A brief review on the assessment of potential joint effects of complex mixtures of contaminants in the environment

Organisms and humans are exposed to a "cocktail" of contaminants in the environment, but methods for mixture assessment, untargeted analysis, and source identification (fingerprinting) are still lagging, which is critically reviewed in this article. Firstly, this paper briefly summarized both the direct and indirect effects of chemical contaminants at multiple levels on the biological responses of wild organisms. Secondly, the choice of a predictive model for chemical mixture assessment can greatly influence the outcome. Therefore, this review emphasizes the limitation of the main methodologies of risk assessments for chemical mixtures. Thirdly, since current environmental toxicology approaches face barriers to realizing the true potential of advances in analytical chemistry for human health or ecology risk assessment, bioanalytical methods, to screen toxic chemicals or identify unknown chemicals at environmentally relevant levels are reviewed. Lastly, Recently developed machine learning models, incorporating non-targeted screening analysis for the suspect and unknown chemicals and machine learning methods, can be trained on complex datasets to better predict interactions among identified chemicals with random combinations, quantification of similar structural chemicals without the presence of analytical standards, and transfer of chemicals based on their physicochemical properties in human tissues. To perform risk assessments for a variety of chemicals, we propose employing a framework that makes use of a range of methods from the toolbox summarized in this review. Many contaminants can have long-term effects on organisms when they are exposed to low concentrations for extended periods. This review presents new methods for identifying the effects of chemical mixtures.