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Sulfide-based pathways for generating nitrite to sustain anaerobic ammonium oxidation (anammox) have garnered increasing attention. However, the presence of sulfide can also impact the anammox process, necessitating a comprehensive understanding of both its short-term and long-term effects on anammox. This study aimed to investigate the influence of sulfide on anammox, including its effects on the microbial community and process kinetics. During long-term operation, the maximum sulfide dosage tested was 30 mg S/L over 50 days of operation, exhibiting good nitrogen removal efficiency of 83.9%±4.8%. Conversely, under short-term exposure to sulfide, nitrogen removal efficiency was notably affected, decreasing to 68.98% at a considerably lower sulfide concentration of only 16 mg S/L. Within the context of long-term sulfide exposure, the maximum contribution of anammox to nitrogen removal reached 86.72% at a sulfide dosage of 25 mg S/L. However, when the influent sulfide concentration was increased to 50 mg/L , the contribution of anammox to nitrogen removal sharply declined to 41.3%. Microbial community analysis revealed as the sulfide concentration increased from 8 to 16 mg S/L , the abundance of anammox bacteria decreased from 2.46×105 to 1.67×105 copies/mL, whereas the abundance of Nitrobacter spp. increased from 2.73×102 to 8.13×102 copies/mL. However, during long-term operation, there was a more pronounced decrease in the microbial abundance of anammox, reducing from 5.3×105 to 3.77×102 copies/mL. Taking this decrease together with the improved efficiency of anammox observed during long-term operation, these findings suggest that sulfide's influence on anammox primarily impacts its metabolic activity rather than its microbial abundance. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models are widely used methods for analyzing data from sensors and satellites to monitor climate change, predict natural disasters, and protect wildlife. However, the application of these technologies for monitoring and managing algal blooms in freshwater environments is relatively new and novel. The commonly used models in algal blooms (ABS) so far are artificial neural networks (ANN), random forests (RF), support vector machine (SVM), data-driven modeling, and long short-term memory (LSTM). In the past, researchers have mostly worked on predicting the effluent parameters, nutrients, microculture, area and weather conditions, meteorological factors, ground waters, energy optimization, and metallic substances in algal blooms using ML models. Most of the studies have employed performance metrics like root mean squared error, mean squared error, peak signal, precision, and determination coefficient as their primary model performance measures for accuracy analysis, and the usage of transfer, and activation function. While there have been some studies on this topic, several research gaps are still to be addressed. The most significant gaps are related to the limited application of ML in different algae bloom scenarios, the interpretability of ML models, and the lack of integration with existing monitoring systems. Keeping these in mind, this review article has been methodically arranged to present an overview of the past studies, their limitations, and the way forward toward the application of ML in the prediction of ABS, thus benefitting future researchers in this area. This review aims to summarize the data that are available, including some benchmarking values. Real-time monitoring of dynamics using ML is essential for mitigating algal blooms. Various complexities hinder applications of current ML algorithms in ABS. Activation and transfer functions can be used for selection of ML to predict ABS. Integrated ML algorithms can drive feature engineering to predict and control ABS. [ABSTRACT FROM AUTHOR]