Some Physiochemical and Heavy Metal Concentration in Surface Water Streams of Tutuka in the Kenyasi Mining Catchment Area

2013 ◽  
Author(s):  
Louis Boateng
Keyword(s):  
Heavy Metal ◽  
Surface Water ◽  
Metal Concentration ◽  
Water Use ◽  
Biochemical Oxygen Demand ◽  
Catchment Area ◽  
Oxygen Demand ◽  
Heavy Metal Concentration ◽  
The People ◽  
Brong Ahafo Region

This research was conducted in the Akantansu stream of Tutuka in Kenyasi in the Brong Ahafo Region of Ghana in the months of October and November 2010 and January 2011. The major objectives of the study were to measure levels of pH, BOD (biochemical oxygen demand), lead, chromium, and arsenic in the Akantansu stream of Tutuka and to find ways that the community could ensure safe water use. To achieve the objectives of the study, sampling was done over a period of three months and data was collected and analyzed into graphs and ANOVA tables. The research revealed that the levels of arsenic and BOD were high as compared to the standards of WHO and EPA. If the people of Tutuka continue to use the stream, they may experience negative health effects (e.g., nausea, vomiting, diarrhea, etc.). The level of pH, chromium and lead was acceptable as compared to the standard of WHO and EPA.

scholarly journals Evaluation of Water Quality and Heavy Metal Concentration across Two Connecting Tropical Lagoons in Lagos, Nigeria

Bio-Research ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 1202-1209
Author(s):  
Gabriel Femi Okunade ◽  
Muyideen Owonire Lawal ◽  
Roland Efe Uwadiae
Keyword(s):  
Water Quality ◽  
Heavy Metal ◽  
Metal Concentration ◽  
Oxygen Demand ◽  
Total Suspended Solid ◽  
Heavy Metal Concentration ◽  
Suspended Solid ◽  
Total Hardness ◽  
Wet Season ◽  
Total Dissolved Solid

Ologe and Badagry Lagoons are important tropical lagoons in Lagos, Nigeria. The water quality and heavy metal concentration were studied for a period of 2 years (Aug. 2016 to Jul. 2018) using standard methods. The least temperature obtained was 28.70±0.05 °C in Ologe Lagoon during the wet season and the maximum recorded was 29.41±0.08 in in Badagry Lagoon during the dry season. During the wet season (May- October) the temperature was steady and similar between the two connecting tropical lagoons. The salinity values vary at different stations in both Lagoon, 0.06 to 0.44 % in Ologe Lagoon and 0.08 to 0.28 % in Badagry Lagoon. Badagry Lagoon showed significant higher values in conductivity, total dissolved solid, chemical oxygen demand, biological oxygen demand, total suspended solid and total hardness across seasons. Heavy metal results showed that except for lead (0.25±0.10 mg/L), Ologe Lagoon had higher concentrations of all examined heavy metals (Zinc, copper, iron, chromium, lead, cadmium, manganese and cobalt) than Badagry Lagoon across season. Furthermore, cadmium, manganese and cobalt were not detected in Badagry Lagoon across season. The two studied connecting Lagoons especially Ologe Lagoon is exposed to dramatic deterioration in its water quality due to different wastes that discharge into the water body. These lagoons are clearly polluted by metals for various utilizations. As a result, the study suggests enforcing the controls on waste discharged into lagoons.

scholarly journals Heavy metal concentration in fish tissues inhabiting waters of “Buško Blato” reservoar (Bosnia and Herzegovina)

2008 ◽  
Vol 145 (1-3) ◽  
pp. 475-475 ◽  
Author(s):  
Elizabeta Has-Schön ◽  
Ivan Bogut ◽  
Gordana Kralik ◽  
Stjepan Bogut ◽  
Janja Horvatić ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Metal Concentration ◽  
Bosnia And Herzegovina ◽  
Heavy Metal Concentration ◽  
Fish Tissues

Remediation-related monitoring of heavy metal concentration of contaminated Technosol using hyperspectral measurements

2021 ◽  
Author(s):  
Friederike Kaestner ◽  
Magdalena Sut-Lohmann ◽  
Thomas Raab ◽  
Hannes Feilhauer ◽  
Sabine Chabrillat
Keyword(s):  
Heavy Metal ◽  
Random Forest ◽  
Metal Concentration ◽  
Strong Dependence ◽  
Scatter Correction ◽  
Heavy Metal Concentration ◽  
Standard Normal Variate ◽  
Multiplicative Scatter Correction ◽  
Second Derivatives ◽  
Standard Normal

<p>Across Europe there are 2.5 million potentially contaminated sites, approximately one third have already been identified and around 15% have been sanitized. Phytoremediation is a well-established technique to tackle this problem and to rehabilitate soil. However, remediation methods, such as biological treatments with microorganisms or phytoremediation with trees, are still relatively time consuming. A fast monitoring of changes in heavy metal content over time in contaminated soils with hyperspectral spectroscopy is one of the first key factors to improve and control existing bioremediation methods.</p><p>At former sewage farms near Ragow (Brandenburg, Germany), 110 soil samples with different contamination levels were taken at a depth between 15-20 cm. These samples were prepared for hyperspectral measurements using the HySpex system under laboratory conditions, combing a VNIR (400-1000 nm) and a SWIR (1000-2500 nm) line-scan detector. Different spectral pre-processing methods, including continuum removal, first and second derivatives, standard normal variate, normalisation and multiplicative scatter correction, with two established estimation models such as Partial Least Squares Regression (PLSR) and Random Forest Regression (RFR), were applied to predict the heavy metal concentration (Ba, Ni, Cr, Cu) of this specific Technosol. The coefficient of determination (R2) shows for Ba and Ni values between 0.50 (RMSE: 9%) and 0.61 (RMSE: 6%) for the PLSR and between 0.84 (RMSE: 0.03%) and 0.91 (RMSE: 0.02%) for the RFR model. The results for Cu and Cr show values between 0.57 (RMSE: 17.9%) and 0.69 (RMSE: 15%) for the PLSR and 0.86 (0.12%) and 0.93 (0.01%) for the RFR model. The pre-processing method, which improve the robustness and performance of both models best, is multiplicative scatter correction followed by the standard normal variate for the first and second derivatives. Random Forest in a first approach seems to deliver better modeling performances. Still, the pronounced differences between PLSR and RFR fits indicate a strong dependence of the results on the respective modelling technique. This effect is subject to further investigation and will be addressed in the upcoming analysis steps.</p>

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