Efficient monitoring of water quality parameters (WQPs) is crucial for environmental health. Drone hyperspectral images have offered the potential for the flexible and accurate retrieval of WQPs. However, a machine learning (ML)-based multi-process strategy for WQP inversion has yet to be established. Taking a typical urban river in Guangzhou city, China, as the study area, this paper proposes a machine learning-based strategy combining spectral preprocessing and ML regression models with ground truth WQP data. Fractional order derivation (FOD) and discrete wavelet transform (DWT) methods were used to explore potential spectral information. Then, multiple methods were applied to select sensitive features. Three modeling strategies were constructed for retrieving four WQPs, including the Secchi depth (SD), turbidity (TUB), total phosphorus (TP), and permanganate index (COD_Mn). The highest R²s were 0.68, 0.90, 0.70, and 0.96, respectively, with corresponding RMSEs of 13.73 cm, 6.50 NTU, 0.06 mg/L, and 0.20 mg/L. Decision tree regression (DTR) was found to have the potential with the best performance for the first three WQPs, and eXtreme Gradient Boosting Regression (XGBR) for the COD_Mn. Moreover, tailored feature selection methods emphasize the importance of fitting processing strategies for specific parameters. This study provides an effective framework for WQP inversion that combines spectra mining and extraction based on drone hyperspectral images, supporting water quality monitoring and management in urban rivers.

This study analyzed the respective advantages and disadvantages by comparing volumetric modulated arc therapy (VMAT) and intensity modulated radiotherapy (IMRT) on the dose distribution and position verification distribution characteristics in esophageal cancer radiotherapy, in order to provide the reference for the clinical radiotherapy technology optimization of esophageal cancer. A total of 56 cases of patients with esophageal cancer were selected and applied to the Pinnacle three-dimensional radiation treatment planning system (TPS), in order to design a VMAT plan and IMRT plan under the guidance of image-guided radiotherapy (IGRT). The dosimetry and position verification difference were compared between the two groups. Revealed that the target dose distribution of the VMAT plan and IMRT plan meets the requirements in clinical dosimetry for all 56 patients in this study. Under the premise of similar target coverage, the conformal index (CI) of the VMAT plan, homogeneity index (HI), target volume, BODY-PTV radiated volume and spinal cord D_max, bilateral lung V5, V20 and mean lung dose (MLD), monitor unit (MU) and treatment time (TT), as well as position verification and others, were obviously superior to those in the IMRT plan; and the difference was statistically significant. CBCT guided VMAT is a potential effective treatment for esophageal cancer and may be more effective and safer than IMRT.

Coupling municipal wastewater treatment plants (WWTPs) with constructed wetlands (CWs) has become an effective strategy for improving effluent quality while providing potential benefits in resource efficiency and environmental sustainability. However, previous studies have predominantly optimized WWTPs and CWs as independent treatment units, whereas coordinated operation of integrated WWTP–CW systems has received comparatively limited attention. This study evaluated a full-scale coupled WWTP–CW system using long-term operational data collected during 2021–2022. Environmental impacts were quantified using scenario-based calculations incorporating electricity consumption, chemical consumption, sludge production, and IPCC-based greenhouse gas accounting. Pollutant removal performance, electricity consumption, chemical consumption, and greenhouse gas (GHG) emissions were comprehensively assessed, and operational scenarios were developed to investigate the environmental benefits of coordinated optimization. The results showed that the WWTP consistently satisfied the Chinese Class 1A discharge standard, whereas the ancillary CW achieved effective polishing for COD and NH₃-N but exhibited relatively low removal efficiencies for TN and TP because of insufficient biodegradable carbon in the influent. Scenario-based simulation demonstrated that moderately relaxing pollutant removal within the WWTP while maintaining compliant effluent quality substantially enhanced the treatment performance of the downstream CW. Under the optimized scenario, the average removal efficiencies of COD, TN, and TP in the CW increased by 28.3%, 17.4%, and 19.8%, respectively. Meanwhile, electricity consumption, sludge production, and dewatering-agent consumption in the WWTP were reduced by approximately 25%. When compared on a harmonized water-volume basis, unit external carbon-source consumption likewise decreased under the optimized scenario. Consequently, overall greenhouse gas emissions decreased by 40.3%, from 527.9 to 315.3 g CO₂-eq·m⁻³ of treated wastewater. These findings demonstrate that coordinated pollutant allocation between engineered and ecological treatment units can substantially improve the environmental sustainability of existing WWTP–CW systems, providing a practical, low-carbon operational strategy without infrastructure expansion.

Cascade hydropower alters river hydrological regimes and threatens aquatic ecosystems, calling for robust ecological risk assessment (ERA). Conventional assessments often rigidly apply the full five-layer Driving Force–Pressure–State–Impact–Response framework, leading to indicator redundancy and unbalanced weighting. Single weighting methods also fail to reconcile expert judgment with data variability. To address these issues, we developed a three-layer (target–element–indicator) evaluation system embedding DPSIR logic without its full structure, focusing on hydrological regime, water environmental quality, and aquatic ecology with ten indicators. We used an improved group AHP-CRITIC coupling method for weighting: AHP aggregates expert judgments via geometric mean, and CRITIC integrates data variability and inter-indicator conflict. Multi-attribute utility theory normalized indicators into a unified security index, applied to four cascade stations in the middle Jinsha River using 66-year (1953–2018) hydrological and seven-year (2013–2019) in situ monitoring data. The evaluation obtained a comprehensive index of 0.71 to 0.74, which is generally safe. River connectivity loss was the primary limiting factor. Hydrological alteration was mild overall with a value of 0.139, while extreme flow decline rate variation reached a high level of 0.83. Weekly regulated stations achieved over 97% ecological flow guarantee, which is much higher than daily regulated stations. This streamlined framework improves interpretability for cascade basins and supports sustainable watershed management.