Estimating ground-level PM2.5 in Texas from remote sensing satellite data with interpolation and regression methods
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The integration of remote sensing satellite data in air quality monitoring system at a regional scale is an important method to provide high spatial / temporal resolution information. This work focuses on estimating high spatial / temporal resolution ground-level information about particulate matter with aerodynamic diameters less than 2.5 um (PM2.5), with the utilization of MODIS aerosol optical thickness (AOT) data and meteorological data. Several missing data reconstruction techniques including Bayesian inversion, regularization and prediction-error filter are employed to estimate PM2.5 from satellite data. The results show that several direct missing data interpolation methods have the capability to estimate some distinctive features on the basis of available ground-based measurements, while the PEF method tends to generate more information with the aid of satellite AOT information. In addition to interpolation methods, general linear regression methods are used to predict ground-level PM2.5 with the consideration of other factors that have been shown to play an important role in predictions. Ordinary Least Square (OLS) method, when natural log taken on dependent and independent variables, is able to reduce the violation of homoscedasticity. The scatterplot of predicted and measured PM2.5 shows a strong correlation over the validation region, indicating the ability of the regression model to predict PM2.5. Weighted Least Square (WLS) method also has advantage in improving homoscedasticity. The predicted and measured PM2.5 has a relatively high correlation.