4.7 Article

Health effects of exposure to particulate matter and physical activity by individual sustainable commuting modes

期刊

JOURNAL OF CLEANER PRODUCTION
卷 397, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2023.136524

关键词

Sustainability transition; Health impact assessment; Individual sustainable modes; Air pollution exposure; Mobile environmental monitoring system

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The promotion of sustainable commuting modes is deemed promising, but it also increases health burdens due to air pollution exposure. This research examines the long-term health impacts of active commuting in Cangzhou, China, and provides valuable information for decision-making regarding sustainable strategies.
Promotion of sustainable commuting modes (e.g., walking, cycling and electric mobility) is considered as a promising solution to achieve sustainability. Active travel may also increase physical activity, which is beneficial for health. But it increases health burdens by directly exposing commuters to air pollution. There is a lack of generalizable quantitative approaches to comprehensively evaluate the health outcomes of sustainable plans during the design period. In this research, we aim to examine the long-term combined health impacts of commuting by walking, cycling, and e-moped in Cangzhou, China, a populous industrial city with high air pollution levels, to inform decision-making prior to the implementation of sustainable strategies. A taxi-based mobile remote sensing system was implemented to create a link-level fine particulate matter (PM2.5) concentration map to overcome the insufficiency of sparse fixing monitoring network, which allows researchers to determine joint relative risks of all-cause mortality at a finer spatial scale. Results show that the benefits always outweighed the risks for active commuting at the case study site, even in high polluted days. E-moped commuters experienced a negative burden. In our case, encouraging these commuters to travel during off-peak periods to reduce exposure might not be effective and could even lead to greater PM2.5 exposure than during peak periods (up to 0.37 mu g/km). The proposed method can be applied to different locations, providing decision makers with the information needed to target interventions to achieve sustainability.

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