Preprints
https://doi.org/10.5194/essd-2021-296
https://doi.org/10.5194/essd-2021-296

  15 Sep 2021

15 Sep 2021

Review status: this preprint is currently under review for the journal ESSD.

Full-coverage 1 km daily ambient PM2.5 and O3 concentrations of China in 2005–2017 based on multi-variable random forest model

Runmei Ma1,, Jie Ban1,, Qing Wang1,, Yayi Zhang1, Yang Yang2, Shenshen Li3, Wenjiao Shi4,5, and Tiantian Li1 Runmei Ma et al.
  • 1China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
  • 2Institute of Urban Meteorology, China Meteorological Administration, Beijing, 100089, China
  • 3State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China
  • 4Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 5College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • These authors contributed equally to this work.

Abstract. The health risks of fine particulate matter (PM2.5) and ambient ozone (O3) have been widely recognized in recent years. An accurate estimate of PM2.5 and O3 exposures is important for supporting health risk analysis and environmental policy-making. The aim of our study was to construct random forest models with high-performance, and estimate daily average PM2.5 concentration and O3 daily maximum 8 h average concentration (O3-8hmax) of China in 2005–2017 at a spatial resolution of 1 km×1 km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on ten-fold cross validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly simulations of PM2.5 gave average model fitting R2 values of 0.85, 0.88 and 0.90, respectively; these R2 values were 0.77, 0.77, and 0.69 for O3-8hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM2.5 and O3-8hmax simulations. During 2005–2017, PM2.5 exhibited an overall downward trend, while ambient O3 experienced an upward trend. Whilst the spatial patterns of PM2.5 and O3-8hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristic. The dataset is accessible to the public at https://doi.org/10.5281/zenodo.4009308, and the shared data set of Chinese Environmental Public Health Tracking: CEPHT (https://cepht.niehs.cn:8282/developSDS3.html).

Runmei Ma et al.

Status: open (until 10 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-296', Anonymous Referee #1, 15 Oct 2021 reply
  • RC2: 'Comment on essd-2021-296', Anonymous Referee #2, 19 Oct 2021 reply

Runmei Ma et al.

Data sets

Full-coverage 1 km daily ambient PM2.5 and O3 concentrations of China in 2005-2017 based on multi-variable random forest model Runmei Ma, Jie Ban, Qing Wang, Yayi Zhang, Tiantian Li https://doi.org/10.5281/zenodo.4009308

Runmei Ma et al.

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Short summary
We constructed multi-variable random forest models based on ten-fold cross validation, and estimated daily PM2.5 and O3 concentration of China in 2005–2017 at a resolution of 1 km. The daily R2 values of PM2.5 and O3 were 0.85 and 0.77. The meteorological variables can significantly affect both PM2.5 and O3 modeling. During 2005–2017, PM2.5 exhibited an overall downward trend, while O3 was on the contrary. The temporal trend of PM2.5 and O3 had spatial characteristic during study period.