The binary metal organic framework (MOF) is composed of two heterometallic ions bonded to an organic ligand. Compared with monometallic MOFs, bimetallic MOFs have greatly improved in terms of structure, porosity, active site, adsorption, selectivity, and stability, which has attracted wide attention. At present, many effective strategies have been designed for the synthesis of bimetallic MOF-based nanomaterials with specific morphology, structure, and function. The results show that bimetallic MOF-based nanocomposites could achieve multiple synergistic effects, which will greatly improve their research in the fields of adsorption, catalysis, energy storage, sensing, and so on. In this review, the main preparation methods of bimetallic MOFs-based materials are summarized, with emphasis on their applications in adsorption, catalysis, and detection of target pollutants in water environments, and perspectives on the future development of bimetallic MOFs-based nanomaterials in the field of water are presented.

Increasing numbers of emerging contaminants (ECs) detected in water environments require a detailed understanding of these chemicals’ fate, distribution, transport, and risk in aquatic ecosystems. Modeling is a useful approach for determining ECs’ characteristics and their behaviors in aquatic environments. This article proposes a systematic taxonomy of EC models and addresses gaps in the comprehensive analysis of EC models and their applications. The reviewed models include conventional water quality models, multimedia fugacity models, and machine learning (ML) models. Conventional water quality models have higher prediction accuracy and spatial resolution; nevertheless, they are limited in functionality and can only be used to predict contaminant concentrations in aquatic environments. Fugacity models are excellent at depicting how contaminants travel between different environmental media, but they cannot be used directly to analyze contaminant variations in different parts of the same environmental media because the fugacity model assumes that contaminant concentrations are constant within the same environmental compartment. Compared to other models, ML models can be applied to more scenarios, such as contaminant identification and risk assessments, rather than being confined to the prediction of contaminant concentrations. In recent years, with the rapid development of artificial intelligence, ML models have surpassed fugacity models and conventional water quality models, becoming one of the newest hotspots in the study of ECs. The primary challenge faced by ML models is that the model outcomes are difficult to interpret and understand, and this influences the practical value of an ML model to some extent.

Accurate water quality prediction is the basis for good water environment management and sustainable use of water resources. As an important time series forecasting model, the Autoregressive Moving Average Model (ARMA) plays a crucial role in environmental management and sustainability research. This study addresses the factors that affect the ARMA model’s forecast accuracy and goodness of fit. The research results show that the sample size used for model parameters estimation is the main influencing factor for the goodness of fit of an ARMA model, and the prediction time is the main factor affecting the prediction error of the model. Constructing a stable and reliable ARMA model requires a certain number of samples for the estimation of model parameters. However, using an excessive number of samples will not further improve the ARMA model’s goodness of fit but rather increase the workload and difficulty of data collection. The ARMA model is not suitable for long-term forecasting because the prediction error of ARMA models increases with the increase of prediction time, and when the prediction time exceeds a certain limit, the fitted values of an ARMA model will almost no longer change with the time, which means the model has lost its significance of prediction. For time series with periodic components, introducing periodic adjustment factors into the ARMA model can reduce the prediction error. These findings enable environmental managers and researchers to apply the ARMA model more rationally, hence developing more precise pollution control and sustainable development plans.

Anomaly detection in water-related time-series data often reveals important environmental problems and serves as a starting point for scientific discoveries. Machine learning has become the mainstream method and a research hotspot for anomaly detection in recent years. This review examines 106 research articles from the Web of Science database published over the past 10 years. Unlike other surveys, this review focuses on anomalies arising from the water-related variables themselves rather than from equipment malfunctions. The work assesses the overall trends in the application and development of machine learning models for water-related anomaly detection. It classifies machine learning-based anomaly-detection models from two dimensions: development stage and anomaly-detection paradigm. Our analysis covers the mechanisms, strengths, limitations, and applications of various machine learning-based anomaly-detection models across different paradigms, highlighting current challenges and prospective research directions in water-related anomaly detection.