• Journal of Environmental Impact Assessment
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• v.22 no.3
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• pp.203-217
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• 2013

This study looked at how to establish effluent limitation standards for formaldehyde, a toxic chemical widely used in industries. To this end, we reviewed Water Quality Based Effluent Limitation (WQBEL), Technology Based Effluent Limitation (TBEL), and water quality criteria for protection of human health and aquatic organism. Based on the results, we estimated formaldehyde effluent limitation standards appropriate to control water quality of industrial wastewater in Korea. However, this study has limits due to the lack of some data necessary in estimating formaldehyde effluent limitation. For example, although water quality criteria based on non-carcinogenic properties of formaldehyde were calculated, those based on carcinogenic properties were not be able to estimate because of the absence of applicable cancer potency factor q1. Without applicable factor, we calculated water quality standards for formaldehyde based on water quality criteria of advanced countries including the United States, while with no water quality standard we referred to applicable drinking water quality standards of other countries. For eco-toxicity based on water quality criteria, proper figures could not be calculated since there have been few reliable data.

• Journal of Environmental Health Sciences
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• v.41 no.2
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• pp.90-101
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• 2015

Objective: This study was conducted to develop a chemical oxygen demand (COD) regression model using water quality monitoring data (January, 2014) obtained from the Han River auto-monitoring stations. Methods: Surface water quality data at 198 sampling stations along the six major areas were assembled and analyzed to determine the spatial distribution and clustering of monitoring stations based on 18 WQPs and regression modeling using selected parameters. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR), cluster analysis (CA) and principal component analysis (PCA) were used to build a COD model using water quality data. Results: A best GA-MLR model facilitated computing the WQPs for a 5-descriptor COD model with satisfactory statistical results ($r^2=92.64$,$Q{^2}_{LOO}=91.45$,$Q{^2}_{Ext}=88.17$). This approach includes variable selection of the WQPs in order to find the most important factors affecting water quality. Additionally, ordination techniques like PCA and CA were used to classify monitoring stations. The biplot based on the first two principal components (PCs) of the PCA model identified three distinct groups of stations, but also differs with respect to the correlation with WQPs, which enables better interpretation of the water quality characteristics at particular stations as of January 2014. Conclusion: This data analysis procedure appears to provide an efficient means of modelling water quality by interpreting and defining its most essential variables, such as TOC and BOD. The water parameters selected in a COD model as most important in contributing to environmental health and water pollution can be utilized for the application of water quality management strategies. At present, the river is under threat of anthropogenic disturbances during festival periods, especially at upstream areas.

• Membrane and Water Treatment
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• v.10 no.1
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• pp.1-11
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• 2019

The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) predicted that recent extreme hydrological events would affect water quality and aggravate various forms of water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed and sunlight) were established using the Representative Concentration Pathways (RCP) 8.5 climate change scenario suggested by the AR5 and calculated the future runoff for each target period (Reference:1989-2015; I: 2016-2040; II: 2041-2070; and III: 2071-2099) using the semi-distributed land use-based runoff processes (SLURP) model. Meteorological factors that affect water quality (precipitation, temperature and runoff) were inputted into the multiple linear regression analysis (MLRA) and artificial neural network (ANN) models to analyze water quality data, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (T-N) and total phosphorus (T-P). Future water quality prediction of the Anseongcheon River basin shows that DO at Gongdo station in the river will drop by 35% in autumn by the end of the $21^{st}$ century and that BOD, COD and SS will increase by 36%, 20% and 42%, respectively. Analysis revealed that the oxygen demand at Dongyeongyo station will decrease by 17% in summer and BOD, COD and SS will increase by 30%, 12% and 17%, respectively. This study suggests that there is a need to continuously monitor the water quality of the Anseongcheon River basin for long-term management. A more reliable prediction of future water quality will be achieved if various social scenarios and climate data are taken into consideration.

• KCID journal
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• v.10 no.2
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• pp.24-35
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• 2003

Physical, chemical and biological water quality indexes of Wanggung agricultural reservoir in Iksan city Korea, were investigated and analyzed to diagnose and assess the water environment from 2000 to 2002. The mean concentrations of SS, COD and T-P satis

• Journal of Environmental Health Sciences
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• v.35 no.6
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• pp.517-525
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• 2009

The water quality of Lake Shihwa had been rapidly deteriorating since 1994 due to wastewater input from the watersheds, limited water circulation and the lack of a wastewater treatment policy. In 2000, the government decided to open the tidal embankment and make a comprehensive management plan to improve the water quality, especially inflowing stream water around Shihwa and Banwol industrial complex. However, the water quality and microbial community have not as yet been fully evaluated. The purpose of this study is to investigate the influent water quality around the industrial area based on chemical and biological analysis, and collected surface water sample from the Siheung Stream, up-stream to down-stream through the industrial complex, Samples were collected in July 2009. The results show that the downstream site near the industrial complex had higher concentrations of heavy metals (Cu, Mn, Fe, Mg, and Zn) and organic matter than upstream sites. A combination of DGGE (Denaturing Gradient Gel Electrophoresis) gels, lists of K-WQI (Korean Water Quality Index), cluster analysis, MDS (Multi-Dimensional Scaling) and PCA (Principal Component Analysis) has demonstrated clear clustering between Siheung stream 3 and 4 and with a high similarity and detected metal reducing bacteria (Shewanella spp.) and biodegrading bacteria (Acinetobacter spp.). These results suggest that use of both chemical and microbiological marker would be useful to fully evaluate the water quality.

• Journal of Environmental Impact Assessment
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• v.8 no.2
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• pp.95-107
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• 1999

The organic contents of reservoir sediment can offer a good indicator to events in history of lake life. Reservoir sediment have many information of the past and future fingerprint about development direction of life cycle in biological animals. So, in this study we made an effort to know the distribution of organic contents in Daecheong reservoir sediment. Items for this investigations are such as follows: Loss on ignition, COD, Organic carbon, TN, TP, heavy metal contents, Loss on ignitions were determined in 6.44~15.91% and COD were determined in 1.606~6.859%, organic carbon in 1,077~3.743%. Contents of TP and TN were in the range of 0.083~0.757%, and 0.645~0.926%, respectively.

• Korean Journal of Ecology and Environment
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• v.41 no.spc
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• pp.1-10
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• 2008

We patterned water quality of agricultural reservoirs according to the differences of six physico-chemical environmental factors (TN, TP, DO, BOD, COD, and SS) using four different ordination methods: Principal Components Analysis (PCA), Detrended Correspondence Analysis (DCA), Nonmetric Multidimensional Scaling (NMS), and Isometric Feature Mapping (Isomap). The data set was obtained from the water quality monitoring networks operated by the Ministry of Agriculture and Forestry and the Ministry of Environments. Chlorophyll-${\alpha}$ displayed the highest correlation with COD, followed by TP, BOD, SS, and TN (p<0.01), while negatively correlated with altitude and bank height of the reservoirs (p<0.01). Although four different ordination methods similarly patterned the reservoirs according to the gradient of nutrient concentration, PCA and NMS appeared to be the most efficient methods to pattern water quality of reservoirs based on the explanation power. Considering variable scores in the ordination map, the concentration of nutrients was positively correlated with Chl-${\alpha}$, while negatively correlated with altitude and bank height. These ordination methods may help to pattern agricultural reservoirs according to their water quality characteristics.

• Korean Journal of Environmental Agriculture
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• v.19 no.5
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• pp.382-393
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• 2000

The principal indicators contributing to water qualities was screened by factor analyses, based on the monitored chemical parameters of water quality for various water resources from 1995 to 1999 in the small agricultural catchments of the Han River Basin. Water samples of streams, groundwaters, and reservoirs were taken four times a year from upper (Daegwanryong), middle (Dunnae and Chunchon) and lower (Guri) reaches of Han River Basin. In these areas, the respective type of farming practiced was alpine agriculture and livestocks raising, typical upland and paddy cultivation, and intensive cropping in the plastic film house. Water quality was monitored for twenty-one water quality parameters, including pH, EC, SS, T-N, T-P, COD, cations, anions, and heavy metals. pH, EC and COD of the stream waters were suitable for the Korea irrigation water quality guidelines. However, T-N and T-P concentrations of water samples in four catchments far exceeded the irrigation water guideline. Concentrations of canons and heavy metals in Wangsuk stream in Guri area were higher than those in streams in other areas. Factor analysis revealed that significant correlation was observed for 81 pairs out of 231 water quality indicators of stream water among the $21\;{\times}\;21$ cross correlation matrix of stream water quality indicators. The first factor accounted for 27.01% of the total variation in stream water quality indicators, and high positive factor loadings were shown on EC, K, Na, $NH_4\;^+-N$, $PO_4\;^{3-}$, $SO_4\;^{2-}$, and COD. Fifty-three water quality indicator pairs were significant out of 190 ground water quality parameters. The first factor accounted for 28.54% of the total variation in ground water quality indicators, and high loadings were revealed on EC, Ca, Mg, K, Na, $NH_4\;^+-N$, and $SO_4$. Twenty-nine pairs of reservoir water quality indicators were significant out of 66 pairs. The first factor accounted for 37.06% of the total variation in reservoir water quality indicators, and high loadings were shown on EC, Mg, K, Na, SS, T-P, Cl, and COD. These results demonstrate that EC was the first factor contributing to water quality.

• Korean Journal of Environmental Agriculture
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• v.33 no.1
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• pp.44-51
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• 2014

BACKGROUND: Water quality of rural areas are susceptible to agricultural nutrient input and supply such as chemical fertilizer and livestock manure. This study was conducted to evaluate the usefulness of nutrient (N and P) indices in understanding spatial variations of water quality across Chonnam province which is a typical agricultural region in Korea. METHODS AND RESULTS: The nutrient indices including chemical fertilizer supply, livestock manure production, and nutrient balance were correlated with water quality data (T-N, T-P, BOD, and COD) for the twenty-two districts of the province. Concentration of T-N were positively correlated with chemical fertilizer supply, livestock manure N production, and nutrient balance (P<0.05 or P<0.01). Meanwhile, T-P concentration was not correlated with these nutrient indices; however, there was a tendency that T-P concentration increases with livestock manure P production (P=0.06) and with nutrient balance (P=0.09). These results suggest that T-N concentration is susceptible to both chemical fertilizer and livestock manure; whereas T-P is likely to be affected by livestock manure rather than chemical fertilizer. The concentrations of BOD and COD were also positively (P<0.05 or P<0.01) correlated with livestock manure production. CONCLUSION: This study shows the usefulness of nutrient indices in understanding spatial variations of water quality and suggests that livestock manure rather than chemical fertilizer can be a more critical water pollution source and thus highlights the need for more attention to livestock manure treatments for rural water quality management.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1194-1196
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• 2003

Variations in soil physical and chemical properties can affect agricultural productivity and the environmental implications of crop production. These variations are present and may be important at regional, field, and sub-field (precision agriculture) scales. Because traditional measurements are time-consuming and expensive, reflectance-based estimates of soil properties such as texture, organic matter content, water content, and nutrient status are attractive. Soil properties have been related to reflectance measured with laboratory, in-field, airborne, and satellite sensors. Both multispectral and hyperspectral instruments have been used, with both natural and artificial illumination. Varying levels of accuracy have been obtained, with the best results (r > 0.95) using hyperspectral reflectance data to estimate soil organic matter and water content.