There are four crucial challenges for environmental toxicologists in the next decade: (1) understanding the mechanisms of molecular and subcellular interactions with pollutant chemicals, including genomic and proteomic aspects; (2) the development of predictive simulation models of toxic effects on complex cellular and physiological processes; (3) linking molecular, cellular and patho-pysiological 'endpoints' with higher level ecological consequences; and (4) precautionary anticipation of possible harmful impacts of novel developments in industrial processes, including biotechnology and nanotechnology. One of the major difficulties in ecotoxicology is to link harmful effects of chemical pollutants in individual animals and plants with the ecological consequences. Consequently, this obstacle has resulted in a 'knowledge-gap' for those seeking to develop policies for sustainable use of resources and environmental protection. The overall problem is: how to develop effective procedures for environmental/ecological impact and risk assessment? However, the use of diagnostic 'clinical-type' tests or 'biomarkers' has started to provide information on the health-status of populations based on relatively small samples of individuals. Also, biomarkers can now be used to begin to link processes of molecular and cellular damage through to the higher levels (i.e. prognostic capability), where they can result in reduced performance and reproductive success. Research effort to meet this challenge must be inter-disciplinary in character, since the key questions mainly involve complex interfacial problems. These include effects of physico-chemical speciation on uptake and toxicity, the toxicity of complex mixtures; and linking the impact of pollutants through the various hierarchical levels of biological organisation to ecosystem and human health. Finally, the development and use of process-based computational simulation models (i.e. 'virtual' cells, organs and animals), illustrated using an endosomal/lysosomal uptake and cell injury model, will facilitate the development of a predictive capacity for estimating risk associated with the possibility of future environmental events.