Heavy Metal Estimations in Coal Slurry Using Reflectance Spectroscopy and WorldView-3 Imagery
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Bowling Green State University / OhioLINK
2020
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1587137504433767 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-bgsu15871375044337672021-08-03T07:14:29Z Heavy Metal Estimations in Coal Slurry Using Reflectance Spectroscopy and WorldView-3 Imagery Gerzan, Mallory N. Soil Sciences Geological Remote Sensing Heavy Metals VHR WorldView-3 Coal Slurry Slurry Remote Sensing Machine Learning Western Kentucky coal mines have long disposed of coal slurry by dumping the material into waterbodies located on property. This fine-grained material contains high amounts of sulfur, iron, and other heavy metals, placing nearby waterways and biota at risk for contamination. This study proposes the implementation of WorldView-3 imagery, reflectance spectroscopy, chemical composition analyses, and multiple Neural Networks to establish a prediction model that would map the extent and concentration of total organic carbon, arsenic, lead, and chromium throughout these slurry deposits. This method of chemometric analysis has proven effective in the determination and prediction of heavy metals but has yet to be applied to WorldView-3 imagery or coal slurry deposits. Worldview-3 imagery provides significantly higher spatial and spectral resolution than most other spaceborne-sensors, as well as allows for a < 1-day return time. Hyperspectral-based predictions of Total Organic Carbon, arsenic, chromium, and lead resulted in R2 values of 0.95, 0.90, 0.77, and 0.75, respectively. WorldView-3 based predictions resulted in Overall Accuracies of 84%, 79%, 70%, and 75%, respectively. This very high resolution (VHR) remote sensing is vital for monitoring complex ecosystems and mapping those substances which pose a risk to soil, biota, and human health, such as coal slurry. By successfully predicting these constituents, coal mines will have a better understanding of contamination extent and can more effectively conduct remediation efforts. 2020-05-06 English text Bowling Green State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1587137504433767 http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1587137504433767 restricted--full text unavailable until 2022-05-15 This thesis or dissertation is protected by copyright: some rights reserved. It is licensed for use under a Creative Commons license. Specific terms and permissions are available from this document's record in the OhioLINK ETD Center. |
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NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Soil Sciences Geological Remote Sensing Heavy Metals VHR WorldView-3 Coal Slurry Slurry Remote Sensing Machine Learning |
| spellingShingle |
Soil Sciences Geological Remote Sensing Heavy Metals VHR WorldView-3 Coal Slurry Slurry Remote Sensing Machine Learning Gerzan, Mallory N. Heavy Metal Estimations in Coal Slurry Using Reflectance Spectroscopy and WorldView-3 Imagery |
| author |
Gerzan, Mallory N. |
| author_facet |
Gerzan, Mallory N. |
| author_sort |
Gerzan, Mallory N. |
| title |
Heavy Metal Estimations in Coal Slurry Using Reflectance Spectroscopy and WorldView-3 Imagery |
| title_short |
Heavy Metal Estimations in Coal Slurry Using Reflectance Spectroscopy and WorldView-3 Imagery |
| title_full |
Heavy Metal Estimations in Coal Slurry Using Reflectance Spectroscopy and WorldView-3 Imagery |
| title_fullStr |
Heavy Metal Estimations in Coal Slurry Using Reflectance Spectroscopy and WorldView-3 Imagery |
| title_full_unstemmed |
Heavy Metal Estimations in Coal Slurry Using Reflectance Spectroscopy and WorldView-3 Imagery |
| title_sort |
heavy metal estimations in coal slurry using reflectance spectroscopy and worldview-3 imagery |
| publisher |
Bowling Green State University / OhioLINK |
| publishDate |
2020 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1587137504433767 |
| work_keys_str_mv |
AT gerzanmalloryn heavymetalestimationsincoalslurryusingreflectancespectroscopyandworldview3imagery |
| _version_ |
1719456890191085568 |