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ABSTRACT Landfill leachate management is a critical challenge for the municipal solid waste (MSW) industry due to its significant environmental impact and high operational costs. In the United States, sanitary landfilling remains the primary MSW disposal method, with more than half of generated waste landfilled as of 2022. The U.S. generates between 7.1 and 11.3 billion gallons of landfill leachate annually, with up to one-third of landfill operational costs dedicated to leachate management. Leachate production can persist for decades after landfill closure, necessitating long-term management strategies. Around 61% of landfill leachate is disposed of at publicly owned treatment works (POTWs). These facilities face challenges in treating hazardous leachate components, including high Total Nitrogen levels, UV quenching substances (UVQS), refractory dissolved organic nitrogen (rDON), elevated temperature landfill (ETLF) leachate, micro- and nanoplastics (MP/NP), and per- and polyfluoroalkyl substances (PFAS). This presentation will explore the historical context of landfill leachate management and the challenges of co-treating leachate at POTWs. It will also identify emerging solutions and technologies aimed at improving treatment processes, enhancing environmental protection, and reducing costs. Addressing these challenges is crucial for minimizing the environmental footprint of landfill operations and ensuring compliance with regulatory standards. 

GenX, the trade name of hexafluoropropylene oxide dimer acid (HFPO-DA) and its ammonium salt, is a short-chain PFAS that has emerged as a substitute for the legacy PFAS perfluorooctanoic acid (PFOA). However, GenX has turned out to be more toxic than people originally thought. In order to monitor and regulate water quality according to recently issued drinking water standards for GenX, rapid and ultrasensitive detection of GenX is urgently needed. For the first time, this study reports ultrasensitive (as low as 1 part per billion (ppb)) and fast detection (in minutes) of GenX in water via surface-enhanced Raman spectroscopy (SERS) using a hierarchical nanofibrous SERS substrate, which was prepared by assembling ~60 nm Ag nanoparticles on electrospun nylon-6 nanofibers through a “hot start” method. The findings in this research highlight the potential of the engineered hierarchical nanofibrous SERS substrate for enhanced detection of short-chain PFASs in water, contributing to the improvement of environmental monitoring and management strategies for PFASs. 

Abstract The treatment of landfill leachate and sewage is crucial for mitigating the environmental impacts of dissolved organic nitrogen (DON) effluent in aquatic ecosystems. This study used three sequencing batch reactors (SBRs) to treat sewage mixed with landfill leachates of varying organic carbon content. While the SBRs significantly removed dissolved inorganic nitrogen (DIN), the effluents were enriched with landfill leachate-induced DON. These landfill leachate-induced DON effluents (R1, R2, and R3) were then photodegraded under simulated summer sunlight conditions based on Greensboro, NC, USA weather data. The study utilized visible light (400–780 nm, 9340 μW/cm²), UVA (365 nm, 1442 μW/cm²), UVB (285 nm, 76 μW/cm²), UVC (254 nm, 315 μW/cm²), and dark controls. Effluents were mixed with Neuse River Estuary water, serving as a natural algal source, and exposed for 90 days under these light conditions. Samples were analyzed every 10 days for DON degradation and algal growth, with molecular changes assessed using FTICR-MS, FTIR, and EEM-PARAFAC. Results showed substantial DON degradation across all light treatments, with UVA achieving the highest reduction (up to 99.07%), followed by UVC (88.85%), visible light (86.19%), and UVB (75.11%), while no degradation occurred under dark conditions. Initial DON levels of 2.69–2.7 mg/L were reduced to as low as 0.025 mg/L under UVA in R3 effluent. UVC treatment led to increased NO3-N concentrations due to the oxidation of DON to NH4-N and its subsequent conversion to NO3-N, reaching 2.66, 2.59, and 2.63 mg/L in R1, R2, and R3, respectively. UVC inhibited algal growth, resulting in no NH4-N uptake and subsequent oxidation to stable, elevated NO3-N levels in the samples. Algal growth responses varied by light treatment, with visible light and UVB promoting the highest algae growth, minimal algae growth observed under UVA, and no growth under UVC or dark conditions. These findings demonstrate the evidence of rDON degradation during the long-term retention in the receiving water bodies and potential impact on the algal growth. 

Per- and polyfluoroalkyl substances (PFAS) contamination has posed a significant environmental and public health challenge due to their ubiquitous nature. Adsorption has emerged as a promising remediation technique, yet optimizing adsorption efficiency remains complex due to the diverse physicochemical properties of PFAS and the wide range of adsorbent materials. Traditional modeling approaches, such as response surface methodology (RSM), struggled to capture nonlinear interactions, while standalone machine learning (ML) models required extensive datasets. This study addressed these limitations by developing hybrid RSM-ML models to improve the prediction and optimization of PFAS adsorption. A comprehensive dataset was constructed using experimental adsorption data, integrating key parameters such as pH, pHpzc, surface area, temperature, and PFAS molecular properties. RSM was employed to model adsorption behavior, while gradient boosting (GB), random forest (RF), and extreme gradient boosting (XGB) were used to enhance predictive performance. Hybrid models—linear, RMSE-based, multiplicative, and meta-learning—were developed and evaluated. The meta-learning HOP-RSM-GB model achieved near-perfect accuracy (R² = 1.00, RMSE = 10.59), outperforming all other models. Surface plots revealed that low pH and high pHpzc maximized the adsorption while increasing log Kow consistently enhanced PFAS adsorption. These findings establish hybrid RSM-ML modeling as a powerful framework for optimizing PFAS remediation strategies. The integration of statistical and machine learning approaches significantly improves predictive accuracy, reduces experimental costs, and provides deeper insights into adsorption mechanisms. This study underscores the importance of data-driven approaches in environmental engineering and highlights future opportunities for integrating ML-driven modeling with experimental adsorption research.