To determine the influence of artificial destratification on nutrient variations in a small eutrophic impoundment, field monitoring and laboratory analyses were conducted in three consecutive summers (2010, 2011, and 2012). The impact of aeration among sampling locations and across the water column of nutrient concentrations, including total and dissolved inorganic nitrogen (N) and phosphorus (P) and water temperature and dissolved oxygen (DO) was evaluated under aerated and non-aerated conditions. Aeration eliminated thermal stratification and DO concentrations of bottom waters increased. Nutrients including soluble reactive phosphorus (SRP), total phosphorus (TP), and total nitrogen (TN) concentrations across the water column did not change significantly during aeration. Nevertheless, under aerated conditions, dissolved inorganic nutrients, TN, TP, and temperature were homogenously distributed throughout the water column as an effect of aeration. Results indicated that artificial destratification resuspended nutrients throughout the water column; however, it did not have a significant effect on nutrient concentrations in the water column, but SRP, TN, and TP concentrations did not reach to the recommended limit as needed by the North Dakota Department of Health requirements. Therefore, alternative aeration methods, for instance, hypolimnetic oxygenation or hypolimnetic aeration are recommended to control nutrient redistribution and/or further releases by existing aeration system.
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J Environ Manage
January 2025
Department of Civil, Environmental and Geodetic Engineering, Ohio State University, Columbus, OH 43210, USA. Electronic address:
Pervious concrete has shown potential in neutralizing wastewater and stormwater, and this study was conducted to determine the effectiveness of pervious concrete at removing heavy metals and neutralizing acid from an AMD source. The removal of aluminum, manganese, iron, and copper from natural and synthetic AMD sources by pervious concrete cubes at beaker scale was tracked. Pervious concrete cylinders were also used to model length requirements for a permeable reactive barrier to treat field-scale AMD.
View Article and Find Full Text PDFAdv Biotechnol (Singap)
November 2024
State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China.
The H9N2 subtype of avian influenza virus (AIV) causes severe immunosuppression and high mortality in view of its frequent co-infection with other pathogens, resulting in significant economic losses in the poultry industry. Current vaccines provide suboptimal immune protection against H9N2 AIV owing to antigenic variations, highlighting the urgent need for safe and effective antiviral drugs for the prevention and treatment of this virus. This study aimed to investigate the inhibitory effects of Hypericum japonicum extract on H9N2 AIV.
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January 2025
Waters Corporation, 34 Maple St., Milford, Massachusetts 01757, United States.
Therapeutic drugs and multivalent vaccines based on the delivery of mRNA via lipid nanoparticle (LNP) technologies are expected to dominate the biopharmaceutical industry landscape in the coming years. Many of these innovative therapies include several nucleic acid components (e.g.
View Article and Find Full Text PDFFront Microbiol
January 2025
Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.
The opportunistic pathogen sp. ATCC 39006 (S39006) is a rod-shaped, motile, Gram-negative bacterium that produces a 𝛽-lactam antibiotic (a carbapenem) and a bioactive red-pigmented tripyrrole antibiotic, prodigiosin. It is also the only known enterobacterium that naturally produces intracellular gas vesicles (GVs), enabling cells to float in static water columns.
View Article and Find Full Text PDFWater Res
January 2025
Department of Chemical Engineering, University of Bath, Claverton Down, Bath BA2 7AY, UK; SWING - Department of Built Environment, Oslo Metropolitan University, St Olavs plass 0130, Oslo, Norway. Electronic address:
Climate resilience in water resource recovery facilities (WRRFs) necessitates improved adaptation to shock-loading conditions and mitigating greenhouse gas emission. Data-driven learning methods are widely utilised in soft-sensors for decision support and process optimization due to their simplicity and high predictive accuracy. However, unlike for mechanistic models, transferring machine-learning-based insights across systems is largely infeasible, which limits communication and knowledge sharing.
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