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A trajectory mining framework for exploring individual concurrent environmental exposure and its association with mental health

Author

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  • Kan, Zihan
  • Wei, Jiaomin
  • Kwan, Mei-Po
  • Yang, Zhenchuan

Abstract

Understanding the health impacts of concurrent environmental exposures presents significant methodological challenges due to the complex interactions between different environmental factors. While extensive research has examined the effects of air pollution, noise, and greenspace individually on health outcomes, their interactions and combined impacts during daily activities remain under-investigated. Traditional studies have primarily relied on residence-based measurements and statistical models, which may not fully capture the dynamic nature of multiple exposures. This paper presents a novel data-driven framework using individual-level data from 414 participants in Hong Kong, incorporating real-time measurements of air pollution, noise, and greenspace exposures along with GPS tracking. Our approach delineates exposure segments with uniform concurrent air pollution, noise, and greenspace levels as individuals go about their daily lives. By analyzing how individuals encounter different combinations of environmental exposures, we identify their naturally occurring patterns and temporal sequences. Concurrent exposure sequences across geographical locations and socioeconomic groups are then examined, and mental health outcomes are analyzed for groups with similar exposure patterns. This study demonstrates that individual daily environmental exposures can be characterized by distinct concurrent patterns, with some of these exposure sequences related to mental health outcomes. The findings highlight the potential value of considering temporal variability and environmental transitions of exposure in environment-health studies. This methodology is particularly valuable for revealing complex, non-linear, and context-dependent interactions between environmental factors that might be overlooked or oversimplified by traditional statistical methods. This study provides new insights into the real-world dynamics of multiple environmental exposures in a high-density urban environment and their potential mental health implications.

Suggested Citation

  • Kan, Zihan & Wei, Jiaomin & Kwan, Mei-Po & Yang, Zhenchuan, 2026. "A trajectory mining framework for exploring individual concurrent environmental exposure and its association with mental health," Social Science & Medicine, Elsevier, vol. 388(C).
    • Handle: RePEc:eee:socmed:v:388:y:2026:i:c:s0277953625010998
    DOI: 10.1016/j.socscimed.2025.118768
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    References listed on IDEAS

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