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Contamination by heavy metals, per- and polyfluoroalkyl substances (PFAS), and other emerging pollutants poses serious risks to environmental and human health. Effective monitoring and tracing require integrating data from diverse sources. A knowledge graph approach enables semantic integration, but relies on an ontology that supports intuitive and robust querying and reasoning. To address this, we present the Contaminant Observations and Samples Ontology (ContaminOSO), a framework for semantically enriching environmental contaminant data. Built on SOSA and QUDT ontologies, ContaminOSO introduces key extensions to meet contamination-specific needs and real-world data challenges. This paper highlights four of its core design solutions: (1) extending SOSA to model multiple features of interest; (2) using QUDT to standardize the representation of contaminants and observed properties; (3) developing a detailed and nuanced pattern for measurement result representation using QUDT and STAD; and (4) adopting a pragmatic approach for connecting to existing taxonomies from the OBO Foundry, such as the NCBI organismal classification and relevant subsets of the Food Ontology (FoodOn), for classifying samples.