Background: Most prior studies adopted a static residence-based approach in the assessment of noise exposure, which may lead to biased exposure estimates and misleading findings in noise-health relationships. Relatively little is known about personal noise exposure based on individuals' space-time behavior and its effect on mental health.
Objectives: This study aims to analyze and geo-visualize personal exposure to noise in various microenvironments based on individuals' space-time trajectories at a very fine resolution and to further investigate the relationships between mental health and personal noise exposure at both the activity/travel episode level and the entire day level.
Methods: Individual-level real-time data were collected with portable noise sensors and GPS trackers from a sample of 117 residents aged 18-60 years old from December 2017 to February 2018 in Beijing, China. Descriptive statistics and geo-visualization methods were used to examine how personal noise exposure varied across different activity types, travel modes, and among residents living in the same residential neighborhood on workdays and weekends based on individuals' space-time behaviors. Logistic regression models were applied to examine the relationships between personal noise exposure and self-reported mental health.
Results: We observed substantial differences in personal noise exposure across different activity types. The equivalent sound levels (Leq, dB(A)) for sleeping were the lowest, while the average Leq for work-related activities was the highest in indoor environments. The noise exposure levels for activities in outdoor environments were higher than indoor noise levels but differed between workdays and weekends. Variations in noise exposure associated with different travel modes were also evident, with the average Leq for public transport being much higher than that of other travel modes. A-weighted equivalent sound pressure level measured over 24 h for each individual (Leq,24h, dB(A)) varied significantly for residents living in the same residential neighborhood, ranging from 36 to 97 dB(A), with the majority of respondents being exposed to noise levels above 55 dB(A) on both workdays and weekends. Regarding the noise-health relationships, the modeling results showed that individual-level objective noise exposure based on space-time behaviors measured over a 24-h period (Leq,24h) was strongly associated with residents' self-reported mental health. Higher exposure to noise was significantly associated with worse mental health. However, personal noise exposure at the activity/travel episode level (Leq) was not significantly associated with mental health on weekdays, but this link turned out to be significant in the weekend model.
Conclusions: There were large variations in personal noise exposure associated with different activity types and travel modes, and the individual-level noise exposure varied significantly across time of day and between residents living in the same residential neighborhood. Variations in personal exposure strongly depend on different space-time behaviors and individual-specific microenvironments experienced in daily life, and they were significantly correlated with mental health.
Keywords: Mental health; Noise; Personal exposure; Sensing technology; Space-time trajectory.
Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.