Deep Learning Hydrodynamic Forecasting for Flooded Region Assessment in Near-Real-Time (DL Hydro-FRAN)

Hydrodynamic flood modeling improves hydrologic and hydraulic prediction of storm events. However, the computationally intensive numerical solutions required for high-resolution hydrodynamics have historically prevented their implementation in near-real-time flood forecasting. This study examines whether several Deep Neural Network (DNN) architectures are suitable for optimizing hydrodynamic flood models. Several pluvial flooding events were simulated in a low-relief high-resolution urban environment using a 2D HEC-RAS hydrodynamic model. These simulations were assembled into a training set for the DNNs, which were then used to forecast flooding depths and velocities. The DNNs' forecasts were compared to the hydrodynamic flood models, and showed good agreement, with a median RMSE of around 2 mm for cell flooding depths in the study area. The DNNs also improved forecast computation time significantly, with the DNNs providing forecasts between 34.2 and 72.4 times faster than conventional hydrodynamic models. The study area showed little change between HEC-RAS' Full Momentum Equations and Diffusion Equations, however, important numerical stability considerations were discovered that impact equation selection and DNN architecture configuration. Overall, the results from this study show that DNNs can greatly optimize hydrodynamic flood modeling, and enable near-real-time hydrodynamic flood forecasting.Comment: 21 pages, 8 figure

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Development and Assessment of a Web-Based National Spatial Data Infrastructure for Nature-Based Solutions and Their Social, Hydrological, Ecological, and Environmental Co-Benefits

Comprehensive datasets for nature-based solutions (NBS), and their diverse relationships have not yet been accumulated into a deployable format. This research describes the development of a novel National Spatial Data Infrastructure (NSDI) system for NBS co-benefits throughout the contiguous United States. Here, we gather and integrate robust geospatial datasets from the social, ecological, environmental, and hydrologic domains using seamless, cloud-based data services to facilitate the trans-disciplinary assessment of NBSs as a function of society and Earth. This research enhances practical decision making and research by assimilating web-based datasets and describing the missing links between national policy and robust adoption of NBSs as a sustainability solution. This NSDI serves to foster participatory planning capabilities and integrate local sustainability goals into decision–support frameworks. Such a platform strengthens the knowledge base necessary for addressing multiple, co-evolving issues of societal relevance, an essential component of fully espousing NBSs within the realm of socio-technological systems and improving policies and implementation regarding sustainable solutions. The efficacy of the proposed platform to serve as a holistic data information system is assessed by exploring important characteristics associated with geospatial NSDI tools, namely, openness, spatial functionality, scalability, and standardization. By placing GIS strengths and weaknesses in the context of transdisciplinary NBSs, we reveal strategic directions toward further co-production of such NSDIs. We conclude with recommendations for facilitating a shared vision of transdisciplinary technologies to strengthen the amalgamation of broad co-benefits and multi-disciplinary influences in sustainability planning

Assessing COVID-19 risk, vulnerability and infection prevalence in communities.

BackgroundThe spread of coronavirus in the United States with nearly five and half million confirmed cases and over 170,000 deaths has strained public health and health care systems. While many have focused on clinical outcomes, less attention has been paid to vulnerability and risk of infection. In this study, we developed a planning tool that examines factors that affect vulnerability to COVID-19.MethodsAcross 46 variables, we defined five broad categories: 1) access to medical services, 2) underlying health conditions, 3) environmental exposures, 4) vulnerability to natural disasters, and 5) sociodemographic, behavioral, and lifestyle factors. The developed tool was validated by comparing the estimated overall vulnerability with the real-time reported normalized confirmed cases of COVID-19.AnalysisA principal component analysis was undertaken to reduce the dimensions. In order to identify vulnerable census tracts, we conducted rank-based exceedance and K-means cluster analyses.ResultsAll of the 5 vulnerability categories, as well as the overall vulnerability, showed significant (P-values ConclusionPolicymakers can use this planning tool to identify neighborhoods at high risk for becoming hot spots; efficiently match community resources with needs, and ensure that the most vulnerable have access to equipment, personnel, and medical interventions

IMPACT OF NON-AQUEOUS PHASE LIQUIDS (NAPLS) ON GROUNDWATER REMEDIATION

Nonaqueous Phase Liquids (NAPLs) are immiscible (undissolved) hydrocarbons in the subsurface that exhibit different behavior and properties than dissolved contaminant plumes. NAPLs have a tremendous impact on the remediation of contaminated aquifers, as it is very difficult or impossible to remove all of the NAPL from a hazardous waste site once released to the subsurface. Although many NAPL removal technologies are currently being tested, to date there have been few field demonstrations where sufficient NAPL has been successfully removed from the subsurface to restore an aquifer to drinking water quality (EPA, 1992a). The residual NAPL that remains trapped in the soil/aquifer matrix acts as a continuing source of dissolved contaminants to ground water, and effectively prevents the restoration of NAPL-affected aquifers for tens or hundreds of years. This is particularly true for groundwater pump-and-treat systems, the most common remediation technology for addressing contaminated aquifers. This technology pumps groundwater out of contaminated zones to remove dissolved contaminants and, if present, to slowly dissolve any trapped NAPLs. The pumped water is then treated on the surface to remove or destroy the dissolved contaminants. To help designers of pump-and-treat systems evaluate the impact of NAPLs on groundwater remediation, a simple design model has been developed that provides the user with the number of recovery/injection wells and time required to reach cleanup standards. The method uses 1) dissolution data from a simplified dissolution model based on the work o

Efficacy of Carbonaceous Materials for Sorbing Polychlorinated Biphenyls from Aqueous Solution

Interest in incorporating nanomaterials into water treatment technologies is steadily growing, driving the necessity to understand the interaction of these new materials with specific water contaminants. In the present study, five different carbonaceous materials: activated carbon (AC), charcoal (BC), carbon nanotubes (CNT), graphene (GE), and graphene oxide (GO) were investigated as sorbent materials for 11 polychlorinated biphenyl (PCB) congeners in aqueous concentrations in the pg-μg/L range. Sorbent-water distribution coefficients (<i>K</i>_s) calculated in aqueous concentrations of ng/L show that AC is superior to GE, GO, CNT, and BC for the 11 PCB congeners investigated by an average of 1.1, 1.1, 1.3, and 2.5 orders of magnitude, respectively. Additionally, maximum capacity and sorption affinity parameters from the Langmuir, Freundlich, and Polanyi–Dubinin–Manes (PDM) models show a similar result. Interestingly, however, the effect of molecular planarity has greater impact on PCB sorption to the nanomaterials, such that the planar congeners form stronger bonds with CNT, GE, and GO compared to AC and BC. This work demonstrated superior PCB sorption by AC as compared with the nanomaterials examined such that substantial post production modifications would be necessary for the nanomaterials to out-perform AC

Estimation of Contaminant Concentration in Ground Water Using a Stochastic Flow and Transport Model

Bioplume II, a widely used computer model developed by researchers at Rice University, models the transport of dissolved hydrocarbons in ground water under the influence of oxygen-limited biodegradation. The model is deterministic, and it was of interest to allow the model to reflect uncertainty in its input variables and to quantify the resulting uncertainty in output contamination estimates. A Monte Carlo interface to the model was developed. This interface gives contaminant concentration information in the form of mean and standard deviation maps. An improved method of estimating contaminant concentrations using this stochastic version of Bioplume II is presented. This method allows contaminant concentration estimates to be obtained for arbitrary spatial locations, not limited to locations at the centers of the cells on the grid used by the model. Additionally, the method allows for calculation of the probability that the contaminant exceeds some user-specified threshold for any loc..

The State of the Bay

TextCharacterization of the Galveston Bay EcosystemGalveston Bay National Estuary ProgramCenter for Research in Water Resource