Geovisualization of Mercury Contamination in Lake St. Clair Sediments

Abstract

The Laurentian Great Lakes of North America contain approximately 20% of the earth's fresh water. Smaller lakes, rivers and channels connect the lakes to the St. Lawrence Seaway, creating an interconnected freshwater and marine ecosystem. The largest delta system in the Great Lakes is located in the northeastern portion of Lake St. Clair. This article focuses on the geovisualization of total mercury pollution from sediment samples that were collected in 1970, 1974 and 2001. To assess contamination patterns, dot maps were created and compared with surfaces that were generated using the kriging spatial interpolation technique. Bathymetry data were utilized in geovisualization procedures to develop three-dimensional representations of the contaminant surfaces. Lake St. Clair generally has higher levels of contamination in deeper parts of the lake, in the dredged shipping route through the lake and in proximity to the main outflow channels through the St. Clair delta. Mercury pollution levels were well above the Probable Effect Level in large portions of the lake in both 1970 and 1974. Lower contaminant concentrations were observed in the 2001 data. Lake-wide spatial distributions are discernable using the kriging technique; however, they are much more apparent when they are geovisualized using bathymetry data.

Irrigation Salinity Risk Assessment and Mapping in Arid Oasis, Northwest China

“…When spatial distribution of each parameter was completed in Arc GIS 10.1 [61], the risk receptor (total soluble salt of 35 sampling point) were added into the each parameter in Arc GIS10.1, then used the extraction tool, calculate the A0, A1, A11, A12, A13, A14 and A15; used the distance measuring tool to calculate the A2 and A8; and the A3, A4, A5, A6, A7, A9, A10 and A16 were the thematic maps in village level, means all point placed in a village has equal futures but during the field observation, we witnessed that a village also has diverse features of eco-environment, in order to match to real condition, the 35 sample points were assigned the values at by following principle: first step was divided the 35 sample point in to curtain group by value of thematic map, assign them equal value, then according to expert suggestions whose familiar well to the Oasis and attended the field investigation, the minor adjustment to sampling point of each group were conducted subjectively. …”

“…So for this reason, normalization is crucial for rational statistics and comparison. There are many normalization methods, in this work, we normalized the data sets by using the Equations (5) and (6) as following [61]: …”

The Effect of Splitting of Raw Data into Training and Test Subsets on the Accuracy of Predicting Spatial Distribution by a Multilayer Perceptron

“…One of the principal advantages of ANNs is their ability to detect patterns in data that demonstrate a significant unpredictable nonlinearity. The predictive accuracy obtained by ANNs is often higher than that of other methods (Schloeder et al 2001;Liu et al 2008;Worsham et al 2010;Guo et al 2012;Forsythe et al 2016). The ANN model might be applied to measured data obtained in monitoring, and can be used to predict the content of pollutants at unmonitored locations (Kanevski 1999;Kanevski et al 2004Kanevski et al , 2009Liu et al 2009;Shaker and Ehlinger 2014).…”

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

“…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).…”

“…Kriging is a geostatistical estimation of unknown values calculated from spatial weights between two or more measured points (Clark 1982;Ouyang et al, 2003;Hessl et al, 2007). The primary goal of kriging is to create a spatially continuous layer of information from isolated point data to better understand spatial phenomena (Forsythe et al, 2016b).…”