Air Pollution Source Tracing Framework: Leveraging Microsensors and Wind Analysis for Pollution Source Identification

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Title:	Air Pollution Source Tracing Framework: Leveraging Microsensors and Wind Analysis for Pollution Source Identification
Authors:	Hung, Chih-Chieh;Hsiao, Hong-En;Lin, Chuang-Chieh;Hsu, Hui-Huang
Keywords:	air pollution;source tracking;Hungrian algorithm;trajectory analysis
Date:	2023-12
Issue Date:	2023-12-13 12:06:08 (UTC+8)
Abstract:	In the face of rapid urbanization, air pollution has emerged as a pressing and pervasive concern. Urban areas experiencing robust development not only contend with intrinsic air pollution sources like high traffic regions, densely inhabited zones, and industrial emissions but also grapple with the impact of external sources of pollution. The ramifications of air pollution extend beyond ecological disruptions, posing a formidable threat to human health. Prolonged exposure to contaminated air and airborne particulate matter in polluted environments exacerbates chronic ailments and elevates mortality risks. Current methods for monitoring air pollution typically revolve around air quality indices and forecasting; however, they fall short in pinpointing pollution sources. Leveraging the widespread deployment of Smart City and Rural Air Quality Microsensors across Taiwan, this study monitors areas affected by air pollution. By analyzing pollution group movement paths through continuous time series of air pollution data and integrating wind factors, the study employs backtracking to identify the emission sources contributing to air pollution. Furthermore, innovative air pollution corridors are formulated to assess the extent of pollution impact. Thus, when air pollution incidents arise, this method can unveil the pollution sources at the specific location, elucidate propagation and movement paths during emission, and outline zones at risk of pollution in the near future. This paper introduces the Air Pollution Source Tracing Problem (APSTP), proposing the APSTF (Air Pollution Source Tracing Framework) to address this challenge. The APSTF encompasses three key phases: Identification, Matching and Backtracking, and Pathway Generation. It effectively identifies stations experiencing air pollution, correlates affected areas across different time slots, and predicts pollution impact areas, thereby shedding light on pollution dynamics and aiding in source identification and future pollution prediction. The APSTF stands as a valuable tool for understanding and mitigating air pollution, leveraging data from air quality micro-sensors, meteorological stations, and advanced mathematical algorithms.
Relation:	Proceedings of the 28th International Conference on Technologies and Applications of Artificial Intelligence
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

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