Patterns And Source Apportionment Of Potentially Toxic Elements Distribution In The Soils Of The Nangodi Area, Northeast Ghana: A Multivariate And Machine Learning Approach.

dc.contributor.authorKazapoe, R.W.
dc.contributor.authorKwayisi, D.
dc.contributor.authorAlidu, S.
dc.contributor.authorFynn, O.F.
dc.contributor.authorSagoe, S.D.
dc.contributor.authoret al.
dc.date.accessioned2025-07-25T17:02:02Z
dc.date.issued2025-03-14
dc.descriptionResearch Article
dc.description.abstractThis study assessed the sources, distribution and pollution status of heavy metals in the Nangodi area of Northwestern Ghana. Cr (120.86 mg/kg) and Co (30.92 mg/kg) had respective average values of 2.4 and 1.2 times higher than their Continental Crustal Averages of 100 mg/kg and 25 mg/kg. The Potential Toxic Elements (PTE) displayed a decreasing trend in the order Ba > Cr > V > Sr > Cu > Zn > Co > Mo. The Metal Index assessment highlighted the significant effect of galamsey on the soil health of the area. The samples were ranked as slightly (26.45 %), moderately (25.18 %), Strongly (21.20 %) and seriously (23.91 %) affected. The positive Matrix Factorization identified three Factors as controlling PTEs in the area. Factor 1/anthropogenic (V = 84 %, Cu = 84 %, Co = 75.5 % and Zn = 58.9 %). Factor 2/geogenic (Ba = 87.5 %, Sr = 83.1 %, Pb = 57.8 %). Factor 3/mixed source (Cr = 91.8 % and Mo = 43.4 %). The Pearson correlation matrix outlined two groups of PTEs; (1) PTEs with moderate to strong correlation (V, Co, Cu and Zn) and (2) PTEs with weak to moderate correlation (Sr, Mo, Ba and Pb). The first group occurs at the southwestern boundary of the study area, reflecting the influence of local geology and mining practices on the levels of potentially toxic elements (PTEs) in the soil. The Self Organising Map (SOM) identified three higher concentration clusters, V, Zn, Cu, and Co, inferred to be the mining activities. Geogenic-sourced Sr and Ba are located centrally. Pb, Mo, and Cr show distinct distributions, suggesting mixed factors affecting their spread. The study identified systematic heavy metal pollution, which could pose a deleterious risk to the environment and inhabitants of the area.
dc.description.sponsorshipNone
dc.identifier.citationKazapoe, R. W., Kwayisi, D., Alidu, S., Fynn, O. F., Sagoe, S. D., Amuah, E. E. Y., & Nyavor, E. (2025). Patterns and source apportionment of potentially toxic elements distribution in the soils of the Nangodi area, Northeast Ghana: A multivariate and machine learning approach. Journal of Hazardous Materials Advances, 18, 100688.
dc.identifier.urihttps://doi.org/10.1016/j.hazadv.2025.100688
dc.identifier.urihttps://ugspace.ug.edu.gh/handle/123456789/43544
dc.language.isoen
dc.publisherJournal of Hazardous Materials Advances
dc.subjectMining
dc.subjectPollution
dc.subjectSelf organising maps
dc.subjectPollution indices
dc.subjectEcological risk
dc.titlePatterns And Source Apportionment Of Potentially Toxic Elements Distribution In The Soils Of The Nangodi Area, Northeast Ghana: A Multivariate And Machine Learning Approach.
dc.typeArticle

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Patterns And Source Apportionment Of Potentially Toxic Elements Distribution In The Soils Of The Nangodi Area, Northeast Ghana A Multivariate And Machine Learning Approach.pdf
Size:
15.66 MB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: