Uncertainty-Informed Deep Transfer Learning of Perfluoroalkyl and Polyfluoroalkyl Substance Toxicity.

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) pose a significant hazard because of their widespread industrial uses, environmental persistence, and bioaccumulation. A growing, increasingly diverse inventory of PFAS, including 8163 chemicals, has recently been updated by the U.S. Environmental Protection Agency. However, with the exception of a handful of well-studied examples, little is known about their human toxicity potential because of the substantial resources required for in vivo toxicity experiments. We tackle the problem of expensive in vivo experiments by evaluating multiple machine learning (ML) methods, including random forests, deep neural networks (DNN), graph convolutional networks, and Gaussian processes, for predicting acute toxicity (e.g., median lethal dose, or LD50) of PFAS compounds. To address the scarcity of toxicity information for PFAS, publicly available datasets of oral rat LD50 for all organic compounds are aggregated and used to develop state-of-the-art ML source models for transfer learning. A total of 519 fluorinated compounds containing two or more C-F bonds with known toxicity are used for knowledge transfer to ensembles of the best-performing source model, DNN, to generate the target models for the PFAS domain with access to uncertainty. This study predicts toxicity for PFAS with a defined chemical structure. To further inform prediction confidence, the transfer-learned model is embedded within a SelectiveNet architecture, where the model is allowed to identify regions of prediction with greater confidence and abstain from those with high uncertainty using a calibrated cutoff rate.

Watching the Grand Ethiopian Renaissance Dam from a distance: Implications for sustainable water management of the Nile water.

Increased demands for sustainable water and energy resources in densely populated basins have led to the construction of dams, which impound waters in artificial reservoirs. In many cases, scarce field data led to the development of models that underestimated the seepage losses from reservoirs and ignored the role of extensive fault networks as preferred pathways for groundwater flow. We adopt an integrated approach (remote sensing, hydrologic modeling, and field observations) to assess the magnitude and nature of seepage from such systems using the Grand Ethiopian Renaissance Dam (GERD), Africa's largest hydropower project, as a test site. The dam was constructed on the Blue Nile within steep, highly fractured, and weathered terrain in the western Ethiopian Highlands. The GERD Gravity Recovery and Climate Experiment Terrestrial Water Storage (GRACETWS), seasonal peak difference product, reveals significant mass accumulation (43 ± 5 BCM) in the reservoir and seepage in its surroundings with progressive south-southwest mass migration along mapped structures between 2019 and 2022. Seepage, but not a decrease in inflow or increase in outflow, could explain, at least in part, the observed drop in the reservoir's water level and volume following each of the three fillings. Using mass balance calculations and GRACETWS observations, we estimate significant seepage (19.8 ± 6 BCM) comparable to the reservoir's impounded waters (19.9 ± 1.2 BCM). Investigating and addressing the seepage from the GERD will ensure sustainable development and promote regional cooperation; overlooking the seepage would compromise hydrological modeling efforts on the Nile Basin and misinform ongoing negotiations on the Nile water management.

Using SWMM for emergency response planning: A case study evaluating biological agent transport under various rainfall scenarios and urban surfaces.

To assist in emergency preparedness for a biological agent terrorist attack or accidental pathogen release, potential contaminant levels and migration pathways of spores spread by urban stormwater were evaluated using a Storm Water Management Model (SWMM) of U.S. Coast Guard Base Elizabeth City, North Carolina. The high temporal-spatial resolution SWMM model was built using spore concentrations in stormwater runoff from asphalt, grass, and concrete collected from a point-scale field study. The subsequent modeled contamination scenarios included a notional plume release and point releases mimicking the field study under three rainfall conditions. The rainfall scenarios included a 6-hour natural rainfall event on Dec. 8, 2021 and two design storms (2-year and 100-year events). The observed spore concentrations from asphalt and concrete from the actual field experiment were applied to calibrate the washoff parameters in the SWMM model, using an exponential washoff function. The calibrated washoff coefficient (c1) and exponent (c2) were 0.01 and 1.00 for asphalt, 0.05 and 1.45 for grass, and 2.45 and 1.00 for concrete, respectively. The calibrated SWMM model simulated spore concentrations in runoff at times and magnitudes similar to the field study data. In the point release modeled scenario, the concrete surface generated 55.6% higher average spore concentrations than asphalt. Similarly, in the field experiment, a 175% (p < 0.05) higher average spore concentration in surface runoff was observed from concrete than from asphalt. This study demonstrates how SWMM may be used to evaluate spore washoff from urban surfaces under different precipitation amounts, intensities, and durations, and how visualized spatial migration pathways in stormwater runoff may be used for emergency planning and remediation.

Assessing regional hydrology and water quality implications of large-scale biofuel feedstock production in the Upper Mississippi River Basin.

A recent U.S. Department of Energy study estimated that more than one billion tons of biofuel feedstock could be produced by 2030 in the United States from increased corn yield, and changes in agricultural and forest residue management and land uses. To understand the implications of such increased production on water resources and stream quality at regional and local scales, we have applied a watershed model for the Upper Mississippi River Basin, where most of the current and future crop/residue-based biofuel production is expected. The model simulates changes in water quality (soil erosion, nitrogen and phosphorus loadings in streams) and resources (soil-water content, evapotranspiration, and runoff) under projected biofuel production versus the 2006 baseline year and a business-as-usual scenario. The basin average results suggest that the projected feedstock production could change the rate of evapotranspiration in the UMRB by approximately +2%, soil-water content by about -2%, and discharge to streams by -5% from the baseline scenario. However, unlike the impacts on regional water availability, the projected feedstock production has a mixed effect on water quality, resulting in 12% and 45% increases in annual suspended sediment and total phosphorus loadings, respectively, but a 3% decrease in total nitrogen loading. These differences in water quantity and quality are statistically significant (p < 0.05). The basin responses are further analyzed at monthly time steps and finer spatial scales to evaluate underlying physical processes, which would be essential for future optimization of environmentally sustainable biofuel productions.

Projected U.S. drought extremes through the twenty-first century with vapor pressure deficit.

Global warming is expected to enhance drought extremes in the United States throughout the twenty-first century. Projecting these changes can be complex in regions with large variability in atmospheric and soil moisture on small spatial scales. Vapor Pressure Deficit (VPD) is a valuable measure of evaporative demand as moisture moves from the surface into the atmosphere and a dynamic measure of drought. Here, VPD is used to identify short-term drought with the Standardized VPD Drought Index (SVDI); and used to characterize future extreme droughts using grid dependent stationary and non-stationary generalized extreme value (GEV) models, and a random sampling technique is developed to quantify multimodel uncertainties. The GEV analysis was performed with projections using the Weather Research and Forecasting model, downscaled from three Global Climate Models based on the Representative Concentration Pathway 8.5 for present, mid-century and late-century. Results show the VPD based index (SVDI) accurately identifies the timing and magnitude short-term droughts, and extreme VPD is increasing across the United States and by the end of the twenty-first century. The number of days VPD is above 9 kPa increases by 10 days along California's coastline, 30-40 days in the northwest and Midwest, and 100 days in California's Central Valley.

Buffering the impacts of extreme climate variability in the highly engineered Tigris Euphrates river system.

More extreme and prolonged floods and droughts, commonly attributed to global warming, are affecting the livelihood of major sectors of the world's population in many basins worldwide. While these events could introduce devastating socioeconomic impacts, highly engineered systems are better prepared for modulating these extreme climatic variabilities. Herein, we provide methodologies to assess the effectiveness of reservoirs in managing extreme floods and droughts and modulating their impacts in data-scarce river basins. Our analysis of multiple satellite missions and global land surface models over the Tigris-Euphrates Watershed (TEW; 30 dams; storage capacity: 250 km3), showed a prolonged (2007-2018) and intense drought (Average Annual Precipitation [AAP]: < 400 km3) with no parallels in the past 100 years (AAP during 1920-2020: 538 km3) followed by 1-in-100-year extensive precipitation event (726 km3) and an impressive recovery (113 ± 11 km3) in 2019 amounting to 50% of losses endured during drought years. Dam reservoirs captured water equivalent to 40% of those losses in that year. Additional studies are required to investigate whether similar highly engineered watersheds with multi-year, high storage capacity can potentially modulate the impact of projected global warming-related increases in the frequency and intensity of extreme rainfall and drought events in the twenty-first century.

Response of deep aquifers to climate variability.

There is a general agreement that deep aquifers experience significant lag time in their response to climatic variations. Analysis of Temporal Gravity Recovery and Climate Experiment (GRACE), Soil Moisture and Ocean Salinity mission (SMOS), satellite altimetry, stable isotopic composition of groundwater, and precipitation and static global geopotential models over the Nubian Sandstone Aquifer System (NSAS) revealed rapid aquifer response to climate variability. Findings include: (1) The recharge areas of the NSAS (Northern Sudan Platform subbasin) witnessed a dry period (2002-2012), where average annual precipitation (AAP) was modest (85 mm) followed by a wet period (2013-2016; AAP: 107 mm), and during both periods the AAP remained negligible (<10 mm) over the northern parts of the NSAS (Dakhla subbasin); (2) the secular trends in terrestrial water storage (TWS) over the Dakhla subbasin were estimated at -3.8 ±â€¯1.3 mm/yr and + 7.8 ±â€¯1 mm/yr for the dry and wet periods, respectively; (3) spatial variations in TWS values and phase are consistent with rapid groundwater flow from the Northern Sudan Platform subbasin and Lake Nasser towards the Dakhla subbasin during the wet period and from the lake during the dry period; and (4) networks of densely fractured and karstified bedrocks provide preferential pathways for groundwater flow. The proposed model is supported by (1) rapid response in groundwater levels in distant wells (>280 km from source areas) and in soil moisture content in areas with shallow (<2 m) groundwater levels to fluctuations in Lake Nasser surface water, and (2) the isotopic composition (O, H) of groundwater along the preferred pathways, consistent with mixing of enriched (Lake Nasser water or precipitation over Sudan) and depleted (NSAS fossil water) endmembers. Findings provide new insights into the response of large, deep aquifers to climate variability and address the sustainability of the NSAS and similar fossil aquifers worldwide.

IT Infrastructure to support the secondary use of routinely acquired clinical imaging data for research.

We propose an infrastructure for the automated anonymization, extraction and processing of image data stored in clinical data repositories to make routinely acquired imaging data available for research purposes. The automated system, which was tested in the context of analyzing routinely acquired MR brain imaging data, consists of four modules: subject selection using PACS query, anonymization of privacy sensitive information and removal of facial features, quality assurance on DICOM header and image information, and quantitative imaging biomarker extraction. In total, 1,616 examinations were selected based on the following MRI scanning protocols: dementia protocol (246), multiple sclerosis protocol (446) and open question protocol (924). We evaluated the effectiveness of the infrastructure in accessing and successfully extracting biomarkers from routinely acquired clinical imaging data. To examine the validity, we compared brain volumes between patient groups with positive and negative diagnosis, according to the patient reports. Overall, success rates of image data retrieval and automatic processing were 82.5 %, 82.3 % and 66.2 % for the three protocol groups respectively, indicating that a large percentage of routinely acquired clinical imaging data can be used for brain volumetry research, despite image heterogeneity. In line with the literature, brain volumes were found to be significantly smaller (p-value <0.001) in patients with a positive diagnosis of dementia (915 ml) compared to patients with a negative diagnosis (939 ml). This study demonstrates that quantitative image biomarkers such as intracranial and brain volume can be extracted from routinely acquired clinical imaging data. This enables secondary use of clinical images for research into quantitative biomarkers at a hitherto unprecedented scale.