气味、空气质量和幸福感:利用众包科学了解城市气味景观

Sahil Bhandari, D. Monticelli, Karen Xie, Andre Ramkairsingh, Rochelle Maher, Angela Eykelbosh, Sarah B Henderson, Naomi Zimmerman, A. Giang
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摘要

由于气味与空气质量、健康、幸福感和生活质量之间的潜在联系,气味是一个新兴的环境健康话题。然而,由于个人敏感度和感知、大气物理化学过程以及气味污染物混合物的排放等方面存在差异,气味研究历来具有挑战性。在此,我们探讨了众包气味报告数据在提高对气味体验的时空模式及其影响的理解方面的潜在作用。我们在加拿大温哥华对一个收集众包气味报告的网络应用程序的 12 个月数据集进行了定量和定性分析,其中包括时空信息、气味和自我报告的影响描述(OSAC:气味、症状、应对措施和疑似原因)以及人口统计数据。用户报告的 OSAC 种类繁多,具有很强的季节性和空间差异性。报告的症状从神经系统症状到与情绪和心情有关的症状,突出了与气味有关的健康和福祉影响的复杂性。气味可能会引发不适应行为,即个人暴露于其他环境压力源(如热应激)或减少健康行为(如户外锻炼),以应对气味的影响。对 OSAC 的聚类分析表明,气味暴露可能通过复杂的机制与健康、幸福和生活质量的影响联系在一起,这不仅与所经历的气味有关,还与感知到的原因有关。报告中的时空模式突显了持久性来源(如废物管理)和瞬时性事件(如事故)的潜在影响。探索性多元线性回归模型表明,对空气质量和气象的监测可能不足以捕捉气味问题。总之,这些结果表明,包含自我报告的健康和福利影响以及行为反应的众包科学可以丰富对大时空范围内气味排放影响的理解,并对传统的空气污染监测起到补充作用。
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Odor, air quality, and well-being: understanding the urban smellscape using crowd-sourced science
Odors are a topic of emerging environmental health interest given their potential links to air quality, health, well-being, and quality of life. However, odors have traditionally been challenging to study given variability in individual sensitivity and perception, atmospheric physico-chemical processes, and emissions of mixtures of odorous contaminants. Here, we explore the potential utility of crowd-sourced odor report data in improving understanding of spatiotemporal patterns of odor experiences and their impacts. We conduct quantitative and qualitative analyses of a 12-month data set from a web application collecting crowd-sourced odor reports, including spatiotemporal information, odor and self-reported impacts description (OSAC: odors, symptoms, actions in response, and suspected causes), and demographics, in Vancouver, Canada. Users report diverse OSAC with strong seasonality and spatial variability. Reported symptoms, ranging from neurological to emotion- and mood-related, highlight the complexity of odor-related health and well-being impacts. Odors can trigger maladaptive actions, where individuals are exposed to other environmental stressors (e.g., heat stress) or curtail healthy behaviors (e.g., exercising outside) to cope with odor impacts. Clustering analysis of OSAC suggests that odor exposures may be linked to health, well-being, and quality of life impacts through complex mechanisms, related not only to the odor experienced but also perceived causes. Spatiotemporal patterns in reports highlight the potential influence of persistent sources (e.g., waste management) and transient events (e.g., accidents). Exploratory multiple linear regression models suggest that monitoring of air quality and meteorology may be insufficient to capture odor issues. Overall, these results suggest that crowd-sourced science incorporating self-reported health and well-being effects and behavioral responses can enrich understanding of the impacts of odorous emissions at large spatiotemporal scales and complement traditional air pollution monitoring.
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