Emission Sector Impacts on Air Quality and Public Health in China From 2010 to 2020

Reconstruction of a century of air pollution history in Nanjing, China, using trace elements in situ leaf specimens of Platanus × hispanica and Pittosporum tobira

Leaves can specifically uptake trace elements from the surrounding environment. And tree leaves are a good biological indicator for air pollution. Therefore, chemical analysis of leaf specifications can be used to reproduce a historical record of air pollution. To better understand the history of urban air pollution from the 1920s to the 2020s in Nanjing, China, leaf samples of two woody plants, Platanus × hispanica and Pittosporum tobira, were collected in this study as environmental indicators from different historical periods. These included historical herbarium specimens and current leaves from live trees. The concentrations of 10 trace elements were determined in the samples using ICP‒MS. Pollution indices were calculated, yielding the key findings. The historical leaf samples showed continuously increasing mean concentrations of the 10 trace elements over time, which significantly correlating with automobile quantities and the number of large-scale industrial enterprises (p < 0.05). Moreover, modern leaf trace element concentrations were significantly correlated with PM10, PM2.5, automobiles, large-scale industrial enterprises, and atmospheric factors, confirming these as sources. In addition to the historical growth trend, spatial heterogeneity was revealed in historical Platanus × hispanica leaf samples from the 14 sites in Nanjing. Changes in heavy metal trace element pollution distributions were consistent with transportation and industrial expansion, with homologous patterns across elements. Specifically, post 1980s increases were observed in the representative NJ2 (Zhongshan Botanical Garden) and the NJ5(Nanjing University) sites, with higher concentrations occurring at in the NJ5 contaminated site than at the NJ2 uncontaminated site. After 2009, the 10 element (except Cd) pollution indices in Platanus × hispanica leaves fluctuated but declined overall. This reconstruction of Nanjing's air pollution history demonstrates that ample environmental information can be extracted from plant leaf markers over time and space.

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

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, 95UI: 1,948,000-2,427,300) premature deaths per year, primarily from PM_2.5 exposure (98%). PM_2.5 exposure and the associated disease burden were most sensitive to industry and residential emissions. We explore the sensitivity of exposure and health to different combinations of emission reductions. The National Air Quality Target (35 μg m^-3) for PM_2.5 concentrations can be attained nationally with emission reductions of 72% in industrial, 57% in residential, 36% in land transport, 35% in agricultural, and 33% in power generation emissions. We show that complete removal of emissions from these five sectors does not enable the attainment of the WHO Annual Guideline (5 μg m^-3) due to remaining air pollution from other sources. Our work provides the first assessment of how air pollution exposure and disease burden in China varies as emissions change across these five sectors and highlights the value of emulators in air quality research.