Smart Thermal Environmental and Noise Monitoring to Enhance
Indoor Comfort for Classrooms:
A Case Study at the International College of Auckland, New Zealand
MUSTAFA I. FADHEL, AYMAN ALDARABIE
Mechanical Engineering Discipline, Department of Engineering,
International College of Auckland, 520 Queen Street, Auckland, 1010,
NEW ZEALAND
Abstract: - The increasing concern about worldwide climate change will necessitate better approaches to daily
life. As a result, indoor thermal comfort and air quality in school classrooms have become a global priority to
improve air quality in educational settings. In this paper, indoor air quality parameters and noise levels were
studied and monitored using an Arduino Uno R3, equipped with a sound sensor, CO2 sensor, and
environmental and humidity sensors to control indoor quality. The performance of the smart thermal
environmental and noise monitoring system was evaluated in a typical classroom at the International College of
Auckland (ICA) in New Zealand. Mechanical analyses were conducted using HAP software for the classroom,
showing that the room requires a 2-ton ceiling air conditioning unit. Parameters were monitered effectivelly,
and the hardware controlled the indoor colling system well. The results demondtrated high efficiency and
reliable performance for the sensors.
Key-Words: - thermal comfort, indoor quality, humidity sensor, CO2 Sensor, noise sensor, ICA college
classroom.
Received: July 11, 2024. Revised: November 19, 2024. Accepted: December 15, 2024. Published: December 31, 2024.
1 Introduction
Designing optimal learning environments is
essential for fostering student success. Factors such
as air quality, thermal comfort, and noise levels
significantly impact students’ concentration,
performance, and overall well-being in the
classroom. To establish these ideal conditions, a
strategic approach to the design and management of
heating, ventilation, and air conditioning (HVAC)
systems is necessary, along with soundproofing
measures to minimize both external and internal
noise disturbances. This will ultimately improve the
overall learning environment and experience, [1].
The air quality and indoor thermal comfort in
school classrooms are of universal importance due
to impact on students' academic performance,
health, and overall productivity. Poor air quality and
high temperatures present many challenges in
school classrooms, especially that students spend a
large part of their day indoors. These issues
increased when the ventilation rates are insufficient
to remove the excessive pollutants and heat,
especially when the doors or windows are closed to
avoid the noise or adverse weather, [2].
the monitoring of indoor air quality and thermal
comfort in the classrooms can be set by measuring
various parameters. In the literature review on smart
control systems, A variety and wide range of air
quality and thermal comfort indicators has been
studied and investigated, [3], [4].
The carbon dioxide (CO2), temperature, relative
humidity (RH), and concentration of pollutants such
as PM 2.5, are the parameters most frequently
studied, [5].
Temperature and relative humidity are crucial
for ensuring thermal comfort in classrooms. The
American Society of Heating, Refrigerating, and
Air-Conditioning Engineers (ASHRAE)
recommends a temperature range from 20.3 - 23.8
oC in the winter and from 23.8 – 27oC in the
summer, [6]. The recommended RH range for
indoor environments is typically between 30% and
60% to prevent mold growth and maintain occupant
comfort, [7].
Carbon dioxide (CO2) concentration is a most
of parameter studied and examined for assessing
IAQ in classrooms. Elevated CO2 levels can
indicate inadequate ventilation, leading to poor air
quality and decreased cognitive performance.
Research has shown that the level of CO2 in
classrooms can increase to very high levels due to
inadequate ventilation rates, [8], [9]. [10], found
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2024.20.84
Mustafa I. Fadhel, Ayman Aldarabie
E-ISSN: 2224-3496
902
Volume 20, 2024
that Schoolchildren exposed to CO2 levels above
1000 ppm face a significantly higher risk for dry
cough and rhinitis. It is generally assumed that the
higher the CO2 concentration, the poorer the air
quality (less dilution). Although CO2 has frequently
been used to characterize air quality in classrooms,
some research has focused on specific pollutants
such as particulate matter or contaminants with
outdoor sources, [11], [12], [13], [14].
Smart technology has revolutionized the
management of indoor air quality (IAQ) by
providing innovative solutions that enhance comfort
and health in various indoor environments. With the
advent of smart sensors and IoT (Internet of Things)
devices, real-time monitoring of IAQ parameters,
such as carbon dioxide (CO2) levels, particulate
matter (PM2.5), humidity, and volatile organic
compounds (VOCs), has become more accessible
and efficient. These devices consistently collect
data and deliver actionable insights, allowing
ventilation systems to be modified to ensure
optimal air quality.
In recent years, the implementation of advanced
control systems for monitoring and managing
indoor air quality (IAQ) in classrooms has gained
considerable focus. These control systems are
engineered to continuously assess, analyze, and
manage air quality metrics, ensuring a healthy and
effective learning environment. They usually
integrate various sensors, data processing units, and
actuators to sustain optimal indoor air quality (IAQ)
levels. After gathering data from the sensors, the
information is processed and analyzed to evaluate
the current indoor air quality (IAQ) status and
forecast future conditions. [15], carried out research
examining the effects of indoor air quality (IAQ)
control systems in schools. The findings indicated
that automated systems, which adjusted ventilation
according to real-time data, were more effective at
enhancing IAQ compared to manual approaches.
The study also emphasized the energy savings
realized through intelligent control strategies that
effectively balance IAQ and energy efficiency.
Studies have shown that control systems
significantly improve Indoor air quality (IAQ) in
classrooms, resulting in improved student
performance and fewer health-related problems,
[16].
The aim of the paper is to design and develop a
smart thermal environmental and noise monitoring
prototype for a typical NZ classroom, case study
International College of Auckland classroom as
shown in Figure 1. The HVAC system's sensible
and latent loads, airflow, and various other
parameters were examined using HAP software to
evaluate temperature, pressure, humidity, gas
exchange, and acoustic modelling for the developed
prototype. This study conducted an in-depth
assessment of classroom environments by
simultaneously evaluating indoor thermal comfort,
air quality, and noise control. It examines the
combined impact of HVAC performance on both
thermal comfort and noise reduction, providing
valuable insights for enhancing classroom design
and creating more effective learning spaces.
Fig. 1: Typical International College of Auckland
classroom
2 Materials and Methods
2.1 Classroom Size and Parameters
The typical international classroom size and
parameters are shown in Figure 2 and Table 1.
Fig. 2: Classroom Size
As seen in Figure 2 the classroom size is 6.4 m
x 4.9 m x 3 m. The classroom features one door,
one window, a floor, a roof, and walls. The
dimensions of each of these components are
provided in Table 1.
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Table 1. Classroom Parameters
2.2 Mechanical Analysis
The analysis of the Heating, Ventilation, and Air
Conditioning (HVAC) system for a typical
classroom at the International College of Auckland
was analyzed using HAP software (Hourly
Analysis Program). The analysis focuses on
important factors, such as sensible and latent heat
loads, airflow, and cooling capacity, to ensure ideal
indoor conditions. The overall thermal load of the
classroom was computed, encompassing two
primary components:
1. Sensible heat load: The energy required to
cool the classroom air to the target
temperature.
2. Latent heat load: The energy needed to
control moisture levels (humidity) within
the classroom.
The thermal performance of the classroom was
calculated based on several factors of
environmental, including equipment, occupancy,
and outside weather conditions. These analyses
enable HVAC professionals to calculate the
necessary airflow to maintain the desired
temperatures and conditions in an ICA classroom.
The heat transfer rate can be calculated as:
 
󰇛󰇜
where (
󰇜 is the heat transfer rate, is the
density, V is the volume, c is the specific heat
capacity,  is the temperature change, and t is the
time over which the heat transfer occurs.
And to convert the equation 1 to cubic feet per
minute (cfm) by integrating constants for air density
and specific heat capacity. The equation 1 will be:
   (2)
and to determine the required airflow (cfm) for
heating or cooling, the following equation 3 is used:

󰇛󰇜
To fully understand the Sound Pressure Level
(SPL), it's important to first grasp the concept of
'Sound Pressure. Sound pressure (p) is the average
variation in atmospheric pressure caused by the
sound. The unit of pressure measurement is the
pascal (Pa). The term 'sound pressure' can be
accompanied by other noise measurement terms
such as 'instantaneous', 'maximum', and 'peak' (e.g.,
peak sound pressure). Sound pressure level (SPL) is
the pressure level of a sound, measured in decibels
(dB). The SPL is the ratio of the absolute sound
pressure against a reference level of sound in the air.
The SPL can be expressed as:
  


󰇛󰇜
where:
 is the root mean square of the sound
pressure.
 is the reference sound pressure
(0.00002 Pa).
2.3 Prototype Design
A smart thermal environmental and noise
monitoring and control system consists of
environmental, humidity, CO2, and noise sensors
with an Arduino microcontroller system. Figure 3
shows the developed system, while Figure 4 shows
the final smart thermal environmental and noise
monitoring system.
Fig. 3: Prototype fabrication
The smart thermal environmental and noise
system components and types are given in Table 2.
The developed system used the Arduino UNO
R03 as a microcontroller and the inputs are sound,
CO2, environmental, and humidity sensors, while
the outputs are a cooling fan and LED as shown in
Figure 5.
Room Parameters
QTY
Size m*m
Door
1
1.5*2
Window
1
3*6.5
Floor
1
4.9*6.8
Roof
1
4.9*6.8
Sidewall
2
3*4.9
Front wall
1
3*6.8
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Mustafa I. Fadhel, Ayman Aldarabie
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Table 2. System Components and Types
Applications
Used to monitor the
temperature
Used to monitor the
humidity
Used to monitor the CO2
level
Used to monitor the noise
Used as a microcontroller
Used to indicate different
sound levels
2.4 Circuit Diagram
Figure 6 displays the circuit diagram of the smart
thermal environmental and noise monitoring
system.
Libraries for CCS811, BMP180, and DHT11
sensors were added to the Arduino IDE.
Components were assigned to specific pins on the
Arduino UNO R03. The setup initialized each
component and the loop function read and
monitored sensor results. Based on the readings,
actions were taken, such as running a cooling fan if
the temperature exceeded a certain threshold or
activating a buzzer if CO2 levels were too high.
LEDs were used to indicate different sound levels.
Fig. 4: Final developed system prototype
Fig. 5: The developed control system block
diagram
Fig. 6: Smart thermal environmental and noise
monitoring system circuit diagram
3 Results and Discussions
3.1 HAP Results for a Typical ICA
Classroom
Figure 7 provides a detailed summary of the air
system sizing necessary to meet the heating and
cooling requirements of the ICA classroom. It
includes calculations for airflow rates and
equipment sizing to ensure an optimal learning
environment.
BMP 180
Environmenta
l Sensor
DHT11
Humidity
Sensor
Arduino
UNO R03
Cooling Fan 12
V
LED
CO2 Sensor
Sound Sensor
Buzzer
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Fig. 7: Air system sizing summary for ICA
classroom
The details of the heating and cooling loads
calculated for the ICA classroom are shown in
Figure 8. It includes data on the thermal energy
required to maintain the desired indoor temperature
throughout different seasons. Figure 9 presents a
comprehensive load calculation, breaking down the
various components contributing to the total HVAC
load. It covers sensible and latent heat gains and
losses, factoring in the occupancy, equipment, and
building envelope characteristics.
Fig. 8: Air system design load for ICA classroom
Fig. 9: HAP results for ICA classroom
Figure 7, Figure 8 and Figure 9 illustrate the
detailed mechanical analysis, providing insights into
the heating, ventilation, and air conditioning
requirements for the ICA classroom. The HAP
software helps in accurately determining these
parameters to ensure a comfortable and efficient
environment. From the HAP analysis, the
classroom's total sensible heat load was calculated
to be 18,000 BTU/hr, while the latent heat load
(accounting for moisture control) was
approximately 6000 BTU/hr, resulting in a total
cooling requirement of 24,000 BTU/hr, or 2 tons of
cooling capacity.
3.2 The Smart Thermal Environmental
and Noise Monitoring System Results
Figure 10 shows the results of temperature readings
from the BMP180 sensor. As seen from the figure
temperature readings fluctuate between 22.5 and
25.8 degrees Celsius. When the temperature
exceeded 25.5 degrees, the cooling fan was
activated.
Fig. 10: Temperature & time monitoring result
For humidity monitoring inside the ICA
classroom, the DHT11 sensor indicated the
classroom humidity was between 50 and 60%,
showing good indoor air quality (Figure 11).
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Fig. 11: Humidity and time monitoring result
According to the standard, a CO2 level range
between 1000-1150 ppm is considered acceptable in
college and university classrooms. In our case, the
CO2 level inside the classroom was below this
threshold for most of the time. The buzzer, which
indicates when CO2 levels exceed 1150 ppm, only
sounded twice during the monitoring period, as
shown in Figure 12. This suggests that the
ventilation rate was adequate to maintain good air
quality throughout the monitoring period.
Fig. 12: CO2 and time monitoring result
The standard noise level for primary and
secondary classrooms is typically set in the range of
35-45 dB, while computer laboratories can go up to
50 dB, [17]. As shown in Figure 13, the sound
levels in the ICA classroom ranged between 45-50
dB. While a noise level range of 40-50 dB might be
acceptable in specific situations, it is generally
considered less than ideal for maintaining optimal
learning conditions. Since the classrooms regularly
experience noise levels above this range, it may be
beneficial to implement soundproofing measures or
modify the classroom layout. Options such as
acoustic panels, ceiling tiles, or even curtains can
significantly reduce noise by absorbing sound waves
and minimizing reverberation. These changes would
create a quieter, more conducive learning
environment.
Fig. 13: Sound level and time monitoring result
4 Conclusion
Indoor Air Quality (IAQ) is a global priority to
ensure healthy living and to monitor the effects of
rising CO2 levels and temperatures due to industrial
growth and global warming. To address these
concerns, a smart thermal environmental and noise
monitoring system has been designed, built, and
tested. The performance of this system was
evaluated in a typical ICA classroom. The
monitored parameters indicated that the hardware
effectively maintained indoor quality conditions.
Mechanical analysis of the classroom suggests that a
2-ton ceiling air conditioning unit is needed. This
study highlights the effectiveness of an integrated
sensor system in optimizing indoor conditions in
educational environments. By seamlessly combining
thermal comfort, indoor air quality (IAQ), and noise
reduction, the system offers a comprehensive
solution. The analysis of the monitored parameters
confirms its high performance, effectively
contributing to the maintenance and improvement of
a comfortable and safe atmosphere in the ICA
classroom.
Declaration of Generative AI and AI-assisted
Technologies in the Writing Process
During the preparation of this work the authors used
GPT4-o for language editing. After using this
service, the authors reviewed and edited the content
as needed and take full responsibility for the content
of the publication.
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DOI: 10.37394/232015.2024.20.84
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problem to the final findings and solution.
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No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare.
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WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2024.20.84
Mustafa I. Fadhel, Ayman Aldarabie
E-ISSN: 2224-3496
909
Volume 20, 2024