Correlating Welding Arc Time and
Field Derived Generation Rates
- PO132 American Industrial Hygiene Association’s
67th Annual Conference and Exposition
June 7, 2007, Philadelphia, Pennsylvania
F.W. Boelter, CIH, PE
C.E. Simmons, CIH
G.A. Vos, PhD
Boelter Associates, Inc.
Park Ridge, IL
Problem Statement
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Would it be feasible to develop a reliable model utilizing
anecdotal information related to welding arc time, coupled with
laboratory sourced fume generation data to reliably predict
concentrations in the breathing zone?
To answer this, we performed this investigation, with the goal of
answering 2 primary questions:
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How reliable are subjective estimates of arc time?
How does arc time relate to breathing zone concentrations?
Potential Variables
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Welding process
Welding consumable
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Base metal - i.e. stainless steel, mild steel, etc.
Presence of coatings or paints on the base metal
Whether performed indoors or outdoors
Configuration of the space
Electrical current and voltage
Amount and type of ventilation
Welder’s skill and speed
Position of the welder
Personal protective equipment
Fume/Gas quantity, dependent upon several parameters:
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Consumable diameter
Consumable composition (usually alloy matched)
Alloy
Amperage
Voltage
Shielding
Polarity
Welder’s arc time or % time welding
Complicating the Measurement of Welding –
Many Related Terms with Differing Definitions
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Arc time
Welding time
Length of time arc is maintained
Non-arc time/handling time
Duty cycle
Operating factor
Operator factor
Hours per shift
Deposited metal/deposition rate
AWS Welding HB–
th
9
Ed, V1
Chung 1999 - pg 111
Castner 1998 – pg 228s
NIOSH 1979 – pg 14
How reliable are subjective estimates of arc time?
Inter-Rater Reliability
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Reliability of subjective measures could have an impact on any
modeling based upon such data
Therefore inter-rater reliability of subjective IH welding
judgments was assessed
19 video clips of single welding cycles were shown to 9
experienced hygienists (including 3 clips shown in this presentation)
Raters were asked to provide judgments for 8 variables per clip:
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Determination of if welding was being performed (Y/N)
Subjective estimate of daily time spent welding in a given job (Hrs.)
Estimates of the arc time for a job, as both:
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Percent of an 8 hour shift (%)
Time in minutes (Min)
The type of welding being performed (e.g. MIG, TIG, SMAW, etc.)
The type of metal being welded (e.g. Steel, Aluminum, etc.)
Whether or not the welder was wearing a welding hood (Y/N)
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And if so, the type of hood being used (e.g. PAPR)
Tee Shirt
Red Shirt
Green Shirt
Inter-Rater Reliability
Variable
Welding
Data Type
Dichotomous
Time Welding (Hrs)*
Arc Time (%)*
Arc Time (Min)*
Welding Type
Metal Involved
Welding Hood*
Continuous
Continuous
Continuous
Nominal
Nominal
Dichotomous
Welding Hood Type
Nominal
Raters
9
9
6*
6*
5*
9
9
8*
9
9
Cases
19
19
19
19
19
19
19
19
19
19
Statistic
KR‐20
Percent Agreement
ICC(2,1) Absolute Agreement, Single Measures
ICC(2,1) Absolute Agreement, Single Measures
ICC(2,1) Absolute Agreement, Single Measures
Percent Agreement
Percent Agreement
KR‐20
Percent Agreement
Percent Agreement
Value
0.97
93%
0.31
0.56
0.46
73%
73%
0.96
88%
73%
95% CI
(71%, 100%)
(0.10, 0.56)
(0.37, 0.76)
(0.24, 0.69)
(47%, 100%)
(38%,100%)
p
< 0.001
< 0.001
< 0.001
(66%, 100%)
(23%, 100%)
* When raters judged any given job as "not welding," other variables were somtimes left blank, resulting in an unbalanced dataset.
Thus some statistcs where calculated using all cases but excluding the missing raters
“Excellent” reliability for:
- Judgments of welding
- Whether a hood was worn
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“Fair to Good” reliability for: “Poor to Fair” reliability for:
- Welding type
- Metal involved
- Hood type
- Time spent welding in hours
- Arc time (both % of shift and minutes)
Reliability measures for all subjective estimates of time related variables were fairly low.
This could negatively impact modeling and other related assessments when subjective time
estimates are used in lieu of actual quantitative measures.
Thus when data is used for modeling and other advanced calculations, actual quantitative
measurements of time related variables should be used rather than subjective estimates.
How does arc time relate to
breathing zone concentrations?
Breathing Zone Concentrations
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Breathing zone concentrations of welding performed by a pipe
fitter doing his normal real world welding tasks in the field were
evaluated
Welding type was SMAW, performed indoors
Multiple 15 minute samples were collected during actual arc
welding tasks, both inside the welding hood and outside the
hood
Samples and Percent Arc Time
Correlation of Arc Time and Concentrations –
In Hood Samples
Sample Percent Arc Time Total Particulate Fe
(mg/m^3)
(mg/m^3)
Mn
(mg/m^3)
1
2
3
4
5
r
p (sig.)
r²
0.04
0.12
0.14
0.18
0.11
0.62
0.26
0.39
20%
37%
30%
30%
23%
4
3.37
3.43
4.47
3.4
-0.22
0.72
0.05
0.42
0.59
0.6
0.8
0.48
0.61
0.28
0.37
Correlation of Arc Time and Concentrations –
Out of Hood Samples
Sample
Percent Arc Time Total Particulate Fe
(mg/m^3)
(mg/m^3)
Mn
(mg/m^3)
1
2
3
4
5
r
p (sig.)
r²
20%
37%
30%
30%
23%
0.11
0.11
0.07
0.16
0.08
0.18
0.77
0.03
4.47
3.47
5.2
-0.49
0.67
0.24
0.95
0.51
0.36
0.72
0.37
-0.40
0.50
0.16
Conclusions
„
How reliable are subjective estimates of arc time?
„ Inter-rater reliability measures for subjective estimates of time related variables were low, which
could have a negative impact on models using such estimates
„ Thus when arc time data is used for modeling and other advanced calculations, actual quantitative
measurements of time related variables would be critical.
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How does arc time relate to breathing zone concentrations?
„ No significant or strong correlations for arc time and breathing zone concentrations were found
„ Arc time on its own appears to be a poor predictor of exposure
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Can we develop a model that utilizes anecdotal information related to welding arc time, coupled
with laboratory sourced fume generation data to reliably predict concentrations in the breathing
zone?
„ Not according to these findings
„ These results emphasize the need for field-derived welding concentrations and emission rates for
estimation of exposures
„ Models based primarily upon arc time and laboratory fume generation rates using self reported
estimates of welding time would likely yield estimates of exposure grossly in error
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Recommendations for future study:
„ Determination of what variables do highly correlate with welding fume exposures, so that reliable
and reproducible models can be developed
„ Lab based fume generation rates are often developed with protocols and parameters that do not
reflect real world welding practices, thus additional research is needed to derive more applicable
values so that they may be included in the development of future models
Literature
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KR-20
„ Streiner, D.L., and G.R. Norman: Health Measurements Scales. A Practical Guide
to Their Development and Use, Second Edition. Oxford, U.K.: Oxford University
Press, 1995. pp. 102–126.
„ Nunnally, J.C., and I.H. Bernstein: Psychometric Theory, Third Edition. New
York: McGraw-Hill, 1994. pp. 83–113.
„ Kuder, G.F., and M.W. Richardson: The theory of the estimation of test reliability.
Psychometrika 2:151–160 (1937).
ICCs
„ Shrout, P.E., and J.L. Fleiss: Intraclass correlations: Uses in assessing rater
reliability. Psychol. Bull. 86:420–428 (1979).
„ McGraw, K.O., and S.P. Wong: Forming inferences about some intraclass
correlation coefficients. Psychol. Meth. 1:30–46 (1996).
Adjectives that can be used in ICC Assessment:
„ Fleiss, J.L.: The Design and Analysis of Clinical Experiments. New York: John
Wiley & Sons, Inc., 1986.
Correlating Welding Arc Time
and Field Derived Generation
Rates
Fred W. Boelter, CIH, PE
Boelter & Yates, Inc.
Park Ridge, Illinois
847/692-4700
fboelter@boelter-yates.com