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( Jaymin Kwon ),( Clifford P. Weisel ),( Maria T. Morandi ),( Thomas H. Stock ),( Barbara Turpin ) 한국환경과학회 2016 한국환경과학회지 Vol.25 No.10
Concentrations of fine particulate matter (PM<sub>2.5</sub>) and several of its particle constituents measured outside homes in Houston, Texas, and Los Angeles, California, were characterized using multiple regression analysis with proximity to point and mobile sources and meteorological factors as the independent variables. PM<sub>2.5</sub> mass and the concentrations of organic carbon (OC), elemental carbon (EC), benzo-[a]-pyrene (BaP), perylene (Per), benzo-[g,h,i]-perylene (BghiP), and coronene (Cor) were examined. Negative associations of wind speed with concentrations demonstrated the effect of dilution by high wind speed. Atmospheric stability increase was associated with concentration increase. Petrochemical source proximity was included in the EC model in Houston. Area source proximity was not selected for any of the PM<sub>2.5</sub> constituents` regression models. When the median values of the meteorological factors were used and the proximity to sources varied, the air concentrations calculated using the models for the eleven PM<sub>2.5</sub> constituents outside the homes closest to influential highways were 1.5-15.8 fold higher than those outside homes furthest from the highway emission sources. When the median distance to the sources was used in the models, the concentrations of the PM<sub>2.5</sub> constituents varied 2 to 82 fold, as the meteorological conditions varied over the observed range. We found different relationships between the two urban areas, illustrating the unique nature of urban sources and suggesting that localized sources need to be evaluated carefully to understand their potential contributions to PM<sub>2.5</sub> mass and its particle constituents concentrations near residences, which influence baseline indoor air concentrations and personal exposures. The results of this study could assist in the appropriate design of monitoring networks for community-level sampling and help improve the accuracy of exposure models linking emission sources with estimated pollutant concentrations at the residential level.
박신영,이혜원,Kwon Jaymin,윤성원,이철민 한국대기환경학회 2024 Asian Journal of Atmospheric Environment (AJAE) Vol.18 No.1
In this study, we developed a prediction model for heavy metal concentrations using PM2.5 concentrations and meteorological variables. Data was collected from five sites, encompassing meteorological factors, PM2.5, and 18 metals over 2 years. The study employed four analytical methods: multiple linear regression (MLR), random forest regression (RFR), gradient boosting, and artificial neural networks (ANN). RFR was the best predictor for most metals, and gradient boosting and ANN were optimal for certain metals like Al, Cu, As, Mo, Zn, and Cd. Upon evaluating the final model’s predicted values against the actual measurements, differences in the concentration distribution between measurement locations were observed for Mn, Fe, Cu, Ba, and Pb, indicating varying prediction performances among sites. Additionally, Al, As, Cd, and Ba showed significant differences in prediction performance across seasons. The developed model is expected to overcome the technical limitations involved in measuring and analyzing heavy metal concentrations. It could further be utilized to obtain fundamental data for studying the health effects of exposure to hazardous substances such as heavy metals.