Understanding Analytical Environmental Data [email protected].

Understanding Analytical

Environmental Data

[email protected]

Complex and Confusing

Interested in low concentrations of targets

Heterogeneous samples - variable results

Matrix interference on analysis

Regulations don’t address these problems


What do you need?

Why do you need it?

How will you use it?

Bad or good decisions can come from it?


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

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

Appendix IB Parameters Total Elements Method 6010B/6020

Volatiles Method 8260B

Method 624

Ask for what you want Communicate, communicate, communicate

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

Much Work Remains to be Done before We Can Announce

Our Total Failure to Make any Progress

•Implementation

Assessment

Environmental Data:

What does this information tell us?

(Reading between the Regulatory Lines)

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.

The soil profile of a dark brown Chernozemic soil formed under native grassland

“A” horizonTopsoil, organic materialZone of leaching

“B” horizonZone of accumulation

“C” horizonParent material ( rock, gravel, sand)

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


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.


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.


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

Background Data

Owner/operator must acquire a minimum of Eight Quarterly SamplesFrom each well and analyzed for Appendix IA and IB constituents.

Owner/operator must specify in the operating record, one or morestatistical 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.

Statistically Significant Increase over Background

Documentation in Operating Record indicating which constituent is above Background, and forward the Documentation to the Department and localGoverning 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.

Statistical Methods and Requirements

Trend analysis

Control charts

Prediction interval (tolerance intervals)

ANOVA comparison with background

Other……………………….

-------------------------------------------------------------------Regulations……..Type I error = 0.01

99 % Certainty (for each constituent in each well)

Statistical Methods and Requirements

Intrawell Statistics, or

Interwell Statistics

(groups and/or Upgradient – Downgradient)

Analyses of Variance (ANOVA)

Trend Analysis

Nitrate

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

0 5 10 15 20 25 30 35

Sampling Event

mil

lig

ram

s/L

iter

Series1

Linear (Series1)

Control ChartsFamily 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 limits2 sd 95 % of data fits within limits3 sd 98.5 % of data fits within limits4 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

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

Control Charts

Control Charts

No rma lize d R a tio

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

S a mp le Nu mb e r

Tolerance Interval

A tolerance interval, also known as a tolerance limit, or prediction interval is an interval within which, with some confidence, a specifiedproportion of a population falls. This differs from a confidence intervalin that the confidence interval bounds a population parameter(the mean, for example) with some confidence, while a tolerance intervalbounds a population proportion.

Criticisms:

Difficult to use and interpret…..takes some experience

Strengths:

Works well on non-parametric data

Tolerance Interval

Tolerance Intervals for the Normal Distribution

Fill in the following information:

If I measured a sample of 8 items,and got a mean of 97.07

and a standard deviation of 1.5then I can be 99.0% certain

that 90.0% of the populationwill be contained…

within the interval from: 90.84983 to 103.2902 (a Two-sided Tolerance Interval)

below the value: 102.7091 (an Upper One-sided Tolerance Interval)

above the value: 91.43086 (a Lower One-sided Tolerance Interval)

You can ignore the following intermediate quantities used in the calculation:z(1-p): 1.281551z(1-g): 2.326342

a: 0.613438b: 0.965889

k1: 3.759429

z((1-p)/2): 1.644853ChiSq(g,n-1): 1.239032

k2: 4.146782

Reference:NIST/Sematech Handbook, Section 7.2.6.3

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

0

0.002

0.004

0.006

0.008

0.01

-500

-375

-250

-125 0

125

250

375

500

Ndist calc

excel t dist

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


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.

Hard to imagine good and bad from Groundwater 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

Well Data on Lead

-100

0

100

200

300

400

500

600

700

1 11 21 31 41 51 61 71 81 91 101

111

121

131

141

151

161

171

181

191

201

Lead Concentration (ug/L)

Pro

bab

ility Upgradient Well

Dow ngradient Well





Reject Ho Accept Ho Accept Ho

Well Data on Lead

-100

0

100

200

300

400

500

600

700

1 11 21 31 41 51 61 71 81 91 101

111

121

131

141

151

161

171

181

191

201


Pro

bab


Dow ngradient Well





Reject Ho Reject Ho Accept Ho

Well Data on Lead

-100

0

100

200

300

400

500

600

700

1 12 23 34 45 56 67 78 89 100

111

122

133

144

155

166

177

188

199


Pro

bab


Dow ngradient Well





Reject Ho Reject Ho Reject Ho

Well Data on Lead

-100

0

100

200

300

400

500

600

700

1 11 21 31 41 51 61 71 81 91 101

111

121

131

141

151

161

171

181

191

201


Pro

bab


Dow ngradient Well

Injecting Common Sense into Statistic Evaluations

If determination is that constituent concentration is > Background

- Is it consequential ?

- Is result above GW standard, or tending toward > 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.

Uggradient WellMW-1 Metals

Parameter Al Sb As Ba Be B Cd Cr Co CuAction Level: 5 0.006 0.050 2.000 0.004 0.750 0.005 0.100 0.050 1.000

A P P P P A P P A SOne-tail 0.05Two-tail 0.10 Avg 0.000 0.258 0.004 0.106 0.000 0.000 0.000 0.000 0.000 0.008

Std Dev 0.000 0.150 0.010 0.102 0.000 0.000 0.000 0.000 0.000 0.015n-1 = 1 6.31 95% UCL 0.000 0.368 0.011 0.181 0.000 0.000 0.000 0.000 0.000 0.019n-1 = 2 2.92 95% UCL Test Pass FAIL Pass Pass Pass Pass Pass Pass Pass Passn-1 = 3 2.35 n samples taken 7.000 7.000 7.000 7.000 7.000 7.000 7.000 7.000 7.000 7.000n-1 = 4 2.13 "n" needed 0.000 1.322 0.186 0.011 0.000 0.000 0.000 0.000 0.000 0.001n-1 = 5 2.02 "n" Test Pass Pass Pass Pass Pass Pass Pass Pass Pass Passn-1 = 6 1.94 t n-1 used 1.940 1.940 1.940 1.940 1.940 1.940 1.940 1.940 1.940 1.940

Above Std PASS FAIL PASS PASS PASS PASS PASS PASS PASS PASS

MW-2Metals

Parameter Al Sb As Ba Be B Cd Cr Co CuAction Level: 5.000 0.006 0.050 2.000 0.004 0.750 0.005 0.100 0.050 1.000

A P P P P A P P A SOne-tail 0.05Two-tail 0.10 Avg 0.000 0.281 0.073 2.636 0.007 0.000 0.000 0.143 0.068 0.102

Std Dev 0.000 0.224 0.104 2.024 0.009 0.000 0.000 0.136 0.070 0.096n-1 = 1 6.31 95% UCL 0.000 0.466 0.150 4.120 0.013 0.000 0.000 0.243 0.120 0.172n-1 = 2 2.92 95% UCL Test Pass FAIL FAIL FAIL FAIL Pass Pass FAIL FAIL Passn-1 = 3 2.35 n samples taken 7.000 6.000 7.000 7.000 7.000 7.000 7.000 7.000 7.000 7.000n-1 = 4 2.13 "n" needed 0.000 2.694 74.997 38.148 41.488 0.000 0.000 38.042 57.619 0.043n-1 = 5 2.02 "n" Test Pass Pass FAIL FAIL FAIL Pass Pass FAIL FAIL Passn-1 = 6 1.94 t n-1 used 1.940 2.020 1.940 1.940 1.940 1.940 1.940 1.940 1.940 1.940

Above Std Pass FAIL FAIL FAIL FAIL Pass Pass FAIL FAIL PassF* #DIV/0! 2.234 102.987 396.614 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 43.022

F table value 4.280 6.390 4.280 4.280 4.280 4.280 4.280 4.280 4.280 4.280F 95% Equiv Variance #DIV/0! Yes No No #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! No9.280 t pooled #DIV/0! -1.505 -6.042 14.058 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! -6.2666.390 tpooled table 1.800 1.800 1.800 1.800 1.800 1.800 1.800 1.800 1.800 1.8005.050 Statisticallydifferent? #DIV/0! No No Yes #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! No4.280 Unequiv Variance #DIV/0! No Yes Yes #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Yes

t se/sd #DIV/0! -2.126 -0.025 2.497 0.007 #DIV/0! #DIV/0! 0.143 0.068 -0.121Statisticallydifferent? #DIV/0! No No Yes No #DIV/0! #DIV/0! No No No

Downgradient Well 1Element Std (mg/L) Std Type P or F Upgradient Variance = Upgrad Statistically differ from Upgrad

Al 5.000 A PassSb 0.006 P PassAs 0.050 P PassBa 2.000 P PassBe 0.004 P PassB 0.750 A PassCd 0.005 P PassCr 0.100 P PassCo 0.050 A PassCu 1.000 S PassFe 0.300 S FAIL FAIL Yes NoPb 0.050 P PassMn 0.050 S FAIL FAIL Yes NoNi 0.200 P PassSe 0.050 P PassAg 0.050 P PassTi 0.002 P PassV 0.100 A PassZn 2.000 A PassLi 2.500 A PassHg 0.002 P Pass

Understanding Analytical Environmental Data [email protected].

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