Comparison of four ground-level PM2.5 estimation models using PARASOL aerosol optical depth data from China

Abstract

Satellite remote sensing is of considerable importance for estimating ground-level PM2.5 concentrations to support environmental agencies monitoring air quality. However, most current studies have focused mainly on the application of MODIS aerosol optical depth (AOD) to predict PM2.5 concentrations, while PARASOL AOD, which is sensitive to fine-mode aerosols over land surfaces, has received little attention. In this study, we compared a linear regression model, a quadratic regression model, a power regression model and a logarithmic regression model, which were developed using PARASOL level 2 AOD collected in China from 18 January 2013 to 10 October 2013. We obtained R (correlation coefficient) values of 0.64, 0.63, 0.62, and 0.57 for the four models when they were cross validated with the observed values. Furthermore, after all the data were classified into six levels according to the Air Quality Index (AQI), a low level of statistical significance between the four empirical models was found when the ground-level PM2.5 concentrations were greater than 75 μg/m3. The maximum R value was 0.44 (for the logarithmic regression model and the power model), and the minimum R value was 0.28 (for the logarithmic regression model and the power model) when the PM2.5 concentrations were less than 75 μg/m3. We also discussed uncertainty sources and possible improvements.