Quantitative analysis of the influence of air temperature variability on thermal perception of Brazilian university students

Abstract

BACKGROUND:

The scientific literature has already identified the influence of thermal conditions on health and performance of students. However, users’ opinions are often overwhelmed by normative evaluations, not receiving the necessary attention.

OBJECTIVE:

To quantitatively compare the influence of air temperature variability on the thermal perception of students from six air-conditioned teaching environments located in four regions of Brazil.

METHODS:

Three-day experiments were carried out in six environments. A thermal condition was proposed for each day. From that, the environmental parameters were measured and a questionnaire about the thermal perception was applied. Then, Generalized Linear Models were applied to obtain a measure of effect and hypothesis test and confidence interval were used to find comfort zones and compare environments.

RESULTS:

The results showed that students from environments A, B, C and E felt less the effects of the increase in air temperature compared to students from environments D and F. In addition, students from environments A, B, C and E showed less perceptual variability compared to students from environments D and F.

CONCLUSIONS:

Students acclimated to higher thermal conditions felt less the effects of the increase in air temperature, showed less perceptual variability and a higher degree of thermal adaptability.

Keywords

Teaching environment students air temperature thermal comfort thermal perception

1 Introduction

Indoor environments are spaces that do not allow occupants to have direct contact with the natural environment and require mechanical systems to maintain good thermal conditions. However, these systems may not meet the thermal needs of the occupants, causing the thermal environment quality to influence their well-being [1].

The thermal conditions of teaching environments demand attention, as students spend more time in school buildings than in any other closed place [2]. The opportunities for adaptation and acclimatization are limited, as students change rooms and take breaks between classes [3]. As a result, the presence of inadequate thermal conditions causes the environmental quality of the classrooms to influence comfort and performance of students [3 , 41].

In these conditions, Environmental Ergonomics becomes important, as it investigates the effects of environmental conditions on health, comfort and human performance [5]. The focus of analyzes is on the environments’ physical variables, with an emphasis on thermal conditions [6]. Despite the importance of teaching environments in people’s development, ergonomics professionals focus their analysis on other work environments, such as industries and hospitals [7].

The evaluation of thermal conditions allows understanding of existing situations and indicating possible changes that increase people’s well-being and satisfaction. For this assessment, the ISO 7730 [8] standard developed from the Fanger’s study [9] indicates the use of two indices: Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD).

PMV seeks to predict the individuals’ thermal sensation and takes into account personal factors (metabolic rate and insulation of clothing) and environmental variables (air temperature, average radiant temperature, air speed and relative humidity) [8]. PPD seeks to predict the percentage of dissatisfied people and is calculated based on PMV [8].

Despite these indices, it is noteworthy that thermal comfort is a mental condition that expresses satisfaction with the thermal environment [10]. It can be understood as a subjective aspect, in which thermal conditions, personal and psychological factors directly affect it [11].

Within this theme, researchers conducted a study in the universities’ classrooms in China and investigated how air temperature influenced thermal comfort of students [12]. In Malaysia and Japan, researchers analyzed comfort temperature and adaptive behavior of college students [13].

Also in university environments, the influence of the relationship between air temperature and radiant temperature was also evaluated in seven air-conditioned environments in Brazil [14]. The relationship between cardiovascular parameters and air temperature was also evaluated in four air-conditioned teaching environments [15]. Finally, the influence of air temperature on heart rate and students’ cognitive performance in air-conditioned environments was also investigated [16].

These studies have shown that thermal conditions in air-conditioned teaching environments influence health and well-being of university students. In addition, the existence of thermal discomfort results in unsatisfactory conditions that compromise performance [2, 17]. This type of analysis is still incipient and requires further investigation [18]. The problem that this article focuses on is the influence of air temperature on the university students’ perception in air-conditioned teaching environments.

The aim of this study is to quantitatively compare the influence of air temperature variability on thermal perception of students from six air-conditioned teaching environments located in four regions of Brazil. Based on this objective, we seek to answer the following questions:

Are there perceptual variations within the same environment?

Is the influence of air temperature variability similar in environments in different regions of Brazil?

Do samples from the warmest regions have greater perceptual variability compared to samples from the other regions?

In literature, analyzes with primary and secondary school students predominate [3 , 19–21] and also naturally ventilated environment assessments [20, 21]. This work is justified because: (i) it analyzes air-conditioned environments frequented by university students and; (ii) presents quantitative measures of the influence of air temperature on thermal perception of these students.

Literature also showed that air temperature influences comfort, health and performance of individuals within an environment. Therefore, quantitatively analyzing the influence of air temperature variability on the students’ thermal perception becomes important. Thus, this article contributes by calculating measures of the effect size of air temperature variability on perception.

This article is limited to air-conditioned environments and higher education students linked to the courses of exact sciences from six educational institutions in Brazil. The experiment was limited to specific temperatures (20°, 24° and 30°C). The environmental variables were limited to Air temperature (°C), Relative humidity (%) and Air velocity (m/s). Thermal perception was limited as (i) thermal sensation; (ii) desire for thermal comfort and (iii) thermal comfort. For statistical analysis was used the Generalized Linear Models.

2 Methodological aspects

This article was subdivided into four stages. Initially, the environments and the sample were defined. In the second stage, an experiment was carried out. The third step was data collection and the last step was data analysis.

2.1 Environments and sample definitions

The research was carried out in six teaching environments (Environments A, B, C, D, E and F) of Higher Education Institutions (HEIs) from different regions of Brazil. The chosen places were computer labs with air conditioning, also known as teaching environments with Video Display Terminal (VDT), as they have technological devices (computers, data-show, wireless) that assist in the teaching-learning process.

The chosen regions for analysis were: North, Northeast, South and Southeast. This choice was to represent different geographic regions of Brazil and cover different climatic conditions.

For example, the northeastern region is characterized by high temperatures throughout the year, while the northern region, in addition to high temperatures, has high air humidity. In the opposite direction, the south and southeast regions have milder temperatures and colder climatic conditions. The thermal characteristics of each city and the data collection period are shown in Table 1.

Table 1
Basic cities characteristics

Environment City Region Annual average temperature (°C) Annual average relative humidity Year of data collection

A João Pessoa Northeast 26.91 75.90 2019

B Petrolina Northeast 26.83 54.32 2019

C Teresina Northeast 27.41 72.50 2019

D Manaus North 27°.01 82.84 2019

E Florianópolis South 20.91 75.47 2019

F São Carlos Southeast 21..22 73.10 2019

Environment	City	Region	Annual average temperature (°C)	Annual average relative humidity	Year of data collection
A	João Pessoa	Northeast	26.91	75.90	2019
B	Petrolina	Northeast	26.83	54.32	2019
C	Teresina	Northeast	27.41	72.50	2019
D	Manaus	North	27°.01	82.84	2019
E	Florianópolis	South	20.91	75.47	2019
F	São Carlos	Southeast	21..22	73.10	2019

To ensure a homogeneous, standardized sample that would allow comparison between environments, some inclusion criteria were defined: (i) students of the exact sciences; (ii) age between 17 and 30 years; (iii) good health conditions, that is, they did not have cardiovascular or chronic diseases; (iv) did not have a Body Mass Index (BMI) greater than 30 and, lastly, (v) who participated in the three days of the survey.

The number of students was calculated based on Equation 1, which stipulates the sample size (n) of a finite population, using a simple random model, in which there is no replacement [22]. The finite population used was the total number of students in the exact sciences from each institution. The significance level used was 95%, margin of error of 5% and proportion of 50%, as it provides the largest sample size. $n = \frac{N \times ρ \times q \times {(Z_{\frac{α}{2}})}^{2}}{(N - 1) \times ɛ^{2} + Z_{\frac{α}{2}}^{2} \times ρ q}$ (1)

in which,

α= Significance level

n = sample size

N = finite population size

$Z_{\frac{α}{2}}$ = is the abscissa (percentile) of the standard normal distribution corresponding to a given significance level

ρ= is the proportion of individuals who have a given characteristic

q = (1 – ρ)

E = maximum tolerable error

2.2 The experiment

The ISO 7730/2005 standard [8] indicates that temperatures between 22° and 24° C provide thermal comfort, temperatures below 22° C provide discomfort due to cold, and temperatures above 24° C result in discomfort due to heat.

Based on this standard [8], students from each environment underwent a three-day experiment. The purpose of this experiment was to subject them to cold discomfort, comfort and heat discomfort. To simulate these three conditions, the temperatures of the air conditioning units were controlled. On the first day the temperature was set at 20° C, on the second day the temperature was set at 24° C and on the third day the temperature was set at 30° C.

The students who were part of the sample arrived 30 minutes before the beginning of the data collection, in order to stabilize the body temperature and acclimate to the proposed thermal conditions [8]. On each day, students used computers to access the thermal perception questionnaire.

This research was approved by the Ethics Committee of the Federal University of Paraíba (Case number 31037614.0.0000.5188). Data collection was carried out in 2019.

2.2.1 Personal variables

Initially, the weight (kilogram) and height (meters) of the participants in the sample were measured. Regarding height, the conventional procedure was used: tape measure. To measure the weight, it was necessary for students to use only their body clothes, so any other type of object was removed at the time of measurement. Therefore, the weight was measured using a digital scale with 150 kg capacity.

The collected data were recorded in a spreadsheet, later tabulated in LibreOffice Calc to calculate the BMI of each participant according to Equation 2. Those who had a BMI > 30, which indicates obesity, were not part of the sample. $BMI = \frac{Weight}{{Height}^{2}}$ (2)

2.2.2 Environmental variables

In an environment, when defining the air temperature through air conditioning, the actual thermal conditions will probably disagree with what has been proposed.

For example, when it is set at 24 ° C, the ambient temperature can be higher or lower than this. This situation occurs due to air infiltration through cracks or occasional door openings, voltage fluctuations, or due to the efficiency loss of the air conditioning system.

To understand whether the conditions existing in the experiment were consistent with the proposed thermal conditions and to identify air temperature variability, the environmental parameters were measured: Air temperature (°C), Relative humidity (%) and Air velocity (m/s).

For this measurement, the thermal stress meter TGD 400 was used. This device was calibrated by the National Institute for Space Research (NISR) of Rio Grande do Norte (RN) and met the requirements of range and precision of ISO 7726 [23].

The equipment has a measuring range of –10°C≤air temperature≤150°C, with a resolution of 0.1°C and accuracy of±0.5°C. For air velocity the measuring range is from 0 to 20 m/s, with a resolution of 0.1 m/s.

Based on ISO 7726 [23], the equipment was installed 30 minutes before the start of collection in the environment’s center. The positioning was compatible with the height of the participants’ torso, 0.6 meters from the ground [23]. The measurement of data related to environmental variables happened from minute to minute during the period of the experiment.

2.2.3 Subjective parameters

The subjective parameters were measured during the three days of data collection to understand the students’ perception of the conditions they were submitted to. The questionnaire was subdivided into three aspects: (i) thermal sensation; (ii) desire for thermal comfort and (iii) thermal comfort.

The questions used (Table 2) for evaluation were based on ISO 10551 [24] and ASHRAE 55 [25]. Cronbach’s alpha was used to ascertain the reliability of this questionnaire.

Table 2
Questionnaire applied

Aspects Question Response category

Thermal sensation What is your thermal sensation at this moment? Very Hot (3)

Hot (2)

Slightly Hot (1)

Neutral (0)

Slightly Cold (–1)

Cold (–2)

Very Cold (–3)

Desire for thermal comfort How would you like the environment to be? Very Hot (3)

Hot (2)

Slightly Hot (1)

Neutral (0)

Slightly Cold (–1)

Cold (–2)

Very cold (–3)

Thermal comfort What is your state of comfort at this moment? Comfortable (0)

Slightly uncomfortable (1)

Uncomfortable (2)

Very Uncomfortable (3)

2.3 Methodological procedure for data analysis

The environmental variables measured and the responses to the perception questionnaires were tabulated using the Libre Office Calc software. Initially, the average temperatures and relative humidity obtained for each environment were identified. In addition, the predominant thermal sensation was identified for each day of analysis.

The statistical software R-Project 3.6.1 was used and the Generalized Linear Models method was applied to evaluate the influence of air temperature on thermal perception of the occupants and to obtain the measures of effect size.

This analysis consisted of three aspects: (1) Influence of air temperature (independent variable) on thermal sensation (multinomial - cold, neutrality, heat - dependent); (2) Influence of air temperature (independent variable) on thermal desire for comfort (multinomial - cold, neutrality, heat -, dependent); and (3) Influence of air temperature (independent variable) on thermal comfort (binomial - comfortable, uncomfortable - dependent);

The models found were approved in the following diagnoses: (i) analysis of the linkage and variance functions; (ii) verification of the response variable distribution and (iii) analysis of residues. From these diagnoses, the chance ratio was used as the quantitative indicator of the influence of air temperature on the students’ perception.

The comfort zone for the environments was estimated in a three-step process. First, the operating temperatures identified by the students indicated the thermal comfort. Subsequently, the hypothesis test (Wilcoxon) was applied to assess the hypothesis that the temperatures identified were conducive to thermal comfort. Lastly, the confidence interval was applied to estimate the comfort interval.

3 Results

The results were subdivided into: (i) general characteristics of the sample; (ii) general characteristics of the environment; (iii) thermal perception analysis; (iv) quantitative analysis of the air temperature influence and (v) comfort temperatures.

3.1 Sample characteristics

The total sample of this study was 210 students. It consisted predominantly of male individuals (70%), with an average age of 21.8 years old and an average BMI of 27.15. The detailed description of the sample characteristics is shown in Table 3.

Table 3
Sample characteristics

Environment Sample Age BMI Male Female

A 40 $\bar{X}$ = 20 $\bar{X}$ = 24 65,7% 34,3%

B 34 $\bar{X}$ = 22 $\bar{X}$ = 23,70 65% 35%

C 36 $\bar{X}$ = 23 $\bar{X}$ = 24 77% 23%

D 37 $\bar{X}$ = 19 $\bar{X}$ = 24,78 56% 44%

E 32 $\bar{X}$ = 22,7 $\bar{X}$ = 22,95 73% 27%

F 31 $\bar{X}$ = 26,3 $\bar{X}$ = 25,06 73% 27%

Total 210 $\bar{X}$ = 21,8 $\bar{X}$ = 24,08 (70%) (30%)

Environment	Sample	Age	BMI	Male	Female
A	40	$\bar{X}$ = 20	$\bar{X}$ = 24	65,7%	34,3%
B	34	$\bar{X}$ = 22	$\bar{X}$ = 23,70	65%	35%
C	36	$\bar{X}$ = 23	$\bar{X}$ = 24	77%	23%
D	37	$\bar{X}$ = 19	$\bar{X}$ = 24,78	56%	44%
E	32	$\bar{X}$ = 22,7	$\bar{X}$ = 22,95	73%	27%
F	31	$\bar{X}$ = 26,3	$\bar{X}$ = 25,06	73%	27%
Total	210	$\bar{X}$ = 21,8	$\bar{X}$ = 24,08	(70%)	(30%)

The sample number presented was made up of students who participated in the three days of the experiment. It is important to note that the sample number for perceptual analysis was 630 questionnaires, being the result of 210 individuals analyzed in three days. Cronbach’s alpha was 0.81.

3.2 Thermal characteristics of environments

To understand the environmental conditions existing during the experiment, the values obtained for the following variables are presented: Average air temperature, Average relative humidity and Average air velocity.

On the first day the lowest temperature was proposed, and the thermal conditions expressed in Table 4 were obtained. On the second day, with the proposed intermediate temperature, the thermal conditions expressed in Table 5 were obtained. On the third day, the highest temperature was proposed, and the thermal conditions expressed in Table 6 were obtained.

Table 4
General environmental characteristics on day 1

Environment Average air temperature (°C) Average relative humidity (%) Average air speed (m/s)

A 20.31 64.53 0.1

B 20.43 64.53 0.1

C 20.07 69.21 0.1

D 20.58 51.54 0.1

E 20.09 65.12 0.1

F 20.72 48.17 0.1

Environment	Average air temperature (°C)	Average relative humidity (%)	Average air speed (m/s)
A	20.31	64.53	0.1
B	20.43	64.53	0.1
C	20.07	69.21	0.1
D	20.58	51.54	0.1
E	20.09	65.12	0.1
F	20.72	48.17	0.1

Table 5

General environmental characteristics on day 2

Environment	Average air temperature (°C)	Average relative humidity (%)	Average air speed (m/s)
A	24.40	59.50	0.1
B	24.04	64.53	0.1
C	24.95	67.54	0.1
D	24.93	55.46	0.1
E	24.30	62.90	0.1
F	24.10	53.92	0.1

Table 6

General environmental characteristics on day 3

Environment	Average air temperature (°C)	Average relative humidity (%)	Average air speed (m/s)
A	27.82	59.50	0.1
B	28.52	64.53	0.1
C	33.72	67.54	0.1
D	30.86	55.46	0.1
E	28.80	57.32	0.1
F	28.48	53.42	0.1

3.3 Thermal perception analysis

The occupants’ thermal perception approach was subdivided in the analysis of sensation and thermal comfort. Table 7 indicates the predominant thermal sensation in each environment according to its respective day.

Table 7
Predominant thermal sensation

Environment Day 1 Day 2 Day 3

A Slightly cold (48%) Neutral (68%) Slightly Hot (38.5%)

B Slightly cold (41%) Neutral (67%) Slightly Hot (44%)

C Slightly cold (50%) Neutral (70%) Hot (62%)

D Neutral (64%) Neutral (43%) Very Hot (58%)

E Neutral (56.25%) Neutral (47.06%) Hot (80%)

F Neutral (58.82%) Neutral (52.63%) Very Hot (53.33%)

Environment	Day 1	Day 2	Day 3
A	Slightly cold (48%)	Neutral (68%)	Slightly Hot (38.5%)
B	Slightly cold (41%)	Neutral (67%)	Slightly Hot (44%)
C	Slightly cold (50%)	Neutral (70%)	Hot (62%)
D	Neutral (64%)	Neutral (43%)	Very Hot (58%)
E	Neutral (56.25%)	Neutral (47.06%)	Hot (80%)
F	Neutral (58.82%)	Neutral (52.63%)	Very Hot (53.33%)

It was observed that the three environments located in the northeast region (environments A, B and C) presented similar results. For Day 1, the feeling that the environment was slightly cold prevailed; for Day 2, the feeling of neutrality predominated; and for Day 3 the feeling of being slightly hot in environments A and B predominated, while in environment C the feeling that the environment was hot predominated.

On the other hand, environments D, E and F also showed similar results. For Days 1 and 2, the sensation of thermal neutrality prevailed; for Day 3, students from environments D and F felt the environment very hot, while students from environment E felt the environment hot.

Table 7 also shows that students’ perception of environments in the northeast region (Environments A, B and C) was different from the perception of students in other regions (Environments D, E and F). On the day when the lowest temperature was proposed (Day 1), students from the Northeast region considered the environment a little cold, while in other environments the students were in thermal neutrality. On the day when the highest temperature was proposed (Day 3), students from the North, South and Southeast regions (Environments D, E and F) felt the warmer environment than students from the Northeast region (Environments A, B and C).

Table 8 presents the data regarding the students’ perception of comfort for each proposed temperature. For the day with the lowest temperature (Day 1), most students in environments A, B, C, D and E, indicated being comfortable (Respectively 51.85%; 52.94%; 61.54%; 64.29% and 60%), while for environment F, most students indicated that the environment was uncomfortable (58.82%).

Table 8

Perception of predominant comfort

Environment	Day 1
	Comfortable	Uncomfortable
A	51.85%	48.15%
B	52.94%	47.06%
C	61.54%	38.46%
D	64.29%	35.71%
E	60.00%	40.00%
F	41.18%	58.82%
Environment	Day 2
	Comfortable	Uncomfortable
A	53.85%	46.15%
B	66.67%	33.33%
C	92.59%	7.41%
D	42.86%	57.14%
E	47.00%	53.00%
F	38.84%	63.16%
Environment	Day 3
	Comfortable	Uncomfortable
A	23.08%	76.92%
B	11.76%	88.24%
C	0%	100%
D	3.85%	96.15%
E	13.00%	87.00%
F	13.33%	88.67%

For the day when the intermediate temperature was proposed (Day 2), it was observed that in the three environments in the northeast region (A, B and C) most students were comfortable with the existing conditions (53.85%; 66.67% and 92.59% respectively). However, students from other environments indicated discomfort with the proposed conditions. For the day with the highest temperature (Day 3), the perception of discomfort prevailed for students from all environments.

3.4 Quantitative analysis of the air temperature influence

3.4.1 Influence of air temperature on the comfort perception

Table 9 shows that all models were consistent for the Z test and for the likelihood ratio test (p-value < 0.05). As a result, it became possible to extract information about these models.

Table 9
Regression model data for thermal comfort

Environment p-value PseudoR2 Chance ratio Chance of feeling discomfort increases in:

A 0.000197 0.478 0.8179 18.20%

B 0.00606 0.251 0.6804 31.96%

C 4.65e–06 0.549 0.7215 27.85%

D 0.000107 0.365 0.6047 39.53%

E 0.0164 0.208 0.6953 30.47%

F 0.0363 0.252 0.5222 47.78%

Environment	p-value	PseudoR2	Chance ratio	Chance of feeling discomfort increases in:
A	0.000197	0.478	0.8179	18.20%
B	0.00606	0.251	0.6804	31.96%
C	4.65e–06	0.549	0.7215	27.85%
D	0.000107	0.365	0.6047	39.53%
E	0.0164	0.208	0.6953	30.47%
F	0.0363	0.252	0.5222	47.78%

The focus of this analysis was on the chance ratio, that is, the student’s chance to feel discomfort by increasing the air temperature by 1° C. For the environment F sample, the chance of feeling discomfort was 47.78% (Chance ratio = 0.5222).

For the environment D sample, the chance of feeling discomfort was 39.53% (Chance ratio = 0.6047). For the environment B sample, the chance of feeling discomfort was 31.96% (Chance ratio = 0.6804). For the sample of environment E, the chance of feeling discomfort was 30.47% (0.6953).

The lowest percentages were identified in samples from environments C and A, respectively 27.85% and 18.20%

3.4.2 Influence of air temperature on thermal sensation

Table 10 shows that all models were consistent for the Z test and for the likelihood ratio test (p-value < 0.05). As a result, it became possible to extract information about these models.

Table 10
Regression model for the thermal sensation

Environment Valor-p PseudoR2 Chance ratio Chance to feel the warmest environment increases by:

A 0.0001 0.452 1.8210 82.10%

B 0.0003 0.385 1.6601 66.01%

C 0.0001 0.850 2.3361 133%

D 0.0001 0.627 2.9921 199%

E 0.0001 0.458 1.8708 87.08%

F 0.0001 0.709 3.5229 252%

Environment	Valor-p	PseudoR2	Chance ratio	Chance to feel the warmest environment increases by:
A	0.0001	0.452	1.8210	82.10%
B	0.0003	0.385	1.6601	66.01%
C	0.0001	0.850	2.3361	133%
D	0.0001	0.627	2.9921	199%
E	0.0001	0.458	1.8708	87.08%
F	0.0001	0.709	3.5229	252%

The focus of this analysis was on the chance ratio, that is, on the student’s chance to feel the warmest environment by increasing the air temperature by 1° C. The highest values observed were for the samples of environments F (Chance ratio = 3.5229), D (Chance ratio = 2.9921) and C (Chance ratio = 2.3361), in which the chance of feeling the warmest environment increased by 252%, 199% and 133%, respectively.

For the environment A sample, this percentage was 82.10%; while for the environment B sample this value was 66.01% and for the environment E sample it was 87.08%.

3.4.3 Influence of air temperature on desired thermal comfort

Table 11 shows that all models were consistent for the Z test and for the likelihood ratio test (p-value < 0.05). As a result, it became possible to extract information about these models.

Table 11
Regression model data for the desire for thermal comfort

Environment valor-p PseudoR2 Chance ratio Chance of wishing for a colder environment increases by:

A 0.0001 0.491 1.4952 49.52%

B 0.0029 0.495 1.4314 43.14%

C 0.0001 0.763 1.5189 51.87%

D 0.0001 0.300 1.6413 64.13%

E 0.0002 0.421 1.5537 55.37%

F 0.0001 0.606 1.3778 37.78%

Environment	valor-p	PseudoR2	Chance ratio	Chance of wishing for a colder environment increases by:
A	0.0001	0.491	1.4952	49.52%
B	0.0029	0.495	1.4314	43.14%
C	0.0001	0.763	1.5189	51.87%
D	0.0001	0.300	1.6413	64.13%
E	0.0002	0.421	1.5537	55.37%
F	0.0001	0.606	1.3778	37.78%

The focus of this analysis was on the chance ratio, that is, on the student’s chance to desire the coldest environment by increasing the air temperature by 1°C. For the sample of environment F, the chance of individuals wanting the coldest environment increases by 37.78% (odds ratio = 1.3778).

For students in environment B, the chance of wanting a cooler environment increases by 43.14% (Chance ratio = 1.4314); whereas, for students in environment A, this percentage was 49.52% (Chance ratio = 1.4952).

For the environment C sample, the chance of wanting the coldest environment increases by 51.89% (Chance ratio = 1.5189); while the sample from environment E the chance was 55.37% (Chance ratio = 1.5537). The highest chance was for the sample of environment D, 64.13% (Chance ratio = 1.6413).

3.5 Comfort zones

Table 12 shows the comfort zones found for the six environments. For environments A, B and C, the comfort zone variation was small and around 22°C. A similar result was observed for environment E, in which the comfort zone was around 24° C. On the other hand, greater comfort zone variations were observed in environments D and F.

Table 12
Comfort ranges obtained

Environment Region Comfort zone (°C)

A Northeast (22.07 – 22.28)

B Northeast (22.73 – 23.04)

C Northeast (22.50 – 22.93)

D North (24.66 – 25.82)

E Southeast (24.13 – 24.31)

F South (24.25 – 25.50)

Environment	Region	Comfort zone (°C)
A	Northeast	(22.07 – 22.28)
B	Northeast	(22.73 – 23.04)
C	Northeast	(22.50 – 22.93)
D	North	(24.66 – 25.82)
E	Southeast	(24.13 – 24.31)
F	South	(24.25 – 25.50)

It was also identified that the comfort temperatures obtained for the environments located in the northeast region (Environments A, B and C) were lower than those obtained for the environments in the northern (Environment D), southern (Environment F) and southeast (Environment E).

4 Discussion

The results indicated that the students showed perceptual differences even when subjected to the same thermal conditions. This is explained by the interpersonal differences resulting from the individual characteristics of each student [20 , 39]. This reiterates the subjectivity of thermal perception and the need to use quantitative aspects to analyze these variables.

Although this difference was verified, among students from environments A, B and C, there were no significant differences in thermal perception. These environments belong to the same region and the adaptation to thermal conditions in that region may have influenced individual and collective thermal perceptions [16 , 40].

Regarding the quantitative analyzes, it was observed that students from environments D and F felt more the effects of the increase in air temperature. On the other hand, students from A, B, C and E environments felt less the effects of increased air temperature. In addition, students acclimated to slightly warmer temperatures (Environments A, B, C and E) showed less perceptual variability.

These results can be explained by two factors. First, due to the thermal adaptation of students with their respective regions [16 , 41]. Environments A, B and C are located in the northeast region, which has higher temperatures throughout the year. The city of the environment E eventually presents climatic conditions similar to the cities of the northeast region environments. Thus, when they were subjected to higher thermal conditions, they felt less the effects of air temperature.

Second, by the degree of thermal adaptability. This adaptability refers to a characteristic of students who are used to different thermal environments [29]. Thus, when they are subjected to different thermal conditions, they feel less the effects of these variations [15, 16]. Therefore, students from environments A, B, C and E are considered to have a higher degree of thermal adaptability than students from environments D and F.

In general, the quantitative results found were in line with studies that identified the influence of air temperature on students’ behavior, health and performance [4 , 31]. This influence happens as follows: unsatisfactory thermal conditions result in thermal discomfort, which influences students’ behavior, decreasing the level of concentration in the activity and compromising performance [2 , 32].

Regarding to comfort zones, environments A, B and C showed lower temperatures and fit within the ranges found by Tham [33] and Lan [34] and Niemela [35]. Environments D, E and F presented comfort zones with higher temperatures and were aligned with the intervals found by Lau [36] and Jaber [37]. In addition, in environments with less perceptual variability (Environments A, B and C) the comfort zones were smaller, facilitating the definition of the ideal temperature in the environment.

As previously mentioned, there are different temperatures in literature related to thermal comfort and increased student performance [33–37 , 43]. This information is important and useful in the optimization of thermal conditions within teaching environments. However, most of these temperatures were defined without taking into account the students’ perception.

As thermal perception is influenced by individual factors, psychological factors, thermal adaptation, degree of adaptability and time of adaptation to the existing conditions, it is important to evaluate the students’ perception [20 , 38]. The results of this study showed how the quantitative analyzes of perception were important for understanding the real needs of students.

5 Conclusions

The thermal adaptation with higher temperatures and degree of thermal adaptability, which reflects a characteristic of students in different thermal environments, minimized the effects of air temperature variability.

These results were reflected in the comfort zone, as the environments with less perceptual variability showed smaller comfort zones. The smaller this comfort zone, the easier it is to estimate the ideal temperature for the environment.

Based on these results, it can be concluded that students acclimated to higher thermal conditions (Environments A, B and C) felt less the effects of the increase in air temperature, showed less perceptual variability and a higher degree of thermal adaptability.

It is also concluded that it is not possible to generalize the comfort conditions for air-conditioned higher education environments in Brazil. As the regions present different climatic conditions, this influences the thermal adaptation of the students and the degree of adaptability. Thus, in addition to taking into account the normative aspects, it is necessary to assess the perception to understand the real needs of users.

As a suggestion for future articles, it is recommended that quantitative assessments of students’ perceptions be carried out in naturally ventilated teaching environments. In addition, it is suggested a comparative analysis of the perception of students who are submitted to naturally ventilated environments and those who are subjected to mechanically acclimatized environments.

Ethical approval

This research was approved by the Ethics Committee of the Federal University of Paraíba (Case number 31037614.0.0000.5188).

Informed consent

Not applicable.

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Acknowledgments

The authors would like to thank the students of all universities included in the study for their help in conducting this study.

Funding

This study was funded by the Coordination for the Improvement of Higher Education Personnel (CAPES) Brazil – Financing Code 001.

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Aspects	Question	Response category
Thermal sensation	What is your thermal sensation at this moment?	Very Hot (3)
		Hot (2)
		Slightly Hot (1)
		Neutral (0)
		Slightly Cold (–1)
		Cold (–2)
		Very Cold (–3)
Desire for thermal comfort	How would you like the environment to be?	Very Hot (3)
		Hot (2)
		Slightly Hot (1)
		Neutral (0)
		Slightly Cold (–1)
		Cold (–2)
		Very cold (–3)
Thermal comfort	What is your state of comfort at this moment?	Comfortable (0)
		Slightly uncomfortable (1)
		Uncomfortable (2)
		Very Uncomfortable (3)

Quantitative analysis of the influence of air temperature variability on thermal perception of Brazilian university students

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1 Introduction

2 Methodological aspects

2.1 Environments and sample definitions

2.2.1 Personal variables

2.2.3 Subjective parameters

3 Results

3.1 Sample characteristics

Table 4 General environmental characteristics on day 1 Environment Average air temperature (°C) Average relative humidity (%) Average air speed (m/s) A 20.31 64.53 0.1 B 20.43 64.53 0.1 C 20.07 69.21 0.1 D 20.58 51.54 0.1 E 20.09 65.12 0.1 F 20.72 48.17 0.1

3.4.1 Influence of air temperature on the comfort perception

Table 12 Comfort ranges obtained Environment Region Comfort zone (°C) A Northeast (22.07 – 22.28) B Northeast (22.73 – 23.04) C Northeast (22.50 – 22.93) D North (24.66 – 25.82) E Southeast (24.13 – 24.31) F South (24.25 – 25.50)

5 Conclusions

Ethical approval

Informed consent

Conflict of interest

Footnotes

Acknowledgments

Funding

References

Table 4
General environmental characteristics on day 1

Environment Average air temperature (°C) Average relative humidity (%) Average air speed (m/s)

A 20.31 64.53 0.1

B 20.43 64.53 0.1

C 20.07 69.21 0.1

D 20.58 51.54 0.1

E 20.09 65.12 0.1

F 20.72 48.17 0.1

Table 12
Comfort ranges obtained

Environment Region Comfort zone (°C)

A Northeast (22.07 – 22.28)

B Northeast (22.73 – 23.04)

C Northeast (22.50 – 22.93)

D North (24.66 – 25.82)

E Southeast (24.13 – 24.31)

F South (24.25 – 25.50)