Oxygen Half-Saturation Constants for Pharmaceuticals in Activated Sludge and Microbial Community Activity under Varied Oxygen Levels

Influence of low oxygen concentrations on biological transformations of trace organic chemicals in sand filter systems

Managed aquifer recharge systems for drinking water reclamation are challenged by trace organic chemicals (TOrCs) since some of them are poorly retained. Although a lot of research has been done to investigate biological transformation of TOrCs in sand filter systems, there are still uncertainties to predict the removal. A laboratory column system with two different filter sands was set up to test TOrC transformation, the influence of low oxygen concentrations as well as the adaptation and influence of spiked TOrC influent concentrations. Bioactivity was quantified with the fluorescence tracer resazurin. In the experiment, a low elimination performance in the first column segment, defined as lag zone, was observed, implying incomplete adaptation or inhibiting co-factors. To assess these lag zones and to determine the dissipation time DT₅₀ for 50% removal, a modified Gompertz model was applied. For acesulfame, formylaminoantipyrine, gabapentin, sulfamethoxazole, and valsartan acid DT₅₀ of less than 10 h were observed, even when influent oxygen concentrations decreased to 0.5 mg/L. In general, TOrC transformations in technical sand with lower bioactivity and especially valsartan acid transformation responded very sensitive to low influent oxygen concentrations of 0.5 mg/L. However, in well adapted sand originating from soil aquifer treatment (SAT) with sufficient bioactivity, TOrC removal was hardly affected by such suboxic conditions. Furthermore, increasing the influent concentrations of TOrCs to 10 μg/L was found to promote adaptation especially for acesulfame and sulfamethoxazole. Benzotriazole, carbamazepine, diclofenac and venlafaxine were recalcitrant under the applied experimental conditions.

Universal Dynamics of Microbial Communities in Full-Scale Textile Wastewater Treatment Plants and System Prediction by Machine Learning

The performance of full-scale biological wastewater treatment plants (WWTPs) depends on the operational and environmental conditions of treatment systems. However, we do not know how much these conditions affect microbial community structures and dynamics across systems over time and predictability of the treatment performance. For over a year, the microbial communities of four full-scale WWTPs processing textile wastewater were monitored. During temporal succession, the environmental conditions and system treatment performance were the main drivers, which explained up to 51% of community variations within and between all plants based on the multiple regression models. We identified the universality of community dynamics in all systems using the dissimilarity-overlap curve method, with the significant negative slopes suggesting that the communities containing the same taxa from different plants over time exhibited a similar composition dynamic. The Hubbell neutral theory and the covariance neutrality test indicated that all systems had a dominant niche-based assembly mechanism, supporting that the communities had a similar composition dynamic. Phylogenetically diverse biomarkers for the system conditions and treatment performance were identified by machine learning. Most of the biomarkers (83%) were classified as generalist taxa, and the phylogenetically related biomarkers responded similarly to the system conditions. Many biomarkers for treatment performance perform functions that are crucial for wastewater treatment processes (e.g., carbon and nutrient removal). This study clarifies the relationships between community composition and environmental conditions in full-scale WWTPs over time.

Biodegradability, environmental risk assessment and ecological footprint in wastewater technologies for pharmaceutically active compounds removal

Several studies have investigated the removal of pharmaceutically active compounds (PhACs) by wastewater treatment technologies due to the risk that these compounds pose to the environment. In this sense, advanced biological processes have been developed for micropollutants removal, such as membrane bioreactors and moving bed biofilm reactors. Thus, this review holistically evaluated the biodegradation of 18 environmentally hazardous PhACs. Biological processes were assessed including removal efficiencies, environmental risk, and ecological footprint (consumption of resources and energy, atmospheric emissions, and waste generation). The maximum concentration of PhACs for a low or negligible risk scenario in treated wastewater and the potential of biological processes to meet this goal were assessed. Among the evaluated PhACs, the most biodegradable was paracetamol, while the most recalcitrant was diclofenac. Combination of conventional processes and advanced biological processes proved to be the most efficient way to remove several PhACs, mainly the osmotic membrane bioreactor.

Insights into the acute effect of anti-inflammatory drugs on activated sludge systems with high solids retention time

The increase in the occurrence of the pharmaceuticals in the environmental compartments is becoming emerging concern as it reflects their inefficient treatment in the wastewater treatment plants which are the main sources of these micropollutants. Non-steroidal anti-inflammatory drugs (NSAIDs) are one of the most commonly prescribed and frequently detected pain medications in wastewater treatment plants. A lab scale sequencing batch reactor (SBR) was operated for seven months and acute inhibitory effect of NSAIDs on activated sludge was tested with respirometry. Culture amendment with different concentrations of NSAIDs in the presence as well as absence of nitrification inhibitor resulted in considerable variation in the oxygen uptake rate (OUR) profiles. The decrease in OUR and nitrate production rate governed with reduced heterotrophic and nitrification activity. The kinetics of half saturation for growth and maximum autotrophic growth rates are determined to be affected negatively by the acute impact of anti-inflammatory pharmaceuticals even at the environmentally relevant concentrations. High removal of tested NSAIDs was observed even for the first time introduce with these compounds.

Methodological Advances to Study Contaminant Biotransformation: New Prospects for Understanding and Reducing Environmental Persistence?

Complex microbial communities in environmental systems play a key role in the detoxification of chemical contaminants by transforming them into less active metabolites or by complete mineralization. Biotransformation, i.e., transformation by microbes, is well understood for a number of priority pollutants, but a similar level of understanding is lacking for many emerging contaminants encountered at low concentrations and in complex mixtures across natural and engineered systems. Any advanced approaches aiming to reduce environmental exposure to such contaminants (e.g., novel engineered biological water treatment systems, design of readily degradable chemicals, or improved regulatory assessment strategies to determine contaminant persistence a priori) will depend on understanding the causal links among contaminant removal, the key driving agents of biotransformation at low concentrations (i.e., relevant microbes and their metabolic activities), and how their presence and activity depend on environmental conditions. In this Perspective, we present the current understanding and recent methodological advances that can help to identify such links, even in complex environmental microbiomes and for contaminants present at low concentrations in complex chemical mixtures. We discuss the ensuing insights into contaminant biotransformation across varying environments and conditions and ask how much closer we have come to designing improved approaches to reducing environmental exposure to contaminants.

Poised potential is not an effective strategy to enhance bio-electrochemical denitrification under cyclic substrate limitations

Bio-electrochemical denitrification (BED) is a promising organic carbon-free nitrate remediation technology. However, the relationship between engineering conditions, biofilm community composition, and resultant functions in BED remains under-explored. This study used deep sequencing and variation partitioning analysis to investigate the compositional shifts in biofilm communities under varied poised potentials in the batch mode, and correlated these shifts to reactor-level functional differences. Interestingly, the results suggest that the proliferation of a key species, Thiobacillus denitrificans, and community diversity (the Shannon index), were almost equally important in explaining the reactor-to-reactor functional variability (e.g. variability in denitrification rates was 51% and 38% attributable to key species and community diversity respectively, with a 30% overlap), but neither was heavily impacted by the poised potential. The findings suggest that while enriching the key species may be critical in improving the functional efficiency of BED, poised potentials may not be an effective strategy to achieve the desired level of enrichment in substrate-limited real-world conditions.

Improved Modeling of Sediment Oxygen Kinetics and Fluxes in Lakes and Reservoirs

To understand water quality degradation during hypoxia, we need to understand sediment oxygen fluxes, the main oxygen sink in shallow hypolimnia. Kinetic models, which integrate diffusion and consumption of dissolved oxygen (DO) in sediments, usually assume a downward flux of DO from the sediment-water interface (SWI) with a zero-flux condition at the lower boundary of the oxic sediment layer. In this paper, we separately account for the oxidation of an upward flux of reduced compounds by introducing a negative flux of DO as a lower boundary condition. Using in situ measurements in two lakes, kinetic models were fit to DO microprofiles using zero-order and first-order kinetics with both zero and non-zero lower boundary conditions. Based on visual inspection and goodness-of-fit criteria, the negative-flux lower boundary condition, -0.25 g O₂ m^-2 d^-1, was found to more accurately describe DO consumption kinetics. Fitted zero-order rate constants ranged from 50 to 510 mg L^-1 d^-1, and first-order rate constants ranged from 60 to 400 d^-1, which agree well with prior laboratory studies. DO fluxes at the SWI calculated from the simulated profiles with the negative-flux lower boundary condition also showed better agreement with the observed DO fluxes than the simulated profiles with the zero-flux lower boundary condition.

Metal Ion-Bridged Forward Osmosis Membranes for Efficient Pharmaceutical Wastewater Reclamation

Membrane performance in separation relies largely on the membrane properties. In this study, metal ions of Cu²⁺, Co²⁺, and Fe³⁺ are used individually as a bridge to develop forward osmosis (FO) membranes via a clean complexation reaction. A metal ion-bridged hydration layer is formed and endows the membrane with a more hydrophilic and smoother surface, higher fouling resistance, and renewability. These improvements make the newly developed membranes superior to the pristine one with better FO performances. The Fe³⁺-bridged membrane produces water fluxes increased up to 133% (FO mode) and 101% (PRO mode) compared with the pristine membrane against DI water with 0.5-2.0 M MgCl₂ as the draw solution. The Fe³⁺-bridged membrane can efficiently reclaim pharmaceuticals such as trimethoprim and sulfamethoxazole from their dilute solutions with good water permeability and a high pharmaceutical retention. This membrane also exhibits a stronger renewability with water flux restored to 98% of its original value after 20 h experiments in trimethoprim-containing water treatment. This study provides a facile and clean approach to develop highly efficient FO membranes for wastewater reclamation and pharmaceutical enrichment.