A machine learning–Powered digital twin framework for adaptive management of urban air quality in Chiang Mai, Northern Thailand

Abstract

Urban air quality management in topographically and meteorologically complex regions such as Chiang Mai, Northern Thailand, is increasingly challenged by diverse emission sources and the limitations of conventional reactive response systems. This study introduces a multi-layer digital twin framework powered by artificial intelligence (AI), integrating real-time Internet of Things (IoT) sensing, deep learning–based spatiotemporal forecasting, and simulation-driven policy optimization to enable predictive and adaptive air quality control. Specifically, a Temporal–Spatial Graph Neural Network (TS-GNN) is employed to capture nonlinear dependencies across both spatial and temporal dimensions, achieving high predictive accuracy (root mean square error, RMSE = 2.8 μg/m³; coefficient of determination, R² = 0.96). For adaptive intervention planning, a hybrid Generative Adversarial Network–Deep Reinforcement Learning (GAN–DRL) algorithm is implemented—resulting in a 57.1 % reduction in fine particulate matter (PM2.5) concentrations, and outperforming state-of-the-art metaheuristics such as the Coot Optimization Algorithm and Red Deer Optimization. These AI-driven policy interventions are assessed through coupled agent-based modeling (ABM) and computational fluid dynamics (CFD) simulations, enabling high-fidelity, multi-source policy testing under realistic urban dynamics. The proposed framework exhibits strong scalability across spatial units, rapid inference capability, and robustness under high-pollution scenarios. Economic analysis confirms its cost-efficiency and policy feasibility. Seasonal simulations further validate sustained environmental benefits across emissions from agricultural, industrial, and transportation sectors. Overall, this work establishes an AI-enhanced, real-time decision-support paradigm combining digital twin technologies, urban simulation, and adaptive environmental governance—contributing a transferable model for data-driven urban air quality management.