scholarly journals FITOREMEDIASI TANAH TERCEMAR LOGAM TIMBAL (Pb) MENGGUNAKAN TANAMAN LIDAH MERTUA (Sansevieria trifasciata) DAN JENGGER AYAM (Celosia plumosa)

2018 ◽  
Vol 3 (2) ◽  
pp. 62-69
Author(s):  
Rhenny Ratnawati ◽  
Risna Dwi Fatmasari
Keyword(s):  
Heavy Metal ◽  
Metal Concentration ◽  
Contaminated Soil ◽  
Heavy Metal Concentration ◽  
Sampling Time ◽  
Species Variation ◽  
Soil Lead ◽  
Plant Parts ◽  
High Concentrations ◽  
Pb Concentration

Soil is a very influential medium of human survival. One of the parameters affecting soil quality is heavy metal concentration in soil, especially heavy metal of lead (Pb). High concentrations of Pb in the soil can treated with phytoremediation techniques. The aims of this research are: 1. To investigate the reduction of heavy metal Pb in the soil by phytoremediation, 2. To investigate the effectiveness of plants to absorb heavy metal Pb in the soil, and 3. To investigate the distribution of Pb concentration in the plant parts. The study variables used in this research are species variation of plants Sansevieria trifasciata and Celosia pulmosa. Phytoremediation test of Pb heavy metal contaminated soil was carried out for 4 weeks with sampling time on days 0, 7, 14, 21, and 28. The parameters analyzed of this research is Pb concentrations on soil and plant parts, namely roots, stems, leaf. Physical observations of plants were also carried out to support this research. The results show that the reactor with Sansevieria trifasciata had a higher effectiveness of removal of Pb in 81.08% (112 mg/kg) than Celosia pulmosa in 59.63% (293 mg/kg). The effectiveness of the absorption of Sansevieria trifasciata was higher 70.50% (418 mg/kg) than Celosia pulmosa 52.40% (311 mg/kg). The distribution of Pb concentrations in the plant of Sansevieria trifasciata and Celosia pulmosa is almost the same, with the most concentration being in the root part and at least scattered in the leaves of the plant. Keywords: Celosia pulmosa, Soil, Lead, Phytoremediation, Sanseviera trifasciata.

The genetic diversity of Asplenium viride (Aspleniaceae) fern colonizing heavy metal-polluted sites

2021 ◽  
Author(s):  
Marcin Woch ◽  
Grzegorz ◽  
Iwona Jedrzejczyk ◽  
Marek Podsiedlik ◽  
Anna Stefanowicz
Keyword(s):  
Heavy Metals ◽  
Genetic Diversity ◽  
Heavy Metal ◽  
Genome Size ◽  
Metal Concentration ◽  
Diploid Species ◽  
Heavy Metal Concentration ◽  
Long Distance ◽  
New Genotype ◽  
High Concentrations

Abstract Heavy metals can affect the morphology, physiology and evolution of plants. Asplenium viride is a diploid species, belonging to the largest genus of the cosmopolitan fern family Aspleniaceae, and occurring on various types of alkaline rocks. It is known to colonize sites with high concentrations of heavy metals, exhibiting changes in frond morphology. Microevolutionary processes, manifesting as changes in genome size and new genotype formation, can ultimately lead to the formation of new subspecies and speciation. This study aimed to evaluate the morphological and genetic diversity of A. viride, and to test for a potential correlation between variability and heavy metal concentration. Analysis of A. viride specimens, from one metalliferous site and five non-metalliferous localities, showed no apparent variation in genome size between plants from affected and non-affected sites. There was no significant correlation between genetic variability and heavy metal concentration. This was possibly due to intragametophytic selfing, caused by patchy habitats and subsequent founder effects, resulting from long-distance colonization by single spores.

scholarly journals Heavy metal concentration in some freshwater fishes during autumn and winter in Mymensingh district of Bangladesh

2021 ◽  
Vol 33 (1) ◽  
pp. 81-87
Author(s):  
MD. ABUL MANSUR ◽  
MD. NURUL HAIDER ◽  
MD. MUBARACK HOSSAIN ◽  
MD. MANIK MIA ◽  
MITHUN KARMAKAR
Keyword(s):  
Heavy Metal ◽  
Metal Concentration ◽  
Fish Species ◽  
Heavy Metal Concentration ◽  
Freshwater Fishes ◽  
Fish Muscle ◽  
Acceptable Level ◽  
Allowable Limit ◽  
Autumn And Winter ◽  
Pb Concentration

Study was conducted to determine the heavy metal concentration in 5 freshwater fishes Heteropneustes fossilis, Clarias batrachus, Anabus testudineus, Oreochromis niloticus, and Mystus gulio during autumn and winter. Most widely eaten five freshwater fish species were selected for this purpose. These fish species were Among the heavy metals estimated in this study (Cd, Cr, Pb, Cu, Zn) the Cd, Cu, Zn were within the acceptable level but Cr and Pb concentration was above the maximum allowable limit. The Cd, Cr, Pb concentration in the fish muscle was higher in winter as compared to that of autumn but the Cu and Zn concentration was higher in autumn when compared to that of winter. Result of the present research indicates that the heavy metal concentration in fish muscle significantly varies with season. Some heavy metal were within the acceptable level in autumn season but above the maximum allowable limit in winter season. In H. fossilis, C. batrachus, and O. niloticus, heavy metal concentration was within the acceptable level but in A. testudineus and M. gulio heavy metal concentration was above the maximum allowable limit. So heavy metal concentration varied with season as well as with species of fish.

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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