Lixin Zheng, Di Wu, Xiu Chen, Yang Li, Anyuan Cheng, Jinrun Yi, Qing Li
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Chemical Profiles of Particulate Matter Emitted from Anthropogenic Sources in Selected Regions of China.
Particulate matter (PM) emissions from anthropogenic sources contribute substantially to air pollution. The unequal adverse health effects caused by source-emitted PM emphasize the need to consider the discrepancy of PM-bound chemicals rather than solely focusing on the mass concentration of PM when making air pollution control strategies. Here, we present a dataset about chemical compositions of real-world PM emissions from typical anthropogenic sources in China, including industrial (power, industrial boiler, iron & steel, cement, and other industrial process), residential (coal/biomass burning, and cooking), and transportation sectors (on-road vehicle, ship, and non-exhaust emission). The data was obtained under the same strict quality control condition on field measurements and chemical analysis, minimizing the uncertainty caused by different study approaches. The concentrations of PM-bound chemical components, including toxic elements and PAHs, exhibit substantial discrepancies among different emission sectors. This dataset provides experimental data with informative inputs to emission inventories, air quality simulation models, and health risk estimation. The obtained results can gain insight into understanding on source-specific PMs and tailoring effective control strategies.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.