• Journal of Environmental Impact Assessment
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• v.29 no.1
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• pp.8-25
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• 2020

Industrialization has increased the production of road-deposited sediments (RDS) and the level of heavy metals in those RDS, which can have a significant impact on the surrounding aquatic environments through non-point pollution. Although the relationship between contamination characteristics and particle size of RDS is important for pollution control, there is very little information on this. In this study, we investigated the characteristics of grain size distribution and heavy metal concentrations in the road-deposited sediments (RDS) collected from 25 stations in Shihwa Industrial Complex. The environmental impact of RDS with particle size is also studied. I_geo, the contamination assessment index of each metal concentration, represents the RDS from Shihwa Industrial Complex are very highly polluted with Cu, Zn, Pb and Sb, and the levels of those metals were 633~3605, 130~1483, 120~1997, 5.5~50 mg/kg, respectively. The concentrations of heavy metals in RDS increased with the decrease in particle size. The particle size fraction below 250 ㎛ was very dominant with mass and contamination loads, 78.6 and 70.4%, respectively. Particles less than 125 ㎛ of RDS were highly contaminated and toxic to benthic organisms in rivers. RDS particles larger than 250 ㎛ and smaller than 250 ㎛ were contaminated by the surrounding industrial facility and vehicle activities, respectively. As a result of this study, the clean-up of fine particles of RDS, smaller than 125-250 ㎛, is very important for the control and reduction of non-point pollution to nearby water in Shihwa Industrial Complex.

• Korean Journal of Environmental Agriculture
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• v.27 no.1
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• pp.35-43
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• 2008

The study was carried out to estimate runoff loads of heavy metals in the valley watershed at the middle of South Korea, during farming season. There were no other pollution sources except agricultural activity. From 27 April 2006 to 31 October 2007, water samples were collected using two methods. The first method was regular sampling wherein water samples were taken every two weeks; and the other method was through regular sampling when water were collected during each rainfall event. Results showed that heavy metals were found in the water from the regular samples, and were highest during May and June. It was presumed that this might have been contributed by farming activities. Heavy metal concentration of the irregular samples was lower than regular samples. The correlation coefficient between each heavy metal of the regular samples were as follows: Fe-Al>Cr-Al>Fe-Cr>Mn-Fe. The correlation coefficient of the irregular samples were the following: Fe-Al>Fe-Cu is positive; and Pb-Cu>Ni-Al is negative. Measured pollutant loads of heavy metals in the valley watershed were : 2.047 kg $day^{-1}$ of Al, 0.008 kg $day^{-1}$ of Cd, 0.034 kg $day^{-1}$ of Cr, 0.311 kg $day^{-1}$ of Cu, 0.601 kg $day^{-1}$ of Fe, and 0.282 kg $day^{-1}$ of Zn in 2006; while in 2007, the following were observed: 2.535 kg $day^{-1}$ of Al, 0.026 kg $day^{-1}$ of Cd, 0.055 kg $day^{-1}$ of Cu, 0.727 kg $day^{-1}$ of Fe, and 0.317 kg $day^{-1}$ of Zn. In the analysis of data gathered, the loading rates of effluents from the valley watershed during the rainy season were : 79.8% of Al, 69.1% of Cu, 82.5% of Fe, and 69.1% of Zn in 2006; while 69.9% of Al, 67.5% of Cu, 70.4% of Fe, and 67.5% of Zn in 2007.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.848-857
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• 2018

Recently, around the world, active development of new renewable energy sources including solar power, waves, and fuel cells, etc. has taken place. Particularly, floating offshore wind farms have been developed for saving costs through large scale production, using high-quality wind power and minimizing noise damage in the ocean area. The development of floating wind farms requires an evaluation of the Maritime Safety Audit Scheme under the Maritime Safety Act in Korea. Floating wind farms shall be assessed by applying the line and area concept for systematic development, management and utilization of specified sea water. The development of appropriate evaluation methods and standards is also required. In this study, proper standards for marine traffic surveys and assessments were established and a systemic treatment was studied for assessing marine spatial area. First, a marine traffic data collector using AIS or radar was designed to conduct marine traffic surveys. In addition, assessment methods were proposed such as historical tracks, traffic density and marine traffic pattern analysis applying the line and area concept. Marine traffic density can be evaluated by spatial and temporal means, with an adjusted grid-cell scale. Marine traffic pattern analysis was proposed for assessing ship movement patterns for transit or work in sea areas. Finally, conceptual design of a Marine Traffic and Safety Assessment Solution (MaTSAS) was competed that can be analyzed automatically to collect and assess the marine traffic data. It could be possible to minimize inaccurate estimation due to human errors such as data omission or misprints through automated and systematic collection, analysis and retrieval of marine traffic data. This study could provides reliable assessment results, reflecting the line and area concept, according to sea area usage.

• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.674-684
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• 2018

The purpose of this study is to construct ecological pond using LID technique in order to create naturally comfortable community space in urban idle space. The specification of the ecological pond is $110m^2$ of surface area, $0.45{\pm}0.02m$ of average depth, and bed material is composed of gravel (diameter ${\leq}60mm$), sand (diameter ${\leq}2mm$) and bentonite. Rainfall and water depth monitoring were conducted to determine the annual characteristics of inflow of the water for the ecological pond, result of total rainfall was 1,287 mm and showed a seasonal imbalance that accounted for 71.3% (918 mm) during July to August, but the annual mean water depth was kept constant at $0.45{\pm}0.02m$ due to the secondary water source. Annual trends of basic water quality showed a significant changes according to the season, such as water temperature ($5.2{\sim}28.8^{\circ}C$), DO (5.0 ~ 13.8 mg/L), EC ($113{\sim}265{\mu}S/cm$). BOD, COD, TN, and TP in physicochemical water quality tended to increase after October, but the ion parameters such as $NH_3$ and $PO_4{^{3-}}$ were generally low. Phytoplankton indicators Chl-a and BGA (blue green algae) showed a sharp increase from July to August, and green algae (Selenastrum bibraianum, Pediastrum boryanum etc.) and filamentous blue green algae (Phormidium sp.) emerged as a dominant species. The ion parameters ($F^-$, $Na^+$, $K^+$, $Mg^{2+}$, $Ca^{2+}$) were strongly correlated with the $Cl^-$ as a conservative substance (R=0.70~0.97, p<0.05). Water quality was influenced by the ambient environment such as seasonal changes or rainfall, and it was closely related to fluctuation of the inflow of the water. In the future, it is necessary to consider ecological connections by referring to the characteristics surveyed in this study in order to effectively manage the water quality and biodiversity of the ecological pond in idle space.

• Korean Journal of Ecology and Environment
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• v.55 no.1
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• pp.60-75
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• 2022

The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.