27th NSF EPSCoR National Conference

Keynote Talk Details:

Define broadly, participatory approaches to research can challenge and change inequity and mistrust in science, particularly when the effort reflects the diversity of publics and does not reinforce existing inequities in science, environmental decision-making, and society. These efforts are transforming investigations, for example, through the development of new monitoring tools, co-production of data, and sharing of results. 

In this presentation, Ramírez-Andreotta will describe participatory research methods to advance exposure science and communication strategies to visualize and translate environmental health research to action. She will present co-generated environmental monitoring and exposure assessment data (e.g., arsenic and heavy metal concentrations in water, soil, locally grown food, dust) from community science projects, Gardenroots and Project Harvest.

Elements of participatory research for environmental health that effectively prompt structural change in environmental justice communities include:

1) having community members hold leadership roles,
2) designing the project with decision-makers and policy goals,
3) sustaining partnerships and funding.

Tactics for successfully sharing results to open the policy window include:

1) building transdisciplinary teams and datasets,
2) community-first reporting,
3) data standardization and interoperability among existing community generated and governmental datasets,
4) ensuring data report-back products serve as boundary objects for use in multiple social spheres.

Together, these efforts can inform how to sustain successful partnerships, build capacity to then endure the unique set of challenges justice projects face when they strive for structural change, and help determine if and how community-level resiliencies may combat environmental health vulnerabilities.

Keynote Speaker Introduction:

Mónica Ramírez-Andreotta, M.P.A., Ph.D. is an Associate Professor of Environmental Science at the University of AZ. Using an environmental justice framework and participatory research methods, she investigates exposure pathways and communication strategies to translate environmental health research to action and achieve structural change.

Using an environmental justice (EJ) framework, Dr. Ramírez-Andreotta’s research program focuses on five primary areas of Environmental Health:

1) Participatory approaches to science to increase environmental health literacy (EHL) and achieve justice
2) Environmental monitoring – Developing a fundamental understanding of the fate and transport of contaminants in plant-soil and plant-atmospheric systems
3) Exposure science – Conducting culturally appropriate risk assessments
4) Science and risk communication – Creating innovative tools and data sharing practices
5) Data Science and Management – Ensuring integration, interoperability, and visualization of community vulnerability and resiliency data.

Dr. Ramírez-Andreotta is pioneering new methods in exposure science, identifying community-level resiliencies to combat environmental health vulnerabilities and devAeloping novel communication strategies. As demonstrated by her flagship co created community science (CS) programs – Gardenroots and Project Harvest, she applies the peer/empowerment education model, strives to elucidate and eradicate environmental health risks, and co-designs public health prevention/intervention programs and communication strategies to engage, build efficacy, and address structural challenges in underrepresented, underserved, and affected populations.

To do this, she integrates pollution and exposure science, sociology, art, and environmental/health communication. Within the context of public health, environmental monitoring and phytotechnologies are a primary prevention strategy and can successfully lead to the mitigation of harmful environmental exposures. Using participatory research methods, she is developing low-cost monitoring and clean up tools, designing effective data sharing strategies, and integrating CS environmental monitoring data with other data sets to enhance discoverability and reuse of data for research translation and better hypothesis generation.