Systematic review: External carbon source for biological denitrification for wastewater

Biodegradation of organosulfur with extra carbon source: Insights into biofilm formation and bacterial metabolic processes

Organosulfur compounds are prevalent in wastewater, presenting challenges for biodegradation, particularly in low-carbon environments. Supplementing additional carbon sources not only provides essential nutrients for microbial growth but also serves as regulators, influencing adaptive changes in biofilm and enhancing the survival of microorganisms in organosulfur-induced stress bioreactors. This study aims to elucidate the biodegradation of organosulfur under varying carbon source levels, placing specific emphasis on functional bacteria and metabolic processes. It has been observed that higher levels of carbon supplementation led to significantly improved total sulfur (TS) removal efficiencies, exceeding 83 %, and achieve a high organosulfur CH3SH removal efficiency of ~100 %. However, in the reactor with no external carbon source added, the oxidation end-product SO42- accumulated significantly, surpassing 120 mEq/m2-day. Furthermore, the TB-EPS concentration consistently increasedwith the ascending glucose concentration. The analysis of bacterial community reveals the enrichment of functional bacteria involved in sulfur metabolism and biofilm formation (e.g. Ferruginibacter, Rhodopeudomonas, Gordonia, and Thiobacillus). Correspondingly, the gene expressions related to the pathway of organosulfur to SO42- were notably enhanced (e.g. MTO increased by 27.7 %). In contrast, extra carbon source facilitated the transfer of organosulfur into amino acids in sulfur metabolism and promoted assimilation. These metabolic insights, coupled with kinetic transformation results, further validate distinct sulfur pathways under different carbon source conditions. The intricate interplay between bacteria growth regulation, pollutant biodegradation, and microbial metabolites underscores a complex network relationship that significantly contributes to efficient operation of bioreactors.

Impact of thermal hydrolysis on VFA-based carbon source production from fermentation of sludge and digestate for denitrification: experimentation and upscaling implications

Stricter nutrient discharge limits at wastewater treatment plants (WWTPs) are increasing the demand for external carbon sources for denitrification, especially at cold temperatures. Production of carbon sources at WWTP by fermentation of sewage sludge often results in low yields of soluble carbon and volatile fatty acids (VFA) and high biogas losses, limiting its feasibility for full-scale application. This study investigated the overall impact of thermal hydrolysis pre-treatment (THP) on the production of VFA for denitrification through the fermentation of municipal sludge and digestate. Fermentation products and yields, denitrification efficiency and potential impacts on methane yield in the downstream process after carbon source separation were evaluated. Fermentation of THP substrates resulted in 37-70 % higher soluble chemical oxygen demand (sCOD) concentrations than fermentation of untreated substrates but did not significantly affect VFA yield after fermentation. Nevertheless, THP had a positive impact on the denitrification rates and on the methane yields of the residual solid fraction in all experiments. Among the different carbon sources tested, the one produced from the fermentation of THP-digestate showed an overall better potential as a carbon source than other substrates (e.g. sludge). It obtained a relatively high carbon solubilisation degree (39 %) and higher concentrations of sCOD (19 g sCOD/L) and VFA (9.8 g VFACOD/L), which resulted in a higher denitrification rate (8.77 mg NOx-N/g VSS∙h). After the separation of the carbon source, the solid phase from this sample produced a methane yield of 101 mL CH4/g VS. Furthermore, fermentation of a 50:50 mixture of THP-substrate and raw sludge produced also resulted in a high VFA yield (283 g VFACOD/kg VSin) and denitrification rate of 8.74 mg NOx-N/g VSS∙h, indicating a potential for reduced treatment volumes. Calculations based on a full-scale WWTP (Käppala, Stockholm) demonstrated that the carbon sources produced could replace fossil-based methanol and meet the nitrogen effluent limit (6 mg/L) despite their ammonium content. Fermentation of 50-63 % of the available sludge at Käppala WWTP in 2028 could produce enough carbon source to replace methanol, with only an 8-20 % reduction in methane production, depending on the production process. Additionally, digestate production would be sufficient to generate 81 % of the required carbon source while also increasing methane production by 5 % if a portion of the solid residues were recirculated to the digester.

Machine Learning-Assisted Optimization of Mixed Carbon Source Compositions for High-Performance Denitrification

Appropriate mixed carbon sources have great potential to enhance denitrification efficiency and reduce operational costs in municipal wastewater treatment plants (WWTPs). However, traditional methods struggle to efficiently select the optimal mixture due to the variety of compositions. Herein, we developed a machine learning-assisted high-throughput method enabling WWTPs to rapidly identify and optimize mixed carbon sources. Taking a local WWTP as an example, a mixed carbon source denitrification data set was established via a high-throughput method and employed to train a machine learning model. The composition of carbon sources and the types of inoculated sludge served as input variables. The XGBoost algorithm was employed to predict the total nitrogen removal rate and microbial growth, thereby aiding in the assessment of the denitrification potential. The predicted carbon sources exhibited an enhanced denitrification potential over single carbon sources in both kinetic experiments and long-term reactor operations. Model feature analysis shows that the cumulative effect and interaction among individual carbon sources in a mixture significantly enhance the overall denitrification potential. Metagenomic analysis reveals that the mixed carbon sources increased the diversity and complexity of denitrifying bacterial ecological networks in WWTPs. This work offers an efficient method for WWTPs to optimize mixed carbon source compositions and provides new insights into the mechanism behind enhanced denitrification under a supply of multiple carbon sources.

Application of artificial intelligence in modeling of nitrate removal process using zero-valent iron nanoparticles-loaded carboxymethyl cellulose

This study explores nitrate reduction in aqueous solutions using carboxymethyl cellulose loaded with zero-valent iron nanoparticles (Fe0-CMC). The structures of this nano-composite were characterized using various techniques. Based on the characterization results, the specific surface area of Fe0-CMC measured by the Brunauer-Emmett-Teller analysis were 39.6 m2/g. In addition, Scanning Electron Microscopy images displayed that spherical nano zero-valent iron particles (nZVI) with an average particle diameter of 80 nm are surrounded by carboxymethyl cellulose and no noticeable aggregates were detected. Batch experiments assessed Fe0-CMC's effectiveness in nitrate removal under diverse conditions including different adsorbent dosages (Cs, 2-10 mg/L), contact time (t, 10-1440 min), initial pH (pHi, 2-10), temperature (T, 10-55 °C), and initial concentration of nitrate (C0, 10-500 mg/L). Results indicated decreased removal with higher initial pHi and C0, while increased Cs and T enhanced removal. The study of nitrate removal mechanism by Fe0-CMC revealed that the redox reaction between immobilized nZVI on the CMC surface and nitrate ions was responsible for nitrate removal, and the main product of this reaction was ammonium, which was subsequently completely removed by the synthesized nanocomposite. In addition, a stable deviation quantum particle swarm optimization algorithm (SD-QPSO) and a least square error method were employed to train the ANFIS parameters. To demonstrate model performance, a quadratic polynomial function was proposed to display the performance of the SD-QPSO algorithm in which the constant parameters were optimized through the SD-QPSO algorithm. Sensitivity analysis was conducted on the proposed quadratic polynomial function by adding a constant deviation and removing each input using two different strategies. According to the sensitivity analysis, the predicted removal efficiency was most sensitive to changes in pHi, followed by Cs, T, C0, and t. The obtained results underscore the potential of the ANFIS model (R2 = 0.99803, RMSE = 0.9888), and polynomial function (R2 = 0.998256, RMSE = 1.7532) as accurate and efficient alternatives to time-consuming laboratory measurements for assessing nitrate removal efficiency. These models can offer rapid insights and predictions regarding the impact of various factors on the process, saving both time and resources.

Optimization of operating parameters and microbiological mechanism of a low C/N wastewater treatment system dominated by iron-dependent autotrophic denitrification

Ferrous iron (Fe2+) reduces the amount of external carbon source used for the denitrification of low-C/N wastewater. The effects of key operating parameters on the efficiency of ferrous-dependent autotrophic denitrification (FDAD) and the functioning mechanism of the microbiome can provide a regulatory strategy for improving the denitrification efficiency of low C/N wastewater. In this study, the response surface method (RSM) was used to explore the influence of four important parameters-the molar ratio of Fe2+ to NO3--N (Fe/N), total organic carbon (TOC), the molar ratio of inorganic carbon to NO3--N (IC/N) and sludge volume (SV, %)-on the FDAD efficiency. Functional prediction and molecular ecological networks based on high-throughputs sequencing techniques were used to explore changes in the structure, function, and biomarkers of the sludge microbial community. The results showed that Fe/N and TOC were the main parameters affecting FDAD efficiency. Higher concentrations of TOC and high Fe/N ratios provided more electron donors and improved denitrification efficiency, but weakened the importance of biomarkers (Rhodanobacter, Thermomonas, Comamonas, Thauera, Geothrix and unclassified genus of family Gallionellaceae) in the sludge ecological network. When Fe/N > 4, the denitrification efficiency fluctuated significantly. Functional prediction results indicated that genes that dominated N2O and NO reduction and the genes that dominated Fe2+ transport showed a slight decrease in abundance at high Fe/N levels. In light of these findings, we recommend the following optimization ranges of parameters: Fe/N (3.5-4); TOC/N (0.36-0.42); IC/N (3.5-4); and SV (approximately 35%).

Electrochemical nitrate reduction over bimetallic Pd-Sn nanocatalysts with tunable selectivity toward benign nitrogen

Nitrate is recognized as a highly impactful water contaminant among various pollutants in water. To address the ever-growing demand for water purification, this work investigates the bimetallic palladium (Pd) and tin (Sn) catalysts, which are electrochemically deposited on stainless steel mesh support (Pd-Sn/SS) for the selective conversion of harmful nitrate (NO3-) into benign nitrogen (N2) gas. Results indicate that the bimetallic composition in Pd-Sn/SS electrodes substantially influenced the reaction route for nitrate reduction as well as the performance of nitrate transformation and nitrogen selectivity. It is found that the electrode prepared from Pd:Sn = 1:1 (mole ratio) demonstrates an outstanding nitrate conversion of 95%, nitrogen selectivity of 88%, and nitrogen yield of 82%, which outperform many reported values in the literature. The electrochemically synthesized bimetallic electrode proposed herein enables a new insight for promoting the reactivity and selectivity of nitrate reduction in water.

Mixotrophic denitrification process driven by lime sulfur and butanediol: Denitrification performance and metagenomic analysis

Heterotrophic sulfur-based autotrophic denitrification is a promising biological denitrification technology for low COD/TN (C/N) wastewater due to its high efficiency and low cost. Compared to the conventional autotrophic denitrification process driven by elemental sulfur, the presence of polysulfide in the system can promote high-speed nitrogen removal. However, autotrophic denitrification mediated by polysulfide has not been reported. This study investigated the denitrification performance and microbial metabolic mechanism of heterotrophic denitrification, sulfur-based autotrophic denitrification, and mixotrophic denitrification using lime sulfur and butanediol as electron donors. When the influent C/N was 1, the total nitrogen removal efficiency of the mixotrophic denitrification process was 1.67 and 1.14 times higher than that of the heterotrophic and sulfur-based autotrophic denitrification processes, respectively. Microbial community alpha diversity and principal component analysis indicated different electron donors lead to different evolutionary directions in microbial communities. Metagenomic analysis showed the enriched denitrifying bacteria (Thauera, Pseudomonas, and Pseudoxanthomonas), dissimilatory nitrate reduction to ammonia bacteria (Hydrogenophaga), and sulfur oxidizing bacteria (Thiobacillus) can stably support nitrate reduction. Analysis of metabolic pathways revealed that complete denitrification, dissimilatory nitrate reduction to ammonia, and sulfur disproportionation are the main pathways of the N and S cycle. This study demonstrates the feasibility of a mixotrophic denitrification process driven by a combination of lime sulfur and butanediol as a cost-effective solution for treating nitrogen pollution in low C/N wastewater and elucidates the N and S metabolic pathways involved.

Carbon source shaped microbial ecology, metabolism and performance in denitrification systems

The limited information on microbial interactions and metabolic patterns in denitrification systems, especially those fed with different carbon sources, has hindered the establishment of ecological linkages between microscale connections and macroscopic reactor performance. In this work, denitrification performance, metabolic patterns, and ecological structure were investigated in parallel well-controlled bioreactors with four representative carbon sources, i.e., methanol, glycerol, acetate, and glucose. After long-term acclimation, significant differences were observed among the four bioreactors in terms of denitrification rates, organic utilization, and heterotrophic bacterial yields. Different carbon sources induced the succession of denitrifying microbiota toward different ecological structures and exhibited distinct metabolic patterns. Methanol-fed reactors showed distinctive microbial carbon utilization pathways and a more intricate microbial interaction network, leading to significant variations in organic utilization and metabolite production compared to other carbon sources. Three keystone taxa belonging to the Verrucomicrobiota phylum, SJA-15 order and the Kineosphaera genus appeared as network hubs in the methanol, glycerol, and acetate-fed systems, playing essential roles in their ecological functions. Several highly connected species were also identified within the glucose-fed system. The close relationship between microbial metabolites, ecological structures, and system performances suggests that this complex network relationship may greatly contribute to the efficient operation of bioreactors.

Mediation of Bacterial Interactions via a Novel Membrane-Based Segregator to Enhance Biological Nitrogen Removal

The regulation of microbial subpopulations in wastewater treatment plants (WWTPs) with desired functions can guarantee nutrient removal. In nature, "good fences make good neighbors," which can be applied to engineering microbial consortia. Herein, a membrane-based segregator (MBSR) was proposed, where porous membranes not only promote the diffusion of metabolic products but also isolate incompatible microbes. The MBSR was integrated with an anoxic/aerobic membrane bioreactor (i.e., an experimental MBR). The long-term operation showed that the experimental MBR exhibited higher nitrogen removal (10.45 ± 2.73 mg/L total nitrogen) than the control MBR (21.68 ± 4.23 mg/L) in the effluent. The MBSR resulted in much lower oxygen reduction potential in the anoxic tank of the experimental MBR (-82.00 mV) compared to that of the control MBR (83.25 mV). The lower oxygen reduction potential can inevitably aid in the occurrence of denitrification. The 16S rRNA sequencing showed that the MBSR significantly enriched acidogenic consortia, which yielded considerable volatile fatty acids by fermenting the added carbon sources and allowed efficient transfer of these small molecules to the denitrifying community. Moreover, the sludge communities of the experimental MBR harbored a higher abundance of denitrifying bacteria than those of the control MBR. Metagenomic analysis further corroborated these sequencing results. The spatially structured microbial communities in the experimental MBR system demonstrate the practicability of the MBSR, achieving nitrogen removal efficiency superior to that of mixed populations. Our study provides an engineering method for modulating the assembly and metabolic division of labor of subpopulations in WWTPs. IMPORTANCE This study provides an innovative and applicable method for regulating subpopulations (activated sludge and acidogenic consortia), which contributes to the precise control of the metabolic division of labor in biological wastewater treatment processes.

Deammonification Potential of Pig Slurries and Vapor Condensates from Sewage Sludge Drying-Substrate Quality and Inhibition

Deammonification is a well-established process for sludge liquor treatment and promising for wastewaters with high nitrogen loads because of its low energy demand compared to nitrification/denitrification. Two wastewaters with high NH4-N concentrations and a rising significance in Germany-pig slurry (12 samples) and condensates from sewage sludge drying (6 samples)-were studied for their deammonification potential. Furthermore, a comprehensive quality assessment is presented. Both wastewaters show a wide range in terms of CODt, CODs, TN and NH4-N, whereby condensates show a greater variability with no direct relation to dryer type or temperature. In the slurries, CODt shows a relative standard deviation of 106% (mean 21.1 g/L) and NH4-N of 33% (mean 2.29 g/L), while in condensates it reaches 148% for CODt (mean 2.0 g/L) and 122% for NH4-N (mean 0.7 g/L). No inhibition of ammonium-oxidizing-bacteria was detected in the slurries, while two out of five condensates showed an inhibition of >40%, one of >10% and two showed no inhibition at all. Since the inhibition could be avoided by mixing, deammonification can be recommended for condensate treatment. For slurry treatment, the importance of employing some form of solid-liquid-separation as a pretreatment was noted due to the associated COD.