An Investigative Analysis of Soil Quality in the United Kingdom Using the TIBCO Spotfire Software for Inorganic Soil Dashboard

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1 A P P L I C AT I O N N O T E Analytics Authors: Kathryn Kuhr Robert Packer PerkinElmer, Inc. Shelton, CT An Investigative Analysis of Soil Quality in the United Kingdom Using the TIBCO Spotfire Software for Inorganic Soil Dashboard Introduction The condition of soil plays a major role in the symbiotic relationship between people and environmental heath by both affecting human sustainability, yet also falling susceptible to the negative impacts of human interaction. Soil contains high quality nutrients in order to sustain the growth of fruits, vegetables and grains. Such dependent vegetation, however, may absorb inorganic elements from agricultural soil through their roots and expose consumers to potential health risks1,2. Other dangers, like the exposure of young children to metals, exist in common locations such as residential neighborhoods and parks3. Regardless of where samples are being collected, one thing remains truepreparing data for communication is equally as important as the steps behind the analysis process. Miscommunication of results may direct people to believe that there are dangers present where none exist, leading to the initiation of costly, superfluous testing4. The TIBCO Spotfire Software for Inorganic Soil Dashboard circumvents miscommunication by simply visualizing complex data in such a way that everybody can understand.

2 This investigation utilized the dashboard to visualize soil quality data from parts of the United Kingdom downloaded from the British Geological Survey. By comparing these test results to established baseline data, it may be shown whether or not variance from the baseline can be attributed to localized interactions while keeping both positive and negative fluctuations in check. The British Geological Survey has been working on their Geochemical Baseline Survey of the Environment (G-BASE) since the late 1960's with annual sample collection at locations throughout the United Kingdom. The samples gathered from the towns specified in this analysis were done individually between the late 1990's and early 2000's. Experimental The goal of analyzing this European soil quality data was to visualize the relative levels of inorganic elements in 11 areas of the United Kingdom and determine which metals, if any, have varied from their established baseline concentration. In total, 14 metals were included in this examination: antimony, arsenic, barium, cadmium, chromium, cobalt, copper, lead, nickel, molybdenum, tin, uranium, vanadium and zinc. The data gathered from these elements were the mean soil concentration values for the locations of Cardiff, Corby, Doncaster, Lincoln, Mansfield, Scunthorpe, Sheffield, Stoke-on-Trent, Swansea, Telford, and York. The TIBCO Spotfire Software for Inorganic Soil Dashboard was customized to show the results. First, the 3D Scatterplot was switched to a 2D Scatterplot and the Map Chart was formatted to properly view latitude and longitude markers. Next, the Bar Chart was customized to view each of the elements and finally, baseline data was entered to determine whether or not the soil concentrations of specific elements tend to be above or below the baseline in each area. Figure 1 displays the Inorganic Dashboard as is upon opening the file, Figure 2 shows the appearance of the dashboard s automatic updates once the old Data Table was replaced and Figure 3 provides a view of the dashboard after it was altered to best view the imported soil data. Results When uploading the new Data Table in place of the default, SpotfireInorganicSoilv1, it was important to match each data type for the appropriate corresponding columns. Once the Data Table was formatted correctly and all appropriate data was displayed, the name of the table was changed to BGSDataUnitedKingdom. When replacing an old Data Table, TIBCO Spotfire loads the new data, however does not update the Data Table with the name of the new file; this must be done manually. The changes that progressed through this analysis can be seen in Figures 1, 2 and 3. The original 3D Scatterplot was designed to view clusters of data. Since there were not enough data points in the new table to give this particular visualization meaning, it was redesigned as a 2D Scatterplot which is shown in Figure 4. The X-axis was set to read the column names of each element chosen along the Y-axis. This format permitted the display of individual markers for each sample area. A color by rule was added to the visualization so each sample area had a unique marker color. This type of visualization makes observations, such as Swansea s concentrations of zinc, lead Figure 1. View of the Inorganic Soil Dashboard before importing new data. Figure 2. View of the Inorganic Soil Dashboard upon importing the BGSDataUnitedKingdom Data Table. Figure 3. Display of the Inorganic Soil Dashboard after altering the visualizations to display new data. 2

3 and copper are far greater than concentrations of the like in other towns, pop out for easy recognition. Preliminary observations gained in this manner may lead alternative investigations in the search for why certain metal are what they are, or what has initiated them to deviate from the norm. Since latitude and longitude coordinates were entered into the new Data Table, the Map Chart, Figure 5, automatically recognized these columns and updated its markers. For this investigation, a color by rule was added that changed all markers over the baseline value of nickel red while those that were under the limit were colored green. According to this visualization, Swansea was the only town in the United Kingdom to have an average nickel value above the baseline. This rule, among other possibilities, makes it easier to identify trends and important data points. Color, size, shape and marker by rules may easily be edited to alter the display for any column of data. The baseline values used for this analysis were averages for the United Kingdom as a whole rather than for each town. Any average results for specific elements above the baseline do not necessarily pose a threat to the environment or its people. Figure 5. Map Chart with markers indicating the average value of nickel at each sampling location. Inorganic elements that were not initially in the Bar Chart, Figure 6, needed to be selected along the Y-axis in order to be displayed. Labels were added to the bars to indicate their values. Whereas the 2D Scatterplot previously discussed displayed the range of sample results for each element, the Bar Chart supplements this data by visualizing the average value of the sample s test results. On the second page of the analysis, additional 2D Scatterplots were updated to view data for individual elements. Here, the British Geological Survey s baseline data was used to create a reference line in order to determine where, in comparison, the average element concentrations fell for each location. Table 1 summarizes some basic statistical measures for both the baseline and sample data. The visualization itself is shown in Figure 7. Figure 4. 2D Scatterplot displaying the range of sample results for each element. A color by rule has been added to display markers for each area a different color. Figure 6. Bar Chart displaying the average value of each inorganic element irrespective of sampling location. Figure 7. 2D Scatterplots displaying data for an individual element vs. each sample area. Sample concentration has been placed in context of baseline data from the British Geological Survey. 3

4 Table 1. Statistical measures of the elements in the sample data and their respective baseline concentrations. Baseline Data Sample Data Element Mean Median Maximum Minimum Mean Median Maximum Minimum Arsenic Barium Cadmium Cobalt Chromium Copper Molybdenum Nickel Lead Antimony Tin Uranium Vanadium Zinc These visualizations were used to compile data for Table 2. Locations that were determined to have an element with average soil concentration over the established baseline were indicated with an X. Data for the town of Corby contained only concentrations of lead and zinc; the elements for which no data was available were shaded gray. According to this analysis, chromium and molybdenum were the only two elements to be under the baseline at all locations. From a different perspective however, there were no towns, besides Corby, that resulted in all average element concentrations falling below their baseline. Since the baseline samples were taken from locations throughout the United Kingdom, lower concentration locations such as scenic country sides may have lowered the overall average. The sample locations for this analysis were populated towns that have a considerable amount of human interaction. It is likely for these locations to have greater concentrations of metals due to construction of infrastructure and other municipal activities. As stated earlier, the presence of these inorganic elements does not alone endanger the environment, but concentrations should be monitored in order to identify problems as they arise. Table 2. Indication of the towns with element averages above established baselines as determined from the 2D Elemental Scatterplots. Element Cardiff Corby* Doncaster Lincoln Mansfield Scunthorpe Sheffield Stokeon-Trent Arsenic X X X X Swansea Telford York Barium X X X X X X X X X X Cadmium X X X X X X X X X X Cobalt X X X X X X X X X X Chromium Copper X X X X X X X Molybdenum Nickel Lead X X X X X X X X X X Antimony X X X X X X X X X X Tin X X X X X X X X X X Uranium X X Vanadium Zinc X X X X X X X X *Lead and zinc were the only two elements to contain data for the town of Corby. X X 4

5 Conclusion The Inorganic Soil Dashboard was developed from a file that mimics a Syngistix data viewer output. Any format of new data may be reloaded in place of the default SpotfireInorganicSoilv1 data table. The fundamental ease of working with TIBCO Spotfire only requires that column matches be made to associate old data table column names with new column names and their data types. This action will automatically update existing visualizations. In this analysis, two types of visualizations were created: those that were comparative in nature and those that were built in context. From the Map Chart, 2D Scatterplot and Bar Chart, basic determinations may be made based upon relative relationships between data points. On the other hand, by comparing the independent elemental 2D Scatterplots with baseline soil data, it is possible to provide insight on which inorganic elements may or may not be endangering the local environment of its respective location within the United Kingdom. If new data is consistently entered into an analysis such as this, it may be determined when fluctuations of metal concentration occur. Having this knowledge will make it possible to associate these fluctuations with potential causal events. Statistical calculations may also be performed in TIBCO Spotfire between sample and baseline data to determine such things as variance and whether the two means are statistically significant. Each of the visualizations provides genuine standalone insight into soil quality data, yet together they accompany one another in seamless multivariate analysis. References 1. Chang, C.Y., Yu, H.Y., Chen, J.J., et al, Accumulation of Heavy Metals in Leaf Vegetables from Soils and Associated Potential Health Risks in the Pearl River Delta, South China, Environmental Monitoring and Assessment 186 (2014) , 5 Sept Antonious, G.F., Turley, E.T., Sikora, F., and Snyder, J.C., Heavy Metal Mobility in Runoff Water and Absorption by Eggplant Fruits From Sludge Treated Soil, Journal of Environmental Science and Health, Part B 43, 6 (2008) , 9 Sept Tsuji, Joyce S., Van Kerkhove, Maria D., Kaetzel, Rhonda S., et al, Evaluation of Exposure to Arsenic in Residential Soil, Environmental Health Perspectives 113, 12 (2005), 5 Sept United States Department of Agriculture, and Natural Resources Conservation Service, Heavy Metal Soil Contamination, Soil Quality Institute 3 (2000) 1-7, 8 Sept 2014 PerkinElmer, Inc. 940 Winter Street Waltham, MA USA P: (800) or (+1) For a complete listing of our global offices, visit Copyright 2014, PerkinElmer, Inc. All rights reserved. PerkinElmer is a registered trademark of PerkinElmer, Inc. All other trademarks are the property of their respective owners _01 PKI