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巴黎地区车内超细颗粒物、黑碳、二氧化氮和一氧化碳浓度的决定因素:PUF-TAXI 研究。

Determinants of ultrafine particles, black carbon, nitrogen dioxide, and carbon monoxide concentrations inside vehicles in the Paris area: PUF-TAXI study.

机构信息

Paris University, CRESS-INSERM UMR_1153, INRAE, HERA Team, Paris, France.

Faculty of Public Health, Lebanese University, Pharmacoepidemiology Surveillance Unit, CERIPH, Fanar, Lebanon.

出版信息

Indoor Air. 2021 May;31(3):848-859. doi: 10.1111/ina.12779. Epub 2020 Dec 22.

Abstract

This study presents real-time concentrations of traffic-related air pollutants during 499 trips conducted by 50 Parisian taxi drivers from PUF-TAXI project. Ultrafine particles (UFP), black carbon (BC), and nitrogen dioxide (NO )/carbon monoxide (CO) were measured inside vehicles by Diffusion Size Classifier Miniature , microAeth , and Gas-Pro , respectively, for nine hours. Vehicle/trip data characteristics were collected by questionnaires and on ambient conditions by monitoring stations. The associations between pollutant levels and their potential determinants were analyzed using generalized estimating equation model. Determinants of in-vehicle pollutants levels were identified: (1) ambient factors (meteorology and ambient pollution)-affecting BC, NO and CO; (2) vehicle characteristics-affecting all pollutants; and (3) trip-related driving habits-affecting UFP, BC, and CO. We highlight that commuters can, therefore, avoid high in-vehicle air pollutant concentrations mainly by (1) closing windows and activating air-conditioning under air recirculation mode in congested traffic; (2) smooth driving; and (3) maintaining cabin air filters.

摘要

本研究展示了来自 PUF-TAXI 项目的 50 名巴黎出租车司机进行的 499 次行程中实时的交通相关空气污染物浓度。通过扩散粒径分类器 Miniature、microAeth 和 Gas-Pro,分别在车内测量超细颗粒物 (UFP)、黑碳 (BC) 和二氧化氮 (NO)/一氧化碳 (CO),时长为 9 小时。通过问卷调查和监测站监测环境条件收集车辆/行程数据特征。使用广义估计方程模型分析污染物水平与其潜在决定因素之间的关系。确定了车内污染物水平的决定因素:(1)环境因素(气象和环境污染物)-影响 BC、NO 和 CO;(2)车辆特征-影响所有污染物;(3)与行程相关的驾驶习惯-影响 UFP、BC 和 CO。因此,我们强调,通勤者可以通过以下方式主要避免车内空气污染物浓度过高:(1)在交通拥堵时关闭窗户并在空气再循环模式下激活空调;(2)平稳驾驶;和(3)保持机舱空气滤清器。

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