3.1 Bottom-up emission inventory

Here, we use the framework of the MEIC (Multi-resolution Emission Inventory for China, http://www.meicmodel.org, last access: 30 September 2018) to estimate China's anthropogenic emissions from 2010 to 2017. MEIC is a bottom-up emission inventory model which covers 31 provinces in mainland China and includes ∼700 anthropogenic sources. Emissions for each source in each province are estimated as follows:

where i represents the province, j represents the emission source, k represents the air pollutants or CO₂, m represents the technologies for manufacturing, n represents the technologies for air pollution control, A is the activity rate, X is the fraction of a specific manufacturing technology, EF is the unabated emission factor, C is the penetration of a specific pollution control technology, and η is the removal efficiency. The details of the technology-based approach and source classifications can be found in Zhang et al. (2007, 2009), Lei et al. (2011), and M. Li et al. (2017a).

The underlying data in the MEIC model are gathered from different sources. Activity rates of energy consumptions by fuel type, by sector, and by province are derived from Chinese Energy Statistics (National Bureau of Statistics, 2018a, b; National Energy Administration, 2018). Productions of various industrial products and penetration of different technologies are collected from a wide variety of statistics (for details, please refer to Lu et al., 2010; Lei et al., 2011). We also use unpublished data from the Ministry of Ecology and Environment to supplement the technology penetration data that are absent in statistics (Qi et al., 2017; Zheng et al., 2017). These data are collected from each plant by local agencies, and then managed and verified by Ministry of Ecology and Environment. The information adopted in this study include pollution control technologies, penetrations, and efficiencies for electric generators, cement factories, iron- and steel-making furnaces, and glass kilns in each province, which are used to calibrate emission control levels (i.e., C and η in Eq. 1) in the bottom-up inventory. Detailed activity rates by province for the year 2017 are not available when the emission trends presented in this work were developed. In this case, we used national activity data for the year 2017 (National Bureau of Statistics, 2018b; National Energy Administration, 2018) and downscaled these national total data to each province using the weighting factors from 2016 data at the provincial level. Unabated emission factors in MEIC are compiled from a wide range of previous studies, for instance, SO₂ from Lu et al. (2010), NO_x from Zhang et al. (2007), NMVOCs from Li et al. (2014), CO from Streets et al. (2006), primary aerosols (PM₁₀, PM_2.5, BC, and OC) from Lei et al. (2011), and CO₂ from Liu et al. (2015a). We then override those data by local emission factors summarized in M. Li et al. (2017b) wherever available, to represent the most recent progress on emission factor developments in China.

Emissions from power plants are estimated following the unit-based approach developed by Liu et al. (2015b). In summary, we track the emissions of each unit from electricity generation, fuel quality, and the progress in emission control using unit-specific parameters. Emissions from on-road vehicles are estimated using a county-level emission model developed by Zheng et al. (2014), which resolves the spatial–temporal variability of vehicle ownership, fleet turnover (i.e., new technology penetration), and emission factors. Detailed documentation of the method and data for power plants and on-road vehicles can be found in Liu et al. (2015b) and Zheng et al. (2014), respectively.

3.2 Index decomposition analysis

We use index decomposition analysis (IDA) to study the driving forces of China's anthropogenic emissions from 2010 to 2017. IDA is one of the major techniques used to analyze the impact of changes in indicators on emission trends (Hoekstra and van den Bergh, 2003). The IDA method is described as follows.

Equation (1) can be converted to a matrix form using the following formula:

The technology distribution factors $X_{i,j,m}$ in Eq. (1) are assembled into the row vector x, and the relevant unabated emission factors EF${}_{i,j,k,m}$ are assembled into a diagonal matrix E. The column vector η represents the average removal efficiencies weighted by the penetration rates $C_{i,j,m,n}$ of all types of pollution control technologies. According to Eq. (2), over a given period of time, any changes in emissions can be decomposed into their component driving factors using Eq. (3).

where Δ is the difference operator. The four multiplicative terms in Eq. (2) are converted into four additive terms in Eq. (3). Each additive term represents the contribution of one driving factor to the changes in emissions, while all other factors are kept constant. For example, Δη is the change in pollutant removal efficiencies and the last term in Eq. (3) represents the change in total emissions caused by end-of-pipe abatement measures, with the activity range A_i, j, technology distribution x, and unabated emission factor E assumed to be constant.

Technically, the decomposition of four factors in Eq. (3) has 4!=24 unique first-order decomposition results. In this study, we use the average of all possible first-order decompositions (Dietzenbacher and Los, 1998) in the analysis of emission drivers. By way of illustration, one of the 24 possible decompositions is shown in Eq. (4).

The decomposition analysis generates a four-dimensional array with dimensions that represent the year (2010–2017), province (size =31), emission source (size >700), and emission drivers (i.e., A, x, E, and η in Eq. 2). This means that for each pollutant, the year-to-year change in emissions can be attributed to the drivers of A, x, E, and η by source and by province. A is the activity effect (e.g., fuel combustion), and the other three factors constitute the overall effect of air pollution control. This study is mainly concerned with source contributions rather than province contributions; hence, we sum the 4-D array of the decomposition analysis results along the province dimension and perform the following analysis at the country scale.

3.3 Satellite-based and in situ observations

We adopt atmospheric observations to evaluate and validate the emission trends estimated in this study. We use NO₂ column retrievals from the DOMINO V2 product (Boersma et al., 2011) and SO₂ column retrievals from the Ozone Monitoring Instrument (OMI) V3 product (Krotkov et al., 2015). The 2010–2017 trends of satellite observations are calculated over eastern China, where anthropogenic sources are dominant relative to natural sources and are compared against the emission trends of NO_x and SO₂, respectively. Several recent papers have used satellite retrievals to infer recent trends in emissions from East Asia or China. These results are summarized in this study and compared to our emission estimates. We also collect surface-level SO₂, NO₂, and PM_2.5 concentration data from national air quality monitoring stations (http://106.37.208.233:20035/, last access: 30 September 2018) for the period of 2013–2017. These real-time monitoring stations were established in 2013 and had the ability to report hourly concentrations of criteria pollutants from over 1400 sites in 2017.

Table 1Anthropogenic emissions of air pollutants and CO₂ in China from 2010 to 2017.

^a The unit of emissions is Tg. ^b TSP is the particulate matter with an aerodynamic diameter of 100 µm or less. ^c CO₂ from fossil fuel use and industrial processes.

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4.1 Emission trends

China's anthropogenic emissions are estimated to have declined by 62 % for SO₂, 17 % for NO_x, 27 % for CO, 38 % for PM₁₀, 35 % for PM_2.5, 27 % for BC, and 35 % for OC since 2010 (Table 1). Most of these emission reductions have been achieved since 2013 when the Clean Air Action was enacted and implemented. SO₂ and NO_x are the only air pollutants that were incorporated into national economic and social development plans with emission reduction targets in China. The 12th 5-Year Plan required the total national emissions of SO₂ and NO_x to be cut by 8 % and 10 % from 2011 to 2015, respectively, while the actual reductions were much larger than planned due to the more stringent pollution control requirements implemented after 2013. During this period, our estimates suggest that CO emissions decreased by 23 %, whereas CO₂ emissions were flat, reflecting China's improved combustion efficiency and emission control. The years since 2013 also observed a sharp drop in particulate emissions, in contrast with the flattening emissions observed before 2013. This trend is more evident for coarse particles because they are more easily removed by end-of-pipe abatement measures. Given that China's economy is growing rapidly, China's emissions are decoupling from population, economic, and energy consumption growth (Fig. 2). China's gross domestic product grew by 7.6 % per year from 2010 and achieved 67 % growth by 2017; however, China's emissions flattened out from 2010 to 2013, followed by a significant decrease after 2013 according to our calculations. In contrast, NMVOC emissions are estimated to have increased by 11 % and NH₃ emissions remained flat from 2010 to 2017; these trends were mainly due to the absence of effective emission control measures.

Figure 2Emission trends and underlying social and economic factors. The coal usage is achieved from Chinese Energy Statistics (National Bureau of Statistics, 2018a, b). The GDP and population data come from the National Bureau of Statistics (2018b, c). Data are normalized by dividing the value of each year by their corresponding value in 2010.

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We present the sectoral trends of China's emissions in Figs. 3 and 4. The most important sector identified by our estimates is the industrial sector, which is the dominant source of SO₂, NO_x, PM₁₀, PM_2.5, and CO₂ emissions during 2010–2017, accounting for average values of 60 %, 38 %, 57 %, 50 %, and 53 % of total emissions, respectively. The industrial sector is the driver of changes in 2010–2017 emissions for these pollutants, except for NO_x and CO₂, and it also drives down CO and BC emissions. The power sector, though accounting for more than half of burning coal, is not a dominant contributor to the emissions of any pollutant. The reason for this is that upgrading plants with pollution control equipment in the 11th 5-Year Plan (2006–2010) significantly reduced SO₂ and particulate emissions from power plants (Liu et al., 2015b), and the remaining part is not compared to industrial emissions. With upgraded emission standards and the spread of the ultralow emission technique, the new emission limit values have further driven down power plant emissions, which is the dominant driving force of the decrease in NO_x emissions, while industrial combustion sources lack an effective control on NO_x.

The residential sector is the dominant source of CO, BC, and OC, to which it contributes average values of 40 %, 49 %, and 79 % to national emissions, respectively, and is the second-most important source of PM₁₀ (31 %) and PM_2.5 (38 %); its relative contributions of these components have increased while industrial emissions have considerably decreased. The residential sector drives OC emissions down and contributes to the reductions of CO, NMVOCs, and particulate matter. The transportation sector accounts for 17 % of CO emissions, 19 % of NMVOC emissions, and 31 % of NO_x emissions. The increase in fuel consumption drives up transport CO₂ emissions, with an increase of 43 % from 2010 to 2017 that is faster than that of any other emission source sector. Solvent use is a major contributor to the increase in NMVOC emissions. Solvent emissions are estimated to have increased by 52 % since 2010, making them the largest contributor (36 %) to NMVOC emissions in 2017, while the share of this sector was only 27 % in 2010. The agricultural sector is the dominant source of NH₃ emissions, as it contributes to 93 % of total emissions. NH₃ emissions have remained constant because agriculture and rural activities showed small interannual variations.

Figure 3China's anthropogenic emissions by sector and year. The species plotted here include (a) SO₂, (b) NO_x, (c) NMVOCs, (d) NH₃, (e) CO, (f) TSP, (g) PM₁₀, (h) PM_2.5, (i) BC, (j) OC, and (k) CO₂. Chinese emissions are divided into six source sectors (stacked column chart): power, industry, residential, transportation, agriculture, and solvent use. Besides the actual emissions data, two emission scenarios are presented to provide emission trajectories when assuming activity (inverted triangle) or pollution control (upright triangle) frozen at 2010 levels.

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Figure 4Changes in China's emissions by sector and year. The species plotted here include (a) SO₂, (b) NO_x, (c) NMVOCs, (d) NH₃, (e) CO, (f) TSP, (g) PM₁₀, (h) PM_2.5, (i) BC, (j) OC, and (k) CO₂. The 2010 emissions are subtracted from the emission data for each year to represent the additional emissions compared to 2010 levels. The emission changes are shown by sector (stacked column chart) and as national totals (black curve).

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Figure 5China's emission pathways from 2010 to 2017. Gaseous pollutants are plotted in (a), and particles are plotted in (b). For each pollutant, the years (circle) are plotted according to the emission changes caused by activity (A in Eq. 2, x axis) and pollution control (the sum of x, E, and η in Eq. 2, y axis). Please refer to Fig. S1 in the Supplement for decomposition analysis results of A, x, E, and η. The intersecting lines y=x and $y=-x$ divide the coordinate plane into four sections. Any point in the section on the right side of the two lines reflects increasing emissions due to activity growth, and the points in the section below the two lines reflect decreasing emissions driven by pollution control.

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4.2 Drivers of China's emissions

The effect of air pollution control can partially or totally offset the additional emissions caused by growing activity rates, and the combination of pollution control and activity growth entirely determines China's emission pathways (Fig. 5). Based on the drivers of emissions, we can classify air pollutants into two categories, namely, activity-driven increasing pollutants and pollution control-driven decreasing pollutants (Fig. 5). NMVOCs and NH₃ belong to the former category. Their emissions have continued to increase at a constant rate from 2010 to 2017, primarily driven by activity growth. Assuming that activity rates are frozen at their 2010 levels (Fig. 3), the NMVOC emissions could decrease by 21 % from 2010 to 2017 due to the emission controls on residential and transport sectors. These emission reductions were far outweighed by the growing use of solvent for paints, coatings, and chemical industry, which consequently drove up their total emissions. The solvent used for paints increased by 110 % from 2010, which is attributed to the increasing demand to coat buildings, cars, and machinery due to the rapid increase in the area of newly built houses (+52 %) and the production of vehicles (+54 %). The solvent used in chemical industry also rose at a fast rate due to the increase in industrial production (e.g., ethylene production grew by 28 % since 2010), which makes them the second largest contributor to NMVOC growth after paints. For NH₃, the lack of control measures has caused its emissions to correlate well with activity; thus, its emissions do not decline, unlike the regulated pollutants that have experienced progressive emission control.

Figure 6Energy consumption of hydrocarbon fuels from 2010 to 2017. Coal includes all coal-based fuels, and oil includes all oil-based fuels. Traditional biofuel includes crop residual and wood.

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Figure 7Drivers of emission changes for different emission species. The species plotted here include (a) SO₂, (b) NO_x, (c) CO, (d) PM_2.5, (e) BC, and (f) OC. For each pollutant, the changes in emissions from 2010 to 2017 (bar) are decomposed into drivers of activity growth (A in Eq. 2) and pollution control (the sum of x, E, and η in Eq. 2) by source sector.

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The other air pollutants followed distinct emission pathways before and after 2013 (Fig. 5). According to our estimates the emissions of these pollutants slightly increased (e.g., SO₂ and NO_x) or remained flat (e.g., CO and particulate matter) during 2010–2013 because emission mitigation just counterbalanced the additional emissions caused by growing activities. China's fuel combustion increased by 15.2 % from 2010 to 2013 (Fig. 6), and its industrial production increased by 14–35 % in different industries. During this period, China's clean air actions mainly focused on upgrading emission standards for the power and industrial sectors. These measures effectively offset the growth in activities but were not stringent enough to reverse the growing trends in emissions; therefore, air pollutant emissions remained stable from 2010 to 2013.

After 2013, emissions of all air pollutants except NMVOCs and NH₃ are found to have reduced as a result of pollution controls. China's fuel combustion and industrial production have flattened out since 2013 (Fig. 6), while high-efficiency mitigation measures have been increasingly implemented in all emission source sectors, as required by the Clean Air Action. Scenario analysis suggests that the effect of pollution control rapidly removes air pollutants and consequently drives down China's emissions (Fig. 3). Assuming that pollution control is frozen at 2010 levels, SO₂ emissions in 2017 could increase by 167 % compared to the actual data, NO_x and TSP emissions could increase by 38 % and 111 %, respectively, and other pollutants could see increases of 23–66 %. The different reduction rates of air pollutant emissions are determined by the source sector distributions and emission mitigation efforts of each sector. For example, this decrease is most notable for SO₂ (emissions are estimated to have decreased by 59 % from 2013 to 2017) because the dominant source sectors (i.e., power and industry) both significantly reduced their emissions. The decrease in emissions is smallest for NO_x (21 % of emissions cut from 2013 to 2017 based on the analysis) because the power sector was the major contributor to emission reduction but only accounted for one-third of total emissions. To understand the underlying drivers of emission reduction, we decompose the avoided emissions due to pollution control (i.e., the sum of contributions from x, E, and η) into sectors (Fig. 7) to identify the main drivers underlying key source categories. We select the year of 2017, which exhibited the largest reduction in emissions according to our calculations, to perform this analysis.

• 1.

  The power sector. The generation of electricity from hydrocarbon fuels in China has increased by 33 % since 2010, which has led to increases of 1.2 Tg SO₂ and 1.7 Tg NO_x in 2017 compared with their levels in 2010 (Fig. 7). Mitigation efforts have yielded reductions of 7.1 Tg SO₂ and 6.1 Tg NO_x and thus totally offset the emissions caused by growing activities. The reduction of emissions was achieved through the ultralow emission standard. To fulfill the stringent standards, flue gas desulfurization (FGD) and selective catalytic reduction (SCR) systems have been increasingly installed at utilities in coal-fired power plants, with penetration rates reaching >95 % in 2017. Of the current power plants, 71 % have operated close to the design performance of ultralow emission levels (China, 2018).

• 2.

  The industrial sector. Mitigation measures have yielded reductions of 9.5 Tg SO₂, 0.9 Tg NO_x, 38.1 Tg CO, 3.4 Tg PM_2.5, 0.3 Tg BC, and 0.3 Tg OC from the industrial sector in 2017 compared with their levels in 2010 (Fig. 7). For SO₂, shutting down small industrial boilers and cleaning larger ones have contributed the most to emission reductions. In particular, small coal boilers (≤7 MW) located in urban areas were eliminated by the end of 2017, and large boilers have extensively used sorbent injection technologies to remove SO₂ from exhaust gases. For other pollutants, the most effective measures include strengthening industrial emission standards, eliminating outdated industrial capacity, and phasing out small high-emitting factories. The improvements in combustion efficiency and oxygen blast furnace gas recycling are the largest drivers of declining CO emissions, and the wide use of high-efficiency dust collectors (e.g., electrostatic precipitators and fabric filters) in manufacturing industries has successfully removed particulate matter. In addition, the desulfurization of sinter plant gases accounts for 8 % of SO₂ emission reductions, and denitrification in cement kilns accounts for 6 % of NO_x emission reductions. The low-sulfur, low-ash coals resulting from fuel quality improvements have also helped reduce SO₂ and particulate emissions.

• 3.

  The residential sector. The emission reductions achieved by the residential sector are primarily driven by the decrease in activities mainly caused by replacing coal with natural gas and electricity (Fig. 7), which yielded reductions of 13.5 Tg CO, 1.0 Tg PM_2.5, 0.1 Tg BC, and 0.7 Tg OC in 2017 compared with their 2010 levels. Additionally, pollution controls caused additional reductions of 0.5 Tg CO, 0.3 Tg PM_2.5, 0.1 Tg BC, 0.1 Tg OC, and 1.0 Tg SO₂. The decreases in activity rates reflect both long-term changes in fuel mixtures, i.e., from traditional biofuels to commercial energy, and short-term measures to replace coal with clean energy. Pollution control policies have promoted the use of clean stoves and the switch from raw coal to clean coal briquettes with lower levels of sulfur and ash.

• 4.

  The transportation sector. Pollution controls on the transportation sector have exactly counterbalanced the growing emissions due to vehicle growth (Fig. 7). China's vehicle ownership reached 209 million in 2017; this value is 2.7 times larger than its 2010 value. Growing activities yielded increases of 22.2 Tg CO, 3.6 Tg NMVOCs, and 1.4 Tg NO_x in 2017 compared with their 2010 levels, while pollution control measures have yielded reductions of 29.0 Tg CO, 4.8 Tg NMVOCs, and 1.3 Tg NO_x. The reduction of emissions is mainly achieved through fleet turnover, which means that old vehicles are being replaced by newer, cleaner models subjected to tougher emission standards. China has scrapped all the old vehicles that do not meet the more stringent emission standards, i.e., “yellow label” vehicles, by the end of 2017. The number of vehicles scrapped in each province is recorded by the local government, and these scrapped vehicles are banned from roads and sent to wrecking yard for recycling. Consequently, the estimated share of fuel consumption by Euro 4 and Euro 5 vehicles increased from 2 % in 2010 to 66 % in 2017. The effects of these changes on reducing particulate emissions are smaller because transport contributes only a small fraction of total particulate emissions. For NMVOCs, the transport sector is the only sector that has seen a deep cut in emissions. More than 80 % of NMVOC emission reductions are achieved from tailpipe exhaust sources, which have caused evaporative emissions to be the primary source (>60 %) of the remaining NMVOC emissions from transport.

4.3 Comparison with observations and implication for uncertainties

Comparison of trends in PM_2.5 precursor emissions with satellite and ground-based PM_2.5 concentrations are presented in Fig. 8a. All data are normalized to the year 2013 because it is the only year that all data are available. Satellite-derived PM_2.5 concentrations presented a relatively flat trend during 2010 and 2013 (e.g., Fontes et al., 2017; Liang et al., 2018; Lin et al., 2018), corresponding to small variations in emissions of different precursors estimated for the same period. Satellite-based PM_2.5 concentrations over China decreased by 18 % from 2013 to 2015 (Lin et al., 2018), in good agreement with the trend in surface PM_2.5 concentrations over 74 cities. During 2013–2017, surface PM_2.5 concentrations over 74 cites decreased by 35 %, while emissions of PM_2.5 precursors over China presented various changing rates. We estimate a faster decrease in SO₂ emissions than the observed surface PM_2.5 concentrations, while the estimated decreasing rates of NO_x and NH₃ emissions were slower than the observed PM_2.5 concentrations. This phenomenon was qualitatively confirmed by an observed large decrease in sulfate and increased relative contribution of nitrate in PM_2.5 compositions from 2013 to 2017 (Shao et al., 2018). However, the quantitative relationship between PM_2.5 concentrations and precursor emissions will require further studies with chemical transport modeling.

Figure 8Emission trends compared with satellite- and ground-based observations. The satellite-retrieved PM_2.5 concentrations (black, the dashed curve in a) (Lin et al., 2018) are compared with emission trends of PM_2.5 precursors in (a). The 2010–2017 trends in SO₂ (red, the solid curve in b) and NO_x (blue, the solid curve in b) emissions are compared with OMI SO₂ (red, the dashed curve in b) and NO₂ (blue, the dashed curve in b) tropospheric columns for eastern China, respectively. Eastern China here includes the provinces of Beijing, Tianjin, Hebei, Shanxi, Shaanxi, Shandong, Henan, Hubei, Anhui, Jiangsu, Shanghai, and Zhejiang. The 2013–2017 trends in ground-based observations of SO₂ (red, the dotted curve in b), NO₂ (blue, the dotted curve in b), and PM_2.5 (black, the dotted curve in a) are also presented. Data are normalized by dividing the value of each year by their corresponding value in 2013.

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Trends in NO_x and SO₂ emissions are generally consistent with satellite- and ground-based observations during 2010–2017 (Fig. 8b and Table 2). Specifically, rapid decreases in SO₂ and NO_x emissions after 2013 are confirmed by satellite-based observations. We estimate that NO_x and SO₂ emissions in eastern China decreased by 21 % and 59 % during 2013–2017 respectively, lower than the 30 % and 73 % decreases in OMI observed NO₂ and SO₂ columns for the same region and time period. Surface SO₂ concentrations decreased by 57 % over eastern China for the period of 2013–2017, in good agreement with the estimated emission trend. In contrast, surface NO₂ concentrations only decreased by 9 % for the same time, significantly lower than the estimated trend in NO_x emissions.

Different trends between emissions and concentrations could be attributed to many factors. First, temporal and spatial patterns of emissions and concentrations are impacted by variations in meteorology, atmospheric transport, and chemical reactions. Interannual variabilities can result in remarkable variations in column and surface concentrations (Uno et al., 2007), which may partly explain the disagreement between changes in emissions and observations for a signal year (e.g., the year 2011 in Fig. 8b). Surface observations are more sensitive to surface emissions than high-stack emissions and satellite-based column observations are more visible to high-stack emissions due to different transport patterns, in which both will contribute to the differences when comparing trends. In addition, chemical partitioning of NO_x into NO₂ may also contribute to discrepancies between trends in emissions and observations (Lamsal et al., 2011, Valin et al., 2011). Taking the above factors into account, inverse modeling (IM) approaches were developed to derive top-down emissions constrained by observations. Table 2 presented recently published estimates on top-down emission trends over China using IM approaches. As shown in Table 2, the discrepancies between bottom-up and top-down emission estimates are not always narrowed, indicating uncertainties from other aspects might exist.

Table 2Comparison of trends in bottom-up emission inventory, satellite-based observations, and top-down emission estimates since 2010.

^a SAT: satellite-based observations; IM: inverse modeling. ^b E China: eastern China. ^c VMR: volume mixing ratio.

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Second, uncertainties in observations can also contribute to the discrepancies. In situ observations are usually thought to be more accurate. However, surface NO₂ concentrations obtained from national monitoring networks relied on chemiluminescence measurements, which can significantly overestimate NO₂ concentrations (Lamsal et al., 2010) and then contribute to discrepancies between emissions and surface observations. Satellite retrievals are subject to larger uncertainties, for instance, tropospheric SO₂ columns are quite uncertain due to difficulties in isolating anthropogenic SO₂ signals from ozone and volcanic SO₂ (Krotkov et al., 2006). Most uncertainties in satellite retrievals are systematic and canceled out when trends are compared. However, influences of aerosols on satellite NO₂ retrievals (Lin et al., 2015) may impact the reliability of the NO₂ column trend due to the large decrease in aerosol concentrations over the discussed period. SO₂ columns are less sensitive to small and near-surface emissions (C. Li et al., 2017), which may lead to an underestimation of the SO₂ budget in China for most recent years and a disagreement between emission and SO₂ column trends when high-stack emissions (e.g., power plants) were significantly reduced.

Last but not least, emissions estimates are uncertain due to incomplete knowledge of underlying data (Zhao et al., 2011; M. Li et al., 2017b). Similar magnitudes of uncertainties are expected considering previous work (e.g., Zhang et al., 2009; Lei et al., 2011; Lu et al., 2011; M. Li et al., 2017b) since similar methodologies and data sources are used. In general, uncertainties are smaller for species whose emissions are dominant by large sources (e.g., SO₂ and NO_x) but larger for species whose emissions are mainly contributed by scattered emitting sources (e.g., BC and OC). Many of the uncertainties in bottom-up emissions are also systematic and may have less impact on emission trends (Lu et al., 2011), but non-compliance with regulations due to lack of inspection will lead to differences between estimated and real-world efficiencies of emission control facilities (e.g., Wang et al., 2015) and impact the validity of estimated emission trend. Specifically, the effectiveness of the measures targeting the small and scattered emitting sources (e.g., phase out small high-emitting factories and eliminate small coal-fired industrial boilers) are difficult to validate, which may lead to higher uncertainty ranges in emission estimates for most recent years.