Exploring the effect of ventilation type on the acoustics of primary andsecondary school classrooms
Kieren H. SMITH(1) and Lily M. WANG(2)(1) University of Nebraska–Lincoln, United States of America, kierenhs@gmail.com(2) University of Nebraska–Lincoln, United States of America, lilywang@unl.edu

Abstract
To better understand the effect of indoor environmental conditions on primary and secondary student performance, measurements over six school days were logged in each of 220 classrooms at schools in the midwesternregion of the United States. Results from statistical analyses demonstrate the effects of classroom conditions
on student achievement scores. Specifically, structural equation modeling has been used to determine the effects of classroom speech levels, non-speech levels, and room reverberation conditions on math and reading
achievement, while controlling for student demographics. Information regarding heating, ventilation and airconditioning (HVAC) system types was also gathered in each classroom. The focus of this paper is to report on
the correlation between HVAC system type and the measured speech or non-speech levels in classrooms during
the school day. Additionally the correlations between measured acoustic and indoor air quality parameters overtime are presented, with specific regard to how HVAC system type might affect the correlations.
Keywords: classrooms, noise, ventilation systems

1

INTRODUCTION

Primary and secondary school students spend significant amounts of time in classrooms. Much work has been
done in exploring the effect of noise on building occupants. Both room acoustic characteristics and backgroundnoise levels contribute to the speech intelligibility of a room, a crucial factor in determining effective communication, such as conversations or instruction. In classrooms specifically, studies have found that increased noisein the classroom is associated with lower achievement in students (1,2).
It is known that ventilation systems within classrooms contribute to background noise levels. Heating, ventilation, and air conditioning (HVAC) system components have noise specs and ratings, but performance can vary
based on installation, age, and any number of factors. This study looks at a large sampling of real classroomsof known HVAC system types, observing the noise levels and their relationship with indoor air quality factors.

2METHODS

To evaluate the indoor environment in primary and secondary schools, 220 classrooms in the midwestern UnitedStates were selected for measurement. A variety of measurement were taken in each classroom, including
acoustic, indoor air quality, thermal comfort, and lighting measurements. Details of these measurements arediscussed in other work within the scope of the grant (3). For this paper, the logged measurements of soundand select indoor air quality parameters will be discussed.
2.1 Sound measurements
Within each classroom, two BSWA 309 Type II sound level meters were deployed for a period of two days,
three times within the school year, leading to six school days of logged measurements per classroom. The
meters were placed in similar locations within each classrooms, with one in a measurement kit placed near theteacher’s desk at the head of the classroom and the other hanging from the ceiling in the classroom. Measurements were taken at 10 second intervals throughout the occupied school day and equivalent continuous5939

A-weighted levels (LA eq), as well as octave band levels, were collected at each time interval.
During the school day, there were times where speech noises were present, either from teacher instruction
or student participation, and times where only background and activity noise were present. In previous workby researchers on this grant, k-means clustering–an unsupervised machine learning technique–was utilized toseparate each time interval into one of two categories: active speech or non–speech based on the level andspectral content (4).
2.2 Indoor air quality measurements
On the same days sound level was logged, a variety of indoor air quality measurements were being logged in
5 minute increments. A select few include carbon dioxide (CO2 ), fine particulate matter, and coarse particulatematter. Carbon dioxide was measured by several sensors: one in the measurement kit by the teacher’s desk, one
in or near the supply duct, and a third in or near the return duct. A CO2 average was calculated for each timeincrement by taking an average of these three meters. For the particle counts, a counter within the measurement
kit took measurements of air particles of difference sizes every 5 minutes. Air particulates smaller than 2.5 µm
were considered to be ’fine’ particulate matter. Particles between 2.5 and 10 µm are considered to be ’coarse’particulate matter.
2.3 Statistical analysis
Analysis of variance was conducted on the sound levels within each classroom, considering both the speechand non–speech levels. Personnel involved in ventilation system design and installation often have opinions andevidence about system noise levels. The question considered in this analysis is the following: In a real–world
sample, is there a significant difference in noise level between different HVAC system types? This question isconsidered for the occupied classroom, looking first at speech times and second at times where no speech ispresent in the classroom.
For each classroom, the HVAC system type was noted and three categories were selected, primarily based on
the secondary system type. The three categories are as follows:• Heat Pumps: systems that include a heat pump (n = 104)
• Centralized: systems that stem from centralized components, utilizing air handling units, variable air volume (VAV) boxes, etc (n = 41)• Unit Ventilators: systems that include a unit ventilator within the classroom (n = 59)
Classrooms with unknown or difficult to characterize systems were eliminated from analysis. Analysis of Variance (ANOVA) was used to determine if the means of the noise values were different between the system
types.
Additionally, an analysis of the effect of HVAC system type on the temporal correlation between sound level
and indoor air quality measures was considered.

3RESULTS

For each classroom, an average sound level was calculated for both the speech and non-speech times. For eachof the 220 classrooms, the time log of speech or non–speech sound levels for each measurement day werelogarithmically averaged, giving each day a single LA eq. These day values were arithmetically averaged byclassroom to give a single value per classroom, indicative of a likely sound level that would be experienced inthat classroom at any given time.
Both speech sound levels and non–speech sound levels were divided into the three HVAC categories.
Figure 1 shows the histograms of the distributions of average speech levels for each HVAC division. While it isclear that more classrooms fall into the heat pump category than either other category, this is just a byproduct5940

of the sample chosen. The takeaway from these histograms is where the peak of the distribution curve falls,
which indicates the average value for that category. For example, for each of the categories, the average speechvalue falls around 66 dBA.

Figure 1. Histograms of the distributions of speech noise levels within a sampling of classrooms (n = 220),
divided into three HVAC system types
Figure 2 shows the distributions of non–speech average levels by system type. Here, the differences in themean values may be more apparent between the categories, especially with the unit ventilators mean value
being higher than the other two categories.

Figure 2. Histograms of the distributions of non–speech noise levels within a sampling of classrooms (n = 220),
divided into three HVAC system types
The sound level averages for each category are presented in Table 1. For the average equivalent continuous
level for speech times (LA eqsp ), all values were near 66 dBA, with centralized systems experiencing the highestlevels, at a 66.5 dBA average. For the non–speech average, unit ventilator classrooms averaged the highestlevels, at 50.6 dBA, with heat pump and centralized systems averaging 48.7 and 48.4 dBA for non–speechnoise, respectively.

5941

Table 1. Average sound levels by system type for speech and non–speech noise within classrooms in dBA
System TypeHeat Pumps
Centralized
Unit Ventilators

LA eqsp
66.1
66.5
65.9

LA eqns
48.7
48.4
50.6

3.1 ANOVAs of noise levels
Analysis of variance was conducted on the average speech levels of each classroom by HVAC system type. No
difference was found between the means of the speech levels within the classrooms, indicating that the HVAC
system type was not significant when determining the sound level during speech times for this sample.
On the other hand, the ANOVA for the non–speech levels was significant for this sample (p = 0.019), indicatinga difference between the non–speech level means of the three HVAC groupings (see Table 2). Post-hoc tests
revealed that classrooms with unit ventilators had significantly higher noise levels than classrooms with heatpumps. No difference was found for the non–speech noise levels between classrooms with centralized systems
and classrooms with unit ventilators.
While the difference in means (see Table 1) is small–only around 2 dB–it should be noted that this is not a
simple decibel difference, but rather an aggregate difference of a larger sampling, so conclusions should not bedrawn regarding just noticeable differences or error propagation without a more in–depth analysis.
Table 2. ANOVA table for non-speech noise divided into HVAC categories
ModelRegression
ResidualTotal

SS
30.558120.885
151.443

df23537MS
15.2793.454F4.424

p.019

3.2 Correlations by HVAC system type
In order to see how strongly the sound level and indoor air quality variables fluctuated together throughout theday, correlations were drawn for each classroom and each day of measurement. Sound level was logarithmicallyaveraged to go from a 10 second resolution to 5 minute increments and all data underwent temporal statisticalde–trending. Discussion of this process and the resulting correlations can be found in previous work (5). Ofinterest to this topic is the likelihood of classrooms with certain system types to see more significant correlations
than others. For the correlations between sound level and CO2 and sound level and coarse particulate matter,no observable difference was found between the system types. However, for the correlations between soundlevel and fine particulate matter, unit ventilators were found to experience a higher percentage of significant
correlations. Table 3 shows the percentage of significant correlations for each system type, showing that inclassrooms with unit ventilators, 58% of observed correlations between LA eq and fine particulate matter werestatistically significant at the Bonferroni corrected level (p < 0.00089).
Table 3. Percentage of significant correlations between fine particulate matter and sound level over time inclassrooms by system type
System TypeHeat Pumps
Centralized
Unit Ventilators

5942% Significant
23%
39%
58%

4

DISCUSSION AND CONCLUSION

An analysis was undertaken to quantify the effect of HVAC system type on the noise levels within a real–world
sample of 220 primary and secondary school classroom. While no difference was observed between levelsof speech within each classroom, the noise with no speech present was different in classrooms with different
system types. Classrooms with unit ventilators were discovered to have quantifiably louder non–speech soundlevels than classrooms with heat pumps or centralized systems. No sound level difference was observed between
the former two.
Additionally, the temporal variation in fine particulate matter in a room was found be more likely to correlatewith the sound level in rooms with unit ventilators, with 58% of cases found to be significant within thiscategory. This is perhaps due to increase in sound level when the units kick on, correlating to the increase inparticle matter pulled in from outside while the units function.
Knowing about these relationships helps characterize the complex indoor environment within classrooms, ultimately aiming to inform design decisions. Future work can explore whether ventilation system type directly
links to student achievement or comfort.

ACKNOWLEDGEMENTS
Thank you to the many faculty and student researchers on this project. This research was supported by EPA
Grant R835633.

REFERENCES[1] Shield, B.; Conetta, R.; Dockrell, J.; Connolly, D.; Cox, R.; Mydlarz, C. A survey of acoustic conditions and noise levels in secondary school classrooms in England. The Journal of the Acoustical Society ofAmerica, Vol 137 (1), 2015, pp 177-188.
[2] Dockrell, J.; Shield, B. The effects of environmental and classroom noise on the academic attainments ofprimary school children. The Journal of the Acoustical Society of America, Vol 123 (1), 2008, pp 133-144.
[3] Lau, J.; Wang, L. M.; Waters, C.; Bovaird, J. A need for evidence-based and multidisciplinary research tostudy the effects of the interaction of school environmental conditions on student achievement. Indoor andBuilt Environment, Vol 25 (6), 2016, pp 869-871.
[4] Brill, L. C. Exploring the Correlation Between Occupied andels in K-12 Classrooms. Architectural Engineering–Dissertations andhttps://digitalcommons.unl.edu/archengdiss/47/

Unoccupied Noise LevStudent Research, 2017.

[5] Smith, K. Explorations of Acoustic Trends in Data Logged in K-12 Classrooms. Architectural Engineering–
Dissertations and Student Research, 2019. https://digitalcommons.unl.edu/archengdiss/55/5943