Evaluating Phytoremediation Potential benchmarks of Medicinal Plants from Ashanti and Eastern Regions of Ghana
Bibliographic record
Abstract
Phytoremediation which is the process of applying plants to extract pollutants from the ecosystem has gained popularity as a possible antidote for cleaning up vicinities since it offers a cost-effective and environmentally friendly treatment.This study supplies benchmarks for hazardous metals (lead, arsenic, copper, cadmium and mercury) depending on extensive assessment of plant variety and effectiveness indicators.Niton XL3 GOLDD+ X-ray fluorescence (XRF) was used to analyze the soils for the presence of hazardous metals (Pb, As, Cu and Cd) while levels (Pb, As, Cu and Cd) in medicinal plants were analyzed using VARIAN SPECTRA AA220 Zeeman Atomic Absorption Spectrometer (AAS) (Varian Canada Inc.). Mercury determination was by Atomic Absorption Spectrometry employed in the RA-915M Zeeman mercury analyzer (Lumex, St. Petersburg, Russia). Soil samples gathered from sites in Ashanti Region manifested mean concentrations of 4.39, 4.85, 11.66, 6.69 and 0.0474 for Pb, As, Cu, Cd and Hg respectively whereas that of Eastern Region had 3.39, 6.40, 13.44,6.02 and 0.06 for Pb, As, Cu, Cd and Hg respectively. The mean soil metal concentrations were below World Health Organization Maximum Permissible Limits (WHO/MPL) for the respective metals. About thirty-eight medicinal plants samples were analyzed. Levels of Pb, As, Cu, Cd evaluated in medicinal plants were below WHO MPL for the respective metals except that of Hg which possessed concentrations above WHO MPL. The translocated factor (TF), bioconcentration factor (BCF) and bioaccumulated coefficient (BAC) were calculated.The range of TF, BCF and BAC were: TF [Ashanti: TF(Ashanti, Pb) = (BDL-1.20), TF(Ashanti, As) = (0.78-3.87),TF(Ashanti, Cu) = (1.07-2.11), TF(Ashanti, Cd) = 0.13-11.88) and TF(Ashanti, Hg) = 0.61-1.69); Eastern: TF(Eastern, Pb) = (BDL-12.62), TF(Eastern, As) = (0.20-4), TF(Eastern, Cd) = (0.15- 36.30) and TF(Eastern, Hg) = BDL-13.17); BCF [Ashanti: BCF(Ashanti, Pb) = (BDL- 0.22), BCF(Eastern, As) = (0.11- 0.32), BCF (Eastern, Cu) = (0.01- 0.11), BCF (Eastern, Cd) = (BDL-0.03) and BCF (Eastern, Hg)= (0.53-2389.41) and BAC [Ashanti: BAC (Ashanti,Pb) = (BDL- 0.14), BAC (Ashanti, As) = (0.04-0.65), BAC (Ashanti,Cu) = (BDL-0.07), BAC (Ashanti,Cd) = BDL-0.20) and BAC (Ashanti,Hg) = (2.01-191.70); Eastern: BAC (Eastern,Pb) (BDL-0.26), BAC (Eastern,As) = (BDL-0.62), BAC (Eastern,Cu) = (BDL-1.05), BAC (Eastern,Cd) = (BDL-0.04) and BAC (Eastern,Hg) = (BDL- 97.15). Calculated TF values showed that most of the medicinal plants from Ashanti Region exhibited high phytoextractive potential for As [3(60%) Ashanti Region > 8(53%) Eastern Region], Cu[(All,100%) Ashanti Region > 6(40%) Eastern Region], Cd [3(60%) Ashanti Region > 4(27%) Eastern Region] and Hg[2(40%) Ashanti Region > 4(27%) Eastern Region] than those of Eastern Region except that of Pb. Computed BCF values demonstrated medicinal plants from the two regions had phytooextractive potential for only Hg. Estimated BAC contents of medicinal plants from Ashanti Region all had phytoextractive potential for only Hg contrary to Eastern Region plants which had 5(15%) phytoextractive potential for only Hg. The medicinal plants from the two regions could serve as phytostabilizers for Hg.There occurred no significance difference in TF, BCF and BAC of medicinal plants from both Ashanti and Eastern Regions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".