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Discussion papers
https://doi.org/10.5194/acp-2019-246
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-246
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 26 Apr 2019

Submitted as: research article | 26 Apr 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Atmospheric Chemistry and Physics (ACP).

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

Dongren Liu1, Baofeng Di1,2, Yuzhou Luo3, Xunfei Deng4, Hanyue Zhang1, Fumo Yang1,5, Michael L. Grieneisen3, and Yu Zhan1,5,6,7 Dongren Liu et al.
  • 1Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China
  • 2Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu 610200, China
  • 3Department of Land, Air, and Water Resources, University of California, Davis, CA95616, USA
  • 4Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
  • 5National Engineering Research Center for Flue Gas Desulfurization, Chengdu 610065, China
  • 6Sino-German Centre for Water and Health Research, Sichuan University, Chengdu 610065, China
  • 7Medical Big Data Center, Sichuan University, Chengdu 610041, China

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. A refined random-forest-spatiotemporal-kriging model (RF-STK) is developed to simulate daily gridded CO concentrations (0.1° grid with 98 341 cells) based on the extensive CO monitoring data and the Measurements of Pollution in the Troposphere CO retrievals (MOPITT-CO). The refined RF-STK model alleviates the negative effects of sampling bias and variance heterogeneity on the model training, resulting in cross-validation R2 of 0.51 and 0.71 for predicting daily and spatial CO concentrations, respectively. The national population-weighted 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 per year. The CO pollution was more severe in North China (1.19 ± 0.30) mg m−3, and the predicted spatial pattern was roughly consistent with the MOPITT-CO. The hotspots in the Central Tibetan Plateau which were overlooked by the MOPITT were revealed by the refined RF-STK predictions. This information has an implication for improving the MOPITT-CO derivation procedure and air quality management.

Dongren Liu et al.
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Short summary
The spatiotemporal distributions of daily ground-level CO concentrations across China during 2013–2016 are derived by fusing the data from remote sensing and ground monitoring. The population-weighted CO were predicted to be 0.99 ± 0.30 mg m−3 and showed a decreasing trend of −0.021 ± 0.004 mg m−3 per year. The CO pollution was the most severe in the North China Plain. The hotspots in the Tibetan Plateau overlooked by the remote sensing were depicted by the data-fusion approach.
The spatiotemporal distributions of daily ground-level CO concentrations across China during...
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