Five emission inventories are collected and incorporated into the mosaic inventory, as listed in the following: REAS inventory version 2.1 for the whole of Asia (referred to as REAS2 hereafter; Kurokawa et al., 2013), the Multi-resolution Emission Inventory for China (MEIC) developed by Tsinghua University (http://www.meicmodel.org), a high-resolution NH3 emission inventory by Peking University (referred to as PKU-NH3 inventory hereafter; Huang et al., 2012), an Indian emission inventory developed by Argonne National Laboratory (referred to as ANL-India hereafter; Lu et al., 2011; Lu and Streets, 2012), and the official Korean emission inventory from the Clean Air Policy Support System (CAPSS; Lee et al., 2011).

We then selected different emission datasets for various species for each country by the following hierarchy. REAS2 was used as the default where local emission data are absent. Emission inventories compiled by the official agencies or developed with more local information are selected to override REAS2, which include MEIC for mainland China, ANL-India for India, and CAPSS for the Republic of Korea. Detailed information and advantages of these inventories are presented in Sect. 2.2. As only a few species (SO2, BC, OC, and power plant NOx) were available from ANL-India, REAS2 was used to supplement the missing species. A mosaic process was then used to combine ANL-India and REAS2 into a single dataset for Indian emissions. It is worth noting that the REAS2 has incorporated local inventories for Japan and Taiwan, which are subsequently adopted in MIX for these two regions. PKU-NH3 was further used to replace MEIC emissions for NH3 over China, given that PKU-NH3 was developed with a process-based model that represented the spatiotemporal variations in NH3 emissions. Table 2 lists the information of each inventory used in MIX.

Figure 2 illustrates the mosaic process for the MIX inventory development. Each dataset was reprocessed to 0.25∘ × 0.25∘ resolution with monthly variations when necessary. We used monthly gridded emissions from each component inventory where available and assumed no monthly variation in emissions when the component inventory only provided annual emissions. The monthly profiles and spatial proxies used in each component emission inventories are summarized in Tables S3 and S4.

For each regional emission inventory, emissions were acquired with subsector information and then aggregated into five sectors: power, industry, residential, transportation, and agriculture. Table S5 presented the sectoral mapping tables from subsectors to the five MIX sectors for each regional inventory. For each subsector, the corresponding IPCC sectors are also provided in Table S5. For agriculture sector, only NH3 emissions are provided in the MIX inventory given that soil NOx emissions and agriculture PM emissions are not available in the regional inventories used for compiling MIX. Emissions from open biomass burning, fugitive dust, aviation, and international shipping were excluded in the MIX inventory because those emissions were only available in a few inventories.

NMVOC emissions were speciated to SAPRC-99 and CB05 mechanisms following the explicit species mapping approach documented in Li et al. (2014) (see Fig. 3). Finally, emissions were aggregated to the five MIX sectors and then assembled to monthly emission grid maps over Asia with a uniform spatial resolution of 0.25∘ × 0.25∘.

ANL-India is available for SO2, BC, and OC for all sectors as well as NOx for power plants. In this work, REAS2 is used to supplement the missing species in ANL-India. To reduce possible inconsistencies from implementation of the two different inventories, we have reprocessed ANL-India and REAS2 emissions over India in the following two steps.

First, for power plants, because ANL-India used CEA reports to derive information of individual power generation units while REAS2 used the CARMA and WEPP databases to get similar information, direct merging of the two products could introduce inconsistency due to a mismatch of unit information in the two databases. In this work, we directly used ANL-India for SO2, NOx, BC, and OC emissions and used REAS2 for CO, NMVOC, PM2.5, PM10, and CO2 but redistributed the total magnitudes of REAS2 power plant emissions by using the spatial distribution of power plants in the ANL-India inventory. We generated the spatial proxies of fuel consumption for each fuel type (coal, oil, and gas) at 0.25 × 0.25∘ by aggregating fuel consumptions of each unit in the ANL-India inventory. We then applied the spatial proxy to the REAS2 estimates by fuel type for species that were not included in ANL-India.

Second, we used BC and OC emissions from ANL-India but used PM2.5 and PM10 emissions from REAS2. In certain grids, the sum of BC and OC emissions may exceed PM2.5 emissions because the two inventories may use different activity data, emission factors, and spatial proxies. The so-called “PMfine” species in chemical transport models are usually calculated by subtracting BC and OC emissions from total PM2.5 emissions, leading to negative emissions of PMfine in those grids. In this case, we adjusted the emissions of PM2.5 to the sum of BC and OC emissions for each sector.

In the MIX inventory, we provide model-ready speciated NMVOC emissions over Asia (except the Republic of Korea) for both CB05 and SAPRC-99 chemical mechanisms, by using the explicit species mapping approach and updated NMVOC profiles developed in Li et al. (2014), as illustrated in Fig. 3. Following Li et al. (2014), NMVOC emissions for CB05 and SAPRC-99 species are calculated as follows: EVOC(i,k,m)=∑j=1nEVOC(i,k)×X(i,j)mol(j)×C(j,m), where k is the region, m is species type in CB05 or SAPRC-99 mechanisms, and n is the number of species emitted from source i. EVOC is the total NMVOC emissions by source type. In this work, emissions in China and other Asian countries were derived from MEIC and REAS2 respectively. X(i,j) is the mass fraction of species j in the total NMVOC emissions for source i, which is taken from the profiles developed by Li et al. (2014). Those profiles were constructed by grouping and averaging multiple profiles from both local measurements and the SPECIATE database (Hsu and Divita, 2009; Simon et al., 2010). Mol(j) is the mole weight of species j and C(j,m) is the conversion factor between j and m obtained from the mapping tables in Carter (2013).

For the Republic of Korea, the SMOKE-Asia model developed by Woo et al. (2012) was used to calculate model-ready NMVOC emissions for both CB05 and SAPRC-99 mechanisms. NMVOC emissions from the CAPSS were mapped to Source Classification Codes (SCCs) and country–state–county (FIPS) code in SMOKE-Asia model and speciated NMVOC emissions were then calculated by linking emissions to speciation profiles with cross references.

We directly used monthly emissions from each regional emission inventory when compiling the MIX inventory. We assume no monthly variation in emissions when monthly profiles are absent from the regional emission inventories. Table S3 presents the monthly profiles used in each component emission inventory for MIX. In summary, monthly profiles for power plant emissions usually developed based on monthly statistics of power generation. Monthly profiles of industrial emissions are derived from monthly output of industrial products or industrial GDP. Residential monthly profiles are estimated from stove operation time based on ambient temperatures by regions (Streets et al., 2003).

We used gridded emissions from each regional emission inventory to compile the grid maps of emissions. Locations of emitting facilities were used to derive gridded emission for large sources, while spatial proxies such as population density, road networks, and land use information are used to allocate emissions of areal sources. Table S4 summarized spatial proxies used in developing gridded emissions for each regional inventory.

Based on the mosaic approach and candidate inventories described in Sect. 2, gridded anthropogenic emissions for 10 species were generated over Asia and called the MIX emission inventory. In the MIX inventory, Asian anthropogenic emissions in 2010 are estimated as follows: 51.3 Tg SO2, 52.1 Tg NOx, 336.6 Tg CO, 67.0 Tg NMVOC, 28.8 Tg NH3, 31.7 Tg PM10, 22.7 Tg PM2.5, 3.5 Tg BC, 8.3 Tg OC, and 17.3 Pg CO2. Figure 4 presents the emission distributions among sectors over Asia in 2010. Among the different sectors, the industrial sector has the largest contribution to SO2 (50 % of total), NMVOC (38 %), PM10 (48 %), and CO2 (40 %) emissions. Power plants have significant contributions for SO2 (38 % of total), NOx (29 %), and CO2 (34 %) emissions.

Asian emissions in 2010 for 10 species are listed in Table 3 by country and the shares of 2010 emissions by each subregion are presented in Fig. 5. China is the largest contributor for most species except NH3, with more than 50 % contribution for SO2, NOx, CO, PM10, PM2.5, and CO2 emissions. Following China, India is the largest contributor for NH3 emissions (34 % of total) and the second largest contributor for all other species. As shown in Fig. 5, Southeast Asia and Other South Asia contribute more than 20 % to NMVOC, NH3, OC, and CO emissions and around 10 % for other species, representing, in particular, a high contribution from biofuel emissions. Contributions from other Asian regions are less than 10 % for all species.

Table 4 presents Asian 2010 emissions by region and by sector. Emissions by country and by sector can be downloaded from the MIX website (http://www.meicmodel.org/dataset-mix.html). China's anthropogenic emissions in 2010 are estimated as follows: 28.7 Tg SO2, 29.1 Tg NOx, 170.9 Tg CO, 23.6 Tg NMVOC, 9.8 Tg NH3, 16.6 Tg PM10, 12.2 Tg PM2.5, 1.8 Tg BC, 3.4 Tg OC, and 10.1 Pg CO2. Overall, industry is the largest emitter of China's anthropogenic emissions, contributing 49 % of the total CO2 emissions and 59, 39, 61, and 50 % of SO2, NOx, NMVOC, and PM2.5 emissions respectively. The dominance of the industrial sector on China's anthropogenic emissions reflects the fact that China has developed a huge industrial capacity, which has led to very high levels of energy use and emissions. For example, China produced 44 and 70 % of global iron and cement, respectively, in 2010 (World Steel Association, 2011; United Nations, 2011). As a result, industrial SO2 emissions in China in 2010 surpassed SO2 emissions from the US and Europe combined. Power plants contributed 32 % of the total CO2 emissions and 28, 33, and 7 % of SO2, NOx, and PM2.5 emissions respectively. Emission ratios of SO2 / CO2 and PM2.5 / CO2 are lower in power plants than in the industrial sector, reflecting better emission control facilities operated in power plants, such as flue-gas desulfurization devices (FGD). The residential sector dominates emissions for pollutants from incomplete combustion, given that large amounts of solid fuels (coal and biomass) were burned in small stoves in China's homes. The residential sector shared 13 % of China's total CO2 emissions in 2010, but contributed to 45 % of CO, 27 % of NMVOC, 51 % of BC, and 81 % of OC emissions respectively. The transportation sector accounted for 25, 12, 11, and 16 % of NOx, CO, NMVOC, and BC emissions respectively. The contribution of the transportation sector to China's CO and NMVOC emissions has substantially decreased during recent years, which will be further discussed in the next section.

In the MIX inventory, Indian emissions in 2010 are estimated as follows: 9.3 Tg SO2, 9.6 Tg NOx, 67.4 Tg CO, 16.9 Tg NMVOC, 9.9 Tg NH3, 7.1 Tg PM10, 5.2 Tg PM2.5, 1.0 Tg BC, 2.5 Tg OC, and 2.3 Pg CO2. In India, the industrial sector has much lower contributions to emissions compared to China, while higher emission contributions from the residential sector are estimated. The differences of the emission patterns between China and India can be attributed to differences in the stage of economic development and the composition of the energy structure. In India, the residential sector is the second largest contributor for CO2 emissions and the largest contributor for CO, NMVOC, PM2.5, BC, and OC emissions, in which more than 70 % of those emissions are contributed by biofuel combustion. With the rapid growth of coal-fired generation units, SO2 emissions from Indian power plants are estimated to be 5.5 Tg in 2010, contributing 59 % of the total Indian SO2 emissions. The SO2 / CO2 emission ratio in Indian power plants is significantly higher than that of China, representing the low penetration rates of FGD in Indian power plants (Lu et al., 2011). The transportation sector contributes 55 % of NOx and 36 % of NMVOC emissions in India. These large shares are caused by the high emission factors used in REAS2, in which relatively poor emission control measures are in place (Kurokawa et al., 2013).

Figure 6 compared per capita emissions by sector and by species in 2010 for each country. Emissions are ranked by GDP per capita of each country. The correlations between emission intensity (per capita emissions) and economic development (GDP per capita) at country level are not always significant because emission intensities are affected by not only economic level but also by other factors such as industrial structure and dominant fuel type. Nevertheless, the changes in emission intensities in general follow the pattern of Kuznets curve for most species except NH3, BC, and OC. Emission intensities tend to increase following the GDP growth first and then tend to decrease for high-income countries. For BC and OC, per capita emissions are higher in developing countries than in developed countries because low-income countries with low incomes tend to use biofuels in which emitted more BC and OC than other fuel types.

Ratios of different species were widely used to inform emission characteristics. For example, SO2 / CO2 ratio was used as an indicator of coal combustion and emission control levels (Li et al., 2007), and ratios of CO / CO2 were used to inform combustion efficiency (Wang et al., 2010). Figure 7 compares regional emission ratios of SO2 / CO2 and CO / CO2 estimated by the MIX inventory. Emission ratios of SO2 / CO2 are lowest in Other East Asia among different regions, which could be attributed to small share of coal use and high penetration of emission control facilities, while high emissions ratios of SO2 / CO2 were found in Russia Asia and Central Asia due to high fraction of coal use and less emission controls. Other East Asia also has the lowest emission ratios of CO / CO2 among different regions, owing to a high contribution from industrial and transportation emissions. In contrast, high emissions from small residential combustions led to low combustion efficiencies and high emission ratio over India and Southeast Asia.

In this work, we also developed Asian emissions for 2006 and 2008 following the same approach of MIX, to illustrate the changes in Asian emissions from 2006 to 2010. Table 5 presents Asian emissions in 2006 and emission ratios of 2010 to 2016 by country. For the whole of Asia, emission growth rates from 2006 to 2010 are estimated as follows: -8.1 % for SO2, +19.2 % for NOx, +3.9 % for CO, +15.5 % for NMVOC, +1.7 % for NH3, -3.4 % for PM10, -1.6 % for PM2.5, +5.5 % for BC, +1.8 % for OC, and +19.9 % for CO2. Growth in CO2 emissions represent the continuously increasing energy use across Asia during 2006–2010, while different trends among species represents differences in the emission control level among sectors and regions. Compared to the increasing emission trends of all species during 2001–2006 (Zhang et al., 2009), the relatively flat or even decreasing emission trends in many species indicate the effectiveness of emission control measures in recent years (Gu et al., 2013; Lin et al., 2010; Wang et al., 2013).

During 2006–2010, CO2 emissions increased in China (+29.4 %), India (+20.4 %), Other South Asia (+15.2 %), and Southeast Asia (+12.3 %) and remained relatively stable for other regions. The increases in CO2 emissions are driven by energy consumption growth stimulated by economic development over Asian regions, especially for China and India. As reported by IEA (International Energy Agency), the total primary energy consumption of Asia has increased by 20.6 % during the period of 2005 and 2010 (IEA, 2013). During the same period, SO2 emissions decreased in China (-17.2 %), Other East Asia (-9.5 %), and Central Asia (-32.8 %) due to effective emission control, while they increased in India (+23.9 %), Other South Asia (+23.9 %), and Southeast Asia (+7.8 %) due to growth in coal use and absence of desulfurization devices. The decrease in SO2 emissions changes in Asian is dominated by changes in China and India. Figure 8a demonstrates the changes in SO2 emissions among Asian regions from 2006 to 2010. Wide installation of FGD in China's coal-fired power plants is the main driving factor of SO2 emission changes over Asia. SO2 emissions in China's power plants decreased from 17.2 Tg in 2006 to 8.2 Tg in 2010, contributing to most of the total SO2 emission reduction over Asia. In contrast, SO2 emissions in India increased by 27 % during 2006–2010, owing to the dramatic construction of new power plants and the lack of emission control facilities (Garg et al., 2001, 2006). As a consequence, the Indian share of the total Asian SO2 emissions increased from 13 % in 2006 to 18 % in 2010. NOx and NMVOCs emissions were increased in all Asian regions except Other East Asia (-17.3 % for NOx and -13.0 % for NMVOCs respectively), indicating lack of effective control measures for those two species over Asia. Increases of NOx and NMVOC emissions are mainly driven by growth in industrial activities and vehicle population. For NOx, remarkable emission increases are observed for China (+22.6 %), India (+27.8 %), Other South Asia (+20.5 %), and Southeast Asia (+23.4 %) during 2006–2010. For NMVOC, emissions increased by 14.0, 15.0, 12.3, 24.9, 23.6, and 9.3 % for China, India, Other South Asia, Southeast Asia, Central Asia, and Russia Asia respectively. Emission changes of other species are relatively small (i.e., within 6 %) during 2006–2010. For CO, PM10, and PM2.5, emission reductions in China were partly offset by increases of emissions in the South and Southeast Asian regions. CO emissions in China decreased by 5 % during 2006–2010 (see Fig. 8b), mainly due to improved combustion efficiency, recycling of industrial coal gases, and strengthened vehicle emission standards. The implementation of new vehicle emission standards and retirement of old vehicles has reduced China's transportation CO and NMVOC emissions by 20 and 30 %, respectively, during 2006–2010. While in India, Other South Asia, and Southeast Asia, CO emissions increased by 21, 11, and 16 %, respectively, between 2006 and 2010.

Satellite observations have shown promising capabilities in detect trends in surface emissions (Streets et al., 2013). The increases in NOx emissions over China and India were confirmed by satellite-based inversions and the growth rates in satellite-based NOx emission trends during 2006–2010 are generally comparable to our estimates in emission inventories (Table 6). For SO2 emissions, the downward trend over China and upward trend over India were also observed by satellite remote sensing, while higher growth rates were detected by OMI than the bottom-up emission inventory (Krotkov et al., 2016). The downward trend of CO emissions over China in recent years has been confirmed by both in situ and satellite observations (Wang et al., 2010; Worden et al., 2013; Yumimoto et al., 2014; Yin et al., 2015). The decreasing rate of CO emissions over China is estimated to be -1.2 % yr-1 from 2006 to 2010 in the MIX inventory, consistent with the rates observed by multiple satellites in range of -1.0 to -3.1 % yr-1 during 2000–2012 (Table 6).

Figure 9 presents 2010 Asian NMVOC emissions of different chemical groups by region and by sector. Similar to Asian emissions estimated in previous work (Klimont et al., 2002; Li et al., 2014), alkanes and alkenes are the largest contributors to the total Asian NMVOC emissions in 2010 (27 and 26 % of the total respectively), followed by oxygenated volatile organic compounds (OVOCs; 20 %), aromatics (17 %), and alkynes (7 %). Regionally, shares of alkanes and aromatics are higher in China, Other East Asia, Central Asia, and Russia Asia than other regions, due to large contributions from the industrial sector. Shares of alkynes in Central Asia and Russia Asia are significantly lower than other regions due to a low contribution from biofuel emissions. Sectoral contribution of emissions varies significantly by different chemical groups. Over Asia, the industrial sector is the major source of emissions of alkanes and aromatics. Alkanes emissions from industrial sector are mainly contributed by gas production and distribution (19.8 % of total industrial emissions), coal combustion (17.1 %), and oil refinery (15.0 %), and aromatics emissions are mainly contributed by architectural paint use (21.0 % of total industrial emissions), other industrial paint use (16.6 %), and gas production and distribution (10.6 %). The residential sector has a high contribution of OVOCs, alkynes, and alkenes, among which mainly contributed by biofuel combustions. The sectoral contribution to different chemical groups also varies with region. For example, the residential sector dominates emissions for all species in the Other South Asia region, as a consequence of the low economic development in that region.

Among different regions, China, India, and Southeast Asia are the largest contributors to NMVOC emissions in Asia, with contributions varying by chemical groups. China contributes more than 40 % of alkanes, alkynes, and aromatics in Asia, compared to 35 % contribution of the total Asian NMVOC emissions. India contributes high to emissions of alkenes, alkynes, and OVOCs, constituting about 30 % of Asian emissions. The high emissions of alkenes in India (and Other South Asia) are mainly from contributions of biofuel combustions and motorcycles, and OVOC emissions in India are dominant by biofuel combustions. Southeast Asia shares around 20 % of the emissions of alkanes, alkenes, aromatics, and OVOCs.

Monthly emissions by sector and by Asian region are provided in Tables S6–S14. Monthly profiles in emissions are highly sector dependent given that monthly activity rates vary among different sectors. Figure 10 illustrates the monthly variations of Asian SO2, CO, PM2.5, and CO2 emissions by sector for the year 2010. Different species generally show similar monthly emission patterns within the same sector, indicating that monthly emission profiles of each sector are dominated by monthly variations in activity rates. For example, industrial emissions are higher in the second half of the year induced by larger industrial productions to meet the annual total production target. The most significant monthly variation with a winter peak was found in the residential sector, reflecting the higher energy demand for residential heating in winter. Residential SO2 emissions in winter are even higher than other species, because SO2 emissions from China dominate residential emissions in Asia (70 % of total), of which coal consumption in winter is higher than other regions for heating. Monthly profiles of CO emissions are different from other species for the transportation sector. This is because the CO emission factor in winter is higher than in other seasons due to additional emissions from the cold-start process (Kurokawa et al., 2013; Zheng et al., 2014).

Figure 11 presents monthly variations of SO2, CO, PM2.5, and CO2 emissions by Asian region. Compared to other species, CO emissions are much higher in winter in high-latitude regions due to residential heating and additional vehicle emissions from cold starts. Winter PM2.5 emissions in China are higher than other regions, representing large emissions from solid fuel use in residential homes.

In the MIX inventory, gridded emissions for 10 gaseous and aerosol species were developed at 0.25 × 0.25∘ resolution. Emission maps of all species in 2010 are shown in Fig. 12. Compared to the previous gridded Asian emission inventories, we believe the spatial patterns are improved because several local high-resolution emission datasets are incorporated, such as CPED for China and JEI-DB and OPRF for Japan. However, for sectors in which emissions are dominated by spatially scattered sources (e.g., residential combustion, solvent use), the spatial distributions in emissions are still uncertain.

MIX emission inventory can be accessed publicly from the website of http://www.meicmodel.org/dataset-mix. Both 2008 and 2010 emissions of 10 species with monthly variation at a spatial resolution of 0.25 × 0.25∘ are available from the website, including SO2, NOx, CO, NH3, NMVOC, PM10, PM2.5, BC, OC, and CO2. Speciated NMVOC Emissions for CB05 and SAPRC-99 chemical mechanisms are provided at the same spatial and temporal resolution. The MIX inventory has been regridded to 0.1 × 0.1∘ resolution using the area-weighting approach and then incorporated to the HTAP v2 gridded emission inventory (Janssens-Maenhout et al., 2015). The HTAP v2 emission inventory can be downloaded from the EDGAR website (http://edgar.jrc.ec.europa.eu/htap_v2/index.php?SECURE=_123).

A comprehensive intercomparison among different emission inventories over Asia was conducted by Kurokawa et al. (2013). In this work, we compare the MIX inventory with REAS2 and EDGAR v4.2 (EC-JRC/PBL, 2011), two widely used inventories, to highlight the new findings from the mosaic inventory and identify the potential sources of uncertainties. We choose the year of 2008 to conduct the comparison because emissions after 2008 are not available in either REAS2 or EDGAR v4.2. Russian Asia was excluded from comparison. Asian anthropogenic emissions of MIX, REAS2, and EDGAR v4.2 in 2008 are tabulated in Table 7. Over Asia, MIX and REAS differ within 10 % for most species, except for NH3 (18 % higher in REAS), PM10 (13 % higher), and BC (13 % lower). It is not surprising that the total Asian emission budgets in MIX and REAS2 are similar given that MIX used emissions estimates in REAS2 for Asian regions except China and India. However, REAS2 has incorporated several recent emission inventories for China (Kurokawa et al., 2013). The differences between REAS and MIX over China and India will be discussed in the following sections.

Remarkable differences are observed between MIX and EDGAR v4.2. Compared to MIX, 2008 Asian emissions in EDGAR are 29 % higher for SO2, but 20, 33, 11, and 27 % lower for NOx, CO, NMVOC, and NH3 respectively. PM10 and CO2 emissions agree well between the two inventories with differences within 3.2 %. Figure 13 details the differences by region and by sector. Regionally, the differences can be largely attributed to disagreements in emission estimates for China and India, as presented in Table 7. Discrepancies are relatively large at the sector level compared to total emissions. EDGAR's estimates for SO2 emissions from power plants are 60 % higher than estimates in MIX. For China, 70 % of power generation capacities were equipped with FGD and the average SO2 removal efficiency was 78 % (Liu et al., 2015). The high estimates in EDGAR v4.2 are most likely due to underestimation of FGD penetration or SO2 removal efficiencies of FGD (Kurokawa et al., 2013). Remarkable differences for the residential and transportation sectors are found for NOx, CO, and NMVOC estimates in the two inventories. For instance, EDGAR v4.2 estimates lower NOx emissions of transportation sector by 27 and by 48 % for the residential sector compared to MIX. Similarly, residential CO emissions in EDGAR v4.2 are 33 % lower than in MIX, leading to 33 % lower estimates of CO emissions in EDGAR v4.2 compared to MIX. Underestimates of CO emissions in EDGAR v4.2 inventory have been confirmed by top-down constraints (Pétron et al., 2004; Fortems-Cheiney et al., 2011). As the statistical differences of energy use are usually within 30 % at sector level (Guan et al., 2012), the discrepancy by sector could only be attributed to differences in the raw emission factors and abatement measures. Although a point-by-point comparison of emission factors between EDGAR v4.2 and MIX is not feasible, we can still speculate that EDGAR v4.2 may overestimate the combustion efficiency and emission control measures in Asia by using an emission factor database from developed countries. NH3 emissions in EDGAR v4.2 are 26 % lower than in MIX, with a large difference in residential emissions. The differences are mainly from high emission estimates of wastewater treatment sources in REAS2, which were incorporated into MIX for Asian regions except China. MIX estimated 3.4 Tg NH3 emissions from wastewater treatment in Asia in 2008, which are more than 2 orders of magnitude higher than EDGAR v4.2 estimates. Differences in PM10 emissions at the sector level are also large; similar estimates of PM10 emissions in the two datasets are rather a coincidence than real agreements.

For CO2 emissions, good agreements are found among MIX, REAS2, and EDGAR v4.2 inventories in Asia with differences in total emissions less than 1 %. CO2 emissions from biofuel combustion are included in the intercomparison. CO2 emission estimates in MIX and REAS2 only differ in China because REAS2 are used in MIX for regions other than China. Total CO2 emissions of China in MIX and REAS2 are quite similar (1.5 % higher in REAS2), while REAS2 estimated higher emissions for power sector (+386 Tg or +13.6 % compared to MIX) but lower emissions for industry (-293 Tg, -6.8 %) emissions. EDGAR v4.2 estimates lower CO2 emissions for China (-308 Tg, -3.4 %) and higher emissions for other regions: +102 Tg (+5.6 %) for Other East Asia, +83.9 Tg (+5.6 %) for Southeast Asia, +204 Tg (+9.7 %) for India, +29.5 Tg (+7.5 %) for Other South Asia, and +25.3 Tg (+6.4 %) for Central Asia. At sector level, EDGAR v4.2 estimates are 29 % (+833 Tg) higher for power, 22 % (-944 Tg) lower for industry, and 31 % (-192 Tg) lower for transportation over China compared to MIX. For Other East Asia, differences between EDGAR v4.2 and MIX are mainly contributed by power sector, with 20 % higher emissions (+130 Tg) in EDGAR v4.2. Residential sector is the main contributor to the differences between EDGAR v4.2 and MIX over India, with 28 % (+177 Tg) higher emissions estimated in EDGAR v4.2. The relatively large discrepancy at sector level can be attributed to differences in energy statistics and emission factors (Guan et al., 2012; Liu et al., 2015) as well as differences in sector definitions. In particularly, EDGAR v4.2 used fuel consumption data from IEA statistics while MIX and REAS2 used provincial level data from Chinese Energy Statistics, which can differ by 20 % at sector level (Hong et al., 2016). Emissions from heating plants are aggregated to the industrial sector in the MIX inventory, while in EDGAR v4.2 heating plants are aggregated to the energy sector and then compared to the power sector in the MIX inventory. In the future, harmonizing the sector (subsector) definition among global and regional inventories would help to reduce the discrepancy of emission estimates at sector level.

For India, MIX used ANL-India for SO2, BC, and OC emissions and REAS2 for other species. Here we compare ANL-India and REAS2 for SO2, BC, and OC emissions, to evaluate the impact of using ANL-India. Both ANL-India and REAS2 used energy consumption data from IEA, and hence the differences are mainly from emission factors. Reasonable agreements are found in total emissions over India (differing by 8–28 %), while discrepancies are large at the sector level. REAS2 estimates 50 % higher SO2 estimates for all sectors except power plants, most likely from different assumptions about the sulfur content of fuels. For BC and OC, the ratio between REAS2 and ANL-India varies from 0.4 to 11.8 at the sector level, indicating large differences in emission factor selections. ANL-India used emission factors from a global database (Bond et al., 2004) with updates of a few recent measurements (Lu et al., 2011), while REAS2 used a local database developed many years ago (Reddy and Venkataraman, 2002a, b). It should be noted that local emission measurements in India are still too few to support accurate emission estimates. More measurements should be conducted in the future to remedy this situation.

When implementing REAS2 to MIX over India, power plant emissions were redistributed using spatial distributions derived from ANL-India at 0.25∘ × 0.25∘ resolution (see Sect. 2.3). We believe that it will improve the accuracy because power plant emissions in ANL-India were estimated by each unit and allocated manually by Google Earth. A total of 68 power plants are identified in REAS2, compared to 145 plants in ANL-India. The two inventories generally agree well for the grids in which both inventories allocate power plant emissions. Lu and Streets (2012) found that the magnitudes and locations of power plant NOx emissions (from ANL-India) are matched well with satellite-based observations over India, providing confidence to the accuracy of ANL-India estimates. From all the comparisons discussed above, we can conclude that emissions are well depicted in MIX due to integration of the most recent regional inventories.