Air Pollution Hotspot Detection and Source Feature Analysis using Cross-Domain Urban Data

Yawen Zhang, Michael Hannigan, Qin Lv

Abstract: Air pollution is a major global environmental health threat, in particular for people who live or work near air pollution sources. Areas adjacent to pollution sources often have high ambient pollution concentrations, and those areas are commonly referred to as air pollution hotspots. In this work, we explore the use of mobile sensing data to detect pollution hotspots. We propose a two-step approach to detect hotspots from unevenly sampled mobile sensing data. To contextualize the detected hotspots and discover potential pollution source characteristics, we explore a variety of cross-domain urban data and extract features from them for hotspot inference. Evaluation results using real-world mobile sensing air quality data as well as cross-domain urban data demonstrate the effectiveness of our approach in detecting and inferring pollution hotspots.