The effects of aeration and stirring systems on the physical properties of sludge were analyzed using a computational fluid dynamics (CFD) model. The aims of this study were to (1) compare the effects of aeration and stirring on sludge properties using the same turbulent mixing intensity, and (2) to reveal the relationship between sludge properties and hydrodynamic indicators to determine how hydrodynamic conditions influence sludge flocculation. Mixing experiments with stirring and aeration were carried out in 2-L beakers with the average velocity gradient (G) set to 90, 190, or 280 s−1. The sludge flocculation performance, zeta potential, and Gibbs free energy (ΔG) were analyzed and the flow rate, turbulence energy, turbulence dissipation rate, and Kolmogorov microscale were calculated as hydrodynamic parameters. The average flow rate and the turbulence dissipation rate were obviously higher in the stirring system than in the aeration system at the same G. However, the turbulence energy and Kolmogorov microscale in the aeration system were much higher than those in the stirring system. Both the zeta potential and ΔG were lower in the aeration system than the stirring system. The zeta potential and ΔG results for the two systems suggest that aeration is more beneficial for sludge flocculation than stirring even though the sludge flocculation performance F/F0 in the stirring and aeration systems showed no obvious differences. Significant relationships between hydrodynamic parameters calculated based on the CFD model and average values of sludge properties in the stable phase showed that the Kolmogorov microscale, average flow rate, and turbulence energy were appropriate hydrodynamic parameters for evaluating the flocculation performance F/F0, zeta potential, and ΔG, respectively.

Lagoon has been used as one of common to treat agricultural wastewater. However, discharging lagoon water without appropriate treatment could because a critical issue that threaten environment and sustainability. It is necessary to monitor the quality of lagoon water before land application. This paper not only proposes an innovative testing method for sewage, but also combines a machine learning methods and spectral processing techniques to analyze the reflectivity of water chemical composition. The nitrogen, phosphorus, bacteria (total coliform), and total solids (TS), are chosen as main indicators for water quality. It was found that the spectral rate of emission or absorption showed a certain correlation with those contaminants. We use machine learning to train three kinds of estimators to prove those correlation, where their core algorithms are normal equation linear regression (LR), stochastic gradient descent (SGD) and ridge regression (R-PLS). At last, the model dataset is evaluated by weight coefficient, function intercept and mean squared error (MSE). The conclusion shows the reflectivity of TS and spectral reflectance of samples close to linear relationship. The MSE of prediction set and decision coefficient are 0.57 and 0.98, respectively. For bacteria, the MSE of prediction set is 0.63, and coefficient R2 is 0.96. The results from this study could provide a versatile method for remote sensing monitoring of agricultural water pollution and a non-destructive spectroscopy technical reference for agricultural wastewater treatment.

Hydraulic fracturing (HF), or “fracking,” is the driving force behind the “shale gas revolution,” completely transforming the United States energy industry over the last two decades. HF requires that 4–6 million gallons per well (15,000–24,000 m3/well) of water be pumped underground to stimulate the release of entrapped hydrocarbons from unconventional (i.e., shale or carbonate) formations. Estimated U.S. production volumes exceed 150 billion gallons/year across the industry from unconventional wells alone and are projected to grow for at least another two decades. Concerns over the environmental impact from accidental or incidental release of produced water from HF wells (“U-PW”), along with evolving regulatory and economic drivers, has spurred great interest in technological innovation to enhance U-PW recycling and reuse. In this review, we analyze U-PW quantity and composition based on the latest U.S. Geographical Survey data, identify key contamination metrics useful in tracking water quality improvement in the context of HF operations, and suggest “fit-for-purpose treatment” to enhance cost-effective regulatory compliance, water recovery/reuse, and resource valorization. Drawing on industrial practice and technoeconomic constraints, we further assess the challenges associated with U-PW treatment for onshore U.S. operations. Presented are opportunities for targeted end-uses of treated U-PW. We highlight emerging technologies that may enhance cost-effective U-PW management as HF activities grow and evolve in the coming decades.

In the aftermath of the lead contamination crisis that plagued the water system in Flint, MI, more than 35,000 water samples were collected from the city's premises. The majority of these samples (>85%) were collected through a voluntary crowdsourced sampling campaign. The samples were analyzed for lead and copper concentrations by the Michigan Department of Environmental Quality (MDEQ). In this study, the crowdsourced sampling data is analyzed by means of spatial autocorrelation analysis to reveal the locations of statistically significant hotspot regions of high water lead levels (WLLs), and to track the spatiotemporal evolution of the WLLs as the city recovered from the lead contamination crisis. The results showed that hotspot regions that experienced high WLLs were consistent with the areas where lead service line (LSL) density is the highest. Yet, galvanized service lines and other lead-containing plumbing components may have also contributed to lead release in hotspot regions. The temporal trend exhibited by the crowdsourced sampling data did not reflect a consistent decrease in the sampled WLLs despite the interventions implemented by MDEQ and EPA. Instead, sampled WLLs remained high for several months after boosting the orthophosphate dose and launching a city-wide residential flushing campaign. The findings of this study suggest that this could be partially attributed to disproportionate sampling from hotspot premises of high WLLs and LSL density.

The aim of this study was to investigate the use of biogas production rate kinetics for the monitoring of anaerobic co-digestion. Recent extensive studies of degradation pathways showed that acetoclastic methanogenesis is not always the main pathway. Hydrogenotrophic methanogenesis and syntrophic acetate oxidation can also dominate, mostly for operating conditions with high concentrations of ammonia or volatile fatty acids … These conditions are also known to cause instability in the digester's operation especially in co-digestion due to substrate variability. Therefore, co-digestion experiments were conducted with several co-substrates using a continuously stirred 35-L tank reactor. Degradation pathways and their potential shifts were identified by monitoring variations in biogas production rate kinetics using a principal component analysis model. The shifts in the degradation pathways were used to monitor the process. These shift points were found to provide early warnings of instabilities in the anaerobic co-digestion process.

High-Solids Anaerobic co-Digestion (HS-AcD) of sewage sludge (biosolids) with the organic fraction of municipal solid waste is a promising waste management alternative due to high methane yields, lower reactor volume requirements, lower energy inputs, and less leachate production than liquid anaerobic digestion. This study evaluated the environmental and economic burdens and benefits of HS-AcD of biosolids, Food Waste (FW), and Yard Waste (YW) using Life Cycle Assessment (LCA) and Life Cycle Cost Analysis (LCCA) methods using Hillsborough County, Florida in the U.S. as a case study. Results for HS-AcD were compared with incineration, composting, and landfilling, with and without landfill gas use. The results showed that HS-AcD of a mixture of biosolids, FW, and YW had the lowest environmental impacts in all categories analyzed (global warming potential, acidification, eutrophication, and ecotoxicity). In terms of economics, HS-AcD had the lowest life cycle cost, with or without considering land acquisition. The results show that HS-AcD is the best choice to manage biosolids and the organic waste in Hillsborough County in terms of both environmental and economic sustainability.