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Graph Laplacian and horseshoe-regularized Bayesian latent component analysis of hydrocarbon fingerprints and carcinogenic PAH indicators in Volta Lake sediments
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DOI：10.1007/s10653-026-03410-6.png)
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Abstract
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Graph Laplacian clustering and horseshoe-regularized Bayesian latent component regression were applied to n-alkane, PAH, toxic equivalent (TEQ), and benzo[a]pyrene equivalent potency (BaPE) data from 12 Volta Lake sediment stations sampled in April 2025. The strongest numerical support was for a two-cluster graph partition (silhouette = 0.343; Davies-Bouldin index = 1.017; modularity = 0.447). Σn-alkanes were highest at VL3, VL5, VL1, and VL6, whereas Total PAHs were highest at VL9, VL11, VL10, and VL8. The aromatic-leaning second component showed a positive posterior direction for TEQ (β = 0.202; Pr[β > 0] = 0.965; posterior odds = 27.30) and BaPE (β = 0.124; Pr[β > 0] = 0.863; posterior odds = 6.29), but both 95% credible intervals crossed zero. Posterior ranking identified VL10, VL8, and VL9 as priorities for repeat sampling, not confirmed regulatory hotspots. The primary two-cluster partition was unchanged across Gaussian-kernel bandwidth multipliers of 0.50–1.50 and across 1000 multiple-imputation datasets; a conservative 30% analytical-perturbation bootstrap reproduced the exact partition in 96.0% of 1,000 replicates. Because the design comprised only 12 stations, one sampling period, a time-integrated 0–60 cm sediment interval, and no measurements of total organic carbon, black carbon, or grain size, the workflow remains an exploratory screening analysis rather than validated source apportionment or risk classification.
Keywords:
Volta Lake
Sediment hydrocarbons
Graph-based clustering
Latent components
Horseshoe regression
Carcinogenic PAHs
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