The effects of hydrodynamics on algae growth have received considerable attention, and flow velocity is one of the most frequently discussed factors. For Euglena gracilis, which aggregates resources and is highly resistant to environmental changes, the mechanism underlying the impact of flow velocity on its growth is poorly understood. Experiments were conducted to examine the response of algae growth to different velocities, and several enzymes were tested to determine their physiological mechanisms. Significant differences in the growth of E. gracilis were found at different flow velocities, and this phenomenon is unique compared to the growth of other algal species. With increasing flow velocity and time, the growth of E. gracilis is gradually inhibited. In particular, we found that the pioneer enzyme is peroxidase (POD) and that the main antioxidant enzyme is catalase (CAT) when E. gracilis experiences flow velocity stress. Hysteresis between total phosphorus (TP) consumption and alkaline phosphatase (AKP) synthesis was observed. Under experimental control conditions, the results indicate that flow velocities above 0.1 m/s may inhibit growth and that E. gracilis prefers a relatively slow or even static flow velocity, and this finding could be beneficial for the control of E. gracilis blooms.

The Three-River Headstreams Region (TRHR) serves as the cradle of China’s three major rivers—the Yangtze, Yellow, and Lancang—rendering its water conservation (WC) capacity quintessentially significant for Asian water resource security. This study integrated the InVEST model and random forest model to holistically elucidate the spatiotemporal characteristics and factors influencing WC in the TRHR from 1980 to 2018. The results revealed that the WC growth rate was 5.65 mm/10a in the TRHR during the study period, yet pronounced regional disparities were observed among different basins, especially with the Lancang River Basin (LRB), which exhibited a decrease at a rate of 5.08 mm per decade despite having the highest WC. Through Theil–Sen trend analysis, the Mann–Kendall abrupt change test, and the cumulative deviation method, meteorological, vegetative, and land use abrupt changes in approximately 2000 were identified as the primary drivers for the abrupt surge in WC within the TRHR. Furthermore, precipitation and the aridity index were the core feature variables affecting WC. However, a positive transition in land use patterns post-2000 was also revealed, and its favorable effect on WC was not as significant as the abrupt climatic changes. This study offers new perspectives on managing multidimensional spatiotemporal data and contributes to laying the groundwork for machine learning applications in water conservation. Additionally, it potentially provides useful references for decision-making processes related to ecosystem security.

With the rapid development of socioeconomics and the continuous advancement of urbanization, water environment issues in plain river networks have become increasingly prominent. Accurate and reliable water quality (WQ) predictions are a prerequisite for water pollution warning and management. Data-driven modeling offers a promising approach for WQ prediction in plain river networks. However, existing data-driven models suffer from inadequate capture of spatiotemporal (ST) dependencies and misalignment between direct prediction strategy assumptions with actual data characteristics, limiting prediction accuracy. To address these limitations, this study proposes a spatiotemporal graph neural network (ST-GNN) that integrates four core modules. Experiments were performed within the Chengdu Plain river network, with performance comparisons against five baseline models. Results suggest that ST-GNN achieves rapid and accurate WQ prediction for both short-term and long-term, reducing prediction errors (MAE, RMSE, MAPE) by up to 46.62%, 37.68%, and 45.67%, respectively. Findings from the ablation experiments and autocorrelation analysis further confirm the positive contribution of the core modules in capturing ST dependencies and eliminating data autocorrelation. This study establishes a novel data-driven model for WQ prediction in plain river networks, supporting early warning and pollution control while providing insights for water environment research.

Ceratophyllum demersum L. and Hydrilla verticillata (L.f.) Royle, two pioneer, submerged plants, effectively remove heavy metals from contaminated water. The present work evaluates the bioaccumulation and defense mechanisms of these plants in the accumulation of lead from contaminated water during their optimal performance period. C. demersum and H. verticillata were investigated after 14 days of exposure to various lead concentrations (5–80 μM). The lead accumulation in both C. demersum and H. verticillata increased with an increasing lead concentration, reaching maximum values of 2462.7 and 1792 mg kg⁻¹ dw, respectively, at 80 μM. The biomass and protein content decreased significantly in C. demersum when exposed to lead. The biomass of H. verticillata exposed to lead had no significant difference from that of the controls, and the protein content increased for the 5–10 μM exposure groups. The malondialdehyde (MDA) content and superoxide dismutase (SOD), peroxidase (POD), and polyphenol oxidase (PPO) activities were much higher in C. demersum, suggesting considerable damage from lipid peroxidation and sensitivity to lead stress. Enzyme inhibition and inactivation were also observed in C. demersum at high lead concentrations (40–80 μM). The excellent growth status, low damage from lipid peroxidation, and high activity of catalase (CAT) and phenylalanine ammonia-lyase (PAL) observed in H. verticillata illustrate its better tolerance under the same lead stress.