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Pumping groundwater from arsenic (As)-contaminated aquifers exposes millions of people, especially those in developing countries, to high doses of the toxic contaminant. Previous studies have investigated cost-effective techniques to remove groundwater arsenic by stimulating sulfate-reducing bacteria (SRB) to form biogenic arsenian pyrite. This study intends to improve upon these past methods to demonstrate the effectiveness of SRB arsenic remediation at an industrial site in Florida. This study developed a ferrous sulfate and molasses mixture to sequester groundwater arsenic in arsenian pyrite over nine months. The optimal dosage of the remediating mixture consisted of 5 kg of ferrous sulfate, ~27 kg (60 lbs) of molasses, and ~1 kg (2 lbs) of fertilizer per 3785.4 L (1000 gallons) of water. The remediating mixture was injected into 11 wells hydrologically upgradient of the arsenic plume in an attempt to obtain full-scale remediation. Groundwater samples and precipitated biominerals were collected from June 2018 to March 2019. X-ray diffraction (XRD), X-ray fluorescence (XRF), electron microprobe (EMP), and scanning electron microscope (SEM) analyses determined that As has been sequestered mainly in the form of arsenian pyrite, which rapidly precipitated as euhedral crystals and spherical aggregates (framboids) 1–30 μm in diameter within two weeks of the injection. The analyses confirmed that the remediating mixture and injection scheme reduced As concentrations to near or below the site’s clean-up standard of 0.05 mg/L over the nine months. Moreover, the arsenian pyrite contained 0.03–0.89 weight percentage (wt%) of sequestered arsenic, with >80% of groundwater arsenic removed by SRB biomineralization. Considering these promising findings, the study is close to optimizing an affordable procedure for sequestrating dissolved As in industry settings.
Alicia Fischer; James Saunders; Sara Speetjens; Justin Marks; Jim Redwine; Stephanie Rogers; Ann Ojeda; Mahfujur Rahman; Zeki Billor; Ming-Kuo Lee. Long-Term Arsenic Sequestration in Biogenic Pyrite from Contaminated Groundwater: Insights from Field and Laboratory Studies. Minerals 2021, 11, 537 .
AMA StyleAlicia Fischer, James Saunders, Sara Speetjens, Justin Marks, Jim Redwine, Stephanie Rogers, Ann Ojeda, Mahfujur Rahman, Zeki Billor, Ming-Kuo Lee. Long-Term Arsenic Sequestration in Biogenic Pyrite from Contaminated Groundwater: Insights from Field and Laboratory Studies. Minerals. 2021; 11 (5):537.
Chicago/Turabian StyleAlicia Fischer; James Saunders; Sara Speetjens; Justin Marks; Jim Redwine; Stephanie Rogers; Ann Ojeda; Mahfujur Rahman; Zeki Billor; Ming-Kuo Lee. 2021. "Long-Term Arsenic Sequestration in Biogenic Pyrite from Contaminated Groundwater: Insights from Field and Laboratory Studies." Minerals 11, no. 5: 537.
Arsenic (As) contamination in groundwater is a global crisis that is known to cause cancers of the skin, bladder, and lungs, among other health issues, and affects millions of people around the world. Due to the time and financial constraints associated with establishing in-depth monitoring programs, it is difficult to monitor and map arsenic concentrations over time and across large areas. The goal of this study was to determine the most accurate Geographic Information Systems (GIS) interpolation method for mapping the effects of bioremediation on groundwater arsenic sequestration across a local-scale study area in northwest Florida (~900 m2) over the duration of a nine-month period (pre-injection, one-month post-injection, and nine-months post-injection). We used groundwater data collected from 2018 to 2019 to visualize arsenic contamination over time. Measured arsenic concentrations from 23 wells were grouped into three categories: (1) decreasing, (2) fluctuating, or (3) largely unaffected by the bioremediation procedure. The accuracy of three interpolation methods was also investigated: Inverse Distance Weighted (IDW), Ordinary Kriging (OK), and Empirical Bayesian Kriging (EBK). Statistical results using the leave-one-out cross validation (LOOCV) process showed that OK consistently provided the most accurate predictions of arsenic concentrations across space and time ([Root Mean Square Error (RMSE) = 0.265] and accurately predicted regulatory arsenic concentrations below 0.05 mg/L in nine of 11 wells, while IDW and EBK only accurately predicted four and five wells, respectively. While it was shown that OK tends to underpredict arsenic maxima, this did not affect the overall accuracy of the interpolation compared to results from EBK (RMSE = 0.297) and IDW (RMSE = 0.272). Overall, these interpolations aided in the interpretation of the extent of bioremediation, revealing the need for repeated injections to continuously remove arsenic from the groundwater. The study will provide guidance and evaluation methods for international and governmental organizations, industrial companies, and local communities on how to understand spatial and temporal distributions of arsenic contamination and inform bioremediation efforts at various scales in the future.
Alicia Fischer; Ming-Kuo Lee; Ann S. Ojeda; Stephanie R. Rogers. GIS interpolation is key in assessing spatial and temporal bioremediation of groundwater arsenic contamination. Journal of Environmental Management 2020, 280, 111683 .
AMA StyleAlicia Fischer, Ming-Kuo Lee, Ann S. Ojeda, Stephanie R. Rogers. GIS interpolation is key in assessing spatial and temporal bioremediation of groundwater arsenic contamination. Journal of Environmental Management. 2020; 280 ():111683.
Chicago/Turabian StyleAlicia Fischer; Ming-Kuo Lee; Ann S. Ojeda; Stephanie R. Rogers. 2020. "GIS interpolation is key in assessing spatial and temporal bioremediation of groundwater arsenic contamination." Journal of Environmental Management 280, no. : 111683.
The technological growth and accessibility of Unoccupied Aerial Systems (UAS) have revolutionized the way geographic data are collected. Digital Surface Models (DSMs) are an integral component of geospatial analyses and are now easily produced at a high resolution from UAS images and photogrammetric software. Systematic testing is required to understand the strengths and weaknesses of DSMs produced from various UAS. Thus, in this study, we used photogrammetry to create DSMs using four UAS (DJI Inspire 1, DJI Phantom 4 Pro, DJI Mavic Pro, and DJI Matrice 210) to test the overall accuracy of DSM outputs across a mixed land cover study area. The accuracy and spatial variability of these DSMs were determined by comparing them to (1) 12 high-precision GPS targets (checkpoints) in the field, and (2) a DSM created from Light Detection and Ranging (LiDAR) (Velodyne VLP-16 Puck Lite) on a fifth UAS, a DJI Matrice 600 Pro. Data were collected on July 20, 2018 over a site with mixed land cover near Middleton, NS, Canada. The study site comprised an area of eight hectares (~20 acres) with land cover types including forest, vines, dirt road, bare soil, long grass, and mowed grass. The LiDAR point cloud was used to create a 0.10 m DSM which had an overall Root Mean Square Error (RMSE) accuracy of ±0.04 m compared to 12 checkpoints spread throughout the study area. UAS were flown three times each and DSMs were created with the use of Ground Control Points (GCPs), also at 0.10 m resolution. The overall RMSE values of UAS DSMs ranged from ±0.03 to ±0.06 m compared to 12 checkpoints. Next, DSMs of Difference (DoDs) compared UAS DSMs to the LiDAR DSM, with results ranging from ±1.97 m to ±2.09 m overall. Upon further investigation over respective land covers, high discrepancies occurred over vegetated terrain and in areas outside the extent of GCPs. This indicated LiDAR’s superiority in mapping complex vegetation surfaces and stressed the importance of a complete GCP network spanning the entirety of the study area. While UAS DSMs and LiDAR DSM were of comparable high quality when evaluated based on checkpoints, further examination of the DoDs exposed critical discrepancies across the study site, namely in vegetated areas. Each of the four test UAS performed consistently well, with P4P as the clear front runner in overall ranking.
Stephanie Rogers; Ian Manning; William Livingstone. Comparing the Spatial Accuracy of Digital Surface Models from Four Unoccupied Aerial Systems: Photogrammetry Versus LiDAR. Remote Sensing 2020, 12, 2806 .
AMA StyleStephanie Rogers, Ian Manning, William Livingstone. Comparing the Spatial Accuracy of Digital Surface Models from Four Unoccupied Aerial Systems: Photogrammetry Versus LiDAR. Remote Sensing. 2020; 12 (17):2806.
Chicago/Turabian StyleStephanie Rogers; Ian Manning; William Livingstone. 2020. "Comparing the Spatial Accuracy of Digital Surface Models from Four Unoccupied Aerial Systems: Photogrammetry Versus LiDAR." Remote Sensing 12, no. 17: 2806.
Stephanie Rogers; Philippe Curdy; Muriel Eschmann-Richon; Ralph Lugon. Glacial Archaeology in the Pennine Alps, Switzerland/Italy, 2011–2014. Journal of Glacial Archaeology 2018, 3, 27 -41.
AMA StyleStephanie Rogers, Philippe Curdy, Muriel Eschmann-Richon, Ralph Lugon. Glacial Archaeology in the Pennine Alps, Switzerland/Italy, 2011–2014. Journal of Glacial Archaeology. 2018; 3 ():27-41.
Chicago/Turabian StyleStephanie Rogers; Philippe Curdy; Muriel Eschmann-Richon; Ralph Lugon. 2018. "Glacial Archaeology in the Pennine Alps, Switzerland/Italy, 2011–2014." Journal of Glacial Archaeology 3, no. : 27-41.
Stephanie Rogers; Mauro Fischer; Matthias Huss. Combining glaciological and archaeological methods for gauging glacial archaeological potential. Journal of Archaeological Science 2014, 52, 410 -420.
AMA StyleStephanie Rogers, Mauro Fischer, Matthias Huss. Combining glaciological and archaeological methods for gauging glacial archaeological potential. Journal of Archaeological Science. 2014; 52 ():410-420.
Chicago/Turabian StyleStephanie Rogers; Mauro Fischer; Matthias Huss. 2014. "Combining glaciological and archaeological methods for gauging glacial archaeological potential." Journal of Archaeological Science 52, no. : 410-420.
Isabelle Schoepfer; Stephanie Rogers. A New Qualitative GIS Method for Investigating Neighbourhood Characteristics Using a Tablet. Cartographica: The International Journal for Geographic Information and Geovisualization 2014, 49, 127 -143.
AMA StyleIsabelle Schoepfer, Stephanie Rogers. A New Qualitative GIS Method for Investigating Neighbourhood Characteristics Using a Tablet. Cartographica: The International Journal for Geographic Information and Geovisualization. 2014; 49 (2):127-143.
Chicago/Turabian StyleIsabelle Schoepfer; Stephanie Rogers. 2014. "A New Qualitative GIS Method for Investigating Neighbourhood Characteristics Using a Tablet." Cartographica: The International Journal for Geographic Information and Geovisualization 49, no. 2: 127-143.
Stephanie Rogers. An Overview of Selected GIS Methods Available for Use in Glacial Archaeology. Journal of Glacial Archaeology 2013, 1, 99 -115.
AMA StyleStephanie Rogers. An Overview of Selected GIS Methods Available for Use in Glacial Archaeology. Journal of Glacial Archaeology. 2013; 1 ():99-115.
Chicago/Turabian StyleStephanie Rogers. 2013. "An Overview of Selected GIS Methods Available for Use in Glacial Archaeology." Journal of Glacial Archaeology 1, no. : 99-115.
Stephanie Rogers; Benno Staub. Standard use of Geographic Information System (GIS) techniques in honey bee research. Journal of Apicultural Research 2013, 52, 1 -48.
AMA StyleStephanie Rogers, Benno Staub. Standard use of Geographic Information System (GIS) techniques in honey bee research. Journal of Apicultural Research. 2013; 52 (4):1-48.
Chicago/Turabian StyleStephanie Rogers; Benno Staub. 2013. "Standard use of Geographic Information System (GIS) techniques in honey bee research." Journal of Apicultural Research 52, no. 4: 1-48.
Analysis of dissolved organic matter (DOM) concentration and composition is essential to quantifying biological and chemical oxygen demand and atmosphere–ocean heat flux exchange in natural waters. However, manual water sampling is costly and time consuming over large areas. The purpose of this research was to analyze the applicability of airborne laser-induced fluorescence light detection and ranging (LiDAR) for the detection of DOM in estuarine ecosystems impacted by agriculture. A fluorescence LiDAR system (Airborne Marine) (FLS-AM) was used to assess the DOM concentration of the Annapolis River and Basin, Nova Scotia, Canada, as well as three rivers and their estuaries in Prince Edward Island, Canada. Two FLS-AM flight missions were conducted in the summers of 2008 and 2009 and the resulting datasets were compared with spectral fluorescence signature (SFS DOM) and dissolved organic carbon (DOC) analysis of in situ water samples. Significant positive correlations were found at five of seven sites between the FLS-AM DOM and SFS DOM relationship which indicates that the FLS-AM sensor is a good surrogate for traditional sample collection of DOM data in estuaries in this region. Positive correlations were also found at all sites between FLS-AM DOM values and DOC. FLS-AM DOM patterns show that DOM values are significantly higher in rivers and estuaries that drain watersheds which are heavily impacted by agricultural practices. The results of this study show that the FLS-AM can be used efficiently as a general indicator for how estuaries are affected by runoff from agricultural watersheds in real time and thus reduce the requirement for traditional water sample collection and laboratory analysis methods.
Stephanie R. Rogers; Tim Webster; William Livingstone; Nelson J. O’Driscoll. Airborne Laser-Induced Fluorescence (LIF) Light Detection and Ranging (LiDAR) for the Quantification of Dissolved Organic Matter Concentration in Natural Waters. Estuaries and Coasts 2012, 35, 959 -975.
AMA StyleStephanie R. Rogers, Tim Webster, William Livingstone, Nelson J. O’Driscoll. Airborne Laser-Induced Fluorescence (LIF) Light Detection and Ranging (LiDAR) for the Quantification of Dissolved Organic Matter Concentration in Natural Waters. Estuaries and Coasts. 2012; 35 (4):959-975.
Chicago/Turabian StyleStephanie R. Rogers; Tim Webster; William Livingstone; Nelson J. O’Driscoll. 2012. "Airborne Laser-Induced Fluorescence (LIF) Light Detection and Ranging (LiDAR) for the Quantification of Dissolved Organic Matter Concentration in Natural Waters." Estuaries and Coasts 35, no. 4: 959-975.