Abstract

There are different complexes of contaminants which are found and regularly introduced into aquatic ecosystems with heavy metals, pesticides, pharmaceuticals and microplastics being the main concern of environmental risk assessment. Traditional toxicological procedures which are mainly conceived to deal with exposures on individual compounds are inadequate in their capability to deal with the interaction and the perceived synergies, antagonisms, or additive effects of several contaminants. Recent developments in the aquatic toxicology field are associated with a new emphasis on computational modeling implementation, omics-based biomarker, and high throughput screening methods to determine the impact of a cocktail of several contaminants on aquatic life. Further possible identification of the toxicity pathways and mechanistic insights of both the organismal stress response and adaptive resilience will be enabled by approaches based on machine learning and systems biology techniques. In addition, the integration of in silico ecotoxicology modeling with next-generation sequencing and metabolomics profiling will allow developing predictive models to overcome environmental variability, species sensitivity, and pollutant transformation dynamics. Such achievements can enable more quantitative measures of ecological risks being taken than ever before to achieve more sustainable water resource management. The present paper discusses the latest strategies and interdisciplinary emerging trends in aquatic toxicology and their relevance to the greater predictability of techniques, policy support, and biodiversity safeguards during an era of escalating anthropogenic pressure.

Keywords: Aquatic toxicology, Multiple pollutants, Ecotoxicology, Predictive modeling, Biomarkers, Machine learning, Environmental risk assessment