This paper proposes conceptual model which can be used to facilitate the discovery, integration and analysis of environmental data in cancer-related risk studies. Persistent organic pollutants were chosen as a model because of their persistence, bioaccumulation potential and genotoxicity. Part dealing with cancer risk is primarily focused on population-based observations encompassing a wide range of epidemiologic studies, from local investigations to national cancer registries. The proposed model adopted multilayer hierarchy working with characteristics of given entities (POPs, cancer diseases as nomenclature classes) and couples “observation – measurement” as content defining classes. The proposal extends formally used taxonomy applying multidimensional set of descriptors including scores of measurement validity and precision. This solution has the potential to aid multidisciplinary data discovery and knowledge mining. The same structure of descriptors used for environmental and cancer part enables the users to integrate different data sources recognizing their methodical origin, time & space coordinates and validity.