Background and objective: The regulatory monitoring data of particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) in Texas have limited spatial and temporal coverage. The purpose of this study is to estimate the ground-level PM2.5 concentrations on a daily basis using satellite-retrieved Aerosol Optical Depth (AOD) in the state of Texas. Methods: We obtained the AOD values at 1-km resolution generated through the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm based on the images retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellites. We then developed mixed-effects models based on AODs, land use features, geographic characteristics, and weather conditions, and the day-specific as well as site-specific random effects to estimate the PM2.5 concentrations (μg/m3) in the state of Texas during the period 2008–2013. The mixed-effects models’ performance was evaluated using the coefficient of determination (R2) and square root of the mean squared prediction error (RMSPE) from ten-fold cross-validation, which randomly selected 90% of the observations for training purpose and 10% of the observations for assessing the models’ true prediction ability. Results: Mixed-effects regression models showed good prediction performance (R2 values from 10-fold cross validation: 0.63–0.69). The model performance varied by regions and study years, and the East region of Texas, and year of 2009 presented relatively higher prediction precision (R2: 0.62 for the East region; R2: 0.69 for the year of 2009). The PM2.5 concentrations generated through our developed models at 1-km grid cells in the state of Texas showed a decreasing trend from 2008 to 2013 and a higher reduction of predicted PM2.5 in more polluted areas. Conclusions: Our findings suggest that mixed-effects regression models developed based on MAIAC AOD are a feasible approach to predict ground-level PM2.5 in Texas. Predicted PM2.5 concentrations at the 1-km resolution on a daily basis can be used for epidemiological studies to investigate short- and long-term health impact of PM2.5 in Texas.
- Land use regression
- Remote sensing