Ambient air quality shows a high degree of spatio-temporal variation. It is known to impact human life and health. Obtaining meaningful estimates of human exposure to air pollutants is key to mitigating its ill effects. To ascertain individual exposure, pollutant information is required at high resolution. Such information would enable policymakers and the public to make informed decisions to combat air pollution and protect themselves. Presently, air quality is measured through sparsely located static monitoring stations. Such a monitoring paradigm results in large spatial gaps in air quality information. This thesis proposes a comprehensive framework for measuring and assessing ambient air pollution at high spatio-temporal resolution through low-cost, IoT (Internet of Things) sensor-based mobile monitoring paradigm. This work introduces strategies and protocols for the standardization of low-cost air quality sensors. Thereafter, a generalised method to assess the adequacy of mobile monitors to cover a cityscape is demonstrated. It is followed by pilot studies to establish a mobile monitoring framework and validate its efficacy for high-resolution, spatiotemporal air quality assessment in an urban setting. The work introduces a novel multi-agent modelling framework that allows for descriptive, predictive, and prescriptive assessment of air pollution at high granularity. Some real-world applications of such hyperlocal air quality information are also explored towards the end.