Moeen Gholami, Aisling D. O’Sullivan, Yunkai Tan, Tianwei Hao, Hamish R. Mackey
Greywater reuse offers a sustainable approach to alleviating freshwater scarcity, but concerns about treatment reliability limit its adoption. This study investigates a two-stage green wall system for greywater treatment, integrating vegetated units planted with Carex secta and vertical flow post-treatment columns (PTCs) packed with waste-derived adsorbents—calcinated kina (sea urchin) shell, ZnCl 2 -activated apricot stone biochar, and natural zeolite. The system’s performance was evaluated for removal of organic matter (COD, TOC), nutrients (ammonia, nitrate, nitrite, phosphate), and xenobiotics (anionic surfactants, aromatic compounds) under varying hydraulic and pollutant loads. Substrate characterization, plant health assessment, microbial analysis, and physicochemical water quality monitoring were used to elucidate plant–microbe–media interactions. PTCs effectively polished residual pollutants after partial removal in green wall units, achieving removal rates of 34% of phosphate, 99.8% of ammonia, 95% of surfactants, and 25%–37% of aromatic compounds (UV (294)). While nitrate leached from green wall units (−34%), PTCs mitigated this by removing 64% of nitrate. TOC and COD were further reduced by 17% and 14%, resulting in overall removal efficiencies of 95% and 96%, respectively, across the two-stage system. Under stress conditions of 2-week drying followed by high pollutant loading, PTCs maintained pollutant removal, whereas green wall units showed reduced nutrient removal. Across the planted green wall, ammonia increased from 0.02 to 1.63 mg/L and phosphate from 3.42 to 4.44 mg/L. The non-planted control deteriorated more strongly, with ammonia increasing to 8.88 mg/L and phosphate to 8.32 mg/L. After stress recovery, vegetated units rapidly regained treatment efficiency, while controls released ammonia tenfold and phosphate twofold. This novel integrated treatment design demonstrates a scalable, low-maintenance solution for effective on-site greywater treatment and reuse.
Umar Munir, Yifei Wang, Xi Zhu, Yuqing Ji, Mussie Y. Habteselassie, Andrea Squartini, Qingguo Huang
6:2 Fluorotelomer sulfonic acid (FTS) has been used as an alternative to perfluorooctane sulfonate (PFOS) as chrome mist suppressant in electroplating, emulsion aid in fluoropolymer processing, etc. Its biotic degradation is slow and incomplete, generating short chain perfluoroalkyl acids (PFAAs) and it is frequently detected in the environment. This proof-of-concept study reports possible transformation of 6:2 FTS by enzyme-catalyzed oxidative humification reactions (ECOHRs) in laccase-mediator systems (LMS) using crude or purified laccases, either mediated by a synthetic chemical (1-hydroxybenzotriazole) or a natural organic material (soybean meal extract; SBMe). The LMS with crude laccase, SBMe and 10 mM CuSO 4 exhibited a 23.8% reduction in concentration (mean of three technical replicates) relative to its control. A 34.8% reduction in adsorbable organic fluorine provided a supporting observation for transformation and potentially degradation. High-resolution mass spectrometry (HRMS) analysis unveiled a suite of tentative transformation products ranging from C8 to C3, including C 3 F 7 O 3 S, a previously unreported candidate formula for 6:2 FTS transformation products and potentially consistent with perfluoropropanesulfonate (PFPrS). Additionally, the calculated bond dissociation energies (BDEs) of the C-C bonds and the C-S bond in the 6:2 FTS were higher than those of PFOS, which could potentially explain the variable degradation observed in different studies. These findings underscore the potential of ECOHRs to transform per-and polyfluoroalkyl substances (PFAS) precursors. Future investigations into the transformation of PFAS mixtures in complex soil systems could provide actionable insights into ECOHRs as a potentially cost-effective remediation technique for vast crop lands, and pastures.
Anton Rossbach, José de Anda, Jörg Karl Janisch, Julius Fabian Müller, Oscar Aguilar Juárez
Large treatment wetlands (TWs) can provide decentralized wastewater treatment at infrastructure scale, but successful implementation requires the integration of site conditions, wastewater characteristics, treatment-train design, operation, financing, and governance across the project lifecycle. We developed an integrated decision-support framework based on a structured qualitative evidence synthesis and a comparative assessment of 64 large-scale TW projects. Project-level data were extracted on location, wastewater source, inflow, population equivalent, wetland area and type, treatment train, vegetation, pretreatment, post-treatment, conventional pollutant removal, data limitations, and special design or operational features. Projects were grouped into 18 configuration categories. Evidence on emerging contaminants was synthesized separately at the compound-group level. Staged vertical-flow/horizontal-flow systems and pretreated vertical-flow systems showed strong removal performance for BOD 5, COD, TSS/SS, nitrogen indicators, and total phosphorus when supported by adequate pretreatment, hydraulic distribution, monitoring, and operation and maintenance. Performance varied with influent strength, treatment objective, system configuration, pollutant-specific data availability, and evidence quality. Micropollutant attenuation was promising but compound-specific and depended on media selection, redox zoning, hydraulic retention time, vegetation, sorption capacity, and multi-barrier design. The resulting matrix links six framework dimensions to key decisions, indicators, red flags, planning outputs, readiness levels, and no-go risks. Large TWs should be planned as governable, adaptive, and multifunctional public infrastructure. The framework provides implementation-oriented support for early project screening, treatment-train selection, governance design, and long-term decentralized water management.
Biswajit Debnath, Ranjan Sasti Charan Pal, Amit K. Chattopadhyay
Wastewater valorisation inherently benefits from predictive models that are accurate, generic, robust and environmentally sustainable. Benchmarked on a publicly available municipal wastewater dataset from Melbourne, Australia, characterised by temporal variability and multivariate complexity, we present a Green Machine Learning (GML) framework to predict Chemical Oxygen Demand (COD) and Total Nitrogen (TN), two variables directly linked to carbon and nutrient recovery pathways. Four widely used machine learning and deep learning models - FFNN, RNN, CNN, and XGBoost - were benchmarked to identify the most optimised approach, defined here as the model providing the best overall balance between predictive accuracy, generalisation, and computational environmental cost rather than the highest training performance alone. XGBoost and RNN emerged as the most optimised algorithmic pathways. RNN captured the temporal behaviour most effectively for COD, achieving an R 2 of 0.71 and an RMSE of 75.55 mg/L, while XGBoost showed the strongest generalisation, with validation/test R 2 values of 0.72/0/71, respectively. For TN, XGBoost was the best-performing model, with validation/test R 2 = 0.59/0.63, outperforming RNN (0.56/0.53) and the other neural architectures. Sustainability analysis using CodeCarbon showed that XGBoost required only 0.000018 kWh and emitted 0.0000129 kg CO 2 -eq for COD, compared to 0.001490 kWh and 0.001063 kg CO 2 -eq for RNN; for TN, XGBoost consumed 0.000028 kWh, compared to 0.002563 kWh for RNN. These results conclusively show that XGBoost delivers comparable or better predictions at 98%–99% lower computational carbon cost, while RNN remains the strongest neural network candidate for COD-related dynamics.
