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Machine learning-based estimation of ground-level NO2 concentrations over China.

Yulei Chi1, Meng Fan2, Chuanfeng Zhao3

  • 1State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.

The Science of the Total Environment
|October 7, 2021
PubMed
Summary

This study introduces a machine learning approach to estimate ground-level nitrogen dioxide (NO2) concentrations in China using satellite data. The method accurately retrieves NO2 levels, revealing pollution patterns and a downward trend from 2018-2020.

Keywords:
ChinaGeographical variationsGround-level NO(2)Seasonal variationTROPOMIXGBoost

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Remote Sensing

Background:

  • Current NO2 remote sensing primarily targets tropospheric columns, not ground-level concentrations.
  • Ground-level NO2 is more directly linked to human health and anthropogenic emissions.

Purpose of the Study:

  • To develop a machine learning model for estimating ground-level NO2 concentrations across China.
  • To analyze the spatial, seasonal, and interannual variations of ground-level NO2.

Main Methods:

  • Utilized TROPOspheric Monitoring Instrument (TROPOMI) satellite data for NO2 column concentrations.
  • Employed the XGBoost machine learning model with multisource geographic data (2018-2020).
  • Validated retrieval accuracy using R² values of 0.67 (validation) and 0.73 (test) datasets.

Main Results:

  • The XGBoost model reliably retrieved ground-level NO2 concentrations.
  • Significant geographical and seasonal variations were observed, with winter peaks and summer lows.
  • High pollution levels were concentrated in major urban agglomerations like BTH, YRD, PRD, and CY.

Conclusions:

  • The proposed machine learning method effectively estimates ground-level NO2.
  • Ground-level NO2 pollution in China showed a continuous decrease from 2018 to 2020.
  • Findings highlight regional pollution hotspots and seasonal trends.