Skip to main content

Full text of "NASA Technical Reports Server (NTRS) 20170009477: NOx Emission Trends over Chinese Cities Estimated from OMI Observations During 2005 to 2015"

See other formats


10 


15 


20 


25 


30 


35 


40 


NO, emission trends over Chinese cities estimated from OMI 
observations during 2005 to 2015 


Fei Liu! ?326, Steffen Beirle’, Qiang Zhang’, Ronald J. van der A, Bo Zheng", Dan Tong! and Kebin 
He'* 


'Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua 
University, Beijing, China 
Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, De Bilt, the Netherlands 
3Max-Planck-Institut fiir Chemie, Mainz, Germany 
“State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 
Beijing, China 
Nanjing University of Information Science & Technology (NUIST), Nanjing, China 
now at: “Universities Space Research Association (USRA), GESTAR, Columbia, MD, USA 
*"NASA Goddard Space Flight Center, Greenbelt, MD, USA 


Correspondence to: Fei Liu (fei.liu@nasa.gov ; liuf1010@ gmail.com) 
Qiang Zhang (qiangzhang @tsinghua.edu.cn) 


Abstract. Satellite NO» observations have been widely used to evaluate emission changes. To determine trends in NO, 
emission over China, we used a method independent of chemical transport models to quantify the NO, emissions from 48 
cities and 7 power plants over China, on the basis of Ozone Monitoring Instrument (OMI) NO; observations during 2005 to 
2015. We found that NO, emissions over 48 Chinese cities increased by 52% from 2005 to 2011 and decreased by 21% from 
2011 to 2015. The decrease since 2011 could be mainly attributed to emission control measures in power sector; while cities 
with different dominant emission sources (i.e. power, industrial and transportation sectors) showed variable emission decline 
timelines that corresponded to the schedules for emission control in different sectors. The time series of the derived NO, 
emissions was consistent with the bottom-up emission inventories for all power plants (r=0.8 on average), but not for some 
cities (r=0.4 on average). The lack of consistency observed for cities was most probably due to the high uncertainty of 
bottom-up urban emissions used in this study, which were derived from downscaling the regional-based emission data to 


cities by using spatial distribution proxies. 


1 Introduction 


Nitrogen oxides (NO,), including nitrogen dioxide (NO>) and nitric oxide (NO), are atmospheric trace gases with a short 
lifetime, and they actively participate in the formation of tropospheric ozone and secondary aerosols and thus harm human 
health and significantly affect climate (Seinfeld and Pandis, 2006). Anthropogenic activities, particularly fossil fuel 
consumption, are the most important sources of NO, emissions. Anthropogenic NO, emissions are clustered over densely 
populated urban areas and suburban/rural industrial areas where large point sources such as power plants are located. 

Tropospheric NO, observations detected from space have been applied to infer the strength of NO, emissions. The 
concentration of NO, in a vertical column of air can be measured via satellite instruments and related to NO, emissions 
according to the mass balance by considering transport and chemical conversion. A pioneering study has used the downwind 
decay of NO, in continental outflow regions to estimate the average NO, lifetime and global NO, emissions (Leue et al., 
2001). Subsequent studies have used chemical transport models (CTMs) to exploit satellite measurements as a constraint to 
improve NO, emission inventories at the global/regional scale (e.g., Martin et al., 2003; Konovalov et al., 2006; Kim et al., 
2009; Lamsal et al., 2011). The spatial and temporal resolution of tropospheric NO, observed from space has increased over 


1 


10 


15 


20 


25 


30 


35 


40 


time, from the Global Ozone Monitoring Experiment (GOME), which was launched in 1995 (Burrows et al., 1999), to the 
Ozone Monitoring Instrument (OMI) (Levelt et al., 2006), which was launched in 2004 and enables the use of satellite 
retrievals to resolve emissions at a finer scale. OMI NO, observations sorted according to wind direction from wind fields 
developed by the European Center for Medium range Weather Forecasting (ECMWF) have been fitted by Beirle et al. 
(2011), who have used the exponentially modified Gaussian function, which allows for a simultaneous fit of the NO, lifetime 
and emissions for megacities without further input from CTMs. In the previous work, we have advanced this method for 
estimating NO, emissions from sources located in a polluted background (Liu et al., 2016a). An alternative approach to 
quantifying urban NO, emissions, proposed by Valin et al. (2013), involves rotating satellite observations according to wind 
directions such that all observations are aligned in one direction (from upwind to downwind), thus increasing the number of 
observations. Subsequent studies have applied the concept of CTM-independent methods for estimating SO, by introducing 
an advanced three-dimensional function (Fioletov et al., 2015; Fioletov et al., 2016; McLinden et al., 2016). 

Satellite observations are particularly suitable for evaluating emission changes because they provide continuous and timely 
tropospheric NO, measurements with global coverage (Lelieveld et al., 2015). Changes in the spatial heterogeneity of NO, 
trends have been observed worldwide, and substantial decreases over Europe and the US (Russell et al., 2012) and 
significant increases over Asia have widely been detected in recent decades (Richter et al., 2005). A linear function 
superposed on an annual seasonal cycle has been introduced by van der A et al. (2008) to derive a quantitative estimate of 
emission trends for a grid with a spatial resolution of 1° x 1° by fitting the corresponding monthly NO, columns. Follow-up 
studies (e.g., Schneider and van der A, 2012; Schneider et al., 2015) have applied similar statistical analyses to time series of 
NO, in finer grid cells located over the center of the city and have quantified the long-term average pattern of NO, for 
megacities. The multi-annual (moving) average is an alternative method of describing local NO, trends. The interannual 
variation in the mass of a chemical species integrated around the source has been used as an indicator of emission changes 
and has been shown to be capable of illustrating the emission changes over US power plants (Fioletov et al., 2011), Canadian 
oil sands (McLinden et al., 2012) and Indian power plants (Lu et al., 2013). In addition, de Foy et al. (2015) and Lu et al. 
(2015) have adopted the fitting function proposed by Beirle et al. (2011) and have provided estimates of NO, emission trends 
from isolated power plants and cities over the US on the basis of 3-year average NO> values obtained through the plume 
rotation technique described by Valin et al. (2013). 

China is one of the largest NO, emitters in the world and is the source of approximately 18% of the global NO, emissions 
(EDGAR 4.2, EC-JRC/PBL, 2011). China has experienced rapid increases in NO, emissions because of its growing 
economy over the past two decades, during which emissions have increased by a factor of three (Kurokawa et al., 2013) and 
have caused severe air pollution. To improve air quality, the Chinese government implemented new emission regulations 
aimed at decreasing the national total NO, emissions by 10% between 2011 and 2015 (The State Council of the People’s 
Republic of China, 2011). Several recent studies (e.g., Duncan et al., 2016; Krotkov et al., 2016) have suggested the 
effectiveness of the air quality policy, as evidenced by a decreasing trend in NO, columns over China since 2012. Miyazaki 
et al. (2017), van der A et al. (2017) and Souri et al. (2017) have further reported a recent decline in national NO, emissions 
on the basis of satellite data assimilation. Liu et al. (2016b) have studied changes in NO, column densities for each province 
from 2005 to 2015 and have performed an intercomparison of a bottom-up inventory and satellite observations; the study 
attributes the decline in regional NO, to decreased emissions from power plants and urban vehicles. However, few analyses 
have been performed for individual cities or power plants, which are the primary targets of the new control measures. Such 
investigations may provide stronger evidence of the effects of control measures on NO, emissions. 

In this work, we quantified NO, emission trends over urban areas in China from 2005 to 2015. Certain widely used 
approaches, including linear trend analysis (e.g., Duncan et al., 2016; Krotkov et al., 2016) and exponentially modified 
Gaussian method (e.g., de Foy et al., 2015; Lu et al., 2015), are difficult to directly apply to hot spots in China. The linear 


trend analysis approach is particularly useful for quantifying changes for cities with a linear trend; however, it is not 
2 


10 


15 


20 


25 


30 


35 


40 


applicable to most Chinese cities, which show a clear turning point of emissions. The exponentially modified Gaussian 
method may introduce significant uncertainties to the fit results because of the heterogeneously polluted background over 
China (de Foy et al., 2014; Liu et al., 2016a). We applied our advanced fitting function to sources located in a polluted 
background (Liu et al., 2016a) to calculate the 3-year moving averages of NO, emissions of pollution hotspots including 
individual cities and power plants, and to relate their variations to bottom-up information. The main purpose of this study 
was not only to demonstrate the recent decrease in NO, levels across the country, as indicated by previous reports (Liu et al., 
2016b) but also to display the diverse emission characteristics among cities and provide in depth interpretations of these 
characteristics. The fitting function and data sets used in this study are detailed in Sect. 2. The interannual variations of NO, 
emissions and the analysis of emission trends for cities derived from the fitting function are provided in Sect. 3.1 and Sect. 
3.2, respectively. The fitting results for cities are presented in Sect. 3.3. The uncertainties associated with the fitting results 


are discussed in Sect. 3.4, and the primary findings of this study are summarized in Sect. 4. 


2 Methodology 
2.1 Fitting method 


We improved the exponentially modified Gaussian method (Beirle et al., 2011) to quantify the multi-year average NO, 
emissions obtained from OMI NO; observations for sources located in a polluted background (Liu et al., 2016a). In this work, 
we adapted the fitting functions of Liu et al. (2016a) to calculate the NO, emissions for individual cities and power plants, 
including adjustments to meet the requirements of the trend analysis. 

Consistently with our previous study (Liu et al., 2016a), we used the OMI tropospheric NO. (DOMINO) v2.0 product 
(Boersma et al., 2011) together with the ECMWF ERA interim reanalysis (Dee et al., 2011) to perform the analysis. We 
calculated the mean NO, tropospheric vertical column densities (TVCDs) for calm wind speeds below 2 m/s and 8 different 
wind direction sectors, by following the approach in Beirle et al. (2011), and for weak-wind conditions (below 3 m/s), by 
following the recommendations in Lu et al. (2015), from 2005 to 2015. We used only non-winter data (from March to 
November) because these data should have larger uncertainties because of larger solar zenith angles and variable surface 
albedo (snow). In addition, the longer NO, lifetimes in winter resulted in a less direct relationship between NO, emissions 
and satellite NO» observations. 

Emissions were derived in a two-step approach in Liu et al. (2016a). The first step was to use NO patterns under calm wind 
conditions as a proxy for the spatial distribution of NO, emissions and determine the effective atmospheric NO, lifetime 
from the change of spatial patterns measured at higher wind speeds. The second step was to derive emissions from the NO» 
mass integrated around the source of interest divided by the corresponding lifetime. 

To perform a trend analysis, we adjusted the method as follows: we based the estimation on NO, columns around the source 
of interest averaged over three years, in agreement with previous studies (e.g., Fioletov et al., 2011; Lu et al., 2015); and then 
the total NO, mass was integrated over the mean TVCDs at weak wind speeds (below 3 m/s) instead of calm winds (below 
2m/s) to balance the need for increasing the number of observations and minimizing interferences by advection. Notably, we 
were not able to derive valid lifetimes on the basis of the 3-year average NO, columns; instead, we fitted the lifetimes on the 
basis of multiple-year data (the entire study period) because of the lack of sufficient observations for different wind sectors 
within a 3-year period. Therefore, the NO, emissions for each 3-year period were calculated by dividing the corresponding 
total NO, mass by the multiple-year average lifetime. In this way, the temporal variations in NO, emissions were merely 
dependent on the changes in the total NO, mass, excluding background pollution, assuming that the lifetimes did not change 
over time. However, we wanted to include the fit over the lifetime in this study to make the comparison of top-down and 
bottom-up estimates more straightforward. Subsequently, we included mountainous sites, which were defined as sites where 


the absolute difference in elevation between ECMWEF and GTOPO data (available at https://lta.cr.usgs.gov/GTOPO30, 
3 


10 


15 


20 


25 


30 


35 


40 


rescaled to 0.05°) was larger than 250 m, in the following analysis. Our previous findings (Sect. 2.6, Liu et al., 2016a) have 
indicated that appropriate wind fields, which are required for accurate lifetime calculations, may not always be achieved 
from the ECMWF simulation over mountainous regions. However, depending on changes in the total NO, mass, the fitted 
emission trends are not as sensitive as the fitted lifetimes to wind fields; thus, we did not exclude mountainous sites from the 
trend analysis. The fitting results with poor performance (i.e., R<0.9, lower bound of confidence interval CI <0, CI width for 
lifetime >10 h, CI width for the NO, mass >0.8xmass) were discarded, in accordance with the criteria in Sect. 2.2 of Liu et al. 
(2016a). 

We selected the Huolin power plant (site 2#, 45.5°N, 119.7°E), which is located in Holingol, a county-level city of Inner 
Mongolia of China (shown in Fig. 1), to demonstrate our approach. The Huolin power plant has a total capacity of 2400 MW 
and dominates the NO, emissions from the city of Holingol, contributing over 80% of the total emissions estimated by using 
the Multi-resolution Emission Inventory for China (see Sect. 2.2), which is a bottom-up emission inventory. Fig. 2(a) 
displays the 3-year average NO. TVCDs around the power plant under weak-wind conditions from 2005 to 2015. For 
simplicity, the 3-year period is represented by the middle year with an asterisk (e.g., 2006* denotes the period from 2005 to 
2007). A significant increase in TVCDs was observed from 2006° to 2010°, which was followed by a subsequent decrease. 
Fig. 2(b) presents the fitted background and NO, emissions. The fitted NO, emissions showed an increase of up to a factor of 
four from 2006° to 2010° and a decrease of 30% from 2011° to 2014", whereas the fitted background was steady and showed 
a standard deviation of less than 10% from 2006° to 2014". The growth of the fitted NO, emissions in the early stage was 
found to be consistent with the construction of new electric-generating units, with the total capacity increasing from 300 
MW to 2400 MW from 2005 to 2009. Subsequently, the fitted NO, emissions remained steady from 2010° to 2012", when no 
new electric-generating units were placed into service, and finally decreased after the installation of Selective Catalytic 
Reduction (SCR) equipment at the power plants. This decrease in emissions indicated the effectiveness of SCR equipment 


for decreasing emissions. 


2.2 Bottom-up information 


We used bottom-up information to pre-select promising sites and to perform a comparison with the fitted top-down emission 
trends. We selected bottom-up emission inventories widely used in the community, in which multi-year gridded estimates 
are provided (more than three years data available from 2005 to 2015). We finally included Emission Database for Global 
Atmospheric Research version 4.3 (EDGAR v4.3, available for 1970-2010, Crippa et al. 2016), Regional Emission 
inventory in Asia version 2.1 (REAS v2.1, available for 2000-2008, Kurokawa et al., 2013) and MEIC 
(http://www.meicmodel.org) compiled by Tsinghua University. The analysis was focused on the MEIC inventory that are 
available for the whole period. Vehicle population and coal consumption at the city level were derived from the China 
Statistical Yearbook for Regional Economy (NBS: CSYRE, 2004-2014) and the China Environment Yearbook (NBS: CEY, 
2004-2015), respectively. We derived the information for the coordinates, unit capacities and technologies for individual 
power plants from the unit-based China coal-fired Power plant Emissions Database (CPED) (Liu et al., 2015) integrated in 
MEIC. 

We calculated the NO, emissions from cities and power plants from 2005 to 2015. Only emissions for non-winter seasons 
were considered, in accordance with the emissions included for the top-down estimates, except for EDGAR in which only 
annual emissions are available. The gridded bottom-up inventories were integrated over a 40 x 40 km? metropolitan area for 
which the proposed top-down method was sensitive to calculate the total urban emissions (Liu et al., 2016a). Emissions for 
individual power plants are derived from CPED and the power plant sector of REAS directly (emissions from individual 
point sources are not available in EDGAR). Notably, the emissions uncertainties associated with power plants derived from 


CPED were much lower (30%) than those for cities (50%~200%) because the former was calculated directly from unit-level 


10 


15 


20 


25 


30 


35 


40 


information whereas the latter was derived by downscaling regional-based emission data to finer grids using spatial proxies 


and integrating emissions from corresponding grids. 


2.3 Selection of locations 


We selected large cities and power plants over China as the pre-selected sites for which bottom-up emission information was 
derived from the and MEIC and CPED inventories, respectively. China classifies its administrative divisions into five 
practical levels (from large to small): province, prefecture, county, township and village. Only prefecture-level cities were 
selected for analysis in this study. Power plants with NO, emission rates greater than 10 Gg/yr were selected for emission 
fitting. Fig. 1 illustrates all investigated sites where the fit results showed good performance. Among over 200 pre-selected 
cities, 48 cities (including 14 mountainous sites) were fitted with good performance (see the definition in Sect.2.1). While 
among over 100 pre-selected power plants, more than half were excluded from the fit procedure, because they are located in 
a radius of 100 km around prefecture-level city centers, on the basis of a visual inspection of satellite imagery from Google 
Earth. Only 7 power plants (including 3 mountainous sites) were fitted with good performance. Detailed information on the 


sites is tabulated in Table S1 of the supplement. 


3 Results and Discussion 
3.1 Interannual trends in OMI NO, emissions for cities 


The trends in the fitted NO, emissions for 48 cities from 2006° to 2014" are shown in Fig. 3a, with an average growth trend 
of 52% prior to 2011” for all investigated cities and a declining trend of 21% from 2011" to 2014". The NO, emissions over 
urban areas essentially represent a marker for combustion-related emissions, including coal combustion for power generation 
and industrial processes and oil combustion for transportation. Fig. 3b further summarizes the statistical data of industrial 
coal consumption (open squares) and vehicle population (proxy of oil consumption, solid squares), which are available for 
only 28 cities. Not surprisingly, the fitted emission trends for the 28 cities (circles) were consistent with those for the overall 
48 cities. The observed sharp growth of 47% in NO, emissions during the period of 2006-2011" was attributed to the 
growth of 75% and 158% in coal consumption and vehicle population, respectively. Coal consumption and vehicle 
population continued to rise and increased by 8% and 26% from 2011 to 2014" respectively; however, a subsequent decline 
in NO, emissions was observed. We further divided these NO, emissions by coal consumption and vehicle population 
because their respective temporal variations can be treated as an approximation of the average emission factor trends in the 
industrial (including power) and transportation sectors, on the basis of the assumption that contributions of NO, emissions 
from corresponding sectors are constant over time. The ratio of emissions to coal consumption and vehicle population has 
diminished over time and decreased by 37% and 65% from 2011° to 2014", respectively. This declining trend was greater 
than the fluctuations in contributions of NO, emissions from the corresponding sectors (ranging from 1%-—24% for individual 
cities) and indicated the effectiveness of emission control measures. 

The time series between fitted emissions and bottom-up inventories are generally consistent in Fig. 3b.The changes in NO, 
emissions from 2005 to 2015 according to sector for the investigated cities on the basis of MEIC estimates are summarized 
in Fig. 3a and indicate the driving force underlying the emission changes. In agreement with previous findings (Liu et al., 
2016b), power plants are the primary component responsible for the decline in NO,, and the associated bottom-up NO, 
emissions decreased by 59% between 2011 and 2015. This finding was further supported by the power plant emission trends 
shown afterwards in Sect. 3.3. The decrease in both fitted and bottom-up emissions has accelerated since 2013 because of the 
implementation of air pollution prevention and control action plans (the State Council of China, 2013). Such plans require 
the deployment of denitration devices for coal-fired boilers and cement precalciners, and the requirements are not limited to 


power plants, as observed in earlier policies. By 2015, 92% of the power plant boilers and cement precalciner kilns in China 
5 


10 


15 


20 


25 


30 


35 


40 


had installed denitration devices. In addition, low-efficiency small coal-fired boilers and even complete factories have been 
phased out. Iron, steel and cement factories with an overall production capacity of 86 Gg, 44 Gg, and 263 Gg were shut 
down in China from 2013 to 2015. Additionally, Chinese cities have pursued a reduction in coal consumption through the 
gradual transformation of the energy system from coal to renewable energy and natural gas. For instance, Beijing has 
outlined plans for “coal-free zones” that ban coal usage, and these plans required the replacement of all coal-fired boilers 
with natural gas in inner suburban districts by 2015 (Clean Air Action Plan 2013-2017, Beijing Municipal Government, 
2013). Accordingly, China reached peak coal consumption in 2013, and a decline of 4% in coal consumption for the 
investigated cities was observed between 2013 and 2014 (Fig. 3b). Moreover, Chinese cities have been required to meet 
more stringent vehicle emission standards. For instance, Euro IV emission standards were widely implemented in 2015, and 
the NO, emission factor is only 2.6% of the Euro 0 standard for gasoline vehicles (Huo et al., 2012). Because of the notable 
success of emission control induced by stricter emission standards, the contributions of high-emitting old vehicles (Euro 0 in 
most cases) to overall emissions are becoming increasingly significant. Reports have indicated that Euro 0 vehicles 
accounted for more than 50% of the total vehicle emissions in China in 2009 (MEP, 2010). Thus, China has marked high- 
emitting vehicles with yellow labels, implemented traffic restrictions and subsidized scrappage programs for these vehicles 
(Wu et al., 2017). A total of 15 million yellow-label vehicles were scrapped between 2013 and 2015. Significant progress in 
controlling vehicle emissions has also been observed with improvements in vehicular fuel combustion efficiency and license 
registration control policies, which allocate quotas for new vehicles through public auction or lottery. Note that all bottom-up 
inventories show lower increase rate around the year 2010, compared to the fitted emissions (Fig. 3b). This is most likely 
caused by the spatial allocation approach adopted in bottom-up inventories, which tends to diminish regional diversity and 


may consequently result in smaller emission growth (see further discussion in Sect. 3.2). 


3.2 Interannual trends of NO, emissions for individual cities 


The fitted results allowed for a closer examination of the trends and causes of emission changes at the individual city level 
instead of at a regional level, as performed in previous studies (e.g., Liu et al., 2016b). Fig. 4 compares the fitted and bottom- 
up emissions for selected cities, which can be considered in 3 broad categories: mega cities with large amount of vehicle 
emissions (Guanzhou and Shanghai in Fig. 4a and b); cities with power plants as the dominant emission source (Wuhai and 
Huainan in Fig. 4c and d); and cities with industrial plants as the dominant emission source (Karamay and Jiayuguan in Fig. 
4e and f). 

Fig. 4a and b show that megacities reached the emission peak prior to the average timeline shown in Fig. 3. Here, we discuss 
in detail the temporal variations in Guangzhou, the largest city in South China. The early decline in emissions was primarily 
related to the stricter regulations on vehicles, which was the only source that showed decreasing emissions, as indicated by 
the bottom-up inventory. Guangzhou implemented Euro III emission standards for all light-duty vehicles and heavy-duty 
diesel vehicles in 2006, which was two years earlier than the national requirement. Traffic restrictions for motorcycles and 
trucks and for yellow-label vehicles have also been implemented since 2007 and 2008, respectively. In addition, alternative 
fuels in buses and taxis have also been promoted in Guangzhou, and 75% and 94% of these vehicles, respectively, were 
using liquefied petroleum gas by 2009 (Zhang et al., 2013). The early decline before 2010 in Shanghai shown in Fig. 4b is 
attributable to similarly strict regulations for vehicle emissions that were implemented before the national schedule. In 
addition, Guangzhou has gradually phased out the high-pollution iron and steel industry since 2008; however, such emission 
reductions were not well presented by the bottom-up inventory. In line with the national denitration procedure, coal-fired 
power plants have remained a significant contributor to emission reductions since 2011. The bottom-up NO, emissions from 
power plants in Guangzhou decreased by over 50% between 2011 and 2015, because of the wider deployment of denitration 


devices at power plants. 


10 


15 


20 


25 


30 


35 


40 


The interannual trends of NO, emissions for the cities of Wuhai and Huainan are displayed in Fig. 4c and d, and power 
plants were the dominant source of NO, emissions. Not surprisingly, the top-down and bottom-up information was more 
consistent than the information for the other two categories, because of the better quality of emission estimates for the power 
sector. The fitted emissions decreased with the decline in emissions from power plants around 2012, and this finding was 
related to the deployment of denitration devices. 

Emission variations for cities for which the industrial sector was the dominant emission source are shown in Fig. 4e and f, 
which indicate significant inconsistencies in the top-down and bottom-up information, even for the total amount. Cities 
belonging to this category were usually medium and small cities. For instance, the city of Jiayuguan (Fig. 4f) has a total 
population of 0.2 million, a vehicle population of 0.03 million and a large-sized industrial enterprise (Jiuquan Iron & Steel 
(Group) Co., Ltd). Industrial activities are the most likely contributor to the recent deceleration and even decline in total 
emissions, because of the small human and vehicle populations and the limited amount of power plant emissions (light blue 
line) in the city. To meet the demands of the air pollution prevention and control action plan (the State Council of China, 
2013), the iron and steel enterprises have been required to regulate their emissions since 2013. Additionally, the city is 
required to meet stricter vehicle emission standards and retire aged vehicles. However, the bottom-up inventory for the city 
of Jiayuguan was consistent with this analysis for only the transportation sector and not the industrial sector as shown in Fig. 
4f. The bottom-up transportation emissions experienced a decline of 10% and a sharp increase of 20% in the vehicle 
population between 2013 and 2015. In addition, the NO, emissions from the industrial sector were fairly steady and showed 
a decrease of only 2% between 2013 and 2015 and account for a small share (20%) of the total emissions. For industrial 
emissions, MEIC first downscaled provincial totals to counties using industrial GDP, and then allocate county emissions to 
grids with population density. Thus uncertainty of emissions from the industrial sector is larger than that from power plants. 
Such changes in the industrial sector most probably represent the regional average level and do not represent the levels for a 
city with a large-sized iron and steel enterprise, because of the uncertainty of downscaling approaches adopted in the bottom- 
up inventory. 

Although we used bottom-up inventories to interpret the changes in NO, emission, certain notable discrepancies occurred 
between the fitted emissions and the bottom-up inventories. We further explored the reasons for these inconsistencies in 
cities by examining the differences in trends between top-down and bottom-up estimates at different spatial scales. Fig. 5 
presents the temporal variations at provincial and city scales from 2006° to 2014" for the top-down and bottom-up data sets. 
The top-down information at the provincial level (Fig. 5a) was the 3-year average OMI NO, column densities for non- 
background regions, where the average annual NO, column densities were larger than 1 x 10'° molec/cm” or the average 
NO, column densities for summer exceeded those for winter in Liu et al. (2016b), and the top-down estimates (Fig. 5c) at the 
city level were derived from this study. The bottom-up emissions were calculated by summing the emissions of the 
corresponding grids belonging to individual provinces (Fig. 5b) or cities (Fig. 5d, see Sect. 2.2). Not surprisingly, the 
comparison of the two independent data sets showed that the trends at both the provincial and city levels were generally 
consistent, with both levels experiencing a sharp rise before 2012" (2011 in Fig. 5c) and a continuous decline thereafter. 
However, a closer examination of the magnitude of relative changes showed that the differences were scale dependent. The 
provincial-level comparison showed a growth trend of 40% + 26% and 34% + 21% from 2006° to 2012" and a subsequent 
declining trend of 9% + 4% and 14% + 6% from 2012° to 2014’ for the top-down and bottom-up data sets, respectively, and 
indicated the acceptable accuracy of provincial totals in bottom-up estimates. However, the city-level comparison exhibited a 
large discrepancy in the magnitude of change rates. For instance, the top-down growth rates reached 45% + 46% in the 
period from 2006° to 2012", whereas the bottom-up rates were only 25% + 27% for the same period. 

We expect that the scale dependence of the differences shown in Fig. 5 may be explained by the spatial allocation approach 
adopted in bottom-up inventories. Current gridded bottom-up emission inventories rely heavily on spatial proxies because 


rare emissions, excluding the emissions from stacks of large point sources, can be directly measured. A variety of spatial 


7 


10 


15 


20 


25 


30 


35 


40 


proxies, such as population density, road density and satellite-observed nightlights, are used to geographically distribute 
emission totals from a large scale down to the scale of geographic grids of various sizes. Several studies (e.g., Hogue et al., 
2016) have indicated that such a spatial distribution approach using proxy data introduces significant uncertainties because 
emissions can be misallocated spatially and temporally. Although the MEIC inventory has substantially improved its 
accuracy, such as by using the high-resolution power plant database CPED (Liu et al., 2015), a lack of data has led to the 
inclusion of other types of point sources (such as industrial boilers) as areal sources of emissions. For example, the MEIC 
first downscales provincial industrial emission totals to county totals according to industrial GDP values and then distributes 
county emissions to grids according to the population density. However, industrial facility locations are likely to be 
decoupled from spatial proxies, because polluted facilities are often required to be located in rural areas with smaller GDP 
and populations (Zheng et al., 2017), and this decoupling may have resulted in the underestimation of emissions from steel 
and iron factories shown in Fig. 4f. In addition, the spatial distribution of proxies cannot easily represent the emission 
changes caused by anti-leapfrogging policies implemented in cities ahead of the national schedule, such as the previously 
discussed new vehicle emission standards in Guangzhou. Thus, regional diversity may have been diminished and 
consequently resulted in the small standard deviation over cities shown in Fig. 5d. 

The correlation coefficients of the pair-wise trends between the fitted NO, emissions and the bottom-up inventory for the 
period 2006° to 2014" are illustrated in Fig. 6. The correlation coefficient of the time series of NO, emissions showed 
remarkable diversity for cities and reached over 0.9 for Urumqi (#9 in Fig. 6) and dropped to less than -0.7 for Jinzhou (#5 in 
Fig. 6), probably because of the high uncertainties of the bottom-up inventories for cities. Notably, the negative correlation 
coefficients do not necessarily correspond to a strong inverse linear relationship and may suggest inconsistency over only 
one or two periods (Fig. 4b). Additionally, the negative correlation coefficients were always observed when the time series 
of fitted emissions experienced a minor fluctuation without a significant trend, as demonstrated in Fig. 6b by cities with a 


correlation coefficient less than -0.4. 


3.3 Interannual trends in OMI NO, emissions for power plants 


The trends in fitted NO, emissions for 7 power plants from 2006 to 2014" are shown in Fig. 7. The changes in the total NO, 
mass and derived NO, emissions were consistent with the addition of new units in individual power plants until the 
installation of denitration devices. The dramatic growth in NO, emissions (red line) prior to 2010", which reached 89% on 
average for all power plants investigated in this study, was driven by increases in the capacity of 84% for the corresponding 
power plants (gray bar). However, the subsequent decline in NO, emissions could not be explained by the simultaneous 
changes in total unit capacities, which increased by 3% from 2010° to 2014", but suggests a good agreement with the wider 
deployment of denitration devices, such as SCR equipment. The installation of SCR devices generally ensures a NO, 
removal efficiency of 80-85% (Forzatti et al., 2001). However, the denitration devices used in Chinese power plants usually 
do not meet this standard efficiency, because of the non-optimal use of catalysts and reductants. The average removal 
efficiency of SCR equipment for 2014 was only 60% on the basis of statistics from the CPED. In this way, the increasing 
penetration of SCR equipment (up to 73%, blue line) corresponded to a decrease of approximately 40% (i.e., 73% x 60%) in 
NO, emissions, a result consistent with the changes in fitted emissions. The fitted emissions were further compared with the 
bottom-up emission estimates, and both values shared a similar trend. The significant decline of 40 + 22% (mean + standard 
deviation) in fitted NO, emissions for individual power plants from 2010° to 2014° was generally consistent with the 
simultaneous decline in bottom-up estimates of 22 + 29%. However, a minor difference in the peak year of emissions was 
detected for a few power plants, and was most probably caused by uncertainty in the fitted emissions related to the lack of 
interannual variations in NO, lifetimes. 

China has implemented the new emission standards for thermal power plants (Ministry of Environmental Protection of China 


(MEP), 2011) in 2012, requiring power plants, particularly large plants, to install denitration devices, such as SCR 
8 


10 


15 


20 


25 


30 


35 


40 


equipment, to control their NO, emissions. The deployment of denitration devices (shown in Fig. 7) was consistent with this 
new policy, and the national average penetration of SCR equipment grew from 18% to 86% between 2011 and 2015 (China 
Electricity Council, 2012-2016). Given that the overall capacity of the power plants investigated in this study was equivalent 
to only 2% of the total national capacity; we may not able to conclude that the temporal variations in NO, emissions derived 
from the 7 power plants reflect the emissions from large power plants at the national level. While for the investigated power 
plants, the derived emissions were consistent with the bottom-up emissions and the time series of the two estimates were 
well correlated, even for mountainous sites where the absolute values of the emission estimates differed significantly (Fig. 
6a). The good consistency increased our confidence that the fitted emission trends accurately represented the real-word 
emission variations, because the uncertainty of the bottom-up emission inventory for power plants is fairly low (Liu et al., 


2015). 


3.4 Uncertainties 


The fitted NO, emissions were compared with the bottom-up emission estimates (Sect. 2.2) for all 48 cities and 7 power 
plants in Fig. 8, and their correlations were consistent with the average emission estimates for multiple years shown in the 
previous work (Liu et al, 2016a). In general, the comparisons indicated consistency among non-mountainous sites, which 
presented a higher correlation coefficient for power plants (blue symbols in Fig. 8a, r=0.89) than cities (blue symbols in Fig. 
8b, r=0.81). The results for the mountainous sites showed higher scatter for both power plants (red symbols in Fig. 8a, 
r=0.79) and cities (red symbols in Fig. 8b, r=0.44), thus confirming that those top-down estimates had higher uncertainties 
because of inaccurate ECMWF wind fields for mountainous sites (Liu et al, 2016a). The comparable correlation among the 
results presented here and in the previous study by Liu et al (2016a) increased our confidence in the accuracy of the fitted 
results. 

We estimated the uncertainty of the fitted NO, emissions and their trends by using a method analogous to that in Liu et al. 
(2016a) because of the consistency in methodology between the two studies. The uncertainty analysis was performed on the 
basis of the fit performance and according to sensitivity studies that have investigated the dependencies on a priori settings, 
which are detailed in the supplement of Liu et al. (2016a). The major sources of errors contributing to the overall 
uncertainties included (a) fit error; (b) choice of fit intervals; (c) tropospheric NO, VCDs and the NO,/NO; ratio; (d) choice 
of wind fields and (e) lifetime variations. The uncertainties arising from (a) — (c) were consistent with those reported in Liu 
et al. (2016a). Here, we briefly discuss the impact of (d) and (e). 

(d) Choice of wind fields. The NO, trends observed under weak-wind conditions may vary from those under all-wind 
conditions (Lu et al., 2015), because higher wind speeds are expected to cause longer NO, lifetimes because of the faster 
dilution of NO, (Valin et al., 2013). A change in the weak-wind conditions by all-wind conditions affects the resulting total 
mass by approximately 10% on average. 

(e) Lifetime variations. We use multiple-year average lifetimes and 3-year average NO, masses to calculate NO, emissions 
trends in this study. The variations in total NO, mass do not necessarily correlate linearly with NO, emissions, because of 
changes in the NO, lifetimes related to variations in meteorology and NO, chemistry. However, the temporal variations in 
lifetimes corresponding to the 3-year moving averages of TVCDs are reduced significantly, as supported by the similar 
decreases in the 3-year mean NO, emissions and OMI NO) observations over urban areas in the US (Lu et al., 2015). In 
addition, we could not unambiguously relate the variability of fitted NO, lifetimes to NO, levels (Liu et al., 2016a). 

The method was applied to the period prior to the row anomaly (the 3-year period from 2005 to 2007), which had a larger 
number of observations than the other periods. The method was successful for 19 sites, and the fitted lifetimes were not 
sensitive to the NO, changes within the studied period, with the lifetimes increasing by only 9% when the average NO2 
increased by ~20% compared with the multiple-year level. The uncertainties caused by lifetime variations were estimated to 


be 10%, and this value was applied to all considered sources. 
9 


10 


15 


20 


25 


30 


35 


40 


The total uncertainty was defined as the root of the quadratic sum of the aforementioned contributions, which were assumed 
to be independent. We estimated that the total uncertainties of the fitted NO, emissions were within 66%-—99% for all 
investigated sites. Notably, this estimate is rather conservative because of the assumption that all the contributors to 
uncertainties are independent. In addition, the uncertainty in emission trends was significantly lower than that of emissions 
because the errors from the choice of fit intervals, wind fields, tropospheric columns and NO,/NO,j ratios were generally 


compensated for in the assessment of trends. 


4 Conclusions 


We quantified the NO, emissions of cities over China obtained from satellite NO. observations for the period 2005 to 2015. 
The lifetimes were determined from the average changes in NO distributions under windy conditions compared with calm 
conditions, and the emissions were subsequently estimated by dividing the total mass of NO, integrated around the source of 
interest in any three consecutive years from 2005 to 2015 by the derived lifetimes. The method was successfully applied to 
48 cities and 7 power plants to obtain the NO, emission trends over China. 

We detected similar temporal variations in the derived NO, emissions for cities and power plants, both of which experienced 
a rapid growth until approximately 2011 and a sharp decline thereafter. The NO, emissions from selected cities experienced 
an average growth of 52% prior to 2011", because of the increase in fuel consumption. The subsequent decline of 21% was 
quantitatively attributed to the successful control of NO, emissions in the power, industrial and transportation sectors. In 
addition to installing denitration devices at power plants and cement plants, China has transformed its industrial structure by 
phasing out heavily polluting industrial factories, decreasing coal consumption, controlling vehicle emissions through stricter 
emission standards and scrapping aged vehicles. The average emission trend fitted by this study is consistent with the 
previous findings, which showed that OMI NO) levels peaked in 2011 over China (Krotkov et al., 2016; Duncan et al., 2016) 
and NO, emissions from satellite data assimilation peaked in 2011/2012 (Miyazaki et al., 2017; van der A et al., 2017; Souri 
et al., 2017) respectively. Additionally, the fitted emission peaks for individual cities showed reasonable agreement with the 
peaks of OMI NO, levels at provincial level (Liu et al., 2016b). Half of the investigated cities reached simultaneous emission 
peaks with the corresponding provinces. For the another half, the majority (over 70%) reached emission peaks prior to the 
average provincial timeline, which are most likely caused by emission control policies implemented in the city ahead of the 
provincial schedule, such as the previously discussed new vehicle emission standards in Guangzhou. 

We further compared the derived NO, emissions with the bottom-up emission estimates for individual cities. Megacities 
with a large amount of vehicle emissions reached the emission peak prior to the average timeline, because of the stricter 
vehicle regulations that were implemented ahead of the national schedule. Cities with power and industrial sectors as the 
dominant emission sources reached the emission peak at dates that were consistent with the schedule for emission control in 
the corresponding sectors. In addition, we found that the derived NO, emissions were significantly less consistent with the 
regional inventory MEIC for cities (=0.4 on average) than the high-resolution power plant inventory CPED, a result related 
to the uncertainties in the spatial allocation technique, in which surrogates were used to break down regional-based emission 
data to the level of cities. However, the discrepancy was strongly scale dependent, and the trends between the top-down and 
bottom-up estimates were consistent at the province level but not at the city level. This finding indicated that the allocation 
technique used in bottom-up inventory misrepresents the spatial and temporal patterns for emissions over cities. 

Our results indicated that OMI NO, observations can be used to estimate NO, emission trends for individual cities and power 
plants, even those with a polluted background. Moreover, this method can be applied to quantify the emission variations 
from various hot spots worldwide. Notably, the lifetimes were derived on the basis of the average NO, columns for the entire 
study period of 2005-2015 because of a lack of statistics for shorter periods. Because future satellite instruments, such as 


TROPOMI (Veefkind et al., 2012), GEMS (Kim et al., 2012), TEMPO (Chance et al., 2012) and Sentinel-4 (Ingmann et al., 


10 


10 


15 


20 


25 


30 


35 


2012), have improved spatial and temporal resolution, the capabilities of this method is expected to be further enhanced. We 
expect that future estimates of interannual lifetimes as well as diurnal cycles from geostationary satellites will be able to 
account for changes in meteorology and NO, chemistry. In addition, the trend analysis for annual and even seasonal NO, 


emissions should be achievable and should serve as a more reliable tool for interpreting emission changes. 


Acknowledgements 


This research was funded by the National Natural Science Foundation of China (41625020, 41571130032), the National 
Key R&D Program (2016YFC0201506), China’s National Basic Research Program (2014CB441301), and the MarcoPolo 
project of the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement number 606953. 


References 


Beirle, S., Boersma, K. F., Platt, U., Lawrence, M. G., and Wagner, T.: Megacity emissions and lifetimes of nitrogen oxides 
probed from space, Science, 333, 1737-1739, 2011. 

Beijing Municipal Government: The Clean Air Action Plan 2013-2017, available at 
http://zhengwu.beijing.gov.cn/ghxx/qtgh/t1324558.htm (last accessed: 30 March 2017), 2013 (in Chinese). 

Boersma, K. F., Eskes, H. J., Veefkind, J. P., Brinksma, E. J., van der A, R. J., Sneep, M., van den Oord, G. H. J., Levelt, P. 


F., Stammes, P., Gleason, J. F., and Bucsela, E. J.: Near-real time retrieval of tropospheric NO, from OMI, Atmos. Chem. 
Phys., 7, 2103-2118, doi: 10.5194/acp-7-2103-2007, 2007. 

Boersma, K. F., Eskes, H. J., Dirksen, R. J., van der A, R. J., Veefkind, J. P., Stammes, P., Huijnen, V., Kleipool, Q. L., 
Sneep, M., Claas, J., Leit&éo, J., Richter, A., Zhou, Y., and Brunner, D.: An improved tropospheric NO, column retrieval 
algorithm for the Ozone Monitoring Instrument, Atmos. Meas. Tech., 4, 1905-1928, doi: 10.5194/amt-4-1905-2011, 2011. 
Burrows, J. P., Weber, M., Buchwitz, M., Rozanov, V., Ladstatter-WeiBenmayer, A., Richter, A., DeBeek, R., Hoogen, R., 
Bramstedt, K., Eichmann, K.-U., Eisinger, M., and Perner, D.: The Global Ozone Monitoring Experiment (GOME): Mission 
Concept and First Scientific Results, Journal of the Atmospheric Sciences, 56, 151-175, 1999. 

Chance, K., Lui, X., Suleiman, R. M., Flittner, D. E., and Janz, S.J.: Tropspheric Emissions: Monitoring of Pollution 
(TEMPO), presented at the 2012 AGU Fall Meeting, San Francisco, USA, 3—7 December 2012, A31B-0020, 2012. 

China Electricity Council (CEC): Annual report on information about flue gas desulfurization and denitrification for thermal 
power plants in 2011, available at http://huanzi.cec.org.cn/dongtai/2012-02-28/80830.html (last accessed: 22 May 2016), 
2012 (in Chinese). 


China Electricity Council (CEC): Annual report on information about flue gas desulfurization and denitrification for thermal 
power plants in 2012, available at www.cec.org.cn/yaowenkuaidi/2013-03-19/98992.html (last accessed: 22 May 2016), 
2013 (in Chinese). 

China Electricity Council (CEC): Annual report on information about flue gas desulfurization, denitrification and dust 
removal for thermal power plants in 2013, available at http://huanzi.cec.org.cn/dongtai/2014-05-07/121302.html (last 
accessed: 22 May 2016), 2014 (in Chinese). 

China Electricity Council (CEC): Annual report on information about environmental protection for thermal power plants in 


2014, available at http://huanzi.cec.org.cn/tuoliu/2015-05-07/137531.html (last accessed: 22 May 2016), 2015 (in Chinese). 


China Electricity Council (CEC): Annual report on information about environmental protection for thermal power plants in 
2015, available at http://huanzi.cec.org.cn/tuoliu/2016-04-25/152005.html (last accessed: 22 May 2016), 2016 (in Chinese). 
Crippa, M., Janssens-Maenhout, G., Dentener, F., Guizzardi, D., Sindelarova, K., Muntean, M., Van Dingenen, R., and 


Granier, C.: Forty years of improvements in European air quality: regional policy-industry interactions with global impacts, 


11 


10 


15 


20 


25 


30 


35 


40 


Atmos. Chem. Phys., 16, 3825-3841, 10.5194/acp-16-3825-2016, 2016.de Foy, B., Wilkins, J. L., Lu, Z., Streets, D. G., and 
Duncan, B. N.: Model evaluation of methods for estimating surface emissions and chemical lifetimes from satellite data, 
Atmos. Environ., 98, 66-77, 2014. 

de Foy, B., Lu, Z., Streets, D. G., Lamsal, L. N., and Duncan, B. N.: Estimates of power plant NO, emissions and lifetimes 
from OMI NO, satellite retrievals, Atmos. Environ., 116, 1-11, 2015. 

Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, 
G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., 
Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hélm, E. V., Isaksen, L., Kallberg, P., KGhler, M., Matricardi, M., 
McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., 
and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Quarterly 
Journal of the Royal Meteorological Society, 137, 553-597, 2011. 

Duncan, B. N., Lamsal, L. N., Thompson, A. M., Yoshida, Y., Lu, Z., Streets, D. G., Hurwitz, M. M., and Pickering, K. E.: 
A space-based, high-resolution view of notable changes in urban NO, pollution around the world (2005-2014), J. Geophys. 
Res., 121, 976-996, doi: 10.1002/2015jd024121, 2016. 

European Commission (EC): Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL), Emission 
Database for Global Atmospheric Research (EDGAR), release version 4.2, available at: http://edgar.jrc.ec.europa.eu (last 
access: 1 December 2013), 2011. 

Fioletov, V. E., McLinden, C. A., Krotkov, N., Moran, M. D., and Yang, K.: Estimation of SO, emissions using OMI 
retrievals, Geophys. Res. Lett., 38, L21811, doi: 10.1029/2011g1049402, 2011. 


Fioletov, V. E., McLinden, C. A., Krotkov, N., and Li, C.: Lifetimes and emissions of SO, from point sources estimated 
from OMI, Geophys. Res. Lett., 42, 2015GL063148, doi: 10.1002/2015g1063148, 2015. 

Fioletov, V. E., McLinden, C. A., Krotkov, N., Li, C., Joiner, J., Theys, N., Carn, S., and Moran, M. D.: A global catalogue 
of large SO, sources and emissions derived from the Ozone Monitoring Instrument, Atmos. Chem. Phys., 16, 11497-11519, 
doi: 10.5194/acp-16-11497-2016, 2016. 

Forzatti, P.: Present status and perspectives in de-NO, SCR catalysis, Applied Catalysis A: General, 222, 221-236, 2001. 
Hogue, S., Marland, E., Andres, R. J., Marland, G., and Woodard, D.: Uncertainty in gridded CO, emissions estimates, 
Earth's Future, 4, 225—239, 2016. 

Huo, H., Yao, Z., Zhang, Y., Shen, X., Zhang, Q., Ding, Y., and He, K.: On-board measurements of emissions from light- 
duty gasoline vehicles in three mega-cities of China, Atmos. Environ., 49, 371-377, 2012. 

Ingmann, P., Veihelmann, B., Langen, J., Lamarre, D., Stark, H., and Courréges-Lacoste, G. B.: Requirements for the GMES 
Atmosphere Service and ESA's implementation concept: Sentinels-4/-5 and-Sp, Remote Sens. Environ., 120, 58-69, 2012. 
Kim, J.: GEMS (Geostationary Enviroment Monitoring Spectrometer) onboard the GGOKOMPSAT to monitor air quality in 
high temporal and spatial resolution over Asia-Pacific region, presented at the 2012 EGU General Assembly, Vienna, 
Austria, 22—27 April 2012, EGU2012-4051, 2012. 

Kim, S. W., Heckel, A., Frost, G. J., Richter, A., Gleason, J., Burrows, J. P., McKeen, S., Hsie, E. Y., Granier, C., and 
Trainer, M.: NO, columns in the western United States observed from space and simulated by a regional chemistry model 
and their implications for NO, emissions, J. Geophys. Res., 114, D11301, doi: 10.1029/2008jd01 1343, 2009. 

Konovalov, I. B., Beekmann, M., Richter, A., and Burrows, J. P.: Inverse modelling of the spatial distribution of NO, 
emissions on a continental scale using satellite data, Atmos. Chem. Phys., 6, 1747-1770, doi: 10.5194/acp-6-1747-2006, 
2006. 

Krotkov, N. A., McLinden, C. A., Li, C., Lamsal, L. N., Celarier, E. A., Marchenko, S. V., Swartz, W. H., Bucsela, E. J., 
Joiner, J., Duncan, B. N., Boersma, K. F., Veefkind, J. P., Levelt, P. F., Fioletov, V. E., Dickerson, R. R., He, H., Lu, Z., and 


12 


10 


15 


20 


25 


30 


35 


40 


Streets, D. G.: Aura OMI observations of regional SO, and NO, pollution changes from 2005 to 2015, Atmos. Chem. Phys., 
16, 4605-4629, doi: 10.5194/acp-16-4605-2016, 2016. 

Kurokawa, J., Ohara, T., Morikawa, T., Hanayama, S., Janssens-Maenhout, G., Fukui, T., Kawashima, K., and Akimoto, H.: 
Emissions of air pollutants and greenhouse gases over Asian regions during 2000-2008: Regional Emission inventory in 
ASia (REAS) version 2, Atmos. Chem. Phys., 13, 11019-11058, doi: 10.5194/acp-13-11019-2013, 2013. 

Lamsal, L. N., Martin, R. V., Padmanabhan, A., van Donkelaar, A., Zhang, Q., Sioris, C. E., Chance, K., Kurosu, T. P., and 
Newchurch, M. J.: Application of satellite observations for timely updates to global anthropogenic NO, emission 
inventories, Geophys. Res. Lett., 38, LO5810, doi: 10.1029/2010g1046476, 2011. 

Lelieveld, J., Beirle, S., H6rmann, C., Stenchikov, G., and Wagner, T.: Abrupt recent trend changes in atmospheric nitrogen 
dioxide over the Middle East, Science Advances, 1, 2015. 

Leue, C., Wenig, M., Wagner, T., Klimm, O., Platt, U., and Jahne, B.: Quantitative analysis of NO, emissions from Global 
Ozone Monitoring Experiment satellite image sequences, J. Geophys. Res., 106, 5493-5505, doi: 10.1029/2000JD900572, 
2001. 

Levelt, P. F., van den Oord, G. H. J., Dobber, M. R., Malkki, A., Huib, V., Johan de, V., Stammes, P., Lundell, J. O. V., and 
Saari, H.: The ozone monitoring instrument, Geoscience and Remote Sensing, IEEE Transactions on, 44, 1093-1101, 2006. 
Liu, F., Zhang, Q., Tong, D., Zheng, B., Li, M., Huo, H., and He, K. B.: High-resolution inventory of technologies, 
activities, and emissions of coal-fired power plants in China from 1990 to 2010, Atmos. Chem. Phys., 15, 13299-13317, doi: 
10.5194/acp-15-13299-2015, 2015. 

Liu, F., Beirle, S., Zhang, Q., Dorner, S., He, K., and Wagner, T.: NO, lifetimes and emissions of cities and power plants in 
polluted background estimated by satellite observations, Atmos. Chem. Phys., 16, 5283-5298, doi: 10.5194/acp-16-5283- 
2016, 2016a. 

Liu, F., Zhang, Q., Ronald, J. van der A., Zheng, B., Tong, D., Yan, L., Zheng, Y., and He, K.: Recent reduction in NO, 
emissions over China: synthesis of satellite observations and emission inventories, Environmental Research Letters, 11, 
114002, 2016b. 

Lu, Z., Streets, D. G., de Foy, B., and Krotkov, N. A.: Ozone Monitoring Instrument observations of interannual increases in 
SO, emissions from Indian coal-fired power plants during 2005—2012, Environ. Sci. Technol., 47, 13993-14000, 2013. 

Lu, Z., Streets, D. G., de Foy, B., Lamsal, L. N., Duncan, B. N., and Xing, J.: Emissions of nitrogen oxides from US urban 
areas: estimation from Ozone Monitoring Instrument retrievals for 2005-2014, Atmos. Chem. Phys., 15, 10367-10383, doi: 
10.5194/acp-15-10367-2015, 2015. 

Martin, R. V., Jacob, D. J., Chance, K., Kurosu, T. P., Palmer, P. I., and Evans, M. J.: Global inventory of nitrogen oxide 
emissions constrained by space-based observations of NO, columns, J. Geophys. Res., 108, 4537, doi: 
10.1029/2003jd003453, 2003. 

McLinden, C. A., Fioletov, V., Boersma, K. F., Krotkov, N., Sioris, C. E., Veefkind, J. P., and Yang, K.: Air quality over the 
Canadian oil sands: A first assessment using satellite observations, Geophys. Res. Lett., 39, L04804, doi: 
10.1029/2011g1050273, 2012. 

McLinden, C. A., Fioletov, V., Shephard, M. W., Krotkov, N., Li, C., Martin, R. V., Moran, M. D., and Joiner, J.: Space- 
based detection of missing sulfur dioxide sources of global air pollution, Nature Geosci, 9, 496-500, 2016. 

Ministry of Environmental Protection of China (MEP): China vehicle emission control annual report 2010, available at 


http://vecc-mep.org.cn/index/20101110nianbao.pdf (last accessed: 22 May 2016), 2010 (in Chinese). 


Ministry of Environmental Protection of China (MEP): Emission Standard of Air Pollutants for Thermal Power Plants: 
GB13223-2011, China Environmental Science, Beijing, China, 2011 (in Chinese). 


10 


15 


20 


25 


30 


35 


40 


Miyazaki, K., Eskes, H., Sudo, K., Boersma, K. F., Bowman, K., and Kanaya, Y.: Decadal changes in global surface NO, 
emissions from multi-constituent satellite data assimilation, Atmos. Chem. Phys., 17, 807-837, doi: 10.5194/acp-17-807- 
2017, 2017. 

National Bureau of Statistics (NBS): China Environment Yearbook (CEY), China Statistics Press, Beijing, China, 2004— 
2015 (in Chinese). 

National Bureau of Statistics (NBS): China Statistical Yearbook for Regional Economy (CSYRE), China Statistics Press, 
Beijing, China, 2004—2014 (in Chinese). 

Richter, A., Burrows, J. P., Nusz, H., Granier, C., and Niemeier, U.: Increase in tropospheric nitrogen dioxide over China 
observed from space, Nature, 437, 129-132, 2005. 

Russell, A. R., Valin, L. C., and Cohen, R. C.: Trends in OMI NO; observations over the United States: effects of emission 
control technology and the economic recession, Atmos. Chem. Phys., 12, 12197-12209, doi: 10.5194/acp-12-12197-2012, 
2012. 

Schneider, P., and van der A, R. J.: A global single-sensor analysis of 2002-2011 tropospheric nitrogen dioxide trends 
observed from space, J. Geophys. Res., 117, D16309, doi: 10.1029/2012jd017571, 2012. 

Schneider, P., Lahoz, W. A., and van der A, R.: Recent satellite-based trends of tropospheric nitrogen dioxide over large 
urban agglomerations worldwide, Atmos. Chem. Phys., 15, 1205-1220, doi: 10.5194/acp-15-1205-2015, 2015. 

Seinfeld, J. H. and Pandis, S. N.: Atmospheric chemistry and physics: From air pollution to climate change, John Wiley and 
Sons, New York, 204-275, 2006. 

Souri, A. H., Choi, Y., Jeon, W., Woo, J.-H., Zhang, Q., and Kurokawa, J.-i.: Remote sensing evidence of decadal changes in 
major tropospheric ozone precursors over East Asia, J. Geophys. Res., 122, 2474-2492, doi: 10.1002/2016JD025663, 2017. 
State Council of the People’s Republic of China: Integrated work plan for energy saving and emission reduction during the 
twelfth five- Year plan, available at http://gov.cn/zwgk/2011-09/07/content_1941731.htm (last accessed: 22 May 2016), 2011 
(in Chinese). 


State Council of the People’s Republic of China: Air pollution prevention and control action plan, available at 


http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm (last accessed: 30 March 2017), 2013 (in Chinese). 


Valin, L. C., Russell, A. R., and Cohen, R. C.: Variations of OH radical in an urban plume inferred from NO, column 
measurements, Geophys. Res. Lett., 40, 1856-1860, doi: 10.1002/gr1.50267, 2013. 

van der A, R. J., Eskes, H. J., Boersma, K. F., van Noije, T. P. C., Van Roozendael, M., De Smedt, I., Peters, D. H. M. U., 
and Meijer, E. W.: Trends, seasonal variability and dominant NO, source derived from a ten year record of NO, measured 
from space, J. Geophys. Res., 113, D04302, doi: 10.1029/2007JD009021, 2008. 

van der A, R. J., Mijling, B., Ding, J., Koukouli, M. E., Liu, F., Li, Q., Mao, H., and Theys, N.: Cleaning up the air: 
effectiveness of air quality policy for SO, and NO, emissions in China, Atmos. Chem. Phys., 17, 1775-1789, doi: 
10.5194/acp-17-1775-2017, 2017. 

Veefkind, J. P., Aben, I., McMullan, K., Forster, H., de Vries, J., Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, 
Q., van Weele, M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., 
Vink, R., Visser, H., and Levelt, P. F.: TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global 
observations of the atmospheric composition for climate, air quality and ozone layer applications, Remote Sens. Environ., 
120, 70-83, 2012. 

Wu, Y., Zhang, S., Hao, J., Liu, H., Wu, X., Hu, J., Walsh, M. P., Wallington, T. J., Zhang, K. M., and Stevanovic, S.: On- 
road vehicle emissions and their control in China: A review and outlook, Sci. Total Environ., 574, 332-349, 2017. 

Zhang, S., Wu, Y., Liu, H., Wu, X., Zhou, Y., Yao, Z., Fu, L., He, K., and Hao, J.: Historical evaluation of vehicle emission 


control in Guangzhou based on a multi-year emission inventory, Atmos. Environ., 76, 32-42, 2013. 


14 


Zheng, B., Zhang, Q., Tong, D., Chen, C., Hong, C., Li, M., Geng, G., Lei, Y., Huo, H., and He, K.: Resolution dependence 
of uncertainties in gridded emission inventories: a case study in Hebei, China, Atmos. Chem. Phys., 17, 921-933, doi: 


10.5194/acp-17-921-2017, 2017. 


aut 
( ee, es 
t— aC 
J 
pene @22 on, eek 
a we : 4 , 
\ A52 : \ Os “--U 
; 48 DN RS 
- © ae fe oo 
AR 4 
- oe oe 
2 SS 
y pee S 
= =x Pe ae 2 
Se i 
: : 
\ — 
od “i, Sh 


Category wu 
@ City 
A Mountainous city 
© Power plant 
A. Mountainous power plant 


Figure 1: Locations of the selected sites in this study. The triangles represent the mountainous sites defined in Sect. 2.1. 
All locations are labeled with their IDs (see Table S1). 


2005-2007 2006-2008 2007-2009 


Lat 


45.5 


2008-2010 2009-2011 2010-2012 


45.5 


Lat 


2011-2013 2012-2014 2013-2015 


119 119.5 120 119 119.5 120 119 119.5 120 
Lon Lon Lon 
NO, Tropospheric Column (10'° molec/cm?) 


0 1 2 3 4 5 


2500 0.100 
Total capacity 

SCR capacity 

—@®— Bottom-up emissions 


—©— Fitted emissions 2000 § 

—O-— Fitted background 0.075 § 

a Ke) 

2 = = 
Oo Oo 

£ 1500 S co 

- = +0050 3 
12) ie) = 

= o =] 

3 1000 & g 

= Oo 2 

WW © 

a 

0.025 5 

500 £ 

ic 


0.000 


1005 29 oo-2IE1-2IBhe- AY Hho: ZY Hho YHA 2YHA2-2GHHG-20> 
Year 
Cb) 


5 Figure 2: (a) OMI NO, TVCD map under weak-wind conditions (<3 m/s) around the Huolin power plant (#2 in Fig. 1) 
during 2005 to 2015 and (b) the corresponding fit results. The red and blue lines denote the fitted emissions and 
background, respectively; the pink line denotes the bottom-up emission estimates; the solid and dashed bars denote 
the total capacity of the generation units and the capacity of generation units that installed SCR equipment, 
respectively. The information on the capacity and SCR equipment is derived from the CPED database (Liu et al., 

10 2015). Error bars show the uncertainties for emissions by using this method and bottom-up inventories (see Sect. 3.4). 


25 
(a) Bottom-up emissions: __ (-_) Fitted emissions (b) ®- Fittedemissions -—*— REAS 

6000 [5 Power Hil Residentyal —— Vehicle population ---- EGDAR 
(5) Industry HM Transportation 


7 la. 


~t+— Coal consumption —4— MEIC 


N 
° 


LC 
ae S 
2 a 
> 4000 o 
E S 
o oN 

s c 6 1.5 
@ 3000 ee 
2 oo 
& gE 

ae B2 410 

2 2000 oo 

z x 2 
a = 
e E 

1000 s 05 

Fitted emissions/vehicle population 
Fitted emissions/coal consumption 
0 0.0 
v1 8, 9 9 4 2 3 4 5 yt 1s} e) iv} 4 2 3 4 5 
2009-299 08-29967 2908-29 o0- OM 0-295 a‘ 2QYA2-295 13-20" 2008-299 08-29967 -20%98-29 90 2910-294 A 2K 2-29543-20 


Year Year 


Figure 3: (a) Fitted (yellow bar) and total anthropogenic NO, emissions by sector for all investigated cities in this study 
during 2005 to 2015. The emissions data are derived from the MEIC model. (b) Interannual trends of the fitted (gray 
line) and bottom-up anthropogenic NO, emissions for selected cities with valid information on the vehicle population 
(blue solid squares) and coal consumption (blue open squares) from 2005-2015. The emissions deriving from the 
MEIC, REAS v2.1 and EDGAR v4.3 inventory are displayed in black, green and purple respectively. The pink lines 
denote the ratio of the fitted NO, emissions in this study to the vehicle population (solid squares) and coal 
consumption (open squares). The relative changes of the vehicle population and coal consumption are indicated by 
right axes. Error bars show the uncertainties for the fitted emissions by using this method (see Sect. 3.4). 


18 


(a) Guangzhou’ (#14, 0.66) (b) Shanghai (#32, -0.33) 
600, 300 
400 
aq a 
° °° 
E 300 £ 
@ % 200 
2 S 
3 ra 
& 200 & 
Oo oO 
Zz = 100 
100 
0 0 
2005 2007 2009 2011 2013 2015 2005 2007 2009 2011 2013 2015 
200 Year 4100 Year 
(c) Wuhai (#38, 0.95) (d) Huainan (#19, 0.87) 
| a 
150 _ 75 /\ | »—< 
g g 
E g bn \ 
Q a 
5 5 
3 100 3 50 V4 
. 7 o—< 
wi uw 
oe Oo 
2 Zz 
50 25 
0 0 
2005 2007 2009 2011 2013 2015 2005 2007 2009 2011 2013 2015 
Year Year 
(e) Karamay (#22, -0.2) (f) Jiayuguan (#47, 0.97) 
gs g 
re) re} 
= £ 
3 2 
2 Ss 
3 g 
& £ 
uw wi 
o oO 
2 z 
0 
2005 2007 2009 2011 2013 2015 2005 2007 2009 2011 2013 2015 
Year Year 


—a— Fitted emissions 


Bottom-up emissions: —©O— Total —©— Industry —-®— Power —@*— Transportation 


Figure 4: Interannual trends in the fitted (gray squares) and bottom-up anthropogenic NO, emissions for 2005 to 2015 
including total (black circles), industrial (pink circles), power plant (light blue circles) and transportation (dark blue 
circles) emissions. Error bars denote the uncertainty of the fitted NO, emissions. The IDs from Fig. 1 and the 
correlation coefficients of the pair-wise trends between the bottom-up and fitted NO, emissions are shown in the 
bracket after the name of the city. 


Guangzhou represents the cities of Guangzhou, Foshan and Dongguan, which are recognized as the same hot spot in the 
map of NO, TVCDs. 


19 


(a) 
S 2.0 4 20 
o oO 
q 9 
oS ra 
[sy 
Beis] Be 45 
oO § oO S 
Se ae 
a8 2 
ae a = 8 
ca ee mae ; % 1.0 
E E 
° (e) 
£ = 
0.5 AS 0.5 —- —- _- _- _- — + 
OP Q® AO rd Ah nd aah nd «( 1 b AS 
og oP oP wo ve 9 hy ® Ao? aS 9X om gy 
9? oP 78 OP gh? go 9 9 90500020 gS 
(c) (d) 
§ 204 £ 20 
2S oS 
Q q 
pt) ite) 
8 S 
BS 28 
io} 
ee 154 Be 45 
[s) § ra) § 
Se gE 
wo ene) 
oa oo 
“ | 88 
= 1.0 5 1.0 a -j--J--+--- 
E = 
ie) ro) 
— Ss 
0.5 0.5 T T T T T T T 
| 6® -CO nO pAN ard bk on x ere ee ee 
eo" Xo SOW Roh x oF NEN ND Ree ol oe a og xe wo “oe 
99 g00" qe qe qu gn q9'" 99 Pq 90! qa qaO” gaan qa" q9 
Year Year 


Figure 5: Comparisons of the trends in satellite observations (left panels) with those in the bottom-up emission 
inventory (right panels) at the province and city level during 2005 to 2015. The box plots show the relative changes in 
(a) the average OMI tropospheric NO, column densities for provinces in China; (b) the anthropogenic NO, emissions 
for provinces in China; (c) the fitted NO, emissions for cities investigated in this study; and (d) the anthropogenic 
NO, emissions for the corresponding cities. The blue horizontal line is the median of the relative differences; the red 
horizontal line is the mean of the relative differences; the box denotes the 25% and 75% percentiles; and the whiskers 
denote the 10% and 90% percentiles. The bottom-up emission data are derived from the MEIC model. 


20 


3. a 
2 i oO 
= 2 mountainous (power) 2 < 
2o non-mountainous (power) =s 
oOo} non-mountainous (city oe 
c oO Ki a nw 
5 2 mountainous (city) 5 2 
eo 3 gr 1: Guiyang 
ie aok- 2: Kunming 
es £ 3: Xiangyang 
8 5 Lu 4: Fuzhou 
5: Jinzhou 
6: Liuzhou 
7: Wuhai 
8: Jiayuguan 
9: Urumgi 
1 10 100 1000 0 20 40 60 80 100 120 
Averaged bottom-up Emissions / (mol/s) Averaged bottom-up Emissions / (mol/s) 


(a) (b) 


Figure 6: (a) Correlation coefficients of the pair-wise trends between the bottom-up anthropogenic and fitted NO, 
emissions for all selected sites during 2006* to 2014*. The results for sites with correlation coefficients less than -0.4 
or larger than 0.9 are indicated by digits. (b) Scatterplots of the fitted NO, emissions for investigated cities versus 
bottom-up anthropogenic emission inventories during 2006* to 2014*. Urban emissions from bottom-up inventories 
are integrated over an area of 40 km x 40 km (see Sect. 2.2). Results with correlation coefficients less than -0.4 or 
larger than 0.9 are color coded by grey and green, respectively. 


21 


10 


3.0 


Total capacity 


——©-— Fitted emissions 

— —e— MEI 

o 257 _» eee 

= penetration 

“N —-@— REAS 

oS 

oO 2.0 c 
OW je) 
oa ae 
5 © ° 
5 & : 
o® 1.5 = 
Ze S 
8 5 
Do 
xe 1.0 a 

© 

= 

2 05 

0.0 


o1 2 A n9\5 
2005 29Y6-29987-29RGv-29iho-20 40-29 1-29\R2-20hg-201 


Year 


Figure 7: Interannual trends of the fitted (red line) and bottom-up NO, emissions for selected power plants during 
2005 to 2015. The emissions deriving from the MEIC and REAS v2.1 inventgory are displayed in pink and green 
respectively. The bar denotes the total capacity of selected power plants. The blue line denotes the penetration of 
power plants with denitration devices (defined as the percentage of unit capacity of power plants installing SCR in 
the total capacity of all the power plants). Error bars show the uncertainties for fitted emissions by this method (see 
Sect. 3.4). 


100 1000 

2005-2007 (0.93) 

2006-2008 (0.97) 

2007-2009 (0.97) 

2008-2010 (1.0) 2008-2010 (0.85) 

2009-2011 (1.0) 2009-2011 (0.84) 

2010-2012 (0.99) 100 2010-2012 (0.86) 

2011-2013 (0.97) 2011-2013 (0.83) 
) 
) 


2005-2007 (0.88) 
2006-2008 (0.87) 
2007-2009 (0.87) 


2012-2014 (0.95) 2012-2014 (0.79 
2013-2015 (0.73) 2013-2015 (0.76 
average saat 


= 
oO 


gO 


Emissions / (mol/s) 
This Study 
r=) 
Emissions / (mol/s) 
This Study 


1 10 100 1 10 100 1000 
Bottom-up Emissions / (mol/s) Bottom-up Emissions / (mol/s) 
non-mountain/mountain non-mountain/mountain 


(a) (b) 


Figure 8: Scatterplots of the fitted NO, emissions for the investigated (a) power plants and (b) cities versus the bottom- 
up emission inventories during 2006* to 2014*. Urban emissions from bottom-up inventories are integrated over an 
area of 40 km x 40 km (see Sect. 2.2). The correlation coefficients of non-mountainous sites for individual 3-year 
periods are shown in brackets. Open circles represent the average emissions for non-mountainous (blue) and 


22 


mountainous (red) sites during the entire period. The correlation coefficients of the average emissions for non- 
mountainous and mountainous sites are color-coded in blue and red, respectively. The straight line represents the 
ratio of 1:1. 


23