Integrating Pollution Indices and Pca-alm for Soil Contamination Assessment at the University of Maiduguri Farm, Nigeria
Year: 2026
Type: article
Abstract: This study evaluated heavy metal contamination in agricultural soils that had received untreated sewage sludge for four years at the University of Maiduguri Research Farm, Nigeria, using pollution indices integrated with Principal Component Analysis and Automatic Linear Modelling (PCA-ALM). Soil samples (n=72) were collected from 36 grid points at two depths (0–15 cm and 15–30 cm) and analysed for Cd, Cr, Cu, Fe, Pb, and Zn. Single-metal indices (Contamination Factor, Geoaccumulation Index, Enrichment Factor, and Ecological Risk Factor) and composite indices (Pollution Load Index, Nemerow Pollution Index, and Potential Ecological Risk Index) were computed. Enrichment factors indicated extremely severe anthropogenic enrichment for Cd (349.2), Pb (321.3), Cu (140.3), and Zn (135.1). Comparison with Nigerian DPR-EGASPIN limits showed that mean Cd (1.06 mg/kg) exceeded the target value (0.8 mg/kg), while the maximum Cu concentration (209.6 mg/kg) exceeded the intervention value (190 mg/kg). ANOVA revealed no significant depth-related differences (p > 0.05). PCA reduced 27 variables to three components, which explained 92.8% of the total variance: PC1 (pollution intensity, 71.5%), PC2 (enrichment patterns, 17.4%), and PC3 (pH influence, 3.9%). PCA-ALM, using component scores as predictors, achieved R² values of 0.81–0.86 with superior stability (VIF = 1.0), thereby avoiding circularity and multicollinearity. The cross-validated Q² values were 0.83 ± 0.04 for PLI, 0.80 ± 0.05 for PIN, and 0.85 ± 0.04 for PERI, which are very close to the corresponding in-sample R² values and therefore suggest minimal overfitting. The PCA-ALM framework provides a robust tool for predicting pollution indices and supporting remediation decisions in agricultural soils affected by urban wastewater.
Source: JOURNAL OF ARID AGRICULTURE
Institution: University of Maiduguri
Language: English
Topic: Heavy metals in environment
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7217720945