Abstract
Personal exposure studies suffer from uncertainty issues, largely stemming from individual behavior uncertainties. Built on spatial-temporal exposure analysis and methods, this study proposed a novel approach to spatial-temporal modeling that incorporated behavior classifications taking into account uncertainties, to estimate individual livestock exposure potential. The new approach was applied in a community-based research project with a Tribal community in the southwest United States. The community project examined the geospatial and temporal grazing patterns of domesticated livestock in a watershed containing 52 abandoned uranium mines (AUMs). Thus, the study aimed to 1) classify Global Positioning System (GPS) data from livestock into three behavior subgroups - grazing, traveling or resting; 2) calculate the daily cumulative exposure potential for livestock; 3) assess the performance of the computational method with and without behavior classifications. Using Lotek Litetrack GPS collars, we collected data at a 20-minute-interval for 2 flocks of sheep and goats during the spring and summer of 2019. Analysis and modeling of GPS data demonstrated no significant difference in individual cumulative exposure potential within each flock when animal behaviors with probability/uncertainties were considered. However, when daily cumulative exposure potential was calculated without consideration of animal behavior or probability/uncertainties, significant differences among animals within a herd were observed, which does not match animal grazing behaviors reported by livestock owners. These results suggest that the proposed method of including behavior subgroups with probability/uncertainties more closely resembled the observed grazing behaviors reported by livestock owners. Results from the research may be used for future intervention and policy-making on remediation efforts in communities where grazing livestock may encounter environmental contaminants. This research also demonstrates a novel robust geographic information system (GIS)-based framework to estimate cumulative exposure potential to environmental contaminants and provides critical information to address community questions on livestock exposure to AUMs.