Geochemical modelling and ecological risk of heavy metals in industrial agricultural soils: a Python-driven approach from southwestern Nigeria

Authors

  • Charles Olubunmi Ogunkoya
    Department of Physics, Ajayi Crowther University, Oyo, P.M.B. 1066, Oyo, Oyo State, Nigeria
  • Gideon Oluyinka Layade
    Department of Physics, Federal University of Agriculture, Abeokuta, P.M.B. 2240, Abeokuta, Ogun State, Nigeria

Keywords:

Agricultural soil contamination, Ecological risk index, Contamination factor, Human health risk assessment, Python geochemistry

Abstract

Heavy metal contamination of agricultural soils near the Ota Industrial Estate, southwestern Nigeria, was assessed using a six-module Python pipeline that integrates geochemical indexing, ecological-risk modelling, and human-health risk assessment. Cu, Pb, Zn, and Cd were analysed at 12 sites. Cu, Pb, and Zn were within WHO/FAO permissible limits. Cd concentrations exceeded the WHO/FAO guideline of 0.80 mg/kg at half of the locations (IN1--IN6, up to 3.09 mg/kg), and two locations exceeded the upper limit of 3.0 mg/kg. Contamination-factor analysis classified Pb at the vehicle-workshop zones (IN7--IN9) as considerable to very high, with CF up to 12.61, and classified Cu/Zn as moderate to very high at fabrication and galvanisation zones. The mean ecological risk index (RI) for the estate was 64.5 ± 24.1, indicating low overall risk, although Cd and Pb fell within moderate Er bands in some sub-zones. Independent recomputation showed that none of five originally proposed metal-pair correlations was significant; however, a strong Pb--Cd correlation was Bonferroni-significant (r = 0.946, p = 0.0012), indicating a shared anthropogenic source. US EPA RAGS non-carcinogenic hazard indices were below unity for adults (HI = 0.014) and children (HI = 0.126). Carcinogenic risk was not quantified because validated oral slope factors for Cd or Pb are unavailable in IRIS. The principal regulatory basis for clean-up is the Cd exceedance. The open-source pipeline provides a reproducible framework for soil-contamination assessment in West African regulatory contexts.

Dimensions

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Published

2026-07-09

How to Cite

Geochemical modelling and ecological risk of heavy metals in industrial agricultural soils: a Python-driven approach from southwestern Nigeria. (2026). Terratmosphera, 1(1), 370. https://doi.org/10.61298/terratmosphera.2026.1.1.372

Issue

Section

Environmental/Pollution Studies

How to Cite

Geochemical modelling and ecological risk of heavy metals in industrial agricultural soils: a Python-driven approach from southwestern Nigeria. (2026). Terratmosphera, 1(1), 370. https://doi.org/10.61298/terratmosphera.2026.1.1.372

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