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

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.