Search for: All records

Hydroclimate and terrestrial hydrology greatly influence the local community, ecosystem, and economy in Alaska and Yukon River Basin. A high‐resolution simulation of the historical climate in Alaska can provide an important benchmark for climate change studies. In this study, we utilized the Regional Arctic System Model (RASM) and conducted coupled land‐atmosphere modeling for Alaska and Yukon River Basin at 4‐km grid spacing. In RASM, the land model was replaced with the Community Terrestrial Systems Model (CTSM) given its comprehensive process representations for cold regions. The microphysics schemes in the Weather Research and Forecast (WRF) atmospheric model were manually tuned for optimal model performance. This study aims to maintain good model performance for both hydroclimate and terrestrial hydrology, especially streamflow, which was rarely a priority in coupled models. Therefore, we implemented a strategy of iterative testing and optimization of CTSM. A multi‐decadal climate data set (1990–2021) was generated using RASM with optimized land parameters and manually tuned WRF microphysics. When evaluated against multiple observational data sets, this data set well captures the climate statistics and spatial distributions for five key weather variables and hydrologic fluxes, including precipitation, air temperature, snow fraction, evaporation‐to‐precipitation ratios, and streamflow. The simulated precipitation shows wet bias during the spring season and simulated air temperatures exhibit dampened seasonality with warm biases in winter and cold biases in summer. We used transfer entropy to investigate the discrepancy in connectivity of hydrologic and energy fluxes between the offline CTSM and coupled models, which contributed to their discrepancy in streamflow simulations. 

Abstract This study investigates the antimicrobial effectiveness of 405 nm light emitting diodes (LEDs) against pathogenicEscherichia coliO157:H7,Listeria monocytogenes,Pseudomonas aeruginosa,SalmonellaTyphimurium, andStaphylococcus aureus, in thin liquid films (TLF) and on solid surfaces. Stainless steel (SS), high density polyethylene (HDPE), low density polyethylene (LDPE), and borosilicate glass were used as materials typically encountered in food processing, food service, and clinical environments. Anodic aluminum oxide (AAO) coupons with nanoscale topography were used, to evaluate the effect of topography on inactivation. The impact of surface roughness, hydrophobicity, and reflectivity on inactivation was assessed. A 48 h exposure to 405 nm led to reductions ranging from 1.3 (E. coli) to 5.7 (S. aureus) log CFU in TLF and 3.1 to 6.3 log CFU on different solid contact surfaces and packaging materials. All inactivation curves were nonlinear and followed Weibull kinetics, with better inactivation predictions on surfaces (0.89 ≤ R² ≤ 1.0) compared to TLF (0.76 ≤ R² ≤ 0.99). The fastest inactivation rate was observed on small nanopore AAO coupons inoculated withL. monocytogenesandS. aureus, indicating inactivation enhancing potential of these surfaces. These results demonstrate significant promise of 405 nm LEDs for antimicrobial applications in food processing and handling and the healthcare industry. 

Abstract Amphiphilic copolymers (AP) represent a class of novel antibiofouling materials whose chemistry and composition can be tuned to optimize their performance. However, the enormous chemistry‐composition design space associated with AP makes their performance optimization laborious; it is not experimentally feasible to assess and validate all possible AP compositions even with the use of rapid screening methodologies. To address this constraint, a robust model development paradigm is reported, yielding a versatile machine learning approach that accurately predicts biofilm formation by Pseudomonas aeruginosa on a library of AP. The model excels in extracting underlying patterns in a “pooled” dataset from various experimental sources, thereby expanding the design space accessible to the model to a much larger selection of AP chemistries and compositions. The model is used to screen virtual libraries of AP for identification of best‐performing candidates for experimental validation. Initiated chemical vapor deposition is used for the precision synthesis of the model‐selected AP chemistries and compositions for validation at solid–liquid interface (often used in conventional antifouling studies) as well as the air–liquid–solid triple interface. Despite the vastly different growth conditions, the model successfully identifies the best‐performing AP for biofilm inhibition at the triple interface. 

Due to the emergence of wide-spread infectious diseases, there is a heightened need for antimicrobial and/or antifouling coatings that can be used to prevent infection and transmission in a variety of applications, ranging from healthcare devices to public facilities. While antimicrobial coatings kill pathogenic bacteria upon contact with the surface, the antimicrobial function alone often lacks long-term effectiveness due to the accumulation of dead cells and their debris on the surface, thus reducing the performance of the coating over time. Therefore, it is desirable to develop coatings with the dual functions of antimicrobial efficacy and fouling resistance, in which antifouling coatings provide the added benefit of preventing the adhesion of dead cells and debris. Leveraging the outstanding antifouling properties of zwitterionic coatings, we synthesized copolymers with this antimicrobial-antifouling dual function by immobilizing lysozyme, a common antimicrobial enzyme, to the surface of a pyridinium-based zwitterionic copolymer. Specifically, poly(4-vinylpyridine- co -pentaflurophenyl methacrylate- co -divinyl benzene) [P(4VP-PFPMA-DVB)] thin films were synthesized by an all-dry vapor deposition technique, initiated Chemical Vapor Deposition, and derivatized using 1,3-propane sultone to obtain sulfobetaine moieties. Lysozyme, known to hydrolyze polysaccharides in the cell wall of Gram-positive bacteria, was immobilized by forming amide bonds with the copolymer coating via nucleophilic substitution of the pentafluorophenyl group. The antifouling and antibacterial performance of the novel lysozyme-zwitterionic coating was tested against Gram-positive Bacillus subtilis and Gram-negative Pseudomonas aeruginosa . A reduction in surface adhesion of 87% was achieved for P. aeruginosa , and of 75% for B. subtilis , when compared to a common poly(vinyl chloride) surface. The lysozyme-zwitterionic coating also deactivated 67% of surface-attached Gram-positive bacteria, B. subtilis . This novel dual-function material can produce anti -infection surfaces for medical devices and surgical tools, personal care products, and surfaces in public facilities. 

Abstract Arctic hydrology is experiencing rapid changes including earlier snow melt, permafrost degradation, increasing active layer depth, and reduced river ice, all of which are expected to lead to changes in stream flow regimes. Recently, long-term (>60 years) climate reanalysis and river discharge observation data have become available. We utilized these data to assess long-term changes in discharge and their hydroclimatic drivers. River discharge during the cold season (October–April) increased by 10% per decade. The most widespread discharge increase occurred in April (15% per decade), the month of ice break-up for the majority of basins. In October, when river ice formation generally begins, average monthly discharge increased by 7% per decade. Long-term air temperature increases in October and April increased the number of days above freezing (+1.1 d per decade) resulting in increased snow ablation (20% per decade) and decreased snow water equivalent (−12% per decade). Compared to the historical period (1960–1989), mean April and October air temperature in the recent period (1990–2019) have greater correlation with monthly discharge from 0.33 to 0.68 and 0.0–0.48, respectively. This indicates that the recent increases in air temperature are directly related to these discharge changes. Ubiquitous increases in cold and shoulder-season discharge demonstrate the scale at which hydrologic and biogeochemical fluxes are being altered in the Arctic. 

Abstract Water quality and freshwater ecosystems are affected by river discharge and temperature. Models are frequently used to estimate river temperature on large spatial and temporal scales due to limited observations of discharge and temperature. In this study, we use physically based river routing and temperature models to simulate daily discharge and river temperature for rivers in 138 basins in Alaska, including the entire Yukon River basin, from 1990–2021. The river temperature model was optimized for ice free months using a surrogate‐based model optimization method, improving model performance at uncalibrated river gages. A common statistical model relating local air and water temperature was used as a benchmark. The physically based river temperature model exhibited superior performance compared to the benchmark statistical model after optimization, suggesting river temperature model optimization could become more routine. The river temperature model demonstrated high sensitivity to air temperature and model parameterization, and lower sensitivity to discharge. Validation of the models showed a Kling‐Gupta Efficiency of 0.46 for daily river discharge and a root mean square error of 2.04°C for daily river temperature, improving on the non‐optimized physical model and the benchmark statistical model, which had root mean square errors of 3.24 and 2.97°C, respectively. The simulation shows that rivers in northern Alaska have higher maximum summer temperatures and more variability than rivers in the Central and Southern regions. Furthermore, this framework can be readily adapted for use across models and regions. 

Abstract Crucial to the assessment of future water security is how the land model component of Earth System Models partition precipitation into evapotranspiration and runoff, and the sensitivity of this partitioning to climate. This sensitivity is not explicitly constrained in land models nor the model parameters important for this sensitivity identified. Here, we seek to understand parametric controls on runoff sensitivity to precipitation and temperature in a state‐of‐the‐science land model, the Community Land Model version 5 (CLM5). Process‐parameter interactions underlying these two climate sensitivities are investigated using the sophisticated variance‐based sensitivity analysis. This analysis focuses on three snow‐dominated basins in the Colorado River headwaters region, a prominent exemplar where land models display a wide disparity in runoff sensitivities. Runoff sensitivities are dominated by indirect or interaction effects between a few parameters of subsurface, snow, and plant processes. A focus on only one kind of parameters would therefore limit the ability to constrain the others. Surface runoff exhibits strong sensitivity to parameters of snow and subsurface processes. Constraining snow simulations would require explicit representation of the spatial variability across large elevation gradients. Subsurface runoff and soil evaporation exhibit very similar sensitivities. Model calibration against the subsurface runoff flux would therefore constrain soil evaporation. The push toward a mechanistic treatment of processes in CLM5 have dampened the sensitivity of parameters compared to earlier model versions. A focus on the sensitive parameters and processes identified here can help characterize and reduce uncertainty in water resource sensitivity to climate change. 

To reduce the severe health risk and the huge economic impact associated with the fomite transmission of SARS-CoV-2, an imidazolium-based zwitterionic polymer was designed, synthesized, and demonstrated to achieve contact deactivation of a human coronavirus under dry ambient conditions that resemble fomite transmission. The zwitterionic polymer further demonstrated excellent antifouling properties, reducing the adhesion of coronavirus and the formation of bacteria biofilms under wetted conditions. The polymer was synthesized using a substrate-independent and solvent-free process, leveraging an all-dry technique named initiated chemical vapor deposition (iCVD). The broad applicability of this approach was demonstrated by applying the polymer to a range of substrates that are curved and/or with high-aspect-ratio nano/microporous structures, which remained intact after the coating process. The zwitterionic polymer and the synthesis approach reported here present an effective solution to mitigate viral transmission without the need for manual disinfection, reducing the health and economic impact of the ongoing pandemic.