Modelling Transport of Heavy Metals in Geita Wetland along Mamubi River

2018 ◽  
Vol 37 (1) ◽  
pp. 72-88
Author(s):  
Ezrael J. Massawe ◽  
Richard Kimwaga
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
Lake Victoria ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Model Parameters ◽  
Prediction Errors ◽  
Water Quality Simulation ◽  
Suspended Particulate ◽  
Monitoring Stations

The modelling of heavy metals in rivers is highly dependent on hydrodynamics, the transport of suspended particulate matter and the partition between dissolved and particulate phases. This paper presents the development of hydrodynamic model DUFLOW, which is a one dimensional flow and water quality simulation package, that describes the processes governing transformations and transport of heavy metals (Hg, Ni and Cu) along Mabubi River in the Geita wetland. Two monitoring stations were established along Mabubi River at the inlet (MBSP1) and outlet (MBSP2) of the wetland. A set of DUFLOW model inputs representative of the water conditions were collected from the established monitoring stations. The model was calibrated and validated for the prediction of flow and heavy metals (Hg, Ni, and Cu) transport, against a set of measured mean monthly monitoring data. Sensitive model parameters were adjusted within their feasible ranges during calibration to minimize model prediction errors. At the gauging station MBSP2, the calibration results showed the model predicted mean monthly flow within 17% of the measured mean monthly flow while the r2 coefficient and Nash-Sutcliffe (NSE) were 0.83 and 0.79 respectively. At the water quality monitoring station MBSP2, the calibration results showed the model predicted heavy metals (Hg, Ni and Cu) concentrations within 13% and 17% of their respective measured mean monthly concentrations. The mean monthly comparisons r 2 values for heavy metals ranged from 0.75 to 0.88 while the NSE values were between 0.70 and 0.82. The model results and field measurements demonstrated that about 40% of the annual heavy metals loadings which would otherwise reach the Lake Victoria are retained in the wetland. The Mabubi river model can therefore be used for prediction of heavy metals (Hg. Ni and Cu) transformation processes in the Geita wetland.

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

Influence of Heavy Metals on the Growth of Chlorella vulgaris

2021 ◽  
Vol 15 (3) ◽  
pp. 374-379
Author(s):  
Bingbing Pang ◽  
Mingzhou Zeng ◽  
Wenjia Zhang ◽  
Fengcai Ye ◽  
Changhua Shang
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
Growth Rate ◽  
Growth Inhibition ◽  
Chlorella Vulgaris ◽  
Specific Growth ◽  
Concentration Level ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Average Specific Growth Rate

Growth inhibition of chromium (Cr), cadmium (Cd) and lead (Pb) to fresh water microalga Chlorella vulgaris (C. vulgaris) FACHB-8 was examined. These results demonstrated that the concentration level (EC50 value) of three heavy metals (Cr, Cd and Pb) could be utilized as an indicator for evaluating the toxicities of Cr, Cd and Pb for microalga growth. The EC50 values of Cr for C. vulgaris were 0.22, 0.07 and 0.04 mg/L at 24, 48 and 72 h based on Algorithm 2 (%Ir, percent inhibition in average specific growth rate), respectively. The EC50 values of Cd for C. vulgaris were 2.76, 1.08 and 0.93 mg/L at 24, 48 and 72 h based on Algorithm 2, respectively. The EC50 values of Pb for C. vulgaris were 73.21, 65.02 and 48.38 mg/L at 24, 48 and 72 h based on Algorithm 2, respectively. The results laid a good foundation for the application of C. vulgaris in the water quality monitoring.

scholarly journals Decline in Transparency of Lake Hongze from Long-Term MODIS Observations: Possible Causes and Potential Significance

2019 ◽  
Vol 11 (2) ◽  
pp. 177 ◽  
Author(s):  
Na Li ◽  
Kun Shi ◽  
Yunlin Zhang ◽  
Zhijun Gong ◽  
Kai Peng ◽  
...  
Keyword(s):  
Water Quality ◽  
Aquatic Vegetation ◽  
Water Quality Monitoring ◽  
Validation Dataset ◽  
Model Parameters ◽  
Potential Significance ◽  
Important Indicator ◽  
Quality Issue ◽  
Mean Square Errors

Transparency is an important indicator of water quality and the underwater light environment and is widely measured in water quality monitoring. Decreasing transparency occurs throughout the world and has become the primary water quality issue for many freshwater and coastal marine ecosystems due to eutrophication and other human activities. Lake Hongze is the fourth largest freshwater lake in China, providing water for surrounding cities and farms but experiencing significant water quality changes. However, there are very few studies about Lake Hongze’s transparency due to the lack of long-term monitoring data for the lake. To understand long-term trends, possible causes and potential significance of the transparency in Lake Hongze, an empirical model for estimating transparency (using Secchi disk depth: SDD) based on the moderate resolution image spectroradiometer (MODIS) 645-nm data was validated using an in situ dataset. Model mean absolute percentage and root mean square errors for the validation dataset were 27.7% and RMSE = 0.082 m, respectively, which indicates that the model performs well for SDD estimation in Lake Hongze without any adjustment of model parameters. Subsequently, 1785 cloud-free images were selected for use by the validated model to estimate SDDs of Lake Hongze in 2003–2017. The long-term change of SDD of Lake Hongze showed a decreasing trend from 2007 to 2017, with an average of 0.49 m, ranging from 0.57 m in 2007 to 0.42 m in 2016 (a decrease of 26.3%), which indicates that Lake Hongze experienced increased turbidity in the past 11 years. The loss of aquatic vegetation in the northern bays may be mainly affected by decreases of SDD. Increasing total suspended matter (TSM) concentration resulting from sand mining activities may be responsible for the decreasing trend of SDD.

monEAU: a platform for water quality monitoring networks

2008 ◽  
Vol 57 (7) ◽  
pp. 1079-1086 ◽  
Author(s):  
L. Rieger ◽  
P. A. Vanrolleghem
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Problem Analysis ◽  
Monitoring Networks ◽  
High Data ◽  
New Concepts ◽  
Set Up ◽  
New Generation ◽  
Monitoring Practice ◽  
Monitoring Stations

Continuous monitoring of water quality creates huge amounts of data and therefore requires new concepts to guarantee high data quality and to prevent data graveyards. Monitoring stations commonly used in practice today suffer from insufficient flexibility and a lack of standardization. That is, although a lot of monitoring tasks are comparable and should lead to robust and powerful platforms, most monitoring stations are case specific developments. In this paper the underlying ideas of a new generation of monitoring networks is described. First a problem analysis of monitoring stations typically seen in current river monitoring practice is outlined, then the monEAU vision on monitoring networks will be discussed together with an overview of a planned system set-up with innovative data evaluation concept.

scholarly journals Mutual relationships of suspended sediment, turbidity and visual clarity in New Zealand rivers

2015 ◽  
Vol 367 ◽  
pp. 265-271 ◽  
Author(s):  
D. J. Ballantine ◽  
A. O. Hughes ◽  
R. J. Davies-Colley
Keyword(s):  
Water Quality ◽  
Suspended Sediment ◽  
Aquatic Ecosystems ◽  
Water Quality Monitoring ◽  
Suspended Particulate ◽  
Sediment Mass ◽  
Data Scatter ◽  
Significant Interest ◽  
Sufficient Precision ◽  
Mass Concentrations

Abstract. Many river water quality monitoring programmes do not measure suspended particulate matter (SPM) mass concentrations despite significant interest in its multiple effects on aquatic ecosystems. Regular monthly sampling usually intercepts rivers in baseflow when suspended sediment mass concentrations and fluxes are relatively low and not of particular interest. New Zealand’s National Rivers Water Quality Network (NRWQN) is probably typical in not measuring SPM mass, although visual clarity and nephelometric turbidity are routinely measured. In order to better characterize SPM in NZ rivers, total suspended sediment (TSS) was temporarily added to the NRWQN. Turbidity, visual clarity and TSS are mutually inter-related over all 77 sites, although with considerable data scatter. However, within individual rivers turbidity and visual clarity are typically fairly closely related to TSS and provide fair to excellent surrogates. Therefore, TSS need not be measured routinely because it can be estimated with sufficient precision for many purposes from visibility or turbidity.

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