The lack of ecological realism is widely recognised as a limitation in current prospective chemical risk assessment, as is the failure of making variability explicit and transparent. The integration of ecological scenarios with chemical effect models to achieve quantitative ERA promises to increase ecological relevance. Probabilistic environmental risk assessment (PERA) has been suggested as a way to account for spatial, temporal, and environmental variability. Probabilistic plots area new way of presenting ecotoxicological data whilst accounting also for ecologically relevant parameters. They provide an indication of the maximum population-relevant impact of an effect of interest (e.g. biomass reduction) and the prevalence of this impact. Essentially they answer two related questions: How strong is the effect? In how many locations will we see the effect? We discuss some of the challenges and opportunities involved in bringing these new concepts into everyday risk assessment for down-the-drain chemicals. One of the key questions revolves around understanding the protection goal for anthropogenic stressors in specific ecological scenarios, and indeed whether certain scenarios require specific modified protection goals. Once the specific protection goal has been established, a metric to suit both the specific ecological scenario and protection goal needs to be defined and agreed. The selection of this endpoint must be carefully considered as different options will lead to different interpretation. We present a framework to integrate probabilistic approaches with mechanistic effect models to assess variable chemical and environmental scenarios. We present a hypothetical case study risk assessment for an ingredient used universally in all laundry products across Europe and illustrate the potential benefits of the framework. To do so, we use an individual based model integrating a dynamic energy budget model to assess the potential impact of chemicals associated with local environmental characteristics. We then map the outcomes based on probabilistic plots and on potential policy makers’ decisions of the maximal ecologically acceptable impact and the maximal prevalence of this impact. This new framework has the potential to better present ecologically relevant risk by using integrated biological endpoints and to aid more transparent risk communication.