Industrial Hygiene Exposure Assessment: The Good, Bad, and

Industrial Hygiene 

Exposure Assessment 

“The Good, Bad, and the Better?”

Exposure Assessments 

� An Introduction 

� What is Exposure Assessment? 

� Why is Exposure Assessment Important? 

� “One of the most important objectives of an industrial hygiene 

program is to accurately assess employees’ occupational 

exposure to airborne contaminants, where necessary by 

exposure measurements.” - Leidel 

� A Common Industrial Hygiene Encounter


� Do we do a good job of assessing occupational exposures? 

� How many follow the AIHA Exposure Assessment Strategy? Or 

know what it is? 

� How many follow NIOSH/OSHA guidance? Or know what it is? 

� How many make Exposure Assessment judgments on fewer 

than 6 samples on a regular basis? 

� How many apply statistics to exposure measurements on a 

regular basis? And report the results?


� Do we do a good job of assessing occupational exposures? 

� Do you ever use area samples to assess employee exposure? 

� Do you always make exposure assessments with quantitative 

measurements? 

� Have you ever made an exposure assessment judgment with 

just one quantitative sample? 

� How many sell equipment that performs area samples as a way 

to assess employees’ occupational exposure?

Where we Started 

� 1977 NIOSH 

Document 

� Leidel, 

Busch, & 

Lynch

1977 NIOSH Exposure Assessment Strategy 

� Identify Contaminants 

� Materials 

� Process 

� Observations 

� Calculate Potential Exposure 

� Modeling 

� Steady State Model 

ppm = 403(sp. gv)(pts/hr.)(10 6 )(K) / (Mole Wt.)(CFM) 

� K Factor 

� Sample Workers 

� Which One?

1977 NIOSH Guidance

1977 NIOSH Guidance 

� Why Highest Risk Employee (Worst Case Scenario) 

� Exposure is Variable 

� 95 % confident captured highest 10% of Exposure 

� Example of a 20 person SEG 

� Need to sample 13 employees


� What is the overall purpose of the NIOSH guidance? 

� OSHA Compliance - 95 %Confidence Interval 

� Decision Making 

� Violation 

� No Violation 

� Possible Over Exposure 

� Coefficient of Variation 

� Error in method and analysis 

� Sampling Period effects CVtotal 

� Full Period Single least error


� LCL95% and UCL95% 

� LCL95% =Mean/PEL -1.645* CVtotal/√n 

� UCL95% =Mean/PEL +1.645* CVtotal/√n 

� UCL ≤ 1 – No violation 

� LCL > 1 – Violation 

� LCL ≤ 1 and UCL > 1 – Possible Overexposure 

� Xylene Example


The Good

AIHA Exposure Assessment Strategy 

� 3 rd Edition – Published 2006


o Exposure Assessment 

Strategy Flow Chart


� Basic Characterization 

� Gather Information 

� Workplace 

� Workforce 

� Agents

Example Basic Characterization Spreadsheet 

Department 

Material Hazard / Agent / 

OEL 

Department Number Equipment Employee Job Title Task Description Material ID Description Agent Chemical CAS# OEL Units 

Adhesives 105 Blender Filters Adhesive Operator Change filter media, bleed and flush pumps 

Using pneumatic pump to charge 700 liters of 

Chemical Cyclohexanone 110-82-7 100 ppm 

Adhesives 105 Blender Pumps Adhesive Operator cyclohexanone from drums into reactor Chemical Cyclohexanone 110-82-7 100 ppm 

Adhesives 105 Blender General Area Maintenance Employee Viscosensor rebuild welding Chemical Nickel 7440-02-0 1.5 mg/m^3 

Adhesives 105 Blender General Area Maintenance Employee Paint area & parts clean up 

Charging latex super mix from storage tank (watching 

Chemical MEK 78-93-3 200 ppm 

Adhesives 105 Blender Tank Adhesive Operator level through open manhole) Chemical Cyclohexanone 110-82-7 200 ppm 

Adhesives 105 Blender Tank Adhesive Operator Package final product through filter system Chemical Cyclohexanone 110-82-7 200 ppm 

Adhesives 105 General Equipment Maintenance Employee Calibration & repair of viscosity meters Chemical Cyclohexanone 110-82-7 200 ppm 

Adhesives 105 Blender Tank Maintenance Employee Reactor equipment maintenance Chemical Cyclohexanone 110-82-7 200 ppm 

Charging 20 - 10 kg bags of TiOx into Reactor w/ local 

Titamium 

Adhesives 105 Blender Tank Adhesive Operator exhaust Chemical Dioxide 13463-67-7 10 mg/m^3 

Adhesives 105 Blender Tank Adhesive Operator Collect a 200 ml QC samples (6) through handhole Chemical Cyclohexanone 110-82-7 200 ppm 

Adhesives 105 Blender Tank Adhesive Operator Monitoring process at control panel Chemical Cyclohexanone 110-82-7 200 ppm 

Adhesives 105 Blender Tank Adhesive Operator Monitoring process at control panel Noise Noise 85 dBA 



Chemical 

Noise 

Physical 

Other

Example Basic Characterization Spreadsheet 

0 (


� Basic Characterization 

� Define Similar Exposure Groups (SEGs) by: 

� Agent / Contaminant and/or a combination of: 

� Process 

� Task 

� Job Classification/Description 

� Define by Observation 

� Define by Sampling


� Exposure Assessment 

� Defining Exposure Profile 

� Prioritizing

Exposure Point Inventories 

Mark Betz


� Exposure Assessment 

� Defining Exposure Profile 

� Prioritizing 

� Initial Exposure Estimates

The AIHA Model 

Exposure Control 

Ratings * 

Cutoff (%OEL) 

1 X 0.95 < 10% 

� AIHA Exposure Control Ratings for TWA 

OELs 2 10% < X0.95 < 50% 

� Which 

3 

control 

50% 

zone 

< 

is 

X 

appropriate? 

0.95 < 100% 

4 X 0.95 > 100% 

Confidence level 

High 

Medium 

Low


� Initial Exposure Estimates 

� Qualitative Exposures 

� Semi-Quantitative Exposures – Modeling 

� Quantitative Exposure Assessments


� Qualitative Exposure Assessments 

� Are we good at this? 

� Not much better than chance (Logan 2009) & (Valadi 2012) 

� Estimates of the 95%tile


� Qualitative Exposure Assessments 

� How do we do this? 

� Results of a recent survey of CIHs

Ranking Qualitative Exposure Determinants

16.0 

14.0 

12.0 

10.0 

8.0 

6.0 

4.0 

2.0 

0.0 

Forced Selection Choices 

Toxicity Phy/Chm 

Prop 

Percent of Time Exposure Determinant Ranked in Top 5 

Time Exp. Quanity LEV How 

Handled 

Wkr Tech PPE Housekpg Maint. 

Gas/Vapor 

Particulate

Statistical Analysis of the Selections 

Exposure Determinant Likert Ranking 

Differences* 

Toxicity 

Gas/Vapor Ranked Higher 

Z = -3.146, p = .002, r = - 

.16 

Forced (Top 5) 

Selection 

Differences** 

Quantity Handled NSD Gas/Vapor Selected More 

Often 

McN X 2 =36.6, p

Qualities that Effect Choice – Gas Vapor 

Determinant # of Exp. 

Assmts. 

Toxicity 

Χ 2 = 12.68, p = .027 

Cramer’s V = .16 

Freq. of Qual. 

Exp. Assmts. 

Χ 2 = 15.37, p = .004 


Χ 2 = 14.18, p = .007 

� Quantity Pertinent Chi Square Findings Cramer’s – Gas V = .17 and Vapor 

Assessments 

Time Exp. 

How Handled 

Phy/Chm Prop 

LEV 

Χ 2 = 14.85, p = .013 


Χ 2 = 11.10, p = .05 


Χ 2 = 20.01, p < .001 


Pharmaceutical 

Industry 

Χ2 = 10.30, p = .001 

Odds Ratio = 2.95 

Χ 2 = 6.33, p = .012 

Odds Ratio = 2.05 

Χ 2 = 5.77, p = .016 

Odds Ratio = 2.04

Qualities that Effect Choice – Particulate 

Determinant # of Exp. 

Assmts. 

Toxicity 

Quantity 

Χ 2 = 13.33, p = .020 


Freq. of Qual. 

Exp. Assmts. 

Χ 2 = 21.49, p


� Semi -Quantitative Exposure Assessments 

� Modeling 

� Physical and Chemical Properties 

� Simple 

� Very Complex


� Quantitative Exposure Assessments 

� Purpose of Monitoring: 

� Baseline 

� Compliance 

� Diagnostic/Evaluation 

� Route of Exposures Accounted for: 

� Inhalation 

� Dermal 

� Ingestion



� Number or Measurements 

� 6 – Estimate of Means 

� 10 – Estimate of Variation



� Applying Statistics 

� Log Normal Distributions – More on this in the “Bad” 

� Mean vs. Upper Tail 

� 95% confident below 95%tile estimate 

� Uncertainty 

� Uncertainty in measurement 

� Uncertainty in OELS 

� Statistics should aid judgment not make the judgment


The Bad

West Michigan Industrial 

Hygiene Society 

Industrial Hygiene Exposure 

Assessments for Small Employers: 

How Many Samples? 

Tuesday, December 4, 2012 

James R. Strobridge, CIH 

36

Exposure Assessments 

The Bad – “STATISTICS”

The Bad - Statistics 

� What do think of or feel when you hear Statistics? 

� Confusion 

� Fear 

� Huh? 

� Confidence?

What is a Log Normal Distribution?

Distributions with a Mean=1, Are they the Same?

Example: Welding fume data - 

estimate m and s


estimate m and s 

Case xi (mg/m3 ) ( xi-x ) 2 

_ 

1 0.84 0.157344 

2 0.98 0.065878 

3 0.42 0.666944 

4 1.16 0.005878 

5 1.36 0.015211 

6 2.66 2.025878 

Sum = 7.42 2.937133 

_ 

x = 

1.24 

sd = 0.77

Sample geometric mean (gm) & 

geometric standard deviation (gsd)


estimate GM and GSD 

Case xi (mg/m3 ) yi=ln(xi) (yi-y) 2 _ 

1 0.84 -0.1744 0.055877 

2 0.98 -0.0202 0.006762 

3 0.42 -0.8675 0.864025 

4 1.16 0.1484 0.007463 

5 1.36 0.3075 0.060248 

6 2.66 0.9783 0.839600 

Sum = 0.3722 1.833976 

_ 

y = 0.0620 

gm = 1.06 

gsd = 1.83


estimate GM and GSD

Sample 95 th Percentile Exposure 

� The focus is on the upper tail of the 

exposure profile. 

� The sample 95 th percentile can be 

considered a “compliance statistic”. 

� The (usual) goal is to determine which 

category the 95 th Percentile most likely falls. 

� It is used to assist in reaching a decision 

that the exposure profile is 

� “Controlled” or “Acceptable” 

� “Unacceptable” 

� or falls in a “Control Category”

Example 

� A sample of six full-shift TWA welding fume 

measurements resulted in the following statistics: 

� (sample) geometric mean is 1.06 mg/m 3 

� (sample) geometric standard deviation is 1.83 

� What is the point estimate (i.e., best estimate) of the 

true 95 th percentile?

90 th , 95 th , and 99 th Percentiles

95 th Percentile

Alternative upper percentile 

formula

Focus on Upper Tail

Upper Confidence Limit – 95% 

Confident 

� Concept 

� Calculate the 95% upper confidence interval for the 

95th percentile statistic (upper tolerance limit) 

� Application 

� 95%UCL can be used to test the following 

hypotheses: 

� H o: 95th percentile > OEL 

� H a: 95th percentile < OEL 

� Interpretation 

� If the 95%UCL is less than the OEL, then we can 

say that we are at least 95% confident that the true 

95th percentile is less than the OEL

95%UCL for the 95 th Percentile 

� Procedure: 

� Calculate the gm and gsd 

� Using n, read the UCL K-value from the appropriate 

table 

�g = confidence level, e.g., 0.95 

�p = proportion, e.g., 0.95 

�n = sample size 

� Using gm, gsd, and k, calculate the 95%UCL 

_ 

�y = ln( gm ) 

�s y = ln( gsd )

Statistical Analysis of Exposure Data 

� There is help to do this automatically with IH-MOD

Industrial Hygiene Statistics 

OEL 

0.15 

Sample 

data 

0.06 Descriptive statistics 

0.1 Number of samples (n) 15 

0.05 Maximum (max) 0.2 

0.1 Minimum (min) 0.01 

0.01 Range 0.19 

0.09 Mean 0.0713 

0.04 Median 0.07 

0.2 Standard deviation (s) 0.0453 

0.04 Geometric mean 0.0584 

0.08 Geometric standard deviation 2.03 

0.08 Percent above OEL 6.7% 

0.03 

0.09 Test for distribution fit 

0.03 W-test of log-transformed data 0.932 

0.07 Lognormal (α = 0.05) ? Yes 

W-test of data 0.870 

Normal (α = 0.05) ? No 

Lognormal parametric statistics 

Estimated Arithmetic Mean - AM est. 0.074 

LCL1,95% - Land's "Exact" 0.055 

UCL1,95% - Land's "Exact" 0.116 

95th Percentile 0.187 

UTL95%,95% 0.359 

Percent above OEL 9.1% 

LCL1,95% %>OEL 2.82 

UCL1,95% %>OEL 23.4 

Normal parametric statistics 

Mean 0.0713 

LCL1,95% - t statistics 0.051 

UCL1,95% - t statistics 0.092 

95th Percentile - Z 0.146 

UTL95%,95% 0.188 

Percent above OEL 4.1% 

C 

D 

Concentration 

0.25 

0.2 

0.15 

0.1 

0.05 

0 

0 

14 

2 4 6 8 10 12 14 

n 

16 

12 

10 

8 

6 

4 

2 

0 

LCL 

est. AM 

UCL 

OEL 

Idealized Lognormal Distribution 

95%ile 

Sequential Data Plot 

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 

Concentration 

UTL

Statistical Analysis of Exposure Data 

� What is the problem with the Upper Tolerance Limit? 

� 95% Confidence limit of the 95%tile point estimate


The Better?

Exposure Assessments – The 

Better? 

� How do we make good estimates of exposure with a 

small data sets? 

� Less than 6 samples 

� Statistical Rules of Thumb

R.O.T. for Estimating the 95 th Percentile 

1. If n is small (i.e., OEL, then decision = Category 4. 

2. Estimate the median and use it as a surrogate of 

the sample GM: 

- Sort the data 

- If n is odd the median is the middle value. 

- If n is even the median is the average of two 

middle values. 

3. Multiply the median by 2, 4, and 6 

- The results comprise an approximate low, 

middle, and high estimate of X 0.95.

The AIHA Model 

Exposure Control 

Ratings * 

Cutoff (%OEL) 

1 < X 0.95 < 10% 

� AIHA 2 Exposure 10% Control < X Ratings for TWA 

0.95 < 50% 

OELs 

3 50% < X0.95 < 100% 

� Which control zone is appropriate? 

4 X 0.95 > 100% 

Confidence level 

High 

Medium 

Low

Examples (assume OEL=100) 

a. X = {5} 

b. X = {68} 

c. X = {7, 34, 57} 

d. X = {1, 1, 2, 5} 

e. X = {4, 5, 8, 23} 

f. X = {0.3, 1, 2, 3, 4, 22} 

g. X = {10, 10, 10, 20, 50, 105} 

h. X = {7, 10, 16, 21, 45, 53} 

For each dataset, determine the appropriate Exposure 

Category – 1, 2, 3, or 4 – using the above Rule-ofthumb.

R.O.T. for Estimating the 95 th Percentile 

� AIHA Moblie App 

� IH Digg - “Game” format to make estimates of exposure 

� https://itunes.apple.com/us/app/ih-dig/id433823028?mt=8

Control Banding 

� Exposure Assessment Bypassed 

� Measurements not needed 

� Chemical Agent and associated hazards drive controls 

� Pharma System 

� Band 1-5 – based on exposure limits 

� COSHH Essentials 

� http://www.coshh-essentials.org.uk/help/Introduction6.htm

Bayesian Decision Analysis - BDA 

� The Promise 

The Prior 

Bayesian 

Calc. 

The Posterior 

The 

Likelihood

Bayesian Decision Analysis - BDA 

� The Reality 

� What did we say about IHs ability to estimate 95 %tile with 

no measurements? 

� Current Recommendations of a Flat Prior 

� What does that mean? 

� Example IHDataAnalyst


� Other tools from AIHA -EASC 

� http://www.aiha.org/INSIDEAIHA/VOLUNTEERGROUPS/EAS 

C/Pages/EASCTopics.aspx 

� Baysian Decision Analysis Tool 

� http://www.oesh.com/

Industrial Hygiene Exposure Assessment: The Good, Bad, and

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