Perspective improvement of regional air pollution burden of disease estimation by machine intelligence.

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_2.5 (from R² = 0.41 to R² = 0.86) and O₃ (from R² = 0.48 to R² = 0.82). The bias quantification results showed that premature deaths in the study region are mainly biased by boundary conditions (Improvement Ratio, IR = 99%) and meteorology (91%), compared with emission and land-use data. The results of further analysis showed using observations only (PM_2.5: 17%; O₃: 56%) or the uncorrected CTM estimations (PM_2.5: -18%; O₃: 171%) contributed more BD biases compared with employing averaged risks without considering urbanization levels (PM_2.5: -5%; O₃: -4%). In conclusion, employing observations only, uncorrected CTM estimations, and homogeneous risks may contribute to non-negligible BD biases and affect regional air quality and risk management. To cope with increasing needs of finer-scale air quality management under climate change, our developed ML-MMF framework can provide a quantitative reference to improve CTM performance and priority to improve input data quality and CTM mechanisms.