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The Impact of Activity-Based Mobility Pattern on Assessing Fine-Grained Traffic-Induced Air Pollution Exposure

Author

Listed:
  • Yizheng Wu

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China)

  • Guohua Song

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Quantifying the air pollution and health impacts of transportation plans provides decision makers with valuable information that can help to target interventions. However, a large number of environmental epidemiological research assumes exposures of static populations at residential locations and does not consider the human activity patterns, which may lead to significant estimation errors. This study uses an integrated modeling framework to predict fine-grained air pollution exposures occurring throughout residents’ activity spaces. We evaluate concentrations of fine particulate matter (PM 2.5 ) under a regional transportation plan for Sacramento, California, using activity-based travel demand model outputs, vehicle emission, and air dispersion models. We use predicted air pollution exposures at the traffic analysis zone (TAZ) level to estimate residents’ exposure accounting for their movements throughout the day to assess the impact of activity-based mobility pattern on air pollution exposure. Results of PM 2.5 exposures estimated statically (at residential locations) versus dynamically (over residents’ activity-based mobility) demonstrates that the two methods yield statistically significant different results ( p < 0.05). In addition, the comparison conducted in different age groups shows that the difference between these two approaches is greater among youth and working age residents, whereas seniors show a similar pattern using both approaches due to their lower rates of travel activity.

Suggested Citation

  • Yizheng Wu & Guohua Song, 2019. "The Impact of Activity-Based Mobility Pattern on Assessing Fine-Grained Traffic-Induced Air Pollution Exposure," IJERPH, MDPI, vol. 16(18), pages 1-13, September.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:18:p:3291-:d:265120
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    Citations

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    Cited by:

    1. Eun-hye Yoo & Qiang Pu & Youngseob Eum & Xiangyu Jiang, 2021. "The Impact of Individual Mobility on Long-Term Exposure to Ambient PM 2.5 : Assessing Effect Modification by Travel Patterns and Spatial Variability of PM 2.5," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    2. JinSoo Park & Sungroul Kim, 2020. "Machine Learning-Based Activity Pattern Classification Using Personal PM 2.5 Exposure Information," IJERPH, MDPI, vol. 17(18), pages 1-11, September.

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