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.

Water security and robust sanitation infrastructure are fundamental pillars supporting public health, ecological integrity, and sustainable urban development

The rapid expansion of machine learning applications in heavy metal risk research has generated a large but fragmented body of literature, necessitating a systematic summary of various methodologies. This survey examines 182 research articles from Web of Science, Scopus, and IEEE Xplore over the past 10 years, providing a comprehensive analysis of the application of prevalent machine learning algorithms across research areas related to heavy metal risks, covering more than 30 algorithms and 9 research areas. The results show that regional risk assessment, risk source analysis, risk driver analysis, and research on the pathogenicity of HMs are the four most frequently applied areas of machine learning, accounting for 80.06% of all applications. Classical machine learning, especially a series of tree-based algorithms, dominates across all applications, accounting for 69.93% and 42.74%, respectively. In addition, some auxiliary algorithms, particularly those for feature analysis, are often used in conjunction with machine learning, primarily to interpret model predictions and analyze risk sources or drivers. Three principal methodological challenges emerge from our review: (1) poor model generalizability across different environmental conditions; (2) insufficient reliability of predictions; and (3) difficulty in obtaining high-quality training data. Some current literature is also constrained by small sample sizes, regional bias, limited field validation, and insufficient integration of multi-omics data with machine learning pipelines. To address these gaps, we advocate for the routine adoption of explainable AI techniques with rigorous stability checks, the development of publicly available, field-validated benchmark datasets, and greater integration of mechanistic knowledge with data-driven models.