STATISTICAL ANALYSIS OF
MINING WASTE SAMPLE
DATA
FINAL REPORT
Submitted by:
Meridian Research, Inc.
818 Roeder Road
Silver Spring, Maryland 20910
Prepared under:
Contract No. 68-01-7053
Subcontract No. 939-1
Work Assignment 3.5
June 30, 1986
-------
STATISTICAL ANALYSIS OF MINING
WASTE SAMPLE DATA
1.0 INTRODUCTION
The Environmental Protection Agency's Report to Congress entitled Wastes
from the Extraction and Beneficiation of Metallic Ores, Phosphate Rock,
Asbestos, Overburden from Uranium Mining, and Oil Shale included results of
mining waste samples that were evaluated for the RCRA Subtitle C hazardous
waste characteristics of corrosivity and EP toxicity and for several other
potentially hazardous characteristics (cyanide content, radioactivity,
asbestos content, and acid formation potential). The majority of the mining
waste sample results used in the Report to Congress were taken from the
following contractor reports prepared for the Environmental Protection Agency:
ERCO/A Division of ENESCO. 1984. Mining Waste Study: Draft Data Report
Prepared for the Office of Solid Waste, U.S. Environmental Protection
Agency under Contract No. 68-01-6467. Cambridge, MA: ERCO/A
Division of ENESCO.
Harty, David M., and Terlecky, Michael P. 1982. Characterization of
Wastewater and Solid Wastes Generated in Selected Ore Mining
Subcategories (Sb, Hg, Al, V, W, Ni, Ti). Report prepared for the
Effluent Guidelines Division, U.S. Environmental Protection Agency,
by Frontier Technical Associates Inc. Contract No. 68-01-5163.
Buffalo, NY: Frontier Technical Associates, Inc.
PEDCo Environmental, Inc. Evaluation of Management Practices for Mine
Solid Waste Storage, Disposal, and Treatment. 3 Vols. Prepared for
the Industrial Environmental Research Laboratory, U.S. Environmental
Protection Agency. Contract No. 68-03-2900. Cincinnati, OH: PEDCo
Environmental, Inc.
These reports contain the results of analyses conducted to measure the
concentration of various elements, anions, radionuclides, and other parameters
in raw mining waste samples and acetic acid extracts of these samples. The
PEDCo report also contains results of analyses conducted on distilled water
extracts of these samples. The results for cyanide presented in these reports
were supplemented by additional data supplied by PEDCo Environmental Inc. to
the Office of Solid Waste in 1984.
-------
These reports presented many more mining waste sampling results than were
necessary to evaluate these wastes for the RCRA Subtitle C hazardous waste
characteristics of corrosivity and EP toxicity and the other potentially
hazardous characteristics of cyanide, radioactivity, asbestos, and acid
formation potential. This report presents an analysis of some of the data not
used in the Report to Congress to aid in the Agency's determination of the
hazard potential of mining and beneficiation wastes. In order to support the
analyses necessary for the Mining Waste Report to Congress, Meridian Research,
Inc. compiled the data from the above-named sources into an automated data
base. The data base was subsequently augmented to support the analyses
presented in this report.
Currently, the data base contains analytical results for 582 mining wastes
samples taken from 99 mines. Analytical results from raw samples, acetic acid
extracts, and distilled water extracts are included in the data base. In
addition, samples are classified as being mine waste, tailings, or from a
leach operation.
This report presents results for two types of analyses conducted by
Meridian. In Section 2.0 of this report, Meridian presents the results of a
comparative analysis between the mining waste sample data and a variety of EPA
standards and criteria for hazardous waste, drinking water, and water
quality. The comparative analysis expands upon the analysis of the mining
waste data presented in the Report to Congress. Meridian also examined the
relationships among the analytical results for raw samples, acid extract
samples, and distilled water samples analyzed for EP-toxic metals. These
relationships were analyzed using linear regressions and are discussed in
Section 3.0. The detailed results of the regression analysis are presented in
Appendices A and B.
-------
2.0 COMPARATIVE ANALYSIS OF MINING WASTE DATA WITH VARIOUS EPA STANDARDS
AND CRITERIA
In the Mining Waste Report to Congress, the potential environmental hazard
posed by mining waste was assessed by comparing the mining waste sample data
to the hazardous waste criteria established under RCRA Subtitle C (40 CFR
261.20-24), as well as to other potentially hazardous characteristics. A
number of other EPA standards and criteria exist that can serve as benchmarks
to aid iVassessing the hazard potential of mining waste. Meridian identified
several EPA standards and criteria that contain allowable or suggested
contaminant limits for some of the parameters contained in the mining waste
data base. These standards and criteria include:
t Prohibitions on the land disposal of liquid hazardous wastes
specified in the 1984 RCRA Amendments
• National Primary Drinking Water Standards (40 CFR 141) promulgated
under Section 1412 of the Safe Drinking Water Act.
• Secondary Drinking Water Standards (40 CFR 143), which, unlike the
primary standards, are only advisory at the Federal level. These
standards were also promulgated under Section 1412 of the Safe
Drinking Water Act.
• Ambient water quality criteria for the protection of freshwater
aquatic life (various Federal Register notices in 1985 and 1986) and
human health (45 FR 79318, November 28, 1980) published under the
aegis of Section 383 of the Clean Water Act.
Table 1 shows the mining waste parameters that were considered in the Report
to Congress and that are considered in this analysis. The standards and
criteria upon which this analysis is based are summarized in Table 2 and
described briefly below.
2.1 DESCRIPTION OF EPA STANDARDS AND CRITERIA USED
2.1.1 RCRA Amendments
The 1984 Hazardous and Solid Waste Amendments to the Resource Conservation
and Recovery Act (RCRA) prohibited all methods of land disposal (except deep
-------
Table 1. Mining Waste Parameters Measured
by EPA Contractors
Considered in Considered in
Parameter9 Report to Congress This Report
Metals
Ag Silver
As Arsenic
Ba Ban urn
Be Beryllium
Ca Calcium
Cd Cadmium
Cr Chromium
Ca Copper
Fe Iron
Hg Mercury
K Potassium
Mg Magnesium
Mn Manganese
Mo Molybdenum
Na Sodium
N1 Nickel
Pb Lead
Th Thallium
V Vanadium
Zn Z1 nc
Anions and Others
PH pH
ALK Alkalinity
ACY Acidity
COND Conductivity
TEMP Temperature
SSOL Settleable Solids
NH3 Ammonia
NOa Nitrate
F Fl uori de
TP Total Phosphorus
$04 Sulfate
TOC Total Organic Carbon
TSS Total Suspended Solids
TDS Total Dissolved Solids
SAR Sodium Adsorption Ratio
TURB Turbidity
Yes
Yes
Yes
No
No
Yes
Yes
No
No
Yes
No
No
No
No
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No
No
Yes
No
No
Yes
Yes
Yes
No
Yes
Yes
No
No
No
No
No
Yes
Yes
Yes
No
Yes
No
No
Yes
No
Yes
Source: Meridian Research, Inc.
a Excludes radionuclides.
-------
Table 2. Hazardous Waste and Water Quality Standards (In mg/1)
Used to Analyze Mining Waste Sample Results
Standards Specifying Limits for These
Mining Waste
Parameters
Measured
Ag (Silver)
As (Arsenic)
Ba (Barium)
Be (Beryllium)
Cd (Cadmium)
Cr (Chromium)
Cu (Copper)
Fe (Iron)
Hg (Mercury)
Mn (Manganese)
N1 (Nickel)
Pb (Lead)
Se (Selenium)
Tl (Thallium)
Zn (Zinc)
Cyanides
NH3 (Ammonia)3
«*
N03 (Nitrate)
F (Fluoride)
SO* (Sulfate)
pH»
TDS (Total dissolved
solids)*
TURB (Turbidity)9
Limits for
Liquids In Landfills
Set by 1984
RCRA Amendments
500
100
500 (VI)
20
134
500
100
130
1.000
<2
RCRA Subtitle C
Characteristics of
Hazardous Waste
5
5
100
1
5
0.2
5
1
<2 or >12.5
Parameters
National Primary National Secondary
Drinking Water Drinking Water
Regulations Regulations
0.05
0.05
1.00
0.01
0.05
0.002
0.05
0.01
10.0
4.0
1 TU (turbidity
unit)3
1.0
0.3
0.05
5.0
2.0
250
6.5 - 8.5
500
Ambient Water
Quality Criteria
for Human Health
0.050
0.0000022
0.0000037
0.010
0.050 (VI)
1.0
0.000144
0.0134
0.050
0.010
5.0
0.20
Ambient Water
Quality Criteria
for Freshwater
Aquatic Life
0.1 9b
0.001 lc
0.011 (VI)°
0.0126
0.00001 2b
0.16° .
0.0032°
0.01B
0.0052°
0.0019(pH=6.5)«
0.035(pH=8)e
Source: Meridian Research, Inc.
a Measured only In liquids.
° Four-day average concentration that should not exceed stated level more than once every three years on the average.
c Four-day average concentration (assuming a water hardness of 100 mg/1 as CaC03) that does not exceed stated level more than once every
three years on the average.
d (1) One turbidity unit (TU), as determined by a monthly average pursuant to §141.22. except that five or fewer turbidity units may be
allowed 1f the supplier of water can demonstrate to the State that the higher turbidity does not create any of the following circumstances:
(a) Interference with disinfection;
(b) Prevention of maintenance of an effective disinfectant agent throughout the distribution system; or
(c) Interference with microbiological determinations.
(2) Five turbidity units based on an average for two consecutive days pursuant to §141.22.
e Standard for water at 25°C where Salmonld-sensltlve species are present.
-------
well injection) of the so-called "California list" of hazardous wastes unless
EPA determines that the prohibition is not required to protect human health
and the environment. In making this determination, the Administrator of EPA
must take into account the long-term uncertainties associated with land
disposal; the goal of managing hazardous waste in an appropriate manner; and
the persistence, toxicity, mobility, and propensity of such hazardous wastes
(and their constituents) to bioaccumulate. The "California list" of hazardous
wastes includes liquid hazardous wastes and sludges containing specified
concentrations of cyanide, heavy metals or arsenic, highly acidic liquids,
liquids containing 50 or more parts per million of polycholorinated biphenyls
(PCBs), and halogenated organic compounds in total concentrations of 1000
mg/kg or greater. The limits for land disposal of wastes containing
constituents that are relevant to the mining waste sample data are listed in
Table 2.
2.1.2 RCRA Subtitle C Characteristics of Hazardous Waste
The RCRA Subtitle C characteristics for identifying wastes as being
hazardous include the following:
• Ignitability. The waste poses a fire hazard during routine
management.
• Corrosivity. The waste has the ability to corrode standard
containers or to dissolve toxic components of other wastes.
• Reactivity. The waste has a tendency to explode under normal
management conditions, to react violently when mixed with water, or
to generate toxic gases.
• EP Toxicity. The waste exhibits the presence of one or more
specified toxic materials at levels greater than those designated in
the Agency's regulations when it is analyzed by a specific
"extraction procedure."
EPA's Report to Congress contained an analysis of the mining waste data
set for the characteristics of corrosivity and EP toxicity. A waste is
-------
considered corrosive and therefore hazardous if it is a liquid and has a pH
less than or equal to 2 or greater than or equal to 12.5, as determined by a
pH meter. EPA chose pH as a "barometer of corrosivity, because wastes
exhibiting tew or high pH can cause harm to human tissue, promote the
migration of toxic contaminants from other wastes, and harm aquatic life"
(45 FR 33109, May 19, 1980).
A solid waste is defined as EP toxic and therefore hazardous if, using
the test methods described in 40 CFR Part 261 (Appendix II), an acetic acid
extract from a representative sample of waste contains certain metals at a
concentration greater than or equal to 100 times the maximum contaminant
levels for these metals as established by EPA's National Primary Drinking
Water Standards. The limits set by RCRA Subtitle C characteristics for
corrosivity and EP toxicity (for metals) are shown in Table 2.
2.1.3 Primary and Secondary Drinking Water Standards
Drinking water standards, mandated under Section 1412 of the Safe
Drinking Water Act (SDWA) (PL 93-523)(42 USC 300f et seq.), apply to public
water systems, which are defined as those systems piping water for consumption
by 25 or more people or those systems having at least 15 service connections.
Under this act, the EPA sets both primary and secondary water standards.
EPA's National Primary Drinking Water Standards (NPDWS) set limits for
contaminants that may affect health. The primary water rules specify
allowable Maximum Contaminant Levels (MCLs) as shown in Table 2.
Regulations for the implementation and enforcement of the National
Primary Drinking Water Regulations are contained in 40 CFR 141. States are
required to monitor and measure the amounts of specified contaminants in
public water supplies and periodically report the results to the EPA. Local
-------
governments and private water companies that supply water to the public must
test water quality to ensure that specified contaminants do not exceed Federal
limits. Suppliers of water must keep records of water quality, notify
consumers if-any MCL 1s being exceeded, and act promptly to correct any rule
violation.
EPA's National Secondary Drinking Water Standards (NSDWS) apply to
drinking water contaminants that primarily affect the aesthetic qualities
relating to the public acceptance of drinking water. These standards are only
advisory, are not Federally enforceable, and are Intended as guidelines for
States. The secondary MCLs for public water systems include those
contaminants listed In Table 2.
2.1.4 Water Quality Criteria
Criteria for water quality are set by EPA to indicate the levels of
specific pollutants that ambient water can contain and still be suitable for
certain purposes. Once these criteria are set, States use them to establish
water quality standards and to determine the discharge limits for individual
National Pollutant Discharge Elimination System (NPDES) permits. The Federal
Government uses the criteria to set general effluent limitation guidelines for
various industrial and municipal pollution sources.
Section 304(a)(l) of the CWA requires EPA to publish and periodically
update ambient water quality criteria in order to reflect the latest
scientific knowledge on the identifiable effects of pollutants on public
health and welfare, aquatic life, and recreation. The ambient water quality
criteria for human health (Table 2) were originally published on November 28,
1980 (45 FR 79318). The freshwater aquatic life criteria cited in Table 2 for
arsenic, cadmium, chromium (VI), copper, lead, mercury, nickel, and selenium
8
-------
replace the criteria previously published in 1980. The freshwater aquatic
life criteria for ammonia replace criteria originally published in 1976.
2.2 RESULTS OF COMPARATIVE ANALYSIS
Table 3 shows, for acid extraction of metals and anions, the number of
mine waste, tailings, or leachate samples that exceed each of the standards or
criteria used in the comparison. The table also shows the total number of
samples that were analyzed for each of the parameters under consideration (not
all samples were chemically analyzed for every parameter). The results of the
analysis conducted on distilled water extracts of mining waste samples appear
in Table 4. In order to provide results comparable to the analysis of
EP-toxic metals in the Report to Congress, data contained in the PEDCo report
for liquid samples were excluded from consideration. In addition, 35 mining
waste samples taken from inactive mines were excluded.
As shown in Table 3, there are fewer acid extracts of tailings samples
than mine waste samples for all parameters measured. However, for most of the
parameters, a greater number of tailings samples than mine waste samples
exceed standards for primary and secondary drinking water and ambient water
quality criteria for human health and aquatic life. For distilled water
extracts of samples, this was only true for most parameters measured when they
were compared to the primary and secondary drinking water standards
(Table 4). These results are not surprising given that tailings are generated
from beneficiation of enriched product rather than from extraction of raw ore.
For a few metals, 100 percent of all acid and water extracts exceed
certain standards; this was noted when samples analyzed for arsenic or
beryllium were compared to respective ambient water quality criteria standards
for human health, or when samples analyzed for mercury were compared to the
-------
Table 3. Number of Acetic Acid Extract Samples that Exceed
Specified Standard, by Parameter Measured and Type of Waste
Standards Specifying Limits for
Mining Waste
Parameters
Measured and
Waste Type
As (Arsenic)
Mine Waste
Tailings
Leachate
Ag (Silver)
Mine Waste
Tailings
Leachate
Ba (Barium)
Mine Waste
Tailings
Leachate
3 Be (Beryllium)
Mine Waste
Tailings
Leachate
Cd (Cadmium)
Mine Waste
Tailings
Leachate
Cr (Chromium)
Mine Waste
Tailings
Leachate
Cu (Copper)
Mine Waste
Tailings
Leachate
Total
Number of
Sampl esa
206
189
18
203
189
18
203
189
18
203
189
18
203
188
18
203
189
18
203
189
18
Limits for
Liquids 1n Landfills
Set by 1984
RCRA Amendments
0
0
0
NA
NA
NA
NA
NA
NA
NA
NA
NA
0
0
0
0
0
0
NA
NA
NA
RCRA Subtitle C
Characteristics of
Hazardous Waste
0
0
1
0
0
0
0
0
0
NA
NA
NA
0
0
1
0
0
0
NA
NA
NA
National
Primary
Drinking Water
Regulations
(IX) (100X)
13
25
6
3
11
3
16
14
2
NA
NA
NA
49
68
10
63
78
11
NA
NA
NA
0
0
1
0
0
0
0
0
0
NA
NA
NA
0
0
1
0
0
0
NA
NA
NA
These Parameters
National
Secondary
Drinking Water
Regulations
(IX) (100X)
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
11
28
12
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0
1
3
Ambient
Water
Quality Criteria
for Human Health
(IX) (100X)
206
189
18
3
11
3
NA
NA
NA
203
189
18
49
68
10
63
78
11
11
28
12
194
188
18
0
0
0
NA
NA
NA
138
141
14
0
0
1
0
0
0
0
1
3
Ambient
Water
Quality Criteria
for Freshwater
Aquatic Life
(IX) (100X)
0
10
5
NA
NA
NA
NA
NA
NA
NA
NA
NA
202
187
18
126
123
11
109
136
17
0
0
0
NA
NA
NA
NA
NA
NA
NA
NA
NA
3
11
3
0
1
3
11
24
12
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Table 3. Number of Acetic Acid Extract Samples that Exceed
Specified Standard, by Parameter Measured and Type of Waste (continued)
Standards Specifying
Mining Waste
Parameters
Measured and
Waste Type
Fe (Iron)
Mine Waste
Tailings
Leachate
Hg (Mercury)
Mine Waste
Tailings
Leachate
Mn (Manganese)
Mine Waste
Tailings
Leachate
N1 (Nickel)
Mine Waste
Tailings
Leachate
Pb (Lead)
Mine Waste
Tailings
Leachate
Se (Selenium)
Mine Waste
Tailings
Leachate
Tl (Thallium)
Mine Waste
Tailings
Leachate
Limits for
Total Liquids In Landfills
Number of Set by 1984
Samples3 RCRA Amendments
203
189
18
205
187
18
203
189
18
203
189
18
203
189
18
206
189
18
203
189
18
NA
NA
NA
0
0
0
NA
NA
NA
0
0
0
0
0
0
0
0
0
0
0
0
Limits for These Parameters
RCRA Subtitle C National Primary
Characteristics of Drinking Water
Hazardous Waste Regulations
(IX) (100X)
NA
NA
NA
0
1
0
NA
NA
NA
NA
NA
NA
6
13
0
1
0
1
NA
NA
NA
NA
NA
NA
20
18
4
NA
NA
NA
NA
NA
NA
158
135
10
82
97
11
NA
NA
NA
NA
NA
NA
0
1
0
NA
NA
NA
NA
NA
NA
6
13
0
1
0
1
NA
NA
NA
National Secondary
Drinking Water
Regulations
(IX) (100X)
49
55
7
NA
NA
NA
174
169
14
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
1
3
5
NA
NA
NA
35
52
8
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Ambient Water
Quality Criteria
for Human Health
(IX) (100X)
NA
NA
NA
184
182
18
NA
NA
NA
185
186
18
158
135
10
82
97
11
NA
NA
NA
NA
NA
NA
8
12
3
NA
NA
NA
1
5
4
6
13
0
1
0
1
NA
NA
NA
Ambient Water
Quality Criteria
for Freshwater
Aquatic Life
(IX) (100X)
NA
NA
NA
205
187
18
NA
NA
NA
43
71
10
203
189
18
82
97
11
NA
NA
NA
NA
NA
NA
24
23
4
NA
NA
NA
0
0
0
13
25
1
1
0
1
NA
NA
NA
-------
Table 3. Number of Acetic Add Extract Samples that Exceed
Specified Standard, by Parameter Measured and Type of Haste (continued)
Standards Specifying Limits for These Parameters
Mining Waste
Parameters
Measured and
Waste Type
Zn (Zinc)
Mine Waste
Tailings
Leachate
N03( Nitrate)
Mine Waste
Tailings
Leachate
F (Fluoride)
Mine Waste
Tailings
Leachate
S04(Sulfate)
Mine Waste
Tailings
Leachate
Total
Number of
Sampl esa
203
188
18
185
158
17
48
54
9
184
156
17
Limits for
Liquids In Landfills
Set by 1984
RCRA Amendments
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
RCRA Subtitle C
Characteristics of
Hazardous Waste
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
National
Drinking
Primary
Water
Regulations
(IX) (100X)
NA
NA
NA
1
6
2
2
8
1
NA
NA
NA
NA
NA
NA
0
0
0
0
0
0
NA
NA
NA
National
Secondary
Drinking Water
Regulations
(IX) (100X)
9
24
5
NA
NA
NA
3
14
1
17
39
9
0
0
1
NA
NA
NA
0
2
0
0
1
3
Ambient
Water
Quality Criteria
for Human
(IX)
9
24
5
NA
NA
NA
NA
NA
NA
NA
NA
NA
Health
(100X)
0
0
1
NA
NA
NA
NA
NA
NA
NA
NA
NA
Ambient
Water
Quality Criteria
for Freshwater
Aquatic
(IX)
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Life
(100X)
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Source: Meridian Research, Inc.
NA ='Not Applicable.
a Excludes PEDCo liquid samples.
-------
Table 4. Number of Distilled Water Extract Samples that Exceed
Specified Standard, by Parameter Measured and Waste Type
Mining Waste
Parameters
Measured and
Waste Type
As (Arsenic)
Mine Waste
Tailings
Leachate
Ag (Silver)
Mine Waste
Tailings
Leachate
Ba (BaHum)
Mine Waste
Tailings
Leachate
<** Be (Beryllium)
Mine Waste
Tailings
Leachate
Cd (Cadmium)
Mine Waste
Tailings
Leachate
Cr (Chromium)
Mine Waste
Tailings
Leachate
Cu (Copper)
Mine Waste
Tailings
Leacha te
Total
Number of
Sampl esa
112
71
7
110
71
7
110
71
7
110
71
7
110
71
7
109
71
7
110
71
7
Limits for
Liquids In Landfills
Set by 1984
RCRA Amendments
0
0
0
MA
NA
NA
' NA
NA
NA
NA
NA
NA
0
0
0
0
0
0
NA
NA
NA
Standards
Specifying
Limits for These Parameters
RCRA Subtitle C National Primary
Characteristics of Drinking Water
Hazardous Waste Regulations
(IX) (100X)
0
0
0
0
0
0
0
0
0
NA
NA
NA
0
0
0
0
0
0
NA
NA
NA
1
3
1
3
3
0
3
0
0
NA
NA
NA
10
10
4
2
3
2
NA
NA
NA
0
0
0
0
0
0
0
0
0
NA
NA
NA
0
0
0
0
0
0
NA
NA
NA
National Secondary
Drinking Water
Regulations
(IX) (100X)
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
3
6
5
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0
1
0
Ambient Water
Quality Criteria
for Human Health
(IX) (100X)
112
71
7
3
3
0
NA
NA
NA
110
71
7
10
10
4
2
3
2
3
6
5
f
104
70
7
0
0
0
NA
NA
NA
72
49
4
0
0
0
0
0
0
0
1
0
Ambient Water
Quality Criteria
for Freshwater
Aquatic Life
(IX) (100X)
1
1
0
NA
NA
NA
NA
NA
NA
NA
NA
NA
109
71
6
52
34
4
21
19
6
0
0
0
NA
NA
NA
NA
NA
NA
NA
NA
NA
1
0
0
0
0
0
3
6
4
-------
Table 4. Number of Distilled Water Extract Samples that Exceed
Specified Standard, by Parameter Measured and Waste Type (continued)
Standards Specifying
Mining Waste
Parameters
Measured and
Waste Type
Fe (Iron)
Mine Waste
Tailings
Leachate
Hg (Mercury)
Mine Waste
Tailings
Leachate
Mn (Manganese)
Mine Waste
Tailings
Leachate
N1 (Nickel)
Mine Waste
Tailings
Leachate
Pb (Lead)
Mine Waste
Tailings
Leachate
Se (Selenium)
Mine Waste
Tailings
Leachate
Tl (Thallium)
Mine Waste
Tailings
Leachate
Limits for
Total Liquids In Landfills
Number of Set by 1984
Samples9 RCRA Amendments
110
71
7
112
71
7
110
71
7
110
71
7
110
71
7
112
71
7
110
71
7
NA
NA
NA
0
0
0
NA
NA
NA
0
0
0
0
0
0
0
0
0
0
0
0
Limits for These Parameters
RCRA Subtitle C National Primary
Characteristics of Drinking Water
Hazardous Waste Regulations
(IX) (100X)
NA
NA
NA
0
0
0
NA
NA
NA
NA
NA
NA
0
0
0
0
0
0
NA
NA
NA
NA
NA
NA
7
9
0
NA
NA
NA
NA
NA
NA
110
71
7
29
25
3
NA
NA-'
NA
NA
NA
NA
0
0
0
NA
NA
NA
NA
NA
NA
0
0
0
0
0
0
NA
NA
NA
National Secondary
Drinking Water
Regulations
(IX) (100X)
7
6
4
NA
NA
NA
55
41
6
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0
1
1
NA
NA
NA
2
3
3
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Ambient Water
Quality Criteria
for Human Health
(IX) (100X)
NA
NA
NA
109
69
7
NA
NA
NA
78
52
6
110
71
7
29
25
3
NA
NA
NA
f
MA
NA
NA
2
5
0
NA
NA
NA
0
0
0
0
0
0
0
0
0
NA
NA
NA
Ambient Water
Quality Criteria
for Freshwater
Aquatic Life
(IX) (100X)
NA
NA
NA
112
71
7
NA
NA
NA
4
5
5
110
71
7
29
25
3
NA
NA
NA
NA
NA
NA
8
9
0
NA
NA
NA
0
0
0
0
0
0
0
0
0
NA
NA
NA
-------
Table 4. Number of Distilled Water Extract Samples that Exceed
Specified Standard, by Parameter Measured and Waste Type (continued)
Standards Specifying Limits for
Mining Waste
Parameters
Measured and
Waste Type
Zn (Zinc)
Mine Waste
Tailings
Leachate
NO 3( Nitrate)
Mine Waste
Tailings
Leachate
F (Fluoride)
Mine Waste
Tailings
Leachate
S04(Sulfate)
Mine Waste
Tailings
Leachate
Total
Number of
Samples'
110
71
7
104
68
4
104
68
4
104
68
4
Limits for
Liquids In Landfills
Set by 1984
RCRA Amendments
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
RCRA Subtitle C
Characteristics of
Hazardous Waste
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
National
Primary
Drinking Water
Regulations
(IX) (100X)
NA
NA
NA
0
0
0
0
2
0
NA
NA
NA
NA
NA
NA
0
0
0
0
0
0
NA
NA
NA
These Parameters
National
Secondary
Drinking Water
Regulations
(IX) (100X)
1
1
1
NA
NA
NA
4
6
1
4
15
1
0
0
0
NA
NA
NA
0
0
0
0
0
0
Ambient
Water
Quality Criteria
for Human Health
(IX) (100X)
1
1
1
NA
NA
NA
NA
NA
NA
NA
NA
NA
0
0
0
NA
NA
NA
NA
NA
NA
NA
NA
NA
Ambient
Water
Quality Criteria
for Freshwater
Aquatic Life
(IX) (100X)
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Source: Meridian Research. Inc.
NA - Not Applicable.
a Excludes PEDCo liquid samples.
-------
ambient water quality criteria standard for aquatic life. In these cases, the
limits specified by the standards are one or two orders of magnitude lower
than the lowest detectable level reported in the sources of the mining waste
data. Table 4 shows a similar finding for water extracts of samples analyzed
for lead, where 100 percent of all samples exceed the primary drinking water
standard and both water quality criteria for human health and aquatic life.
This result arose because the lowest level of lead reported for water extracts
of samples is 0.06 mg/1, which is higher than the limits* specified by these
standards for lead (0.05 mg/1) and one or two orders of magnitude higher than
the detection limit reported for any other metal. Sixty-three percent of the
water extracts analyzed for lead content exceed the reported detection limit
of 0.06 mg/1. This result is comparable to the 74 percent of acid extracts of
lead samples that exceed the limits for lead specified by the primary drinking
water standards and the water quality criteria for human health, suggesting
that acid and water extraction are equally efficient in leaching out small
quantities of lead.
However, when the lead content of water and acid extracts are compared to
100 times the limits specified by the primary drinking water standard and
water quality criteria (i.e., 5 mg/1), a different pattern emerges. Five
percent of the acid extract samples have lead content exceeding 5 mg/1, while
none of the water extract samples had lead content exceeding this level. This
suggests that acid extraction is more efficient than water extraction for
leaching out larger quantities of lead.
Little difference in the percentage of samples exceeding the various
standards was observed between acid and water extracts of samples analyzed for
silver, beryllium, mercury, or selenium. For each of the other metals and
anions analyzed, a greater percentage of acid extracts exceed the limits
16
-------
specified by the various standards (and 100 times these limits) than do water
extracts. The parameters for which this observation was most pronounced
include arsenic, barium, cadmium, chromium, copper, iron, zinc, fluoride, and
sulfate. To a lesser extent, acid extraction was more effective than water
extraction for leaching nickel and selenium, particularly at levels exceeding
100 times the limits specified for nickel and selenium. In general, the
results of the comparative analysis show that, with few exceptions, acetic
acid extraction is a more aggressive leaching technique for metals and anions
than is water extraction.
The number of liquid samples analyzed for ammonia, pH, total dissolved
solids (TDS), turbidity (TURB), or cyanide that exceed the various standards
and criteria are presented in Table 5. Most of the samples analyzed for
ammonia, TDS, TURB, or cyanide exceed the various standards and criteria.
Approximately half of the samples analyzed for pH fall outside the limits
specified by the secondary drinking water standard, but few samples would be
characterized as being hazardous by the criteria established under RCRA
Subtitle C or the 1984 RCRA Amendments. All samples analyzed for free cyanide
exceed the limit specified by the ambient water quality criteria for aquatic
life, while half the samples exceed the water quality criteria for human
health.
3.0 ANALYSIS OF RELATIONSHIPS BETWEEN METAL CONTENT OF RAW SAMPLES, ACID
EXTRACTS, OR WATER EXTRACTS
Data on solid mining waste samples reported by PEDCo included data on the
metal content of raw samples, acetic acid extracts, and distilled water
extracts. In this section of the report, Meridian.examines whether or not any
relationships exist between metal content of raw samples and that of the
extracts.
17
-------
Table 5. Number of Liquid Samples that Exceed Specified Standard,
by Parameter Measured and Waste Type
oo
Standards Specifying
Mining Waste
Parameters
Measured and
Waste Type
NH3 (Ammonia)
Mine Waste
Tailings
Leachate
pH Mine Waste
Tailings
Leachate
IDS Mine Waste
Tailings
Leachate
TURB (Turbidity)
Mine Waste
Tailings
Leachate
CN (Cyanide)
All Samples
Limits for
Total Liquids 1n Landfills
Number of Set by 1984
Samples RCRA Amendments
40
42
5
63
83
13
65
73
13
40
41
5
27
NA
NA
NA
0
1
3
NA
NA
NA
NA
NA
NA
0
Limits for These Parameters
RCRA Subtitle C National Primary
Characteristics of Drinking Water
Hazardous Waste Regulations
(IX) (100X)
NA
NA
NA
0
2
3
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
40
41
5
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
32
31
3
NA
Ambient Water
National Secondary Quality Criteria Ambient Water
Drinking Water for Freshwater Quality Criteria
Regulations Aquatic Life for Human Health
(IX) (100X) (IX) (100X) (IX) (100X)
NA
NA
NA
21
37
10
49
50
12
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0
0
4
NA
NA
NA
NA
40a (25)b
42a (31)b
5a (4)b
NA
NA
NA
NA
NA
NA
NA
NA
NA
27
36a (3)b
36a (12)b
4a (3)b
NA
NA
NA
NA
NA
NA
NA
NA
NA
12
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
12
NA
MA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
4
Source: Meridian Research, Inc.
NA = Not Applicable.
a Number of samples that exceed ammonia standard In water with a pH =» 6.5.
b Number of samples that exceed ammonia standard In water with a pH = 8.0.
-------
3.1 METHODS USED
To conduct this analysis, Meridian collapsed the mining waste data base
to include only those solid samples that were analyzed for metal content in
raw samples, acid extracts, and distilled water extracts. Samples analyzed
for six of the seven EP toxic metals were included in the analysis (there were
no silver samples that were analyzed concurrently for silver content in raw
samples, acid extracts, and water extracts). Least-squares regression
analyses were used to determine the degree of correlation between metal
content in raw samples or the extracts. All regressions were performed using
the regression analysis routine contained in Lotus 1-2-3 (Version 2). For
each metal, the following relationships were examined:
MA • a0 * aiMT
MA = a0 + aiMTn/2)
MA = a0 +
MA = a0 *
MA = a0 +
InMfl = a0 +
My = a0
MW = a0
MM = a0 + aiMT0/3)
MW = a0 +
My = a0 +
InMy = a0 +
"A = «o
= a0 +
19
-------
where
My = total metal content in raw sample
MA = metal content in acid extract
My = metal content in water extract
a0 = constant (y-intercept)
al» a2> a3 = x-coefficient(s).
The output generated by the regression program provided the values of the
constant (aQ) and x-coefficients (a1§ a2> a3), the standard error of
a , the standard error of the x-coefficients, the number of samples, the
?
degrees of freedom, and the correlation coefficient (R~). Regressions that
2
resulted in an R value greater than 0.3 were considered to be indicative of
a true relationship between the terms examined (M,, M., or M.,). This is
generally the case for cross-sectional data such as that represented by the
mining waste data set. All regressions reported in this section allowed for
the calculation of the constant value a ; forcing the y-intercept through
2
zero resulted, in most cases, in a small change in the R value or,
2
occasionally, in an R value of 0. The outputs from all regressions
performed appear in Appendix A. Appendix B contains graphic representations
of regressions with the highest degree of correlation between the regression
terms. The results of the more significant regression analyses are summarized
below, by metal.
3.2 REGRESSION RESULTS
3.2.1 Barium
The results of the regression analysis indicate that there is a
correlation between barium content in raw samples and barium content in acid
2
extract; the correlation coefficient (R ) for this relationship was 0.38. A
20
-------
correlation between barium content in raw samples and in water extract was
2 2
less evident (R = 0.19). An excellent degree of correlation (R = 0.70)
was observed between barium content in acid extract versus water extract. The
graph depicting the latter reltaionship (Figure 2, Appendix B) reveals that,
in general, acid extract samples will contain approximately six times more
barium than will water extract samples. This relationship was also reflected
by the analysis presented in Section 2.2, which shows, that 8 percent of acid
extract samples exceed the primary drinking water standard for barium,
compared to only 2 percent of water extract samples.
3.2.2 Chromium
No correlation was observed between chromium content in raw samples and
2 2
either acid extract (R = 0.004) or water extract (R = 0.003). A high
2
degree of correlation (R = 0.75) was observed between chromium content in
acid extract versus water extract, with the x- and y-coefficients indicating
an approximate one-to-one reltaionship. This finding appears to contradict
the result of the comparative analysis, which shows that 37 percent of acid
extract samples exceed the limits for chromium specified by the primary
drinking water standards and water quality criteria for human health, compared
to only 4 percent of water extracts (see Tables 3 and 4). Examination of
Figure 3 in Appendix B suggests an explanation for this discrepancy. At
chromium concentrations below 0.05 mg/1 (the limits of the above-mentioned
standards), the relationship between acid extract and water extract samples
approaches a one-to-one relationship; this relationship does not hold for
samples above 0.05 mg/1, where there are comparatively few water extract
samples. Thus, in the case of chromium samples, it appears that the
regression is being driven by the samples that contain chromium levels below
the limits specified by the drinking water and water quality criteria.
21
-------
3.2.3 Lead
Regression analyses conducted on samples analyzed for lead indicate a
correlation between the natural logarithm of lead content in raw samples
o
versus the natural logarithm of lead content in acid extract (R - 0.56).
No other correlations between the results for the various types of lead
samples were evident. These results suggest that the lead content in acid
extracts reflects (in a non-linear fashion) the lead content in the raw
samples. On the other hand, the lead content in water extracts does not
reflect the lead content present in the raw samples. Despite the different
results obtained in the regressions of acid and water extract samples, there
was little difference in the percentage of acid and water extract samples that
exceeds the 0.05 mg/1 specified by the primary drinking water standards and
water quality criteria for human health. (See discussion in Section 2.2)
3.2.4 Analyses of Samples of Other Metals
Regressions performed on samples analyzed for silver, cadmium, and
mercury failed to identify any correlation between metal content in raw
samples; acid extracts, or water extracts.
3.2.5 Conclusions
For four of the six metals examined, there was no clear relationship
between the amount of metal in raw mining waste samples and the metal content
of acid extracts of these samples. The results indicate that there is a
linear relationship between barium content in raw samples versus acid
extracts, and a non-linear relationship between lead content in raw samples
versus acid extracts. There does not appear to be any simple relationship
between metal content in raw samples and metal content in water extracts.
22
-------
Metal content of add extracts correlated with metal content of water extracts
for only two of the six metals examined (barium and chromium).
These results suggest that, In general, the metal content of acetic add
or distilled water extracts will not reflect, according to any simple formula,
the presence of the metal 1n the raw waste sample. This Is perhaps not
surprising, given that the mining waste data were generated by analyzing
several types of mining waste at 99 mine and mill operations. Thus, these
data represent a mineraloglcally diverse set of samples with different
chemical and physical properties that are likely to affect the susceptibility
of various metals to leaching.
23
-------
APPENDIX A
-------
REGRESSION ANALYSIS FOR ARSENIC SAMPLES
Regression Outout:
Constant
Std Err of V Est
3 Scuared
;.'o. of Observations
Decrees of Freedom
0.01553130
0.04084793
0.02366382
213
211
ASA =
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedon
0.01379011
0.04116361
0.00851522
213
211
X Ccefhcient(s)
Etd Err of Coaf.
3.03283963
0.BB001752
X Coefficient(s)
Std Err of Coef.
0.00207345
0.00154027
•'I/:;
R-grsssian Out;ut:
Constant
Stj Err of Y Est
R Squared
rJo. of Observations
Dezreet of Freec'a?
C.81217449
3.B4079159
B.02635545
213
111
ln!ASAJ =
Regression Outout:
Constant
Std Err of Y Est
R Scuared
No. cf Observations
Decrees of Freedo.ii
-5.3578673
1.66515854
0.03723404
213
211
X Ccsfficieatis)
Std Err of Ccef.
0.30142394
0.20259582
X Coefficient^)
Std Err of Coef.
0.07617587
0.06230758
ASA =
(1/3)
Regression Output:
Constant
Std Err of Y Est
ft Scuared
No. of Observations
Decrees of Freedom
0.0409Z379
0.02099182
213
211
Regression Output:
Constant
Std Err of Y Est
R Souared
No. of Observations
Degrees of Freedom
0.00974169
06552777
0.16254482
213
211
X Coefficients;
Std Err of Coef.
0.0C391249
0.00183941
X Coefficients!
Std Err of Coef.
0.00017993
511
ASA =
(1/2)
ion Output:
Constant
Std Err sf Y Est
R Scuared
Nc. of Observations
Decrees of Freedom
(1/3)
0.03019641
0.04C80B64
0.03476837
213
2B9
(1/2)
ASy = ag + ajAST
Regression Output:
Constant
Std Err of Y Est
R Souared
No. of Observations
Degrees of Freedom
-0.0098147
0.06491819
0.17805346
213
211
X Coefficient!;}
Std Err cf Coef.
-0.0300991 0.01233731 -0.0250464
0.00209:56 O.BE3S6667 0.01904753
X Coefficient^)
Std Err of Coef.
0.00641074
0.B0094B22
-------
REGRESSION ANALYSIS FOR ARSENIC SAMPLES (CONTINUED)
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-0.0248550
0.06637241
0.14081664
213
211
0.01755214
0.00298472
In(ASy) = a,, «
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-5.6761669
1.20728745
0.09243847
213
211
0.20942359
0.04517477
ASg = ag * ajASy + i
Regression Output:
Constant
Std Err of Y Est
R Sauared
No. of Observations
Degrees of Freedom
X Coefficient^)
Etd Err of Coef.
AS --
a,ASu
0.07120485
0.06294024
0.23470055
213
209
-0.0004345 0.05484621 -0.1116844
0.00014277 0.01336677 0.02937737
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
0.01548863
0.03982970
0.07173206
213
211
0.15462597
0.03B29316
ASU = *a *
Regression Output:
Constant
Std Err of Y Est
R Squared
Mo. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-0.0007652
0.06988571
0.04745059
213
211
0.00847797
0.002ol501
InlASJ = aB +
n H
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-3.4036592
1.61363303
0.06753459
213
211
0.34267222
0.0B765786
-------
REGRESSION ANALYSIS FOR BARIUM SAMPLES
Regression Ojtout:
Constant
Std Err of Y Est
R Sauared
No. of Observations
Degrees of Freedom
I Coefficiett(s)
Std Err of Ccef.
-2.87693651
3.623232526
0.362311721
289
207
0.0B8854359
0.000782254
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-3.23217589
4.497614267
0.048209514
209
207
0.775566234
0.239517924
= V
(!/:>
Regression Output:
Constant
Std Err of Y Est
R Soared
No. of Observations
Degrees cf Freedom
X Coefficient(s)
Std Err of Coef.
-2.82432553
4.214577628
0.164233302
209
287
0.226117823
0.835453698
ln(2AA) = 30 «•
Regression Cutout:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-3.01450736
1.635606058
0.025734462
209
207
0.203675401
0.087103283
BAA = aa *
(1/3)
Regression Output:
Constant
Etd Err of Y Est
R Sauared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-3.46468575
4.340476546
0.113555040
209
287
0.69347838
8.134668288
Regression Cutout:
Constant
Std Err of Y Est
R Sauared
No. of Observations
Degrees of Freedom
X Coefficients)
Std Err of Coef.
-0.03303S46
0.407416451
0.195148515
209
207
0.000623160
0.000087961
FA« = an * diBA-r + «
H V • i
Seccession Output:
Constant
Std Err of Y Est
P Scuared
No. of Observations
Degrees of Freedom
X Coefficients!
Std Err of Coef.
(1/2) . * ,. (1/3)
-10.4211735
2.568:92593
0.692638594
289
285
0.05400281! -4.91068511 11.77151754
0.084223263 0.615572477 1.745167831
(1/2)
Regression Output:
Constant
Std Err of Y Est
R Sauared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-0.11669029
0.4:9543442
0.105350730
209
207
0.017839887
0.003613387
-------
REGRESSION ANALYSIS FOR BARIUM SAMPLES (CONTINUED)
v
(1/3)
Regression Outout:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficients)
Std Err of Coef.
-0.17900600
0.436049317
0.078044737
209
207
0.0566324B2
0.013528932
InfBAyl = a» + ailn
-------
REGRESSION ANALYSIS FOR CADMIUM SAMPLES
CDA =
Regression Output:
Constant
Std E.-r of Y Est
R Squared
No. of Observations
Degrees of Freedoa
0.0187:0630
0.849130775
8.052254679
207
205
CDA = ag * ajln(CDj)
Regression Output:
Constant
Std Err of Y Est
R Souared
No. of Observations
Degrees of Freedom
-0.01810196
0.047369141
0.100304191
207
205
X Coefficient(s)
Std Err of Coef.
0.000102974
0.0000J0629
X Coefficient(s)
Std Err of Coef.
0.015132817
0.003165418
CDA =
(1/2)
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Deorees of Freedom
0.007232276
0.048263165
0.085431962
207
205
ln(CDA) = 30 +
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Decrees of Freedom
-4.93393808
0.761975261
0.101443765
207
205
X Coefficient(s)
Std Err of Coef.
0.003318175
0.000758263
X Coefficient(s)
Std Err of Coef.
0.242400427
0.050386756
CDA =
(1/3)
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
-0.00623910
0.048084715
0.092182582
207
205
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
0.012335524
0.015199044
0.004793978
207
205
X Coefficient(s)
Std Err of Coef.
0.010925308
0.002394592
X Coefficient^)
Std Err of Coef.
0.000009475
CDA = aa * a,C
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficients)
Std Err of Coef.
«2CDT(1/2) * a3CDT
'1/3)
(1/2)
-0.13869807
0.047958074
0.105768272
207
203
0.002266331 HJ.0476B450 0.140457244
3.0EB469353 0.056150555 0.143961266
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
0.011014330
0.015151290
0.011037903
207
205
0.000360067
0.000238042
-------
REGRESSION ANALYSIS FOR CADMIUM SAMPLES (CONTINUED)
-(I/3) InlCDg) = aj +
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedon
X Coefficient(s)
Std Err of Coef.
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient^)
Std Err of Coef.
0. 009461822
0.815139331
8.812598498
207
205
0.881219328
0.000753930
-4. 45601 MB
0.556686100
11.019242031]
207
205
0.025961313
0.036911702
CDU = ae * a,CDT * a2CDT(1/2) * a3CDTll/3)
Regression Output;
Constant
Std Err of V Est
R Souared
No. oF Observations
Degrees of Freedom
CD =
X Coefficients)
Std Err of Coef.
0.0016:5413
B.B15172587
8.017931275
207
203
-0.00036337 -0.30172581 0.0072AB690
B.BB014849B 0.0177644570.345545:99
Regression Output:
Constant
Std Err of Y Est
R Sauared
No. of Observations
Degrees of Freedom
X Coefficient (s)
Std Err of Coef.
0.915129746
e.049Sol01B
0.0355331901
287
35
0.624S40663
8.227197472
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient (5)
Std Err of Coef.
0.307974663
0.015123213
0.014699067
287
205
B. B81748868
B.BB180BB45
ln(CCA) = aa +
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-2.37711818
B.770B53529
B.082290241
267
235
0.413719341
B.896495575
-------
CR
REGRESSION ANALYSIS FOR CHROMIUM SAMPLES
= a0 + ajCRT
Regression Output:
Constant
Std Err of V Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
8.838965683
8.895036466
8.804433413
208
206
8.888824968
8.808026868
CRA =
ajln(CRT)
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freed™
X Coefficient(s)
Std Err of Coef.
8. 001523330
0.094B6B71B
8.089155821
288
286
8.008553543
0.006199913
(1/2)
CRfi = aj, * W""
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
0.021449779
0.094806621
0,010:38868
208
206
0.001559412
0.001068234
ln(CRA)
Regression Output:
Constant
Std Err of Y Est
R Sauared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-3. 642361 38
1.628871347
0.030482608
208
206
-0.27093669
0.106459887
CRA =
a,CRT
(1/3)
Regression Outout:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
8.011351614
0.094768487
0.011082496
208
206
8.005927186
0.003981002
Cf^ = a» * a< CRT
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
0.017508632
0.075592165
0.003492004
208
206
0.800017607
0.000020724
CRA =
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std £T of Coef.
(1/2) + ^(1/3)
0.201087593
0.093867160
0.839221667
208
284
-0.00860376 0.074155197 -0.18208754
0.000248619 0.032109662 0.034298676
CR = a +
(1/2)
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedon
X Coefficient(s)
Std Err of Coef.
0.009107285
0.075282694
0.011634613
208
206
.0.001320881
0.000848228
-------
REGRESSION ANALYSIS FOR CHROMIUM SAMPLES (CONTINUED)
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-€.00334303
0.075198907
0.013833427
208
206
0.005261959
0.003095452
In(CRy) = ag +
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedon
X Coefficient(s)
Std Err of Coef.
-4.10530988
1.467660972
0.033773641
208
206
-0.25739972
0.095923488
ajCRT + a2CRT
(1/2)
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Caef.
a3CRT
(I/3)
0.146381198
0.074189817
0.049441695
203
204
-0.0B0519B5 0.060910954 -0.14611740
0.0E0196501 0.C25378523 0.06662084S
M- « i . /*n
A ~ 0 1 H
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedon
X Coefficient(s)
Std Err of Coef.
0.012495216
0.048025304
0.746035420
208
206
1.086993337
0.044187570
CRy = ag +
Regression Output:
Constant
Std Err of Y Est
R Souared
No. of Observations
Degrees of Freedon
X Coefficient(s)
Std Err of Coef.
-0.01173451
0.075204586
0.013684490
208
206
.008309665
.004915226
ln(CRA> = a0 +
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedon
X Coefficient(s)
Std Err of Coef.
-1.75516416
1.416354143
0.266445596
208
206
0.571909023
0.066115795
-------
REGRESSION ANALYSIS FOR MERCURY SAMPLES
A = aa * aiHGr
Regression Output:
Constant
Std Err of Y Est
R Sauared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
0.003231429
0.008337632
0.001445948
210
208
0.01905196541
0.800922981
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedoa
X Coefficient(s)
Std Err of Coef.
0.000970364
0.008228526
0.027409015
210
-0.190095087
0.000392743
(1/2)
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
0.004007058
0.008323574
0.004810415
210
208
-0.00173116
0.001726509
ln(H6A) = ag +
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-7.56980480
1.461227190
0.016590022
210
208
-0.13064506
0.06974373
HSA =
(1/3)
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedon
X Coefficient(s)
Std Err of Coef.
0.005154627
a.008207221
0.013484397
210
-8.00367958
0.002182246
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
0.002919363
0.012170813
0.005128028
210
208
0.001395060
0.001347317
HGA - aQ + ajHGj +
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-------
REGRESSION ANALYSIS FOR MERCURY SAMPLES (CONTINUED)
HGy =
(1/3)
Regression Output:
Constant
Std Err af Y Est
R Squared
Nc. of Observations
Dearses of Freedom
0.000537054
0.012112780
0.014592981
210
208
In(HGy) = ag +
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
-6.92387684
1.267533133
0.034977682
210
208
X Coefficient(s)
Stc' Err of Coef.
0.005598020
0.003189617
X Coefficient(s)
Std Err of Coef.
0.166113621
0.060498798
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficients)
Std Err of Coef.
-0.00210889
0.012150414
0.017994347
210
206
0.00042582 -0.00990408 0.018838501
.01061948B 0.061400604 0.056341306
ur • i. LJC
nUA * flfl **" O l^HLJ
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedoa
X Coefficient!*)
Std Err of Coef.
0.003520392
0.008321645
0.035271539
210
208
-0.04964661
0.047286975
HGU = ae +
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedon
X Coefficient(s)
Std Err of Coef.
0.003839499
0.012197686
0.000729968
210
208
0.000226938
0.000582190
InlHGJ = aa +
n v
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedoa
X Coefficient(s)
Std Err of Coef.
-B.73931330
1.449793213
0.031913328
210
208
-0.20400736
0.077908573
-------
DEGRESSION ANALYSIS FOR LEAD SAMPLES
Regression Output:
Constant
Std Err of V Est
R Squared
No. of Observations
Degrees of Freedro
X Coefficientls)
Std Err of Coef.
e. 43306113
6.89818445
B.B7722B25
289
287
8.08024255
PEA =
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedon
X Coefficients)
Std Err of Coef.
-14.820143
6.56871399
0.15815963
289
207
3.11307998
0.49919725
PBA =
(1/2)
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Decrees of Freedom
X Coefficient(s)
Std Err of Coef.
-1.4746949
6.69055893
B.13193299
209
207
.15996134
.82851870
ln(PBJ = a» +
H V
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedon
X Coefficients]
Std Err of Coef.
-7.0276499
0.77917859
0.55560801
209
287
B.94971842
B.B59034B6
PBA =
(1/3)
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(si
Std Err of Coef.
-4.2378602
6.62701834
B.14834272
209
207
B.89544940
0.14912662
PBy = df) +
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedon
X Coefficient(s)
Std Err of Coef.
B.08291346
B.23017815
0.06922148
209
207
0.00003175
0.BBB00809
PBA = aa
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedon
X Coefficient is)
Std Err of Coef.
a2PBT(1/2J + a3PBT(1/3)
22.3367626
6.337522B3
0.22B6509B
289
285
-8.8121416 4.86999743 -13.276657
B.B0315069 1.22291117 4.596699B2
PBy = 3j * 3jPi
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedon
X Coefficient(s)
Std Err of Coef.
0.02895323
0.22633091
0.10007581
209
207
0.00462868
0.08896474
-------
REGRESSION ANALYSIS FOR LEAD SAMPLES (CONTINUED)
ajPBr
(1/3)
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficipnt(s)
Std Err of Coef.
-0.0443370
0. 22599956
0.10270881
289
207
0.02475522
0.00503562
InlPBy) =
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-3.12S5543
0.37674641
0.07444731
209
207
B. 11647420
0.02854436
PBU = aB + ajPBy + c
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedoa
X Coefficient(s>
Std Err of Coef.
a3PB/^>
1.77912594
0.20859505
0.242975B7
209
205
-0.0006386 0.24207094 -0.8673466
0.00010370 0.04025125 0.15129712
PBA = aB + ajP!H
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedoa
X Coefficient(s)
Std Err of Coef.
0.80364462
7.17454682
209
207
1.27710833
2.09010638
t ajln(PBT)
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s)
Std Err of Coef.
-0.2989114
0.22744031
0.09123186
209
207
0.07855446
0.01723213
ln(PBft) = ag + ,
Regression Output:
Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedoa
X Coefficient(s)
Std Err of Coef.
-0.1854435
1.12605070
0.07187156
209
207
0.80017301
0.19985923
-------
APPENDIX B
-------
BA CONTENT-RAW SAMPLE ve. ACID EXTRACT
i
REGRESSION ANALYSIS
BARUM CONTENT IN RAW S4MR_E (ua/q)
Q REPORTED VALUES REGRESS
FIGURE 1
5
£
BA CONTENT-WATER ve. ACID EXTRACT
RCCREfifilON
BARIUM CONTENT IN WATER EXTRACT (mo/I)
O REPORTED VALUES REdRESSlON UNE
FIGURE 2
-------
r
2
a
8
CR CONTENT-WATER vs. ACID EXTRACT
REGRESSION ANALYSIS
CHRDMUM CONTENT WATER EXTRACT (mo/I)
D REPORTED VALUES REGRESSION
UNE
FIGURE 3
r
9
PB CONTENT-RAW SAMPLE vs. ACID EXTRACT
REGRESSION ANALYSIS
LN REPORTED VALUES
LN LEAD CONTENT W RAW SAMPLE ("j^J9JI
LINE
FIGURE 4
------- |