This study assessed the feasibility of bioaugmentation with Bacillus amyloliquefaciens for degrading dissolved organic nitrogen (DON). The aim was to improve DON conversion and support more efficient nitrogen removal under stringent discharge requirements. Both qualitative and quantitative analyses were conducted to characterize DON transformation in the bioaugmented system and to explore the possible role of B. amyloliquefaciens in this process. Results indicated that inoculation of domesticated B. amyloliquefaciens improved reactor performance. The optimal treatment effect was achieved at a 10
In sustainable cities, forecasting effluent properties within wastewater treatment plants (WWTPs) offers significant benefits by decreasing sampling frequency, energy consumption, and operational expenses. Overcoming these problems necessitates establishing a suitable advanced nitrogen removal framework for municipal WWTPs. Therefore, this research primarily aims to create precise prediction models for nitrogen elimination in municipal WWTPs through sophisticated machine learning methodologies. The investigation proposes ten powerful metaheuristic optimization algorithms to develop enhanced support vector regression (SVR) frameworks. The research employed a structured workflow where models first underwent development using an extensive database, then faced rigorous testing to determine their accuracy and prediction quality. To ensure dependable total nitrogen forecasting, each approach was evaluated under eight different combinations of input parameters. SHAP and LIME interpretability frameworks were also employed in this investigation, with their primary function being to reveal which variables drive nitrogen concentration forecasts. Through these analytical tools, the study gained insights into feature hierarchies and discovered the fundamental relationships connecting input data to model predictions. Moreover, to assess model reliability, evaluation was conducted through four key statistical metrics, which facilitated comprehensive assessment of predictive performance for every model. According to RMSE analysis, the three highest-performing models were as follows: SVR-HFA exhibited the best results for total nitrogen effluent prediction (TNeff) with an RMSE of 1.285 mg/L and correlation coefficient of 0.905, making it the leading approach. SVR-GWO achieved second place, delivering an RMSE of 1.293 mg/L and R-value of 0.902, followed by SVR-TSA in third position with an RMSE of 1.316 mg/L and R-value of 0.898. These results offer practical guidance for WWTPs, highlighting reliable models for forecasting TNeff. By adopting these predictive tools, facilities can optimize operational performance, streamline monitoring tasks, and reduce reliance on laboratory testing. This investigation created ten integrated metaheuristic SVR frameworks for predicting TNeff within WWTPs. TNeff estimation employed eight distinct input variable combinations. SHAP and LIME were deployed to identify the most impactful and significant input variables. The SVR-HFA approach achieved the highest prediction accuracy for TNeff among the evaluated methods, with an RMSE of 1.285 mg/L. SHAP analysis demonstrated that TN7, COD6, TNinf, and COD6 were the most influential parameters for modeling TNeff.
Unraveling the groundwater recharge process is crucial for water resource sustainability. However, systematic studies of groundwater recharge in large river basins are still limited due to the strong anthropogenic impacts and complex hydrogeologic conditions. This study investigated the spatio-temporal variations and driving mechanisms of groundwater recharge in the Inner Mongolia Reaches of the Yellow River Basin (IMR-YRB) based on 396 shallow groundwater samples collected from 132 sites during the wet, dry, and normal seasons, and combined with hydrogen (δD) and oxygen (δ18O) isotopes and statistical methods. The backward wind trajectory analysis indicated that the water vapor firstly reached the high elevation area, with over 70
Potato processing wastewater (PPW) is known for its high organic load and suspended solids, which pose considerable environmental and operational challenges for the food industry. This research presents an integrated, near zero-waste treatment strategy that not only enhances wastewater quality but also facilitates the recovery of renewable energy. A continuous two-stage anaerobic digestion system was employed for the sequential production of Biohydrogen and Biomethane, utilizing graphite rods coated with Nano-nickel ferrite as conductive catalytic media to boost microbial activity and electron transfer. The initial stage was optimized for Biohydrogen production under mesophilic conditions with controlled pH, while the subsequent stage transformed the effluents from the hydrogen stage into Biomethane. The treated effluents underwent further polishing through reverse osmosis (RO) and UV disinfection to allow for water reuse. The incorporation of Nano-nickel ferrite–coated graphite markedly improved system performance in comparison to control reactors. The yield of Biohydrogen rose from 148 to 273 mL H₂ g⁻¹ VS, with peak daily production nearing 3000 mL. Similarly, the Biomethane yield increased from 143 to 274 mL CH₄ g⁻¹ VS, reflecting the effective conversion of volatile fatty acids during the second stage. The integrated system achieved high removal efficiencies for total and soluble chemical oxygen demand, total solids, volatile solids, and nitrogen compounds, showcasing effective stabilization of organic matter. An energy recovery analysis indicated a significant enhancement in net energy output attributable to the combined production of hydrogen and methane. After RO treatment, the final effluent satisfied quality standards for reuse in potato processing activities. In summary, the findings illustrate that the integration of Nano-enabled conductive materials with two-stage anaerobic digestion and membrane filtration constitutes an effective and sustainable approach for the treatment of PPW. This methodology aligns with circular economy principles by merging wastewater reclamation, renewable energy recovery, and resource recovery.
The rapid expansion of transport infrastructure has driven significant population growth in the Lobo District, increasing the need for sustainable water resource management. This study delineates zones favorable for groundwater recharge through predictive spatial analysis, validated against geophysical datasets and observations from selected existing boreholes. Groundwater aquifer potential zone maps were generated by applying two models: the Analytic Hierarchy Process (AHP) and the Frequency Ratio (FR), based on geological, topographical, hydrological, and climatic factors. The AHP model assigned the highest weights to lithology and lineament density, followed by altitude, slope, and precipitation. Similarly, the FR model emphasized lithology and lineament density but demonstrated stronger correlations with specific altitude ranges, slope degrees, and drainage density. Despite the fact that the FR model exhibits slightly superior performance compared to the AHP model in delineating groundwater recharge zones, these two predictive spatial analysis approaches remain complementary. The former is associated with areas favorable for infiltration and recharge, whereas the latter identifies zones conducive to groundwater accumulation and storage. Vertical electrical soundings revealed curve types A, H, K, KH, HK, and HA, leading to the identification of two stratigraphic models: three-layer and four-layer formations. Lithological logs confirmed four distinct subsurface layers: topsoil, weathered/fissured bedrock, fractured bedrock, and fresh bedrock, with resistivity values ranging from 74.72 to 9,214.55 Ω·m. The aquiferous zones correspond to the second and third layers, exhibiting resistivities between 74.72 and 2,314.03 Ω·m, thicknesses ranging from 1.12 to 6.88 m, and depths between 3.9 and 42.97 m. This work elucidates the main controlling factors governing aquifer dynamics and advances the understanding of aquifer structural architecture in metamorphic basement settings. These findings provide robust evidence to guide conservation strategies and inform investment prioritization in the Lobo District water sector, thereby supporting the sustainable satisfaction of the increasing water demand driven by population growth.
