June 18, 2014 – 10am-noon
Sponsors

• 10:00 to 10:10am
• 10:10 to 10:35am
• 10:35 to 11:00am
• 11:00 to 11:25am
• 11:25 to 11:50am
• 11:50 to Noon
Opening Comments and Introduction
– Anna Haines; Center for Land Use Education
Ability for Frac Sand to Emit Crystalline Silica – Potential
Health Risks:
– Dr. Crispin Pierce UW- Eau Claire
EOG Resources Air Emissions Air Monitoring for Crystalline
Silica at PM4 Preliminary data for four facilities in Chippewa
& Barron Counties:
– Dr. John Richards - Air Control Techniques
Study to Evaluate the Impact of Frac Sand Mining on
Groundwater & Overview of Other Geology of Frac Sand
Research Projects
– Mike Parsen & Jay Zambito - Wisconsin Geological and Natural
History Survey
Economic Impact of Frac Sand Mining:
– Dr. Steve Deller - UW Extension
Wrap-up and next steps: Anna Haines

• Sandstone formations

• What is non-metallic mining?
• The extraction of mineral aggregates or nonmetallic minerals for
“sale or use by the operator.”

• What is the sand mining process?
– Removal of sand (possible blasting)
– Rough screening
– Wash sand to remove fines
– Sand then goes to drying or a stock pile
– Further screening
– Possible resin coating
– Transport

• Who regulates NMM?
– Local governments
• Zoning
• Operational requirements
• Reclamation plans per NR
135
– State DNR
• Water permits
• Air permits
• Other possible regulations
– Wastewater
– Stormwater
– Hazardous waste
– Safe drinking water

• What are the potential health risks?

• How does air monitoring happen and what are the implications?

• What is the impact on groundwater?

• What is the economic impact of frac sand mining?

Frac Sand Mining – Environmental
Research Webinar
Crispin Pierce PhD
University of Wisconsin-Eau Claire

• Summary
• How do Particles/Silica Get into the Air?
• Health Risks
• Regulation
• PM/Silica Data
– Minnesota Pollution Control Agency (MPCA)
– Mine Safety and Health Administration (MSHA)
– National Institute for Occupational Safety and Health
(NIOSH)
– Industry Measurements
– Our studies
• Conclusions

• Frac sand mining, processing, and transportation increase fine dust particle levels (PM2.5, which include crystalline silica) in the air.
• These particles are known to cause cardiovascular disease, lung disease and lung cancer.
• Our measurements have found higher levels around sand plants, compared to regional levels, often above the EPA standard.
• Monitoring of local PM2.5 and silica is essential to protect public health.

As of May 2014, there are 142 frac sand facilities in Wisconsin.
Image: WisconsinWatch / Google Maps

Image: upstreamonline.com
• Frac sand mining and processing generate PM and silica through blasting, loading, and hauling; processing activities such as crushing; and transporting frac sand and “waste sand.”

Photo: Pat Popple

Photo: Vaughn Nagahashi

Photo: Vaughn Nagahashi

“Pattison Sand South Main Street older conveyor spout not properly sealed to railcar (01/09/2013)”

Residence Time in the Atmosphere
(Jaenicke, 1978)
1 µ m ~ 15 days
10 µ m ~ 1 day
Husar 2003

• Airborne pollutants
• Waterborne pollutants
• Noise pollution
• Light pollution
• Wetland loss that affects local water quality.
• Truck traffic that affects road safety.
• Greenhouse gas generation that increases climate change.

• Increased respiratory symptoms, such as irritation of the airways, coughing, or difficulty breathing, asthma;
• Development of chronic bronchitis;
• Irregular heartbeat;
• Nonfatal heart attacks;
• Premature death in people with heart or lung disease; and
• Lung cancer.

Image: Modified from http://www.riverpartners.org

• Silicosis –a fibrosis (scarring) of the lungs.
Silicosis is progressive and leads to disability and death.
• Kidney, autoimmune diseases

Lung Cancer – Crystalline silica (quartz) is classified as a human carcinogen by the following regulatory agencies:
• International Agency for Research on Cancer (IARC)
• National Toxicology Program
• California Proposition 65
• American Conference of Governmental Industrial
Hygienists (ACGIH)
• Occupational Safety and Health Administration
(OSHA) - Potential Cancer Hazard
• National Institute for Occupational Safety and Health
(NIOSH) – Potential Cancer Hazard

• Silicosis: Crude mortality rates by state, U.S. residents age 15 and over, 1991-
• estimates from U.S. Bureau of the Census. http://www.cdc.gov/niosh/docs/96-134/pdfs/96-134e.pdf

• The National Institute for Occupational Safety and Health reported 75 deaths in Wisconsin between 1996 and 2005 from silicosis, primarily among workers in manufacturing, construction and mining (Smathers, 2011).
• About 200 people in the US will die this year due to workplace exposure to silica (NIOSH 2008).
• Between 8-18 people are expected to die in
Wisconsin from workplace silicosis in 2014.
SOURCE: National Center for Health Statistics multiple cause of death data. Population estimates from U.S. Bureau of the Census, http://www.cdc.gov/niosh/docs/96-
134/pdfs/96-134e.pdf

• Six states (but not Wisconsin) are now regulating crystalline silica exposure: the State of California
OEHHS has done a careful job of establishing a non-cancer risk threshold of 3 ug/m3 to protect the public from silicosis (Myers 2010).
– New Jersey: 3 ug/m3 measured as PM10.
– New York: 0.06 ug/m3 measured as PM10.
– Texas: 0.27 ug/m3 measured as PM4.
– Vermont: 0.12 ug/m3 measured as PM10.
– Minnesota: 3 ug/m3 measured as PM4.

• Uses EPA AERMOD computer model to predict increase in air levels of pollutants.
– Amount of sand processed per day
– Unit emission rates for different kinds of stacks
– Pollution control equipment (e.g., baghouses)
• PM10 monitoring “required” but often waived.
• “Fugitive dust control plan” for emissions not from a stack.

2.5

• Jeffrey Johnson, an environmental engineering supervisor at the DNR … said there are "a couple of [frac sand plants] that would exceed the [federal] PM 2.5 standards." (Source:
Inside Climate News, 5 Nov. 2013 )

• Does not include fugitive dust emissions in prediction of pollutant levels.
• Does not consider cumulative effects from nearby sources of pollutants (e.g., other sand plants).
• Has declined to establish a limit for crystalline silica exposure.
• Does not require monitoring of PM2.5 or crystalline silica.

Across the 30 Minnesota Pollution Control
Agency air quality monitors, the Winona monitor, near 9 frac sand mining, transporting and processing sites -- has documented the poorest air quality in Minnesota to date in 2014 based on PM2.5 levels

Mine Safety and Health Administration (MSHA) monitoring of 41 sand mines and processing plants in Wisconsin.
Date Location
EOG Resources, Chippewa Falls, WI
Electrician
Job Contaminant
Quartz, respirable, >1% Qtz
Concentration PEL PPE
0.13
0.56
Y
Contractor
ID
Action
6/2012 S - General
27/2012
27/2012
4/2012
4/2012
4/2012
M - Washing & Screening
S - General
S - Ore Processing
S - Ore Processing
S - Ore Processing
Washer Operator
27/2012 M - Washing & Screening Washer Operator
27/2012
27/2012
S - General
S - General
Electrician
Electrician
27/2012 Laboratory Lab Technician
27/2012 M - Washing & Screening Washer Operator
Noise dosimeter, 80dBA threshold dose
Noise dosimeter, 90dBA threshold dose
Noise dosimeter, 80dBA threshold dose
Noise dosimeter, 90dBA threshold dose
Quartz, respirable, >1% Qtz
Quartz, respirable, >1% Qtz
Electrician Quartz, respirable, >1% Qtz
Building Repair/Maint.
Quartz, respirable, >1% Qtz
Building Repair/Maint.
Quartz, respirable, >1% Qtz
Washer Operator Quartz, respirable, >1% Qtz
52.40
50.00
Y
37.46
100.00
Y
32.85
50.00
Y
19.96
100.00
Y
0.23
0.60
0.40
0.53
Y
Y
0.82
0.47
0.70
0.54
0.57
0.60
0.74
0.69
Y
N
N
N
E
L
C

0.14
47% 0.12
0.1
OSHA PEL
0.098 mg/m3
0.08
0.06
NIOSH REL
0.050 mg/m3
0.04
32%
21%
0.02
0
MSHA NIOSH

EPA found levels <3 ug/m3 in major cities (EPA/600/R95/115) .

• 909 PM10 Samples reported from 10 frac sand operations:
– Average is 13.8 ug/m3 (PM10 standard is 150 ug/m3)
• Estimated PM2.5 levels (assuming 68% of
PM10 is PM2.5):
– Average across all samples is 11.3 ug/m3 (PM2.5 standard is 12 ug/m3)
– Average across operations is 11.9 ug/m3

EPA Annual
Standard

• Local sand mining, processing, and transport sites in the Chippewa Valley area
• Data collection of PM2.5 and PM10 sized particulates concentrations in air around active and inactive sites
– 1 to 2 minute ‘snapshots’ and 24-hour filter collections
• Also recorded longitude, latitude, relative humidity, wind direction, wind speed, and time
(actual/duration)

• A 1995 American Cancer Society study, 2002 follow-up, and published 2012 study of six cities found that each 10-microgram per-cubic-meter increase in long-term average PM2.5 concentration was associated with,
– a 4-14% increased risk of death from all natural causes,
– a 6-26% increased risk of death from cardiopulmonary/cardiovascular disease (including stroke), and
– an 8-37% increased risk of death from lung cancer.
References: http://toxicology.usu.edu/endnote/1132.pdf
, http://dx.doi.org/10.1289/ehp.1104660
, http://www.ncbi.nlm.nih.gov/pubmed/23647842

• About 15% of PM4 is silica (MSHA inspections)
• Using the PM2.5 standard, 12 ug/m3 x 15% =
1.8 ug/m3.
• This is below the State of California silica standard of 3 ug/m3.

DustTrak I model 8520 aerosol monitor, zero-calibrated, flow rate set to 1.7 L/min, PM 2.5,
PM 10 or cyclone respirable size filter used. Samples of
1min–24 hours were collected.

DustTrak II model 8530 aerosol monitor, zero-calibrated, PM
2.5, PM 10 filter used. Samples of 1 min–24 hours were collected.

SKC DPS particulate gravimetric sampler, calibrated 10 L/min,
PM 2.5 sampling head, and 47 mm PVC filter.

• US EPA federal reference method
PM2.5/PM10 and silica particulate sampler.

• Collect and analyze particle-based filters for
PM2.5, PM10 and crystalline silica levels in each of these particle sizes.

10
5
0
25
20
15
45
40
35
30
n=16 n=4 n=4 n=16
EPA PM2.5 annual limit
EOG Construction (PM 4) EOG Beginning of Plant
Operation (PM 4)
EOG Plant Open But No
Operations (PM 2.5)
EOG Plant Operating (PM
2.5)

150
PM 10
125
100
75
50
25
0
DNR Predicted Max. Offsite EOG 24-hr UWEC Offsite 1-min Samples
EPA
Standard

25
20
15
10
5
0
45
41.30
40
35
30
9
13.10
12/17/12
EOG No wind or activity.
4.5
6.20
12/28/12
SSS Slight wind, frequent truck activity, snowing.
19.5
1/3/13
EOG Slight wind, regular truck & train activity.
6.9
0
1/9/13
Fairmount
Strong wind, low truck activity.
DNR Eau Claire PM 2.5 (ug/m3)
DustTrak PM 2.5 (ug/m3)
EPA Annual PM 2.5 Standard

60
50
40
30
20
10
0
N44.6560278 W091.0950278 N44.25718 W091.40034
N45.2094444 W091.5598333 N44.055164, W091.703249
N45.2094444 W091.5598333
Locations and Measured PM
2.5
concentrations near frac sand mining and processing sites (error bars represent mean +/- s.d.).
EPA
Annual
PM 2.5
Standard

30
25
20
15
40
35
45
50
Frac Sand Train
Non-Frac Sand Train
Train Maintenance
Cars
10
5
0
12:00 12:28 12:57 13:26 13:55 14:24 14:52
Time
PM
2.5
concentrations next to a frac sand rail line with and without train traffic.

20
18
16
14
12
10
8
6
4
2
0
0
16
14
12
10
8
6
4
2
0
0 2 4
5
Dicot vs. DT I
Dicot vs. DT II
6 8 10
Dicot PM2.5 (ug/m3)
12 14 16
16
14
12
10
8
6
4
2
0
0 2 4 6 8 10
Dicot PM2.5 (ug/m3)
Dicot vs. DPS Filter
Dicot vs. DNR Background
10
Dicot PM2.5 (ug/m3)
15 20
3.5
3
2.5
2
1.5
1
0.5
0
0 0.5
1 1.5
2 2.5
Dicot PM2.5 (ug/m3)
12
3
14
3.5
16

• PM2.5 (particles with diameters of 2.5 micrometers and less) are of most concern to public health;
• Measurement and enforcement of the current
EPA annual PM2.5 standard of 12 micrograms/m3 is likely to protect against silicosis risk from respirable crystalline silica;

• Minnesota (MPCA) data show average levels just below the PM2.5 standard in some locations (e.g., Tiller Sand) but much higher levels in other locations (e.g., Winona).
• MSHA has found exceedances of the workplace silica standard in Wisconsin frac sand plants.
• NIOSH has found exceedances of workplace silica standards at hydraulic fracturing sites.

• Industry measurements have found PM10 levels at about 10% of the standard, and silica levels below the California standard.
– Estimation of PM2.5 from these data indicate levels at about the exposure standard.

• Measured levels of PM2.5 at EOG, Superior Silica
Sands (New Auburn, Auburn), Fairmount mine
(Menomonie), and Hi-Crush (Bridge Creek) were
1.7-22 micrograms/m3 higher than concurrent
DNR regional levels and often higher than the EPA standard.
• 24-Hour filter samples have also shown elevation of PM2.5 around sand plants, compared to DNR regional levels and often higher than the standard.
• Inter-instrument comparisons are underway.

• Crispin Pierce, PhD
• Associate Professor, University of Wisconsin-
Eau Claire
• 715-836-5589
• piercech@uwec.edu
• http://www.uwec.edu/Watershed/enph/silica
/index.htm

John Richards, Ph.D., P.E., QSTI
Air Control Techniques, P.C.
Cary, North Carolina
85

Speaker’s Background
Quick Summary
Ambient Crystalline Silica Characteristics
Sampling and Analytical Techniques
Upwind and Downwind Ambient PM4 Crystalline
Silica Concentrations at EOG Resources
Facilities
Conclusions
86

Speaker’s Background
Chemical/Environmental Engineer
Work Areas
Air pollution control equipment engineering
Stack testing
Agency training
PM10 and PM2.5 emission factor testing
Education
B.S. Chemical Engineering, Penn State
B.S. Chemistry, Penn State
M.S. Environmental Engineering, UNC Chapel Hill
Ph.D. UNC Chapel Hill
87

Why Measure
PM4 Ambient Crystalline Silica
Concentrations?
Wisconsin DNR has stated that there were little relevant data concerning localized concentrations of ambient PM4 crystalline silica near possible sources.
No one wants to wait 20 years to determine whether or not there were air quality issues in localized areas.

Data and Conclusions to be Presented
Sampling was conducted upwind and downwind of four
EOG facilities for 13 to 15 months.
1,176 daily average concentrations were measured
(28,224 hours of sampling).
Average PM4 crystalline silica concentration was
0.24 micrograms per cubic meter.
Sampling methods were based on a well-established EPA reference method sampler, and the samples were analyzed for crystalline silica by a well-established NIOSH method.

Crystalline
Silica
Characteristics
7/2/2014
“ Crystalline silica, usually in the form of alpha quartz, is everywhere. It is in every part of every continent. It occurs plentifully in nature and is used commonly in industry.”
Crystalline Silica
Primer, U.S. Department of the Interior
Crystalline silica is the second most common mineral in soil and rock. It comprises 12% of the Earth’s crust.
Crystalline silica particles can be released to the air whenever soil or rock are disturbed.
90

Sources of Ambient
PM4 Crystalline Silica
Wind blown dust from arid land
Farms
Global transport of desert dust
Forest fires
Public unpaved roads
Industrial sources
Construction
• 91

Crystalline Silica is not a new issue
.
Industrial efforts to minimize employee exposure have been on-going for more than
30 years.
Agencies have previously concluded that compliance with established air quality standards protects public health with respect to ambient crystalline silica.
92

EOG Facilities
CHIPPEWA COUNTY
• Chippewa Falls Sand
Processing Plant
• DS Mine
• S&S Mine
BARRON COUNTY
• DD MINE

Sand Mine and Processing Plant
Ambient PM4 Crystalline Silica Studies
EOG used eight U.S. EPA
Reference Method PM2.5 samplers with adjusted operating conditions to capture PM4 particulate matter for crystalline silica analyses.
Samplers operated for 24 hours per day from midnight-to-midnight once every three days.
94

Sand Mine and Processing Plant
Ambient PM4 Crystalline Silica Studies
PVC Filter media were used to facilitate X-Ray
Diffraction analyses of crystalline silica in PM4 particulate matter using
NIOSH Method 7500.
95

UPWIND-
DOWNWIND
SAMPLING
10-Meter Wind
Direction and Wind
Speed Monitors
Sampling Location 2
(Usually Upwind)

Results-Chippewa Falls Plant
Number of 24 hour Samples –153
Max. Value—1.44 µg/m 3
Above Detection Limit—31%
Number of 24 hour Samples –154
Max. Value—1.44 µg/m 3
Above Detection Limit – 13%

Results-DS Mine
Number of 24 hour Samples –149
Max. Value—1.06 µg/m 3
Above Detection Limit – 13%
Number of 24 hour Samples –150
Max. Value—0.63 µg/m 3
Above Detection Limit – 11%

Results-S&S Mine
Number of 24 hour Samples –149
Max. Value—0.63 µg/m 3
Above Detection Limit-9%
Number of 24 hour Samples –149
Max. Value—8.06 µg/m 3
Above Detection Limit – 17%

Results-DD Mine
Number of 24 hour Samples –137
Max. Value—0.69 µg/m 3
Above Detection Limit-13%
Number of 24 hour Samples –136
Max. Value—1.31 µg/m 3
Above Detection Limit – 12%

Daily Variations in PM4 (EOG)
and PM2.5 (Wisconsin DNR )

Summary
The data set compiled at EOG facilities represents
1,176 days (28,224 hours) of sampling.
Measured ambient concentrations of PM4 crystalline silica were very low at all four facilities, and all were at less than 10% of the California reference exposure level.
.
Upwind-to-downwind fenceline concentrations differences were extremely small.
103

104

Mike Parsen hydrogeologist michael.parsen@wgnhs.uwex.edu
Jay Zambito geologist jay.zambito@wgnhs.uwex.edu www.WisconsinGeologicalSurvey.org

Today’s outline
• Who we are, what we do…
• Chippewa Co. groundwater study
– Study overview
– Hydrostratigraphy
– Water-use data collection
• WGNHS bedrock geology capabilities
– Wisconsin bedrock mapping status
– Frac sand-related bedrock geology research
• Online resources
• Questions

• Jamie Robertson
State Geologist
• 9 Faculty
• 15 Academic/Other Staff
• 3 Program Areas
• Bedrock Geology
• Hydrogeology
• Quaternary Geology

Wisconsin Idea
Extending the boundaries of the university to the boundaries of the state.
Mission Statement
The Survey conducts earth-science surveys, field studies, and research.
We provide objective scientific information about the geology, mineral resources, water resources, soil, and biology of Wisconsin.
We collect, interpret, disseminate, and archive natural resource information. We communicate the results of our activities through publications, technical talks, and responses to inquiries from the public. These activities support informed decision making by government, industry, business, and individual citizens of Wisconsin.

Study overview
Western Chippewa Co., WI
5-year study , started in 2012
Project group :
- WGNHS
- USGS
- Chippewa County
Stakeholders include :
- Local citizens
- Wisconsin DNR
- Wisconsin Farmers Union
- Trout Unlimited
- All industrial sand mining companies in study area

Study overview
Objectives
– Modeling - develop a groundwater flow model to evaluate current and future water use and landscape changes on the hydrologic system
– Outreach - disseminate the study results to stakeholders and the general public
– Transferability - transfer the study results to similar geologic and hydrologic settings as appropriate

Why do we care?
Study overview
– Pumping in upland areas near headwaters of streams
– Intensifying water-use practices
– Changes to landscape and implications for recharge
– Long-term water resource management and sustainability

Subsurface data collection
Geophysical logs
• Superior Silica (2011)
• Preferred Sands (2011)
• Dan Stiehl farm (2013)
Provides detailed information about geology and hydrogeology
Allows for detailed hydrogeological characterization

Subsurface data collection
Collecting a geophysical log
High-capacity well - western Chippewa County
Water table
Well casing

Subsurface data collection
Geophysical log of a 320’ irrigation well
High-capacity well - western Chippewa County
Video log still: looking down hole
Picture taken 200’ below surface
4”
Video log stills: looking at wall of hole

Hydrostratigraphic interpretation
Dan Stiehl Farm Superior Silica Preferred Sands

Hydrostratigraphic interpretation
GP logs hung from same elevation
Wonewoc
Eau
Claire
Mount
Simon 1
MS - 2
MS - 3
MS – 4 ??
Precambrian

Cross
Section
West
Hydrostratigraphic interpretation
Stratigraphic framework
Hydrostratigraphic framework
Tunnel City Tunnel City
East
Wonewoc
Water table
Wonewoc
Eau Claire
Mount
Simon
Precambrian
Eau
Claire
Mount
Simon 1
MS - 2
MS - 3
MS – 4 ??
Precambrian
(No-flow boundary

1980
Water-use practices
2014
Recent intensification in water use

Groundwater Use in Wisconsin:
2012 Withdrawals
Courtesy of Robert Smail (WDNR)

Industrial sand mining
Municipal supply
Irrigated agriculture
Well information provided by WDNR
Water-use practices
Well Depth
250 - 400’
150 - 300’
100 - 300’
Pump rate
20 - 95 million gal. per year, per well
80 - 90 million gal. per year, per well
<10 - 30 million gal. per year, per well
31
6
# Wells
5
Season
10 months
∼ Feb - Nov
Year round
5 months
∼ May - Sept

WGNHS bedrock mapping
Regional scale (1:250,000) County scale (1:100,000)

WGNHS bedrock mapping
West Central Wisconsin (1:250,000)

BOTTOM
WGNHS bedrock mapping
TOP

• Publications and Resources
• State, Region, and County Maps
• General Interest Maps
• Fact Sheets
• Reports and Journal Articles
• Website www.WisconsinGeologicalSurvey.org

Data, methods, and references
• Subsurface data
– WDNR well construction report database
– WGNHS well cuttings repository
– WGNHS geophysical log repository
• Water-use data
– WDNR well construction report database
– WDNR 2012 water-use database
• Bedrock Geology
– WGNHS map catalog
– WGNHS Mount Horeb Research and Educational Center
– WGNHS 2014 frac sand fact sheet
– Roadside Geology of Wisconsin (Dott and Attig, 2004)

To find out more visit the
WGNHS website: www.WisconsinGeologicalSurvey.org
Or,
Chippewa County website: co.chippewa.wi.us/lcfm and
click on the link “Chippewa County Groundwater Study”
Questions?

Frac Sand Mining
Environmental Research Webinar
T HE C OMMUNITY E CONOMIC
I MPACTS OF M INING
Steven Deller
Department of Agricultural and Applied Economics
University of Wisconsin-Madison/Extension

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
The discussion will proceed in three parts:
1.A focus on Wisconsin.
2.What can we learn from a national perspective?
3.Local community policy in-sights.

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
50 plus employees
Number of Construction Sand and Gravel Mining Firms
20-49 employees
10-19 employees
5-9 employees
1-4 employees
Total establishments
0 10 20 30
2012 2007 2000
40 50 60 70

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
50 plus employees
Number of Industrial Sand Mining Firms
20-49 employees
10-19 employees
5-9 employees
1-4 employees
Total establishments
0 2 4 6 8
2012 2007 2000
10 12 14 16

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
Sand Mine Status 2013
Proposed 18
Permitted - no development
6
Operational 72
In Development 20
0 20 40 60 80
Source: Wisconsin Watch

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
• One company can own more than one mine.
• Not all the companies are Wisconsin firms.
Fairmount Minerals
Superior Silica Sands
EOG Resources
Chardon, OH
Kosse, TX
Houston, TX
5
4
4
About $40 of the $110-per-ton price is pure profit
(Chicago Tribune)
Difficult to assess how much of the profit is leaving Wisconsin.

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
WEDC estimates 10 jobs per frac sand mine and 50 to 80 jobs for every processing facility or combined operation. Using the lower end estimates, these 86 facilities could employ 2,780 people (Wisconsin
Watch)

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
Total Income Industry Sales
Direct Effect
Indirect Effect
Induced Effect
Total Effect
Multiplier
2,780
978
1,736
5,494
1.976
($000)
$206,186
$54,164
$70,685
$331,035
1.606
($000)
$384,991
$89,445
$129,007
$603,443
1.567
($000)
$591,654
$178,861
$208,676
$979,191
1.655
Agriculture 11
Mining 2,915
Construction 23
Manufacturin 60
TIPU
Trade
Service
152
426
1,873
Government 33
Sales Taxes
Property Taxes
Income Taxes
Other
State & Local Gov Revenue
$9,699
$12,944
$8,226
$6,312
$37,180
$659
$210,506
$1,377
$3,965
$11,116
$15,973
$85,134
$2,304
$708
$393,662
$1,844
$6,026
$24,777
$24,950
$148,857
$2,620
$1,595
$626,398
$3,648
$25,093
$43,314
$36,978
$237,385
$4,781

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
Map 1a: Mining Employment-Population Ratio

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
Map 1b: Mining Employment-Population Ratio Spatial Clustering

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING





Simple
Correlation
Analysis





 


Variable One

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
Table 1. Simple Correlations: Mining Employment to Population Ratio, Economics
Poverty Rate
Children in Poverty Rate
Income Inequality (GINI Coefficient)
Unemployment Rate
Violent Crime Rate
Property Crime Rate
Persons over 25 with a High School Degree (%)
Spearman Kendall Tau b
-0.0572
(0.0109)
-0.0505
(0.0245)
-0.1209
(0.0001)
0.0937
(0.0001)
0.0202
(0.3688)
0.0676
(0.0026)
0.0537
-0.0401
(0.0125)
-0.0349
(0.0292)
-0.0860
(0.0001)
0.0670
(0.0001)
0.0133
(0.4071)
0.0468
(0.0035)
0.0366
• Lower Poverty
• Lower Income Inequality
• Higher Rates of
Unemployment
• Higher Property Crime
• Higher Share of People with a HS Degree
• Lower Share of People with a College Degree
(0.0169) (0.0220)
Persons over 25 with a Bachelor Degree (%) -0.0540
(0.0163)
-0.0389
(0.0153)
Marginal significance in parentheses.

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
Table 2. Simple Correlations: Mining Employment to Population Ratio, Health
Under 18 Without Health Insurance (%)
Premature death (Years of Potential Life Lost)
Poor or fair health (%)
Poor physical health days
Poor mental health days
Low birthweight (%)
Marginal significance in parentheses.
Spearman Kendall Tau b
-0.1585
(0.0001)
-0.0217
(0.3463)
0.0582
(0.0190)
0.0946
(0.0001)
0.1080
(0.0001)
-0.0437
(0.0657)
-0.1124
(0.0001)
-0.0142
(0.3840)
0.0419
(0.0168)
0.0683
(0.0001)
0.0777
(0.0001)
-0.0271
(0.1054)
• Lower Rates of Youth
Without Health
Insurance
• Higher Rates of People with “Poor or Fair”
Health
• Higher Rates of People with “Poor Physically
Healthy Days”
• Higher Rates of People with “Poor Mental
Health Days”

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
Table 2. Simple Correlations: Mining Employment to Population Ratio, Health
Adult smoking (%)
Adult obesity (%)
Binge drinking (%)
Chlamydia rate (per 100k)
Teen Birth Rate
Single-Parent Households (%)
Spearman Kendall Tau b
0.1058
(0.0001)
-0.0377
(0.0929)
0.0017
(0.9478)
-0.0609
(0.0066)
-0.1269
(0.0001)
-0.0068
0.0752
(0.0001)
-0.0273
(0.0891)
0.0010
(0.9548)
-0.0477
(0.0029)
-0.0886
(0.0001)
-0.0082
• Higher Smoking Rates
• Lower Obesity Rates
(weak)
• Lower Rates of STDs
• Lower Teen Birth Rates
• Higher Number of Poor
Air Quality (Ozone) Days
Ozone Days
(0.7607)
0.1215
(0.0001)
(0.6087)
0.1016
(0.0001)
Marginal significance in parentheses.

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
A simple Growth Model for U.S. Rural Counties
2000 to 2007
• Change in Population
• Change in Employment
• Change in Per Capita Income
(1) Nonmetro, (2) Adjacent, (3) Non-Adjacent (Remote)

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
Table 7. Growth Models Nonmetro Not Adjacent Counties
Population Growth Employment Growth Per Capita Income Growth
Intercept
Population 2000
Employment 2000
Per Capita Income 2000
Percent of the Population Over Age 65 2000
Ethnic Diversity Index 2000
Percent of the Population Over Age 25 with a Bachelor Degree 2000
Percent of the Population Foreign Born 2000
Percent of the Population Speaks A Language Other than English at Home 2000
Percent of the Population Living in Same Residence in 2000 as in 1995
Poverty Rate 2000
Mining Employment to Population Ratio
0.3276
(0.0001)
0.0150
(0.0062)
-0.0119
(0.1904)
-0.0127
(0.0187)
-0.5975
(0.0001)
-0.0111
(0.4540)
0.0270
(0.6071)
-0.0862
(0.3283)
-0.0214
(0.4438)
-0.3529
(0.0001)
-0.1561
(0.0061)
-0.0037
(0.0451)
─
0.3300
(0.0001)
0.0155
(0.0047)
-0.0128
(0.1612)
-0.0127
(0.0192)
-0.6092
(0.0001)
-0.0129
(0.3870)
0.0251
(0.6308)
-0.0919
(0.2923)
-0.0219
(0.4308)
-0.3517
(0.0001)
-0.1568
(0.0056)
─
20.9079
(0.0003)
2.6353
(0.0013)
-4.6696
(0.0012)
-3.2734
(0.0035)
-59.4042
(0.0001)
0.6594
(0.8013)
66.6074
(0.0001)
-12.1358
(0.4522)
13.8382
(0.0165)
-13.8276
(0.0364)
-27.0966
(0.0039)
1.8481
(0.0001)
─
19.9805
(0.0005)
2.4187
(0.0030)
-4.2826
(0.0029)
-3.3050
(0.0031)
-54.7190
(0.0001)
1.4322
(0.5881)
67.2757
(0.0001)
-9.5433
(0.5551)
14.0079
(0.0165)
-14.3198
(0.0309)
-26.9276
(0.0041)
─
0.1557
(0.0654)
-0.0298
(0.0094)
0.0237
(0.2072)
-0.0684
(0.0119)
0.3117
(0.0622)
0.0141
(0.6338)
0.9162
(0.0001)
-0.1149
(0.6135)
0.1851
(0.0285)
0.3446
(0.0018)
0.0258
(0.8743)
0.0163
(0.0135)
─
0.1448
(0.0838)
-0.0319
(0.0055)
0.0275
(0.1441)
-0.0687
(0.0117)
0.3638
(0.0330)
0.0223
(0.4528)
0.9247
(0.0001)
-0.0893
(0.6941)
0.1872
(0.0268)
0.3394
(0.0022)
0.0289
(0.8589)
─
Mining Employment as a Share of Total Employment -0.0548
(0.1125)
23.4688
(0.0006)
0.2450
(0.0015)
Ṝ 2
F statistic
0.4017
59.65
(0.0001)
0.4023
59.81
(0.0001)
0.1921
21.77
(0.0001)
0.1959
22.28
(0.0001)
0.1262
13.62
(0.0001)
0.1296
14.00
(0.0001)
Marginal significance in parentheses.

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
Table 7. Growth Models Nonmetro Not Adjacent Counties
Population Growth Employment Growth Per Capita Income Growth
Mining Employment to Population Ratio
Mining Employment as a Share of Total Employment
-0.0037
(0.0451)
─
─
-0.0548
(0.1125)
1.8481
(0.0001)
─
─
23.4688
(0.0006)
0.0163
(0.0135)
─
Results:
Higher Dependency on Mining for Employment in 2000 is
Associated with:
• Slower Rates of Population Growth 2000 to 2007
• Faster Rates of Employment Growth 2000 to 2007
• Faster Rates of Per Capita Income Growth 2000 to 2007
─
0.2450
(0.0015)

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
Summary of Findings:
• Mining, as an industry within the U.S., remains inherently unstable through the “flickering effect” but the level of instability seems to be declining over time.
• Ownership structure of the mining companies and the resource itself greatly influence the degree of economic impact and subsequent growth. Non-local ownership is generally associated with smaller economic impacts and lower local growth levels.

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
Summary of Findings:
• The growing pool of “resource curse” literature suggests that robust economic growth and development from resource extraction activities should be considered the exception rather than a general rule.
• Communities that are more heavily dependent on mining for employment tend to experience greater negative impacts after the mines close than positive impacts while the mines are in operation.

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
Summary of Findings:
• One must guard against making blanket generalizations about the impact of mining on the local community. In many ways mining can provide well-paying jobs leading to lower levels of poverty. But on the other hand, mining activity appears to be associated with poorer overall health levels within the community.
• For remote rural counties we have weak evidence that counties more heavily dependent on mining for employment will tend to have a slower population growth rate. There is more consistent evidence that mining has a positive impact on employment and income growth rates.

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
Issues to Consider:
• Are mining operations consistent with other sources of economic activity within the region?
• Is the public infrastructure (transportation networks) sufficient to support the mining operations? Are sufficient public resources (i.e., tax revenues) available to maintain infrastructure in the face of increased deterioration through usage?
• Is there a sufficient pool of labor to meet the needs of the mining operations and replace workers who transfer into the mining industry?

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING
Issues to Consider:
• Is the community making adequate investments to build on the economic activity generated by mining operations?
• Is the community implementing strategies to adjust to mine closures? In other words, are post-mine plans in place and being acted upon?
• Is the community learning from the experiences of other communities that have experienced this type of development?

T HE C OMMUNITY E CONOMIC I MPACTS OF M INING

• Thank you to speakers:
– Dr. Crispin Pierce UW- Eau Claire
– Dr. John Richards - Air Control Techniques
– Mike Parsen & Jay Zambito - Wisconsin Geological and Natural History Survey
– Dr. Steve Deller - UW Extension
• Reminder: Can receive CE through APA-WI
• Thank you to sponsors