Estimating 2013–2019 NO₂ exposure with high spatiotemporal resolution in China using an ensemble model

Air pollution has become a major issue in China, especially for traffic-related pollutants such as nitrogen dioxide (NO₂). Current studies in China at the national scale were less focused on NO₂ exposure and consequent health effects than fine particulate exposure, mainly due to a lack of high-quality exposure models for accurate NO₂ predictions over a long period. We developed an advanced modeling framework that incorporated multisource, high-quality predictor data (e.g., satellite observations [Ozone Monitoring Instrument NO₂, TROPOspheric Monitoring Instrument NO₂, and Multi-Angle Implementation of Atmospheric Correction aerosol optical depth], chemical transport model simulations, high-resolution geographical variables) and three independent machine learning algorithms into an ensemble model. The model contains three stages: (1) filling missing satellite data; (2) building an ensemble model and predicting daily NO₂ concentrations from 2013 to 2019 across China at 1×1 km² resolution; (3) downscaling the predictions to finer resolution (100 m) at the urban scale. Our model achieves a high performance in terms of cross-validation to assess the agreement of the overall (R² = 0.72) and the spatial (R² = 0.85) variations of the NO₂ predictions over the observations. The model performance remains moderately good when the predictions are extrapolated to the previous years without any monitoring data (CV R² > 0.68) or regions far away from monitors (CV R² > 0.63). We identified a clear decreasing trend of NO₂ exposure from 2013 to 2019 across the country with the largest reduction in suburban and rural areas. Our downscaled model further improved the prediction ability by 4%–14% in some megacities and captured substantial NO₂ variations within 1-km grids in the urban areas, especially near major roads. Our model provides flexibility at both temporal and spatial scales and can be applied to exposure assessment and epidemiological studies with various study domains (e.g., national or citywide) and settings (e.g., long-term and short-term).

• Record URL:
  https://doi.org/10.1016/j.envpol.2021.118285
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0269749121018674
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/02697491
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Huang, Conghong
  • 0000-0002-0180-0806
  • Sun, Kang
  • Hu, Jianlin
  • Xue, Tao
  • Xu, Hao
  • Wang, Meng
• Publication Date: 2022-1-1

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Serial:
  • Environmental Pollution
  • Volume: 292
  • Issue Number: 0
  • Publisher: Elsevier Publishing Company, Limited
  • ISSN: 0269-7491
  • Serial URL: http://www.journals.elsevier.com/environmental-pollution

Subject/Index Terms

• TRT Terms: Air pollution; Nitrogen oxides; Predictive models; Roads; Trend (Statistics)
• Geographic Terms: China
• Subject Areas: Environment; Highways;

Filing Info

• Accession Number: 01788168
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 17 2021 2:25PM