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International Journal ofBiometeorology ISSN 0020-7128 Int J BiometeorolDOI 10.1007/s00484-012-0522-1

Improved predictive ability of climate–human–behaviour interactions withmodifications to the COMFA outdoorenergy budget model

J. K. Vanos, J. S. Warland, T. J. Gillespie& N. A. Kenny

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ORIGINAL PAPER

Improved predictive ability of climate–human–behaviourinteractions with modifications to the COMFA outdoorenergy budget model

J. K. Vanos & J. S. Warland & T. J. Gillespie & N. A. Kenny

Received: 28 June 2011 /Revised: 19 November 2011 /Accepted: 22 January 2012# ISB 2012

Abstract The purpose of this paper is to implement currentand novel research techniques in human energy budgetestimations to give more accurate and efficient applicationof models by a variety of users. Using the COMFA model,the conditioning level of an individual is incorporated intooverall energy budget predictions, giving more realisticestimations of the metabolism experienced at various fitnesslevels. Through the use of VO2 reserve estimates, errors arefound when an elite athlete is modelled as an unconditionedor a conditioned individual, giving budgets underpredictedsignificantly by −173 and −123 Wm−2, respectively. Suchunderprediction can result in critical errors regarding heatstress, particularly in highly motivated individuals; thus thisrevision is critical for athletic individuals. A further im-provement in the COMFA model involves improved adap-tation of clothing insulation (Icl), as well clothing non-uniformity, with changing air temperature (Ta) and metabol-ic activity (Mact). Equivalent Ta values (for Icl estimation) arecalculated in order to lower the Icl value with increasingMact

at equal Ta. Furthermore, threshold Ta values are calculatedto predict the point at which an individual will change froma uniform Icl to a segmented Icl (full ensemble to shorts and a

T-shirt). Lastly, improved relative velocity (vr) estimates werefound with a refined equation accounting for the degree angleof wind to body movement. Differences between the originaland improved vr equations increased with higher wind andactivity speeds, and as the wind to body angle moved awayfrom 90°. Under moderate microclimate conditions, and windfrom behind a person, the convective heat loss and skintemperature estimates were 47 Wm−2 and 1.7°C higher whenusing the improved vr equation. These model revisions im-prove the applicability and usability of the COMFA energybudget model for subjects performing physical activity inoutdoor environments. Application is possible for other sim-ilar energy budget models, and within various urban and ruralenvironments.

Keywords Human comfort . Clothing insulation . Relativewind velocity . Heat stress . COMFAmodel . Physical activity

Introduction

Thermal energy budgets sum the gains and losses of themajor energy streams interacting with the human body, i.e.convective and evaporative heat losses, radiative exchanges,and metabolic heat production within the body (Brown andGillespie 1986; Parsons 2003). When assessing the thermalenergy budgets of humans, many personal factors, such asclothing and metabolic heat production (Mact), becomeprominent components in modelling (Havenith et al.2002). Unlike the four microclimatic parameters of air tem-perature (Ta), wind velocity (vw), radiant temperature (TRT)and relative humidity (RH), clothing and Mact are user-dependent, and thus difficult to incorporate in a model.However, accurately modelling energy budgets is essentialfor assessing heat and cold stress, outdoor bioclimatic

J. K. Vanos (*) : J. S. Warland : T. J. GillespieSchool of Environmental Sciences, University of Guelph,Guelph, ON, Canadae-mail: vanosj@uoguelph.ca

J. S. Warlande-mail: jwarland@uoguelph.ca

T. J. Gillespiee-mail: tgillesp@uoguelph.ca

N. A. KennyTeaching Support Services, University of Guelph,Guelph, ON, Canadae-mail: nkenny@uoguelph.ca

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design, and overall thermal sensation (TS) predictions. Heatstress situations are much more likely to occur in a motivat-ed and/or elite individual exercising at levels that seemacceptable to the body, yet may push the limits of heatillness. Human biometeorology research highlights the in-creasing importance being placed on the interrelationshipsbetween the environment and human health (Vanos et al.2010a), with emphasis on urban areas due to findings ofoverall decreased health and functionality (Galea and Vlahov2005; Johansson and Rohinton 2006; Eliasson et al. 2007;Watkins et al. 2007). Such studies call for a considerableamount of research, with strong focus on vulnerable popula-tions who show the highest heat mortality and risk of heatstrokes (Smoyer et al. 2000; Basu and Samet 2002).

This paper is one of the few studies in human biomete-orology that has been completed for exercising individualsin realistic outdoor field settings. The COMFA energy bud-get model (Brown and Gillespie 1986; Kenny et al. 2009a,b;Vanos et al. 2010b) is used to integrate transient microcli-matic variables with physiological and environmental datain order to estimate the energy budget of an exercisingindividual. Studies involving indoor and/or sedentary/near-sedentary activity are not accurate when applied as an actualTS prediction tool for outdoor conditions and intense exer-cise (Hoppe 2002; Djongyang et al. 2010; Vanos et al.2010a). To be thermally comfortable, an exercising individ-ual would wear less clothing than a sedentary individual atthe same Ta due to the endogenous heat produced duringexercise. Inclusion of such human behaviour is often diffi-cult to incorporate into energy budget models.

The COMFAmodel has performed well in various climaticconditions for sedentary individuals (Brown and Gillespie1986), and has been improved for subjects performingmoderate-to-high physical activity (Kenny et al. 2009a, b)and for skin temperature prediction during moderate activity(Vanos et al. 2010b). In these studies, subjects wore standard-ized clothing for the weather, with intrinsic static insulations(Icl) input into the COMFA model by the researchers. How-ever, when attempting to predict behavioural choices byhumans for input, an accurate, consistent, and comparativemethod is needed.

Therefore, the purpose of this paper is to implementcurrent and novel results and research techniques from theliterature into the COMFA model, allowing a more accurateand simpler application of human energy budget estima-tions. Improvements are presented and applied to theCOMFA outdoor model in order to better account for thehuman behaviours associated with clothing and metabolicvariables. This paper will present the following changes: (1)incorporation of conditioning level—or fitness—into energybudget predictions; (2) intrinsic clothing insulation estima-tions based on changing air temperature (Ta) and Mact, withaccurate consideration of bare and clothed body parts; and

(3) improved prediction of wind and activity speed effectson TS.

Modelling methods and modifications

Energy budgets and the associated components [metabo-lism, convection (CONV), evaporation, emitted terrestrialradiation and absorbed radiation (Rabs, W m−2)], are calcu-lated using the most recent version of the COMFA energybudget model (Vanos et al. 2010b) with the current study’simprovements.

Radiation modelling

Precise estimates of all radiation fluxes needed for estima-tion of Rabs can be difficult to obtain due to the complexitiesof short- and long-wave radiation flux behaviours; however,Kenny et al. (2008) provide a method for reliable estimatesin outdoor environments. The Rabs model of Kenny et al.(2008) is applied using a cylindrical estimation approach(Monteith and Unsworth 1990; Campbell and Norman1998). See Kenny et al. (2008) for in-depth methods.

The total radiation absorbed by a human, in W m−2, iscalculated based on the absorbed short- and long-waveradiation as:

R ¼ AeffKin absð Þ þ Kup absð Þ þ La absð Þ þ Lg absð Þ

Acylð1Þ

where Kin(abs) and Kup(abs) are incoming and groundreflected solar radiation absorbed by the person, respec-tively. La(abs) and Lg(abs) are absorbed atmospheric andground surface longwave radiation, respectively. Acyl isthe outer surface area of the body cylinder (m2), andAeff is the effective area factor of a human, which is0.78 and 0.70 when standing and sitting, respectively(Campbell and Norman 1998).

Metabolism and activity speed

Average levels of physical activity and heart rate (HR) fordifferent genders and ages can be quantified using a generalenergy expenditure estimation method by Strath et al.(2000), which is appropriate for application to the averagepopulation.

According to Havenith (1997), there is evidence thatcardiovascular fitness, measured as maximum oxygenuptake ( VO2max ), may be more crucial than age indetermining heat stress vulnerability. This would beparticularly evident when an individual attempts a de-manding cardiovascular activity. Therefore, Mact is esti-mated by considering VO2 for both runners and cyclists

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who are unconditioned (VO2max < 40 mL kg−1 min−1)and conditioned (VO2max > 40 mL kg−1 min−1) to thegiven activity. To gain cardiorespiratory benefits fromexercise, an individual must be working at their percentof VO2 reserve (%VO2R , Eq. 2), which is the percentdifference between maximum and resting HR (ACSM2006). For unconditioned or conditioned individuals,%VO2R

is a minimum of 30% and 45%, respectively (ACSM 2006).An ‘elite’ category for running is added to estimate budgets ofelite runners using parks for training. These athletes frequentlytrain at %VO2R > 90% to achieve improvements in cardiovas-cular fitness (ACSM 2006). Using the appropriate%VO2R, theactivity VO2 (VO2act ) is determined from Eq. 2 (Strath et al.2000).

%VO2R ¼VO2act � VO2rest

VO2max � VO2rest� 100 ð2Þ

VO2act is divided by 3.5 to provide output in metabolic

equivalent of task (MET) units, where 1 MET058.15 Wm−2. VO2max is based on gender, age and physical activity,andVO2rest is the energy expenditure at rest (1 MET), equaling3.5 mL (kg min)−1 (Strath et al. 2000). Data pertaining to Mact

and va estimates are shown in Table 1, using a mean age of 25and Mact for ‘brisk walking’ from Ainsworth et al. (2000). Allva are based on calculated Mact values, listed as METs, withthe corresponding va found in Ainsworth et al. (2000).

Clothing properties

In order for thermal budget models to be accurate, theinfluences of bare and clothed body fractions must be con-sidered (Havenith et al. 2002; Psikuta et al. 2011), as well asmetabolic variations caused by changing activity. The

intrinsic clothing insulation (Icl, clo), referring to the wholebody, was estimated based on ambient Ta using Eq. 3.

Icl ¼ 1:372� 0:01866Ta � 0:0004849T2a

� 0:000009333T3a ð3Þ

where Ta is air temperature in °C, and Icl is clothing insula-tion in units of clo, which is an arbitrary unit (1 clo0186.6 sm−100.1555 m2 °C−1 W−1). This equation was developedfor the UTCI-Fiala model (UTCI 2010; Havenith et al.2011; Psikuta et al. 2011) through regression analysis ofseasonal clothing adaptation studies and numerous outdoorfield surveys of Europeans (The Eurowinter Group 1997;Donaldson et al. 1998; Goodwin et al. 2000; Donaldson etal. 2001; Mäkinen et al. 2006) as well as clothing surveysfrom COST730 (Jendritzky 2004). Studies were completedwhen individuals were adapted to their local climates(Havenith et al. 2011). The top curve in Fig. 1 displaysEq. 3 (Havenith et al. 2011), showing how the overallIcl relates to Ta, since individuals adjust clothing withrespect to the ambient Ta (UTCI 2010; Havenith et al.2011). For Ta>27°C, an Icl of 0.31 clo (0.048 m2 °C−1 W−1)was used. This corresponds to an ensemble of lightweightshorts and a tank top generated with ISO9920 (2007) stand-ards, and is the lower limit of clothing expected to be worn inpublic.

The Ta-dependent Icl from Eq. 3 is assumed to be uniformover the whole body surface. However, when a personbecomes overheated enough to expose arms and legs forextra cooling, separate consideration must be given to bareand clothed body parts (Campbell and Norman 1998; Vanoset al. 2010b; Zolfaghari and Maerefat 2010) for propersimulation of heat exchanges from different segments ofthe body. In this situation, we use Icl values from Eq. 3 (or0.31 clo for Ta>27°C) for covered segments, and assume Iclis zero for bare segments. This weighting was used tocalculate total evaporative, convective, and emitted radia-tion heat fluxes using a cylindrical body segment approach(Campbell and Norman 1998). An overall value for eachenergy flux was determined by giving body fractions cov-ered by clothing a weighting of 0.698 [torso and shoulders(0.413), thighs and pelvis (0.285)] and uncovered fractions aweighting of 0.302 [arms (0.192), and lower legs (0.110);Tikuisis et al. 2001). Many energy budget prediction studiesnote increased accuracy in skin temperature and budgetestimations using such a multi-segment approach (Fiala etal. 2001, 2003; Huizenga et al. 2001; Tanabe et al. 2002;Arens et al. 2006; Zolfaghari and Maerefat 2010; Vanos etal. 2010b; Psikuta et al. 2011). The following sectiondescribes a metabolically based method for shifting theoriginal UTCI-Icl to a lower clo-value at equal Ta, as wellas deciding the point at which a person will switch from theuniform Icl to the segmented Icl.

Table 1 List of activities and corresponding metabolic rates (Mact) andactivity speeds (va) used for energy budget prediction. Male (female)values are listed

Activity Mact (W m−2) va (m s−1)

Running

Unconditioneda 320 (262) 1.5 (1.4)

Conditionedb 436 (326) 2.1 (1.7)

Elitec 756 (582) 3.5 (3.0)

Cycling

Unconditioned 320 (262) 4.5 (4.0)

Conditioned 436 (326) 5.1 (4.5)

Brisk Walking

Average 221 (221) 1.3 (1.3)

a %VO2R 0 35b %VO2R 0 50c %VO2R 0 95.

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Metabolically dependent intrinsic clothing insulation

Exercising in outdoor conditions dramatically changes one’sthermal sensation, yielding larger neutral budget rangeresponses by users (Kenny et al. 2009b; Vanos et al.2010b). Additionally, there are increased complexities inevaporative heat exchanges leading to heat stress (Epsteinand Moran 2006), with overestimations of predicted thermalconditions at high Mact, particularly when clothing evapora-tion is not considered (Havenith et al. 2002). Equation 3,and further examples by the UTCI-Fiala model, have beenfound to have limitations for predicting the physiologicalresponses of well-trained exercising individuals (UTCI2010), where the effects of metabolism on clothing choiceare recognised, yet not incorporated. According to Huang(2007), individuals will have activity-dependent Icl, wherethey will adjust their clothing not only according to Ta, butalso to balance extra heat produced by Mact with additionalheat loss through the skin and clothing. Exercising individ-uals generally wear less clothing than a sedentary individualat a similar Ta; thus, Icl is lowered to compensate for thecomparatively high metabolic heat.

Accordingly, the current study implements a method(Steps 1–5 below) to solve for the change in Icl (ΔIcl) thata highly active individual—compared to minimally active—would be expected to wear at an equal Ta. This involvesshifting the magnitude of the original UTCI-Icl curve to theleft, giving a lower Icl value at the same Ta. The magnitudeof the shift is determined using the temperature difference(ΔTa), which is solved for using equivalent Icl (Icleq ) values

and Eq. 3. In the same way, we determine the Ta atwhich exercising individuals, at various levels of Mact,will change from a uniform Icl to a segmented Icl (seesection on Clothing properties) by altering a full ensem-ble to shorts and a T-shirt.

As a reference, walking leisurely (Mact0175 Wm−2) wasused, and it was assumed an individual would be wearingcomfortably a one-layer full outdoor ensemble. This

corresponds to an average Icl of ~0.11 m2 °C−1 W−1 (0.71clo) (ISO9920 2007). From Eq. 3, this Icl gives a referenceTa of 20.5°C. It was also assumed that an exercising personwould switch to shorts and a T-shirt if the Ta rose above thislevel, as a lower clo-value is needed for continued comfort.With these references, fractional analysis methodology(Steps 1–5) can be employed to find the threshold Ta fordistinct activity levels.

1. Using an example of an unconditioned male runner(Mact0320 Wm−2), obtain a metabolic fraction (Eq. 4)with the reference value of 175 Wm−2.

Macto

Mactx¼ 175

320¼ 0:55 ð4Þ

2. Modify the reference Icl using the metabolic fraction toobtain an equivalent Icl (Icleq ). Therefore, 0.71 clo×0.550

0.39 clo. The sole purpose of this Icleq is to find theΔTa (Step

4); hence, these values are not used in any further calcula-tions. The Icl values input into the COMFA model will neverfall below 0.31 clo, which is the lowest value an individualwill acceptably wear publicly outdoors.3. From Eq. 3 or Fig. 1, determine an equivalent air tem-perature (Taeq ). For 0.39 clo, this is ~26°C.

4. Find the difference (ΔTa) between the reference air tem-perature and Taeq . Therefore, (26−20.5)°C05.5°C. This

represents the change in the threshold Ta for uncoveringbody parts due to increased metabolic heat production, andthe subsequent left shift required to obtain the curve forMact0350 Wm−2 in Fig. 1.5. Apply ΔTa to the reference Ta (20.5°C) to obtain the newthreshold Ta at which the reference person would change toshorts and a T-shirt. Hence, (20.5−5.5°C)015.0°C.

Therefore, if Ta reaches 15.0°C, an unconditioned malerunner would be expected to reduce body coverage to only69%, due to such high heat generated by metabolism. Exam-ples and calculations for eachMact given in Table 1 are listed

−10 −5 0 5 10 15 20 25 300.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Air Temperature (oC)

Clo

thin

g In

sula

tio

n (

clo

)

175Wm−2

221Wm−2

320Wm−2

436Wm−2

756Wm−2Original UTCI Equation

Fig. 1 Relationship betweenambient air temperature (Ta)and overall intrinsic clothinginsulation (Icl) used in thecurrent study based on UTCI(2010) and Havenith et al.(2011). Each line is shifted leftwith increasing Mact (listed)based onΔT listed in Table 2 togive a lower Icl. The horizontaldashed line displays thereference Icl of 0.71, connectingwith each polynomial at the Xcorresponding to the thresholdTa

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in Table 2, and threshold Tas for reducing body coverage aremarked by ‘X’ symbols in Fig. 1. It is important to note herethat the given references for Ta and Icl are assumed for ahigh-to-midlatitude city in a cold continental climate, suchas Toronto (Canada), Southern Finland, or the Netherlands,as examples. Such thresholds would differ in the variousclimate zones throughout the world.

Wind and activity speeds

The heat transfer from a person to the surrounding environ-ment is affected greatly by wind speed at human height andactivity speed (vw and va, respectively), plus pumpingeffects on clothing (Nielsen et al. 1985; Havenith et al.1990b, 2002; Havenith and Nilsson 2004; ISO9920 2007;Huang 2007). These depend on the speed and direction atwhich the person moves during the activity. A simple tech-nique for calculating an effective relative velocity (vr) wasgiven by Holmer and Parsons (1999) and incorporated intothe COMFA model by Kenny et al. (2009b) for movingindividuals, as shown in Eq. 5 (denoted vr1 ). However,clothing heat transfer must be corrected for the directionsand velocities of both vw and va to accurately predict TS.Therefore, Eq. 6 (denoted vr2 ) was incorporated fromISO9920 (2007) into the COMFA model to find the relativeair velocity considering the combined effects of vw, va, andthe degree angle (α) between the wind direction and bodymovement (0° if same direction).

vr1 ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

v2a þ v2w

q

ð5Þ

vr2 ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

va � vw cos að Þð Þ2 þ vw sin að Þð Þ2h i

r

ð6Þ

According to Havenith et al. (2002), the resulting dynamiceffects on clothing are very important to include in correctTS prediction, as they can reduce Icl and vapour resistancedramatically, yet are commonly neglected. The overallclothing dry heat and vapour resistances decrease exponen-tially based on va and vr, respectively (Kenny et al. 2009b).Therefore, decay equations incorporating this effect, plusclothing correction factors (Nielsen et al. 1985; Havenithet al. 1990a,b) were implemented into the COMFA modelby Kenny et al. (2009b) and are used in all analyses withinthe current study.

Results and discussion

This section will display and discuss comparisons of origi-nal methods with new revisions proposed in the currentstudy. Such analysis justifies the above noted modificationsto the COMFA model, and similar energy budget models,for improved behavioural predictions related to clothing,athletic conditioning incorporation, and dynamic windeffects on clothing resistances. Bivariate correlation analysisis reported as the Pearson correlation coefficient (r) assess-ing the linear dependence of one variable on another. Pairedt-tests were used to compare the means of two variables—such as gender or activity—to assess if the average devia-tions differed significantly from zero.

Conditioning levels applied to metabolic activityestimations

The sensitive nature of the budget output to Mact (Kenny et al.2009b) requires accurate model prediction of Mact for input,with an emphasis on application to athletic individuals. Basedonmethods presented in the section onMetabolism and activityspeed, the impacts of incorporating levels of conditioning andgender into energy budget predictions can be assessed. Model-ing a human’s energy budget based on the VO2 needed tobenefit from exercise in their specific fitness category will moreaccurately predict theirMact. Differences are displayed in Fig. 2,where unconditioned, conditioned and elite male and femaleathletes are compared directly under two environmental con-ditions—moderate (20°C) and hot (30°C)—both with 40%RHand 0.2 ms−1 vw. Modelling of radiation is for July at a mid-latitude of 43°40′N. From this graph, we can see that when aperson has the fitness ability and motivation to reach a higherMact, their risk of heat stress is much greater. For example,when an elite athlete (VO2R 0 95%) is modelled as an uncon-ditioned (VO2R 0 35%) or conditioned individual (VO2R 0

50%), budgets are significantly underpredicted by −173and −123 Wm−2, respectively (P<0.05). This shows howcritical information regarding heat stress may be missed if the

Table 2 Threshold air temperatures (Ta, °C) at which an individualexercising at a given metabolic activity (Mact, W m−2) is predicted tochange from full clothing to shorts and a T-shirt, approximated fromequivalent temperatures (Taeq ) for a reference intrinsic clothing insula-tion (Icl) of 0.71clo. ΔTa is also the magnitude of each shift used toproduce the new polynomial curves in Fig. 1 from the original curvefor 175 Wm−2

Mact Fractiona Icleqb Taeq ΔTa Tc

a

175 1 0.71 20.5 0 20.5

221 0.79 0.56 23.0 2.5 18.0

320 0.55 0.39 26.0 5.5 15.0

436 0.40 0.28 27.5 7.0 13.5

756 0.23 0.16 29.5 9.0 11.5

a 175 Wm−2 divided by Mact

b Reference Icl of 0.71 clo multiplied by fractionc Threshold Ta where an individual will remove or add clothing.

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type of person/group being modelled is not considered in heat-health prediction and design application (i.e. design for an elitefitness outdoor complex versus a low-level recreational area).

The motivational instincts of elite athletes and outdoorenthusiasts are a psychological characteristic that can beproblematic on high heat days and/or during intense trainingor competition, which may prevail over physiological sensesand result in heat injury (Roberts 2007; Brotherhood 2008).Figure 2 shows the elite athletes to be in dangerous budgetzones (>250 Wm−2), even under moderate microclimateconditions. Additionally, there are also physiological varia-tions between genders with respect to metabolic differences;however, these differences are not found to be significant inthe current analysis. Hence, a generalised model may not beconcerned with gender differences, yet an individual-specific model should include gender and fitness-specificVO2 values in its calculations.

Vulnerability to heat increases in the young, sick, unac-climatised, and those unconditioned to exercise, all of whommay have compromised thermoregulation (Smoyer et al.2000); however, special attention must also be given tohighly motivated conditioned or elite individuals. As withmodel variations found in fitness and gender, differencesalso exist among individuals acclimatised to the exercise inthe heat. A negative impact of exercising in the heat isbehavioural modification at the expense of performance(Tucker et al. 2006), such as decreasing intensity or ceasingexercise. However, during intense training or competitionand under higherMact, a ‘motivational effect’may overcomephysiological perceptions resulting in heat injury (Roberts2007; Brotherhood 2008). Particular attention should begiven as the warm season quickly progresses in mid-to-high latitude cities where individuals are generally highlysensitive to these initial changes (Gosling et al. 2007). Heatacclimatisation is beneficial as it causes a lower threshold tothe onset of sweating, thus internal temperature will bereduced by efficient evaporative cooling (Shibasaki et al.2006). Adaptation to heat can cause detectable changes in

TS responses by individuals; thus, adaptive coefficientshave been suggested to account for time of year and warmand cool conditions (deDear and Brager 1998; Yao et al.2009). These generalised coefficients could be applied toCOMFA; however, further development should considereach climate zone and/or city for improvements in accuracyof TS predictions for exercising individuals.

Dependence of clothing insulation on metabolism and airtemperature

Energy budget outputs at five metabolic levels and varying airtemperatures are presented in Fig. 3. Each linemakes use of theIcl value from the corresponding polynomial based on Mact inFig. 1. Values for relative humidity, absorbed radiation, activityspeed, wind speed, and wind angle to body were set at constantvalues of 50%, 300 Wm−2, 0 ms−1, 0.2 ms−1, and 90°, respec-tively, in order to avoid external adjustments affecting thebudget output values. Strong contrasts are present with increas-ing Mact, with changing budget values dependent upon Icl andits adaption with respect toMact and Ta. The sharp decrease ineach energy budget indicates the threshold Ta at which anindividual exercising at a given Mact will uncover body partsand decrease Icl. This, plus the shifted UTCI regression equa-tions for Icl (Eq. 3), incorporates human behaviour (adding orremoving clothing) into the COMFA energy budget model.

At lower Ta, a human will generally wear a higher clothinginsulation and cover a higher percentage of their body than athigh Ta. This figure shows the drastic changes felt when thethreshold Ta is reached to provoke skin exposure of the calves,knees, and arms. Applying metabolically dependent Icland Ta thresholds displays how excess body heat leadsto a lower threshold Ta for exposing body segments tothe air (e.g. 11.5°C at Mact of 756 Wm−2 versus 18°C at221 Wm−2). The slight energy budget decrease shown justbefore the clothing change occurs due to an increased surfacetemperature (Tsf) (caused by Ta). This results in a greateramount of emitted longwave radiation as Tsf is raised to the

1 2 3 4 5 60

50

100

150

200

250

300

350

400

Activity and Conditioning

En

erg

y B

ud

get

(W

m−2

) &

Th

erm

al S

ensa

tio

n Ta = 20oC

Ta = 30oC

Hot

Neutral

Warm

Fig. 2 Energy budget analysisof subjects under variousconditioning (con) levels listedin Table 1 in moderate and hotenvironments. Dashedhorizontal lines indicate femalelevels for identical activity

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fourth power in Stephan-Boltzmann’s equation. The verticalsolid line displays the point at which Icl will no longer bereduced, but remain at 0.31 clo. Hence, budget lines display aslight increase in slope beyond this point due to a constant,rather than decreasing, Icl. The magnitudes of both Ta thresh-olds and Icl shifts will differ with climate zone, latitude, andtime of season, and hence can be adapted for these variables.

Dynamic effects in relative wind velocity

The degree angle (α) between vw and va is an importantconsideration affecting the relative air velocity impinging onthe human body. As α increases (0–180°), the wind rotates

from the back to the front, therefore a person experienceshigher vr when moving into the wind. Differences betweenthe two equations [vr1 (original) or vr2 (improved)] can beevaluated by altering the magnitudes of va, vw and α. Figure 4shows the deviations of the vr values using the improvedequation compared to the original for α00°–180°. Whileholding va constant at 1, 2, 3, and 4 ms−1 (dotted grey lines)and allowing vw to range from 0.1–2 ms−1, the averagedifference (vr1 − vr2 ) was calculated at angles of 0–180° in30° intervals. In the sameway, holding vw constant at 0.4 ms−1

intervals from 0 to 2 ms−1 (solid lines) and allowing va torange from 1 to 4 ms−1, the vr difference was calculated at thesame angles.

−5 0 5 10 15 20 25 30 350

50

100

150

200

250

300

350

400

450

500

Air Temperature (oC) E

ner

gy

Bu

dg

et (

Wm

−2)

175Wm−2

221Wm−2

320Wm−2

436Wm−2

756Wm−2

Fig. 3 Line graph displayingthe effects of metabolicallydependent clothing insulationchanges on the energy budgetoutput with changing airtemperature (°C). Linesrepresent energy budgets atlisted metabolic rates (Mact).The solid vertical line displaysthe point at which Icl will nolonger be reduced, but remainsat 0.31 clo

0 20 40 60 80 100 120 140 160 180−2

−1.5

−1

−0.5

0

0.5

1

1.5

2

Degree angle of wind to body

Rel

ativ

e W

ind

Vel

oci

ty D

iffe

ren

ce (

m s

−1)

2 ms−1

1.6 ms−1

1.2 ms−1

0.8 ms−1

0.4 ms−1

0 ms−1

vw

va = 4 ms−1

3 ms−1

2 ms−1

1 ms−1

Fig. 4 Difference values of relative wind velocities (vr2 − vr1), showing deviations with changing windspeed, vw (solid black lines), activity speed,va (dotted grey lines) and degree angle (x-axis)

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Generalised trends indicate that the absolute differenceincreases as α moves away from 90° (±). This graph alsoshows that, with the original equation, vr was overpredictedat α<90° and underpredicted at α>90°, with absolute devi-ations increasing with windspeed. These deviations alsoincreased with va; however, the angle at which over- andunder-prediction occurred was dependent on the va, with alarger va of 4 ms−1 giving rise to over-prediction at α of 40°,yet a lower va of 1 ms−1 resulting in a constantly decreasingunder-prediction.

The convective heat loss (CONV) and mean skin tem-

perature (Tsk) are affected by vr. Table 3 displays CONVand

Tsk results for various degree angles modelled at a constantva, vw, Ta, RH and Mact of 3 ms−1, 2 ms−1, 25°C, 50% and

436 Wm−2, respectively. Using vr2 , Tsk was found todecrease with increasing vr, so an individual experiences a

higherTsk (2.1°C) with wind at their back than when moving

against the wind (α0180°). Tsk is an important predictor ofactual thermal sensation (Bulcao et al. 2000; Yao et al.2007) due to heat exchanges at the skin surface; hence,accurate modelling is essential when estimating thermoreg-

ulatory responses. Tsk was found to be more sensitive tochanging α as Ta decreases and as vw increases, with allother variables held constant. The corresponding CONVdifference found using vr1 and vr2 at α030° is 30 Wm−2

(129 Wm−2 and 99 Wm−2, respectively). However, withidentical input data modelled at α of 180° (front) or 0°,the differences are −13 Wm−2 and 47 Wm−2, respectively.The listed CONV results can be explained because vr1assumes wind is hitting the side of the body at α090°,which is not always the case. Therefore, the two equationswill give equal results at 90° with va and vw held constant.Variance in CONV becomes more apparent with increasingwind and/or activity speeds, as demonstrated in Fig. 4 withlines va04 ms−1 and vw02 ms−1. Although vw up to 4 ms−1

has been found not to disturb the air layer between skin andclothing (Havenith et al. 1990a), when combined with va,

higher relative wind velocities are found. This result showshow the pumping effect caused by movement is accountedfor in vr2 , hence reducing the Icl (Kenny et al. 2009b).

Several studies highlight that dynamic heat transfer isaffected significantly by vw and va (Havenith et al. 1990a,b,2002; Holmer and Gavhed 1991), as well as Ta (Ben Shabatand Shitzer 2011); however, the former two variables arecommonly neglected. Convection is a greater contributor toheat loss under colder temperatures (e.g. −10°C), with con-vective heat transfer coefficients found to be more dependenton vr rather than Ta, as well as to increase exponentially withvw (Ben Shabat and Shitzer 2011). These coefficients can bedetermined for different body parts, yet are more crucial toknow for uncovered body portions (such as the face), espe-cially in cold and windy conditions.

The current study conveys that when modelling body TSat higher activity and air velocities with various angles,accuracy of the model inputs is critical for convectiveexchanges. However, inconsistencies may occur if the airvelocity is not unidirectional, or if the direction of move-ment is not constant. Under these circumstances, ISO9920(2007) suggests that the relative wind velocity be calculatedas the average of all possible angles between wind andmovement directions within in a given time period, thusresulting in a time-averaged convection and budget. Addi-tionally, it is important to note that wind speeds and direc-tions should be at human height of 1.5 m (Souch and Souch1993). Thus, if the 1.5 m wind measurement is not in closeproximity to the subject, a nearby weather station can beused, provided that the wind velocity is reduced to humanheight.

Conclusion

The current study improved the COMFA multi-segmentmodel, displaying the impacts of each revision under a widerange of environmental conditions and activity levels. Noveluse of research techniques has been incorporated to ensureaccurate thermophysical estimations of heat and moistureexchange between the body and environment. The changesallow for simpler and more efficient application of theCOMFA model for widespread use. From this study, wecan objectively account for human behaviour related toclothing and metabolic variables.

Utilization of %VO2R prescribed for fitness gain based onthe VO2max of an individual (ACSM 2006) allowed forcategorical prediction of Mact using the method of Strath etal. (2000). The two additions allow the COMFA model toaccurately predictMact for various levels of activity (or typesof activity) and conditioning level. The next step in improv-ing this specific method involves inclusion of psychological

Table 3 Convection (CONV, W m−2) and mean skin temperature (Tsk,°C) model results at varying degree angles of wind to body. Variablesof va, vw, Ta, RH and Mact are held constant at 3 ms−1, 2 ms−1, 25°C,50% and 436 Wm−2, respectively

Degree angle CONVa CONVb T1sk T2

sk

0 129 82 33.1 34.8

30 129 99 33.1 34.2

60 129 117 33.1 33.5

90 129 129 33.1 33.1

180 129 142 33.1 32.7

a Original equation (vr1 )b Improved angle equation (vr2 )

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research in motivation to exercise and/or competition, aswell as acclimatisation to heat or cold with respect to timeof year and climate zone. The second modification in thecurrent study modelled clothing behaviour based on Ta andMact. The original UTCI-Icl curve is shifted to give a lowerIcl value at equal Ta due to increasing metabolic heat gener-ation. Additionally, an equivalent Icl value is derived todetermine a threshold Ta for prediction of (un)covering bodyparts. Therefore, the COMFA model can now account for aperson removing clothing when overwhelmed with heat, yetstill attempting to exercise at the same intensity level.

Lastly, integration of the degree angle of wind to thebody (α) allows accurate predictions of the relative airvelocity (Havenith et al. 2002), which is an important com-ponent of accurate convective heat losses (CONV) and skintemperature predictions. Assessment of differences in rela-tive air velocity calculations with and without inclusion of αdisplayed increased deviations with higher vw and va, as wellas when α moved away from 90°, with CONV being sig-nificantly affected (P<0.05) when incorporating α. Withrealistic energy budget modelling of humans in a varietyof environments, and undergoing various activity levels basedon conditioning, more accurate results can be achieved forheat stress prediction, effective bioclimatic design, and hu-man–climate interactions. The current study has displayedimproved efficiency concerning the interactions of microcli-mate and physiology with human behaviour for the predictionof human energy budgets.

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