The
Beijing–Tianjin–Hebei (BTH) region is a metropolitan area with the most
severe fine particle (PM
The Beijing–Tianjin–Hebei (BTH) region is the political, economic, and
cultural center of China. According to China National Environmental
Monitoring Centre (2018), in 2017, the annual average concentrations of
PM
The spatial distribution is one of the most uncertain components of emission
inventories considering the diverse source categories and complex emission
characteristics. The traditional method of spatial allocation is to
distribute the emissions by administrative region into grids based on spatial
proxies such as population, gross domestic product (GDP), road map, land use
data, and nighttime lights (Geng et al., 2017; Oda and Maksyutov, 2011;
Streets et al., 2003). The results may deviate significantly from the actual
spatial distributions of many sources (Zhou and Gurney, 2011), especially the
power and industrial sources, which contribute over 50 % of the total
PM
A number of studies have developed the emission inventory in the BTH region (Li et al., 2017; Wang et al., 2014), whereas others have provided emission estimates for this region as part of national or larger-scale emission inventories (Ohara et al., 2007; Stohl et al., 2015). However, only limited studies have estimated the emissions from individual point sources (i.e., a unit-based emission inventory). Zhao et al. (2008), Chen et al. (2014), and Liu et al. (2015) established unit-based emission inventories of coal-fired power plants in China. K. Wang et al. (2016) and Wu et al. (2015) developed an emission inventory for the steel industry. Lei et al. (2011) and Chen et al. (2015) established an emission inventory for the cement industry in China. Qi et al. (2017) established an emission inventory in the BTH region in which power and major industrial sources were treated as point sources. These studies usually focused on one or several major industries, and did not cover all industrial sectors in the BTH region. Moreover, these previous studies seldom validated the unit-based emission inventory or evaluated the improvement it brings to air quality simulation.
In this study, we developed a unit-based emission inventory of industrial sectors for the BTH region. A three-domain nested simulation by the WRF-CMAQ (Weather Research and Forecasting–Community Multi-scale Air Quality) model was applied to evaluate the emission inventory. In order to study the influence of the point sources, we compared the simulation results of this emission inventory with those of a traditional proxy-based emission inventory.
A unit-based method is applied to quantify the emissions from industrial
sectors such as power plants, industrial boilers, iron and steel production,
non-ferrous metal smelters, coking plants, cement, glass, brick, lime, ceramics,
refineries, and chemical industries in 2014. The product yields used for
estimating emissions of each sector are shown in Table S4 in the Supplement. The pollutant
emissions from each industrial enterprise are calculated from activity level
(energy consumption for power plants and industrial boilers, and product
yield for other sectors), the emission factor, and the removal efficiency of control
technology, as shown in the following equation:
Some industrial sources involve multiple production processes, such as iron
and steel production and cement production. Using cement production as an
example, emissions are calculated using the following equation:
The production processes represented by the first and second terms of
Eq. (2) are frequently performed in different enterprises. For example,
for cement production, clinker may be produced in one enterprise and
subsequently processed in another enterprise, which is very common. For each
enterprise, we calculate the emission of each production process.
Specifically, the total emission of enterprise
In this study, we collected detailed information for all power and industrial
sources except industrial boilers, including latitude/longitude, annual
product, production technology/process, and pollution control facilities from
a compilation of power industry statistics (China Electricity Council,
2015b), the China Iron and Steel Industry Association
(
Plume rise is caused by the buoyancy effect and momentum rise (Briggs, 1982).
Therefore, stack information, including stack height, flue gas temperature,
chimney diameter, and flue gas velocity, is essential for plume rise
calculation. For power plants, we get the stack height from the “Compilation
of Power Industry Statistics” (China Electricity Council, 2015b). For the
stack height of cement factories, we refer to the emission standard of air
pollutants for the cement industry (Ministry of Environmental Protection of
China, 2013). For the stack height of glass, brick, lime, and ceramic
industries, we refer to the emission standard of air pollutants for
industrial kilns and furnaces (Ministry of Environmental Protection of China,
1997). For the stack height of non-ferrous metal smelters, coking plants,
refineries, and chemical industries, as well as the flue gas temperature,
chimney diameter, and flue gas velocity for all industrial sectors, we refer
to the national information platform of pollutant discharge permits
(
The emission inventory for other sources, including residential sources,
transportation, solvent use, and open burning, is developed based on the
“top-down method” following our previous work (Fu et al., 2013; Wang et
al., 2014; Zhao et al., 2013b). The method is the same as Eq. (1) except that
the emissions are calculated for an individual prefecture-level city rather than
individual enterprises. The activity data and technology distribution for each
sector are derived based on the statistical yearbooks (Beijing Municipal Bureau
of Statistics, 2015; Hebei Municipal Bureau of Statistics, 2015; National
Bureau of Statistics, 2015h, g, f, e, i, j, a, b, c, d; Tianjin Municipal
Bureau of Statistics, 2015), a wide variety of Chinese technology reports
(China Electricity Council, 2015a; National Bureau of Statistics, 2012), and
an energy demand modeling approach. Figure S1 shows the energy consumption in
the BTH region in 2014. We compared the sum of the energy consumption for
each plant with the energy statistics. The sum of individual plants accounts
for over 90 % of the energy consumption or product yield reported in the
statistics. For the plants not included in the preceding data sources, we
calculate the emission using the top-down method. The emission factors are
obtained from Zhao et al. (2013b). The speciation of PM
In this work, we use CMAQ version 5.0.2 to simulate the concentration of
pollutants. A three-domain nested simulation is established as shown in
Fig. 1a. The first domain covers almost the entire area of China, Korea,
Japan, and parts of India and Southeast Asia with a horizontal grid
resolution of 36 km
The three-domain nested CMAQ domains
In order to minimize the influence of the initial conditions, we choose a 5-day spin-up period. The Carbon Bond 05 (CB05) and AERO6 (Sarwar et al., 2011) are chosen as the gas-phase and aerosol chemical mechanisms, respectively. The simulation periods are January and July of 2014, representing winter and summer, respectively.
We use the Weather Research and Forecasting (WRF) model version 3.7.1
(Skamarock et al., 2008) to simulate the meteorological fields. The physics
options for the WRF simulation are the Kain–Fritsch cumulus scheme (Kain,
2004), the Morrison double-moment scheme for cloud microphysics (Morrison et
al., 2005), the Pleim–Xiu land surface model (Xiu and Pleim, 2001), the
Pleim–Xiu surface layer scheme (Pleim, 2006), the Asymmetric Convective
Model (ACM2; Pleim) boundary layer parameterization (Pleim, 2007), and the
Rapid Radiative Transfer Model for GCMs radiation scheme (Mlawer et al.,
1997). The meteorological initial and boundary conditions
are generated from the Final Operational Global Analysis data (ds083.2) of
the National Center for Environmental Prediction (NCEP) at
In order to evaluate the high-resolution emission inventory with the unit-based industrial sources, we develop a traditional proxy-based emission inventory with the same amount of emissions and compare the simulation results of these two emission inventories. In the proxy-based emission inventory, all sectors are allocated as area sources using spatial proxies such as population, GDP, road map, and land use data. The proxies used for each sector are described in detail in Table S2. In order to separate the influences of the horizontal and vertical distributions of the emissions, we developed another unit-based inventory with emission heights the same as the proxy-based inventory; we call this inventory the “hypo-unit-based inventory”. The anthropogenic emission inventories for other provinces in China were developed in our previous studies (Wang et al., 2014; Zhao et al., 2018). The emissions outside China are obtained from the MIX emission inventory (Li et al., 2017) for 2010, which is the most current year available. In the simulation with the unit-based inventory, plume rise is calculated using the built-in algorithm in CMAQ. Meteorological data are used to calculate plume rise for all point sources. Then, the plume is distributed into the vertical layers that the plume intersects based on the pressure in each layer.
In the BTH region, the emissions of sulfur dioxide (
Sectoral contributions to emissions in the BTH region in 2014.
Locations and emissions of industrial sources in the BTH region. The industrial plants are divided into four groups for the sake of clarity.
Power plants account for 13 %, 16 %, and 4 % of the total
The emissions from industrial boilers account for 27 %, 19 %, 8 %,
1 %, and
The emissions from cement contribute 6 %, 9 %, and 10 % of the total
The emissions from steel production represent 8 %, 3 %, and 22 % of the total
Besides the aforementioned sectors, 8 %, 8 %, 13 %, 36 %, and
In total, in the BTH region, industrial sectors (power plants, industrial
boilers, cement, steel plants, and other industrial processes) contributed 61 %,
55 %, 62 %, 56 %, 58 %, 22 %, 36 %, and 0 % of the
total respective
Emission rate of PM
Considering the large contribution of industrial sources to the total emissions,
the application of a unit-based method results in remarkable changes in the
spatial distribution of air pollutant emissions. The emission rates of
PM
In order to study the accuracy of the unit-based inventory, the simulation
results of
The statistics for model performance of PM
The statistics for model performance of 1 h-peak
For
Spatial distribution of the monthly (January and July) mean
concentrations of
The simulated PM
Figure 5 further shows the spatial distribution of
For
Observed and simulated concentration gradients of
The differences (unit:
The spatial distribution of the concentrations of these pollutants are
significantly heterogeneous. The NME and MFE of most pollutants averaged over
a 2-month period are lower with the unit-based inventory than with the
proxy-based inventory; this means that the spatial distribution with the
unit-based inventory agrees more with the observations than that of the
proxy-based inventory. For
The mean concentrations (unit:
To further elucidate the reasons for the difference between the PM
The results for secondary inorganic aerosols are quite different. From Fig. 7
and Table 3 we can see that the
In this study, we developed a high-resolution emission inventory of major
pollutants for the BTH region for the year 2014 using unit-based emissions from
industrial sectors. The emissions of
The emissions in the unit-based emission inventory are lower than those in the
proxy-based emission inventory in most urban centers in the BTH region
due to the concentrated emissions from point sources. The application of
the unit-based emission inventory improves model–observation agreement for
most pollutants. The accurate location of point sources leads to lower
concentrations of primary pollutants in urban areas and higher concentrations in suburban
areas. Plume rise accounts for the lower concentrations over the whole
region. For
The unit-based industrial emission inventory enables more accurate source apportionment and more reliable research on the mechanism of air pollution formation; therefore, it contributes to the development of more precisely targeted control policies. To further improve the emission inventory, it is necessary to improve the spatial allocation of emissions from non-industrial sectors, such as the residential and commercial sectors. Our previous study provides an example of the development of a village-based residential emission inventory in rural Beijing (Cai et al., 2018). Such studies on high-resolution emission inventories, for both industrial and non-industrial sources, are highly needed and should also be extended to other provinces and/or regions. In addition, the plume-in-grid approach might help to further improve model performance, which merits further in-depth study.
All data needed to evaluate the conclusion of this paper are provided in the main text and the Supplement. Additional related data are available upon request.
The supplement related to this article is available online at:
SW and BZ designed the research. HZ, SC, and XC performed the research. HZ, SC, BZ, SW, and XC analyzed the results. HZ, SC, BZ, SW, XC, and JH wrote the paper. HZ and SC contributed equally to this study.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Regional assessment of air pollution and climate change over East and Southeast Asia: results from MICS-Asia Phase III”. It is not associated with a conference.
This research has been supported by the National Key Research and Development Program of the Ministry of Science and Technology of China (grant no. 2017YFC0213005), the National Natural Science Foundation of China (grant no. 21625701), the Strategic Priority Research Program of Chinese Academy of Sciences (grant no. XDA20040502), the National Research Program for Key Issues in Air Pollution Control (grant no. DQGG0301), and the Beijing Municipal Commission of Science and Technology (grant no. D171100001517001). The simulations were completed on the “Explorer 100” cluster system of Tsinghua National Laboratory for Information Science and Technology.
This paper was edited by Yafang Cheng and reviewed by two anonymous referees.