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
'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,
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 (email@example.com ; 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.
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
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
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.
2.1 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,
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.
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
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
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
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.
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-
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
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
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.,
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
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.
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.
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.,
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.
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.
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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).
(a) Bottom-up emissions: __ (-_) Fitted emissions (b) ®- Fittedemissions -—*— REAS
6000 [5 Power Hil Residentyal —— Vehicle population ---- EGDAR
(5) Industry HM Transportation
~t+— Coal consumption —4— MEIC
> 4000 o
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@ 3000 ee
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2 2000 oo
z x 2
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Fitted emissions/vehicle population
Fitted emissions/coal consumption
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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
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).
(a) Guangzhou’ (#14, 0.66) (b) Shanghai (#32, -0.33)
E 300 £
@ % 200
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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)
150 _ 75 /\ | »—<
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3 100 3 50 V4
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2005 2007 2009 2011 2013 2015 2005 2007 2009 2011 2013 2015
(e) Karamay (#22, -0.2) (f) Jiayuguan (#47, 0.97)
2005 2007 2009 2011 2013 2015 2005 2007 2009 2011 2013 2015
—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.
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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.
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
1 10 100 1000 0 20 40 60 80 100 120
Averaged bottom-up Emissions / (mol/s) Averaged bottom-up Emissions / (mol/s)
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.
——©-— Fitted emissions
— —e— MEI
o 257 _» eee
“N —-@— REAS
oO 2.0 c
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xe 1.0 a
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2005 29Y6-29987-29RGv-29iho-20 40-29 1-29\R2-20hg-201
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
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)
2012-2014 (0.95) 2012-2014 (0.79
2013-2015 (0.73) 2013-2015 (0.76
Emissions / (mol/s)
Emissions / (mol/s)
1 10 100 1 10 100 1000
Bottom-up Emissions / (mol/s) Bottom-up Emissions / (mol/s)
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
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.