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Title	Air Pollution Source Tracing Framework: Leveraging Microsensors And Wind Analysis For Pollution Source Identification
ID_Doc	7143
Authors	Hung C.-C.; Hsiao H.-E.; Lin C.-C.; Hsu H.-H.
Year	2024
Published	Communications in Computer and Information Science, 2075 CCIS
DOI	http://dx.doi.org/10.1007/978-981-97-1714-9_12
Abstract	In the context of rapid urbanization, air pollution has become a significant concern, particularly in densely developed urban areas. This pollution stems from various sources, including traffic, industry, and external contributors. Its implications go beyond ecological disruption, posing a substantial threat to human health, with prolonged exposure leading to chronic ailments and increased mortality risks. Current air pollution monitoring methods, centered on air quality indices and forecasting, often lack the ability to pinpoint pollution sources. To address this, our study employs Smart City and Rural Air Quality Microsensors deployed across Taiwan. By analyzing pollution movement patterns through continuous time series data and integrating wind data, we utilize backtracking to identify emission sources and create innovative air pollution corridors to assess pollution's extent. 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. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Author Keywords	air pollution; hungarian algorithm; source tracking; trajectory analysis

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