Africa is increasingly being exposed to the negative impacts of climate and environmental change, while having less capacity to respond compared to other continents. The vulnerability partially results from unprecedented demographic growth, urbanization, and industrialization. However, the continent has still largely been underserved by the broader Earth system science (ESS) community, as evidenced by the limited amount of ESS data and research that cover Africa compared to other areas of the world. Here, we present the recent University Corporation for Atmospheric Research (UCAR) Africa Initiative that aims to enhance environmental sustainability in Africa by fostering international collaborative research partnerships coled by African scientists. Specifically, we outline urgent challenges and opportunities identified through an international workshop in six areas of ESS, namely, 1) air quality and health, 2) weather, 3) climate, 4) land and water, 5) social science perspectives, and 6) developing equitable collaboration and sustainable infrastructure. We highlight examples of successful partnerships and conclude with recommendations to advance collaborative, actionable ESS research that addresses Africa's critical environmental challenges.
Increasing residential segregation of affluent families is widespread internationally, raising concerns about "opportunity hoarding" and the perpetuation of social inequalities. Yet, we do not know whether this phenomenon is driven by families selecting neighborhoods characterized by their profiles of earnings or wealth. We model neighborhood selection during the 1993-2017 period by native Norwegian parents of children born 1992-2003, using a conditional logit analysis. We employ population register data from the Oslo metropolitan area that allows the calculation of neighborhood wealth profiles. We find that neighborhood residents' real capital and financial capital distributions are empirically distinct from their earnings distribution and strongly predict residential selections, but with considerable heterogeneity depending on wealth of the moving family and child's gender. Economic homophily dominates the neighborhood selections of wealthy families; others appear driven by avoiding status discrepancy. Neighborhood economic profiles are stronger predictors when moving with girls.
The Zebra Mussel Dreissena polymorpha (Pallas, 1771) has invaded surface waters throughout North America, and it alters benthic habitats by depositing dense layers of shells. Although these shell deposits can increase habitat complexity in some systems, they may also lead to habitat homogenization, particularly in streams with coarse substrates. We sought to understand how these shell deposits may affect macroinvertebrate assemblages in the Rouge and Huron watersheds (Michigan, USA; August 2017). We examined whether shell deposit density is related to macroinvertebrate community diversity and relative abundance and if those relationships might differ between rural and urban streams, noting changes in taxa sensitive to habitat structure, such as Ephemeroptera and Trichoptera (mayflies and caddisflies). In one rural stream, the % abundance of specific mayfly and caddisfly families decreased with increasing shell density, suggesting a localized negative response to shell-induced substrate changes. However, this pattern was not consistently observed across other rural streams, indicating that site-specific factors may mediate these effects. Regardless, shell deposits in urban streams, where baseline habitat complexity is often low, may offer structural refuge and benefit specific invertebrate taxa. Our findings highlight the context-dependent nature of Zebra Mussel relationships with stream invertebrate community metrics and suggest that the ecological consequences of shell deposits may vary with stream habitat characteristics.
Urban flooding is a matter of pressing concern in the city of Detroit. The increase in precipitation in recent years, together with the city's aging infrastructure put many residents at risk of financial losses, displacement, and detrimental health outcomes. The current study aims to identify potential determinants of residents' engagement or a lack thereof in flood prevention. The study found that the extent of flooding that residents had previously experienced was significantly associated with more at-home precautionary measures against future flooding. Prior flooding experiences were also linked to support for flood prevention policies, although this relationship was no longer significant when we considered additional demographic factors. Specifically, housing status, income, and racial identity were important predictors of prior flooding experiences and engagement in flood prevention. These findings highlight the importance of targeted communication and facilitation of low-to-no cost interventions to effectively support communities who are susceptible to flooding risks. The paper offers a further discussion of practical implications, limitations, and future directions.
Unsigned distance functions (UDFs) have been a vital representation for open surfaces. With different differentiable renderers, current methods are able to train neural networks to infer a UDF by minimizing the rendering errors with the UDF to the multi-view ground truth. However, these differentiable renderers are mainly handcrafted, which makes them either biased on ray-surface intersections, or sensitive to unsigned distance outliers, or not scalable to large scenes. To resolve these issues, we present a novel differentiable renderer to infer UDFs more accurately. Instead of using handcrafted equations, our differentiable renderer is a neural network which is pre-trained in a data-driven manner. It learns how to render unsigned distances into depth images, leading to a prior knowledge, dubbed volume rendering priors. To infer a UDF for an unseen scene from multiple RGB images, we generalize the learned volume rendering priors to map inferred unsigned distances in alpha blending for RGB image rendering. To reduce the bias of sampling in UDF inference, we utilize an auxiliary point sampling prior as an indicator of ray-surface intersection, and propose novel schemes towards more accurate and uniform sampling near the zero-level sets. We also propose a new strategy that leverages our pretrained volume rendering prior to serve as a general surface refiner, which can be integrated with various Gaussian reconstruction methods to optimize the Gaussian distributions and refine geometric details. Our results show that the learned volume rendering prior is unbiased, robust, scalable, 3D aware, and more importantly, easy to learn. Further experiments show that the volume rendering prior is also a general strategy to enhance other neural implicit representations such as signed distance function and occupancy. We evaluate our method on both widely used benchmarks and real scenes, and report superior performance over the state-of-the-art methods.
