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Estimating ground-level CO concentrations across China based on the national monitoring network and MOPITT: potentially overlooked CO hotspots in the Tibetan Plateau

文献类型： 外文期刊

作者： Liu, Dongren 1 ; Di, Baofeng 1 ; Luo, Yuzhou 3 ; Deng, Xunfei 4 ; Zhang, Hanyue 1 ; Yang, Fumo 1 ; Grieneisen, Michael 1 ;

作者机构： 1.Sichuan Univ, Dept Environm Sci & Engn, Chengdu 610065, Sichuan, Peoples R China

2.Sichuan Univ, Inst Disaster Management & Reconstruct, Chengdu 610200, Sichuan, Peoples R China

3.Univ Calif Davis, Dept Land Air & Water Resources, Davis, CA 95616 USA

4.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Zhejiang, Peoples R China

5.Natl Engn Res Ctr Flue Gas Desulfurizat, Chengdu 610065, Sichuan, Peoples R China

6.Sichuan Univ, Sino German Ctr Water & Hlth Res, Chengdu 610065, Sichuan, Peoples R China

7.Sichuan Univ, Med Big Data Ctr, Chengdu 610041, Sichuan, Peoples R China

期刊名称：ATMOSPHERIC CHEMISTRY AND PHYSICS （ 影响因子：6.133； 五年影响因子：6.546 ）

ISSN： 1680-7316

年卷期： 2019 年 19 卷 19 期

页码：

收录情况： SCI

摘要： 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 degrees 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 R-2 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) (mu +/-sigma) 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.

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