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大气中有机污染物空间集中变异的典型模式:对识别来源和绘制气态户外吸入暴露组的启示。

Archetypes of Spatial Concentration Variability of Organic Contaminants in the Atmosphere: Implications for Identifying Sources and Mapping the Gaseous Outdoor Inhalation Exposome.

机构信息

Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, ON M1C 1A4, Canada.

Air Quality Processes Research Section, Environment and Climate Change Canada, Toronto, ON M3H 5T4, Canada.

出版信息

Environ Sci Technol. 2024 Oct 15;58(41):18273-18283. doi: 10.1021/acs.est.4c05204. Epub 2024 Oct 3.

DOI:10.1021/acs.est.4c05204
PMID:39359192
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11485095/
Abstract

Whereas inhalation exposure to organic contaminants can negatively impact human health, knowledge of their spatial variability in the ambient atmosphere remains limited. We analyzed the extracts of passive air samplers deployed at 119 unique sites in Southern Canada between 2019 and 2022 for 353 organic vapors. Hierarchical clustering of the obtained data set revealed four archetypes of spatial concentration variability in the outdoor atmosphere, which are indicative of common sources and similar atmospheric dispersion behavior. "Point Source" signatures are characterized by elevated concentration in the vicinity of major release locations. A "Population" signature applies to compounds whose air concentrations are highly correlated with population density, and is associated with emissions from consumer products. The "Water Source" signature applies to substances with elevated levels in the vicinity of water bodies from which they evaporate. Another group of compounds displays a "Uniform" signature, indicative of a lack of major sources within the study area. We illustrate how such a data set, and the derived spatial patterns, can be applied to support the identification of sources, the quantification of atmospheric emissions, the modeling of air quality, and the investigation of potential inequities in inhalation exposure.

摘要

虽然吸入有机污染物会对人类健康产生负面影响,但大气环境中它们的空间变异性知之甚少。我们分析了 2019 年至 2022 年期间在加拿大南部 119 个独特地点部署的被动空气采样器中采集的 353 种有机蒸气提取物。所得数据集的层次聚类揭示了户外大气中空间浓度变化的四种原型,这表明存在共同的来源和相似的大气扩散行为。“点源”特征是主要释放位置附近浓度升高的特征。“人口”特征适用于其空气浓度与人口密度高度相关的化合物,这些化合物与消费品的排放有关。“水源”特征适用于在其蒸发的水体附近水平较高的物质。另一组化合物表现出“均匀”特征,表明在研究区域内没有主要来源。我们说明了如何使用这样的数据集和得出的空间模式来支持源识别、大气排放量化、空气质量建模以及吸入暴露潜在不平等的调查。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a7c/11485095/afbe94af91ce/es4c05204_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a7c/11485095/6161d4887ed1/es4c05204_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a7c/11485095/6022bd1592b2/es4c05204_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a7c/11485095/34f1a0c307f6/es4c05204_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a7c/11485095/afbe94af91ce/es4c05204_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a7c/11485095/6161d4887ed1/es4c05204_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a7c/11485095/6022bd1592b2/es4c05204_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a7c/11485095/34f1a0c307f6/es4c05204_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a7c/11485095/afbe94af91ce/es4c05204_0004.jpg

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