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

摘要

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)concentrations for a given input configuration of emission changes. PM_(2.5)concentrations are primarily sensitive to residential (51%–94% of first‐order sensitivity index), industrial (7%–31%), and agricultural emissions (0%–24%). Sensitivities of PM_(2.5)concentrations to land transport and power generation emissions are all under 5%, except in South West China where land transport emissions contributed 13%. The largest reduction in winter PM_(2.5)exposure for changes in the five emission sectors is by 68%–81%, down to 15.3–25.9?μg m~(?3), remaining above the World Health Organization annual guideline of 10?μg m~(?3). The greatest reductions in PM_(2.5)exposure are driven by reducing residential and industrial emissions, emphasizing the importance of emission reductions in these key sectors. We show that the annual National Air Quality Target of 35?μg m~(?3)is unlikely to be achieved during winter without strong emission reductions from the residential and industrial sectors. Key Points We developed accurate and fast emulators to predict air quality in China from emission changes Winter ambient fine particulate matter concentrations were primarily sensitive to residential, industrial, and agricultural emissions The National Air Quality Target is unlikely to be met in winter without large emission reductions in the residential and industrial sectors