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Article

Real-Time Monitoring of Environmental Parameters in Schools to Improve Indoor Resilience Under Extreme Events

by
Salit Azoulay Kochavi
1,
Oz Kira
1,2,* and
Erez Gal
1,2
1
The Department of Civil and Environmental Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
2
The Goldman Sonnenfeldt School of Sustainability and Climate Change, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
*
Author to whom correspondence should be addressed.
Smart Cities 2025, 8(1), 7; https://doi.org/10.3390/smartcities8010007
Submission received: 30 October 2024 / Revised: 2 December 2024 / Accepted: 11 December 2024 / Published: 3 January 2025
(This article belongs to the Topic Smart Cities: Infrastructure, Innovation, Technology, Governance and Citizenship)
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Abstract

:

Highlights

What are the main findings?
  • The study emphasizes the critical need for real-time IAQ monitoring in educational facilities, especially during extreme weather events. By closely tracking air pollutants, particularly PM2.5, temperature, and humidity, the study reveals that indoor air quality can deteriorate significantly during such events, potentially impacting vulnerable children’s health.
  • The study observed that during sandstorms, indoor PM2.5 levels rose by over 16%, and temperatures increased by more than 5% compared to normal conditions. These findings underline the significant infiltration of outdoor pollutants indoors, even with windows and doors closed, and call for enhanced ventilation and filtration systems in schools.
What is the implication of the main finding?
  • This research aligns with the objectives of smart cities by calling for intelligent, real-time IAQ monitoring in schools, especially those in regions susceptible to climate extremes. The study supports policy initiatives focused on implementing centralized ventilation, monitoring systems, and air quality regulations in school environments, enhancing urban resilience to environmental stressors.
  • To mitigate health risks, the study suggests that policy frameworks establish indoor air quality guidelines for educational settings, especially in arid or polluted regions. It also highlights the importance of further research to refine IAQ models and recommends installing low-cost, routinely calibrated sensors for ongoing IAQ assessment, aiming to create safer, healthier indoor environments for children.

Abstract

Climatic changes lead to many extreme weather events throughout the globe. These extreme weather events influence our behavior, exposing us to different environmental conditions, such as poor indoor quality. Poor indoor air quality (IAQ) poses a significant concern in the modern era, as people spend up to 90% of their time indoors. Ventilation influences key IAQ elements such as temperature, relative humidity, and particulate matter (PM). Children, considered a vulnerable group, spend approximately 30% of their time in educational settings, often housed in old structures with poorly maintained ventilation systems. Extreme weather events lead young students to stay indoors, usually behind closed doors and windows, which may lead to exposure to elevated levels of air pollutants. In our research, we aim to demonstrate how real-time monitoring of air pollutants and other environmental parameters under extreme weather is important for regulating the indoor environment. A study was conducted in a school building with limited ventilation located in an arid region near the Red Sea, which frequently suffers from high PM concentrations. In this study, we tracked the indoor environmental conditions and air quality during the entire month of May 2022, including an extreme outdoor weather event of sandstorms. During this month, we continuously monitored four classrooms in an elementary school built in 1967 in Eilat. Our findings indicate that PM2.5 was higher indoors (statistically significant) by more than 16% during the extreme event. Temperature was also elevated indoors (statistically significant) by more than 5%. The parameters’ deviation highlights the need for better indoor weather control and ventilation systems, as well as ongoing monitoring in schools to maintain healthy indoor air quality. This also warrants us as we are approaching an era of climatic instability, including higher occurrence of similar extreme events, which urge us to develop real-time responses in urban areas.

1. Introduction

As we enter an era of climatic instability, indoor air quality and environmental comfort face a heightened risk of deterioration. With extreme events becoming more frequent [1], the resilience of the indoor environment in maintaining its atmosphere becomes increasingly crucial [2]. This study focuses on the Middle East, a region historically characterized by extreme conditions, such as high temperatures, low precipitation, and dust storms, impacting human health. Recent climatic changes and ongoing desertification have led to high uncertainty and may result in more frequent and severe dust storms. These events are expected to become even more common as we move into an increasingly unstable climatic future.
In recent years, scientific evidence has highlighted that indoor air quality (IAQ) can be compromised [3]. Poor air quality can stem from various indoor air pollutants, including biological, chemical, and physical sources. It can adversely affect the occupants’ health, leading to symptoms such as sneezing, teary eyes, coughing, shortness of breath, dizziness, fatigue, and digestive problems. Furthermore, it can exacerbate conditions such as asthma and angina and even lead to life-threatening illnesses or sudden death. Poor IAQ can also promote the spread of biological and chemical factors, impacting occupants’ perception of stuffiness and comfort [4]. Given that people now spend up to 90% of their time indoors [5], the health risks associated with poor IAQ are becoming increasingly significant. Particularly vulnerable groups include children, the elderly, and individuals with pre-existing lung or cardiovascular conditions [6,7]. The quality of the indoor environment is influenced by various factors, including site conditions, climate, building design, mechanical systems, construction methods, sources of pollution, occupants, and external transportation [8,9].
Children spend nearly 30% of their day at school, with 70% of that time spent in classrooms. However, overcrowded classrooms and aging school buildings with inadequate planning and maintenance often lead to poor ventilation and insulation. This exposes this vulnerable population to increased risks [10,11,12], which may impact their health and reduce their academic performance [13]. Moreover, children’s underdeveloped respiratory systems, breathing patterns, and close physical interactions put them at risk of potential long- and short-term health issues [14,15,16,17].
Understanding the relationship between school building characteristics, their surroundings, and indoor air pollutants is crucial due to its potential implications for health and well-being, especially under harsh conditions and extreme events. This underscores the importance of investigating air quality in classrooms, as poor IAQ can lead to decreased attendance, hindered learning achievements, and reduced teacher productivity [8,16], and ultimately lead to health issues. Air quality concerns in school buildings are widespread globally. For instance, half of U.S. schools reported internal air quality issues, while in Finland, 75–85% of school occupants experienced comfort issues affecting their performance levels [9]. Proper monitoring and continuous management of IAQ, such as by ensuring proper ventilation, can prevent or mitigate these issues. Additionally, adequate ventilation can reduce the spread of infectious diseases and enhance students’ and teachers’ health and performance [8,18,19].
To estimate the influence of extreme events on indoor environmental conditions, we tracked four measures in an elementary school: temperature (T), relative humidity (RH), carbon dioxide (CO2), and fine particulate matter (PM2.5). These measures are influenced by external environmental conditions and the type and effectiveness of ventilation in the building [8,20]. Maintaining stable thermal conditions by avoiding frequent fluctuations in temperature and relative humidity, ensuring proper air mixing through ventilation systems, and adequate insulation are crucial for the thermal comfort of young children [7,21]. Poor maintenance of buildings and equipment can significantly contribute to poor indoor air quality, leading to discomfort and adverse health effects among occupants [17].
A relative humidity (RH) level greater than 60% can lead to moisture damage and promote the growth of microorganisms, exacerbating respiratory issues such as asthma symptoms in students and teachers [22]. Conversely, an RH level lower than 30% can cause the release of fungal spores and irritation of the eyes and upper respiratory system due to dryness [9,17]. Indoor CO2 levels are primarily influenced by the number of occupants and ventilation rates, with sufficient ventilation being crucial for maintaining a healthy learning environment and preventing adverse health effects among children [23]. While CO2 is not considered a pollutant indoors, it serves as an indicator of adequate ventilation levels.
The World Health Organization (WHO) sets the permissible concentration of CO2 in enclosed spaces up to 1000 ppm. Beyond this threshold, occupants may experience headaches, nose and throat irritation, decreased concentration, and fatigue. In modern automatic ventilation systems, elevated CO2 levels trigger ventilation processes [24,25,26]. According to Israeli standard 5281 for “Construction of Sustainable Buildings (Green Buildings)”—Section 5 “Health and Welfare”, automatic ventilation should activate when CO2 levels exceed 300 ppm [27]. A comparative study examining naturally ventilated classrooms in different climates (Spain and Poland) found that CO2 concentrations exceeded recommended levels within one hour of full occupancy [25]. Another study monitoring CO2 levels in a conference room at Bialystok University (far from industrial areas) showed a linear increase in CO2 levels, attributed to occupants due to the supposed clean outdoor environment [28].
Indoor PM2.5 originates from various outdoor sources such as motor vehicles, power plants, dust storms, and smoking, as well as indoor sources like building occupants and materials [5]. It can travel long distances and is a leading cause of global mortality due to air pollution [6,29]. The Organization for Economic Co-operation and Development (OECD) considers PM2.5 the most abundant and critical factor affecting human health, with PM alone linked to approximately 2.1 million premature deaths annually worldwide [30,31,32].
Ambient PM concentration, exposure duration, individual physical characteristics (e.g., age, gender, health status), and lung development and breathing rates influence intake levels and the severity of harm [15,33]. A 2019 preschool study reported high exposure levels to PM2.5 in 69.0% of classrooms, primarily in urban areas, related to seasonal factors, occupancy, and activity patterns [23]. In efforts to protect young children during warm and cold seasons, Brazilian educational teams inadvertently sealed spaces, halting ventilation and exposing children to harmful pollutants, particularly respiratory particles [15,34]. Epidemiological studies have linked daily PM2.5 exposure to illness and even immediate deaths, with no safe limit set by WHO [34].
In most cases, ASHRAE (the American Society of Heating, Refrigerating, and Air-Conditioning Engineers) standards are used to determine the recommended levels of each parameter screened in this study. The IMOH advises maintaining indoor thermal factors within certain ranges: a T of 20–25 °C and a RH of 30–60%. This aligns with the EPA (the U.S. Environmental Protection Agency) recommendation of keeping RH between 30% and 50% in indoor school environments, or at least below 60%, to prevent mold growth [35,36].
Our study was conducted in Eilat (Figure 1), a city uniquely positioned on the shores of the Red Sea, bordering Asia, Africa, and the Mediterranean Sea. The city’s location exposes it to dust and sandstorms from Africa and aerosols from Europe, all contributing to PM levels [31,37]. Eilat’s climate, characterized as hyper-arid, features warm and dry desert conditions with limited precipitation. In July–August, average daily maximum temperatures reach 39.2 °C, with relative humidity at around 16% [38,39,40].
Desert dust storms (DDS) are natural events common in arid regions like Eilat, causing reduced visibility and haze formation due to airborne sand particles [41]. Israel, situated in the Dust Belt, experiences frequent dust storms originating from the Arabian Peninsula and North Africa, posing significant public health risks due to elevated PM levels [42]. Sandstorms raise PM levels and can cause respiratory diseases. A study examining the impact of sandstorms on air pollution and health in our region found that PM levels grew exceedingly higher than the WHO recommendation due to these events. The study found a causal relationship between an increase in PM10 levels and a rise in hospitalizations for respiratory problems [43].
Despite numerous studies highlighting the importance of indoor air quality (IAQ), real-time monitoring of IAQ factors remains underutilized. Furthermore, the significance of such real-time systems is rarely demonstrated in terms of daily variations in IAQ. These systems are particularly crucial in regions prone to extreme weather events that can significantly impact IAQ, especially in areas with limited climate control systems. In our study, we installed a sensor network in a public elementary school to monitor four key environmental factors: temperature and relative humidity (T and RH%) for comfort and carbon dioxide (CO2) and fine particulate matter (PM2.5) for health. To highlight the critical need for such a system, this paper focuses on one month’s measurements, including an extreme dust storm, and its impact on indoor environmental conditions.

2. Materials and Methods

2.1. Study Site and Data Collection Periods

Our study was conducted at Ye’elim Elementary School in Eilat. This school, constructed in 1967, is situated in a quiet urban area with low socio-economic status. The city’s geographical location, bordering the Arava Valley to the south and the northern coast of the Red Sea, along with its hyper-arid climate, imparts distinctive characteristics [38] that may exacerbate indoor air quality issues at the school.
A modular monitoring system (Section 2.2) was installed at the school to enable real-time monitoring of air quality and various environmental parameters. To make this data accessible to students, a user-friendly interface was created (see Figure 2), allowing live data to be displayed for each classroom. Additionally, a color-coded display was used to help young students quickly identify which measurements were outside the recommended ranges.
Measurements of IAQ comfort parameters, temperature (T) and relative humidity (RH), and indoor environment atmospheric concentrations of CO2 and PM2.5 were taken continuously. We chose to present measurements during the month of May 2023, which had a very big extreme weather event in the area. These assessments aimed to evaluate the levels of these parameters in unique extreme conditions characterized by an arid climate and during a sandstorm coupled with poorly ventilated spaces. We measured four classrooms (detailed in Table 1) situated on various floors of the school building, including the ground floor, second floor, and third floor adjacent to the roof. These classrooms featured varied occupancy levels, ranging from full occupancy to empty. Classrooms with distinct characteristics were treated as separate microenvironments, while others were deemed representative of the same microenvironment.
The classrooms were chosen to represent various conditions and orientations. Classes 2 and 3 are situated at the corners of the buildings, while Class 4 is near the roof. Class 1 is positioned in the center of the second floor. Other factors influencing IAQ parameters analyzed in this study include the building floor on which a class is located, temperature, and humidity patterns [16,17]. In Class 3, designated as special needs classes, the student density was lower than the maximum recommended density of 25 students per 100 m2 according to the ASHRAE standard. In contrast, in the remaining four classes, this number significantly exceeded recommendations [23].
A comparative analysis was conducted by juxtaposing the indoor data with corresponding information obtained from a monitoring station to assess the indoor–outdoor coupling (I/O ratio). This Ministry of Environmental monitoring station, measuring PM10, is situated in another district of Eilat called the “Shachmon neighborhood” (coordinates: 34°56.08′ N 29°50.32′ E) located ~3 km from the school. Data collected by this official station is accessible on their websites, enabling retrieval at any time based on the specified dates and hours.

2.2. Monitoring and Analysis Infrastructure

A modular monitoring system applied in this study was designed so that the omission or addition of components would not interfere with its overall performance. The system includes wireless control for collecting and retaining information in the cloud provided by the device manufacturers. Multifunctional instruments are used for ongoing monitoring of indoor levels of CO2, PM2.5, and comfort parameters T and RH. The sensor’s specifications are detailed in Table 2.
Sensors were strategically positioned in coordination with Eilat’s municipality and the school’s management, ensuring their approval and placement two meters above the floor. Factors such as the age of the building and the necessity to safeguard the equipment from unintentional damage were also considered during sensor placement. Measurements were conducted continuously for the entire duration of the experiment, 24 h per day. The selected days were retrieved from this database, enabling a comprehensive analysis of indoor air quality (IAQ) parameters. Before installation on-site, the indoor sensors and communication systems underwent verification to ensure that their performance met the manufacturer’s specifications, as detailed in Table 2. An interim assessment of the study sensors’ measurements of T and RH at Ye’elim school was conducted against certified data to confirm their adherence to the manufacturer’s specifications. Additionally, the sensors were placed together and compared to each other as well as to another device’s PM2.5 measurements (Dylos DC1700, Dylos Corporation, Riverside, CA, USA). Due to minor deviations (up to 5%), the sensors were recalibrated against the Dylos device to ensure they showed similar magnitudes. The Dylos itself is not a reference device; however, the used Dylos device is constantly calibrated with PALAS Promo 2000 (Palas GmbH, Karlsruhe, Germany), a much more accurate device. The calibration was based on a linear regression between the Tuya air device (Tuya Inc., Hangzhous, China) and the Dylos device. To enhance the robustness of the measurements, the positions of each Tuya Air device were rotated and switched between classrooms.

2.3. Data Analysis

Data collected from the calibrated monitoring station (as detailed in Section 2.1) and indoor sensors were subjected to analysis. Subsequently, the average values of the environmental factors measured were compared against state-of-the-art international and national recommendations and guidelines, as delineated in Table 3.
The study data were compared against the respective target values, which are believed to have health implications as per the Clean Air Act law. The target values for CO2 and PM2.5 represent the maximum allowable concentration of pollutants in the air for specified durations. Anomalous results observed on a single day are considered isolated events. A deviation lasting up to four days a year is deemed within normal limits, while deviations beyond this period are considered significant and warrant attention.

3. Results

3.1. General Conditions in the School Under an Arid Climate

The values of the observations from May 2023 are presented in Figure 3. Regarding thermal comfort parameters, the measured temperatures (Figure 3A) range between 17 °C and 33 °C. During occupancy hours (8:00–14:00), temperatures are generally cooler due to the operation of local air conditioning systems. The relative humidity (Figure 3B) ranges from 20% to 60%. We observed some deviations: all the maximum temperature values and most of the minimum values during study hours (8:00–14:00) fall outside the recommended IMOH range (Figure 3A). However, the relative humidity values are mostly within the recommended range (Figure 3B).
Interestingly, the classroom with the lowest occupancy, Class 3, had significantly higher temperatures (95% confidence level) compared to the other classrooms (Figure 4A). It should be noted that classroom differences in temperature and relative humidity can arise from various factors besides occupancy levels, such as different air conditioning systems, varying habits of opening and closing doors and windows, structural differences, and different itineraries.
The PM2.5 observations revealed levels higher than the recommended WHO exposure values. This is unsurprising, as most of the world experiences PM concentrations exceeding WHO recommendations. During the 30 days of measurement, two irregular events with elevated PM concentrations were identified: days 136–139 (Event 1) and 153–155 (Event 2).
Event 1 is likely a local occurrence, as there is no evidence of it in the PM10 measurements from the Ministry of Environmental Protection monitoring station located approximately 2 km from the school (Figure 5C). In contrast, Event 2 is clearly visible in Figure 3, indicating a regional dust storm. Furthermore, the Israeli Ministry of Environmental Protection classified the days of Event 2 as extreme sandstorm days, with high temperatures, low relative humidity, and high atmospheric PM concentrations, which are deemed extremely harmful to human health.
The PM data presented in Figure 4C shows that the minimum and average concentrations in the tracked classrooms are similar, with no significant differences found (95% confidence level). However, the maximum values differ, with Class 1 exhibiting the highest concentrations, reaching 81 μg/m3. Higher PM2.5 concentrations were observed when the school was vacant; however, this study concentrated on actual exposure, and thus it was not included here. There is no evident cause for the higher concentrations of PM2.5 in Class 1 compared to the other classrooms. This emphasizes the need to track air quality in real time and act to mitigate high indoor air pollution caused by outdoor events.
Unlike temperature, relative humidity, and PM2.5, which depend greatly on outdoor conditions, CO2 concentrations mainly depend on the number of students occupying the classroom and the ventilation system. The CO2 concentrations (Figure 3D) support this, showing higher values when the classrooms are occupied (8:00–14:00). Given that the school lacks a central ventilation system, the main reason for high CO2 concentrations is the number of students. This is clearly shown in Figure 4D, where Class 3, which has a much lower student number per area (Table 1), produced lower CO2 levels. All classrooms experienced higher than recommended CO2 levels (Table 3), coinciding with the lack of a central ventilation system and the tendency to shut doors and windows due to high heat and PM concentrations.

3.2. Impact of the Sandstorm on the Indoor Environment

The impact of the sandstorm is shown in Figure 6. This event resulted in elevated PM levels and higher temperatures. First, it is evident that CO2 concentrations, which do not substantially differ outdoors during a sandstorm, were not affected by the storm. At this time of year, temperatures are generally higher (Figure 5A), prompting students to close doors and windows regardless of the extreme event.
Temperature and relative humidity were significantly different (95% confidence level) during the sandstorm compared to regular conditions. Temperatures were approximately 5% higher during the sandstorm, likely because air conditioning systems had to work against higher outdoor temperatures. This increase pushed the average classroom temperature beyond the maximum recommended level. Relative humidity values, while still within the IMOH recommended range, were slightly lower during the sandstorm, coinciding with the lower relative humidity levels measured outside (Figure 5B) and the impact of a prolonged air conditioning activity that dries the indoor air.
The most significant change caused by the sandstorm was the elevated indoor PM levels. The average daily (8:00–14:00) concentrations increased by more than 16% due to the sandstorm. These significant changes indicate a substantial outdoor air pollution event that leaked into the indoor environment.
The correlation between indoor PM2.5 and outdoor PM10 measurements is presented in Figure 7. This figure illustrates the differences in correlation between regular and extreme conditions across various classrooms. When examining these correlations, we observe that the slopes of the linear regressions are steeper under regular conditions. Although maximum outdoor PM10 concentrations during extreme conditions were approximately five times higher than those under regular conditions, indoor PM2.5 concentrations during extreme conditions were only slightly higher than those during regular conditions.
The correlations under regular conditions are stronger compared to those under extreme conditions. However, this could be attributed to the larger dataset for regular conditions (240 data points) versus extreme conditions (60 data points). Additionally, differences between classrooms are evident in their dependence on outdoor concentrations. A higher regression slope indicates a greater dependency of indoor PM concentrations on outdoor PM levels. The fourth classroom has the steepest slope, suggesting it is more sensitive to outdoor concentrations. This classroom is the only one studied that is located on the third floor, with less interference from adjacent buildings. Conversely, the first classroom shows the lowest dependency on outdoor concentrations.

4. Discussion

4.1. General Comparison of the Measured Parameters

Eilat is situated in the heart of a desert area, regularly experiencing harsh environmental conditions, including high temperatures and low precipitation, even without the consideration of extreme weather events. These conditions force local residents to remain indoors for much of the daytime, which, while improving overall comfort, does not shield them from more severe indoor conditions compared to other regions. The average temperature and relative humidity levels, measured in Ye’elim school, are mostly within recommended ranges. However, Class 3 is an exception, with an average temperature exceeding the upper limit recommended by IMOH. Examining the differences between classrooms, we find that Class 3 has the lowest occupancy rate, which does not coincide with the higher temperatures. However, another notable feature of this classroom is its two outer walls, compared to only one outer wall in the other classrooms. This likely contributes to the higher temperatures in Class 3, as it is more exposed to heat conductance from the outdoor environment.
Maintaining thermal comfort for children during the school day can be crucial for effective learning. The average temperatures at Ye’elim School are higher than the temperatures children worldwide typically find comfortable [46]. Our data show that average hourly temperatures exceed 25 °C—considered the upper limit for thermal comfort in children—36% of the time, indicating that students at Ye’elim School are learning under suboptimal conditions.
However, several studies suggest that thermal comfort should be based on outdoor temperatures, implying that each climate has its own optimal comfort range [47,48,49]. Using the children-based adaptive comfort model, the calculated comfort temperature for May is approximately 26 °C [47]. In this context, classroom temperatures exceed this comfort level 30% of the time, which is still significant. It should be noted that adults’ comfort temperatures are generally about 2 °C higher. Since teachers, who are responsible for operating the air conditioning, may set temperatures to their comfort level, this could further compromise the children’s comfort [47].
The average CO2 levels measured were mostly below the recommended upper threshold of 1000 ppm (ASHRAE). However, there were several instances where CO2 concentrations spiked significantly, which should have alerted the teachers if these measures were being monitored. While CO2 is not classified as an air pollutant, it serves as an indicator of poor ventilation [50]. A significant increase in CO2 concentrations suggests reduced oxygen levels, which are known to diminish learning effectiveness and cause headaches [51,52]. This implies that children’s performance may be impaired due to the harsh climate in Eilat, as there is no ventilation system in place, and the doors and windows are usually closed, preventing natural ventilation.
Under “regular” environmental conditions, the air quality and thermal comfort parameters at Ye’elim School partly deviate from the recommended values. While temperature, relative humidity, and CO2 are primarily considered comfort parameters, PM is classified by the WHO as a major air pollutant. The school’s PM monitoring revealed that the children’s exposure to PM2.5 exceeds WHO recommendations.
In 2023, the annual average PM10 concentration, measured by the Israeli Ministry of Environmental Protection station, was 39.9 µg/m3 (with a standard deviation of 26.8 µg/m3), significantly higher than the WHO’s recommended 15 µg/m3. Assuming the ratio between outdoor PM10 and indoor PM2.5 remains consistent throughout the year, the corresponding annual average of indoor PM2.5 at the school would be approximately 10 µg/m3, which is twice the WHO’s recommended annual PM2.5 concentration of 5 µg/m3. This situation necessitates that decision-makers promote the monitoring of the air quality within schools and establish appropriate guidelines for building ventilation and filtration systems.

4.2. Indoor/Outdoor PM Correlations

The results presented in Figure 7 show a direct connection between indoor and outdoor PM concentrations. The correlations between indoor PM2.5 and outdoor PM10 were positive, with an increase in outdoor PM levels leading to a corresponding increase in indoor PM levels. Given the students’ activity and the absence of significant indoor PM sources in the classrooms, the fluctuations in these correlations are most likely related to the operation of the air conditioning system and the opening or closing of windows and doors.
Figure 7 reveals two main differences: varying regression slopes between the classrooms and different slope magnitudes between regular and extreme conditions. Focusing on the regular conditions, the classroom on the highest floor (Classroom 4) exhibited the steepest regression slope (0.0795), indicating that outdoor PM had the greatest impact on indoor PM levels. Classroom 1, located on the second floor, had the lowest slope (0.0489), while another second-floor classroom (Classroom 2) showed a higher slope (0.0644).
Although both second-floor classrooms face the same direction (south), they have significantly different student population densities (54.7 vs. 37.7 students per 100 m2). Students act as a filter for particulate matter (PM), and a higher student density theoretically leads to more efficient air filtration. Assuming a breathing rate of 0.42 m3/h [53], the students in Classroom 1 can “filter” the entire classroom volume (118.75 m3) in ~652 min, while in Classroom 2, with a volume of 172.5 m3, it takes them approximately 948 min.
The classroom on the first floor exhibited a slope of 0.0634. Given the interference from adjacent buildings, a lower slope would be expected. However, this classroom has the lowest student population density (14.5 students per 100 m2) and more windows compared to the other examined classrooms, which may account for the observed results.
The differences in regression slopes under regular and extreme conditions may indicate behavioral changes. During the apparent sandstorm, students and teachers were likely more inclined to stay indoors and close windows and doors more thoroughly. Katra and Krasnov (2020) measured PM10 levels across Beer Sheva during dust storms and found similar results, where the correlation between indoor and outdoor PM was stronger under regular conditions than under extreme conditions [54]. However, in a different measurement campaign, the same research group observed the opposite trend: a lower correlation between outdoor and indoor PM10 under regular conditions than during extreme conditions [55]. Due to the limitations of our equipment, we were unable to measure outdoor PM2.5 and, therefore, focused on comparing PM2.5 with PM10.

4.3. Sandstorm Occurrence and Their Impact on the Indoor Environment

The extreme sandstorm event was thoroughly documented across Israel, reaching peak levels of PM and temperature. The Israeli Ministry of Environmental Protection and the Ministry of Health advised the public to stay indoors to avoid exposure to harmful PM concentrations. However, its impact on the indoor environment was not investigated.
To better understand the occurrences of these windstorms, we analyzed the PM10 measurements from the Israeli Ministry of Environmental Protection monitoring station. The analysis followed the Ministry’s guidelines based on a study by Ganor et al. (2009) [56]. Due to the absence of a PM2.5 measuring device at the monitoring station, our analysis was limited to PM10. Ganor et al.’s study included a comparison of PM10 and PM2.5 concentrations, adding robustness to their decision-making process, which we were unable to replicate [56].
According to the PM10 analysis, there were 16 sandstorms in 2023, with some lasting a few hours and others a few days (Figure 8). In total, these sandstorm events accounted for approximately 250 h throughout the year. These windstorms occurred year-round, except during the summer months when schools were not in session.
This analysis (Figure 8) underscores the significance of our research, highlighting the substantial occurrence of sandstorms and their impact on the indoor environment. Our monitoring during the sandstorm event revealed notable changes, elevated temperatures (~5% on average) and increased PM concentrations (~16% on average), both of which were significantly different statistically from values under “regular” conditions. This indicates that the sandstorm was the cause of these changes. Additionally, the slight decrease in relative humidity, significantly different in all but one classroom, aligns with the drop in outdoor humidity due to the sandstorm. As expected, CO2 concentrations remained relatively stable, as they depend primarily on the number of occupants in the classroom. The habits of opening and closing windows and doors did not change significantly during the sandstorm, likely because the high temperatures in May already encourage staying indoors with the air conditioning on.
Our study focuses on an arid region, but its conclusions are also applicable to other areas experiencing climatic changes. Many settlements in temperate regions, such as European countries, are facing rising temperatures due to climate change. While hotter periods in the past required little attention, the current temperature increases are making indoor environments, particularly schools, less thermally comfortable. These buildings often lack efficient cooling systems, similar to the situation in schools in Eilat, which negatively affects the learning experience.
Furthermore, the growing issue of desertification is expected to increase the frequency of dust storms, even in regions where they were previously uncommon. Addressing these challenges requires real-time environmental monitoring coupled with efficient indoor climate control systems to enhance climate resilience.

4.4. Research Limitations

In preparation for this research, we examined several low-cost sensors. The chosen sensor exhibited high stability, and when comparing it to reference devices, it yielded reasonable results. However, this sensor is still a low-cost sensor, and its performance is still somewhat limited [57]. In addition, we could not place this sensor outdoors for reference due to its low durability, which left us with the PM10 measurements of the monitoring station located ~3 km from the school. The center of the research was sandstorms, a multiregional phenomenon, so the monitoring station is close enough. However, for comparison to local events, one of which apparently happened during our measurements, the 2 km distance is too far and limits our analysis in this aspect.
Another limitation is our inability to monitor the opening and closing of the doors and windows, as well as to track the occupancy levels. Filming all the classrooms was prohibited due to privacy regulations and installing open/close sensors was not applicable. Nevertheless, this information is important for the understanding of the connection between the outdoor and indoor environmental parameters.

5. Conclusions

This study investigates the importance of real-time indoor air quality (IAQ) monitoring systems, with a focus on extreme weather conditions, using a public elementary school in Eilat as a case study. A modular sensor network was deployed to measure key environmental parameters, including temperature (T), relative humidity (RH%), carbon dioxide (CO2), and particulate matter (PM2.5). Over a one-month monitoring period, including an extreme sandstorm, data were analyzed to evaluate indoor environmental conditions and their interaction with outdoor influences.
Despite the recognized importance of IAQ, real-time monitoring remains underutilized. The sandstorm event that was measured in this research demonstrated the critical role of such systems in detecting significant IAQ deviations, including elevated PM2.5 levels and temperature spikes beyond comfort thresholds. Real-time monitoring provides actionable insights into daily IAQ variations and abnormal conditions, supporting timely interventions.
Temperature and RH analyses revealed that indoor comfort levels were often suboptimal, with temperatures exceeding recommended limits 36% of the time. Thermal comfort differences between classrooms were influenced by factors such as occupancy, building orientation, and air conditioning performance. High CO2 concentrations during occupancy hours highlighted insufficient ventilation, emphasizing the need for improved air circulation systems.
Indoor PM2.5 levels consistently surpassed WHO guidelines, underscoring the susceptibility of indoor environments to outdoor pollution. During the sandstorm, PM2.5 concentrations increased by over 16%, reflecting substantial outdoor air infiltration. The sandstorm exacerbated indoor conditions, with heightened temperatures, reduced RH, and increased PM levels. Behavioral responses, such as closing windows and doors, partially mitigated outdoor infiltration but compounded ventilation deficiencies. These findings highlight the increased risk of poor IAQ in extreme weather, particularly in arid climates like Eilat.
The study underscores the need for targeted guidelines to improve school ventilation and air filtration, particularly in hyper-arid regions. Real-time IAQ monitoring systems can guide decision-makers in protecting vulnerable populations, such as children, from adverse health and comfort impacts. Integrating these systems with improved building designs can enhance IAQ resilience during extreme weather events, promoting sustainable indoor environments.
This should encourage schools to use monitoring sensors to track indoor environmental conditions. Proper regulations are required for the installation and maintenance of such systems to ensure a high level of accuracy. Even low-cost sensors can effectively monitor the indoor environment if regularly maintained and calibrated. Additionally, policymakers should continue and promote studies to estimate children’s exposure to harmful pollutants, establish proper regulations, and create a safer and healthier school environment.

Author Contributions

Conceptualization, E.G. and S.A.K.; methodology, E.G., S.A.K. and O.K.; validation, S.A.K. and O.K.; formal analysis, O.K.; investigation, S.A.K. and O.K.; resources, E.G.; data curation, S.A.K.; writing—original draft preparation, O.K.; writing—review and editing, O.K.; visualization, O.K.; supervision, E.G. and O.K.; funding acquisition, E.G. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the European Union under the ENI CBC Mediterranean Sea Basin, under the project “Berlin—Cost-effective rehabilitation of public buildings into smart and resilient nano-grids using storage” [grant ref. A_B.4.3_0034].

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors thank the Eilat Municipality and the school management and administration staff of Ye’elim School for facilitating the measurement procedures conducted within the school premises. Furthermore, appreciation is extended to government entities, namely the Israeli Ministry of Agriculture and the Israeli Ministry of Environmental Protection, for providing unrestricted access to station data, encompassing periodic datasets and associated parameters.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. (A) A map of the general region of Eilat on the shores of the Red Sea (Landsat). (B) A land surface temperature map of the region of Eilat during the time of measurements (Landsat).
Figure 1. (A) A map of the general region of Eilat on the shores of the Red Sea (Landsat). (B) A land surface temperature map of the region of Eilat during the time of measurements (Landsat).
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Figure 2. The accessible display of monitored environmental parameters, which provides an effective way to communicate measurements to students.
Figure 2. The accessible display of monitored environmental parameters, which provides an effective way to communicate measurements to students.
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Figure 3. Observations of temperature (A), relative humidity (B), PM2.5 (C), and CO2 (D) during 130 and 160 day of year (DOY) 2023. The observations are class-specific (four classes in total).
Figure 3. Observations of temperature (A), relative humidity (B), PM2.5 (C), and CO2 (D) during 130 and 160 day of year (DOY) 2023. The observations are class-specific (four classes in total).
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Figure 4. Daily (8:00–14:00) average, maximum and minimum values of temperature (A), relative humidity (B), PM2.5 (C), and CO2 (D) during 130 and 160 days of year (DOY) 2023. The observations are class-specific (four classes in total). In (D), the maximum values of classrooms 1 and 3 reached the maximum detection level.
Figure 4. Daily (8:00–14:00) average, maximum and minimum values of temperature (A), relative humidity (B), PM2.5 (C), and CO2 (D) during 130 and 160 days of year (DOY) 2023. The observations are class-specific (four classes in total). In (D), the maximum values of classrooms 1 and 3 reached the maximum detection level.
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Figure 5. Corresponding temperature (A), relative humidity (B), and PM10 (C) measurements. Temperature and relative humidity were measured by a station of the Israeli Meteorological Service. The PM10 was measured by a monitoring station operated by the Israeli Ministry of Environmental Protection.
Figure 5. Corresponding temperature (A), relative humidity (B), and PM10 (C) measurements. Temperature and relative humidity were measured by a station of the Israeli Meteorological Service. The PM10 was measured by a monitoring station operated by the Israeli Ministry of Environmental Protection.
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Figure 6. Daily (8:00–14:00) average values of temperature (A), relative humidity (B), PM2.5 (C), and CO2 (D) under regular conditions and under extreme conditions. The bars represent standard deviation, and above each two bars is the p-value for the test of significant difference (95% confidence level).
Figure 6. Daily (8:00–14:00) average values of temperature (A), relative humidity (B), PM2.5 (C), and CO2 (D) under regular conditions and under extreme conditions. The bars represent standard deviation, and above each two bars is the p-value for the test of significant difference (95% confidence level).
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Figure 7. Indoor PM2.5 compared to outdoor PM10 at the test site. Data representing regular conditions were collected from DOY 140–150, while data representing extreme conditions was collected from DOY 153–155. The dotted line represents a linear regression based on the data.
Figure 7. Indoor PM2.5 compared to outdoor PM10 at the test site. Data representing regular conditions were collected from DOY 140–150, while data representing extreme conditions was collected from DOY 153–155. The dotted line represents a linear regression based on the data.
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Figure 8. Maximum PM10 half-hourly concentrations during a sandstorm event. The data were taken from a monitoring station operated by the Israeli Ministry of Environmental Protection. A separate sandstorm event was defined when there was at least a 12 h difference with no sandstorms before and after the event.
Figure 8. Maximum PM10 half-hourly concentrations during a sandstorm event. The data were taken from a monitoring station operated by the Israeli Ministry of Environmental Protection. A separate sandstorm event was defined when there was at least a 12 h difference with no sandstorms before and after the event.
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Table 1. Class characteristics: maximal number of children per class, class density, window directions, and building floor.
Table 1. Class characteristics: maximal number of children per class, class density, window directions, and building floor.
Class1234
Size (m2)47.569.069.047.5
Number of students26261026
Students # per 100 m2 a54.737.714.554.7
Window direction
North b/South c		SouthSouthNorth and SouthSouth
Floor2nd2nd1st (Ground)3rd
a Recommended number of students per 100 m2 (ASHRAE) is 25; b sun all day long; c shadow all day long.
Table 2. Sensor specification.
Table 2. Sensor specification.
Device Type (Location)ParameterMeasurement PrinciplesRange
Tuya_Air (Indoor)Temperature and Relative Humiditysolid-state sensorsDetection T:
−10–50 °C
RH: 20−85%
CO2infra-red detectorCO2: 0–5000 ppm
PM2.5laser scatteringNA µg/m3
Table 3. Recommended ranges or maximum levels of the various measured indoor parameters.
Table 3. Recommended ranges or maximum levels of the various measured indoor parameters.
ParameterAuthorityAdvised Level
TIMOH a20–25 °C
RHIMOH a30–60%
CO2ASHRAE b1000 ppm
PM2.5WHO c5 µg/m3 annual average
15 µg/m3 24 h average
PM10WHO c15 µg/m3 annual average
45 µg/m3 24 h average
a Israeli Ministry of Health—https://www.health.gov.il/English/Topics/EnviroHealth/Environmental_Contaminants/Pages/intern-structural.aspx (accessed on 1 August 2024); b ASHRAE—American Society of Heating, Refrigerating and Air-Conditioning Engineers [44]; c WHO—World Health Organization [45] https://www.who.int/news-room/feature-stories/detail/what-are-the-who-air-quality-guidelines (accessed on 1 August 2024).
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Azoulay Kochavi, S.; Kira, O.; Gal, E. Real-Time Monitoring of Environmental Parameters in Schools to Improve Indoor Resilience Under Extreme Events. Smart Cities 2025, 8, 7. https://doi.org/10.3390/smartcities8010007

AMA Style

Azoulay Kochavi S, Kira O, Gal E. Real-Time Monitoring of Environmental Parameters in Schools to Improve Indoor Resilience Under Extreme Events. Smart Cities. 2025; 8(1):7. https://doi.org/10.3390/smartcities8010007

Chicago/Turabian Style

Azoulay Kochavi, Salit, Oz Kira, and Erez Gal. 2025. "Real-Time Monitoring of Environmental Parameters in Schools to Improve Indoor Resilience Under Extreme Events" Smart Cities 8, no. 1: 7. https://doi.org/10.3390/smartcities8010007

APA Style

Azoulay Kochavi, S., Kira, O., & Gal, E. (2025). Real-Time Monitoring of Environmental Parameters in Schools to Improve Indoor Resilience Under Extreme Events. Smart Cities, 8(1), 7. https://doi.org/10.3390/smartcities8010007

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