Alleviating Environmental Health Disparities Through Community Science and Data Integration

Frontiers in Sustainable Food Systems

Ramona Walls¹  Kai Blumberg²  Ken Youens-Clark²  Katherine E. Isaacs³  Raina M. Maier⁴  Dorsey Kaufmann⁴  Mónica D. Ramírez-Andreotta⁵ 

[1] BIO5 Institute, University of Arizona, Tucson, AZ, United States;Department of Biosystems Engineering, University of Arizona, Tucson, AZ, United States;Department of Computer Science, University of Arizona, Tucson, AZ, United States;Department of Environmental Science, University of Arizona, Tucson, AZ, United States;Mel and Enid Zuckerman College of Public Health's Division of Community, Environment and Policy, University of Arizona, Tucson, AZ, United States;

关键词: citizen science;    community science;    interoperability;    FAIR principles;    environmental health;    community resiliency;   

DOI  :  10.3389/fsufs.2021.620470

来源: DOAJ

【 摘 要 】

Environmental contamination is a fundamental determinant of health and well-being, and when the environment is compromised, vulnerabilities are generated. The complex challenges associated with environmental health and food security are influenced by current and emerging political, social, economic, and environmental contexts. To solve these “wicked” dilemmas, disparate public health surveillance efforts are conducted by local, state, and federal agencies. More recently, citizen/community science (CS) monitoring efforts are providing site-specific data. One of the biggest challenges in using these government datasets, let alone incorporating CS data, for a holistic assessment of environmental exposure is data management and interoperability. To facilitate a more holistic perspective and approach to solution generation, we have developed a method to provide a common data model that will allow environmental health researchers working at different scales and research domains to exchange data and ask new questions. We anticipate that this method will help to address environmental health disparities, which are unjust and avoidable, while ensuring CS datasets are ethically integrated to achieve environmental justice. Specifically, we used a transdisciplinary research framework to develop a methodology to integrate CS data with existing governmental environmental monitoring and social attribute data (vulnerability and resilience variables) that span across 10 different federal and state agencies. A key challenge in integrating such different datasets is the lack of widely adopted ontologies for vulnerability and resiliency factors. In addition to following the best practice of submitting new term requests to existing ontologies to fill gaps, we have also created an application ontology, the Superfund Research Project Data Interface Ontology (SRPDIO).

【 授权许可】

Unknown