Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China.

ABSTRACT: Air pollution exposure remains a leading public health problem in China. The use of chemical transport models to quantify the impacts of various emission changes on air quality is limited by their large computational demands. Machine learning models can emulate chemical transport models to provide computationally efficient predictions of outputs based on statistical associations with inputs. We developed novel emulators relating emission changes in five key anthropogenic sectors (residential, industry, land transport, agriculture, and power generation) to winter ambient fine particulate matter (PM_2.5) concentrations across China. The emulators were optimized based on Gaussian process regressors with Matern kernels. The emulators predicted 99.9% of the variance in PM_2.5

SUBMITTER: Conibear L 

PROVIDER: S-EPMC8095364 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature