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Building characteristics, indoor environmental quality, and mathematics achievement in Finnish elementary schools

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Building characteristics, indoor environmental quality, and mathematics achievement
in Finnish elementary schools
Oluyemi Toyinbo, Richard Shaughnessy, Mari Turunen, Tuula Putus, Jari
Metsämuuronen, Jarek Kurnitski, Ulla Haverinen-Shaughnessy
PII: S0360-1323(16)30151-2
DOI: 10.1016/j.buildenv.2016.04.030
Reference: BAE 4473
To appear in: Building and Environment
Received Date: 15 November 2015
Revised Date: 28 April 2016
Accepted Date: 29 April 2016
Please cite this article as: Toyinbo O, Shaughnessy R, Turunen M, Putus T, Metsämuuronen J,
Kurnitski J, Haverinen-Shaughnessy U, Building characteristics, indoor environmental quality, and
mathematics achievement in Finnish elementary schools, Building and Environment (2016), doi:
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Building characteristics, indoor environmental quality, and mathematics achievement in
Finnish elementary schools
Oluyemi Toyinbo
, Richard Shaughnessy
, Mari Turunen
, Tuula Putus
, Jari
, Jarek Kurnitski
, Ulla Haverinen-Shaughnessy
1 National Institute for Health and Welfare, P.O. Box 95, FI-70701 Kuopio Finland
2 Indoor Air Program, the University of Tulsa, 800 South Tucker drive, Oklahoma, USA
3 University of Turku, P.O. Box 7245, FI-01051 Turku, Finland
4 Finnish Education Evaluation Centre (FINEEC), P.O. Box 28, FI-00101 Helsinki, Finland
5 Aalto University, P.O. Box 11000, FI-00076 Aalto, Finland
Corresponding author: Oluyemi Toyinbo
( National Institute for Health and
Welfare (THL), Department of Health Protection, Living Environment and Health Unit, P.O.
Box 95, FIN-70701 Kuopio, Finland. Tel.:+358406764200.
Objective: To study indoor environmental quality (IEQ) in elementary school buildings and
its association with students’ learning outcomes.
Methods: Measurements of ventilation rates and temperatures were recorded during school
days in 108 classrooms in 60 schools in the spring and summer of 2007; background
information on 3514 school buildings was retrieved from the Finnish population register.
Data on school environment and students’ health were collected by questionnaires from 4248
students as well as from 1154 school principals. Results from a national student achievement
assessment program were used to assess learning.
Results: Upgrades to heating,
ventilation, and
air conditioning (HVAC) systems correlated
significantly with airflow measurement, ventilation rate per student and per area, and mean
temperature (r
= 0.642, r
= 0.654, r
= 0.647 and r
= -0.325 & r = 0.481, r = 0.483, r =
0.574, r = -0.271 respectively). The ventilation rate per student correlated with the number of
students in classrooms (r
= -0.360 & r = -0.387) and mean temperature (r
= -0.333 & r =
0.393). Only schools with a mechanical supply and exhaust type of ventilation met the
recommended ventilation rate per student of 6 l/s per person. An association was found
between lower mathematics test results and schools that did not meet the recommended
ventilation rate.
Conclusion: Ventilation is associated with thermal comfort and students’ learning outcomes.
The ventilation system requires scheduled maintenance or replacement as well as ongoing
ventilation adjustment to accommodate the number of students at any one time.
1. Introduction
Since children spend quality time in school trying to learn, it is important to study the
effects of their classroom environment on their health and performance. Due to inadequate
funding, the operations and maintenance of school facilities are often neglected and this leads
to a persistence of environmental problems in schools [1], even while research has shown
children to be more affected by indoor environmental quality (IEQ) compared to adults [2].
According to Zhao et al. [3], various pollutants such as bacteria, mould, volatile
organic compounds (VOCs), allergens, and particulate matter (PM) can decrease indoor air
quality (IAQ) in classrooms. VOCs can be emitted from materials used in the building
construction or operation [4], mould and bacteria may grow due to dampness and moisture
damage [5], while allergens and particulate matter (PM) may be brought into school by
ventilation or students and staff. Interaction between these compounds can also alter IAQ [6].
Proper and adequate ventilation improves IAQ by diluting the concentration of
pollutants present indoor, introducing fresh air from outdoors and removing polluted indoor
air. Mechanical ventilation can be so-called mechanical exhaust ventilation, in which an
amount of indoor air is continually extracted by the ventilation system, or mechanical supply
and exhaust ventilation, in which the ventilation system continually introduces and removes
an amount of air from the indoor environment [7]. Mechanical ventilation can also improve
IAQ by bringing in filtered air, while direct ventilation by opening windows and doors can
concurrently increase the amount of outdoor pollutants that enter indoors, especially in highly
polluted urban areas [8, 9].
Several studies have reported associations between inadequate classroom ventilation
and students’ health and learning outcomes. Sundell et al. [10] found that reduced ventilation
leads to asthma symptoms and lower respiratory functions, and Mendell et al. [11] related
insufficient ventilation with students’ absenteeism. Based on the literature, adverse health
outcomes can be reduced by adequate classroom ventilation [12]. Recent studies have also
reported associations between inadequate ventilation and learning outcomes [13-16].
addition to improved IAQ, proper classroom ventilation could also improve thermal comfort
[17, 18].
Classroom temperature is also important for students’ performance [14, 19, 20].
Although it is difficult to define thermal comfort, it is generally accepted that 80% of
occupants should be thermally comfortable in their environment [18], and it has been
suggested that indoor temperature should always be below 24
C in order to achieve thermal
comfort for 85% of occupants [19].
The issues of low ventilation and thermal discomfort may be related to building
characteristics and energy conservation. A poorly maintained school building may have its
ventilation, and
air conditioning (HVAC) systems working below capacity [21],
thereby leading to lower ventilation per student and/or per area. A low ventilation rate
translates to low air flow, which may correspond with thermal discomfort in a hot season,
especially if the building does not have cooling or air conditioning systems in place.
Ventilation is sometimes purposefully reduced in order to decrease the need for heating or
cooling. On the other hand, discomfort may occur during the cold season with a faulty or
unadjusted heating system.
The European HealthVent project has proposed source control of ambient air by
cleaning outdoor air that enters indoor ventilation (e.g. with periodical changes of HVAC
filters) [22]. In addition, passive measures can also improve students’ thermal comfort:
elimination of radiant heat sources (such as printers in the classroom); installation of low-
energy windows that reduce solar heat gain in warm weather and keep radiant heat inside in
cold weather conditions without blocking outdoor views; the use of green roofing for
insulation; as well as classroom furniture that discourage heat build-up and improve air
movement [23, 24]. These passive methods should be preferred for controlling IAQ and
thermal comfort, since they can help to conserve energy. Mechanical systems could be used
as a last resort, when other methods fail and the expected benefits (for example improved
student achievement) outweigh the potential costs. Knowledge is needed about the optimal
level of HVAC operation while taking into account energy consumption, IEQ, and students’
health and performance.
This study is a part of a broad research project that started in 2007 and has
investigated Finnish elementary schools, their IEQ, and students’ health and learning
outcomes (Figure 1). Within the schools, the studies are focused on sixth grade students
(mean age 12.5 years) and their classrooms.
Figure 1. Conceptual diagram of the research project conducted in Finnish elementary
schools 2007–2016.
The most recent study was focused on associations between IEQ, health, and building
characteristics using data from student and principal questionnaires [25]. Other studies have
assessed the school level prevalence of students’ health symptoms [26], associations between
students’ health and learning outcomes [27], ventilation adequacy [21] and temperature
control [28]. This paper focuses on associations between measured IEQ parameters and both
building characteristics and learning outcomes, and forms a synopsis of the entire research
2. Materials and methods
2.1. Data collection
The database for the research project was described in Haverinen-Shaughnessy et al. [27].
Prior to data collection, the Institutional review board of the National Public Health Institute,
Finland gave ethical approval for the study [25, 26]. Participation was voluntary.
Building characteristics such as building age, size, and type of ventilation and heating
systems were retrieved from the Finnish population register center (FPRC). The FPRC had
information about 3514 buildings out of a total of 3749 buildings, representing 2802 Finnish
elementary schools (some schools occupy more than one building).
Questionnaires were sent to all Finnish elementary school principals in December 2007.
Some principals are responsible for more than one school. A total of 2769 principals were
identified, of which 1154 completed the questionnaire (response rate 42%). Some of the
questions asked were about the school building condition and renovation, ventilation, thermal
comfort, heating, and presence of dampness and mould.
A stratified random sample of 334 Finnish elementary schools participated in a national,
grade learning assessment of mathematics in March 2007 [29]. These schools, comprising
6787 students in the 6
grade, were also invited to participate in a questionnaire study about
their school and home environment, health, and social economic status in May 2007. A total
of 4248 students from 297 schools responded to the questionnaire (estimated response rate
63%). The students were encouraged to answer the questionnaire with their parents. Some of
the questions asked students about their perception of their school’s IEQ, such as the
frequency (daily/weekly/occasionally/never) of perceived poor indoor air quality or high
indoor temperature [25, 26]. Some other questions such as “Do you think your child is
especially gifted or more advanced than other children of the same age mathematically?”
were directed to the parents.
From the schools participating in the national learning assessment, 108 classrooms
(serving 6
grade pupils) from 60 schools predominantly from Southern Finland were
selected for further investigations and measurements. For the measurements, classrooms that
had more than 15 students were selected (considered to have adequate sample size for
statistical analyses).
Indoor temperatures were measured for several weeks in the spring and summer of
2007 (between March and June and August respectively). The measurements were conducted
with Gemini Tinytag Plus data loggers (reading range -25°C to +85°C, resolution 0.01°C and
accuracy 0.5 °C or better within the range of 0 °C to 40 °C). Daily average classroom
temperatures were calculated using data from the school time period (from 8 am to 2 pm on
weekdays). At the end of heating season, average outdoor temperature was 13.0°C (range 5.0
- 21.0°C), and it was 16.6°C (range 5.6-25.6°C) in the beginning of the school semester (right
after the summer holidays in August). More detailed information about temperature
measurements are reported by Kurnitski et al. [28].
From the same classrooms, ventilation rates were measured based on exhaust air flow
or carbon dioxide (CO
) measurements. The measurements were conducted during school
days (normal occupancy conditions). In the classrooms with mechanical ventilation, exhaust
air flow measurements were conducted on one occasion using a calibrated anemometer. (The
prevailing ventilation system was a constant air volume system with constant supply air). In
classrooms with natural (passive stack) ventilation, CO
measurements were made for five to
ten days, based on which, the air change rate was estimated from tracer decay curves after the
final school hour when classrooms were unoccupied [30].
More detailed information about
ventilation rate measurements are reported by Palonen et al. [21].
2.2 Data analysis
IBM SPSS statistics version 21 was used to analyze the data. The data were
preliminarily assessed using descriptive statistics related to building characteristics (e.g. data
from FPRC), as well as data from the measurements (using school-level average or median
values). Spearman’s rho and Pearson correlations (together with their 95% confidence
intervals) between building characteristics and the measured variables were calculated,
together with effect sizes (Cohen’s d). A test of correlation measures how related two sets of
data are. The Pearson correlation shows the strength of any linear association between two
normally distributed, independent variables, whereas Spearman’s rho is used for variables
that are not normally distributed [31]. Both correlation types have a range from +1 to -1, with
a value greater than 0 indicating a positive relationship, those less than 0 indicating a negative
relationship and a value of 0 showing no association between two sets of variables. A 95 %
confidence interval gives a range of values that encompasses the actual population parameter
in about 95 % of instances [31, 32]. An effect size (Cohen’s d) shows the magnitude of the
dependability (or correlation) between the variables. An effect size < 0.20 shows no effect
(no correlation), 0.20 to 0.35 shows a small effect, 0.35 to 0.65 shows a medium effect, 0.65
to 0.80 shows a fairly large effect, and 0.80 or higher shows a large effect between groups
[33, 34]. The above methods have been used in several environmental health studies [13, 14,
In addition, the associations between students’ test results in mathematics and
measured IEQ parameters in classrooms were studied using linear mixed modelling (LMM).
The estimation was based on the Restricted Maximum Likelihood (REML) method and the
Expected Maximum (EM) algorithm. The school and classroom codes were used as subject
variables, and the covariance type was identity (covariance structure for a random effect with
only one level). Only main effects are studied, while the factorial design with all interaction
effects was not used.
First we studied a null model, which included only the outcome variable without any
predictors, so as to examine the variance between student and school levels as well as to
calculate the intra class correlation (ICC) (i.e. proportion of the total variance associated with
differences among schools). Secondly, we selected socioeconomic status (SES) and
background variables as the student-level covariates from a larger pool of variables selected
for a LMM model fitted for mathematics achievement, as shown by Haverinen-Shaughnessy
et al. [27]. Students’ attitude towards mathematics was an additional variable obtained from
the Finnish Education Evaluation Center (FINEEC), together with the results from
mathematics tests. This variable was based on 15 statements on a 5-point Likert-scale
centralized around 0 (-2 - +2) [29]. Next, the number of variables was reduced based on the
Akaike information criterion (AIC), which is a measure of the relative quality of statistical
models for a given set of data, considering that the number of schools and students in the sub-
sample of measured schools was smaller as compared to the number of schools and students
participating in a national mathematics testing and questionnaire study. Finally, IEQ
indicators (ventilation rate and thermal conditions) were fitted to the model.
3. Results and discussion
Table 1 shows a summary of data retrieved from the FPRC (all schools) and those
measured (60 schools). Information from the FPRC include year of construction, number of
floors, and floor area. The rest of the data were from on-site investigation of the school
buildings. Based on the FPRC data, the average year of construction was 1957 and the floor
area was 2098 m
, whereas the corresponding numbers for measured schools are 1967 and
3116 m
, i.e. the measured schools are about 10 years newer and 30% larger, possibly related
to their predominant location near larger municipalities with growing populations.
Table 1. Descriptive statistics of all Finnish elementary schools and a sub-sample of 60
schools with measurement data from 2007.
All schools
N = 2802 Measured schools
Attribute Mean
SD Min. Max
Year constructed 1957 1958 1967 1971 23.8
1875 2001
Floor area (m
) 2098 1152 3116 3414 2060
100 8730
Number of floors 1.7 2.0 1.9 2.0 0.9 1.0 4.0
Volume (m
) - - 12743
13460 8439
600 36677
HVAC upgraded (year) - - 1986 1998 22 1914 2006
Classrooms - - N=108
Number of students - - 24.0 24.0 5.3 8.0 47.0
Floor area (m
) - - 61.0 60.0 9.2 40.0 99.0
Room height (cm) - - 319.5
320.0 23.2
265.0 385.0
Airflow design (l/s) - - 166.4
173.5 50.8
56.0 400.0
Airflow measurement (l/s) - - 127.9
125.0 70.7
30.0 400.0
) - - 2.2 2.0 1.1 0.5 5.0
Ventilation per student
(l/s/student) - - 5.7 4.7 3.8 1.0 20.0
Mean temperature (
C) - - 22.4 22.3 1.0 20.4 24.5
Max. temperature (
C) - - 23.7 23.5 1.2 21.4 28.3
Min. temperature (
C) - - 21.2 21.2 1.1 18.7 23.5
The average year of HVAC upgrade in the measured schools was 1986 (range from
1914 to 2006). In the event that the ventilation system had not been upgraded, the year
corresponded with the original system installation. The mean number of students was 24
(range 8–47) and mean temperature (during school hours) was 22.4 °C (20.4–24.5 °C). Mean
airflow was 127.9 (range 30.0–400.0) l/s, and ventilation rate per area was 2.2 (0.5–5.0) l/s
per m
, whereas ventilation rate per student was 5.7 (1-20) l/s per person.
Finnish children start school (grade 1) at age 7 years [35]. Most of the 6
students had been in the same school since the 1
grade, and the number of boys and girls
was nearly equal (47% boys and 53% girls) with an average age of 12.5 years. The classroom
height and net area complied with the National Building Code of Finland (NBCF) regulations
for habitable rooms, which stipulates 250 cm and 7m
as the minimum height and floor area
for habitable rooms respectively [36].
The difference of 38.5 l/s between mean airflow as designed and measured may mean
that ventilation systems are not functioning properly [37]. Indeed, it was estimated that
between 25 and 30 % of the schools studied have a ventilation system working below optimal
performance and were in need of renovation or outright replacement [21]. A reduced airflow
will possibly translate to a lower ventilation rate. This may be why the average ventilation
rates per area and per student and ventilation per m
are lower than the standards of 3 l/s per
or 6 l/s per person, respectively [36].
Based on the temperature measurements, the observed range was within 4
C. None of
the classrooms had temperatures below 18
C, which is considered the lowest acceptable
indoor temperature in Finland [38, 39]. The maximum temperature was 28
C, which could
have been due to outdoor conditions. In Finland, this can happen for a very limited and short
period compared with some other countries in which this temperature or higher can last for
somewhat longer periods, both day and night. Based on mean temperatures, about 25 % of
classrooms had temperatures higher than 23
C and about 10 % had temperatures higher than
C. While these results concur with the results from the principal questionnaire, with 18 %
reporting temperatures being too high outside the heating season, the results are anyhow
rather inconclusive, due to the timing and duration of the measurements.
Spearman’s rho and Pearson correlations as well as the effect size (Cohen’s d)
between background (e.g. those from FPRC) and measured variables are presented in Tables
2 and 3, respectively. Classroom volume was associated with mean temperature, while
airflow measurement, ventilation rates, and mean temperature have significant correlations
with the year of HVAC upgrade. This indicates the need for regular adjustment, maintenance,
upgrade and/or replacement of faulty HVAC systems.
Table 2. Spearman’s rho correlation between building characteristics and the measured variables.
No. of students Air flow measurement Ventilation/m
Ventilation rate/student Mean temperature
95%CI Sig. d r
95%CI Sig. d r
95%CI Sig. d r
95%CI Sig. d r
95%CI Sig. d
-.15 -.42–.15 .33 .29 .64** .43–.79 .00 1.68 .65** .44–.79 .00 1.70 .65** .45 –.80 .00 1.73 -.33* -.57– -.03 .03 .69
.09 -.22–.38 .57 .18 -.26 -.53– .05 .10 .55 -.23 -.51–.09 .15 .48 -.30 -.56– -.02 .06 .62 .11 -.22–.41 .52 .22
.22 -.09–.49 .16 .45 -.00 -.31–.31 .99 .00 -.06 -.25–.37 .71 .12 -.08 -.38–.24 .62 .16 -.23 -.51–.10 .17 .47
.23 -.10– .51 .17 .47 .10 -.23–.41 .56 .20 .18 -.16–.48 .29 .36 .00 -.32–.33 .99 .01 -.36* -.62–-.03 .04 .77
-.04 -.35–.28 .80 .08 .09 -.24–.40 .60 .18 .169 -.16–.47 .32 .34 .11 -.22–.42 .52 .22 -.04 -.37–.30 .81 .08
* Correlation is significant at the 0.05 level (2-tailed).
** Correlation is significant at the 0.01 level (2-tailed).
(1 = HVAC upgrade, 2 = Number of floors, 3 = Floor area, 4 = Volume and 5 = Year of construction)
Table 3. Pearson correlation between building characteristics and the measured variables.
No. of students Air flow measurement Ventilation/m
Ventilation rate/student Mean temperature
r 95%CI Sig. d r 95%CI Sig. d r 95%CI Sig. d r 95%CI Sig. d r 95%CI Sig. d
-.10 -.38–.19 .49 .21 .48** .22–.68 .00 1.10 .57** .34–.74 .00 1.40 .48** .22–.68 .00 1.10 -.27 -.53–.03 .08 .56
.11 -.20–.40 .48 .23 -.26 -.53– -.06 .11 .53 -.18 -.47–.14 .26 .37 -.23 -.50–.09 .16 .47 .09 -.24–.34 .58 .19
.09 -.22–.39 .56 .19 -.04 -.34–.28 .83 .07 .04 -.27–.35 .80 .08 -.08 -.38–.24 .64 .16 -.28 -.55–.05 .09 .58
.11 -.22–.41 .51 .22 .04 -.29– .36 .82 .08 .12 -.22– .42 .50 .23 -.01 -.34– .31 .95 .02 -.38* -.64– -.05 .03 .82
-.09 -.39–.23 .59 .18 .03 -.30– .35 .22 .05 .11 -.23– .42 .54 .21 .01 -.31– .34 .93 .03 -.20 -.50– .15 .26 .40
* Correlation is significant at the 0.05 level (2-tailed).
** Correlation is significant at the 0.01 level (2-tailed).
(1 = HVAC upgrade, 2 = Number of floors, 3 = Floor area, 4 = Volume and 5 = Year of construction)
In addition, there is a negative significant correlation between the number of students and
ventilation rate per student (r
= -0.360, p < 0.008, d = 0.771 & r = -0.387, p < 0.004, d = 0.839), as
well as between ventilation rate per student and mean temperature (r
= -0.333, p = 0.018, d = 0.706
& r = -0.393, p = 0.005, d = 0.855).
The data were further analyzed based on ventilation system type: 1) mechanical ventilation,
including both mechanical supply and exhaust ventilation and mechanical exhaust only and 2)
natural (passive stack) ventilation. The results for mechanical ventilation still show a significant
negative correlation between number of students and ventilation rate per student (r
= -0.441, p =
0.002, d = 0.983 & r = -0.432, p = 0.002, d = 0.958) and also between ventilation rate per student
and mean temperature (r
= -0.316, p = 0.014, d = 0.666 & r = -0.413, p = 0.004, d = 0.907). For
naturally ventilated classrooms, there was no statistically significant correlation between ventilation
rate per student and number of students (r
= -0.232, p = 0.658, d = 0.477 & r = -0.619, p = 0.190, d
= 1.57) or the mean classroom temperature (r
= -0.400, p = 0.600, d = 0.873 & r = -0.319, p =
0.681, d = 0.673). This is probably because only a small number of schools (seven schools with 18
classrooms) relied on natural ventilation. However, Cohen’s d values denote medium to large
effects between the variables. These results indicate that regular ventilation adjustment is required
to meet classroom population at any given time, while discouraging overcrowding.
From another perspective, there is a significant correlation between student’s daily
perception of poor air quality in the classroom and both mean temperature (r
= 0.409, p = 0.004, d
= 0.896 & r = 0.343, p = 0.017, d = 730) and ventilation rate per student (r
= -0.300, p = 0.029, d =
0.629 & r = -0.306, p = 0.026, d = 0.643). It appears that low ventilation is related to both perceived
poor air quality and high indoor temperature, which has also been reported in other studies [17, 26,
As shown in Table 4, there are differences in the estimated ventilation rates by the type of
ventilation system. The ventilation rates per student were below the recommended level of 6 l/s per
person in all schools with mechanical exhaust or passive stack ventilation, and were exceeding the
recommended level only in 52% of schools (44 out of 84 classrooms) with a mechanical supply and
exhaust ventilation system.
Table 4. Ventilation rates by type of ventilation system in classrooms.
Type of
Mechanical supply
and exhaust (n=84) Mechanical exhaust
(n=6) Natural ventilation
Ventilation rate (l/s) Ventilation rate (l/s) Ventilation rate (l/s)
/student /m
/student /m
2.4 6.5 1.2 3.0 1.1 3.0
2.4 6.1 1.3 3.2 1.1 3.0
1.1 3.9 .6 1.8 .3 .9
.5 1.2 .5 1.0 .7 1.8
5.0 20.0 1.7 4.6 1.5 4.7
Based on the LMM models, school explains about 15 % of the total variance (ICC = .15) in the
mathematics test result and school * classroom explains 17%. By including student background
variables as fixed effects, the variance component within subjects diminishes by 67% and the
variance component between subjects diminishes by 32%, while school * classroom explains 23%
of the remaining variance. By including the continuous classroom ventilation rate and temperature
variables as fixed effects, estimates (95%CI) are 0.1 (-0.4 - 0.7) for the ventilation rate and -0.3 (-
2.2 - 1.6) for temperature, i.e. small and statistically non-significant. However, preliminary analyses
suggested significantly lower mathematics test results in schools where the ventilation rate was
lower than the recommended value of 6 l/s per person. This association is statistically significant
(Table 5), and by including the dichotomized ventilation rate variable, ICC is reduced to .21, which
corresponds to a 9% reduction in the variance component between subjects (i.e. school *
classroom). We also ran the model for schools with mechanical supply and exhaust ventilation,
which were the only schools exceeding the recommended level. The results are comparable,
indicating that the association is not confounded by the type of ventilation system.
Table 5. Linear mixed model for % of correct answers in mathematics test, including students’
attitude, statistically significant SES and background variables, as well as 2-category classroom
ventilation rate (based on measurements) and thermal comfort (self-reported).
All students
sample Estimates for fixed effects
N=4248 N=1055 Estimate 95%CI
Attitude towards mathematics 0.54 0.62 8.5 6.9-9.4
First language Finnish 90.4 96.0 11.9 7.2-16.5
Swedish 7.4 0.3 8.2 -7.6-23.9
Other 2.2 3.7 0
Mother’s education primary
school 10.5 11.4 -5.4 -(8.6-2.1)
high school / equivalent 36.1 29.6 -4.9 -(7.1-2.7)
college / university 53.4 59.0 0
Father’s education primary
school 17.9 14.9 -6.8 -(9.8-3.8)
high school / equivalent 45.0 36.0 -3.1 -(5.2-1.0)
college / university 37.1 49.1 0
Takes naps during the day 10.7 10.8 -3.7 -(0.9-6.4)
Gifted in mathematics
d, e
18.4 20.5 11.8 9.4-14.1
Gifted linguistically
20.5 22.4 5.7 3.6-7.9
Gifted in sports
22.9 25.9 -2.6 -(4.6-0.6)
Needs personal tutoring
8.3 7.6 -14.3 -(10.9-17.7)
High indoor temperature daily
3.0 3.7 -4.8 -(0.4-9.2)
Ventilation rate <6 l/s per
- 61.8 -3.6 -(7.0-0.2)
Mean value referring to students’ attitude towards mathematics on a continuous scale -2- +2
Percent of students with the following attributes
This parameter is set to zero because it is redundant
Based on questionnaire data
Questions formulated as follows: “Do you think your child is especially gifted or more advanced
than other children of the same age mathematically?”
In schools with mechanical supply and exhaust, the corresponding estimate is -3.6, 95%CI -(7.2 -
0.1), p = 0.047
It should be noted that both methods used for estimating ventilation rates (including an
estimation based on air flow measurements and CO
concentrations) entail some uncertainties. Air
flow measurements probably underestimate the actual ventilation rate, which in reality is increased
by opening windows and doors as well as air leakage through the building envelope. Estimation
based on CO
decay may also lead to underestimating the actual ventilation rate, since after school
hours, windows and doors are more likely to be closed than during school days. In addition, the
measurements were done in each school during the spring term; therefore we could not evaluate the
effect of seasonal variation in detail. Seasonal variation could be greater in schools with passive
stack ventilation, which is more dependent on outdoor temperatures, as well as other conditions
such as wind direction and velocity.
Indeed, uncertainty related to the ventilation rate estimates may be the most limiting factor
for drawing more definite conclusions on the ventilation adequacy and its associations with learning
outcomes: further studies are recommended with longer follow-up periods and continuous (real-
time) monitoring during normal occupancy situations throughout the school year. A larger sample
size would also give more statistical power for performing more detailed analyses and modelling of
the associations. However, in practical terms it appears that ventilation rates in classrooms should
meet the required minimum of 6 l/s per person. It also appears that mechanical exhaust and the
passive stack type of ventilation systems may not be able to provide adequate ventilation for
classrooms in the Finnish climate throughout the year. Based on the results related to learning
outcomes, this study did not indicate a need to increase the ventilation rate above the current
standards in Finnish schools, since we did not find a continuous, linear association between
ventilation rate and mathematics test results.
The potential to reduce the ventilation rate should also be considered for energy
conservation purposes; however, it would require more information about actual indoor air
pollutants, as ventilation is merely an indicator of IAQ. Future studies could benefit from utilizing
multi-pollutant assessment to thoroughly characterize IAQ in schools, which would improve
understanding of the children's exposure to indoor air pollutants and the most effective means to
reduce these exposures. Such studies have also been useful in terms of developing strategies (such
as source control) for preventing adverse health consequences for children in schools [41, 42].
Whereas mean indoor temperature was not associated with mathematics test results, we have
previously found an association between math test results and students’ reporting too high
temperature daily [27]. While the percentage of students reporting daily discomfort is only 4%, this
association remained significant also in the LMM model including the 2-categorical ventilation rate.
Therefore, it appears that in these data, subjectively perceived temperature is related to mathematics
achievement. It was noticed that of those students who perceived too high indoor temperatures
daily, some 42% had a classroom temperature >23
C, whereas 74% had a classroom ventilation
rate of <6 l/s per person. A possible explanation is that the mean indoor temperature during our
measurement period (spring semester) was not representative of the temperature during the recall
period used in the questionnaire (i.e. 12 months). Another explanation is that the students’
perception of high temperature in our data could be related to the low ventilation rate rather than to
high temperature. Although the data for this study only allowed us to compare perceived thermal
comfort with the measured ventilation rate and temperature, further studies should compare
perceived temperature with contemporaneous thermal environmental conditions, such as relative
humidity, air velocity, and radiant heat.
In summary, a broad research project involving Finnish elementary schools was carried out.
Based on the previously published results, noise (11%) and stuffiness/poor IAQ (7%) in the
classroom are the leading cause of daily discomfort as perceived by the students [26]. It was found
that self-reported thermal discomfort (too high temperature daily), missed school days due to
respiratory infections, headache, and difficulties in concentration was associated with mathematics
test results [27].
Principals reported inadequate ventilation (38%), dampness or moisture damage (27%), and
unsatisfactory temperatures (11-18%) in schools [25]. We found that these school-level IEQ
indicators could explain a large part of the school-level variations observed in self-reported upper
respiratory symptoms among students. In addition, a higher number of missed school days due to
respiratory infections were found in schools in which inadequate ventilation had been reported by
the principal. The current study completes the research project by reporting results from combined
analysis based on data from objective measurements as well as national assessment of mathematics.
4. Conclusions
Based on the current study, ventilation rate per area and per student were below standard in
a majority of the classrooms measured (67% and 58 % respectively). The standard was not met in
any of the classrooms with passive stack ventilation or with mechanical exhaust only: in these
cases, ventilation systems may need adjusting, maintenance, or upgrading. In addition, adjustment
of the ventilation rate should always be done to accommodate the maximum number of students at
any given time. Adequate ventilation is also related to thermal comfort and appears to be associated
with mathematics test results, which emphasizes the importance of meeting standards. Based on the
results related to learning outcomes, this study did not indicate a need to increase the ventilation
rate above the current standard in Finnish schools. However, further studies with more schools and
longer follow-up periods are recommended for more in-depth assessment.
The authors wish to thank the school principals, students and parents who responded to the
This study was financially supported by the Academy of Finland (grant 109062). In addition, data
analyses were partially supported by the INSULAtE-project, which is co-financed by the EU Life+
-programme (LIFE09 ENV/FI/000573) and Finnish Energy Industries (THL/1759/6.00.00/2010).
Competing interests
Authors declare no conflict of interest.
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- Based on a large dataset involving Finnish elementary schools, 6
grade students in classrooms
fulfilling the required ventilation rate of 6 l/s per person had significantly higher mathematics
test results as compared to students in classrooms failing the national standards.
- Ventilation rates were also associated with both perceived air quality and thermal comfort
among students.
- Some 52% of the classrooms with both mechanical supply and exhaust air fulfilled the current
standards, whereas all classrooms with mechanical exhaust or natural ventilation failed.
- Other factors related to ventilation adequacy were HVAC upgrade and number of students in
classrooms. For schools failing the guidelines, upgrading the ventilation system and / or reducing
class sizes may be necessary.
- The results did not indicate a need to increase ventilation rate above the current standards.
... Multiple studies [4][5][6][7][8][9][10][11][12][13][14][15] have shown the importance of maintaining appropriate IAQ in classrooms for the comfort, health and wellbeing of both pupils and teachers. The World Green Building Council has published a factsheet about IAQ for schools, and how bad air affects children [16]. ...
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Healthy indoor environments influence the comfort, health and wellbeing of the occupants. Monitoring the indoor temperature, relative humidity and CO2 levels in primary schools during the COVID-19 pandemic was mandated by a local authority in Scotland. The aim was to investigate the comfort and safety of the teachers and their pupils. This paper presents the measurements of indoor climate in 20 classrooms in four different primary schools in Scotland. The schools were of different architypes. The classrooms were of different sizes, orientations and occupancy, and had different ventilation systems. Ventilation was achieved either by manually opening the windows, or by a mechanical ventilation system. Indoor air temperature, relative humidity and carbon dioxide (CO2) concentrations were continuously monitored for one week during the heating season 2020/21. Occupancy and opening of the windows were logged in by the teachers. The ventilation rates in the classrooms were estimated by measuring the CO2 concentrations. On the 20 classrooms of the study, data of 19 were analysed. The results show that four of the five mechanically ventilated classrooms performed better than natural ventilation, which indicates that opening the windows depended on the customs and habits. Classrooms in naturally ventilated Victorian buildings have the worst average ventilation rate (4.38 L/s per person) compared to the other classrooms (5.8 L/s per person for the more recent naturally ventilated ones, and 6.08 L/s per person for the mechanically ventilated ones). The results of this preliminary study will be used as the basis to find ways to ensure adequate ventilation in natural ventilated classrooms.
... A poor classroom acoustic environment makes speech reception harder, with students having to allocate additional cognitive resources to hear the teacher's teaching content clearly (Phillips 2016;Visentin et al. 2018). Maintaining such a state of high concentration for extended periods can easily cause fatigue among students, which, in turn, can affect acoustic comfort (Lee et al. 2012;Haverinen-Shaughnessy et al. 2015;Toyinbo et al. 2016;Ricciardi and Buratti 2018) and learning efficiency (Valente et al. 2012;Yang et al. 2013). The acoustic environment also affects teachers' subjective judgments about the classroom (Brunskog et al. 2009). ...
The acoustic environment of the classroom is one of the most important factors influencing the teaching and learning effects of the teacher and students. It is critical to ensure good speech intelligibility in classrooms. However, due to some factors, it may not be easy to achieve an ideal classroom acoustic environment, especially in large-scale multimedia classrooms. In a real renovation project of 39 multimedia classrooms in a university, seven typical rooms were selected, and the acoustic environment optimisation design and verification for these multimedia classrooms were performed based on simulation. First, the acoustic and sound reinforcement design schemes were determined based on the room acoustics software ODEON. Next, the effects of the optimisation design were analysed, and the simulated and measured results were compared; the accuracy of using the reduced sound absorption coefficients, which were determined empirically, was also examined. Finally, the recommended reverberation times (RTs) in multimedia classrooms corresponding to speech intelligibility were discussed, the effectiveness of the speech transmission index (STI) as a primary parameter for classroom acoustic environment control was considered, and the acoustic environment under the unoccupied and occupied statuses was compared. The results revealed that although there are many factors influencing the effect of classroom acoustic environment control, an adequate result can be expected on applying the appropriate method. Considering both the acoustic design and visual requirements also makes the classroom likely to have a good visual effect in addition to having a good listening environment.
... There is a continuous international interest in investigating thermal comfort in educational buildings (Auliciems, 1969;Cheng et al., 2008;Chung & Tong, 1990;de Dear et al., 2015;De Giuli et al., 2014;Humphreys, 1977;Hwang et al., 2006Hwang et al., , 2009Issa et al., 2011;Kim & de Dear, 2018;Liang et al., 2012;Liu et al., 2017;Teli et al., 2013;Wong & Jan, 2003;Yao et al., 2010;Zhang et al., 2007;Zomorodian et al., 2016) due to its well-documented effect on students' performance (Ferreira & Cardoso, 2014;Haverinen-Shaughnessy et al., 2015;Montazami & Nicol, 2013;Puteh et al., 2012;Toyinbo et al., 2016;Wong & Khoo, 2003). For instance, a recent study revealed that both high clothing level and restrictions on adaptive actions are related to thermal dissatisfaction among primary school students. ...
Thermal comfort has a regional nature as it is affected by people’s climatic and cultural background. Under the extreme climate of Oman, the absence of local standards combined with the lack of thermal comfort studies in educational buildings, and the relatively long-time students spend daily in classrooms, highlights the importance of conducting such studies. In this research, the indoor and outdoor measurements of air temperature (Ta), globe temperature (Tg), relative humidity (RH), and air velocity (AV) were combined with the findings of four questionnaires distributed among female school students to explore their responses to the classrooms' thermal environments. The findings indicated that around 66–83% of the students expressed satisfaction with the thermal conditions in their classrooms with around 61–79% feeling comfortable. The neutral temperature was computed as 24.9 ± 2.48°C applying Griffiths’ method and a slope of 0.5/K. The acceptability limits for satisfaction levels of 80% and 90% were 23.3–26.6°C and 23.9–25.9°C, respectively. The research findings provide information that may be used to manage how the classrooms are managed in relation to the use of air conditioning and window opening, as well as valuable information about how the students adjust their clothing to maintain thermal comfort.
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At present, 35% of all primary schools and 15% of all secondary schools in Pakistan do not have access to electricity, severely impacting student participation and performance. Earlier literature exploring the effects of electrification of schools through solar electricity on educational access and outcome has been very limited, but recently it has gained attention. By examining data of more than 20,000 schools across 176 districts of Pakistan from the years 2013 until 2018, this paper quantifies the effectiveness of installing solar panels at schools to generate electricity, and thus increasing student participation through higher enrolment. The results show that a school where a solar panel was installed as an education policy initiative witnessed an increase in enrolment, when compared to a school that did not have a solar panel installed under the education policy initiative. This research highlights an immediate need of electrification of schools in order to improve learning outcomes. It also quantifies the effects of using solar electricity at schools that otherwise may not have access to electricity via the conventional grid system. Finally, as Pakistan ranks second in the list of countries with the worst pollution in the world, this study provides evidence for policymakers, and urges them to focus on expanding the use of renewable energy resources in all fields of socioeconomic activity in order to reverse the detrimental effects of climate change.
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Background: The aim of this paper was to examine associations between school building characteristics, indoor environmental quality (IEQ), and health responses using questionnaire data from both school principals and students. Methods: From 334 randomly sampled schools, 4248 sixth grade students from 297 schools participated in a questionnaire. From these schools, 134 principals returned questionnaires concerning 51 IEQ related questions of their school. Generalized linear mixed models (GLMM) were used to study the associations between IEQ indicators and existence of self-reported upper respiratory symptoms, while hierarchical Zero Inflated Poisson (ZIP)-models were used to model the number of symptoms. Results: Significant associations were established between existence of upper respiratory symptoms and unsatisfactory classroom temperature during the heating season (ORs 1.45 for too hot and cold, and 1.27 for too cold as compared to satisfactory temperature) and dampness or moisture damage during the year 2006-2007 (OR: 1.80 as compared to no moisture damage), respectively. The number of upper respiratory symptoms was significantly associated with inadequate ventilation and dampness or moisture damage. A higher number of missed school days due to respiratory infections were reported in schools with inadequate ventilation (RR: 1.16). Conclusions: The school level IEQ indicator variables described in this paper could explain a relatively large part of the school level variation observed in the self-reported upper respiratory symptoms and missed school days due to respiratory infections among students.
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Using a multilevel approach, we estimated the effects of classroom ventilation rate and temperature on academic achievement. The analysis is based on measurement data from a 70 elementary school district (140 fifth grade classrooms) from Southwestern United States, and student level data (N = 3109) on socioeconomic variables and standardized test scores. There was a statistically significant association between ventilation rates and mathematics scores, and it was stronger when the six classrooms with high ventilation rates that were indicated as outliers were filtered (> 7.1 l/s per person). The association remained significant when prior year test scores were included in the model, resulting in less unexplained variability. Students’ mean mathematics scores (average 2286 points) were increased by up to eleven points (0.5%) per each liter per second per person increase in ventilation rate within the range of 0.9–7.1 l/s per person (estimated effect size 74 points). There was an additional increase of 12–13 points per each 1°C decrease in temperature within the observed range of 20–25°C (estimated effect size 67 points). Effects of similar magnitude but higher variability were observed for reading and science scores. In conclusion, maintaining adequate ventilation and thermal comfort in classrooms could significantly improve academic achievement of students.
Objective of this paper is to examine whether the available epidemiological evidence provides information on the link between outdoor air ventilation rates and health, and whether it can be used for regulatory purposes when setting ventilation requirements for non-industrial built environments. Effects on health were seen for a wide range of outdoor ventilation rates from 6 to 7 L/s per person, which were the lowest ventilation rates at which no effects on any health outcomes were observed in field studies, up to 25e40 L/s per person, which were in some studies the lowest outdoor ventilation rates at which no effects on health outcomes were seen. These data show that, in general, higher ventilation rates in many cases will reduce health outcomes, and that there are the minimum rates, at which some health outcomes can be avoided. But these data have many limitations, such as crude estimation of outdoor ventilation rates, diversity and variability of ventilation rates at which effects were seen, a diversity of outcomes (in case of health otcomes being mainly acute not chronic). Among other limitations there are incomplete data on the strength of pollution sources and exposures as well as a wide range of sensibility of the exposed populations. The available data do not provide a sound basis for determining specific outdoor air ventilation rates that can be universally applicable in different public and residential buildings to protect against health risks. They cannot be used for regulative purposes, unless the required ventilation rates are related to actual exposures and are prescribed only when full advantage of other methods for controlling exposures has been taken. Copyright © 2015 Elsevier Ltd. All rights reserved.