Assessing Mobility-Based Real-Time Air Pollution Exposure in Space and Time Using Smart Sensors and GPS Trajectories in Beijing.

Using real-time data from portable air pollutant sensors and smartphone Global Positioning System trajectories collected in Beijing, China, this study demonstrates how smart technologies and individual activity-travel microenvironments affect the assessment of individual-level pollution exposure in...

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Publicado en:Annals of the American Association of Geographers Vol. 110; no. 2; pp. 434 - 449
Autores principales: Ma, Jing, Tao, Yinhua, Kwan, Mei-Po, Chai, Yanwei
Formato: Artículo
Publicado: Taylor & Francis Ltd Mar2020
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Mar2020
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      pub: Taylor & Francis Ltd
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        10.1080/24694452.2019.1653752
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        atl: Assessing Mobility-Based Real-Time Air Pollution Exposure in Space and Time Using Smart Sensors and GPS Trajectories in Beijing.
      aug:
        au:
          Ma, Jing
          Tao, Yinhua
          Kwan, Mei-Po
          Chai, Yanwei
        affil:
          Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University
          College of Urban and Environmental Sciences, Peking University
          Department of Geography and Resource Management and Institute of Space and Earth Information Science, Chinese University of Hong Kong
      su:
        Beijing (China)
        Air pollution
        Real-time computing
        Intelligent sensors
        Global Positioning System
      sug:
        subj:
          Air pollution
          Beijing (China)
          Real-time computing
          Intelligent sensors
          Global Positioning System
      keyword:
        ambiente interior
        exposición a la contaminación aérea en tiempo real
        modos de viaje
        problema de contexto geográfico incierto
        tecnologías inteligentes
        ambiente interior
        exposición a la contaminación aérea en tiempo real
        modos de viaje
        problema de contexto geográfico incierto
        tecnologías inteligentes
        ambiente interior
        exposición a la contaminación aérea en tiempo real
        modos de viaje
        problema de contexto geográfico incierto
        tecnologías inteligentes
        ambiente interior
        exposición a la contaminación aérea en tiempo real
        modos de viaje
        problema de contexto geográfico incierto
        tecnologías inteligentes
      ab: Using real-time data from portable air pollutant sensors and smartphone Global Positioning System trajectories collected in Beijing, China, this study demonstrates how smart technologies and individual activity-travel microenvironments affect the assessment of individual-level pollution exposure in space and time at a very fine resolution. It compares three different types of individual-level exposure estimates generated by using residence-based monitoring station assessment, mobility-based monitoring station assessment, and mobility-based real-time assessment. Further, it examines the differences in personal exposure to PM associated with different activity places and travel modes across various environmental conditions. The results show that the exposure estimates generated by monitoring station assessment and real-time sensing assessment vary substantially across different activity locations and travel modes. Individual-level daily exposure for residents living in the same community also varies significantly, and there are substantial differences in exposure levels using different approaches. These results indicate that residence- or mobility-based monitoring station assessments, which cannot account for the differences in air pollutant exposures between outdoor and indoor environments and between different travel-related microenvironments, could generate considerably biased estimates of personal pollution exposure. Key Words: indoor environment, real-time exposure to air pollution, smart technologies, travel modes, the uncertain geographic context problem.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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