The implementation of autotrophic anaerobic ammonium oxidation processes for the removal of nitrogen from municipal wastewater (known as "mainstream anammox") bears the potential to bring wastewater treatment plants close to energy autarky. The aim of the present work was to assess the long-term stability of partial nitritation/anammox (PN/A) processes operating at low temperatures and their reliability in meeting nitrogen concentrations in the range of typical discharge limits below 2 mgNH4-N·L-1 and 10 mgNtot·L-1. Two main 12-L sequencing batch reactors were operated in parallel for PN/A on aerobically pre-treated municipal wastewater (21 ± 5 mgNH4-N·L-1 and residual 69 ± 19 mgCODtot·L-1) for more than one year, including over 5 months at 15 °C. The two systems consisted of a moving bed biofilm reactor (MBBR) and a hybrid MBBR (H-MBBR) with flocculent biomass. Operation at limiting oxygen concentrations (0.15-0.18 mgO2·L-1) allowed stable suppression of the activity of nitrite-oxidizing bacteria at 15 °C with a production of nitrate over ammonium consumed as low as 16% in the MBBR. Promising nitrogen removal rates of 20-40 mgN·L-1·d-1 were maintained at hydraulic retention times of 14 h. Stable ammonium and total nitrogen removal efficiencies over 90% and 70% respectively were achieved. Both reactors reached average concentrations of total nitrogen below 10 mgN·L-1 in their effluents, even down to 6 mgN·L-1 for the MBBR, with an ammonium concentration of 2 mgN·L-1 (set as operational threshold to stop aeration). Furthermore, the two PN/A systems performed almost identically with respect to the biological removal of organic micropollutants and, importantly, to a similar extent as conventional treatments. A sudden temperature drop to 11 °C resulted in significant suppression of anammox activity, although this was rapidly recovered after the temperature was increased back to 15 °C. Analyses of 16S rRNA gene-targeted amplicon sequencing revealed that the anammox guild of the bacterial communities of the two systems was composed of the genus "Candidatus Brocadia". The potential of PN/A systems to compete with conventional treatments for biological nutrients removal both in terms of removal rates and overall effluent quality was proven.@en

Biofilm formation in membrane systems negatively impacts the filtration system performances. This study evaluated how biofilm compression driven by permeate flow increases the hydraulic resistance and leads to reduction in permeate flux. We analysed the effect of biofilm compression on hydraulic resistance and permeate flux through computational models supported by experimental data. Biofilms with homogeneous surface structure were subjected to step-wise changes in flux and transmembrane pressure during compression and relaxation tests. Biofilm thickness under applied forces was measured non-invasively in-situ using optical coherence tomography (OCT). A numerical model of poroelasticity, which couples water flow through the biofilm with biofilm mechanics, was developed to correlate the structural deformation with biofilm hydraulics (permeability and resistance). The computational model enabled extracting mechanical and hydrological parameters corresponding to the experimental data. Homogeneous biofilms under elevated compression forces experienced a significant reduction in thickness while only a slight increase in resistance was observed. This shows that hydraulic resistance of homogeneous biofilms was affected more by permeability decrease due to pore closure than by a decrease in thickness. Both viscoelastic and elastoplastic models could describe well the permanent biofilm deformation. However, for biofilms under study, a simpler elastic model could also be used due to the small irreversible deformations. The elastic moduli fitting the measured data were in agreement with other reported values for biofilm under compression. Biofilm stiffening under larger flow-driven compression forces was observed and described numerically by correlating inversely the elastic modulus with biofilm porosity. The importance of this newly developed method lies in estimation of accurate biofilm mechanical parameters to be used in numerical models for both membrane filtration system and biofouling cleaning strategies. Such model can ultimately be used to identify optimal operating conditions for membrane system subjected to biofouling.@en

Poroelastic fluid-structure interaction models were coupled to experimental data to determine the effects of biofilm spatial distribution of mechanical and hydraulic properties on the biofilm hydraulic resistance and compressibility in membrane filtration processes. Biofilms were cultivated on ultrafiltration membranes for 20 and 30 days under high (0.28 bar) and low (0.06 bar) transmembrane pressure (TMP), in dead-end filtration mode. Subsequently, biofilms were subjected to a compression/relaxation cycles by step-wise TMP changes. Structural deformation of biofilms during compression was observed in-situ using optical coherence tomography. Experimental results show that the observed increase in the biofilm hydraulic resistance during compression is not necessarily accompanied by a detectable biofilm thickness reduction. A dual-layer biofilm model with a dense base and porous top layer could explain these observed results. Because porosity controls indirectly the mechanical response of biofilms under compression, results could be described without assuming a gradient in mechanical properties within the biofilm. The biofilm surface roughness did not significantly influence the water flux in this study. However, the fraction of biofilm base layer directly exposed to bulk liquid could be a good indicator in the determination of water flux. The main implications of this study for the design and operation of low-pressure membrane systems (e.g., MF and UF with fouling layer being the main filtration resistance) lays in the selection of favorable operational TMP and biofilm morphology.@en

Biofilms are complex biostructures that appear on all surfaces that are regularly in contact with water. They are structurally complex, dynamic systems with attributes of primordial multicellular organisms and multifaceted ecosystems. The presence of biofilms may have a negative impact on the performance of various systems, but they can also be used beneficially for the treatment of water (defined herein as potable water, municipal and industrial wastewater, fresh/brackish/salt water bodies, groundwater) as well as in water stream-based biological resource recovery systems. This review addresses the following three topics: (1) biofilm ecology, (2) biofilm reactor technology and design, and (3) biofilm modeling. In so doing, it addresses the processes occurring in the biofilm, and how these affect and are affected by the broader biofilm system. The symphonic application of a suite of biological methods has led to significant advances in the understanding of biofilm ecology. New metabolic pathways, such as anaerobic ammonium oxidation (anammox) or complete ammonium oxidation (comammox) were first observed in biofilm reactors. The functions, properties, and constituents of the biofilm extracellular polymeric substance matrix are somewhat known, but their exact composition and role in the microbial conversion kinetics and biochemical transformations are still to be resolved. Biofilm grown microorganisms may contribute to increased metabolism of micro-pollutants. Several types of biofilm reactors have been used for water treatment, with current focus on moving bed biofilm reactors, integrated fixed-film activated sludge, membrane-supported biofilm reactors, and granular sludge processes. The control and/or beneficial use of biofilms in membrane processes is advancing. Biofilm models have become essential tools for fundamental biofilm research and biofilm reactor engineering and design. At the same time, the divergence between biofilm modeling and biofilm reactor modeling approaches is recognized.@en

The control of nitrite-oxidizing bacteria (NOB) challenges the implementation of partial nitritation and anammox (PN/A) processes under mainstream conditions. The aim of the present study was to understand how operating conditions impact microbial competition and the control of NOB in hybrid PN/A systems, where biofilm and flocs coexist. A hybrid PN/A moving-bed biofilm reactor (MBBR; also referred to as integrated fixed film activated sludge or IFAS) was operated at 15 °C on aerobically pre-treated municipal wastewater (23 mg NH4-N L −1 ). Ammonium-oxidizing bacteria (AOB) and NOB were enriched primarily in the flocs, and anammox bacteria (AMX) in the biofilm. After decreasing the dissolved oxygen concentration (DO) from 1.2 to 0.17 mg O2 L −1 - with all other operating conditions unchanged - washout of NOB from the flocs was observed. The activity of the minor NOB fraction remaining in the biofilm was suppressed at low DO. As a result, low effluent NO 3 − concentrations (0.5 mg N L −1 ) were consistently achieved at aerobic nitrogen removal rates (80 mg N L −1 d −1 ) comparable to those of conventional treatment plants. A simple dynamic mathematical model, assuming perfect biomass segregation with AOB and NOB in the flocs and AMX in the biofilm, was able to qualitatively reproduce the selective washout of NOB from the flocs in response to the decrease in DO-setpoint. Similarly, numerical simulations indicated that flocs removal is an effective operational strategy to achieve the selective washout of NOB. The direct competition for NO 2 − between NOB and AMX - the latter retained in the biofilm and acting as a “NO 2 -sink” - was identified by the model as key mechanism leading to a difference in the actual growth rates of AOB and NOB (i.e., μ NOB < μ AOB in flocs) and allowing for the selective NOB washout over a broad range of simulated sludge retention times (SRT = 6.8–24.5 d). Experimental results and model predictions demonstrate the increased operational flexibility, in terms of variables that can be easily controlled by operators, offered by hybrid systems as compared to solely biofilm systems for the control of NOB in mainstream PN/A applications. @en

The formation and application of aerobic granules for the treatment of real wastewaters still remains challenging. The high fraction of particulate organic matter (XS) present in real wastewaters can affect the granulation process. The present study aims at understanding to what extent the presence of XS affects the granule formation and the quality of the treated effluent. A second objective was to evaluate how the operating conditions of an aerobic granular sludge (AGS) reactor must be adapted to overcome the effects of the presence of XS. Two reactors fed with synthetic wastewaters were operated in absence (R1) or presence (R2) of starch as proxy for XS. Different operating conditions were evaluated. Our results indicated that the presence of XS in the wastewater reduces the kinetic of granule formation. After 52 d of operation, the fraction of granules reached only 21% in R2, while in R1 this fraction was of 54%. The granules grown in presence of XS had irregular and filamentous outgrowths in the surface, which affected the settleability of the biomass and therefore the quality of the effluent. An extension of the anaerobic phase in R2 led to the formation of more compact granules with a better settling ability. A high fraction of granules was obtained in both reactors after an increase of the selection pressure for fast-settling biomass, but the quality of the effluent remained low. Operating the reactors in a simultaneous fill-and-draw mode at a low selection pressure for fast-settling biomass showed to be beneficial for substrate removal efficiency and for suppressing filamentous overgrowth. Average removal efficiencies for total COD, soluble COD, ammonium, and phosphate were 87 ± 4%, 95 ± 1%, 92 ± 10%, and 87 ± 12% for R1, and 72 ± 12%, 86 ± 5%, 71 ± 12%, and 77 ± 11% for R2, respectively. Overall our study demonstrates that the operating conditions of AGS reactors must be adapted according to the wastewater composition. When treating effluents that contain XS, the selection pressure should be significantly reduced.@en

Greywater reuse is a strategy to address water scarcity, necessitating the selection of treatment processes that balance cost-efficiency and human health risks. A key aspect in evaluating these risks is understanding pathogen contamination levels in greywater, a complex task due to intermittent pathogen occurrences. To address this, faecal indicator organisms like E. coli are often monitored as proxies to evaluate faecal contamination levels and infer pathogen concentrations. However, the wide variability in faecal indicator concentrations poses challenges in their modelling for quantitative microbial risk assessment (QMRA). Our study critically assesses the adequacy of parametric models in predicting the variability in E. coli concentrations in greywater. We found that models that build on summary statistics, like medians and standard deviations, can substantially underestimate the variability in E. coli concentrations. More appropriate models may provide more accurate estimations of, and uncertainty around, peak E. coli concentrations. To demonstrate this, a Poisson lognormal distribution model is fit to a data set of E. coli concentrations measured in shower and laundry greywater sources. This model estimated arithmetic mean E. coli concentrations in laundry waters at approximately 1.0E + 06 MPN 100 mL−1. These results are around 2.0 log10 units higher than estimations from a previously used hierarchical lognormal model based on aggregated summary data from multiple studies. Such differences are considerable when assessing human health risks and setting pathogen reduction targets for greywater reuse. This research highlights the importance of making raw monitoring data available for more accurate statistical evaluations than those based on summary statistics. It also emphasizes the crucial role of model comparison, selection, and validation to inform policy-relevant outcomes.@en

Greywater reuse is a strategy to address water scarcity, necessitating the selection of treatment processes that balance cost-efficiency and human health risks. A key aspect in evaluating these risks is understanding pathogen contamination levels in greywater, a complex task due to intermittent pathogen occurrences. To address this, faecal indicator organisms like E. coli are often monitored as proxies to evaluate faecal contamination levels and infer pathogen concentrations. However, the wide variability in faecal indicator concentrations poses challenges in their modelling for quantitative microbial risk assessment (QMRA). Our study critically assesses the adequacy of parametric models in predicting the variability in E. coli concentrations in greywater. We found that models that build on summary statistics, like medians and standard deviations, can substantially underestimate the variability in E. coli concentrations. More appropriate models may provide more accurate estimations of, and uncertainty around, peak E. coli concentrations. To demonstrate this, a Poisson lognormal distribution model is fit to a data set of E. coli concentrations measured in shower and laundry greywater sources. This model estimated arithmetic mean E. coli concentrations in laundry waters at approximately 1.0E + 06 MPN 100 mL−1. These results are around 2.0 log10 units higher than estimations from a previously used hierarchical lognormal model based on aggregated summary data from multiple studies. Such differences are considerable when assessing human health risks and setting pathogen reduction targets for greywater reuse. This research highlights the importance of making raw monitoring data available for more accurate statistical evaluations than those based on summary statistics. It also emphasizes the crucial role of model comparison, selection, and validation to inform policy-relevant outcomes.@en

Greywater reuse is a strategy to address water scarcity, necessitating the selection of treatment processes that balance cost-efficiency and human health risks. A key aspect in evaluating these risks is understanding pathogen contamination levels in greywater, a complex task due to intermittent pathogen occurrences. To address this, faecal indicator organisms like E. coli are often monitored as proxies to evaluate faecal contamination levels and infer pathogen concentrations. However, the wide variability in faecal indicator concentrations poses challenges in their modelling for quantitative microbial risk assessment (QMRA). Our study critically assesses the adequacy of parametric models in predicting the variability in E. coli concentrations in greywater. We found that models that build on summary statistics, like medians and standard deviations, can substantially underestimate the variability in E. coli concentrations. More appropriate models may provide more accurate estimations of, and uncertainty around, peak E. coli concentrations. To demonstrate this, a Poisson lognormal distribution model is fit to a data set of E. coli concentrations measured in shower and laundry greywater sources. This model estimated arithmetic mean E. coli concentrations in laundry waters at approximately 1.0E + 06 MPN 100 mL−1. These results are around 2.0 log10 units higher than estimations from a previously used hierarchical lognormal model based on aggregated summary data from multiple studies. Such differences are considerable when assessing human health risks and setting pathogen reduction targets for greywater reuse. This research highlights the importance of making raw monitoring data available for more accurate statistical evaluations than those based on summary statistics. It also emphasizes the crucial role of model comparison, selection, and validation to inform policy-relevant outcomes.@en

Greywater reuse is a strategy to address water scarcity, necessitating the selection of treatment processes that balance cost-efficiency and human health risks. A key aspect in evaluating these risks is understanding pathogen contamination levels in greywater, a complex task due to intermittent pathogen occurrences. To address this, faecal indicator organisms like E. coli are often monitored as proxies to evaluate faecal contamination levels and infer pathogen concentrations. However, the wide variability in faecal indicator concentrations poses challenges in their modelling for quantitative microbial risk assessment (QMRA). Our study critically assesses the adequacy of parametric models in predicting the variability in E. coli concentrations in greywater. We found that models that build on summary statistics, like medians and standard deviations, can substantially underestimate the variability in E. coli concentrations. More appropriate models may provide more accurate estimations of, and uncertainty around, peak E. coli concentrations. To demonstrate this, a Poisson lognormal distribution model is fit to a data set of E. coli concentrations measured in shower and laundry greywater sources. This model estimated arithmetic mean E. coli concentrations in laundry waters at approximately 1.0E + 06 MPN 100 mL−1. These results are around 2.0 log10 units higher than estimations from a previously used hierarchical lognormal model based on aggregated summary data from multiple studies. Such differences are considerable when assessing human health risks and setting pathogen reduction targets for greywater reuse. This research highlights the importance of making raw monitoring data available for more accurate statistical evaluations than those based on summary statistics. It also emphasizes the crucial role of model comparison, selection, and validation to inform policy-relevant outcomes.@en

Greywater reuse is a strategy to address water scarcity, necessitating the selection of treatment processes that balance cost-efficiency and human health risks. A key aspect in evaluating these risks is understanding pathogen contamination levels in greywater, a complex task due to intermittent pathogen occurrences. To address this, faecal indicator organisms like E. coli are often monitored as proxies to evaluate faecal contamination levels and infer pathogen concentrations. However, the wide variability in faecal indicator concentrations poses challenges in their modelling for quantitative microbial risk assessment (QMRA). Our study critically assesses the adequacy of parametric models in predicting the variability in E. coli concentrations in greywater. We found that models that build on summary statistics, like medians and standard deviations, can substantially underestimate the variability in E. coli concentrations. More appropriate models may provide more accurate estimations of, and uncertainty around, peak E. coli concentrations. To demonstrate this, a Poisson lognormal distribution model is fit to a data set of E. coli concentrations measured in shower and laundry greywater sources. This model estimated arithmetic mean E. coli concentrations in laundry waters at approximately 1.0E + 06 MPN 100 mL−1. These results are around 2.0 log10 units higher than estimations from a previously used hierarchical lognormal model based on aggregated summary data from multiple studies. Such differences are considerable when assessing human health risks and setting pathogen reduction targets for greywater reuse. This research highlights the importance of making raw monitoring data available for more accurate statistical evaluations than those based on summary statistics. It also emphasizes the crucial role of model comparison, selection, and validation to inform policy-relevant outcomes.@en

New-generation bioprocesses using granular sludge aim for a high-rate removal of nutrients from wastewater with low footprint. Achieving enhanced biological phosphorus removal (EBPR) relies on the design of sludge beds and wastewater feeding conditions to optimally load the biomass and to select for polyphosphate- (PAOs) over glycogen-accumulating organisms (GAOs) and over other heterotrophs. A hydraulic–metabolic mathematical model was developed to elucidate the impact of hydraulic transport patterns and environmental conditions on the PAO/GAO competition during up-flow feeding through an EBPR granular sludge bed. Tracer experiments highlighted plug-flow regimes with dispersion under both rapid (9 m h−1, Rebed = 1.6, Pez = 7.2, Pet = 4.6) and slow (0.9 m h−1, Rebed = 0.2, Pez = 21.3, Pet = 3.4) feeding. Non-turbulent regimes (Rebed << 103) promote a safe implementation of simultaneous fill/draw. Feeding time, pH, and temperature significantly impacted bacterial competition for carbon uptake under anaerobic slow feeding. Feeding duration should be designed to avoid full depletion of intracellular storage polymers within static granules. PAOs bear twice longer feeding than GAOs by using both polyphosphate and glycogen hydrolysis to sustain anaerobic C-uptake. Alkaline conditions (pH 7.25–8.0) by, e.g., dosing lime in the feed select for PAOs independently of temperature (10−30°C). A twice higher bed is required for full anaerobic conversions at 10 rather than 20°C. Biosystem responses for anaerobic C-uptake can be anticipated using the model toward designing robust anaerobic selectors to manage the microbial resource in EBPR granular sludge. Biotechnol. Bioeng. 2017;114: 1688–1702.@en

A researcher or practitioner can employ a biofilm model to gain insight into what controls the performance of a biofilm process and for optimizing its performance. While a wide range of biofilmmodeling platforms is available, a good strategy is to choose the simplest model that includes sufficient components and processes to address the modeling goal. In most cases, a onedimensional biofilm model provides the best balance, and good choices can range from handcalculation analytical solutions, simple spreadsheets, and numerical-method platforms. What is missing today is clear guidance on how to apply a biofilm model to obtain accurate and meaningful results. Here, we present a five-step framework for good biofilm reactor modeling practice (GBRMP). The first four steps are (1) obtain information on the biofilm reactor system, (2) characterize the influent, (3) choose the plant and biofilm model, and (4) define the conversion processes. Each step demands that the model user understands the important components and processes in the system, one of the main benefits of doing biofilm modeling. The fifth step is to calibrate and validate the model: System-specific model parameters are adjusted within reasonable ranges so that model outputs match actual system performance. Calibration is not a simple 'by the numbers' process, and it requires that the modeler follows a logical hierarchy of steps. Calibration requires that the adjusted parameters remain within realistic ranges and that the calibration process be carried out in an iterative manner. Once each of steps 1 through 5 is completed satisfactorily, the calibrated model can be used for its intended purpose, such as optimizing performance, trouble-shooting poor performance, or gaining deeper understanding of what controls process performance.@en

A researcher or practitioner can employ a biofilm model to gain insight into what controls the performance of a biofilm process and for optimizing its performance. While a wide range of biofilmmodeling platforms is available, a good strategy is to choose the simplest model that includes sufficient components and processes to address the modeling goal. In most cases, a onedimensional biofilm model provides the best balance, and good choices can range from handcalculation analytical solutions, simple spreadsheets, and numerical-method platforms. What is missing today is clear guidance on how to apply a biofilm model to obtain accurate and meaningful results. Here, we present a five-step framework for good biofilm reactor modeling practice (GBRMP). The first four steps are (1) obtain information on the biofilm reactor system, (2) characterize the influent, (3) choose the plant and biofilm model, and (4) define the conversion processes. Each step demands that the model user understands the important components and processes in the system, one of the main benefits of doing biofilm modeling. The fifth step is to calibrate and validate the model: System-specific model parameters are adjusted within reasonable ranges so that model outputs match actual system performance. Calibration is not a simple 'by the numbers' process, and it requires that the modeler follows a logical hierarchy of steps. Calibration requires that the adjusted parameters remain within realistic ranges and that the calibration process be carried out in an iterative manner. Once each of steps 1 through 5 is completed satisfactorily, the calibrated model can be used for its intended purpose, such as optimizing performance, trouble-shooting poor performance, or gaining deeper understanding of what controls process performance.@en

Nitrogen r emoval based on anaerobic a mmonium oxidation (anammox) has g ained tremendous popularity as it is energy efficient and environmentally friendly. In this case, anammox is c oupled w ith p artial n itrification (collectively called p artial n itritation-anammox or P N/A systems) t o a ccomplish ne arly 80–90% t otal i norganic ni trogen removal. Despite s ignificant k nowledge gained ab out t he p rocess en gineering an d microbiology related to PN/A systems, very little is known about the effect of external perturbations on P N/A s ystems e specially in a s ystem b iology f ramework. I n this research w e first evaluated t he effects o f m etal an d t oxic l oading o n an en riched anammox community. Thereafter, we operated a single-stage PN/A lab-scale reactor to evaluate the effect of temperature on t he process efficiency and system microbiology. Mercury led to a 50% reduction and lead caused a 20% reduction in specific anammox activity a t 0.8 m g/L concentrations. Cyanide, used as a t oxic ch emical, caused significant anammox activity inhibition at 50 pp b concentration. The results related to temperature changes showed that ammonium oxidizing bacteria (Nitrosomonas sp.) can maintain a relatively stable activity at lower temperatures (13~21°C), while anammox bacteria (Candidatus Brocadia) are more sensitive to temperature reductions.@en

Nitrogen r emoval based on anaerobic a mmonium oxidation (anammox) has g ained tremendous popularity as it is energy efficient and environmentally friendly. In this case, anammox is c oupled w ith p artial n itrification (collectively called p artial n itritation-anammox or P N/A systems) t o a ccomplish ne arly 80–90% t otal i norganic ni trogen removal. Despite s ignificant k nowledge gained ab out t he p rocess en gineering an d microbiology related to PN/A systems, very little is known about the effect of external perturbations on P N/A s ystems e specially in a s ystem b iology f ramework. I n this research w e first evaluated t he effects o f m etal an d t oxic l oading o n an en riched anammox community. Thereafter, we operated a single-stage PN/A lab-scale reactor to evaluate the effect of temperature on t he process efficiency and system microbiology. Mercury led to a 50% reduction and lead caused a 20% reduction in specific anammox activity a t 0.8 m g/L concentrations. Cyanide, used as a t oxic ch emical, caused significant anammox activity inhibition at 50 pp b concentration. The results related to temperature changes showed that ammonium oxidizing bacteria (Nitrosomonas sp.) can maintain a relatively stable activity at lower temperatures (13~21°C), while anammox bacteria (Candidatus Brocadia) are more sensitive to temperature reductions.@en

This research investigated the short term effects of temperature changes (lasting 2–4 weeks each) from 35 ± 2 °C to 21 ± 2 °C and 13 ± 2 °C and sulfide toxicity on partial nitrification-anammox (PN/A) system. Temperatures below 20 °C and sulfide content as low as 5 mgS L−1 affected both aerobic and anaerobic catabolic activities of ammonia oxidation and the expression of related functional gene markers. The activity of AOB was inversely correlated with ammonium monooxygenase (amoA) gene expression. In contrast, the activity of AMX bacteria was positively correlated with the expression of their hydrazine synthase (hzsA) gene. Although the overall activities of AMX bacteria decreased at lower temperatures, the AMX bacteria were still active at the low temperatures. The inverse correlation between amoA gene expressions and the corresponding AOB activities was surprising. 16S rDNA based high throughput amplicon sequencing revealed the dominance of Chloroflexi, Planctomycetes and Proteobacteria phyla the distribution of which changed with temperature changes.@en

This research investigated the short term effects of temperature changes (lasting 2–4 weeks each) from 35 ± 2 °C to 21 ± 2 °C and 13 ± 2 °C and sulfide toxicity on partial nitrification-anammox (PN/A) system. Temperatures below 20 °C and sulfide content as low as 5 mgS L−1 affected both aerobic and anaerobic catabolic activities of ammonia oxidation and the expression of related functional gene markers. The activity of AOB was inversely correlated with ammonium monooxygenase (amoA) gene expression. In contrast, the activity of AMX bacteria was positively correlated with the expression of their hydrazine synthase (hzsA) gene. Although the overall activities of AMX bacteria decreased at lower temperatures, the AMX bacteria were still active at the low temperatures. The inverse correlation between amoA gene expressions and the corresponding AOB activities was surprising. 16S rDNA based high throughput amplicon sequencing revealed the dominance of Chloroflexi, Planctomycetes and Proteobacteria phyla the distribution of which changed with temperature changes.@en

This research investigated the short term effects of temperature changes (lasting 2–4 weeks each) from 35 ± 2 °C to 21 ± 2 °C and 13 ± 2 °C and sulfide toxicity on partial nitrification-anammox (PN/A) system. Temperatures below 20 °C and sulfide content as low as 5 mgS L−1 affected both aerobic and anaerobic catabolic activities of ammonia oxidation and the expression of related functional gene markers. The activity of AOB was inversely correlated with ammonium monooxygenase (amoA) gene expression. In contrast, the activity of AMX bacteria was positively correlated with the expression of their hydrazine synthase (hzsA) gene. Although the overall activities of AMX bacteria decreased at lower temperatures, the AMX bacteria were still active at the low temperatures. The inverse correlation between amoA gene expressions and the corresponding AOB activities was surprising. 16S rDNA based high throughput amplicon sequencing revealed the dominance of Chloroflexi, Planctomycetes and Proteobacteria phyla the distribution of which changed with temperature changes.@en

This research investigated the short term effects of temperature changes (lasting 2–4 weeks each) from 35 ± 2 °C to 21 ± 2 °C and 13 ± 2 °C and sulfide toxicity on partial nitrification-anammox (PN/A) system. Temperatures below 20 °C and sulfide content as low as 5 mgS L−1 affected both aerobic and anaerobic catabolic activities of ammonia oxidation and the expression of related functional gene markers. The activity of AOB was inversely correlated with ammonium monooxygenase (amoA) gene expression. In contrast, the activity of AMX bacteria was positively correlated with the expression of their hydrazine synthase (hzsA) gene. Although the overall activities of AMX bacteria decreased at lower temperatures, the AMX bacteria were still active at the low temperatures. The inverse correlation between amoA gene expressions and the corresponding AOB activities was surprising. 16S rDNA based high throughput amplicon sequencing revealed the dominance of Chloroflexi, Planctomycetes and Proteobacteria phyla the distribution of which changed with temperature changes.@en