Environmental Chemistry•2026•DOI: 10.7524/j.issn.0254-6108.2025011302
Heavy metal contamination in soil severely compromises the quality and safety of Alisma orientale medicinal materials, and consumption of contaminated herbal preparations poses health risks. To characterize contamination and risks in Sichuan's genuine producing areas, 159 paired soil and plant samples were collected. Concentrations of Cu, Zn, Pb, Cd, and Ni were determined via ICP-OES. Soil pollution was assessed using the Single Pollution Index (Pi), Nemerow Comprehensive Index (Pn), and Potential Ecological Risk Index (RI). Human health risks from heavy metals in Alisma were evaluated via Target Hazard Quotient (THQ) and Hazard Index (HI). Mean soil concentrations were Cu 29.57, Zn 61.86, Pb 29.51, Cd 1.77, and Ni 28.08 mg·kg−1. Except for Cd, all elements were below agricultural soil screening values. Pi and Pn confirmed Cd contamination, with Cd posing slight to strong potential ecological risks. Cu, Cd, Pb, and Ni showed highly significant positive correlations, indicating common origins. Heavy metal concentrations in Alisma did not exceed pharmacopeial limits. The plant exhibited strong Zn enrichment but weak accumulation of Cu, Cd, and Pb, and negligible Ni enrichment. THQ and HI values indicated no potential health risks under current exposure. Quantitative assessment is critical for soil pollution control, safe cultivation, and medication safety.
Environmental Chemistry•2026•DOI: 10.7524/j.issn.0254-6108.2025012401
Traditional excitation-emission matrix (EEM) fluorescence spectroscopy suffers from prolonged scanning times, data redundancy, high instrument cost, and bulkiness, hindering rapid on-site water pollution source tracing. This study proposes a novel classification method combining fixed characteristic excitation wavelength scanning with support vector machine (SVM) to enhance efficiency. A total of 180 EEM samples were collected from six pollution source categories: chemical fiber dyeing and finishing, wool textile dyeing and finishing, leather processing, metal surface processing, papermaking, and domestic sewage. Parallel factor analysis (PARAFAC) identified characteristic fluorescent components and excitation wavelengths. Correlation analysis and feature importance analysis further reduced these to seven characteristic excitation wavelengths. SVM and random forest (RF) models were constructed using both the reduced and original EEM datasets. Results demonstrated that models based on the seven characteristic excitation wavelengths maintained high recognition accuracy while significantly improving efficiency. The SVM model achieved the best performance, with runtime reduced from 243.05 s to 34.56 s (an 86% decrease) and recognition accuracy reaching 94.4%. Precision, recall, and F1-score metrics confirmed the robust performance of SVM with characteristic wavelengths, particularly for metal surface processing wastewater. This study provides an efficient and reliable method for rapid water pollution tracing by simplifying EEM scanning and integrating SVM, offering high application value. Future work will optimize feature selection strategies and explore additional sample categories and model combinations to broaden applicability.