Preprints
https://doi.org/10.5194/essd-2022-110
https://doi.org/10.5194/essd-2022-110
 
14 Apr 2022
14 Apr 2022
Status: this preprint is currently under review for the journal ESSD.

Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China

Junting Zhong1, Xiaoye Zhang1,4, Ke Gui1, Jie Liao2, Ye Fei2, Lipeng Jiang3, Lifeng Guo1, Liangke Liu5, Huizheng Che1, Yaqiang Wang1, Deying Wang1, and Zijiang Zhou2 Junting Zhong et al.
  • 1State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
  • 2National Meteorological Information Center, Beijing, 100081, China
  • 3Earth System Numerical Prediction Center, Beijing, 100081, China
  • 4Center for Excellence in Regional Atmospheric Environment, IUE, Chinese Academy of Sciences, Xiamen, 361021, China
  • 5Department of Earth System Science, Tsinghua University, Beijing 100084, China

Abstract. Fine particulate matter (PM2.5) has altered radiation balance on earth and raised environmental and health risks for decades, but only been monitored widely since 2013 in China. Historical long-term PM2.5 records with high temporal resolution are essential but lacking for both research and environmental management. Here, we reconstruct a site-based PM2.5 dataset at 6-hour intervals from 1960 to 2020 that combines long-term visibility, conventional meteorological observations, emissions, and elevation. The PM2.5 concentration at each site is estimated based on an advanced machine learning model, LightGBM, that takes advantage of spatial features from 20 surrounding meteorological stations. Our model's performance is comparable or even better than those of previous studies in by-year cross validation (CV) (R2=0.7) and spatial CV (R2=0.76), and more advantageous in long-term records and high temporal resolution. This model also reconstructs a 0.25°×0.25°, 6-hourly, gridded PM2.5 dataset by incorporating spatial features. The results show PM2.5 pollution worsens gradually or maintains before 2010 from an interdecadal scale but mitigates in the following decade. Although the turning points vary in different regions, PM2.5 mass concentrations in key regions decreased significantly after 2013 due to clean air actions. In particular, the annual average value of PM2.5 in 2020 is nearly at the lowest value in history since 1960. These two PM2.5 datasets (publicly available at https://doi.org/10.5281/zenodo.6372847) provide spatiotemporal variations at high resolution, which lay the foundation of research studies associated with air pollution, climate change, and atmospheric chemical reanalysis.

Junting Zhong et al.

Status: open (until 09 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-110', Anonymous Referee #1, 22 Apr 2022 reply
  • RC2: 'Comment on essd-2022-110', Anonymous Referee #2, 28 Apr 2022 reply

Junting Zhong et al.

Data sets

Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China Zhong, Junting, Zhang, Xiaoye, Gui, Ke, Liao, Jie, Fei, Ye, Jiang, Lipeng, Guo, Lifeng, Liu, Liangke, Che, Huizheng, Wang, Yaqiang, Wang, Deying, & Zhou, Zijiang https://doi.org/10.5281/zenodo.6372846

Junting Zhong et al.

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Short summary
Historical long-term PM2.5 records with high temporal resolution are essential but lacking for research and environmental management. Here, we reconstruct site-based and gridded PM2.5 datasets at 6-hour intervals from 1960 to 2020 that combine visibility, meteorological data, and emissions based on a machine learning model with extracted spatial features. These two PM2.5 datasets will lay the foundation of research studies associated with air pollution, climate change, and aerosol reanalysis.