Utilization of bathymetry data to examine lead sediment contamination distributions in Lake Ontario

Forsythe, K. Wayne; Märvin, Chris H.; Mitchell, Danielle E.; Aversa, Joseph; Swales, Stephen; Burniston, D.; Watt, James P.; Jakubek, Daniel; McHenry, Meghan H.; Shaker, Richard R.

doi:10.3934/environsci.2016.3.347

Cited by 4 publications

(12 citation statements)

References 21 publications

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“…Polluted sediments may be used as a proxy indicator of water quality (Forsythe et al, 2016a) and the overall health of aquatic ecosystems (Adams and Stadler-Salt, 2016). Since 2002, a group of scientists and academic researchers have mapped spatially interpolated sediment contamination values throughout many Great Lakes and tributaries (Forsythe et al, 2004;Forsythe et al, 2005;Forsythe and Marvin, 2009;Forsythe et al, 2010;Forsythe et al, 2013;Forsythe et al, 2015;Forsythe et al, 2016a;Forsythe et al, 2016b;Forsythe et al, 2016c;Gawedzki and Forsythe, 2012). The main objective of their research was to generate continuous data layers from sediment sample point data using the kriging spatial interpolation method (Forsythe et al, 2016a).…”

Section: Research Contextmentioning

confidence: 99%

“…Overtime, Cd, and Pb contaminated sediments are likely to sink deeper into the sediment column, or be redistributed throughout the lake basin (Forsythe et al, 2016c).…”

Section: Defining Contaminationmentioning

confidence: 99%

“…Contamination patterns can be observed in point form, however trends and underlying factors for contaminant distribution (i.e. bathymetry, and lake circulation patterns) are not recognizable or available to assist with interpretation (Forsythe et al, 2016c). Due to advances in geospatial computation, spatial interpolation methods such as kriging are now used to model estimated contamination in a continuous data layer (Goovaerts, 1999;Lark and Ferguson, 2004;Forsythe et al, 2016b;Forsythe et al, 2016c).…”

Section: Geovisualization Of Sediment Contaminationmentioning

confidence: 99%

“…bathymetry, and lake circulation patterns) are not recognizable or available to assist with interpretation (Forsythe et al, 2016c). Due to advances in geospatial computation, spatial interpolation methods such as kriging are now used to model estimated contamination in a continuous data layer (Goovaerts, 1999;Lark and Ferguson, 2004;Forsythe et al, 2016b;Forsythe et al, 2016c). Not only are interpolated surfaces cost-and time-effective (Simpson and Wu, 2014), they also work to translate disjunctive point data into communicable visualizations of spatial patterns (Hessl et al, 2007).…”

Section: Geovisualization Of Sediment Contaminationmentioning

confidence: 99%

“…Doing so also expands the communication of scientific research to a much broader audience. For example, geovisualizations of trends, patterns, and processes are significant pieces of information in policy development (Lark and Ferguson, 2004;Forsythe et al, 2016a;Forsythe et al, 2016b), pollution control and management (Wu et al, 2011;Khosh Eghbal, 2014), environmental science (Forsythe et al, 2016b;Forsythe et al, 2016c) and risk assessment (Markus and McBratney, 2001).…”

Section: Geovisualization Of Sediment Contaminationmentioning

confidence: 99%

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Visualizing The Big Three: Geospatial Interpolation Of Heavy Metal Sediment Contamination In Lake Erie

Mitchell¹

2021

Preprint

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A wealth of resources for economic prosperity have driven development along the shorelines of the Great Lakes for over 150 years. The rapid growth of industrial, agricultural, and residential land use has degraded many natural components of lake ecosystems, including sediments and water quality. In this dissertation, spatiotemporal patterns of non-essential heavy metal sediment contamination in Lake Erie will be examined from historic and contemporary sediment surveys. Three inter-related studies explore innovative methods for improving the validity and overall usefulness of sediment contamination maps that could be used by a variety of stakeholders in pollution control efforts throughout the Lake Erie basin. First, sediment survey designs are analyzed for their utility in creating valid interpolated surfaces from which spatiotemporal comparisons of mercury sediment contamination can be compared over time. The next study explores how ancillary sediment variables and contamination categorization methods can support interpolated maps of cadmium sediment contamination from low-density sediment surveys. The final study introduces a novel method of three-dimensional geovisualization to enhance the geographic representation of lead sediment contamination patterns throughout the Lake Erie basin. Innovative research methodologies designed for this dissertation may be applied to sediment contamination studies in other Great Lakes. The visualization techniques employed in mapping sediment contamination patterns provide strong scientific evidence for spatiotemporal change in non-essential heavy metal pollution throughout Lake Erie. Combined, the research findings and maps produced throughout this dissertation can contribute to the growing body of knowledge used in environmental decision making for pollution control in the Great Lakes basin

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Section: Research Contextmentioning

confidence: 99%

“…Overtime, Cd, and Pb contaminated sediments are likely to sink deeper into the sediment column, or be redistributed throughout the lake basin (Forsythe et al, 2016c).…”

Section: Defining Contaminationmentioning

confidence: 99%

Section: Geovisualization Of Sediment Contaminationmentioning

confidence: 99%

Section: Geovisualization Of Sediment Contaminationmentioning

confidence: 99%

Section: Geovisualization Of Sediment Contaminationmentioning

confidence: 99%

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Visualizing The Big Three: Geospatial Interpolation Of Heavy Metal Sediment Contamination In Lake Erie

Mitchell¹

2021

Preprint

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Assessing statistical and spatial validity of sediment survey design and sampling densities: examples from Lake Erie

Mitchell

Forsythe

Märvin

et al. 2018

Water Quality Research Journal

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Spatial interpolation methods translate sediment contamination point data into informative area-based visualizations. Lake Erie was first sampled in 1971 based on a survey grid of 263 locations. Due to procedural costs, the 2014 survey was reduced to 34 sampling locations mostly located in deep offshore regions of the lake. Using the 1971 dataset, this study identifies the minimum sampling density at which statistically valid, and spatially accurate predictions can be made using ordinary kriging. Randomly down-sampled subsets at 10% intervals of the 1971 survey were created to include at least one set of data points with a smaller sample size than that of the 2014 dataset. Regression analyses of predicted contamination values assessed spatial autocorrelation between kriged surfaces created from the down-sampled subsets and the original dataset. Subsets at 10% and 20% of the original data density accurately predicted 51% and 75% (respectively) of the original dataset's predictions. Subsets representing 70%, 80% and 90% of the original data density accurately predicted 88%, 90% and 97% of the original dataset's predictions. Although all subsets proved to be statistically valid, sampling densities below 0.002 locations/km2 are likely to create very generalized contamination maps from which environmental decisions might not be justified.

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Multivariable 3D Geovisualization of Historic and Contemporary Lead Sediment Contamination in Lake Erie

et al. 2021

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Lead sediment contamination in Lake Erie stems from a long history of natural and synthetic resource production. Sediment samples with variable sampling densities were collected by the Canada Centre for Inland Waters in 1971, 1997/1998, and 2014. The kriging interpolation method was used to create continuous sediment contamination surfaces for time/space comparisons. Change detection analyses identified an overall decreasing trend in high lead pollution levels from 1971 to 2014, while sediments with the lowest concentrations increased in surface area. Lake-wide circulation patterns and bathymetric data were added to interpolated contamination surfaces to enhance the understanding of interrelated hydrodynamic processes and geophysical features in the movement of contaminated sediments. Utilizing visualization tools in Esri’s ArcScene, bathymetric data were employed to enhance the geographic context of contamination maps. The physical barriers to sediment transportation created by bathymetric features can be visualized in three-dimensions. Elevated features between lake basins are easily recognized as impedances to lake currents when circulation directions are draped over the bathymetric model. By using illumination tools and techniques, geovisualizations of lead sediment contamination throughout Lake Erie create a scientific communication tool for a wide audience to use in multiple-criteria decision making for environmental remediation of sediment contamination.

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Utilization of bathymetry data to examine lead sediment contamination distributions in Lake Ontario

Cited by 4 publications

References 21 publications

Visualizing The Big Three: Geospatial Interpolation Of Heavy Metal Sediment Contamination In Lake Erie

Visualizing The Big Three: Geospatial Interpolation Of Heavy Metal Sediment Contamination In Lake Erie

Assessing statistical and spatial validity of sediment survey design and sampling densities: examples from Lake Erie

Multivariable 3D Geovisualization of Historic and Contemporary Lead Sediment Contamination in Lake Erie

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