광양만권 대기 중 초미세먼지 원인별 기여율 평가
Assessment of Regional Source Contribiution of PM2.5 in the Gwangyang Bay Area
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Fine particulate matter (PM-2.5) samples were collected over 15 months in the Yeosu and Gwangyang areas from March, 2019 to June 2020. Samples were analyzed for organic carbon, elemental carbon, water-soluble ionic compounds, and trace elements. Source apportionment model (Positive Matrix Factorization) was applied to the data understand monthly and seasonal contributing sources of PM-2.5. Nine source categories with reasonably stable profiles were identified in the Yeosu area such as secondary sulfate (38%), oil combustion (14%), mobile (14%), biomass burning (13%), roadway emission (10%), secondary nitrate (6%), industry activity (3%), sea salt (2%), and coal combustion (0.7%). In the Gwangyang area, the main and relatively different source contributions were secondary sulfate (37%), biomass burning (21%), mobile (13%), industry activity (13%), secondary nitrate (5%), oil combustion (3%), sea salt (3%), and roadway emission (0.7%). The companion model such as conditional probability function, revealed that the sources of secondary sulfate, nitrate, and biomass burning facilities can significantly affect the pollution levels at the specific locations.
Abstract
Fine particulate matter (PM-2.5) samples were collected over 15 months in the Yeosu and Gwangyang areas from March, 2019 to June 2020. Samples were analyzed for organic carbon, elemental carbon, water-soluble ionic compounds, and trace elements. Source apportionment model (Positive Matrix Factorization) was applied to the data understand monthly and seasonal contributing sources of PM-2.5. Nine source categories with reasonably stable profiles were identified in the Yeosu area such as secondary sulfate (38%), oil combustion (14%), mobile (14%), biomass burning (13%), roadway emission (10%), secondary nitrate (6%), industry activity (3%), sea salt (2%), and coal combustion (0.7%). In the Gwangyang area, the main and relatively different source contributions were secondary sulfate (37%), biomass burning (21%), mobile (13%), industry activity (13%), secondary nitrate (5%), oil combustion (3%), sea salt (3%), and roadway emission (0.7%). The companion model such as conditional probability function, revealed that the sources of secondary sulfate, nitrate, and biomass burning facilities can significantly affect the pollution levels at the specific locations.
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