SOUTHWEST An Integrated Approach for Immediate and Long-Term Air Quality Regulation and Monitoring in Mexico

Overview

Air pollution is among the leading causes of premature deaths across the world and disproportionately impacts the Global South due to lesser air quality regulation and research on regional air pollution monitoring when compared to the United States, Europe, and East Asia. This project consists of three models to address air quality in different ways in Mexico. The first program includes data cleanup, visualization, training, and testing for a machine-learning model to provide hourly air pollution forecasts up to 48 hours in advance as an immediate means of minimizing the harm of hazardous air pollution events for numerous cities, with high seasonal accuracy scores up to R² = 0.77. Second, physical model simulations validated by a statistical analysis of historical relationships between ozone and its precursors present long-term policy suggestions to safely reduce ozone levels at each city, a challenge due to ozone’s counter-intuitive behavior (highlighted during the pandemic lockdowns). Finally, to provide continuous estimates of air pollution in regions without monitoring stations, a machine-learning model that incorporates historical ground monitoring, meteorological parameters, and physical model simulations is developed and tested, with cross-validated R² scores up to 0.78 - the first of its kind covering all of Mexico.

Competition history

• JSHS 2023 Category not listed

Resources

• National JSHS abstract book ↗

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