Wastewater environmental DNA-based mapping of sulfate-reducing bacteria for network-scale sewer corrosion screening using species distribution modeling.

The deterioration of extensive underground sewer networks presents a critical global challenge, as traditional inspection methods are difficult to scale under resource constraints. This study evaluates whether wastewater environmental DNA (eDNA)-derived habitat potentials of sulfate-reducing bacteria (SRB) can provide additional predictive information for network-scale sewer corrosion screening. We applied a species distribution modeling (SDM) framework to three targets: the two literature-selected and locally detected SRB genera, Desulfovibrio spp., Desulfobulbus spp., and the functional gene dsrB, using wastewater samples collected across the predefined target network over two years, achieving an optimal Area Under the Receiver Operating Characteristic Curve (ROC-AUC) of 0.925 for Desulfobulbus spp. Given the established role of SRB in sewer sulfide production, the SDM-derived habitat potentials were subsequently used to train a machine learning model to classify pipeline corrosion status defined by the municipal CCTV assessment. Using the two-year mean habitat potentials, a Random Forest (RF) model achieved the highest test-set Area Under the Precision-Recall Curve (PR-AUC) of 0.670. For the selected RF model, test-set permutation importance, measured as the decrease in PR-AUC after feature permutation, was highest for pipe age. Among the microbial predictors, the SDM-derived habitat-potential features for Desulfobulbus spp. and dsrB showed comparable importance, followed by that for Desulfovibrio spp. These findings demonstrate a scalable, data-driven framework for sewer corrosion screening and support inspection prioritization across unsampled pipe spans within the investigated target network.