In the rst instance we observe that the correlation between θin and Ein (the driving variables for thermal and visual comfort) is low both in the overall dataset (ρ = 0.150, Figure 2.6(b)) and in the questionnaire dataset (ρ = 0.043). There is thus no problematic correlation between these driving variables that would undermine our study of the impacts of environmental comfort in one domain over that of another.
173
18 20 22 24 26 28 30
0.00.20.40.60.81.0
Indoor temperature (°C)
Proportion thermally comfortable Visual sensation
Too dark Comfortable Too bright
(a) In uence of visual sensation on thermal comfort probability
18 20 22 24 26 28 30
0.00.20.40.60.81.0
Indoor temperature (°C)
Proportion thermally comfortable
Olfactory sensation Acceptable or worse Rather good or better
(b) In uence of olfactive sensation on thermal com-fort probability
0 500 1000 1500 2000 2500 3000 3500
0.00.20.40.60.81.0
Indoor illuminance (lx)
Proportion visually comfortable Thermal sensation
Too cold Comfortable Too hot
(c) In uence of thermal sensation on visual comfort probability
0 500 1000 1500 2000 2500 3000 3500
0.00.20.40.60.81.0
Indoor illuminance (lx)
Proportion visually comfortable
Olfactory sensation Acceptable or worse Rather good or better
(d) In uence of olfactive sensation on visual comfort probability
Figure B.1: Interactions between thermal, visual and olfactory sensations: tted comfort probabilities with standard errors (dashed lines) and observed proportions of comfortable votes
B.3. DISCUSSION 175
Impact on thermal comfort. Using the same method as in Section 6.3.2, we derive the probabilities of thermal comfort with respect to visual (P (Sth= 0|θin, Svis)) and olfactory sensation (P (Sth = 0|θin, Solf)). The obtained distributions are displayed in Figures B.1(a)-B.1(b).
The tted curves P (Sth = 0|θin, Svis) dier slightly between dierent visual comfort conditions. It can be noticed that thermal comfort probability decreases more sharply at low temperatures when occupants are visually uncomfortable, and that the maximum reached by P (Sth = 0|θin, Svis) is lower when occupants are visually uncomfortable. In other words, when we are visually comfortable, we are also more thermally comfortable, but when it is too dark we prefer a higher temperature. On the other hand, there is no sign that olfactory sensation in uences thermal comfort.
To test the hypothesis of a signi cant impact of Svison thermal comfort, we attempted to t models for pcold and phot that include Svis as a multiple-level factor, but this latter is not signi cant according to the Wald test, likewise Solf.
Impact on visual comfort. Similarly, we infer visual comfort probabilities with respect to thermal (P (Svis = 0|Ein, Sth)) and olfactory sensation (P (Svis= 0|Ein, Solf)), shown in Figures B.1(c)-B.1(d). We again notice a small deviation in the case of uncomfortable visual conditions with lower maximum comfort probability, so that visual comfort would improve when occupants are thermally comfortable, while visual comfort probability re-mains practically unchanged in the case of olfactory sensation. A formal t of models for pdark and pbright including Sth and Solf leads to the same conclusions as above.
Olfactory comfort. We do not display any probability of olfactory comfort and do not study the impact of other comfort variables on this latter, as no associated environmental variable could be measured during our survey. However, an approach similar as above could be used in this case if some simultaneous measure of indoor air quality (eg. CO2
concentration) was available.
B.3 Discussion
Based on the analysis of our data, no clear evidence of interaction between thermal, visual and olfactory sensation could be identi ed. The only noticeable trend lies in the observation that both visual and thermal comfort probabilities reach a smaller maximum when their counterpart is estimated uncomfortable, although our data cannot provide strong statistical evidence to rigorously support this observation. Nevertheless, further data particularly from less comfortable buildings could con rm such a trend, which may be considered as an expression of the revenge eect observed by Leaman and Bordass [117].
Another interesting approach would be to also survey global indoor environment accep-tance as performed by Wong et al. [177] and precisely assess the impact of each sensation variable and ultimately examine the statistical signi cance of their interactions on the global outcome.
Appendix C
Productivity of occupants
We made that cut by applying the rule that everything and everybody must produce or get out.
Henry Ford (1863-1947), My Life and Work (1922) [178]
With the decline of agriculture and industry coupled with a rise in the services sector in developed economies, a large part of the population performs their work indoors a trend reinforced by the development of computer-based work. The link between the indoor environment and productivity at work is thus of increasing economical importance. We investigate such relationships in this nal annex.
In this we begin with a short review of previously published research on the link between the indoor environment and productivity at work (Section C.1). Based on our measure-ments and questionnaire, we present the associations found between perceived productivity and surveyed environmental variables (Section C.2) and discuss the scope and the limita-tions of these results (Section C.3).
C.1 Introduction
Motivation
Ensuring the conditions leading to highest productivity at work has longsince attracted attention, not only in o ce environments. The ideas of Henry Ford in the context of industrial environments [178] are amongst the most famous historical examples.
In the o ce environment, a relationship between indoor environment quality and pro-ductivity has longsince been hypothesised. As pointed out by Kosonen and Tan [179, 180], this is an issue of increasing interest since the salaries of o ce workers are many times greater than the cost of operating a building in developed countries and that there is a potential monetary gain due to improved workers' productivity.
Previous research
Leaman and Bordass [117] performed a questionnaire similar1 to ours and observed the positive impact on perceived productivity induced by a good degree of perceived personal
1The formulation was slightly dierent: Please estimate how you productivity at work is increased or decreased by the environmental conditions in the building.
177
control (on heating, cooling, ventilation and noise) and a rapid environmental response following from control actions.
Several studies concluded that excess temperatures decrease perceived [3, 4] and ob-served productivity in the context of o ce [181, 182, 183] and manual work [184]. Based on these observations, Seppanen and Fisk [6] proposed a simple model formulated as a linear productivity decrease of 2% per degree above 25◦C. There is less research available on the eects of low temperatures; however Meese et al. [185] found that these cause a reduction of the performance of manual tasks. Jensen et al. [186] also observed that providing good thermal comfort in o ce environments increases productivity.
Nicol et al. [187] surveyed perceived productivity in o ces, in conjunction with a rel-evant set of physical variables and thermal and visual sensation. In general, observed associations between physical variables were not found to be signi cant, except with illu-minance (negative association). Some weak correlation between productivity and the use of controls was found to be statistically signi cant: the use of arti cial light would have a negative eect while open blinds would have a positive impact. On the other hand, the
ndings of the eld survey conducted in a factory by Juslén et al. [188] found the opposite result that productivity increases with horizontal illuminance on the workplane.
The application of these ndings in building design is straightforward with potentially important implications. For instance, in their theoretical study Kosonen and Tan [179, 180]
use the previously observed link between environmental comfort and productivity to assess its improvement for dierent scenarios of building operation (eg. ventilation rate), based on Fanger's predicted percentage of dissatis ed with respect to thermal [106] and olfactory [73]
environment.
C.2 Results
Method
In order to detect the set of predictors impacting on productivity, we observe the variation of this latter with respect to all measured variables. This includes all surveyed environmen-tal variables (Table 2.1) but it is also of interest to examine the impact of daily aggregated values of these latter, as occupants were asked to estimate their productivity at the scale of the present day. For this purpose we will also check for the in uence of mean, minimal and maximal indoor temperatures during the day and the degree-hours over a threshold value of 25◦Cduring the occupied period. Occupancy-related patterns are potentially of interest, such as rst arrival time, total presence duration, day of the week and month. We also study the impact of mean and extreme reported thermal and visual sensation votes, likewise occupants' clothing level.
To do this, we t a linear model of self-reported productivity to all considered variables, and analyse the association between them using the obtained R2 and the signi cance of this eect with the F test. The assessment of the scale of the eect and necessary graphical checks complete the procedure.
In uential variables
Table C.1 shows values of R2, F and the associated p-values for the tted linear models using the 545 entries for productivity in our database. We observe signi cant variations amongst months, and that several environmental variables are signi cantly associated with
C.2. RESULTS 179
20 22 24 26
−20−1001020
Daily minimum indoor temp. (°C)
Productivity
20 22 24 26
−20−1001020
Daily mean indoor temp. (°C)
Productivity
−5 0 5 10 15 20 25
−20−1001020
Daily mean outdoor temp. (°C)
Productivity
1 2 3 4 5 6 7 8 9 10 11 12
−20−1001020
Month
Productivity
3 4 5 6 7
−20−1001020
Daily max. thermal comfort vote
Productivity
0.3 0.4 0.5 0.6 0.7 0.8 0.9
−20−1001020
Daily mean clothing level (clo)
Productivity
Figure C.1: Perceived productivity versus a selection of signi cant variables (the points of the bottom charts have been jittered to break ties)
Variable R2 F p-value Variable R2 F p-value θout 0.024 9.241 0.003 Svis,mean 0.003 1.507 0.220
θout,dm 0.033 17.871 0.000 Svis,min 0.002 0.980 0.323
θout,rm 0.035 19.211 0.000 Svis,max 0.000 0.042 0.838
θout,wm 0.036 19.712 0.000 Sth,mean 0.005 2.292 0.131
θout,mm 0.027 14.536 0.000 Sth,min 0.000 0.038 0.845
θin 0.020 7.766 0.006 Sth,max 0.022 11.022 0.001
θin,mean 0.036 11.884 0.001 Icl,min 0.036 18.119 0.000
θin,max 0.016 5.283 0.022 Icl,max 0.024 12.079 0.001
θin,min 0.048 15.789 0.000 Icl,mean 0.032 16.393 0.000
DH > 25◦C 0.029 2.371 0.128 Tpres 0.005 1.523 0.218
Ein,mean 0.000 0.032 0.858 Weekday 0.004 0.467 0.760
First arr. 0.000 0.142 0.706 Month 0.078 3.940 0.000 Table C.1: Values for R2, F and p-value based on F test obtained from linear models for productivity with each listed variable
a decrease in perceived productivity (θoutand its averages, θinand its mean, minimum and maximum and the daily maximum thermal sensation Sth,max) while Icl,mean, Icl,min and Icl,max grow together with productivity; which suggests that we are more productive during colder outside conditions as Icland θoutare inversely correlated (Section A.1). These trends are visible in Figure C.1.
Variables linked with indoor temperature (θin,mean and θin,min) produce the highest values of R2. These results indicate that the thermal history of the current day has the heaviest impact on productivity: a daily mean indoor temperature of 20◦Cwould increase productivity by 6% compared to 26◦C. The use of the daily minimum indoor temperature θin,min results in a slightly higher quality of adjustment than θin,mean.
The addition of any individually signi cant variable to θin,min or θin,mean in a model with two parameters is not statistically signi cant. We can conclude that the observed association between productivity and variables such as θout, Icl,mean and Sth,max is due to their own correlation with indoor thermal stimuli and that they do not display an intrinsic eect. We did not nd any signi cant association with visual stimuli and sensation.
C.3 Discussion
We have isolated the clear adverse in uence of a hot indoor environment on occupants' perceived productivity as observed in previous studies. However, the surveyed indoor conditions do not include uncomfortably cold indoor conditions, where a decrease in pro-ductivity may be expected. It can thus be hypothesised that curvilinear relations, including for instance polynomial terms (although these appear not to be signi cant according to our data), would better describe the association between productivity and temperature or daily extreme comfort votes; but this would need con rmation from further observations.
We point out that surveyed variations of perceived productivity might not fully re ect increases or decreases to actual performance. However, assuming coherent answers from the surveyed subjects, we may assume that these latter are proportional.
A large number of other factors are likely to impact productivity, such as access to environmental controls, but our survey did not address these issues. Non-environmental
C.3. DISCUSSION 181
factors, such as management, motivation, o ce size and occupancy density are possibly more important, so that a large database including a wide range of building types would be necessary to understand all the key mechanisms in uencing o ce productivity. We also underline that the obtained eect is valid in the context of usual o ce tasks and it is not necessarily valid for other work activities.
Finally, we point out that we have merely found evidence for a signi cant association, but not attempted to provide a predictive model or any form of validation; the spread being too large and the explained variance too small for such purposes.
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