Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202604009
Biological nitrogen removal in wastewater treatment plants (WWTPs) is often limited by insufficient influent carbon sources, necessitating external carbon addition to enhance denitrification. Conventional single carbon sources, such as sodium acetate, frequently fail to meet the metabolic demands of complex microbial communities, compromising nitrogen removal efficiency and stability. Composite carbon sources, by providing multiple electron donors, can improve metabolic cooperation among microorganisms, yet their underlying microbial mechanisms remain insufficiently understood. In this study, activated sludge from a municipal WWTP was used to investigate the microbial mechanisms of composite carbon sources during denitrification. Batch denitrification experiments were conducted in combination with metagenomic and metatranscriptomic analyses to systematically characterize microbial community structure and functional gene expression under different carbon source conditions. Results showed that, compared with sodium acetate as the single carbon source, the composite carbon source system (sodium acetate: sodium succinate: ethanol = 2:1:3) increased the denitrification rate from (6.822 ± 0.141) mg/(L·h) to (8.370 ± 0.186) mg/(L·h), representing a 22.7% improvement, while reducing N2O accumulation by approximately 55%. Metagenomic analysis revealed that Ottowia, Rubrivivax, Thauera, and Zoogloea were the dominant denitrifying genera. Metatranscriptomic results further demonstrated that the composite carbon sources significantly upregulated the transcription of key denitrification genes, with nirS, norB, and nosZ increasing by 37.8%, 27.4%, and 48.6%, respectively. In addition, the composite carbon sources promoted complementary carbon metabolic strategies among different microbial communities, enhancing electron donor supply and improving denitrification efficiency. These findings indicate that composite carbon sources synergistically enhance denitrification performance through regulation of functional gene transcription in complex microbial communities, providing a theoretical basis for carbon source optimization in WWTPs.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202604003
Emerging contaminants (ECs) in wastewater from urban transportation infrastructure remain poorly characterized. This study employed high-resolution mass spectrometry (HRMS)-based non-target screening to systematically identify the composition and spatial distribution of ECs in wastewater from three functional zones of a metro maintenance depot: storeroom (S1), office/residential area (S2), and final discharge outlet (S3). A total of 417 contaminants were detected, spanning eight categories including industrial materials, pharmaceuticals, pesticides, and natural products. Among these, 48 substances were identified with Level 1 confidence via spectral matching. Pesticides exhibited the highest detection frequency and concentration levels, representing the primary contaminant load. Semi-quantitative concentration heatmaps of 24 pesticides revealed significant spatial variation: S2 showed the highest number and concentration of contaminants, reflecting inputs from landscaping and vector control; S1 and S3 showed lower levels, indicating dilution, migration, and attenuation. Representative pesticide bifenox displayed a clear concentration gradient (S2 > S1 > S3), suggesting transport mechanisms such as surface runoff, hydraulic transfer, and sorption. These findings underscore the complexity and diversity of EC sources in metro depot wastewater, highlight the need to prioritize pesticides in regulatory management, and provide fundamental data for understanding EC environmental behavior and informing water environment risk assessment.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202605005
Water quality prediction is essential for river basin management, yet existing models often struggle with non-stationary, noisy monitoring data. This study collected water quality data from two city-level control sections in southern China from December 2020 to June 2024, including eight indicators: water temperature, turbidity, pH, conductivity, dissolved oxygen (DO), ammonia nitrogen (NH4+-N), total phosphorus (TP), and permanganate index (CODMn). To predict four key indicators (DO, NH4+-N, TP, CODMn), we developed hybrid models combining seasonal trend decomposition (STD), Bayesian hyperparameter optimization, and either random forest (RF) or XGBoost. STD smoothed and denoised the data while extracting seasonal factors; Bayesian optimization tuned model hyperparameters. Evaluation showed that the STD-Bayesian-XGBoost model achieved smaller bias errors and higher prediction accuracy than STD-Bayesian-RF. Specifically, XGBoost reduced root mean square error (RMSE) by 15-20% across all four indicators and improved the coefficient of determination (R²) to above 0.90, compared to RF's 0.85-0.88. The models were validated on southern river data, but the methodology is generalizable to other climatic and hydrological settings. This work provides a technical reference for pollution reduction and carbon management in regional watersheds.