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PM2.5 Modeling in Mexican Cities using using Aerosol Optical Depth and Meteorological Reanalysis Data

environmental-sciences
public-health
data-science
climate-change-biology
spatial-and-geographic-information-science

Abstract

Air pollution is a significant global health risk, making the necessity of accurate spatial and temporal concentration estimates essential. However, due to the high cost of deploying and maintaining comprehensive monitoring networks, their coverage is often limited, especially in low-resource settings. This study addresses the challenge of obtaining reliable PM2.5 estimations in four Mexican cities with limited monitoring by developing a model that integrates satellite Aerosol Optical Depth (AOD) and other data sources with location measurements.

These findings highlight a robust approach for estimating PM 2.5 concentrations that mitigates the limitations of ground-based monitoring networks, leveraging data obtained by remote perception inputs. The use of reliable meteorological data to obtain estimations, even in regions distant from monitoring stations, provides a valuable tool for public health assessments and generating information to aid decision-making in the design of future monitoring networks and the implementation of mitigation strategies.

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