Browsing by Author "Jordan, Cynthia (TTU)"
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Item Comparative analysis and visualization of soil profiles at the meter spatial scale utilizing novel matrix and volume rendering techniques(2023) Gonzalez, Jake (TTU); Siebecker, Matthew (TTU); Pham, Vung; Jordan, Cynthia (TTU); Weindorf, David C. (TTU); Dang, Tommy (TTU)This research introduces a soil characterization technique involving four data visualization tools to help researchers and stakeholders interpret high dimensional soil data at the field scale. This technique involves visualizing a reduced dimensionality representation of elemental concentration and color data gathered via portable X-ray fluorescence (pXRF) spectrometer and NixPro color proximal sensors, respectively. Soil cores were collected from sites located in Lubbock and Lamb Counties, West Texas, USA. Thirteen core samples were collected from these sites in a star pattern with readings from proximal sensors at depths ranging between 0 and 100 cm at 10 cm intervals. The dimensionality reduction techniques utilize four visualization tools to represent soil composition data through multiple user-adjustable variables (i.e., mg kg−1 elemental concentrations and soil profiles), offering more insight and control compared to a single-variable approach. Through these tools and techniques, qualitative and quantitative conclusions regarding soil characteristics (e.g., elemental concentration variation, delineation of soil horizons, changes in soil color) can be formulated from the data and used in various applications. Areas where these novel software tools can be utilized potentially include rapid contaminant mapping in soils, characterization of diagnostic soil horizons (e.g., calcic, spodic, gypsic, etc.), micronutrient distribution at a field scale for precision agricultural purposes, and pedometrics.Item Rapid quantification of lignite sulfur content: Combining optical and X-ray approaches(2019) Kagiliery, Julia; Chakraborty, Somsubhra; Acree, Autumn (TTU); Weindorf, David C. (TTU); Brevik, Eric C.; Jelinski, Nicolas A.; Li, Bin; Jordan, Cynthia (TTU)Coal is an important natural resource for global energy production. However, certain types of coal (e.g., lignite) often contain abundant sulfur (S) which can lead to gaseous sulfur dioxide (SO2) emissions when burned. Such emissions subsequently create sulfuric acid (H2SO4), thus causing highly acidic rain which can alter the pH of soil and surface waters. Traditional laboratory analysis (e.g., dry combustion) is commonly used to characterize the S content of lignite, but such approaches are laborious and expensive. By comparison, proximal sensing techniques such as portable X-ray fluorescence (PXRF) spectrometry, visible near infrared (VisNIR) spectroscopy, and optical sensors (e.g., NixPro) can acquire voluminous data which has been successfully used to elucidate fundamental chemistry in a wide variety of matrices. In this study, four active lignite mines were sampled in North Dakota, USA. A total of 249 samples were dried, powdered, then subjected to laboratory-based dry combustion analysis and scanned with the NixPro, VisNIR, and PXRF sensors. 75% of samples (n = 186) were used for model calibration, while 25% (n = 63) were used for validation. A strong relationship was observed between dry combustion and PXRF S content (r = 0.90). Portable X-ray fluorescence S and Fe as well as various NixPro color data were the most important variables for predicting S content. When using PXRF data in isolation, random forest regression produced a validation R2 of 0.80 in predicting total S content. Combining PXRF + NixPro improved R2 to 0.85. Dry combustion S + PXRF S and Fe correctly identified the source mine of the lignite at 55.42% via discriminant analysis. Adding the NixPro color data to the PXRF and dry combustion data, the location classification accuracy increased to 63.45%. Even with VisNIR reflectance values of 10–20%, spectral absorbance associated with water at 1940 nm was still observed. Principal component analysis was unable to resolve the mine source of the coal in PCA space, but several NixPro vectors were closely clustered. In sum, the combination of the NixPro optical sensor with PXRF data successfully augmented the predictive capability of S determination in lignite ex-situ. Future studies should extend the approach developed herein to in-situ application with special consideration of moisture and matrix efflorescence effects.