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Abstract Current U.S. policies aim to establish domestic supply chains of critical minerals for the energy transition. The Iron Creek deposit in the Idaho cobalt belt (ICB) is one of the most promising cobalt (Co) targets. Our case study illustrates the importance of mineralogy in strategic evaluations of critical mineral potential. Most of the Co at Iron Creek occurs as Fe substitution in pyrite, with lattice-bound and inclusion-hosted Ag, As, Bi, Ni, Pb, Se, Te ± trace Au and Sb. Cobalt also occurs in minor cattierite-vaesite. The Co minerals are intergrown with Co-poor chalcopyrite hosting Cu ± minor In and Zn. Worldwide, most Co is recovered from deposits mineralogically distinct from the ICB, and the United States currently lacks infrastructure to recover this Co and its associated metals. ICB ore minerals could be processed by autoclave, roaster, smelter, bioleach, or heap leach. Recovery of the Ag, As, Au, Bi, In, Pb, Se, Te, and Zn would be costly by autoclave, and construction of a custom smelter for ICB ores is likely uneconomic, so these elements would become waste irrespective of criticality. The Co-Fe and Co-As sulfide minerals are most suitable for Co and Ni recovery by a hydrometallurgical autoclave process, with potential pretreatment of cobaltiferous pyrite/arsenopyrite in an inert-atmosphere roaster, in new domestic or anticipated international facilities. The ICB is the second largest known Co resource in the United States. Consideration of ore mineralogy in the ICB is essential in strategies for domestic production. 

RCMs produced at ~0.5° (available in the NA-CORDEX database esgf-node.ipsl.upmc.fr/search/cordex-ipsl/) address issues related to coarse resolution of GCMs (produced at 2° to 4°). Nevertheless, due to systematic and random model errors, bias correction is needed for regional study applications. However, an acceptable threshold for magnitude of bias correction that will not affect future RCM projection behavior is unknown. The goal of this study is to evaluate the implications of a bias correction technique (distribution mapping) for four GCM-RCM combinations for simulating regional precipitation and, subsequently, streamflow, surface runoff, and water yield when integrated into Soil and Water Assessment Tool (SWAT) applications for the Des Moines River basin (31,893 km²) in Iowa-Minnesota, U.S. The climate projections tested in this study are an ensemble of 2 GCMs (MPI-ESM-MR and GFDL-ESM2M) and 2 RCMs (WRF and RegCM4) for historical (1981-2005) and future (2030-2050) projections in the NA-CORDEX CMIP5 archive. The PRISM dataset was used for bias correction of GCM-RCM historical precipitation and for SWAT baseline simulations. We found bias correction improves historical total annual volumes for precipitation, seasonality, spatial distribution and mean error for all GCM-RCM combinations. However, improvement of correlation coefficient occurred only for the RegCM4 simulations. Monthly precipitation was overestimated for all raw models from January to April, and WRF overestimated monthly precipitation from January to August. The bias correction method improved monthly average precipitation for all four GCM-RCM combinations. The ability to detect occurrence of precipitation events was slightly better for the raw models, especially for the GCM-WRF combinations. Simulated historical streamflow was compared across 26 monitoring stations: Historical GCM-RCM outputs were unable to replicate PRISM KGE statistical results (KGE>0.5). However, the Pbias streamflow results matched the PRISM simulation for all bias-corrected models and for the raw GFDL-RegCM4 combination. For future scenarios there was no change in the annual trend, except for raw WRF models that estimated an increase of about 35% in annual precipitation. Seasonal variability remained the same, indicating wetter summers and drier winters. However, most models predicted an increase in monthly precipitation from January to March, and a reduction in June and July (except for raw WRF models). The impact on hydrological simulations based on future projected conditions was observed for surface runoff and water yield. Both variables were characterized by monthly volume overestimation; the raw WRF models predicted up to three times greater volume compared to the historical run. RegCM4 projected increased surface runoff and water yield for winter and spring by two times, and a slight volume reduction in summer and autumn. Meanwhile, the bias-corrected models showed changes in prediction signals: In some cases, raw models projected an increase in surface runoff and water yield but the bias-corrected models projected a reduction of these variables. These findings underscore the need for more extended research on bias correction and transposition between historical and future data. 

Abstract Building cooling loads are driven by heat gains through enclosures. This research identifies possible ways of reducing the building cooling loads through vegetative shading. Vegetative shading reduces heat gains by blocking radiation and by evaporative air cooling. Few measured data exist, so we gathered thermal data from a vegetative wall grown in front of a Mobile Diagnostics Lab (MDL), a trailer with one conditioned room with instrumentation that collects thermal data from heat flux sensors and thermistors within its walls. In spring 2020 a variety of plants were cultivated in a greenhouse and planted in front of the south façade of the MDL, which was placed in direct sunlight to collect heat flux data. The plants acted as a barrier for solar radiation and reduced the amount of thermal energy affecting the trailer surface. Data were collected through the use of 16 heat flux sensors and development of continuous infrared (IR) images indicating surface temperature with and without plant cover. The façade surface beneath the plants was 10-30 °C cooler than exposed façade areas. In further analyses, the heat-flux data were compared to IR temperature data. 

Polycyclic aromatic hydrocarbons (PAHs) are produced by the burning of biomass, with molecular weights reflecting combustion conditions. After being formed, PAHs are transported downward through soil and bedrock by infiltrating rainwater (Perrette et al., 2013), and in karst areas can become incorporated into stalagmites as they crystallize from dripwater in underlying caves (Perrette et al., 2008; Denniston et al., 2018). Thus, when stalagmite growth is high, infiltration times short, and fluid mixing minimized, there exists the potential for PAHs in stalagmites to preserve evidence of the presence and intensity of fire through time. We have previously reported a high-resolution analysis of PAH distributions in two non-overlapping aragonite stalagmites from cave KNI-51, tropical Western Australia, that together span the majority of the last 900 years. The geologic conditions of this site make it well suited for the transmission of discrete pulses of fire-derived compounds from the land surface to the stalagmite. Soils are thin to absent above the stalagmite chamber and the cave is shallow. As a result, homogenization of infiltrated water (and thus PAHs) is expected to be small on interannual time scales. In addition, intense summer monsoon rains flush fire debris from the hillsides over the cave. These characteristics, coupled with the fast growth rates (1-2 mm/yr) and precise radiometric dates (±1-30 years 2 s.d. over the last millennium) of KNI-51 stalagmites suggest that they hold the potential for extremely high resolution paleofire reconstruction. Here we provide the first test of replication of PAH abundances, ratios, and trends in coeval stalagmites. Samples were analyzed at Ca’ Foscari University using methods of Argiriadis et al. (2019) and the results validated by comparing them with fire activity detected through satellite images. Stalagmites KNI-51-F and -G overlap in age from CE 1310-1630, allowing an examination of the consistency of the PAH signal along different infiltration pathways. References Argiriadis, E. et al. (2019) European Geosciences Union Annual Meeting, Vienna, Australia. Denniston, R.F. et al. (2018) American Geophysical Union Annual Meeting, Washington, D.C. Perrette, Y. et al. (2008) Chemical Geology, 251, 67-76. Perrette, Y. et al. (2013) Organic Geochemistry, 65, 37-45.