Introduction
Emission inventories are crucial for atmospheric science research,
particularly chemical transport modeling (CTM), and for air quality
policymaking that seeks to identify and control pollution sources. Given
China's important role in the origins and transport of air pollutants in east
Asia and beyond, a number of emission inventories at national scale have been
established in recent years. These include the Transport and Chemical
Evolution over the Pacific mission (TRACE-P, Streets et al., 2003), the
Intercontinental Chemical Transport Experiment-Phase B (INTEX-B, Zhang et
al., 2009), the Regional Emission inventory in ASia (REAS, Ohara et al.,
2007; Kurokawa et al., 2013), and the Multi-resolution Emission Inventory for
China (MEIC, http://www.meicmodel.org/). Based on “bottom-up”
principles and frameworks similar to those described in Streets et
al. (2003), more detailed source categories and expanded domestic information
on emission factors and activity levels have been integrated into most recent
work, yielding improved interannual trends in national estimates of
anthropogenic air pollutant emissions (e.g., MEIC; Zhao et al., 2012a, b,
2013). Aside from the national-level work, regional emission inventories have
also been established with improved understanding of local conditions for key
areas with high densities of population, industry, and energy consumption,
e.g., the Jing–Jin–Ji region including Beijing and Tianjin (JJJ; S. Wang et
al., 2010), the Yangtze River Delta (YRD; Fu et al., 2013; Huang et al.,
2011), and the Pearl River Delta (PRD; Zheng et al., 2009).
There is still need for improvement of bottom-up emission inventories
however, particularly at smaller spatial scales. First, data for activity
levels and emission factors used in current Chinese inventories come mostly
from coarse statistics or surveys at the provincial level, except for select
sectors (e.g., power generation, Zhao et al., 2008). Underlying information
that is crucial to emission levels (e.g., combustion or manufacturing
technologies, fuel qualities, and penetrations and removal efficiencies of
various emission control devices) has often been overlooked or assumed to be
uniform, inevitably reducing the accuracy and reliability of emission
estimates. Without sufficient source-specific information, in particular, it
is hard to identify and characterize “super-emitting” sources, which can
strongly affect aggregate emission estimates. Second, the spatial and
temporal distributions of emissions are often not well characterized.
Emissions from most sectors are spatially allocated according to proxies,
e.g., population or economic densities. However, the distribution of large
sources in China is changing quickly because much industrial production is
being relocated outside of urban cores, and the correlations between emissions
and the usual proxies like population density are getting weaker,
particularly in developed cities (Zhang et al., 2012a; Zhao et al., 2013).
This introduces error into the spatial allocations. The time distribution of
emissions is commonly based on expert judgment, with little reliance on
real-time data sources, e.g., continuous emission monitoring systems (CEMS).
Finally, some sources that may play important roles in local emissions are
often missed in regional emission inventories, e.g., fugitive dust from
construction and road transportation, and volatile organic compounds (VOCs)
from gas stations, mainly due to data limits on such sources. Such
limitations weaken capacities to research atmospheric chemistry using
inventories as inputs to CTMs, and also undermine the efficacy of pollution
control decision-making.
Several studies have illustrated the benefits of city-scale emission
inventories that integrate more detailed local information. Timmermans et
al. (2013), for example, concluded that the results of NO2 and PM10
simulations were more consistent with observations for Paris when a local
emission inventory was used. In the US, model simulations of NO2 columns
based on actual hourly NOx emissions from CEMS agreed better with
satellite observations than those based on default emission inputs from the
US Environmental Protection Agency (US EPA; Kim et al., 2009). In China,
vehicle emission inventories resolved at county level, rather than
provincial level, were shown to better support air quality simulations for
small regions (Zheng et al., 2014).
China is experiencing frequent severe haze episodes (Zhang et al., 2012b;
Wang et al., 2013; Wang et al., 2014; R. J. Huang et al., 2014). Cities are
being required to expand efforts to control air pollution, and use of CTMs
for air quality assessment and policy making is becoming more routine. Due in
part to weak emission inputs, however, atmospheric simulations for cities are
often disappointingly inaccurate (Zifa Wang, personal communication,
Institute of Atmospheric Physics, Chinese Academy of Science, 2014). Without
integrating more detailed local information, some cities simply downscale a
national emission inventory into high-resolution emission inputs for a CTM or
develop local inventories based on the same source data and methods used for
national ones. Despite improvements from some information (e.g., the precise
location of large point sources), the quality and reliability of those
inventories have not been well evaluated using, for example, integrated
observational data as top-down constraints. Thus the emission estimates
introduce large uncertainties into the city-scale air quality simulations.
It should be noted that such improvements in emission inventories for a few
megacities, including Beijing and Shanghai, have been driven by air quality
planning for major events like the 2008 Summer Olympic Games and 2010 World
Expo. During recent years, however, satellite observations have detected that
the most significant growth in air pollution (indicated for example by
vertical column densities of tropospheric NO2) across the country is
occurring not within such megacities but in the less-developed regions around
them, due to faster growth in the economies and emission sources in those
areas (Zhang et al., 2012a). This finding highlights the importance of
developing and assessing air pollutant emission inventories for regions other
than China's much-studied megacities.
We select Nanjing, a typical large city in the YRD, to establish and evaluate
a high-resolution emission inventory at city scale. As shown in Fig. S1a in
the Supplement, Nanjing is the capital city of the province of Jiangsu and the
second largest city in central east China following Shanghai, with a total
area of 6587 km2 and population of 8 million in 2012 (NJNBS, 2013).
Intensively industrial, Nanjing consumes much more coal than many other
Chinese cities with economies of similar size (e.g., Hangzhou, Qingdao, and
Shenyang), and in 2012 suffered the highest number of days of haze (226) of
all of China's provincial capital cities (data provided by the Nanjing
Meteorological Bureau). The share of coal use in primary energy reached
89 % in 2012 (Environmental Statistics, an internal database of the
Nanjing Environmental Protection Bureau, NJEPB; NJNBS, 2013), much higher
than the national level of 71 % (NBSC, 2013). Large coal consumption and
industrial and chemical production have resulted in high emissions of
anthropogenic atmospheric pollutants. As the host city of the 2nd Asian Youth
Games (AYG) in August 2013, Nanjing undertook various measures to control
emissions in industry but allowed construction activities at thousands of
sites, leading to large changes in the levels and the temporal and spatial
distributions of emissions. Such changes are captured by a comprehensive
city-scale emission inventory.
Data and methods
The basic methodology
The annual emissions in Nanjing from 2010 to 2012 are estimated with a
bottom-up approach for 10 atmospheric pollutants (SO2, NOx, total
suspended particulates (TSP), PM10, PM2.5, black carbon (BC) or
elemental carbon (EC), organic carbon (OC), CO, VOCs, and NH3) and the
greenhouse gas CO2. At the largest scale, sources fall into six main
categories: coal-fired power plants (CPP), industry (IND), transportation
(TRA, including on-road and non-road subcategories), the residential and
commercial sector (RES, including fossil fuel, biofuel, and biomass open
burning subcategories, along with gas stations for the estimation of VOCs only),
agriculture (AGR, including livestock farming and fertilizer use for NH3
estimation only), and fugitive dust (FUD, including that from construction
sites and roads). IND is further divided into cement plants (CEM), iron and
steel plants (ISP), refineries and chemical plants (RCP), solvent use (SOL;
although some solvent is not used in industry, we include it in this category
for classification simplicity) and other industry plants (OIN).
To improve the accuracy and reliability of the city-level emission
inventory, new data are collected from various sources and modified methods
are applied compared to previous studies, as briefly summarized below.
With sufficient information related to emission estimation now available,
more sources can be characterized as point sources in the current inventory.
These include power plants (total number in 2012, similarly for subsequent
categories: 18), cement plants (23), iron and steel plants (2), chemical
plants (173), non-ferrous metal smelters (14), lime plants (9), brick
plants (31), factories representing a portion of other industrial
sectors (434), and gas-fueling stations (269). The locations of point sources
by category are shown in Fig. S1b and c in the Supplement. The annual
emissions of point sources are calculated with Eq. (1):
Ei=∑j,mAi,j,m×EFi,j,m×1-ηi,j,m,
where i, j, and m represent the species, individual plant, and
fuel/technology type, respectively; A is the activity level data; EF is the
uncontrolled emission factor; and η is removal efficiency of air
pollutant control device (APCD).
For all the point sources, information from the Environmental Statistics
database and Pollution Source Census (internal data of NJEPB) is collected
and compiled to obtain the activity levels (energy consumption or industrial
production) and parameters related to emission factors, plant by plant.
Moreover, we conduct on-site surveys individually for all of the CPP, CEM,
ISP, and RCP sources (labeled “key sources” in this paper), to get further
information that is crucial for emission estimation but not covered by the
official census or statistics (see details in Sect. 2.2). In 2012, all of the
point sources and all of the key sources in Nanjing accounted for 97
and 96 % of the city's total coal consumption, respectively, reflecting
the highly centralized energy use of Nanjing. The annual activity levels for
the key sources for 2010–2012 are summarized in Table S1 in the Supplement.
Besides annual levels, monthly energy consumption, industrial production, and
flue-gas concentrations from CEMS are obtained whenever possible through our
plant-by-plant on-site investigations. The monthly distribution of emissions
from key sources is then revised based on these data.
Emissions from on-road transportation are calculated using COPERT 4 (version
9.0) (EEA, 2012). The parameters required by the model, including vehicle
population by type, fleet composition by control stage (China I–IV,
equivalent to Euro I–IV), and annual average kilometers traveled (VKT), are
taken from investigations by NJEPB. The detailed information for 2012 is
summarized in Tables S2 and S3 in the Supplement. The traffic flows in the
city are compiled from the observations of the Intelligent Traffic Violation
Monitoring System (internal data of NJEPB). In the system, cameras are used
to continuously record the real-time traffic flows for most arterial roads
and highways, and some residential roads in the city. The system does not
cover all the roads (particularly for residential roads), and the traffic
flows in those roads were simulated from the real-time monitoring data for
similar roads that are covered by the system. Combining the information of
traffic flows and the road net, the spatial and diurnal distributions of
emissions from CORPERT can be derived. It should be noted, however, that
uncertainty exists in the allocation of vehicle emissions, particularly for
areas with fewer cameras installed.
For area sources including other small industry, solvent use, residential
combustion, agricultural activity, and non-road transportation, emissions are
estimated following previous work (Zhao et al., 2012a, b, 2013), with
up-to-date emission factors from domestic measurements and city-scale
activity levels. The energy consumption data are taken from the Environmental
Statistics (internal data of NJEPB) and the agricultural and industrial
outputs are mainly from the Nanjing Statistical Yearbook (NJNBS, 2013). Fire
counts and intensity observed from MODIS (Moderate Resolution Imaging
Spectroradiometer,
https://earthdata.nasa.gov/data/near-real-time-data/firms) are used to
determine the spatial and temporal distribution of emissions from biomass
open burning. Regarding fugitive dust, information about individual
construction sites in the city is obtained from NJEPB to improve the
estimation of emission levels and spatial and temporal distributions. This
includes location, period of operations, construction area, and amount of
earthworks). The largest 221 construction sites in Nanjing in 2012
(accounting for 45 % of the total construction area) are shown in
Fig. S1c in the Supplement. The annual activity levels for the main area
sources for 2010–2012 are summarized in Table S1.
The capacity penetrations, average removal efficiencies of APCDs,
and flue gas release ratios for key sources in Nanjing.
CPP-FGD
CPP-SCR/SNCR
CEM-dust collectora
Penetration
SO2 removal efficiency
Penetration
NOx removal efficiency
TSP removal efficiency
2010
92.4 %
66.0 %
43.7 %
17.7 %
96.9 %
2011
97.0 %
78.5 %
66.6 %
41.8 %
97.0 %
2012
98.3 %
81.2 %
67.4 %
77.0 %
99.6 %
ISP coke oven
ISP blast furnace
ISP sintering-FGD
ISP pig iron production-fabric filter
ISP steelmaking-dust collectorc
Gas release ratio b
Gas release ratio b
SO2 removal efficiency
TSP removal efficiency
TSP removal efficiency
2010
0.5 %
1.5 %
70.0 %
98.9 %
97.3 %
2011
0.5 %
1.5 %
70.0 %
98.9 %
96.7 %
2012
0.5 %
1.5 %
70.0 %
98.8 %
96.7 %
a Including the fabric filter and the electrostatic precipitator.
b The fraction of flue gas that is not recycled or collected for
purification treatment, and is thus directly released to the atmosphere.
c Including the fabric filter and the wet scrubber.
Emission factors
As mentioned previously, parameters related to emission factors for key
sources (CPP, CEM, ISP, and RCP) are obtained through on-site surveys for each
plant. The parameters for CPP include boiler type, combustion technology,
fuel quality (sulfur, ash, and volatile matter contents), types and pollutant
removal efficiencies of APCDs (flue gas desulfurization (FGD), selective
catalytic reduction (SCR)/selective non-catalytic reduction (SNCR), dust
collection). Emission factors can then be determined or calculated based on
the method described by Zhao et al. (2010). For cement production, the kiln
type, PM removal efficiency of dust collectors, and fuel quality are
investigated and emission factors are calculated following Lei et al. (2011)
and Zhao et al. (2012a, 2013). For ISP, key parameters in four main processes
(coking, sintering, pig iron production, and steelmaking) are obtained,
including the SO2 removal rate of FGD for sintering, PM removal rate of
dust collectors, and the gas release ratios of coke ovens, blast furnaces,
and basic oxygen furnaces. Emission factors for each process are then
calculated following Zhao et al. (2012a, 2013). For the chemical industry,
the surveyed parameters include the types and amounts of raw materials and
products, the types and volumes of tanks, and technologies applied for the control of
VOCs. In particular, the emission factors for refineries are determined by
industrial processes, including production, storage, loading, and unloading
(Wei et al., 2008; US EPA, 2002; EEA, 2013). Interannual variations in these
parameters for major sources are tracked in the survey so that changing
emission factors over time (2010–2012) can be determined. Table 1 summarizes
the penetrations and removal efficiencies of APCDs for CPP and typical
industries in Nanjing from 2010 to 2012. For other industrial sources,
emission factors are taken mainly from the database by Zhao et al. (2011,
2013), after incorporating the most recent results from domestic
measurements. Table S4 in the Supplement summarizes the emission factors for
main industrial processes.
For other sources for which local emission factors are currently unavailable,
emission factors are determined based on existing studies in other parts of
China. If no domestic studies are available, recommendations of US EPA (2002)
are applied. Road dust emissions are estimated following US EPA (2002), based
mainly on the average weight of vehicles, silt loading of the road surface,
and traffic flow. Those parameters are taken from Fan et al. (2007) and Huang (2006),
with some adjustments of road types for Nanjing. Emission
factors of construction dust recommended by US EPA (2002) are used in this
work, i.e., 0.026, 0.106, and 0.191 (kg m-2) month-1 for PM2.5, PM10, and TSP,
respectively. The mass fractions of BC and OC in construction PM2.5 are
assumed to be 2.4 and 3.4 %, respectively, from measurements by Zhao et
al. (2009).
For gasoline stations, Nanjing completed installation of vapor recovery
systems at all stations at the end of 2012. VOC emission factors for gas
storage, loading, unloading and sales are determined at 0.03, 0.87, 0.10 and
2.44 g kg-1,
respectively (Wei et al., 2008; Fu et al., 2013). Solvents include paints for
buildings and furniture, ink, fabric coating adhesives, and pesticides. VOC
emission factors for decorative adhesives, interior wall paints, and wood
paints are taken from Fu et al. (2013), while those for other solvent use
come mainly from Wei et al. (2008).
Emission factors for non-road transportation are mainly from Zhang et
al. (2010) and Ye et al. (2014). Emission factors for household biofuel use
are estimated based on results of various domestic measurements as summarized
in Zhao et al. (2013) and Cui et al. (2015), while those for biomass open
burning are from Li et al. (2007). NH3 emissions from livestock farming
and fertilizer use are taken from Dong et al. (2010), Yang (2008), and Yin et
al. (2010).
Results
Interannual variability and sector distribution of emissions
The annual emissions of various air pollutants and CO2 from
anthropogenic sources in Nanjing are shown in Fig. 1a for 2010–2012. In
2010, the total emissions of SO2, NOx, CO, VOCs, NH3,
PM2.5, PM10, TSP, CO2, BC, and OC are estimated to be 165, 216,
774, 224, 21, 71, 94, 158, 79 976, 6.2, and 6.7 gigagrams (Gg), respectively.
Note the numbers here for PM emissions do not include fugitive dust from
construction and transportation, to facilitate comparison with inventories
that omit the source. Despite large growth in coal consumption from 2010 to
2012, the emissions of SO2 and NOx in 2012 are estimated to be
smaller than those in 2010, implying the effectiveness of emission control
measures for the city in recent years. These measures mainly include the
increased use of flue gas desulfurization (FGD) and selective catalytic
reduction (SCR) systems in the power generation sector (see the detailed
information in Table 1). The slight increase in SO2 emissions between
2011 and 2012 resulted mainly from the growth in coal consumption in
industries other than power generation, where FGD systems have not been
widely deployed. For NOx, the large increase in coal consumption from
31 million metric tons (Mt) in 2010 to 35 in 2011 dominated the growth of
NOx emissions, even with improved use of SCR in the power sector. From 2011
to 2012, the growth in coal consumption was limited while the average removal
efficiencies of SCR are significantly improved (as shown in Table 1), leading
to reduced NOx emissions for the whole city. PM emissions are estimated
to be quite stable for the 3 years, with small increases in PM2.5
and PM10. Rising mass fractions of PM2.5 to TSP (from 45 % to
48 %) indicate the difficulty in controlling emissions of finer primary
particles compared to coarser ones. For VOCs and NH3, which have not
been well regulated in national action plans for air pollution prevention and
control (Zhao et al., 2014), the interannual variabilities of emissions are
small and driven mainly by relative stability in chemical and agricultural
production, respectively. While CO2 continues to rise, no growth is
estimated for CO from 2011 to 2012, implying improved overall combustion
efficiency in the city.
(a) The interannual variability of Nanjing emissions for 2010–2012
and (b) comparisons in annual emissions with other studies for 2010. The
left-hand vertical axis indicates SO2, NOx, CO, VOCs, NH3,
PM2.5, PM10, TSP, and CO2, while the right-hand axis indicates BC
and OC. 2010(A) and 2010(B) refer to the emissions of current estimates with
and without fugitive dust, respectively.
Figure S2 in the Supplement shows the sector contributions to total emissions
by year and species. From 2010 to 2012, power plants, iron and steel plants,
and other industrial plants are the largest SO2 sources, contributing
41–42, 14–19, and 32–23 % of total emissions, respectively. NOx
emissions come mainly from power plants (45 %) and on-road transportation
(20 %) throughout the time period. The shares of SO2 and NOx
emissions from the power sector are clearly smaller than its shares of coal
consumption (57–64 %) or CO2 emissions (48–57 %), due largely
to relatively stringent emission controls in the sector. It can be found that
the sector distribution of NOx emissions did not change much annually,
even with clear enhancement of NOx control in coal-fired power plants as
shown in Table 1. The growth in coal consumption of power plants partly
offsets the benefits of improved penetration and removal efficiency of SCR on
NOx control, and the NOx emissions from the power sector did not vary
much for the 3 years (97, 112 and 94 Gg for 2010, 2011, and 2012,
respectively). Besides the power sector, in addition, emission controls were also
improved for other sources, including the increased use of retrofitted
low-NOx burners for industrial boilers, and the implementation of strict
emission standards for vehicles. For example, the emissions from other
industrial combustion (OIN) were estimated to decrease from 22 to 14 Gg from
2010 to 2012, and the emissions from on-road vehicles did not change a lot
despite vehicle population growth, leading to relatively small variation
in sector distribution of emissions for the 3 years.
Fugitive dust, particularly that of road origin, is identified as the
dominant anthropogenic source of PM emissions. The fugitive dust shares of
TSP are estimated to range 64–70 % during the research period, while
smaller fractions are found for finer particles and carbonaceous aerosols.
Apart from fugitive dust, iron and steel production plays a significant role
in PM emissions in Nanjing, with its shares of TSP, PM10, and PM2.5
calculated to be 15–16, 20–23, and 35–41 %, respectively. This mainly results from the large coal use by the sector, and relatively poor PM control
measures of certain plants compared to other major coal-consuming sources,
e.g., power plants. Iron and steel production is also identified as the
biggest contributor of CO emissions for the city, with its share reaching
60 % in 2012, even though emission factors for the sector in Nanjing
(based on field investigations) are smaller than the national average (Zhao
et al., 2012a). This is partly attributed to relatively little inefficient
coal combustion at other sources in the city (e.g., in small industry and
residential use), resulting in much lower fractions of CO emissions from
those sources than the national averages. VOCs come mainly from chemical
production (52 %) and solvent use (29–30 %). With vapor recovery
systems increasingly applied, VOC emissions from gas stations decline during
the research period. Despite an increase in vehicle population, the fractions
of on-road transportation emissions for most species decrease from 2010 to
2012, attributed mainly to implementation of increasingly strict vehicle
emission standards. From effective prohibition of burning of agricultural
waste, the emission contributions of this source, mainly of particles,
carbonaceous aerosols, and CO, are also considerably reduced.
Spatial and temporal distribution
For simulation of atmospheric transport and chemistry, the emission
inventories are allocated into a 3 × 3 km grid system. For sources
lacking specific location information, their emissions are assumed to be
correlated with population density, with the exception of NH3, which is
allocated based on the density of agricultural GDP. Shown in Fig. 2 are the
spatial distributions of SO2, NOx, PM2.5 (excluding fugitive
dust from construction and roads) and VOC emissions for Nanjing in 2012, and
the locations of the 10 largest point sources of each species. Relatively
high emission densities are found in the urban area, particularly around
certain large power generation and industrial sources. As illustrated in Fig. 3,
the fractions of emissions from point sources for all concerned species are
estimated to exceed 50 %, as are those from the collective four key
source types, with the exception of BC, at 38 %.
Spatial distribution of emissions for Nanjing 2012, with locations
of largest point sources indicated. (a) SO2; (b) NOx;
(c) PM2.5 (fugitive dust from construction and road sources excluded); and
(d) VOCs.
Monthly distributions of SO2 emissions by sector and that of total
emissions of all species for 2012 are respectively shown in Fig. S3a and b in
the Supplement. Note again that fugitive dust from construction sites and
roads is excluded. The results of MEIC are also provided in Fig. S3a for
comparison. It can be seen that the temporal distributions of the two studies
are similar except for residential emissions, which are smaller overall in
this work compared to MEIC. As indicated by MODIS fire counts, over 90 %
of biomass open burning occurred in May–July, leading to much higher OC
emissions in those 3 months compared to any other time of the year. For
other species, the temporal distributions of emissions correlate closely with
those of activity levels, with a drop in February attributed mainly to
reduced energy supply and industrial production during the Spring Festival.
Pronounced diurnal variations of on-road transportation emissions are
illustrated in Fig. S4 in the Supplement, with two peaks at the rush hours.
The daily shares of CO and VOC emissions in the morning rush hour (16 %)
are slightly higher than those of NOx (14 %) and PM2.5
(15 %). Based on the assumptions of COPERT, the cold start of most
vehicles occurs in the morning, leading to larger CO and VOC emission factors
during this time compared to those during stable operation of vehicles. The
influence of vehicle cold starts on emissions of NOx and PM2.5 is
smaller.
The emission fractions of point sources, area sources, and on-road
transportation, and those of key sources of total emissions in Nanjing,
2012.
Comparisons with other studies in emission estimates
Figure 1b compares our estimates of Nanjing emissions with those from other
inventories (Fu et al., 2013; MEIC) for a common year, 2010. In the other
studies, national or regional average levels for some parameters related to
emissions, e.g., the penetrations and pollutant removal rates of emission
control devices, are applied. These values can vary considerably from those
based on plant-by-plant field investigations, leading to clear differences in
emission estimates compared to the current work.
Our estimate of SO2 emissions for Nanjing is 25 and 22 % higher than
those of Fu et al. (2013) and MEIC, respectively, even though the
plant-by-plant survey indicates an FGD penetration rate of 92 % of
installed power-generating capacity, higher than the provincial average of
85 % used in Fu et al. (2013). The higher estimate results because:
(1) the total coal consumption from the Environmental Statistics applied in
this work is 14 % larger than that provided by the Nanjing Almanac used
in other studies (NJCLCC, 2011; see Sect. 4.6 for more discussion); and (2) a
relatively lower removal efficiency of FGD is obtained from the on-site survey
for 2010. Similar NOx emission levels are found between the current work and
MEIC, while lower emissions were provided by Fu et al. (2013). According to
a field survey, the penetration rate of SCR/SNCR increased from 44 to 67 %,
and the NOx removal efficiency increased from 18 to 77 % during
2010–2012 (Table 1). The penetration rate is much larger compared to the
provincial average of 22 % applied in MEIC and Fu et al. (2013), partly
offsetting a discrepancy in estimated emissions caused by larger activity
levels used in the current city-scale inventory.
Our estimates for PM2.5, PM10, and BC emissions (without fugitive
dust emissions) are larger than those of Fu et al. (2013) or MEIC in 2010.
This results mainly from larger emissions from industry (particularly iron
and steel production), as the survey revealed that relatively old and
inefficient wet dust collectors were still used at some plants. VOC emissions,
however, are estimated to be lower than MEIC indicates, due mainly to very
little coal or biomass burning in the city-level statistics.
VOC emissions estimated in this work in 2010 are 34 % larger than Fu et
al. (2013) and 36 % larger than MEIC. In particular, emissions from
refineries and chemical plants, calculated using detailed information on each
plant's inputs of raw materials and the product types and amounts, are
116 % higher than those in regional inventories (Fu et al., 2013). Thus
the fraction of total VOC emissions attributed to industrial processes is
estimated to be 48 % by us, larger than the YRD average level of 34 %
(Fu et al., 2013). Given Nanjing is a city with large petroleum refining and
chemical industries, and that much higher production of crude oil, gasoline,
diesel and liquefied petroleum gas is reported than in other YRD cities in
2010 (NJNBS, 2013), the higher VOC emissions indicated by the plant-based
inventory is believed to better reflect the city's true industrial structure.
For CO, our estimates are 12 % higher for industry than those of MEIC,
but 26 and 37 % lower respectively for residential and transportation
sectors, resulting in 2 % lower emissions for anthropogenic sources as a
whole. The discrepancy in sector contributions is caused mainly by the high
percentage of centralized coal combustion in the city: power, iron and steel,
cement, and chemical plants consumed over 95 % of the city's coal, based
on our field survey. Our CO2 emission estimate is 22 % higher than
that of MEIC, mainly resulting from the difference in coal consumption
reported by the Environmental Statistics database and the city almanac.
Assessment of the city-scale emission inventory
The current inventory is assessed to gauge improvements of emission
estimates using a city-scale framework. The interannual variability,
spatial distributions, and correlations of a number of species of the
inventory are evaluated by comparison to available satellite and ground
observations, and to downscaled national emission inventories.
Evaluation of interannual trends and spatial distribution of
NOx emissions with satellite observations
The interannual trend in NOx emissions estimated bottom-up is compared
with that of NO2 vertical column densities (VCDs) based on satellite
observations. The VCDs of tropospheric NO2 are retrieved from the Ozone
Monitoring Instrument (OMI) by the Royal Netherlands Meteorological Institute
(Boersma et al., 2007, 2011), using monthly data with a spatial resolution of
0.125∘ × 0.125∘ (data source:
http://www.temis.nl/airpollution/no2col/no2regioomimonth_v2.php).
Illustrated in Fig. 4 are annual emissions estimated in this work for Nanjing
from 2010 to 2012 and VCDs from 2005 to 2012 for four regions: Nanjing,
Shanghai, four provinces in the YRD (including Jiangsu, Zhejiang, Anhui, and
Shanghai), and a rectangular region containing Nanjing (see Fig. S1a for
reference). The first two represent NO2 at city levels, while the latter
two represent regional levels. To eliminate seasonal variations, NO2
VCDs are presented as 12-month moving averages, calculated as the means of
the data for the previous and subsequent 6 months. All the data are
normalized to the 2010 level of Nanjing. While NO2 VCDs started
declining around 2008 for Shanghai, it kept increasing for the rest of the
YRD region including Nanjing until 2012. Clearly higher than the regional
levels, the average NO2 VCD for Nanjing approached that of Shanghai
after 2010. This implies, on the one hand, the benefits of Shanghai's strict
emission controls of on-road vehicles and big power plants (K. Huang et al.,
2014), implemented in advance of other regions. On the other hand, the growth
of NO2 for the rest of the YRD demonstrates the spread of air pollution
source regions from major metropolitan areas to less developed cities nearby,
and suggests the need for increased efforts in emission and pollution
abatement in those areas, as indicated by Zhang et al. (2012a). Decreased
emissions in Nanjing are clearly indicated by this work after 2011,
attributed mainly to the national policy of compulsory installation and
running of SCR devices in the power sector. This interannual variation shows
good consistency with that of OMI NO2 VCD.
The interannual trends in NO2 vertical column density (VCD)
from OMI for selected regions (see Fig. S1a for reference) and the
bottom-up NOx emissions for 2010–2012. All the data are normalized to
2010 level in Nanjing.
It should be noted that uncertainties exist in the comparison between the
interannual variations in emissions and VCDs and the results should be
interpreted cautiously. First, the biases of NO2 VCD retrieval from OMI
were reported to reach 40 % attributed probably to the errors in the air
mass factor calculations (Boersma et al., 2007, 2011). Such uncertainties are
potentially larger than the interannual changes in NOx emissions of
Nanjing between 2010 and 2012, and thus weaken the comparison. However, since
the bottom-up emissions are not evaluated by the absolute values but the
relative trends of VCDs, the effects of NO2 VCD retrieval uncertainties
on the comparison could partly be mitigated. Another uncertainty comes from
meteorology. As shown in Fig. S5 in the Supplement, the interannual changes
in meteorological parameters were small, except for precipitation during
2010–2012 in Nanjing. The varied precipitation could change the data
sampling of retrieval, and thereby influence the NO2 VCD levels.
Finally, the period for emission and VCD comparison is relatively short. Even
the NO2 VCDs kept decreasing slightly after 2012, the benefits of
NOx emission control could not be fully confirmed as the emission
inventory for the most recent years are still unavailable due to the delay of
activity data report. Analysis of the long-term trends in emissions from the
bottom-up method is further suggested for better understanding the NOx
pollution in the area.
To further assess possible improvement of emission estimates by the current
city-level inventory, the spatial distribution of monthly means of OMI
NO2 VCD in summer (June–August) 2010 over Nanjing is compared with that
of two emission studies: (1) city-level emissions at a spatial resolution of 3×3 km given in the current work, and (2) MEIC emissions developed at the
provincial level with a resolution of 5×5 km. For the purpose of
visualization and further analysis, the emissions are reallocated to a
0.125∘ × 0.125∘ grid system from the original
spatial distributions, consistent with the resolution of retrieved OMI
NO2 VCD. We assume that the NO2 VCD levels from satellite observations
reflect the anthropogenic NOx emissions of the city for the following
reasons. NOx emissions in east China are predominantly anthropogenic
(Mijling et al., 2013); lightning and soil sources as a share of total
emissions are estimated to peak in July, when they account for 9 % and
12 %, respectively (Lin, 2012). NOx emissions in Nanjing are clearly
larger than in surrounding areas (Huang et al., 2011), and the NO2 VCD
level over the city is believed to be most influenced by local emissions.
(a) Spatial distribution of city-scale NOx emissions in this
study, (b) summer NO2 vertical column density (VCD) from OMI, and
(c) NOx emissions from MEIC for Nanjing, 2010. The resolution is
0.125∘ × 0.125∘.
As shown in Fig. 5, a similar spatial pattern of NOx is captured by the
gridded emissions and satellite observations, and relatively higher pollution
in the urban area in the center of the city is indicated, attributed mainly
to the combined effects of intensive transportation and large point sources.
The emission inventories, however, underestimate the high-pollution areas
compared to OMI observations, particularly MEIC. To further gauge improvement
in spatial distribution by the city-scale emissions, correlations between the
gridded emissions and the VCD are analyzed. As shown in Fig. 6a, the
correlation coefficients (R) between the emissions and the VCDs are
calculated to be 0.450 and 0.408 for this work and MEIC, respectively,
indicating better agreement by the city-scale inventory. Moreover, a
sensitivity test on the correlation coefficients is conducted through
step-wise exclusion of the grid cells with the largest emissions. Along with
the increase in excluded grid cells, the R for this city-scale emission
inventory remains above 0.43, while those of MEIC sharply decrease (Fig. 6b).
In order to estimate emissions of the whole country, the MEIC is based mainly
on energy and economic statistics at the provincial level, though it includes
a limited number of major point sources, e.g., power plants with relatively
good documentation and large emissions. Emissions from other point sources
are based on coarser inputs due to constraints of time, labor, and data
availability. The current study, in contrast, compiled detailed information
for all power plants and most other industrial sources in Nanjing through
comprehensive survey investigation, as described in Sect. 2. Better estimates
of emission levels and spatial distributions should thus be expected,
particularly for small- or medium-sized emission sources. Once the grid cells
dominated by major power plants are excluded from the two inventories, as
shown in Fig. 6c, the current city-scale emissions still correlate well with
satellite observations (R=0.436), while MEIC shows little correlation (R=0.085). The results reflect that inventories compiled at the provincial or
regional level better estimate emissions of large sources than small- or
medium-sized ones, due to relative availability of information on power
plants but much poorer nationwide data availability for other industrial
plants. When focusing on smaller regions like cities, however, detailed
information on more emission sources from on-site surveys becomes crucial for
improving emission estimates.
Spatial correlation between NOx emissions from city- and
national-scale inventories and NO2 vertical column density (VCD) from
OMI, in Nanjing, 2010 for (a) all grids, (b) step-wise exclusion of grid
cells with largest emissions, and (c) grid cells without power plant
emissions.
It should be noted that high NO2 VCDs are found over the Yangtze River
by OMI (roughly following the dark red zone in Fig. S6 in the Supplement),
while current emission inventories cannot capture this. Possible
underestimation of emissions from ships is indicated. Due to data limits,
only ships arriving or leaving the port of Nanjing are taken into account in
the current city-scale inventory, while those passing through Nanjing are
omitted. Further investigation of the vessel flow along the Yangtze River is
thus necessary to improve the estimation of ship emissions, which may be
particularly influential at small spatial scales. Besides the ships, the
emissions from factories along the river could also contribute to the high
VCDs.
Spatial correlations between pollutant emissions and ambient
concentrations from ground observations
Ambient concentrations for selected pollutants from ground observations are
used to test the city-scale emission inventory. Daily averages of SO2,
NO2, CO, and PM2.5 concentrations for 2012 are obtained from the
nine state-operated monitoring stations in urban/suburban Nanjing, mapped in
Fig. S1d. The SO2, NO2, CO, and PM2.5 concentrations were
measured by Ecotech EC9850B, Ecotech EC9841B, Ecotech EC9830B, and Met One
1020 analyzers, respectively. The emissions of specific pollutants around
each site with a grid cell size of 0.04∘ × 0.04∘ are
calculated from the 3×3 km gridded inventories, and correlations
with annual mean concentrations of corresponding species are analyzed. Since
none of the city's key sources (CPP, CEM, ISP, or RCP) are located in those
grid cells, the effects of individual big sources on the correlation between
emissions and observation are assumed to be limited.
As shown in Fig. 7a, modest agreement is found in spatial patterns between
the observed concentrations and the emissions for SO2 and NOx
(NO2), with the R calculated to be 0.58 and 0.46, respectively. SO2
and NOx have average atmospheric lifetimes of several days and 1 day,
respectively, thus the ambient concentrations are expected to partly reflect
emission intensities nearby, and the correlation analysis adds support for the
reliability of the city-scale emission inventory. The y intercepts in
Fig. 7a can be considered as the approximations of regional background levels
of SO2 and NO2, and the value for NO2 is relatively high
compared to SO2. Since the YRD is a developed region with a large economy,
high energy consumption, and, particularly large vehicle population and
intensive transportation, the background levels of NO2 could be
enhanced, and the local emissions in Nanjing city thus have a less
significant impact on NO2 concentrations. Moreover, in urban areas with
plenty of emission sources and relatively large emission density, NO was
found to account for a large fraction of NOx in the ambient atmosphere
(Zhou et al., 2008). As the dominating component of primary NOx directly
from emission sources, NO is more difficult to be oxidized to NO2 in
urban than in rural or remote regions, attributed to less resident time for
chemistry conversion in urban regions. Given the nine state-operated monitoring sites
are all located in urban/suburban Nanjing, the observed NO2
concentrations were less sensitive or correlated to the NOx emissions,
compared to the case of SO2, and it leads to the smaller slope of
ambient NO2 levels to NOx emissions and the lower correlation
coefficient in Fig. 7a.
Linear regression of emissions and concentrations at the nine state-operated stations in Nanjing, 2012. (a) SO2 and NOx / NO2;
(b) CO.
As shown in Fig. 7b, the correlation coefficient for CO is calculated to be
0.61, and it reaches 0.86 when the observation of the Caochangmen site is
excluded, where extremely high emissions are calculated but low ambient
levels were observed (to be further discussed in Sect. 4.4). Even with a
longer lifetime (weeks to months) than SO2 or NOx, CO in the
atmosphere over Nanjing results mainly from primary emissions from incomplete
combustion, implying reasonable agreement between emissions and
concentrations. However, emissions from small coal combustion sources still
cannot be fully tracked or precisely quantified, and this evidence is thus
tentative.
Evaluation of emissions against top-down constraints from
observations
For certain pairs of pollutants that come from common sources and thus share
emission characteristics, or weakly reactive species that are relatively
stable in the atmosphere, correlations of ambient concentrations can provide
useful “top-down” constraints on “bottom-up” estimates of primary
emissions. In this work, the correlations of three pairs of species in the
atmosphere – BC and CO, OC and EC, and CO2 and CO – are analyzed based
on daily mean concentrations from ground observations in 2012. Combining the
mass or molar ratios of emissions for corresponding species allows further
evaluation of the city-scale inventory.
BC and CO
BC and CO both result from incomplete combustion of solid fuels and certain
industrial processes such as coking. With a relatively long atmospheric
lifetime, CO is usually recognized as a tracer of pollution transport.
Combined with BC levels, it can also be used to test emission inventories of
the two species (most at regional or national scale), which is particularly
useful for BC given its relatively large emission uncertainties (Kondo et
al., 2011; Wang et al., 2011; Zhao et al., 2011, 2012a). We follow the method
presented in Wang et al. (2011) but focus on evaluating the city-level,
top-down emission ratio of BC to CO based on observations at Caochangmen in
Nanjing (point A in Fig. S1d in the Supplement). We choose this site for
emission evaluation for two main reasons. First, it is an urban site and thus
assumed to be more representative of the city emissions, compared to
suburban/rural sites that are more influenced by emissions from broader
areas. Second, Caochangmen is the biggest and the most comprehensive
state-operated station in the city. Among all the nine state-operated sites in
Nanjing, it is the one and only station that conducts observations not only for
the six criterion pollutants (i.e., SO2, NO2, CO, O3,
PM10, and PM2.5), but also for certain species including BC used
here and CO2 used later. Daily means of BC and CO concentrations are
calculated based on the hourly data from continuous observations using Magee
AE 31 and Ecotech EC9830B analyzers, respectively, and the correlation
between the two species are then evaluated and used to check the bottom-up
emission inventories. Since ambient levels of BC and CO do not only depend on
emissions but also on atmospheric processes (e.g., wet and dry depositions of
BC, chemical reactions of CO with OH, and mixing of both BC and CO) that
exert different influences on the two species (Wang et al., 2011), the
top-down emission ratio of BC to CO (BC / CO|E,top-down) is
calculated from the observed BC / CO (dBC / dCO|t) by excluding
the influence of the above-mentioned atmospheric processes, as indicated in
Eq. (2):
dBC/dCO|t=BC/CO|E,top-downFdryFchemFmixingFwet.
Fwet indicates the wet deposition screening. Based on precipitation
data from the Weather Underground website
(http://www.wunderground.com/history/), the data in precipitation days
were excluded to eliminate the effects of wet deposition. Fdry,
Fchem, and Fmixing indicate the screening of dry
deposition of BC, chemical reactions of CO with OH, and mixing of both BC and
CO, respectively. Following the methods by Wang et al. (2011),
Fmixing is set at 1 and Fchem+dry is calculated
to be 0.88, based on the lifetime of BC and CO in the atmosphere.
The average concentrations of BC and CO and correlation of BC to CO
from observations at Caochangmen and the ratios of bottom-up BC to CO
emissions by season in Nanjing, 2012.
Urban observation
Bottom-up inventory
Avg. BCa
Avg. COa
Avg. BCb
Avg. COb
BC / COa
BC / COb
BC / CO|E,top-downc
BC / CO|E,bottom-up
(µg m-3)
(ppbv)
(µg m-3)
(ppbv)
(µg m-3 ppbv-1)
(µg m-3 ppbv-1)
(µg m-3 ppbv-1)
(µg m-3 ppbv-1)
Spring
2.946
661.3
3.009
684.9
0.0070
0.0072
0.0082
0.0101
Summer
2.644
490.3
3.000
491.0
0.0083
0.0085
0.0097
0.0096
Autumn
4.206
619.6
3.822
627.1
0.0081
0.0081
0.0092
0.0095
Winter
3.007
615.0
3.068
637.7
0.0051
0.0060
0.0068
0.0096
Overall
3.156
588.0
3.264
600.5
0.0071
0.0074
0.0084
0.0097
a All the observation data included.
b The influence of wet deposition excluded.
c The influence of wet and dry deposition, chemical reactions with OH
radicals, and mixing excluded.
As shown in Fig. 8, the annual ratio of BC to CO from observations is
estimated to be 0.0071 µg m-3 ppbv-1 by linear regression
with the reduced major axis method (Hirsch and Gilroy, 1984), and it is
0.0073 µg m-3 ppbv-1 if the days of wet deposition are
excluded. Once influence from other atmospheric processes is further
eliminated, BC / COE,top-down rises to 0.0084, lower than the
ratio from the city-scale bottom-up emission inventory at 0.0097, or that
from the MEIC national inventory at 0.0095. It should be noted that the
downtown observation site is influenced heavily by local transportation,
particularly gasoline vehicles that have relatively high CO but low BC
emissions. Therefore, the top-down ratio of BC to CO observed at the site is
expected to be somewhat lower than that of emissions over the entire city.
The comparison of top-down and bottom-up results is thus roughly consistent
with the city-scale emission inventory, although possible overestimation of
BC, or underestimation of CO emissions is indicated.
The correlation of daily BC and CO concentrations at Caochangmen
site and the emission ratios of BC to CO from bottom-up inventories for
Nanjing, 2012.
Aside from mean annual levels, comparisons are also conducted for seasonal BC
to CO ratios, as summarized in Table 2. The highest
BC / CO|E,top-down is found in summer, while the lowest is in
winter. Such seasonal variation, however, is not indicated in the current
bottom-up emission inventory, for the following possible reasons. First, as
described in Sect. 2, the temporal distribution of emissions is based on the
investigation of large and medium enterprises. However, the species of
concern here, especially BC, come largely from small industrial and
residential sources, for which temporal information is still lacking. For
transportation, the increased cold start of vehicles in winter also leads to
higher CO emissions that cannot be fully captured by COPERT (Cai and Xie,
2010; Xiao et al., 2004) and could then lead to an overestimate of
BC / CO from the bottom-up method. Second, although Caochangmen is
located in urban Nanjing, it is inevitably influenced by emissions from
wider regions outside the city that are not quantified in the city-scale
inventory. For example, biomass burning, which has a higher BC to CO ratio
than exists in the ambient atmosphere, occurs more frequently in
less-developed areas such as northern Jiangsu and Anhui provinces than in
Nanjing. According to MODIS, 79 % of agricultural fire points in Jiangsu
2012 were found in summer, elevating the ambient BC / CO in that season.
Third, uncertainty exists about the estimation of Fchem. In winter,
the lowest OH densities in the boundary layer resulting from the weakest
radiation lead to the smallest CO sink, and the opposite is true in summer
(Seiler et al., 1984; Huang et al., 2013). The elevated Fchem in
summer should thus lead to reduced BC / CO|E,top-down. In this
work, however, the seasonal difference in Fchem cannot be precisely
quantified precisely based on existing studies, and the same Fchem
has to be used for all seasons, leading to possible overestimation of
BC / CO|E,top-down for summer and underestimation for winter.
OC and EC
EC and primary OC result from incomplete combustion, and the ratio of OC to
EC concentrations is used to evaluate carbonaceous aerosol emissions and the
formation of secondary organic aerosols (SOA) through the EC tracer method
(Castro et al., 1999). From 2011 to 2013, ambient EC and OC in downwind
Nanjing were collected using quartz filters and analyzed with a DRI Model
2001 thermal/optical carbon analyzer by season (Li et al., 2015). The ratios
of primary OC to EC, (OC / EC)pri, were then determined based
on the observation. Attributed to limited sampling size, Li et al. (2015)
chose the lowest daily OC/EC during the sampling period for each season as
the seasonal (OC / CE)pri, to exclude the effects of SOA (see
Fig. S7 for the observed time series of ambient OC to EC ratio). The
(OC / EC)pri values were then estimated to be 1.70, 1.27, 1.53, and 1.85
for spring, summer, autumn, and winter, respectively, with an annual average
of 1.59. In this work, we adopt those results and assume that they serve as a
top-down constraint of carbonaceous aerosol emissions. From the bottom-up
estimates, the emission ratios of OC to BC in our city-scale emission
inventory and MEIC are respectively 1.38 (for 2012) and 1.24 (for 2010, the
most recent year for which MEIC emissions are available), both of which are
lower than the top-down (OC / EC)pri from observations. With
few emission sources nearby, the observation site is thought to be less
influenced by local sources (e.g., on-road transportation that has a
relatively low emission OC to BC ratio) compared to the regional transport of
pollutants (Li et al., 2015). Thus some sources with high OC to BC ratios
that are uncommon in Nanjing but more dispersed outside the city contribute
significantly to the observed concentrations at the site. Those sources
include residential fossil and biomass combustion and biomass open burning.
The OC to BC emission ratios of Jiangsu and Anhui provinces surrounding
Nanjing are estimated respectively at 1.91 and 2.13 (Zhao et al., 2013),
clearly larger than the local emission ratio of Nanjing. Moreover, the
carbonaceous aerosol sampling procedure used by Li et al. (2015) would lead
to positive artifacts of OC measurement and elevated OC to EC, since usage of
quartz filters adsorbs some semivolatile organic compounds (SVOC) in the
ambient atmosphere (Cheng et al., 2009). The system bias of OC quantification
from this sampling approach was estimated to reach 50 % in US (Chow et
al., 2010; McDonald et al., 2015), and it was found to be 100 % in
Beijing, China (Hu et al., 2008). However, the bias can vary largely
between cities, and it has not been well quantified for Nanjing, making the
evaluation of emission inventories of carbonaceous aerosols less conclusive.
Finally, uncertainty also exists in the (OC / EC)pri
determination by Li et al. (2015), as the sample size from off-line
measurements was small. To evaluate the city-level OC and BC
emissions better, therefore, more observational research with improved (e.g.,
long-term and continuous) measurements is strongly recommended at sites where
local sources dominate, and the artifacts of the measurement should be
sufficiently analyzed.
CO2 and CO
CO2 is a well-known greenhouse gas, with the main anthropogenic sources
fossil energy combustion and industrial processes. The ratios of CO2 to
CO emissions differ between source types, reflecting varying combustion
efficiencies. The observed ratio of CO2 to CO levels in the atmosphere
can thus be used as an indicator of energy efficiency, as well as a top-down
test of emissions estimated bottom-up. The annual molar ratios of CO2 to
CO emissions in Nanjing are calculated to be 65.7, 73.6, and 76.1 for 2010,
2011, and 2012, respectively, significantly higher than those in Beijing in
2008 (32.8, Zhao et al., 2012a). They are also higher than the mixing ratios
observed in rural Beijing in 2008 (26.8, Y. Wang et al., 2010) or at Hatetuma
Island (HAT), a remote site located off the coast of continental east Asia
and influenced by air masses transported from east Asian countries from late
fall to early spring (34.5, Tohjima et al., 2014). Given the large
discrepancy, data from measurements in urban Nanjing (Caochangmen, Point A in
Fig. S1d) are further analyzed to test the emissions. Daily mean
concentrations of CO2 and CO are derived from hourly observations using
Thermo 410i and Ecotech EC9830B analyzers, respectively, for all of 2012. To
exclude the effects of biogenic emissions that prevail in warm seasons, data
for the winter months (January, February, and December) are used. The
prevailing wind directions over Nanjing in winter are east and northeast, and
large point sources, accounting for 64 % of the city's CO2
emissions, are located to the east and northeast of Caochangmen, supporting
use of the observational data for the current purpose.
The average observed concentrations of CO2 and CO in winter were
421 ppmv and 608 ppbv, respectively. Based on the cumulative probability
distribution of daily CO concentrations (as shown in Fig. S8 in the
Supplement), the whole data set is divided into three subsets: (1) below the
30th percentile (with average CO and CO2 concentrations at 350 ppbv and
410 ppmv, respectively), (2) between the 30th and 95th percentile (677 ppbv
for CO and 424 ppmv for CO2), and (3) above the 95th percentile (above
1200 ppbv for CO and 448 ppmv for CO2). We consider that subset
(1) represents air masses from relatively clean areas outside Nanjing, and
subset (2) a well-mixed blend of sources of CO2 and CO over Nanjing.
Subset (3) is assumed to indicate extremely serious regional pollution
episodes, in which pollutants were trapped in a shallow inversion layer
(Y. Wang et al., 2010). Subset (2) is believed to best reflect the typical
effects of local emissions and is used for bottom-up emission comparisons.
Illustrated in Fig. 9 is the CO2–CO correlation estimated with the
reduced major axis method based on surface observations and the CO2 to
CO ratios from bottom-up emission inventories for Nanjing. Our estimate of
the CO2 to CO ratio (76.1) is closer to observations (86.9) than MEIC
(52.8), implying improvement in the current city-scale inventory. The
observed CO2 to CO ratio, however, should theoretically be lower than
that from emissions for the following three reasons. First, compared to CO,
the observation of CO2 at an urban site would be more influenced by
sources within a broader region than the city, as CO2 has a longer
lifetime in the atmosphere. Thus it is not fully representative of the very
centralized and large CO2 emissions inside the city, particularly those
from large point sources (e.g., 17 power plants and 2 iron and steel plants,
which are estimated to account for 78 % of total CO2 emissions in
Nanjing), and the CO2 to CO ratio from observations should be reduced.
Second, the current emission inventory only includes the primary CO
emissions, while there may be a fair amount of secondary CO from the oxidation of NMVOC (non-methane VOC).
Duncan et al. (2007) estimated that CO from NMVOC oxidation equaled nearly
50 % of global primary CO emissions. Given the intensive refineries and
chemical plants and thereby elevated NMVOC emissions in Nanjing, considerable
secondary CO from NMVOC oxidation can be expected, leading to a lower ratio
of CO2 to CO from observations than that from primary emissions. To
partly exclude the effects of NMVOC oxidation, we recalculated the ambient
CO2 to CO ratio at 91.9 based on the hourly concentration data from
18:00 to 06:00 LT for subset (2), when
the temperature was lower and photochemistry was slower. The value is larger
than 86.9 based on daily average concentration data, attributed mainly to
less formation of secondary CO. Third, as discussed previously, the
Caochangmen site is influenced heavily by local transportation that exhibits
a lower CO2 to CO emission ratio than industry. We believe that the
higher CO2 to CO ratio from observations than bottom-up emissions
reflect the uncertainties from both approaches. On the one hand, emissions from
certain species and sectors need to be further improved, e.g., CO from
vehicles might be underestimated by the current work, since relatively poor
management of vehicle emissions in China cannot be tracked by COPERT. On the
other hand, we speculate that possible bias also exists in observations, with
more discussion to follow in Sect. 4.4.
The larger molar ratios of CO2 to CO in Nanjing than in Beijing, both
from observations and emissions, are attributed mainly to the structure of
emission sources. Nanjing is a city with intensive heavy industry, and over
90 % of coal was consumed by power plants, iron and steel plants, cement
plants, and large chemical enterprises with relatively high energy
efficiencies, leading to elevated ratios of CO2 to CO in emissions, and
thereby also in concentrations. Transportation, particularly gasoline vehicles, is
additionally a significant emission source of CO compared to CO2, and
plays important roles in molar ratios of CO2 to CO. For instance,
Y. Wang et al. (2010) found significantly increased ambient CO2 / CO
in September 2008 (46.4 ppmv ppmv-1) compared to September 2005–2007
(23–29 ppmv ppmv-1) in Beijing, resulting mainly from the temporary
ban of vehicles for the Beijing Olympic Games and thereby decreased CO
emissions. In 2012 the vehicle population in Beijing was 3.5 times that of
Nanjing (NJNBS, 2013). Transportation was estimated to contribute
29–37 % of anthropogenic CO emissions in Beijing from various studies
(MEIC; Zhao et al., 2013), but the value is much smaller in Nanjing
(10 %), elevating the molar ratio of CO2 to CO in the city.
Evaluation of local ground observations based on the city-scale
emission inventory
While ground observations can be used as top-down constraints on emissions,
we suggest that a high-resolution emission inventory can also be used to
evaluate observational data. Although detailed local information demonstrably
improves emission estimation, inconsistencies still exist between the
city-scale emission inventory and ground observations of CO at Caochangmen.
These inconsistencies include (1) the significant increase in correlation
coefficients between CO emissions and ambient concentrations at
state-operated monitoring sites when the data at Caochangmen are excluded
(from 0.61 to 0.86), as described in Sect. 4.2 and Fig. 7b; and (2) the
higher CO2 / CO from observations at Caochangmen than that from
city-scale emissions, which contradicts expectations based on atmospheric
chemistry principles, as described in Sect. 4.3 and shown in Fig. 9. This
suggests the possibility of instrumental or other error reflected in the
relatively low CO concentrations observed at Caochangmen in 2012. Thus we
conduct a comparison of CO concentrations between Caochangmen and another
state-operated site, Shanxilu, for 2012 and 2014. Similar to Caochangmen,
Shanxilu is also an urban site, 3.5 km from Caochangmen. Frequency
histograms of hourly CO concentrations at the two sites for 2012 and 2014 are
shown in Fig. S9. It can be seen that CO levels at Caochangmen were
significantly lower than those at Shanxilu in 2012 (Fig. S9a) but the CO
levels were quite similar at the two sites in 2014 (Fig. S9b). Moreover, a
clear difference (∼ 30 %) in CO levels between 2012 and 2014 was
found at Caochangmen (Fig. S9c) but not at Shanxilu (Fig. S9d). Given the
very close distance and similar characteristics of the two sites, we
tentatively assume that there should not be a significant difference in CO
levels between them. Thus we conduct a sensitivity test by increasing the CO
concentrations at Caochangmen by 30 % in 2012, and repeat the assessment
of the city-scale emission inventory with the revised CO data set. The
correlation coefficient between CO emissions and ambient concentrations at
the nine state-operated sites would be increased substantially, from 0.62 to
0.83. The ratio of CO2 to CO in winter from the revised observational
data would decrease from to 86.9 to 66.8, close to and lower than the ratio
from the bottom-up city-scale inventory (76.1), consistent with the
expectation that observed CO2 / CO should be smaller than emissions.
If only hourly data from 18:00 to 06:00 LT are applied to mitigate the effects of secondary CO formation from
NMVOC oxidation, an even closer ambient CO2 to CO ratio to emission
inventory result would be estimated to be 70.7. Such data revision is clearly
speculative, but encourages further analysis when observational data for a
longer period become available at both sites. The city-scale emission
inventory may thus provide a basis to raise questions about the quality of
local ground observations, which should not be taken for granted.
The correlation of daily CO2 and CO concentrations at
Caochangmen site and the emission ratios of CO2 to CO from bottom-up
inventories for Nanjing, 2012. The wintertime concentrations with CO between
the 30th and 90th percentiles are used for the correlation
analysis.
Comparison between city and national inventories for certain
sources
To further explore the effects of methods and data employed in emission
estimation at city and national levels, we conduct comparisons of emission
levels and spatial distributions between the current inventory and MEIC for
given pollutants from typical sources, including SO2 from power
generation, NOx from transportation, and PM2.5 from industry, for
2010 in Nanjing. Our estimates are reallocated to a resolution of
5 × 5 km, the same as MEIC, so that spatial correlations between
the two inventories can be quantified.
As shown in Fig. 10a, relatively good correlation in the spatial
distributions of SO2 emissions from power generation is found for the
two inventories, with the R estimated to be 0.74. The result indicates
consistency between the emission estimates of the two studies for large point
sources, as might be expected given their shared reliance on relatively
transparent, publicly available information on power plants nationwide.
Since detailed field investigations of individual sources are lacking, however, national
inventory studies have to rely on standard information for which routine
updates or revisions are not guaranteed, and the latest changes in individual
plants, including the closure of small power units or relocation of some
power plants, cannot be tracked fully or on a timely basis. This is reflected
by a number of data points in Fig. 10a with positive emission values on one
axis but zero on the other. Regarding the total emission levels, MEIC is
35 % lower than our estimate, attributed mainly to the different SO2
removal efficiencies of FGD applied in the two studies. Based on the field
measurement data that were reported by individual plants and verified by the
local environmental protection bureau, the average removal efficiency of FGD
for power plants in Nanjing in 2010 is estimated to be 66 %, lower than the
values commonly applied by researchers in national emission assessments
(e.g., above 70 %, Zhao et al., 2013). The discrepancy reveals the value
of site-specific investigation of key parameters influencing emission
estimates, including the SO2 removal rate of FGD.
Spatial distribution and linear regression of emissions from
city-scale and national inventories (MEIC) for (a) SO2 from power
generation, (b) NOx from transportation, and (c) PM2.5 from
industry for Nanjing, 2010.
For NOx from transportation, the spatial R is calculated to be 0.652
between the two estimates, and the value would rise to 0.75 if the two grid
cells with the largest emissions in the city-scale inventory were excluded,
as shown in Fig. 10b. Similar to the power sector, the general spatial
pattern of emissions from transportation for the two inventories is largely
consistent. The emissions in MEIC, however, are much more concentrated in
downtown urban regions compared to our estimate, resulting from differences
in spatial densities of population versus transportation flows based on road
networks. The former is commonly applied in spatial distribution of national
emission inventories, while the latter, when available through field
investigations or real-time recordings, are used in city-scale inventories like ours.
The total NOx emissions from transportation estimated by MEIC are
27 % lower than those by the city-scale inventory, suggesting
introduction of considerable uncertainty when emissions estimated to be the
national level are downscaled to the city level based on proxies like
population or economic activity.
In contrast to the above two cases, little correlation is found between the
two estimates in the spatial distribution of PM2.5 emissions from
industrial sources (Fig. 10c). Shown in the maps of Fig. 10c are not only the
PM2.5 emissions but also the locations of the 20 largest emitting
industrial enterprises. A clear discrepancy is observed between the
distribution of those sources and emissions from MEIC, while much stronger
consistency is found in the current work. Without sufficient information on
individual sources, inventories developed at the national level tend to
allocate large fractions of emissions into urban regions with relatively high
densities of population and/or economic activity, assuming good spatial
correlation between emissions and those proxies. Such correlation, however,
likely weakens as pollution control in urban regions is implemented because
it includes significant relocation of emission sources to suburban or rural
areas (a primary element of urban pollution control policy in China). The
total PM2.5 emissions from industrial sources estimated by MEIC are
50 % lower than our estimate, moreover, because: (1) a national emission
inventory based on the sector-average levels of controls and emission factors
cannot capture atypical, extremely large sources (super emitters); and
(2) coal consumption from the official statistics used by MEIC is much lower
than the aggregate of individual sources evaluated in the field survey (3.0
vs. 5.0 Mt for Nanjing, 2010). Comparisons and correlation analyses
between inventories developed at different spatial scales, therefore, show
the advantages of thorough investigation of individual emission sources,
particularly for cities with many large industrial enterprises like Nanjing.
Uncertainty assessment of the city-scale emission inventory
The uncertainties of China's national emission inventories have been
estimated using a Monte-Carlo simulation, as described in our previous studies
(Zhao et al., 2011, 2013). Targeting city scale, however, the uncertainty of
current inventory for Nanjing is more difficult to be systematically
quantified, as many emission factors are city- or device-dependent and their
probability distributions could not be fully defined without sufficient field
measurement records. In general, the uncertainties of emissions from power
and industry sectors are expected to be reduced compared to the national
level, as the “key sources” in the city contribute significantly to the
fuel consumption and production of those sectors. As described in Sect. 2,
the activity levels and emission factors for those key sources were complied
plant by plant or obtained through on-site surveys, leading to relatively small
biases in emission estimation. For most area sources, the emission factors
used in this work were hardly improved compared to previous national/regional
inventories; thus large uncertainties remain in those sources. Given the tiny
fractions of emissions by those sources in Nanjing, however, their
contribution on the uncertainty of total city emissions is expected to be
limited. Very high uncertainty is expected for fugitive dusts that are
generally not included in national/regional inventories, as little local
information is available to improve the emission factors. Given the large
shares of fugitive dusts to primary PM emissions (Fig. S2), field
measurements on construction sites and road dusts are suggested for better
emission estimation in Nanjing.
We try further to identify some common sources of uncertainty in the
development of city-level emission inventories, including (1) the
inconsistencies of activity-level data from various sources; and (2) the
downscaling of activity data or emissions due to a lack of city-level
information.
There has been continuing concern about the accuracy and reliability of
China's energy statistics for more than a decade (Sinton, 2001). Statistics
from various sources report divergent energy consumption levels for the
country, and the choice of activity-level data for emission inventories
continues to be debated. For example, China's total energy use from national
statistics has been inconsistent with that aggregated from provincial
statistics, driving considerable differences in national emission estimates
(Akimoto et al., 2006; Guan et al., 2012; Zhao et al., 2013). While
Akimoto
et al. (2006) concluded that an emission inventory based on
province-by-province statistics were in better agreement with satellite
observations, Guan et al. (2012) indicated that over-reporting in provincial
energy statistics could be a factor. At the city level, however, there are
far fewer evaluations of the accuracy of energy statistics. We find a clear
discrepancy in energy consumption data in statistical sources for Nanjing:
the total coal consumption in 2010 was reported at 27.9 Mt in the Nanjing
Almanac (NJCLCC, 2011), while the value from the Environmental Statistics was
14 % higher, 31.9 Mt. The disparity results mainly from differences in
data collection for small emission sources (enterprises). While data
reporting systems and resulting data quality have been gradually improved for
large- and medium-scale enterprises, many small-scale ones still do not
maintain well-documented records on energy consumption, and the energy use of
those enterprises is poorly captured by the city almanac (personal
communications with officials from Nanjing Municipal Commission of Economy
and Information Technology, 2014). Aimed at pollution control, the
environmental statistical system obtains and verifies energy data for each
enterprise through field surveys, and we thus believe that these energy
consumption data are more complete and reliable for emission inventory
development. The uncertainty from such varied statistical sources could be
reduced as retirement of small boilers and/or closure of small enterprises
increases. Although the Nanjing Almanac stopped reporting coal consumption
for the city after 2010, the Environmental Statistics indicates that the
combined share of coal consumption by large- and medium-sized sources
increased from 84 % in 2010 to 91 % in 2012, attributed to the closure of
small enterprises, reporting highly uncertain energy data during the period.
Comparisons of emission estimations using method A (plant-by-plant
survey) and B (downscaling from provincial levels) for brick, lime, and
copper production in Nanjing, 2012.
Productiona
SO2 emissions
NOx emissions
PM2.5 emissions
CO emissions
Method A
Method B
B/A
(B-A)/OIN2
B/A
(B-A)/OIN
B/A
(B-A)/OIN
B/A
(B-A)/OIN
Brick
14
29
2.0
10 %
2.0
19 %
2.0
13 %
Lime
207
2050
9.9
6 %
9.9
22 %
3.6
15 %
9.9
24 %
Copper
1.3
39
31.1
24 %
3.6
13 %
a The units are 109 bricks, 103 t-lime and 103 t-copper,
respectively.
b Recall from Sect. 2.1 that OIN indicates emissions from other
industries (iron and steel, cement production, and chemical industry excluded)
estimated at the city level.
Besides problems in the energy data, uncertainty in the city-scale emission
inventory can also result from a lack of information on certain industrial
sectors in the city statistics. If field surveys of individual sources cannot
be conducted due to labor or time constraints, emissions have to be estimated
by downscaling national or provincial estimates. To evaluate the resulting
uncertainty, air pollutant emissions from non-ferrous metal smelting and the
production of brick and lime in Nanjing 2012 are recalculated by the
downscaling provincial estimates method (method B). In this method, emissions
in the Jiangsu province are first calculated based on the provincial statistics
and provincial average levels of emission control. Emissions in Nanjing are
then obtained according to Nanjing's fraction of certain proxy (industrial
GDP in this case) out of the whole province. The results are compared with
those based on detailed source investigations (method A). Shown in Table 3
are the product output (activity level) and pollutant emissions estimated by
methods A and B. The activity levels estimated from provincial-level
information are much higher than the actual industrial production aggregated
from individual plants, suggesting that downscaling produces emission
overestimates. For example, gaseous pollutant emissions calculated with
method B are 2, 10, and 30 times larger than those produced by method A for
brick, lime, and copper production, respectively. For PM emissions, the
discrepancies in emissions between the two methods are smaller, attributed
partly to the compensating effects of divergent removal efficiencies of dust
collectors applied in the two methods, obtained either from plant-by-plant
surveys (method A) or from national or provincial average levels (method B).
The differences are believed to reflect disparities in the considerable
fractions of total emissions from OIN (i.e., industrial sources excluding
iron and steel, cement, and chemical plants, for which information is
relatively clear, as defined in Sect. 2.1), specifically 6, 22, 15, and
24 % of SO2, NOx, PM2.5, and CO for lime production, as
shown in Table 3. The results suggest relatively large uncertainties in
city-level emission estimates lacking sufficient individual source
information. In this case, moreover, the overestimates in Nanjing's emissions
from downscaling provincial emissions would inevitably lead to underestimates
for other cities within the province, weakening understanding of emission
sources and the quantitative basis of regional control policies.
Policy implications
The effects of pollution control policies on emission abatement
and air quality
Substantial efforts have been undertaken in specific Chinese sectors to
achieve national targets in both energy conservation and emission reduction
(Zhao et al., 2013). Under the air pollution control measures, clear benefits
in emission abatement, particularly in the power and transportation sectors,
are found for Nanjing from 2010 to 2012, a relatively short period. In the
power sector, electricity generation increased by 58 % during 2010–2012
while that specifically from coal-fired plants grew by 47 %, reflecting
the switch of coal to gas combustion and other diversification of power
generation in the city. Meanwhile, coal consumption of the power sector
increased by only 25 %, much slower than the resulting electricity
generation, reflecting improved energy efficiency due to replacement of small
and old power units with larger and more energy-efficient ones. As shown in
Fig. S10a in the Supplement, the capacity share of large units (above
300 MW (megawatts)) increased from 72 % in 2010 to 78 % in 2012, while that of
small ones (below 100 MW) decreased from 20 to 16 %. The penetration of
large units raised as well the use of APCD for SO2, NOx, and PM,
leading to significant reduction of emission factors by 30, 23, and 22 %,
respectively. The decrease in emission factors can also be seen for vehicles.
With the implementation of staged emission standards for new vehicles from 2010
to 2012, the emission factors of SO2, NOx, CO, and VOCs are
estimated to have declined by 66, 33, 34, and 37 % for gasoline vehicles
(Fig. S10b); those of NOx, CO, and VOCs by 12, 13, and 24 % for
diesel vehicles (Fig. S10c); and those of CO and VOCs by 25 and 34 % for
motorcycles (Fig. S10d), respectively. The SO2, CO, and VOC emissions
from on-road transportation decreased by 39, 11, and 27 %, respectively,
while the vehicle population increased by 27 % in the city over the 3
years.
The benefits of emission control on air quality can be partly confirmed by
comparisons of changes in emissions and ambient concentrations. During
16–24 August 2013 when the 2nd Asian Youth Games (AYG) were held in Nanjing,
a series of extra emission controls measures were undertaken by the
government to improve air quality for the Games. Those measures included
increasing use of low-sulfur coal at power plants, closing small factories
with relatively large pollutant emissions, stopping construction in some
regions, and restricting traffic. Taking these extra measures into account,
the emissions of SO2, NOx, PM2.5, PM10, and CO over
16–24 August 2013 are estimated to have declined 23, 31, 21, 14, and
33 %, respectively, compared to those in the same period in 2012, based
on the monthly distributions described in Sect. 3.2 (see Table S5 in the
Supplement). Correspondingly, the daily average concentrations for those
pollutants in Nanjing during the AYG period were found to decline by 22, 27,
10, 5, and 22 %, respectively, compared to the same period in 2012 (Yu et
al., 2014). Although changes in other factors including meteorological
conditions also influenced air quality, the consistency between the reduced
emissions and concentrations suggests that local emission abatement played a
primary role in the air quality improvement.
Effects of super emitters and small sources on emission levels and
spatial distributions
For cities with intensive, heavily polluting industries like Nanjing, large
point sources with significant energy consumption and/or industrial
production are estimated to dominate the levels and spatial distribution of
emissions of the city. As shown in Fig. 3, the areas with high emission
densities in Nanjing are in good agreement with geographical locations of
point sources for all pollutants. The 10 largest point sources of SO2
emissions are estimated to account for 54 % of total emissions in the
city (Fig. 2a), and the analogous number for NOx is 43 % (Fig. 2b).
For PM2.5, as shown in Fig. 2c, the 10 largest sources are estimated to
be responsible for 75 % of total primary emissions in Nanjing (excluding
construction and road dust). In particular, extremely high emissions are
found for iron and steel plants, resulting mainly from the high production of
steel and reliance on wet scrubbers with relatively low removal efficiencies
(annual average of 85 %) in the exhaust streams of basic oxygen furnaces.
Similarly, the 10 largest refineries and chemical plants shown in Fig. 2d
are responsible for 52 % of VOC emissions in Nanjing. The dominant roles
of these big sources on emission levels and spatial distributions indicate
that careful investigation and analysis of source-specific parameters
relevant to emissions from these super emitters (e.g., removal efficiency of
APCDs) are particularly crucial to the reliability of city-scale emission
inventories.
Although large point sources dominate emissions at the city level, the
contributions from scattered small sources cannot be overlooked. As shown in
Fig. 3, the fractions of air pollutant emissions from power, cement, iron and
steel, and chemical plants to the city's total emissions are estimated to
range from 38 to 88 %, significantly lower than that of coal consumption
(96 %). Despite the tiny share of coal use, decentralized small coal
combustion sources have a relatively high proportion of emissions, resulting
from poorer emission control technologies and management than big
enterprises. Regarding emission abatement and air quality improvement, it is
imperative to expand pollution control from large sources to small- and
medium-sized enterprises, as the potential for further reductions from the
major sources is diminishing due to near-saturation of APCDs. As for
improvement of emission inventories, more varied and uncertain emission
factors for small boilers and kilns result from much greater diversities of
manufacturing technologies. This necessitates more field measurements in the
future to inform the application of emission factors in inventories and to
better understand the emission characteristics of small sources.
Conclusions
With updated methods and substantial new data on local emission sources, a
city-scale emission inventory of air pollutants and CO2 has been developed for
Nanjing for 3 years, 2010–2012. Through plant-by-plant on-site surveys,
emission factors, spatial and temporal emission distributions, and estimates
of total emissions for major sources (power, cement, iron and steel, and
refineries and chemical plants) are especially improved. Emissions in the
city are dominated by large point sources, or super emitters. Despite large
increases in energy consumption and industrial production, the emissions of
most concerned pollutants were largely stable during the period, particularly
SO2 and NOx, attributed to increased use and air pollutant removal
rates of APCDs under national policies of air pollution control. The current
estimates are consistent with the interannual variability of NO2 VCD
observed from OMI. The improvement of emission estimates by city-scale
assessment is further indicated by analyses of spatial correlations with
observations by satellite (for NOx) and ground stations (for SO2,
NOx, and CO), as well as by top-down constraints (BC / CO,
OC / EC, and CO2 / CO) provided by ground-based observations.
Analyses of bottom-up emission inventories, moreover, can identify possible
errors in observational data sets, encouraging further investigation.
Limitations remain in the current work. First, some available and potentially
valuable information cannot yet be fully exploited to improve emission
estimates. CEMS data help to determine the time distribution of emissions,
for example, but are currently less useful for estimating absolute emission
levels, due to incompleteness and systematic errors in relevant parameters
(e.g., flue gas flow rate). Second, since emission factors for some sources
are still based on provincial or national assessments due to lack of local
information, the uncertainties of the city-scale emission inventory have not
yet been systematically quantified. In particular, the degree of uncertainty
in the city-scale inventory compared to that of national ones remains
unknown. Finally, it is currently difficult to assess emissions of some
species believed to have high emission uncertainty, e.g., VOC and NH3,
due to lack of sufficient instrumental observations. More field measurements
of both emissions and ambient levels of these species are thus recommended in
the future.