Interpretable air quality classification for public health using machine learning

Abstract

Air pollution remains a critical public health and environmental challenge, especially in rapidly urbanizing regions. Accurate classification of air quality levels is essential for proactive environmental management and timely health interventions. This study presents an interpretable machine learning approach for multi-class air quality classification using logistic regression. We utilize a real-world dataset comprising 23,463 records, integrating pollutant concentrations (PM2.5, PM10, NO₂, SO₂, CO), meteorological data (temperature and humidity), and demographic features (industrial zoning and population distribution). Data preprocessing includes median imputation for missing values, feature normalization, and appropriate encoding of categorical variables. To address class imbalance, class weighting is applied, and model evaluation is conducted using 5-fold stratified cross-validation. Results show that the model achieves strong overall accuracy (87%) and a macro F1-score of 0.72, with particularly high performance for the dominant “Good” and “Moderate” categories. Feature selection methods, including Pearson correlation and recursive feature elimination, highlight PM2.5 as the most influential predictor (r = 0.98 with overall AQI). The model’s transparency and computational efficiency make it suitable for real-time deployment and policy decision-making. This work contributes a robust, interpretable baseline for air quality forecasting and highlights the importance of addressing underrepresented but critical pollution categories. Future directions include real-time data integration and comparative evaluation against more complex machine learning models.

Received on, 11 June 2025

Accepted on, 25 July 2025

Published on, 07 October 2025