To evaluate the improved emission estimates from online monitoring, we
applied the Models-3/CMAQ (Community Multiscale Air Quality) system to
simulate the air quality of the Yangtze River Delta (YRD) region using two
emission inventories with and without incorporated data from continuous emission
monitoring systems (CEMSs) at coal-fired power plants (cases 1 and 2,
respectively). The normalized mean biases (NMBs) between the observed and
simulated hourly concentrations of SO
Due to swift economic development and associated growth in demand for
electricity, coal-fired power plants have played an important role in energy
consumption and air pollutant emissions for a long time in China. For
example, Zhao et al. (2008) for the first time developed a “unit-based”
emission inventory of primary air pollutants from the coal-fired power
sector in China and found that the sector contributed 53 % and 36 % to
the national total emissions of SO
Evaluations of emission estimates and the changed air quality from emission
abatement provide useful information on the sources of air pollution and the
effectiveness of pollution control measures. Air quality modeling is an
important tool for evaluating emission inventories, by comparing simulation
results with available observation data. Developed by the US Environmental
Protection Agency (US EPA), the Models-3/Community Multiscale Air Quality
(CMAQ) system has been widely used in China (Li et al., 2012; An et al.,
2013; Wang et al., 2014; Han et al., 2015; Zheng et al., 2017; Zhou et al.,
2017; Chang et al., 2019). Han et al. (2015) conducted CMAQ simulations with
different emission inventories for East Asia and found that the simulated
NO
Besides air quality, the health risk caused by air pollution exposures in
China is a major concern, especially to PM
As one of the most densely populated and economically developed regions, the YRD region encompassing Shanghai and Anhui, Jiangsu, and Zhejiang provinces is a key area for air pollution prevention and control in China (Huang et al., 2011; Li et al., 2011, 2012). It is also one of the regions with the earliest implementation of the ultra-low emission policy on the power sector in the country. Quantification of emission reductions as well as subsequent changes in air quality is crucial for full understanding of the environmental benefits of the policy. To test the possible improvement in the regional emission inventory, this study evaluated the air quality modeling performance without and with CEMS data incorporated in the estimation of emissions of the coal-fired power sector for the YRD region. The changes in regional air quality and health risk resulting from the implementation of the ultra-low emission policy for key industries were quantified combining the air quality modeling and the health risk model. The results provide scientific support for incorporation of online monitoring data to improve the estimation of air pollutant emissions and for better design of emission control policies based on their simulated environmental effects.
The modeling domain and the locations of the concerned provinces
and their capital cities. The numbers 1–4 represent the cities of Nanjing,
Hefei, Shanghai, and Hangzhou, respectively. The map data, provided by the
Resource and Environment Data Cloud Platform, are freely available for
academic use (
In this study, we adopted CMAQ version 4.7.1 (UNC, 2010) to conduct air
quality simulations and to evaluate various emission inventories for the YRD
region. The model has performed well in Asia (Zhang et al., 2006; Uno et
al., 2007; Fu et al., 2008; Wang et al., 2009). Two one-way nested domains
were adopted for the simulations, and the horizontal resolutions were set at
27 and 9 km square grid cells, respectively, as shown in Fig. 1. The mother
domain (D1, 177
The Weather Research and Forecasting (WRF) Model version 3.4 (
The anthropogenic emissions from industry, residential, and transportation
sectors for D1 and D2 were obtained from the national emission inventory
developed in our previous work (Xia et al., 2016). The total emissions
excluding those of the power sector of SO
The air pollutant emissions by sector for cases 1–5 in the YRD (unit: Gg).
Note that for case 1, the emissions of coal-fired power sector were estimated based on the emission factor method without CEMS data. For case 2, the emissions of coal-fired power sector were estimated based on the improved method by Y. Zhang et al. (2019), with CEMS data incorporated. For case 3, all the coal-fired power plants in the YRD region were assumed to meet the requirement of the ultra-low emission policy. For case 4, all the coal-fired power plants and certain industrial sources including boilers, cement, and iron and steel factories in the YRD region were assumed to meet the requirement of the ultra-low emission policy. For case 5, the emissions of all coal-fired power plants were set at zero.
For the power sector in the YRD region specifically, we adopted the
unit-level emission estimates from our previous study and allocated the
emissions according to the actual locations of individual units (Y. Zhang et
al., 2019). As described in that study, the detailed information at the
power unit level was compiled based on official environmental statistics
including the geographic location, installed capacity, fossil fuel
consumption, combustion technology, and APCDs. Besides the commonly used
method, Y. Zhang et al. (2019) developed a new method of examining,
screening and applying CEMS data to improve the estimates of power sector
emissions. CEMS data were collected for over 1000 power units, including
operation condition; monitoring time; flue gas flow; and hourly
concentrations of SO
The air pollutant emissions for all the cases are summarized by sector in
Table 1. With the CEMS data for the power sector incorporated, the total
emissions of SO
We applied the IER model of the Global Burden of Disease (GBD) study 2015
(Cohen et al., 2017) and quantified the impact of emission control policy on
the human health risk due to long-term exposure of PM
The health risks in the different emission cases were estimated following
Gao et al. (2018) with the updated information for 2015. First, the relative
risk (RR) for each disease was calculated using Eq. (1):
Secondly, the population attributable fractions (PAFs) were calculated with
RR following Eq. (2) by disease, age, and gender subgroup:
Finally, the year of life lost (YLL) due to PM
Air quality simulations based on emission inventories with and without
incorporation of CEMS data for the coal-fired power sector (cases 1 and 2,
respectively) were conducted to test the improvement of emission estimates.
Because of the combined influences of regional transport and chemical
reactions of air pollutants in the atmosphere, nonlinear relationships were
found between the changes of primary emissions and ambient concentrations of
air pollutants. Compared to case 1, the simulated annual average
concentrations of SO
Comparison of the observed and simulated hourly SO
Note that the arrows indicate that the simulation values in case 2 were improved
compared to case 1. The
The model performance was evaluated with available ground observation. The
hourly concentrations were observed at 230 state-operated air quality
monitoring stations within YRD, and the averages of hourly concentrations of
those sites were compared with the simulations in cases 1 and 2, as
summarized in Table 2. Similar model performances were found for the two
emission cases, with overestimation of SO
The spatial distributions of the simulated monthly SO
In general, better modeling performance in the YRD region was found in case 2 than case 1. The NMBs between the simulated and observed concentrations of
SO
Figure 2 illustrates the spatial patterns of the simulated monthly SO
The relative (%) and absolute changes (
The spatial distributions of the relative changes (%) in the
simulated monthly SO
The spatial distributions of the relative changes (%) in the
simulated monthly SO
Table 3 summarizes the absolute and relative changes of the simulated
monthly concentrations of the concerned air pollutants in cases 3–5 compared
to the base case (case 2). The average contributions of the power sector to
the total ambient concentrations of SO
The spatial distributions of the annual PM
The relative changes in the simulated pollutant concentrations varied by
month, due to the combined influences of meteorology and secondary
chemistry, and larger relative changes were found for SO
Figures 3 and 4 illustrate the spatial distributions of the relative changes
of simulated pollutant concentrations in cases 3 and 4 compared to case 2,
respectively. As shown in Fig. 3, the overall changes across the region
due to ultra-low emission controls in the power sector only were less than
10 % for primary pollutants SO
In case 4, where both power plants and selected industrial sources meet the
ultra-low emission requirement, the average reduction rates of simulated
SO
The population fractions exposed to different levels of PM
Figure 5 illustrates the spatial distributions of PM
We further calculated the fractions of the population with different annual
average PM
The estimated mortality and YLL attributable to PM
The spatial distributions of the mortality
The mortality and YLL caused by atmospheric PM
Comparisons of the estimated mortality attributable to PM
Many studies have focused on the human health risks attributable to air
pollution in China, with considerable disparities between them due to
different estimation methods and health endpoints selected. Figure 8
compares the estimates of premature deaths caused by PM
The reduced attributable deaths (persons) and rates (in parentheses) resulting from implementation of the ultra-low emission policy in the YRD region.
The reduced cases and rates (in parentheses) of YLL resulting from implementation of the ultra-low emission policy in the YRD region.
Tables 5 and 6, respectively, summarize the avoided premature deaths and YLL
by disease and region that would result from implementation of the ultra-low
emission control policy and thereby reduced PM
The spatial distributions of the avoided deaths and YLL
attributable to the reduced PM
Figure 9 illustrates the spatial distributions of the avoided deaths and YLL
from the ultra-low emission policy in the YRD region. When the policy was
implemented only for coal-fired power plants, the health benefits were small
and the regional differences relatively insignificant, with the avoided
deaths and YLL smaller than 10 persons and 100 years, respectively, for all of
the grid cells (Fig. 9a and b). When the policy was implemented both in
power and industry sectors, more avoided deaths (
We evaluated the improvement of emission estimation by incorporating CEMS
data for the power sector, and we explored the air quality and health benefits
from the ultra-low emission control policy for the YRD region through air
quality modeling. In general, the bias between ground observations and
simulations based on the emission inventory with CEMS data incorporated was
smaller than that without, suggesting that appropriate use of online
monitoring information helped improve the emission estimation and model
performance. Compared to the base case in which CEMS data were incorporated
in emission estimation, the simulated monthly concentrations of all the
concerned species (SO
Nearly 305 premature deaths and 8744 years of YLL would be avoided if the
policy were implemented for the power sector alone, and benefits would reach
10 651 premature deaths and 316 562 YLL avoided with the policy enacted for
both power and industrial sectors. The study revealed the limited potential
for further emission reduction and air quality improvement via controls in
the power sector alone. Along with stringent emission control in that
sector, the coordinated control of emissions from industrial
sources (other than the power industry) would be essential to effectively improve air quality and reduce
associated human health risks. Moreover, more attention needs to be paid to
control of VOCs to limit O
All data in this study are available from the authors upon request.
The supplement related to this article is available online at:
YZhang developed the strategy and methodology of the work and wrote the draft. YZhao improved the methodology and revised the article. MG provided useful comments on the health risk analysis. XB provided emission monitoring data. CPN revised the article.
The authors declare that they have no conflict of interest.
This work was sponsored by the Natural Science Foundation of China (41922052 and 91644220), National Key Research and Development Program of China (2017YFC0210106), a Harvard Global Institute award to the Harvard-China Project on Energy, Economy and Environment, and the Key Program for Coordinated Control of PM
This research has been supported by the National Natural Science Foundation of China (grant nos. 41922052 and 91644220), the National Key Research and Development Program of China (grant no. 2017YFC0210106), and the Key Program for Coordinated Control of PM
This paper was edited by Min Shao and reviewed by three anonymous referees.