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

• URL: http://arxiv.org/abs/2211.08400v1
• Date: Tue, 15 Nov 2022 18:44:03 GMT
• Title: Air Pollution Hotspot Detection and Source Feature Analysis using Cross-domain Urban Data
• Authors: Yawen Zhang, Michael Hannigan, Qin Lv
• Abstract summary: Areas adjacent to pollution sources often have high ambient pollution concentrations, and those areas are commonly referred to as air pollution hotspots. We propose a two-step approach to detect hotspots from mobile sensing data, which includes local spike detection and sample-weighted clustering. As a soft-validation, we build hotspot inference models for cities with and without mobile sensing data.
• Score: 2.458537954999774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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