Western Kentucky University Fundamentals of Industrial Hygiene Essay

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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