The riparian zone has a proven ability to reduce agricultural nonpoint-source nitrogen pollution. However, prior studies have only assessed nitrification and denitrification and their influencing factors, such as hydrology, climate, vegetation, and soil physicochemical properties, and overlooked the role of pesticide accumulation, which is an important nonpoint-source anthropogenic pollutant. This study investigated the response of the soil microbial nitrogen cycle in riparian zones to 60 days of acetochlor (ACE) exposure at doses of 0.5, 2.5, and 5.0 mg/kg. The results showed that ACE inhibited the transformation of soil NH₃-N, NO₃⁻-N, and NO₂⁻-N and also decreased potential nitrification and denitrification rates by affecting the respective enzyme activities and related microbial communities. Metagenomic sequencing revealed that the expression of functional genes associated with NO₃⁻-N transformation processes, including denitrification, dissimilatory nitrate reduction to ammonium, and assimilatory nitrate reduction, declined the most. The recommended field dose (0.5 mg/kg) showed a slight effect on soil microbial nitrogen transformation and the related microbial communities, whereas doses 5 and 10 times the recommended dose were highly inhibitory. This is a novel study exploring the effects of ACE on the microbial nitrogen cycle in riparian soils, also suggesting that pesticide accumulation in riparian zones could affect the health of aquatic ecosystems.

Frequent algal blooms in lakes pose a serious threat to aquatic ecosystems. It is of great significance to quickly and accurately monitor the distribution of algae in lakes for the regulation of algal blooms. While remote sensing techniques and machine learning methods can be used in combination to identify algae and analyze their spatial and temporal distribution, these methods still face challenges in practical applications due to uncertainties in lake boundaries and imbalances between algae and non-algae. In order to overcome these difficulties, we studied the dynamic open water range of Ulansuhai Lake and used a non-equilibrium data processing method to identify its algae. We also performed a spatiotemporal analysis of the algal range over a long time series. The results show that (1) the spectral characteristics of Landsat 8 images are very suitable for algal identification based on remote sensing, especially in the random forest method, where the fourth band plays an important role. (2) Among various machine learning methods, the accuracy of the random forest method on the training set and validation set is more than 90%. This indicates that the random forest method is suitable for the long-term monitoring of algal blooms. This study provides scientific and technical support for the management of Ulansuhai Lake, which will be helpful in guiding future management and control work.

The release process of endogenous phosphorus (P) in the sediments of large ecological wetlands and their connected rivers in the plain river network area shows temporal and spatial differences. This study investigated P dynamics of the sediments in a large ecological wetland and its connected rivers in a plain river network area. Sample collection occurred across three periods (October 2024, March 2025, and July 2025). P source-sink characteristics and microbial regulatory mechanisms were analyzed to clarify differences in the P release processes between wetland (SS) and river (SH) sediments. The results showed that the total phosphorus (TP) concentration in overlying water was highest in July (0.16 mg/L), while the TP content in SS was relatively low, with a mean value of 514.1 mg/kg. SS generally acted as a P sink, with its zero equilibrium P concentrations (EPC₀) significantly lower than those of river sediments (SH), reaching a minimum of 0.01 mg/L, and its maximum P sorption capacity (Q_max) higher, with a maximum value of 1.413 mg/g. In contrast, SH mainly served as a P source, with a particularly high release risk in spring and summer. Seasonal changes significantly influenced P behavior, and sorption capacity was highest in spring (March), while the high EPC₀ of SH still facilitated P release under actual water conditions. In autumn, elevated microbial diversity enhanced organic matter mineralization to increase EPC₀ and P release risk (p < 0.05), while in summer, specific functional phyla (Proteobacteria and Bacteroidota) simultaneously regulated both adsorption capacity (Q_max) and release threshold (EPC₀) through organic matter mineralization, iron reduction, and competitive sorption (p < 0.05). This study provides scientific support for internal pollution control in ecological wetlands and watershed phosphorus management in plain river network areas.

Reservoir regulation changes downstream flow regimes, which affects pollutant dilution, residence time, transport, and ecological exposure in aquatic ecosystems. This study developed a Bayesian-network-assisted ecological reservoir operation framework to support monthly decisions under inflow uncertainty while balancing water-supply reliability and flow-regime protection. A genetic-algorithm optimization model was used to minimize a weighted objective combining the water-supply shortage index and the hydrological alteration degree. Optimized monthly release, water supply, and storage samples were then used to train a Bayesian network, with inflow and initial storage as input variables and release and water supply as output variables. The framework was applied to the Tanghe Reservoir, China, using the monthly inflow records from 1950 to 1969; the records from 1950–1964 were used for training and those from 1965–1969 for validation. Under the equal-weight baseline scenario, the optimization produced a shortage index of 0.060 and a hydrological alteration degree of 0.276. The Bayesian network reproduced the main optimized operating patterns and gave clear release probabilities under different inflow–storage combinations. These results show that the method can support reservoir operation decisions that balance the water supply and flow-regime protection. The method is most suitable for reservoirs where ecological risk is strongly affected by flow alteration and direct pollutant observations are limited.