PGA: Physics Guided and Adaptive Approach for Mobile Fine-Grained Air Pollution Estimation.

Large-scale fine-grained air pollution information has both financial for city managers and health benefits for all city residents. Sensors installed on fleet of vehicles (like taxis) to collect air pollution data provides a low cost, low-maintenance and a potentially high coverage approach. The challenges here are: 1) Sensing coverage is sparse in some areas, and 2) it changes over time. This paper presents PGA, a physics guided and adaptive approach to estimate fine-grained air pollution with vehicle fleets. We combine the advantages of a physics guided model and a data-driven model to achieve high accuracy with high spatial-temporal resolution. To evaluate the system, we deploy our air pollution sensing hardware on 29 taxis in the city of Shenzhen, and collected around 26.3 million data samples within 14 days. The results show that our system achieves up to 4.0× reduction on average error compared to state-of-the-art approaches.