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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615805
• 关键词: 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，这在健康影响研究中很重要。因此，流行病学家经常使用不同的方法来量化人口暴露。其中包括个人暴露测量、固定地点监测站的数据、土地利用回归技术和弥散模型。所有这些方法都有一些局限性。个人暴露测量通常限于在几天到几周的时间内测量特定个体样本的暴露。随着样本量的增加，它们很快变得繁重起来。从固定地点监测站获得的空气污染数据已被确定为城市内部变化的较差标记，并且与道路附近的空气污染水平相关性较差。已为许多大都市地区开发了LUR模型，但其在准确预测人口暴露模式方面的可靠性取决于已建立的监测网络的密度，因此需要在广泛的空间和时间范围内进行昂贵的运动。最后，用于暴露评估的色散建模提出了许多挑战，特别是与排放输入的有效性有关。使用具有gps功能的智能手机的迅速激增开辟了新的研究领域，利用这些数据流来推断人口流动决策、景点和紧急疏散策略。此外，许多研究实验室和初创公司正在投资生产精度高、读数限制低、范围可与高端监视器相媲美的空气质量传感器。以人为中心的空气污染感测的可能性为开发随时间变化的空气污染暴露图提供了无与伦比的机会，流行病学家可以使用这些地图研究慢性健康影响，最重要的是用于测试急诊就诊与随时间变化的空气污染发作之间的关系。拟议的研究将为制定旨在减少空气污染的创新政策和准则以及信息系统提供必要的资料。具体来说，我将解决三个研究问题：城市环境的哪些因素对道路附近的空气质量影响最大？我们如何相应地设计微环境，以最大限度地减少与交通有关的空气污染？信息和通信技术在精细时空尺度上监测环境空气污染的作用是什么？我们能否选择最优路线，将暴露在空气污染中的机会降到最低，从而将研究成果传递给广大公众？