ArticleFrontiers in public health2025
Perspective improvement of regional air pollution burden of disease estimation by machine intelligence.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
As air pollution events increasingly threaten public health under climate change, more precise estimations of air pollutant exposure and the burden of diseases (BD) are urgently needed. However, current BD assessments from various sources of air pollutant concentrations and exposure risks, and the derived uncertainty still needs systematic assessment. Owing to growing health and air quality data availability, machine learning (ML) may provide a promising solution. This study proposed an ML-measurement-model fusion (MMF) framework that can quantify the air pollutant biases from the Chemical Transport Modeling (CTM) inputs, and further analyze the BD biases concerning various sources of air pollutant estimations and exposure risks. In our study region, the proposed ML-MMF framework successfully improves CTM-modeled PM
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