Crystal zonations provide valuable snapshot of the dynamic changes within a magma reservoir. However, crystal zonations are often heterogeneous down to the hand-sample scale, such that deciphering their signatures becomes convoluted. Crystals are reactively precipitated and dissolving as a function of temperature, pressure, and composition. In this manuscript, we investigate what temperature histories crystals experience in a magma lens after its injection into a cooler magma reservoir. We simulate the cooling interface in either hot basaltic or dacitic magmas after their injection into a cooler magma reservoir. We couple fluid dynamics to thermodynamics by resolving flow at the crystalline-scale and allowing crystals with constant density and size to precipitate and dissolve based on ambient melt properties. We infer zonations in our simulated crystals by tracking the magma temperatures they sample over time. Our results show that when thermodynamics and fluid dynamics are coupled, a reactive, crystal-driven instability arises, because the negative buoyancy of crystals pulls along the cooler-than-ambient melt in which they precipitated. As crystals continue to precipitate along the cooling boundary, the instability develops into a sustained convective flow. Our results show that crystals record complex and unique zonations in this crystalline-scale domain, suggesting that zonations and their heterogeneity can be indicative of local instead of system scale processes. Also, our results show that many of the crystals in the instability dissolve and lose their thermal record of the instability. These results highlight the challenges of deciphering system-scale process from crystalline data.

COVID-19 success stories from countries using contact tracing as an intervention tool for the pandemic have motivated US counties to pilot opt-in contact tracing applications. Contact tracing involves identifying individuals who came into physical contact with infected individuals. Recent studies show the effectiveness of contact tracing scales with the number of people using the applications. We hypothesize that the effectiveness of contact tracing also depends on the occupation of the user with a large-scale adoption in certain at risk occupations being particularly valuable for identifying emerging outbreaks. We build on an agent-based epidemiological simulator that resolves spatiotemporal dynamics to model San Francisco, CA, USA. Census, OpenStreetMap, SafeGraph, and Bureau of Labor Statistics data inform the agent dynamics and site characteristics in our simulator. We test different agent occupations that create the contact network, e.g. educators, office workers, restaurant workers, and grocery workers. We use Bayesian Optimization to determine transmission rates in San Francisco, which we validate with transmission rate studies that were recently conducted for COVID-19 in restaurants, homes and grocery stores. Our sensitivity analysis of different sights show that the practices that impact the transmission rate at schools have the greatest impact on the infection rate in San Francisco. The addition of occupation dynamics into our simulator increases the spreading rate of the virus, because each occupation has a different impact on the contact network of a city. We quantify the positive benefits of contact tracing adopted by at risk occupation workers on the community and distinguish the specific benefits on at risk occupation workers. We classify to which degree a certain occupation is at risk by quantifying the impact (a) the number of unique contacts and (b) the total number of contacts an individual has for any given work day on the virus spreading rate. We also attempt to constrain if, when, and for how long certain sites should be shut down once exposed to positive cases. Through our research, we are able to identify the occupations, like educators, that are at greatest risk. We use common geophysical data analysis techniques to bring a different set of insights into COVID-19 and policy research.

Fractional crystallization is an essential process proposed to explain worldwide compositional abundances of igneous rocks. It requires crystals to precipitate from the melt and segregate from its residual melt, or crystal fractionation. The compositional abundances of volcanic systems show a bell-curve distribution suggesting that the process has variable efficiencies. We test crystal fractionation efficiency in convective flow in low to intermediate crystallinity regime. We simulate the physical segregation of crystals from their residual melt at the scale of individual crystals, using a direct numerical method. We find that at low particle Reynolds numbers, crystals sink in clusters. The relatively rapid motion of clusters strips away residual melt. Our results show cluster settling can imprint observational signatures at the crystalline scale. The collective crystal behavior results in a crystal convection that governs the efficiency of crystal fractionation, providing a possible explanation for the bell curve distribution in volcanic systems.