In this study, we investigated the characteristics of Volatile Organic Compounds(VOCs) emission from painting and printing facilities in the Pyeongdong industrial complex in Gwangju. In addition, the objective was to understand the distribution characteristics of VOCs in the ambient air in industrial complexes affected by painting and printing facilities. The painting facility mainly emitted toluene, acetone, butyl acetate, 4-methyl-2-pentanone, ethyl acetate, 1-butanol, methyl ethyl ketone, m,p-xylene, o-xylene, 4-ethyltoluene, ethylbenzene, 3-ethyltoluene, and 1,2,4-trimethylbenzene. The main emission components in printing facilities were methyl ketone, ethyl acetate, acetone, 2-propanol, toluene, heptane, and butyl acetate. Ethyl acetate, toluene, 2-butanone, acetone, butyl acetate, 2-propanol, xylenes, and 4-methyl-2-pentanone were detected in the ambient air of the Pyeongdong industrial complex, consistent with the VOCs emitted by painting and printing facilities. The average concentration of seasonal TVOCs followed an order of winter > fall > spring > summer, whereas the concentrations of daytime and nighttime TVOCs were generally higher at night than those during the day, and the wind speed was greater during the day than it was at night. Based on a factor analysis of VOCs in the ambient air of Pyeongdong industrial complex, it is considered that organic solvents used in coating, printing, and electronics manufacturing facilities, as well as diesel vehicle emissions played a major role.

Industrial emissions, mainly from industrial complexes, are important sources of ambient Volatile Organic Compounds (VOCs). Identification of the significant VOC sources from industrial complexes has practical significance for emission reduction. VOC samples were collected from July 2019 to June 2020. A Positive Matrix Factorization (PMF) receptor model was used to evaluate the VOC sources in the area. Four sources were identified by PMF analysis, including coating-1, coating-2, printing, and vehicle exhaust. The coating-1 source was revealed to have the highest contribution (41.5%), followed by coating-2 (23.9%), printing (23.1%), and vehicle exhaust (11.6%). The source showing the highest contribution was coating emissions, originating from the northwest to southwest of the sample site. It also relates to facilities that produce auto parts. The major components of VOC emissions from the coating facilities were toluene, m,p-xylene, ethylbenzene, o-xylene, and butyl acetate. Industrial emissions should be the top priority to meet the relevant control criteria, followed by vehicular emissions. This study provides a strategy for VOC source apportionment from an industrial complex, which is helpful in the development of targeted control strategies.

The purpose of this study is to investigate the relationship of fine dust PM10 and heavy metals in PM10 in Asian dust flowing into Gwangju from 2013 to 2018. The migration pathways of Asian dust was analyzed by backward trajectory analysis using HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model, and the change of heavy metal concentration and heavy metal content per 1 μg/m3 of fine dust PM10 in Gwangju area were analyzed. Also, the characteristics of the heavy metals were analyzed using the correlation between the heavy metals in PM10. As a result of analyzing Asian dust entering the Gwangju region for 6 years, the average concentration of PM10 measured in Asian dust was 148 μg/m3, which was about 4.5 times higher than in non-Asian dust, 33 μg/m3. A total of 13 Asian dust flowed into the Gwangju during 6 years, and high concentration of PM10 and heavy metals in that were analyzed in the C path flowing through the Gobi/Loess Plateau-Korean Peninsula. As a result of the correlation analysis, in case of Asian dust, there was a high correlation between soil components in heavy metals, so Asian dust seems to have a large external inflow. On the other hand, in case of non-Asian dust, the correlation between find dust PM10 and artificial heavy metal components was high, indicating that the influence of industrial activities in Gwangju area was high.

The objective of this study was to estimate the trends of air quality in the study area by analyzing monthly and seasonal concentration trends obtained from sampled data. To this aim, the mass concentrations of PM2.5 in the air were analyzed, as well as those of metals, ions, and total carbon within the PM2.5. The mean concentration of PM2.5 was 22.7 ㎍/㎥. The mass composition of PM2.5 was as follows: 31.1% of ionic species, 2.2% of metallic species, and 26.7% of carbonic species (EC and OC). Ionic species, especially sulfate, ammonium, and nitrate, were the most abundant in the PM2.5 and exhibited a high correlation coefficient with the mass concentration of PM2.5. Seasonal variations of PM2.5 showed a similar pattern to those of ionic and metallic species, with high concentrations during winter and spring. PM2.5 also had a high correlation with the ionic species NO3 - and NH4 +. In addition, NH4 + was highly correlated with NO3 -. Through factor analysis, we identified four controlling factors, and determined the pollution sources using the United States Environmental Protection Agency(U.S. EPA) pollution profile. The first factor, accounting for 19.1% of PM2.5 was attributed to motor vehicles and heating-related sources: the second factor indicated industry-related sources and secondary particles, and the other factors indicated soil, industry-related and marine sources. However, the pollution profile used in this study may be somewhat different from the actual situation in Korea, since it was obtained from US EPA. Therefore, to more accurately estimate the pollutants present in the air, a pollution profile for Korea should be produced.

The objective of this study was to estimate air quality trends in the study area by surveying monthly and seasonal concentration trends. To do this, the mass concentration of PM10 samples and the metals, ions, and total carbon in the PM10 were analyzed. The mean concentration of PM10 was 33.9 ㎍/㎥. The composition of PM10 was 39.2% ionic species, 5.1% metallic species, and 26.6% carbonic species (EC and OC). Ionic species, especially sulfate, ammonium, and nitrate, were the most abundant in the PM10 and had a high correlation coefficient with PM10. Seasonal variation of PM10 showed a similar pattern to those of ionic and metallic species. with high concentration during the winter and spring seasons. PM10 showed high correlation with the ionic species NO3 - and NH4 +. In addition, NH4 + was highly correlated with SO4 2- and NO3 -. We obtained four factors through factor analysis and determined the pollution sources using the United States Environmental Protection Agency(U.S. EPA) pollution profile. The first factor accounted for 51.1% of PM10 from complex sources, that is, soil, motor vehicles, and secondary particles: the second factor indicated marine sources; the third factor, industry-related sources; and the last factor, heating-related sources. However, the pollution profile used in this study may be somewhat different from the actual situation in Korea because it was from US EPA. Therefore, to more accurately estimate the pollutants present, it is necessary to create a pollution profile for Korea.