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

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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?