Investigating the potential of mobile air quality monitoring in urban areas in the development of real-time air pollution maps and applications to reduce population exposure

研究城市地区移动空气质量监测在开发实时空气污染地图和应用程序以减少人口暴露方面的潜力

• 批准号: RGPIN-2016-05615
• 负责人: Hatzopoulou, Marianne
• 金额: $ 2.26万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709780
• 关键词: Investigating; potential; mobile; air; quality; Investigating; potential; mobile; air; quality

In urban areas, air pollution exhibits substantial seasonal and spatial variability that is important to capture in health effects studies. As a result, epidemiologists often use different methods to quantify population exposure. These include personal exposure measurements, data from fixed-site monitoring stations, land-use regression (LUR) techniques, and dispersion models. All of these methods are associated with a number of limitations. Personal exposure measurements are normally limited to measuring the exposure of a specific sample of individuals over a time period ranging from a few days to a few weeks. They quickly become onerous as the sample size increases. Air pollution data obtained from fixed-site monitoring stations has been identified as a poor marker of intra-urban variability and correlate poorly with near-road air pollution levels. LUR models have been developed for many metropolitan areas however their reliability in predicting accurate population exposure patterns depends on the density of the established monitoring network thus entailing costly campaigns with extensive spatial and temporal coverage. Finally dispersion modelling for the purposes of exposure evaluation poses many challenges especially related to the validity of emission inputs. The rapid surge in the use of GPS-enabled smart-phones has opened-up new areas of research making use of these data streams to infer population mobility decisions, places of attraction, and emergency evacuation strategies. In addition, a number of research labs and start-up companies are investing in the production of air quality sensors with precisions, lower reading limits, and ranges comparable with high-end monitors. The possibility of human-centric air pollution sensing provides unparalleled opportunities for the development of time-dependent air pollution exposure maps which can be used by epidemiologists for studies of chronic health effects and most importantly for testing the association between emergency hospital visits and time-dependent air pollution episodes. The proposed research will provide essential information to develop innovative policies and guidelines and information systems that aim at reducing air pollution exposure. Specifically, I will address three research questions: What elements of the urban environment have the highest effect on near-road air quality and how can we accordingly design microenvironments which minimize traffic-related air pollution? What is the role of information and communication technologies in monitoring ambient air pollution at a fine spatial and temporal scale? Can we optimally select routes that minimize exposure to air pollution, thereby translating research results to the public at large?

在城市地区，空气污染表现出很大的季节性和空间变异性，对于捕获健康效应研究很重要。结果，流行病学家经常使用不同的方法来量化人口暴露。这些包括个人暴露测量，固定地点监控站的数据，土地利用回归（LUR）技术和分散模型。所有这些方法都与许多局限性有关。个人暴露测量通常仅限于测量一个时间段的特定样本的暴露，范围从几天到几周。随着样本量的增加，它们很快变得繁重。从固定位置监测站获得的空气污染数据已被确定为城市内变异性的较差标记，并且与近路空气污染水平的相关性很差。 LUR模型已经针对许多大都市地区开发，但是它们在预测准确的人口暴露模式方面的可靠性取决于已建立的监视网络的密度，因此具有昂贵的广告系列，并具有广泛的空间和时间覆盖范围。最终，出于暴露评估的目的，分散建模构成了许多挑战，尤其是与排放输入的有效性有关。使用基于GPS的智能手机的使用迅速激增，为使用这些数据流以推断人口流动性决策，吸引力和紧急撤离策略的新研究领域。此外，许多研究实验室和初创公司都在投资于具有精确度，较低读取限制和可与高端监视器相当的范围的空气质量传感器的生产。以人为中心的空气污染传感的可能性为开发时间依赖性空气污染图的开发提供了无与伦比的机会，而流行病学家可以将其用于研究慢性健康效应，最重要的是测试紧急医院就诊与时间依赖性空气污染事件之间的关联。拟议的研究将提供基本信息，以制定旨在减少空气污染暴露的创新政策，准则和信息系统。具体来说，我将解决三个研究问题：哪些城市环境的要素对近乎公路的空气质量具有最高影响，以及如何相应地设计微环境，从而最大程度地减少与交通相关的空气污染？信息和通信技术在监测良好的空间和时间尺度监测环境空气污染中的作用是什么？我们可以最佳选择最大程度地减少空气污染的途径，从而将研究结果转化为整个公众？