Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation.

ABSTRACT: Machine learning models can emulate chemical transport models, reducing computational costs and enabling more experimentation. We developed emulators to predict annual-mean fine particulate matter (PM_2.5) and ozone (O₃) concentrations and their associated chronic health impacts from changes in five major emission sectors (residential, industrial, land transport, agriculture, and power generation) in China. The emulators predicted 99.9% of the variance in PM_2.5 and O₃ concentrations. We used these emulators to estimate how emission reductions can attain air quality targets. In 2015, we estimate that PM_2.5 exposure was 47.4 μg m^-3 and O₃ exposure was 43.8 ppb, associated with 2,189,700 (95% uncertainty interval, 95U

SUBMITTER: Conibear L 

PROVIDER: S-EPMC9207901 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature