Per- and polyfluorinated alkyl substances (PFAS) are an environmentally persistent group of chemicals that can pose an imminent threat to human health through groundwater and surface water contamination. In this review, we evaluate the subsurface behavior of a variety of PFAS chemicals with a focus on aqueous film forming foam (AFFF) discharge sites. AFFF is the primary PFAS contamination risk at sites such as airports and military bases due to use as a fire extinguisher. Understanding the fate and transport of PFAS in the subsurface environment is a multifaceted issue. This review focuses on the role of adsorbent, adsorbate, and aqueous solution in the fate and transport of PFAS chemicals. Additionally, other hydrogeological, geochemical, ecological factors such as accumulation at air–water interfaces, subsurface heterogeneity, polyfluorinated PFAS degradation pathways, and plant interactions are discussed. This review also examines several case studies at AFFF discharge sites in order to examine if the findings are consistent with the broader PFAS literature. We present the most crucial future research directions and trends regarding PFAS and provide valuable insights into understanding PFAS fate and transport at AFFF discharge sites. We suggest a more comprehensive approach to PFAS research endeavors that accounts for the wide variety of environmental variables that have been shown to impact PFAS fate and transport.

UVA and UVB can be applied to solar disinfection of water. In this study, the inactivation and photoreactivation of viruses and bacteria in the UVA-B range were analyzed. MS2 and T4 bacteriophages, and Escherichia coli were used as surrogates to quantify dose-response behaviors. Inactivation in UVC was used to validate the methodology and to expand the inactivation action spectra. The results showed log-linear inactivation for MS2 and T4 in the 254–320 nm wavelength range. T4 inactivation was consistently faster than MS2 (except at 320 nm), and for both phages, inactivation decreased with increasing wavelength. The dose-response of bacteria exhibited a lag at low doses, possibly because the photons must strike a discrete number of critical targets before growth stops. A tail was present at high doses for some wavelengths, perhaps due to clumping or the presence of subgroups with higher resistance. The inactivation action spectra for bacteria exhibited a reduction in inactivation as wavelength increased. No bacterial inactivation was observed beyond 320 nm at doses applied. After inactivation at 297 nm (UVA), bacteria regained viability through photoreactivation, and repair increased with increase in photoreactivating light exposure time. This implies additional doses above inactivation thresholds are required to cause irreversible damage. These results are useful for designing solar disinfection systems.

Rapidly increasing solid waste generation and energy demand are two critical issues of the current century. Plasma gasification, a type of waste-to-energy (WtE) technology, has the potential to produce clean energy from waste and safely destroy hazardous waste. Among plasma gasification technologies, microwave (MW)-driven plasma offers numerous potential advantages to be scaled as a leading WtE technology if its processes are well understood and optimized. This paper reviews studies on modeling experimental microwave-induced plasma gasification systems. The system characterization requires developing mathematical models to describe the multiphysics phenomena within the reactor. The injection of plasma-forming gases and carrier gases, the rate of the waste stream, and the operational power heavily influence the initiation of various chemical reactions that produce syngas. The type and kinetics of the chemical reactions taking place are primarily influenced by either the turbulence or temperature. Navier–Stokes equations are used to describe the mass, momentum, and energy transfer, and the k-epsilon model is often used to describe the turbulence within the reactor. Computational fluid dynamics software offers the ability to solve these multiphysics mathematical models efficiently and accurately.

Switchgrass‐derived ethanol has been proposed as an alternative to fossil fuels to improve sustainability of the US energy sector. In this study, life cycle analysis (LCA) was used to estimate the environmental benefits of this fuel. To better define the LCA environmental impacts associated with fertilization rates and farm‐landscape topography, results from a controlled experiment were analyzed. Data from switchgrass plots planted in 2008, consistently managed with three nitrogen rates (0, 56, and 112 kg N ha⁻¹), two landscape positions (shoulder and footslope), and harvested annually (starting in 2009, the year after planting) through 2014 were used as input into the Greenhouse gases, Regulated Emissions and Energy use in transportation (GREET) model. Simulations determined nitrogen (N) rate and landscape impacts on the life cycle energy and emissions from switchgrass ethanol used in a passenger car as ethanol–gasoline blends (10% ethanol:E10, 85% ethanol:E85s). Results indicated that E85s may lead to lower fossil fuels use (58 to 77%), greenhouse gas (GHG) emissions (33 to 82%), and particulate matter (PM2.5) emissions (15 to 54%) in comparison with gasoline. However, volatile organic compounds (VOCs) and other criteria pollutants such as nitrogen oxides (NOx), particulate matter (PM10), and sulfur dioxides (SO_x) were higher for E85s than those from gasoline. Nitrogen rate above 56 kg N ha⁻¹ yielded no increased biomass production benefits; but did increase (up to twofold) GHG, VOCs, and criteria pollutants. Lower blend (E10) results were closely similar to those from gasoline. The landscape topography also influenced life cycle impacts. Biomass grown at the footslope of fertilized plots led to higher switchgrass biomass yield, lower GHG, VOCs, and criteria pollutants in comparison with those at the shoulder position. Results also showed that replacing switchgrass before maximum stand life (10–20 years.) can further reduce the energy and emissions reduction benefits.

Microwave-driven plasma gasification technology has the potential to produce clean energy from municipal and industrial solid wastes. It can generate temperatures above 2000 K (as high as 30,000 K) in a reactor, leading to complete combustion and reduction of toxic byproducts. Characterizing complex processes inside such a system is however challenging. In previous studies, simulations using computational fluid dynamics (CFD) produced reproducible results, but the simulations are tedious and involve assumptions. In this study, we propose machine-learning models that can be used in tandem with CFD, to accelerate high-fidelity fluid simulation, improve turbulence modeling, and enhance reduced-order models. A two-dimensional microwave-driven plasma gasification reactor was developed in ANSYS (Ansys, Canonsburg, PA, USA) Fluent (a CFD tool), to create 644 (geometry and temperature) datasets for training six machine-learning (ML) models. When fed with just geometry datasets, these ML models were able to predict the proportion of the reactor area with temperature above 2000 K. This temperature level is considered a benchmark to prevent formation of undesirable byproducts. The ML model that achieved highest prediction accuracy was the feed forward neural network; the mean absolute error was 0.011. This novel machine-learning model can enable future optimization of experimental microwave plasma gasification systems for application in waste-to-energy.