Understanding Analytical Environmental Data kenneth'niswongerstate'co'us

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Understanding Analytical Environmental
Datakenneth.niswonger_at_state.co.us
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Complex and Confusing

• Interested in low concentrations of targets
• Heterogeneous samples - variable results
• Matrix interference on analysis
• Regulations dont address these problems

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Complex and Confusing

• What do you need?
• Why do you need it?
• How will you use it?
• Bad or good decisions can come from it?

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Complex and Confusing

• All data have error.
• Nobody can afford absolute certainty.
• Tolerable error rates (99 vs. 95 certainty)
• Without DQOs, decisions are uninformed.
• Uninformed decisions - conservative and expensive

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Appendix IA Parameters

• Dissolved Anions Method 300 or 9056 (pay
  attention to hold times) Alkalinity Method
  310.1
• 48 hour hold on NO3- and NO2- (May need 353.1,
  353.2, 353.3)
• Dissolved Cations Method 6010B/6020
• Field Parameters
• Specific Conductance Method 160.1
• pH Method(s) 150.1 or 9040B
• Temperature Method 170.1
• TOC (Not field parameter) Lab Method 9060
• Ask for what you need and want

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Appendix IB Parameters

• Total Elements Method 6010B/6020
• Volatiles
• Method 8260B
• Method 624
• Ask for what you want
• Communicate, communicate, communicate

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DQO Approach 3 Phases

• Planning
• Data Quality Objectives (Why sample?)
• Quality Assurance Project Plan (QAPP)
• Implementation
• Field Data Collection (Sampling)
• Quality Assurance/Quality Control Activities
• Assessment
• Data Validation
• Quality Assurance/Quality Control Activities

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Much Work Remains to be Done before We Can
Announce Our Total Failure to Make any Progress
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• Implementation

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Assessment
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Environmental Data What does this
information tell us?(Reading between the
Regulatory Lines)
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Why monitor? Why do statistical analysis?
Understand the hydrological setting. Detect and
deal with environmental impacts. Understand
risks and liabilities. Focus resources. Reduce
monitoring costs.
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A horizon Topsoil, organic material Zone of
leaching
B horizon Zone of accumulation
C horizon Parent material ( rock, gravel, sand)
The soil profile of a dark brown Chernozemic soil
formed under native grassland
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Detection Monitoring
Includes all Appendix I parameters (Appendix IA
and IB). May be modified, in consultation with
local governing body to delete any Appendix I
parameter on a Site Specific Basis, if Removed
constituents not reasonably expected to be
derived from waste
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Detection Monitoring
May add parameters, if Acceptable analytical
method, Commercially available calibration
standard, Analyte is chemically
stable, Reasonable sample collection and
preservation technique Reasonable expectation
of detection, and is a good indicator and
possible precursor to other more hazardous
constitutents that might Be released later.
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Detection Monitoring
Department considerations in modifying Appendix I
parameters Types, quantities, and
concentrations of constituents in waste managed
at the SWDS and facilities Mobility, stability,
and persistence of constituents, or their
reaction products in the unsaturated zone beneath
the MSWLF unit.
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Detection Monitoring
Department may specify a monitoring frequency
during the active life and post-closure. Minimum
of semi-annually, unless approved by the
Department. Considerations Lithology of the
saturated and unsaturated zone Hydraulic
conductivity of groundwater Groundwater flow
rates and minimum distance of travel Resource
value of the groundwater
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Background Data

Owner/operator must acquire a minimum of Eight
Quarterly Samples From each well and analyzed for
Appendix IA and IB constituents. Owner/operator
must specify in the operating record, one or
more statistical tests for each hazardous
constituent. Changes in these statistical tests
shall be reviewed and approved within two weeks
of the request and entered into the operating
record.
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Background Data

Owner/operator must acquire a minimum of Eight
Quarterly Samples From each well and analyzed for
Appendix IA and IB constituents. Owner/operator
must specify in the operating record, one or
more statistical tests for each hazardous
constituent. Changes in these statistical tests
shall be reviewed and approved within two weeks
of the request and entered into the operating
record.
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Statistically Significant Increase over Background
Documentation in Operating Record indicating
which constituent is above Background, and
forward the Documentation to the Department and
local Governing Body within 14 days. Begin
Assessment Monitoring, or Provide an
Alternative Source Demonstration Error in
sampling, analysis, or natural variations in
water Certified by a qualified groundwater
scientist If not successfully demonstrated begin
Assessment Monitoring in 90 days.

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Statistical Methods and Requirements
Trend analysis Control charts Prediction
interval (tolerance intervals) ANOVA comparison
with background Other. ----------------
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- Regulations..Type I error 0.01 99
Certainty (for each constituent in each well)
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Statistical Methods and Requirements
Intrawell Statistics, or Interwell
Statistics (groups and/or Upgradient
Downgradient)
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Analyses of Variance (ANOVA)

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Trend Analysis

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Control Charts
Family of Charts Shewhart used 3 sigma (3
standard deviations, 98.5 probability, others
have used the Standard error of the Estimate,
etc.) 1 sd 67 of data fits within limits 2
sd 95 of data fits within limits 3 sd 98.5
of data fits within limits 4 sd 99 of data
fits within limits .the fact that the
criterion which we happen to use has a fine
ancestry in highbrow statistical theorems does
not justify its use. Such justification must come
from empirical evidence that it works. As the
practical engineer might say, the proof of the
pudding is in the eating. Walter A.
Shewhart
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Control Charts
Criticisms Controversial. Operators expected
to determine if a special case has
occurred. Process in control 0.27
probability that a point will be out of
specs (1/0.0027 or 1 in 370.4) Good at
detecting large changes, does not detect small
changes efficiently Strengths May work well
for non-parametric data Special control chart
CUMSUM does detect small changes
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Control Charts
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Control Charts
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Tolerance Interval
A tolerance interval, also known as a tolerance
limit, or prediction interval is an interval
within which, with some confidence, a
specified proportion of a population falls. This
differs from a confidence interval in that the
confidence interval bounds a population
parameter (the mean, for example) with some
confidence, while a tolerance interval bounds a
population proportion. Criticisms Difficult
to use and interpret..takes some
experience Strengths Works well on
non-parametric data
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Tolerance Interval
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Analyses of Variance (ANOVA)
Parametric populations behave as a Normal
Distribution

Non-parametric population does not behave
Normally Can it be mathematically transformed
to behave Normally ? log, antilog, power
transformation
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Hypothesis Testing Probability and Inferential
Statistics
Hypothesis Ho The Landfill is
contributing pollutants in excess of standards,
and background.   Ha The Landfill is not
contributing pollutants in excess of standards,
and background.   There are two decisions
possible   (1). Accept the null hypothesis
(Ho), (2). Reject the null hypothesis (Ho ),
equivalent to accept the alternate hypothesis
(Ha).   There are two possible situations
either the null hypothesis (Ho ) is true, or it
is false. Because of these facts the possible
errors are Situation   Ho
is True Ho is False_ Decision

  Accept
Ho correct Type II error (Beta) Reject
Ho Type I error (alpha) correct

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Hypothesis Testing Probability and Inferential
Statistics
  The Type I (alpha) error occurs when Ho is
true, but we reject it. This error would occur
when the Landfill is contributing pollutants to
water above standards and background, but we
conclude that it is not. The consequences of
the Type I (alpha) error are the most severe.
This error would mislead an understanding of the
actual impacts to water resources and public
health. In addition, the Type I (alpha) error
would be the most embarrassing error to the
agency.   The Type II (Beta) error occurs when
Ho is false, but we accept it. This error would
occur when the Landfill is not contributing
pollutants above standards and background, but we
conclude that it is. The Type II error (Beta) is
less embarrassing to the organization, but
carries a large opportunity cost by unnecessarily
alarming residents of the area and possibly
causing unnecessary remediation activities.  

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Hard to imagine good and bad from Groundwater
Statistics !!!!!!!
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Hypothesis Testing Probability and Inferential
Statistics

Ho - The two well populations are not
statistically equivalent Ha - The two well
populations are statistically equivalent 90
Certainty 95 Certainty 99 Certainty Accept
Ho Accept Ho Accept Ho
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Hypothesis Testing Probability and Inferential
Statistics

Ho - The two well populations are not
statistically equivalent Ha - The two well
populations are statistically equivalent 90
Certainty 95 Certainty 99 Certainty Reject
Ho Accept Ho Accept Ho
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Hypothesis Testing Probability and Inferential
Statistics

Ho - The two well populations are not
statistically equivalent Ha - The two well
populations are statistically equivalent 90
Certainty 95 Certainty 99 Certainty Reject
Ho Reject Ho Accept Ho
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Hypothesis Testing Probability and Inferential
Statistics

Ho - The two well populations are not
statistically equivalent Ha - The two well
populations are statistically equivalent 90
Certainty 95 Certainty 99 Certainty Reject
Ho Reject Ho Reject Ho
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Injecting Common Sense into Statistic Evaluations
If determination is that constituent
concentration is gt Background - Is it
consequential ? - Is result above GW standard,
or tending toward gt GW standard ? - Look over
the data, is it cogent? - Is there a failure,
or misrepresentation of the statistical
protocol? - Resample, errors happen and GW
variations are the norm.

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