Browsing by Subject "Precipitation"

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    Artificial Gut Simulator for Simultaneous Evaluation of Drug Dissolution and Absorption
    (2021-08) Harish Jain, Krutika
    Over the past three decades, high-throughput screening has resulted in a discovery pipeline consisting mostly of highly potent but lipophilic compounds exhibiting poor aqueous solubility and classified as Biopharmaceutics Classification System (BCS) Class II. Since poor solubility limits absorption and bioavailability, efforts have been made to develop supersaturating delivery systems such as amorphous solid dispersions (ASD) that enhance the apparent solubility of the drug without sacrificing its thermodynamic activity. The performance of these dispersions is often tested in ‘closed’, non-sink compendial dissolution testing apparatus that lacks an absorptive sink. The supersaturated solution generated upon ASD dissolution is metastable with respect to the stable crystalline phase and can undergo amorphous and/or crystalline precipitation. The rate of precipitation depends upon the degree of supersaturation. In the absence of absorption, during non-sink dissolution testing, high supersaturation can drive more precipitation than that which occurs in vivo where continuous drug absorption from the intestinal lumen decreases drug concentration, which in turn decreases the driving force for precipitation. Unsurprisingly, many in vitro studies with non-sink dissolution testing have failed to predict the in vivo performance of formulations of BCS-II drugs, which by definition, have high intestinal permeability. A simultaneous dissolution and absorption testing apparatus called the side-by-side diffusion cell allows drug to diffuse from the donor to a receiver compartment across a membrane that separates the two. However, small surface area of the membrane results in very low rates of drug absorption and very long, unphysiological experimental time scales. The first goal of this study was to develop and validate an artificial gut simulator apparatus (AGS) consisting of a hollow fiber-based absorption module suspended in the drug donor. The hollow fibers provide a large surface area for absorption, significantly improving mass transfer rate of drugs from the donor into the aqueous receiver media in the hollow fiber lumen. Continuous pumping of the drug-free receiver media into the lumen helps maintain an absorptive sink. A theory for mass transfer across the hollow fiber membrane was developed and validated using caffeine. Physiological rate of drug absorption was attained by tuning the AGS operating parameters per the theoretical model. This is an important step in developing a biorelevant test for BCS-II drugs. The next goal of this project was to understand how absorption impacts dissolution of ASDs and subsequent crystallization from supersaturated solutions of a model BCS-II compound, ketoconazole. Relative to a non-sink ‘control’, continuous drug removal by absorption enhanced ASD dissolution and significantly decreased both amorphous and crystalline precipitation. This can be attributed to both a decreased driving force for precipitation due to lower drug concentration in the AGS donor as well as to redissolution of any precipitate that is formed to replenish the drug in solution lost to absorption. On the other hand, polymer excipient added to the ASD to stabilize the drug against crystallization during storage and dissolution reduced the drug’s absorption rate by possibly interacting favorably with the free drug species and reducing the drug’s thermodynamic activity. Simple analytical techniques used in conjunction with the AGS helped decouple and understand the impact of dissolution, precipitation and speciation on absorption and vice-a-versa. The final goal of this project was to implement a scheme to establish in vitro/in vivo correlation with another BCS-II drug, dipyridamole, by inputting the drug concentration absorbed by the AGS receiver media into a compartment-based disposition model to ultimately predict the in vivo plasma concentration-time profile of the drug. The human intestinal absorption rate constant of dipyridamole, determined from Caco-2 cell monolayer permeability coefficient, was used to tune the AGS. Gastric emptying was simulated at a physiological rate to ensure a physiological rate of supersaturation generation as the weakly basic dipyridamole is solubilized and emptied from acidic gastric compartment into a neutral duodenum. This methodology of simulating gastric emptying and absorption enabled accurate prediction of drug in vivo intestinal and plasma concentration-time profiles. This approach and apparatus is anticipated to be of great utility during drug product development for screening and optimization of potential oral formulations.
  • Thumbnail Image
    Item
    Climate and Land Use Change Impacts on N-Loads in Iowa Rivers and Remediation of Tile Water with an Anion-Exchange Resin
    (2017-12) Wolf, Kari
    This research was conducted to (1) better understand the underlying reasons for a continuous increase in nitrate loads in the Gulf of Mexico, and (2) if an industrial anion resin can be used at a field scale to reduce N losses from tile-drained watersheds to the rivers. The first objective was accomplished through statistical analyses of climate and land use change impacts on streamflow, baseflow, flow weighted nitrate-N concentrations (FWNC) and nitrate-N-loads in three major rivers of Iowa. The rivers included the Des Moines River, the Iowa River, and the Raccoon River. The results from this analysis showed that natural log of annual streamflow, baseflow, and N-loads were primarily controlled by the precipitation in the corresponding watersheds. For streamflow and baseflow, this precipitation corresponded to the current years as well as previous year precipitation. Previous year precipitation reflected the lack or excess presence of stored water in the soil and its consequences in terms of increased or decreased overland flow, infiltration, and percolation processes. For N loads, the precipitation effect was limited to one-year precipitation for the Des Moines and the Iowa Rivers and two-year precipitation for the Raccoon River. There were individual years when streamflow, baseflow, and N loads were impacted by up to three previous years’ precipitation. Effect of land use change, in terms of increased soybean area, had no effect on annual streamflow, annual baseflow, annual flow-weighted N concentrations or annual N-loads in all three rivers. Additional regression analysis of FWNC and N-loads from 1987-2001 showed no effect of N fertilizer use as an explanatory variable for any of the three watersheds. Statistical analysis of the combined annual data from all three rivers showed that there was a unique relationship between the natural log of streamflow, the baseflow, and the N-yield (N-loads/watershed area) versus the precipitation. The precipitation effects were both in terms of current year precipitation and the previous year precipitation. The coefficient of determination (R2) of Ln(streamflow), Ln(baseflow) and Ln(N load) with precipitation for the combined data were 0.74, 0.70 and 0.54, respectively. Limited scatter in the N-yield data at a given annual precipitation level over three rivers suggested that variation in annual precipitation has much bigger impact on N losses than the differences in cultural or cropping practices between the three river watersheds over the study period. Considering that there has been a 10-15% increase in precipitation in the Upper Midwestern United States in recent years, the combined N Yield relationship with precipitation would suggest that the recent increases in N-loads or increased hypoxic area in the Gulf of Mexico are likely due to increased precipitation. Statistical analysis of N-loads over a shorter period of time (1987-2001) also showed that changes in fertilizer use had no effect on river N-loads. Regression analysis of monthly streamflow, baseflow, N-loads and FWNC concentration showed that natural log of streamflow, baseflow, and N-loads were generally linearly related to precipitation in a given month and a few prior months. In some cases earlier in the season, these variables were also related to previous year’s precipitation, an indication that some of the past water stored in the soil both above and below the drain tile is interacting with current months precipitation and affecting the streamflow and baseflow. In most cases, there was no effect of soybean area on natural log of monthly streamflow, baseflow, or N-loads. A field test on the use of anion exchange resin to remediate tile water for nitrate showed that nitrate adsorption by the resin is instantaneous. The efficiency of the resin to retain nitrate varied 7-46%. This efficiency generally decreased with time due to the presence of sulfate, bicarbonates, and organic anions in tile water, which competed with nitrate ions for adsorption to the resin. In some instances, nitrate concentration in the percolating water was higher than the tile water most likely due to the expulsion of adsorbed nitrate ions on the resin by sulfate ion in the tile water. The results also showed that potassium chloride (KCl) is an effective resin-regenerating agent and provides a means to recycle wastewater as KNO3 fertilizer back on land. Although the use of anion exchange resin is an attractive alternative to passive technologies like bioreactors, saturated buffers, control drainage, etc. for remediating nitrate in tile water, it also presents some challenges in its use under field conditions. These challenges include the fouling up of the resin by sediment, sulfate, bicarbonate, and organic anions in tile water; costs associated with buying of resin and regenerating salt (KCl versus NaCl); need for a large volume of clean water for cleaning of resin; and the difficulty of treating large volume of tile water in-situ. However, the feasibility study shows that small-scale units similar to home water softener can be developed for individual homes in rural area where groundwater may be high in NO3-N concentration and NO3-N remediation is needed.
  • Thumbnail Image
    Item
    Climate of Minnesota Part VII - Areal Distribution and Probabilities of Precipitation in the Minneapolis-St. Paul Metropolitan Area
    (Minnesota Agricultural Experiment Station, 1973) Baker, Donald G.; Kuehnast, Earl L.
  • Thumbnail Image
    Item
    Hydro-meteorological inverse problems via sparse regularization: advanced frameworks for rainfall spaceborne estimation
    (2013-09) Ebtehaj, Mohammad
    The past decades have witnessed a remarkable emergence of new spaceborne and ground-based sources of multiscale remotely sensed geophysical data. Apart from applications related to the study of short-term climatic shifts, availability of these sources of information has improved dramatically our real-time hydro-meteorological forecast skills. Obtaining improved estimates of hydro-meteorological states from a single or multiple low-resolution observations and assimilating them into the background knowledge of a prognostic model have been a subject of growing research in the past decades. In this thesis, with particular emphasis on precipitation data, statistical structure of rainfall images have been thoroughly studied in transform domains (i.e., Fourier and Wavelet). It is mainly found that despite different underlying physical structure of storm events, there are general statistical signatures that can be robustly characterized and exploited as a prior knowledge for solving hydro-meteorological inverse problems such rainfall downscaling, data fusion, retrieval and data assimilation. In particular, it is observed that in the wavelet domain or derivative space, rainfall images are sparse. In other words, a large number of the rainfall expansion coefficients are very close to zero and only a small number of them are significantly non-zero, a manifestation of the non-Gaussian probabilistic structure of rainfall data. To explain this signature, relevant family of probability models including Generalized Gaussian Density (GGD) and a specific class of conditionally linear Gaussian Scale Mixtures (GSM) are studied. Capitalizing on this important but overlooked property of precipitation, new methodologies are proposed to optimally integrate and improve resolution of spaceborne and ground-based precipitation data. In particular, a unified framework is proposed that ties together the problems of downscaling, data fusion and data assimilation via a regularized variational approach, while taking into account the underlying sparsity in an appropriately chosen transform domain. This framework seeks solutions beyond the paradigm of the classic least squares by imposing a proper regularization. The results suggest that sparsity-promoting regularization can reduce uncertainty of estimation in hydro-meteorological inverse problems of downscaling, data fusion, and data assimilation. In continuation of the proposed methodologies, we also introduce a new data driven framework for multisensor spaceborne rainfall retrieval problem and present some preliminary and promising results.
  • Thumbnail Image
    Item
    Machine Learning for Advancing Spaceborne Passive Microwave Remote Sensing of Snowfall
    (2022-09) Vahedizade, Sajad
    Falling snow is one of the key elements of the water and energy cycle that occurs in response to a complex cascade of macro and microphysical processes. While an accurate observation of spatiotemporal variability of snowfall is lacking due to the sparse network of ground-based gauges and their intrinsic challenges, remote sensing from spaceborne satellites has provided a global picture of snowfall through near-global observations. Bayesian passive microwave (PMW) retrievals of snowfall have been developed to detect precipitation phase and retrieve its rate using coincident data from the active and passive sensors onboard the CloudSat and the Global Precipitation Measurement (GPM) satellites. These Bayesian techniques often rely on mathematical matching of the observed vectors of brightness temperature (TB) with an a priori database of precipitation profiles and their corresponding TBs. In the present dissertation, we analyzed the effects of surface and atmospheric state variables on the PMW retrievals through Silhouette Coefficient (SC) analysis. The Neyman-Pearson (NP) hypothesis testing is employed to improve these retrievals by conditioning them to the associated physical variables that affect the PMW signatures including the cloud total liquid (LWP) and ice water path (IWP). The presented approach determines thresholds for IWP and LWP that enable identification of non-snowing and snowing clouds, which can mislead the retrieval algorithms to falsely detect or miss the snowfall events. Inspired by advances in deep learning approaches, a dense and deep neural network architecture is proposed. The presented framework first detects the precipitation occurrence and its phase, and then estimates its intensity using key physical variables including those capturing cloud microphysical properties. The results suggest the proposed framework could effectively reduce the uncertainties in the retrievals and improve their accuracy compared to the existing reanalysis data and official GPM products.
  • Thumbnail Image
    Item
    Modeling the impact of iIrrigation on precipitation over the Great Plains.
    (2011-08) Harding, Keith John Iliff
    Since World War II, the rapid expansion of irrigation throughout the Great Plains has threatened the sustainability of the Ogallala Aquifer. Irrigation has been shown to modify the surface energy and water budgets over the Great Plains by altering the partitioning of latent and sensible heating. An increase in latent heating from irrigation contributes to a cooler and more humid surface, which has competing impacts on convection. In this study, the Weather Research and Forecasting model was modified to simulate the effects of irrigation at sub-grid scales. Nine April-October simulations were completed for different hydrologic conditions over the Great Plains. Data from these simulations was assimilated into a back-trajectory analysis to identify where evapotranspired moisture from irrigated fields predominantly falls out as precipitation. May through September precipitation increased on average over the Great Plains by 4.97 mm (0.91%), with the largest increases during wet years (6.14 mm; 0.98%) and the smallest increases during drought years (2.85 mm; 0.63%). Large precipitation increases occurred over irrigated areas during normal and wet years, with decreases during drought years. On average, only 15.8% of evapotranspired moisture from irrigated fields fell out as precipitation over the Great Plains, resulting in 5.11 mm of May-September irrigation-induced precipitation. The heaviest irrigation-induced precipitation occurred over north-central Nebraska, coincident with simulated and observed precipitation increases. While irrigation resulted in localized and region-wide increases in precipitation, large evapotranspiration increases suggest that irrigation contributes to a net loss of water in the Great Plains.