• Membrane and Water Treatment
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• v.10 no.1
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• pp.99-104
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• 2019

In Korea, nonpoint pollutants have a significant effect on rivers' water quality, and they are discharged in very different ways depending on rainfall events. Therefore, preparing an optimal countermeasure against nonpoint pollutants requires much monitoring. The present study was conducted to help prepare a method for installing an automatic nonpoint pollutant measurement system for the cost-effective monitoring of the effect of nonpoint pollutants on rivers. In the present study, monitoring was performed at six sites of a river passing through an urban area with a basin area of $454.3km^2$. The results showed that monitoring could be performed for a relatively long time interval in the upstream and downstream regions, which are mainly comprised of forests, regardless of the rainfall amount. On the contrary, in the urban region, the monitoring had to be performed at a relatively short time interval each time when the rainfall intensity changed. This was because the flow rate was significantly dependent on the rainfall's intensity. The appropriate sites for installing an automatic measurement system were found to be a site before entering the urban region, a site after passing through the urban region, and the end of a river where the effects of nonpoint pollutant sources can be well-decided. The analysis also showed that the monitoring time should be longer for the rainfall events of a higher rainfall class and for the sites closer to the river end. This is because the rainfall runoff has a longer effect on the river. However, the effect of nonpoint pollutant sources was not significantly different between the upstream and the downstream in the cases of rainfall events over 100 mm.

• KCID journal
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• v.10 no.2
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• pp.118-122
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• 2003

• KCID journal
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• v.9 no.2
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• pp.110-118
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• 2002

• Journal of Korean Society of Water and Wastewater
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• v.20 no.4
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• pp.573-584
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• 2006

This study was carried out to investigate the causes of fish kills in the Yudeung Stream in Daejeon, Korea using literature reviews, governmental and our water quality monitoring data of the study site, rainfall data, intensive water quality monitoring during rainfall events, sediment pollutant contents and laboratory bioassay tests. Fish kill in urban streams can be caused by combined effect of reduction in dissolved oxygen concentration, increase in toxic material or increase in turbidity in waterbody due to introduction of surface runoff or effluent of combined sewer overflows after rainfall from the watershed areas. Despite of extensive and intensive field surveys and laboratory tests, it was found that those conventional methods have limitations to identify causes of fish kills in urban streams. It would be necessary to use dynamic water quality modeling to predetermine the range and level of water pollution in the stream and automatic water quality monitoring system that can collect water samples and detect water quality continuously.

• Journal of Korean Society on Water Environment
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• v.26 no.6
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• pp.994-999
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• 2010

The toxin of Cyanobacteria (blue-green algae) during summer season has been a problem and early prevention should be considered. A variety of methods can be used to forecast algal blooms and this study aims at examining feasibility of chlorophyll-a. The real-time data were collected by automatic water quality monitoring system (AWQMS) in Daecheong reservoir and invalid data were sorted by experts. And then, the sorted data were filled using linear interpolation. When the concentration of chlorophyll-a increased by $15mg/m^3$, water temperature and pH exceeded $26.8^{\circ}C$ and 9.5 respectively. As a result of correlation between chlorophyll-a and other parameters(i.e. water quality items and hydrological data), temperature (r=0.502 - 0.574), pH (r=0.583 - 0.681), total organic carbon (TOC, r=0.583 - 0.681) comparably had higher values. Meanwhile, the data around a day or two showed the highest correlation. In addition, chlorophyll-a is considered to be significantly effected by precipitation and inflow.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.957-968
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• 2021

To improve the data on reliability of the onshore fish farm water quality monitoring system and operate the system efficiently, the water quality data of the onshore seawater fish farms which are progressing test operation, and the marine environmental information network(Wando tidal station) were compared and analyzed. Furthermore, data validation, data range filters, and data displacement checks were applied to analyze the data in a way that eliminates the data errors in water quality monitoring systems and increases the reliability of measurement data.

• Journal of Korean Society on Water Environment
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• v.26 no.5
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• pp.796-801
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• 2010

Water quality index (WQI) can be a great tool that allows experts to translate large amount of complex water quality data into a format more easily understood by the public and policy makers. Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) can be calculated with the three factors (Scope: $F_1$, Frequency: $F_2$, Amplitude: $F_3$). After all, the WQI for a specific site is produced as a number between 0 to 100; the scale is also divided into five categories, i.e., Excellent, Good, Fair, Marginal and Poor. The WQI was found to be highly related to Chl-a, pH, temperature among the collected items. When the more input parameters were used, the range of variation generally became smaller. $F_3$ among the factors of WQI was influenced by algae. It showed a similar variation tendency between WQI and algal bloom in 2008.

• Journal of Biosystems Engineering
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• v.42 no.4
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• pp.350-357
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• 2017

Purpose: In-situ monitoring of water quality is fundamental to most environmental applications. The high cost and long delays of conventional laboratory methods used to determine water quality, including on-site sampling and chemical analysis, have limited their use in efficiently managing water sources while preventing environmental pollution. The objective of this study was to develop an on-site water monitoring system consisting mainly of an Arduino board and a sensor array of multiple ion selective electrodes (ISEs) to measure the concentration of $NO_3$ ions. Methods: The developed system includes a combination of three ISEs, double-junction reference electrode, solution container, sampling system consisting of three pumps and solenoid valves, signal processing circuit, and an Arduino board for data acquisition and system control. Prior to each sample measurement, a two-point normalization method was applied for a sensitivity adjustment followed by an offset adjustment to minimize the potential drift that could occur during continuous measurement and standardize the response of multiple electrodes. To investigate its utility in on-site nitrate monitoring, the prototype was tested in a facility where drinking water was collected from a water supply source. Results: Differences in the electric potentials of the $NO_3$ ISEs between 10 and $100mg{\cdot}L^{-1}$ $NO_3$ concentration levels were nearly constant with negative sensitivities of 58 to 62 mV during the period of sample measurement, which is representative of a stable electrode response. The $NO_3$ concentrations determined by the ISEs were almost comparable to those obtained with standard instruments within 15% relative errors. Conclusions: The use of the developed on-site nitrate monitoring system based on automatic sampling and two-point normalization was feasible for detecting abrupt changes in nitrate concentration at various water supply sites, showing a maximum difference of $4.2mg{\cdot}L^{-1}$ from an actual concentration of $14mg{\cdot}L^{-1}$.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.527-534
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• 2017

The purpose of this study is to develop unmanned equipment that can automatically move to the desired point and measure water quality at the correct depth. For this purpose, we constructed a water sampling lift and water sampling container, an unmanned vessel equipped with a VRS-GPS, an acoustic echo sounder, and a water quality sensor. Also, we developed an automatic navigation algorithm and program, an automatic water sampling program, and a water quality map generation program. As a result of the experiment in the detention pond, the unmanned vessel sailed along the planned route with an accuracy of about 93% within the error range of 3m. In addition, the water quality sensor installed in the lift was able to acquire the water quality of the target area in real time and transmit it to the server via wireless Internet, and it was possible to monitor the water quality of each site in real time. Through field experiments, the water sampling lift was able to control the desired length with an accuracy of about 94%. The stretch length accuracy experiment of the water sampling lift was impossible to measure directly in the water, so it was replaced land-based experiment. We also found some unstable problems due to the weight of the water sampling lift and the weight of the air compressor to operate the water container. Except these two problems, we accomplished purpose of this study. An automated water quality measurement method using an unmanned vessel can be used to measure the quality of water in a difficult to access area and to secure the safety of the worker.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.