Estimation of PM2.5 using satellite and meteorological data

Abstract

Motivation: Air pollution is measured by the amount of PM2.5 the air contains. These are fine particles with a diameter less than 2.5 micrometres that can penetrate deep into the lungs and trigger severe respiratory diseases. The concentration of PM2.5 in the air can be measured using ground-based monitoring stations, but there is a considerable deficit in the number of stations required for reliable measurements as air quality varies spatially and temporally across a given region. Given the non-trivial costs of installing and maintaining ground-based PM2.5 sensors, previous research has looked at using satellite retrievals for estimating PM2.5 data from visual features. Problem Statement: The goal is to predict PM2.5 from aerosol optical thickness (AOT), which is a measure of how much light is attenuated by the aerosols (e.g. haze, smoke particles, desert dust) as it passes the atmosphere. Previous studies have shown that higher amount of PM2.5 reduces the light transmission and increases attenuation and thereby causes higher AOT [2].We further examine the addition of the meteorological factors as predictor variables and its effect on the correlation with PM2.5.