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Fires in the wildland-urban interface (WUI) are a global issue with growing importance. However, the impact of WUI fires on air quality and health is less understood compared to that of fires in wildland. We analyze WUI fire impacts on air quality and health at the global scale using a multi-scale atmospheric chemistry model—the Multi-Scale Infrastructure for Chemistry and Aerosols model (MUSICA). WUI fires have notable impacts on key air pollutants [e.g., carbon monoxide (CO), nitrogen dioxide (NO₂), fine particulate matter (PM_2.5), and ozone (O₃)]. The health impact of WUI fire emission is disproportionately large compared to wildland fires primarily because WUI fires are closer to human settlement. Globally, the fraction of WUI fire–caused annual premature deaths (APDs) to all fire–caused APDs is about three times of the fraction of WUI fire emissions to all fire emissions. The developed model framework can be applied to address critical needs in understanding and mitigating WUI fires and their impacts. 

"Monkey see monkey do" is an age-old adage, referring to naive imitation without a deep understanding of a system's underlying mechanics. Indeed, if a demonstrator has access to information unavailable to the imitator (monkey), such as a different set of sensors, then no matter how perfectly the imitator models its perceived environment (See), attempting to directly reproduce the demonstrator's behavior (Do) can lead to poor outcomes. Imitation learning in the presence of a mismatch between demonstrator and imitator has been studied in the literature under the rubric of causal imitation learning (Zhang et. al. 2020), but existing solutions are limited to single-stage decision-making. This paper investigates the problem of causal imitation learning in sequential settings, where the imitator must make multiple decisions per episode. We develop a graphical criterion that is both necessary and sufficient for determining the feasibility of causal imitation, providing conditions when an imitator can match a demonstrator's performance despite differing capabilities. Finally, we provide an efficient algorithm for determining imitability, and corroborate our theory with simulations. 

With rapid innovations in drone, camera, and 3D photogrammetry, drone-based remote sensing can accurately and efficiently provide ultra-high resolution imagery and digital surface model (DSM) at a landscape scale. Several studies have been conducted using drone-based remote sensing to quantitatively assess the impacts of wind erosion on the vegetation communities and landforms in drylands. In this study, first, five difficulties in conducting wind erosion research through data collection from fieldwork are summarized: insufficient samples, spatial displacement with auxiliary datasets, missing volumetric information, a unidirectional view, and spatially inexplicit input. Then, five possible applications—to provide a reliable and valid sample set, to mitigate the spatial offset, to monitor soil elevation change, to evaluate the directional property of land cover, and to make spatially explicit input for ecological models—of drone-based remote sensing products are suggested. To sum up, drone-based remote sensing has become a useful method to research wind erosion in drylands, and can solve the issues caused by using data collected from fieldwork. For wind erosion research in drylands, we suggest that a drone-based remote sensing product should be used as a complement to field measurements.