首页|A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data

A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data

扫码查看
原文链接
  • NSTL
  • Elsevier
Research of PM2.5 chronic health effects requires knowledge of large-scale and long-term exposure that is not supported by newly established monitoring networks due to their sparse spatial coverage and lack of historical measurements. Estimating PM2.5 using satellite-derived aerosol optical depth (AOD) can be used to fill the data gap left by the ground monitors and extend the PM2.5 data coverage to suburban and rural areas over long time periods. Two approaches have been applied in large-scale and long-term satellite remote sensing of PM2.5, i.e., the scaling and statistical approaches. Compared to the scaling method, the statistical approach has greater prediction accuracy and has been widely used. There is a gap in the current literature and review papers on how the statistical methods work and specific considerations to best utilize them, especially for large-scale and longterm estimates. In this critical review, we summarize the evolution of large-scale and long-term PM2.5 statistical models reported in the literature. We describe the framework and guidance of large-scale and long-term satellite based PM2.5 modeling in data preparation, model development, validation, and predictions. Sample computer codes are provided to expedite new model-building efforts. We also include useful considerations and recommendations in covariates selection, addressing the spatiotemporal heterogeneities of PM2.5-AOD relationships, and the usage of cross validation, to aid the determination of the final model.

Satellite remote sensingAerosol optical depthPM2.5 estimatesStatistical methodsAEROSOL OPTICAL DEPTHGROUND-LEVEL PM2.5AIR-POLLUTION CONTROLRANDOM FOREST MODELPARTICULATE MATTERTEMPORAL TRENDSWEIGHTED REGRESSIONMORTALITY BURDENCHINARESOLUTION

Ma, Zongwei、Dey, Sagnik、Christopher, Sundar、Liu, Riyang、Bi, Jun、Balyan, Palak、Liu, Yang

展开 >

Nanjing Univ

Indian Inst Technol Delhi

Univ Alabama Huntsville

Emory Univ

展开 >

2022

10.1016/j.rse.2021.112827

Remote Sensing of Environment

Remote Sensing of Environment

EISCI
ISSN：0034-4257
年,卷(期)：2022.269
  • 20
  • 131