The role of glutathione S-transferase Mu 1 (GSTM1) in the biomonitoring of polycyclic aromatic hydrocarbons (PAHs) is not clear. Our purpose has been to evaluate the influence of GSTM1 genotypes on 1-hydroxypyrene (1-OHP), deoxyribonucleic acid (DNA) adducts, and micronucleus frequency in both...

Background: Urban wastewater integrates chemical signals from human activities and provides a composite biochemical representation of the communities it serves. However, whether wastewater metabolomes exhibit stable spatial organization at the catchment scale remains insufficiently characterized. Methods: Untargeted LC–HRMS was applied to 56 influent wastewater samples collected from seven urban catchments over eight consecutive days to evaluate spatial structuring of the community metabolome. Results: A total of 3248 reproducible molecular features were detected. Permutational multivariate analysis of variance indicated that catchment identity explained 62% of total metabolomic variation (R² = 0.62, p < 0.001), while multivariate dispersion did not differ significantly among sites (p = 0.082). Within-catchment compositional distances were significantly lower than between-catchment distances (p < 0.001), and centroid separation exceeded internal dispersion (ratio = 2.55), indicating short-term stable, site-specific configurations in high-dimensional chemical space. Random Forest classification achieved 98% accuracy under strict spatial cross-validation and 98.2% under leave-one-day-out validation (p < 0.001). Classification performance remained high using annotated-only (98.2%), unannotated-only (98.2%), or pharmaceutical-excluded datasets (96.5%), demonstrating that spatial identity was encoded in a distributed manner across the metabolome rather than driven by a limited subset of compounds. Conclusions: These findings provide quantitative evidence that urban wastewater metabolomes exhibit short-term stable, reproducible catchment-level structure and establish a methodological foundation for system-level characterization of urban populations using untargeted metabolomics.