVOCs in air pollution control, NMVOC emissions have received increasing attention in recent years. Due to the rapid economic development and the lack of strong control measures, the national emissions of NMVOCs in China rose continuously from 2000 to 2015. The annual growth rate associated with this rapid increase in emissions from 2000 to 2005 is estimated to have been 7.0% and 8.9% [101,13] (MEIC v1.2). From 2005 to 2010, the emissions of NMVOCs increased at a relatively slow annual rate of 3.4–4.6% [8,169] (MEIC v1.2). The results of Wu et al. [102] reflect emissions of 22.4 Tg in 2008 and 29.8 Tg in 2012 and an annual growth rate of 7.4%—higher than the 5.3% estimated by MEIC v1.2. The contributions of different sources to total NMVOC emissions vary among different studies. Industrial processes and transportation are two of the primary contributors, and they account for 29.3–39.9% and 25.6–26.9%, respectively, from 2008 to 2013, as estimated by Wu et al. [102,236]. In contrast, MEIC v1.2 indicates that NMVOC emissions are produced primarily by industry (35% in 2010), residential biofuel combustion (22%) and solvent utilization (32%) [3]. In MEIC, the contributions from the residential sector and solvent use (22% and 32%, respectively) are much higher than in the results presented by Wu et al. [102] (10% and 15%), whereas the proportion of transportation is much lower in MEIC (10%). These discrepancies in the sector-based NMVOC emissions can be attributed to the differences in the source classification system, activity rates and EFs used in the two inventories. Combining the results of current studies, we conclude that the industrial sector (especially the petroleum-related industry and the coke industry) makes the largest contribution to the total emissions and was also the major driving source of the rapid increase in emissions since 2008. The combustion of biofuels in the residential sector, the use of solvents (especially in paint, adhesives and pesticide use) and transportation are other key contributors. More reliable EFs are needed to reduce the uncertainties associated with NMVOC emissions. In recent years, increasing numbers of local EFs have been measured; these factors address biofuel combustion, coke production, rubber products and other emission sources. However, most of the relevant EFs still rely on foreign databases, such as AP-42 or the EEA handbook (see the ‘Petroleum industry’ and ‘Solvent use’ sections). More measurements and investigations of local EFs must be conducted. The government of China has released standards to control the emissions of NMVOCs that result from the exploitation and distribution of fossil fuels and several sources associated with solvent use. Under current regulations, emissions of NMVOCs are projected to increase by 27% over the 2010 level by 2030, with emission reductions in the transportation and residential sectors. The enforcement of energy-saving policies and the implementation of end-of-pipe control measures would likely reduce the emissions of NMVOCs by 16% and 26%, respectively, in 2030 compared to the baseline scenario [8]. NH3 NH3 can lead to the formation of secondary fine particulates and has impacts on ecosystems. Huang et al. [238] developed a high-resolution inventory of NH3 emissions for 2006 using a process-based model that represents EFs on a grid; these EFs are parameterized based on multiple factors, such as temperature and soil properties. Following the same framework, Kang et al. [240] compiled a long-term NH3 emission inventory from 1980 to 2012. NH3 emissions in China are estimated to have increased from 10.1 Tg in 2000 to 10.7 Tg in 2005; they then decreased to 9.3 Tg in 2012. The good agreement of the bottom-up emission estimates with the top-down inversion results [244] confirms the reliability and accuracy of these data. In China, NH3 emissions are dominated by livestock manure and the application of synthetic fertilizer and account for 80–90% of the total emissions. Livestock manure is the largest contributor, with proportions of approximately 50%. From 2000 to 2005, the emissions of livestock manure increased by 0.81 Tg (15%), leading to a rapid rise in total emissions [240]. Due to the reduced numbers of beef cattle and sheep, as well as promotion of the intensive rearing system, livestock emissions decreased by 18% from 2005 to 2012. The application of synthetic fertilization is responsible for 30–43% of the total emissions. From 2000 to 2012, the emissions from this source decreased from 3.79 to 2.81 Tg, which can be attributed to the substitution of urea for highly volatile ammonium bicarbonate (ABC). The estimated total NH3 emissions agree well among the different inventories, but variations by sector still exist, as shown in Kang et al. [240]. Additional local field measurements are needed to reduce the uncertainties in EFs and thus those of emission estimates. PM We present the changes in emissions of primary PM10, PM2.5, BC and OC in this subsection. PM degrades air quality and visibility, affects the climate system through increasing the radiative forcing and damages human health. Lei et al. [34] quantified the historical emissions of PM components using a technology-based framework and is an example of a pioneering native research study. The development of primary PM emission inventories for China has been a key topic of research that supports the assessment of haze-control policies since 2013. PM10 and PM2.5 The emissions of PM10 and PM2.5 are estimated to have been 16 and 12 Tg in 2010, respectively, according to MEIC v1.2. The trends in the emissions of PM10 and PM2.5 are similar among the different inventories. The emissions of PM10 and PM2.5 increased by 15–17% and 15–20% from 2000 to 2005 [5,34] (MEIC v1.2) and decreased at rates of 10–17% and 6–15% from 2005 to 2010, respectively [6,8] (MEIC v1.2). Klimont et al. [5] arrived at an increasing trend in emissions for the latter period, likely due to different assumptions regarding technological evolution. Since 2012, the emissions of both PM10 and PM2.5 have shown decreasing trends. Cement production and the combustion of biofuels in residential stoves are the largest emitters of PM2.5, and these sources account for 11–33% and 24–28% of the total emissions during 2000 and 2015, respectively (MEIC v1.2). Power plants and transportation are minor contributors (less than 10%) to the total emissions. From 2000 to 2005, a boom in the production of iron and steel, cement and aluminium offset the effects of efficient PM control technologies, leading to the increase in emissions [34]. During 2005 to 2010, the use of highly effective PM abatement measures in the production of cement led to a reduction in emissions in this subsector of approximately 50%. The increase in large power plants equipped with efficient end-of-pipe dust collectors (e.g. electrostatic precipitators or fabric filters) reduced the EF of PM2.5 by 46% from 2005 to 2010 [6]. Nevertheless, the emissions from iron and steel plants increased by 24–39% during the same period, due to the huge growth of steel production [6]. Local measurements of the PM EFs for the brick and coke industries, as well as coal and biofuel burning in the residential sector, are still limited. In addition to end-of-pipe control measures, energy-saving policies could play a key role in reducing the emissions of PM10 and PM2.5 by 2030. The effects of advanced energy-saving policies on PM2.5 emissions (approximately 29% reduction, compared to the baseline scenario) exceed those of the planned end-of-pipe control measures (approximately 25% reduction) [8]. BC and OC Emissions of BC and OC are estimated based on the mass ratio of each component in PM2.5 for each source category. The measurements of the mass ratios for various sources before 2010 are summarized in Lei et al. [34]. BC emissions increased from 1.4 Tg in 2000 to 1.8 Tg in 2006, then showed a relatively flat and then a decreasing trend since 2006 (MEIC v1.2). Similarly, OC emissions increased from 2.7 Tg in 2000 to 3.5 Tg in 2005, remained at approximately 3.2 Tg from 2006 to 2012, then decreased to 2.5 Tg in 2015. The estimates of emissions reported by the different inventories show relatively good agreement, probably because they use similar EFs for sources for which few local measurements are available (e.g. brick and coke production). BC and OC emissions are dominated by the residential consumption of biofuels, which accounts for 23–33% and 57–70% of the total, respectively. The significant increase between 2000 and 2005 occurred primarily due to the combustion of biofuels (+0.13 Tg of BC, +0.51 Tg of OC), followed by the coke industry (+0.09 Tg of BC, +0.11 Tg of OC) and the transportation sector (+0.04 Tg of BC, +0.02 Tg of OC) [34]. Since 2005, the sectoral distributions of both species have been relatively stable. Toxic heavy metals. The development of emission inventories that are more complete in terms of the source categories and hazardous air pollutants are becoming increasingly important for protecting human health in the future. Quantitative assessments of the emissions and spatial distributions of toxic heavy metals, including Hg, As, Se, Pb, Cd, Cr, Ni, Sb, Mn, Co, Cu and Zn; the attribution of these emissions to particular sources; and policy controls on the releases of these metals from anthropogenic sources have been extensively studied in a series of papers [272–276]. The historical results for the period of 2000–12 show increases in the emissions of Hg, Cd, Sb, Cu and Zn that exceed a factor of two; increases in the emissions of As, Se, Cr, Ni, Mn and Co of 20–59% and a 29% reduction in Pb emissions reached approximately 526.9–22 319.6 Mg (varying among species) in 2012 [276]. Brake wear, which is the main source of hazardous metals from the transportation sector, should be considered in future work; no emission standard has been released that addresses this source of hazardous metals. MODEL-READY EMISSION INVENTORY Spatial allocation In addition to the estimation of total emissions, gridded emissions are also needed for application in chemical transport models. Allocating emissions according to the geographic coordinates of emission sources is the most accurate method of generating gridded emissions. However, such information is not usually available, especially for mobile and areal emission sources, for which exact locations are unknown. Therefore, certain parameters that may represent the spatial distributions of emissions are employed in the gridding process; these parameters are referred to as spatial proxies. After reviewing several global and regional emission inventories covering China, we find that the spatial proxies used in current inventories are usually shared among the different pollutant species; however, they differ among sectors. In the power sector, early studies used latitudes and longitudes to allocate the emissions from large-capacity power units, whereas population density was used for small power plants [1,2,12,22]. Recently, more detailed power plant databases have been developed, such as CARMA [37] and CPED [19], which contain more information on the locations of power emissions than was provided by previous work. For the industrial sector, total population [1,12,13] or urban population [2] are most commonly used as spatial proxies, based on the assumption that such emissions are closely related to human activities. However, Geng et al. [284] have shown that this method may incorrectly allocate greater amounts of emissions to rural areas and underestimate urban emissions. Using county-level industrial GDP data as a constraint to map emissions from provinces to counties before gridding [36] could enhance the accuracy of estimated urban emissions and yield better model performance at the county level [284]. Recent studies have adopted large datasets indicating the locations of manufacturing facilities that produce products including coke, iron, steel and cement to enable the generation of high-resolution (e.g. 0.05°  × 0.05°) and more accurate emission inventories [15,285,286]. In the transportation sector, pollutants are emitted from objects that follow trajectories; thus, road networks are frequently used to distribute on-road vehicle emissions [1,12] and ship lanes are used to apportion shipping emissions [1]. The hidden assumption in this method is that traffic volumes are homogeneous within provinces. To reduce the biases produced by this assumption, some studies have used county-level GDP [137] or county-level vehicle populations [16] as a first-pass spatial proxy to allocate emissions from provinces to counties before using the road networks. Zheng et al. [16] also utilized different types of road networks (e.g. highways and national, provincial and county roads); the total VKT data were used to weight each road type to further improve the allocation accuracy. For the residential sector, total and rural population density have been widely used for gridding [2,36], though large uncertainties persist. Review of the spatial proxies used in the Chinese emission inventory shows that the selection of spatial proxies is primarily empirical and work has been carried out to assess the uncertainties in gridded emissions introduced by the use of spatial proxies [284,285]. Additional efforts are needed in the future to improve the spatial distribution of emissions. Temporal variation Temporally resolved emissions with an hourly resolution are typically required by regional chemical transport models (CTMs). The temporal variations in emissions are largely driven by the activity strength or emission characteristics of their sources. For the open burning of agricultural waste, fire data derived from MODIS are used to estimate emissions at daily resolution [238]. The monthly emissions due to other anthropogenic activities are estimated by assigning the monthly profiles for each source category; further allocation to daily and hourly emissions is carried out based on the weekly and diurnal profiles, respectively. Emission processing systems that carry out temporal allocation have been proposed and validated using the Sparse Matrix Operator Kernel Emissions (SMOKE) model for China [287,288]. Monthly profiles by source are developed based on the monthly statistics of fuel consumption, industrial production or other relevant indicators [3,13,19,287]. In summary, for power plants, emissions are allocated to months based on monthly electricity generation. Industrial monthly profiles are derived from the yields of industrial products or industrial GDP for each month derived from statistical reports. For residential sources, monthly profiles are estimated from the stove operation time, according to ambient temperatures [1–3,13]. Zhu et al. [112] and Chen et al. [289] set up regression models to characterize the temporal variations of energy use in the residential sector based on temperature-related variables and socio-economic parameters. For agricultural activities, monthly emissions are estimated using monthly parameters, such as soil pH values and surface temperatures [238]. The available weekly and hourly profiles are restricted to specific regions (e.g. PRD) and limited source categories for which field investigations have been performed (e.g. urban and highway on-road vehicles) [290]. For the other sectors, the weekly and diurnal profiles were developed using working schedules, source characteristics, research reports and preliminary field measurements [287]. Information on the weekly and diurnal variations in each source is rarely available. Profiles are always shared among source categories that have similar temporal characteristics. Accurate databases of weekly and diurnal profiles covering complete source categories within different regions in China are needed. Source profiles and chemical speciation Speciation of NMVOCs NMVOCs consist of a variety of chemical species that differ significantly in their chemical structures and reactivity in producing ozone and secondary organic aerosols (SOAs). In CTMs, NMVOCs are usually characterized by a specific chemical mechanism that represents theses differences. The most commonly used mechanisms include the 1999 version of the State Air Pollution Research Center (SAPRC-99) [291] and its updated version SAPRC-07 [292]; Carbon Bond Mechanism version IV (CB-IV) [293], Carbon Bond Mechanism version Z (CBMZ) [294] and CB05 [295]; the Regional Acid Deposition Model chemical mechanism (RADM2) [296]; and the Regional Atmospheric Chemistry Mechanism (RACM and RACM2) [297,298]. Individual species are lumped together based on similarities in their carbon bond types (CB-IV, CB05, CBMZ) or functional groups (SAPRC-99, SAPRC-07, RADM2, RACM2). Therefore, NMVOC emissions should be speciated into the specific chemical mechanism configured in CTM. Zhang et al. [2] developed a speciated NMVOC emission inventory for a variety of chemical mechanisms (e.g. SAPRC-99, CB05, RADM2) using an explicit speciation assignment approach. Following the same method, a unified framework for NMVOC speciation with an updated composite profile development method has been proposed by Li et al. [28] to generate model-ready emissions of multiple chemical mechanisms for Asia, as shown in Fig. 5. First, composite profiles were developed in this work by averaging the profiles of the same source category to reduce the potential uncertainty associated with the selection of a single-source profile. The emissions of individual species were then developed using the composite profile for each source. Second, the individual species were lumped to different chemical mechanisms using the corresponding species mapping tables. Li et al. [28] pointed out that the OVOCs (oxygenated VOCs) were missing from some locally measured profiles due to improper sampling and analysis methods, especially for biofuel combustion and diesel engine operation. A framework for correcting the fractions of OVOCs in composite profiles was also developed by Li et al. [28]. Figure 5. Open in new tabDownload slide Schematic methodology of NMVOCs speciation (adapted from Li et al. [28]). Source profiles are the most important source of uncertainties in the speciation of NMVOCs. Mo et al. [299] compiled a source profile database for hydrocarbons and OVOCs in China by summarizing recent available profiles. In the last several decades, increasing numbers of local profiles have been measured that cover important sources, including residential fuel combustion [114,126,179,299], solvent use [179,300,301], the paint industry [302], the petrochemical industry [179,303,304], the coking industry [305], on-road vehicles [179,299,306–313] and fuel evaporation [180]. Recent profiles have included OVOCs in their measurements, thus making the results more complete and reliable. The emission characteristics of NMVOCs produced by various sources have been presented by numerous studies [10,21,28,100,169,236,299,237,314]. The emission distributions of chemical species are relatively consistent among investigations. For China as a whole, alkanes, alkenes, aromatics and OVOCs are the main contributors to the total emissions, and these classes of compounds have mass fractions of 24–30%, 19–28%, 15–30% and 12–18%, respectively. OVOCs made up 54% of the total VOCs within heavy-duty diesel vehicle exhaust and 12–46% of those produced by residential biofuel and coal burning, demonstrating the importance of OVOCs for combustion-related sources. Ethene, xylene, toluene, propene, 2-methyl-2-butene, 1,2,4-trimethylbenzene, butene and OVOC species (formaldehyde and glyoxal) are the main contributors to ozone formation that are produced by anthropogenic sources [21,28]. Measurements of local industrial profiles are still insufficient. Additional reliable domestic profiles for industrial facilities (e.g. chemical plants, iron and steel plants and oil refineries) and for both stack and fugitive sources are needed. Apart from non-methane hydrocarbons, OVOCs and halocarbons should be sampled and analysed in profile measurements. Source profiles are better reported in the absolute mass (e.g. g/kg) instead of mass fraction (%) because the latter may be highly uncertain, due to the incompleteness of the list of species measured. The EFs primarily used in emission inventories are for non-methane hydrocarbons, leading to underestimates in total VOC emissions by up to 30%, due to the omission of OVOCs [299]. The EFs based on source profiles that include OVOC measurements must be revised to improve the accuracy of emission estimates. Speciation of PM2.5 The composition of primary PM2.5, including BC, OC, sulphate, nitrate and other trace elements, play key roles in haze formation and climate change. In CTMs, PM is speciated into over 10 chemical species in the updated aerosol modules (such as AERO6) used in regional models, providing a basis for further simulations of the partitioning between gases and aerosols, SOA formation, aerosol ageing and other processes [315]. BC and OC are always inventoried, together with PM2.5, whereas PM speciation is required to estimate the emissions of other species for application in CTMs. The framework used in PM speciation mainly follows that used with NMVOCs. According to the source profiles for PM2.5 for each source category, PM2.5 is first speciated into various components and then mapped to the model-compatible species. Reff et al. [316] developed the first speciated PM2.5 emission inventory with 37 trace elements based on the US National Emissions Inventory and the US SPECIATE profile database (available at https://www.epa.gov/air-emissions-modeling/speciate-version-45-through-40). Compared to the speciation of NMVOCs, research on the speciation of PM in China is still lagging. Fu et al. [23] speciated PM2.5 into 18 species using available local source profiles from the YRD region. The covered species include EC, OC, sulphate, nitrate, H2O, Na, Cl, NH4+, non-carbon organic matter, Al, Ca, Fe, Si, Ti, Mg, K, Mn and others. Nationwide model-ready emissions with PM speciation are still lacking. Reliable source profiles of PM2.5 are essential to the speciation process. Several local source profiles have been measured in China [23]; these profiles cover power plants [317,318], coal-fired boilers [319], industrial combustion [95,320], residential coal combustion [116,117], biomass burning [321], vehicles [322,323], cement production [324], iron and steel production [325] and coking [326]. The USA-based SPECIATE database can supplement the sources that are lacking in the local profiles. Given the differences in the composition of PM between China and Western countries that may occur due to differences in fuel quality, technology and control policies, more measurements and investigations of local source profiles of various sources for PM are needed. SUMMARY OF UNCERTAINTIES AND LIMITATIONS The uncertainty ranges of emission estimates are calculated through the propagation of error [1,13] or the Monte Carlo approach [6,103,327]. Table 10 presents the emission uncertainties estimated by different studies in China. The uncertainties in emissions by sector were also estimated by Zhao et al. [6] using the Monte Carlo framework. The estimated emissions of SO2 and NOx are found to have low uncertainties of –15–26% and –15–35%, respectively ([6], 95% CIs). A moderate uncertainty range is assigned to CO (–18–42%). The uncertainties in the emissions of primary aerosols (PM10, PM2.5, BC, OC) are much higher than those of the gaseous species, due to the highly uncertain contributions from the residential sector. The EFs are the main contributing parameters to the final emission uncertainties [6]. As indicated in the previous sections, the current limitations of emission estimation by sectors are summarized as follows. Table 10. Uncertainties of emission estimates (95% confidence intervals, unit: %). SO2 . NOx . CO . NMVOC . NH3 . PM10 . PM2.5 . BC . OC . Year . References . ±12 ±31 ±70 ±68 ±132 ±130 ±208 ±258 2006 Zhang et al. [2] ±91 ±107 ±187 ±229 2005 Lei et al. [34] −14–13 −13–37 −14–45 −17–54 −25–136 −40–121 2005 Zhao et al. [103] −16–17 −41–80 −44–92 2010 Lu et al. [36] ±31 ±37 ±86 ±78 ±153 ±114 ±133 ±176 ±271 2008 Kurokawa et al. [13] −15–26 −15–35 −18–42 −15–54 −15–63 −28–126 −42–114 2010 Zhao et al. [6] SO2 . NOx . CO . NMVOC . NH3 . PM10 . PM2.5 . BC . OC . Year . References . ±12 ±31 ±70 ±68 ±132 ±130 ±208 ±258 2006 Zhang et al. [2] ±91 ±107 ±187 ±229 2005 Lei et al. [34] −14–13 −13–37 −14–45 −17–54 −25–136 −40–121 2005 Zhao et al. [103] −16–17 −41–80 −44–92 2010 Lu et al. [36] ±31 ±37 ±86 ±78 ±153 ±114 ±133 ±176 ±271 2008 Kurokawa et al. [13] −15–26 −15–35 −18–42 −15–54 −15–63 −28–126 −42–114 2010 Zhao et al. [6] Open in new tab Table 10. Uncertainties of emission estimates (95% confidence intervals, unit: %). SO2 . NOx . CO . NMVOC . NH3 . PM10 . PM2.5 . BC . OC . Year . References . ±12 ±31 ±70 ±68 ±132 ±130 ±208 ±258 2006 Zhang et al. [2] ±91 ±107 ±187 ±229 2005 Lei et al. [34] −14–13 −13–37 −14–45 −17–54 −25–136 −40–121 2005 Zhao et al. [103] −16–17 −41–80 −44–92 2010 Lu et al. [36] ±31 ±37 ±86 ±78 ±153 ±114 ±133 ±176 ±271 2008 Kurokawa et al. [13] −15–26 −15–35 −18–42 −15–54 −15–63 −28–126 −42–114 2010 Zhao et al. [6] SO2 . NOx . CO . NMVOC . NH3 . PM10 . PM2.5 . BC . OC . Year . References . ±12 ±31 ±70 ±68 ±132 ±130 ±208 ±258 2006 Zhang et al. [2] ±91 ±107 ±187 ±229 2005 Lei et al. [34] −14–13 −13–37 −14–45 −17–54 −25–136 −40–121 2005 Zhao et al. [103] −16–17 −41–80 −44–92 2010 Lu et al. [36] ±31 ±37 ±86 ±78 ±153 ±114 ±133 ±176 ±271 2008 Kurokawa et al. [13] −15–26 −15–35 −18–42 −15–54 −15–63 −28–126 −42–114 2010 Zhao et al. [6] Open in new tab Power plants: Local measurements of PM EFs are still limited, compared to SO2 and NOx, leading to relatively high uncertainties. Industry: Very few local measurements of EFs for industrial boilers can be found. The consumption of fuel by industrial boilers is still highly uncertain. There are very large gaps in the local EFs for NMVOCs for most industrial processes, especially those associated with oil refineries, carbon black production and the chemical industry. Residential: EFs show large variations among different measurements. The official statistics that describe the amounts of coal and biofuel consumed in residential stoves are highly uncertain. Both fuel consumption and the relevant EFs are assigned high uncertainties, due to the lack of reliable underlying local data. Transportation: Current EFs of on-road vehicles are calculated based on models developed for Western countries, such as the USA-developed IVE and MOVES models. Most existing EF measurements were taken in the megacities, reducing their representativeness for producing national emission estimates. Provincial-scale and national-scale surveys for determining vehicle activities are still limited. Few measurements or surveys have been conducted for off-road engines, leading to high uncertainties in this subsector. Solvent use: Investigations into the amounts of solvent used are limited. Real-world measurements of EFs are lacking for most emission sources associated with solvent use. Agriculture: Local measurements of EFs for NH3 are rare. In-field crop residue burning: The amounts of crop residues burned in fields and the corresponding EFs are still uncertain. With regards to model-ready processing, the spatial and temporal allocations of emissions and the speciation of NMVOC/PM to model-configured mechanisms are investigated and summarized in the previous sections. Limitations include the lack of local measurements of temporal profiles (especially weekly and diurnal profiles), reliable spatial surrogates, and complete and reliable source profiles. OUTLOOK Based on the efforts made in previous studies, we have gained increased knowledge of the emission characteristics of large point sources, including power plants and cement plants, and other key sources including on-road vehicles, shipping, residential combustion and agriculture. More reliable statistics and survey-based data have been used to reduce the uncertainties in activity rates and technology distributions. Local EFs and source profiles covering various sources have been measured and reported. Independent validations including satellite-based and in-situ observations have been introduced for better constraints of emissions during the last decade. Further efforts are required to improve the accuracy of emission inventories in China based on local data with high resolutions. Recent studies demonstrate that treating sources as point sources significantly improves the accuracy of both emission estimates and the inferred spatial distributions of emissions and thus model performance [285,328]. The development of inventory models that include greater numbers of point sources, such as cement plants, iron and steel plants and oil refineries, is anticipated to make key contributions. Given the gradual installation of continuous emission monitoring system (CEMS) in power plants in China, the accuracy of emission estimates can be further improved by including the real-time data. Integrating data describing traffic flow into the development of vehicular emission inventories should improve the accuracy of emission estimates significantly. For sources that lack adequately detailed information, a mosaic of different statistical data may provide improved emission estimates [285]. Future work should focus on surveys of activity rates and measurements of EFs and source profiles in places where few such assessments have been carried out locally in China. Top-down validations based on ground and satellite observations continue to play important roles in improving the temporal and spatial characterizations contained within emission inventories in China. Public access to emission inventories is another important issue to be addressed. As essential inputs to CTMs, public access to emission inventories and high transparency of such inventories can benefit both modellers and the inventory community. With wide application and independent validation, the uncertainties of emission inventories can be identified and further reduced. We encourage inventory developers to provide online access to emissions data grouped by sector and subsector, together with information on temporal variations, spatial distributions and chemical speciation (where possible) and the necessary documentation. Acknowledgements This work was supported by the National Key R&D program (2016YFC0201506), the National Natural Science Foundation of China (41625020, 41571130032 and 41571130035), and the National Basic Research Program of China (2014CB441301). REFERENCES 1. Streets DG , Bond TC, Carmichael GR et al. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000 . J Geophys Res 2003 ; 108 : 8809 . 10.1029/2002JD003093 Google Scholar Crossref Search ADS WorldCat 2. Zhang Q , Streets DG, Carmichael GR et al. Asian emissions in 2006 for the NASA INTEX-B mission . Atmos Chem Phys 2009 ; 9 : 5131 – 53 . 10.5194/acp-9-5131-2009 Google Scholar Crossref Search ADS WorldCat 3. Li M , Zhang Q, Kurokawa JI et al. MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP . Atmos Chem Phys 2017 ; 17 : 935 – 63 . 10.5194/acp-17-935-2017 Google Scholar Crossref Search ADS WorldCat 4. Janssens-Maenhout G , Crippa M, Guizzardi D et al. HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution . Atmos Chem Phys 2015 ; 15 : 11411 – 32 . 10.5194/acp-15-11411-2015 Google Scholar Crossref Search ADS WorldCat 5. Klimont Z , Kupiainen K, Heyes C et al. Global anthropogenic emissions of particulate matter including black carbon . Atmos Chem Phys 2017 ; 17 : 8681 – 723 . 10.5194/acp-17-8681-2017 Google Scholar Crossref Search ADS WorldCat 6. Zhao Y , Zhang J, Nielsen CP. The effects of recent control policies on trends in emissions of anthropogenic atmospheric pollutants and CO2 in China . Atmos Chem Phys 2013 ; 13 : 487 – 508 . 10.5194/acp-13-487-2013 Google Scholar Crossref Search ADS WorldCat 7. Zhao Y , Zhang J, Nielsen CP. The effects of energy paths and emission controls and standards on future trends in China's emissions of primary air pollutants . Atmos Chem Phys 2014 ; 14 : 8849 – 68 . 10.5194/acp-14-8849-2014 Google Scholar Crossref Search ADS WorldCat 8. Wang S , Zhao B, Cai SY et al. Emission trends and mitigation options for air pollutants in East Asia . Atmos Chem Phys 2014 ; 14 : 6571 – 603 . 10.5194/acp-14-6571-2014 Google Scholar Crossref Search ADS WorldCat 9. Kato N , Akimoto H. Anthropogenic emissions of SO2 and NOx in Asia: emission inventories . Atmospheric Environment Part A General Topics 1992 ; 26 : 2997 – 3017 . 10.1016/0960-1686(92)90291-R Google Scholar Crossref Search ADS WorldCat 10. Klimont Z , Streets DG, Gupta S et al. Anthropogenic emissions of non-methane volatile organic compounds in China . Atmos Environ 2002 ; 36 : 1309 – 22 . 10.1016/S1352-2310(01)00529-5 Google Scholar Crossref Search ADS WorldCat 11. Bond TC , Bhardwaj E, Dong R et al. Historical emissions of black and organic carbon aerosol from energy-related combustion, 1850–2000 . Global Biogeochem Cycles 2007 ; 21 : GB2018 . 10.1029/2006GB002840 Google Scholar Crossref Search ADS WorldCat 12. Ohara T , Akimoto H, Kurokawa J et al. An Asian emission inventory of anthropogenic emission sources for the period 1980–2020 . Atmos Chem Phys 2007 ; 7 : 4419 – 44 . 10.5194/acp-7-4419-2007 Google Scholar Crossref Search ADS WorldCat 13. Kurokawa J , Ohara T, Morikawa T et al. Emissions of air pollutants and greenhouse gases over Asian regions during 2000–2008: Regional Emission inventory in ASia (REAS) version 2 . Atmos Chem Phys 2013 ; 13 : 11019 – 58 . 10.5194/acp-13-11019-2013 Google Scholar Crossref Search ADS WorldCat 14. Zhao Y , Wang S, Duan L et al. Primary air pollutant emissions of coal-fired power plants in China: current status and future prediction . Atmos Environ 2008 ; 42 : 8442 – 52 . 10.1016/j.atmosenv.2008.08.021 Google Scholar Crossref Search ADS WorldCat 15. Lei Y , Zhang Q, Nielsen C et al. An inventory of primary air pollutants and CO2 emissions from cement production in China, 1990–2020 . Atmos Environ 2011 ; 45 : 147 – 54 . 10.1016/j.atmosenv.2010.09.034 Google Scholar Crossref Search ADS WorldCat 16. Zheng B , Huo H, Zhang Q et al. High-resolution mapping of vehicle emissions in China in 2008 . Atmos Chem Phys 2014 ; 14 : 9787 – 805 . 10.5194/acp-14-9787-2014 Google Scholar Crossref Search ADS WorldCat 17. Huang Y , Shen H, Chen H et al. Quantification of global primary emissions of PM2.5, PM10, and TSP from combustion and industrial process sources . Environ Sci Technol 2014 ; 48 : 13834 – 43 . 10.1021/es503696k Google Scholar Crossref Search ADS PubMed WorldCat 18. Huang Y , Shen H, Chen Y et al. Global organic carbon emissions from primary sources from 1960 to 2009 . Atmos Environ 2015 ; 122 : 505 – 12 . 10.1016/j.atmosenv.2015.10.017 Google Scholar Crossref Search ADS WorldCat 19. Liu F , Zhang Q, Tong D et al. High-resolution inventory of technologies, activities, and emissions of coal-fired power plants in China from 1990 to 2010 . Atmos Chem Phys 2015 ; 15 : 13299 – 317 . 10.5194/acp-15-13299-2015 Google Scholar Crossref Search ADS WorldCat 20. Meng W , Zhong Q, Yun X et al. Improvement of a global high-resolution ammonia emission inventory for combustion and industrial sources with new data from the residential and transportation sectors . Environ Sci Technol 2017 ; 51 : 2821 – 9 . 10.1021/acs.est.6b03694 Google Scholar Crossref Search ADS PubMed WorldCat 21. Zheng J , Shao M, Che W et al. Speciated VOC emission inventory and spatial patterns of ozone formation potential in the Pearl River Delta, China . Environ Sci Technol 2009 ; 43 : 8580 – 6 . 10.1021/es901688e Google Scholar Crossref Search ADS PubMed WorldCat 22. Zhao B , Wang P, Ma JZ et al. A high-resolution emission inventory of primary pollutants for the Huabei region, China . Atmos Chem Phys 2012 ; 12 : 481 – 501 . 10.5194/acp-12-481-2012 Google Scholar Crossref Search ADS WorldCat 23. Fu X , Wang S, Zhao B et al. Emission inventory of primary pollutants and chemical speciation in 2010 for the Yangtze River Delta region, China . Atmos Environ 2013 ; 70 : 39 – 50 . 10.1016/j.atmosenv.2012.12.034 Google Scholar Crossref Search ADS WorldCat 24. Yin S , Zheng J, Lu Q et al. A refined 2010-based VOC emission inventory and its improvement on modeling regional ozone in the Pearl River Delta Region, China . Sci Total Environ 2015 ; 514 : 426 – 38 . 10.1016/j.scitotenv.2015.01.088 Google Scholar Crossref Search ADS PubMed WorldCat 25. Guan D , Liu Z, Geng Y et al. The gigatonne gap in China's carbon dioxide inventories . Nat Clim Change 2012 ; 2 : 672 – 5 . 10.1038/nclimate1560 Google Scholar Crossref Search ADS WorldCat 26. Hong C , Zhang Q, He K et al. Variations of China's emission estimates: response to uncertainties in energy statistics . Atmos Chem Phys 2017 ; 17 : 1227 – 39 . 10.5194/acp-17-1227-2017 Google Scholar Crossref Search ADS WorldCat 27. Peng L , Zhang Q, Yao Z et al. Missing coal consumption from statistics: a survey-based solid fuel usage and emission inventory for rural residential sector in China . (By personal communication) . OpenURL Placeholder Text WorldCat 28. Li M , Zhang Q, Streets DG et al. Mapping Asian anthropogenic emissions of non-methane volatile organic compounds to multiple chemical mechanisms . Atmos Chem Phys 2014 ; 14 : 5617 – 38 . 10.5194/acp-14-5617-2014 Google Scholar Crossref Search ADS WorldCat 29. Klimont Z , Cofala J, Schöpp W et al. Projections of SO2, NOx, NH3 and VOC emissions in East Asia up to 2030 . Water, Air, and Soil Pollution 2001 ; 130 : 193 – 8 . Google Scholar Crossref Search ADS WorldCat 30. Hao J , Tian H, Lu Y. Emission inventories of NOx from commercial energy consumption in China, 1995−1998 . Environ Sci Technol 2002 ; 36 : 552 – 60 . 10.1021/es015601k Google Scholar Crossref Search ADS PubMed WorldCat 31. Zhang Q , Streets DG, He K et al. Major components of China's anthropogenic primary particulate emissions . Environ Res Lett 2007 ; 2 : 045027 . 10.1088/1748-9326/2/4/045027 Google Scholar Crossref Search ADS WorldCat 32. Zhang Q , Streets DG, He K et al. NOx emission trends for China, 1995–2004: the view from the ground and the view from space . J Geophys Res 2007 ; 112 : D22306 . 10.1029/2007JD008684 Google Scholar Crossref Search ADS WorldCat 33. Klimont Z , Cofala J, Xing J et al. Projections of SO2, NOx and carbonaceous aerosols emissions in Asia . Tellus B 2009 ; 61 : 602 – 17 . Google Scholar Crossref Search ADS WorldCat 34. Lei Y , Zhang Q, He KB et al. Primary anthropogenic aerosol emission trends for China, 1990–2005 . Atmos Chem Phys 2011 ; 11 : 931 – 54 . 10.5194/acp-11-931-2011 Google Scholar Crossref Search ADS WorldCat 35. Klimont Z , Smith SJ, Cofala J. The last decade of global anthropogenic sulfur dioxide: 2000–2011 emissions . Environ Res Lett 2013 ; 8 : 014003 . 10.1088/1748-9326/8/1/014003 Google Scholar Crossref Search ADS WorldCat 36. Lu Z , Zhang Q, Streets DG. Sulfur dioxide and primary carbonaceous aerosol emissions in China and India, 1996–2010 . Atmos Chem Phys 2011 ; 11 : 9839 – 64 . 10.5194/acp-11-9839-2011 Google Scholar Crossref Search ADS WorldCat 37. Wheeler D , Ummel K. Calculating CARMA: Global Estimation of CO2 Emissions from the Power Sector , Working Paper 145. : Center for Global Development. Washington, D.C., USA , 2008 . OpenURL Placeholder Text WorldCat 38. Oda T , Maksyutov S. A very high-resolution (1 km×1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights . Atmos Chem Phys 2011 ; 11 : 543 – 56 . 10.5194/acp-11-543-2011 Google Scholar Crossref Search ADS WorldCat 39. Wang R , Tao S, Ciais P et al. High-resolution mapping of combustion processes and implications for CO2 emissions . Atmos Chem Phys 2013 ; 13 : 5189 – 203 . 10.5194/acp-13-5189-2013 Google Scholar Crossref Search ADS WorldCat 40. Zhao Y , Wang S, Duan L et al. Primary air pollutant emissions of coal-fired power plants in China: current status and future prediction . Atmos Environ 2008 ; 42 : 8442 – 52 . 10.1016/j.atmosenv.2008.08.021 Google Scholar Crossref Search ADS WorldCat 41. Wang SW , Zhang Q, Streets DG et al. Growth in NOx emissions from power plants in China: bottom-up estimates and satellite observations . Atmos Chem Phys 2012 ; 12 : 4429 – 47 . 10.5194/acp-12-4429-2012 Google Scholar Crossref Search ADS WorldCat 42. Tian H , Liu K, Hao J et al. Nitrogen oxides emissions from thermal power plants in China: current status and future predictions . Environ Sci Technol 2013 ; 47 : 11350 – 7 . 10.1021/es402202d Google Scholar Crossref Search ADS PubMed WorldCat 43. Chen L , Sun Y, Wu X et al. Unit-based emission inventory and uncertainty assessment of coal-fired power plants . Atmos Environ 2014 ; 99 : 527 – 35 . 10.1016/j.atmosenv.2014.10.023 Google Scholar Crossref Search ADS WorldCat 44. Lu Z , Streets DG, Zhang Q et al. Sulfur dioxide emissions in China and sulfur trends in East Asia since 2000 . Atmos Chem Phys 2010 ; 10 : 6311 – 31 . 10.5194/acp-10-6311-2010 Google Scholar Crossref Search ADS WorldCat 45. Li C , Zhang Q, Krotkov NA et al. Recent large reduction in sulfur dioxide emissions from Chinese power plants observed by the Ozone Monitoring Instrument . Geophys Res Lett 2010 ; 37 : L08807 . 10.1029/2010GL042594 Google Scholar OpenURL Placeholder Text WorldCat Crossref 46. Xu Y , Williams RH, Socolow RH. China's rapid deployment of SO2 scrubbers . Energy Environ Sci 2009 ; 2 : 459 – 65 . 10.1039/b901357c Google Scholar Crossref Search ADS WorldCat 47. Xu Y. China's functioning market for sulfur dioxide scrubbing technologies . Environ Sci Technol 2011 ; 45 : 9161 – 7 . 10.1021/es202333t Google Scholar Crossref Search ADS PubMed WorldCat 48. Yao W. Experiment on the SO2 removal efficiency of wet scrubbers (in Chinese) . Environmental Protection 1989 ; 2 : 11 – 13 . Google Scholar OpenURL Placeholder Text WorldCat 49. Xie Z. Wet dedusting, desulfurization and denitrification without using additives for coal-fired boilers (in Chinese) . China Environmental Protection Industry 1995 ; 3 : 22 – 3 . Google Scholar OpenURL Placeholder Text WorldCat 50. SEPA . Emission Standard of Air Pollutants for Thermal Power Plants: GB 13223–1996 . Beijing: China Environmental Science Press , 1996 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 51. Klimont Z , Cofala J, Bertok I et al. Modeling Particulate Emissions in Europe: A Framework to Estimate Reduction Potential and Control Costs , Interim Report, IR-02-076. Laxenburg: International Institute for Applied Systems Analysis , 2002 . OpenURL Placeholder Text WorldCat 52. Amann M , Bertok I, Borken-Kleefeld J et al. Cost-effective control of air quality and greenhouse gases in Europe: Modeling and policy applications . Environ Modell Softw 2011 ; 26 : 1489 – 501 . 10.1016/j.envsoft.2011.07.012 Google Scholar Crossref Search ADS WorldCat 53. Qian C. Study on air staged low NOx emission combustion technology (in Chinese) . Shanghai Electricity 2010 ; 1 : 38 – 40 . Google Scholar OpenURL Placeholder Text WorldCat 54. Cao D , Liu H. Study on NOx emission control technology of boilers (in Chinese) . Water Conservancy & Electric Power Machinery 2011 ; 33 : 69 – 72 . Google Scholar OpenURL Placeholder Text WorldCat 55. Zhu L. Modification of low nitrogen oxide combustion for 300MW coal-fired boiler (in Chinese) . Guangdong Electric Power 2009 ; 22 : 64 – 7 . Google Scholar OpenURL Placeholder Text WorldCat 56. Wang X , Luan T, Cheng L. Experimental study on performance and NOx emission of large coal-fired boilers (in Chinese) . Power Equipment 2008 ; 6 : 467 – 72 . Google Scholar OpenURL Placeholder Text WorldCat 57. Yi HH , Hao JM, Duan L et al. Characteristics of inhalable particulate matter concentration and size distribution from power plants in China . JAPCA J Air Waste MA 2006 ; 56 : 1243 – 51 . 10.1080/10473289.2006.10464590 Google Scholar Crossref Search ADS WorldCat 58. Zhu F , Liu D, Wang S. Overview of NOx emissions and control measures from thermal power plants (in Chinese) . Environmental Protection 2009 ; 21 : 40 – 1 . Google Scholar OpenURL Placeholder Text WorldCat 59. Xie J , Zhang S, Xia Y. Analysis of performance and NOx emission of 600MW boilers (in Chinese) . Dongfang Boiler 2008 ; 3 : 12 – 6 . Google Scholar OpenURL Placeholder Text WorldCat 60. Wang X , Xin H, Luan T et al. Research and test on influence of boiler combustion adjusting on NOx emission of 330MW unit (in Chinese) . Power Sys Eng 2007 ; 23 : 7 – 10 . Google Scholar OpenURL Placeholder Text WorldCat 61. Bi Y , Chen G. Countermeasures and suggestions for controlling NOx emission of utility boilers (in Chinese) . Electricity 2004 ; 15 : 41 – 5 . Google Scholar OpenURL Placeholder Text WorldCat 62. Tian H. Studies on present and future emissions of nitrogen oxides and its comprehensive control policies in China (in Chinese). Ph.D. Dissertation . Tsinghua University 2003 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 63. Zhu F. Study on NOx Emission Reduction Potential and Gross Control Measures of Thermal Power Plants in China (in Chinese) . Nanjing : State Power Environmental Protection Research Institute , 2011 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 64. Zhu F , Wang S, Zheng Y. NOx emitting current situation and forecast from thermal power plants and countermeasures (in Chinese) . Energy Environ Prot 2004 ; 18 : 1 – 5 . Google Scholar OpenURL Placeholder Text WorldCat 65. Feng M , Yan H. Research on the concentration value conversion of NOx in the flue gas from thermal power plants (in Chinese) . Shanxi Eletric Power 2007 ; 143 : 14 – 20 . Google Scholar OpenURL Placeholder Text WorldCat 66. Zhao Y , Wang S, Nielsen CP et al. Establishment of a database of emission factors for atmospheric pollutants from Chinese coal-fired power plants . Atmos Environ 2010 ; 44 : 1515 – 23 . 10.1016/j.atmosenv.2010.01.017 Google Scholar Crossref Search ADS WorldCat 67. Huang W , Luo ZY, Xu H et al. Research on distribution of aerosol and PAHs in power plant stack (in Chinese) . Energ Eng 2003 ; 2 : 29 – 32 . Google Scholar OpenURL Placeholder Text WorldCat 68. Liu JZ , Fan HY, Zhou JH et al. Experimental studies on the emissions of PM10 and PM2.5 from coal-fired boiler (in Chinese) . Proceedings of the CSEE 2003 ; 23 : 145 – 9 . Google Scholar OpenURL Placeholder Text WorldCat 69. Zhao B , Wang SX, Liu H et al. NOx emissions in China: historical trends and future perspectives . Atmos Chem Phys 2013 ; 13 : 9869 – 97 . 10.5194/acp-13-9869-2013 Google Scholar Crossref Search ADS WorldCat 70. Hua S , Tian H, Wang K et al. Atmospheric emission inventory of hazardous air pollutants from China's cement plants: temporal trends, spatial variation characteristics and scenario projections . Atmos Environ 2016 ; 128 : 1 – 9 . 10.1016/j.atmosenv.2015.12.056 Google Scholar Crossref Search ADS WorldCat 71. Wang K , Tian H, Hua S et al. A comprehensive emission inventory of multiple air pollutants from iron and steel industry in China: temporal trends and spatial variation characteristics . Sci Total Environ 2016 ; 559 : 7 – 14 . 10.1016/j.scitotenv.2016.03.125 Google Scholar Crossref Search ADS PubMed WorldCat 72. Wang X , Yan L, Lei Y et al. Estimation of primary particulate emissions from iron and steel industry in China (in Chinese) . Acta Scientiae Circumstantiae 2016 ; 36 : 3033 – 9 . Google Scholar OpenURL Placeholder Text WorldCat 73. US Geological Survey (USGS) . Mineral Commodity Summaries 2014 . Washington : United States Government Printing Office , 2014 . Google Scholar PubMed PubMed OpenURL Placeholder Text Google Preview WorldCat COPAC 74. Zhang Q , Klimont Z, Streets D et al. An anthropogenic PM emission model for China and emission inventory for the year 2001 (in Chinese) . 2006 ; 16 : 223 – 31 . 75. Zhang C , Wang S, Zhao Y et al. Current status and future prospects of anthropogenic particulate matter emissions in China (in Chinese) . Environ Sci 2009 ; 30 : 1881 – 7 . Google Scholar OpenURL Placeholder Text WorldCat 76. Lei Y , He K, Zhang Q et al. Technology-based emission inventory of particulate matters (PM) from cement industry (in Chinese) . Huan Jing Ke Xue 2008 ; 29 : 2366 – 71 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 77. Woo J-H , Baek JM, Kim JW et al. Development of a multi-resolution emission inventory and its impact on sulfur distribution for Northeast Asia . Water, Air, and Soil Pollution 2003 ; 148 : 259 – 78 . 10.1023/A:1025493321901 Google Scholar Crossref Search ADS WorldCat 78. Liu H. Control of SO2 from cement kiln systems (in Chinese) . China Cement 2006 ; 11 : 74 – 7 . Google Scholar OpenURL Placeholder Text WorldCat 79. Wu H. NOx emission limits of policy analysis in cement industry (in Chinese) . Sichuan Cement 2012 ; a01 : 36 – 8 . Google Scholar OpenURL Placeholder Text WorldCat 80. Yuan W , Chen Z, Cao W et al. Implementing policy for new pollutants emission standards in cement industry (in Chinese) . Jiangsu Building Materials 2005 ; 2 : 16 – 27 . Google Scholar OpenURL Placeholder Text WorldCat 81. Ren C , Jiang M, Zou L et al. Study on feasible emission control level of air pollutions for cement industry (in Chinese) . Environ Sci 2014 ; 35 : 3632 – 8 . Google Scholar OpenURL Placeholder Text WorldCat 82. Guo B , Tian W. Selection of examining methods for total emission control of SO2 and NOx in cement industry (in Chinese) . Shanghai Environ Sci 2012 ; 31 : 38 – 46 . Google Scholar OpenURL Placeholder Text WorldCat 83. Liu X. emission characteristics of gaseous pollutants (SO2, NOx and fluoride) in cement industry (in Chinese) . Environ Sustain Development 2016 ; 41 : 239 – 40 . Google Scholar OpenURL Placeholder Text WorldCat 84. Chen L , Liu S, Zhao W et al. The emission and control of NOx for new dry cement kilns (in Chinese) . Air Pollution Prevention and Control 2016 ; 38 : 40 – 3 . Google Scholar OpenURL Placeholder Text WorldCat 85. Su D , Gao D, Ye H. Harmful gases pollution and its remedy in cement kiln (in Chinese) . Chongqing Environmental Science 1998 ; 20 : 20 – 3 . Google Scholar OpenURL Placeholder Text WorldCat 86. Ding X , Wang Y, Yu X et al. Air pollutants emission and management research in China's cement industry (in Chinese) . China Cement 2010 ; 11 : 47 – 50 . Google Scholar OpenURL Placeholder Text WorldCat 87. Li L. In the environment statistical work of cement industry nitrogen oxide and sulfur dioxide emissions factors of cement industry in the environment statistical work (in Chinese) . Statistics and Consulting 2007 ; 5 : 29 – 30 . Google Scholar OpenURL Placeholder Text WorldCat 88. Wang Y , Xue Z, Chai F et al. Estimation of air pollutants emissions of cement industry in China (in Chinese) . Res Environ Sci 2008 ; 21 : 207 – 12 . Google Scholar OpenURL Placeholder Text WorldCat 89. Wang S , Xing J, Chatani S et al. Verification of anthropogenic emissions of China by satellite and ground observations . Atmos Environ 2011 ; 45 : 6347 – 58 . 10.1016/j.atmosenv.2011.08.054 Google Scholar Crossref Search ADS WorldCat 90. Chen W , Hong J, Xu C. Pollutants generated by cement production in China, their impacts, and the potential for environmental improvement . J Clean Prod 2015 ; 103 : 61 – 9 . 10.1016/j.jclepro.2014.04.048 Google Scholar Crossref Search ADS WorldCat 91. Wu X , Zhao L, Zhang Y et al. Primary air pollutant emissions and future prediction of iron and steel industry in China . Aerosol Air Qual Res 2015 ; 15 : 1422 – 32 . 10.4209/aaqr.2015.01.0029 Google Scholar OpenURL Placeholder Text WorldCat Crossref 92. Ge S , Bai Z, Liu W et al. Boiler briquette coal versus raw coal: Part I—Stack gas emissions . JAPCA J Air Waste MA 2001 ; 51 : 524 – 33 . 10.1080/10473289.2001.10464293 Google Scholar Crossref Search ADS WorldCat 93. Zhang Y , Schauer JJ, Zhang Y et al. Characteristics of particulate carbon emissions from real-world Chinese coal combustion . Environ Sci Technol 2008 ; 42 : 5068 – 73 . 10.1021/es7022576 Google Scholar Crossref Search ADS PubMed WorldCat 94. Li C , Li X-H, Duan L et al. Emission characteristics of PM10 from coal-fired industrial boiler (in Chinese) . Huan Jing Ke Xue 2009 ; 30 : 650 – 5 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 95. Wang S , Zhao X-J, Li X-H et al. Emission characteristics of fine particles from grate firing boilers (in Chinese) . Huan Jing Ke Xue 2009 ; 30 : 963 – 8 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 96. UNEP . A Review of Air Pollution Control in Beijing 1998–2013 . Nairobi : United Nations Environment Programme (UNEP) , 2016 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 97. UNEP . UNEP Environmental Assessment: EXPO 2010 Shanghai, China . Nairobi : United Nations Environment Programme (UNEP) , 2010 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 98. Qiu K , Yang L, Lin J et al. Historical industrial emissions of non-methane volatile organic compounds in China for the period of 1980–2010 . Atmos Environ 2014 ; 86 : 102 – 12 . 10.1016/j.atmosenv.2013.12.026 Google Scholar Crossref Search ADS WorldCat 99. Wu X , Huang W, Zhang Y et al. Characteristics and uncertainty of industrial VOCs emissions in China . Aerosol Air Qual Res 2015 ; 15 : 1045 – 58 . Google Scholar OpenURL Placeholder Text WorldCat 100. Wei W , Wang S, Chatani S et al. Emission and speciation of non-methane volatile organic compounds from anthropogenic sources in China . Atmos Environ 2008 ; 42 : 4976 – 88 . 10.1016/j.atmosenv.2008.02.044 Google Scholar Crossref Search ADS WorldCat 101. Bo Y , Cai H, Xie SD. Spatial and temporal variation of historical anthropogenic NMVOCs emission inventories in China . Atmos Chem Phys 2008 ; 8 : 7297 – 316 . 10.5194/acp-8-7297-2008 Google Scholar Crossref Search ADS WorldCat 102. Wu R , Bo Y, Li J et al. Method to establish the emission inventory of anthropogenic volatile organic compounds in China and its application in the period 2008–2012 . Atmos Environ 2016 ; 127 : 244 – 54 . 10.1016/j.atmosenv.2015.12.015 Google Scholar Crossref Search ADS WorldCat 103. Zhao Y , Nielsen CP, Lei Y et al. Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China . Atmos Chem Phys 2011 ; 11 : 2295 – 308 . 10.5194/acp-11-2295-2011 Google Scholar Crossref Search ADS WorldCat 104. Yang X , Jiang Y, Yang M et al. Energy and environment in Chinese rural housing: current status and future perspective . Front Energy Power Eng China 2010 ; 4 : 35 – 46 . 10.1007/s11708-010-0001-5 Google Scholar Crossref Search ADS WorldCat 105. Duan X , Jiang Y, Wang B et al. Household fuel use for cooking and heating in China: results from the first Chinese Environmental Exposure-Related Human Activity Patterns Survey (CEERHAPS) . Appl Energ 2014 ; 136 : 692 – 703 . 10.1016/j.apenergy.2014.09.066 Google Scholar Crossref Search ADS WorldCat 106. Liu W , Spaargaren G, Heerink N et al. Energy consumption practices of rural households in north China: basic characteristics and potential for low carbon development . Energ Policy 2013 ; 55 : 128 – 38 . 10.1016/j.enpol.2012.11.031 Google Scholar Crossref Search ADS WorldCat 107. Sun E , Wang L, Lai X et al. Impacts and solutions of northern China winter rural heating on PM2.5 . Environ Sci Manag 2013 ; 38 : 90 – 3 . Google Scholar OpenURL Placeholder Text WorldCat 108. Zhang Y , Jiang J, Ye J et al. Testing the evolution of crude oil market efficiency: data have the conn . Energ Policy 2014 ; 36 : 39 – 52 . 10.1016/j.enpol.2013.12.052 Google Scholar Crossref Search ADS WorldCat 109. Zhao W , Xu Q, Li L et al. Estimation of air pollutant emissions from coal burning in the semi-rural areas of Beijing Plain (in Chinese) . Res Environ Sci 2015 ; 28 : 869 – 76 . Google Scholar OpenURL Placeholder Text WorldCat 110. Zhi GR , Yang JC, Tao Z et al. Rural household coal use survey, emission estimation and policy implications (in Chinese) . Res Environ Sci 2015 ; 28 : 1179 – 85 . Google Scholar OpenURL Placeholder Text WorldCat 111. Cheng M , Zhi G, Tang W et al. Air pollutant emission from the underestimated households’ coal consumption source in China . Sci Total Environ 2017 ; 580 : 641 – 50 . 10.1016/j.scitotenv.2016.12.143 Google Scholar Crossref Search ADS PubMed WorldCat 112. Zhu D , Tao S, Wang R et al. Temporal and spatial trends of residential energy consumption and air pollutant emissions in China . Appl Energ 2013 ; 106 : 17 – 24 . 10.1016/j.apenergy.2013.01.040 Google Scholar Crossref Search ADS WorldCat 113. Zhang J , Smith KR, Ma Y et al. Greenhouse gases and other airborne pollutants from household stoves in China: a database for emission factors . Atmos Environ 2000 ; 34 : 4537 – 49 . 10.1016/S1352-2310(99)00450-1 Google Scholar Crossref Search ADS WorldCat 114. Tsai SM , Zhang J, Smith KR et al. Characterization of non-methane hydrocarbons emitted from various cookstoves used in China . Environ Sci Technol 2003 ; 37 : 2869 – 77 . 10.1021/es026232a Google Scholar Crossref Search ADS PubMed WorldCat 115. Chen Y , Sheng G, Bi X et al. Emission factors for carbonaceous particles and polycyclic aromatic hydrocarbons from residential coal combustion in China . Environ Sci Technol 2005 ; 39 : 1861 – 7 . 10.1021/es0493650 Google Scholar Crossref Search ADS PubMed WorldCat 116. Chen Y , Zhi G, Feng Y et al. Measurements of emission factors for primary carbonaceous particles from residential raw-coal combustion in China . Geophys Res Lett 2006 ; 33 : L20815 . 10.1029/2006GL026966 Google Scholar Crossref Search ADS WorldCat 117. Zhi G , Chen Y, Feng Y et al. Emission characteristics of carbonaceous particles from various residential coal-stoves in China . Environ Sci Technol 2008 ; 42 : 3310 – 5 . 10.1021/es702247q Google Scholar Crossref Search ADS PubMed WorldCat 118. Li X , Wang S, Duan L et al. Carbonaceous aerosol emissions from household biofuel combustion in China . Environ Sci Technol 2009 ; 43 : 6076 – 81 . 10.1021/es803330j Google Scholar Crossref Search ADS PubMed WorldCat 119. Shen G , Yang Y, Wang W et al. Emission factors of particulate matter and elemental carbon for crop residues and coals burned in typical household stoves in China . Environ Sci Technol 2010 ; 44 : 7157 – 62 . 10.1021/es101313y Google Scholar Crossref Search ADS PubMed WorldCat 120. Shen G , Wei S, Wei W et al. Emission factors, size distributions, and emission inventories of carbonaceous particulate matter from residential wood combustion in rural China . Environ Sci Technol 2012 ; 46 : 4207 – 14 . 10.1021/es203957u Google Scholar Crossref Search ADS PubMed WorldCat 121. Shen G , Tao S, Wei S et al. Field measurement of emission factors of PM, EC, OC, parent, nitro-, and oxy- polycyclic aromatic hydrocarbons for residential briquette, coal cake, and wood in rural Shanxi, China . Environ Sci Technol 2013 ; 47 : 2998 – 3005 . 10.1021/es304599g Google Scholar Crossref Search ADS PubMed WorldCat 122. Shen G , Xue M, Wei S et al. Influence of fuel moisture, charge size, feeding rate and air ventilation conditions on the emissions of PM, OC, EC, parent PAHs, and their derivatives from residential wood combustion . J Environ Sci 2013 ; 25 : 1808 – 16 . 10.1016/S1001-0742(12)60258-7 Google Scholar Crossref Search ADS WorldCat 123. Shen G , Chen Y, Xue C et al. Pollutant emissions from improved coal- and wood-fuelled cookstoves in rural households . Environ Sci Technol 2015 ; 49 : 6590 – 8 . 10.1021/es506343z Google Scholar Crossref Search ADS PubMed WorldCat 124. Ge S , Xu X, Chow JC et al. Emissions of air pollutants from household stoves: honeycomb coal versus coal cake . Environ Sci Technol 2004 ; 38 : 4612 – 8 . 10.1021/es049942k Google Scholar Crossref Search ADS PubMed WorldCat 125. Chen Y , Zhi G, Feng Y et al. Measurements of black and organic carbon emission factors for household coal combustion in China: implication for emission reduction . Environ Sci Technol 2009 ; 43 : 9495 – 500 . 10.1021/es9021766 Google Scholar Crossref Search ADS PubMed WorldCat 126. Wang S , Wei W, Du L et al. Characteristics of gaseous pollutants from biofuel-stoves in rural China . Atmos Environ 2009 ; 43 : 4148 – 54 . 10.1016/j.atmosenv.2009.05.040 Google Scholar Crossref Search ADS WorldCat 127. Wei W , Zhang W, Hu D et al. Emissions of carbon monoxide and carbon dioxide from uncompressed and pelletized biomass fuel burning in typical household stoves in China . Atmos Environ 2012 ; 56 : 136 – 42 . 10.1016/j.atmosenv.2012.03.060 Google Scholar Crossref Search ADS WorldCat 128. Kelly FJ , Zhu T. Transport solutions for cleaner air . Science 2016 ; 352 : 934 – 6 . 10.1126/science.aaf3420 Google Scholar Crossref Search ADS PubMed WorldCat 129. Wang F , Lin T, Feng J et al. Source apportionment of polycyclic aromatic hydrocarbons in PM2.5 using positive matrix factorization modeling in Shanghai, China . Environmental Science: Processes & Impacts 2015 ; 17 : 197 – 205 . Google Scholar Crossref Search ADS PubMed WorldCat 130. Wang G , Cheng S, Li J et al. Source apportionment and seasonal variation of PM2.5 carbonaceous aerosol in the Beijing-Tianjin-Hebei Region of China . Environ Monit Assess 2015 ; 187 : 143 . 10.1007/s10661-015-4288-x Google Scholar Crossref Search ADS PubMed WorldCat 131. Cui H , Chen W, Dai W et al. Source apportionment of PM2.5 in Guangzhou combining observation data analysis and chemical transport model simulation . Atmos Environ 2015 ; 116 : 262 – 71 . 10.1016/j.atmosenv.2015.06.054 Google Scholar Crossref Search ADS WorldCat 132. Guo Z , Shi L, Chen S et al. Sulfur isotopic fractionation and source appointment of PM2.5 in Nanjing region around the second session of the Youth Olympic Games . Atmos Res 2016 ; 174 : 9 – 17 . 10.1016/j.atmosres.2016.01.011 Google Scholar Crossref Search ADS WorldCat 133. Cuddihy RG , Griffith WC, McClellan RO. Health risks from light-duty diesel vehicles . Environ Sci Technol 1984 ; 18 : 14A – 21A . 10.1021/es00119a720 Google Scholar Crossref Search ADS PubMed WorldCat 134. Crump KS , Van Landingham C, Moolgavkar SH et al. Reanalysis of the DEMS nested case-control study of lung cancer and diesel exhaust: suitability for quantitative risk assessment . Risk Anal 2015 ; 35 : 676 – 700 . 10.1111/risa.12371 Google Scholar Crossref Search ADS PubMed WorldCat 135. Anenberg SC , Miller J, Minjares R et al. Impacts and mitigation of excess diesel-related NOx emissions in 11 major vehicle markets . Nature 2017 ; 545 : 467 – 71 . 10.1038/nature22086 Google Scholar Crossref Search ADS PubMed WorldCat 136. Walsh M . Global trends in motor vehicle use and emissions . Annu Rev Energy 1990 ; 15 : 217 – 43 . 10.1146/annurev.eg.15.110190.001245 Google Scholar Crossref Search ADS WorldCat 137. Cai H , Xie S. Estimation of vehicular emission inventories in China from 1980 to 2005 . Atmos Environ 2007 ; 41 : 8963 – 79 . 10.1016/j.atmosenv.2007.08.019 Google Scholar Crossref Search ADS WorldCat 138. Cai H , Xie SD. Tempo-spatial variation of emission inventories of speciated volatile organic compounds from on-road vehicles in China . Atmos Chem Phys 2009 ; 9 : 6983 – 7002 . 10.5194/acp-9-6983-2009 Google Scholar Crossref Search ADS WorldCat 139. Huo H , Zhang Q, He K et al. High-resolution vehicular emission inventory using a link-based method: a case study of light-duty vehicles in Beijing . Environ Sci Technol 2009 ; 43 : 2394 – 9 . 10.1021/es802757a Google Scholar Crossref Search ADS PubMed WorldCat 140. Wang H , Fu L, Zhou Y et al. Trends in vehicular emissions in China's mega cities from 1995 to 2005 . Environ Pollut 2010 ; 158 : 394 – 400 . 10.1016/j.envpol.2009.09.002 Google Scholar Crossref Search ADS PubMed WorldCat 141. Wu Y , Wang R, Zhou Y et al. On-road vehicle emission control in Beijing: past, present, and future . Environ Sci Technol 2011 ; 45 : 147 – 53 . 10.1021/es1014289 Google Scholar Crossref Search ADS PubMed WorldCat 142. Wu Y , Zhang S, Hao J et al. On-road vehicle emissions and their control in China: a review and outlook . Sci Total Environ 2017 ; 574 : 332 – 49 . 10.1016/j.scitotenv.2016.09.040 Google Scholar Crossref Search ADS PubMed WorldCat 143. Lang J , Cheng S, Zhou Y et al. Air pollutant emissions from on-road vehicles in China, 1999–2011 . Sci Total Environ 2014 ; 496 : 1 – 10 . Google Scholar Crossref Search ADS PubMed WorldCat 144. Yang X , Liu H, Man HY et al. Characterization of road freight transportation and its impact on the national emission inventory in China . Atmos Chem Phys 2015 ; 15 : 2105 – 18 . 10.5194/acp-15-2105-2015 Google Scholar Crossref Search ADS WorldCat 145. Zhou Y , Shuiyuan C, Lang J et al. A comprehensive ammonia emission inventory with high-resolution and its evaluation in the Beijing–Tianjin–Hebei (BTH) region, China . Atmos Environ 2015 ; 106 : 305 – 17 . 10.1016/j.atmosenv.2015.01.069 Google Scholar Crossref Search ADS WorldCat 146. Sun S , Jiang W, Gao W. Vehicle emission trends and spatial distribution in Shandong province, China, from 2000 to 2014 . Atmos Environ 2016 ; 147 : 190 – 9 . 10.1016/j.atmosenv.2016.09.065 Google Scholar Crossref Search ADS WorldCat 147. Zhang S , Wu Y, Huang R et al. High-resolution simulation of link-level vehicle emissions and concentrations for air pollutants in a traffic-populated eastern Asian city . Atmos Chem Phys 2016 ; 16 : 9965 – 81 10.5194/acp-16-9965-2016 Google Scholar Crossref Search ADS WorldCat 148. Hao J , He D, Wu Y et al. A study of the emission and concentration distribution of vehicular pollutants in the urban area of Beijing . Atmos Environ 2000 ; 34 : 453 – 65 . 10.1016/S1352-2310(99)00324-6 Google Scholar Crossref Search ADS WorldCat 149. Fu L , Hao J, He D et al. Assessment of vehicular pollution in China . JAPCA J Air Waste MA 2001 ; 51 : 658 – 68 . 10.1080/10473289.2001.10464300 Google Scholar Crossref Search ADS WorldCat 150. Liu H , He K, Lents JM et al. Characteristics of diesel truck emission in China based on portable emissions measurement systems . Environ Sci Technol 2009 ; 43 : 9507 – 11 . 10.1021/es902044x Google Scholar Crossref Search ADS PubMed WorldCat 151. Zhang S , Wu Y, Wu X et al. Historic and future trends of vehicle emissions in Beijing, 1998–2020: a policy assessment for the most stringent vehicle emission control program in China . Atmos Environ 2014 ; 89 : 216 – 29 . 10.1016/j.atmosenv.2013.12.002 Google Scholar Crossref Search ADS WorldCat 152. Wang G , Cheng S, Lang J et al. On-board measurements of gaseous pollutant emission characteristics under real driving conditions from light-duty diesel vehicles in Chinese cities . J Environ Sci 2016 ; 46 : 28 – 37 . 10.1016/j.jes.2015.09.021 Google Scholar Crossref Search ADS WorldCat 153. Huo H , Yao Z, Zhang Y et al. On-board measurements of emissions from light-duty gasoline vehicles in three mega-cities of China . Atmos Environ 2012 ; 49 : 371 – 7 . 10.1016/j.atmosenv.2011.11.005 Google Scholar Crossref Search ADS WorldCat 154. Fu M , Ge Y, Wang X et al. NOx emissions from Euro IV busses with SCR systems associated with urban, suburban and freeway driving patterns . Sci Total Environ 2013 ; 452 : 222 – 6 . 10.1016/j.scitotenv.2013.02.076 Google Scholar Crossref Search ADS PubMed WorldCat 155. Huang C , Lou D, Hu Z et al. A PEMS study of the emissions of gaseous pollutants and ultrafine particles from gasoline- and diesel-fueled vehicles . Atmos Environ 2013 ; 77 : 703 – 10 . 10.1016/j.atmosenv.2013.05.059 Google Scholar Crossref Search ADS WorldCat 156. Wang X , Yin H, Ge Y et al. On-vehicle emission measurement of a light-duty diesel van at various speeds at high altitude . Atmos Environ 2013 ; 81 : 263 – 9 . 10.1016/j.atmosenv.2013.09.015 Google Scholar Crossref Search ADS WorldCat 157. Guo J , Ge Y, Hao L et al. On-road measurement of regulated pollutants from diesel and CNG buses with urea selective catalytic reduction systems . Atmos Environ 2014 ; 99 : 1 – 9 . 10.1016/j.atmosenv.2014.07.032 Google Scholar Crossref Search ADS WorldCat 158. Shen X , Yao Z, Huo H et al. PM2.5 emissions from light-duty gasoline vehicles in Beijing, China . Sci Total Environ 2014 ; 487 : 521 – 7 . 10.1016/j.scitotenv.2014.04.059 Google Scholar Crossref Search ADS PubMed WorldCat 159. Shen X , Yao Z, Zhang Q et al. Development of database of real-world diesel vehicle emission factors for China . J Environ Sci 2015 ; 31 : 209 – 20 . 10.1016/j.jes.2014.10.021 Google Scholar Crossref Search ADS WorldCat 160. Zhang Y , Yao Z, Shen X et al. Chemical characterization of PM 2.5 emitted from on-road heavy-duty diesel trucks in China . Atmos Environ 2015 ; 122 : 885 – 91 . 10.1016/j.atmosenv.2015.07.014 Google Scholar Crossref Search ADS WorldCat 161. Liu J , Ge Y, Wang X et al. On-board measurement of particle numbers and their size distribution from a light-duty diesel vehicle: Influences of VSP and altitude . J Environ Sci 2017 ; 57 : 238 – 48 . 10.1016/j.jes.2016.11.023 Google Scholar Crossref Search ADS WorldCat 162. Xie C-q , Parker D. A social psychological approach to driving violations in two Chinese cities . Transport Res F-Traf 2002 ; 5 : 293 – 308 . 10.1016/S1369-8478(02)00034-7 Google Scholar Crossref Search ADS WorldCat 163. Liu H , He K, Wang Q et al. Comparison of Vehicle Activity and Emission Inventory between Beijing and Shanghai . JAPCA J Air Waste MA 2007 ; 57 : 1172 – 7 . 10.3155/1047-3289.57.10.1172 Google Scholar Crossref Search ADS WorldCat 164. Liu H , Man H, Cui H et al. An updated emission inventory of vehicular VOCs and IVOCs in China . Atmos Chem Phys 2017 ; 17 : 12709 – 24 . 10.5194/acp-17-12709-2017 Google Scholar Crossref Search ADS WorldCat 165. Liu H , He K, Barth M. Traffic and emission simulation in China based on statistical methodology . Atmos Environ 2011 ; 45 : 1154 – 61 . 10.1016/j.atmosenv.2010.10.051 Google Scholar Crossref Search ADS WorldCat 166. Cao G , Zhang X, Gong S et al. Emission inventories of primary particles and pollutant gases for China . Chin Sci Bull 2011 ; 56 : 781 – 8 . 10.1007/s11434-011-4373-7 Google Scholar Crossref Search ADS WorldCat 167. Li W , Fu L, Hao JM et al. Emission inventory of 10 kinds of air pollutants for road traffic vehicles in China (in Chinese) . Urban Env Urban Ecol 2003 ; 16 : 36 – 8 . Google Scholar OpenURL Placeholder Text WorldCat 168. Liu JF , Zhao J, Li TT et al. Establishment of Chinese anthropogenic source volatile organic compounds emission inventory (in Chinese) . China Environ Sci 2008 ; 28 : 496 – 500 . Google Scholar OpenURL Placeholder Text WorldCat 169. Wei W , Wang S, Hao J et al. Trends of chemical speciation profiles of anthropogenic volatile organic compounds emissions in China, 2005–2020 . Front Environ Sci Eng 2014 ; 8 : 27 – 41 . 10.1007/s11783-012-0461-4 Google Scholar Crossref Search ADS WorldCat 170. Song Y , Shao M, Liu Y et al. Source apportionment of ambient volatile organic compounds in Beijing . Environ Sci Technol 2007 ; 41 : 4348 – 53 . 10.1021/es0625982 Google Scholar Crossref Search ADS PubMed WorldCat 171. Wang S , Zhao M, Xing J et al. Quantifying the air pollutants emission reduction during the 2008 Olympic Games in Beijing . Environ Sci Technol 2010 ; 44 : 2490 – 6 . 10.1021/es9028167 Google Scholar Crossref Search ADS PubMed WorldCat 172. Wang M , Shao M, Chen W et al. Trends of non-methane hydrocarbons (NMHC) emissions in Beijing during 2002–2013 . Atmos Chem Phys 2015 ; 15 : 1489 – 502 . 10.5194/acp-15-1489-2015 Google Scholar Crossref Search ADS WorldCat 173. Yamada H. Contribution of evaporative emissions from gasoline vehicles toward total VOC emissions in Japan . Sci Total Environ 2013 ; 449 : 143 – 9 . 10.1016/j.scitotenv.2013.01.045 Google Scholar Crossref Search ADS PubMed WorldCat 174. Liu H , Man H, Tschantz M et al. VOC from vehicular evaporation emissions: status and control strategy . Environ Sci Technol 2015 ; 49 : 14424 – 31 . 10.1021/acs.est.5b04064 Google Scholar Crossref Search ADS PubMed WorldCat 175. Gee IL , Sollars CJ. Ambient air levels of volatile organic compounds in Latin American and Asian cities . Chemosphere 1998 ; 36 : 2497 – 506 . 10.1016/S0045-6535(97)10217-X Google Scholar Crossref Search ADS WorldCat 176. Na K , Kim YP. Seasonal characteristics of ambient volatile organic compounds in Seoul, Korea . Atmos Environ 2001 ; 35 : 2603 – 14 . 10.1016/S1352-2310(00)00464-7 Google Scholar Crossref Search ADS WorldCat 177. Ho KF , Lee SC, Guo H et al. Seasonal and diurnal variations of volatile organic compounds (VOCs) in the atmosphere of Hong Kong . Sci Total Environ 2004 ; 322 : 155 – 66 . 10.1016/j.scitotenv.2003.10.004 Google Scholar Crossref Search ADS PubMed WorldCat 178. Ting M , Yue-si W, Hong-hui X et al. A study of the atmospheric VOCs of Mount Tai in June 2006 . Atmos Environ 2009 ; 43 : 2503 – 8 . 10.1016/j.atmosenv.2009.02.013 Google Scholar Crossref Search ADS WorldCat 179. Liu Y , Shao M, Fu L et al. Source profiles of volatile organic compounds (VOCs) measured in China: Part I . Atmos Environ 2008 ; 42 : 6247 – 60 . 10.1016/j.atmosenv.2008.01.070 Google Scholar Crossref Search ADS WorldCat 180. Zhang Y , Wang X, Zhang Z et al. Species profiles and normalized reactivity of volatile organic compounds from gasoline evaporation in China . Atmos Environ 2013 ; 79 : 110 – 8 . 10.1016/j.atmosenv.2013.06.029 Google Scholar Crossref Search ADS WorldCat 181. Harley RA , Coulter-Burke SC, Yeung TS. Relating liquid fuel and headspace vapor composition for California reformulated gasoline samples containing ethanol . Environ Sci Technol 2000 ; 34 : 4088 – 94 . 10.1021/es0009875 Google Scholar Crossref Search ADS WorldCat 182. Hwa M-Y , Hsieh C-C, Wu T-C et al. Real-world vehicle emissions and VOCs profile in the Taipei tunnel located at Taiwan Taipei area . Atmos Environ 2002 ; 36 : 1993 – 2002 . 10.1016/S1352-2310(02)00148-6 Google Scholar Crossref Search ADS WorldCat 183. Man H , Liu H, Niu H et al. VOC evaporative emissions from vehicles in China: Chemical characters and source apportionment (By personal communication) . 184. Pang Y , Fuentes M, Rieger P. Trends in the emissions of Volatile Organic Compounds (VOCs) from light-duty gasoline vehicles tested on chassis dynamometers in Southern California . Atmos Environ 2014 ; 83 : 127 – 35 . 10.1016/j.atmosenv.2013.11.002 Google Scholar Crossref Search ADS WorldCat 185. Mellios G , Samaras Z, Martini G et al. A vehicle testing programme for calibration and validation of an evaporative emissions model . Fuel 2009 ; 88 : 1504 – 12 . 10.1016/j.fuel.2009.03.018 Google Scholar Crossref Search ADS WorldCat 186. U.S. Environmental Protection Agency (U.S. EPA) . Development of Evaporative Emissions Calculations for the Motor Vehicle Emissions Simulator MOVES2010 . 2012 . Assessment and Standards Division, Office of Transportation and Air Quality, U.S. Environmental Protection Agency, EPA-420-R-12-027, available at https://nepis.epa.gov 187. Huang C , Wang HL, Li L et al. VOC species and emission inventory from vehicles and their SOA formation potentials estimation in Shanghai, China . Atmos Chem Phys 2015 ; 15 : 11081 – 96 . 10.5194/acp-15-11081-2015 Google Scholar Crossref Search ADS WorldCat 188. Dong X , Tschantz M, Fu JS. Estimating evaporative vapor generation from automobiles based on parking activities . Environ Pollut 2015 ; 202 : 104 – 11 . 10.1016/j.envpol.2015.03.010 Google Scholar Crossref Search ADS PubMed WorldCat 189. Yang X , Liu H, Cui H et al. Vehicular volatile organic compounds losses due to refueling and diurnal process in China: 2010–2050 . J Environ Sci 2015 ; 33 : 88 – 96 . 10.1016/j.jes.2015.01.012 Google Scholar Crossref Search ADS WorldCat 190. Mellios G , Samaras Z. An empirical model for estimating evaporative hydrocarbon emissions from canister-equipped vehicles . Fuel 2007 ; 86 : 2254 – 61 . 10.1016/j.fuel.2007.01.031 Google Scholar Crossref Search ADS WorldCat 191. Kean AJ , Sawyer RF, Harley RA. A fuel-based assessment of off-road diesel engine emissions . JAPCA J Air Waste MA 2000 ; 50 : 1929 – 39 . 10.1080/10473289.2000.10464233 Google Scholar Crossref Search ADS WorldCat 192. Wang F , Li Z, Zhang K et al. An overview of non-road equipment emissions in China . Atmos Environ 2016 ; 132 : 283 – 9 . 10.1016/j.atmosenv.2016.02.046 Google Scholar Crossref Search ADS WorldCat 193. United Nations Conference on Trade and Development . Review of Maritime Transport 2016 , 2016 . 194. Yang D-q , Kwan SH, Lu T et al. An emission inventory of marine vessels in Shanghai in 2003 . Environ Sci Technol 2007 ; 41 : 5183 – 90 . 10.1021/es061979c Google Scholar Crossref Search ADS PubMed WorldCat 195. Jin T-s , Yin X, Xu J et al. Air pollutants emission inventory from commercial ships of Tianjin Harbor (in Chinese) . Marine Environ Sci 2009 ; 06 : 623 – 25 . Google Scholar OpenURL Placeholder Text WorldCat 196. Zhang L-j , Zheng J-y, Yin S-s et al. Development of non-road mobile source emission inventory for the Pearl River Delta region (in Chinese) . Environ Sci Technol 2010 ; 31 : 886 . Google Scholar OpenURL Placeholder Text WorldCat 197. Fu Q , Shen Y, Zhang J. On the ship pollutant emission inventory in Shanghai port (in Chinese) . J Safety Environ 2012 ; 05 : 57 – 64 . Google Scholar OpenURL Placeholder Text WorldCat 198. Ye S , Zheng J, Pan Y et al. Marine emission inventory and its temporal and spatial characteristics in Guangdong Province (in Chinese) . Acta Scientiae Circumstantiae 2014 ; 34 : 537 – 47 . Google Scholar OpenURL Placeholder Text WorldCat 199. Li Z , He L. Emission inventory estimation methods study of ship pollutants (in Chinese) . Guangxi Journal of Light Industry 2011 ; 27 : 79 – 80 . Google Scholar OpenURL Placeholder Text WorldCat 200. Zhang Y , Yang X, Brown R et al. Shipping emissions and their impacts on air quality in China . Sci Total Environ 2017 ; 581 : 186 – 98 . 10.1016/j.scitotenv.2016.12.098 Google Scholar Crossref Search ADS PubMed WorldCat 201. Liu H , Fu M, Jin X et al. Health and climate impacts of ocean-going vessels in East Asia . Nat Clim Change 2016 ; 6 : 1037 – 41 . 10.1038/nclimate3083 Google Scholar Crossref Search ADS WorldCat 202. Chen D , Wang X, Li Y et al. High-spatiotemporal-resolution ship emission inventory of China based on AIS data in 2014 . Sci Total Environ 2017 ; 609 : 776 – 87 . 10.1016/j.scitotenv.2017.07.051 Google Scholar Crossref Search ADS PubMed WorldCat 203. Fu M , Liu H, Jin X et al. National- to port-level inventories of shipping emissions in China . Environ Res Lett 2017 ; 12 : 114024 . 10.1088/1748-9326/aa897a Google Scholar Crossref Search ADS WorldCat 204. Fan Q , Zhang Y, Ma W et al. Spatial and seasonal dynamics of ship emissions over the Yangtze River Delta and East China Sea and their potential environmental influence . Environ Sci Technol 2016 ; 50 : 1322 – 9 . 10.1021/acs.est.5b03965 Google Scholar Crossref Search ADS PubMed WorldCat 205. Li C , Yuan Z, Ou J et al. An AIS-based high-resolution ship emission inventory and its uncertainty in Pearl River Delta region, China . Sci Total Environ 2016 ; 573 : 1 – 10 . 10.1016/j.scitotenv.2016.07.219 Google Scholar Crossref Search ADS PubMed WorldCat 206. Mao X , Cui H, Roy B et al. Distribution of air pollution from oceangoing vessels in the Greater Pearl River Delta, 2015 . The International Council on Clean Transportation 2017 ; 10 : 1 – 8 . Google Scholar OpenURL Placeholder Text WorldCat 207. International Maritime Organization (IMO) . Second IMO GHG Study 2009, London, UK , 2009 . 208. International Maritime Organization (IMO) . Third IMO GHG Study 2014 Executive Summary and Final Report .\ London: International Maritime Organization, 2015 . 209. Entec UK Limited . Quantification of Emissions from Ships Associated with Ship Movements between Ports in the European Community .\ 2002 . Online available at: http://ec.europa.eu/environment/air/pdf/chapter1_ship_emissions.pdf. 210. European Environment Agency (EEA) . The impact of international shipping on European air quality and climate forcing . Luxembourg: Publications Office of the European Union, 2013 . 211. U.S. Environmental Protection Agency (U.S. EPA) . Proposal to Designate an Emission Control Area for Nitrogen Oxides, Sulfur Oxides and Particulate Matter .\ 2009 . Online available at: https://www.epa.gov/sites/production/files/2016-09/documents/420r09007.pdf. 212. ICF International . Current Methodologies and Best Practices for Preparing Ocean Going Vessel Emission Inventories Used in Preparing the U.S. ECA Proposal for U.S.EPA. 2010 . Online available at: https://www.epa.gov/sites/production/files/2014-05/documents/annexvi.pdf. 213. Fu M , Ding Y, Ge Y et al. Real-world emissions of inland ships on the Grand Canal, China . Atmos Environ 2013 ; 81 : 222 – 9 . 10.1016/j.atmosenv.2013.08.046 Google Scholar Crossref Search ADS WorldCat 214. Lou D , Shi L, Hu Z et al. Experimental study on emissions characteristics of inland ships based on different fuel qualities (in Chinese) . Ship Engineering 2016 ; 07 : 49–53+7 . Google Scholar OpenURL Placeholder Text WorldCat 215. Peng Z , Ge Y, Tan J et al. Emissions from several in-use ships tested by portable emission measurement system . Ocean Eng 2016 ; 116 : 260 – 7 . 10.1016/j.oceaneng.2016.02.035 Google Scholar Crossref Search ADS WorldCat 216. Zhang F , Chen Y, Tian C et al. Emission factors for gaseous and particulate pollutants from offshore diesel engine vessels in China . Atmos Chem Phys 2016 ; 16 : 6319 – 34 . 10.5194/acp-16-6319-2016 Google Scholar Crossref Search ADS WorldCat 217. LMIS . The Lloyd's Maritime Database (CD-ROM). Ltd L s R F Editor . 2002 . 218. Jalkanen JP , Johansson L, Kukkonen J. A comprehensive inventory of ship traffic exhaust emissions in the European sea areas in 2011 . Atmos Chem Phys 2016 ; 16 : 71 – 84 . 10.5194/acp-16-71-2016 Google Scholar Crossref Search ADS WorldCat 219. Yin P , Huang Z, Zheng D et al. Marine vessel emission and its temporal and spatial distribution characteristics in Ningbo-Zhoushan Port (in Chinese) . China Environ Sci 2017 ; 37 : 27 – 37 . Google Scholar OpenURL Placeholder Text WorldCat 220. Song S. Ship emissions inventory, social cost and eco-efficiency in Shanghai Yangshan port . Atmos Environ 2014 ; 82 : 288 – 97 . 10.1016/j.atmosenv.2013.10.006 Google Scholar Crossref Search ADS WorldCat 221. Yao X , Mou J, Chen P et al. Ship emission inventories in estuary of the Yangtze River using terrestrial AIS data . TransNav 2016 ; 10 : 633 – 40 . 10.12716/1001.10.04.13 Google Scholar Crossref Search ADS WorldCat 222. Yang J , Yin P-l, Ye S-q et al. Marine emission inventory and its temporal and spatial characteristics in the city of Shenzhen . Huan Jing Ke Xue 2015 ; 36 : 1217 – 26 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 223. Ng SKW , Loh C, Lin C et al. Policy change driven by an AIS-assisted marine emission inventory in Hong Kong and the Pearl River Delta . Atmos Environ 2013 ; 76 : 102 – 12 . 10.1016/j.atmosenv.2012.07.070 Google Scholar Crossref Search ADS WorldCat 224. Yau PS , Lee SC, Corbett JJ et al. Estimation of exhaust emission from ocean-going vessels in Hong Kong . Sci Total Environ 2012 ; 431 : 299 – 306 . 10.1016/j.scitotenv.2012.03.092 Google Scholar Crossref Search ADS PubMed WorldCat 225. Xing H , Duan S, Huang L et al. AIS data-based estimation of emissions from sea-going ships in Bohai Sea areas . China Environ Sci 2016 ; 36 : 953 – 60 . Google Scholar OpenURL Placeholder Text WorldCat 226. Song Y , Ge Y, Beijing Institute of Technology. Research of Emission Inventory and Emission Character of Inland and Offshore Ship . Report for the Energy Foundation (in Chinese) 2015 . Google Scholar OpenURL Placeholder Text WorldCat 227. Chen D , Zhao Y, Nelson P et al. Estimating ship emissions based on AIS data for port of Tianjin, China . Atmos Environ 2016 ; 145 : 10 – 18 . 10.1016/j.atmosenv.2016.08.086 Google Scholar Crossref Search ADS WorldCat 228. Chen D , Wang X, Nelson P et al. Ship emission inventory and its impact on the PM 2.5 air pollution in Qingdao Port, North China . Atmos Environ 2017 ; 166 : 351 – 61 . 10.1016/j.atmosenv.2017.07.021 Google Scholar Crossref Search ADS WorldCat 229. Liu J . The Establishment and application of ship emissions inventory in Qingdao Port (in Chinese) . Environmental Monitoring in China 2011 ; 27 : 50 – 3 . Google Scholar OpenURL Placeholder Text WorldCat 230. Tan JW , Song YN, Ge YS et al. Emission inventory of oceangoing vessels in Dalian Coastal area . Res Environ Sci 2014 ; 12 : 1426 – 31 . Google Scholar OpenURL Placeholder Text WorldCat 231. Cullinane K , Tseng P-H, Wilmsmeier G. Estimation of container ship emissions at berth in Taiwan . Int J Sustain Transp 2016 ; 10 : 466 – 74 . 10.1080/15568318.2014.975303 Google Scholar Crossref Search ADS WorldCat 232. Fan S , Nie L. Fuel consumption based exhaust emissions estimating from agricultural equipment in Beijing . J Saf Environ 2011 ; 11 : 145 – 8 . Google Scholar OpenURL Placeholder Text WorldCat 233. Fu M , Ding Y, Yin H et al. Characteristics of agricultural tractors emissions under real-world operating cycle . Transactions of the Chinese Society of Agricultural Engineering 2013 ; 29 : 42 – 8 . Google Scholar OpenURL Placeholder Text WorldCat 234. Ge Y , Liu H, Ding Y et al. Experimental study on characteristics of emissions and fuel consumption for combines . Transactions of the Chinese Society of Agricultural Engineering 2013 ; 29 : 41 – 7 . Google Scholar OpenURL Placeholder Text WorldCat 235. Li D-L , Wu Y, Zhou Y et al. Fuel consumption and emission inventory of typical construction equipments in China (in Chinese) . Huan Jing Ke Xue 2012 ; 33 : 518 – 24 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 236. Wu R , Xie S. Spatial distribution of ozone formation in China derived from emissions of speciated volatile organic compounds . Environ Sci Technol 2017 ; 51 : 2574 – 83 . 10.1021/acs.est.6b03634 Google Scholar Crossref Search ADS PubMed WorldCat 237. Zhao Y , Mao P, Zhou Y et al. Improved provincial emission inventory and speciation profiles of anthropogenic non-methane volatile organic compounds: a case study for Jiangsu, China . Atmos Chem Phys 2017 ; 17 : 7733 – 56 . 10.5194/acp-17-7733-2017 Google Scholar Crossref Search ADS WorldCat 238. Huang X , Song Y, Li M et al. A high-resolution ammonia emission inventory in China . Global Biogeochem Cycles 2012 ; 26 : GB1030 . 10.1029/2011GB004161 Google Scholar OpenURL Placeholder Text WorldCat Crossref 239. Gu B , Ju X, Chang J et al. Integrated reactive nitrogen budgets and future trends in China . Proc Natl Acad Sci USA 2015 ; 112 : 8792 – 7 . 10.1073/pnas.1510211112 Google Scholar Crossref Search ADS PubMed WorldCat 240. Kang Y , Liu M, Song Y et al. High-resolution ammonia emissions inventories in China from 1980 to 2012 . Atmos Chem Phys 2016 ; 16 : 2043 – 58 . 10.5194/acp-16-2043-2016 Google Scholar Crossref Search ADS WorldCat 241. Huo Q , Cai X, Kang L et al. Estimating ammonia emissions from a winter wheat cropland in North China Plain with field experiments and inverse dispersion modeling . Atmos Environ 2015 ; 104 : 1 – 10 . 10.1016/j.atmosenv.2015.01.003 Google Scholar Crossref Search ADS WorldCat 242. Van Damme M , Clarisse L, Heald CL et al. Global distributions, time series and error characterization of atmospheric ammonia (NH3) from IASI satellite observations . Atmos Chem Phys 2014 ; 14 : 2905 – 22 . Google Scholar OpenURL Placeholder Text WorldCat 243. Warner JX , Dickerson RR, Wei Z et al. Increased atmospheric ammonia over the world's major agricultural areas detected from space . Geophys Res Lett 2017 ; 44 : 2875 – 84 . 10.1002/2016GL072305 Google Scholar Crossref Search ADS WorldCat 244. Paulot F , Jacob DJ, Pinder RW et al. Ammonia emissions in the United States, European Union, and China derived by high-resolution inversion of ammonium wet deposition data: Interpretation with a new agricultural emissions inventory (MASAGE_NH3) . J Geophy Res Atmos 2014 ; 119 : 4343 – 64 . Google Scholar Crossref Search ADS WorldCat 245. Streets DG , Yarber KF, Woo JH et al. Biomass burning in Asia: annual and seasonal estimates and atmospheric emissions . Global Biogeochem Cycles 2003 ; 17 : 1099 . 10.1029/2003GB002040 Google Scholar Crossref Search ADS WorldCat 246. Song Y , Liu B, Miao W et al. Spatiotemporal variation in nonagricultural open fire emissions in China from 2000 to 2007 . Global Biogeochem Cycles 2009 ; 23 : GB2008 . 10.1029/2008GB003344 Google Scholar Crossref Search ADS WorldCat 247. Randerson JT , Chen Y, van der Werf GR et al. Global burned area and biomass burning emissions from small fires . J Geophys Res 2012 ; 117 : G04012 . 10.1029/2012JG002128 Google Scholar Crossref Search ADS WorldCat 248. Wooster MJ. Small-scale experimental testing of fire radiative energy for quantifying mass combusted in natural vegetation fires . Geophys Res Lett 2002 ; 29 : 23-1–4 . 10.1029/2002GL015487 Google Scholar OpenURL Placeholder Text WorldCat Crossref 249. Wooster MJ , Roberts G, Perry GLW et al. Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release . J Geophys Res 2005 ; 110 : D24311 . 10.1029/2005JD006318 Google Scholar Crossref Search ADS WorldCat 250. Vermote E , Ellicott E, Dubovik O et al. An approach to estimate global biomass burning emissions of organic and black carbon from MODIS fire radiative power . J Geophys Res 2009 ; 114 : D18205 . 10.1029/2008JD011188 Google Scholar Crossref Search ADS WorldCat 251. Kaiser JW , Heil A, Andreae MO et al. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power . Biogeosciences 2012 ; 9 : 527 – 54 . 10.5194/bg-9-527-2012 Google Scholar Crossref Search ADS WorldCat 252. Liu M , Song Y, Yao H et al. Estimating emissions from agricultural fires in the North China Plain based on MODIS fire radiative power . Atmos Environ 2015 ; 112 : 326 – 34 . 10.1016/j.atmosenv.2015.04.058 Google Scholar Crossref Search ADS WorldCat 253. Xu W , Wooster MJ, Kaneko T et al. Major advances in geostationary fire radiative power (FRP) retrieval over Asia and Australia stemming from use of Himarawi-8 AHI . Remote Sens Environ 2017 ; 193 : 138 – 49 . 10.1016/j.rse.2017.02.024 Google Scholar Crossref Search ADS WorldCat 254. Saikawa E , Kim H, Zhong M et al. Comparison of emissions inventories of anthropogenic air pollutants and greenhouse gases in China . Atmos Chem Phys 2017 ; 17 : 6393 – 421 . 10.5194/acp-17-6393-2017 Google Scholar Crossref Search ADS WorldCat 255. Xia Y , Zhao Y, Nielsen CP. Benefits of China's efforts in gaseous pollutant control indicated by the bottom-up emissions and satellite observations 2000–2014 Atmos Environ 2016 ; 136 : 43 – 53 . 10.1016/j.atmosenv.2016.04.013 Google Scholar Crossref Search ADS WorldCat 256. van Donkelaar A , Martin RV, Leaitch WR et al. Analysis of aircraft and satellite measurements from the Intercontinental Chemical Transport Experiment (INTEX-B) to quantify long-range transport of East Asian sulfur to Canada . Atmos Chem Phys 2008 ; 8 : 2999 – 3014 . 10.5194/acp-8-2999-2008 Google Scholar Crossref Search ADS WorldCat 257. Yan H , Chen L, Su L et al. SO2 columns over China: temporal and spatial variations using OMI and GOME-2 observations . IOP C Ser Earth Env 2014 ; 17 : 012027 . Google Scholar Crossref Search ADS WorldCat 258. Krotkov NA , McLinden CA, Li C et al. Aura OMI observations of regional SO2 and NO2 pollution changes from 2005 to 2015 . Atmos Chem Phys 2016 ; 16 : 4605 – 29 . 10.5194/acp-16-4605-2016 Google Scholar Crossref Search ADS WorldCat 259. van der A RJ , Mijling B, Ding J et al. Cleaning up the air: effectiveness of air quality policy for SO2 and NOx emissions in China . Atmos Chem Phys 2017 ; 17 : 1775 – 89 . 10.5194/acp-17-1775-2017 Google Scholar Crossref Search ADS WorldCat 260. Shi Y , Xia Y-f, Lu B-h et al. Emission inventory and trends of NOx for China, 2000–2020 . J Zheijang Univ-Sci 2014 ; 15 : 454 – 64 . 10.1631/jzus.A1300379 Google Scholar Crossref Search ADS WorldCat 261. Richter A , Burrows JP, Nusz H et al. Increase in tropospheric nitrogen dioxide over China observed from space . Nature 2005 ; 437 : 129 – 32 . 10.1038/nature04092 Google Scholar Crossref Search ADS PubMed WorldCat 262. Berezin EV , Konovalov IB, Ciais P et al. Multiannual changes of CO2 emissions in China: indirect estimates derived from satellite measurements of tropospheric NO2 columns . Atmos Chem Phys 2013 ; 13 : 9415 – 38 . 10.5194/acp-13-9415-2013 Google Scholar Crossref Search ADS WorldCat 263. Gu D , Wang Y, Smeltzer C et al. Reduction in NOx emission trends over China: regional and seasonal variations . Environ Sci Technol 2013 ; 22 : 12912 – 9 . 10.1021/es401727e Google Scholar Crossref Search ADS WorldCat 264. Mijling B , van der A RJ, Zhang Q. Regional nitrogen oxides emission trends in East Asia observed from space . Atmos Chem Phys 2013 ; 13 : 12003 – 12 . 10.5194/acp-13-12003-2013 Google Scholar Crossref Search ADS WorldCat 265. Itahashi S , Uno I, Irie H et al. Regional modeling of tropospheric NO2 vertical column density over East Asia during the period 2000–2010: comparison with multisatellite observations . Atmos Chem Phys 2014 ; 14 : 3623 – 35 . 10.5194/acp-14-3623-2014 Google Scholar Crossref Search ADS WorldCat 266. Miyazaki K , Eskes H, Sudo K et al. Decadal changes in global surface NOx emissions from multi-constituent satellite data assimilation . Atmos Chem Phys 2017 ; 17 : 807 – 37 . 10.5194/acp-17-807-2017 Google Scholar Crossref Search ADS WorldCat 267. Liu F , Zhang Q, Ronald JvdA et al. Recent reduction in NOx emissions over China: synthesis of satellite observations and emission inventories . Environ Res Lett 2016 ; 11 : 114002 . 10.1088/1748-9326/11/11/114002 Google Scholar Crossref Search ADS WorldCat 268. Streets DG , Zhang Q, Wang L et al. Revisiting China's CO emissions after the Transport and Chemical Evolution over the Pacific (TRACE-P) mission: synthesis of inventories, atmospheric modeling, and observations . J Geophys Res 2006 ; 111 : D14306 . 10.1029/2006JD007118 Google Scholar OpenURL Placeholder Text WorldCat Crossref 269. Worden HM , Deeter MN, Frankenberg C et al. Decadal record of satellite carbon monoxide observations . Atmos Chem Phys 2013 ; 13 : 837 – 50 . 10.5194/acp-13-837-2013 Google Scholar Crossref Search ADS WorldCat 270. Yumimoto K , Uno I, Itahashi S. Long-term inverse modeling of Chinese CO emission from satellite observations . Environ Pollut 2014 ; 195 : 308 – 18 . 10.1016/j.envpol.2014.07.026 Google Scholar Crossref Search ADS PubMed WorldCat 271. Yin Y , Chevallier F, Ciais P et al. Decadal trends in global CO emissions as seen by MOPITT . Atmos Chem Phys 2015 ; 15 : 13433 – 51 . 10.5194/acp-15-13433-2015 Google Scholar Crossref Search ADS WorldCat 272. Streets DG , Hao J, Wu Y et al. Anthropogenic mercury emissions in China . Atmos Environ 2005 ; 39 : 7789 – 806 . 10.1016/j.atmosenv.2005.08.029 Google Scholar Crossref Search ADS WorldCat 273. Streets DG , Devane MK, Lu Z et al. All-time releases of mercury to the atmosphere from human activities . Environ Sci Technol 2011 ; 45 : 10485 – 91 . 10.1021/es202765m Google Scholar Crossref Search ADS PubMed WorldCat 274. Wu QR , Wang SX, Zhang L et al. Update of mercury emissions from China's primary zinc, lead and copper smelters, 2000–2010 . Atmos Chem Phys 2012 ; 12 : 11153 – 63 . 10.5194/acp-12-11153-2012 Google Scholar Crossref Search ADS WorldCat 275. Chen Y , Wang R, Shen H et al. Global mercury emissions from combustion in light of international fuel trading . Environ Sci Technol 2014 ; 48 : 1727 – 35 . 10.1021/es404110f Google Scholar Crossref Search ADS PubMed WorldCat 276. Tian H , Zhu CY, Gao JJ et al. Quantitative assessment of atmospheric emissions of toxic heavy metals from anthropogenic sources in China: historical trend, spatial distribution, uncertainties, and control policies . Atmos Chem Phys 2015 ; 15 : 10127 – 47 . 10.5194/acp-15-10127-2015 Google Scholar Crossref Search ADS WorldCat 277. Smith SJ , van Aardenne J, Klimont Z et al. Anthropogenic sulfur dioxide emissions: 1850–2005 . Atmos Chem Phys 2011 ; 11 : 1101 – 16 . 10.5194/acp-11-1101-2011 Google Scholar Crossref Search ADS WorldCat 278. Li J , Li LY, Wu RR et al. Inventory of highly resolved temporal and spatial volatile organic compounds emission in China . In: 24th International Conference on Modelling, Monitoring and Management of Air Pollution (AIR 2016), 2016 . Abstract Air Pollution XXIV. WIT Transactions on Ecology and The Environment . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 279. Cao G , Zhang X, Zheng F. Inventory of black carbon and organic carbon emissions from China . Atmos Environ 2006 ; 40 : 6516 – 27 . 10.1016/j.atmosenv.2006.05.070 Google Scholar Crossref Search ADS WorldCat 280. Qin Y , Xie SD. Spatial and temporal variation of anthropogenic black carbon emissions in China for the period 1980–2009 . Atmos Chem Phys 2012 ; 12 : 4825 – 41 . 10.5194/acp-12-4825-2012 Google Scholar Crossref Search ADS WorldCat 281. Dong W , Xing J, Wang S. Temporal and spatial distribution of anthropogenic ammonia emissions in China: 1994–2006 . Environ Sci 2010 ; 31 : 1457 – 63 . Google Scholar OpenURL Placeholder Text WorldCat 282. Gu B , Ge Y, Ren Y et al. Atmospheric reactive nitrogen in China: sources, recent trends, and damage costs . Environ Sci Technol 2012 ; 46 : 9420 – 7 . 10.1021/es301446g Google Scholar Crossref Search ADS PubMed WorldCat 283. Xu P , Zhang Y, Gong W et al. An inventory of the emission of ammonia from agricultural fertilizer application in China for 2010 and its high-resolution spatial distribution . Atmos Environ 2015 ; 115 : 141 – 8 . 10.1016/j.atmosenv.2015.05.020 Google Scholar Crossref Search ADS WorldCat 284. Geng G , Zhang Q, Martin RV et al. Impact of spatial proxies on the representation of bottom-up emission inventories: a satellite-based analysis . Atmos Chem Phys 2017 ; 17 : 4131 – 45 . 10.5194/acp-17-4131-2017 Google Scholar Crossref Search ADS WorldCat 285. Zheng B , Zhang Q, Tong D et al. Resolution dependence of uncertainties in gridded emission inventories: a case study in Hebei, China . Atmos Chem Phys 2017 ; 17 : 921 – 33 . 10.5194/acp-17-921-2017 Google Scholar Crossref Search ADS WorldCat 286. Zhou Y , Zhao Y, Mao P et al. Development of a high-resolution emission inventory and its evaluation and application through air quality modeling for Jiangsu Province, China . Atmos Chem Phys 2017 ; 17 : 211 – 33 . 10.5194/acp-17-211-2017 Google Scholar Crossref Search ADS WorldCat 287. Wang SS , Zheng J, Fu F et al. Development of an emission processing system for the Pearl River Delta Regional air quality modeling using the SMOKE model: methodology and evaluation . Atmos Environ 2011 ; 45 : 5079 – 89 . 10.1016/j.atmosenv.2011.06.037 Google Scholar Crossref Search ADS WorldCat 288. Woo J-H , Choi K-C, Kim HK et al. Development of an anthropogenic emissions processing system for Asia using SMOKE . Atmos Environ 2012 ; 58 : 5 – 13 . 10.1016/j.atmosenv.2011.10.042 Google Scholar Crossref Search ADS WorldCat 289. Chen H , Huang Y, Shen H et al. Modeling temporal variations in global residential energy consumption and pollutant emissions . Appl Energ 2016 ; 184 : 820 – 9 . 10.1016/j.apenergy.2015.10.185 Google Scholar Crossref Search ADS WorldCat 290. Zheng J , Zhang L, Che W et al. A highly resolved temporal and spatial air pollutant emission inventory for the Pearl River Delta region, China and its uncertainty assessment . Atmos Environ 2009 ; 43 : 5112 – 22 . 10.1016/j.atmosenv.2009.04.060 Google Scholar Crossref Search ADS WorldCat 291. Carter WPL . Documentation of the SAPRC-99 Chemical Mechanism for VOC Reactivity Assessment, Report to the California Air Resources Board . C A : University of California , Riverside, USA , 2000 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 292. Carter WPL. Development of the SAPRC-07 chemical mechanism . Atmos Environ 2010 ; 44 : 5324 – 35 . 10.1016/j.atmosenv.2010.01.026 Google Scholar Crossref Search ADS WorldCat 293. Gery MW , Whitten GZ, Killus JP et al. A photochemical kinetics mechanism for urban and regional scale computer modeling . J Geophys Res 1989 ; 94 : 12925 – 56 . 10.1029/JD094iD10p12925 Google Scholar Crossref Search ADS WorldCat 294. Zaveri RA , Peters LK. A new lumped structure photochemical mechanism for large-scale applications . J Geophys Res 1999 ; 104 : 30387 – 415 . 10.1029/1999JD900876 Google Scholar Crossref Search ADS WorldCat 295. Yarwood G , Rao S, Yocke M et al. Updates to the carbon bond chemical mechanism: CB05 , Final Report to the US EPA, RT-0400675 , Novato, CA , 2005 . Online available at: http://www.camx.com/files/cb05_final_report_120805.aspx Google Scholar OpenURL Placeholder Text WorldCat 296. Stockwell WR , Middleton P, Chang JS et al. The second generation regional acid deposition model chemical mechanism for regional air quality modeling . J Geophys Res 1990 ; 95 : 16343 – 67 . 10.1029/JD095iD10p16343 Google Scholar Crossref Search ADS WorldCat 297. Stockwell WR , Kirchner F, Kuhn M et al. A new mechanism for regional atmospheric chemistry modeling . J Geophys Res 1997 ; 102 : 25847 – 79 . 10.1029/97JD00849 Google Scholar Crossref Search ADS WorldCat 298. Goliff WS , Stockwell WR, Lawson CV. The regional atmospheric chemistry mechanism, version 2 . Atmos Environ 2013 ; 68 : 174 – 85 . 10.1016/j.atmosenv.2012.11.038 Google Scholar Crossref Search ADS WorldCat 299. Mo Z , Shao M, Lu S. Compilation of a source profile database for hydrocarbon and OVOC emissions in China . Atmos Environ 2016 ; 143 : 209 – 17 . 10.1016/j.atmosenv.2016.08.025 Google Scholar Crossref Search ADS WorldCat 300. Yuan B , Shao M, Lu S et al. Source profiles of volatile organic compounds associated with solvent use in Beijing, China . Atmos Environ 2010 ; 44 : 1919 – 26 . 10.1016/j.atmosenv.2010.02.014 Google Scholar Crossref Search ADS WorldCat 301. Wang H , Qiao Y, Chen C et al. Source profiles and chemical reactivity of volatile organic compounds from solvent use in Shanghai, China . Aerosol Air Qual Res 2014 ; 14 : 301 – 10 . Google Scholar OpenURL Placeholder Text WorldCat 302. Zheng J , Yu Y, Mo Z et al. Industrial sector-based volatile organic compound (VOC) source profiles measured in manufacturing facilities in the Pearl River Delta, China . Sci Total Environ 2013 ; 456 : 127 – 36 . 10.1016/j.scitotenv.2013.03.055 Google Scholar Crossref Search ADS PubMed WorldCat 303. Wei W , Cheng S, Li G et al. Characteristics of ozone and ozone precursors (VOCs and NOx) around a petroleum refinery in Beijing, China . J Environ Sci 2014 ; 26 : 332 – 42 . 10.1016/S1001-0742(13)60412-X Google Scholar Crossref Search ADS WorldCat 304. Mo Z , Shao M, Lu S et al. Process-specific emission characteristics of volatile organic compounds (VOCs) from petrochemical facilities in the Yangtze River Delta, China . Sci Total Environ 2015 ; 533 : 422 – 31 . 10.1016/j.scitotenv.2015.06.089 Google Scholar Crossref Search ADS PubMed WorldCat 305. He Q. Preliminary study on profiles of VOC_S emitted from coking (in Chinese) . Environmental Monitoring in China 2005 ; 21 : 61 – 5 . Google Scholar OpenURL Placeholder Text WorldCat 306. Tsai WY , Chan LY, Blake DR et al. Vehicular fuel composition and atmospheric emissions in South China: Hong Kong, Macau, Guangzhou, and Zhuhai . Atmos Chem Phys 2006 ; 6 : 3281 – 8 . 10.5194/acp-6-3281-2006 Google Scholar Crossref Search ADS WorldCat 307. Wang J , Jin L, Gao J et al. Investigation of speciated VOC in gasoline vehicular exhaust under ECE and EUDC test cycles . Sci Total Environ 2013 ; 445 : 110 – 6 . 10.1016/j.scitotenv.2012.12.044 Google Scholar Crossref Search ADS PubMed WorldCat 308. Dong D , Shao M, Li Y et al. Carbonyl emissions from heavy-duty diesel vehicle exhaust in China and the contribution to ozone formation potential . J Environ Sci 2014 ; 26 : 122 – 8 . 10.1016/S1001-0742(13)60387-3 Google Scholar Crossref Search ADS WorldCat 309. Ou J , Feng X, Liu Y et al. Source characteristics of VOCs emissions from vehicular exhaust in the Pearl River Delta region . Acta Scientiae Circumstantiae 2014 ; 34 : 827 – 34 . Google Scholar OpenURL Placeholder Text WorldCat 310. Yao Z , Shen X, Ye Y et al. On-road emission characteristics of VOCs from diesel trucks in Beijing, China . Atmos Environ 2015 ; 103 : 87 – 93 . 10.1016/j.atmosenv.2014.12.028 Google Scholar Crossref Search ADS WorldCat 311. Yao Z , Wu B, Shen X et al. On-road emission characteristics of VOCs from rural vehicles and their ozone formation potential in Beijing, China . Atmos Environ 2015 ; 105 : 91 – 6 . 10.1016/j.atmosenv.2015.01.054 Google Scholar Crossref Search ADS WorldCat 312. Cao X , Yao Z, Shen X et al. On-road emission characteristics of VOCs from light-duty gasoline vehicles in Beijing, China . Atmos Environ 2016 ; 124 : 146 – 55 . 10.1016/j.atmosenv.2015.06.019 Google Scholar Crossref Search ADS WorldCat 313. Yue T , Yue X, Chai F et al. Characteristics of volatile organic compounds (VOCs) from the evaporative emissions of modern passenger cars . Atmos Environ 2017 ; 151 : 62 – 9 . 10.1016/j.atmosenv.2016.12.008 Google Scholar Crossref Search ADS WorldCat 314. Ou J , Zheng J, Li R et al. Speciated OVOC and VOC emission inventories and their implications for reactivity-based ozone control strategy in the Pearl River Delta region, China . Sci Total Environ 2015 ; 530 – 1 : 393 – 402 . Google Scholar OpenURL Placeholder Text WorldCat 315. Appel KW , Napelenok SL, Foley KM et al. Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1 . Geosci Model Dev 2017 ; 10 : 1703 – 32 . 10.5194/gmd-10-1703-2017 Google Scholar Crossref Search ADS WorldCat 316. Reff A , Bhave PV, Simon H et al. Emissions inventory of PM2.5 trace elements across the United States . Environ Sci Technol 2009 ; 43 : 5790 – 6 . 10.1021/es802930x Google Scholar Crossref Search ADS PubMed WorldCat 317. Yi HH . Studies on emission characteristics of PM10 from power plants. Ph.D. Thesis . Tsinghua University 2006 . 318. Wang W . Studies on emissions characteristics of PM for typical stationary combustion source . Master’s Thesis . Chinese Research Academy of Environmental Sciences 2007 . Google Scholar OpenURL Placeholder Text WorldCat 319. Pei B , Wang X, Zhang Y et al. Emissions and source profiles of PM2.5 for coal-fired boilers in the Shanghai megacity, China . Atmos Pollut Res 2016 ; 7 : 577 – 84 . 10.1016/j.apr.2016.01.005 Google Scholar Crossref Search ADS WorldCat 320. Li C , Li X-H, Duan L et al. Emission characteristics of PM10 from coal-fired industrial boiler . Huan Jing Ke Xue 2009 ; 30 : 650 – 5 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 321. Li X , Wang S, Duan L et al. Particulate and trace gas emissions from open burning of wheat straw and corn stover in China . Environ Sci Technol 2007 ; 41 : 6052 – 8 . 10.1021/es0705137 Google Scholar Crossref Search ADS PubMed WorldCat 322. He L-Y , Hu M, Zhang Y-H et al. Fine particle emissions from on-road vehicles in the Zhujiang Tunnel, China . Environ Sci Technol 2008 ; 42 : 4461 – 6 . 10.1021/es7022658 Google Scholar Crossref Search ADS PubMed WorldCat 323. Cheng Y , Lee SC, Ho KF et al. Chemically-speciated on-road PM2.5 motor vehicle emission factors in Hong Kong . Sci Total Environ 2010 ; 408 : 1621 – 7 . 10.1016/j.scitotenv.2009.11.061 Google Scholar Crossref Search ADS PubMed WorldCat 324. Ma JY . Research on emission characteristics of air pollutants from cement industry . Master’s Thesis . Hebei University of Engineering 2010 . 325. Ma JH . Emission characteristics of particulates from typical production process of iron and steel enterprises . Master’s Thesis . Southwest University 2009 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 326. Li CQ . Characterization of air pollutants emitted from coking production . Master’s Thesis . Southwest University 2009 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 327. Zhao Y , Nielsen CP, McElroy MB et al. CO emissions in China: uncertainties and implications of improved energy efficiency and emission control . Atmos Environ 2012 ; 49 : 103 – 13 . 10.1016/j.atmosenv.2011.12.015 Google Scholar Crossref Search ADS WorldCat 328. Zhao Y , Qiu LP, Xu RY et al. Advantages of a 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 . Atmos Chem Phys 2015 ; 15 : 12623 – 44 . 10.5194/acp-15-12623-2015 Google Scholar Crossref Search ADS WorldCat Author notes Equally contributed to this work. © The Author(s) 2017. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. © The Author(s) 2017.
TI - Anthropogenic emission inventories in China: a review
JF - National Science Review
DO - 10.1093/nsr/nwx150
DA - 2017-11-01
UR - https://www.deepdyve.com/lp/oxford-university-press/anthropogenic-emission-inventories-in-china-a-review-4WsG3UMLWb
SP - 834
EP - 866
VL - 4
IS - 6
DP - DeepDyve
ER -