High-Resolution Satellite Mapping of Fine Particulates Based on Geographically Weighted Regression

Bin Zou, Qiang Pu, Muhammad Bilal, Qihao Weng, Liang Zhai, Janet Elizabeth Nichol

Research output: Journal article publicationJournal articleAcademic researchpeer-review

105 Citations (Scopus)

Abstract

Satellite-retrieved aerosol optical depth (AOD) has been increasingly utilized for the mapping of fine particulate matter (PM2.5) concentrations. An accurate estimation and mapping of PM2.5concentrations depends on the high-resolution AOD data and a robust mathematical model that takes into account the spatial nonstationary relationship between PM2.5and AOD. Take the core portion of the Beijing-Hebei-Tianjin (Jing-Jin-Ji) urban agglomeration as case study (the most seriously polluted region in China). Land use, population, meteorological variables, and simplified aerosol retrieval algorithm-retrieved AOD at 1-km resolution are employed as the predictors for the geographically weighted regression (GWR) and the ordinary least squares (OLS) model to map the spatial distribution of PM2.5concentrations. The GWR model shows significant spatial variations in PM2.5concentrations over the region than the traditional OLS model, which reveals relative homogeneous variations. Validation with ground-level PM2.5concentrations demonstrates that PM2.5concentrations predicted by the GWR model (R2= 0.75, RMSE = 10 μg/m3) correlate better than those by the OLS model (R2= 0.53, RMSE = 16 μg/m3). These results suggest that the GWR model offered a more reliable way for the prediction of spatial distribution of PM2.5concentrations over urban areas.
Original languageEnglish
Article number7421977
Pages (from-to)495-499
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume13
Issue number4
DOIs
Publication statusPublished - 1 Apr 2016

Keywords

  • Aerosol optical depth (AOD)
  • geographically weighted regression (GWR)
  • moderate resolution imaging spectroradiometer (MODIS)
  • PM 2.5
  • simplified aerosol retrieval algorithm (SARA)
  • urban area

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering

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