Abstract Vegetated landfill covers (VLC) are extensively applied to block rainwater infiltration and reduce the leachate generation. However, the associated analytical works remain limited. This study, for the first time to our best knowledge, develops analytical solutions for transient infiltration of water in the VLC (consisting of rooted and unrooted layers) under a time-varying rainfall scenario, incorporating the natural evaporation effect. Then, key expressions of an analytical solution under several common rainfall patterns are presented. On this basis, the obtained solutions are validated through two comparisons. Finally, the conducted application study finds that, among the five rainfall patterns with equal total rainfall, the “prepeak” rainfall pattern induces the largest cumulative bottom percolation C Q b . An increase in evaporation rate decreases the C Q b , and such effect gradually grows over time. Among different initial water content forms, the form with a larger bottom water content will lead to a larger C Q b , but this impact mainly occurs in the early stage of the rainfall event. For four classic root architectures considered, the VLC is most effective in blocking bottom water percolation with the uniform root architecture, while it is least effective with the exponential root architecture. Further, the VLC’s effectiveness in blocking bottom water percolation has been observed to become stronger with the increase of rooted layer thickness and transpiration rate, and such function is more effective than that of a soil-based landfill cover with no root. Overall, this analytical investigation can take into account the combined effects of time-varying rainfall feature and natural evaporation and contribute to guiding the application and design of VLCs.
Abstract The issue of dust pollution in mine roadways and tunnel excavation operations has become increasingly severe. This study systematically investigates, for the first time, to the best of our knowledge, the influence mechanisms of partition plates and tube sheet structures on the internal flow field uniformity of a sunk-airway filter plate dust collector. Using a three-dimensional numerical model with the realizable k - ε turbulence model and discrete phase model (DPM), gas-solid two-phase flow under different structural parameters was simulated. The results demonstrate that eliminating the side partition ( L = 0 mm ) achieves optimal flow field uniformity, significantly reducing interfilter plate mass flow deviation, airflow velocity range, and dust capture nonuniformity (coefficient of variation reduced by 6%–11%). Furthermore, a stepped tube sheet with a moderate step height ( H = 50 mm ) reduces total ventilation resistance by approximately 6.3% and enhances flow field uniformity by 3%. This study elucidates the positive role of structural optimization in balancing dust load distribution. The findings provide a theoretical basis and numerical reference for designing high-efficiency dust removal equipment in long, narrow spaces, with significant engineering application value for improving occupational safety and equipment longevity in underground mining and tunnel construction.
Abstract Struvite recovery in the form of magnesium ammonia phosphate (MAP) from high-strength nitrogenous wastewater promotes recycling of nutrients. Moreover, it prevents release of ammonia to the environment. The present study focused on MAP recovery from synthetic wastewater containing ammonia nitrogen ( NH 4 + ─ N ) concentration of 5 g / L by chemical precipitation method. The key operating parameters, i.e., pH and [ Mg 2 + ] ∶ [ NH 4 + ─ N ] ∶ [ PO 4 3 − ─ P ] molar ratio, for the precipitation process were optimized using the central composite design (CCD) to achieve effective MAP recovery. From the batch experimental runs, the following optimum conditions were found: pH = 9.6 and [ Mg 2 + ] ∶ [ NH 4 + ─ N ] ∶ [ PO 4 3 − ─ P ] molar ratio = 1 ∶ 1 ∶ 1.3 . Under these conditions, the overall NH 4 + ─ N removal was ∼ 88 % , and a total of ∼ 140 g / L precipitate was recovered. Visual MINTEQ 4.0 software was used to identify various chemical species that are expected in an aqueous solution under the optimum conditions. Moreover, nitrogen mass balance was performed to understand possible nitrogen removal pathways. The struvite (or MAP) precipitation was confirmed using scanning electron microscopy with energy dispersive X-ray spectroscopy and X-ray diffraction analyses. The estimated cost for recovering 1 kg of struvite was USD 0.42–1.85; hence, the findings can be useful to industries producing nitrogenous wastewater and promote a circular economy.
Abstract During the potable reuse of reclaimed water, nitrosodimethylamine (NDMA), as a highly carcinogenic disinfection by-product (DBP), requires critical control. In this study, copper-doped nano zero-valent iron supported on granular activated carbon (nZVI-Cu@GAC) was synthesized via an impregnation-chemical liquid-phase reduction method. Column filtration experiments were performed to evaluate the effects of Cu/Fe loading ratios (0, 5, 10% by weight) and filtration velocities (0.5, 1.0, 2.0 m / h ). Coupled with high-resolution mass spectrometry for degradation pathway identification, this study aimed to explore the removal efficiency and mechanism of nitrosodimethylamine and its typical precursor, dimethylamine (DMA), in reclaimed water. The results showed that after chloramine disinfection, the nitrosodimethylamine concentration in reclaimed water reached 158.4 ng / L , primarily generated from hydrophilic nitrogen-containing organic matter with a molecular weight 1 kDa . The removal rate of NDMA by nZVI-Cu@GAC first increased and then decreased with increasing Cu/Fe ratio, reaching the maximum efficiency at 5% by weight. When the Cu/Fe loading ratio increased from 0 to 5% by weight, the removal efficiency of NDMA rose initially from 19.4% to 29.3%. As the Cu/Fe loading ratio was further increased to 10% by weight, the removal efficiency of nitrosodimethylamine decreased to 22.4%. Decreasing the filtration velocity significantly enhanced the removal rate, with the best performance at 0.5 m / h . Mechanistically, nZVI-Cu@GAC achieved the stepwise degradation of nitrosodimethylamine into intermediates (e.g., unsymmetrical dimethylhydrazine (UDMH) and dimethylamine) via the synergistic effects of GAC adsorption, nZVI-mediated chemical reduction, and nZVI-Cu microelectrolysis. These intermediates were ultimately converted into inorganic ions (e.g., ammonium and nitrate) and low-molecular-weight organic acids [e.g., formic acid (FA)]. In conclusion, this material provides an effective control approach for N-nitrosamines (NAs) pollutants in reclaimed water.
Abstract Landfill leachate is a complex, high-concentration organic wastewater that poses challenges for stable and efficient degradation using conventional treatment methods. Advanced oxidation technologies based on sulfate radicals ( SO 4 − · ) have garnered significant attention due to their strong oxidizing capacity and long half-life. This study developed a micro-nano bubble (MB) coupled with ultraviolet (UV)-activated potassium metabisulfite ( KHSO 5 ) system ( MBs / UV / KHSO 5 ). Simulated leachate (with hexanoic acid as the target compound) and ultrafiltration effluent from actual landfill leachate were used as treatment subjects. to investigate the impact of varying reaction conditions on chemical oxygen demand (COD) removal efficiency. Radical quenching experiments and electron paramagnetic resonance spectroscopy were employed to characterize reactive species and elucidate the reaction mechanism. Results demonstrated that the MBs / UV / KHSO 5 system exhibited significant synergistic degradation of simulated hexanoic acid wastewater, achieving a COD removal rate of 99.25% after 180 min of reaction, with a synergistic index of 43.76. Optimal process conditions were determined as a KHSO 5 dosage of 6 g · L − 1 , an MBs gas flow rate of 30 mL · min − 1 , a UV power of 10 W, and an initial pH of 4, achieving a total organic carbon (TOC) removal rate of 73.15%. Radical identification experiments confirmed that sulfate radicals ( SO 4 − · ) and hydroxyl radicals ( · OH ) were the dominant active species in the system. The introduction of micro-nano bubbles significantly enhanced radical yield by improving mass transfer, providing physical activation, and enriching reactants. In practical leachate validation experiments, the MBs / UV / KHSO 5 system also demonstrated excellent treatment performance. At a KHSO 5 dosage of 12 g · L − 1 , the COD removal rate reached 84.56% after 180 min of reaction. This study elucidates the synergistic mechanism of the MBs / UV / KHSO 5 system and validates its feasibility in practical wastewater treatment, providing an efficient and viable technological solution for advanced treatment of landfill leachate.
Abstract This study explores the impact of glucose as a second source of organic carbon on the denitrification coupled to methane oxidation (DOM) process under anoxic conditions. For this purpose, batch tests were conducted varying the C:N ratios to 0.25 ∶ 1 , 1 ∶ 1 , and 3.8 ∶ 1 . During the tests, the removal efficiencies for nitrite, nitrate, methane, and total organic carbon were monitored, and the biomass growth was observed. The highest nitrogen removal efficiency and biomass growth were obtained without glucose, with a C:N ratio of 0.25 ∶ 1 , whereas the higher C:N ratios resulted in reduced nitrite and nitrate removal rates, 54.4% and 47.9%, respectively, and lower biomass yield with longer doubling times. These findings suggest that additional organic carbon, such as glucose, may affect DOM activity, for which it is important to emphasize the need of careful control of C:N ratios for sustainable wastewater treatment applications.
Abstract In subtropical climate wastewater stabilization ponds, the treatment process relies on consistent temperatures and microbial activity. This case study aims to report the findings of an atypical snow event in the state of Louisiana that impacted wastewater treatment processing. A multivariate statistical analysis was performed on the average yearly, seasonal, and single-event datasets to show changes in water parameter correlations for stabilization-based ponds. During the atypical weather event, water temperatures decreased to 9°C, halting microbial activity and emphasizing increased nitrogen species correlations in the primary settling pond. The strong Pearson correlation coefficient for conductivity/ammonium (0.910) and moderate negative correlation of pH/nitrate ( − 0.622 ) indicate an accumulation in nitrogen species, while canonical correlation analysis illustrates a lag time for processing after the atypical snow. This data analysis can provide facility personnel with valuable insight into the shift of pond treatment during extreme events to forecast variability in water quality dynamics and drive operation changes to be proactive in response to environmental conditions.
Abstract Microcystis aeruginosa (M. aeruginosa) , a prevalent cyanobacterial species in eutrophic waters, threatens aquatic ecosystems and public health through bloom formation and toxin release. This study investigates the use of natural pyrite for controlling M. aeruginosa , examining both removal efficiency and underlying mechanisms. Within 240 min of pyrite treatment, turbidity, cell density, and chlorophyll a (Chl-a) decreased significantly to 0.94 ± 0.06 NTU, 0.08 ± 0.01 × 10 6 cells / mL , and 8.2 ± 0.6 μ g / L , corresponding to removal rates of 93.8%, 97.9%, and 98.2%, respectively, effectively ending eutrophic conditions. Mechanistic analyses indicated that FeOOH and Fe 2 O 3 , formed during pyrite oxidation, serve as binding sites that promote M. aeruginosa aggregation and sedimentation. Meanwhile, hydroxyl radicals ( • OH) generated via Fenton-like reactions disrupted cellular integrity and facilitated the degradation of dissolved extracellular organic matters and microcystin-LR (MC-LR). By Day 7, the MC-LR concentration was reduced to 0.37 ± 0.02 μ g / L , below the regulatory limit, thus avoiding secondary pollution. This study demonstrates the potential of pyrite as a sustainable, mineral-based strategy for mitigating M. aeruginosa blooms, offering both theoretical and practical insights for environmentally friendly water remediation.
Abstract This study investigates the innovative use of perforated screens as an aeration method in an open channel wastewater treatment system. Although screens are typically used in primary treatment to eliminate solids, their use in aeration has not yet been investigated. To evaluate this potential, 189 experimental tests were conducted using acrylic screens with different perforations/jets, such as circular, triangular, and square. Input parameters such as number of jets, discharge, angle of inclination of tilting flume ( α ), hydraulic radius of each geometry perforated on screens, Froude number, and shape factor (SF) were used to predict aeration efficiency ( E 20 ) using three artificial intelligence techniques, including artificial neural network (ANN), random forest, and support vector machine with Pearson VII kernel. With a correlation coefficient of 0.9635, ANN outperformed the other developed models. Sensitivity analysis revealed that SF was the most important component affecting E 20 . The dependability of the results was further supported by the great accuracy ( R 2 = 0.9116 ) of a mathematical model that used the same variables. The results lay the groundwork for further study and field-scale validation of this innovative aeration technique.
Stormwater infrastructure experiences uncontrolled and unsteady hydraulic and pollutant loadings driven by episodic rainfall-runoff events. Evaluating its in situ treatment dynamics and performance remains a fundamental challenge for cost-effective system design, planning, and management. Computational fluid dynamics (CFD) offers robust predictive capabilities of these systems by resolving the underlying turbulent transport and pollutant fate physics; however, its high computational cost constrains its application, particularly over extended periods. To address this limitation, this study developed a composite neural network (CPNN) framework to predict the three-dimensional (3D), transient dynamics of stormwater infrastructure under unsteady hydraulic and particulate matter (PM) loading. This framework was evaluated on a common urban stormwater treatment system, a hydrodynamic separator (HS). A total of 496 CFD simulations of the HS under various storm events were used for training, 62 for validation, and 62 for test. The results demonstrate a high predictive capability of the proposed CPNN framework relative to CFD, achieving an R2 score above 0.8 in 95.2% of test cases for hydrodynamic predictions. For PM concentration predictions, the R2 score exceeds 0.8 in 72.6% of test cases and remains between 0.4 and 0.8 in 22.6%. The study highlights challenges in learning spatial-temporal dynamics under complex loading conditions spanning multiple orders of magnitude. Furthermore, CPNN’s automatic differentiation capability is leveraged to assess the sensitivity of PM transport to loading conditions. The potential of CPNN for evaluating stormwater infrastructure performance over long-term periods is discussed. The framework is transferable to other water systems and represents a critical step toward enabling robust and climate-aware stormwater infrastructure planning and management.
Abstract Few studies have systematically examined the predictive capacity of digital search data for forecasting COVID-19 cases on a global scale, particularly in terms of search queries in different languages. Additionally, limited research has explored the correlations and temporal dynamics between digital search data in different languages and clinical cases. Therefore, this study employed Google Trends using COVID-19-related search terms in English and local languages to predict World Health Organization reported cases across 75 countries, spanning Africa, the Americas, Asia and Oceania, and Europe. Correlations between Google Trends and clinical case data were evaluated, and time-lagged cross-correlation analysis was employed to estimate the temporal dynamics of search data relative to cases. We also developed a machine learning-based random forest model to predict clinical cases using combined English and local language Google Trends. Our results identified leading time lags in most countries, suggesting that Google Trends can provide early warnings of reported clinical case surges. Furthermore, our random forest model successfully predicted COVID-19 cases using combined English and local language search data in a retrospective analysis, with identified relative contributions of English versus local-language searches. This study demonstrates that digital search data provide an openly accessible source of information that may assist forecasting of infectious disease outbreak surges prior to clinical data reporting. Digital epidemiology-based approaches could support public health decision-making by potentially complementing existing clinical and wastewater surveillance systems for infectious diseases. This work directly contributes to the United Nations sustainable development goals (SDGs), particularly SDG 3 (Good Health and Well-Being) and SDG 11 (Sustainable and Resilient Cities and Communities).
