Heavy Metal Concentration in Fruit Samples During the Dry Season From three Major Markets in Enugu, Nigeria

2020 ◽  
Vol 5 (1) ◽  
pp. 83-91
Author(s):  
G.I Ameh ◽  
L.C Ogbodo ◽  
C.D Nwani
Author(s):  
G. I. Ameh ◽  
L. C. Ogbodo

The effect of seasonal changes on heavy metals concentration in three commonly edible fruits in Enugu State was analyzed. Banana, pineapple and watermelon samples were collected during three rainy season months (June, July and August) from three markets in the three districts of Enugu state (Enugu North, Enugu West and Enugu East). Heavy metals evaluated during the study include lead, cadmium, cobalt, nickel, zinc and copper. Metals in the samples were quantified using atomic absorption spectrophotometry (AAS) at specific wave lengths and values reported in mg/kg. The result of the study showed the maximum and minimum values of heavy metals observed in all the samples were; 0.28 – 0.03 mg/kg, 0.22 – 0.01 mg/kg, 0.13 – 0.01 mg/kg, 0.64 – 0.33 mg/kg, 0.69 – 0.01 mg/kg and 13.88 – 1.42 mg/kg for lead, cadmium, nickel, cobalt, copper and zinc respectively. The quantity of metals in all the samples, on average, reduces as the rainy season month progresses. The order of heavy metal concentrations in the fruit samples from the markets were in the following decreasing order; Nsukka market> Ogbete market> Ezeagu market. Banana fruit showed the highest concentration of heavy metals while watermelon showed the least heavy metal concentration. Values obtained were compared to WHO maximum permissible limit for each metal. Environmental pollution should be prevented in market areas to avoid food poisoning from consumption of contaminated food.


PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8990
Author(s):  
Şeyma Demirhan Aydın ◽  
Mine Pakyürek

This study was carried out to determine the possible heavy metal accumulation in fruits and leaves of Zivzik pomegranate (Punica granatum L.) grown in two different roadside orchards located in Pirinçli and Kapılı villages of Siirt province, Turkey. Leaf and fruit samples were collected from trees located at 0, 50, 100 m distances from the main roads. Plant samples were analyzed for cobalt (Co), nickel (Ni), cadmium (Cd), lead (Pb) and chromium (Cr) concentrations. The Co, Ni, Cd, Pb and Cr concentrations of fruit samples collected from Pirinçli village were ranged from 0.082 to 0.238 mg kg−1, from 1.160 to 1.559 mg kg−1, from 0.087 to 0.179 mg kg−1, 0.326 to 0.449 mg kg−1 and 0.606 to 1.054 mg kg−1, respectively. The Co, Ni, Cd, Pb and Cr concentrations of fruit samples from Kapılı village were between 0.085 and 0.137 mg kg−1, 1.042 and 1.123 mg kg−1, 0.037 and 0.076 mg kg−1, 0.277 and 0.520 mg kg−1 and 0.762 and 0.932 mg kg−1, respectively. Heavy metal concentrations of leaf samples from Pirinçli village varied from 0.191 to 0.227 mg Co kg−1, 2.201 to 3.547 mg Ni kg−1, 0.051 to 0.098 mg Cd kg−1, 0.535 to 0.749 mg Pb kg−1 and from 1.444 to 2.017 mg Cr kg−1. Similarly, the heavy metal concentration of leaf samples from Kapılı villages were between 0.213 and 0.217 mg Co kg−1, 2.160 and 2.511 mg Ni kg−1, 0.058 and 0.114 mg Cd kg−1, 0.579 and 0.676 mg Pb kg−1 and 1.688 and 1.518 mg Cr kg−1. The Co, Ni and Cr concentrations in fruit samples collected from 0, 50 and 100 meters to the main road in Pirinçli village were at statistically significant level, while only Ni concentration in leaf samples collected from 0, 50 and 100 meters to the main road was at significant level. In contrast, heavy metal concentrations in fruit and leaf samples collected from 0, 50 and 100 m to the main road in Kapılı village were not statistically significant level.


2008 ◽  
Vol 145 (1-3) ◽  
pp. 475-475 ◽  
Author(s):  
Elizabeta Has-Schön ◽  
Ivan Bogut ◽  
Gordana Kralik ◽  
Stjepan Bogut ◽  
Janja Horvatić ◽  
...  

2021 ◽  
Author(s):  
Friederike Kaestner ◽  
Magdalena Sut-Lohmann ◽  
Thomas Raab ◽  
Hannes Feilhauer ◽  
Sabine Chabrillat

<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>


Sign in / Sign up

Export Citation Format

Share Document