• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.8-14
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• 2020

The human impact of particulate matter are revealed and demand for improved forecast accuracy is increasing. Recently, efforts is made to improve the accuracy of PM10 predictions by using machine learning, but prediction performance is decreasing due to the particulate matter data with a large rate of low concentration occurrence. In this paper, separation prediction model by concentration is proposed to improve the accuracy of PM10 particulate matter forecast. The low and high concentration prediction model was designed using the weather and air pollution factors in Cheonan, and the performance comparison with the prediction models was performed. As a result of experiments with RMSE, MAPE, correlation coefficient, and AQI accuracy, it was confirmed that the predictive performance was improved, and that 20.62% of the AQI high-concentration prediction performance was improved.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.119-128
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• 2020

An ultrasonic multiple scattering simulation using cross-section of fine dust particles were proposed. These days, along with awareness of air pollution, social interest in fine dust is increasing. In the construction field, awareness of fine dust is increasing, and research on preparing various countermeasures is underway. The light scattering method fine dust meter currently in use is affected by environmental factors such as relative humidity, and reliability problems in terms of accuracy are continuously reported. However, the transmission of ultrasonic waves can directly reflect the physical change of the medium based on the mechanical wave. Using these advantages of ultrasonic waves, fine dust measurement simulation was performed using the scattering cross section and ultrasonic multiple scattering theory. The shape data of the fine dust particles were collected using a SEM (Scanning Electron Microscope), and a cross-section according to the fine dust particles was derived through numerical analysis. As a result of signal processing, the error for the number density corresponding to each cross-section is minimum 19, maximum 3455.

• Korean Journal of Artificial Intelligence
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• v.8 no.2
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• pp.17-22
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• 2020

Purpose: After entering the 21st century, a new industrial revolution, i.e. industrial revolution 4.0, which is characterized by intelligence, automation and networking, has opened the curtain of the "industry 4.0" era. In recent years, "low-carbon economy" has been a development goal that has been paid close attention to and adhered to at home and abroad. As a major economic province, Shandong Province has not only brought about rapid economic growth, but also caused rapid environmental deterioration due to its high energy consumption, high dependence and high environmental pollution. In this environment, low-carbon economy has become an inevitable trend in the development of foreign trade in Shandong Province. Based on the current situation of foreign trade in Shandong Province and various existing problems, this paper explores the relationship between low-carbon economy and foreign trade in Shandong Province under this strategic background. Research design, data and methodology: By selecting the data from 2008 to 2017, using the carbon emission coefficient method to measure the CO2 emissions in the past decade, analyzing the impact of ecological factors on trade, selecting the most representative GDP and total imports for regression analysis, it is proved that they have a real impact on CO2 emissions. The total GDP is positively correlated with carbon emissions, while the total import is negatively correlated with carbon emissions. Results:This paper discusses the impact of low-carbon economy on foreign trade of Shandong Province from the perspective of foreign trade. Especially in today's "low-carbon economy" background. Conclusions:it is helpful for relevant departments to formulate relevant policies and promote the sustainable development of foreign trade in Shandong Province.

• Food Science and Industry
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• v.54 no.4
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• pp.293-300
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• 2021

Food safety accidents have been increasing by 2% over 5,000 cases every year since 2009. Most people know that the best method to prevent food safety accidents is a quick inspection, but there is a lack of inspection technology that can be used at the non-analytic level to food production and distribution sites. Among the recent on-site diagnostic technologies, the methods for testing gene-based food poisoning bacteria were introduced with the STA technology, which can range from sample to detection. If food safety information can be generated without forgery by directly inspecting food hazard factors by remote, unmanned, not human, pollution sources can be managed by predicting risks more accurately from current big-data and artificial intelligence technology. Since this information processing can be used on smartphones using the current cloud technology, it is judged that it can be used for food safety to small food businesses or catering services.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.343-350
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• 2022

Purpose: The Taean oil spill in 2007 taught us a great lesson and is a representative example of a social disaster. It was overcome through the dazzling dedication and service of volunteers. However, behind the volunteers, they were directly or indirectly exposed to the spilled oil, resulting in health problems such as headaches and safety accidents. Safety accidents were caused by unsafe behavior, and unsafe behavior was caused by lack of safety awareness or ignorance. We want to find an education and training program to systematically raise safety awareness for volunteers in connection with the Occupational Safety and Health Act. Method: The occupational safety and health law, the laws related to coastal clean-up, and the unsafe behavior factors in the statistics of occupational accidents in the past year were mainly identified. Result: The contents of education and training hours to be provided for volunteers involved in coastal clean-up were presented in comparison with workers under the Occupational Safety and Health Act. Conclusion: Safety and health education for volunteers and volunteer managers is directly related to safety awareness and can prevent unsafe behavior.

• Journal of the Society of Disaster Information
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• v.19 no.1
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• pp.231-240
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• 2023

Purpose: The purpose of this study is to analyze air quality and carbon emissions in developing countries, particularly Myanmar, and explore the impact of transportation on CO2 emissions during peak hours relative to free-flow conditions. Method: This study conducted a traffic survey in two major cities in Myanmar to quantify carbon dioxide emissions from the transportation sector, using IPCC's tier 1 and tier 2 approaches, with statistical analysis performed using Python 3 and Microsoft Excel for comparative analysis of critical factors in CO2 emissions. Result: The result of this study is an estimate of the vehicle kilometers traveled (VKT) and fuel consumption in Yangon city for the year 2019, based on data from various sources including the Myanmar Statistical data base, YUTRA project survey, and Ministry of Electric and Energy. The study also analyzes the average travel time index (TTI) for the four roads in Yangon, which indicates the impact of congestion on vehicle travel time and CO2 emissions. Overall, the study provides important insights into the transport sector in Yangon city and can be used to inform policies aimed at reducing greenhouse gas emissions and improving traffic conditions. Conclusion: The study concludes that congestion plays a significant role in increasing fuel use and emission levels in the road transport sector in Myanmar. The analysis provides valuable insights into the impact of the sector on the environment and emphasizes the importance of addressing congestion to reduce fuel use and emissions. However, the study's scope is limited to Yangon city and Mandalay city, and some mean values may not accurately represent the entire country and other developing countries.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• Environmental and Resource Economics Review
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• v.31 no.4
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• pp.619-644
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• 2022

The purpose of this study is to estimate the economic benefit of improving ground water quality in Jeju Island, where groundwater pollution has recently become a social issue and various water quality improvement projects are being promoted. By applying the contingent valuation method, an online survey was conducted on Jeju Island residents to analyze the response data of 542 respondents and estimate the mean willingness to pay using 16 models. The estimation of the double-bounded dichotomous choice model confirmed that each household was willing to pay 28,008 won per year, with the willingness to pay estimated at a minimum of 17,762 won and a maximum of 37,416 won based on different models. The total annual benefit for Jeju Island's ground water quality improvement was estimated to be about 8.66 billion won , and socioeconomic factors influencing willingness-to-pay were investigated. This study is expected to serve as a foundation for the development of environmental improvement policies by assisting in the understanding of Jeju Island's unique water resource environment.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.2
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• pp.35-45
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• 2023

The DOR (Drainage outlet raising) in the paddy field has been suggested as one of the most important best management practices for the TMDL (Total maximum daily load) management in the technical guidelines by the NIER (National institute of environmental research). However, this method is underestimated and is not well adopted by local governments for the TMDL. The purpose of this study is to evaluate the unit load reduction equation according to the application of DOR in order to expand this equation. The original equation in the guideline was derived using the HSPF (Hydrological Simulation Program-Fortran) model for 1 year in Changnyeong. We analyzed the reduction effect of the original equation application by collecting additional long-term monitoring data from the Buan, Icheon, Iksan, and Jeonju. When comparing the reduction loads between the original equation and monitoring results, the evaluation results of the original equation were 11% of the monitoring analysis results, which was underestimated. This means that the original equation needs to be improved. For assessing the equation, the HSPF Paddy-RCH model was established according to the NI ER guideline and evaluated for applicability. The performance results of the model showed a reasonable range by the statistical criteria. Modified equations 1 and 2 were proposed based on the monitoring and modeling results. Modified equation 1 was the method of modifying the original equation's main factors, and modified equation 2 was the method of applying the non-point pollution reduction efficiency according to the rainfall class using the long-term modeling results. At the level of 58.6~64.6% of monitoring data, the difference between them could be further reduced compared to the original equation. The suggested approach will be more reasonable and practicable for decision-makers and will contribute to the TMDL management plans.