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This study introduces a semi-empirical algorithm to estimate the extent of the phycocyanin (PC) concentration in eutrophic freshwater bodies; this is achieved by studying the reflectance characteristics of the red and near-red spectral regions, especially the shifting of the peak near 700 nm towards longer wavelengths. Spectral measurements in a darkroom environment over the pure-cultured cyanobacteria Microcystis showed that the shift is proportional to the algal biomass. A similar proportional trend was found from extensive field measurement data. The data also showed that the correlation of the magnitude of the shift with the PC concentration was greater than that with chlorophyll-a. This indicates that the characteristic can be a useful index to quantify cyanobacterial biomass. Based on these observations, a new PC algorithm was proposed that uses the remote sensing reflectance of the peak band around 700 nm and the trough band around 620 nm, and the magnitude of the peak shift near 700 nm. The efficacy of the algorithm was tested with 300 sets of field data, and the results were compared to select algorithms for the PC concentration prediction. The new algorithm performed better than the other algorithms with respect to most error indices, especially the mean relative error, indicating that the algorithm can reduce errors when PC concentrations are low. The algorithm was also applied to a hyperspectral dataset obtained through aerial imaging, in order to predict the spatial distribution of the PC concentration in an approximately 86 km long reach of the Nakdong River.
Gibeom Nam; Hyunjoo Shin; Rim Ha; Hyunoh Song; Jaehyun Yoo; Hyuk Lee; Sanghyun Park; Taegu Kang; Kyunghyun Kim. Quantification of Phycocyanin in Inland Waters through Remote Measurement of Ratios and Shifts in Reflection Spectral Peaks. Remote Sensing 2021, 13, 3335 .
AMA StyleGibeom Nam, Hyunjoo Shin, Rim Ha, Hyunoh Song, Jaehyun Yoo, Hyuk Lee, Sanghyun Park, Taegu Kang, Kyunghyun Kim. Quantification of Phycocyanin in Inland Waters through Remote Measurement of Ratios and Shifts in Reflection Spectral Peaks. Remote Sensing. 2021; 13 (16):3335.
Chicago/Turabian StyleGibeom Nam; Hyunjoo Shin; Rim Ha; Hyunoh Song; Jaehyun Yoo; Hyuk Lee; Sanghyun Park; Taegu Kang; Kyunghyun Kim. 2021. "Quantification of Phycocyanin in Inland Waters through Remote Measurement of Ratios and Shifts in Reflection Spectral Peaks." Remote Sensing 13, no. 16: 3335.
Owing to urbanization, impervious areas within watersheds have continuously increased, distorting healthy water circulation systems by reducing soil infiltration and base flow; moreover, increases in surface runoff deteriorate water quality by increasing the inflow of nonpoint sources. In this study, we constructed a Hydrological Simulation Program—Fortran (HSPF) watershed model that applies the impervious area and can set medium- and long-term water circulation management goals for watershed sub-areas. The model was tested using a case study from the Yeongsan River watershed, Korea. The results show that impervious land-cover accounts for 18.47% of the upstream reach in which Gwangju City is located; approximately twice the average for the whole watershed. Depending on the impervious area reduction scenario, direct runoff and nonpoint source load could be reduced by up to 56% and 35%, respectively; the water circulation rate could be improved by up to 16%. Selecting management goals requires the consideration of both policy objectives and budget. For urban areas with large impervious cover, the designation of nonpoint source management areas is required. For new cities, it is necessary to introduce water circulation systems (e.g., low impact development techniques) to improve rainwater penetration and recharge and activate preemptive water circulation.
Jong Lee; Minji Park; Bae Park; Jiyeon Choi; Jinsun Kim; Kyunghyun Kim; Yongseok Kim. Evaluation of Water Circulation by Modeling: An Example of Nonpoint Source Management in the Yeongsan River Watershed. Sustainability 2021, 13, 8871 .
AMA StyleJong Lee, Minji Park, Bae Park, Jiyeon Choi, Jinsun Kim, Kyunghyun Kim, Yongseok Kim. Evaluation of Water Circulation by Modeling: An Example of Nonpoint Source Management in the Yeongsan River Watershed. Sustainability. 2021; 13 (16):8871.
Chicago/Turabian StyleJong Lee; Minji Park; Bae Park; Jiyeon Choi; Jinsun Kim; Kyunghyun Kim; Yongseok Kim. 2021. "Evaluation of Water Circulation by Modeling: An Example of Nonpoint Source Management in the Yeongsan River Watershed." Sustainability 13, no. 16: 8871.
This study aimed to estimate pollutant unit loads for different landuses and pollutants that reflected long-term runoff characteristics of nonpoint source (NPS) pollutants and recent environmental changes. During 2008–2014, 2026 rainfall events were monitored. The average values of antecedent dry days, total rainfall, rainfall intensity, rainfall duration, runoff duration, and runoff coefficient for each landuse were 3.8–5.9 d, 35.2–65.0 mm, 2.9–4.1 mm/h, 12.5–20.4 h, 12.4–27.9 h, and 0.24–0.45, respectively. Uplands (UL) exhibited high suspended solids (SS, 606.2 mg/L), total nitrogen (TN, 7.38 mg/L), and total phosphorous (TP, 2.27 mg/L) levels, whereas the runoff coefficient was high in the building sites (BS), with a high impervious surface ratio. The event mean concentration (EMC) for biological oxygen demand (BOD) was the highest in BS (8.0 mg/L), while the EMC was the highest in BS (in the rainfall range 50 mm). The unit loads for BOD (1.49–17.76 kg/km2·d), TN (1.462–10.147 kg/km2·d), TP (0.094–1.435 kg/km2·d), and SS (15.20–327.70 kg/km2·d) were calculated. The findings can be used to manage NPS pollutants and watershed environments and implement relevant associated management systems.
Jiyeon Choi; Baekyung Park; Jinsun Kim; Soyoung Lee; Jichul Ryu; Kyunghyun Kim; Yongseok Kim. Determination of NPS Pollutant Unit Loads from Different Landuses. Sustainability 2021, 13, 7193 .
AMA StyleJiyeon Choi, Baekyung Park, Jinsun Kim, Soyoung Lee, Jichul Ryu, Kyunghyun Kim, Yongseok Kim. Determination of NPS Pollutant Unit Loads from Different Landuses. Sustainability. 2021; 13 (13):7193.
Chicago/Turabian StyleJiyeon Choi; Baekyung Park; Jinsun Kim; Soyoung Lee; Jichul Ryu; Kyunghyun Kim; Yongseok Kim. 2021. "Determination of NPS Pollutant Unit Loads from Different Landuses." Sustainability 13, no. 13: 7193.
To determine the high-priority tributaries that require water quality improvement in the Nakdong River, which is an important drinking water resource for southeastern Korea, data collected at 28 tributaries between 2013 and 2017 were analyzed. To analyze the water quality characteristics of the tributary streams, principal component analysis and factor analysis were performed. COD (chemical oxygen demand), TOC (total organic carbon), TP (total phosphorus), SS (suspended solids), and BOD (biochemical oxygen demand) were classified as the primary factors. In the self-organizing maps analysis using the unsupervised learning neural network model, the first factor showed a highly relevant pattern. To perform the grade classification, 11 parameters were selected. Six parameters are concentrations of the main parameters for the water quality standard assessment in South Korea. We added the pollution load densities for the selected five primary factors. Joochungang showed the highest pollution load density despite its small watershed area. According to the results of the grade classification method, Joochungang, Topyeongcheon, Hwapocheon, Chacheon, Gwangyeocheon, and Geumhogang were selected as tributaries requiring high-priority water quality management measures. From this study, it was concluded that neural network models and grade classification methods could be utilized to identify the high-priority tributaries for more directed and effective water quality management.
Kang-Young Jung; Sohyun Cho; Seong-Yun Hwang; Yeongjae Lee; Kyunghyun Kim; Eun Na. Identification of High-Priority Tributaries for Water Quality Management in Nakdong River Using Neural Networks and Grade Classification. Sustainability 2020, 12, 9149 .
AMA StyleKang-Young Jung, Sohyun Cho, Seong-Yun Hwang, Yeongjae Lee, Kyunghyun Kim, Eun Na. Identification of High-Priority Tributaries for Water Quality Management in Nakdong River Using Neural Networks and Grade Classification. Sustainability. 2020; 12 (21):9149.
Chicago/Turabian StyleKang-Young Jung; Sohyun Cho; Seong-Yun Hwang; Yeongjae Lee; Kyunghyun Kim; Eun Na. 2020. "Identification of High-Priority Tributaries for Water Quality Management in Nakdong River Using Neural Networks and Grade Classification." Sustainability 12, no. 21: 9149.
In this study, we propose the application of struvite precipitation for the sustainable recovery of nitrogen (N) and phosphorus (P) from anaerobic digestion (AD) effluents derived from swine manure. The optimal conditions for four major factors that affect the recovery of N and P were derived by conducting batch experiments on AD effluents obtained from four AD facilities. The optimal conditions were a pH of 10.0, NH4-N:Mg:PO4-P molar ratio of 1:1.4:1, mixing intensity of 240 s−1, and mixing duration of 2 min. Under these optimal conditions, the removal efficiencies of NH4-N and PO4-P were approximately 74% and 83%, respectively, whereas those of Cu and Zn were approximately 74% and 79%, respectively. Herein, a model for swine manure treatment that incorporates AD, struvite precipitation, and biological treatment processes is proposed. We applied this model to 85 public biological treatment facilities in South Korea and recovered 4722 and 51 tons/yr of NH4-N and PO4-P, respectively. The economic analysis of the proposed model’s performance predicts a lack of profitability due to the high cost of chemicals; however, this analysis does not consider the resulting protection of the hydrological environment. Field-scale studies should be conducted in future to prove the effectiveness of the model.
Hong-Duck Ryu; Do Lim; Sun-Jung Kim; Un-Il Baek; Eu Chung; Kyunghyun Kim; Jae Lee. Struvite Precipitation for Sustainable Recovery of Nitrogen and Phosphorus from Anaerobic Digestion Effluents of Swine Manure. Sustainability 2020, 12, 8574 .
AMA StyleHong-Duck Ryu, Do Lim, Sun-Jung Kim, Un-Il Baek, Eu Chung, Kyunghyun Kim, Jae Lee. Struvite Precipitation for Sustainable Recovery of Nitrogen and Phosphorus from Anaerobic Digestion Effluents of Swine Manure. Sustainability. 2020; 12 (20):8574.
Chicago/Turabian StyleHong-Duck Ryu; Do Lim; Sun-Jung Kim; Un-Il Baek; Eu Chung; Kyunghyun Kim; Jae Lee. 2020. "Struvite Precipitation for Sustainable Recovery of Nitrogen and Phosphorus from Anaerobic Digestion Effluents of Swine Manure." Sustainability 12, no. 20: 8574.
Machine learning modeling techniques have emerged as a potential means for predicting algal blooms. In this study, synthetic spatio-temporal water quality data for a river section were generated with a 3D water quality model and used to investigate the capability of a convolutional neural network (CNN) for predicting harmful cyanobacterial blooms. The CNN model displayed a reasonable capacity for short-term predictions of cyanobacteria (Microcystis) biomass. In the nowcasting of Microcystis, the CNN performance had a Nash-Sutcliffe Efficiency (NSE) of 0.87. An increase in the forecast lead time resulted in a decrease in the prediction accuracy, reducing the NSE from 0.87 to 0.58. As the spatial observation density increased from 20% to 100% of the input image grids, the CNN prediction NSE had improved from 0.70 to 0.84. Adding noise to the data resulted in accuracy deterioration, but even at the noise amplitude of 10%, the accuracy was acceptable for some applications, with NSE = 0.76. Visualization of the CNN results characterized its performance variations across the studied river reach. Overall, this study successfully demonstrated the capability of the CNN model for cyanobacterial bloom prediction using high temporal frequency images.
Jongcheol Pyo; Lan Joo Park; Yakov Pachepsky; Sang-Soo Baek; Kyunghyun Kim; Kyung Hwa Cho. Using convolutional neural network for predicting cyanobacteria concentrations in river water. Water Research 2020, 186, 116349 .
AMA StyleJongcheol Pyo, Lan Joo Park, Yakov Pachepsky, Sang-Soo Baek, Kyunghyun Kim, Kyung Hwa Cho. Using convolutional neural network for predicting cyanobacteria concentrations in river water. Water Research. 2020; 186 ():116349.
Chicago/Turabian StyleJongcheol Pyo; Lan Joo Park; Yakov Pachepsky; Sang-Soo Baek; Kyunghyun Kim; Kyung Hwa Cho. 2020. "Using convolutional neural network for predicting cyanobacteria concentrations in river water." Water Research 186, no. : 116349.
The present study was designed to identify recently (or rarely) recognized or unreported substances (RRS or URS) contained in the effluents from water treatment plants in two industrialized urban areas, Gumi and Daegu, in Korea. In addition to 30 initial targets, 72 substances were identified through suspect and non-target screening (SNTS). Among them were 4 RRSs and 22 URSs, respectively. The quantitative analyses were applied to 35 pharmaceuticals, 15 pesticides, 13 poly-/perfluorinated alkyl substances (PFASs), 2 organophosphate flame retardants (OPFRs), 2 corrosion inhibitors, and 3 metabolites. The highest average concentration was observed for benzotriazole, followed by those for niflumic acid, and metformin. Effluents from Gumi mainly contained benzotriazole and metformin whereas niflumic acid and tramadol were the major components in effluents from Daegu. According to a scoring system based on risk relevant parameters, higher priorities were given to telmisartan, PFOA, and cimetidine. Yet, priorities for some substances were area specific (e.g., benzotriazole from Gumi, PFASs from Daegu), reflecting differences in industry profiles and populations. Many of the RRSs and URSs were recognized as potential hazards. The new identifications and evaluations should be taken into consideration for constant monitoring and management, as do the previously recognized contaminants.
Younghun Choi; Ji-Ho Lee; Kyunghyun Kim; Hyunsaing Mun; Naree Park; Junho Jeon. Identification, quantification, and prioritization of new emerging pollutants in domestic and industrial effluents, Korea: Application of LC-HRMS based suspect and non-target screening. Journal of Hazardous Materials 2020, 402, 123706 .
AMA StyleYounghun Choi, Ji-Ho Lee, Kyunghyun Kim, Hyunsaing Mun, Naree Park, Junho Jeon. Identification, quantification, and prioritization of new emerging pollutants in domestic and industrial effluents, Korea: Application of LC-HRMS based suspect and non-target screening. Journal of Hazardous Materials. 2020; 402 ():123706.
Chicago/Turabian StyleYounghun Choi; Ji-Ho Lee; Kyunghyun Kim; Hyunsaing Mun; Naree Park; Junho Jeon. 2020. "Identification, quantification, and prioritization of new emerging pollutants in domestic and industrial effluents, Korea: Application of LC-HRMS based suspect and non-target screening." Journal of Hazardous Materials 402, no. : 123706.
Despite the implementation of intensive phosphorus reduction measures, periodic outbreaks of cyanobacterial blooms in large rivers remain a problem in Korea, raising the need for more effective solutions to reduce their occurrence. This study sought to evaluate whether phosphorus or nitrogen limitation is an effective approach to control cyanobacterial (Microcystis) blooms in river conditions that favor this non-nitrogen-fixing genus. These conditions include nutrient enrichment, high water temperature, and thermal stratification during summer. Mesocosm bioassays were conducted to investigate the limiting factors for cyanobacterial blooms in a river reach where severe Microcystis blooms occur annually. We evaluated the effect of five different nitrogen (3, 6, 9, 12, and 15 mg/L) and phosphorus (0.01, 0.02, 0.05, 0.1, and 0.2 mg/L) concentrations on algae growth. The results indicate that nitrogen treatments stimulated cyanobacteria (mostly Microcystis aeruginosa) more than phosphorus. Interestingly, phosphorus additions did not stimulate cyanobacteria, although it did stimulate Chlorophyceae and Bacillariophyceae. We conclude that phosphorus reduction might have suppressed the growth of Chlorophyceae and Bacillariophyceae more than that of cyanobacteria; therefore, nitrogen or at least both nitrogen and phosphorus control appears more effective than phosphorus reductions alone for reducing cyanobacteria in river conditions that are favorable for non-nitrogen-fixing genera.
Kyunghyun Kim; Hyunsaing Mun; Hyunjoo Shin; Sanghyun Park; Chungseok Yu; Jaehak Lee; Yumi Yoon; Hyeonsu Chung; Hyeonjeong Yun; Kyunglak Lee; GeonHee Jeong; Jin-A Oh; Injung Lee; Haejin Lee; Taewoo Kang; Hui Seong Ryu; Jonghwan Park; Yuna Shin; Doughee Rhew. Nitrogen Stimulates Microcystis-Dominated Blooms More than Phosphorus in River Conditions That Favor Non-Nitrogen-Fixing Genera. Environmental Science & Technology 2020, 54, 7185 -7193.
AMA StyleKyunghyun Kim, Hyunsaing Mun, Hyunjoo Shin, Sanghyun Park, Chungseok Yu, Jaehak Lee, Yumi Yoon, Hyeonsu Chung, Hyeonjeong Yun, Kyunglak Lee, GeonHee Jeong, Jin-A Oh, Injung Lee, Haejin Lee, Taewoo Kang, Hui Seong Ryu, Jonghwan Park, Yuna Shin, Doughee Rhew. Nitrogen Stimulates Microcystis-Dominated Blooms More than Phosphorus in River Conditions That Favor Non-Nitrogen-Fixing Genera. Environmental Science & Technology. 2020; 54 (12):7185-7193.
Chicago/Turabian StyleKyunghyun Kim; Hyunsaing Mun; Hyunjoo Shin; Sanghyun Park; Chungseok Yu; Jaehak Lee; Yumi Yoon; Hyeonsu Chung; Hyeonjeong Yun; Kyunglak Lee; GeonHee Jeong; Jin-A Oh; Injung Lee; Haejin Lee; Taewoo Kang; Hui Seong Ryu; Jonghwan Park; Yuna Shin; Doughee Rhew. 2020. "Nitrogen Stimulates Microcystis-Dominated Blooms More than Phosphorus in River Conditions That Favor Non-Nitrogen-Fixing Genera." Environmental Science & Technology 54, no. 12: 7185-7193.
Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash–Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application.
Jongcheol Pyo; Hongtao Duan; Mayzonee Ligaray; Minjeong Kim; Sangsoo Baek; Yong Sung Kwon; Hyuk Lee; Taegu Kang; Kyunghyun Kim; Yoonkyung Cha; Kyung Hwa Cho. An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery. Remote Sensing 2020, 12, 1073 .
AMA StyleJongcheol Pyo, Hongtao Duan, Mayzonee Ligaray, Minjeong Kim, Sangsoo Baek, Yong Sung Kwon, Hyuk Lee, Taegu Kang, Kyunghyun Kim, Yoonkyung Cha, Kyung Hwa Cho. An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery. Remote Sensing. 2020; 12 (7):1073.
Chicago/Turabian StyleJongcheol Pyo; Hongtao Duan; Mayzonee Ligaray; Minjeong Kim; Sangsoo Baek; Yong Sung Kwon; Hyuk Lee; Taegu Kang; Kyunghyun Kim; Yoonkyung Cha; Kyung Hwa Cho. 2020. "An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery." Remote Sensing 12, no. 7: 1073.
Data assimilation in complex water quality modeling is inevitably multivariate because several water quality variables interact and correlate. In ensemble Kalman filter applications, determining which variables to include and the structure of the relationships among these variables is important to achieve accurate forecast results. In this study, various analysis methods with different combinations of variables and interaction structures were evaluated under two different simulation conditions: synthetic and real. In the former, a synthetic experimental setting was formulated to ensure that issues, including incorrect model error assumption problem, spurious correlation between variables, and observational data inconsistency, would not distort the analysis results. The latter did not have such considerations. Therefore, this process could demonstrate the undistorted effects of the different analysis methods on the assimilated outputs and how these effects might diminish in real applications. Under synthetic conditions, updating a single active variable was found to improve the accuracy of the other active variables, and updating multiple active variables in a multivariate manner mutually enhanced the accuracy of the variables if proper ensemble covariance and observation data consistency were ensured. The results of the real case indicated a weakened mutual enhancement effect, and the methods in which variable localization were applied yielded the best analysis results. However, the multivariate analysis methods produced more accurate forecasting results, indicating that these methods could be superior. Therefore, it is suggested that multivariate analysis methods be considered first for water quality modeling, and the application of variable localization should be considered if significant spurious correlations and data inconsistency are present.
Sanghyun Park; Kyunghyun Kim; Changmin Shin; Joong-Hyuk Min; Eun Hye Na; Lan Joo Park. Variable update strategy to improve water quality forecast accuracy in multivariate data assimilation using the ensemble Kalman filter. Water Research 2020, 176, 115711 .
AMA StyleSanghyun Park, Kyunghyun Kim, Changmin Shin, Joong-Hyuk Min, Eun Hye Na, Lan Joo Park. Variable update strategy to improve water quality forecast accuracy in multivariate data assimilation using the ensemble Kalman filter. Water Research. 2020; 176 ():115711.
Chicago/Turabian StyleSanghyun Park; Kyunghyun Kim; Changmin Shin; Joong-Hyuk Min; Eun Hye Na; Lan Joo Park. 2020. "Variable update strategy to improve water quality forecast accuracy in multivariate data assimilation using the ensemble Kalman filter." Water Research 176, no. : 115711.
In this study, we investigated the activity concentrations of natural (226Ra, 232Th, 40K) and artificial (137Cs) radionuclides of soils in Jeju Island, and radiation hazard indices arising from the activity concentrations. The activity concentrations of 232Th and 40K based on soil color were in the order of black volcanic ash soils (BVAS) < very dark brown volcanic ash soils (VDBAS) < dark brown soils, but 137Cs was highest in BVAS, and 226Ra was lowest in VDBAS. The radiation hazard indices calculated from the activity concentrations were negligible. In terms of annual outdoor effective dose rate (AEDRout), the contribution of radionuclides to the soils was 137Cs (5.1%) < 40K (24.7%) < 226Ra (29.0%) < 232Th (41.3%), i.e., dominated by natural radionuclides, but contributions were dependent on soil properties.
Tae-Woo Kang; Won-Pyo Park; Young-Un Han; Ki Moon Bong; Kyunghyun Kim. Natural and artificial radioactivity in volcanic ash soils of Jeju Island, Republic of Korea, and assessment of the radiation hazards: importance of soil properties. Journal of Radioanalytical and Nuclear Chemistry 2020, 323, 1113 -1124.
AMA StyleTae-Woo Kang, Won-Pyo Park, Young-Un Han, Ki Moon Bong, Kyunghyun Kim. Natural and artificial radioactivity in volcanic ash soils of Jeju Island, Republic of Korea, and assessment of the radiation hazards: importance of soil properties. Journal of Radioanalytical and Nuclear Chemistry. 2020; 323 (3):1113-1124.
Chicago/Turabian StyleTae-Woo Kang; Won-Pyo Park; Young-Un Han; Ki Moon Bong; Kyunghyun Kim. 2020. "Natural and artificial radioactivity in volcanic ash soils of Jeju Island, Republic of Korea, and assessment of the radiation hazards: importance of soil properties." Journal of Radioanalytical and Nuclear Chemistry 323, no. 3: 1113-1124.
River water quality is one of the main challenges that societies face during the 21st century. Accurate and reliable real-time prediction of water quality is an effective adaptation measure to counteract water quality issues such as accidental spill and harmful algae blooms. To improve accuracy and skill of water quality forecasts along the Yeongsan River in South Korea three different ensemble data assimilation (DA) methods have been investigated: the traditional Ensemble Kalman Filter (EnKF) and two related algorithms (Dud-EnKF and EnKF-GS) that offer either possibilities to improve initial conditions for non-linear models or reduce computation time (important for real-time forecasting) by using a (smaller) time-lagged ensemble to estimate the Kalman gain. Twin experiments, assimilating synthetic observations of three algae species and phosphate concentrations, with relatively small ensemble sizes showed that all three DA methods improved forecast accuracy and skill with only subtle difference between the methods. They all improved the model accuracy at downstream locations with very similar performances but due to spurious correlation, the accuracy at upstream locations was somewhat deteriorated. The experiments also showed no clear trend of improvement by increasing the ensemble size from 8 to 64. The real world experiments, assimilating real observations of three algae species and phosphate concentrations, showed that less improvement was achieved compared to the twin experiments. Further improvement of the model accuracy may be achieved with different state variable definitions, use of different perturbation and error modelling settings and/or better calibration of the deterministic water quality model.
Sibren Loos; Chang Min Shin; Julius Sumihar; Kyunghyun Kim; Jaegab Cho; Albrecht H. Weerts. Ensemble data assimilation methods for improving river water quality forecasting accuracy. Water Research 2019, 171, 115343 .
AMA StyleSibren Loos, Chang Min Shin, Julius Sumihar, Kyunghyun Kim, Jaegab Cho, Albrecht H. Weerts. Ensemble data assimilation methods for improving river water quality forecasting accuracy. Water Research. 2019; 171 ():115343.
Chicago/Turabian StyleSibren Loos; Chang Min Shin; Julius Sumihar; Kyunghyun Kim; Jaegab Cho; Albrecht H. Weerts. 2019. "Ensemble data assimilation methods for improving river water quality forecasting accuracy." Water Research 171, no. : 115343.
This study examined the distribution of pharmaceuticals in Yeongsan River and at point sources (PSs) in the associated water system, and performed a risk assessment based on our findings. The samples included effluents collected from three sewage treatment plants (PS1, PS2, and PS3) and two industrial complexes (PS4 and PS5) as well as surface water collected from seven mainstreams and 11 tributaries of the river. The target pharmaceuticals were acetylsalicylic acid, carbamazepine, clarithromycin, naproxen, sulfamethazine, sulfamethoxazole, sulfathiazole, and trimethoprim, which were detected by liquid chromatography–tandem mass spectrometry. All pharmaceuticals except acetylsalicylic acid and sulfathiazole were found in PS1, PS2, and PS3 samples, whereas acetylsalicylic acid, carbamazepine, sulfamethazine, and sulfamethoxazole were found in PS4, most of the pharmaceuticals were not present in PS5. The rank order of pharmaceutical concentration in surface water was carbamazepine (97.2%, 0.2067 μg/L) > sulfamethoxazole (88.9%, 0.1132 μg/L) > naproxen (51.4%, 0.0516 μg/L) > clarithromycin (43.1%, 0.0427 μg/L). The distribution of pharmaceuticals in the Yeongsan River at PSs and non-PSs differed, and higher concentrations of human pharmaceuticals were detected in upstream and midstream areas whereas higher concentrations of animal pharmaceuticals were found downstream. Hazard quotients (HQs) evaluated at each sites based on mean concentration and 95% upper confidence limits (95% UCLs) were all less than one, indicating a low risk of toxicity. The findings of this study are expected to be useful for risk assessment of aquatic ecosystems.
Tae Woong Na; Tae-Woo Kang; Kyoung-Hee Lee; Soon-Hong Hwang; Hee-Jung Jung; Kyunghyun Kim. Distribution and ecological risk of pharmaceuticals in surface water of the Yeongsan river, Republic of Korea. Ecotoxicology and Environmental Safety 2019, 181, 180 -186.
AMA StyleTae Woong Na, Tae-Woo Kang, Kyoung-Hee Lee, Soon-Hong Hwang, Hee-Jung Jung, Kyunghyun Kim. Distribution and ecological risk of pharmaceuticals in surface water of the Yeongsan river, Republic of Korea. Ecotoxicology and Environmental Safety. 2019; 181 ():180-186.
Chicago/Turabian StyleTae Woong Na; Tae-Woo Kang; Kyoung-Hee Lee; Soon-Hong Hwang; Hee-Jung Jung; Kyunghyun Kim. 2019. "Distribution and ecological risk of pharmaceuticals in surface water of the Yeongsan river, Republic of Korea." Ecotoxicology and Environmental Safety 181, no. : 180-186.
Hyperspectral imagery (HSI) provides substantial information on optical features of water bodies that is usually applicable to water quality monitoring. However, it generates considerable uncertainties in assessments of spatial and temporal variation in water quality. Thus, this study explored the influence of different optical methods on the spatial distribution and concentration of phycocyanin (PC), chlorophyll-a (Chl-a), and total suspended solids (TSSs) and evaluated the dependence of algal distribution on flow velocity. Four ground-based and airborne monitoring campaigns were conducted to measure water surface reflectance. The actual concentrations of PC, Chl-a, and TSSs were also determined, while four bio-optical algorithms were calibrated to estimate the PC and Chl-a concentrations. Artificial neural network atmospheric correction achieved Nash-Sutcliffe Efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively. Moderate resolution atmospheric transmission 6 (MODTRAN 6) showed an NSE value >0.8; whereas, atmospheric and topographic correction 4 (ATCOR 4) yielded a negative NSE value. The MODTRAN 6 correction led to the highest R2 values and lowest root mean square error values for all algorithms in terms of PC and Chl-a. The PC:Chl-a distribution generated using HSI proved to be negatively dependent on flow velocity (p-value = 0.003) and successfully indicated cyanobacteria risk regions in the study area.
Jong Cheol Pyo; Mayzonee Ligaray; Yong Sung Kwon; Myoung-Hwan Ahn; Kyunghyun Kim; Hyuk Lee; Taegu Kang; Seong Been Cho; Yongeun Park; Kyung Hwa Cho. High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery. Remote Sensing 2018, 10, 1180 .
AMA StyleJong Cheol Pyo, Mayzonee Ligaray, Yong Sung Kwon, Myoung-Hwan Ahn, Kyunghyun Kim, Hyuk Lee, Taegu Kang, Seong Been Cho, Yongeun Park, Kyung Hwa Cho. High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery. Remote Sensing. 2018; 10 (8):1180.
Chicago/Turabian StyleJong Cheol Pyo; Mayzonee Ligaray; Yong Sung Kwon; Myoung-Hwan Ahn; Kyunghyun Kim; Hyuk Lee; Taegu Kang; Seong Been Cho; Yongeun Park; Kyung Hwa Cho. 2018. "High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery." Remote Sensing 10, no. 8: 1180.