Simulation of organic carbon loading using MIKE 11 model: a case of River Nzoia, Kenya

2015 ◽  
Vol 10 (2) ◽  
pp. 298-304 ◽  
Author(s):  
Edwin K. Kanda ◽  
Job R. Kosgei ◽  
Emmanuel C. Kipkorir
Keyword(s):  
Lake Victoria ◽  
Agricultural Runoff ◽  
Point Sources ◽  
Low Flow ◽  
Quality Data ◽  
Coefficient Of Determination ◽  
Lake Victoria Basin ◽  
Water Quality Data ◽  
Mike 11 ◽  
Mike 11 Model

River Nzoia is the largest river draining into the Kenyan portion of Lake Victoria. This river receives both point sources of pollution from industrial and municipal wastes, and non-point sources from agricultural runoff in the catchment. The objective of this study was to simulate dissolved oxygen (DO) and biochemical oxygen demand (BOD) of the middle section of River Nzoia using MIKE 11 model. The model was calibrated using discharge and water quality data for 2009 and validated with March–April 2013 data. The model performance was good with coefficient of determination (R2) values of between 0.845 and 0.995, Nash–Sutcliffe efficiency values of between 0.748 and 0.993 and percent bias of less than 10 for both calibration and validation of electrical conductivity (EC), DO and BOD. EC and BOD values were lower for April compared to March which could be attributed to dilution during high flows. DO values were above the recommended minimum level of 4 mg/l in all the sections of the river in the wet period but some sections had lower than 4 mg/l during low flow period. The government agencies such as Water Resources Management Authority and National Environment Management Authority should enforce the effluent standards to ensure that industries and wastewater treatment plants adhere to the maximum allowable limit for BOD and also improve their treatment efficiencies of wastewater plants so as to improve the quality of River Nzoia which is important in the overall management of the Lake Victoria basin.

scholarly journals Modelling of nitrates in River Nzoia using MIKE 11

2017 ◽  
Vol 12 (1) ◽  
pp. 217-223 ◽  
Author(s):  
Edwin K. Kanda ◽  
Emmanuel C. Kipkorir ◽  
Job R. Kosgei
Keyword(s):  
Industrial Wastewater ◽  
Lake Victoria ◽  
Model Performance ◽  
Quality Data ◽  
Strong Positive Correlation ◽  
Nitrogen And Phosphorus ◽  
Water Quality Data ◽  
Mike 11 ◽  
Run Off ◽  
The Relationship

Eutrophication is a serious problem in Lake Victoria as a result of enrichment by nutrients transported by the rivers draining into it. River Nzoia is one of the main rivers draining into the lake. The main aim of this study was to simulate the level of nitrates using MIKE 11 and to establish relationship between nitrogen and phosphorus. The model was calibrated using water quality data for 2009 and validated with March 2013 data and then it was used to simulate nitrate concentration for the wet month of April 2013. The model performance was good with R2 values of between 0.87 and 0.98 and EF values of between 0.73 and 0.96. From the simulations, the effluent discharge from municipal and industrial wastewater ponds elevated the concentration of the nitrates in the river. Analysis of the concentrations of nitrates for wet and dry periods showed significant variations indicating significant contributions from the catchment through run-off. The relationship between total nitrogen and total phosphorus was analysed and found to have a strong positive correlation (r = 0.714, p < 0.05) indicating that both originate from similar sources or are influenced by the same factors such as agriculture.

Assessment of the Klang River Quality Using the Water Quality Indices

2012 ◽  
Vol 599 ◽  
pp. 237-240 ◽  
Author(s):  
Faridah Othman ◽  
Mohamed Elamin Alaa Eldin
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Water Quality Index ◽  
Quality Index ◽  
River Mouth ◽  
Peninsular Malaysia ◽  
Point Sources ◽  
Quality Data ◽  
Commercial Activity ◽  
Water Quality Data

The Klang river basin is located within the state of Selangor and Kuala Lumpur, Malaysia. The Klang River drains an area of 1,288 km2 from the steep mountain rain forests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, covering a distance of 120 km. It originates from the northern part of Selangor, drains the Klang Valley, and finally discharges itself into the Straits of Malacca. The pollution discharges for various locations along the river basin was obtained from the Water Quality and GIS group. The pollutants can come from point sources (PS) such as industrial wastewater, municipal sewers, wet market, sand mining and landfill. Pollutants can also come from non-point sources (NPS) such as agricultural or urban runoff, and commercial activity such as forestry, and construction due to rainfall event. Mathematical–computational modeling of river water quality is possible but requires an extensive validation. Besides it requires previous knowledge of hydraulics and hydrodynamics. To overcome these difficulties, a water quality index (WQI) was developed. The water quality index (WQI) is a mathematical instrument used to transform large quantities of water quality data into a single number. The purpose of this research is to classify the upstream and downstream of the Klang main river based on WQI value.

scholarly journals Pioneering water quality data on the Lake Victoria watershed: effects on human health

2015 ◽  
Vol 13 (3) ◽  
pp. 920-930 ◽  
Author(s):  
Tamie J. Jovanelly ◽  
Julie Johnson-Pynn ◽  
James Okot-Okumu ◽  
Richard Nyenje ◽  
Emily Namaganda
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Lake Victoria ◽  
Fecal Coliforms ◽  
World Health ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set ◽  
Forest Reserves ◽  
Elevated Exposure

Four forest reserves within 50 km of Kampala in Uganda act as a critical buffer to the Lake Victoria watershed and habitat for local populations. Over a 9-month period we capture a pioneering water quality data set that illustrates ecosystem health through the implementation of a water quality index (WQI). The WQI was calculated using field and laboratory data that reflect measured physical and chemical parameters (pH, dissolved oxygen, biological oxygen on demand, nitrates, phosphates, fecal coliform, and temperature turbidity). Overall, the WQI for the four forest reserves reflect poor to medium water quality. Results compared with US Environmental Protection Agency and World Health Organization drinking water standards indicate varying levels of contamination at most sites and all designated drinking water sources, with signatures of elevated nitrates, phosphates, and/or fecal coliforms. As critical health problems are known to arise with elevated exposure to contaminants in drinking water, this data set can be used to communicate necessary improvements within the watershed.

scholarly journals Dissolved oxygen modelling using artificial neural network: a case of River Nzoia, Lake Victoria basin, Kenya

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Kanda Edwin Kimutai ◽  
Kipkorir Emmanuel Chessum ◽  
Kosgei Job Rotich
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dissolved Oxygen ◽  
Network Architecture ◽  
Lake Victoria ◽  
Agricultural Runoff ◽  
Lake Victoria Basin ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

River Nzoia in Kenya, due to its role in transporting industrial and municipal wastes in addition to agricultural runoff to Lake Victoria, is vulnerable to pollution. Dissolved oxygen is one of the most important indicators of water pollution. Artificial neural network (ANN) has gained popularity in water quality forecasting. This study aimed at assessing the ability of ANN to predict dissolved oxygen using four input variables of temperature, turbidity, pH and electrical conductivity. Multilayer perceptron network architecture was used in this study. The data consisted of 113 monthly values for the input variables and output variable from 2009–2013 which were split into training and testing datasets. The results obtained during training and testing were satisfactory with R2 varying from 0.79 to 0.94 and RMSE values ranging from 0.34 to 0.64 mg/l which imply that ANN can be used as a monitoring tool in the prediction of dissolved oxygen for River Nzoia considering the non-correlational relationship of the input and output variables. The dissolved oxygen values follow seasonal trend with low values during dry periods.

scholarly journals Longitudinal River Monitoring and Modelling Substantiate the Impact of Weirs on Nitrogen Dynamics

Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 189
Author(s):  
Geovanni Teran-Velasquez ◽  
Björn Helm ◽  
Peter Krebs
Keyword(s):  
Water Quality ◽  
Nitrogen Dynamics ◽  
Low Flow ◽  
Quality Data ◽  
Quality Model ◽  
Spatiotemporal Resolution ◽  
Water Quality Data ◽  
River Monitoring ◽  
The Impact

The fluvial nitrogen dynamics at locations around weirs are still rarely studied in detail. Eulerian data, often used by conventional river monitoring and modelling approaches, lags the spatial resolution for an unambiguous representation. With the aim to address this knowledge gap, the present study applies a coupled 1D hydrodynamic–water quality model to a 26.9 km stretch of an upland river. Tailored simulations were performed for river sections with water retention and free-flow conditions to quantify the weirs’ influences on nitrogen dynamics. The water quality data were sampled with Eulerian and Lagrangian strategies. Despite the limitations in terms of required spatial discretization and simulation time, refined model calibrations with high spatiotemporal resolution corroborated the high ammonification rates (0.015 d−1) on river sections without weirs and high nitrification rates (0.17 d−1 ammonium to nitrate, 0.78 d−1 nitrate to nitrite) on river sections with weirs. Additionally, using estimations of denitrification based on typical values for riverbed sediment as a reference, we could demonstrate that in our case study, weirs can improve denitrification substantially. The produced backwater lengths can induce a means of additional nitrogen removal of 0.2-ton d−1 (10.9%) during warm and low-flow periods.

SPATIAL CHARACTERIZATION AND IDENTIFICATION SOURCES OF POLLUTION USING MULTIVARIATE ANALYSIS AT TERENGGANU RIVER BASIN, MALAYSIA

2015 ◽  
Vol 77 (1) ◽  
Author(s):  
Ahmad Firdaus Kamaruddin ◽  
Mohd Ekhwan Toriman ◽  
Hafizan Juahir ◽  
Sharifuddin Md Zain ◽  
Mohd Nordin Abdul Rahman ◽  
...  
Keyword(s):  
Water Quality ◽  
Principal Component ◽  
Organic Pollution ◽  
Agricultural Runoff ◽  
Pollution Sources ◽  
Anthropogenic Sources ◽  
Quality Data ◽  
Water Quality Data ◽  
Hierarchical Agglomerative Cluster ◽  
Sources Of Pollution

The spatial water quality data (281x22) obtained from 12 sampling stations located along the Terengganu River and its main tributaries were evaluated with environmetric methods. Principal component analysis was used to investigate the origin of each variable due to land use and human activities based on the three clustered regions obtained from the hierarchical agglomerative cluster analysis. Six principal components (PCs) were obtained, where six varimax factor (VF) of values more than 0.70 that considered strong loading are discussed. The possible pollution sources identified are of anthropogenic sources, mainly municipal waste, surface runoff, agricultural runoff, organic pollution and urban storm runoff. As a conclusion, the application of environmetric methods could reveal important information on the spatial variability of a large and complex river water quality data in order to control pollution sources.

Dynamic Multivariate Analysis for Pollution Profiling and Abatement Recommendation in La Buong Watershed of Vietnam

2021 ◽  
Author(s):  
Luan Hong Pham ◽  
Dung Duc Tran ◽  
Hien Dieu Thi Le ◽  
Toan Quang Dinh ◽  
Quan Hong Nguyen ◽  
...  
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Separation Of Variables ◽  
Temporal Patterns ◽  
Point Sources ◽  
Quality Data ◽  
Dynamic Factor ◽  
Water Quality Data ◽  
Run Off ◽  
Local Point

Abstract Analysis of temporal patterns of high-dimensional time-series water quality data is essential in informing better pollution management. In this study, Dynamic Factor Analysis (DFA) and Cluster Analysis (CA) were adopted to analyze time-series water quality data monitored at five stations SB1, SB2, SB3, SB4 and SB5 on La Buong river in the Southern Vietnam. Application of DFA identified two temporal patterns in SB1 and SB2 and three temporal patterns in SB3, SB4 and SB5. Analysis of factor loadings of water variables revealed run-off-driven patterns with the contribution of Total Suspended Solid (TSS), turbidity or Fe at all stations. The association of other variables like BOD5, COD at SB1, SB2, SB4, and SB5 to this run-off pattern exposed their sharing of common driver. On the contrary, separation of variables like Phosphate (PO43−) in SB3, SB4 and SB5 from run-off pattern suggested their local point-source origin. The derived factors from DFA were later used in time-point CA to explore temporal distribution of pollution intensities. Comparisons between clusters’ value and two regulatory benchmarks A2 and B1 for drinking and irrigation water respectively suggested land-use approach for abating TSS, Fe and BOD5, COD at most sites. The control of point sources of BOD5 and COD pollutants is needed at SB3 along with PO43−, Ammonium (NH4+) and Escherichia coli (E.coli) at SB1 and SB4.

Novel bacterial ratio for predicting faecal age

2003 ◽  
Vol 47 (3) ◽  
pp. 45-49 ◽  
Author(s):  
J. Nieman ◽  
G.M. Brion
Keyword(s):  
Water Quality ◽  
River Water ◽  
Total Coliform ◽  
River Water Quality ◽  
Low Flow ◽  
Quality Data ◽  
Water Quality Data ◽  
Ongoing Research ◽  
Kentucky River ◽  
Survival Studies

This study presents an extension of ongoing research into the utility of the ratio of colonies isolated on membrane filters during the total coliform test using m-Endo broth media. Investigations into the relative shifts in concentrations of indicator bacterial populations over time, in laboratory-based survival studies conducted with filtered river water, were undertaken. Also, analysis of Kentucky River water quality data collected from the inlet of a local water treatment plant was carried out. Survival studies found that the ratio between the raw concentrations of atypical colonies (AC) and total coliform colonies (TC) was directly related to the amount of time coliform spiked river water had been held in open jars in the laboratory. The AC/TC ratio in the jars would rise from <1 at the time of coliform spiking to >200 within 4d. The rise in AC/TC ratio with time in river water was confirmed in the analysis of two years of Kentucky River water quality data where the average AC/TC ratio during months with high river flow (rain) was 3.37 and rose to an average of 27.58 during months with low flow. The average AC/TC ratio during high flow months compared to that of raw human sewage (3.9) and the ratio increased to values associated with animal impacted urban runoff (18.9) during low flow months.

scholarly journals Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1273
Author(s):  
Jianzhuo Yan ◽  
Jiaxue Liu ◽  
Yongchuan Yu ◽  
Hongxia Xu
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Prediction Algorithm ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Quality Prediction ◽  
Water Quality Data ◽  
Water Quality Prediction

The current global water environment has been seriously damaged. The prediction of water quality parameters can provide effective reference materials for future water conditions and water quality improvement. In order to further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new comprehensive deep learning water quality prediction algorithm. Firstly, the water quality data are cleaned and pretreated by isolation forest, the Lagrange interpolation method, sliding window average, and principal component analysis (PCA). Then, one-dimensional residual convolutional neural networks (1-DRCNN) and bi-directional gated recurrent units (BiGRU) are used to extract the potential local features among water quality parameters and integrate information before and after time series. Finally, a full connection layer is used to obtain the final prediction results of total nitrogen (TN), total phosphorus (TP), and potassium permanganate index (COD-Mn). Our prediction experiment was carried out according to the actual water quality data of Daheiting Reservoir, Luanxian Bridge, and Jianggezhuang at the three control sections of the Luan River in Tangshan City, Hebei Province, from 5 July 2018 to 26 March 2019. The minimum mean absolute percentage error (MAPE) of this method was 2.4866, and the coefficient of determination (R2) was able to reach 0.9431. The experimental results showed that the model proposed in this paper has higher prediction accuracy and generalization than the existing LSTM, GRU, and BiGRU models.

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