Chemical Profiles of Particulate Matter Emitted from Anthropogenic Sources in Selected Regions of China.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-08 DOI:10.1038/s41597-024-04058-6

Lixin Zheng, Di Wu, Xiu Chen, Yang Li, Anyuan Cheng, Jinrun Yi, Qing Li

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Abstract

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.

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中国部分地区人为源排放颗粒物的化学特征。

人为污染源排放的颗粒物（PM）是造成空气污染的主要原因。源排放的可吸入颗粒物对健康造成的不利影响是不平等的，这就强调了在制定空气污染控制策略时，需要考虑与可吸入颗粒物结合的化学物质的差异，而不是仅仅关注可吸入颗粒物的质量浓度。在此，我们介绍了中国典型人为源的实际可吸入颗粒物排放化学成分数据集，包括工业（电力、工业锅炉、钢铁、水泥和其他工业过程）、居民（燃煤/生物质燃烧和烹饪）和交通部门（道路车辆、船舶和非废气排放）。数据是在同样严格的现场测量和化学分析质量控制条件下获得的，从而最大限度地减少了不同研究方法造成的不确定性。包括有毒元素和多环芳烃在内的可吸入颗粒物化学成分的浓度在不同排放部门之间存在很大差异。该数据集为排放清单、空气质量模拟模型和健康风险评估提供了实验数据和信息输入。所获得的结果可帮助人们深入了解特定来源的可吸入颗粒物，并制定有效的控制策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

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.