When air pollutants are generated, the form and concentration of air pollutants change depending on advection, diffusion, reaction, and deposition from the source into the atmosphere. By using characteristic information such as a chemical composition ratio or a special element, the source profile and the contribution in PM2.5 can be calculated. Positive matrix factorization (PMF) modeling is typically used for source identification. In this study, the contribution of industrial sources of PM2.5 in Siheung, Republic of Korea, was calculated using PMF 5.0, which is distributed by the US Environmental Protection Agency (EPA). The PM2.5 samples were collected every three or four days over a 24-h period from November 2019 to December 2020 at the Jeongwnag-dong national air quality measuring station (37.3472°N, 126.7399°E) from the rooftop of a building that is approximately 10 m above ground level. The three samplers (PMS-204, APM engineering, South Korea) with a 47 mm filter were used to collect ambient PM2.5 and quantify its chemical species. The mass concentration, carbon component, ionic component, and trace element component were analyzed by measuring the weight change, thermal optical transmittance, ion chromatography, energy dispersive X-ray fluorescence, respectively. The uncertainty for PMF modeling was calculated by US-EPA guidelines. Also, to compare the derived type of pollutant and meteorological data, a polar plot (OpenAir package in R) was used. The contribution of each pollution source according to the wind direction and wind speed was analyzed. As a result of the modeling, the contribution of PM2.5 from industrial sources excluding secondary formation was calculated about 1.29 μg per cubic meter in the daily average. Major chemical components from the derived source profile were Cu, Zn, Cr, Mn, Ni, Fe, V. It is suggested that the usage of heavy fuel oil and the metal smelting factories were the main sources. As a result of the time series data analysis, the industrial pollutant sources were affected evenly throughout the year, with little variation by season. In addition, by analyzing the polar plot results, the contribution of industrial pollutants was high when the southeast wind blows, which seems to reflect the influence of the surrounding industrial areas well. Siheung city has a large-scale national industrial complex and power plant in the southeast. Based on these modeling results, intensive management of hot-spots by type is necessary to reduce PM2.5 emission. However, since the concentration of PM2.5 in a specific area varies greatly depending on the weather conditions, and the constituent components are also affected by various factors, complex consideration with other types of pollutants is required for more accurate estimation.
