Yapay Zeka Hızlı Bakış & Çıkarımlar
AI InsightThe results of this analysis demonstrated that, when using large PM2.5 datasets, segmenting tasks expands the potential ability to increase the number of specified sources using PMF analysis.
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Özet
Hourly PM2.5 datasets and corresponding meteorological parameters were obtained from the National Institute of Environmental Research (NIER) in Korea. Initially the datasets at the Seoul Intensive Monitoring Station (SIMS) contained 12,376 samples, each with 24 chemical variables, from Jan. 1, 2013 to Dec. 31, 2014. To identify site-specific sources, to clarify vague or unknown source types, and to develop a database containing abundance patterns for tracers for each source, three stage-by-stage positive matrix factorization (PMF) modeling tasks were performed based on a dataset segmentation scheme. In this study, the PM2.5 datasets were segmented using meteorological parameters of wind direction, wind speed, and precipitation. After performing 18 independent PMF modeling simulations, 10 and 14 sources were identified before and after use of the segmentation scheme, respectively. As the number of identified sources increased, the overall contributions from ubiquitous sources continued to show both increasing and decreasing trends. This behavior was noticeable in cases where the contribution of major sources, such as secondary aerosols and coal burning sources, were observed to decrease, and their contributions were reassigned in various burning sources, such as local secondary nitrate and oil sources. Moreover, the contributions of various waste burning sources, such as incineration and biomass burning, moved into secondary nitrate and Br-related waste burning sources. The results of this analysis demonstrated that, when using large PM2.5 datasets, segmenting tasks expands the potential ability to increase the number of specified sources using PMF analysis.