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Area of Science:

  • Environmental Health
  • Data Science
  • Public Health Surveillance

Background:

  • Environmental contamination significantly impacts human health and food security.
  • Current public health surveillance efforts are fragmented across agencies, hindering holistic environmental exposure assessment.
  • Citizen science (CS) provides valuable site-specific data but faces interoperability challenges with existing datasets.

Purpose of the Study:

  • To develop a common data model for integrating disparate environmental health datasets, including citizen science data.
  • To facilitate data exchange and enable new research questions across different scales and domains.
  • To address environmental health disparities and promote environmental justice through ethical data integration.

Main Methods:

  • Employed a transdisciplinary research framework to develop a data integration methodology.
  • Integrated citizen science data with governmental environmental monitoring and social attribute data from 10 federal and state agencies.
  • Developed the Superfund Research Project Data Interface Ontology (SRPDIO) to address the lack of standardized ontologies for vulnerability and resiliency factors.

Main Results:

  • A novel method and common data model were developed to integrate diverse environmental health data sources.
  • The methodology successfully integrated citizen science data with governmental and social attribute data.
  • The SRPDIO provides a standardized ontology for key environmental health research variables.

Conclusions:

  • The developed common data model enhances holistic environmental exposure assessment and supports addressing environmental health disparities.
  • Ethical integration of citizen science data is crucial for achieving environmental justice.
  • The methodology and ontology offer a scalable solution for managing and analyzing complex environmental health data.