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Urban air pollution is believed to be a major contributor to premature deaths and chronic illnesses worldwide. Current systems for urban air pollution monitoring rely on static sites with low spatial resolution, and moreover, lack the means to estimate exposures for (potentially mobile) individuals in order to make medical inferences. This paper describes the design and evaluation of a low-cost participatory sensing system called HazeWatch that uses a combination of portable mobile sensor units, smart-phones, cloud computing, and mobile apps to measure, model, and personalize air pollution information for individuals. Our contributions are three-fold: we architect, prototype, and compare multiple hardware devices and software applications for collecting urban air pollution data with high spatial density in real-time; we develop web-based tools and mobile apps for the visualization and estimation of air pollution …