Evaluating Phytoremediation Potential benchmarks of Medicinal Plants from Ashanti and Eastern Regions of Ghana
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».