scholarly journals Evaluation of Toxicity and Bioremediation of Contaminated Drinking Water Sources in Delta State, Nigeria

2020 ◽  
pp. 8-16
Author(s):  
Daniel Olorunfemi ◽  
Richard Uzakah ◽  
Romeo Ofomata ◽  
Charles Okoruwa
Keyword(s):  
Heavy Metal ◽  
Drinking Water ◽  
Metal Concentration ◽  
Water Samples ◽  
Cost Effective ◽  
Bacterial Count ◽  
Heavy Metal Concentration ◽  
Total Bacterial Count ◽  
Potential Cost ◽  
Permissible Limits

Drinking water samples were collected from boreholes in six locations in Ughelli and environs in Delta State of Nigeria and were treated by filtration through a substrate colonized with mycelium of Pleurotus tuber-regium. Water samples were analysed for pH, heavy metal concentration and microbiological content before and after filtration. Results obtained showed that the pH of unfiltered water samples were acidic (5.0 – 5.8) and below the WHO and SON permissible limits for drinking water. The same trend was followed by the concentrations of heavy metals such as lead, iron, zinc and chromium. Water samples from all six locations also had high total bacterial and coliform counts. Filtration through the mycelium colonized substrate showed adjustment of pH to a range within the WHO permissible limits. Reduction in heavy metal concentration ranged from  45.0 – 100%. Total bacterial count of mycofiltered water samples was impressively reduced by 77.3 – 100% and coliform count was not detected. The results obtained in this study makes mycofiltration a potential cost-effective and efficient technique for the treatment of potable water for domestic use.

scholarly journals Ultra-portable, smartphone-based spectrometer for heavy metal concentration measurement in drinking water samples

2021 ◽  
Vol 11 (11) ◽  
Author(s):  
Satyam Srivastava ◽  
Vinay Sharma
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Drinking Water ◽  
Data Collection ◽  
Metal Concentration ◽  
Water Samples ◽  
Heavy Metal Concentration ◽  
Concentration Measurement ◽  
Visible Radiation ◽  
Drinking Water Samples

AbstractHeavy metals are very toxic and hazardous for human health. Onsite screening of heavy metal contaminated samples along with location-based automation data collection is a tedious job. Traditionally high-end equipment’s such as gas chromatography mass spectrometer (GC–MS) and atomic absorption spectrometers have been used to measure the concentration of different heavy metals in water samples but most of them are costly, bulky, and time consuming, and requires expert human intervention. This manuscript reports an ultra-portable, rapid, cost-effective, and easy-to-use solution for onsite heavy metal concentration measurement in drinking water samples. Presented solution combines off-the-shelf available chemical kits for heavy metal detection and developed spectrometer-based readout for concentration prediction, quality judgment, and automatic data collection. Two chemical kits for copper and iron detection have been imported form Merck and have been used for overall training and testing. The developed spectrometer has capability to work with smartphone-based android app and also can work in standalone mode. The developed spectrometer uses white light-emitting diode as a source and commercially imported spectral sensor (AS7262) for visible radiation reception. A low-power sub-GHZ-based wireless embedded platform has been developed and interfaced with source and detector. A power management module also has been designed to monitor the battery status and also to generate low battery indication. Overall modules has been packaged in custom designed enclosure to avoid external light interference. The developed system has been trained using standard buffer samples with known heavy metal concentrations and further tested for water samples collected from institute colony and nearby villages. The obtained results have been validated with commercially imported system from HANNA instruments, and it has been observed that developed system has shown excellent accuracy to predict heavy metal concentration (tested for Fe and Cu) in water samples.

Detection of Coliform, faecal Coliform And Total Bacterial Count From Drinking Water Of Varanasi (U.P.) India

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
Alok Kumar Singh ◽  
Anand Prakash Singh ◽  
Sanjay Srivastava
Keyword(s):  
Drinking Water ◽  
Water Samples ◽  
Tap Water ◽  
Bacterial Count ◽  
Most Probable Number ◽  
Total Bacterial Count ◽  
Indicator Organisms ◽  
Cross Sectional ◽  
Probable Number ◽  
Hand Pump

In India source of drinking water at Varanasi city for common people are tap water, well, hand pump, Ganga river and stored tank water collected from bore well. All water samples were studied to assess their bacteriological characteristics and suitability for potable purposes. A cross-sectional epidemiological method was adopted to investigate the drinking water of six different sites of Varanasi city. The bacteriological examination of water samples included the most probable number of presumptive coliforms, faecal coliforms, and total bacterial count. The results showed that the total coliform count was detected in all the site. In all the methods coliforms presence was indicated. Maximum number of coliform observed in all the seasons, were from river and well water followed by hand pump, tap water and stored tank. The most common group of indicator organisms used in water quality monitoring are coliforms. These organisms are representative of bacteria normally present in the intestinal tract of mammals including human. Contamination of water may occur through different way like sewage disposal in the river, seepage of bathing near sites, fecal excreta of human, bird and other animals. Improving and expanding the existing water treatment and sanitation systems are more likely to provide good, safe and sustainable sources of water in the long term.

scholarly journals The Relation between Bacterial and Heavy Metal Water Pollution and Blood Micronuclei as Biomarkers in the Tigris River Fish

2017 ◽  
Vol 14 (1) ◽  
pp. 126-134 ◽  
Author(s):  
Baghdad Science Journal
Keyword(s):  
Heavy Metal ◽  
River Water ◽  
Fish Species ◽  
Water Samples ◽  
Bacterial Count ◽  
Heavy Metal Concentration ◽  
Viable Count ◽  
Tigris River ◽  
Quality Of Water ◽  
Garra Rufa

The objective of this study is to evaluate the bacterial count and heavy metal concentration of river water on fish micronuclei. Fish and water samples are carried out in 1 May to 1 June 2013 from Tigris River. A total of fifty three fish sample are studied. The bacteriological quality of water showed that the total viable count is ranged from 150×103 to 352×103 cfu/ml and fecal coliform counts was 1250 cell/100ml during the study period. All the metals (Cu, Hg, Pb, and Zn) are within the normal limit, but Cd was slightly elevated in river water samples. The appearance of micronuclei in red blood cells of all fish species is detect , by recording a larger number of it, in ( Abu Alsomere , Hishne , Bannini Kaber al fam & Karkoor ahmar) species compared with (Abu AL hakam , Nabbash , Kattan , Himri & Tela shami ) species. There is a difference in the percentages of the leukocytes types in different fish species, the highest percentage (12.3) of lymphocyte is recorded in Barbus xanthopterus and the lowest (1.5) is in Garra rufa

scholarly journals Heavy Metal Concentration and Mutagenic Assessment of Pond Water Samples: a Case Study from India

2019 ◽  
Vol 29 (1) ◽  
pp. 789-798
Author(s):  
Sneh Rajput ◽  
Tajinder Kaur ◽  
Saroj Arora ◽  
Rajinder Kaur
Keyword(s):  
Heavy Metal ◽  
Metal Concentration ◽  
Water Samples ◽  
Heavy Metal Concentration ◽  
Pond Water

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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