analytical chemistry and statistics in exposure science

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RTI International is a trade name of Research Triangle Institute. www.rti.org1

Analytical Chemistry and Statistics in Exposure ScienceDo Physical Scientists Evolve to Statisticians or Is It the Other Way Around?

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Mr. Wizard’s Lesser-Known Apprentice

From a B.S. in Chemistry at Moravian College (Bethlehem, PA) in 1972 …

…to the University of North Carolina at Chapel Hill

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Not Exactly Carnival Cruise Lines

M.S. Environmental Analytical Chemistry – 1974– Electrochemistry of

trace metal – organic interactions in marine systems

– Anodic stripping voltammetry and Rotated ring-disk mercury film electrodes

R/V Columbus Iselin in the Sargasso Sea

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Finally… A Real Job!?

Joined Research Triangle Institute on July 3, 1976

Organics method development in separation science– Purge and trap with VOCs

through the “Master Analytical Scheme”

– Pesticides, PCBs, PCNs, Nitrosamines, “you name it – we measured it”

– Air, water, soil, sludge, sediments, food, blood, urine, etc.

Transite® Hoods in Hermann Bldg. with all the utility services conveniently located in the rear.

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If You Build It… They Will Come RTI’s critical role in the

EPA’s First Superfund Site

Love Canal in Niagara Falls, NY

The year was 1979 Concrete block

basements in the first-perimeter homes

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The “Essentials” in Human Exposure Field Studies

Name all three RTI staff and win an all expense paid trip to El Dorado, Arkansas!

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Sample Collection Was a Snap

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Systems Administration in ACS

Chromatography Data Systems Integrated processing and storage of raw and processed

GC and LC data Nelson Analytical - HP Basic on 3 node HP Series 200

cluster Fisons Multichrom - VMS on 5 node DEC/VAX cluster Labsystems Atlas – Windows NT on 6+ node Windows

cluster

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From Systems Administration to Programming

Gas Chromatography/Mass Spectrometry data processing.

Developed the logical and algebraic algorithms to process multiple m/z measurements from nearly every instrument we had.– Quattro spreadsheets– SAS

Incorporated method detection limit and sample size computations.

Graphical and tabular support for data quality assurance. Participant and client reporting.

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A Lab Rat No More

National Human Exposure Assessment Survey– “First” comprehensive multimedia and probability-based human

exposure study– RTI-EPA cooperative agreement in EPA Region 5– Personal sample collection from ~300 participants– Data Manager for field and laboratory results from 8 laboratories

generating data from air, water, soil, dust, urine, dermal, and blood samples.

– Automated Field Data Entry Program “Information Shell” used by a great many projects inside and outside of ACS

– > 2000 samples; > 100,000 SAS data records Primary interface between analytical laboratories and

statistical analyses in RTI-StatEpi– Andy Clayton and Barbara Alexander

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Human Exposure at a Glance

Human Exposure and Metabolism

URINARY METABOLITES(Biomarkers)

ELIMINATION VIA OTHER ROUTES

INHALATION

INGESTION

DERMAL

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Back in the Saddle Again

UNC Biostatistics – 2002-2010– Continuing Studies for 2 years – MPH degree candidate for 6 more– 42 credits and working full-time at RTI

No shortage of statistical application opportunities– Children’s Dietary Modeling– Environmental Conditions and Children’s Cognitive Outcomes– NATTS– AutoGC

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Dietary Pesticide Exposure Modeling EPA-ORD/Cincinnati: Lisa

Melnyk RTI: Larry Michael, Gordon

Brown, Andy Clayton, Michelle McCombs

2004-2012

Develop and evaluate a model for estimating children’s exposure to pesticides from household surfaces

Computation of F-statistics from Monte Carlo simulations of variance ratios

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Children’s Dietary Intake ModelI = R•FT+[CS•FS•TSF•ASF]+[(CS•SHR•TSH•ASH)(THF•AHF•FH)]

Intake = Food Residue + Surface → Food + Surface → Hand → Food

I = total dietary intake (μg) of pesticideR = pesticide residue (μg/g food) in food itemFT = total amount (g) of the single piece of contaminated food consumed CS = pesticide residue on the contacted surface (μg/cm2 of hardwood floor)FS= total food surface area in contact with surface (cm2)TSF = surface-to-food transfer efficiency (dimensionless)ASF = surface-to-food contact frequency (number/time)SHR = factor to account for surface area contacted by hand (=1 if no wiping effect;

>1 otherwise)TSH = surface-to-hand transfer efficiency (dimensionless)ASH = surface-to-hand contact frequency (number/time)THF = hand-to-food transfer efficiency (dimensionless)AHF = hand-to-food transfer frequency (number/time)FH = total food surface area in contact with contaminated portion of hand (cm2)

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CDIM Sensitivity Results

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CDIM Sensitivity Results (cont’d)

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National Air Toxics Trends Station Network EPA-OAQPS/RTP: Dennis Mikel, Mike Jones RTI: Larry Michael, Jeff Nichol, Ed Rickman 2008-2012 Accuracy,

precision, method detection limits, completeness for the NATTS network QC results

VOCs, Metals, PAHs, Carbonyls

Air Quality Systems database

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NATTS - Completeness

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NATTS - Precision

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NATTS - Bias

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NATTS – Method Detection Limits

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NATTS – Detection Compliance

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Environmental Conditions and Children’s Cognitive Outcomes

NIEHS Grant Denni Fishbein, Larry Michael, Jim Raymer, Susan

McRitchie, Christine Carr 2010-2014 Assess relationships between childhood Mn exposure,

measured in environmental and biologic samples and cognitive function, motor performance, and dysregulated moods and behavior (e.g., anxiety, irritability, impulsivity, aggression);

Evaluate the relative utility of environmental (house dust) and biomarker (nails, hair) measures of Mn exposure in relation to these outcomes.

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Mn Exposure Study – Participant Locations

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Mn Exposure Study - Objectives

Linear Modeling of Cognitive Outcomes with dust and biological measures was largely inconclusive.

Importance of the initial premise that cognition or metals results might be different among the study participants in the two geographical locations.

Effect of metals associated with PM2.5 in air Effect of the “age” of the dust

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Mn Exposure Study – Metals in Dust, Nails, and Hair

Sample Matrix Analyte DF Geo. MeanRatio (IL/IN) Pr > |t|

Dust Cd 61.315 0.9312 0.1200  Mn 64.485 0.3784 0.0005  Pb 63.693 0.6007 0.1669

Nail Cd 64.981 1.4703 0.1206  Mn 64.922 1.0708 0.7426  Pb 60.776 1.2661 0.3686

Hair Cd 52.58 1.9625 0.0071  Mn 58.573 2.1170 0.0191  Pb 57.401 2.4098 0.0010

• Significant differences for Mn in dust, with higher levels found in IN, and for all three metals in hair with higher concentrations universally found in IL .

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Mn Exposure Study – Cognitive DifferencesCognitiveAssess. STATE

No.Obs.

MeanMeas. Median

GeometricMean

p-value

RVP_APRIM IL 36 1.0 1.0 1.0415 0.0292  IN 30 0.9 1.0    RVP_BPRIM IL 35 0.7 0.9 1.3012 0.0462  IN 24 0.4 0.8    RVP_CORRREJECT IL 37 257.1 267.0 1.0803 0.0058  IN 30 246.5 260.0    SOC_SOLVMINMOVE IL 36 7.2 7.0 1.2663 0.0090  IN 27 5.7 5.0    SWM_BTWNERR IL 37 37.7 40.0 0.7244 0.0031  IN 30 48.7 47.5    

• indicate higher levels of cognitive functioning (visual sustained attention, spatial planning and motor control)  for participants in DuPage County, IL relative to those in Lake County, IN  

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Pb

Cd Mn

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Mn Exposure Study – Dust Age

Outcome N Parameter Estimate p-Value MetalMN_DUST 66 MAXAGE 173.399 0.0231 MNCD_DUST 66 MAXAGE 12.411 0.1977 CDPB_DUST 66 MAXAGE 120.143 0.0220 PB

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Auto Gas Chromatograph Evaluation Study

EPA –OAQPS: Kevin Cavender, Dave Shelow Jeff Nichol, Larry Michael, Eric Poitras, Seung-Hyun Cho 2013-2015 Photochemical Assessment Monitoring Stations (PAMS) Monitoring agencies are permitted to measure VOCs

using either:– an automated gas chromatograph suitable for field use– collect samples in the field (canisters) and ship them to a

laboratory for analysis.

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autoGC Study

EPA contract to evaluate the current state and availability of auto-GCs and determine their suitability for use in the PAMS programs.

Evaluated bias, precision, completeness, detectability among 8 vendor-supplied instruments in a laboratory setting

4 or 5 instruments will be selected to go on to a field evaluation phase

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autoGC Study – Bias

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AutoGC Study – Vendor Performance Bias and Precision

Vendor ID Direction p-Value

1   0.1497

2   0.7125

3 + <0.0001

4   0.4343

5 - <0.0001

6   0.8519

7 + <0.0001

8   0.8873

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autoGC Study - Overall Vendor Score

1 2 3 4 5 6 7 80

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Bias Precision CompletenessRelative Measurement Threshold Temp/RH Effect Overall

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Insufficient Thanks

There’s no way I could acknowledge the contributions of all the bright and motivated scientists who have made contributions to my career and successes in Exposure Science.

So, if you’re sitting in this room, you’ve probably worked along side me and for that I give great thanks.

If we haven’t worked together, I hope we can soon.

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In the end …… there’s nothing like tailgating with a friend.