Urban air quality exhibits significant spatial and temporal heterogeneity at hyperlocal scales, necessitating advanced modeling paradigms that can bridge the gap between computationally intensive physics-based models and empirically driven statistical approaches. This paper introduces a novel agent-based modeling framework specifically designed for hyperlocal air quality assessment, capable of providing descriptive, predictive, and prescriptive analyses. The proposed framework discretizes urban environments into interacting agents, with pollutant dynamics governed by a parameterized mass balance that preserves fundamental physics while maintaining computational efficiency. The framework is demonstrated through a mobile monitoring case study in Chennai, India. Geospatial features are encoded into agent properties through physically interpretable parameters enabling attribution of pollution sources and transport pathways, thereby strengthening the framework’s descriptive capabilities. The approach successfully captures complex spatio-temporal pollution dynamics and describes pollution hotspots by attributing them to sourcing and transport influences. Predictive capabilities are demonstrated through spatio-temporal interpolation and forecasting. Spatio-temporal formulation of the framework enables it to outperform regular unidimensional methods. The discrete agent structure facilitates prescriptive applications, demonstrated here through the identification of the least-exposure route between locations. The unification of descriptive, predictive, and prescriptive capabilities within a single interpretable framework makes it a valuable tool for urban environmental management and real-time decision support systems.
Swaminathan et al. (Tue,) studied this question.