Alleviating Environmental Health Disparities Through Community Science and Data Integration

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.