Abstract
Abstract. 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 PM2.5 concentrations in terms of statistical performance. Compared with the observed PM2.5 concentrations, the mean bias (MB), normalized mean bias (NMB) and correlations of the total PM2.5 concentrations are −8.1 µg m−3, −18 % and 0.6, respectively. The statistical performance is considered to be satisfactory for a reduced-complexity air quality model and remains consistent with that evaluated in the USA. The underestimation of total PM2.5 concentrations was mainly caused by its composition, primary PM2.5. In terms of the ability to quantify source contributions of PM2.5 concentrations, InMAP-China presents similar results to those based on the CMAQ model, with variation mainly caused by the different treatment of secondary inorganic aerosols in the two models. Focusing on the health impacts, the annual PM2.5-related premature mortality estimated using InMAP-China in 2017 was 1.92 million, which was 250 000 deaths lower than estimated based on CMAQ simulations as a result of the underestimation of PM2.5 concentrations. This work presents a version of the reduced-complexity air quality model over China that provides a powerful tool to rapidly assess the air quality and health impacts associated with control policy and to quantify the source contribution attributable to many emission sources.
Highlights
With rapid urbanization and industrialization, fine particulate matter pollution less than 2.5 μm in diameter (PM2.5) has become a major environmental issue in China
Compared with the observed annual average PM2.5 concentrations, the total PM2.5 concentrations are moderately underpredicted by Intervention Model for Air Pollution (InMAP)-China, with an mean bias (MB) of −8.1 μg m−3 and an normalized mean bias (NMB) of −18.1 %
Significant overpredictions of PM2.5 concentrations can be observed over mountainous areas across northern China, and the complex terrain and high emission intensity increase the challenge of predicting PM2.5 concentrations using the reduced-complexity air quality model in this region
Summary
With rapid urbanization and industrialization, fine particulate matter pollution less than 2.5 μm in diameter (PM2.5) has become a major environmental issue in China. 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. 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) This model is convenient when conducting multiple simulations of PM2.5 concentrations due to air pollutant emissions in 2017. 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 PM2.5 air quality and PM2.5-related health impacts in several simulations; and, Sect. 4 summarizes the conclusions and limitations of this study
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