Satellite data geoprocessing to estimate PM2.5 over the Megalopolis of Central Mexico

Abstract

The Megalopolis of Central Mexico experiences high levels above the Official Mexican Standard (NOM) of PM_2.5, leading to various respiratory diseases ranging from acute symptoms to chronic illnesses such as asthma and lung cancer. It is crucial to measure PM_2.5 levels accurately to warn the public about the risks of exposure to particulate matter. Unfortunately, the Megalopolis of Central Mexico has a shortage of monitoring sites, limiting data availability. This study addresses this issue using satellite data to develop a multiple linear regression model. Our model uses aerosol optical depth (AOD), relative humidity (RH), temperature (T), the planetary boundary layer height (PBLH), and the normalized difference vegetation index (NDVI) as independent variables to estimate PM_2.5 concentrations in the region under study. The relationship between AOD and PM_2.5 concentrations was found to be strongly influenced by RH and T. However, this effect is compensated for by a low PBLH (< 400 m), which enables AOD and PM_2.5 measurements to be similar in magnitude. Our findings have important implications for estimating PM_2.5 concentrations using satellite data. This study could help improve air quality monitoring in the Megalopolis of Central Mexico by providing more spatial and temporal data on particle concentrations in the atmosphere.