Bisphenol A (BPA) is easily enriched in many human-disturbed watersheds, particularly lakes with poor water mobility, which is posing a threat to aquatic biota. While previous studies have focused on the concentration of BPA in water and its toxicity to aquatic organisms, a small amount of measured data is not enough to reveal the temporal and spatial distribution and threats of BPA, and estimate the ecological risk in watersheds. Therefore, we collected 164 measured BPA data points from Taihu Lake to develop machine learning models using random forest (RF), support vector machine (SVM) and least square regression (LSR) and created month-by-month watershed prediction maps in temperate lakes to estimate the spatiotemporal distribution and threats of BPA. Due to RF's superior robustness to noisy data, the RF model exhibits the best performance among the three algorithms. The RF model showed acceptable predictive performance on the modeling dataset (coefficients of determination and root-mean-square error for the training set were 0.927 and 17.499, respectively, and 0.607, 39.645 for the validation set, respectively). The maps indicated that areas susceptible to anthropogenic activities were more severely polluted by BPA, and rainy climate may favor the migration of BPA to aquatic ecosystems. The model was also applied to predict 42 data points of BPA collected from Dianchi Lake, and the results showed that most predicted data were within a factor of 10 of the measured data, but the prediction accuracy of the model has declined. The ecological risks in the two lakes were evaluated and attention should be paid to the regions with higher risks. Our study provided a novel idea for comprehensive monitoring of an unconventional trace pollutant with endocrine disrupting effects in aquatic ecosystems and analyzing their spatiotemporal distribution, which will contribute to the scientific assessment of the ecological risk of BPA.

Water pollution and its ecological consequences have become central concerns in watershed governance and aquatic ecosystem conservation. Conventional ecotoxicological research on water pollution has long relied on physicochemical monitoring, laboratory-based single-species exposure tests, and morphology-based biological surveys. Although these approaches have provided essential support for pollutant identification, toxicity characterization, and environmental standard setting, they remain insufficient for resolving community-level responses, food-web perturbations, and ecosystem degradation under multiple-stressor conditions. Environmental DNA (eDNA) has emerged as a promising molecular tool because it is non-invasive, highly sensitive, high-throughput, and capable of detecting multiple taxa simultaneously. In aquatic systems, eDNA applications have expanded from biodiversity detection to pollution diagnosis, ecological health assessment, restoration monitoring, and early warning of ecological risk, while increasingly being integrated with eRNA, multi-omics approaches, machine learning, hydrological modeling, and ecological network analysis. However, several challenges still constrain its broader application, including incomplete methodological standardization, false-positive and false-negative detections, insufficient reference databases, limited quantitative capacity, scale mismatches caused by transport and mixing, and difficulties in causal attribution. This review synthesizes recent progress in the use of eDNA for water-pollution research, with emphasis on its technical workflow, major application domains, integrative analytical frameworks, and methodological boundaries. More specifically, three main points are highlighted: (1) eDNA is shifting water-pollution research from single-species toxicity characterization toward community- and ecosystem-level ecological interpretation; (2) its greatest value lies in its integrative role at the interface of biodiversity monitoring, ecological risk assessment, and management-oriented decision support; and (3) future progress will depend on improvements in standardization, quantitative inference, regional reference databases, and multi-source data integration. Overall, this review clarifies how eDNA can contribute to more robust, ecologically meaningful, and management-relevant assessment of water pollution.

Emerging organic pollutants (EOPs) in aquatic environments have attracted increasing attention because many occur at trace levels, undergo transformation during environmental transport, and contribute to poorly resolved mixture risks. Traditional targeted analysis is inherently restricted to predefined compounds, whereas high-resolution mass spectrometry (HRMS)-based full-scan workflows provide broader opportunities for discovering known unknowns and previously unrecognized contaminants. This review critically synthesizes an omics-based analytical framework for aquatic environments, covering sample digitalization, instrumental analysis and acquisition modes, chemical fingerprint/non-target screening, suspect screening, effect-directed analysis, and confidence-based structural identification. Particular emphasis is placed on practical decision points and trade-offs, including dissolved versus particulate-associated analytes, LC-HRMS versus GC-HRMS coverage, hard versus soft ionization, DDA- versus DIA-type acquisition, database dependence, and the persistent difficulty of linking analytical features to toxicological relevance. The review also discusses emerging directions involving artificial intelligence, chemometrics, organometallic contaminants, and microplastic-associated chemicals. By clarifying conceptual boundaries and highlighting current limitations, this article aims to support the development of more critical, transparent, and risk-oriented workflows for the discovery and prioritization of emerging pollutants in aquatic environments.

Late blight, caused by Phytophthora infestans, is one of the most destructive diseases in potato cultivation. Chemical fungicides are currently the primary means of controlling it, but they pose significant issues, including environmental pollution and the development of resistant strains. Plant jiaosu (PJ), derived from the direct fermentation of various plants, plays a vital part in various fields such as environmental protection, agriculture, daily chemicals, and livestock. This study illustrates that PJ, fermented from lettuce leaves, significantly inhibits the growth of P. infestans. An isolated anti-oomycete bacterium, designated X-1, has 100% homology to Bacillus subtilis in the NCBI database, shown through 16S rDNA sequence analysis. B. Subtilis X-1 effectively inhibits the hyphal growth and sporangia germination of P. infestans, induces morphological changes in the hyphae, and can promote the growth of potato. PJ exerts a synergistic effect with the chemical fungicide Infinito (the main active ingredients are fluopicolide and propamocarb hydrochloride). In summary, this study provides a novel approach for the reutilization of fruit and vegetable waste and offers an environmentally friendly and effective alternative to chemical fungicides for controlling potato late blight.