Assessment of Water Quality Target Attainment and Influencing Factors Using the Multivariate Log-Linear Model in the Nakdong River Basin, Republic of Korea

Because identifying the factors affecting water quality is challenging, water quality assessment of an individual component based on the arithmetic mean method cannot adequately support management policies. Therefore, in this study, we assessed the water quality target attainment at 24 sites in the Nakdong River Basin by applying multivariate log-linear models to identify factors influencing water quality, including flow and season. The temporal and seasonal water quality trend and flow were also analyzed using the calculated model coefficients. Specifically, weekly data on biological oxygen demand (BOD), total phosphorous (TP), and flow during 2013–2018 were used to investigate the 2018 water quality target attainment level for this river. The significance and suitability of the models were analyzed using the F-test, root mean squared error (RMSE), mean absolute percent error (MAPE), and adjusted R2 values. All 24 models applied in this study showed statistical significance and suitability for the prediction of BOD and TP concentrations. Moreover, flow was identified as the main factor affecting water quality and had a predominant effect on BOD and TP concentrations in tributaries and the main stream, respectively. Furthermore, among the 24 sites, BOD and TP targets were evidently attained at 18 and 17 sites, respectively.

Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River

Process-based modeling for predicting harmful cyanobacteria is affected by a variety of factors, including the initial conditions, boundary conditions (tributary inflows and atmosphere), and mechanisms related to cyanobacteria growth and death. While the initial conditions do not significantly affect long-term predictions, the initial cyanobacterial distribution in water is particularly important for short-term predictions. Point-based observation data have typically been used for cyanobacteria prediction of initial conditions. These initial conditions are determined through the linear interpolation of point-based observation data and may differ from the actual cyanobacteria distribution. This study presents an optimal method of applying hyperspectral images to establish the Environmental Fluid Dynamics Code-National Institute of Environment Research (EFDC-NIER) model initial conditions. Utilizing hyperspectral images to determine the EFDC-NIER model initial conditions involves four steps that are performed sequentially and automated in MATLAB. The EFDC-NIER model is established using three grid resolution cases for the Changnyeong-Haman weir section of the Nakdong River Basin, where Microcystis dominates during the summer (July to September). The effects of grid resolution on (1) water quality modeling and (2) initial conditions determined using cumulative distribution functions are evaluated. Additionally, the differences in Microcystis values are compared when applying initial conditions using hyperspectral images and point-based evaluation data. Hyperspectral images allow detailed initial conditions to be applied in the EFDC-NIER model based on the plane-unit cyanobacterial information observed in grids, which can reduce uncertainties in water quality (cyanobacteria) modeling.

Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach

A Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) combined with a deep learning approach was created by combining CNN and LSTM networks simulated water quality including total nitrogen, total phosphorous, and total organic carbon. Water level and water quality data in the Nakdong river basin were collected from the Water Resources Management Information System (WAMIS) and the Real-Time Water Quality Information, respectively. The rainfall radar image and operation information of estuary barrage were also collected from the Korea Meteorological Administration. In this study, CNN was used to simulate the water level and LSTM used for water quality. The entire simulation period was 1 January 2016–16 November 2017 and divided into two parts: (1) calibration (1 January 2016–1 March 2017); and (2) validation (2 March 2017–16 November 2017). This study revealed that the performances of both of the CNN and LSTM models were in the “very good” range with above the Nash–Sutcliffe efficiency value of 0.75 and that those models well represented the temporal variations of the pollutants in Nakdong river basin (NRB). It is concluded that the proposed approach in this study can be useful to accurately simulate the water level and water quality.

Evaluation of Water Quality Interaction by Dam and Weir Operation Using SWAT in the Nakdong River Basin of South Korea

The purpose of this study was to evaluate the streamflow and water quality (SS, T-N, and T-P) interaction of the Nakdong river basin (23,609.3 km2) by simulating dam and weir operation scenarios using the Soil and Water Assessment Tool (SWAT). The operation scenarios tested were dam control (Scenario 1), dam control and weir gate control (Scenario 2), dam control and sequential release of the weirs with a one-month interval between each weir (Scenario 3), dam control and weir gate full open (Scenario 4), dam control and weir gate sequential full open (Scenario 5), weir gate control (Scenario 6), weir gate full open (Scenario 7), and weir gate sequential full open (Scenario 8). Before evaluation, the SWAT was calibrated and validated using 13 years (2005–2017) of daily multi-purpose dam inflow data from five locations ((Andong Dam (ADD), Imha Dam (IHD), Hapcheon Dam (HCD), Namkang Dam (NKD), and Milyang Dam (MYD))multi-function weir inflow data from seven locations (Sangju Weir (SJW), Gumi Weir (GMW), Chilgok Weir (CGW), Gangjeong-Goryeong Weir (GJW), Dalseong Weir (DSW), Hapcheon-Changnyeong Weir (HCW), and Changnyeong-Haman Weir (HAW)), and monthly water quality monitoring data from six locations (Andong-4 (AD-4), Sangju (SJ-2), Waegwan (WG), Hapcheon (HC), Namkang-4 (NK-4), and Mulgeum (MG). For the dam inflows and dam storage, the Nash-Sutcliffe efficiency (NSE) was 0.59~0.78, and the coefficient of determination (R2) was 0.71~0.90. For water quality, the R2 values of SS, T-N, and T-P were 0.58~0.83, 0.53~0.68, and 0.56~0.79, respectively. For the eight dam and weir release scenarios suggested by the Ministry of Environment, Scenarios 4 and 8 exhibited water quality improvement effects compared to the observed data.

Assessment of Nakdong River Basin Management: Target Water Quality Achievement and Future Challenges

In 2006, the Korean Ministry of Environment established <The 1st Water Environment Management Master Plan>. The plan aimed at “Clean Water, Eco River 2015” and guided water quality protection and strengthened water management. This study evaluated the achievement of the target water quality among the 33 mid-level basins in the Nakdong River basin and assessments of the causes of non-achievement of the target water quality by mid-level basins. According to the 2015 water quality data, only 16 of the 33 mid-level basins achieved the target water quality. The low achievement of the target water quality was attributed to the failure to predict the pollutant load at the time of planning, problems with the management of tributaries, implementation of the <Four major river restoration project>, and problems with the representativeness of the water quality representative points. In addition, feasibility studies on the water quality monitoring representative point used in each mid-level basin were also performed; some mid-level basins required improvement or change of the representative points. This study also suggested further research to improve water quality, such as detailed studies of the management of pollutant load, mainstream tributaries, and water quality indicators, for the revision of the current ongoing <The 2nd Water Environment Management Master Plan>.

A Study on River Representation for Water Impaired Analysis of Measurement Data at TPLMs

Objectives:This study assessed the applicability of the total pollutant load management (TPLM) data on the analysis of impaired waterbody and pollutant source characterization. To achieve the study objective, the TPLM data collected in the downstream of Yeongcheon-Dam within the Geumho A unit-basin, Nakdong River basin for 6 years (2013-2018) were used to analyze the accountability for precipitation conditions, seasons, and flow rates. Methods:The study area is the downstram of Yeongcheon Dam within the Geumho A unit-basin, Nakdong River basin. The TPLM data used in this study were obtained from the two monitoring stations in the mainstream of Geumho River (i.e., Jahocheon and Geumho A station) and the three tributary streams (i.e., Gochoncheon, Sillyeongcheon, and Bugancheon). Precipitation conditions were classified into rainy (>10 mm/day) and non-rainy days (≤10 mm/day) to assess the TPLM data accountability for the two precipitation conditions. Seasonal accountabilities of the TPLM data were assessed for spring (March-May), summer (June-August), autumn (September-November), and winter (December-February). The TPLM data accountability for stream flow rates was assessed using the standardized daily flow rates which were estimated by % of maximum flow rate. The daily flow rates were simulated using the Hydrologic Simulation Program-Fortran (HSPF).Results and Discussion:During the study period (2013-2018), TPLM data were collected 35-41 times throughout a year in the two Geumho River mainstream stations (i.e., Jahocheon, and Geumho A), while the tributary streams (i.e., Gochoncheon, Sillyeongcheon, and Bugancheon) were monitored 36 times per year excluding every January, February, and December. The tributary streams shown the missing data months because those stations were monitored through the implementation assessment for total maximum daily load (TMDL) management. But, the mainstream data were continuously collected by the Water Environment Research Institute. The accountability assessment results shown that 28%, 25%, 28%, and 19% of TPLM data for the two mainstream stations represents spring, summer, autumn, and winter seasons. The three TPLM station data in the tributary streams could not account for winter season with the seasonal data ratios of 29% for spring, 32% for summer, 34% for autumn, and 4% for winter. Secondly, 37.9% and 23.1% of TPLM data for the two mainstream stations and the three tributary streams, respectively, were collected during rainy days. Comparing to 22% of rainy days throughout the study period, the two mainstream stations were monitored more frequently during rainy days than the tributary stations. Lastly, accountability for stream flow rates shown that the annual TPLM data cannot account for mainstream flow conditions. For the tributary stream flow rates, six years composite TPLM data could not represent the flow conditions. Therefore, the TPLM data need to be carefully reviewed before assessing impaired waterbody and pollutant source characterization. Conclusions:TPLM data was widely used in TMDL implementation and research activities. Thus, detailed TPLM monitoring plans should be developed to improve accountability for various stream characteristics including precipitation, season, and flow rate, etc. In addition, further research is needed to represent wide ranges of flow rate which are hard to be predicted due to the large uncertainties induced by precipitation, precipitation intensity, precedent rain days, and other factors (e.g., dams, wastewater treatment plants, etc.).

Occurrence and Behavior of Residual Pharmaceuticals in the Nakdong River Basin

Objectives:This study was conducted to evaluate the detection characteristics of Nakdong River basin (wastewater treatment plant (WWTP) effluents, tributaries and main streams) by selecting 18 kinds of pharmaceuticals which were detected frequently. It was intended to be used as basic data for advanced treatment of WWTP and securing safe water source.Methods:The selected 18 pharmaceuticals investigated in this study were acetaminophen, atenolol, carbamazepine, caffeine, diclofenac, iopromide, lincomycin, metoprolol, primidone, propranolol, sulfachloropyridazine, sulfamethazine, sulfamethoxazole, tetracycline, trimethoprim, chlorotetracycline, oxytetracycline and sulfathiazole. The residual pharmaceuticals was analyzed LC-MS/MS equipped with on-line solid phase extraction (SPE) system and the limit of quantifications (LOQ) of 18 residual pharmaceuticals were 1~5 ng/L. Sampling points were 27 from the upstream to the downstream, including mainstream, tributary, and WWTP effluents. Sampling was conducted four times in Feb., Jun., Aug. and Nov. 2018.Results and Discussion:The most frequently detected residual pharmaceutical in all WWTP effluents, tributaries and mainstream sites was caffeine, followed by carbamazepine, lincomycin, sulfamethoxazole and iopromide. In addition, iopromide was detected as the highest mean concentration of 44.9~985.4 ng/L followed by caffeine (63.2~337.1 ng/L) and carbamazepine (13.8~145.0 ng/L), sulfamethoxazole (12.3~114.8 ng/L) and diclofenac (3.1~144.1 ng/L). In particular, the proportion ratio of caffeine gradually decreased as going to the downstream, whereas iopromide gradually increased. However, the concentrations of caffeine in the sampling sites of the downstream were higher than those of the upstream sites. The total concentrations of detected residual pharmaceuticals gradually increased from the upstream to the middle stream, showed the highest detected concentration at the Goryeong area in the middle stream of the Nakdong River, and then gradually decreased due to the dilution effect. Residual pharmaceuticals in the Nakdong River basin, which could not be completely removed from the WWTPs, entered into the water system and contaminated source water of the downstream. This process is a typical contaminant pattern of the source water by the emerging micropollutants.Conclusions:The most frequently detected residual pharmaceutical in all WWTP effluents, tributaries and mainstream sites was caffeine, followed by carbamazepine, lincomycin, sulfamethoxazole and iopromide. In addition, iopromide was detected as 44.9~985.4 ng/L, showing the highest mean concentration at the sampling points, and caffeine (63.2~337.1 ng/L) and carbamazepine (13.8~145.0 ng/L), sulfamethoxazole (12.3~114.8 ng/L) and diclofenac (3.1~144.1 ng/L) in order. The total concentrations of detected residual pharmaceuticals gradually increased from the upstream to the middle stream, showed the highest concentration at the Goryeong area in the middle stream of the Nakdong River basin, and then gradually decreased due to the dilution effect.