The Diesel Exhaust in Miners Study: IV. Estimating Historical Exposures to Diesel Exhaust in Underground Non-metal Mining Facilities

Article (PDF Available)inAnnals of Occupational Hygiene 54(7):774-88 · September 2010with44 Reads
DOI: 10.1093/annhyg/meq025 · Source: PubMed
Abstract
We developed quantitative estimates of historical exposures to respirable elemental carbon (REC) for an epidemiologic study of mortality, including lung cancer, among diesel-exposed miners at eight non-metal mining facilities [the Diesel Exhaust in Miners Study (DEMS)]. Because there were no historical measurements of diesel exhaust (DE), historical REC (a component of DE) levels were estimated based on REC data from monitoring surveys conducted in 1998-2001 as part of the DEMS investigation. These values were adjusted for underground workers by carbon monoxide (CO) concentration trends in the mines derived from models of historical CO (another DE component) measurements and DE determinants such as engine horsepower (HP; 1 HP = 0.746 kW) and mine ventilation. CO was chosen to estimate historical changes because it was the most frequently measured DE component in our study facilities and it was found to correlate with REC exposure. Databases were constructed by facility and year with air sampling data and with information on the total rate of airflow exhausted from the underground operations in cubic feet per minute (CFM) (1 CFM = 0.0283 m³ min⁻¹), HP of the diesel equipment in use (ADJ HP), and other possible determinants. The ADJ HP purchased after 1990 (ADJ HP₁₉₉₀(+)) was also included to account for lower emissions from newer, cleaner engines. Facility-specific CO levels, relative to those in the DEMS survey year for each year back to the start of dieselization (1947-1967 depending on facility), were predicted based on models of observed CO concentrations and log-transformed (Ln) ADJ HP/CFM and Ln(ADJ HP₁₉₉₀(+)). The resulting temporal trends in relative CO levels were then multiplied by facility/department/job-specific REC estimates derived from the DEMS surveys personal measurements to obtain historical facility/department/job/year-specific REC exposure estimates. The facility-specific temporal trends of CO levels (and thus the REC estimates) generated from these models indicated that CO concentrations had been generally greater in the past than during the 1998-2001 DEMS surveys, with the highest levels ranging from 100 to 685% greater (median: 300%). These levels generally occurred between 1970 and the early 1980s. A comparison of the CO facility-specific model predictions with CO air concentration measurements from a 1976-1977 survey external to the modeling showed that our model predictions were slightly lower than those observed (median relative difference of 29%; range across facilities: 49 to -25%). In summary, we successfully modeled past CO concentration levels using selected determinants of DE exposure to derive retrospective estimates of REC exposure. The results suggested large variations in REC exposure levels both between and within the underground operations of the facilities and over time. These REC exposure estimates were in a plausible range and were used in the investigation of exposure-response relationships in epidemiologic analyses.
Ann. Occup. Hyg., Vol. 54, No. 7, pp. 774–788, 2010
Ó The Author 2010. Published by Oxford University Press
[on behalf of the British Occupational Hygiene Society].
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doi:10.1093/annhyg/meq025
The Diesel Exhaust in Miners Study: IV. Estimating
Historical Exposures to Diesel Exhaust in
Underground Non-metal Mining Facilities
ROEL VERMEULEN
1,4
, JOSEPH B. COBLE
1,5
, JAY H. LUBIN
1
,
LU
¨
TZEN PORTENGEN
2
, AARON BLAIR
1
, MICHAEL D. ATTFIELD
3
,
DEBRA T. SILVERMAN
1
*
and PATRICIA A. STEWART
1,6†
1
Division of Cancer Epidemiology and Genetics, US National Cancer Institute, Bethesda, MD, 20892,
USA;
2
Institute for Risk Assessment Sciences, Utrecht University, Utrecht, 3584 CK, The Netherlands;
3
Surveillance Branch, Division of Respiratory Disease Studies, US National Institute for Occupational
Safety and Health, Morgantown , WV, 26505, USA
Received 21 November 2009; in final form 13 February 2010; published online 27 September 2010
We developed quantitative estimates of historical exposures to respirable elemental carbon
(REC) for an epidemiologic study of mortality, including lung cancer, among diesel-exposed
miners at eight non-metal mining facilities [the Diesel Exhaust in Miners Study (DEMS)].
Because there were no historical measurements of diesel exhaust (DE), historical REC (a com-
ponent of DE) levels were estimated based on REC data from monitoring surveys conducted in
1998–2001 as part of the DEMS investigation. These values were adjusted for underground
workers by carbon monoxide (CO) concentration trends in the mines derived from models
of historical CO (another DE component) measurements and DE determinants such as engine
horsepower (HP; 1 HP 5 0.746 kW) and mine ventilation. CO was chosen to estimate historical
changes because it was the most frequently measured DE component in our study facilities and
it was found to correlate with REC exposure. Databases were constructed by facility and year
with air sampling data and with information on the total rate of airflow exhausted from the un-
derground operations in cubic feet per minute (CFM) (1 CFM 5 0.0283 m
3
min
21
), HP of the
diesel equipment in use (ADJ HP), and other possible determinants. The ADJ HP purchased
after 1990 (ADJ HP
19901
) was also included to account for lower emissions from newer, cleaner
engines. Facility-specific CO levels, relative to those in the DEMS survey year for each year
back to the start of dieselization (1947–1967 depending on facility), were predicted based on
models of observed CO concentrations and log-transformed (Ln) ADJ HP/CFM and Ln(ADJ
HP
19901
). The resulting temporal trends in relative CO levels were then multiplied by facility/
department/job-specific REC estimates derived from the DEMS surveys personal measure-
ments to obtain historical facility/department/job/year-specific REC exposure estimates. The
facility-specific temporal trends of CO levels (and thus the REC estimates) generated from
these models indicated that CO concentrations had been generally greater in the past than dur-
ing the 1998–2001 DEMS surveys, with the highest levels ranging from 100 to 685% greater
(median: 300%). These levels generally occurred between 1970 and the early 1980s. A compar-
ison of the CO facility-specific model predictions with CO air concentration measurements
from a 1976–1977 survey external to the modeling showed that our model predictions were
slightly lower than those observed (median relative difference of 29%; range across facilities:
*
Author to whom correspondence should be addressed. Tel: þ1-301-435-4716; fax: þ1-301-402-1819;
e-mail: silvermd@mail.nih.gov
y
Co-senior authors
4
Present address: Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
5
Present address: 1412 Harmony Lane, Annapolis, MD 21409, USA
6
Present address: Stewart Exposure Assessments, LLC, Arlington, VA 22207, USA
774
49 to –25%). In summary, we successfully modeled past CO concentration levels using selected
determinants of DE exposure to derive retrospective estimates of REC exposure. The results sug-
gested large variations in REC exposure levels both between and within the underground oper-
ations of the facilities and over time. These REC exposure estimates were in a plausible range
and were used in the investigation of exposure–response relationships in epidemiologic analyses.
Keywords: elemental carbon; miners; exposure assessment; carbon monoxide; diesel exhaust
INTRODUCTION
In response to concerns about potential health effects
associated with long-term exposure to diesel exhaust
(DE), in particular lung cancer, the US National
Cancer Institute (NCI) and the US National Institute
for Occupational Safety and Health (NIOSH) initi-
ated an epidemiologic study of DE among non-metal
miners, known as the Diesel Exhaust in Miners
Study (DEMS). One of the principal aims of the
study was to develop retrospective estimates of
workers’ exposure to respirable elemental carbon
(REC), a constituent of DE, for all years in which
diesel-powered equipment was used in the eight
mining facilities. The resulting quantitative exposure
assessment for REC for underground workers from
the first year of underground diesel usage (1947–
1967, depending on the facility) to the study end of
follow-up (31 December 1997) was based on data
from monitoring surveys conducted as part of the
DEMS combined with time trend prediction models
using historical carbon monoxide (CO, another com-
ponent of DE) data and DE determinants. The expo-
sure assessment for surface workers is described
elsewhere (Coble et al., 2010; Stewart et al., 2010).
Personal REC exposure measurements from the
DEMS surveys, each of which took place during
a four to five consecutive day period between 1998
and 2001 depending on the facility, provided the
basis for estimating average facility/department/
job-specific REC exposure levels for the reference
period of 1998–2001 (Coble et al., 2010). Analysis
of the area measurements from the DEMS surveys
found that REC concentrations correlated with CO
levels and that CO concentrations increased approx-
imately linearly in log-log space with REC concen-
trations (Vermeulen et al., 2010). These findings
supported the use of past CO measurements to esti-
mate historical REC exposure levels. Additional jus-
tification came from the fact that there were more
detectable CO data historically than for any other
DE component and that EC and CO are both pro-
duced by incomplete combustion of diesel fuel.
In this paper, we describe the development of the
quantitative estimates of historical exposures to REC
for underground workers in the DEMS. First, infor-
mation on measurements and on possible DE deter-
minants was collected and summarized by year.
Subsequently, facility-specific time trend predic-
tion models were developed using historical CO
measurements and important exposure determinants
(e.g. power of diesel engines and rates of airflow ex-
hausted from the underground operations). These
time trend prediction models were then linked with
the facility/department/job-specific REC averages
from the DEMS surveys to estimate annual historical
facility/department/job REC exposure levels from
1998 to 2001 to the date of dieselization in each fa-
cility. Estimates were compared with external mea-
surement data not used in the exposure modeling.
METHODS
The mining facilities
The mining facilities included three potash opera-
tions in New Mexico (B, D, and J), one rock salt
facility in Ohio (E), one limestone facility in Mis-
souri (A), and three trona (trisodium hydrogen dicar-
bonate dihydrate) operations in Wyoming (G, H, and
I). Characteristics of the underground operations of
these facilities can be found elsewhere (Coble
et al., 2010; Stewart et al., 2010).
Data collection
A detailed description of the data collection ef-
forts can be found in Stewart et al. (2010). In brief,
three rounds of visits were made to the seven operat-
ing mining facilities to collect: DE-related measure-
ments [including REC, CO, carbon dioxide (CO
2
),
and nitrogen dioxide (NO
2
)] in 1998–2001 (i.e. the
DEMS surveys) (Coble et al., 2010; Vermeulen
et al., 2010), information on past exposure levels,
and information on diesel equipment, job tasks,
and locations. This and other production-related in-
formation was obtained from records and from inter-
views with long-term workers, including union or
worker representatives and the management staff,
who had held a broad spectrum of jobs. Because
one facility (J) had closed before 1998, we were
Historical diesel exhaust exposures in underground mining 775
unable to conduct a monitoring survey. However, we
were able to obtain extensive stored records on pro-
duction parameters from the facility. In addition, we
interviewed former employees from that facility
concerning relevant aspects of the mining environ-
ment. Measurement data on all facilities were also
acquired from other sources (see Description of the
Measurement Database).
Description of the measurement database
An overview of the measurement database can be
found in Stewart et al. (2010). The database con-
tained underground and surface measurements taken
in the eight study facilities from: the Mine Safety
and Health Administration (MSHA) Mine Informa-
tion Data System (MIDAS), a computerized data-
base composed primarily of area measurements
generated from compliance visits (years covered
1976–2001) (MSHA, 2006); the DEMS surveys of
the seven operating facilities in the study (1998–
2001) (Coble et al., 2010; Vermeulen et al., 2010);
and a monitoring survey of Facility B (1994) con-
ducted as part of a feasibility study for this project
(Stanevich et al., 1997). In addition, measurement
data were extracted from hard-copy reports from
several monitoring surveys by the Mine Enforce-
ment and Safety Administration/Bureau of Mines
(MESA/BoM) (1976–1977) (Sutton et al., 1979)
and from reports predominantly evaluating compli-
ance that were available from MSHA, the mining fa-
cilities, and the State of New Mexico for the three
facilities located there (1954–1996). Accompanying
documentation for these monitoring data [the mining
facility, duration, date, time, type of measurement
(area, personal), agent, job or location description,
and the sampling and analytic method] was entered
into the database. The personal measurements were
coded using the same job coding system as used
for the work histories (Stewart et al., 2010). The
area measurements were coded using the location
codes in the MIDAS database (Watts and Parker,
1995). All coding was done without reference to
the measurement results.
Description of the determinants database
Information on possible determinants of DE con-
centrations was assembled into a database by year
and facility (Stewart et al., 2010). The determinant
information covered the years from the date of die-
selization in the underground operations to the refer-
ence year (1998–2001). Two of the major a priori
determinants of DE were HP of the diesel-powered
equipment used in each facility and the total airflow
rates exhausted from the underground operation. HP
was deemed the critical characteristic of diesel
equipment in that, together with emissions rates
(gram per horsepower-hour), it determines exhaust
emissions. HP of the diesel-powered equipment
was available on inventories of diesel-powered
equipment used underground, extending back as
far as the early 1970s from the facilities. Inventories
generally were available for a few years in the 1970s
and the 1990s but rarely in the 1980s. The lack of in-
ventories was compensated by a careful scrutiny of
each facility’s production characteristics, trends over
time in the number of diesel pieces used (for all the
facilities, there was generally little change in equip-
ment from year to year), and the number of years
equipment was used, as well as being supplemented
by information from the interviews. The specific sec-
tion of the mine where the equipment was used was
usually not identified. HP was directly available for
80% of the diesel equipment. For the other 20%,
HP was estimated based on the same or similar
equipment purchased about the same time in the
same or in other facilities. From this information,
the annual sum of the HP of all diesel-powered
equipment in each facility was calculated based on
all diesel engines used in a particular year.
Diesel equipment was not, however, operated con-
tinuously over a full work shift. For example, diesel-
powered mantrips, after transporting the work crews
to the production face, were often turned off. To ac-
count for actual use, the average percentage of a work
shift that each type and model of diesel equipment
was used (%USE
i
) was estimated, based on inter-
views, to derive the adjusted HP (ADJ HP
i
), for year
i for each facility (Fig. 1).
ADJ HP
i
5
X
HP
ij
%USE
ij
;
where HP
ij
5 HP estimate for a specific piece of die-
sel equipment j in year i and %USE
ij
5 average
percentage of use for a specific piece of diesel equip-
ment j in year i.
We used purchase dates to assign adjusted HP of
equipment acquired after 1990 to account for possi-
ble lower emissions from newer (e.g. cleaner) direct
injection engines (ADJ HP
1990þ
)(Haney and
Saseen, 2000).
Annual facility-specific estimates of total airflow
rates exhausted from all operations within each un-
derground mine, in cubic feet per minute (CFM)
(1 CFM 5 0.0283 m
3
min
1
), also were compiled.
Records were available identifying the average total
airflow rates exhausted from the underground opera-
tions when each exhaust shaft was installed since the
beginning of diesel use and occasionally for other
776 R. Vermeulen et al.
years. Information was not, however, available for
sections of the underground mine. We also included
year-specific information (if available) on fuel
use (either in gallons or dollars); ore production (ton-
nage); mining methods [conventional, continuous
(bore, drum), long wall]; ore haulage methods (con-
veyors, diesel shuttle cars, or electric shuttle cars),
use of ammonium nitrate/fuel oil as the explosive;
and various engineering controls and work practices
(e.g. installation of scrubbers or catalytic converters
on the diesel equipment, existence of a preventive
maintenance program, and sealing of side passages
from the main haulage ways) and other work place
characteristics.
Building predictive models to estimate levels of DE
exposure over time
Most of the collected measurements in our study
were area measurements (n 5 29 211; 78% of total)
(Stewart et al., 2010). Of these, CO (n 5 11 124;
38% of the area measurements), CO
2
(n 5 9651;
33%), and NO
2
(n 5 5045; 17%) were the most fre-
quently measured agents, and as they are all compo-
nents of DE, they were considered as the primary
candidates for empirical modeling. CO area meas-
urements were available for all facilities, with sub-
stantial proportions of the measurements above the
limit of detection (LOD) (median of the CO meas-
urements .LOD 61%; range 40–80% by facility).
These historical CO concentration measurements,
almost exclusively covering the years 1976 to the
reference year, were used to develop the primary
facility-specific time trend prediction models. Pre-
diction models using NO
2
concentration measure-
ments were developed for three facilities (A, D,
and E), but the model fit was poor and the models
were not used. Most NO
2
measurements from the
other four facilities were below the LOD (up to
90%), which precluded model development. A
model for facility J was not developed because
there were no monitoring data from the DEMS sur-
veys. Historical CO
2
area measurements were not
used due to uncertainty regarding data quality,
i.e. .70% of the measurement data were below
2000
19901980
197019601950
0 2000 4000 6000 8000
Facility A
Calendar year
Adjusted Horse Power
2000
1990
19801970
1960
1950
0 500 1500 2500 3500
Facility B
Calendar year
Adjusted Horse Power
Airflow (kCFM)
0 200 400 600 800
2000199019801970
19601950
0 500 1500 2500 3500
Facility D
Calendar year
Adjusted Horse Power
0 200 400 600 800
Airflow (kCFM)
2000
1990
1980197019601950
0 500 1500 2500 3500
Facility E
Calendar year
Adjusted Horse Power
Airflow (kCFM)
0 200 400 600 800
0
002099
1
08
9
10791069
1
0
5
91
0 500 1500 2500 3500
Facility G
Calendar year
Adjusted Horse Power
Airflow (kCFM)
0 200 400 600 800
200019901980197019601950
0 500 1500 2500 3500
Facility H
Calendar year
Adjusted Horse Power
0 500 1000 1500 2000
Airflow (kCFM)
200019901980197019601950
0 500 1500 2500 3500
Facility I
Calendar year
Adjusted Horse Power
0 500 1000 1500 2000
Airflow (kCFM)
200019901980197019601950
0 500 1500 2500 3500
Facility J
Calendar year
Adjusted Horse Power
0 200 400 600 800
Airflow (kCFM)
Adjusted HP
Adjusted HP after 1990
Airflow
Fig. 1. Overview of the primary model variables [i.e. adjusted HP (ADJ HP; solid line), total airflow exhaust rates (in CFM; dotted
line), and adjusted HP after 1990 (ADJ HP1990þ; dashed line)] from date of dieselization of the underground operation to 1998 by
facility. Facility A relied primarily on natural ventilation, and therefore, no estimates of the airflow exhaust rates were available.
Historical diesel exhaust exposures in underground mining 777
typical background CO
2
levels of 375 parts per
million (ppm) (Tans, 2009).
Natural log (Ln) transformation of the measure-
ment data was used in the regression analyses, as
the measurements were approximately log-normally
distributed. We conducted a multiple imputation ap-
proach based on maximum-likelihood estimation to
assign values that were below the LOD (Helsel,
1990; Helsel, 2005). This procedure was done sepa-
rately for the historical data (i.e. MIDAS, MESA/
BoM survey, State of New Mexico compliance data,
and MSHA and facility hard-copy reports) and the
feasibility (Stanevich et al., 1997) and DEMS
surveys to account for differences in LODs (LOD
CO: historical data 5 1 ppm; feasibility/DEMS
surveys 5 0.3 ppm). This imputation procedure
was repeated 100 times to obtain plausible values
to represent the uncertainty of the LOD values, ren-
dering 100 data sets consisting of the same (mea-
sured) values for CO measurements above the
LOD and different (imputed) values for measure-
ments below the LOD. Each of these data sets was
used in the modeling procedure (see below).
The area CO measurements taken for compliance
purposes were primarily short-term samples using
a reusable specialized vacuum sampler (i.e. bistable)
analyzed by gas chromatography (MSHA, 2006)or
a direct-reading detector tube. CO measurements
taken as part of the DEMS surveys and feasibility
study were long-term direct-reading detector tubes
(NCI/NIOSH, 1997; Stanevich, 1997). Only area
measurements collected in the production face area
were used for the modeling since these were more di-
rectly related to routine activities. All face area CO
measurements were used in the modeling from all
sources except for those from the MESA/BoM sur-
vey data, which were set aside to serve as an evalu-
ation data set (see below Evaluation of Predictive
Models).
A multiple linear regression model (PROC GLM;
SAS) for CO was initially constructed for each facil-
ity incorporating that facility’s estimates of ADJ HP,
total exhaust airflow rates (CFM), and ADJ HP
1990þ
.
Due to the relatively high correlation of HP and
airflow rates in some of the underground operations
(n 5 3; Pearson r: Facility D 5 0.86; Facility G 5
0.54; Facility H 5 0.53) and for model consistency
in the other facilities, we computed the ratio of the
adjusted HP and the airflow rate (ADJ HP/CFM).
Facility A relied primarily on natural airflow to ven-
tilate the underground operations, so we could not
estimate the airflow rate. Therefore, we used only
the ADJ HP (Coble et al., 2010). ADJ HP/CFM
and ADJ HP
1990þ
were log transformed in all the
models to ease interpretability of the model by as-
suming proportional changes between these factors
and CO concentrations. Because ADJ HP
1990þ
by
default was zero before 1990, we added 1 to the
ADJ HP
1990þ
estimate for all years to allow Ln trans-
formation of the data. Additional work place charac-
teristics, as well as measurement methods (bistable
versus detector tube) were added in a forward step-
wise manner to the facility-specific models, begin-
ning with the variable having the smallest P value
in the univariate analysis. Newly added variables
were kept in the models if they were statistically sig-
nificant (P , 0.05) and if alterations in the regres-
sion coefficients for the other variables appeared to
be interpretable. The source of the measurement data
(survey) and the season [winter (October to March)
versus summer (April to September)] were forced
in the models based on a priori decisions to adjust
the models. Survey was included to correct for dif-
ferences in measurement methods and strategies.
Season was included to account for possible differ-
ences in ventilation efficiency and/or seasonal pro-
duction rates.
The general structure of the CO regression models,
therefore, was:
LnðCOÞ 5 a þ b
1
Ln
ADJ HP=CFM
þ b
2
Ln
ADJ HP
1990 þ
Þþb
3
Season þ b
4
Survey þ b
5...i
ðadditional facility-specific determinantsÞ
5...i
þ e:
Multiple linear regression analyses were run on
the 100 generated data sets from the LOD imputa-
tion procedure, and the results were combined to
derive the parameter estimates, standard errors,
95% confidence limits, and associated P values
(PROC MIANALYZE; SAS). These were consid-
ered our primary prediction models used in the
epidemiologic analyses.
Application of the models to predict CO
concentrations over time
The facility-specific models were employed to
predict annual CO concentrations from the date of
dieselization to the reference year (i.e. the year of
each facility’s DEMS survey) by inputting into the
models the determinant information for these years.
Then, the facility-specific predicted CO concentration
for the reference year was set at 100% and all the prior
predicted annual CO concentration estimates were
scaled relative to the reference. For Facility J, because
the underground operations had closed in 1993 and
therefore was not in the DEMS survey, it had no
778 R. Vermeulen et al.
1998–2001 CO reference measurements. Therefore,
we applied the model coefficients of Facility B to
the determinant information of Facility J [e.g.
Ln(ADJ HP/CFM)] to derive annual CO estimates.
Facility B was selected because of similarities in geo-
graphic location, type of mining, the amount of ADJ
HP present, and the airflow rates.
Predicting job-specific historical respirable
elemental carbon exposure levels over time
REC estimates had been developed for the refer-
ence year for each mining facility/department/job
based on the arithmetic means of the REC full-shift
personal measurements (Coble et al., 2010; Stewart
et al., 2010). As part of that process, the percentage
of time worked in each of four major areas of the un-
derground operations (face, haulage or travel ways,
shop/office area, and crusher area) had been
estimated for each underground job in the study.
To estimate historical concentrations, these time al-
locations were used to derive estimates of the total
percentage of time subjects worked in areas that re-
ceived air that had already traveled through some
portion of the mine, herein called ‘Mine air’, and
for areas, such as the shop/office or, in some facili-
ties, the crusher area that received intake air directly
from the surface, herein called ‘Intake air’. Differen-
tiation was made because changes in HP or CFM,
and thus, DE concentrations were expected in the
areas receiving mine air. In contrast, because few
diesel pieces were run in the areas receiving fresh
air, changes in HP or CFM should have had little
or no effect in these areas. Thus, the relative histor-
ical trends were assumed to be applicable to the per-
centage of time in ‘Mine air’ but not for the
percentage of time in ‘Intake air’.
To estimate the REC concentrations we used:
REC
ik
5 REC
kR

RELtrend
i
%T
Mine air
%T
Underground

þ
%T
Intake air
%T
Underground

;
where REC
ik
5 REC exposure estimate for year i
and job k; REC
kR
5 reference REC exposure esti-
mate assigned to job k, i.e. the 1998–2001 REC
estimate; RELtrend
i .
5 CO concentration estimate
for year i relative to the reference CO concentra-
tion of 1998–2001, where the reference 5 100%;
% T
Mine air
5 total percentage of time of an average
work shift job spent in underground areas receiving
mine air (ranging from 0 to 100%); and %T
Intake air
5
total percentage of time of an average work shift job
spent in underground areas receiving intake air
(ranging from 0 to 100%).
For example, if a job spent 100% of an average
work shift in areas receiving mine air, the equation
reduces to REC
ik
5REC
kR
RELtrend
i
. For a job that
was located 100% in areas ventilated by intake air,
the equation is REC
ik
5REC
kR
. For workers who
worked in areas ventilated by both mine and intake
air, the REC
kR
values were adjusted according to
the percentages of the time of a work shift ventilated
by each source of air.
For Facility J, the 1993 department/job-specific
REC estimates for Facility B were used as the
reference department/job-specific REC estimates
(REC
kR
). These estimates were subsequently multi-
plied with Facility J’s relative time trend predictions
where 1993 was set to 100%.
To prevent the estimated underground REC expo-
sure levels from being lower than surface bystander
exposure levels, the underground levels were
bounded by the facility’s REC estimate for surface
bystander exposure to DE (exposure group B, see
Coble et al., 2010). This procedure was followed be-
cause the use of diesel equipment in the relatively
enclosed spaces in the underground operations
would generally result in higher DE exposure levels
than would be expected from outside surface opera-
tions involving diesel equipment. This occurred only
for a few jobs and years in Facility D (four jobs,
years 1950–1957) and Facility E (five jobs, year
1959).
Evaluation of predictive models
We had set aside two data sets when developing
the models for use in evaluation of the modeling.
In 1976–1977, short-term CO area measurements
were taken by MESA/BoM to obtain a representative
sample of air concentrations in six of our study facil-
ities (B, D, E, H, I, and J) (Sutton et al., 1979). We
compared average CO concentrations measured
at the production face during these surveys with
the estimated CO concentrations derived from our
prediction models for 1976–1977. In a separate com-
parison, we contrasted the average of the personal
REC measurements collected in Facility B in 1994
(Stanevich et al., 1997) with the 1994 REC estimates
we derived from the model for this facility for
two primary jobs (i.e. the continuous miner and the
foreman). The 1994 survey was conducted as part
of the feasibility study of the DEMS. REC measure-
ments were collected according to NIOSH analytical
method 5040 (Schlecht and O’Connor, 2003)over
three days on miners working at two representative
production sections. For both sets of comparisons
Historical diesel exhaust exposures in underground mining 779
(1976–1977 CO and 1994 REC), facility- or job-
specific differences and relative differences were
calculated, where the difference was defined as the dif-
ference between the mean of the observed measure-
ments minus the predicted value and the relative
difference as the difference divided by the observed
mean.
Alternative models of DE exposure estimates over
time
Our primary facility-specific trend models used in
the epidemiologic analysis were based on CO con-
centration measurements and selected determinants
of DE exposure as described. We also, however, ex-
plored two alternative sets of time trend models. The
first set of models was based on actual 5-year aver-
age CO concentration levels (5-year average CO
models) in each of the facilities without any modifi-
cation by determinants after 1975 when measure-
ment data were available. The CO averages for
these time trends after 1975 were based only on
the MIDAS measurements collected in the produc-
tion face area as the MIDAS data were the only data
that were truly longitudinal (1976–2001). Because
there were only four CO concentration measure-
ments before 1976, we extrapolated the 1976 CO
mean concentration level in each facility by year
back to the date of dieselization using the facility-
specific annual changes in ADJ HP/CFM relative
to the 1976 ADJ HP/CFM values. Relative changes
in the CO concentrations were applied to the job-
specific reference REC exposure levels, as done with
the CO concentrations from the primary models.
In our primary time trend models, we assumed
that a relative change in historical CO levels
was directly translated to an identical change in
REC levels over all the years of the study. Results
from Yanowitz et al. (2000) suggest that indeed the
CO-REC relationship probably changed little from
1976 to 1997. However, in the cross-sectional DEMS
surveys, we observed that the relation between CO
and REC in 1998 to 2001 might not be strictly pro-
portional. The regression of Ln(REC) on Ln(CO)
rendered a parameter estimate of 0.58, indicating
that REC concentrations would increase with CO
concentration to the power of 0.58 (Vermeulen
et al., 2010). In our second set of alternative time
trend models, therefore, we explored the influence
of using the 0.58 power estimate by modifying the
facility-specific relative time trends based on CO
concentrations to estimate a different set of relative
time trends in REC exposure levels (herein called
the CO Model
0.58
).
All work was done blind to the epidemiologic
results. Analyses were done using SAS version 9.0
software (SAS Institute, Cary, North Carolina,
USA) and R version 2.9.0 (The R foundation for
Statistical Computing).
RESULTS
Table 1 shows the summary statistics of CO area
concentrations by 5-year time periods and major sur-
veys in the production face area of the eight study
facilities. The geometric means (GMs) of CO meas-
urements from the MIDAS database were highest in
1980–1984 (1.2–3.6 ppm) in most facilities and de-
clined thereafter to ,1 ppm in most facilities in
the 1990s. An exception was Facility A, where the
GMs increased over time. Large differences were ob-
served between the CO concentrations from the three
major surveys. The GMs for the MESA/BoM sur-
veys in 1976–1977 were 4-fold higher than those
from the MIDAS survey in the same time period
(range of differences across the facilities: 3.5–7.8).
The GMs from the DEMS surveys were 2-fold
higher than the GMs from the MIDAS 1995–1999
data and displayed a wider range of differences by
facility (0.9–5.0) than seen in 1976–1977.
Figure 1 shows that generally there was a very
rapid increase in the estimated amount of ADJ HP
in the facilities starting in 1960s to 1980. After
1980, ADJ HP continued to increase in two facilities
(A and D) but leveled off (G, H, and I) or declined in
others (B, E, and J). The proportion of ADJ HP from
diesel engines introduced after 1990, as compared to
the total ADJ HP, varied by facility, with a minimum
of 32% for Facility H to a maximum of 77% for Fa-
cility A at the time of the DEMS surveys. The rates
of total airflow exhausted from the underground
operations generally increased with increasing
ADJ HP.
The parameter estimates for Ln(ADJ HP/CFM)
varied from 0.68 to 2.72 and were statistically signif-
icant (P , 0.05) for all facilities except Facility G
(P 5 0.31) (Table 2). The Ln(ADJ HP
1990þ
) param-
eter estimates were all negative, indicating that the
increase in CO concentrations relative to HP was
lower for the newer engines compared to older en-
gines. This factor achieved statistical significance
(P , 0.05) only for Facilities D, G, and I. It was
not possible to estimate the coefficients for Ln(ADJ
HP
1990þ
) for Facilities A and H due to collinearity
with Ln(ADJ HP/CFM) after 1990 (Fig. 1). Most
other facility-specific determinants considered did
not improve model fit. The use of the long wall
780 R. Vermeulen et al.
mining method for Facilities H and I, which were the
only facilities where this mining technique was used
(Coble et al., 2010; Stewart et al., 2010), was found
to be significant. The long wall operation was asso-
ciated with about three times higher CO levels than
other mining operations in Facility I, while in Facil-
ity H the CO measurements associated with long
wall operations were 40% lower. In addition, for
Facility H, there were unusually high CO concentra-
tions in 1981 and 1982 (about five times the concen-
trations in adjacent years). We corrected for these
two types of unexplained phenomena by including
indicator variables to improve overall model fit.
Temporal analyses of the facility-specific CO
models to evaluate model fit over time indicated
that, in general, there was no association between
the model residuals and the 5-year time periods
[Fig. 2: the shaded 5-year average periods denote
those periods when there was a significant difference
(P , 0.05) between the mean of the CO area concen-
tration measurements and the primary estimates
from the facility-specific models for the same time
period (i.e. mean of residuals differed significantly
from zero)]. The exception was for Facility H for
the three periods of 1980–1984, 1990–1994, and
1995–1998, where the model significantly under-
estimated and overestimated (for two periods)
the Ln(CO) concentrations, respectively. In addi-
tion, the CO model significantly overestimated
the Ln(CO) concentrations for the two periods of
1975–1984 of Facility E and the periods of 1980–
1984 and 1990–1994 of Facility G.
The parameter estimates indicated in Table 2 were
used to predict annual CO concentrations. For Facil-
ities H and I, the CO concentrations were estimated
for non-long wall operations, as the work histories
indicated relatively few subjects at the long wall.
In addition, the parameter estimate for the observed
high period in Facility H was not included in the pre-
diction model because the high concentrations could
not be explained and only occurred for 2 years. The
maximum predicted CO levels relative to the refer-
ence 1998–2001 CO means ranged from 100% for
Facility A (i.e. all earlier years had lower levels than
the reference period because of lower estimates of
ADJ HP from smaller haulage trucks) to 685% in
1971 for Facility G (Fig. 2), with the median of the
maximum relative increases among the operations
being 300%. According to the models, the highest
exposure levels occurred between 1970 and the early
1980s for most facilities.
The alternative set of facility-specific time trend
models based on the 5-year average CO concentra-
tion levels were independent of any determinant data
Table 1. Summary statistics of measured CO (ppm) area concentrations by 5-year time period and survey
a
in the production face of the eight study facilities
b
Time period Survey Facility
AB D E G H I J
c
n GM GSD n GM GSD n GM GSD n GM GSD n GM GSD n GM GSD n GM GSD n GM GSD
1976–1977 MESA/
BoM
90 4.9 2.6 136 7.1 3.0 148 6.8 2.1 100 4.7 3.0 122 5.6 2.6 217 5.1 2.8
1976–1979 MIDAS 37 1.7 3.4 53 1.4 2.8 66 1.6 3.7 24 1.4 2.6 16 1.6 6.4 242 0.6 3.3 111 1.2 3.1 35 1.2 3.0
1980–1984 MIDAS 95 2.4 3.0 265 2.1 2.4 116 1.7 3.2 95 3.1 2.6 71 2.1 4.1 1010 1.4 4.8 806 3.6 3.4 109 1.2 3.0
1985–1989 MIDAS 72 2.4 3.5 62 1.2 2.4 83 1.1 3.2 38 1.8 2.7 110 1.3 4.5 400 0.6 3.2 411 2.3 4.4 34 0.9 2.4
1990–1994 MIDAS 31 4.3 2.5 8 1.0 2.0 3 0.4 1.0 23 0.8 1.8 41 0.3 1.7 409 0.4 1.8 417 0.5 1.9
1995–1999 MIDAS 2 5.0 1.0 41 0.8 2.0 38 0.5 1.3 1 2.5 21 0.4 2.4 277 0.4 1.7 234 0.5 2.1
1998–2001 DEMS
surveys
11 4.5 3.7 15 3.5 1.7 17 1.9 2.3 21 3.1 1.6 13 0.4 2.6 23 0.8 4.6 21 2.5 3.0
n, Number of measurements; GSD, geometric standard deviation.
a
MESA/BoM surveys were conducted in 1976–1977; DEMS surveys were conducted in 1998–1999 except for Facility B which was conducted in 2001; MIDAS comprises compliance
measurements collected between 1976 and 2001.
b
In addition to the CO measurements presented here, 12 CO measurements from other sources were used in the statistical modeling.
c
Facility J closed in 1993.
Historical diesel exhaust exposures in underground mining 781
after 1975. These models revealed very similar
trends as our primary facility-specific CO models
based on exposure determinants except for most of
the periods just indicated in the temporal analysis
of the residuals and for the years 1980–1990 in Facil-
ity I, which, although diverging from the primary
model, were not statistically significant (Fig. 2).
The second set of facility-specific alternative time
trends, which assumed that REC increased to the
power 0.58 instead of a proportional 1:1 increase
as assumed in the primary CO models, also are de-
picted in Figure 2 (CO Model
0.58
). Again, the time
trends were similar, although, as expected, they
resulted in lower historical relative levels (e.g. the
maximum increase relative to 1998–2001 was
305% in Facility G, based on the 0.58 power param-
eter model, in contrast to 685% based on the primary
model.
The estimated REC exposure levels derived by
applying the relative CO time trends from our primary
models to the reference REC estimates are shown in
Figure 3 for the mine operator, a representative face
job (as Facility A did not have a mine operator the
loader operator is depicted). REC exposure levels
for the mine operator ranged across the operations be-
tween 100 and 600 lgm
3
in the 1970–1980s. In ad-
dition, as can be seen, there was substantial variability
in the REC estimates over time.
A comparison of the predicted CO concentrations
for 1976–1977 with the average observed CO face
concentrations from the MESA/BoM survey data
of 1976–1977 showed that the facility-specific CO
models underestimated the CO-measured concentra-
tions in 1976–1977 by 24–49% (Table 3), except in
Facility E, where the CO concentrations were over-
estimated (25%). Only two of four jobs previously
measured in the feasibility study in Facility B could
be exactly matched with the coded job titles in the
DEMS survey. A small difference was observed be-
tween the predicted and observed REC levels, i.e.
10% for the continuous miner and 6% for the
foreman (Table 4).
DISCUSSION
In our study, job-specific exposure levels to DE
were estimated from personal REC measurements
collected in 1998–2001 during the DEMS surveys
(Coble et al., 2010; Stewart et al., 2010). However,
almost no REC or other EC monitoring data were
available prior to these surveys, prohibiting us from
estimating past DE levels based on EC measure-
ments. As a consequence, the historical estimation
of REC for underground jobs relied on back-
extrapolation of the REC estimates from the DEMS
data using predictive time trend models. CO was
Table 2. Facility-specific parameter estimates and 95% CIs of the primary facility-specific models based on CO area
concentrations and exposure determinants
a
Mining
facility
n (% ,LOD) Ln(ADJ HP/CFM)
b
(95% CI)
Ln(ADJ HP
1990þ
)
c
(95% CI)
Long wall mining
technique
d
(95% CI)
High period
e
(95% CI)
A 248 (45) 1.90 (0.27 to 3.53) NC
f
NA
g
NA
B 447 (39) 1.05 (0.52 to 1.58) 0.04 (0.13 to 0.04) NA NA
D 323 (38) 0.74 (0.02 to 1.46) 0.13 (0.22 to 0.04) NA NA
E 207 (20) 1.29 (0.08 to 2.51) 0.03 (0.14 to 0.09) NA NA
G 276 (30) 0.68 (0.64 to 2.01) 0.20 (0.36 to 0.05) NA NA
H 2361 (60) 0.75 (0.45 to 1.05) NC 0.55 (0.77 to 0.32) 1.65 (1.47 to 1.84)
I 2000 (46) 2.72 (1.38 to 4.05) 0.07 (0.11 to 0.04) 1.08 (0.95 to 1.02) NA
J
h
NA 1.05 (0.52 to 1.58) 0.04 (0.13 to 0.04) NA NA
CI, confidence interval; n, number of measurements; Ln, log transformed; ADJ HP, adjusted HP for percentage of a work shift
used; ADJ HP
1990þ
, adjusted HP after 1990.
a
All models were corrected for season and survey. Additionally, the primary models for Facilities A, B, E, and I were corrected
for measurement technique (detector tube versus bistable).
b
Ln(Adj HP/CFM) was statistically significant (P , 0.05) for all facilities except Facility G.
c
Ln(ADJ HP
1990þ
) was statistically significant (P , 0.05) for Facilities D, G, and I.
d
An indicator variable was used to improve model fit but it was not used in the prediction models because there were few subjects
in the epidemiologic study who worked at the long wall.
e
The variable for high period, which occurred in 1981–1982, was not included in the prediction models as it could not be
explained by any of the known determinants.
f
Not calculated: Ln(ADJ HP
1990þ
) could not be fitted due to collinearity with Ln(ADJ HP/CFM).
g
Not applicable: The variable was not included in the particular facility-specific model.
h
Parameter estimates of Facility B are displayed as these were used in the prediction of the CO concentrations for Facility J
because no facility-specific model was developed for Facility J.
782 R. Vermeulen et al.
1950 1960 1970 1980 1990 2000
0 100 200 300 400 500 600
Mine Operator
Calendar year
REC (µg/m
3
)
FACILITIES
A *
B
D
E
G
H
I
J
Fig. 3. REC historical predictions (lg/m
3
) for the mine operator are shown, based on the primary facility-specific CO models, by
mining facility. Footnote (*) Facility A had no mine operator and therefore the loader operator is depicted.
1950 1960 1970 1980 1990 2000
0 200 600
Facility A
Relative trend (%)
1950 1960 1970 1980 1990 2000
02
00 600
Facility B
Calendar year
1950 1960 1970 1980 1990 2000
0200
600
Facility D
Calendar yearCalendar year
Relative trend (%)
Relative trend (%)
1950 1960 1970 1980 1990 2000
0 200 600
Facility E
1950 1960 1970 1980 1990 2000
0 200 600
Facility G
1950 1960 1970 1980 1990 2000
0 200 600
Facility H
Calendar year
Relative trend (%)
Calendar yearCalendar year
Relative trend (%)
1950 1960 1970 1980 1990 2000
0 200 600
Facility I
Calendar year
1950 1960 1970 1980 1990 2000
0 200 600
Facility J
CO-model
CO-model^0.58
5-yr CO average
CO-model vs. 5-yr CO average P < 0.05
Relative trend (%)
Calendar year
Relative trend (%)
Relative trend (%)
Fig. 2. Changes in CO concentrations (from date of dieselization to 1998–2001) relative to 1998–2001 (1998–2001 5 100%)
predicted by the primary facility-specific models used in the epidemiologic analyses (CO Model; solid line) and the two alternative
set of models: one based on a less than proportional increase in REC relative to CO (CO Model
0.58
; dashed line) and one based on
5-year average CO measurements (5-year CO average Model; dotted line). The estimates prior to 1976 from the 5-year average CO
models were not based on actual measurements but were extrapolated from the 1976 CO values based on relative changes in ADJ
HP/CFM. The shaded 5-year average periods denote those periods when there was a significant difference (P , 0.05) between the
mean of the CO area concentration measurements and the primary estimates from the facility-specific models for the same time
period (i.e. mean of residuals differ significantly from zero). Note, due to the relative scaling of the time trends, absolute
differences in predicted CO concentrations cannot be read from figure 2.
Historical diesel exhaust exposures in underground mining 783
chosen to estimate relative changes in historical REC
levels because of its frequent use in the past as
a proxy of DE exposure (Pronk et al., 2009) and be-
cause it was the most frequently measured DE com-
ponent in our study facilities EC and CO are both
produced by incomplete combustion of diesel fuel.
This decision was supported by our finding that
CO area concentrations were correlated with REC,
that CO increased approximately linearly in log-
log space with REC area concentrations in the
DEMS surveys, and that it loaded on the same factor
as EC and DE gases (Vermeulen et al., 2010).
There were, however, several limitations to the use
of CO to predict historical DE levels. First, except
for four measurements from 1972 for one mine
(G), CO measurements were only available after
1975 while we needed to predict exposures from
the date of dieselization, which varied by facility
from 1947 to 1967, to the reference year, which var-
ied from 1998 to 2001. The prediction for all years,
including prior to 1976, was achieved by applying
the derived model parameter estimates for Ln(ADJ
HP/CFM), Ln(ADJ HP
1990þ
), and the other variables
to the determinant data available for all years. This
procedure, because the model was developed from
measurement data after 1975, assumed that the pa-
rameter estimate for Ln(ADJ HP/CFM) was applica-
ble to the time period prior to 1976. This assumption
seemed reasonable since engine and ventilation tech-
nology did not differ substantially from the 1960s to
the 1990s and there was little diesel equipment prior
to the 1960s (ADJ HP ranged from ,1 to 10% of the
maximum ADJ HP in the five facilities that were in
existence before 1960).
A second possible limitation was that CO might
have arisen from sources in underground mining
other than diesel engines. Another source of CO
was likely from the explosives used to blast the face
(Douglas and Beaulieu, 1983; Jacobsen et al., 1988).
This source, however, was unlikely to have influ-
enced our results as we only used CO face measure-
ments, of which 95% were from inspection data.
MSHA inspectors did not routinely monitor at the
face immediately after a blasting because of the
Table 4. Assessment of differences and relative differences between the primary facility-specific predicted personal REC
estimates in Facility B in 1994 and the arithmetic means of the REC personal measurements collected in 1994 (Stanevich et al.,
1997)
Job Feasibility study (1994) Estimated
REC AM
(lgm
3
)
n REC AM
(lgm
3
)
Difference
(lgm
3
)
a
Relative
difference (%)
b
Continuous miner 26 248.4 272.7 24.3 10
Foreman 6 166.3 175.9 9.6 6
n, Number of measurements; AM, arithmetic mean of the personal REC measurements.
a
Difference between the AM of the measured REC exposure levels in the 1994 feasibility study and the estimated REC exposure
level.
b
Relative difference is the difference divided by the AM of the measured REC exposure levels in the 1994 feasibility study.
Table 3. Assessment of differences and relative differences between the primary facility-specific CO prediction model estimates
and the arithmetic means of the CO measurement data for 1976–1977
Mining
facility
MESA/BoM (1976–1977) Facility-specific CO models
n Measured CO
concentration
AM (ppm)
Estimated CO
concentration in
1976–1977 (ppm)
Difference
(ppm)
a
Relative
difference (%)
b
B 90 7.23 5.15 2.08 29
D 136 10.50 7.98 2.52 24
E 148 8.50 10.60 2.10 25
H 100 7.68 3.90 3.78 49
I 122 7.73 4.85 2.88 37
J 217 8.09 4.36 3.73 46
Overall median difference 29
n, Number of measurements; AM, arithmetic mean of the CO area measurements at the production face.
a
Difference between the AM of the measured CO concentrations in the MESA/BoM surveys and the estimated CO concentration.
b
Relative difference is the difference divided by the AM of the measured CO concentrations in the MESA/BoM surveys.
784 R. Vermeulen et al.
possible high exposures to the blasting fumes. Thus,
the inspectors would not have entered the face areas
until the blasting fumes were exhausted and diluted.
This assumption was further supported by additional
statistical analyses of the MIDAS data that did not
find any indication of higher CO air concentrations
shortly after the time of blasting compared to other
times during the day (data not shown). Therefore,
these data suggest that CO was a specific proxy for
DE in underground mining operations in our study
facilities.
We acquired CO measurement data from several
data sources. Of these sources, MIDAS was the only
source with truly longitudinal data, and it contrib-
uted .95% of the CO data that were used in the
modeling. MIDAS measurements taken at the pro-
duction face found average CO concentrations gen-
erally ranging from 1.2 to 1.6 ppm in 1975–1979,
0.9–3.1 ppm in the 1980s, and ,1 ppm in the
1990s. The major exception was Facility A where
concentrations increased over time. The difference
for this facility was consistent with the increasing
size (and therefore HP) and number of diesel equip-
ment, particularly haulage trucks, used in the under-
ground operations over time (Coble et al., 2010;
Stewart et al., 2010). Although the year of introduc-
tion of the first diesel equipment varied considerably
between the underground operations of the facilities
(1947–1967), in general, the largest increase in die-
sel equipment usage occurred between 1960 and
1980 in all facilities (when the facilities rapidly die-
selized their underground mining equipment) and
use peaked in the early 1980s (Facility A being the
exception). After the mid-1980s, diesel usage gener-
ally changed little or decreased slightly. Increases in
exhausted airflow rates followed the increase in HP
in almost all operations to control gaseous contami-
nant levels and rose or remained unchanged as HP
remained the same or decreased, thus resulting in
lower exposure levels. We also estimated the amount
of HP that was introduced after 1990, the period
which corresponded to the introduction of cleaner
direct injection engines and cleaner fuels (Haney
and Saseen, 2000). At the time of the DEMS surveys,
the ADJ HP of the newer engines ranged between 32
and 77% of the total ADJ HP, per facility, suggesting
that for most of the mines this additional parameter
was important.
Our facility-specific CO models used ADJ HP, to-
tal exhaust airflow rates (CFM) and ADJ HP
1990þ
as
predictors and CO concentration measurements as
the dependent variable. These a priori selected pre-
dictors were similar to the main parameters of the
deterministic model developed by MSHA to esti-
mate diesel particulate matter (DPM) exposure at
the faces of underground operations (Haney and
Saseen, 2000). The MSHA model had basically three
components: the quantity of exhaust emissions (i.e.
estimated based on engine HP, engine DPM emis-
sion rates, the number of engines, and the length of
the work shift), efficiency of exhaust control emis-
sions (i.e. fuel properties and the efficiency of ap-
plied control technology), and the quantity of air
exhausted from the face. A difference between the
MSHA deterministic model and our predictive mod-
els is that we lacked airflow rate or ADJ HP data at
each operating face of our facilities. For each face,
however, a minimal set of equipment was generally
necessary, so that the types and amount of equipment
used for the production operations were not likely to
differ substantially between faces within a facility at
a given point in time. Support for this assumption
was also found in the fact that the REC measure-
ments on the jobs, which were taken in different
areas, were relatively homogeneous within each fa-
cility, as were the area measurements, which also
were taken in different places (Coble et al., 2010).
It seemed reasonable, therefore, to use mine-wide
ADJ HP estimates and total exhaust airflow rates
to model area-specific CO concentrations.
Ln(ADJ HP/CFM) was significantly associated
with area CO concentrations in six of the seven
facility-specific models. The median parameter esti-
mate of Ln(ADJ HP/CFM) in the seven facilities
was 1 (range: 0.74–2.72). Except for Facility I,
the 95% confidence intervals of the parameter esti-
mates for all facilities included 1. The global P value
for the test of homogeneity of the Ln(ADJ HP/CFM)
parameter estimates for all facilities was 0.03, while
if Facility I was excluded, the P value was 0.64, in-
dicating that the coefficient was indeed similar
across the facilities except for Facility I. A parameter
estimate of 1 indicates that the change for CO and
ADJ HP/CFM are directly proportional and equal,
i.e. that a doubling in the ratio ADJ HP/CFM corre-
sponds to a doubling of the CO concentration. This is
consistent with the MSHA model where direct pro-
portionality is assumed for HP and an inverse pro-
portionality is assumed for airflow rate (Haney and
Saseen, 2000). The explanation for the large param-
eter estimate for Ln(ADJ HP/CFM) in Facility I is
unclear and might indicate incorrect data for HP or
airflow rate in 1980–1990.
The negative estimates for Ln(ADJ HP
1990þ
) in all
facility-specific models where this parameter could
be fitted indicates that the increase in CO concentra-
tions relative to HP was lower for the newer engines
compared to older engines. The global P value for
Historical diesel exhaust exposures in underground mining 785
the test of homogeneity of the Ln(ADJ HP
1990þ
) pa-
rameter estimates for all facilities was 0.08, suggest-
ing that this parameter estimate was not statistically
different across the facilities. The observed reduced
emission of newer engines in combination with
cleaner fuels is in agreement with other reports
(Haney and Saseen, 2000), which indicated that en-
gines introduced after 1990 resulted in lower DE
emissions (including CO) per unit of HP.
Because we found such similar results for the
main model parameters [i.e. Ln(ADJ HP/CFM) and
Ln(ADJ HP
1990þ
)] in the various facilities, it can
be assumed that the widely varying HP, loads, effi-
ciency of the engines, and other characteristics
tended to cancel those characteristics that affect the
emission characteristics of an individual engine.
Since the CO models were developed to predict
REC levels over time, the absence of temporal varia-
tions in the residuals was an important confirmation
of internal model validity. We observed significant
deviations in residuals for seven 5-year time periods
in 35 facility/5-year time period combinations. In
two instances, the models predicted higher levels
than the measurements, and in five instances, the
models predicted lower levels than the measure-
ments. Thus, we found that the CO predictions were
free of any marked temporal bias.
This finding was further corroborated by our alter-
native set of models using the 5-year average CO
concentration time trends that showed temporal pat-
terns similar to our primary CO models, indicating
the good temporal fit of our models. We also used
the CO models to extrapolate the DEMS average
CO concentrations back to 1976–1977 to compare
with average face concentrations measured during
the MESA/BoM 1976–1977 survey. This compari-
son showed a median relative difference of 29%,
which varied between 49 and -25% by facility. These
differences were close to what would be expected if
side-by-side measurements were taken (Zey et al.,
2002). Noteworthy is the difference estimate of Fa-
cility J (46%). This facility was closed in 1993 and
could not be measured in the DEMS surveys, so
the model parameter estimates of Facility B were
used. The relative difference seen for this facility
was within the range for the other facilities, indicat-
ing that this approach for exposure estimation in Fa-
cility J was satisfactory.
Few industry-based studies of chemical agents
have been able to evaluate historical quantitative
exposure estimates in comparison to external mea-
surement data. Investigators of a study of ethylene
oxide workers found a relative difference of 24%
when comparing predictions with measurements
over a 6-year period (Hornung et al., 1994). In an
acrylonitrile study, the relative difference for two
estimation methods was 17% and 66% when
compared to measurement data over an 11-year
period (Stewart et al., 2003). A relative difference
of 2% was found in a study of textile workers
between the estimates and the measurements over
a 15-year period (Astrakianakis et al., 2006). Inves-
tigators of an asphalt paving study found relative
differences of 70 and 51% for bitumen fume
and benzo(a)pyrene, respectively, when comparing
measurements to estimates for up to 12 years back
in time (Burstyn et al., 2002). The difference seen
in our evaluation (median of 29%) is comparable
to these earlier studies, even though our comparison
covered over 20 years, whereas the other studies
covered 15 years or less.
The main variables in our models were Ln(ADJ
HP/CFM) and Ln(ADJ HP
1990þ
), where the effect
estimate of ADJ HP was conditioned on CFM. We
therefore explored alternative model specifications
in which Ln(ADJ HP), Ln(ADJ HP
1990þ
), and
Ln(CFM) were introduced as separate variables in
the regression models. As previously indicated, the
inclusion of CFM and ADJ HP as separate determi-
nants in the models proved to be difficult for some of
our facility-specific models (D, G, and H), due to
collinearity between these two variables, resulting
in implausible estimates of the parameters (i.e. in-
creasing exposure levels with increasing airflow
rates). However, for the models where collinearity
was not a problem, the results were quite similar to
the results based on the primary model specifications
(data not shown). In addition, we explored models
in which we modeled Ln(ADJ HP
1990
/CFM) and
Ln(ADJ HP
1990þ
/CFM) separately (where ADJ
HP
1990
designates ADJ HP of equipment acquired
before 1990). These models resulted in very similar
fits and predictions as our primary models (data not
shown). Based on these results, we concluded that
our facility-specific models are reasonably robust
toward differences in model specification.
The model coefficients were subsequently used to
predict the relative CO concentrations from the
reference year to the date of dieselization. These rel-
ative trends were used to back-extrapolate 1998–
2001 personal exposure estimates by assuming that
a relative change in historical CO levels could be di-
rectly translated to an identical change in REC over
all the years of the study. A study by Yanowitz et al.
(2000) suggests that indeed the CO-REC relation-
ship probably changed little from 1976 to 1997.
In that study, emission data based on laboratory
tests of various engines with varying model years
786 R. Vermeulen et al.
(1976–1997) were analyzed using regression analy-
ses on emissions per mile of grams of CO and of
grams of DPM. The authors found that CO and
DPM emissions increased back through time at
only slightly different rates [parameter estimate dif-
ference 5 0.003, i.e. DPM increased slightly less
than CO (US EPA, 2002)]. A comparable relation-
ship was also likely to hold true for EC, given that
it is a significant proportion of DPM (Birch and Noll,
2004).
However, in our cross-sectional DEMS surveys
we observed that the relation between CO and
REC might not be strictly proportional because the
regression of Ln(REC) on Ln(CO) rendered a param-
eter estimate of 0.58, indicating that REC concentra-
tions increased with CO concentration to the power
of 0.58 (Vermeulen et al., 2010). We previously
noted that, given the cross-sectional nature of this
survey, it is possible that the observed parameter es-
timate might not apply longitudinally to past condi-
tions (Vermeulen and Kromhout, 2005; Vermeulen
et al., 2010). However, to assess the sensitivity of
the epidemiologic findings to our decision of making
the REC changes over time directly proportional to
temporal changes in CO, we developed an alterna-
tive set of facility-specific models using the 0.58
power parameter to modify the relative CO concen-
trations. As expected, this change resulted in lower
historical REC levels but had little effect on the rank-
ing of the subjects’ cumulative exposure (Pearson
correlation . 0.9) (Stewart et al., 2010).
To estimate mining facility/department/job/year-
specific REC exposure levels, the facility-specific
relative changes in CO were subsequently multiplied
with the reference REC exposure estimates of the
underground jobs. In Stewart et al. (2010),we
showed that the hierarchical grouping strategy was
successful in explaining the between-job variability
of the REC measurements. Of course, this approach
assumes that these grouping remained valid histori-
cally, which is reasonable as long as job locations
did not differ significantly over time. For illustrative
purposes, we presented the predicted REC levels for
the mine operator. REC exposure levels for this job,
one of the highest exposed jobs in the DEMS sur-
veys, ranged between 100 and 600 lgm
3
in the
1970–1980s. There are few previously published
data with which to compare the results of our study.
However, recent studies have reported personal EC
measurement levels between 20 and .500 lgm
3
(Ramachandran and Watts, 2003; Backe et al.,
2004; Birch and Noll, 2004; Adelroth et al., 2006;
Burgess et al., 2007; Dahmann et al., 2007; Noll
et al., 2007). The wide range among these studies
is likely due to different types and amounts of diesel
equipment, airflow rates, and work practices, just
as these differences influenced exposure levels in
our facilities. The levels reported in the literature
suggest that our historical REC estimates of up
to 600 lgm
3
20 years ago are within plausible
ranges of REC exposure levels.
In summary, the facility-specific models were
developed based on the regression of diesel use, total
exhaust airflow rates, and engine technology on his-
torical CO area concentration measurements to pre-
dict relative changes in DE exposure levels. The
models indicate substantial changes over time, with
the highest DE exposure levels for most underground
operations between 1970 and the early 1980s. Com-
parisons of the CO estimates from our time trend
models with external CO measurement data showed
that the estimates had a median relative difference of
29% in 1976–1977, a difference that is comparable
to what other epidemiologic studies have found.
We concluded that the predictions derived from the
time trend models were plausible, resulting in
time-varying REC exposure estimates that were in
a range observed in other published monitoring stud-
ies. The subsequently derived time-varying REC
job-specific exposure estimates were used in the in-
vestigation of exposure–response relationships in the
epidemiologic evaluation.
FUNDING
Intramural Research Program of the National In-
stitutes of Health, National Cancer Institute, Division
of Cancer Epidemiology and Genetics, and the Na-
tional Institute for Occupational Safety and Health,
Division of Respiratory Disease Studies.
Acknowledgements—We thank the management, the represen-
tatives of the labor unions, and the employees of the facilities
who participated in this study. We also thank Dr Noah Seixas at
University of Washington for his valuable comments, and
Rebecca Stanevich and Daniel Yereb formerly of NIOSH
and Dr Mustafa Dosemeci formerly of NCI for their work on
the DEMS surveys.
Disclaimer—The findings and conclusions in this report/
presentation have not been formally disseminated by the Na-
tional Institute for Occupational Safety and Health and should
not be construed to represent any agency determination or policy.
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788 R. Vermeulen et al.
    • "These ELRs exceed the U.S. Occupational Safety and Health Administration and the European Union Scientific Committee on Occupational Exposure Limits typical goal of limiting ELR of disease for exposed workers to below 1/1,000 based on a lifetime exposure at an average exposure level. Workers in the trucking, railroad, and mining industries have been and still are often exposed to EC levels in these exposure ranges (Coble et al. 2010; Davis et al. 2011; Pronk et al. 2009; Vermeulen et al. 2010). With millions of workers currently exposed to such levels, and likely higher levels in the past, the impact on the current and future lung cancer burden could be substantial. "
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    • "predominantly less recent data. Searching for unpublished data has been recommended and previously proved to be a highly rewarding effort (Kauppinen et al., 1997; Burstyn et al., 2000; De Vocht et al., 2005; Vermeulen et al., 2010). Many of the researchers involved in obtaining the non-digitized exposure data are, or will soon be, retired. "
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    • "Both these databases were analysed using mixed model analysis (Symanski et al., 2001). In a cancer study of US miners and their exposure to diesel exhaust, Vermeulen et al. (2010) developed quantitative estimates of historical exposures to respirable elemental carbon based on modelling measurement data and determinants for diesel exhausts from the same time period and using multiple linear regression analyses to create primary prediction models for epidemiological analyses (Vermeulen et al., 2010). Related issues include whether exposure data routinely collected by companies are a good representation of exposure patterns and whether exposure models are sufficiently accurate to be used for predicting exposure patterns for periods not covered by available measurements, a situation typically encountered in cohort studies. "
    [Show abstract] [Hide abstract] ABSTRACT: Background: Swedish foundries have a long tradition of legally required surveys in the workplace that, from the late 1960s onwards, included measurements of quartz. The availability of exposure data spanning almost 40 years presents a unique opportunity to study trends over that time and to evaluate the validity of exposure models based on data from shorter time spans. The aims of this study were (i) to investigate long-term trends in quartz exposure over time, (ii) using routinely collected quartz exposure measurements to develop a mathematical model that could predict both historical and current exposure patterns, and (iii) to validate this exposure model with up-to-date measurements from a targeted survey of the industry.
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