PI Nishad Jayasundara– The Nicholas School of the Environment
Christina Wyatt – The School of Medicine, Nephrology
Truls Ostbye – The School of Medicine, Community and Family Medicine & the Duke Global health Institute
Lee Ferguson – The Pratt School of Engineering & The Nicholas School of the Environment
Claudia Gunsch – The Pratt School of Engineering
Drew Shindell – The Nicholas School of the Environment
Michael Bergin – The Pratt School of Engineering
Robert Tighe – The School of Medicine
We propose to predict and mitigate climate change and pollution driven health outcomes in low- and middle-income communities in Asia, Africa, and America by leveraging bioassays, low-cost sensor networks, and webtools (internet of things) to monitor air and water quality. We will utilize existing sensor networks and novel approaches for biomonitoring to investigate potential changes in the chemosphere associated with climate change. For example, water quantity and quality will diminish with increasing temperature and drought seasons, concentrating water contaminants. Air quality is predicted to worsen with climate change (e.g., increasing particulate matter in the air and changes in volatilized chemical compound levels). Low- and middle-income communities (LMIC), specially agricultural communities are heavily and disproportionately burdened by extensive chemical usage, while also being directly impacted by climate change driven shifts in temperature, rain, drought, and habitat suitability patterns. However, human health outcomes linked to shifts in the chemosphere with climate change in LMIC communities remain a critical knowledge gap. To address this important health disparity, we have assembled a team of researchers conducting research in global and local domains with expertise in air and water quality monitoring as well as community and occupational health. We propose to leverage existing research in (i) the CAFOS (Confined Animal Feeding Operations) in North Carolina, (ii) sugarcane and rice farming communities in South Asia, and (iii) agricultural regions in Kenya. We will utilize global satellite data, social media data, and ground sensor data to determine long-term climate trends. Sensor data, biomonitoring data (e.g., microbial community shift data and in vitro exposureassay data), non-target chemical composition data will be assessed for identifying exposome shifts. To determine adverse health outcomes potentiating from climate change induced chemosphere shifts, community health records will be mined and compared with climatological and exposome data. Results will develop a globally relevant robust framework and a sensor network for predicting and mitigating synergistic adverse health effects of anthropogenic environmental change.
