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The presence and distribution of antibiotics and antibiotic resistance genes (ARGs) in rivers have attracted significant global concern. However, research on the contamination of typical rivers in Sichuan Province, China, remains limited. This study aimed to assess the residual levels of antibiotics across 42 national and provincial monitoring sites in nine rivers within Sichuan using UPLC-MS/MS. Ecological risk levels were evaluated through established risk assessment methods, and antibiotic resistance in Escherichia coli(E.coli) isolated from these waters was determined using the Kirby-Bauer disk diffusion method. Additionally, redundancy analysis (RDA) was conducted to explore the impact of residual antibiotics on the microbial community structure in the Minjiang River basin. Antibiotics were detected in all nine rivers studied, with the Minjiang, Tuojiang, and Jialingjiang rivers exhibiting particularly severe contamination, with concentrations ranging from 0.29 to 2233.71 ng/L. The level of antibiotic pollution in the Sichuan Basin was significantly higher than in other regions of Sichuan, likely due to the area's high population density. Furthermore, 9.77% of E. coli isolates from the nine rivers exhibited antibiotic resistance, with over 5.8% demonstrating multidrugs resistance. Norfloxacin, amoxicillin, ampicillin, and tetracycline were identified as the primary contributors to the high ecological risk at 26 of the 42 monitoring sites. A strong correlation was observed between residual antibiotics and changes in microbial community structure. These findings provide critical insights into the distribution of antibiotics and ARGs in the rivers of Sichuan Province and highlight the urgent need for targeted strategies to mitigate antibiotic pollution. Addressing this issue is essential to protect both ecological integrity and public health. [ABSTRACT FROM AUTHOR]

Building energy consumption management significantly impacts energy efficiency, environmental effects, and economic benefits throughout a building's life cycle. Optimizing building energy consumption has become a great challenge in the field of green buildings. This paper proposes an automated simulation method that integrates the EnergyPlus energy consumption simulation tool with Python scripting. This approach efficiently generates large volumes of energy consumption data and supports the development of machine learning surrogate models, thereby enhancing simulation efficiency and reducing computational costs. Based on this foundation, the multi-objective optimization algorithm NSGA-III is introduced to achieve a balanced optimization of three primary objectives: building energy consumption, photovoltaic electricity generation, and thermal comfort. Through a systematic analysis of a case study involving an office building and dormitory at a construction site in China, the effects of building envelope, air conditioning systems, and occupant behavior on energy consumption are examined. The optimization results indicate that energy consumption is reduced by 41% for the office building and 38% for the dormitory. Additionally, photovoltaic electricity generation increases by 176% and 169% compared to the baseline model, while thermal comfort improves by 19% and 6%, respectively. These improvements significantly enhance energy self-sufficiency and residential comfort. [ABSTRACT FROM AUTHOR]

The damage of expansion joints is a common problem of bridges, which adversely affects not only driving comfort and safety but also the durability of the structure. Jointless bridges can fundamentally solve this fragile problem. However, how to accurately assess its environmental and economic benefits has not been studied, specifically as climate change intensifies. To address these issues, this study first examines the effects of climate change on design expansion allowance of bridge expansion joints based on climate projections. Subsequently, the life-cycle environmental and economic benefits of two jointless bridges located in Beijing–Hong Kong–Macau Expressway are quantified under climate change scenarios. Moreover, uncertainty and random forest algorithm-based importance analyses are carried out to help decision-makers identify major environmental and economic factors and develop eco-friendly plans to facilitate jointless bridge construction. Results indicate the following: (1) the design expansion allowance of bridge expansion joints tends to increase with the time-dependent temperature and humidity; (2) the avoidance of traffic detours caused by the maintenance and replacement of expansion joints is a major source of life-cycle environmental and economic benefits; (3) traffic detour distance is the most important factor influencing the life-cycle environmental and economic benefits. [ABSTRACT FROM AUTHOR]