• Title/Summary/Keyword: Diffuse pollution loads

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A Study on Estimating Diffuse Pollution Loads Removal by Road Vacuum Cleaning (도로청소에 의한 비점오염부하 삭감량 산정방법 연구)

  • Lee, Taehwan;Cho, Hong-Lae;Jeong, Euisang;Koo, Bhon K.;Park, Baekyung;Kim, Yongseok
    • Journal of Korean Society on Water Environment
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    • v.33 no.2
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    • pp.123-129
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    • 2017
  • The purpose of this study is to identify potential methodologies to reasonably estimate the effectiveness of road vacuum cleaning in terms of pollution loads reduction. In this context, this study proposes two empirical equations to estimate the amount of diffuse pollution loads removed by road vacuum cleaning. The proposed equations estimate the removed amount of pollution loads respectively taking into consideration of: a) the distance of road vacuum cleaning; and b) the amount of road-deposited sediment(RDS). All of the parameters in these equations were evaluated based on results of field monitoring and laboratory analyses, except for the RDS generation rate. The results of this study suggest that pollutant removal efficiency is 46.3% for $BOD_5$ and 56.4% for TP; discharge ratios for particulate and dissolved $BOD_5$ are 35.0% and 21.2%, respectively; discharge ratios for particulate and dissolved TP are 35.0% and 19.4%, respectively. Average concentrations of pollutants in RDS are $BOD_5$ 977.3 mg/kg and TP 317.6 mg/kg. Some results of a case study imply that both equations can be potentially useful if the adopted parameters are reasonably evaluated. In particular, the RDS generation rate should be evaluated based on monitoring data collected from various road conditions.

Application of BASIN 4.0 and WinHSPF to a Small Stream in Total Water Pollution Load Management Area and Calibration of Model Parameter using Genetic Algorithm (오염총량관리지역내 소하천에 대한 BASINS 4.0 및 WinHSPF의 적용과 유전알고리즘을 이용한 매개변수의 보정)

  • Cho, Jae-Heon;Yun, Seoung-Jin
    • Journal of Environmental Impact Assessment
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    • v.21 no.1
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    • pp.161-169
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    • 2012
  • Recently various attempts have been made to apply HSPF model to calculate runoff and diffuse pollution loads of stream and reservoir watersheds. Because the role of standard flow is very important in the water quality modelling of Total Water Pollution Load Management, HSPF was used as a means of estimating standard flow. In this study, BASINS 4.0 and WinHSPF was applied to the Gomakwoncheon watershed, genetic algorithm(GA) and influence coefficient algorithm were used to calibrate the runoff parameters of the WinHSPF. The objective function is the sum of the squares of the normalized residuals of the observed and calculated flow and it is optimized using GA. Estimates of the optimum runoff parameters are made at each iteration of the influence coefficient algorithm. The calibration results showed a relatively good correspondence between the observed and the calculated values. The standard flow(Q275) of the Gomakwoncheon watershed was estimated using the ten years of weather data.

Simultaneous Estimation of Diffuse Pollution Loads and Model Parameters for River Water Quality Modeling (하천 수질모형에 의한 비점 오염 부하량과 모형 매개변수의 동시 추정)

  • Jun, Kyung-Soo;Kang, Ju-Whan
    • Journal of Korea Water Resources Association
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    • v.37 no.12
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    • pp.1009-1018
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    • 2004
  • A systematic method using an optimal estimation algorithm is presented for simultaneous estimation of diffuse pollution distributed along a stream reach and model parameters for a stream water quality model. It was applied with the QVAL2E model to the South Han River for optimal estimation of kinetic constants and diffuse loads along the river. Initial calibration results for kinetic constants selected from a sensitivity analysis reveal that diffuse source inputs for nitrogen and phosphorus are essential to satisfy the system mass balance. Diffuse loads for total nitrogen and total phosphorus were estimated solving the expanded inverse problem. Comparison of kinetic constants estimated simultaneously with diffuse sources to those estimated without diffuse loads, suggests that diffuse sources must be included in the optimization not only for its own estimation but also for adequate estimation of the model parameters. Application of optimization method to river water quality modeling is discussed in terms of the sensitivity coefficient matrix structure.