ALBERT

Description: Data-driven decision making is the key to providing effective and efficient wildfire protection and sustainable use of natural resources. Due to the complexity of natural systems, management decision(s) require clear justification based on substantial amounts of information that are both accurate and precise at various spatial scales. To build information and incorporate it into decision making, new analytical frameworks are required that incorporate innovative computational, spatial, statistical, and machine-learning concepts with field data and expert knowledge in a manner that is easily digestible by natural resource managers and practitioners. We prototyped such an approach using function modeling and batch processing to describe wildfire risk and the condition and costs associated with implementing multiple prescriptions for risk mitigation in the Blue Mountains of Oregon, USA. Three key aspects of our approach included: (1) spatially quantifying existing fuel conditions using field plots and Sentinel 2 remotely sensed imagery; (2) spatially defining the desired future conditions with regards to fuel objectives; and (3) developing a cost/revenue assessment (CRA). Each of these components resulted in spatially explicit surfaces describing fuels, treatments, wildfire risk, costs of implementation, projected revenues associated with the removal of tree volume and biomass, and associated estimates of model error. From those spatially explicit surfaces, practitioners gain unique insights into tradeoffs among various described prescriptions and can further weigh those tradeoffs against financial and logistical constraints. These types of datasets, procedures, and comparisons provide managers with the information needed to identify, optimize, and justify prescriptions across the landscape.

Description: One in three homes in Utah (USA) contains dangerous levels of radon. Except for a radon mitigation certification law, Utah’s radon laws are nonexistent. To determine public perception of state policies on radon testing and mitigation, a social cognitive theory-based 52-item questionnaire was administered to residents (N = 307) who visited the Utah County Health Department (UCHD) during the study period. Respondents were divided into an Environmental Health Group (n = 110), who purchased a radon kit, and Vital Records Control Group (n = 197), who filed/obtained birth/death certificates at UCHD. Ninety percent responded they had never tested their homes for radon, and 99% were not aware of state policies regarding radon. Support for various radon policies was significantly associated with older age (odds ratios (OR): 0.37–0.52), being female (OR: 2.60–7.79), lower annual family income (OR: 2.27), and theoretical constructs of behavioral modeling (OR: 2.31–2.55) and risk perception (OR: 2.55–3.71). To increase awareness, testing, and remediation, respondents suggested increasing public education/awareness, requiring testing in homes, businesses, and public buildings, and increasing access to testing. Multi-sectoral radon risk reduction programs could incorporate behavioral modeling and risk perception as components to create a radon testing and mitigation culture in Utah.