Michael Bellmann, Herrmann Remmers ITAP GmbH, Institute of Technical and Applied Physics D Oldenburg, Germany

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1 1 st International Conference on Experiments/Process/System Modelling/Simulation/Optimization 1 st IC-EpsMsO Athens, -9 July, 0 IC-EpsMsO MODELLING THE IMPACT OF ENVIRONMENTAL CONDITIONS ON COMFORT, TASK LOAD AND MOTIVATION IN THE MOCK UP AND IN REAL FLIGHTS Michael Trimmel, Christoph Goger, and Elisabeth Groll-Knapp Institute for Environmental Health at the Centre for Public Health Unit for Psychology of Ergonomics, Environment, Health and Performance (PEEHP) Medical University of Vienna Kinderspitalgasse 1, A-9 Vienna, Austria michael.trimmel@meduniwien.ac.at; michael.trimmel@univie.ac.at Volker Mellert, Ingo Baumann, Nils Freese, Reinhard Weber Carl-von-Ossietzky University of Oldenburg, Department of Physics Carl-von-Ossietzky Str. 9-11, D-29 Oldenburg, Germany volker.mellert@uni-oldenburg.de Michael Bellmann, Herrmann Remmers ITAP GmbH, Institute of Technical and Applied Physics D-29 Oldenburg, Germany bellman@itap.de Michael Grünewald, Peter Faulhaber, Arno Röder EADS, European Aeronautic Defence and Space Company, CRC Germany D-1 Munich, Germany michael.gruenewald@eads.net John Seller, Claire Aizlewood, Helen Emms, Lorna Hamilton BRE; Garston, Watford WD2 9XX, UK sellerj@bre.co.uk Demos Tsahalis LFME: Laboratory of Fluid Mechanics and Energy University of Patras G-200 Patras, GREECE tsahalis@lfme.chemeng.upatras.gr Keywords: Modeling, Environment, Task load, Comfort, Aeronautics, Mock Up, Real Flight, Flight attendants. Abstract. The impact of environmental conditions on comfort, task load and motivation of flight attendants are investigated. Data from a full factorial experimental investigation in a mock up are compared to real flight data. Heterogenous results demonstrate methodological constraints for both research strategies. Wheres in the mockup experiments theoretically expected results were gained, results from real flights demonstrate the relatively small range of the investigated veriables and also confoundings by other sources of load, however, they give a realistic picture. For further investigations the combination of mock up studies with real flights is suggested. 1 INTRODUCTION Health and well being of flight attendants and pilots is a topic of growing interest. The continuous growth of air traffic, duration of flights, multi haul flights, workload during flights optimized climate and other issues are relevant factors to draw attention on that issue by stakeholders like aircraft manufacturers, airlines, unions, health organizations, authorities responsible for regulations and others. Empirical investigations on the impact of environmental conditions on perceived environmental qualities, 9

2 First A. Author, Second B. Author, and Third C. Coauthor. health issues and aspects of comfort were done either in real flights or in mock ups [2,, ]. Investigating environmental working conditions of real flights has the advantage of investigating real working situations, not only as found in the environmental variables, but also by the real work load. However, by this approach only a very limited number of theoretically possible combinations of environmental conditions are usually found. This limitation implies that theoretical models which asses single and combined effects of the impact of environmental parameters on health, well being, work load and further dependent measures cannot be easily formed. A second approach to investigate the impact of environmental conditions on parameters like well being and comfort was often done in so called mock ups [, ]. In such investigations an experimental setting can relatively easily be applied by i.e., varying a number of relevant environmental factors according to a full factorial design (1) to gain data on the impact of single environmental factors and of information of the impact of their interaction on the dependent measures. However, the disadvantage of mock up studies came from the fact that they are never as realistic as real flights by a number of reasons. Even if they would include air pressure as in real flights one can assume, that if humans know they are exposed to a mock up, they will response differently compared to a real flight situation. In the present paper the impact of environmental conditions of a mock up study is compared to the impact of environmental conditions in real flight for perceived symptoms and ratings of comfort. (Note: Physiological measures are not included in this paper.) The data were collected in the EU funded project HEACE ( Health Effects of Cabin Environment, funded by the European Community under the "Competitive and Sustainable Growth" Program, th Framework, Project Nr. GRD , 02 0), coordinated by University of Oldenburg (Prof. Volker Mellert). In this project mock up studies were done at the airport Vienna, Austria (in cooperation with Austrian Airlines) and at Watford, GB (at BRE as a partner in this project). After mock up studies real flight data were collected in outgoing and incoming flights to Tokyo and in outgoing and incoming flights to Delhi (in cooperation with Austrian Airlines). 2 METHOD 2.1 Mock up According to a full factorial X X Analysis of Variance (ANOVA) design with the between-factors temperature (21, 2, 27 C), humidity (, 1, 2 % relative humidity) and noise as a within-factor (70, 7, 7 db(a)) 70 [1] flight attendants and 1 pilots were investigated on 1 simulated flights. Nine with a systematic increase of noise level after 0 min of exposition and 9 with a systematic decrease of noise level with an exposure time of 0 min each. In each 0 min condition flight attendants had to serve passengers and work in galley for about 2 min, thereafter they filled in questionnaires (comfort, discomfort, perceived environment, task-load, mood, health symptoms), performed a selective attention task and experimenters took samples of salivary cortisol and measured blood pressure. 2.2 In-Flight Measurements In long-haul outgoing and incoming flights (three h-flights to Tokyo and three 7h-flights to Delhi) environmental conditions and dependent measures of comfort, mood, environmental satisfaction, continuous physiological recordings, spot measures of physiological states, performance and health symptoms were investigated from 1 data sets of flight attendants (N = 7) and of 2 data sets of pilots (n = 1) in cooperation with Austrian Airlines on Airbuses. 2. Dependent variables Aspects of environmental satisfaction was assessed by ratings (7 point scale) of satisfaction with environmental aspects (like Temperature, Humidity, Air Quality, Vibration and Noise), rated by the discrepancy from expectations and transformed to satisfaction. Comfort was rated on a 7 point scale. Furthermore a scale (consisting of three point scales each) for Motivation (Cronbach alpha =.7) was computed. Symptoms were assessed by the weighted mean of number of reported symptoms of health impairment. RESULTS Ratings from the mock up will be compared to ratings in real flights. Ratings are plotted in dependence of two other qualities (x-axes and y-axes) to demonstrate the impact of interactions. 0

3 .1 Satisfaction by air temperature in mock up First A. Author, Second B. Author, and Third C. Coauthor. D-Konturenplot (Trimmel2_HEACE1_Inflight_U_Comfort_SYMP_01-22v*990c) AirTemp_Satisfw = Distanz-gewichtete kleinste Quadrate , 2 1, 1 0, Figure 1. Satisfaction with air temperature in mock up was high at a temperature of 2 C (x-axes) and highest combined with high humidity (y-axes). Satisfaction was low for low temperature..2 Satisfaction by air temperature in real flight D-Konturenplot (Trimmel2_HEACE1_Inflight_U_Comfort_SYMP_01-22v*990c) 1 AirTemp_Satisfi = Distanz-gew ichtete kleinste Quadrate Figure 2. Satisfaction with air temperature in the real flight was highest at high temperature (x-axes) and relative high humidity (y-axes) and very low for low temperature with low humidity. 2, 2 1

4 . Rated comfort in the mock up First A. Author, Second B. Author, and Third C. Coauthor. D-Konturenplot (Trimmel2_HEACE1_Inflight_U_Comfort_SYMP_01-22v*990c) Comfort_Gallyw = Distanz-gew ichtete kleinste Quadrate Figure. Rated comfort in the mock up by temperature (x-axes) and relative humidity (y-axes). Comfort was high by mean temperature with high humidity and low for low and high temperature.. Rated comfort in real flights 2 D-Konturenplot (Trimmel2_HEACE1_Inflight_U_Comfort_SYMP_01-22v *990c) 1 Comfort_Gallyi = Distanz-gewichtete kleinste Quadrate Figure. Rated comfort in real flights by temperature (x-axes) and relative humidity (y-axes). Comfort was rated lower than in the mock up and relatively independent of the environmental conditions as they appeared in the cabin. 2

5 First A. Author, Second B. Author, and Third C. Coauthor.. Task load in mock up by temperature and noise level D-Konturenplot (Trimmel2_HEACE1_Inflight_U_Comfort_SYMP_01-22v*990c) NASATLXw = Distanz-gew ichtete kleinste Quadrate dba Figure. Task load in mock up by temperature (x-axes) and noise level (y-axes). Task load was rated low for 2 C and less dependent by air temperature for conditions of low noise Task load in real flights by temperature and noise level D-Konturenplot (Trimmel2_HEACE1_Inflight_U_Comfort_SYMP_01-22v *990c) 2 NASATLXi = Distanz-gewichtete kleinste Quadrate 0 7 dba Figure. Task load in real flights by temperature (x-axes) and noise level (y-axes). For high noise levels task load was rated high, independent of temperature.

6 First A. Author, Second B. Author, and Third C. Coauthor..7 Task load in mock up by temperature and humidity D-Konturenplot (Trimmel2_HEACE1_Inflight_U_Comfort_SYMP_01-22v*990c) NASATLXw = Distanz-gew ichtete kleinste Quadrate Figure 7. Task load in mock up by temperature (x-axes) and humidity (y-axes). Task load was rated low for 2 C and was lowest for high levels of humidity Task load in mock up by temperature and humidity D-Konturenplot (Trimmel2_HEACE1_Inflight_U_Comfort_SYMP_01-22v*990c) NASATLXi = Distanz-gewichtete kleinste Quadrate Figure. Task load in real flights by temperature (x-axes) and humidity (y-axes). Only for lower temperature and high humidity, task load was modeled lower.

7 First A. Author, Second B. Author, and Third C. Coauthor. D-Konturenplot (Trimmel2_HEACE1_Inflight_U_Comfort_SYMP_01-22v*990c) 7 Motivationw = Distanz-gew ichtete kleinste Quadrate dba Rated motivation in mock up by temperature and noise level Figure 9. Rated motivation in mock up by temperature (x-axes) and noise level (y-axes). Motivation was modeled low for higher levels of noise independent of temperature, however for low levels of noise temperature was modeled to affect motivation.. Rated motivation in real flights by temperature and noise level D-Konturenplot (Trimmel2_HEACE1_Inflight_U_Comfort_SYMP_01-22v*990c) Motivationi = Distanz-gewichtete kleinste Quadrate dba Figure. Rated motivation in mock up by humidity (x-axes) and noise level (y-axes). Motivation was generally high and modeled to be lower for conditions with high noise level combined with low air temperature

8 DISCUSSION First A. Author, Second B. Author, and Third C. Coauthor. The presented data on ratings of satisfaction with temperature, environmental comfort, task load and motivation in the mock up study and in real flights demonstrate different occurrences in dependent measures. The models suggest that results from the mock up studies differ from the results from the in flight investigations. In general results from the mock up studies are more related to the expected effects of environmental conditions [1]. In addition models from real flights are less differentiated than models coming from an experimental investigation. This could be a sign that in real flights more (than the displayed) variables affect the dependent variables. One can speculate that real flight conditions are associated with a different/higher level of load originating from a variety of sources like lower air pressure, different work load, unexpected load coming from the passengers, pre-conditions like time of day or preceding flights or by other reasons. One should consider that the presented results are based on a distance least square weighted model as provided by Statistica. If one would apply e.g. a linear approach, the results will probably be different. Another methodological issue refers to the fact that in the experimental approach the full space of relevant environmental conditions (precisely: levels of the varied factors) is investigated. This is obviously not the case in the real flight date, as only the environmental condition which appeared in the investigated flights are the basis of the modeling. Also the differences of environmental condition in the airplane are confounded by different workloads coming from different sections in the plane. As humidity is lower in the front of the airplane this condition is nearly always associated with the specific workload in the business class. Another example is the association of a certain noise level at the rear of the airplane with the specific workload in the last part of the economy section. Moreover, in real flights exposition took up to hours whereas in the mock up exposition time was all together about hours. Such differences in the exposition could be a reason for the observed differences in the models. However, up to now there are also investigations considered with much lower exposition times, so that a hour exposition for an environmental conditions investigation is seen as a long time exposition. Such a view seems to be questioned by the presented data. But there also seems to be a limitation of exposure time in mock ups, because participants know that they are not traveling (and it may be much more boring for the participants than real traveling) and therefore much longer expositions (i.e., hours) in the mock up must be proven to be more valid. Because of the limited available space, in this paper only results of ratings are presented. The inclusion of physiological data and of performance data (as it was done in the HEACE project) permits a much more entire view on the impact of environmental conditions. Although mentioned in the method section, these data are solely coming from flight attendants. If one considers to optimize the cabin environment one has to take into account that comfort and satisfaction by the environment may be somewhat different for passengers (and also for pilots). One can conclude that empirical investigations on the impact of environmental conditions on comfort, task load and motivation of people in the airplane needs to have both, experimental investigations in to get data on the whole space of possible environmental conditions and real flight data to validate the outcome of the models in real life conditions. Furthermore the more realistic (i.e., in the sense of physical and psychological conditions) the mock up test will take place the more valid the mock up data should be. REFERENCES [1] Baumann, I., Bellmann, M., Buss, S., Freese, N., Groll-Knapp, E., Hallmann, S., König, C., Kruse, R., Leitmann, T., Mellert, V., Remmers, H., Schulte-Fortkamp, B., Trimmel, M. and Weber, R. (0), HEACE: Versuchsdesign zur Erfassung der Belastung, der Beanspruchung und des Komforts von Passagieren, und der Crew in einem Kabinensimulator, DAGA 0, Aachen Germany. [2] Bauman, I., Bellmann, M., Buss, S., Freese, N., Groll-Knapp, E., E., Hallmann, S., König, C., Kruse, R., Leitmann, T., Meixner-Pendleton, M., Mellert, V., Remmers, H., Schöls, M., Schulte-Fortkamp, B., Trimmel, M. and Weber, R. (0), The assessment of load, strain, and comfort of flight crew and cabin crew in a cabin simulator-test design Euronoise, Naples Italy. [] Trimmel, M., Goger, C., Vouk, E.M., Kritz, M., Klaus, A., and Groll-Knapp, E. (0), Heart rate of pilots during long haul flights indicate low arousal states, Psychophysiology, Vol. 2, p. 2S. [] Trimmel, M., Goger, C., Vouk, E.M., Kritz, M., Klaus, A., and Groll-Knapp, E. (0), Physiological and psychological response on environmental parameters inside aircraft, International Symposium on Health Effects in Aircraft Cabin Environment. Oldenburg Germany, 22-2 June.