This paper presents the first development and evaluation
of a reduced-complexity air quality model for China. In this study, the
reduced-complexity Intervention Model for Air Pollution over China (InMAP-China)
is developed by linking a regional air quality model, a reduced-complexity
air quality model, an emission inventory database for China, and a health
impact assessment model to rapidly estimate the air quality and health
impacts of emission sources in China. The modeling system is applied over
mainland China for 2017 under various emission scenarios. A comprehensive
model evaluation is conducted by comparison against conventional Community Multiscale Air Quality (CMAQ) modeling system
simulations and ground-based observations. We found that InMAP-China
satisfactorily predicted total PM
With rapid urbanization and industrialization, fine particulate matter
pollution less than 2.5
State-of-the-art three-dimensional air quality models (AQMs) have been
widely used in China as tools to simulate regional PM
The reduced-complexity Intervention Model for Air Pollution (InMAP) was
developed by Tessum et al. (2017) to rapidly assess the air
pollution, health and economic impacts resulting from marginal changes in
air pollutant emissions. Compared with conventional air quality models,
InMAP has the advantage of being temporally efficient: it can predict annual average
PM
In this work, based on the source code of version 1.6.1 of InMAP, the
reduced-complexity Intervention Model for Air Pollution over China (InMAP-China)
is developed to rapidly predict the air quality and estimate the health
impacts of emission sources in China. The total simulation time for the year 2017 using the InMAP-China model established in this study
is approximately 1 h with a single 24-node central processing unit (CPU). Therefore, this model is
convenient when conducting multiple simulations of PM
The paper is organized as follows: Sect. 2.1 presents the components of
InMAP-China, including the interface development between WRF-CMAQ and InMAP
to generate the base atmospheric state parameters, the preprocessing
process for emission input data and the exposure–response functions employed
in this model; Sect. 2.2 introduces the evaluation protocol, including the
statistical variables adopted and the simulation design in this study;
Sect. 3 presents the evaluation of the InMAP-China predictions of PM
InMAP has been widely used in studies (Sergi et
al., 2020; Thind et al., 2019; Goodkind et al., 2019; Dimanchevi et al.,
2019) focusing on PM
Model framework of InMAP-China.
In this work, a Chinese version of the reduced-complexity Intervention Model for Air Pollution (InMAP-China) is developed to rapidly estimate the
PM
Model configurations in InMAP-China.
Table 1 presents the basic configurations of InMAP-China. The simulation domain is over East Asia and covers mainland China. The spatial resolution is 36 km. Fourteen vertical layers are used in InMAP-China, ranging from the surface layer to the top level of the tropospheric layer.
We develop a preprocessed interface to calculate physical and chemical process parameters using WRF-CMAQ output variables for simplified simulation in InMAP-China based on work from the Environmental Protection Agency (EPA) (Baker et al., 2020). Two NetCDF (Network Common Data Form) files containing the key parameters for simplified simulation are generated using the parameter interface developed here: one is at a 36 km resolution across the entire mainland of China and another is at a 4 km resolution over the Beijing–Tianjin–Hebei (BTH) region. The main step of the preprocessed interface includes meteorological and chemical variable extraction and merging, unit conversion, vertical layer mapping, physical and chemical process parameter calculation, and average processing. The hourly chemical and meteorological variable outputs from the WRF-CMAQ modeling system are converted into the annual average physical and chemical process parameters required for simplified simulation.
The relationship between parameters for simplified simulation and original variables.
A NetCDF file containing the three-dimensional annually averaged parameters
to characterize atmospheric advection, dispersion, mixing, chemical
reaction and deposition is generated. Table 2 shows the relationship
between the annually averaged parameters for simplified simulation and the
original hourly variables. In InMAP-China, the annually averaged component and
the deviation of wind speed to represent advection are calculated using
hourly elements. The offset of wind vectors in different directions may
result in some uncertainties in this process. The parameters of eddy
diffusion and convective transport are pre-calculated using hourly elements,
including temperature, pressure and boundary layer height, among others. The annual wet
deposition rate is determined by the rainwater mixing ratio and cloud
fractions. The annual dry deposition rate of particles and gaseous
pollutants at the surface level is pre-calculated using friction speed, heat
flux, radiation flux and land cover. The simplification of chemical
reactions is different among pollutants. For NO
To generate the meteorological and chemical parameters required by
InMAP-China, a 1-year WRF-CMAQ simulation covering the entirety of mainland of
China is conducted to output hourly meteorological and chemistry-related
variables in 2017. Furthermore, a nested WRF-CMAQ simulation over the BTH
region is also conducted and validated using observed data. The corresponded
output data are used to generate the meteorological and chemical parameters
required by InMAP-China for the 4 km resolution simulations in the BTH
region. Tables S1 and S2 in the Supplement show the major configurations of the WRF-CMAQ
modeling system. The WRF model (Skamarock et al., 2008) is driven by the National Centers for
Environmental Prediction Final Analysis (NCEP-FNL)
(
Table S3 summarizes the performance statistics of meteorological variables,
including surface temperature, relative humidity and wind speed, in China
in 2017, as simulated by the WRF model. The hourly observed data of major
meteorological variables derived from the National Climatic Data Center
(NCDC) are utilized here. The results show that the meteorological variables
simulated by the WRF model agree well with the surface observations, which
is consistent with previous studies (Wu et al., 2019; Zheng et al., 2015;
Hong et al., 2017). The model performs well with respect to the prediction of surface
temperature, with a mean bias (MB) of
The SO
We develop the preprocessed module to generate vector emission input for the InMAP-China simulation. This module can allocate air pollutant emissions to vertically and horizontally supply the missing parameters for the emission file and convert them into a shapefile vector format. The shapefile vector format's 36 km resolution emission data for the entirety of mainland of China and 4 km resolution data for the BTH region in 2017 are preprocessed using this module.
In this module, the emission data are preprocessed by source and altitude.
The anthropogenic emissions of five sectors in China in 2017 from the MEIC
inventory (
In more detail, the 0.3
To rapidly estimate the premature mortality attributable to PM
In this study, the performance of the InMAP-China predictions are evaluated by comparison against CMAQ simulations and surface observations. Model–model and model–observation comparison have both been used to evaluate the performance of reduced-complexity air quality models in previous studies (Tessum et al., 2017; Gilmore et al., 2019).
The following aspects are considered when carrying out an evaluation. First, we
examine the ability of InMAP-China to predict PM
Four key regions are defined in this study: the Beijing–Tianjin–Hebei region, the Yangtze River Delta region, the Pearl River Delta region, and the Fenwei Plain region.
For the observed PM
Simulation experiments conducted using InMAP-China.
We designed 12 simulations to examine the model ability of InMAP-China in this study. Table 3 shows the sequence of simulations.
“InMAP_TOT” represents the baseline simulation with the maximum input of combined emissions. Five sectoral anthropogenic emissions derived
from the MEIC inventory, natural emissions derived from the MEGANv2.10
model and Asian emissions outside of mainland China derived from the
MIX-2010 inventory are utilized in the simulation. Five sectoral and five
abatement simulations are also conducted to examine the ability of
InMAP-China to predict concentration changes in response to sectoral
emissions and abatement emissions. The emission inputs for these 10
simulations are given in Table 3. The annual average physical and
chemical process parameters are calculated based on the output variables of the
WRF-CMAQ model, which has already been mentioned in Sect. 2.1.2. Based on
the above input, the particle continuity equations are solved by the InMAP-China
model to obtain the annual average PM
To carry out a comparison with the InMAP-China simulations, 11 CMAQ
simulations are also performed using the same emission inputs. The hourly
PM
The spatial pattern and statistical metrics of total
PM
Figure 3 shows the performance evaluation of total PM
Scatterplot comparing the PM
Figure 4 shows a comparison of PM
The spatial pattern of PM
The difference in the spatial pattern of
PM
The ability of InMAP-China to predict PM
Comparison of PM
Figure 8 compares the InMAP-China and CMAQ predictions of
population-weighted PM
Marginal change in the nationwide annual average
population-weighted PM
Figure 8b–f compare the predictions of PM
The regional performance of the changes in PM
Comparison of the proportions of sectoral contributions to
PM
Comparison of source contributions to population-weighted
PM
Scatterplot comparing the PM
Figure 9 shows the nationwide and regional-scale contributions of each sector to PM
Scatterplot comparing the PM
The results based on the two models indicate that the industrial and residential sectors are the first and second contributors among the five sectors. The contribution of the electricity sector is comparable when using the two models, whereas the contributions of transportation and agriculture are moderately different, which is mainly due to the difference in the model mechanism and the treatment of secondary inorganic aerosols in the two models. At the regional scale, the difference in the sectoral contribution caused by the mechanism in the two models is more significant than at the national scale.
We also conducted a simulation with a higher spatial resolution of 4 km in
the BTH region using InMAP-China and compared the results to the
WRF-CMAQ nested simulation for the same area. Figures 10 and
11 show the performance evaluation of total PM
The spatial pattern of PM
Further comparison with the nested CMAQ predictions shows that the total PM
Comparison of PM
To examine the performance of the predictions of PM
At the provincial level, the PM
This work develops a reduced-complexity air quality intervention model over
China, InMAP-China, and presents a comprehensive evaluation by comparing CMAQ simulations
and surface observations. InMAP-China aims at providing a simplified
modeling tool to rapidly predict PM
InMAP-China has moderately satisfactory performance in this study; however,
this model has decreased accuracy compared with conventional CTMs.
Overall, InMAP-China satisfactorily predicts total PM
This study is subject to some limitations and uncertainties. In InMAP-China, the annual average chemical and physical process parameters are calculated using hourly parameters from WRF-CMAQ. Complicated seasonal and daily variations affecting the formation and transportation of particulate matter are challenging to retain. The intensity of advection of the air mass is supposed to be weakened due to the offset of the wind vector in the averaging process, which was also pointed out in a previous study. Moreover, InMAP-China has difficulty predicting SOA concentrations because reaction pathways for SOA are insufficient in this modeling system. Further research is suggested to improve the model performance; for instance, the combination of a machine learning technique and a reduced complexity air quality model may improve the model performance in China.
The source code of InMAP-China, the user manual, and data related to this study are all available at
The supplement related to this article is available online at:
QZ and RW designed the research, and RW carried it out. RW, CWT and YaZ contributed to model develop- ment. CH and YZ contributed to data analysis. XQ and SL contributed to data preprocessing. RW and QZ interpreted the results. RW prepared the paper with contributions from all co-authors.
The contact author has declared that neither they nor their co-authors have any competing interests.
The views expressed in this paper are those of the authors alone and do not necessarily reflect the views and policies of the U.S. EPA. The EPA does not endorse any products or commercial services mentioned in this publication. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was supported by the National Natural Science Foundation of China (grant nos. 41625020 and 41921005). It was also funded under Assistance Agreement No. RD835871 awarded by the U.S. EPA to Yale University.
This research has been supported by the National Natural Science Foundation of China (grant nos. 41921005 and 41625020) and by the U.S. Environmental Protection Agency (assistance agreement no. RD835871 awarded to Yale University through the SEARCH – Solutions for Energy, AiR, Climate, and Health – Center).
This paper was edited by Gunnar Luderer and reviewed by two anonymous referees.