Preprints
https://doi.org/10.5194/gmd-2021-87
https://doi.org/10.5194/gmd-2021-87

Submitted as: model description paper 26 Apr 2021

Submitted as: model description paper | 26 Apr 2021

Review status: a revised version of this preprint is currently under review for the journal GMD.

Reduced-complexity air quality intervention modelling over China: development of the InMAPv1.6.1-China and comparison with the CMAQv5.2 model

Ruili Wu1, Christopher W. Tessum2, Yang Zhang3, Chaopeng Hong4, Yixuan Zheng5, and Qiang Zhang1 Ruili Wu et al.
  • 1Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
  • 2Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
  • 3Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115, United States
  • 4Department of Earth System Science, University of California, Irvine, California 92602, United States
  • 5Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing 100012, China

Abstract. This paper presents the first development and evaluation of the reduced-complexity air quality model for China. In this study, a reduced-complexity air quality intervention model over China (InMAPv1.6.1-China, hereafter, 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 modelling system is applied over mainland China for 2017 under various emission scenarios. A comprehensive model evaluation is conducted by comparison against conventional CMAQ 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/m3, −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 United States. 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 in comparison with those based on the CMAQ model, the difference is mainly caused by the different mechanism and the 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 25 ten thousand deaths lower than that estimated based on CMAQ simulations as a result of underestimation of PM2.5 concentrations. This work presents a version of the reduced-complexity air quality model over China, 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.

Ruili Wu et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-87', Juan Antonio Añel, 17 May 2021
    • AC3: 'Reply on CEC1', Ruili Wu, 16 Jul 2021
    • AC4: 'Reply on CEC1', Ruili Wu, 18 Jul 2021
  • RC1: 'Comment on gmd-2021-87', Anonymous Referee #1, 26 May 2021
    • AC1: 'Reply on RC1', Ruili Wu, 16 Jul 2021
  • RC2: 'Comment on gmd-2021-87', Anonymous Referee #2, 03 Jun 2021
    • AC2: 'Reply on RC2', Ruili Wu, 16 Jul 2021

Ruili Wu et al.

Model code and software

The source code of InMAPv1.6.1-China model Ruili Wu https://zenodo.org/record/4686431#.YH0pf5MzYkg

Ruili Wu et al.

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Short summary
Reduced-complexity air quality models are less computationally intensive and easier to use. We developed a reduced-complexity air quality intervention model over China, InMAPv1.6.1-China, to rapidly predict the air quality and estimate the health impacts of emission sources in China. We believe that this work will be of great interest to a broad audience, including environmentalists in China, and scientists in relevant fields in both national and local institutes.