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印度城乡地区高分辨率机动车尾气和非尾气排放分析。

High resolution vehicular exhaust and non-exhaust emission analysis of urban-rural district of India.

机构信息

Centre for Rural Development & Technology, Indian Institute of Technology Delhi, New Delhi 110016, India.

World Resources Institute, New Delhi 110016, India.

出版信息

Sci Total Environ. 2022 Jan 20;805:150255. doi: 10.1016/j.scitotenv.2021.150255. Epub 2021 Sep 16.

Abstract

Air quality deterioration due to vehicular emissions in smaller Indian cities and rural areas remains unacknowledged, even though the situation is alarmingly similar to megacities. The resulting lack of knowledge on travel behavior and vehicle characteristics impacts accuracy of emission studies in these regions. This study uses a novel approach and appropriate primary and secondary data sets to allocate vehicular activities (vehicle population and vehicle kilometer travelled) and associated emissions at a high spatial resolution for estimation and dispersion analysis of vehicular exhaust and non-exhaust PM emission in an Indian urban-rural landscape. The study indicates that using approaches that do not allocate vehicles kilometers travelled to areas of their expected travel results in underestimating the percent share of PM emissions from rural roads and motorways while overestimating overall PM emissions. Particulate matter resuspension is the dominant form of PM emissions from the vehicular sector on all road types, constituting an even higher fraction on rural roads. Two-wheelers contribute a high fraction of PM emissions (exhaust and non-exhaust combined), followed by heavy commercial vehicles and four-wheelers on urban roads. Light commercial vehicles, especially agricultural tractors dominate these emissions on rural roads. PM hotspots are prevalent in urban areas, but several rural areas also experience heavy particulate matter concentrations. Thus, vehicle movement incorporation results in more accurate emission estimation, especially in an urban-rural landscape.

摘要

在印度较小的城市和农村地区,由于车辆排放导致的空气质量恶化仍然未被认识到,尽管情况与特大城市惊人地相似。由于对出行行为和车辆特征缺乏了解,这对这些地区的排放研究的准确性产生了影响。本研究采用一种新方法和适当的原始和二手数据集,以高空间分辨率分配车辆活动(车辆数量和车辆行驶里程)和相关排放,以估计和分析印度城乡景观中车辆尾气和非尾气 PM 排放的扩散。研究表明,使用不将车辆行驶里程分配到预期行驶区域的方法会导致低估农村道路和高速公路的 PM 排放量的百分比,而高估总体 PM 排放量。颗粒物再悬浮是所有道路类型中车辆排放的主要 PM 排放形式,在农村道路上的比例更高。两轮车(包括尾气和非尾气)贡献了 PM 排放量的很大一部分,其次是城市道路上的重型商用车和四轮车。在农村道路上,轻型商用车,特别是农用拖拉机,是这些排放的主要来源。PM 热点在城市地区很普遍,但一些农村地区也经历了严重的颗粒物浓度。因此,车辆运动的纳入会导致更准确的排放估算,特别是在城乡景观中。

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