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参与2019年秋季巴黎地区Polluscope活动的参与者的氮氧化物、黑碳和颗粒物暴露情况。

NO, BC and PM Exposure of Participants in the Polluscope Autumn 2019 Campaign in the Paris Region.

作者信息

Bouillon Laura, Gros Valérie, Abboud Mohammad, El Hafyani Hafsa, Zeitouni Karine, Alage Stéphanie, Languille Baptiste, Bonnaire Nicolas, Naude Jean-Marc, Srairi Salim, Campos Y Sansano Arthur, Kauffmann Anne

机构信息

Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL), UMR CNRS-CEA-UVSQ, 91191 Gif-Sur-Yvette, France.

Laboratoire DAVID, Université Saint-Quentin-en-Yvelines, 78035 Versailles, France.

出版信息

Toxics. 2023 Feb 23;11(3):206. doi: 10.3390/toxics11030206.

DOI:10.3390/toxics11030206
PMID:36976970
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10051186/
Abstract

The Polluscope project aims to better understand the personal exposure to air pollutants in the Paris region. This article is based on one campaign from the project, which was conducted in the autumn of 2019 and involved 63 participants equipped with portable sensors (i.e., NO, BC and PM) for one week. After a phase of data curation, analyses were performed on the results from all participants, as well as on individual participants' data for case studies. A machine learning algorithm was used to allocate the data to different environments (e.g., transportation, indoor, home, office, and outdoor). The results of the campaign showed that the participants' exposure to air pollutants depended very much on their lifestyle and the sources of pollution that may be present in the vicinity. Individuals' use of transportation was found to be associated with higher levels of pollutants, even when the time spent on transport was relatively short. In contrast, homes and offices were environments with the lowest concentrations of pollutants. However, some activities performed in indoor air (e.g., cooking) also showed a high levels of pollution over a relatively short period.

摘要

Polluscope项目旨在更深入地了解巴黎地区个人接触空气污染物的情况。本文基于该项目的一次活动,该活动于2019年秋季开展,63名参与者配备了便携式传感器(即监测一氧化氮、黑碳和颗粒物),为期一周。在经过数据整理阶段后,对所有参与者的结果以及个别参与者用于案例研究的数据进行了分析。使用机器学习算法将数据分配到不同环境(如交通、室内、家庭、办公室和室外)。活动结果表明,参与者接触空气污染物的情况在很大程度上取决于他们的生活方式以及附近可能存在的污染源。研究发现,即使在交通上花费的时间相对较短,个人的交通方式使用也与更高的污染物水平相关。相比之下,家庭和办公室是污染物浓度最低的环境。然而,在室内空气中进行的一些活动(如烹饪)在相对较短的时间内也显示出高污染水平。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc76/10051186/42011bdb8897/toxics-11-00206-g006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc76/10051186/b37e6a05bb5b/toxics-11-00206-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc76/10051186/c5525541963f/toxics-11-00206-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc76/10051186/7971336a0fd2/toxics-11-00206-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc76/10051186/42011bdb8897/toxics-11-00206-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc76/10051186/3ebc50102e3c/toxics-11-00206-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc76/10051186/e59891fe5e7d/toxics-11-00206-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc76/10051186/b37e6a05bb5b/toxics-11-00206-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc76/10051186/c5525541963f/toxics-11-00206-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc76/10051186/7971336a0fd2/toxics-11-00206-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc76/10051186/42011bdb8897/toxics-11-00206-g006.jpg

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