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© 2019. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Given its relatively long lifetime in the troposphere, carbon monoxide (CO) is commonly employed as a tracer for characterizing airborne pollutant distributions. The present study aims to estimate the spatiotemporal distributions of ground-level CO concentrations across China during 2013–2016. We refined the random-forest–spatiotemporal kriging (RF–STK) model to simulate the daily CO concentrations on a 0.1 grid based on the extensive CO monitoring data and the Measurements of Pollution in the Troposphere CO retrievals (MOPITT CO). The RF–STK model alleviated the negative effects of sampling bias and variance heterogeneity on the model training, with cross-validation R2 of 0.51 and 0.71 for predicting the daily and multiyear average CO concentrations, respectively. The national population-weighted average CO concentrations were predicted to be 0.99±0.30 mg m-3 (μ±σ) and showed decreasing trends over all regions of China at a rate of -0.021±0.004 mg m-3 yr-1. The CO pollution was more severe in North China (1.19±0.30 mg m-3), and the predicted patterns were generally consistent with MOPITT CO. The hotspots in the central Tibetan Plateau where the CO concentrations were underestimated by MOPITT CO were apparent in the RF–STK predictions. This comprehensive dataset of ground-level CO concentrations is valuable for air quality management in China.

Details

Title
Estimating ground-level CO concentrations across China based on the national monitoring network and MOPITT: potentially overlooked CO hotspots in the Tibetan Plateau
Author
Liu, Dongren 1 ; Baofeng Di 2 ; Luo, Yuzhou 3 ; Deng, Xunfei 4 ; Zhang, Hanyue 1 ; Yang, Fumo 5 ; Grieneisen, Michael L 3 ; Yu, Zhan 6   VIAFID ORCID Logo 

 Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China 
 Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China; Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu 610200, China 
 Department of Land, Air, and Water Resources, University of California, Davis, CA 95616, USA 
 Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China 
 Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China; National Engineering Research Center for Flue Gas Desulfurization, Chengdu 610065, China 
 Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China; National Engineering Research Center for Flue Gas Desulfurization, Chengdu 610065, China; Sino-German Centre for Water and Health Research, Sichuan University, Chengdu 610065, China; Medical Big Data Center, Sichuan University, Chengdu 610041, China 
Pages
12413-12430
Publication year
2019
Publication date
2019
Publisher
Copernicus GmbH
ISSN
16807316
e-ISSN
16807324
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2301765702
Copyright
© 2019. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.