Geospatially Conditioned Diffusion Models for Urban Environmental Exposure Mapping and Spatiotemporal Health Risk Prediction
Keywords:
urban environmental exposure, diffusion models, geospatial artificial intelligence, health risk assessment, environmental governance, spatiotemporal modelingAbstract
Urban environmental exposure mapping has traditionally relied on deterministic interpolation, land use regression, and atmospheric dispersion models. These approaches provide useful regional estimates but often fail to represent fine-grained spatiotemporal variability, microenvironmental peaks, and uncertainty. Generative diffusion models, when conditioned on geospatial structure, offer a new pathway for synthesizing high-resolution exposure fields and supporting health risk assessment. This paper presents a system-level analysis of geospatially conditioned diffusion models for urban environmental exposure mapping and spatiotemporal health risk prediction. We examine architectural design decisions, conditioning mechanisms, data infrastructure, uncertainty quantification, and downstream epidemiological integration. The central argument is that the primary challenge is not solely model realism but the construction of trustworthy inference systems that align generative outputs with public health evidence, governance requirements, and equity commitments. The discussion covers trade-offs between centralized and edge deployment, computational sustainability, robustness under sensor dropout and concept drift, and the risk of encoding historical environmental injustice into synthetic exposure surfaces. We further consider policy implications for health risk communication, regulatory use, and community oversight. Recent examples from transport microenvironment measurements and geospatially guided diffusion are used to illustrate wider structural issues.
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