scholarly journals Mathematical Modeling for Water Quality Management under Interval and Fuzzy Uncertainties

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
J. Liu ◽  
Y. P. Li ◽  
G. H. Huang
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Water Quality Management ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Interval Numbers ◽  
Nonpoint Sources ◽  
Xiangxi River ◽  
Constrained Programming ◽  
System Benefit

In this study, an interval fuzzy credibility-constrained programming (IFCP) method is developed for river water quality management. IFCP is derived from incorporating techniques of fuzzy credibility-constrained programming (FCP) and interval-parameter programming (IPP) within a general optimization framework. IFCP is capable of tackling uncertainties presented as interval numbers and possibility distributions as well as analyzing the reliability of satisfying (or the risk of violating) system’s constraints. A real-world case for water quality management planning of the Xiangxi River in the Three Gorges Reservoir Region (which faces severe water quality problems due to pollution from point and nonpoint sources) is then conducted for demonstrating the applicability of the developed method. The results demonstrate that high biological oxygen demand (BOD) discharge is observed at the Baishahe chemical plant and Gufu wastewater treatment plant. For nonpoint sources, crop farming generates large amounts of total phosphorus (TP) and total nitrogen (TN). The results are helpful for managers in not only making decisions of effluent discharges from point and nonpoint sources but also gaining insight into the tradeoff between system benefit and environmental requirement.

Water Quality Management of the Hooghly Estuary – A Linear Programming Model

1988 ◽  
Vol 20 (6-7) ◽  
pp. 235-242 ◽  
Author(s):  
S. K. Bose ◽  
P. Ray ◽  
B. K. Dutta
Keyword(s):  
Water Quality ◽  
Linear Programming ◽  
Quality Management ◽  
Programming Model ◽  
Water Quality Management ◽  
Treatment Plant ◽  
Linear Programming Model ◽  
Cost Of Treatment ◽  
Hooghly Estuary ◽  
Wide Range

Rapid and widespread deterioration of water quality in surface water systems has rendered mathematical modelling for predicting water quality indispensable especially in terms of bio-chemical oxygen demand (BOD) and dissolved oxygen (DO) under various system parameters. Model output under a prescribed set of conditions indicates the degree of treatment necessary to make the waste load acceptable. It also analyses the consequences of changes in water quality objectives from a cost/benefit viewpoint. The most appropriate model in this regard would be the one which takes into account the cost of treatment plant installations and their locations. This paper proposes a linear programming model for water quality management of the Hooghly estuary. The linear objective function for the total cost of treatment at selected terminals has been expressed in terms of quantity of BOD removed. Computed data have been presented under reasonably wide range of parameters.

scholarly journals Identification of High-Priority Tributaries for Water Quality Management in Nakdong River Using Neural Networks and Grade Classification

2020 ◽  
Vol 12 (21) ◽  
pp. 9149
Author(s):  
Kang-Young Jung ◽  
Sohyun Cho ◽  
Seong-Yun Hwang ◽  
Yeongjae Lee ◽  
Kyunghyun Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Quality Management ◽  
Water Quality Management ◽  
Oxygen Demand ◽  
Principal Component ◽  
Pollution Load ◽  
Nakdong River ◽  
Neural Network Models ◽  
Grade Classification

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.

Research on IFP Method for Water Quality Management

2013 ◽  
Vol 848 ◽  
pp. 259-262
Author(s):  
Yao Ji ◽  
Guo He Huang ◽  
Wei Sun
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Water Quality Management ◽  
Fuzzy Programming ◽  
Environmental Issues ◽  
Management Problem ◽  
Interval Numbers ◽  
Fuzzy Variables ◽  
Fuzzy Parameters ◽  
Management Issues

In this paper, an inexact fuzzy programming (IFP) method has been proposed to deal with nonpoint-resource pollution water quality management issues with interval and fuzzy parameters. In IFP method, parameters presented as interval numbers and fuzzy variables can be reflected simultaneously. This study introduced IFP into water quality management problem, and is also significant to other environmental issues under the similar situation.

scholarly journals Nitrate and biochemical oxygen demand change in a typical Midwest headwater stream in the past two decades

2020 ◽  
Vol 3 (1) ◽  
pp. 519-537
Author(s):  
M. D. Shahin Alam ◽  
Bangshuai Han ◽  
Amy Gregg ◽  
John Pichtel
Keyword(s):  
Water Quality ◽  
Biochemical Oxygen Demand ◽  
Water Quality Management ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Agricultural Runoff ◽  
Low Flow ◽  
Organic Contamination ◽  
Novel Approach ◽  
Rich Data

Abstract Nitrate and organic contamination from Midwest rivers, including the White River at Muncie, IN, has been an on-going concern and contributes to the hypoxic zone in the Gulf. Despite rich data, recent water quality changes have rarely been investigated. This study employed 16 years of continuous monitoring data, including biochemical oxygen demand (BOD), dissolved oxygen (DO), and nitrate–nitrite as nitrogen (NN) from five sites near Muncie, and analyzed the water quality trend and pollution sources. A novel approach, Weighted Regression on Time, Discharge and Seasons that allows for the representation of long-term water quality patterns by considering seasonal variance and discharge-related effects over time, is adopted. Flow-normalized BOD and NN concentration and flux both increased, and DO concentration and flux decreased. However, the changes vary among sites. Muncie wastewater treatment plant and combined sewage outflows (CSOs) contribute remarkably to NN pollution during low-flow seasons. Urban and agricultural runoff, and CSOs impact BOD levels. Agricultural runoff contribution to BOD is increasing in recent years. Seasonal patterns of nitrate and BOD in the river are also analyzed. The results are helpful for watershed managers to re-think conservation practices and have indications to water quality management beyond the study area.

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