Yifan Shi

Antibiotic resistance genes (ARGs) have emerged as globally concerning environmental contaminants, posing serious threats to ecosystem health and public safety. This systematic review summarizes global research trends on ARGs across three key aspects: (i) identification and distribution in river and lake ecosystems, (ii) sources and environmental behaviors, and (iii) ecological and human health risks. Concentration data of ARGs in various rivers and lakes across China were compiled to reveal their spatial distribution patterns. The analysis of ARGs sources and environmental behaviors provides essential insights for designing effective mitigation strategies. Furthermore, this review highlights the potential ecological and human health hazards of ARGs and discusses limitations and improvement directions of current risk assessment methodologies. The main findings indicate that ARGs are widely present in rivers and lakes across China; higher abundances occur in eastern and southern regions compared with central–western and northern areas, such as 4.93 × 10²–8.10 × 10³ copies/mL in Qinghai Lake and 6.7 × 10⁷–1.76 × 10⁸ copies/mL in Taihu Lake. The environmental behaviors of ARGs are highly complex, involving multiple mechanisms and influenced by climatic conditions, nutrient levels, and additional environmental factors. Based on these findings, future efforts should prioritize long-term site-specific monitoring, evaluate their prolonged impacts on aquatic ecosystems, and develop integrated risk assessment models to support evidence-based environmental management.

The encroachment of buildings into the waters of rivers and lakes can lead to increased safety hazards, but current semantic segmentation algorithms have difficulty accurately segmenting buildings in such environments. The specular reflection of the water and boats with similar features to the buildings in the environment can greatly affect the performance of the algorithm. Effectively eliminating their influence on the model and further improving the segmentation accuracy of buildings near water will be of great help to the management of river and lake waters. To address the above issues, the present study proposes the design of a U-shaped segmentation network of buildings called RDAU-Net that works through extraction and fuses a convolutional neural network and a transformer to segment buildings. First, we designed a residual dynamic short-cut down-sampling (RDSC) module to minimize the interference of complex building shapes and building scale differences on the segmentation results; second, we reduced the semantic and resolution gaps between multi-scale features using a multi-channel cross fusion transformer module (MCCT); finally, a double-feature channel-wise fusion attention (DCF) was designed to improve the model’s ability to depict building edge details and to reduce the influence of similar features on the model. Additionally, an HRI Building dataset was constructed, comprising water-edge buildings situated in a riverine and lacustrine regulatory context. This dataset encompasses a plethora of water-edge building sample scenarios, offering a comprehensive representation of the subject matter. The experimental results indicated that the statistical metrics achieved by RDAU-Net using the HRI and WHU Building datasets are better than those of others, and that it can effectively solve the building segmentation problems in the management of river and lake waters.

Heavy metal pollution is a critical issue affecting the safety of drinking water sources. However, the impact of human activities on heavy metal risk levels in water-carrying lakes remains unclear. This study aims to explore the risk mechanisms of heavy metals in Luoma Lake, an important water-carrying lake for the South-to-North Water Diversion Project. We explored the spatial and temporal differences in the distribution of heavy metals in Lake Luoma using methods such as the heavy metal pollution index (HPI) and assessed the risk variations using a health assessment model. The results indicated that heavy metal concentrations in water-carrying lakes generally decreased during the dry season, with Mn and Zn levels decreasing by 89.3% and 56.2%, respectively. The comprehensive score of HPI decreased by 13.16% following the retreating polder compared to the control area (Non-retreating polder area). Furthermore, the HPI at the drinking water intake was lower, which is closely associated with the elevated dissolved oxygen (DO) and oxidation–reduction potential (ORP) resulting from water diversion. The annual average health risk across the entire lake was not significant, with higher levels observed in the control area. The annual non-carcinogenic risk levels of Mn, Ni, Cu, Zn, and Pb range from 10⁻¹³ to 10⁻⁹, which are considered negligible risk levels. Notably, the carcinogenic risk posed by arsenic (As) through the drinking pathway reached 10⁻⁵ a⁻¹, exceeding the maximum levels recommended by certain organizations. These findings provide a critical foundation for managing heavy metals in water-carrying drinking water sources.

Quantifying agricultural greenhouse gas (GHG) emissions is important for addressing global warming. In this regard, empirical models were constructed to evaluate the feasibility of using rice yield and canopy spectral properties for estimating paddy cumulative methane (CH₄) emissions (CCE). A field experiment with shading treatments was conducted in 2017. A static chamber‐gas chromatography was used to measure CH₄ fluxes during the growing season. Canopy hyperspectral reflectance was measured and then used to calculate the multispectral normalized difference vegetation index (NDVI), the ratio vegetation index (RVI), and the enhanced vegetation index (EVI). The results show that CH₄ emissions were positively correlated to rice yield and biomass, indicating that higher biomass provided more substrates for CH₄ generation. O₂ availability maybe a main factor in the CCE differences under shading treatments. RVI and EVI showed a stronger positive relationship to CH₄ emissions than NDVI. The empirical model including yield and EVI‐JS (EVI of jointing‐booting stage) as input variables performed better (adj‐R² = 0.85, root mean square error [RMSE] = 12.26 kg ha⁻¹) than the model that included only yield (adj‐R² = 0.5, RMSE = 22.5 kg ha⁻¹). This study suggests that it is feasible to apply remote sensing in paddy CH₄ estimation, providing a referable attempt for future regional agricultural GHG emissions quantification. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.

Agricultural greenhouse gas (GHG) has a significant effect on climate change. The ability to quantify agricultural GHG and implement practices to mitigate its emissions is of great significance. Optimal GHG mitigation practices in the middle and lower reaches of the Yangtze River, China, have not been well documented. In this study, historical (2000–2014) and projected (2015–2049) methane (CH₄) and nitrous oxide (N₂O) emissions from paddy‐upland crop rotations were simulated at a county scale using the denitrification–decomposition model. The results demonstrated (1) historical mean emissions of CH₄, N₂O, and global warming potential (GWP) were 158.6 kg‐C ha⁻¹ year⁻¹, 0.75 kg‐N ha⁻¹ year⁻¹, and 22.15 Tg CO₂‐eq year⁻¹ in the study area (1 Tg = 10¹² g); (2) CH₄ emission rates significantly increased, while there was no significant change in N₂O under projected RCP4.5 and RCP8.5 climate scenarios, and RCP8.5 induced 10% more CH₄ than RCP4.5; (3) marginal flooding coupled with reduced‐tillage practice was the optimal strategy to mitigate GHG emissions due to its lowest GWP. This study would provide insight into the agricultural GHG emissions, as well as the impact on climate change. Also, a theoretical basis for policymaking would be provided by this study. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.