Forests have the ability to sequester carbon from our atmosphere. Soil respiration (Rs) plays a role in a forest's ability to do so as it is a significant source of carbon dioxide back to the atmosphere. Therefore, understanding the process of Rs under varying conditions is gaining more attention. As of now we have a relatively good understanding of Rs under managed forest ecosystems such as pine plantations. This particular study examined Rs under different overstories and understories in a high elevation Southern Appalachian forest in order to get a better understanding of Rs under a natural hardwood system. The four vegetation types under consideration were an eastern hemlock (Tsuga canadensis L. Carriere) dominated overstory, a hardwood overstory with little to no understory, a mountain laurel (Kalmia latifolia L.) dominated understory, and a cinnamon fern (Osmundastrum cinnamomeum (L.) C.Presl) dominated understory. Differing temporal variations of Rs were observed under the vegetation types. We found monthly differences in rates among vegetation type however, an overall annual difference in Rs rates between vegetation types was not observed. This simply indicates the importance of observing Rs under different time scales to get a better understanding of its variation. We also calculated vegetation indices from remotely-sensed data to explore any relationships to Rs as well as if the indices themselves could improve out model. A vegetation index is a number that is calculated for every pixel in a remotely sensed image and represents plant vigor or abundance. Few significant relationships were found between the indices and Rs. Future work may want to better understand vegetation indices' spatial extent and accuracy in order to find whether they may be beneficial in Rs estimation. Understanding the influence of varying vegetation type and soil temperature and moisture on Rs will ultimately improve our ability to predict what drives changes in carbon fluxes. / Master of Science / Forests have the ability to sequester carbon from our atmosphere. Soil respiration (Rs) plays a role in a forest’s ability to do so as it is a significant source of carbon dioxide back to the atmosphere. Therefore, understanding the process of Rs under varying conditions is gaining more attention. As of now we have a relatively good understanding of Rs under managed forest ecosystems such as pine plantations. This particular study examined Rs under different overstories and understories in a high elevation Southern Appalachian forest in order to get a better understanding of Rs under a natural hardwood system. The four vegetation types under consideration were an eastern hemlock (Tsuga canadensis L. Carriere) dominated overstory, a hardwood overstory with little to no understory, a mountain laurel (Kalmia latifolia L.) dominated understory, and a cinnamon fern (Osmundastrum cinnamomeum (L.) C.Presl) dominated understory. Differing temporal variations of Rs were observed under the vegetation types. We found monthly differences in rates among vegetation type however, an overall annual difference in Rs rates between vegetation types was not observed. This simply indicates the importance of observing Rs under different time scales to get a better understanding of its variation. We also calculated vegetation indices from remotely-sensed data to explore any relationships to Rs as well as if the indices themselves could improve out model. A vegetation index is a number that is calculated for every pixel in a remotely sensed image and represents plant vigor or abundance. Few significant relationships were found between the indices and Rs. Future work may want to better understand vegetation indices’ spatial extent and accuracy in order to find whether they may be beneficial in Rs estimation. Understanding the influence of varying vegetation type and soil temperature and moisture on Rs will ultimately improve our ability to predict what drives changes in carbon fluxes.

Moisture from concrete has been estimated to be responsible for over $1 billion annually from damages in floor coverings. To prevent damages, flooring manufacturers require installers to test concrete moisture levels to determine if the concrete has dried sufficiently to receive flooring or covering. Two of the main tests used in the United States to determine concrete moisture levels are moisture vapor emissions rate (MVER) tests and relative humidity (RH) tests. Changes in ambient temperature can affect the results of both RH and MVER tests. The goal of this study was to investigate the effects of ambient temperature changes on the RH of concrete, and compare the sensitivity of RH measurements to the results of MVER tests at the same ambient temperature. The RH of concrete was measured at 20%, 40%, 60%, and 80% of depth in each sample and tracked over a period of 24 days to develop drying curves at each depth, and drying profiles of each sample. The changes in concrete RH due to a change in ambient temperature were predicted using the psychrometric process and a model developed during this study. Due to size constraints on the concrete samples, ASTM 1869 had to be altered during the MVER tests. Typical RH change in the concrete samples was under 4% RH after either an increase or decrease in an ambient temperature of 5.5°C (10°F). The psychrometric process predicted that the concrete RH would change between 20% - 40% RH after the ambient temperature changed by 5.5°C. Psychrometric properties were not able to full describe the behavior of air in concrete pores so a new model was created to better predict the change in concrete RH after a change in ambient temperature. The developed model was able to predict concrete RH change within 5% error over the range of tested temperatures. / Master of Science

Many applications of metal-organic frameworks (MOFs) are highly dependent on their structures. The type and consistency of structure inform their properties. Zinc-based MOFs are applicable in different fields because of the low toxicity of zinc materials and are therefore also useful for catalysis. While MOF-5, a zinc-based MOF with carboxylate linkers is moisture intolerant, a variant of this is moisture tolerant. The introduction of a nitrogen-based linker in the zinc MOF which renders the structure moisture-tolerant. This material has not been explored as much, despite its multifunctional properties. Furthermore, the growth of Zn-based bimetallics of this MOF has not yet been explored. In this work, I studied the synthesis of this zinc-based moisture-tolerant MOF-5 as a thin film using a simple, fast, and cost-effective layer-by-layer wet synthesis method on different substrate surfaces. I successfully synthesized a series of bimetallics of this MOF as thin films on an untreated silicon wafer substrate. The successful synthesis of these materials was confirmed using X-ray photoelectron spectroscopy, X-ray diffraction, and Raman spectroscopy techniques. Additionally, some software data analysis tools were used for the characterization of the surface of the thin films to quantify the chemical composition. Future applications of these materials will be as sorbent materials for the capture of CO2 and its subsequent conversion to CO which is a synthesis gas for different useful materials like fuel and other chemical materials.

Accurate measurement of soil moisture (SM) has a significant impact on agricultural production, hydrological modeling, forestry, horticulture, waste management, and other environmental fields. Particularly in precision agriculture (PA), high spatiotemporal resolution information about surface SM is crucial. However, the use of invasive SM probes and other sensors is expensive and requires extensive manpower. Moreover, these intrusive techniques provide point measurements and are unsuitable for large agricultural fields. As an alternative, this dissertation explores the remote sensing of surface SM by utilizing the surface reflectivity estimated from global navigation satellite systems reflectometry (GNSS-R) data acquired through smartphones and off-the-shelf, cost-effective U-blox global navigation satellite systems (GNSS) receivers. To estimate surface reflectivity, the GNSS receivers are attached underneath a small unmanned aircraft system (UAS), which flies over agricultural fields. Additionally, this dissertation investigates a fully custom UAS-based dual-polarized L-band microwave radiometric measurement technique over agricultural areas to estimate surface brightness temperature (����). The radiometer measures surface emissivity as ����, allowing for the estimation of surface SM while considering the detection and removal of radio frequency interference (RFI) from the radiometric measurements. This radiometer processes the data in near real-time onboard the UAS, collecting raw in-phase and quadratic (I&Q) signals across the study field. This feature mitigates the RFI onboard and significantly reduces post-processing time. In summary, this study highlights the utilization of smartphones and semi-custom GNSS receivers in conjunction with UAS-based GNSS-R techniques and UAS-based L-band microwave radiometry for the estimation of surface reflectivity and ����. The radiometric measurement of surface emissivity is related to surface reflectivity through the relationship (Emissivity = 1 -Reflectivity).

<p dir="ltr"><a href="" target="_blank">Watermelon [<i>Citrullus lanatus </i>(Thunb.) Matsum. & Nakai] is one of the world's top three most consumed fruits.</a> Indiana cultivates approximately 7,000 acres of watermelons every year, ranking 6^th in the nation. More than 70% of this production is concentrated in and around Knox County, making Southern Indiana a key region for watermelon production in the States. Despite its significance, watermelon production faces many challenges, including erratic rainfall patterns exacerbated by climate change. Enhanced irrigation management has emerged as a critical strategy in mitigating negative environmental effects and in optimizing fertilizer applications.</p><p dir="ltr">Currently, Southern Indiana farmers have incorporated different irrigation and fertilization practices into watermelon production, yet the effects on production outcomes remain poorly understood. To bridge this gap in knowledge, this study aims to explore the effects of existing practices on watermelon yield and develop irrigation guidelines for optimal production in the region. The experiment was conducted at Southwest Purdue Agricultural Center, Vincennes, Indiana, in 2022 and 2023. Four treatments were applied: High Irrigation, Low Irrigation, No Irrigation, and Fertigation. Fertigation treatment received the same water application as the High Irrigation treatment. Fertilizers were applied pre-plant in the High, Low, and No irrigation treatments, while frequent fertigation was applied to the Fertigation Treatment. Soil moisture sensors measuring volumetric water content were used for irrigation decisions. In 2022, the irrigation thresholds were set at 15% water depletion at 1-ft depth for High Irrigation and Fertigation treatment, and 2-ft depth for Low Irrigation. In 2023, the irrigation threshold for Low Irrigation was adjusted to 40% water depletion at 1-ft depth.</p><p dir="ltr">While soil moisture levels in the bed at the different depths varied notably among treatments, no significant differences in yield by weight were observed. The minimal impact of irrigation on watermelon yield suggests that rainfall provides sufficient water, preventing yield-reducing stress. However, the Fertigation and High Irrigation treatments yielded more fruit than the Low Irrigation and No Irrigation treatments. The dry periods in both years coincided with the watermelon fruit setting stages that may have contributed to the lower fruit set in the Low Irrigation and No Irrigation treatments. Fertigation showed a higher early yield than the other treatments in 2022. Analysis of soil and tissue nitrogen levels indicated that sole nitrogen application before planting could result in excessive soil nitrogen levels during vegetative growth. This excess nitrogen might delay flowering and harvest. This project offers insights into enhancing irrigation and fertilization practices for watermelon production in southern Indiana.</p>

Hitherto, most research conducted to monitor agricultural drought on the African continent has focused only on meteorological aspects, with less attention paid to soil moisture, which describes agricultural drought. Satellite missions dedicated to soil moisture monitoring must be used with caution across various scales. The rainfed sector of Sudan takes great importance due to it is high potential to support national food security. El Gedaref state is significant in Sudan given its potentiality of the agricultural sector under a mechanized system, where crop cultivation supports livelihood sources for about 80% of its population and households, directly through agricultural production and indirectly through labor workforce. The state is an essential rainfed region for sorghum production, located within Sudan's Central Clay Plain (CCP). Enhancing soil moisture estimation is key to boosting the understanding of agricultural drought in the farming lands of Sudan. Soil moisture measuring stations/sensors networks do not exist in the El Gedaref agricultural rainfed sector. The literature shows a significant gap in whether soil moisture is sufficient to meet the estimated water demands of cultivation or the start of the growing season. The purpose of this study is to focus principally on agricultural drought. The soil moisture data retrieved from the Soil Moisture Active Passive (SMAP) mission launched by NASA in 2015 were compared against in situ data measurements over the agricultural lands. In situ points (at 5 cm, 10 cm, and 20 cm depths) corresponding to 9×9 km SMAP pixel foot-print are rescaled to conduct a point-to-pixel evaluation of SMAP product over two locations, namely Samsam and Kilo-6, during the rainy season 2018. Four errors were measured; Root Mean Squared Error (RMSE), Mean Bias Error (MBE), unbiased RMSE (ubRMSE), Mean Absolute Bias Error (MABE), and the coefficient of determination R2. SMAP improve (significantly at the 5% level for SM). The results indicated that the SMAP product meets its soil moisture accuracy requirement at the top 5 cm and in the root zone (10 and 20 cm) depths at Samsam and Kilo-6. SMAP demonstrates higher performance indicated by the high R2 (0.96, 0.88, and 0.97) and (0.85, 0.94, and 0.94) over Samsam and Kilo-6, respectively, and met its accuracy targeted by SMAP retrieval domain at ubRMSE 0.04 m3m-3 or better in all locations, and most minor errors (MBE, MABE, and RMSE). The possibility of using SMAP products was discussed to measure agricultural drought and its impacts on crop growth during various growth stages in both locations and over the CCP entirely. The croplands of El Gedaref are located within the tropical savanna (AW, categorization following the Köppen climate classification), warm semi-arid climate (BSh), and warm desert climate (BWh). The areas of interest are predominantly rainfed agricultural lands, vulnerable to climate change and variability. The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), SMAP at the top surface of the soil and the root zone, and Soil Water Deficit Index (SWDI) derived from SMAP were analyzed against the Normalized Difference Vegetation Index (NDVI). The results indicate that the NDVI val-ues disagree with rainfall patterns at the dekadal scale. At all isohyets, SWDI in the root zone shows a reliable and expected response of capturing seasonal dynamics concerning the vegetation index (NDVI) over warm desert climates during 2015, 2016, 2017, 2018, and 2019, respectively. It is concluded that SWDI can be used to monitor agricultural drought better than rainfall data and SMAP data because it deals directly with the available water content of the crops. SWDI monitoring agricultural drought is a promising method for early drought warning, which can be used for agricultural drought risk management in semi-arid climates. The comparison between sorghum yield and the spatially distributed water balance model was assessed according to the length of the growing period. Late maturing (120 days), medium maturing (90-95 days), and early maturing variety (80-85 days). As a straightforward crop water deficit model. An adapted WRSI index was developed to characterize the effect of using different climatic and soil moisture remote sensing input datasets, such as CHIRPS rainfall, SMAP soil moisture at the top 5 cm and the root zone, MODIS actual evapotranspiration on key WRSI index parameters and outputs. Results from the analyses indicated that SMAP best captures season onset and length of the growing period, which are critical for the WRSI index. In addition, short-, medium-, and long-term sorghum cultivar planting scenarios were con-sidered and simulated. It was found that over half of the variability in yield is explained by water stress when the SMAP at root zone dataset is used in the WRSI model (R2=0.59–0.72 for sorghum varieties of 90–120 days growing length). Overall, CHIRPS and SMAP root zone show the highest skill (R2=0.53–0.64 and 0.54–0.56, respectively) in capturing state-level crop yield losses related to seasonal soil moisture deficit, which is critical for drought early warning and agrometeorological risk applications. The results of this study are important and valuable in supporting the continued development and improvement of satellite-based soil moisture sensing to produce higher accuracy soil moisture products in semi-arid regions. The results also highlight the growing awareness among various stakeholders of the impact of drought on crop production and the need to scale up adaptation measures to mitigate the adverse effects of drought.

An experimental investigation was carried out to determine the feasibility of using thermal conductivity measurements to detect moisture concentrations in a highly porous glass fiber insulation. A new technique employing thermistor probes was used to measure thermal conductivity over a range of low moisture contents. The results indicate that the material's thermal conductivity is a strong nonlinear function of the moisture concentration. The sensitivity of the moisture content to thermal conductivity is greatest for moisture contents less than 25 per cent for the material tested. A numerical procedure for predicting the temperature and moisture distributions in a highly porous material is detailed. / Ph. D.

The costs of cleaning fine coal are substantially higher than those of cleaning coarse coal. Therefore, many many coal companies in the U.S. choose to discard fine coal (150 micron x 0) by means of 6-inch diameter hydrocyclones. The cyclone overflows are stored in fine coal impoundments, which create environmental concerns and represent loss of valuable national resources. The major component of the high costs of cleaning fine coal is associated with the difficulty in fine coal dewatering. Therefore, the availability of efficient of fine coal dewatering methods will greatly benefit companies. In the present study, three different novel dewatering aids have been tested. These include Reagents W (RW), Reagent U (RU), and Reagent V (RV). These reagents are designed to increase the contact angles of the coal samples to be dewatered, which should help decrease the Laplace pressure of the water trapped in filter cake and, hence, increase dewatering rate. They were tested on i) the fresh coal samples from Consolidation Coal Corporation's Buchanan Preparation Plant, ii) a composite drill core sample from the Smith Branch Impoundment, Pinnacle Mine Mining Company, and iii) a blend of coals from the Smith Branch Impoundment, thickener underflow, and thickener feed. The coal samples were used initially for laboratory-scale tests using a 2.5-inch diameter Buchner vacuum filter. The results showed that the use of the novel dewatering aids can reduce the cake moisture up to 50% over what can be achieved without using any dewatering aid. The use of the dewatering aids also increased the kinetics of dewatering by up to 6 times, as measured by cake formation times. On the basis of the laboratory test results, pilot-scale continuous vacuum filtration tests were conducted using a 2-feet diameter Peterson vacuum disc filter. The cake moistures obtained in the pilot-scale test work were similar to those obtained in the laboratory tests, while the fast dewatering kinetics observed in the laboratory tests was manifested as higher throughput. It was found that high-shear agitation is essential for achieving low cake moistures and high throughput. / Master of Science

With the advent of the personal computer, mechanistic nutrient uptake models have become widely used as research and teaching tools in plant and soil science. Three models NST 3.0, SSAND, and PCATS have evolved to represent the current state of the art. There are two major categories of mechanistic models, transient state models with numerical solutions and steady state models. NST 3.0 belongs to the former model type, while SSAND and PCATS belong to the latter. NST 3.0 has been used extensively in crop research but has not been used with woody species. Only a few studies using SSAND and PCATS are available. To better understand the similarities and differences of these three models, it would be useful to compare model predictions with experimental observations using multiple datasets from the literature to represent various situations for woody species. Therefore, the objectives of this study are to: (i) compare the predictions of uptake by the NST 3.0, SSAND, and PCATS models for a suite of nutrients against experimentally measured values, (ii) compare the behavior of the three models using a one dimensional sensitivity analysis; and (iii) compare and contrast the behavior of NST 3.0 and SSAND using a multiple dimensional sensitivity analysis approach. Predictions of nutrient uptake by the three models when run with a common data set were diverse, indicating a need for a reexamination of model structure. The failure of many of the predictions to match observations indicates the need for further studies which produce representative datasets so that the predictive accuracy of each model can be evaluated. Both types of sensitivity analyses suggest that the effect of soil moisture on simulation can be influential when nutrient concentration in the soil solution (CLi) is low. One dimensional sensitivity analysis also revealed that Imax negatively influenced the uptake estimates from the SSAND and PCATS models. Further analysis indicates that this counter intuitive response of Imax is probably related to low soil nutrient supply. The predictions of SSAND under low-nutrient-supply scenarios are generally lower than those of NST 3.0. We suspect that both of these results are artifacts of the steady state models and further studies to improve them, such as incorporating important rhizospheric effects, are needed if they are to be used successfully for the longer growth periods and lower soil nutrient supply situations more typical of woody species. / Master of Science

A detailed design was developed of an apparatus to measure moisture transfer in porous materials. The apparatus is to be used to collect data to aid in the development of mathematical models which accurately describe this phenomena. The apparatus consists of dual environmental chambers between which a specimen material is sealed. The temperature of each chamber is controlled separately allowing nonisothermal test conditions. The relative humidity is maintained without the use of saturated salt solutions. The moisture transfer rate is measured by periodically weighing a desiccant column used to absorb moisture as result of diffusion across the specimen. The apparatus was built and used to verify a heat transfer model written to predict its thermal characteristics. The chamber temperature capabilities are 5°C to 60°C with up to a 20°C temperature difference across the specimen. The relative humidity limits are based on the heat transfer into or out of the system. High relative humidities (75 to 85 percent) are possible at chamber temperatures close to ambient, but decrease sharply at the extremely high or low temperatures and during nonisothermal operation. The apparatus maintains a constant temperature within ±0.4°C of the setpoint when subjected to varying ambient temperatures. The spatial temperature variation close to the sample (within 25 mm) is within approximately ​​​​±1°C of the average chamber temperature. The relative humidity can be manually controlled to within ±.7 percent RH. Automated control, complicated by a response lag, was within ±1 percent RH. / Master of Science