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Quantifying the contribution of activity patterns to PM2.5 exposure inequity between urban and rural residents by a novel method

  • Wei Du
  • , Zhanpeng Cui
  • , Jinze Wang
  • , Yuqiong Wang
  • , Yungui Li*
  • , Xiaoan Li
  • , Yan Zhou
  • , Tao Jiang*
  • , Kang Mao
  • , Xianbiao Lin
  • , Jianwu Shi
  • , Dengzhou Gao
  • , Yiming Qin
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

PM2.5 pollution variations in different microenvironments would result in PM2.5 exposure inequity between rural and urban residents. In this study, the real-time PM2.5 exposure of urban and rural residents in China was examined based on portable PM2.5 sensors together with activity patterns derived from questionnaire surveys, with a focus on students and senior citizens who are sensitive to air pollution. The results showed that PM2.5 exposure varied significantly among different resident groups, with higher PM2.5 exposure of rural residents than those of urban residents. PM2.5 exposure peaks mostly occurred during (Accompanied) cooking activities owing to strong emissions. Sleeping and resting were the main activities that affected PM2.5 exposures of different resident groups, accounting for 60.7%–94.5% of total daily exposures. Furthermore, the long duration of sleeping makes it the predominant activity contributing to PM2.5 exposure inequity. It is necessary to obtain point-to-point respiratory volume (respiratory rate) data when measuring real-time PM2.5 exposure data and incorporate respiratory volume (respiratory rate) into the analysis of PM2.5 exposure. For the first time, this study quantified the PM2.5 exposure inequality based on a novel method and can provide useful information for further studies on the exposure inequity. © Tsinghua University Press 2024.
Original languageEnglish
Pages (from-to)1323–1333
JournalBuilding Simulation
Volume17
Issue number8
Online published27 Aug 2024
DOIs
Publication statusPublished - Aug 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Research Keywords

  • activity pattern
  • environmental inequity
  • PM2.5 exposure
  • urban and rural difference

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