Waste Pile and Water Sampling - USGS · Waste Pile and Water Sampling ... ¾Due to the fact that...
Transcript of Waste Pile and Water Sampling - USGS · Waste Pile and Water Sampling ... ¾Due to the fact that...
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Waste Pile and WaterSampling
Kathleen S. Smith, USGSBillings Symposium / ASMR Annual Meeting
Assessing the Toxicity Potentialof Mine-Waste Piles Workshop
June 1, 2003
U.S. Department of the Interior U.S. Geological Survey
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Flow Chart for Ranking and Prioritization
Characterization
Regulatory
Detailed
Reconnaissance
Waste Pile
& Water Sampling
Requirements vary Protocols exist for
some regulatory tests
If water is present, collect it
Probabilistic vs. nonprobabilistic
Record keeping and field notes
Degree of confidence vs. cost
Probabilistic vs. nonprobabilistic
Average properties vs. hot spots
Surficial vs. 3-D Water filtration and
filter size Sampling plan Sampling design Protocols (USEPA,
USGS, etc.) Record keeping
and field notesScale
Site Watershed
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What? Why?
¾ What to sample • Define target population
¾ Reasons for sampling ¾ Question(s) to be answered ¾ Desired degree of confidence
in the answer(s)
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Examples of Questions
¾ Are there hot spots? ¾ What is the average behavior of a
mine-waste pile? ¾ Are these two waste piles different? ¾ Are concentrations above baseline
conditions? ¾ Is a remediation approach working?
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Target Population
¾ Defined by objectives of study ¾ Must be identified prior to sampling ¾ Scale of observation matters
Target Population
Sample
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Desired Degree of Confidence
¾ Must be identified prior to sampling
¾ Low degree of confidence can lead to erroneous data and flawed decisions
¾ High degree of confidence can be expensive
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Precision versus Accuracy
Precise, unbiased, Precise, biased, and accurate and inaccurate
Imprecise, biased, Imprecise, unbiased, and inaccurate and inaccurate
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Sampling Concerns
¾ Sampling error ¾ Precision requirements • Field sampling methods and
equipment • Sample preparation • Laboratory subsampling • Analyses
¾ Sample containers ¾ Sample preservation and storage ¾ Sample holding times
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Sampling Error
¾ Improper collection • Target population • Sampling location • Spatial or temporal changes • Sampling media • Sampling tools • Sample containers
¾ Contamination ¾ Sample preservation and storage ¾ Inadequate sample mass
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Sampling Error Example—Diel Cycling(from Nimick, 2001)
Arsenic Cadmium Manganese Zinc Copper22-33 ug/L 1.4-3.0 ug/L 35-142 ug/L 214-634 ug/L 3.0-4.3 ug/L
50% 110% 306% 196% 43%
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Fundamental Error
¾ The source of most sampling errors
¾ Cannot be eliminated, but can be estimated
¾ Due to the fact that not all particles have the same composition
¾ Results in variability and a lack of precision
¾ Particle size, sample mass, and degree of heterogeneity are important factors
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Desired Particle degree of
size Heterogeneity confidence
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Fundamental Error
¾ Mineralogical factor
¾ Liberation factor
¾ Shape factor
¾ Granulometric factor
¾ Maximum particle size
¾ Sample mass
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Grouping and Segregation Error
¾ Due to the fact that not all particles are randomly distributed
¾ Size, shape, concentration
¾ Temporal differences
¾ Waste-pile segregation
¾ Can be reduced
¾ Random sampling
¾ Collection of multiple increments
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How many samples?
0
0.2
0.4
0.6
0.8
1
1.2
1 6 11 16 21 26 31 36 41 46 51 56
Number of Samples
Wid
th o
f Con
fiden
ce In
terv
al
Assumes a normal distribution
30 50
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Sampling Media
¾ Solid ¾ Liquid ¾ Biological ¾ Air
Choice of media depends on: • Regulatory requirements • Anticipated sources • Transport mechanisms • Receptors • Climate
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Solid Sampling Objectives
¾ Assess potential for acid generation ¾ Assess potential for contaminant release ¾ Identify contaminant source(s) ¾ Provide input for modeling ¾ Determine lithologic variability ¾ Establish baseline conditions ¾ Meet regulatory requirements
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Liquid Sampling Objectives
¾ Assess water quality ¾ Provide data for contaminant
loading calculations ¾ Identify contaminant source(s) ¾ Provide input for modeling ¾ Use in toxicological testing ¾ Establish baseline conditions ¾ Meet regulatory requirements
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Biological Sampling Objectives
¾ Detect changes in community composition
¾ Trace contaminant pathways ¾ Provide input for modeling ¾ Use in toxicological testing
or bioassays ¾ Establish baseline conditions ¾ Meet regulatory requirements
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Air Sampling Objectives
¾ Monitor for hazardous gases, vapors, or particulates
¾ Monitor for combustible gases or vapors
¾ Monitor for oxygen deficiency ¾ Determine total suspended
particulates ¾ Establish exposure levels ¾ Meet regulatory requirements
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Things to Keep in Mind
¾ Focusing sampling activities solely on regulated constituents often results in incomplete or incorrect characterization, which could lead to costly problems later
¾ Most modeling requires complete information (e.g., Biotic Ligand Model, geochemical speciation models)
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Sampling Methods
¾ Probabilistic (Each member of the target population has a
known probability of being selected)
• Random sampling • Systematic random sampling • Stratified random sampling
¾ Nonprobabilistic • Convenience sampling • Purposive sampling
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Sampling Methods
¾ Adaptive sampling • Sampling regions are selected
based on values of the variables of interest observed during a sampling survey
• Because sampling is based on prior data, different estimators must be used in the adaptive sampling technique to guarantee lack of bias
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Sampling Methods
¾ Composite sampling • Use when average values are of
interest
• Can significantly reduce analytical costs
¾ Hot spot sampling • Use when need to distinguish
areas of different concentrations
• Can result in expensive sampling and analytical costs
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Mining Wastes Heterogeneity
Distributional
Morphological (size and shape)
Compositional
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Mining Wastes Heterogeneity
3-D variation
Gold King Mine, Cripple Creek, Colorado (photo by William Henry Jackson; Western History/Genealogy Dept., Denver Public Library)
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Sampling Strategy for ScreeningMining Wastes
¾ Screening and prioritizing for AML studies ¾ Regional or watershed-based assessments ¾ Average properties of mine-waste pile ¾ Statistically based ¾ Field friendly ¾ Cost effective
¾ Heterogeneity • Compositional, spatial, particle size
¾ Sampling errors
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Sampling Strategy for ScreeningMining Wastes
¾ Surficial material (upper 15 cm) ¾ < 2 mm size fraction
¾ Pitard (1993) • Interplay between particle size and
sample mass • Collect many small increments • Awareness of segregation mechanisms
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Sampling Strategy for ScreeningMining Wastes
Divide mine-waste dump into at least 30 cells of roughly equal surface area
Collect a surficial sample from each cell (multiple samples from each cell if possible
and a total weight of at least 100 g)
Combine cell samples into a mine-dump composite sample
Dry sieve the mine-dump composite sample to < 2 mm (final composite sample should weigh
at least 1,000 g (1 kg) after sieving)
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Sampling Strategy for ScreeningMining Wastes
Stainless steel trowel
Plastic bucket
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Sampling Strategy for ScreeningMining Wastes
One 30-increment dump-composite sample collected using this sampling strategy contains as much information, relative to average value, as 30 individual grab samples at 1/30 of the analytical cost
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Sampling Strategy for Screening
This sampling strategy could be adapted to the sampling of other target populations, such as
¾ Individual waste-dump lobe ¾ Pit bench ¾ Dump lift ¾ Geologic unit ¾ Other "operational" units
Mining Wastes
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Particle Size Distribution
from Smith, Ramsey, and Hageman (2000)
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pH versus Particle Size
from Smith, Ramsey, and Hageman (2000)
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Zinc versus Particle Size
from Smith, Ramsey, and Hageman (2000)