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1. Scope and Objectives of Analysis
The analysis of the physiological data recorded in the tests at Selby Mines Rescue Station is split into two sections. The first section, the major subsection, deals essentially with the analysis of core body temperature and its relationship to environmental conditions, use or otherwise of a respiratory protective device, and treadmill speed and distance covered. The second section reviews heart rate data and discusses this in relation to oxygen consumption models and whether there are any ensuing implications at high temperatures. This analysis provides a significant insight into the following:
· how thermal regulation is lost under conditions of severe thermal stress. · the core body temperature rise characteristic during a simulated evacuation.· how the temperature gradient varies if a respiratory protective device is or is not worn.· the average speeds and distances covered by subjects during these conditions.· the model of core body temperature rise during thermoregulatory breakdown, including
heat storage effects. · based on the above, a projection of how distance covered, and SCSR run out time, may
be affected by ambient temperature. · the implications for oxygen consumption modelling associated with high heart rates
during evacuation in conditions of high heat and humidity.
The trials at Selby MRS covered a variety of environmental test conditions and test permutations, with an objective of observing behaviour for a wide range of conditions. As a consequence, certain observations are associated with a small number of data points, which are not sufficient to express statistical significance. This is noted as and where it arises.
2. Overview of Data Obtained
Each subject undertook a self-paced two-stage exercise protocol on a treadmill. All either completed the protocol, withdrew, or were stopped by the study physician, Dr Andrew Booth. The two consecutive stages involved a warm-up phase (Phase 1) and a rescuer-wearing phase (Phase 2, A/B/C). In the latter phase, individuals were assigned either an SCSR (‘C’), a hot air FSR (‘B’), or baseline (no respirable protective device, RPD, ‘A’).
The monitoring instrumentation provided data on the following:
· Chamber dry bulb temperature; measured at mean abdomen height, floor and ceiling zones.
· Core body (aural canal) temperature. · Heart rate.
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· Treadmill odometer pulse rate.
Whirling hygrometer and electronic humidity instruments were used to confirm the Test chamber maintained a fully saturated atmosphere (dry bulb temperature = wet bulb temperature).
A pool of 25 volunteers, drawn from the rescue stations of MRSL, was made available. For operational and proximity reasons, subjects from Selby and Mansfield rescue stations were mainly used in the trials.
After the pilot trial phase, some 42 runs were undertaken at mean chamber temperatures of 27, 29, 30, 31, 32, 34, 35 and 37 °C, air fully saturated. The majority of the runs were concentrated at 29 and 34/35 °C.
It should be noted that the primary physiological data recorded during the trials is shown as graphical plots of core body temperature, heart rate and treadmill speed against time for each subject within Section 6. Environmental chamber ambient temperature data is also included in the Annex.
3. Typical Behaviour of Physiological Response Indicators
A characteristic behaviour of the core body temperature and heart rate physiological response indicators was observed for all test subjects. Reference is made to Figure 4.1, which shows the two periods of exercise with a short intervening period of rest. Figure 4.1 indicates the typical physiological response of a subject, in this case undertaken at 34°C BET (air fully saturated). The graph indicates readings for core body temperature, heart rate and odometer activity from the treadmill. It can be observed that the subject at rest had a resting pulse rate of ~75 bpm and an initial core temperature of ~37°C. The subject commenced exercise and was instructed to stop after a warm-up period of approximately 7 minutes. During the warm-up period, the subject's heart rate rose to stabilise at ~130 bpm. The core body temperature, after an initial period of compensation, rose progressively and stabilised at 37.5°C during the period of rest between the exercise phases. Heart-rate during the rest period showed a rapid recovery to ~110 bpm. There is evidence of slight cooling taking place during the rest period. On commencing the second phase of the test, heart rate increased rapidly to ~135 bpm and then increased slowly to reach a maximum of 170 bpm. Neglecting heart rate monitor spikes attributed to electrode connection problems, all subjects were observed to be within safe heart rate limits. Core body temperature increased steadily until the physician withdrew the subject at 38.6°C.
In generalising the trial observations, the primary risk factor for the test subjects was exceedence of safe core body temperature limits. It is noted that even where individuals attempted to pace themselves and reduce their energy expenditure, there was still evidence of heat gain. As an example, the Figure 4.2 graphs core body temperature characteristic and treadmill odometer pulse rate for a trial conducted at 29°C BET. During the second phase of this trial, the subject witnessed some discomfort from the hot air training model filter self-rescuer and compensated by progressively reducing his walking pace. Over a period of approximately 25 minutes, the subject, an experienced rescue brigadesman, reduced his walking pace by one third and substantially reduced the rate of core body heat gain. However thermoregulation was still not achieved. This observation of progressive loss of thermoregulation during exercise held for all subjects.
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4. Trial Development
Individuals generally show large variations in their response to exercise under conditions of high heat and humidity. It was acknowledged that a comprehensive study of individual heat stress response was not feasible at this stage in that it would entail a large-scale investigation of influence of individual characteristics against a range of stressor conditions. The literature review undertaken cites studies that have examined aerobic power (VO2 max), body morphology (height, mass, body surface area, fat content), acclimatisation state, hydration state, age and climate type. A large-scale study of individual heat stress response could entail a study of several hundred test runs and a large volunteer population. Clearly, a test programme of modest size and timescale would entail a reduction in scope, and/or reduction in statistical precision, and would need, as far as possible, to exploit previous research.
In modelling underground climate, basic effective temperatures (BET) of > 30 °C were considered representative of deep UK coal mine conditions. It was judged that conditions of 3234 °C air fully saturated would greatly restrict evaporative heat loss for moderate to high metabolic rates. Previous work to determine breathing apparatus safe wearing times, including the effects of pre-heating, demonstrated that thermoregulation deteriorates rapidly above 3032 °C (air fully saturated) in the test populations. In order to simplify matters, exercise work rate was restricted to a uniform self-paced treadmill walking activity, and initially, at the one test temperature.
The initial trial objective was to establish whether the use of a respiratory protective device (SCSR or hot air FSR) contributed significantly to baseline physiological stress response under fixed environmental conditions of heat and humidity (34 °C fully saturated). The intention was to use a group of about 15 subjects, each undertaking A/B/C protocol tests on separate occasions. This would entail a minimum of 45 individual trial runs. The number of subjects and the constraints on hypothesis testing were to ensure intra-subject test variation and workforce population characteristics could be accounted for.
Following completion of the pilot phase and initial test runs at 34/35 °C, there was clear evidence that all test subjects were being withdrawn prematurely due to core body temperature exceedence. The length of the warm-up phase (Phase 1) had to be progressively reduced to ensure subjects exercising with SCSRs could reach oxygen run out. A decision was made to examine physiological behaviour at various lower temperatures and to concentrate on the use of the industry standard respirable protective device, the Filter Self-Rescuer. It was accepted that this could compromise confirmation of the original hypothesis (due to a reduction in the size of the data set), but would offer indicative data for a wider range of environmental conditions and circumstances (including for example, influence of clothing).
With a change to the original experimental objectives, it was agreed that a smaller group of subjects would be used, with individuals repeating a range of tests. Twelve subjects were used in all, with six individuals undertaking four or more tests.
Throughout the trial programme, problems were experienced with the heart-rate monitor electrode connections, which prevented heart-rate data from being consistently acquired. The study physician did however confirm that all individuals were exercising within their individual safe heart rate limits. Given heart rate data was essentially a safety measure, and was not as relevant as core body temperature to the study, this situation was acceptable and no alternative
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measurement means was sought. A limited analysis of the available heart rate data is provided (§12 et seq).
5. Statistical Testing Objectives
Even within the limitations of a small-scale trial, it was considered important to attempt to address the followin points, since this information is of direct relevance to regulatory authorities and mine operators:
1. Is there a substantial physiological stress contribution from wearing a respiratory protective device in conditions of high heat and humidity? Previous research indicates a ~15 per cent relative increase in heart rate, but thermal physiological response is unknown.
2. How does the loss of thermoregulation take place with subjects walking with a moderate associated metabolic rate, and how is loss of thermoregulation linked to BET?
3. In general terms, can guidance be given on the extrapolated time and distance for the test population to reach critical core body temperatures if they were to continue exercising in the prevailing conditions?
4. Does clothing (e.g. overalls) substantially increase the rate of core body temperature rise whilst exercising?
5. Is there any indication that SCSR oxygen run-out time may be reduced at high BET?
There is also the issue of the extrapolation of the programme findings to an acclimatised underground workforce. Given that the use of mineworkers was precluded from the trial, this could not be confirmed by practical testing. However, the statistical significance factor and the projection of findings to the general underground workforce was acknowledged.
Given that the scheduled test chamber time could accommodate 50-60 individual subject test runs, there was inevitably a limitation on the number and type of tests that could provide results of statistical significance within the volunteer test subject population. A result is considered statistically significant, when for example, in examining two sets of samples, the result would be unexpected if the populations were identical. It is possible for a study to miss a small effect due to small sample size and/or large scatter. The power to find various hypothetical differences, if they exist, depends on the sample size and amount of variation within the groups, quantified by the standard deviation.
In biological processes, sample distribution may not follow a Gaussian (Normal) distribution, and non-parametric tests, which do not assume Normality, are often used. However, the power of these tests is low for small sample sizes (say <12 samples). Given that some test conditions would involve a very small number of samples (e.g. 2 samples points to gauge effects of additional clothing), it was accepted that, at best, these tests would provide a useful indication of behaviour, possibly warranting more detailed observation.
A discussion of the statistical methods employed is given in Annex 1 of Section 4. The principal statistical method employed was regression analysis.
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6. Core Body Temperature Analytical Model
The following is a brief discussion of the core temperature/treadmill activity/heart rate data supplied separately. Analysis of this information was used to provide information on:
1. Comparative response between wearing respiratory protective devices and the baseline case (no RPD).
2. Rate of core body temperature rise and its relationship to BET.
Using this data it is possible to extrapolate mean time and distance for the test population to reach critical core body temperatures. There is also some indication from the data on how SCSR oxygen run out time might be affected at high BET, and the response of additional clothing being worn on thermoregulatory response.
Inspection of the plotted raw data sets indicates there is, in general, some non-linearity between the core temperature variable with time. In order to provide better visualisation of the core body temperature trends for the complete data set, relative core body temperature rise has been plotted for all Phase 1 and 2 data in Figures 4.3 and 4.4. The data has been constructed by normalising the temperature at the start of each treadmill activity phase to a datum. This removes offset variations in individuals' resting body temperature and the variation in temperatures seen at commencement of Phase 2. The latter is in part due to varying durations of warm-up period necessitated during the trials (made in an attempt to prevent withdrawal due to over-temperature occurring prior to SCSR run-out). It is observed that the non-linearity is pronounced at the start of Phase 1, and to a somewhat lesser extent, at the start of Phase 2. It is postulated that the initial delay in core body temperature rise in Phase 1 is associated with heat storage effects, as the individual’s cardio-vascular system attempts to respond to the heat stress. This results in the onset of thermoregulatory breakdown being progressive, taking place over several minutes. After this, for a constant work rate, the rate of core body temperature rise appears to be linear.
The concept of the heat balance equation is useful for providing an understanding of how internal body temperature is maintained and in explaining the thermoregulatory breakdown characteristic. All heat balance equations have the same underlying concept; heat generation within the body, heat transfer, and heat storage. Equations 1 and 2 below show the conceptual heat balance equation where metabolic rate (M) provides energy enabling the body to perform mechanical work (W). The remainder of the energy is given off as heat (M-W). The ways that heat transfer can be achieved involve evaporation (E), radiation (R), convection (C) and conduction (K). The resultant heat production and loss provide the storage (S), where in heat balance, S = zero.
M - W = E + R + C + K + S [1]
and when S=0
M - W - E - R - C - K = 0 [2]
Heat produced is in proportion to the work rate (metabolic rate). Core temperature in a steady state is dependent upon work rate, while under severe environmental conditions thermoregulation fails. Heat loss is small in fully saturated atmospheres, especially at 34-37 °C. Assuming constant biomechanical efficiency, the rate of core body temperature rise will be a function of work rate. At a fixed work rate, core body temperature should continue to rise linearly, once
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initial heat storage effects have been overwhelmed. This simple linear model does appear to explain core body behaviour once thermoregulatory breakdown is established. The two runs with overalls clothing (suffix –O, Figures 4.5 and 4.6) are almost linear, associated with a small contribution from variable heat loss effects to the characteristic. Inspection of the resting period between the protocol phases in the other data sets suggests evidence of limited cooling taking place, depending on environmental conditions.
As a point of technical detail, analysis of the core temperature characteristic has to take account of any variation in individual work rate/metabolic rate within the test run. This variability in work rate is considered in a following sub-section. Deviations from constant work rate will impact on the linearity of the core temperature characteristic. This can be observed in Figure 4.2, where in Phase 2 there is a progressive reduction in temperature gradient in response to a slowing of walking pace. For the purpose of these trials, it was not considered practical to determine metabolic rates for a range of treadmill speeds and to introduce correction factors. Each individual would have a differing response, varying in part with speed, gait and biomechanical efficiency. In the tests undertaken, efforts were made however to ensure consistency between the respective treadmills, and that walking effort was self-paced and broadly representative of underground conditions.
Within the range of test environmental conditions and metabolic rates, all subjects exhibited a sustained loss of thermoregulation during exercise activity, with a monotonically increasing core body temperature characteristic observed. These observations may be contrasted with the core body temperature characteristic in a thermally neutral environment. Malchaire et el [2000], commenting on the work of Saltin and Hermansen [1966], suggest that in a neutral thermal environment, the mean core temperature, tco increases progressively towards an equilibrium core temperature (teq in °C) which varies as a function of metabolic rate, M in Watts, according to:
teq = 0.002M + 36.6
Their test data also shows that tco reaches this teq with an exponential behaviour and a time constant of approximately 10 minutes.
7. Regression Analysis of Core Body Temperature Data
Taking into account the above observations and argument, it is reasonable to assume where the subject is unable to lose heat through sweating, that a linear model can be used to describe the core body temperature characteristic at constant work rate. On this basis, regression analysis was performed on Phase 1 (warm-up) and Phase 2 (A/B/C) data. The regression analysis has been restricted to the data interval where there is treadmill activity. Various forms of regression were examined and compared; linear, Deming, Passing and Bablok, and polynomial regression. Whilst polynomial regression can provide a precise fit within the test run temperature data (example Figure 4.7), it cannot be used as a basis for extrapolation.
For the Phase 2 data set, the mean R2 coefficient is 0.977, indicating a fair level of fit with a linear model.
For the Phase 1 data set, the initial regression analysis indicated that a linear model was not acceptable, with somewhat lower R2 coefficients recorded. In order to use a linear regression model, it was considered necessary to discount the effects of heat storage over the first few minutes. A decision was made to rebase the Phase 1 temperature data, and discount the
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first 4 minutes of data when determining the regression equation. This adjustment is considered technically justified - we are seeking to use a linear regression model, and the linear data segment from 4 minutes onwards is the section of data relevant to observations of thermoregulatory breakdown.
Two examples of linear regression results from the Phase 1 complete data and the rebased data set are given in Figure 4.8 and Figure 4.9. It can be seen that the heat storage 'time constant' is of the order of 3 to 5 minutes. The single linear regression analysis results for the complete and rebased Phase 1 data sets are given in Table 4.1, together with R2 coefficients for the rebased data regression equations. The mean R2 coefficient is 0.942, again indicating a fair level of fit with a linear model. Regression analysis gradient data for rebased Phase 1 and Phase 2 data is given in Table 4.2 in descending order of test chamber mean temperature.
8. Difference Assessment - Respiratory Protective Devices versus Baseline
The linear regression analysis results of the Phase 2 and rebased (+4 minutes on) Phase 1 data were used to assess differences in the physiological response between the wearing of respiratory protective devices (SCSR and FSR) versus the baseline reference case (no RPD). The original experiment design intended that separate Phase 2 A/B/C protocol test runs would be undertaken for each test subject. This is a classical experimental approach but requires a significant number of test runs for each test chamber temperature.
An alternative approach to assessing difference was adopted. This entailed using the rebased Phase 1 (warm-up ) data as a baseline reference for the following Phase 2 period of the run. Technically, this has a number of advantages:
1. The provision of a reference case within each experimental run eliminates the variations in day-to-day physiological response observed for individuals.
2. The consecutive nature of Phase 1 and Phase 2 ensures the variations in test chamber environmental conditions are sensibly minimised.
3. Individuals are more likely to maintain a constant treadmill speed across consecutive phases.
The principal disadvantage is that the duration of Phase 1 is relatively short compared with Phase 2, reducing the number of measurement points for analysis. The rebasing of Phase 1 data reduces the available measurements further still. On balance, the approach of using Phase 1 data as a reference for Phase 2 was considered technically robust.
To investigate the significance of physiological (core body temperature) response between the wearing of an SCSR/hot air FSR, and the baseline reference case, the following methods were used. These methods, including non-parametric methods, examined differences in the populations and related data correlation:
Method 1. The core temperature graph plots were visually inspected for noticeable differences between Phase 1 and Phase 2 gradient characteristics (a subjective pattern recognition assessment method).
Method 2. A Mann-Whitney U test was conducted on the regression analysis gradient data derived for Phase 1 and Phase 2 to assess population median differences.
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Method 3. A paired samples t-test was conducted on the regression analysis gradient data to compare population means and t statistic.
Method 4. Phase 1 and Phase 2 related data pairs were assessed using Pearson correlation, and via Deming, and Passing and Bablok comparison methods.
The above tests are premised on the assumption that there is no significant difference in the work rates experience between Phase 1 and Phase 2 in each test run (unless this is separately accounted for). The issue of treadmill work rate variation is considered in the following section. The results from the test methods 1-5 above are summarised as follows:
Method 1. Visual inspection does not reveal a significant pattern of differences between baseline and SCSR or FSR core body temperature response.
Method 2. The Mann-Whitney U test results are tabulated in Table 4.3. The P values are 0.3821 and 0.7642. This infers the overall medians do not differ to a significant extent between the two populations.
Method 3. The paired samples t-test indicates a mean of 0.074 for the Phase 1 data set and 0.072 for the Phase 2 data set. The t statistic is 0.55. The difference between the means, 0.002, is noted to be small.
Method 4. Comparison of the pair (related) data using correlation/method comparison techniques is less straightforward. Refer to Figures 4.10 and 4.11. There is reasonable correspondence of fitted gradient lines with the x=y identity line (y=1.0995x, Figure 4.10 and y=0.975x, Figure 4.11). However, the scatter of the data points indicates the standardised residuals have a wide spread.
The above analysis results require further interpretation. As a general point, there must be a consistently high level of covariation between the temperature gradient variables measured for Phase 1 and Phase 2 for a conclusion to be reached that there is no discernible difference between wearing a respiratory protective device or otherwise (in terms of core body temperature response). Within the data set, a high level of correlation has not been shown. The above tests infer that any difference in the thermal physiological response in wearing a self-rescuer (compared with a baseline) is modest. However, both respirator types have the potential to present a substantial thermal burden, and the associated second order effects would probably emerge from a larger data sets used in conjunction with more discriminating methods of statistical analysis.
9. Analysis of Treadmill Speed and Distance Data
Analysis of treadmill speed and distance data provided the following for the test subjects:
· Distance travelled prior to SCSR run-out or core body temperature exceedence. · Information on average speed, and confirmation of any differences between Phase 1 and
Phase 2 components of the test run.
The treadmill odometer provides a pulse output (eight pulses equivalent to 1.1 metre); with the data acquisition package recording the number of pulses received each second. The odometer pulse rate data, when plotted, exhibits a significant scatter with in a 4-12 pulses per second band, as exemplified in Figure 4.2. To gain a better appreciation of underlying trends of treadmill speed across the test run, moving averages (10 and 50 period) were applied to the raw pulse data. Figure 4.12 illustrates examples of underlying trends resulting from application of the moving
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average method. These trends are generally consistent with the associated core body temperature characteristic.
Inspection of plots of treadmill speed over the complete data set suggests a high degree of consistency between Phase 1 and Phase 2. This was checked using Pearson correlation and the Passing and Bablok method (Figure 4.13). There is reasonable correspondence about the x=y line, albeit with a fair degree of scatter. The descriptive statistics for Phase 1 and Phase 2 average speed data are given below:
Phase 1 Average Speed Data Descriptive Statistics:
Phase 2 Average Speed Data Descriptive Statistics:
MeanStandard Error MedianModeStandard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count
3.167142857 0.049814156
3.215 2.83
0.322832628 0.104220906 -0.164756617 -0.450388288
1.33 2.42 3.75
133.02 42
MeanStandard Error MedianMode Standard Deviation Sample Variance KurtosisSkewness RangeMinimum MaximumSum Count
3.293095238 0.051874825
3.325 3.09
0.336187289 0.113021893
-0.357371131 -0.124699443 1.4 2.55 3.95 138.31
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The difference in the means of the set of average speed data for Phase 1 and Phase 2 is ~4%, which is relatively small. Data for distance covered, average speed and time is tabulated in Table 4.4. The mean of the total distances covered during the test runs is 1448 metres.
The influence of chamber test temperature on total distance covered can only be established as an indication. This is due to the limited number of test temperatures and the small number of data points for some test temperatures. Total distance covered in each run was compared with the prevailing test chamber ambient temperature using linear regression and the Passing and Bablok method (Figure 4.14 and Figure 4.15 respectively). Whilst the regression relationship is relatively tenuous, the data indicates a reduction in distance covered reducing at a rate of between 90-155 metres per °C increase in chamber temperature, within the ambient temperature range of 27-37 °C BET. Whilst approximately 15% of test subjects completed the test protocol with some latitude in core body temperature (from the 38.5 °C withdrawal temperature), the margin was not judged to be substantial in each case. It is speculated that more detailed observation would confirm a relationship of a similar order to that indicated.
10. Analysis of Core Body Temperature and SCSR Run-out Period versus Chamber Temperature
Alongside distance covered and SCSR run-out time, the influence of test temperature on rate of rise of core body temperature is an important parameter. Core body temperature gradient data for Phase 1 and Phase 2 was compared against chamber test temperature using the linear regression method. The results are given in Figures 4.16 and 4.17. The regression equations infer a
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positive relationship between BET and rate of increase in gradient, of between 0-5 mK per °C increase in chamber temperature. Again, the same statistical limitations apply as in section 9.
The issue of SCSR run-out time versus test temperature could not be addressed satisfactorily due to a lack of data. Of the 17 test runs involving SCSRs, seven were terminated before oxygen runout due to core body temperature exceedence. Given that there is no simple means of determining residual oxygen content of a chemical self-rescuer, the data set is limited to 10 points. This is not considered sufficient for statistical analysis. The data was assessed using the various means of regression testing, with the results summarised in Figure 4.18. The regression equations indicate a potential reduction in run-out time of between 30-90 seconds for each 1 °C increase in BET for the MSA Auer SSR30 type SCSR. Given the small data set, this can at best, be considered a potential indicator of oxygen run-out behaviour. Further study is required to confirm this relationship.
11. Summary of Test Subject Medical Information
Anonymised medical information was made available for the test subject group against the following parameters:
· Age · Height · Weight · BMI* · BP · FEV1 % of predicted · FVC % of predicted · FEV1/FVC · Treadmill score
Specific parameters from the above, averaged across the subject group, are given as follows:
Age (years): 39 (Range 27-48) Height (cm): 174.2 (Range 167-179) Weight (kg): 81.6 (Range 69.3-104.6) BMI*: 26.7 (Range 22-33) Treadmill score; 86.5 (Range 76-108)
These same parameters, but weighted by the number of trial runs undertaken by each individual, are as follows:
Age (years): 36.7 Height (cm): 174.5 Weight (kg): 83.6 BMI*: 27.5 Treadmill score; 85.6
Further research is required to compare the trial cohort medical records against data available for the general underground mine workforce.
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*Body Mass Index: The Body Mass Index (BMI), or Quetelet index, examines body weight relative to height. It is calculated by dividing body weight by height squared (kg.m-2). BMI is a useful indicator of total body composition in population-based studies and is related to health outcomes. As BMI increases, mortality from heart disease, cancer, and diabetes also increases. Significant increases in risk begin at a BMI of about 27.8 kg.m-2 for men and 27.3 kg.m-2 for women. Other general anthropometric definitions include circumference, waist-to-hip ratio and body composition [Jackson and Pollock 1978, Sloan and Weir 1970, Wilmore and Behnke 1969].
12. Discussion of Heart Rate Data and Oxygen Consumption Models
A previous section of the analysis indicated a link between observed SCSR run-out time and chamber temperature. This final section of the analysis uses the heart rate and body weight data as the basis of input to various oxygen consumption models to establish what implications there might be for the use of SCSRs in conditions of high thermal stress. The discussion considers various empirical oxygen consumption models, the test conditions on which they are based (where known), and the influence of work rate on oxygen consumption predictions.
It is noted that the discussion involves a limited data set, where the dependent variable (heart rate) exhibited significant variance due to instrumentation problems. As noted elsewhere, problems with the heart rate measurement arrangement were experience throughout the trials. These problems were associated with intermittent ECG electrode detachment and electrode gel depletion due to profuse sweating. Consequently, the data set for the trials was reduced to 11 partial and complete heart rate data logs that were judged acceptable for analysis.
Heart Rate Data Filtering
Inspection of the raw heart rate data for the 11 test runs (refer to Section 6) shows a number of outlier data points associated with ECG electrode problems. In order to estimate mean heart rate during Phase 2 of the trial (associated with the wearing of SCSR or FSR devices) it was necessary to discount the outlier data points.
The heart rate data recordings were inspected for short-term “spikes”, which were consistent with electrode detachment and the heart rate monitor responding to random electrical noise pick-up. These data points were removed and substituted with interpolated data (actually a constant y value, equal to the short term mean heart rate). This approach inevitably involves a subjective assessment of what constitutes spurious noise, and results in some data loss. The nett error in the calculation of mean heart-rate is estimated to be within +5% for the degree of data substitution involved, which is considered acceptable. The filtered heart rate data and associated treadmill activity (speed actual, and with a 10 period averaging period) is presented graphically for each of the 11 Test runs (see Section 6).
Analysis of Filtered Heart Rate Data
The average heart rate and range of heart rates associated with the filtered data for Phase 2 of the trial protocol is presented in Table 4.5. The mean Phase 2 heart rate for the 11 Test runs is 156 bpm. This is a high heart rate, but is consistent with the severe heat and humidity conditions present, and for subjects with a high core body temperature. A brief discussion on heart rate elevation mechanisms follows.
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Under conditions of heat stress, a greater volume of blood must flow through the skin each minute to achieve the same rate of heat exchange as in a neutral environment. When there are external heat loads concurrent with the performed work, both the central drive for increased conductance and the rise in local skin temperature cause dilation of skin vessels, thereby increasing their blood capacity and reducing their resistance. This is however at the cost of reducing venous return flow to the heart, resulting in a smaller stroke volume. In order to meet the oxygen demand of the working muscles, cardiac output can be maintained only by further constriction of splanchnic vessels and an increase in heart rate.
At very high effective temperatures, typically >34 - 35 °C BET, subjects will progress from the time-limited thermoregulatory compensation zone to the uncompensated heat storage zone. Subjects who are less fit or less well acclimatised for work will reach limiting levels for thermal balance at lower core temperatures and at corresponding lower levels of external heat stress for a particular work rate. Figure 4.19 [US Naval Aerospace Medical Institute, 1991, Minard 1973], indicates the thermoregulatory responses to heat stress in zone A (full compensation), B (timelimited compensation), and C (uncompensated heat storage). The effector responses (SR, BFs), circulatory strain (HR) correspond to a highly acclimatised man working at one-third VO2 max at levels of heat stress up to limits of tolerance. Progression to heart rates approaching 180 bpm and above occurs in the uncompensated heat storage zone where thermoregulation has failed.
It is noted that results from climatic chamber treadmill tests on SCSR run-out characteristic, conducted by Aziz et al [2000], show a typical heart rate characteristic similar to that observed in the current trials. Figure 4.20 gives typical individual records of heart rate versus duration for climatic conditions of 22°C, 50-75% RH, and 32°C, 100% RH as recorded by Aziz et al. In these trials, heart rates of ~160 bpm were observed at 32°C BET, compared with 125-135 bpm for similar work under benign environmental conditions.
Escape Oxygen Cost Discussion
Manufacturers’ nominal durations for SCSRs are typically defined at BS EN401: 1993 test breathing rates. Typically, 70 litres of oxygen is supplied by a 0.3 kg charge of KO2 in an SCSR [Aziz et al, 2000]. In Figure 4.21, nominal duration versus breathing minute volume is given for the SSR30/100 SCSR device. The actual duration of an SCSR is a function of a cardiovascular fitness level, work rate/metabolic rate and body weight. There is then a complex interaction with state of anxiety/panic, speed of walking, heat and humidity stress, device breathing resistance increase, CO2 ventilation stimulus at high work rate, and condition of the SCSR.
Underground run-out tests of SCSRs have been conducted in several countries. Generally, these tests are not conducted where miners undertake a simulated escape along mine roadways that are significantly inclined, involve difficult walking conditions, or require participation in extreme climatic conditions. Similarly, no published data has been identified for underground measurements of oxygen consumption in simulated escapes measured directly by means of laboratory respiratory gas analysis systems. It is conjectured that exercises conducted under severe conditions could lead to a significant modification of the available escape oxygen consumption models. It is noted that the physiological impacts of a panic response cannot be factored into these models.
A survey of oxygen cost during simulated escape, employing a laboratory grade respiratory gas analysis instrument, has recently been completed by the Institute of Silicosis in Asturias, Spain. Results have not yet been published, but the Institute was kind enough to discuss the initial
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findings of the underground oxygen consumption tests. An example sample result from these trials was given for a mineworker of 78 kg weight, age 38 against the following test conditions:
Phase 1: Ascent of 100 m incline (41° inclination), rough steps, followed by 3 minutes rest.Phase 2: Descent of above incline.Phase 3: Walk along a horizontal roadway, 800 metres length, and 3 minutes rest.
Within these test phases, maximum heart rate and oxygen consumption were recorded as followsfor the test subject:
Test Phase Max. Heart Rate Oxygen Consumption, VO2 bpm l/min
1 155 3.4 2 134 2.1 3 117 1.8
The Spanish tests infer that evacuation, involving climbing sub-level caving gallery drifts, entails nearly double the oxygen consumption compared with local horizontal galleries. This may be contrasted with a mean VO2 of 1.7 l/min (range 1.3 – 2.2 l/min) for 37 subjects making a trial evacuation at four Australian collieries [Aziz et al, 2000]. The distribution of run-out times recorded by Aziz et al (nominally 60 min run-out time) is given in Figure 4.22.
The Australian trials, referenced above, comprised two sets of laboratory treadmill tests together with underground field trials. Within a limited scale trial, the Australian trials were extended to consider the impact of high heat and humidity on oxygen run-out time. These tests compared two test conditions; (i) 22°C at 50-75% relative humidity and (ii) 32°C at 100% relative humidity. The authors claim there was no significant difference in run-out time between the two test conditions. A question is raised over these findings, in that the Australian data confirms a significant increase in cardiac rate at the higher temperature and humidity, which their own oxygen consumption model suggests, should lead to a quasi-linear increase in VO2.
Other Factors
Work rate is intrinsically linked to the type of activity and the associated mechanical efficiency. Kovak et al (1992) suggest oxygen consumption as follows:
Walking upright : 0.3 ml/kg.m Bent posture: 0.5 ml/kg.m Crawling: 0.7 ml/kg.m
This indicates that escape planning allowances should be made for low seam conditions, or where travelling conditions are difficult. It is noted that each miner will in practice carry approximately 15 kg in terms of equipment, lamp, clothing, boots etc., which will contribute to oxygen demand over and above laboratory run-out tests.
Work physiologists have measured the maximum efficiency of walking, without a load, to be 2730 per cent. However, efficiency, expressed as energy consumption in kilojoules per unit of walking effort (kg.m) varies as a function of walking speed, pace length and footwear weight and resistance. The most efficient walking speed of 4-5 km/h reduces the to around 3 km/h with heavy footwear [Grandjean 1993].
83
A limited study of relevant scientific literature was also undertaken to ascertain whether there are physiological benefits attributable to breathing pure oxygen from SCSRs or oxygen BA during modest exercise, and indeed if there are any oxygen toxicity issues.
The physics of partial pressure of gases identifies the impact of altitude on the partial pressure of oxygen: PAIR = PO2 + PCO2 + PN2 + PH20
At sea level: PAIR = 760 mm Hg, PO2 = 160 m Hg At 5500m altitude: PAIR = 380 mm Hg, PO2 = 80 mm Hg
However, for healthy, fit subjects underground, we are essentially dealing with oxygen at maximum saturation. This suggests that benefits from breathing oxygen during submaximal exercise are likely to be modest, primarily due to the oxygen saturation of haemoglobin being maintained for a wide range of oxygen tension. A basic explanation is as follows. Oxygen enters and carbon dioxide leaves the blood due to partial pressure gradients. The carbon dioxide level is a major regulator of respiration. Small changes in carbon dioxide level (5 mm Hg increase in PCO2, hypercapnia) in the blood cause large increases in the rate and depth of respiration (100 % increase in ventilation). The effects of PO2 (if the changes occur within the normal range) on respiration are minor. A decrease in PO2 (hypoxia) produces significant changes in respiration only after 50 % decrease in PO2. The nature of oxygen-haemoglobin saturation accounts for the stable respiration characteristic, in that at any PO2 level above 80 mm Hg, haemoglobin is saturated with oxygen. This can be observed from the oxyhaemoglobin dissociation curve, Figure 4.23. Consequently only large changes in PO2 produce symptoms; otherwise it is compensated by oxygen that is bound within the haemoglobin. The theoretical oxygen-carrying capacity of haemoglobin (Hüfner’s constant) is 1.39 ml per gramme. Venous blood (PO2 = 46 mm) is still >75% saturated, providing additional oxygen available for high demand times. If a subject has been breathing 100% oxygen, a higher functional residual capacity exists within the lung.
It is probably reasonable to conclude that at low-moderate levels of metabolic activity, the benefits from breathing pure oxygen or oxygen-enriched air are likely to be modest. As a general physiological observation, it would be reasonable to expect heart rate to be lower for breathing pure oxygen than for breathing air under comparable circumstances. This aspect of oxygen selfrescuer use requires further research.
The onset of pulmonary oxygen toxicity under normobaric conditions is not well defined. The only recent studies of the effects of high inspired concentrations of oxygen to organisms, without confounding pre-existing lung disease or injury, have come from animal laboratory work or the use of cultured human pulmonary artery endothelial cells. Martin and Kachel [1989] demonstrated human pulmonary endothelial cytotoxicity after 8 hours exposure to 95% oxygen. A review of pulmonary oxygen toxicity is provided by Simes [1998].
84
Oxygen Consumption Models
Oxygen consumption, lung ventilation and heart-rate had been measured at various work rates by a number of researchers, with an example as follows [Grandjean 1993]:
Indicative Work Rate
Oxygen Consumption l/min
Lung Ventilation l/min
Heart Rate BPM
Resting 0.25-0.3 6-7 60-70 ‘Low’ 0.5-1.0 11-20 75-100 ‘Moderate’ 1.0-1.5 20-31 100-125 ‘High’ 1.5-2.0 31-43 125-150 ‘Very High’ 2.0-2.5 43-56 150-175 ‘Extreme’ 2.4-4.0 60-100 over 175
Various predictive models have been devised to link oxygen consumption, VO2 to average heart rate, HR and body weight, W. Heart rate is the primary parameter within four simple predictive models used to estimate oxygen consumption, VO2 in relation to average heart rate, HR, and in some models, body weight, W. [Bernard et al 1979, Berry et al 1983, Buskirk et al 1975]. These models; the PSU, Foster, NIOSH, and University of Wollongong (UoW) models are empirically based. The NIOSH and UoW models take into account the body mass in predicting oxygen consumption. However, no information was obtained on the climatic conditions relating to these models, or whether the NIOSH model is based on physiological tests where a respirator is worn. The UoW model is derived from mine evacuation trial data with a mean walking speed of 2 km/hr. This is contrasted with more typical walking speeds of 3-4 km/hr. The NIOSH and UoW models are given as follows:
University of Wollongong model: VO2 = 6.0W + 1.5HR + 0.332 500
NIOSH model: VO2 = W(HR – 61.25) 3230
Oxygen Consumption Model Predictions Using Selby Trial Heart Rate Data
The limited heart rate data set was used as input to the UoW and NIOSH oxygen consumption models. Using Phase 2 average heart rate and subject body mass data, oxygen consumption projections for the UoW and NIOSH predictive models are tabulated in Table 4.5. The mean oxygen consumption predicted by the UoW model is 1.82 l/min (range 1.66-2.02 l/min). Using the NIOSH model, the predicted consumption figures are higher; mean 2.47 l/min (range 2.04-3.03 l/min).
Examination of the Phase 2 treadmill speed data for the 11 Test runs, confirms a mean speed of 3.25 km/hr (range 2.94-3.74 km/hr), which is 63% higher than the normative speed of 2 km/hr used by the UoW model. In order to make a correction for speed in the UoW model, the
85
following simplified estimate for energy expenditure during walking was used [ACSM 2000, Swain and Leutholtz 1997]:
Walking: VO2 = 3.5 + 1.7(speed) + 0.3(speed)(%gradient)
VO2 in ml.kg-1.min-1, Speed in km.h-1
The correction for mean treadmill speed is a source of potential error. However, without further complication, it is difficult to make an accurate compensation for increased energy expenditure associated with the higher mean walking speed in the Selby trials compared with the nominal 2 km/hr on which the UoW model is predicated. Using the above relationship, and assuming negligible gradient, the oxygen consumption prediction from the UoW model needs to be increased by 30% to correct for a mean speed of 3.25 km/hr. Revised figures for a 30% and 40% increase over the UoW model predictions are tabulated in Table 4.5. It is observed that there is reasonable agreement between the uplifted UoW model figures and the NIOSH model predictions.
References
ACSM (2000)Guidelines for exercise testing and prescription. American College of Sports Medicine, (Sixth Edition). Baltimore, Pub. Williams and Wilkins, 2000
Aziz NI et al (2000)Self-contained self-rescuers – New strategies for mine escape in Australia.IMM Journal, Vol. 3, No. 25, Jan. 2000, pp 19-25.
Bernard TE et al (1979)Interrelationships of respiratory variables of coal miners during work. Ergonomics, Vol 22, No 10, pp 1097-1104.
Berry DR et al (1983)Recommended guidelines for oxygen self-rescuers, Vol III, Escape time studies in underground coal mines.Foster-Miller Associates, Inc and US Bureau of Mines. Contract No. J0199118, 49p.
Buskirk ER et al (1975)Measurement of work metabolism - Use of cardiorespiratory parameters for estimating metabolic heatproduction in hot environments.National Institute for Occupational Safety and Health, Cincinnati, Ohio. Contract No. HSM 99-72-70, 35p.
Cornbleet PJ, Gochman N (1979)Incorrect least-squares regression coefficients in method comparison analysis.Clin. Chem. 1979; 25-3: pp432-38.
Grandjean E (1993)Fitting the task to the man – A text book of occupational ergonomics.Burgess Science Press, Basingstoke, 4th Edn., p. 90, 1993.
86
Jackson AS, Pollock ML (1978)Generalized equations for predicting body density of men. British Journal of Nutrition, 40, pp 497-504, 1978
Kovac JG et al (1992)Probability of making a successful mine escape while wearing a self-contained self-rescuer. Jrnl. Int. Soc. for Resp. Protn., Vol. 10, Issue IV, pp 18-27, 1992
Linnet K (1990) Evaluation of the linear relationship between the measurements of two methods with proportional errors. Stat. Med. 1990; 9: pp1463-73.
Malchaire J et al (2000)Criteria for estimating acceptable exposure times in hot working environments: a review.Int. Arch. Occup. Environ. Health (2000) 73: pp 215-220
Mann HB, Whitney DR (1947)On a test whether one of two random variables is stochastically larger than the other.Ann. Math. Statist. 1947, 18, pp 50-60.
Martin WJ, Kachel DL (1989)Oxygen-mediated impairment of human pulmonary endothelial cell growth: Evidence for a specific threshold of toxcity. Jrnl Lab Clin Med 1989; 113: pp 413-421.
Minard D (1973)Physiology of Heat Stress. In Department of Health, Education, and Welfare, The Industrial Environment -Its Evaluation and Control. Washington, DC: U.S. Government Printing Office, 1973. pp. 399-412.
Passing H, Bablok W (1983)A new biometrical procedure for testing the equality of measurements from two different analyticalmethods.J Clin. Chem. Clin. Biochem. 1983; 21: pp709-20.
Passing H, Bablok W (1988)A general regression procedure for method transformation.J Clin. Chem. Clin. Biochem. 1988; 26: pp783-90.
Saltin B, Hermansen L (1966)Esophageal, rectal and muscle temperature during exercise. J. Appl. Physiol. 21: pp 1757-1762, 1966
Simes DC (1998)Elimination of Pulmonary Oxygen ToxicityAustralasian Anaesthesia 1998, Ed. Keneally J, Australian and New Zealand College of Anaesthetists, 1998
Sloan AW, Weir JB (1970)Nomograms for prediction of body density and total body fat from skinfold measurements.Jrnl. Appl. Physiol. 28(2): pp 221-222, 1970.
Swain DP, Leutholtz BC (1997)Metabolic calculations simplified.Baltimore, Pub. Williams and Wilkins, 1997
87
US Naval Aerospace Medical Institute (1991) Naval Flight Surgeon's Manual: Third Edition 1991: Chapter 20: Thermal Stresses and Injuries
Wilmore JH, Behnke AR (1969)An anthropometric estimation of body density and lean body weight in young men.Jrnl. Appl. Physiol. 27(1): pp 25-31, 1969.
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Section 4, Annex 1:
Overview of Statistical Tests Employed
The data acquisition and logging instrumentation provided tab-delimited text format data files, which could be imported directly into Microsoft Excelâ spreadsheets. On this basis, statistical analysis tools were used which operated in conjunction with Excel, as follows:
General & Clinical Laboratory Statistical Analysis Package, Ver. 1.65 Analyse-it Software Ltd. (http://www.analyse-it.com) P.O. Box 77 Leeds LS12 5XA
Spreadsheet Assistant Add-in, Release 6.11 Macro System Products (http://www.add-ins.com) 144 Dewberry Drive Hockessin, DE, USA 19707
An overview of the statistical tests available is given in Figure 1. A brief overview of the statistical tests employed is given below. Further details are referenced [Cornbleet and Gochman 1979, Linnet 1990, Mann and Whitney 1947, Passing and Bablok 1983, Passing and Bablok 1988]. The principal statistical method employed was regression analysis.
Single and Multiple Linear Regression
This is formally used to predict values for a response variable (Y) based on 1 or more predictor variables (referred to as independent or X variables).
Data and assumptions:
The relationship between the X and Y variables must be linear; one response variable (Y) and one or more predictor variables (X) measured on a continuous scale. X variables must be free of measurement error. Measurement error in the Y variable must be normally distributed and have constant variance over the sampling range.
The linear relation between the Y and X variables is expressed as: y = a + bx + cx + dx, where “a” is the y-intercept (where the regression line cuts the y-axis), and “b”, “c”, “d” (and so on..) are the partial regression coefficients for each X. For simple linear regression, with only one predictor variable, the formula is y = a + bx.
Linear regression computes residuals (the vertical distance between observations and the fitted regression line). Standardised residuals are the raw residuals divided by their (estimated) standard deviation. Positive residuals indicate observations above the regression line; negative residuals below the regression line. A plot of the residuals for each observation can be used to verify assumptions of the regression are met, and examine how closely the computed regression line fits the variables.
Residuals should be normally distributed. The histogram shows the frequency of residuals, with a superimposed normal curve. If the residuals are normally distributed the curve should match the histogram well. Residuals should not form clusters or long runs above or below the line, otherwise the relation between the variables may be non-linear. Residuals should be randomly scattered in a constant width horizontal band. If the points converge or diverge, then the Y variable does not have constant variance across the sampling range. Residuals outside +/- 3 SD may be possible outliers.
The R² statistic summarises how well the regression line fits the relation between Y and X. It indicates how much of the variation within the sample is accounted for by the fitted regression line. High values close to 1.0 indicate much variation in Y has been accounted for by the predictors - the regression is a good fit; lower values indicate much variation is still not accounted for - the regression line is a poor fit.
89
Deming Regression/Method Comparison
This formally tests for agreement between two methods, or is used to predict values of a response variable (Y) based on a predictor variable (X). Deming regression has been widely adopted in clinical medicine, where unlike least squares linear regression, imprecision can be present in both variables (linear regression allows only for imprecision in the response variable, Y).
Data and assumptions:
Two related variables or two methods, measured in single or duplicate, with observations measured on a continuous scale. Imprecision in the observations must be normally distributed and have constant variance over the sampling range. When duplicate measurements are made for each variable, the imprecision is calculated from the duplicates, otherwise estimates of the imprecision standard deviation must be given for X and Y.
Passing & Bablok Regression/Method Comparison
This formally tests for agreement between two clinical methods, or is used to predict values of a response variable (Y) based on a predictor variable (X). Like Deming regression it is useful when imprecision occurs in both variables. However, the imprecision need not be normally distributed, and can have non-constant variance over the sampling range. A restriction is that the ratio of the imprecision X / Y must be equal to the slope squared. A useful feature of the procedure is that the regression line is not unduly biased by extreme values.
Data and assumptions:
Two related variables or two methods, measured on a continuous scale. See above for further assumptions.
Pearson Correlation
Correlation determines the degree of association between two variables X and Y. A plot of the observations generally helps to visualise whether the variables are correlated. Flaring or narrowing of the spread of observations may suggest that the variance over the sample is not constant. The correlation coefficient, with a value between +1 and -1, expresses the degree of association between X and Y. For values close to zero, no correlation exists between X & Y. The variables are independent.
Formally tests for an association between two related variables. The Pearson correlation, also known as the linear or product-moment correlation, is a parametric test to measure how linearly related variables are. The degree of association is expressed by the correlation coefficient, r. The Pearson correlation coefficient is computed, with the confidence interval around the coefficient. The p-value is computed using the t- approximation.
Data and assumptions:
Two related variables measured on a continuous scale. For a valid confidence interval around the correlation coefficient, both variables must be normally distributed.
t- Tests of Difference
Formally tests for a difference between the mean of 1 sample against a hypothesised mean, or between the means of two related samples or two independent samples. The t-tests are similar to ztests except they do not require knowledge of the shape of the underlying population distribution.
Data and assumptions:
Depends on the variation used. Either:
(i) One sample t-test: one sample, normally distributed, measured on a continuous scale and a hypothesised mean.
(ii) Independent samples t-test: two independent samples, normally distributed with similar shape distributions, measured on a continuous scale.
90
(iii) Paired samples t-test: two related samples measured on a continuous scale. Only the differences between the samples need be normally distributed.
Mann-Whitney U Test
The Mann-Whitney U test, also called the rank sum test, is a non-parametric test that compares two unpaired groups for differences in median. The median is a more robust measure of central tendency than the mean. Given samples from two populations, the Mann-Whitney U test is used to test the hypothesis:
H0: the two populations follow the same distribution or H0: there is no significant difference between the populations.
The key result is a P value that answers this question - If the populations have the same median, what is the chance that random sampling would result in medians as far apart (or more so) as observed? If the P value is small, one can reject the idea that the difference is coincidence, and conclude instead that the populations have different medians. If the P value is large, the data do not give reason to conclude the overall medians differ. However, this does not confirm the medians are the same; rather there is no evidence that they differ. For small samples, the Mann-Whitney test has little power. If the total sample size is seven or less, the Mann-Whitney test will always return a P value greater than 0.05, no matter how the groups differ.
Data and assumptions:
Two independent samples, with similar shape distributions, measured on at least ordinal scale.
91
Annex 1, Figure 1: Overview of Statistical Tests
92
Table 4.1: Regression Analysis Equations, Phase 1 Data
Test Run Regression Equation (Complete Phase 1)
Regression Equation (Phase 1, Rebased)
R2
Coefficient (Phase 1, Rebased)
R2
Coefficient (Phase 2)
01-08_06-C-35 y = 0.0897x + 36.812 y = 0.1050x + 36.706 0.99 0.97 01-08_18-B-35 y = 0.0891x + 36.537 y = 0.1130x + 36.373 0.99 0.97 01-08_21-B-35 y = 0.0812x + 36.770 y = 0.1067x + 36.606 0.99 0.97 01-08_25-C-35 y = 0.0805x + 37.454 y = 0.1306x + 37.188 0.98 0.98 03-10_15-B-31 y = 0.0549x + 36.367 y = 0.0847x + 36.154 0.97 0.99 03-10_18-B-31 y = 0.0508x + 36.538 y = 0.0680x + 36.417 0.98 0.99 06-08_13-C-35 y = 0.0548x + 36.855 y = 0.0839x + 36.739 0.71 0.98 06-08_15-A-35 y = 0.0440x + 36.880 y = 0.0836x + 36.674 0.96 0.97 06-08_17-C-35 y = 0.0173x + 36.890 y = 0.0403x + 36.769 0.87 0.96 08-08_16-B-35 y = 0.0376x + 36.781 y = 0.0681x + 36.634 0.82 1.00 08-08_20-B-35 y = 0.0401x + 36.920 y = 0.0800x + 36.718 0.95 0.99 08-10_21-C-27 y = 0.0439x + 36.504 y = 0.0768x + 36.276 0.97 0.99 08-10_23-C-27 y = 0.0032x + 36.411 y = 0.0382x + 36.166 0.87 0.99 12-09_18-C-32 y = 0.0424x + 36.855 y = 0.0673x + 36.732 0.91 0.98 12-09_21-C-32 y = 0.0693x + 36.694 y = 0.0994x + 36.539 0.96 0.99 17-09_20-C-30 y = 0.0314x + 36.617 y = 0.0675x + 36.438 0.88 0.91 17-09_21-C-30 y = 0.0401x + 36.630 y = 0.0644x + 36.516 0.89 0.99 20-09_20-B-32 y = 0.0364x + 36.666 y = 0.0730x + 36.491 0.91 0.99 20-09_23-B-32 y = 0.0058x + 36.837 y = 0.0549x + 36.597 0.88 0.99 25-07_16-C-37 y = 0.0906x + 36.620 y = 0.1130x + 36.396 0.99 0.96 25-07_21-C-37 y = 0.0324x + 37.415 y = 0.0626x + 37.258 0.91 0.99 27-09_16-B-29 y = 0.0434x + 36.803 y = 0.0493x + 36.762 0.96 0.99 27-09_17-B-29 y = 0.0281x + 36.465 y = 0.0429x + 36.360 0.94 0.99 27-09_19-B-29 y = 0.0604x + 36.495 y = 0.0856x + 36.308 0.99 0.95 27-09_20-B-29 y = 0.0513x + 36.405 y = 0.0625x + 36.327 0.97 1.00 27-09_22-B-29 y = 0.0403x + 36.281 y = 0.0583x + 36.155 0.97 0.85 29-07_15-C-35 y = 0.0388x + 37.183 y = 0.0595x + 37.009 0.97 0.99 29-07_16-C-35 y = 0.0450x + 36.837 y = 0.0521x + 36.782 0.98 0.99 29-07_18-C-35 y = 0.0625x + 37.101 y = 0.0860x + 36.926 0.99 0.99 29-07_19-C-35 y = 0.0309x + 37.411 y = 0.0415x + 37.327 0.95 0.96 29-07_21-C-35 y = 0.0743x + 36.800 y = 0.0917x + 36.661 0.99 0.98 29-07_22-A-35 y = 0.0598x + 36.898 y = 0.0784x + 36.747 0.98 0.96 29-08_15-A-35 y = 0.0218x + 37.117 y = 0.0585x + 36.943 0.87 0.98 29-08_18-A-34 y = 0.0409x + 37.046 y = 0.0515x + 36.985 0.92 0.98 29-08_21-A-34 y = 0.0813x + 36.948 y = 0.1169x + 36.753 0.98 0.96 29-08_23-A-35 y = 0.0088x + 37.187 y = 0.0453x + 37.009 0.84 0.97 30-09_13-B-29 y = 0.0507x + 36.852 y = 0.0625x + 36.769 0.97 0.99
30-09_17-B-29-O y = 0.0563x + 36.070 y = 0.0703x + 35.963 0.98 1.00 30-09_18-B-29 y = 0.0561x + 36.371 y = 0.0826x + 36.173 0.97 0.99 30-09_21-B-29 y = 0.0757x + 36.369 y = 0.1045x + 36.155 0.99 0.97
30-09_22-A-29-O y = 0.0483x + 36.202 y = 0.0704x + 36.040 0.98 1.00 30-09_23-B-29 y = 0.0515x + 36.734 y = 0.0683x + 36.616 0.98 0.99
Mean R2 Coefficient (Phase 1, Rebased) = 0.942 Mean R2 Coefficient (Phase 2) = 0.977
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Table 4.2: Core Temperature Gradient Data reordered by Chamber Test Temperature
Phase 1 (4 min onwards) and Phase 2 Data
TEST RUN Gradient, Phase 1, °C/min Gradient, Phase 2, °C/min
25-07_16-C-37 0.1130 0.0713 25-07_21-C-37 0.0626 0.1222 06-08_15-A-35 0.0836 0.0556 29-07_22-A-35 0.0784 0.0817 29-08_15-A-35 0.0585 0.0931 29-08_23-A-35 0.0453 0.0992
0.1130 0.0918 0.1067 0.0953 0.0681 0.0927 0.0800 0.0871 0.1050 0.0749 0.1306 0.1502 0.0839 0.1131 0.0403 0.0544 0.0595 0.0723 0.0521 0.0761 0.0860 0.0831 0.0415 0.0652 0.0917 0.0923
01-08_18-B-3501-08_21-B-3508-08_16-B-3508-08_20-B-3501-08_06-C-3501-08_25-C-3506-08_13-C-3506-08_17-C-3529-07_15-C-3529-07_16-C-3529-07_18-C-3529-07_19-C-3529-07_21-C-3529-08_18-A-34 0.0515 0.0744 29-08_21-A-34 0.1169 0.0828 20-09_20-B-32 0.0730 0.0492 20-09_23-B-32 0.0549 0.0749 12-09_18-C-32 0.0673 0.0525 12-09_21-C-32 0.0994 0.0696 03-10_15-B-31 0.0847 0.0711 03-10_18-B-31 0.0680 0.0754 17-09_20-C-30 0.0675 0.0293 17-09_21-C-30 0.0644 0.0672 30-09_22-A-29-O 0.0704 0.0568 27-09_16-B-29 0.0493 0.0387 27-09_17-B-29 0.0429 0.0506 27-09_19-B-29 0.0856 0.0514 27-09_20-B-29 0.0625 0.0591 27-09_22-B-29 0.0583 0.0370 30-09_13-B-29 0.0625 0.0436 30-09_17-B-29-O 0.0703 0.0529 30-09_18-B-29 0.0826 0.0719 30-09_21-B-29 0.1045 0.0842 30-09_23-B-29 0.0683 0.0708 08-10_21-C-27 0.0768 0.0540 08-10_23-C-27 0.0382 0.0443
94
n
Table 4.3: Mann-Whitney Test: Temperature Gradient Data, Phase 1 (+4min) & Phase 2
P1 = 42; nP2 = 42
Gradient Data and Ranks:
1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
76 78.5 77 83 63 36 61 60 5
37 55 53 3
34 74 35 31 47 21
78.5 30 12 7
64 28.5 24 27 16 65 6
67 54 25 15 81 10
28.5 40 57 75 41 38
49.5 68 72 84 43 51 80 22 20 70 66 19 9
17 39 1
33 11
49.5 44 82 4
13 14 26 2
46 52 59 32 69 56 71 48 58 73 8
18 45 62 23 42
43.3 41.7
Ranks for Gradient Data Count Phase 1 Phase 2 Phase 1 Phase 2
0.1050 0.1130 0.1067 0.1306 0.0847 0.0680 0.0839 0.0836 0.0403 0.0681 0.0800 0.0768 0.0382 0.0673 0.0994 0.0675 0.0644 0.0730 0.0549 0.1130 0.0626 0.0493 0.0429 0.0856 0.0625 0.0583 0.0595 0.0521 0.0860 0.0415 0.0917 0.0784 0.0585 0.0515 0.1169 0.0453 0.0625 0.0703 0.0826 0.1045 0.0704 0.0683
0.0749 0.0918 0.0953 0.1502 0.0711 0.0754 0.1131 0.0556 0.0544 0.0927 0.0871 0.0540 0.0443 0.0525 0.0696 0.0293 0.0672 0.0492 0.0749 0.0713 0.1222 0.0387 0.0506 0.0514 0.0591 0.0370 0.0723 0.0761 0.0831 0.0652 0.0923 0.0817 0.0931 0.0744 0.0828 0.0992 0.0436 0.0529 0.0719 0.0842 0.0568 0.0708
Mean Ranks for Phase 1 Phase 2
UP1 = 848 z = 0.3 P(1) = 0.3821 P(2) = 0.7642
95
Tab
le 4
.4:
Com
pila
tion
of T
read
mill
Pri
mar
y D
ata
Tes
t Run
D
ista
nce
Cov
ered
D
ista
nce
Cov
ered
T
otal
Dis
tanc
e A
ve. S
peed
A
ve. S
peed
T
ime,
Pha
se 2
C
omm
ent
Phas
e 1,
m
Phas
e 2,
m
m
Phas
e 1,
km
/hr
Phas
e 2,
km
/hr
min
01-0
8_06
-C-3
5 58
1 81
7 13
98
01-0
8_18
-B-3
5 45
9 63
0 10
89
01-0
8_21
-B-3
5 44
7 69
0 11
42
01-0
8_25
-C-3
5 43
3 15
8
592
03-1
0_15
-B-3
1 54
9 12
01
1750
03-1
0_18
-B-3
1 54
7 11
35
1683
06-0
8_13
-C-3
5 31
4 78
2 10
96
06-0
8_15
-A-3
5 33
2 11
16
1448
06-0
8_17
-C-3
5 35
1 11
60
1511
08-0
8_16
-B-3
5 26
9 84
0 11
09
08-0
8_20
-B-3
5 33
1 65
3
984
08-1
0_21
-C-2
7 51
8 10
10
1528
08-1
0_23
-C-2
7 50
3 14
06
1909
12-0
9_18
-C-3
2 35
1 11
08
1459
12-0
9_21
-C-3
2 34
4 10
82
1426
17-0
9_20
-C-3
0 26
7 83
4 11
01
17-0
9_21
-C-3
0 32
3 87
2 11
95
20-0
9_20
-B-3
2 26
2 10
72
1334
20-0
9_23
-B-3
2 35
1 12
53
1604
3.55
3.95
15
Tem
pera
ture
with
draw
al
2.88
2.
64
20
Stop
ped
at c
ompl
etio
n of
20
min
3.05
2.89
17
Tem
pera
ture
with
draw
al
3.75
3.
69
not w
orn
Tem
pera
ture
with
draw
al
3.39
3.
32
22
Tem
pera
ture
with
draw
al
3.38
3.
13
21
Tem
pera
ture
with
draw
al
3.73
3.82
13
SCSR
Run
-out
2.95
3.
33
20
Stop
ped
at c
ompl
etio
n of
20
min
3.12
3.
46
20
SCSR
Run
-out
2.74
3.13
16
Tem
pera
ture
with
draw
al
2.83
2.
95
14
Tem
pera
ture
with
draw
al
3.3
3.45
18
SC
SR R
un-o
ut
3.18
3.
41
25
SCSR
Run
-out
3.27
3.
06
21
SCSR
Run
-out
3.21
3.
41
18
SCSR
Run
-out
2.53
2.55
20
SCSR
Run
-out
3.06
3.39
16
SCSR
Run
-out
2.5
2.69
20
St
oppe
d at
com
plet
ion
of 2
0 m
in
3.36
3.
74
20
Stop
ped
at c
ompl
etio
n of
20
min
96
25-0
7_16
-C-3
7
25-0
7_21
-C-3
7
27-0
9_16
-B-2
9
27-0
9_17
-B-2
9
27-0
9_19
-B-2
9
27-0
9_20
-B-2
9
27-0
9_22
-B-2
9
29-0
7_15
-C-3
5
29-0
7_16
-C-3
5
29-0
7_18
-C-3
5
29-0
7_19
-C-3
5
29-0
7_21
-C-3
5
29-0
7_22
-A-3
5
29-0
8_15
-A-3
5
29-0
8_18
-A-3
4
29-0
8_21
-A-3
4
29-0
8_23
-A-3
5
30-0
9_13
-B-2
9
30-0
9_17
-B-2
9-O
30-0
9_18
-B-2
9
30-0
9_21
-B-2
9
30-0
9_22
-A-2
9-O
30-0
9_23
-B-2
9
20C
35 P
ilot
851
368
525
485
570
503
527
546
434
581
480
622
624
348
385
413
346
572
495
610
560
539
549
220
424
1515
1753
1281
1495
283
751
1107
607
933
748
709
885
1055
524
797
1771
1712
1301
887
1538
1244
560
1071
792
2040
2238
1851
1998
810
1297
1541
1188
1413
1370
1333
1233
1440
937
1143
2343
2207
1911
1447
2077
1793
3.63
3.26
3.1
3.04
3.38
3.12
3.31
2.83
2.42
3.02
2.67
2.83
3.31
3.41
3.19
3.39
3.36
3.49
2.9
3.62
3.32
3.22
3.42
3.02
3.71
3.2
3.5
3.09
3.05
3.16
2.94
3.16
3.15
3.09
3.52
3.92
3.61
3.37
2.83
3.55
3.67
3.45
3.38
3.27
3.09
3.57
5
not w
orn
30
32
26
30
16
21
12
18
13
11
14
18
11
13
30
30
23
16
30
21
10
Tem
pera
ture
with
draw
al
Tem
pera
ture
with
draw
al
With
draw
al e
stim
ated
at 3
4min
Tem
pera
ture
with
draw
al
Tem
pera
ture
with
draw
al
Tem
pera
ture
with
draw
al
With
draw
al, i
ntol
eran
ce o
f FSR
Tem
pera
ture
with
draw
al
SCSR
Run
-out
Tem
pera
ture
with
draw
al
Tem
pera
ture
with
draw
al
SCSR
Run
-out
Tem
pera
ture
with
draw
al
Tem
pera
ture
with
draw
al
Tem
pera
ture
with
draw
al
Tem
pera
ture
with
draw
al
Tem
pera
ture
with
draw
al
Tem
pera
ture
with
draw
al
With
draw
al e
stim
ated
at 3
2min
Tem
pera
ture
with
draw
al
Tem
pera
ture
with
draw
al
Tem
pera
ture
with
draw
al
Tem
pera
ture
with
draw
al
SCSR
Run
-out
97
Tab
le 4
.5:
Sum
mar
y of
Hea
rt R
ate,
Tre
adm
ill S
peed
and
Oxy
gen
Con
sum
ptio
n Pr
edic
tions
TE
ST R
UN
Ph
ase
2 A
ve. H
R,
bpm
Phas
e 2
HR
Ran
ge,
bpm
Phas
e 2
Ave
. Tre
adm
ill
Spee
d,
km/h
r
SCSR
R
un-o
ut T
ime,
m
in
UoW
Mod
el
Pred
icte
d O
2 C
onsu
mpt
ion,
l/m
in
UoW
Mod
el
+ 30
% u
plift
, l/m
in
UoW
Mod
el
+ 40
% u
plift
, l/m
in
NIO
SH M
odel
Pr
edic
ted
O2
Con
sum
ptio
n,
l/min
1.6
l/min
Ref
. C
ase
as %
of
NIO
SH M
odel
C
onsu
mpt
ion
08-0
8_16
-B-3
5 15
1 11
4 -1
74
3.13
-
1.84
2.
39
2.58
2.
45
65%
08-0
8_20
-B-3
5 14
5 11
4 -1
86
2.95
-
2.02
2.
63
2.83
2.
71
59%
12-0
9_18
-C-3
2 15
1 13
2 -1
86
3.06
21
1.
92
2.50
2.
69
2.62
61
%
20-0
9_23
-B-3
2 16
4 11
4 -2
04
3.74
-
1.67
2.
17
2.34
2.
24
71%
25-0
7_16
-C-3
7 17
2 15
0 -1
86
3.02
W
ithdr
awn
1.91
2.
48
2.67
3.
03
53%
25-0
7_21
-C-3
7 17
7 14
4 -2
04
3.71
W
ithdr
awn
1.86
2.
42
2.60
2.
97
54%
29-0
7_15
-C-3
5 15
0 12
6 -1
74
2.94
W
ithdr
awn
1.68
2.
18
2.35
2.
06
78%
29-0
7_16
-C-3
5 14
0 10
8 -1
74
3.16
21
1.
81
2.35
2.
54
2.15
74
%
29-0
7_19
-C-3
5 16
2 12
4 -2
06
3.09
W
ithdr
awn
1.66
2.
16
2.32
2.
18
73%
29-0
8_15
-A-3
5 14
9 10
8 -1
68
3.61
-
1.68
2.
18
2.35
2.
04
78%
29-0
8_18
-A-3
4 15
3 10
2 –
192
3.37
-
1.92
2.
50
2.69
2.
68
60%
MEA
N O
F 11
TE
ST R
UN
S 15
6 3.
25
1.82
2.
36
2.54
2.
47
65%
98
Figure 4.1: Trial Example and Physiological Parameters
Figure 4.2: Second Example Showing Influence of Exercise Rate
99
Figure 4.3: Phase 1 Data Set, Core Body Temperature
Figure 4.4: Phase 2 Data Set, Core Body Temperature
100
Figure 4.5: Regression Analysis, Phase 2, 30-09_22-A-29-O
Figure 4.6: Regression Analysis, Phase 2, 30-09_17-A-29-O
101
Figure 4.7: Polynomial Regression Analysis, Phase 2, 27-09_19-B-29
Figure 4.8: Regression Analysis, Phase 1, 08-10_21-C-27
102
Figure 4.9: Regression Analysis, Phase 1, 29-07_15-C-35
103
Figure 4.10: Temperature Gradient Data Comparison, Deming
Figure 4.11: Temperature Gradient Data Comparison, Passing & Bablok
104
Figure 4.12: Treadmill Speed Characteristic Examples, Moving Average
105
Figure 4.13: Treadmill Speed Data Comparison, Passing & Bablok
Figure 4.14: Distance versus Chamber Temperature, Linear Regression
106
Figure 4.15: Distance versus Chamber Temperature, Passing & Bablok
Figure 4.16: Phase 1 Gradient versus Chamber Temperature, Linear Regression
107
Figure 4.17: Phase 2 Gradient versus Chamber Temperature, Linear Regression
Figure 4.18: SCSR Run-out Time versus Chamber Temperature, Regression
108
Figure 4.19: Effector Responses for Acclimatised Men Versus Effective Temperature
Figure 4.20: Comparison of Heart Rate Behaviour in Australian Hot and Humid Tests
109
Figure 4.21: MSA Auer SSR30/100 SCSR, Nominal Duration versus Breathing Rate
Figure 4.22: SCSR Run-out Time Distribution [Aziz et al 2000]
110
Figure 4.23: Oxyhaemoglobin Dissociation Curve
111
SSEECCTTIIOONN 55DDIISSCCUUSSSSIIOONN OOFF TTRRIIAALL RREESSUULLTTSS
The research study essentially comprised three related research areas. Sections 1 and 2 of the research study concerned a review of UK deep mine underground climatic conditions and the development of a hot air simulation device with due consideration of thermal inspired air tolerance limits. Discussion on the findings from Sections 1 and 2 is addressed within each section respectively. The research within Section 3 primarily involved establishing a climate controlled chamber facility with appropriate physiological monitoring, and then undertaking a series of approximately 60 individual wearing tests. The results of the climate chamber trials are reviewed here, together with discussion on how the results might relate to the general mine workforce.
Scope and Development of Trial Objectives
Initially, it was proposed that the climate chamber trials should concentrate on investigating the following hypothesis:
“In conditions of high heat and humidity, does the wearing of an oxygen self-rescuer, or the wearing of a filter self-rescuer in a mine atmosphere containing a high CO content, have any noticeable effect compared with the baseline reference case, in terms of physiological stress response?”
Previous mining research had indicated that the wearing of escape respiratory protective devices could increase heart rate by ~15%. However, the thermal physiological response to the wearing of these devices was virtually unknown, particularly in conditions of high heat and humidity. The emphasis of the work was to reflect industry practice, i.e. concentrate on the wearing of filter self-rescuers, with appropriate comparative tests undertaken with chemical oxygen SCSRs.
The test programme involved medically assessed MRSL volunteer staff, each undertaking three wearing trials; a hot air filter self-rescuer training model run, a wearing trial of a representative SCSR (MSA SSR30), and, a baseline reference test run without an escape respiratory protective device. To arrive at results that had acceptable statistical accuracy, the subject group size was nominally defined to be between 11 and 16 subjects. Initially the test conditions used a standard temperature of 34°C (air fully saturated).
After initial trial runs, it became evident that loss of thermoregulation and premature withdrawal from the trials was occurring for all test subjects, and that physiological response needed to be assessed for a wider range of climatic conditions. Further spot tests were then undertaken at temperatures ranging between 27°C to 37°C basic effective temperature (BET). This range of conditions was considered representative of mine emergency situations in deep, laterally extended mines, including situations where ventilation has been disrupted. With agreement that the scope of the trials should be broadened, an objective was to gain a broad base of information, towards addressing the following questions:
1 Is there a substantial additional physiological stress contribution from wearing mining industry escape respiratory protective devices in conditions of high heat and humidity?
2 How does loss of thermoregulation take place for subjects walking with a moderate associated metabolic rate, and how is loss of thermoregulation linked to BET?
112
3 In general terms, can guidance be given on the extrapolated time and distance for the test population to reach critical core body temperatures if they were to continue exercising in the prevailing conditions?
4 Does clothing (e.g. overalls) increase the rate of core body temperature rise whilst exercising? 5 Is there any indication that SCSR oxygen run-out time may be reduced at high BET? 6 What comments can be made on the extrapolation of the programme findings to an
acclimatised underground workforce? 7 What other related evidence exists, linking high heat and humidity to stress response?
Summary of Key Trial Results
In examining the trial data, one key finding is that there is no statistical evidence of either of the devices tested contributing significantly to the loss of thermoregulation. There is evidence however, at the prevailing conditions of temperature, humidity and work rate, that after a brief period of compensation, all subjects progressed monotonically towards excessive core body temperature and subsequent withdrawal from the trials.
One further key issue, was the degree to which SCSR oxygen consumption rates are influenced by climatic conditions. There was limited evidence that for high ambient temperatures and humidity, there is an increase in oxygen consumption. This aspect of the work requires further investigation.
The findings from the climatic chamber tests can be summarised as follows:
1 Withdrawal criteria were set as end of protocol reached, exceedence of age-adjusted maximum heart rate, core body temperature (38.5 - 38.6°C), SCSR run-out, or physician's assessment.
2 Of these criteria, exceedence of core body temperature limits was the predominant reason for withdrawal. All subjects were withdrawn within 1 hour of entering the chamber. In nearly all cases, the subjects’ core temperatures would have continued to rise above 38.6ºC had they continued to exercise. This could have implications for emergency evacuation planning.
3 The initial warm-up period (Phase 1), initially set at 20 minutes, had to be reduced to 10 minutes and subsequently 6 minutes to avoid premature withdrawal from the tests occurring. In some subjects, an increase in core body temperature of 2ºC was observed after a total of 30 minutes of exercise.
4 Withdrawal due to excessive temperature was observed for the three protocol cases noted above, i.e. exercising under the prevailing conditions with or without a respiratory protective device led to loss of thermoregulation.
5 Subjects paced themselves at between 2– 4 km/h, but in all cases, core body temperature continued to rise during exercise. The difference in the means of the average speed data for Phase 1 and Phase 2 (rescuer wearing phase) was ~4%, which is relatively small.
6 As noted above, there was no statistical evidence for either of the test devices exacerbating loss of thermoregulation relative to the base case. The effects may be observable with a larger data set and more discriminating statistical analysis methods. It is noted that the hot air training model does not replicate the inhalation temperature of a filter self-rescuer used in a high CO concentration atmosphere.
7 Against the given withdrawal criteria, the maximum distance covered by any subject was 2350m and the minimum distance covered was 590m. The mean of the total distance covered during the test runs was 1448m. The distance covered was dependent on the prevailing climate stress.
113
8 The test data tentatively indicates a reduction in distance covered reducing at a rate of between 90-155 metres per °C increase in chamber temperature, within the ambient temperature range of 27-37 °C BET (but note §16 below).
9 Of the 17 test runs involving SCSRs, seven were terminated before oxygen run-out due to core body temperature exceedence. Where subjects reached run-out (10 test runs), SCSR life was in all cases less than the manufacturer’s nominal duration.
10 The observed run-out times for SCSRs varied from 10–25 minutes, with equivalent distances of between 560m and 1400m covered wearing the SCSR.
11 The regression equations indicate a reduction in SCSR run-out time of between 30-90 seconds for each 1 °C increase in BET. Given the small data set, this result is no more than a potential indicator of oxygen run-out behaviour, and requires further investigation.
12 Work rate criteria cannot account for the reduced SCSR run-out periods observed at high temperatures. Possible mechanisms associated with thermoregulatory breakdown, increased heart rate and possibly individual sensitivity to hypercapnia are noted.
13 Temperature fluctuations and stratification of chamber temperature were observed. It is likely that other climatic chamber designs exhibit this behaviour to some extent.
14 Given the variable temperature stratification, it is difficult to ascertain directly the mean effective chamber temperature. It is considered reasonable to use mean torso height temperature regression analysis results to determine the effective chamber temperature.
15 Only a limited subset of heart rate data was obtained, due to electrode connection problems. The mean heart rate recorded during Phase 2 was 156 bpm. This figure is high but is consistent with the high levels of imposed heat stress.
16 Whilst it is not appropriate to directly compare SEFA breathing apparatus safe wearing times under hot and humid conditions with the chamber trial durations, it is reasonable to suggest that if a large scale study of temperature sensitivity were conducted, comparable trends would be observed. This matter is considered further below.
Discussion on Individual Heat Stress Response and Safety Guidelines
The purpose of this study has primarily been to appraise the physiological response of subjects when wearing escape respiratory protective devices under relatively severe conditions of heat and humidity. The scope of the study did not include assessment of operational implications, for example the compounding effects of low visibility, or the requirement or otherwise for changes in emergency preparedness provisions and practice. It is considered appropriate however, to briefly review individual heat stress response, and any occupational safety guidelines of relevance. The discussion progresses via consideration of thermal exposure criteria for various national mines rescue services, through to the development of more generalised industrial guidelines on preventing heat exhaustion and heat stroke. Finally, comment is offered the role and effectiveness of cooling.
The margin for safety when subjects start losing thermoregulation is relatively small. An example is cited of heat casualty core body temperature characteristics observed in a US military exercise. In unpublished experiments conducted by the US Army Research Institute on Environmental Medicine [Kraning 1997], a group of 696 subjects were subjected to exercise in a hot climate that resulted in a number of casualties. Within the test cohort, approximately 50% of the total number of casualties had a core temperature of <38.4ºC. At a core temperature of 39.2ºC, over 95% of heat casualties within the cumulative frequency distribution are accounted for. The relationship is presented graphically in Figure 5.1.
114
Guidance has been developed in a number of countries concerning permissible limits for rescue operations, where breathing apparatus is worn in hot microclimates. This includes; Australia, Czech Republic, France, Germany, Poland, RSA, UK and Ukraine [Goldstein and Nowak, 2001]. It is not possible to directly compare recommended thermal exposure criteria for each country, since there are various assumptions made on work rate (which has a major impact on tolerance time), apparatus type, standard clothing type, and degree of permitted risk. For light clothing, without cooling jackets, and average-moderate work rates, indicative guidelines for various countries are given in Table 5.1 [Bresser and Kampmann 2000, DeKlerk 2000, Gaman and Tanasa 2000, Goldstein and Nowak 2001, Smolanov and Klimenko 2001]. Inspection of Table 5.1 data shows the following:
1. Whilst there are significant differences in permitted duration at lower BETs between national standards, there is increasing convergence at higher BETs. This is a reflection on limits increasingly being imposed by physiological response rather than by equipment performance issues etc. A similar convergence is observed between safe wearing times for various breathing apparatus types [Forster 1999].
2. The mathematical descriptive function of safe wearing time with temperature in fully saturated atmospheres typically follows a polynomial characteristic rather than a linear characteristic. The safe wearing time for SEFA apparatus in fully saturated atmospheres is described mathematically in Figure 5.2. It is conjectured that with a larger test data set, it would be reasonable to expect endurance limits for escape respiratory protective devices to follow a similar characteristic.
In addition to the above, they have been various studies on individual heat stress response. One notable Thesis is that of Havenith [1997]. Havenith's PhD concerns work undertaken at TNO (Human Factors Research Institute, Soesterberg) to examine individual variation and susceptibilities to heat strain and develop relevant heat stress prediction models. Literature on the assessment of stress response points to aerobic power, morphology (height, mass, body surface area, surface to mass ratio), body fat content, acclimatisation state, hydration state, gender and age to be relevant parameters [Kenney 1985].
Havenith's study used multiple regression techniques to assess the influence of aerobic power, anthropometrics and morphology, gender and age. Assessment of a heterogeneous subject group (with no correlation of age with aerobic power) showed age not to have a significant effect on rectal temperature, body heat storage or sweat loss. However, whilst chronological age has a negligible effect on body temperature and sweating, it does have a significant effect on both central and peripheral cardiovascular effector response.
Again referring to Havenith’s study, assessment of exercise in warm humid heat (for a group of young subjects, mean age 26 years) contrasted the relative importance of maximal oxygen uptake, adiposity, Du Bois body surface area, surface to mass ratio and body mass. The subjects' aerobic power, or exercise intensity expressed as a percentage of VO2max, was shown to have greatest influence on heartrate and core body temperature (approximately 50% of effect). Body size related parameters had about half the effect of VO2max. In general, the higher the VO2max and/or the bigger the subject, the lower the heat strain observed. The premise that core body temperature is determined by exercise intensity expressed as %VO2max, and sweat loss by absolute heat load, was only partially supported. It is noted that individual heat stress response can be highly variable on a day-to-day basis [Livingstone et al 1982].
Apart from the guidelines noted above for the mining industry, safety guidelines have been proposed for the general workplace. Core body temperature limits for prolonged daily exposure to heavy work originated with the World Health Organisation, which put forward a recommendation for a limit of 38ºC [WHO 1969]. This criterion has been extended to consider limit values associated with avoiding heat exhaustion and physiological sequela (heat stroke). Malchaire et al [2000], suggest acceptable
115
probabilities for any person to reach these two maximum core temperatures could be defined as follows:
- For 39.2 ºC, less than 10-3 (i.e. less than one person at risk per 1000 shifts). - For 42 ºC, less than 10-6 (i.e. less than 1 severe heat stroke every four years per 1000 workers).
However, Malchaire et al point out that there is limited published data describing the standard deviation and skewness of the distribution of core body temperature for the workforce population athigh heat stress levels. Wyndham and Heyn (1973) derived experimentally, distributions of core (rectal) temperature for acclimatised and non-acclimatised male mining subjects. Using this data, revised limit values can be set for the population of non-acclimatised and acclimatised workers, where mean rectal temperature should be:
-For non-acclimatised workers Lower than 38.2 ºC for P<10-3 of anyone reaching 39.2 ºC. Lower than 38.7 ºC for P<10-6 of anyone reaching 42 ºC.
-For acclimatised workers Lower than 38.3 ºC for P<10-3 of anyone reaching 39.2 ºC. Lower than 39.4 ºC for P<10-6 of anyone reaching 40 ºC.
More recent mining-based data [Kampmann 1997] has confirmed that distribution of core bodytemperature for a population after exposure to heat is very close to Gaussian, and that where the mean it is equal to 38 ºC, the probabilities of reaching 39.2 ºC and 42 ºC are 10-4 and 10-7 respectively.
Predictive safety models, such as ISO 7933, are used to predict the safe group response (i.e. 38 ºCmaximum core body temperature) for 95-99% of the population. When actual workplaces in themining industry have been evaluated, it has been shown that many workplaces significantly exceed the ISO modelled safety limits [Havenith 1997]. However, at these workplaces, few heat related problems were encountered. Havenith [1997] conjectures that underground workers are fitter than the general population and are also acclimatised, resulting in lower strain for the same climatic stress. InFigure 5.3, Havenith [1997] indicates a hypothetical distribution for the general population and a subgroup of acclimatised miners. Under the given conditions, the mining workforce sub-group would tolerate conditions with lower core body temperatures. However, the benefits of acclimatisation are greatly diminished if members of the workforce are subject to hypohydration (significant body fluid deficit). In this case, the response of unacclimated and heat-acclimated personnel who are hypohydrated is broadly similar. The core body temperature response of unacclimated and heatacclimated personnel to work in euhydrated and hypohydrated states is given in Figure 5.4. Thisreinforces the critical requirement for underground staff to have ready access to drinking water, andfor drinks to be taken at regular intervals in hot and humid conditions. This unfortunately, is not an option in a prolonged escape.
Development of guidelines which will protect the whole mine workforce, whether they work regularlyor infrequently underground, is not straightforward. Furthermore, the protection of persons who are most heat intolerant would entail adopting conservative and possibly restrictive heat stress limits. Afew pointers are noted in this regard. Assessment of heat stroke in the South African gold miningindustry by Stewart [1982] suggests that occupational incidence of heat stroke increased for ages >40. Kielblock et al [1982], commenting on the same industry, cite annual incidence rate of heat strokemorbidity and mortality increasing rapidly for wet-bulb temperature >34ºC. In terms of the relationship between BET and productivity, Pickering and Tuck [1997] state that adverse effects on work efficiency commence at a BET of 27ºC, and productivity declines noticeably at BETs >30ºC.
Mines rescue staff are trained to recognise initial signs of heat strain, arising both individually and inteam members. However, the onset of heat strain can be difficult to detect and can present limited symptomatic evidence before problems arise. More reliable methods involve continuous monitoring
116
of core body temperature and rate of core body temperature rise. However, the method selected for core temperature measurement has to be reliable, non-invasive and incur minimum disruption and discomfort in use. In the US, NIOSH has recommended that worker-acceptable physiological monitoring may be an effective alternative to environmental monitoring in extreme environmental conditions. Associated study has been undertaken to evaluate the accuracy of commercial personal heat strain monitoring systems with clinical standard methods of body core temperature measurement. However tests of devices have demonstrated an unacceptably high number of false negatives in use [Turner et al 2000]. Alternative techniques have been investigated, including the use of postural sway measurement as a safety monitoring technique to indicate the level of neurophysiological strain on a worker wearing personal protective equipment and subject to high heat stress [Kincl et al 1998].
Mining heat stress research conducted by NIOSH is making use of a new heat stress index, the Physiological Strain Index - PSI , which is based on personal reaction to heat. The NIOSH researchers argue that conventional heat stress indices are fairly complicated to apply accurately, and are not well suited to dynamic or changing conditions and activities. The index, developed by the US military to evaluate a person's fitness for duty and to evaluate soldiers in the field, examines the body's physical reaction based on changes in core temperature and heart rate, and uses a rating of condition on a scale of 0 to 10. The system rates a person's strain level based on how far they have progressed from their starting temperature and heart rate to maximum values of 180 beats per minute and 38.6ºC. The approach has been field-tested using swallowable core temperature sensing technology ("CorTemp" measuring system supplied by HQI Technology Inc). The application and value of this index warrants consideration in any future research, and the temperature measurement approach may have value in undertaking any underground trials.
In the UK, a Code of Practice for work in hot and humid conditions has been proposed by Hanson and Graveling [1997]. This study measured the physiological response of miners at work to identify the extent of heat strain and determine environmental temperature levels above which action should be taken to reduce the risk of heat strain. The application of heat stress models is generally not fully understood across industry. HSE has published a research report that examines critically heat stress indices, and the development of a heat stress assessment methodology, which may be more practical to apply [Bethea and Parsons 2002]. It is noted that the development of rational heat stress indices and methodologies for the design and evaluation of hot working environments needs to treat a number of parameters and account for their interaction [Bethea and Parsons 2002]. These parameters are broadly circumscribed as follows:
Factors determining imposed heat: · Air temperature. · Temperature of structures (viz. radiant heat). · Air water vapour pressure. · Air velocity. · Metabolic heat production. · Body surface area exposed/posture. · Clothing insulation/permeability.
Factors determining resulting strains: · Heat tolerance and consequences of the increase of core temperature. · Exposure duration. · Skin wettedness and other consequences of sweating. · Vasodilation and consequences of increased peripheral blood flow.
The environmental chamber test conditions have circumscribed a number of the above parameters, specifically air temperature and humidity, air velocity, clothing and exposure, and to a lesser extent metabolic heat production rates. The observations from the chamber tests indicate that in the test environment with fully saturated atmospheres and zero air velocity (i.e. basic effective temperature,
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BET = dry bulb temperature), that all subjects exhibited a progressive increase in core body temperature. The range of test chamber temperatures of 27-37 ºC BET covers the great majority of workplace locations underground.
It is not clear from the test observations, if self-pacing of work rate together with brief periods of rest would result in an equilibrium core temperature being established. At high test chamber BETs, body cooling was observed to be negligible during short periods of rest (< 5minutes) and heat storage continued as soon as activity recommenced. In this case, the use of a FSR in a low CO concentration challenge atmosphere may still have a consequence of loss of core body temperature thermoregulation. At lower test chamber BETs, the heat storage was less pronounced, and some cooling took place during periods of rest. However, any cooling was heavily modified by the presence of clothing. Even at relatively benign temperatures of 27-29 ºC BET, the wearing of an ensemble consisting of boots, socks, long-sleeved overalls and shirt is probably sufficient to mask any cooling. The thermal resistance of overalls is typically twice that of a T-shirt and shorts, 0.143 ºCm2W-1 versus 0.076 ºCm2W-1 [Pickering and Tuck 1997]. Personal protective clothing can greatly exacerbate heat stress due to its low permeability [Havenith 1999, Hanson 1999, Kampmann and Bresser 1999]. Even if cooling could be made available, there are issues relating to the length of time available to effect significant cooling in an evacuation situation. A brief discussion on observations of cooling period with reference to ice jackets follows, primarily as an example of possible cooling options.
The use of ice jackets in reducing heat load on men working in hot conditions has been investigated for a number of years, by Strydom et al [1974] and Van Rensburg et al [1972] in South African gold mines, by Mucke (1982) on behalf of the German mining industry and by Kamon et al (1986) for the US nuclear industry. Strydom et al [1974] considered that unacclimatised personnel, such as supervisors, would benefit considerably from wearing pre-frozen jackets in wet bulb temperatures as low as 31°C. The possible application of ice-cooled garments for use by mines rescue teams has been examined by DeRosa and Stein [1976] in the United States, and by Sweetland and Love [1974] for the UK Mines Rescue Service.
One notable application consideration concerning the application of cooled garments is the cooling recovery time required after a period of activity. Pasternack [1982] carried out research on a Draeger jacket under identical conditions to those reported by Engel [1982], but shortened the alternate work and rest phases from 30 minutes to 15 minute intervals. Pasternack found that in the case of the 15 minute interval, cooling was less effective and the subject’s core temperature dropped substantially less. This indicates that the physiological cooling response has a relatively long time constant, and that a staged evacuation using safe havens for resting and recovery, may require quite lengthy periods to accomplish appreciable cooling. The safe haven could serve an important physiological function in providing an opportunity to rehydrate evacuating mineworkers and possibly to permit a cooling microclimate to be established. It is noted that the use of safe havens has several established benefits, which include:
· Provision of a recognised mustering point for further instruction and to help address any disorientation.
· Provision of two-way speech communication with a control centre or rescuers, which can help control panic.
· The possibility of excluding toxic or irrespirable gases for an extended period.
Several researchers argue that the thermal performance criteria of underground refuges and safe havens are arguably as critical as the maintenance of breathable atmospheres [Brake and Bates 1999, Kielblock et al 1988, Venter et al 1998].
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REFERENCES
Bethea D, Parsons K (2002) The development of a practical heat stress assessment methodology for use in UK industry.HSE Research Report 008, undertaken by Department of Human Sciences, Loughborough University214 pages, 2002, ISBN 0 7176 2533 8
Brake DJ, Bates GP (1999) Criteria for the design of emergency refuge stations for an underground metal mine. Australian IMM Proceedings, No. 2, 1999, pp 1-7
Bresser G, Kampmann B (2000) Working time (duration limited exposure) standards for mine rescue operations under hot and wet ambientconditions in German coalmines. Proc. Int. Conf. on Mine Rescue Works, CMRS, Bytom Poland, 8 -9 May 2000, pp 47-52.
DeKlerk C (2000) Criteria for selecting rescue brigades in South Africa. Proc. Int. Conf. on Mine Rescue Works, CMRS, Bytom Poland, 8 -9 May 2000, pp 71-77
Derosa MI, Stein RL (1976) An ice-cooling garment for mine rescue teams. US Bureau of Mines, USBM Report of Investigations RI 8139, Washington DC, 1976.
Engel P (1982) op cit Pasternak A (1982)
Forster JA (1999)Survival, escape and rescue including factors governing the selection of self-rescuers and siting of safe havens. Int. Mining and Minerals, Jrnl Inst Mining and Metallurgy, Vol. 2, No. 6, pp 95-106, 1999
Gaman GA, Tanasa M (2000)Experimental determination for the selection and training methods of mine rescuers operating at hightemperatures and humidity.Proc. Int. Conf. on Mine Rescue Works, CMRS, Bytom Poland, 8 -9 May 2000, pp 57-62
Goldstein Z, Nowak A (2001) Criteria of safe work time of mine rescuers participating in rescue operations performed it difficult conditions ofhot microclimate. Proc. 29th Safety in Mines Research Institutes Conference, GIG, Katowice Poland, 8 -11 October 2001, pp 133143.
Hanson MA (1999) Development of a draft British Standard: the assessment of heat strain for workers wearing personal protectiveequipment. Annals of Occupational Hygiene, Vol. 43, No. 5 , pp 309-320, 1999
Hanson MA, Graveling R (1997) Development of a code of practice for work in hot and humid conditions. Institute of Occupational Medicine (IOM), Edinburgh, Report of work conducted on behalf of British CoalCorporation.
Havenith G (1997) Individual heat stress response.PhD thesis, Catholic University of Nijmegen, the Netherlands, 1997.
Havenith G (1999) Heat balance when wearing protective clothing. Annals of Occupational Hygiene, Vol. 43, No. 5 , pp 289-296, 1999
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ISO 7933 (1989) Hot environments. Analytical determination and interpretation of thermal stress using calculations of requiredsweat rate (SWreq). International Standards Organisation (ISO), Geneva, 1989.
Kamon E et al (1986) Readdressing personal cooling with ice. American Industrial Hygiene Association Journal, 47, 5, pp293-298, 1986.
Kampmann B (1997) Risk analyses for one colliery of Ruhrkohle AG in 1995. EC BIOMED project, BMH4-CT96-0648 HEAT, Working Paper GT2/13, 1995.
Kampmann B, Bresser G (1999) Heat stress and flame protective clothing in mine rescue brigadesmen: inter- and intra-individual variation of strain.Annals of Occupational Hygiene, Vol. 43, No. 5 , pp 357-366, 1999
Kenney WL (1985) A review of comparative responses of men and women to heat stress.Environ. Res. 37: pp 1-11, 1985
Kielblock AJ et al (1982) Heat acclimatization: perspectives and trends. Jrnl. Mine Vent. Soc. South Africa, Vol. 35, No. 7, pp 53-58, 1982
Kielblock AJ et al (1988) The functional performance of formal gold mine and colliery refuge bays with special reference to air supplyfailure.Jrnl. Mine Vent. Soc. of South Africa, Vol., No., pp 58-69, 1988
Kincl LD et al (1998) The use of postural sway measurement as a safety monitoring technique for workers wearing personal protective equipment. In: Proceedings of American Industrial Hygeine Conference 1998, Abstract 172, 1998.
Kraning KK (1997) Analysis of data from 696 subject observations during experiments conducted at US Army Research Institute ofEnvironmental Medicine, Natick, Mass., USA. Unpublished, 1997.
Livingstone SD et al (1992) Variability of body temperature response to standardised stress conditions. Proc. 5th Int. Conf. Environmental Ergonomics, Eds.: Lotens WA and Havenith G, 1992.
Malchaire J et al (2000) Criteria for estimating acceptable exposure times in hot working environments: a review. Int. Arch. Occup. Environ. Health (2000) 73: pp 215-220
Mucke G (1982) op cit Nicholl A G Mck et al, 1985 Personal cooling garments for the alleviation of thermal stress in mining. In: Proc. 9th Congress Int. Ergonomics Association, Bournemouth,September 1985, Ed.: Brown ID et al. Taylor and Francis, London, pp 568-570, 1985
Pasternack A (1982) The ice cooling vest. Draeger Review 49, May 1982, Draegerwerk A G, Germany.
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Pickering AJ, Tuck MA (1997) Heat: sources, evaluation, determination of heat stress, and heat stress treatmentMining Technology, Trans. I. Min E., Vol. 79, No. 910, June 1997, pp 147-156
Smolanov SN, Klimenko YV (2001) Heat protection of mineworkers and mine rescuers. Proc. 29th Safety in Mines Research Institutes Conference, GIG, Katowice Poland, 8 -11 October 2001, pp 161164.
Stewart JM (1982) Practical aspects of human heat stress.In: Environmental Engineering in South African Mines, Mine Vent. Soc. South Africa, 1982, p 556.
Strydom NB et al (1974) The design, construction, and use of a practical ice-jacket for miners. Journal of South African Institute of Mining and Metallurgy, 75, 2, pp22-27, 1974
Sweetland KF, Love RG (1974) A pilot trial of pre-frozen jackets for use in mines rescue work. Institute of Occupational Medicine (IOM), Edinburgh, Report TM/74/14, 1974.
Turner N et al (2000) Laboratory evaluation of three personal heat strain monitors in young and older wearers of protective clothing. In: Proceedings of American Industrial Hygeine Conference 2000, Abstract 159, 2000.
Van Rensburg AJ et al (1972)Physiological reactions of men using microclimate cooling in hot humid environments. British Journal of Medicine 29, pp387-393, 1972.
Venter J et al (1998) Portable refuge chambers: aid or tomb in underground escape strategies. In: Proc. Mine Rescue - Into the New Millennium, pp 55-78, 1998, Mine Vent. Soc. of South Africa.
WHO, World Health Organisation (1969) Health factors involved in working under conditions of heat stress.WHO Scientific Group on health factors involved in working under conditions of heat stress, Technical Report 412, 1969.
Wyndham CH, Heyn AJ (1973)The probability of heat stroke developing at different levels of heat stress. Arch. Sci. Physiol. 27: A, pp 545-562, 1973.
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Table 5.1: Permissible work duration at high temperatures (air fully saturated)
Ukraine RSA* UK Germany Poland Roumania
Safe Working Duration, minutes
TWB ºC
26 87
27 150
28 110 63
29 85
30 70 230 46 95 78
31 60 180 65 67
32 50 140 33 55 60 58
33 43 110 50 60 49
34 38 85 27 45 60 42
35 34 60 40 55 36
36 29 40 22 35 45 31
37 25 25 30 40 27
38 22 Hard work 19 30 35 23
39 20 (20 minutes prohibited.
25 30 20
40 18 ‘Average’ work 25 30 17
41 16 permitted at 43°C) 20 25 15
42 15 20 20 13
43 14 15 20 11
* RSA use Emergency Heat Stress Index (EHSI), where EHSI = (TDB + TWB)/2
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Figure 5.1: Experimental relationship between core temperature and cumulative occurrence of exhaustion from heat strain.
( 3 2 )
10 20 30 40 50 60 70 80 90
23 33
)
Safe
Per
iod
(min
utes
)
SEFA Safe Wearing Times in Fully Saturated Atmospheres
Polynom ial Trend Line: y = - 0.0265x + 3.076x - 120.11x + 1596.5
100 110 120
28 38
Wet Bulb Tem perature (deg. C
Figure 5.2: Mathematical Description of SEFA Safe Wearing Time Characteristic
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Figure 5.3: Relative response of general population and acclimatised mineworkers
Figure 5.4: Impact of Hydration Status on Core Body Temperature During Work
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SSEECCTTIIOONN 66::
TTRRIIAALL PPHHYYSSIIOOLLOOGGIICC DDAATTAA GGRRAAPPHH PPLLOOTTSS
This Annex contains graphical plots of physiologic data (core body temperature and heart rate) together with associated treadmill activity/speed. The file naming convention is as follows:
Example: 29-07_15-C-35
29-07 - Date (Day/Month of 2002) _15 - Subject Number -A/B/C - A, No RPD worn in Phase 2; B, FSR worn in Phase 2; C, SCSR worn in Phase 2-35 - Mean chamber temperature, deg C (100% RH) -O - Overalls worn rather than standard clothing.
The plots provide core body temperature and treadmill activity versus time, with heart rate shown where this was recorded reliably. The graph key is as follows, where Channel 1 records core bodytemperature, Channel 13 records heart rate, and Channel 14 records treadmill odometer pulse output:
Specific additional plots of heart rate and treadmill speed versus time are also given. In this case, a moving average trend (10 periods) is superimposed on the raw recorded data. The heart rate trends are filtered where necessary to remove outlier data points associated with loss of electrode contact.
For each test run, chamber temperature trends are given, recorded from four fixed thermistors at approximately ceiling height, mid torso height, and floor level (see main body text). In this case, red is the ceiling thermistor, green/cyan are thermistors placed at either end of the row of treadmills at mid-torso height, and blue is the floor level thermistor. Linear regression lines are superimposed.
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