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Regional PM2.5 Estimation for Southern Ontario through Geographically Weighted Regression

K. Huang1 *

  1. Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan S4S 0A2, Canada

*Corresponding author. Tel.: +1 306-501-8706. E-mail address: (K. Huang).


In this study, a geographically weighted regression (GWR) approach was adopted to forecast regional concentration of particulate matter 2.5 (PM2.5) for the southern Ontario based on both in situ meteorological measurement and Satellite retrievals of aerosol optical depth (AOD). The correlation between monitored concentration of PM2.5 and Satellite-retrieved AOD would be quantified. The ground-level PM2.5 for South Ontario area was then predicted using GWR with AOD and meteorological variables considered as inputs. The results indicated that performance of GWR was slightly better than the ordinary least squares (OLS) model, indicating spatial variations between independent and dependent variables. Consequently, the GWR model can help us to predict the PM2.5 concentration in terms of time or region with satellite data, and also help improve satellite data inversion.

Keywords: geographically weighted regression, air pollution, particulate matter, Southern Ontario

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