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sidenav header background[10月26日]能源与气候变化经济学workshop
发布日期：2018-10-24 04:20 来源：北京大学国家发展研究院
时间：2018年10月26（周五）12:00-14:00
地点：致福轩会议室
主讲人：夏凡（国家发展研究院）
主持人：徐晋涛、王敏、黄卓
题目：Traffic Congestion and Air Quality in Beijing: Utilizing Taxi Trajectory Data
摘要：Extant studies, either indirectly or directly, on impacts of traffic congestion on air quality, have used city level data, leading to a potential reverse causality problem in that air quality in a city affects people’s travel behaviors which in turn impact traffic congestion. Our study employs a big data of real-time driving trajectories of all taxies in Beijing in 2015, to construct measures of local traffic congestion surrounding air quality monitoring stations. This allows us to utilize spatial and temporal variations across monitoring stations and over time, to examine casual effects of traffic congestion on air pollution, at monitoring station and hourly basis. We find significant effects of traffic congestion on ambient NO2, CO and O3. Traffic congestion also increases the concentration of PM2.5 and PM10, but in a lagged manner: it takes on average two hours for congestion to affect ambient PM2.5 and PM10. The impacts of congestion are more significant in less windy days, less polluted days, for stations closer to roads, and for areas with more road lanes. Our results could be used to understand the cost and benefit of urban traffic policies.
主讲人简介：夏凡，北京大学国家发展研究院4年级博士生，研究领域为交通、空气污染与健康。
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首页
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教学项目展开 / 收起
sidenav background教学项目
sidenav header background[10月26日]能源与气候变化经济学workshop
发布日期：2018-10-24 04:20 来源：北京大学国家发展研究院
时间：2018年10月26（周五）12:00-14:00
地点：致福轩会议室
主讲人：夏凡（国家发展研究院）
主持人：徐晋涛、王敏、黄卓
题目：Traffic Congestion and Air Quality in Beijing: Utilizing Taxi Trajectory Data
摘要：Extant studies, either indirectly or directly, on impacts of traffic congestion on air quality, have used city level data, leading to a potential reverse causality problem in that air quality in a city affects people’s travel behaviors which in turn impact traffic congestion. Our study employs a big data of real-time driving trajectories of all taxies in Beijing in 2015, to construct measures of local traffic congestion surrounding air quality monitoring stations. This allows us to utilize spatial and temporal variations across monitoring stations and over time, to examine casual effects of traffic congestion on air pollution, at monitoring station and hourly basis. We find significant effects of traffic congestion on ambient NO2, CO and O3. Traffic congestion also increases the concentration of PM2.5 and PM10, but in a lagged manner: it takes on average two hours for congestion to affect ambient PM2.5 and PM10. The impacts of congestion are more significant in less windy days, less polluted days, for stations closer to roads, and for areas with more road lanes. Our results could be used to understand the cost and benefit of urban traffic policies.
主讲人简介：夏凡，北京大学国家发展研究院4年级博士生，研究领域为交通、空气污染与健康。
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