  Air pollution is a major global environmental health threat, in particular
for people who live or work near pollution sources. Areas adjacent to pollution
sources often have high ambient pollution concentrations, and those areas are
commonly referred to as air pollution hotspots. Detecting and characterizing
pollution hotspots are of great importance for air quality management, but are
challenging due to the high spatial and temporal variability of air pollutants.
In this work, we explore the use of mobile sensing data (i.e., air quality
sensors installed on vehicles) to detect pollution hotspots. One major
challenge with mobile sensing data is uneven sampling, i.e., data collection
can vary by both space and time. To address this challenge, we propose a
two-step approach to detect hotspots from mobile sensing data, which includes
local spike detection and sample-weighted clustering. Essentially, this
approach tackles the uneven sampling issue by weighting samples based on their
spatial frequency and temporal hit rate, so as to identify robust and
persistent hotspots. To contextualize the hotspots and discover potential
pollution source characteristics, we explore a variety of cross-domain urban
data and extract features from them. As a soft-validation of the extracted
features, we build hotspot inference models for cities with and without mobile
sensing data. 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. Furthermore, the
empirical analysis of hotspots and source features yields useful insights
regarding neighborhood pollution sources.
