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Spatio-temporal patterns of traffic-related air pollutant emissions in different urban functional zones estimated by real-time video and deep learning technique

Song, Jinchao (author)
University of the Chinese Academy of Sciences,University of Copenhagen
Zhao, Chunli (author)
Lund University,Lunds universitet,Trafik och väg,Institutionen för teknik och samhälle,Institutioner vid LTH,Lunds Tekniska Högskola,Transport and Roads,Department of Technology and Society,Departments at LTH,Faculty of Engineering, LTH,K2–The Swedish Knowledge Centre for Public Transport
Lin, Tao (author)
University of the Chinese Academy of Sciences
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Li, X. (author)
University of the Chinese Academy of Sciences
Prishchepov, Alexander V. (author)
University of Copenhagen
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 (creator_code:org_t)
Elsevier BV, 2019
2019
English.
In: Journal of Cleaner Production. - : Elsevier BV. - 0959-6526. ; 238
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • The aim of this paper is to explore the relationship between spatial-temporal patterns of vehicles types and numbers in different urban functional zones and traffic-related air pollutant emissions with real-time traffic data collected from traffic surveillance video and image recognition. The data were analyzed by using video-based detection technique, while the air pollution was quantified via pollutant emission coefficients. The results revealed that: (1) the order of traffic-related pollutant emissions was expressway > business zone > industrial zone > residential zone > port; (2) daily maximum emissions of each pollutant occurred in different functional zones on weekdays and weekends. With the exception of expressway, the business zones had the highest emissions of CO, HC and VOC on weekdays, while the highest emissions of all the pollutants (CO, HC, NOx, PM2.5, PM1.0, and VOC) were at the weekend. The industrial zone had the highest emissions of NOx, PM2.5 and PM1.0 on weekdays; (3) pollutant emissions (CO, HC, NOx, PM2.5, PM1.0 and VOC) in all functional zones peaked in the morning and evening peak except at port sites; (4) cars and motorcycles represented the major source of traffic-related pollutant emissions. Collecting data through video-based vehicle detection with finer spatio-temporal resolution represents a cost-effective way of mapping spatio-temporal patterns of traffic-related air pollution to contribute to urban planning and climate change studies.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Miljövetenskap (hsv//swe)
NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Environmental Sciences (hsv//eng)

Keyword

Deep learning
Pollutant emissions
Urban functional zones
Video-based vehicle detection

Publication and Content Type

art (subject category)
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Song, Jinchao
Zhao, Chunli
Lin, Tao
Li, X.
Prishchepov, Ale ...
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ENGINEERING AND TECHNOLOGY
ENGINEERING AND ...
and Civil Engineerin ...
and Transport System ...
NATURAL SCIENCES
NATURAL SCIENCES
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and Environmental Sc ...
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Journal of Clean ...
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