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

在城市地区，空气污染表现出很大的季节性和空间变异性，这对健康影响研究很重要。因此，流行病学家经常使用不同的方法来量化人群暴露。这些方法包括个人接触测量、来自固定地点监测站的数据、土地使用回归技术和扩散模型。所有这些方法都有一些局限性。个人接触测量通常限于测量特定样本的个人在几天到几周的时间内的接触。随着样本量的增加，它们很快变得繁重。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不良标志，与近路空气污染水平的相关性很差。已为许多大都市地区开发了LUR模型，但其在预测准确的人口接触模式方面的可靠性取决于已建立的监测网络的密度，因此需要开展具有广泛空间和时间覆盖范围的昂贵运动。最后，为暴露评估目的的扩散建模提出了许多挑战，特别是与排放输入的有效性有关的挑战。具有GPS功能的智能手机的使用迅速激增，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空气质量传感器，这些传感器具有精度、较低的阅读限制和与高端监测器相当的范围。以人为中心的空气污染感测的可能性提供了前所未有的机会，发展时间依赖的空气污染暴露地图，可用于流行病学家的慢性健康影响的研究，最重要的是用于测试急诊医院就诊和时间依赖的空气污染事件之间的关联。拟议的研究将提供必要的信息，以制定旨在减少空气污染暴露的创新政策和指南以及信息系统。具体来说，我将解决三个研究问题：城市环境的哪些元素对近路空气质量的影响最大，以及我们如何相应地设计微环境，最大限度地减少与交通有关的空气污染？信息和通信技术在监测精细时空尺度的环境空气污染方面发挥什么作用？我们能否最佳地选择最大限度地减少暴露于空气污染的路线，从而将研究成果转化为广大公众？

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?