DescriptionENV 380 Environmental Toxicology

Exercise 5.1 – Exploration of the AIHA Exposure Assessment Tool – IHSTAT

50 points

Overview:

For this exercise we will be reviewing the Industrial Hygiene statistical tool available from the

AIHA website. This tool is specifically for calculating exposure statistics from IH air monitoring

data. Typically, the data will represent time weighted average values.

For the exercise your task is to download the excel spreadsheet that has statistical models for

evaluating exposures. Next your task will be to enter the data from monitoring events below to

estimate exposures and compare to the occupational exposure limits. Finally, your task will be

to interpret the results and explain if an employee exposed to the given airborne time weighted

averages would be at risk of health impacts.

Remember, the goal of IH monitoring is to evaluate potentially hazardous conditions and

ensure that employees are not exposed to harmful environments. This means if you calculate

average values and use these to compare to occupational exposure limits you will make many

mistakes that will put employees in harmful environments. This tool from AIHA can be used to

evaluate the distribution of the data and model a 95% limit that can then be evaluated against

the occupational exposure limit. Thus, you are reducing your potential error and using a

conservative value to compare to exposure limits. In this manner, you will reduce errors and be

protective. If you need more information on this tool you can arrange a meeting with me via

Zoom or a conference call.

Finally, the link to the IH tools available from the American Industrial Hygiene Association can

be found at https://www.aiha.org/public-resources/consumer-resources/topics-of-interest/ihapps-tools . You will see that there are several tools available to assist the EOHS professional.

Also, there is a stepwise procedure for evaluating and assessing workplaces. Each of these tools

can be an extremely helpful resource. The resource we will use for this exercise is IHSTAT.

This resource is at the bottom of the page under STEP 5. IHSTAT is an Excel application that

you may use to calculate a variety of exposure statistics. Likewise, you can use this application

to perform goodness of fit tests, and graph and explain exposure data. The book you have access

through to in the WKU Library “A Strategy for Assessing and Managing Occupational

Exposures” accompanies this tool and will give guidance on the use of this application. You can

find the book at this link. Also, you can go to WKU Libraries and do a search for the title.

Analysis:

Your task is to evaluate the Scenarios below with the IHSTAT tool. There will be a video to

guide you. Then, your task will be to assess the exposures based on the data analysis produced.

You will need to answer the questions that follow for each scenario. The video will guide you

through the analysis of the first dataset. Your task is to evaluate the other two datasets on your

own.

Data to Analyze:

Scenario 1: Xylene, TLV = 100 ppm, monitoring data (ppm) as 8-hour TWA values – 21,38, 41,

48, 109, 21, 68, 14, 22, 18, 65, 52, 29

Scenario 2: Ethanol, OEL = 1000 ppm, monitoring data (ppm) as 8-hour TWA values – 215, 52,

395, 700, 75, 98, 300, 454, 102

Scenario 3: Welding Fumes, OEL = 5 mg/M3, monitoring data (mg/M3) as 8-hour TWA values –

0.84, 0.98, 0.42, 1.16, 1.36, 2.66

Questions to Answer for each Scenario:

1. Describe the descriptive statistics for the Scenario. Include the number of samples (n),

maximum, minimum and range. Also, please list the percent above the OEL, the mean,

median, and the standard deviation. You may include a table of these results, if you

choose.

2. What do the tests for distribution fit indicate? Are the data normally distributed? Are

the data lognormally distributed? Please discuss.

3. How does the 95th percentile compare to the OEL? Now, compare the Upper Tolerance

Limit (UTL) for the 95th percentile to the OEL. This is the modeled upper limit for the

95ht percentile. In other words, we are 95% sure the 95th percentile is this value or less.

So, if this value exceeds the OEL the exposure would be unacceptable. Also, if the 95 th

percentile exceeds the OEL the exposure is unacceptable.

4. An important value to review for each scenario is the %>OEL. This is in relation to the

exposures. In essence, given the exposure data, the prediction is that this percentage of

exposures will be above the OEL. The lower and upper limits of this estimate give us

some confidence in this value. Also, these estimates will suggest uncertainty. What

percent of exposures are predicted to be above the OEL?

5. Based on these results, would you rate the exposure in the scenario as acceptable or

unacceptable?

Industrial Hygiene Statistics

Data Description:

Sample Data

(max n = 50)

No less-than ()

1.3

1.8

1.2

4.5

2

2.1

5.5

2.2

3

2.4

2.5

2.5

3.5

2.8

2.9

DESCRIPTIVE STATISTICS

Number of samples (n)

Maximum (max)

Minimum (min)

Range

Percent above OEL (%>OEL)

Mean

Median

Standard deviation (s)

Mean of logtransformed data (LN)

Std. deviation of logtransformed data (LN)

Geometric mean (GM)

Geometric standard deviation (GSD)

15

5.5

1.2

4.3

6.667

2.680

2.500

1.138

0.908

0.407

2.479

1.502

TEST FOR DISTRIBUTION FIT

W-test of logtransformed data (LN)

Lognormal (a = 0.05)?

0.974

Yes

W-test of data

Normal (a = 0.05)?

0.904

Yes

LOGNORMAL PARAMETRIC STATISTICS

Estimated Arithmetic Mean – MVUE

LCL1,95% – Land’s “Exact”

2.677

2.257

UCL1,95% – Land’s “Exact”

95th Percentile

UTL95%,95%

Percent above OEL (%>OEL)

LCL1,95% %>OEL

3.327

4.843

7.046

4.241

0.855

Sequential Data Plot

6

5

4

Concentration

OEL

5

3

2

1

0

UCL1,95% %>OEL

0

2

4

6 Sample 8

Number 10

12

14

16

Logprobability Plot and

Least-Squares Best-Fit Line

99%

98%

95%

15.271

90%

84%

NORMAL PARAMETRIC STATISTICS

Mean

LCL1,95% – t statistics

2.680

2.162

75%

UCL1,95% – t statistics

95th Percentile – Z

UTL95%,95%

Percent above OEL (%>OEL)

3.198

4.553

5.60

2.078

50%

25%

16%

10%

Linear Probability Plot and

Least-Squares Best-Fit Line

5%

2%

1%

0

1

Concentration

99%

98%

0.5

95%

0.45

90%

84%

0.4

75%

0.35

10

Idealized Lognormal Distribution

AM and CI’s

95%ile

3

4

Concentration

5

0.3

50%

0.25

-5

0

5

Concentration

10

25%

0.2

16%

10%

0.15

5%

0.1

2%

1%

0.05

0

15

0

1

2

6

7

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