Evaluation of pollutants removal efficiency to achieve successful urban river restoration

2009 ◽  
Vol 59 (11) ◽  
pp. 2101-2109 ◽  
Author(s):  
Sung Min Cha ◽  
Young Sik Ham ◽  
Seo Jin Ki ◽  
Seung Won Lee ◽  
Kyung Hwa Cho ◽  
...  
Keyword(s):  
Water Quality ◽  
Stream Restoration ◽  
Time Window ◽  
Water Quality Management ◽  
Oxygen Demand ◽  
Urban Stream ◽  
Pollution Level ◽  
Fecal Indicator ◽  
Discharge Point ◽  
Restoration Planning

Greater efforts to provide alternative scenarios are key to successful urban stream restoration planning. In this study, we discuss two different aspects of water quality management schemes, biodegradation and human health, which are incorporated in the restoration project of original, pristine condition of urban stream at the Gwangju (GJ) Stream, Korea. For this study, monthly monitoring of biochemical oxygen demand (BOD5) and fecal indicator bacteria (FIB) data were obtained from 2003 to 2008 and for 2008, respectively, and these were evaluated to explore pollutant magnitude and variation with respect to space and time window. Ideal scenarios to reduce target pollutants were determined based on their seasonal characteristics and correlations between the concentrations at a water intake and discharge point, where we suggested an increase of environmental flow and wetland as pollutants reduction drawing for BOD5 and FIB, respectively. The scenarios were separately examined by the Qual2E model and hypothetically (but planned) constructed wetland, respectively. The results revealed that while controlling of the water quality at the intake point guaranteed the lower pollution level of BOD5 in the GJ Stream, a wetland constructed at the discharge point may be a promising strategy to mitigate mass loads of FIB. Overall, this study suggests that a combination of the two can be plausible scenarios not only to support sustainable urban water resources management, but to enhance a quality of urban stream restoration assignment.

Resident perceptions of urban stream restoration and water quality in South Korea

2018 ◽  
Vol 34 (5) ◽  
pp. 481-492 ◽  
Author(s):  
Chang-Yu Hong ◽  
Heejun Chang ◽  
Eun-Sung Chung
Keyword(s):  
Water Quality ◽  
South Korea ◽  
Stream Restoration ◽  
Urban Stream ◽  
Resident Perceptions

scholarly journals Role of humic substances as refractory organic matter in sustainable water quality assessment and management

2019 ◽  
Vol 20 (2) ◽  
pp. 538-549
Author(s):  
Maoqing Duan ◽  
Xia Du ◽  
Wenqi Peng ◽  
Cuiling Jiang ◽  
Shijie Zhang
Keyword(s):  
Water Quality ◽  
Chemical Oxygen Demand ◽  
Humic Substances ◽  
River Water ◽  
Anthropogenic Activities ◽  
Water Quality Management ◽  
National Development ◽  
Oxygen Demand ◽  
Study Results ◽  
The Impact

Abstract In northern China, river water originating from or flowing through forests often contains large amounts of oxygen-consuming organic substances, mainly humic substances. These substances are stable and not easily biodegradable, resulting in very high detection values of chemical oxygen demand. However, under natural conditions, the dissolved oxygen demand is not as high. Using experimental values to evaluate river water quality and the impact of human activities on water quality is thus unscientific and does not meet national development goals. In this study, the potential sources of high-concentration chemical oxygen demand in river water in two areas exposed to virtually no anthropogenic activities and strongly affected by humic substances, were analysed. The chemical oxygen demand contributed by humic substances (COD-HSs) was quantified using three methods. The results of water quality monitoring in 2017 and 2018 revealed that the chemical oxygen demand concentrations (5–44 mg/L) predominantly exceeded the standard (15 mg/L). The study results suggest that COD-HSs should be considered separately for objective evaluation and management of water quality, particularly in areas that are seriously affected by COD-HSs, to provide a scientific basis for formulating sustainable water quality management policies.

scholarly journals A Multivariate and Spatiotemporal Analysis of Water Quality in Code River, Indonesia

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Mochamad A. Pratama ◽  
Yan D. Immanuel ◽  
Dwinanti R. Marthanty
Keyword(s):  
Water Quality ◽  
Land Cover ◽  
Agricultural Land ◽  
Water Quality Management ◽  
Temporal Trends ◽  
Oxygen Demand ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data

The efficacy of a water quality management strategy highly depends on the analysis of water quality data, which must be intensively analyzed from both spatial and temporal perspectives. This study aims to analyze spatial and temporal trends in water quality in Code River in Indonesia and correlate these with land use and land cover changes over a particular period. Water quality data consisting of 15 parameters and Landsat image data taken from 2011 to 2017 were collected and analyzed. We found that the concentrations of total dissolved solid, nitrite, nitrate, and zinc had increasing trends from upstream to downstream over time, whereas concentrations of parameter biological oxygen demand, cuprum, and fecal coliform consistently undermined water quality standards. This study also found that the proportion of natural vegetation land cover had a positive correlation with the quality of Code River’s water, whereas agricultural land and built-up areas were the most sensitive to water pollution in the river. Moreover, the principal component analysis of water quality data suggested that organic matter, metals, and domestic wastewater were the most important factors for explaining the total variability of water quality in Code River. This study demonstrates the application of a GIS-based multivariate analysis to the interpretation of water quality monitoring data, which could aid watershed stakeholders in developing data-driven intervention strategies for improving the water quality in rivers and streams.

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.

scholarly journals Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction

2020 ◽  
Vol 12 (13) ◽  
pp. 5374 ◽  
Author(s):  
Stephen Stajkowski ◽  
Deepak Kumar ◽  
Pijush Samui ◽  
Hossein Bonakdari ◽  
Bahram Gharabaghi
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Water Temperature ◽  
Real Time ◽  
River Water ◽  
Time Window ◽  
Water Quality Management ◽  
Water Quality Monitoring ◽  
Lstm Network ◽  
River Water Temperature

Advances in establishing real-time river water quality monitoring networks combined with novel artificial intelligence techniques for more accurate forecasting is at the forefront of urban water management. The preservation and improvement of the quality of our impaired urban streams are at the core of the global challenge of ensuring water sustainability. This work adopted a genetic-algorithm (GA)-optimized long short-term memory (LSTM) technique to predict river water temperature (WT) as a key indicator of the health state of the aquatic habitat, where its modeling is crucial for effective urban water quality management. To our knowledge, this is the first attempt to adopt a GA-LSTM to predict the WT in urban rivers. In recent research trends, large volumes of real-time water quality data, including water temperature, conductivity, pH, and turbidity, are constantly being collected. Specifically, in the field of water quality management, this provides countless opportunities for understanding water quality impairment and forecasting, and to develop models for aquatic habitat assessment purposes. The main objective of this research was to develop a reliable and simple urban river water temperature forecasting tool using advanced machine learning methods that can be used in conjunction with a real-time network of water quality monitoring stations for proactive water quality management. We proposed a hybrid time series regression model for WT forecasting. This hybrid approach was applied to solve problems regarding the time window size and architectural factors (number of units) of the LSTM network. We have chosen an hourly water temperature record collected over 5 years as the input. Furthermore, to check its robustness, a recurrent neural network (RNN) was also tested as a benchmark model and the performances were compared. The experimental results revealed that the hybrid model of the GA-LSTM network outperformed the RNN and the basic problem of determining the optimal time window and number of units of the memory cell was solved. This research concluded that the GA-LSTM can be used as an advanced deep learning technique for time series analysis.

A Fuzzy Inference System for Water Quality of Chunnambar River, Puducherry

2015 ◽  
Vol 787 ◽  
pp. 322-326 ◽  
Author(s):  
V. Nirmala ◽  
K.R. Leelavathy ◽  
Sivapragasam Sowndharya ◽  
Parthiban Bama
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Fuzzy Inference System ◽  
Quality Index ◽  
Fuzzy Inference ◽  
Oxygen Demand ◽  
Pollution Level ◽  
Inference System ◽  
Indian Water ◽  
Proposed Model

A Fuzzy Inference System (FIS) is considered as an effective tool for solution of many complex engineering systems when ambiguity and uncertainty is associated with the systems. The water quality is an important issue of relevance in the context of present times. The proposed model is designed to predict Water Quality Index (WQI) for Chunnambar, Ariyankuppam, Puducherry Region, Southern India. A systematic investigation of the pollution level at Chunnambar from March 2013 to February 2014 was carried out. The untreated domestic wastes from various parts of the Ariyankuppam town are directly discharged into the river which leads to increased level of pollution. The present studies emphasis on the magnitude of pollution by monitoring key water quality parameters such as Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), pH and Temperature. FIS simplifies and speed up the computation of WQI as compared to the currently existing standards. In this paper, the proposed model is compared with Indian Water Quality Index (IWQI) and it is found that the designed model predicts accurately.

Water quality and prospects for revitalization of an urban stream in Recife, Brazil

2020 ◽  
Vol 11 (5) ◽  
pp. 284-294
Author(s):  
José Luís Said Cometti ◽  
Jaime Joaquim da Silva Pereira Cabral ◽  
Taylse Marielly da Conceição
Keyword(s):  
Water Quality ◽  
Riparian Forest ◽  
Stream Water ◽  
Oxygen Demand ◽  
Urban Stream ◽  
The European Union ◽  
Stream Water Quality ◽  
Anthropogenic Pressures ◽  
Untreated Sewage ◽  
Lake Zone

The urbanization of Recife characterized an occupation of the Capibaribe River banks and its tributaries. This caused the grounding, rectification and degradation of several streams. Thus, this paper presents a diagnosis of the Cavouco stream water quality and suggests measures for its restoration. The collections were performed between 2016 and 2017 in three sampling points. Analysis adopted the Standard Methods for the Examination of Water and Wastewater and calculated the Water Quality Index (WQI). A correlation test between the parameters was applied to understand the phenomenon. Actions to revitalize it followed the European Union Water Framework Directive. The WQI of the Cavouco stream had a good presentation in the small lake zone; it was poor in the Federal University (UFPE) region and awful in the Caxangá Avenue section.  Dissolved Oxygen (OD) concentration was negatively correlated with Biochemical Oxygen Demand (BOD), decreasing from upstream to downstream.  Water quality degradation is associated with untreated sewage discharge along the stream. The proposal to its recovery is to collect and treat domestic sewage, remove irregular housing, restore the riparian forest, control erosion, create linear parks and search for governance mechanisms with public participation. The proposed interventions are fundamental for the restoration of Cavouco's ecological potential, with improved water quality and reduced anthropogenic pressures.

Modelling of biochemical oxygen demand from limited water quality variable by ANFIS using two partition methods

2017 ◽  
Vol 53 (1) ◽  
pp. 24-40 ◽  
Author(s):  
Belouz Khaled ◽  
Aidaoui Abdellah ◽  
Dechemi Noureddine ◽  
Heddam Salim ◽  
Aguenini Sabeha
Keyword(s):  
Water Quality ◽  
Biochemical Oxygen Demand ◽  
Inorganic Nitrogen ◽  
Water Quality Management ◽  
Oxygen Demand ◽  
Performance Criteria ◽  
Subtractive Clustering ◽  
Time Step ◽  
Inference System ◽  
Mean Square Errors

Abstract This paper aims to: (1) develop models based on adaptive neuro-fuzzy inference system (ANFIS) able to predict five-day biochemical oxygen demand (BOD5) in Ouizert reservoir; (2) demonstrate the capability of the ANFIS in the practical issues of water quality management; (3) choose the optimal combination of input variables to improve the model performance; (4) compare two ANFIS partition methods, namely subtractive clustering called ANFIS-SC and grid partitioning, called ANFIS-GP. The models were developed using experimental data which were gathered during a ten-year period, at a mean monthly time step (scale). The input data used are total inorganic nitrogen, chemical oxygen demand (COD), total dissolved solid, dissolved oxygen and phosphate; the output is five-day biochemical oxygen demand (BOD5). Results reveal that ANFIS-SC models gave a higher correlation coefficient, a lower root mean square errors (RMSE) and mean absolute errors than the corresponding ANFIS-GP models. We can conclude that ANFIS-SC has supremacy over ANFIS-GP in terms of performance criteria and prediction accuracy for BOD5 estimation. The results showed that COD is the more effective variable for BOD5 estimating than other parameters, hence COD is the major driving factor for BOD5 modelling through ANFIS.

scholarly journals Investigation of the Soan River Water Quality Using Multivariate Statistical Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Zakaullah ◽  
Naeem Ejaz
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Management ◽  
Oxygen Demand ◽  
River Water Quality ◽  
Quality Data ◽  
Spatiotemporal Pattern ◽  
Multivariate Statistical ◽  
Water Quality Data

Evaluating the quality of river water is a critical process due to pollution and variations of natural or anthropogenic origin. For the Soan River (Pakistan), seven sampling sites were selected in the urban area of Rawalpindi/Islamabad, and 18 major chemical parameters were examined over two seasons, i.e., premonsoon and postmonsoon 2019. Multivariate statistical approaches such as the Spearman correlation coefficient, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the water quality of the Soan River based on temporal and spatial patterns. Analytical results obtained by PCA show that 92.46% of the total variation in the premonsoon season and 93.11% in the postmonsoon season were observed by only two loading factors in both seasons. The PCA and CA made it possible to extract and recognize the origins of the factors responsible for water quality variations during the year 2019. The sampling stations were grouped into specific clusters on the basis of the spatiotemporal pattern of water quality data. The parameters dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), turbidity, and total suspended solids (TSS) are among the prominent contributing variations in water quality, indicating that the water quality of the Soan River deteriorates gradually as it passes through the urban areas, receiving domestic and industrial wastewater from the outfalls. This study indicates that the adopted methodology can be utilized effectively for effective river water quality management.

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