Exercise 8: Raster Modeling

Introduction

For this assignment the goal was to look at suitable sand mining locations in southern Trempealeau County, Wisconsin; sand mining risk model; and ultimately use the two models to determine locations for sand mining with minimal environmental and communal impact.

Methods/ Results


Suitability Model

The first step to assessing suitability for a frac sand mine was to determine the different criteria being assessed. This criteria consisted of having Wonewoc or Jordan formation quartz sands, suitable landcover, nearby railroad terminals, a gradual slope, and a shallow water table. This way, the mine has the right type of sand, easy removal of top soil, minimal travel distance from the mine to a railroad terminal, an appropriate surface incline at the site, and easy access to water, all of which are crucial to operating a successful frac sand mine. Figure 1 shows the model for calculating these factors into one *comprehensive suitability model (*shown in the far right corner of figure 2).

Figure 1: Suitability analysis ModelBuilder tool.

Figure 2 shows a map of all criteria that went into determining suitable areas within the study area.

Figure 2: Map showing different characteristics of suitability as well as comprehensive suitability model.

In figure 2, for the "Suitable Geology" map, the prime frac sand values represented Wonewoc and Jordan quartz sand formations, while the other values were non-suitable geologic formations.

In the "Landcover" map, suitable values were areas of either barren land (rock/sand/clay), grassland/herbaceous, pasture/hay, or cultivated crops as these were landcovers that could easily be temporarily re-purposed for frac sand mining. The poor values were areas of woody wetlands, shrub/scrub, forests, developed land, or open water as these landcovers would be difficult to access the underlying sand deposits.

For the "Distance to Rail" map, the distances were calculated using the euclidean distance tool and were reclassified using the "natural breaks (jenks)" classification to produce three proximity values.

In the "Suitable Slope" map, the slopes were calculated with the "slope" tool and, again, reclassified into steep and gradual classes using the "natural breaks (jenks)" classification method. The gradual slopes class shows optimal areas for frac sand mining as extracting resources across a steep slope is more difficult than mining across a gradual slope.

For the "Water Table Elevation" map, a water table elevation contour coverage file was used to create a digital elevation raster model (DEM) using the "topo to raster" tool. The resulting DEM was the basis for a following rank and reclassification, using the "natural breaks (jenks)" classification method, which produced three classes, ranging from shallow to deep water table elevation. Due to the amount of water used in frac sand mining, it is important for a mine site to have easy access to water (usually from wells reaching down to the water table) and having a shallow water table depth facilitates this need better than a large water table depth.

Lastly, the "Calculated Suitability" map was created using the "raster calculator" tool to obtain the sum of all the rasters used in this analysis. Then that sum was multiplied by the "Landcover" raster to produce the final map. Poor areas, shown in blue, represent areas of  non Wonewoc/Jordan geologic formations, non-suitable landcovers, far away from railroad terminals, steep slopes, and/or deep water tables. Best areas, shown in brown, represent areas of Wonewoc/Jordan geologic formations, suitable landcovers, near railroad terminals, gradual slopes, and/or shallow water tables.

Impact Model

Once suitability was calculated, the next step was to calculate potential risk of environmental impacts. The criteria assessed for this model included: How far away the mine site is from perennial streams, how much impact will the mine have on farmland, how far away the site will be from residential areas, how far away the site will be from schools, and how far away the site will be from sanctioned wildlife areas. The environmental and cultural impacts of these factors were then used to create a comprehensive* impact model (*shown in lower right corner of figure 4) in Model Builder (figure 3).

Figure 3: Impact analysis ModelBuilder tool.

Figure 4 shows a map of all the criteria that went into determining the environmental and cultural impacts of frac sand mining.

Figure 4: Map showing different characteristics of impact/ risk as well as a calculated impact model.

In figure 4, for the "River Proximity" map, the blue values (3) represent high-risk areas that are within 984 meters of any primary or secondary perennial streams over land or into water. The brown values (1) represent low-risk areas that are greater than 4,048 meters away from any primary or secondary perennial stream over land or into water. These values were chosen due to their presence year-round and were determined by using the natural breaks (jenks) classification method.

In the "Farm Proximity" map, the blue values (3) represent high-risk areas that are within 775 meters of any primary farmland. The brown values (1) represent low-risk areas that are greater than 1,946 meters away from primary farmland.

For the "Residential Proximity" map, the blue values (3) represent high-risk areas that are within 640 meters of residential areas. This high-risk distance was determined by the minimum distance requirement for all mines according to standard zoning laws and is what is known as a "noise/ dust shed." The brown values (1) represent low-risk areas that are greater than 28,594 meters away from residential areas. The medium and low-risk values were re-adjusted using the natural breaks (jenks) classification method after the high-risk value was set. The residential areas were derived from the Trempealeau County Zoning Districts layer. The residential areas selected consisted of: Residential Public Utilities, Residential - 20, Residential - 8, and Rural Residential fields.

In the "School Proximity" map, the blue values (3) represent high-risk areas that are within 640 meters of all schools. These high-risk distance, again was chosen due to the minimum distance requirement for residential areas. The brown values (1) represent low-risk areas that are greater than 10,875 meters from all schools. Again, the medium and low-risk values were re-adjusted using the natural breaks (jenks) classification method after the high-risk value was set.

For the "Wildlife Proximity" map, the blue values (3) represent high-risk areas that are within 15,298 meters of any official wildlife area. The brown values (1) represent low-risk areas that are within 26,790 meters of any designated wildlife area. These values were also determined by the natural breaks (jenks) classification method.

Lastly, the "Calculated Impact" map was produced by using the Raster Calculator tool to add up all of the criteria. High Impact areas, shown in blue, represent areas that are: within 984 meters of any primary or secondary perennial stream over land or into water, within 775 meters of any primary farm land, within 640 meters of a school or residential area, and/or within 15,298 meters of any designated wildlife area. Low Impact areas, shown in brown, represent areas that are greater than: 4,048 meters away from any perennial stream, 1,946 meters away from any primary farmland, 28,594 meters away from a school or residential area, and/or 26,790 meters away from any designated wildlife area.

Figure 5 shows an excel table that gives detailed distances for each risk characteristic displayed in figure 4.

Figure 5: Impact data values.

Once a suitability and impact model were made, the raster calculator tool was again used to add up the two models and create a final risk model. The data flow model is shown in figure 6 and the resulting map is shown in figure 7.

Figure 6: Calculated risk ModelBuilder tool.

Figure 7: Calculated risk and suitability model.

Viewshed Model

One thing much of western Wisconsin relies on for income and overall enjoyment by its citizens, is recreation. The appearance of a frac sand mine could be potentially harmful to certain tourist attractions and therefore is important to avoid mining in these areas at all costs. For the next model and map, a scenic bike route, chummily named "Breathtaking Highs and Lows" by the Trempealeau County tourist site*, was selected and the "viewshed" tool was run based on that loop. Figure 8 displays the data flow model and figure 9 displays the resulting map.

Figure 8: Viewshed ModelBuilder tool.

Figure 9: Viewshed map.

The Trempealeau county digital elevation model, in conjunction with the bike loop, was used as inputs in the "viewshed" tool. In figure 9, blue values represent non-visible areas from the bike loop, whereas beige areas represent visible areas from the bike loop. Although this map was not incorporated into the risk and suitability models, it very well could be and would further help the mine site selection process.

Discussion

Looking at the results it's clear that raster analysis can be extremely beneficial in determining site location for something such as a frac sand mine. Seeing all of the factors come together and be used to develop a map that shows prime locations and poor locations, really puts the importance and power of this technology into perspective. Figure 9, which shows the viewshed of a bike trail in Trempealeau county also supports the claim that analysis is never fully complete. The viewshed analysis wasn't even factored into the final risk and suitability model, but very well could've been, and in doing so, would've further narrowed down the areas suitable for frac sand mine locations. What about network analysis? Air particulate modeling? Return on investment analysis? These additional analyses, along with countless others, could all be factored into a comprehensive risk and suitability model, but were not. So then it's a matter of what to include and what not to include. For the purpose of this assignment, as well as time constraints, I do feel as though the factors chosen for this analysis were sufficient and relevant to the project's end goal - to find suitable and low impact locations for frac sand mines in southern Trempealeau county.

I found the raster calculator tool to be extremely useful and a fascinating method of analyzing multiple factors. The tool works by overlaying multiple rasters with classified values to create one raster with all of those inputs factored in. In figure 7, the effectiveness of this tool is shown, as the areas that are more suitable and have less impact (specifically in the southern and western portions of Trempealeau county), correlate heavily with the layers that went into making the final model.

Overall, the final risk and suitability raster is effective in serving its purpose. It would make for a great map to be used by mining companies, resources management companies, or government agencies in locating acceptable and beneficial locations for frac sand mine sites in Trempealeau county. Although there is a potential for more investigation to be done for this particular topic, the analyses completed on the given data does a good job of showcasing the power of GIS in site location and produced a well-thought-out model of good mine locations. 

Exercise 7: Network Analysis

Introduction

For this assignment, the goal was to build upon some of the other assignments completed throughout the semester that looked at sand mine locations in western Wisconsin. To do that, a network analysis was used to figure out the distance from each sand mine without on-site rail access to the nearest rail terminal, then estimate a cost that each county would incur due to the excess use of dump trucks on those particular roads.

All figures and calculations in this assignment are hypothetical and should only be used for educational purposes.

Methods

This assignment was completed in two parts, which made completing all required tasks a bit easier to tackle. The first part determined which mines would be used in the network analysis that would be completed in part two. To do this, a python script was used to query and select mines based on a variety of criteria:

-The mine must be active.
-The mine must not have a rail loading station on-site.
-The mine must be further away than 1.5 km from any rail terminal.

The following script (figure 1) was used to first use SQL statements to select the desired mine features, then intersect the output layer with the Wisconsin boundary, and finally remove mines that have a rail terminal within 1.5 km of the site. (See Python Scripts post for more information on how the script works.)

Figure 1: Python script that selects mines based on specified criteria.

Once the desired mines existed in their own shapefile, they were brought into ArcMap and used for the second part of this assignment; the network analysis.

In order to run a network analysis, ArcMap needs to have a network brought into the map document, so the roads network from the ESRI Geospatial database was used. Then the other necessary layers were brought in such as a Wisconsin counties shapefile and the mine locations. A tool was then created which performed a network analysis on the data and returned the total miles of road each county could expect frequent sand transportation on (figure 2).

Figure 2: Tool made in Model Builder.

Lastly, to determine cost that each county could expect to be charged with due to sand transportation was calculated by bringing the data table output from the last step into excel. In figure 3, the formula for calculating the cost incurred by county per year was miles times one hundred trips per year, all times twenty-two cents per mile:

= ((miles*100)*0.022)

Results

Figure 3: Calculating cost incurred by county in Excel.

Figure 4: Final Map.

The counties that could expect to incur the most cost on their roads due to frac sand transportation are Barron, Chippewa, Dunn, Eau Claire, and Wood. These counties are green in figure 4.

Discussion

After looking at the results, it is clear that counties with few rail terminals but many mines nearby are the ones that would expect to have the heaviest and costliest road wear. Counties like Trempealeau, Jackson, and Clark, which have only one or no rail terminals at all, have multiple mines and are the sixth, seventh, and eighth most expensive counties for sand transport. Counties like Burnett, Douglas, Waupaca, and Winnebago are among the cheapest counties for sand transport. All four have mines or rail terminals near their boarders and have routes that are mostly completed in other counties. The patterns displayed in figures 3 and 4 are quite distinguishable and showcase the logistics of a predominant industry in western Wisconsin.

Overall, network analysis can be used in many different applications and is an incredible technology. Whether it's routing your drive cross country or improving efficiency and eliminating costs of a delivery service, network analysis can help users to understand the fastest way to get somewhere, estimate costs or times of routes, planning efficient routes with multiple way points, and the list goes on. So long as the data being used in the network analysis is accurate, the analysis produces quite accurate results as well.

Exercise 6: Data Normalization, Geocoding, and Error Assessment

Introduction

The goals of this assignment were to normalize a data table containing addresses and PLSS locations for mines in western Wisconsin, geocode the addresses of the mines using the address locator tool in ArcMap, and compare the locations I picked for the mine sites to the locations picked by my classmates and the official locations from the DNR. This assignment provided insight into how a geocoding process might look in the professional world.

Methods

The first step in this assignment was to normalize the provided data table. This was done in excel and the normalization techniques included: breaking up the provided addresses into PLSS location, street address, city, city/town/village, county, and state. This helped to ensure that each field hadn't too much information and that the information was more organized and easy to navigate when geocoding.

Figure 1: The highlighted fields indicate normalized data fields.

Once the table was normalized, the next step was to geocode the addresses of the mines in ArcMap. First, the table was imported into the geocoding toolbar and the input fields were established.

Figure 2: The sheet containing the normalized address data was used and the highlighted fields were selected from the normalized data table.

Once the tool was set up, the addresses were automatically matched by ArcMap and put into the interface as a shapefile.

Figure 3: Matching locations in ArcMap.

From there, the Rematch Addresses function of the geocoding tool was used to look at where the potential addresses of the points are located. Then they could either be verified by the user or edited to have a manually selected point be the new location for that mine in the shapefile.

Figure 4: Editing and verifying address points in the mines shapefile. The mine in this photo is highlighted by a red circle.

Upon completion of editing and verifying the locations of mine addresses, the changes were saved as a shapefile layer. Next, the shapefiles of three classmates who were assigned mutual mine locations were imported into my map as well as the DNR's official mine locations shapefile. The merge tool was used to join each of my classmate's and my attribute tables together based on the mine unique ID field.

Figure 5: Merge tool inputs.

After the merge tool completed, a composite table was created and added to the map as a feature layer to which the "Mine_Uniqu" field was sorted by ascending order. Then the table was sifted through measuring the distance from my mine location to my other classmate's using the measure tool. These values were recorded in a separate table and used to analyze the error in my locations versus the legitimate ones. 

Results

After following all of the procedures described in the methods section, a map of the locations I used, a screen grab of my classmate's locations, and a comparison table were created.

Figure 6: Map of my mine locations.

Figure 7: Screen grab of my mine locations (red) and my classmate's mine locations.

Figure 8: Comparison table.

Discussion

Looking at the results and using the fourth chapter of CP Lo's Concepts and Techniques in Geographic Information Systems, the quality and accuracy of the resulting data is examined. Clearly, when referencing figure 8, the average distances between what I had as my mine location and what the class had or what the DNR had, were over 8,000 meters and 12,000 meters respectively. This is an enormous difference especially considering the sheer amount of sand mining that occurs in western Wisconsin. A difference that large could mean a location of a mine is in fact a completely different mine, and in some cases, it was. This however could also potentially be the result of these gross errors. Gross errors refer to large errors that can be easily detected and are usually caused by inadequate training or failing to adhere to standard procedures. With this assignment, there were some gross errors in that there was a level of uncertainty with the data as well as there was perhaps some confusion with using the PLSS addresses to locate the mines. Again, since there were so many mines throughout the imagery of western Wisconsin, there could be multiple mines within the same section and on the same road. Some other sources of error were in misuse or blunders with the geocoding tool. Both my classmates and I had run into some errors or confusion when selecting a point from the map. Sometimes a point on the map would be selected and then it appeard that the tool didn't recognize the selection, so the user clicked again, making two points for the address without realizing it. There were also times when the tool itself wouldn't actually use the point the user selected, but would place the point a few meters off. 

Determining the accuracy of locations in this exercise was difficult. For example, the DNR dataset containing the "actual mine locations" had a few points that clearly were inaccurate (i.e. in the middle of a crop field 200 meters away from the mine). Obviously, some of the points that I selected were also inaccurate, so it can be difficult to truly tell. The only way to do so would be to consult multiple sources and find the most recurring point or go to the location yourself and get the site's geographic coordinates. This brings up issues with time and resources to collecting that information, however.  

Conclusion

Overall, this dataset would need another comb-through if it were to be used for further analysis, due to the somewhat inaccurate data and uncertainties that are associated with it. In order to assure data accuracy, the geographer would need to go to these locations themselves and get the geographic coordinates, due to the clear lack of precision within the datasets used. On the other hand, I thought I walked away from this assignment with a better understanding of the issues associated with imperfect datasets and how to better understand correcting inaccurate data. I also feel as though I'm more comfortable with using the geocoding/ address locator tools in ArcMap after having done this assignment.

Python Scripts

Python Script for Exercise 8
#-------------------------------------------------------------------------------
# Name:        Ex 8 - Part Two
# Purpose:     Calculates
#
# Author:      Zach Miller
#
# Created:     05/15/2017
# Copyright:   (c) millerzm 2017
#-------------------------------------------------------------------------------

#import spatial settings
import arcpy
from arcpy import env
from arcpy.sa import*
arcpy.CheckOutExtension("spatial")

#set workspace
arcpy.env.workspace = "Q:\StudentCoursework\CHupy\GEOG.337.001.2175\MILLERZM\ex8\Exercise_8.gdb"
arcpy.env.overwriteOutput = True
print "{}".format(env.workspace)

#Set up variables
streams = arcpy.Raster("dist2river_reclass")
farms = arcpy.Raster("dist2farms_reclass")
schools = arcpy.Raster("dist2schools_reclassify")
people = arcpy.Raster("dist2people_reclass")
wildlife = arcpy.Raster("dist2WLA_reclassify")

#Calculating the most important variable
outweight = (schools*1.5)

#Overlaying rasters with weighted variable
weighted = (outweight + streams + farms + people + wildlife)
weighted.save("weighted_result")


print "The script is complete"

Python Script for Exercise 7

#-------------------------------------------------------------------------------
# Name:        Ex 7 - part 1
# Purpose: To perform a network analysis on frac sand transportation
#
# Author:      millerzm
#
# Created:     10/04/2017
#-------------------------------------------------------------------------------

#import system modules and set up workspace
import arcpy
from arcpy import env
arcpy.env.overwriteOutput=True
arcpy.env.workspace = "Q:\StudentCoursework\CHupy\GEOG.337.001.2175\MILLERZM\Ex7\ex7.gdb"

#set up variables
all = "all_mines"
active = "active_mines"
status = "status_mines"
railess = "mines_norail"
wi ="wi"
wtm = "rails_wtm"
final = "mines_norail_final"

#Add field delimiters for the SQL statements
field1 = arcpy.AddFieldDelimiters(all, "Site_Statu")
field2 = arcpy.AddFieldDelimiters(all, "Facility_T")

#SQL statements to select active mines, have mine facility type, and do not have rail
activeSQL = field1 + "=" + "'Active'"
statusSQL = field2 + "LIKE" + "'%Mine%'"
norailSQL = "NOT" + field2 + "LIKE" + "'%Rail%'"

#create feature layer from SQL statement
arcpy.MakeFeatureLayer_management(all, "active_mines", activeSQL)
arcpy.MakeFeatureLayer_management(active, "status_mines", statusSQL)
arcpy.MakeFeatureLayer_management(status, "mines_norail", norailSQL)

#select all mines from mines_norail layer within wi layer
arcpy.SelectLayerByLocation_management(railess, "INTERSECT", wi)

#remove all mines within 1.5 km of rails within mines_norail_final layer
arcpy.SelectLayerByLocation_management(railess, "WITHIN_A_DISTANCE", wtm, "1.5 KILOMETERS", "REMOVE_FROM_SELECTION")

#save selected features
arcpy.CopyFeatures_management(railess, final)


print "The script is complete"

Discussion

This script works by first bringing in the neccessary system modules and setting up the workspace so python knows where to grab the data from and where to put the outputs once tools in the script have been ran. Nest, variables were assigned to be defined throughout the script. *** To my pleasure, the script worked the first time and I ran into no real issues with it. The script selected 44 mines to be used for the network analysis.

Python Script for Exercise 5


#-------------------------------------------------------------------------------
# Name:        Ex 5
# Purpose:      Project, clip, and load data into geodatabase
#
# Author:      Zach Miller
#
# Created:     08/03/2017
# Copyright:   (c) millerzm 2017
# Licence:     <your licence>
#-------------------------------------------------------------------------------

#import spatial settings
import arcpy
from arcpy import env
from arcpy.sa import*
arcpy.CheckOutExtension("spatial")

#set workspace
arcpy.env.workspace = "Q:\StudentCoursework\CHupy\GEOG.337.001.2175\MILLERZM\Ex5\Work"
arcpy.env.overwriteOutput = True
print "{}".format(env.workspace)

#get a list of rasters
ListOfRasters = arcpy.ListRasters()
print "{}".format(ListOfRasters)

#loop through the rasters
for raster in ListOfRasters:
    #define the outputs
    rasterOut = "{}_Out.tif".format(raster)
    rasterExtract = "{}_Extract.tif".format(raster)

    #project the rasters
    arcpy.ProjectRaster_management(raster, rasterOut, "Q:\StudentCoursework\CHupy\GEOG.337.001.2175\MILLERZM\Ex5\TrempWebDATA.gdb\Boundaries\County_Boundary")

    #Extract the raster and copy the raster into the geodatabase
    outExtractByMask = ExtractByMask(rasterOut, "Q:\StudentCoursework\CHupy\GEOG.337.001.2175\MILLERZM\Ex5\TrempWebDATA.gdb\Boundaries\County_Boundary")
    outExtractByMask.save(rasterExtract)
    arcpy.RasterToGeodatabase_conversion(rasterExtract, "Q:\StudentCoursework\CHupy\GEOG.337.001.2175\MILLERZM\Ex5\Work")
    print "Raster to Geodatabase conversion {} successful".format(rasterExtract)


print "Script is complete"

Exercise 5: Data Downloading, Interoperability, and Working With Projections in Python

Introduction

The goal of this assignment was to become familiar with downloading data from a multitude of internet sources, importing the data into ArcGIS applications, and automating the projection of the data using python. This data collection and preparation will be used in a latter assignment which will focus on frac sand mining in Trempealeau County.

Methods

In order to ensure an organized and concise method of downloading data from multiple sources, a temporary folder was created in which all of the .zip files downloaded would be stored until unzipped into the assignment's working folder. The data for the digital elevation model (DEM) and the land cover came from the USGS National Map. The data for the cropland map was found in the USDA Geospatial Gateway. The data for the soil survey was obtained from the USDA NRCS Soil Survey. Lastly, the Trempealeau County database was derived from the Trempealeau County Land Records Department.

Once the data was downloaded and unzipped into the working folder, python scripter was used to automate projecting the shapefiles and rasters into the same projection (NAD 83 Trempealeau County US Feet) and any shapefiles or rasters that were not just the county already were clipped.

 #-------------------------------------------------------------------------------
# Name:        Ex 5
# Purpose:      Project, clip, and load data into geodatabase
#
# Author:      Zach Miller
#
# Created:     08/03/2017
#
#-------------------------------------------------------------------------------

#import spatial settings
import arcpy
from arcpy import env
from arcpy.sa import*
arcpy.CheckOutExtension("spatial")

#set workspace
arcpy.env.workspace = "Q:\StudentCoursework\CHupy\GEOG.337.001.2175\MILLERZM\Ex5\Work"
arcpy.env.overwriteOutput = True
print "{}".format(env.workspace)

#get a list of rasters
listOfRasters = arcpy.ListRasters()
print "{}".format(listOfRasters)

#loop through the rasters
for raster in listOfRasters:
    #define the outputs
    rasterOut = "{}_Out.tif".format(raster)
    rasterExtract = "{}_Extract.tif".format(raster)

    #project the rasters
    arcpy.ProjectRaster_management(raster, rasterOut, "Q:\StudentCoursework\CHupy\GEOG.337.001.2175\MILLERZM\Ex5\TrempWebDATA.gdb\Boundaries\County_Boundary")

    #Extract the raster and copy the raster into the geodatabase
    outExtractByMask = ExtractByMask(rasterOut, "Q:\StudentCoursework\CHupy\GEOG.337.001.2175\MILLERZM\Ex5\TrempWebDATA.gdb\Boundaries\County_Boundary")
    outExtractByMask.save(rasterExtract)
    arcpy.RasterToGeodatabase_conversion(rasterExtract, "Q:\StudentCoursework\CHupy\GEOG.337.001.2175\MILLERZM\Ex5\Work")
    print "Raster to Geodatabase conversion {} successful".format(rasterExtract)

print "Script is complete"

Next, the accuracy of the data was determined by looking at the metadata of the downloaded datasets.

Lastly, three maps were made to show the landcover, cropland, and DEM of Trempealeau County.

Results

Conclusions

After making the maps and reviewing the accuracy of the data, it is clear that there is quite a bit of information missing. In the instance of this assignment, the sources used to collect the data are fairly reputable and credible. Time-wise the lack of temporal information might be critical in some situations but for the context of this assignment, again, it's not. As far as the usability of these maps, the landcover is relatively useful. For the croplands map, there is an overwhelming amount of classified values and would require a much smaller scale to really be effective.

Sand Mining in Western Wisconsin Overview

Introduction

Frac sand mining is the process of extracting well-rounded quartz sand from sandstone of a very particular size (425 to 212 microns). This process starts by site location; finding where these deposits exist and if they're economically viable. In order to be deemed economically viable and profitable, a deposit must have no more than 50 feet of overburden (the amount of ground to be removed in order to access the deposit) for a particular deposit and relatively easy access to water. Once the sand is extracted, it undergoes washing, sorting, drying, and is then shipped to a hydraulic oil fracking site to be used in that process.

Where in Wisconsin?

Due to the geologic history of western Wisconsin, much of the country’s frac sand is mined in Wisconsin, but there are also deposits in Minnesota, Michigan, Missouri, and in the eastern United States.

Figure 1. Quartz sandstone formations and frac sand sites in Wisconsin

Environmental and Public Health Concerns

With the excavation and processing methods associated with mining for frac sand (or really any type of mining) comes potential health risks to the workers, citizens in the area, and the environment. One of the major issues associated with frac sand mining is air pollutants, more specifically: silica. Because frac sand is comprised of almost pure quartz (SiO2), drilling, processing, and transporting the sand creates silica dust. These silica particles are then dispersed in the air and can then be inhaled by workers, nearby citizens, and nearby wildlife. This prolonged exposure to silica particles in the air can lead to silicosis.

Another issue concerned with frac sand mining is groundwater depletion and/or contamination. Due to the processing involved with frac sand mining, the operation requires a significant amount of water to wash the sand. Depending on the size, duration, and location of the operation, groundwater wells could be used and therefore, might deplete the wells used, if not contribute to lowering of the water table. This water is also sometimes mixed with chemicals for the purpose of sanitizing or further washing the sand. When the company is done with the washing/sanitizing process, the mixture is dumped and could seep into the ground water. This contamination could affect wildlife, citizens who use water from the same aquifer, and streams that are fed by that aquifer.

GIS and Frac Sand Mining

Using a geographic information system could be extremely beneficial when it comes to mining frac sand in terms of managing deposit data, modeling mine sites and geological deposits, and modeling potential environmental and public health hazards associated with this industry.

Sources

http://www.fracsandinsider.com/mmi_ConferenceDL/2013FSI/FSIThurAlan%20Bennetts%20(2).pdf

https://iisc.uiowa.edu/sites/iisc.uiowa.edu/files/project/files/assessment_of_site_suitability_for_frac_sand_mining_in_winneshiek_county_final_report.pdf

http://wcwrpc.org/frac-sand-factsheet.pdf

http://geology.com/minerals/quartz.shtml

http://www.lung.org/lung-health-and-diseases/lung-disease-lookup/silicosis/silicosis-symptoms-causes-risk.html?referrer=https://www.google.com/

HalfmoonSeminar-Exploring%20Impacts.pdf