A BiLSTM-driven framework for operational PM2.5 forecasting: integrating meteorological kinematics for urban air quality management
The accelerating impact of climate change on meteorological dynamics and air quality across India poses a pressing challenge for urban sustainability and public health resilience. In this study, we present a data-driven AI framework based on Bidirectional Long Short-Term Memory (BiLSTM) networks to forecast PM₂.₅ concentrations using multivariate environmental data, including temperature, humidity, wind speed, UV index, and particulate matter levels. Comparative analysis with both conventional deep learning models (LSTM, GRU) and statistical baselines (ARIMAX, MLR) demonstrates the BiLSTM’s superior capacity in learning long-range temporal dependencies. Among the evaluated models, the proposed BiLSTM framework achieved the highest predictive performance with an R 2 of 0.8113, MAE of 0.2988, and RMSE of 0.4359. Consequently, the model supports not only enhanced predictive accuracy but also operational daily, localized decision-making for air quality management, offering actionable insights for climate-aware urban planning and smart city governance. By integrating advanced AI techniques with high-dimensional environmental datasets, this work underscores the transformative role of computational intelligence in shaping adaptive, evidence-based sustainability strategies for future-ready cities.