Predicting Air Pollution in Metropolitan Lima Using Gaussian Naïve Bayes (2025): An Efficient Model for Urban Environmental Management

Air pollution episodes in Metropolitan Lima pose persistent challenges for urban health protection and timely environmental decision-making. However, many machine learning approaches for air-quality prediction remain difficult to operationalize due to high latency, extensive hyperparameter tuning, and limited interpretability. This study addresses this gap by adopting an engineering-driven predictive knowledge modeling approach grounded in the Knowledge Discovery in Databases (KDD) framework to evaluate an efficient probabilistic classifier—Gaussian Naïve Bayes (GNB)—for predicting regulatory air-quality categories in Metropolitan Lima. A total of 768,185 hourly observations from SENAMHI monitoring stations covering the 2020–2025 period were analyzed, considering PM 10 , PM 2.5 , NO 2 concentrations, and the Air Quality Index (AQI). Data were preprocessed through validity checks, explicit outlier handling, and categorical encoding based on regulatory thresholds, while a time-based train–test split preserved temporal structure and prevented data leakage. The proposed model achieved strong predictive performance (global accuracy ≥ 0.925) and excellent probabilistic calibration (overall Brier Score ≈ 0.023; AQI Brier Score ≈ 0.010). These results demonstrate that GNB provides a robust, interpretable, and computationally efficient solution for operational air-quality management and early warning support, contributing to evidence-based urban environmental decision-making aligned with Sustainable Development Goal 13 (Climate Action).