Modeling biological denitrification in the presence of metal ions and elevated chloride content: Insights into abiotic and biotic mechanisms regulating metal bioprecipitation

Biological denitrification is a critical process in which microorganisms convert nitrate to nitrogen gas. Metal ions, such as those found in industrial wastewater, can be toxic to microorganisms and impede denitrification. It is critical to identify the mechanisms that allow microorganisms to tolerate metal ions and understand how these mechanisms can be utilized to improve denitrification efficiency by modeling the process. This study presents a mathematical model of biological denitrification in the presence of metal ions. The model includes key biotic and abiotic mechanisms and is based on pilot scale results. The model predicts the bioprecipitation of metal ions due to pH shift and alkalinity production during the metabolic activity of microorganisms. The model parameters are estimated to fit the experimental results and the mechanisms regulating metal detoxification via biological metal precipitation are presented. The model provides a valuable tool for understanding the behavior of denitrification systems in the presence of metal ions and can be used to optimize these systems for more efficient and effective treatment of industrial wastewater.

Integrated mathematical model to simulate the performance of a membrane bioreactor

Membrane bioreactor technology includes the integration of biological wastewater treatment and physical separation by membrane filtration. When analyzing the system performance, efficiency of biological processes, physical separation and membrane fouling must be taken into consideration. Over the years, mathematical modelling of wastewater treatment has evolved and is being used extensively to optimize the performance of treatment systems. A Number of attempts have been made towards the development of mathematical models for membrane bioreactors and most of these models have not considered the effect of soluble microbial products on membrane fouling. Also the effect of periodic membrane cleaning was neglected. In this study, an integrated mathematical model was developed for the membrane bioreactor. A biological model based on activated sludge processes (extended with biopolymer kinetics) and a physical model with cake layer kinetics and membrane fouling have been combined. In order to overcome the drawbacks of previous attempts of modelling, the influence of soluble microbial products and extracellular polymeric substances are considered in the model integration. Further, the physical processes of the sludge removal and membrane cleaning which have strong influence on membrane fouling are considered in the model. "AQUASIM", a computer program for the identification and simulation of aquatic systems, was used for solving the processes. Calibrated and validated model enables the prediction of the system performance and membrane fouling under different operating conditions.

Coupling Suspect and Nontarget Screening with Mass Balance Modeling to Characterize Organic Micropollutants in the Onondaga Lake-Three Rivers System

Characterizing the occurrence, sources, and fate of organic micropollutants (OMPs) in lake-river systems serves as an important foundation for constraining the potential impacts of OMPs on the ecosystem functions of these critical landscape features. In this work, we combined suspect and nontarget screening with mass balance modeling to investigate OMP contamination in the Onondaga Lake-Three Rivers system of New York. Suspect and nontarget screening enabled by liquid chromatography-high-resolution mass spectrometry led to the confirmation and quantification of 105 OMPs in water samples collected throughout the lake-river system, which were grouped by their concentration patterns into wastewater-derived and mixed-source clusters via hierarchical cluster analysis. Four of these OMPs (i.e., galaxolidone, diphenylphosphinic acid, N-butylbenzenesulfonamide, and triisopropanolamine) were prioritized and identified by nontarget screening based on their characteristic vertical distribution patterns during thermal stratification in Onondaga Lake. Mass balance modeling performed using the concentration and discharge data highlighted the export of OMPs from Onondaga Lake to the Three Rivers as a major contributor to the OMP budget in this lake-river system. Overall, this work demonstrated the utility of an integrated screening and modeling framework that can be adapted for OMP characterization, fate assessment, and load apportionment in similar surface water systems.

A computational model for the catalytic hydrogel membrane reactor

The catalytic hydrogel membrane reactor (CHMR) is a promising new technology for hydrogenation of aqueous contaminants in drinking water. It offers numerous benefits over conventional three-phase reactors, including immobilization of nano-catalysts, high reactivity, and control over the hydrogen (H₂) supply concentration. In this study, a computational model of the CHMR was developed using AQUASIM and calibrated with 32 experimental datasets for a nitrite (NO₂^-)-reducing CHMR using palladium (Pd) nano-catalysts (~4.6 nm). The model was then used to identify key factors impacting the behavior of the CHMR, including hydrogel catalyst density, H₂ supply pressure, influent and bulk NO₂^- concentrations, and hydrogel thickness. Based on the model calibration, the reaction rate constants for the NO₂^- steady-state adsorption Hinshelwood reaction equation, k₁ and k₂, were 0.0039 m³ mole-Pd^-1 s^-1 and 0.027 (mole-H₂ m³)^1/2 mole-Pd^-1 s^-1, respectively. The reactant flux, which is the overall NO₂^- removal rate for the CHMR, is affected by the NO₂^- reduction rate at each catalyst site, which is in turn controlled by the available NO₂^- and H₂ concentrations that are regulated by their mass transport behavior. Reactant transport in the CHMR is counter-diffusional. So for thick hydrogels, the concurrent concentrations of NO₂^- and H₂ are limiting in the middle region along the x-y plane of the hydrogel, which results in a low overall NO₂^- removal rate (i.e., flux). Thinner hydrogels provide higher concurrent reactant concentrations throughout the hydrogel, resulting in higher fluxes. However, if the hydrogel is too thin, the flux becomes limited by the amount of Pd that can be loaded, and unused H₂ can diffuse into the bulk and promote biofilm growth. The hydrogel thickness that maximized the NO₂^- flux ranged between 30 and 150 μm for the conditions tested. The computational model is the first to describe CHMR behavior, and it is an important tool for the further development of the CHMR. It also can be adapted to assess CHMR behavior for other contaminants or catalysts or used for other types of interfacial catalytic membrane reactors.

Simulation of batch-operated experimental wetland mesocosms in AQUASIM biofilm reactor compartment

In this study, a mathematical biofilm reactor model based on the structure of the Constructed Wetland Model No.1 (CWM1) coupled to AQUASIM's biofilm reactor compartment has been used to reproduce the sequence of transformation and degradation of organic matter, nitrogen and sulphur observed in a set of constructed wetland mesocosms and to elucidate the development over time of microbial species as well as the biofilm thickness of a multispecies bacterial biofilm in a subsurface constructed wetland. Experimental data from 16 wetland mesocosms operated under greenhouse conditions, planted with three different plant species (Typha latifolia, Carex rostrata, Schoenoplectus acutus) and an unplanted control were used in the calibration of this mechanistic model. Within the mesocosms, a thin (predominantly anaerobic) biofilm was simulated with an initial thickness of 49 μm (average) and in which no concentration gradients developed. The biofilm density and area, and the distribution of the microbial species within the biofilm were evaluated to be the most sensitive biofilm properties; while the substrate diffusion limitations were not significantly sensitive to influence the bulk volume concentrations. The simulated biofilm density ranging between 105,000 and 153,000 gCOD/m(3) in the mesocosms was observed to vary with temperature, the presence as well as the species of macrophyte. The biofilm modeling was found to be a better tool than the suspended bacterial modeling approach to show the influence of the rhizosphere configuration on the performance of the constructed wetlands.

Monochloramine cometabolism by Nitrosomonas europaea under drinking water conditions

Chloramine is widely used in United States drinking water systems as a secondary disinfectant, which may promote the growth of nitrifying bacteria because ammonia is present. At the onset of nitrification, both nitrifying bacteria and their products exert a monochloramine demand, decreasing the residual disinfectant concentration in water distribution systems. This work investigated another potentially significant mechanism for residual disinfectant loss: monochloramine cometabolism by ammonia-oxidizing bacteria (AOB). Monochloramine cometabolism was studied with the pure culture AOB Nitrosomonas europaea (ATCC 19718) in batch kinetic experiments under drinking water conditions. Three batch reactors were used in each experiment: a positive control to estimate the ammonia kinetic parameters, a negative control to account for abiotic reactions, and a cometabolism reactor to estimate the cometabolism kinetic constants. Kinetic parameters were estimated in AQUASIM with a simultaneous fit to all experimental data. The cometabolism reactors showed a more rapid monochloramine decay than in the negative controls, demonstrating that cometabolism occurs. Cometabolism kinetics were best described by a pseudo first order model with a reductant term to account for ammonia availability. Monochloramine cometabolism kinetics were similar to those of ammonia metabolism, and monochloramine cometabolism was a significant loss mechanism (30-60% of the observed monochloramine decay). These results suggest that monochloramine cometabolism should occur in practice and may be a significant contribution to monochloramine decay during nitrification episodes in drinking water distribution systems.