  People are increasingly concerned with understanding their personal
environment, including possible exposure to harmful air pollutants. In order to
make informed decisions on their day-to-day activities, they are interested in
real-time information on a localized scale. Publicly available, fine-scale,
high-quality air pollution measurements acquired using mobile monitors
represent a paradigm shift in measurement technologies. A methodological
framework utilizing these increasingly fine-scale measurements to provide
real-time air pollution maps and short-term air quality forecasts on a
fine-resolution spatial scale could prove to be instrumental in increasing
public awareness and understanding. The Google Street View study provides a
unique source of data with spatial and temporal complexities, with the
potential to provide information about commuter exposure and hot spots within
city streets with high traffic. We develop a computationally efficient
spatiotemporal model for these data and use the model to make short-term
forecasts and high-resolution maps of current air pollution levels. We also
show via an experiment that mobile networks can provide more nuanced
information than an equally-sized fixed-location network. This modeling
framework has important real-world implications in understanding citizens'
personal environments, as data production and real-time availability continue
to be driven by the ongoing development and improvement of mobile measurement
technologies.
