Indoor Environmental Quality in Tanzanian Secondary Schools: Objective Baseline Measurements

Abstract

This study assessed the baseline indoor environmental quality (IEQ) of secondary school classrooms in Tanzania by measuring temperature, relative humidity, noise, lighting, and indoor air quality. Objective measurements were conducted using calibrated sensors in 14 classrooms across five schools, with data collected during occupied school hours and additional noise measurements during unoccupied periods. All classrooms are naturally ventilated through operable windows and doors. The findings reveal a pattern of cumulative IEQ deficiencies: classroom temperatures frequently exceeded the recommended 20–24 °C range, reaching means as high as 30.4 °C, while relative humidity varied widely, with levels occasionally surpassing 65%. Noise levels consistently exceeded the World Health Organization (WHO)’s recommended 35 dBA threshold, with significant differences observed between occupied and unoccupied periods (p = 0.02). Light distribution was uneven, with significantly higher lux levels near windows than at classroom centers (p < 0.001), and artificial lighting was generally insufficient due to infrastructure limitations. Although CO₂ concentrations remained below the 1000 ppm threshold, indicating adequate ventilation, particulate matter levels were often elevated, with PM2.5 reaching up to 58.80 µg/m³ and PM10 up to 96.90 µg/m³, exceeding health-based guidelines. Together, these findings suggest that students are exposed to multiple environmental stressors that may impair health, comfort, and academic performance. This study provides a critical baseline for future research and context-specific interventions aimed at improving learning environments in Tanzanian schools and similar settings in East Africa.

1. Introduction

Indoor environmental quality (IEQ) in schools is a critical issue that can affect the health, comfort, and well-being of students, teachers, and other school personnel. IEQ refers to the different environmental conditions (physical, chemical, or biological) inside a building that can influence the health and productivity of its occupants, including factors such as indoor air quality (IAQ), temperature, relative humidity, lighting, and noise levels [1].

Poor IEQ in schools can lead to a variety of health problems, including allergies, asthma, respiratory infections, headaches, and fatigue [2]. It can also negatively impact students’ academic performance and overall quality of life [3,4,5]. Therefore, it is crucial to ensure that schools provide a healthy and comfortable indoor environment for students. One of the primary factors affecting IEQ in schools is IAQ. Poor IAQ can result from inadequate ventilation to mix, dilute, and eventually remove indoor pollutants [6]. IAQ can be affected by outdoor pollutants infiltrating the indoor space, especially in naturally ventilated schools that are close to sources of pollutants such as busy roads and fossil fuel energy-generating companies [7,8]. They can be from building materials such as carpets and furniture that emit pollutants like volatile organic compounds (VOCs) [9], and from cleaning products that can release harmful chemicals such as ammonia, phthalates, and VOCs into the classroom environment [10,11]. In addition to pollutant sources, the geographical location and setting of a school can significantly influence its IEQ. For example, schools in tropical regions may face consistently high temperatures and humidity levels, which are associated with thermal discomfort and the proliferation of bacteria and fungi in indoor environments [12]. In contrast, schools in temperate regions may experience seasonal shifts that affect window use and create a need for mechanical ventilation and heating, particularly in tightly sealed school buildings with low relative humidity during cold seasons [13]. Low humidity has been linked to increased transmission of airborne viruses such as influenza among students [14]. Furthermore, urban schools located near traffic or industrial sources may be exposed to higher levels of outdoor air and noise pollution, while rural schools may face challenges such as limited infrastructure and less frequent maintenance [15,16]. School buildings in developed countries often have access to consistent artificial lighting when daylight is insufficient, whereas energy poverty in lower-income regions may limit the availability of artificial lighting [17]. These differences must be considered when interpreting IEQ findings or comparing results across regions.

Exposure of students to pollutants like particulate matter (PM), mold, bacteria, and VOCs can lead to a range of health problems, including asthma and allergies [2,18]. To improve IAQ, schools should ensure proper classroom ventilation, use low-emission building materials and cleaning products, and regularly maintain their heating, ventilation, and air conditioning (HVAC) systems.

The type of ventilation system used in schools, whether natural, mechanical, or a combination of both, plays an important role in shaping IAQ in classrooms [19,20]. Natural ventilation relies on pressure and temperature differences across openings such as windows and doors, and it is commonly used in schools in low-income and tropical settings due to its simplicity and cost effectiveness. It functions best when windows are positioned on opposite walls to allow for cross ventilation [21]. In this arrangement, fresh air enters the classroom from one side and spent air exits through the other side. However, the performance of natural ventilation depends heavily on building orientation, weather conditions, and user behavior. These factors influence air exchange rates, which are directly related to outdoor wind speed [22].

While some studies in temperate climates, such as Zapata Lancaster et al. [23] and Toyinbo et al. [13], have shown that naturally ventilated classrooms can exhibit elevated concentrations of CO₂, a commonly used proxy for ventilation adequacy, studies conducted in tropical settings, including Talarosha et al. [24] and Toyinbo et al. [12], report classroom carbon dioxide (CO₂) levels consistently below 1000 ppm, suggesting adequate ventilation. However, both tropical studies also reported classroom temperatures above the recommendation, highlighting a tradeoff between ventilation adequacy and thermal comfort. Mechanical ventilation systems can provide more stable and adequate ventilation through different configurations such as mechanical supply only, mechanical exhaust only, or systems that include both supply and exhaust [25]. Despite these advantages, their use is limited in many tropical African schools due to financial and infrastructure constraints [26]. Similar limitations apply to combined or hybrid systems, which may offer a balanced solution but are rarely implemented in tropical African school environments.

Temperature extremes can lead to discomfort, distraction, and reduced productivity among students and staff. To create a conducive indoor environment, ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) suggests that a minimum of 85% of individuals within a building should experience thermal comfort. If a higher standard of thermal comfort is desired, such as in schools, ASHRAE recommends aiming for at least 90% of occupants being comfortable [27]. Thermal comfort in classrooms is typically achieved when temperatures range between 20 °C and 24 °C [27], with some studies recommending a narrower range of 20 °C to 22 °C for improved focus and learning (e.g., [28]). Relative humidity (RH) levels should also be maintained at a comfortable level as high humidity can encourage the growth of mold and other harmful microorganisms, while low humidity can result in negative health outcomes such as itchy eyes, dry skin, and respiratory symptoms [29,30]. ASHRAE Standard 62.1 [31] recommends keeping RH below 65% to minimize such risks, with no lower limit suggested. Other studies, such as Arundel et al. [32], suggest that RH should ideally be maintained between 40% and 60% to minimize the health risks associated with both low and high humidity and to support overall comfort and well-being.

Proper lighting levels in schools can improve concentration, reduce eye strain and headaches, and improve overall mood [33]. Schools should ensure that classrooms have access to natural light and that artificial lighting is adequate when required and professionally designed to prevent glare and flicker. Excessive noise levels can lead to hearing loss, communication difficulties, and reduced academic performance [34,35]. The World Health Organization (WHO) proposed a background noise level below 35 A-weighted decibels for appropriate teaching and learning conditions [35].

The existing body of literature establishes an association between IEQ in schools and its significant impact on students’ health and academic performance. However, there is a noticeable dearth of research in this area within developing countries in Africa [12]. This research gap hampers the standardization of IEQ parameters essential for creating conducive classroom environments. The majority of existing standards and guidelines are derived from studies conducted in developed countries, potentially inadequately addressing the specific requirements of African nations [36,37].

This discrepancy is particularly evident in tropical climates, prevalent in many African countries, where the environmental conditions differ markedly from the temperate regions where most IEQ studies have been conducted. Furthermore, the variances in climatic conditions necessitate distinct architectural designs for school buildings, coupled with variations in the construction materials employed. Consequently, it is imperative to bridge this knowledge gap and develop context-specific standards tailored to the unique climatic and structural considerations of African schools with school IEQ research.

The Improving Learning Through Classroom Experience (ILCE) program is a research initiative aimed at investigating the impact of cost-effective modifications to the school’s built environment, including temperature, light intensity, and acoustics, on the creation of optimal learning environments. This multi-faceted research project comprises two distinct teams: Team 1, which focuses on assessing overall environmental conditions and their correlation with learning outcomes, and Team 2, which is dedicated to practical approaches for improving IEQ through retrofitting and environmental modifications [38].

This paper pertains to Team 2 of the ILCE program. The study is structured around two distinct phases: the Discovery phase and the Alpha phase. The ‘Discovery’ phase involved gathering both quantitative environmental data and qualitative subjective comfort data from Tanzanian school buildings and students, respectively. The subsequent ‘Alpha’ phase aims to test cost-effective retrofit options in selected classrooms, with the potential for further assessment of their efficacy and scalability in the future.

While the Discovery phase recognizes the importance of understanding both measured IEQ conditions and student comfort, this paper focuses exclusively on sensor-based baseline data. When available, the results of comfort surveys and walkthrough assessments will be reported separately. The insights from this baseline study contributed to identifying retrofit strategies tailored to improving IEQ and student comfort in these classrooms. Specifically, this study seeks to answer the following questions: What are the baseline levels of temperature, relative humidity, noise, lighting, and indoor air pollutants CO₂, PM2.5, PM10, and formaldehyde (HCHO)) in Tanzanian secondary school classrooms? How do these measured levels compare with internationally recommended standards for IEQ in learning environments? And what do these findings reveal about the current state of learning environments in Tanzanian secondary schools and similar settings?

Literature Review

Globally, poor IEQ, manifested through inadequate air quality, extreme temperatures, excessive noise, or suboptimal lighting, has been shown to negatively affect students’ cognitive function, concentration, learning outcomes, and overall well-being [2,4,39]. However, empirical research on IEQ within the African context remains limited, despite increasing evidence that schools across the continent often face environmental challenges distinct from those in temperate, well-resourced settings [12]. This gap is likely due to several factors, including insufficient funding for built environment research in Africa and the lingering perception highlighted by Toyinbo et al. [12] that classroom environmental conditions have minimal influence on student health and academic performance.

Consequently, the development of contextually appropriate IEQ standards has been constrained, and many African schools continue to operate under environmental conditions that would be deemed inadequate in other regions. Even when school-based IEQ studies are conducted, they are often not carried out extensively and involve a limited number of schools or classrooms. For example, while a study in the southwestern United States involved 70 schools over two academic years to investigate the link between IEQ and student performance [3], a Finnish study assessed over 4000 students from approximately 300 schools, focusing on mathematics achievement and respiratory health outcomes [13,40]. In contrast, a study in Nigeria assessed a few IEQ parameters in just 15 classrooms across five schools [12], and a study in the Democratic Republic of Congo examined IAQ parameters in two schools [41]. These disparities highlight the limited scale and focus of IEQ research in Africa compared to developed contexts, with many studies not exploring the links between IEQ, educational outcomes, and health.

Thermal discomfort in African classrooms is a major concern due to inadequate infrastructure and the absence of climate-responsive design. In tropical Nigeria, Toyinbo et al. [12] reported classroom temperatures as high as 34 °C and relative humidity (RH) levels up to 89%, with minimal awareness of their impacts on student health and learning. In South Africa’s subtropical Eastern Cape Province, Pule et al. [42] recorded indoor temperatures ranging from 11 °C to 30 °C, often exceeding outdoor temperatures by 5 °C due to the absence of insulation or mechanical ventilation. Absenteeism was highest when apparent temperatures (Tapp) dropped below 15 °C or rose above 25 °C, highlighting the importance of maintaining classroom temperatures within recommended thresholds. Similarly, in the Western Cape Province, a Mediterranean-type climate, van der Walt et al. [43] monitored 24 classrooms over 16 months and found that indoor temperatures regularly exceeded 35 °C in container and prefabricated structures, despite the presence of air conditioning. CO₂ concentrations exceeded 1000 ppm on over 50% of school days in container classrooms, underscoring poor ventilation. In Cairo, Egypt, a dense arid urban setting, Afifi et al. [44] assessed naturally ventilated primary school classrooms and found seasonal discomfort despite adaptive ventilation strategies. Students experienced discomfort during both cold (e.g., January) and warm (e.g., April) periods. While open windows improved airflow and daylight access, they also introduced outdoor pollutants, dust, and noise, especially in schools near busy roads or markets. Students sitting near windows often felt more alert due to fresh air and daylight but also experienced glare and distractions, particularly in government schools.

Acoustic quality is another under-researched yet critical aspect of IEQ. In South Africa, Goldschagg and Bekker [34] found that classroom noise negatively impacted students’ learning, especially for second-language speakers, whose increased language processing demands made them more vulnerable to noise-related disruptions. In Tanzania, Likuru and Mwila [45] identified overcrowding and poor classroom design as key contributors to excessive noise, frequently cited by teachers as major barriers to effective instruction. These findings are consistent with earlier work by Ijaiya [46] in Nigeria, which emphasized noise and classroom density as impediments to teacher and student interaction. Ijaiya [46] also proposed additional infrastructure, such as providing more furniture and classrooms, to alleviate these issues and improve the learning environment.

Lighting levels in classrooms have been highlighted as an important yet often overlooked aspect of IEQ. Ibhadode et al. [47] conducted an extensive study across 180 classrooms in 60 schools throughout Nigeria, evaluating classroom illumination under various sky conditions. The study revealed significant disparities in indoor lighting, with illuminance levels on desks near windows exposed to direct sunlight ranging from 1243 lux to 4486 lux, well above the global standard of 300 lux for classroom tasks [47,48]. Meanwhile, desks located in the center of classrooms had much lower levels of illumination, ranging from 101 lux to 449 lux, far below the recommended 300 lux. In many cases, classroom board illuminance ranged from 110 lux to 494 lux, falling short of the recommended 500 lux for reading tasks. These discrepancies highlight the impact of both excessive and inadequate lighting in classrooms, which can contribute to eye strain, reduced academic performance, and overall student discomfort [49]. In South Africa, Booysen et al. [50] found that replacing fluorescent lights with energy-efficient LEDs led to savings of 21% to 39% in energy usage, significantly reducing lighting costs. However, energy poverty may hinder such interventions in many African schools, where insufficient access to reliable energy sources and limited funding can prevent the adoption of energy-efficient technologies [17].

Despite these valuable contributions, IEQ research in sub-Saharan Africa remains fragmented and often limited to single-parameter assessments such as lighting, noise, or ventilation. Comprehensive, multi-parameter studies that reflect the complex interplay of environmental conditions and student comfort are rare. Additionally, longitudinal and intervention-based studies, which are critical for identifying cost-effective improvements, are virtually absent.

2. Methodology

2.1. Sampling Approach

To ensure homogeneity in school environments and structures, the study focused on randomly selecting five schools within the Temeke district of Dar es Salaam, Tanzania. The selection process deliberately avoided initial direct contact with schools to prevent potential biases. Instead, collaboration with the Temeke district administrator was established, from where the schools were mapped and selected randomly for inclusion in the study. This approach is aimed at enhancing the objectivity of the school selection process.

Five selected schools in the Temeke district underwent further scrutiny, with three Form 1 classrooms (the first year of secondary education in Tanzania) chosen from four schools and two classrooms from one school for investigation. This selection allows for potential longitudinal studies to assess the impacts of interventions over time.

All selected schools are situated in a coastal urban area of Dar es Salaam, within a tropical savannah climate zone (Aw) according to the Köppen Geiger classification [51]. This climate is marked by a distinct wet and dry season, with generally high temperatures and humidity levels. The surrounding environment of the schools includes residential areas, open land, and local roads, with no schools located near major highways or industrial sources.

All classrooms had similar construction features, including uninsulated masonry walls, corrugated metal roofs, four windows (two on opposite walls), and a single entry door (Figure 1). The windows were permanently open during normal classroom use and served as the means for passive (natural) cross ventilation. None of the schools had mechanical HVAC. Additional details such as floor area, room volume, and window size for each classroom are presented in Appendix A to provide further context. The measured indoor environmental parameters across classrooms did not show statistically significant differences, supporting the comparability of classroom conditions for the purpose of analysis. All classrooms relied entirely on daylight for lighting during teaching hours.

2.2. Tool Development and Configurations

The tools used in this study included various sensors to objectively measure classroom IEQ and building parameters.

Various sensors were used to measure classroom IEQ parameters in this study. Specifically, industrially calibrated sensors were used to measure temperature, RH, sound, lighting, and IAQ metrics, such as PM, HCHO, and CO₂. Additionally, a custom sensor package developed by Open Development and Education (OpenDevEd, London, UK) was incorporated, which simultaneously measured temperature, RH, and lighting. The IAQ, temperature, and humidity sensors were placed at a height between 1.2 and 1.5 m from the floor, positioned at the middle back of each classroom, and kept away from windows and doors to minimize the influence of external airflow or direct sunlight [31]. This location was chosen to avoid interfering with normal classroom activities. Sound sensors were placed centrally at a similar height. Lighting measurements were taken at two specific points within each classroom, the window side and the center, to capture spatial variation in light intensity. All sensors were industry calibrated before deployment, and their performance was verified through two pilot studies conducted prior to the main data collection. These pilots confirmed the accuracy and consistency of the sensor readings and informed the final sensor deployment strategy [52,53].

Temperature and humidity measurements were collected using two types of industrially calibrated sensors and the OpenDevEd sensor box. The Onset HOBO MX1101 (470 MacArthur Blvd., Bourne, MA 02532 USA) offers an accuracy of ±0.21 °C within a range of 0 °C to 50 °C for temperature and ±2% for humidity. The Lascar EasyLog EL-SIE-2 (Lascar Electronics Ltd., Module House, Whiteparish, Salisbury, Wiltshire SP5 2SJ, UK) provides an accuracy of ±0.2 °C for temperatures ranging from −180 °C to 55 °C and ±1.5% for humidity from 0 to 100%. All sensors are battery-powered.

For lighting conditions, the ATP DT-8809A Lux sensor data logger (ATP Electronics Taiwan Inc., 10F, No. 185, Tiding Boulevard, Section 2, Neihu District, Taipei 11493, Taiwan, China) was used. It has an accuracy of ±3% of the reading ±0.5% full-scale below 10,000 Lux. This device includes a silicon photodiode sensor and a spectral response filter and can be powered by batteries or electricity. Measurements were taken from two window sides and the central region of each classroom.

Noise levels were measured using the ATP ET-958 sound level meter (ATP Electronics Taiwan Inc., 10F, No. 185, Tiding Boulevard, Section 2, Neihu District, Taipei 11493, Taiwan, China), which is accurate to ±1.4 dB (94 dB @ 1 kHz) and can operate on batteries or electrical power. Noise measurements were taken both when students were in the classroom and when they were not.

IAQ parameters were measured with a Temtop M2000 2nd generation device (Temtop Inc., 2528 Qume Drive, Suite 2, San Jose, CA 95131, USA), which features a CO₂ accuracy of ±50 ppm + 5% reading, PM2.5 accuracy of ±10 μg/m³ (0–100 μg/m³), ±10% (>100 μg/m³), PM10 accuracy of ±15 μg/m³ (0–100 μg/m³), ±15% (>100 μg/m³), and HCHO accuracy of ±0.03 mg/m³ (0–0.3 mg/m³), ±10% (>0.3 mg/m³). This sensor is powered by a rechargeable battery. IAQ assessments were conducted in one representative classroom per school, and outdoor CO₂ spot measurements were also taken at each school.

After two rounds of piloting the sensors (both the commercially available and the OpenDevEd sensor box), it was observed that: (1) temperature and humidity showed very similar results across all devices, with a difference of 0.2 to 0.5 °C and 1%, respectively; and (2) luminance readings between the ATP DT-8809A Lux sensor (a commercial and industrially calibrated sensor) and the OpenDevEd sensor were not comparable. We believe this discrepancy is due to the lack of a photodiode sensor in the OpenDevEd sensor, which makes the commercially available sensor more sensitive to light. Consequently, data for light intensity were not recorded using the OpenDevEd sensors.

2.3. Data Analysis

Based on prior literature and classroom conditions observed in similar contexts (e.g., [12]), it was anticipated that some environmental parameters such as temperature, noise levels, and particulate matter may not meet recommended standards. This assumption informed the use of descriptive statistics, independent sample t-tests, paired sample t-tests, and correlation analysis to examine the extent and significance of any deviations from international standards.

Data collection occurred in August 2023 (a period that is perceived to have mild temperature conditions by the locals in Dar es Salam, Tanzania), with measurements conducted over two consecutive days in each school. Only data from occupied periods were analyzed, except for noise, which included both occupied and unoccupied data. The schools are labeled A to E, with classrooms numbered 1 to 3 within each school, except for School A, where two classrooms (A1 and A2) were investigated. Therefore, the classrooms included in the study were labeled A1, A2, B1, B2, B3, C1, C2, C3, and so on through E3. The collected data were analyzed using open-source Python (version 3.12.6, Python Software Foundation, Wilmington, NC, USA) and Microsoft Excel analytic tools (Microsoft Headquarters, One Microsoft Way, Redmond, WA, USA). The analyses performed include descriptive statistics, independent sample t-tests, paired sample t-tests, and Spearman’s rho correlation.

Table 1. IEQ standards, guidelines and recommendations.

T (°C)	RH (%)	CO2 (ppm)	PM2.5 (µg/m3)	PM10 (µg/m3)	HCHO (mg/m3)	Students’ Density/100 m2	Sound (dBA)	Light Intensity (lux)
20–24 (Winter) 23–27 (Summer)	≤65	<1000	10	20	0.1	35	35	300
ASHRAE standard 55 [27]	ASHRAE standard 62.1 [31]	ASHRAE standard 62 [54]	South African domestic IAQ guideline [55]			ASHRAE standard 62.1 [56]	WHO [35]	CIBSE [48]

presents the reference standards used for comparison. After a thorough search of the web and other available documents, including the open database for national and international guidelines [36,37], no specific guidelines, standards, or regulations for IEQ in Tanzania were found. In light of this, we selected internationally recognized standards that are relevant to school environments and warm climate regions. These include guidelines from ASHRAE, WHO, and the South African domestic IAQ standard. Where multiple standards existed, we prioritized those most applicable to naturally ventilated schools and comparable environmental contexts. This combined approach offers a consistent and practical baseline for assessing IEQ in Tanzanian classrooms, despite the lack of national benchmarks.

2.4. Ethical Considerations

During the measurement process in the schools, ethical considerations were prioritized in accordance with the Declaration of Helsinki, and all necessary approvals were obtained from relevant authorities. Participation by the schools was voluntary, with the option to opt out at any time. A detailed explanation of the study’s purpose was provided, and a poster outlining the research was displayed within each school. Additionally, an interpreter was present throughout the process to communicate effectively in the local Swahili language.

3. Results and Discussion

3.1. Temperature (T) and Relative Humidity (RH) Measurement

Table 2. Descriptive statistics for temperature and humidity measurements in the classrooms.

Classroom	Number of Students	Mean T (°C)	Min. T (°C)	Max. T (°C)	SD. T (°C)	Mean RH (%)	Min. RH (%)	Max. RH (%)	SD RH (%)
A1	70	27.50	23.20	30.00	1.90	67.40	44.00	91.00	12.80
A2	70	27.40	23.30	29.70	1.90	66.8	46.00	90.00	12.50
B1	80	28.50	25.20	30.70	1.40	65.2	54.10	85.20	8.60
B2	78	29.50	26.90	30.80	0.70	56.6	30.80	69.50	7.90
B3	80	28.20	23.80	31.10	1.90	68.7	56.00	95.00	10.90
C1	98	29.00	24.40	31.70	2.30	61.3	45.90	83.10	12.30
C2	98	30.40	28.90	32.70	0.90	48.7	34.40	62.30	6.90
C3	98	28.50	23.10	31.70	2.80	66.2	47.00	90.00	14.10
D1	69	29.40	26.90	31.70	1.00	52.4	38.70	64.30	5.50
D2	65	29.10	26.20	31.80	1.60	58.1	41.50	73.20	8.90
D3	80	28.20	25.20	31.80	1.70	62.1	43.00	77.00	9.30
E1	81	26.40	23.40	29.00	1.50	76.9	60.00	97.00	9.80
E2	78	27.00	24.00	29.40	1.50	73.4	57.20	92.70	9.90
E3	77	27.90	24.50	29.40	1.10	62.0	34.80	76.00	9.30

presents the descriptive statistics for temperature and RH in all classrooms, along with the number of students present during the measurements. The mean and maximum temperatures recorded in the classrooms generally exceeded the ASHRAE winter/cold season recommendation of 20 to 24 °C [27]. The lowest mean temperature across all classrooms, from school A to E, was 26.40 °C in Classroom E1. Conversely, the highest mean temperature was 30.40 °C, observed in Classroom C2. This indicates a significant temperature range of 4.0 °C among the sampled classrooms. Additionally, the lowest individual temperature recorded was 23.10 °C in Classroom C3, while the highest was 32.70 °C in Classroom C2.

The lowest mean RH observed was in Classroom C2, with 48.70%. Conversely, Classroom E1 exhibited the highest mean RH at 76.90% and also recorded the maximum RH value at 97%. On the other hand, the lowest RH recorded was in Classroom B2, measuring 30.80%. ASHRAE recommends an upper limit of 65% for RH but does not specify a lower limit [31].

Similar indoor conditions were reported in southwestern Nigerian primary schools, where average indoor temperatures ranged from 29 °C to 32 °C and RH typically exceeded 60% [12]. These findings align with this study and illustrate the shared thermal challenges in naturally ventilated classrooms in tropical regions. A more recent study in South Africa’s Western Cape Province, which has hot dry summers, also found that classroom temperatures often reached up to 35 °C, even where air conditioning was installed [43]. Although the climate and infrastructure differ, the study supports a broader concern across warm regions: the need for climate-responsive classroom design and improved ventilation to support indoor comfort and learning.

A previous study has demonstrated that lowering classroom temperatures can improve students’ test performance by an average of 20%, with the optimal temperature for peak performance being below 22 °C [4]. It is important to note that this association has been established in temperate climates. Therefore, it is crucial for future East African school IEQ research to build on these findings through experimental studies. These studies should aim to identify the optimal classroom temperature range that enhances student performance in the tropical climates of Tanzania and East Africa. Supporting this need, a study conducted in South African primary schools found that student absenteeism increased when indoor classroom temperatures exceeded 25 °C, which may be linked to thermal discomfort and reduced motivation to attend classes under unfavorable indoor conditions [42].

High RH levels in schools can promote the growth of indoor microorganisms, such as fungi and bacteria [57], which can negatively impact students’ health. Conversely, low RH can facilitate the spread of viruses and is associated with adverse health outcomes, including itchy eyes and respiratory symptoms [30].

3.2. Acoustic/Noise Measurement

The acoustic/noise values that provide a summary of the sound levels in each school under different conditions, with students in the classroom (WS) and no students in the classroom (NS), are presented in

Table 3. Descriptive statistics for sound measurements in schools.

School	Mean (dBA)	Min. (dBA)	Max. (dBA)	SD
School A (NS)	54.17	30.40	83.60	4.87
School B (NS)	59.34	31.20	87.30	6.07
School B (WS)	69.80	56.90	97.90	5.36
School C (NS)	60.01	53.90	82.50	2.70
School C (WS)	63.14	56.20	87.90	4.74
School D (NS)	43.58	28.30	73.50	6.29
School D (WS)	65.98	53.80	93.90	5.97
School E (NS)	51.30	30.00	91.30	7.16
School E (WS)	78.71	57.50	122.50	7.55

WS = with students in the classroom; NS = no student in the classroom.

.

School D exhibits the lowest mean acoustic value, registering at 43.58 dBA, which includes the overall minimum acoustic value recorded at 28.30 dBA and the lowest median acoustic level observed at 41.50 dBA during the baseline assessment when classrooms were not in session. During the school period, School C records the lowest mean and median acoustic levels at 63.14 dBA and 61.70 dBA, respectively, while School D maintains the lowest recorded value at 53.80 dBA. The overall maximum acoustic level measured was 122.50 dBA at School E during the school period.

According to the World Health Organization, background noise levels in classrooms should not exceed 35 dBA to support clear speech communication and an effective learning environment [35]. This limit refers to unoccupied classroom conditions. In this study, measured noise levels exceeded the WHO threshold in all classrooms, both with and without students present. The levels were substantially higher during teaching periods, indicating significant acoustic challenges, likely driven by both internal classroom activities and external environmental sources. These elevated noise levels may disrupt speech perception, reduce student concentration, and hinder academic performance.

An independent sample t-test comparing noise levels between occupied (WS) and unoccupied (NS) periods showed a statistically significant difference (p = 0.02). This difference may be associated with overcrowding, as all classrooms had student densities exceeding 184 students per 100 m², far above the recommended limits outlined by ASHRAE Standard 62.1 [56]. Figure 2 presents an overview of the distribution of sound measurements across all schools during unoccupied periods.

The findings are consistent with other research. A 2020 study in South African schools reported that elevated classroom noise interferes with students’ learning and cognitive function [34]. In a systematic review by Lamotte et al. [58], 89% of the studies reviewed found that noise, particularly chatter, significantly reduced test performance. In Nigeria, Ijaiya [46] reported that both students and teachers identified noise as the most serious classroom problem, reducing the quality of teaching and learning. Similarly, a study of Tanzanian secondary schools [45] attributed key teaching and learning challenges to overcrowding and associated noise levels. Siddiqui [59] emphasized that noise in crowded classrooms can impair communication, make it difficult to support struggling students, and disrupt classroom management. To address these concerns, Ijaiya [46] suggests constructing additional classrooms and providing more furniture for students, recommendations that would suit the schools under study.

3.3. Light Measurement

Measurements of classroom light intensity at the window sides and central areas are presented in

Table 4. Descriptive statistics for measured light intensity in the classrooms.

Classroom	Window Area (lux)			Central Area (lux)
	Mean	Min.	Max.	Mean	Min.	Max.
A1	677	348	1690	543	326	896
A2	1907	1240	3690	1179	650	1490
B1	1731	517	4295	707	420	969
B2	2143	175	7370	659	401	781
B3	1466	351	4740	731	487	921
C1	2035	289	3670	863	414	1410
C2	2448	1030	3900	1156	828	1470
C3	2409	312	4710	710	506	909
D1	1606	1100	3010	1346	946	1610
D2	491	304	785	278	177	334
D3	972	612	1770	570	407	662
E1	271	46	657	106	55	163
E2	1499	185	3890	520	199	785
E3	1138	181	3130	499	346	643

. More light intensity was observed on the side of the classroom adjacent to windows, compared to the central region, with the mean lux consistently higher at the window side across all classrooms. A paired samples t-test assessed disparities in mean lux levels between the window side and classroom center. Results showed a statistically significant difference, with mean lux at the window side (M = 1485.21, SD = 694.41) higher than the center (M = 704.79, SD = 342.95); t(13) = 5.70, p < 0.001. This discrepancy can be attributed to the prevalence of natural lighting facilitated by windows and doors. Ibhadode et al. [47] similarly found that in naturally ventilated Nigerian classrooms, mean indoor illuminance levels near windows ranged from 1243 to 4486 lux, while central desk areas recorded significantly lower levels between 101 and 449 lux. This pattern reflects uneven light distribution within classrooms, a common issue in naturally lit spaces without adequate artificial lighting [60]. Poorly distributed lighting can affect visual comfort, increase eye strain, and reduce learning efficiency [61], particularly for students seated farther from light sources. Uniformity in lighting is essential to ensure that all students have equal access to appropriate visual conditions, regardless of seating position [60].

Despite the availability of electricity in the sampled schools, it is noteworthy that the primary beneficiaries are the administrative blocks. In the majority of classrooms, issues with the electrical system persist. Common concerns include misalignment of classrooms with power sources and malfunctioning light bulbs. These challenges contribute to the observed variation in light intensity between the window side and the center of the classrooms. Addressing these electrical infrastructure issues is important to achieve uniform illumination throughout educational spaces.

The mean lux levels exceeded the recommended threshold of 300 lux [48] for classroom lighting in most classrooms. However, Classroom E1 was a notable exception, where lux levels fell below the recommended standard. This deviation was primarily due to the presence of a tree acting as a sunshade for Classroom E1, which also partially affected Classroom E2.

While the recommended lux level for classroom illumination is set at 300 lux [48] or more, exceeding levels above 1000 lux can significantly compromise students’ visual comfort [61]. The prevalent design of the classrooms, characterized by perpetually open windows and minimal to no shading from the roof, exacerbates this issue. Excessive lux levels not only reduce visual comfort but also contribute to glare, further impeding students’ ability to focus and learn effectively [62,63]. To address this concern, retrofitting windows in classrooms experiencing excessive lux with shading devices is essential. However, the introduction of such shading devices may disrupt the lighting distribution within the classroom, particularly in central areas. Consequently, this underscores the necessity of integrating artificial lighting solutions or alternative passive lighting infrastructure to ensure consistent and optimal illumination levels conducive to learning.

In a comprehensive review of the literature on the impact of lighting on students’ academic performance conducted by Mogas-Recalde and Palau [64], it was found that students’ cognitive processes are significantly influenced by insufficient classroom lighting. This inadequacy has repercussions for various aspects of academic performance, including achievement levels, attention rates, working speed, productivity, and accuracy. The study revealed that implementing artificial lighting, specifically LED lights, to address insufficient lighting in the classroom led to improvements in both psychological and cognitive processes. The findings underscore the significance of a dynamic lighting system that can adapt and respond to changes in atmospheric conditions. In another study by Singh et al. [65], the lighting conditions in classrooms were observed to significantly impact students’ focus and academic performance. The study found that maintaining classroom illuminance within the range of 250 to 500 lux led to improved concentration among students, resulting in higher scores and enhanced overall performance.

3.4. Indoor Air Quality Measurement

The descriptive statistics for measured IAQ parameters for each of the schools are presented in

Table 5. Descriptive statistics of measured IAQ parameters.

	Mean	Median	Min.	Max.	SD	Outdoor CO2 (ppm)
School A						379.00
CO2 (ppm)	405.90	404.00	396.00	420.00	6.79
PM10 (µg/m3)	20.48	13.90	7.80	53.90	13.37
PM2.5 (µg/m3)	12.01	8.00	4.30	32.00	8.02
HCHO (mg/m3)	0.001	0.001	0.001	0.002	0.0004
School B						380.00
CO2 (ppm)	435.08	426.00	389.00	511.00	36.98
PM10 (µg/m3)	20.88	18.40	7.90	44.20	10.04
PM2.5 (µg/m3)	11.93	10.30	4.60	23.80	5.41
HCHO (mg/m3)	0.001	0.001	0.001	0.002	0.0004
School C						393.00
CO2 (ppm)	417.22	425.00	382.00	504.00	31.09
PM10 (µg/m3)	21.44	13.70	8.40	63.90	15.15
PM2.5 (µg/m3)	12.51	8.00	5.10	38.40	8.87
HCHO (mg/m3)	0.001	0.001	0.001	0.002	0.0005
School D						424.00
CO2 (ppm)	674.00	484.50	405.00	2410.00	559.19
PM10 (µg/m3)	10.46	9.55	5.50	24.00	4.57
PM2.5 (µg/m3)	5.95	5.55	3.20	12.90	2.38
HCHO (mg/m3)	0.001	0.001	0.001	0.002	0.0005
School E						488.00
CO2 (ppm)	467.92	469.00	344.00	531.00	33.77
PM10 (µg/m3)	30.13	25.55	15.40	96.90	17.78
PM2.5 (µg/m3)	17.79	14.90	9.20	58.80	10.74
HCHO (mg/m3)	0.001	0.001	0.001	0.002	0.0004

. The overall analysis of indoor CO₂ concentrations in the sampled schools shows that the mean, median, minimum, and maximum values consistently remained below 1000 ppm. School D was a notable exception, with a maximum CO₂ concentration of approximately 2410 ppm. This unusually high value is likely an outlier caused by someone breathing directly onto the sensor during measurement, which can lead to localized spikes in CO₂ levels, especially in naturally ventilated spaces without forced air mixing. The overall mean CO₂ concentration in School D remained within acceptable limits, supporting this interpretation. A study of Nigerian primary schools reported a similar pattern, with CO₂ concentrations in cross-ventilated classrooms also remaining below 1000 ppm [12]. A recent study from two naturally ventilated primary schools in the Democratic Republic of Congo reported considerably higher indoor CO₂ concentrations, with peak values reaching up to 9459 ppm and weekly averages consistently above 1000 ppm [41]. These elevated levels were linked to poor ventilation performance, high occupancy, and closed windows during morning sessions to reduce dust and noise intrusion from nearby mining and traffic sources. This supports the idea that cross ventilation, when effective and unobstructed, may facilitate air mixing and reduce CO₂ buildup in naturally ventilated school environments.

Indoor CO₂ is recognized as a crude measurement for assessing ventilation adequacy. Levels below 1000 ppm indicate sufficient air change and movement, characterized by the continuous expulsion of spent air and the introduction of fresh outdoor air [2,54,66]. The spot measurements of outdoor CO₂ concentration consistently revealed levels around 400 ppm. This baseline concentration is indicative of typical ambient outdoor air conditions [66].

The formaldehyde measurements showed a consistent pattern across all sampled schools, with a mean value of 0.001 mg/m³ (ranging from 0.001 to 0.002 mg/m³). This uniformity indicates a generally low and stable concentration of formaldehyde in the indoor air of the assessed classrooms, likely resulting from adequate ventilation. The low CO₂ levels and the absence of VOC-emitting materials such as carpets and cleaning materials in the classrooms further support this assessment [67].

In contrast to the uniform formaldehyde levels, the measurements for PM2.5 varied between schools. School D recorded a minimum value of 3.20 µg/m³, while School E recorded a maximum of 58.80 µg/m³. Similarly, PM10 measurements ranged from a minimum of 5.50 µg/m³ in School D to a maximum of 96.90 µg/m³ in School E. The fact that PM2.5 constitutes part of PM10 explains the observed similarities and differences in the ranges for these PM measurements across the sampled schools [68]. The mean and maximum values for PMs generally exceeded the South African domestic IAQ guideline recommended levels of 10 µg/m³ for PM2.5 and 20 µg/m³ for PM10 [55]. Elevated PM concentrations have been shown to impact students’ health and learning outcomes adversely. A study by Pham and Roach [69] revealed that exposure to PM2.5 levels above recommended values is associated with a reduction in learning outcomes, with students experiencing up to a 7.5% standard deviation decline in performance due to PM2.5 exposure. Additionally, Roy et al. [70] found that both PM2.5 and PM10 are associated with negative health outcomes in students, as evidenced by lower lung function values.

Spearman’s rho correlation test was used to assess the association between IAQ parameters, including temperature and RH, as shown in

Table 6. Spearman’s rho correlation between measured IAQ parameters including temperature and RH.

	PM2.5		PM10		CO2		HCHO		T		RH
	r	p-Value	r	p-Value	r	p-Value	r	p-Value	r	p-Value	r	p-Value
1	1.000		0.998	<0.001	0.289	<0.001	0.088	0.319	−0.073	0.411	0.439	<0.001
2	0998	<0.001	1.00		0.290	<0.001	0.091	0.304	−0.070	0.427	0.432	<0.001
3	0.289	<0.001	0.290	<0.001	1.00		−0.025	0.780	0.226	0.010	0.119	0.178
4	0.088	0.319	0.091	0.304	−0.025	0.780	1.00		−0.182	0.038	0.126	0.156
5	−0.073	0.411	−0.070	0.427	0.226	0.010	−0.182	0.038	1.00		−0.788	<0.001
6	0.439	<0.001	0.432	<0.001	0.119	0.178	0.126	0.156	−0.788	<0.001	1.00

(1 = PM2.5, 2 = PM10, 3 = CO₂, 4 = HCHO, 5 = T and 6 = RH).

.

Classroom indoor temperature shows a robust and statistically significant negative correlation with RH (r = −0.788, p < 0.001). This indicates that as the temperature increases, RH decreases, and vice versa. As retrofitting interventions were anticipated in the classrooms to lower their temperature, it is crucial to ensure that the RH levels in the classrooms remain within recommended limits. This precaution is necessary to prevent the classrooms from becoming breeding sites for microorganisms that can adversely affect students’ health, especially when the temperature is lowered.

Both PM2.5 and PM10 show positive and statistically significant associations with RH (r = 0.439 for PM2.5, r = 0.432 for PM10) and CO₂ (r = 0.289 for PM2.5, r = 0.290 for PM10), all with p < 0.001. The effect sizes of these associations range from moderate, in the case of PM with RH, to weak to moderate for the relationship between PM and CO₂. PM concentrations tend to be higher when RH or CO₂ levels are elevated and lower when these environmental factors decrease. An increase in CO₂ concentration, often associated with reduced ventilation, can lead to insufficient dilution and expulsion of PMs in the classrooms. RH is typically high in the morning, coinciding with students cleaning their classrooms with brooms. This cleaning activity may resuspend PMs, contributing to higher PM concentrations [71]. In addition, high RH conditions can promote hygroscopic growth of fine particles, especially in naturally ventilated environments where moisture exchange is unregulated. When RH increases, PM particles may absorb moisture, grow in size, and remain suspended for longer due to reduced settling velocity, thereby contributing to elevated concentrations [72]. As RH decreases throughout the day, the concentration of resuspended PMs also diminishes. This dynamic may explain the statistically significant positive correlation observed between PM concentrations and RH. It is important to note that the relationship between RH and particulate matter is complex, involving multiple factors that interact to influence their correlation. Notably, the association between PM2.5 and PM10 is nearly perfect (r = 0.998, p < 0.001), which reflects their intrinsic relationship, as PM2.5 is a component of PM10 [68].

To mitigate the elevated levels of PM2.5 and PM10 observed in the classrooms, immediate strategies should be considered. First, classrooms should be cleaned using damp methods (e.g., mopping) instead of dry sweeping to reduce the resuspension of PMs. Additionally, cleaning should be scheduled after school hours to allow resuspended particles to settle overnight before students return the following day. Finally, rotating cleaning duties among students is recommended to minimize repeated exposure to PM for any single individual.

3.5. Study Limitation and Future Directions

This study offers initial insights into the IEQ of Tanzanian secondary schools; however, it does carry limitations that should be noted. The data were collected from a relatively small sample of 14 classrooms within five schools in the Temeke district, limiting the generalizability of the findings across Tanzania or broader East African contexts. Although various sensors were utilized for objective IEQ measurements and subjective comfort surveys were conducted, the results of the subjective surveys are reserved for detailed discussion in a forthcoming manuscript. This current separation might limit the comprehensive understanding of IEQ impacts until the subjective data are analyzed to complement the current objective sensor assessment. Additionally, the study’s short duration does not capture seasonal variations, which could significantly influence IEQ.

Future research directions should expand the scope of this study by including a broader geographic sample and extending the duration to capture seasonal and long-term effects of IEQ on student health and academic performance. Currently, interventions using locally sourced materials are being tested [73] for their efficacy in improving classroom IEQ. Both objective and subjective measurements will be conducted after these interventions (Alpha phase) to assess their efficacy, with a long measurement campaign planned to ensure adequate assessment. Such comprehensive assessment will be crucial for formulating effective, contextually appropriate IEQ improvement strategies. Furthermore, the publication of the integrated Alpha phase results will offer a more complete conclusion on the interaction between physical IEQ measures and the subjective comfort of school occupants, potentially guiding policy and practical implementations for healthier educational environments. Although this study documents key passive design features, including reliance on daylight for lighting, permanently open windows and doors for ventilation, and the use of uninsulated masonry structures with zinc roofs, it does not fully explore the influence of broader bioclimatic factors. Future research should build on this baseline by examining the effects of classroom orientation, solar exposure, and other climate-responsive design variables to support the development of context-specific strategies for improving IEQ in Tanzanian and East African schools.

3.6. Summary of Findings in Relation to Research Questions

This study aimed to answer three research questions concerning IEQ in Tanzanian secondary school classrooms. The first question focused on identifying the baseline levels of temperature, relative humidity, noise, lighting, and indoor air pollutants, which were presented in Section 3.1, Section 3.2, Section 3.3, Section 3.4. The second question addressed how these levels compare with internationally recommended standards, as shown in Table 1 and discussed within each subsection. The third question sought to interpret what these findings reveal about current classroom conditions in Tanzania.

The results show that many classrooms exceed recommended thresholds for temperature, noise, and particulate matter. On the other hand, CO₂ levels generally indicate adequate ventilation, suggesting that air exchange may be sufficient in many cases. This evidence highlights areas that require urgent attention to create healthier learning environments.

Research on IEQ in schools is still emerging in Tanzania, a country with a large and growing student population. To the best of our knowledge, this study represents the first objective assessment of IEQ in Tanzanian schools using direct environmental measurements. As such, it offers a foundational reference for future research and serves as a useful source of information for environmental scientists, education policymakers, school leaders, and parents. The findings help to clarify the types of environmental exposures students may face in classrooms, which may influence both learning and well-being. This contribution is expected to stimulate further investigations in Tanzania and other countries with similar climates and school infrastructure. These considerations emphasize the significance of the research questions addressed in this study.

4. Conclusions

This study has provided an assessment of IEQ in Tanzanian secondary schools, highlighting several critical areas that require attention to improve the learning environment. The findings indicate that classroom temperatures generally exceed the recommended levels, which can negatively impact student performance and comfort. Relative humidity, while varying, also sometimes surpassed the recommended upper limits, particularly in the mornings. Noise levels frequently exceeded the WHO’s recommended thresholds, likely due to overcrowding, posing significant disruptions to the learning process.

Lighting conditions in classrooms showed considerable variation, with areas near windows receiving more light compared to central areas, and most classrooms lacking sufficient artificial lighting due to electrical issues. IAQ assessment revealed that while CO₂ levels generally remained below the 1000 ppm threshold, indicating adequate ventilation, particulate matter (PM2.5 and PM10) levels often exceeded recommended values, posing potential health risks.

The study underscores the need for targeted interventions to address these issues. Specifically, retrofitting classrooms with better insulation to manage temperatures, soundproofing materials, and more effective lighting solutions could significantly enhance IEQ. Such improvements are essential for creating a conducive learning environment that supports students’ health, well-being, and academic performance.

These preliminary findings from the ILCE program provide a crucial baseline for future studies and interventions. They emphasize the importance of having context-specific standards and guidelines for IEQ in East African schools, tailored to the unique climatic and structural conditions of the region, since the measured IEQ parameters were compared with standards from outside the region. The ILCE program currently focuses on the implementation and evaluation of cost-effective retrofitting measures to determine their impact on student comfort and learning outcomes.

The results of this study can support practical decision-making for school retrofitting, classroom design, and ventilation strategies tailored to tropical school environments. The findings may serve as an initial evidence base to support future policy discussions related to minimum IEQ conditions in schools, prioritization of retrofitting in public investment strategies, and the eventual development of context appropriate school design guidelines for schools in East Africa or tropical regions. By addressing these IEQ challenges, significant strides can be made toward providing healthier, more comfortable, and more effective learning environments for students in Tanzania and similar settings across East Africa.