Frac Sand Mining Environmental Research Webinar

Current status of research findings

June 18, 2014 – 10am-noon

Sponsors

Agenda • 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

Some Background

Location

Location

• 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?

Next speaker

Ability for Frac Sand to Emit Crystalline Silica – Potential Health Risks

Frac Sand Mining – Environmental Research Webinar Crispin Pierce PhD

University of Wisconsin-Eau Claire

Our Partners

OUTLINE • 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

SUMMARY

• 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.

Image: WisconsinWatch / Google Maps

As of May 2014, there are 142 frac sand facilities in Wisconsin.

How Do Particulates and Silica Get into the Air During Sand Operations?

• 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.”

Image: upstreamonline.com

Photo: Pat Popple

Conveyor Belt Sand Leakage

Photo: Vaughn Nagahashi

Blasting

Photo: Vaughn Nagahashi

Frac Sand Trail Derailment

DNR Violations of Truck-to-Train Transfer

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

PM2.5 is in the Air for 10-15 Days

Residence Time in the Atmosphere (Jaenicke, 1978)

1 µm ~ 15 days

10 µm ~ 1 day

Husar 2003

Overview of Health Risks

• 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.

Particulate Matter (PM)

• 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.

Particle Size is Important

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

Crystalline Silica (Quartz)

• 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, 1991-1992.

• 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

• 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

Regulation

• 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.

DNR Regulation

• 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.

AERMOD Model Predictions

Sand Plants Add Significant PM2.5 Pollution to the Air

WDNR Believes PM2.5 Standards are Being Exceeded

• 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)

Critique of DNR Approach

• 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.

Measurement

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

MSHA Findings

Mine Safety and Health Administration (MSHA) monitoring of 41 sand mines and processing plants in Wisconsin.

EOG Resources, Chippewa Falls, WI

Date Location Job Contaminant Concentration PEL PPE Contractor ID Action

6/2012 S - General Electrician Quartz, respirable, >1% Qtz 0.13 0.56 Y 27/2012 M - Washing & Screening Washer Operator Noise dosimeter, 80dBA threshold dose 52.40 50.00 Y E 27/2012 M - Washing & Screening Washer Operator Noise dosimeter, 90dBA threshold dose 37.46 100.00 Y 27/2012 S - General Electrician Noise dosimeter, 80dBA threshold dose 32.85 50.00 Y 27/2012 S - General Electrician Noise dosimeter, 90dBA threshold dose 19.96 100.00 Y 27/2012 Laboratory Lab Technician Quartz, respirable, >1% Qtz 0.23 0.40 Y 27/2012 M - Washing & Screening Washer Operator Quartz, respirable, >1% Qtz 0.60 0.53 Y L 27/2012 S - General Electrician Quartz, respirable, >1% Qtz 0.82 0.57 Y C 4/2012 S - Ore Processing Building Repair/Maint. Quartz, respirable, >1% Qtz 0.47 0.60 N 4/2012 S - Ore Processing Building Repair/Maint. Quartz, respirable, >1% Qtz 0.70 0.74 N 4/2012 S - Ore Processing Washer Operator Quartz, respirable, >1% Qtz 0.54 0.69 N

NIOSH (National Institute for Occupational Safety and Health)

Results: Onsite Silica Above Limits

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14Si

O2

Conc

entr

atio

n (m

g/m

3)

47%

32%

21%

OSHA PEL 0.098 mg/m3

NIOSH REL 0.050 mg/m3

MSHA NIOSH

Industry Studies

Industry Study Found Low Levels of Silica (Richards 2013)

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

Industry Data Published by DNR

• 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

Industry Data Published by MPCA

EPA Annual Standard

Our Research

• 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)

PM2.5 Levels May be the Best Indicator of Public Health Risk

• 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

Measurement and Enforcement of the Current EPA 12 ug/m3 PM2.5 Standard is Likely to Protect Against Silicosis Risk • 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.

Sampling Instruments: DustTrak Aerosol Monitor

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.

Sampling Instruments: SKC DPS Gravimetric Sampler

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

Sampling Instruments: Andersen Dichotomous Sampler

• 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.

Measured EOG PM2.5/4 Increased During Operation

0

5

10

15

20

25

30

35

40

45

EOG Construction (PM 4) EOG Beginning of PlantOperation (PM 4)

EOG Plant Open But NoOperations (PM 2.5)

EOG Plant Operating (PM2.5)

PM C

once

ntra

tion

(ug/

m3)

n=16

n=4

EPA PM2.5 annual limit

n=16

n=4

No Activity vs. Regular Activity

PM10 Levels Higher Than DNR Predicted or EOG Measured

0

25

50

75

100

125

150

PM 1

0 (m

icro

gram

s/m

3)

PM 10

EOG 24-hr DNR Predicted Max. Offsite UWEC Offsite 1-min Samples

EPA Standard

9

4.5

19.5

0

13.10

6.20

41.30

6.9

0

5

10

15

20

25

30

35

40

45

PM 2

.5 (u

g/m

3)

DNR Eau Claire PM 2.5 (ug/m3)

DustTrak PM 2.5 (ug/m3)

12/17/12 EOG No wind

or activity.

12/28/12 SSS Slight wind, frequent truck

activity, snowing.

1/3/13 EOG Slight

wind, regular truck & train

activity.

1/9/13 Fairmount

Strong wind, low truck activity.

EPA Annual PM 2.5 Standard

PM 2.5 Increases Over Background at Sand Plants

24-Hour Sampling

0

10

20

30

40

50

60

Conc

entr

atio

n (u

g/m

3)

N44.6560278 W091.0950278 N44.25718 W091.40034 N45.2094444 W091.5598333

N45.2094444 W091.5598333 N44.055164, W091.703249

Locations and Measured PM2.5 concentrations near frac sand mining and processing sites (error bars represent mean +/- s.d.).

EPA Annual PM 2.5

Standard

Frac Sand Train Sampling

0

5

10

15

20

25

30

35

40

45

50

12:00 12:28 12:57 13:26 13:55 14:24 14:52

PM2.

5 C

once

ntra

tion

(ug/

m3)

Time

Train Maintenance Cars

Non-Frac Sand Train

Frac Sand Train

PM2.5 concentrations next to a frac sand rail line with and without train traffic.

PM2.5 Levels Along Rail Line

Data Validation

Inter-Instrument Testing

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12 14 16

DT I

PM2.

5 (u

g/m

3)

Dicot PM2.5 (ug/m3)

Dicot vs. DT I

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12 14 16

DT II

PM

2.5

(ug/

m3)

Dicot PM2.5 (ug/m3)

Dicot vs. DT II

02468

101214161820

0 5 10 15 20

DPS

Filte

r PM

2.5

(ug/

m3)

Dicot PM2.5 (ug/m3)

Dicot vs. DPS Filter

0

0.5

1

1.5

2

2.5

3

3.5

0 0.5 1 1.5 2 2.5 3 3.5

DNR

PM2.

5 Ba

ckgr

ound

(ug/

m3)

Dicot PM2.5 (ug/m3)

Dicot vs. DNR Background

Conclusions

• 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.

Questions?

• 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

Next speaker

Ambient PM4 Crystalline Silica Concentrations at

EOG Sand Producing Facilities in Wisconsin

John Richards, Ph.D., P.E., QSTI Air Control Techniques, P.C.

Cary, North Carolina

85

Road Map of the Presentation 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 PM10, PM2.5, and PM4 crystalline silica ambient air monitoring

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.

90 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.

Crystalline Silica Characteristics

Sources of Ambient PM4 Crystalline Silica

• 91

Wind blown dust from arid land

Farms

Global transport of desert dust

Forest fires

Public unpaved roads

Industrial sources

Construction

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.

Crystalline Silica is not a new issue.

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.

94

Samplers operated for 24 hours per day from midnight-to-midnight once every three days.

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/m3

Above Detection Limit—31%

Number of 24 hour Samples –154 Max. Value—1.44 µg/m3

Above Detection Limit – 13%

Results-DS Mine

Number of 24 hour Samples –149 Max. Value—1.06 µg/m3

Above Detection Limit – 13%

Number of 24 hour Samples –150 Max. Value—0.63 µg/m3

Above Detection Limit – 11%

Results-S&S Mine

Number of 24 hour Samples –149 Max. Value—0.63 µg/m3

Above Detection Limit-9%

Number of 24 hour Samples –149 Max. Value—8.06 µg/m3

Above Detection Limit – 17%

Results-DD Mine

Number of 24 hour Samples –137 Max. Value—0.69 µg/m3

Above Detection Limit-13%

Number of 24 hour Samples –136 Max. Value—1.31 µg/m3

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

Thanks for your attention.

104

Next speaker

Frac Sand Mining Environmental Research Webinar

www.WisconsinGeologicalSurvey.org

Mike Parsen hydrogeologist michael.parsen@wgnhs.uwex.edu

Jay Zambito geologist jay.zambito@wgnhs.uwex.edu

• 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

Today’s outline

• 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.

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

Study overview

Study overview Why do we care?

– 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

Provides detailed information about geology and hydrogeology

Allows for detailed hydrogeological characterization

Subsurface data collection Geophysical logs

• Superior Silica (2011)

• Preferred Sands (2011)

• Dan Stiehl farm (2013)

Collecting a geophysical log High-capacity well - western Chippewa County

Subsurface data collection

Water table

Well casing

Video log still: looking down hole Picture taken 200’ below surface

High-capacity well - western Chippewa County

4”

Subsurface data collection

Video log stills: looking at wall of hole

Geophysical log of a 320’ irrigation well

Hydrostratigraphic interpretation

Dan Stiehl Farm Superior Silica Preferred Sands

Mount Simon 1

Eau Claire

MS - 2

MS - 3

MS – 4 ??

Wonewoc

Precambrian

GP logs hung from same elevation

Hydrostratigraphic interpretation

Stratigraphic framework

Tunnel City

Eau Claire

Mount Simon

Precambrian

Mount Simon 1

Eau Claire

MS - 2 MS - 3 MS – 4 ??

Precambrian (No-flow boundary

Hydro-stratigraphic framework

Wonewoc

Tunnel City

Wonewoc Water table

Qua

tern

ary

Sand

and

gra

vel

Hydrostratigraphic interpretation

East

Cross Section

West

Water-use practices

Recent intensification in water use

1980 2014

Groundwater Use in Wisconsin: 2012 Withdrawals

Courtesy of Robert Smail (WDNR)

Well Depth

250 - 400’

150 - 300’

100 - 300’

Water-use practices

Industrial sand

mining

Municipal supply

Irrigated agriculture

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

# Wells

5

6

31

Season

10 months

∼ Feb - Nov

Year round

5 months

∼ May - Sept

Well information provided by WDNR

WGNHS bedrock mapping

Regional scale (1:250,000) County scale (1:100,000)

WGNHS bedrock mapping

West Central Wisconsin (1:250,000)

WGNHS bedrock mapping TOP

BOTTOM

inch

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

www.WisconsinGeologicalSurvey.org

• 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)

Data, methods, and references

Frac Sand Mining Environmental Research Webinar

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?

Next speaker

THE COMMUNITY ECONOMIC IMPACTS OF MINING

Steven Deller Department of Agricultural and Applied Economics

University of Wisconsin-Madison/Extension

Frac Sand Mining Environmental Research Webinar

THE COMMUNITY ECONOMIC IMPACTS OF MINING

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.

THE COMMUNITY ECONOMIC IMPACTS OF MINING

0 10 20 30 40 50 60 70

Total establishments

1-4 employees

5-9 employees

10-19 employees

20-49 employees

50 plus employees

2012 2007 2000

Number of Construction Sand and Gravel Mining Firms

THE COMMUNITY ECONOMIC IMPACTS OF MINING

0 2 4 6 8 10 12 14 16

Total establishments

1-4 employees

5-9 employees

10-19 employees

20-49 employees

50 plus employees

2012 2007 2000

Number of Industrial Sand Mining Firms

THE COMMUNITY ECONOMIC IMPACTS OF MINING

20

72

6

18

0 20 40 60 80

In Development

Operational

Permitted - nodevelopment

Proposed

Sand Mine Status 2013

Source: Wisconsin Watch

THE COMMUNITY ECONOMIC IMPACTS OF MINING

Fairmount Minerals Chardon, OH 5 Superior Silica Sands Kosse, TX 4 EOG Resources Houston, TX 4

About $40 of the $110-per-ton price is pure profit (Chicago Tribune)

• One company can own more than one mine. • Not all the companies are Wisconsin firms.

Difficult to assess how much of the profit is leaving Wisconsin.

THE COMMUNITY ECONOMIC IMPACTS OF MINING

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)

THE COMMUNITY ECONOMIC IMPACTS OF MINING Employment Labor Income

($000)Total Income

($000)Industry Sales

($000)Direct Effect 2,780 $206,186 $384,991 $591,654 Indirect Effect 978 $54,164 $89,445 $178,861 Induced Effect 1,736 $70,685 $129,007 $208,676 Total Effect 5,494 $331,035 $603,443 $979,191

Multiplier 1.976 1.606 1.567 1.655

Agriculture 11 $659 $708 $1,595 Mining 2,915 $210,506 $393,662 $626,398 Construction 23 $1,377 $1,844 $3,648 Manufacturin 60 $3,965 $6,026 $25,093 TIPU 152 $11,116 $24,777 $43,314 Trade 426 $15,973 $24,950 $36,978 Service 1,873 $85,134 $148,857 $237,385 Government 33 $2,304 $2,620 $4,781

Sales Taxes $9,699Property Taxes $12,944Income Taxes $8,226Other $6,312State & Local Gov Revenue $37,180

THE COMMUNITY ECONOMIC IMPACTS OF MINING

Map 1a: Mining Employment-Population Ratio

THE COMMUNITY ECONOMIC IMPACTS OF MINING

Map 1b: Mining Employment-Population Ratio Spatial Clustering

THE COMMUNITY ECONOMIC IMPACTS OF MINING

Simple Correlation Analysis

Variable One

Varia

ble

Two

THE COMMUNITY ECONOMIC IMPACTS OF MINING

Table 1. Simple Correlations: Mining Employment to Population Ratio, Economics

Spearman Kendall Tau b

Poverty Rate -0.0572 -0.0401(0.0109) (0.0125)

Children in Poverty Rate -0.0505 -0.0349(0.0245) (0.0292)

Income Inequality (GINI Coefficient) -0.1209 -0.0860(0.0001) (0.0001)

Unemployment Rate 0.0937 0.0670(0.0001) (0.0001)

Violent Crime Rate 0.0202 0.0133(0.3688) (0.4071)

Property Crime Rate 0.0676 0.0468(0.0026) (0.0035)

Persons over 25 with a High School Degree (%) 0.0537 0.0366(0.0169) (0.0220)

Persons over 25 with a Bachelor Degree (%) -0.0540 -0.0389(0.0163) (0.0153)

Marginal significance in parentheses.

• 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

THE COMMUNITY ECONOMIC IMPACTS OF MINING

• 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”

Table 2. Simple Correlations: Mining Employment to Population Ratio, HealthSpearman Kendall Tau b

Under 18 Without Health Insurance (%) -0.1585 -0.1124(0.0001) (0.0001)

Premature death (Years of Potential Life Lost) -0.0217 -0.0142(0.3463) (0.3840)

Poor or fair health (%) 0.0582 0.0419(0.0190) (0.0168)

Poor physical health days 0.0946 0.0683(0.0001) (0.0001)

Poor mental health days 0.1080 0.0777(0.0001) (0.0001)

Low birthweight (%) -0.0437 -0.0271(0.0657) (0.1054)

Marginal significance in parentheses.

THE COMMUNITY ECONOMIC IMPACTS OF MINING

• Higher Smoking Rates

• Lower Obesity Rates (weak)

• Lower Rates of STDs

• Lower Teen Birth Rates

• Higher Number of Poor Air Quality (Ozone) Days

Table 2. Simple Correlations: Mining Employment to Population Ratio, HealthSpearman Kendall Tau b

Adult smoking (%) 0.1058 0.0752(0.0001) (0.0001)

Adult obesity (%) -0.0377 -0.0273(0.0929) (0.0891)

Binge drinking (%) 0.0017 0.0010(0.9478) (0.9548)

Chlamydia rate (per 100k) -0.0609 -0.0477(0.0066) (0.0029)

Teen Birth Rate -0.1269 -0.0886(0.0001) (0.0001)

Single-Parent Households (%) -0.0068 -0.0082(0.7607) (0.6087)

Ozone Days 0.1215 0.1016(0.0001) (0.0001)

Marginal significance in parentheses.

THE COMMUNITY ECONOMIC IMPACTS OF MINING

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)

THE COMMUNITY ECONOMIC IMPACTS OF MINING Table 7. Growth Models Nonmetro Not Adjacent Counties

Population Growth Employment Growth Per Capita Income Growth

Intercept 0.3276 0.3300 20.9079 19.9805 0.1557 0.1448(0.0001) (0.0001) (0.0003) (0.0005) (0.0654) (0.0838)

Population 2000 0.0150 0.0155 2.6353 2.4187 -0.0298 -0.0319(0.0062) (0.0047) (0.0013) (0.0030) (0.0094) (0.0055)

Employment 2000 -0.0119 -0.0128 -4.6696 -4.2826 0.0237 0.0275(0.1904) (0.1612) (0.0012) (0.0029) (0.2072) (0.1441)

Per Capita Income 2000 -0.0127 -0.0127 -3.2734 -3.3050 -0.0684 -0.0687(0.0187) (0.0192) (0.0035) (0.0031) (0.0119) (0.0117)

Percent of the Population Over Age 65 2000 -0.5975 -0.6092 -59.4042 -54.7190 0.3117 0.3638(0.0001) (0.0001) (0.0001) (0.0001) (0.0622) (0.0330)

Ethnic Diversity Index 2000 -0.0111 -0.0129 0.6594 1.4322 0.0141 0.0223(0.4540) (0.3870) (0.8013) (0.5881) (0.6338) (0.4528)

Percent of the Population Over Age 25 with a Bachelor Degree 2000 0.0270 0.0251 66.6074 67.2757 0.9162 0.9247(0.6071) (0.6308) (0.0001) (0.0001) (0.0001) (0.0001)

Percent of the Population Foreign Born 2000 -0.0862 -0.0919 -12.1358 -9.5433 -0.1149 -0.0893(0.3283) (0.2923) (0.4522) (0.5551) (0.6135) (0.6941)

Percent of the Population Speaks A Language Other than English at Home 2000 -0.0214 -0.0219 13.8382 14.0079 0.1851 0.1872(0.4438) (0.4308) (0.0165) (0.0165) (0.0285) (0.0268)

Percent of the Population Living in Same Residence in 2000 as in 1995 -0.3529 -0.3517 -13.8276 -14.3198 0.3446 0.3394(0.0001) (0.0001) (0.0364) (0.0309) (0.0018) (0.0022)

Poverty Rate 2000 -0.1561 -0.1568 -27.0966 -26.9276 0.0258 0.0289(0.0061) (0.0056) (0.0039) (0.0041) (0.8743) (0.8589)

Mining Employment to Population Ratio -0.0037 ─ 1.8481 ─ 0.0163 ─(0.0451) (0.0001) (0.0135)

Mining Employment as a Share of Total Employment ─ -0.0548 ─ 23.4688 ─ 0.2450(0.1125) (0.0006) (0.0015)

Ṝ2 0.4017 0.4023 0.1921 0.1959 0.1262 0.1296F statistic 59.65 59.81 21.77 22.28 13.62 14.00

(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)Marginal significance in parentheses.

THE COMMUNITY ECONOMIC IMPACTS OF MINING

Table 7. Growth Models Nonmetro Not Adjacent CountiesPopulation Growth Employment Growth Per Capita Income Growth

Mining Employment to Population Ratio -0.0037 ─ 1.8481 ─ 0.0163 ─(0.0451) (0.0001) (0.0135)

Mining Employment as a Share of Total Employment ─ -0.0548 ─ 23.4688 ─ 0.2450(0.1125) (0.0006) (0.0015)

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

THE COMMUNITY ECONOMIC IMPACTS OF MINING

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.

THE COMMUNITY ECONOMIC IMPACTS OF MINING

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.

THE COMMUNITY ECONOMIC IMPACTS OF MINING

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.

THE COMMUNITY ECONOMIC IMPACTS OF MINING 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?

THE COMMUNITY ECONOMIC IMPACTS OF MINING 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?

THE COMMUNITY ECONOMIC IMPACTS OF MINING

Final Comments • 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