• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.943-953
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• 2021

The purpose of this study is to investigate sediment recovery after the relocation of fish cage farms, by examining the changes in sediments and the benthic polychaete community. A preliminary survey was carried out in October 2017, before the relocation of the farms, and monthly surveys were conducted from November 2017 to October 2018 after the farms were moved. Subsequently, it was conducted every 2-3 months until October 2020. The survey was carried out at three stations (Farm1-3) at the location of the removed fish farms and at three control stations (Con1-3) without farms. The overall organic carbon content of the farm stations was higher than the control stations, but it gradually decreased after the farm was demolished, and there was no statistically significant difference about one year after the relocation of the farms (p<0.05). In the benthic polychaete community, abiotic community appeared at the farm stations in the summer, and consequently, the community transitioned to a low-diversity region with the predominant species Capitella capitata, which is an indicator of pollution. Until the abiotic period in the summer of the next year, the species diversity increased and the proportion of indicator species decreased, showing a tendency of recovering the benthic polychaete community, and these changes were repeated every year. In this study, the abiotic community appeared every year owing to the topographical characteristics, but as the survey progressed, the period of abiotic occurrence became shorter and the process of community recovery progressed expeditiously. Biological recovery of sediments after the relocation of the fish farms is still in progress, and it is imperative to study recovery trends through continuous monitoring.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.528-536
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• 2023

In Korea, from January 2023, the Act on Labeling and Advertising of Food was revised to reflect the use-by-date rather than the sell-by-date. Hence, the purpose of this study was to establish a system for calculating the safety factor and determining the recommended use-by-date for each food type, thereby providing a scientific basis for the recommended use-by-date labels. A safety factor calculation technique based on scientific principles was designed through literature review and simulation, and opinions were collected by conducting surveys and discussions including industry and academia, among others. The main considerations in this study were pH, Aw, sterilization, preservatives, packaging for storage improvement, storage temperature, and other external factors. A safety factor of 0.97 was exceptionally applied for frozen products and 1.0 for sterilized products. In addition, a between-sample error value of 0.08 was applied to factors related to product and experimental design. This study suggests that clearly providing a safe use-by-date will help reduce food waste and contribute to carbon neutrality.