Simplifying Air Quality Forecasting: Logistic Regression for Predicting Particulate Matter in Chile

Widespread residential wood burning in southern Chile combined with cold climate conditions cause severe episodes of particulate matter (PM_2.5 and PM₁₀) pollution. In this study, we used logistic regression to predict daily exceedances of fine (PM_2.5) and coarse (PM₁₀) particulate levels at multiple urban sites, assessing model performance under different air quality standards. Results showed a clear latitudinal gradient in air pollution, with communities further south experiencing significantly higher PM levels and more frequent threshold exceedances, likely due to higher per capita firewood use and cooler temperatures. The logistic models achieved their best predictive accuracy under the strictest European (ESP) air quality standards (F1-scores up to ~0.72 for PM₁₀ and ~0.59 for PM_2.5), while Chile’s national (NCh) thresholds significantly underestimated pollution events. Additionally, annual per capita wood energy consumption in the far south was several times higher than in central Chile, contributing to disproportionately high emissions. These findings highlight the need to adopt more protective air quality standards and reduce wood-fueled emissions to improve early warning systems and decrease particulate exposure in southern Chile.