scholarly journals Bacteriological and heavy metal evaluation of abandoned crude oil–contaminated sites in Gio community, Ogoniland, Nigeria

2021 ◽  
Vol 16 (2) ◽  
pp. 267-273
Author(s):  
Maryjoy Chidinma Maduwuba ◽  
Gideon Chijioke Okpokwasili ◽  
Abiye Anthony Ibiene
Keyword(s):  
Heavy Metal ◽  
Crude Oil ◽  
Ground Water ◽  
Metal Concentration ◽  
Heterotrophic Bacteria ◽  
Heavy Metal Concentration ◽  
Contaminated Sites ◽  
Bacterial Counts ◽  
Sediment Samples ◽  
Ph Values

The environmental pollution in the Niger Delta has been a course of concern. Microorganisms such as bacteria have proved to be of great benefit in the degradation of petroleum derived hydrocarbons. This study evaluated the bacteriological and heavy metal concentration of abandoned crude oil–contaminated sites in Gio community, Ogoniland, Nigeria. Soil, water, and sediment samples were collected from the sites. pH and selected heavy metals in the samples were monitored. Isolation and biochemical characterization were done to determine the heterotrophic and hydrocarbon utilizing bacteria present in the samples. Soil and sediment samples had pH values of 4.80±0.04 and 4.8±0.07 respectively while the surface and ground water samples had pH values of 6.40±0.216 and 6.50±0.01. Iron had the highest heavy metal concentration in all the samples, especially the sediment (1000.80±0.01 mg/kg) while copper and lead had the lowest concentration of < 0.001mg/kg in all the samples except sediment sample. The total petroleum hydrocarbon in the soil (9114.86±0.036 mg/kg), exceeded DPR intervention limit while sediment (1034.46±0.022 mg/kg), surface water (2.515±0.003 µg/L) and ground water sample (32.38±0.99 µg/L) were below DPR’s limit. The soil sample had the highest total culturable heterotrophic bacterial counts and total culturable hydrocarbon utilizing bacterial counts of 5.20 ± 0.21 X 108 CFU/g and 4.00 ± 0.11 X 107 CFU/g, respectively. The following heterotrophic bacteria were isolated and identified from the samples; Pseudomonas spp, Bacillus spp, Acidiphilium spp, Acidibrevibacterium spp and Leptospirillum spp. This study has shown the presence of indigenous resident bacteria which possess the ability to degrade hydrocarbons. These bacteria can be improved through bioaugmentation and bio stimulation for the bioremediation of these sites.

scholarly journals ASSESSMENT OF HEAVY METAL CONCENTRATIONS IN GROUND WATER IN CRITICAL AREAS OF VISAKHAPATNAM CITY, AP, INDIA

2018 ◽  
Vol 6 (9) ◽  
pp. 239-245
Author(s):  
M. Sudhakar Reddy ◽  
T. Byragi Reddy ◽  
CH. Venkataramana
Keyword(s):  
Heavy Metal ◽  
Ground Water ◽  
Metal Concentration ◽  
Urban Areas ◽  
Heavy Metal Concentration ◽  
Metal Concentrations ◽  
Mean Values ◽  
Heavy Metal Concentrations ◽  
Critical Areas ◽  
Icp Ms

Presence of heavy metal concentration in the ground water may cause health problems during intake of through different ways. Present study focused on heavy metal concentration of ground water in the sub-urban areas of Visakhapatnam City, AP, India. Most of heavy metals i.e., Aluminum (Al), Chromium (Cr), Manganese (Mn), Iron (Fe), Nickel (Ni), Zinc (Zn), Arsenic (As), Cadmium (Cd), Mercury (Hg) and Lead (Pb) were analyzed using Inductive Coupled Plasma Mass Spectroscopy (ICP-MS). Mean values of Zn (1.845) > Mn (1.203) > Fe (0.664) > Al (0.334) > Pb (0.245) > Ni (0.082) > Cr (0.066) > As (0.028) > Cd (0.012) > Hg (0.010) results respectively. Results shows that all heavy metal concentrations were exceeded the water quality permissible limit and this area were not suitable for domestic purpose and use before proper treatment.

scholarly journals Contamination of Stream Sediment With Heavy Metals in the Awetu Watershed of Southwestern Ethiopia

2021 ◽  
Vol 9 ◽  
Author(s):  
Higemengist Astatkie ◽  
Argaw Ambelu ◽  
Embialle Mengistie
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Metal Concentration ◽  
Contamination Factor ◽  
Heavy Metal Concentration ◽  
Emission Spectrometry ◽  
Sediment Samples ◽  
Southwestern Ethiopia ◽  
High Degree ◽  
Very High

Surface sediment samples were collected from different streams of Awetu Watershed in southwestern Ethiopia. Sediment samples were analyzed for As, Cd, Cr, Pb, and Hg levels using inductively coupled plasma optical emission spectrometry. The heavy metal concentration ranged from 183.60 to 1,102.80 mg/kg for As (mean 623.32 ± 291.65 mg/kg), 4.40–303.20 mg/kg for Cd (151.09 ± 111.5 mg/kg), 149.20–807.20 mg/kg for Cr (375 ± 212.03 mg/kg), 485.60–3,748.80 mg/kg for Pb (2005.94 ± 954.99 mg/kg) and 3.6–5.6 mg/kg for Hg (4.64 ± 0.59 mg/kg). The mean heavy metal concentration in the streams followed the decreasing order of Pb &gt; As &gt; Cr &gt; Cd &gt; Hg. As, Cr and Pb are detected at high concentrations with values of 623.32, 375.00, and 2,005.94 mg/kg respectively. A low level of heavy concentration (3.6 mg/kg) was recorded for Hg. The contamination factor (CF) of all the studied heavy metals ranged from a low degree (CF &lt; 1) to a very high degree (CF ≤ 6). Mainly, Dololo and Kito streams show a very high degree of contamination (CF ≤ 6) than Awetu and Boye streams. Specifically, As, Cd and Cr in the Dololo and Kito streams have significantly elevated concentrations than others. Geo-accumulation index (Igeo) shows low to moderate contamination level with As, Pb, and Hg; uncontaminated to heavily contaminated by Cr; and moderate to extreme contamination by Cd. Untreated solid waste, garages and farmlands were sources of contamination. Streams receiving wastewater effluents from teaching institutions had higher heavy metal concentrations. Dumping of electronic wastes and car washing discharges also identified as another source of pollution.

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

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document