Model study on real-time aeration based on nitrite for effective operation of single-stage anammox

Maximizing the efficiency of single-stage partial nitrification/Anammox granule processes and balancing microbial competition using insights of a numerical model study

Granulation is an efficient approach for the rapid growth of anaerobic ammonia oxidation (Anammox) bacteria (X ANA $$ {X}_{ANA} $$ ) to limit the growth of nitrite-oxidizing bacteria (X NOB $$ {X}_{NOB} $$ ). However, the high sensitivity of Anammox bacteria to operational conditions and the competition with other microorganisms lead to a critical challenge in maintaining sufficientX ANA $$ {X}_{ANA} $$ population. In this study, a one-dimensional steady-state model was developed and calibrated to investigate the kinetic constants ofX ANA $$ {X}_{ANA} $$ growth and mass transport in individual granules, including the liquid film. According to the model calibration results, the range of the maximum specific growth rate constant ofX ANA $$ {X}_{ANA} $$ (μ ANA $$ {\mu}_{ANA} $$ ) was 0.033 to 0.10 d-1. In addition the other kinetic constants ofX ANA $$ {X}_{ANA} $$ were 0.003 d-1 for decay rate constant (b ANA $$ {b}_{ANA} $$ ), 0.10 mg-O2/L for oxygen half-saturation constant (K O 2 ANA $$ {K}_{O_2}^{ANA} $$ ), 0.07 mg-N/L for ammonia half-saturation constant (K NH 4 ANA $$ {K}_{NH_4}^{ANA} $$ ), and 0.05 mg-N/L for nitrite half-saturation constant (K NO 2 ANA $$ {K}_{NO_2}^{ANA} $$ ). The model simulation results showed that the dissolved oxygen of about 0.10 mg-O2/L was found to be optimal to maintain highX ANA $$ {X}_{ANA} $$ population. In addition, minimal COD concentration is required to control heterotrophs (X H $$ {X}_H $$ ) and improve ammonia oxidation by ammonia-oxidizing bacteria (X AOB $$ {X}_{AOB} $$ ). It was also emphasized that moderate mixing conditions (L f $$ {L}_f $$ ≅ $$ \cong $$ 100 μm) are preferable to decrease the diffusion of oxygen to the deep layers of the granules, controlling the competition betweenX ANA $$ {X}_{ANA} $$ andX NOB $$ {X}_{NOB} $$ . A single-factor relative sensitivity analysis (RSA) on microbial kinetics revealed thatμ ANA $$ {\mu}_{ANA} $$ is the governing factor in the efficient operation of the single-stage PN/A processes. In addition, it was found that nitrite concentration is a rate-limiting parameter on the success of the process due to the competition betweenX ANA $$ {X}_{ANA} $$ andX NOB $$ {X}_{NOB} $$ . These findings can be used to enhance our understanding on the importance of microbial competition and mass transport in the single-stage PN/A process. PRACTITIONER POINTS: A one-dimensional steady-state model was developed and calibrated for simulating the single-stage partial nitrification/Anammox (PN/A) granule process. Moderate liquid films (L f $$ {L}_f $$ ≅ $$ \cong $$ 100 μm) are preferable for better performance of Anammox growth in single-stage PN/A processes. Moderate dissolved oxygen (DO≅ $$ \cong $$ 0.10 mg-O2/L) is highly recommended for efficient growth of Anammox bacteria in single-stage PN/A granulation. Minimal COD (COD≅ $$ \cong $$ 0) is preferable for successful operation of the single-stage PN/A granule process. Nitrite concentration is a rate-limiting parameter on the competition between Anammox and nitrite-oxidizing bacteria in the single-stage PN/A processes.

Machine learning models for prediction of nutrient concentrations in surface water in an agricultural watershed

Prediction and quantification of nutrient concentrations in surface water has gained substantial attention during recent decades because excess nutrients released from agricultural and urban watersheds can significantly deteriorate surface water quality. Machine learning (ML) models are considered an effective tool for better understanding and characterization of nutrient release from agricultural fields to surface water. However, to date, no systematic investigations have examined the implementation of different classification and regression ML models in agricultural settings to predict nutrient concentrations in surface water using a group of input variables including climatological (e.g., precipitation), hydrological (e.g., stream flow) and field characteristics (i.e., land and crop use). In the current study, multiple classification (e.g., decision trees) and regression (e.g., regression trees) ML models were applied on a dataset pertaining to surface water quality in an agricultural watershed in southern Ontario, Canada (i.e., Upper Parkhill watershed). The target variables of these models were the nutrient concentrations in surface water including nitrate, total phosphorus, soluble reactive phosphorus, and total dissolved phosphorus. These target variables were predicted using physical and chemical water parameters of surface water (e.g., temperature and DO), climatological, hydrological, and field conditions as the input variables. The performance of these different models was assessed using various evaluation metrics such as classification accuracy (CA) and coefficient of determination (R2) for classification and regression models, respectively. In general, both the ensemble bagged trees and logistic regression (CA ≥ 0.72), and exponential Gaussian process regression (R2≥ 0.93) models were the optimal classification and regression ML algorithms, respectively, where they resulted in the highest prediction accuracy of the target variables. The insights and outcomes of the current study demonstrates that ML models can be employed to effectively predict and quantify the nutrient concentrations in surface waters to supplement field-collected monitoring data in agricultural watersheds, assisting in maintaining high quality of the available surface water resources.

Enhanced nitrogen removal in sewage treatment is achieved by using kitchen waste hydrolysate without a significant increase in nitrous oxide emissions

Kitchen waste hydrolysate (KWH) is an effective replacement for commonly used carbon sources such as sodium acetate (NaAc) and glucose (Glu), in wastewater treatment plants (WWTPs) to enhance the total nitrogen (TN) removal efficiency in sewage and reduce the operating cost of WWTPs. However, KWH utilization introduces complex organic matter that may lead to increased nitrous oxide (N2O) emissions, compared with that of NaAc and Glu, causing significant damage to the atmosphere. Therefore, this study aims to compare the effects of KWH, Glu, and NaAc on N2O emissions in sewage treatment. The results indicated that KWH introduction did not lead to a significant increase in N2O emissions, with a conversion rate of only 5.61 %. Compared with raw sludge, the addition of only Glu and NaAc significantly increased the abundance of the nar G gene, indicating that the readily degradable carbon sources initiated denitrification at a faster rate than KWH. When KWH was added, there was a notable increase in the abundance of genes associated with partial nitrification and denitrification (nir K, hzo, and nos Z). In contrast, Glu and NaAc did not have a significant effect on the nos Z gene. The results suggested that KWH supplementation was more effective to reduce N2O to N2. Moreover, the KWH addition significantly increased the microbial diversity in the sludge and promoted the presence of shortcut nitrification and denitrification bacteria (Comamonadaceae) and denitrification bacteria (Rhodobacteraceae), further indicating the potential of KWH for enhanced denitrification and reduced N2O emissions. Overall, to the best of our knowledge, this is the first study that demonstrated KWH, as a novel and complex organic carbon source, can be safely used in sewage treatment processes to improve the pollutant removal efficiency without causing a significant increase in N2O emissions.

Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed

Excess nutrients in surface water and groundwater can lead to water quality deterioration in available water resources. Thus, the classification of nutrient concentrations in water resources has gained significant attention during recent decades. Machine learning (ML) algorithms are considered an efficient tool to describe nutrient loss from agricultural land to surface water and groundwater. Previous studies have applied regression and classification ML algorithms to predict nutrient concentrations in surface water and/or groundwater, or to categorize an output variable using a limited number of input variables. However, there have been no studies that examined the application of different ML classification algorithms in agricultural settings to classify various output variables using a wide range of input variables. In this study, twenty-four ML classification algorithms were implemented on a dataset from three locations within the Upper Parkhill watershed, an agricultural watershed in southern Ontario, Canada. Nutrient concentrations in surface water were classified using geochemical and physical water parameters of surface water and groundwater (e.g., pH), climate and field conditions as the input variables. The performance of these algorithms was evaluated using four evaluation metrics (e.g., classification accuracy) to identify the optimal algorithm for classifying the output variables. Ensemble bagged trees was found to be the optimal ML algorithm for classifying nitrate concentration in surface water (accuracy of 90.9%), while the weighted KNN was the most appropriate algorithm for categorizing the total phosphorus concentration (accuracy of 87%). The ensemble subspace discriminant algorithm gave the highest overall classification accuracy for the concentration of soluble reactive phosphorus and total dissolved phosphorus in surface water with an accuracy of 79.2% and 77.9%, respectively. This study exemplifies that ML algorithms can be used to signify exceedance of recommended concentrations of nutrients in surface waters in agricultural watersheds. Results are useful for decision makers to develop nutrient management strategies.

The efficient utilization of thiocyanate on simultaneous removal of ammonium and nitrate through thiosulfate-driven autotrophic denitrifiers and anammox

The efficient utilization of thiocyanate remain be an important bottleneck in the low-cost nitrogen removal for wastewaters containing thiocyanate. The study aimed to investigate the feasibility of thiocyanate in removal of nitrate and ammonium through anammox (AN) and thiosulfate-driven autotrophic denitrifiers (TSAD). The results showed that removal of nitrate and ammonium were achieved rapidly utilizing thiocyanate, which was attributed to degradation of thiocyanate by TSAD and cooperation with AN. The utilization efficiency of thiocyanate in nitrogen removal was increased by 250% due to the microbial cooperation. Excess thiocyanate and ammonium did not influence the nitrogen removal amount. However, the nitrogen removal were affected obviously by the biomass ratio (X_AN/X_TSAD) between AN and TSAD Moreover, the dynamics related to removal of pollutants was described successfully by a modified Monod model with time constraints. These findings offer an insight for efficient utilization of thiocyanate in nitrogen removal via microbial cooperation.