This study was conducted to analyze the characteristics of odorous components that have been generated from the downtown sewer system based on twenty-three survey items for complex odor and designated offensive odor. As a result of the research, the contribution rates for the causative materials of the odor indicated 73.5% of hydrogen sulfide, 26.0% of methyl mercaptan, 0.4% of dimethyl sulfide, and 0.1% of dimethyl disulfide. The occurrence for the odorous materials according to sampling site revealed data of which contribution rates showed 56.9% of hydrogen sulfide and 36.8% of methyl mercaptan from the combined sewer system in the business district; whereas the combined sewer system in the residential area showed 16.4% of dimethyl sulfide and 4.3% of dimethyl disulfide. The seasonal occurrence rate of the odor materials was observed higher in summer and lower in winter And, the combined sewer system in the business district recorded the highest concentration of 4.61 ppm of hydrogen sulfide among the sampling site. An hourly occurrence rate for the odor materials consistently showed the greatest increase between 11:00 and 14:00 at each location and showed a decreasing tendency afterward.

Chironomid communities are indicators of water pollution because of their ability to thrive under freshwater conditions. However, it is difficult to distinguish between chironomid larvae based on morphology. DNA barcoding, based on nucleotide sequences of marker genes, can be used to identify chironomid larvae. Samples of chironomid larvae were collected from Gwangju Stream and Pungyeongjeong Stream, tributaries of the Yeongsan River in South Korea. We identified 3 subfamilies, 13 genera, 16 species, and 1 cryptic species. There were 7 genera and 10 species from the subfamily Chironominae, 5 genera and 5 species from subfamily Orthocladiinae, 1 genus and 1 species from subfamily Tanipodinae, and the cryptic chironomid species of the family Chironomidae. There were 21 individuals from, 7 species and 1 cryptic species from the Gwangju Stream and 24 individuals, belonging to 10 species from the Pungyeongjeong Stream. The only species detected in both streams was Cricotopus bicinctus. The relationship between water quality and the species detected was difficult to explain, but the number of species showed a tendency to increase at sites where water quality was poor. Additional investigations and studies are needed to understand the relationship between water quality and the chironomid species occurring in these two streams.

This study was conducted to propose a way to increase the accuracy and precision of β-ray measurement equipment. Statistical processing results of equivalent evaluation data from 2016 to 2021 confirmed that the concentration of micro dust measured by β-ray measurement equipment was higher than that of micro dust sampler. According to quarterly data, it was confirmed that the data from the third quarter (July to September) showed a different trend from other periods, which is assumed to be due to weather conditions. This study indicates that automatic micro-dust measurement equipment evaluation at air pollution measuring stations during the third quarter should be excluded. The evaluation cycle should be changed from once every two years to quarterly. In addition, when the criterion for determining equivalence evaluation falls within the range of the slope and intercept values of the existing trend line, it is necessary to evaluate the R2 value together and reduce the slope from 0.9-1.1 to 0.9-1.0.

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.

This study analyses the characteristics of volatile organic compounds (VOCs) emissions from the painting and printing facilities, as well as ambient VOCs at industrial complexes in Gwangju. The major components of VOCs emissions from painting facilities were toluene, acetone, 2-butanone, ethyl acetate, ethyl benzene, o-xylene and m,p-xylene. The printing facilities mostly emitted ethyl acetate, 2-butanone, acetone and toluene. Aromatics (49.9%) and oxygenated VOCs (43.6%) were dominant in painting facilities, while oxygenated VOCs (92.7%) were the largest group in printing facilities. The total hydrocarbon concentration (THC) in printing facilities was approximately six times higher than in the painting facilities. The painting and printing facilities use many solvents. Their THC concentrations differed considerably depending on the type of prevention facilities. To reduce THC, it is necessary to improve the prevention facilities and operating conditions. The dominant species of ambient VOCs in industrial complexes were investigated with toluene, ethyl acetate, 2-butanone, ethyl benzene, m,p-xylene, butyl acetate, o-xylene, hexane and acetone. Factor analysis of ambient VOCs showed that the main sources of the VOCs were organic solvents used in painting, coating, and printing, as well as automobile emissions.

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.