Environmental proteomics as a useful methodology for early-stage detection of stress in anammox engineered systems.

Anammox bacteria are widely applied worldwide for denitrification of urban wastewater. Differently, their application in the case of industrial effluents has been more limited. Those frequently present high loads of contaminants, demanding an individual evaluation of their treatability by anammox technologies. Bioreactors setting up and recovery after contaminants-derived perturbations are slow. Also, toxicity is frequently not acute but cumulative, which causes negative macroscopic effects to appear only after medium or long-term operations. All these particularities lead to relevant economic and time losses. We hypothesized that contaminants cause changes at anammox proteome level before perturbations in the engineered systems are detectable by macroscopic analyses. In this study, we explored the usefulness of short-batch tests combined with environmental proteomics for the early detection of those changes. Copper was used as a model of stressor contaminant, and anammox granules were exposed to increasing copper concentrations including previously reported IC50 values. The proteomic results revealed that specific anammox proteins involved in stress response (bacterioferritin, universal stress protein, or superoxide dismutase) were overexpressed in as short a time as 28 h at the higher copper concentrations. Consequently, EPS production was also increased, as indicated by the alginate export family protein, polysaccharide biosynthesis protein, and sulfotransferase increased expression. The described workflow can be applied to detect early-stage stress biomarkers of the negative effect of other metals, organics, or even changes in physical-chemical parameters such as pH or temperature on anammox-engineered systems. On an industrial level, it can be of great value for decision-making, especially before dealing with new effluents on facilities, deriving important economic and time savings.

Challenges of aerobic granular sludge utilization: Fast start-up strategies and cationic pollutant removal.

Aerobic granular sludge (AGS) is a self-aggregated microorganism consortium with pollutant removal properties. The aim of this work is to study and review the application of aerobic granules for water treatment with special focus on new applications and methodologies. Carbon-nitrogen containing pollutants are the classic targets of AGS technology. Carbon and nitrogen removal of AGS are classified as a biodegradation process. More recently, the AGS granules have been studied as sorbent materials for wastewater treatment. In particular, the sorption of cationic pollutants has been studied through biosorption and bioaccumulation mechanisms without distinguishing when one or the other process is involved. AGS conformation made them suitable for complex wastewater treatment. Indeed, several studies have demonstrated the removal of polyvalent cationic pollutants even with higher capacity than conventional sorbent materials. However, this was achieved almost exclusively for synthetic substrates, with single cation evaluation and using in some cases only qualitative measures. For successful industrial AGS application in complex substrates, it is necessary to evaluate and demonstrate the technology in real industrial conditions and reduce the currently long start-up times which limits its utility. Two new strategies have been proposed: autoinducer molecules and the production of artificial granular from common active sludge with commercial alginate. Finally, the increase of research on AGS cations assimilation properties will allow a new point of view, where granules will be materials for the recovery of valuable metals from industrial wastewater streams.

Characterization of Arsenite-Oxidizing Bacteria Isolated from Arsenic-Rich Sediments, Atacama Desert, Chile.

Arsenic (As), a semimetal toxic for humans, is commonly associated with serious health problems. The most common form of massive and chronic exposure to As is through consumption of contaminated drinking water. This study aimed to isolate an As resistant bacterial strain to characterize its ability to oxidize As (III) when immobilized in an activated carbon batch bioreactor and to evaluate its potential to be used in biological treatments to remediate As contaminated waters. The diversity of bacterial communities from sediments of the As-rich Camarones River, Atacama Desert, Chile, was evaluated by Illumina sequencing. Dominant taxonomic groups (>1%) isolated were affiliated with Proteobacteria and Firmicutes. A high As-resistant bacterium was selected (Pseudomonas migulae VC-19 strain) and the presence of aio gene in it was investigated. Arsenite detoxification activity by this bacterial strain was determined by HPLC/HG/AAS. Particularly when immobilized on activated carbon, P. migulae VC-19 showed high rates of As(III) conversion (100% oxidized after 36 h of incubation). To the best of our knowledge, this is the first report of a P. migulae arsenite oxidizing strain that is promising for biotechnological application in the treatment of arsenic contaminated waters.

The prediction of partial-nitrification-anammox performance in real industrial wastewater based on granular size.

To date, the partial nitrification-Anammox (PN-A) granular sludge size has been exclusively analyzed in synthetic substrates. In this work, different ranges of granular size of PN-A sludge were studied at low oxygen concentration using real industrial wastewater as, well as a synthetic substrate. The granular sludge was characterized by the specific nitrification activity (SNA), specific anammox activity (SAA), and granule sedimentation rate. The relative abundance of the bacterial consortium was assessed for each range of diameters through the fluorescence in situ hybridization (FISH) technique. SNA exhibits a direct association with the specific surface of granules, which proves the importance of the outer layer in the nitrification process. Even more critical, the flocculent sludge allowed the stability of the nitrifying activity. The SAA showed different performances faced the real industrial and synthetic substrates. With the synthetic substrate, the SAA decreased at higher diameter ranges, whereas with the industrial substrate, the SAA increased at higher diameter ranges. This situation is explained by the oxygen protection in the sludge maintained with industrial wastewater. The relative abundance of heterotrophic bacteria increased from 9.6 to 22%, due to the presence of organic matter in the industrial substrate. The granular sedimentation rate increased with the diameter of the granules with a linear correlation (R2 > 0.98). Thus, granular sizes can be selected through sedimentation rate control. A linear correlation between SAA and granular sludge diameter ranges was observed. With this correlation, an error of less than 11% in the prediction of SAA was achieved. The use of diameter measurement and granular sedimentation rate as routine techniques could contribute to the control and start-up of PN-A reactors. In the same sense, organic matter present in defined concentrations, can be beneficial for the granular sludge stability, and thus, for nitrogen removal.

Efficient poultry manure management: anaerobic digestion with short hydraulic retention time to achieve high methane production.

The efficient treatment or appropriate final disposal of poultry manure (PM) to avoid serious environmental impacts is a great challenge. In this work, the optimization of a 2-stage anaerobic digestion system (ADS) for PM was studied with the aim of reaching a maximal methane yield with a short hydraulic retention time (HRT). Three activities were performed: The first activity, ADS 1, consisted of evaluating the effect of the substrate concentration and the HRT on the process, with a constant organic loading rate (OLR) of 3.66 ± 0.21 gVS L-1 d-1. The second activity, ADS 2, consisted of decreasing the HRT from 9.09 to 2.74 d with a constant substrate concentration. In the third activity, ADS 3, the substrate concentration was increased from 10.09 ± 1.41 to 35.25 ± 6.20 gVS L-1 with an average HRT of 4.66 ± 0.11 d. Maximal methane yields of 0.22, 0.21, and 0.22 LCH4 gVS-1 were reached for ADS 1, ADS 2, and ADS 3, respectively, at a low HRT (3.38 to 4.66 d) and high free ammonia concentration (between 323.05 ± 56.48 and 460.93 ± 135.40 mgN-NH3 L-1). These methane yields correspond to the production of 40.36 and 42.28 cubic meters of methane per ton of PM, respectively, and a laying hen produces between 47.45 and 54.75 kg of PM per year in Chile. Finally, this is the first study of the separate and combined effects of OLR, HRT and substrate concentration on the anaerobic digestion of PM. The results demonstrate the technical feasibility of the two-stage ADS treatment of PM with a short HRT; the system tolerates variations in the total ammonia nitrogen concentration of PM throughout the year and achieves a high methane yield when the correct operational conditions are selected.

Correction to: Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques.

The original publication of this paper contains a mistake. Unfortunately, an author was inadvertently missed out, Constanza Arriagada had participated in the operation of the anaerobic digesters cited in the work and now as a PhD student, she is involved in the production of other publication.

Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques.

Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. The analytical method considers the protein as the only source of ammonia production in AD after degradation. Total ammonia nitrogen (TAN), total solids (TS), chemical oxygen demand (COD), and total volatile solids (TVS) were measured in the influent and effluent of the process. The TAN concentration in the effluent was predicted, this being the most inhibiting and polluting compound in AD. Despite the limited data available, the SVM-based model outperformed the analytical method for the TAN prediction, achieving a relative average error of 15.2% against 43% for the analytical method. Moreover, SVM showed higher prediction accuracy in comparison with Artificial Neural Networks. This result reveals the future promise of SVM for prediction in non-linear and dynamic AD processes. Graphical abstract ᅟ.

Technical and economical optimization of a full-scale poultry manure treatment process: total ammonia nitrogen balance.

A full-scale process for the treatment of 80 tons per day of poultry manure was designed and optimized. A total ammonia nitrogen (TAN) balance was performed at steady state, considering the stoichiometry and the kinetic data from the anaerobic digestion and the anaerobic ammonia oxidation. The equipment, reactor design, investment costs, and operational costs were considered. The volume and cost objective functions optimized the process in terms of three variables: the water recycle ratio, the protein conversion during AD, and the TAN conversion in the process. The processes were compared with and without water recycle; savings of 70% and 43% in the annual fresh water consumption and the heating costs, respectively, were achieved. The optimal process complies with the Chilean environmental legislation limit of 0.05 g total nitrogen/L.

Startup and oxygen concentration effects in a continuous granular mixed flow autotrophic nitrogen removal reactor.

The startup and performance of the completely autotrophic nitrogen removal over nitrite (CANON) process was tested in a continuously fed granular bubble column reactor (BCR) with two different aeration strategies: controlling the oxygen volumetric flow and oxygen concentration. During the startup with the control of oxygen volumetric flow, the air volume was adjusted to 60mL/h and the CANON reactor had volumetric N loadings ranging from 7.35 to 100.90mgN/Ld with 36-71% total nitrogen removal and high instability. In the second stage, the reactor was operated at oxygen concentrations of 0.6, 0.4 and 0.2mg/L. The best condition was 0.2 mgO2/L with a total nitrogen removal of 75.36% with a CANON reactor activity of 0.1149gN/gVVSd and high stability. The feasibility and effectiveness of CANON processes with oxygen control was demonstrated, showing an alternative design tool for efficiently removing nitrogen species.