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Article

Real-Time Insights into Indoor Air Quality in University Environments: PM and CO2 Monitoring

by
Dan-Marius Mustață
1,
Daniel Bisorca
2,*,
Ioana Ionel
1,
Ahmed Adjal
1 and
Ramon-Mihai Balogh
1
1
Faculty of Mechanical Engineering, Universitatea Politehnica Timisoara, 300006 Timisoara, Romania
2
Faculty of Civil Engineering, Universitatea Politehnica Timisoara, 300006 Timisoara, Romania
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(8), 972; https://doi.org/10.3390/atmos16080972
Submission received: 29 June 2025 / Revised: 28 July 2025 / Accepted: 13 August 2025 / Published: 16 August 2025
(This article belongs to the Special Issue Physical and Chemical Characterization of Particulate Matter: Ambient, Personal, and Indoor Perspectives)
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Abstract

This study presents real-time measurements of particulate matter (PM1, PM2.5, PM10) and carbon dioxide (CO2) concentrations across five university indoor environments with varying occupancy levels and natural ventilation conditions. CO2 concentrations frequently exceeded the 1000 ppm guideline, with peak values reaching 3018 ppm and 2715 ppm in lecture spaces, whereas one workshop environment maintained levels well below limits (mean = 668 ppm). PM concentrations varied widely: PM10 reached 541.5 µg/m3 in a carpeted amphitheater, significantly surpassing the 50 µg/m3 legal daily limit, while a well-ventilated classroom exhibited lower levels despite moderate occupancy (PM10 max = 116.9 µg/m3). Elevated PM values were strongly associated with flooring type and occupant movement, not just activity type. Notably, window ventilation during breaks reduced CO2 concentrations by up to 305 ppm (p < 1 × 10−47) and PM10 by over 20% in rooms with favorable layouts. These findings highlight the importance of ventilation strategy, spatial orientation, and surface materials in shaping indoor air quality. The study emphasizes the need for targeted, non-invasive interventions to reduce pollutant exposure in historic university buildings where mechanical ventilation upgrades are often restricted.

1. Introduction

Indoor air quality (IAQ) is a critical factor in maintaining a healthy learning environment, especially in university classrooms where students and faculty spend significant amounts of time. The quality of indoor air significantly influences health, comfort, and occupant performance. Poor IAQ has been linked to various health issues, including respiratory problems, allergies, and decreased cognitive function, which can adversely affect both students’ learning outcomes and faculty performance [1,2,3]. Recently, increased attention has been given to monitoring specific IAQ parameters such as particulate matter (PM1, PM2.5, and PM10), carbon dioxide (CO2) levels, light intensity, temperature, and humidity in educational settings [4,5].
Particulate matter (PM) is a key component of indoor air pollution and is commonly classified based on aerodynamic diameter: PM1 (particles smaller than 1 µm), PM2.5 (smaller than 2.5 µm), and PM10 (smaller than 10 µm). These particles can infiltrate deep into the respiratory tract, with finer particles reaching the alveolar region, thereby posing significant health risks. PM2.5 exacerbates conditions such as asthma and other respiratory diseases, with prolonged exposure linked to severe outcomes, including cardiovascular disease [6,7,8,9,10]. The accumulation of PM in classrooms, where ventilation may be inadequate, poses significant health risks [11,12,13,14,15].
CO2 concentration is another important IAQ parameter, often used to gauge ventilation effectiveness. Elevated indoor CO2 levels result primarily from human respiration and poor ventilation [16,17,18]. High CO2 levels can cause headaches, dizziness, shortness of breath, and impaired cognitive function, which are especially concerning in learning environments. Cognitive functions such as decision-making and problem-solving can be significantly affected when CO2 concentrations exceed 1000 ppm, a common occurrence in poorly ventilated classrooms [19,20,21,22,23,24].
Apart from air quality, environmental factors like light intensity, temperature, and humidity play crucial roles in maintaining a productive classroom environment. Poor lighting can cause eye strain and fatigue, while inappropriate temperature and humidity affect comfort and concentration [25,26,27,28,29]. High humidity can promote mold and dust mites, further degrading IAQ and exacerbating allergies [30,31,32,33,34,35,36].
The adverse health effects of poor IAQ in university classrooms are of significant concern, given the vulnerability of students and faculty who spend long hours in these environments. Exposure to elevated PM levels can lead to acute and chronic respiratory conditions, such as increased bronchitis and asthma rates, especially in individuals with pre-existing respiratory issues. Long-term exposure to high PM10 levels has been linked to several health issues, including cardiovascular diseases and reduced lung function [37,38,39].
Universities must regularly monitor and manage environmental parameters within classrooms, ensuring appropriate ventilation, pollutant control, lighting, temperature, and humidity. Effective measures are essential for fostering a healthy and productive learning environment [40].
The purpose of this study was to investigate the concentration of PM and carbon dioxide CO2 levels in multiple university environments over different experimental conditions.

2. Materials and Methods

The measurement episode was conducted in several distinct areas of the university campus—amphitheaters, laboratories, workshops, and classrooms—at Universitatea Politehnica Timisoara, Romania. These rooms are located across multiple buildings within the central university complex in downtown Timisoara. The measurement episodes were performed on separate days throughout the year.
Detailed layout and window positioning for each room are as follows:
AMPH-1 is located on the ground floor of a heritage-listed main building. The room faces the main road and has large, single-sided windows aligned along the longer wall, offering limited cross-ventilation. The windows are tall but spaced far apart, reducing the efficiency of natural airflow.
AMPH-2, also on the ground floor of the same building as AMPH-1, has a similar street-facing orientation. However, the windows are more evenly distributed and aligned on both sides of the room, providing slightly improved air circulation during the monitoring period.
TOOL-1 is situated on the second floor of a rear technical workshop building facing an internal courtyard. The windows are wide and run across one side of the room, providing modest but continuous natural ventilation.
CLASS-1 is located on the first floor, at the corner of an academic building. It features windows on two perpendicular walls, allowing for effective cross-ventilation and optimal air exchange during the break periods.
LAB-1 is also on the first floor of the same building as CLASS-1 but is located in the middle of the corridor row. It has windows only on one side, limiting airflow to a single direction.
Sampling was conducted on the following specific dates, covering both spring and early spring-to-summer transition periods over 2 academic years (2024–2025).
Table 1 shows the distinct measurement areas with their spatial characteristics.
Measurements were performed for detecting the concentration of CO2 and PM (PM1; PM2.5; PM10). For CO2, measurements were recorded at 15 s intervals, whereas for particulate matter (PM), a 6 s sampling interval was employed during the measurement process.
Each area is naturally ventilated through open windows, which facilitate air exchange. Natural ventilation is preferable to mechanically enhanced systems, as it avoids additional energy consumption. Furthermore, the majority of the buildings are of historical significance, where the installation of air conditioning units on building façades is prohibited.
Break periods are scheduled every 50 min, allowing for a 10 min interval between classes. This timing has been adopted over the years, in part to address the accumulation of CO2 during class sessions, promoting air exchange and maintaining indoor air quality.
A key factor influencing the results of this study is the spatial orientation of the classrooms in relation to the main road. Table 2 illustrates the positioning of each room relative to this road, which typically experiences heavy traffic congestion. Classrooms with windows facing the main road are expected to exhibit higher concentrations of CO2 and particulate matter (PM) compared to those located on the opposite side of the buildings, adjacent to an internal courtyard area.
In addition to vehicle emissions from continuous traffic along the main boulevard, vehicles often idle or stop near the building façade due to nearby traffic light-controlled intersections, contributing to outdoor pollutant buildup. PM infiltration may also be influenced by wind direction, which often carries roadside dust and fine particles toward the street-facing windows. These windows, particularly in AMPH-1 and AMPH-2, are typically opened during breaks, allowing such pollutants to enter directly. In contrast, TOOL-1, CLASS-1, and LAB-1 face the quieter, greener internal courtyard, with fewer external sources of pollution. The courtyard also benefits from vegetative cover, which may help reduce incoming dust and particles by acting as a natural buffer.
These factors likely explain the notable differences observed between indoor spaces with identical ventilation practices but differing outdoor exposures.
For the analysis of indoor air quality, two different types of sensors were employed: one for measuring CO2 concentration and another for measuring PM.
CO2 was measured using a Testo infrared (IR) sensor, product no. 0632 1543, manufactured by Testo SE & Co. KGaA (Titisee-Neustadt, Baden-Württemberg, Germany), specifically designed for accurate, real-time CO2 monitoring. The Testo IR sensor operates based on the non-dispersive infrared (NDIR) technology, which detects CO2 by measuring the absorption of infrared light at specific wavelengths [41]. The sensor consists of an infrared light source, a gas sample chamber, and a detector. As the infrared light passes through the air sample, CO2 molecules absorb specific wavelengths of light, and the amount of absorbed light is directly correlated with the CO2 concentration. This method allows for highly reliable and precise CO2 measurements, making it ideal for monitoring indoor air quality in both static and dynamic environments.
PM concentrations were measured using a DLS-type (Dynamic Light Scattering) sensor, part of a portable aerosol spectrometer, model number 1.109, manufactured by DURAG GROUP (Hamburg, Germany). This type of sensor is commonly used for detecting and quantifying particulate matter, including PM1, PM2.5, and PM10. The DLS sensor works by scattering light as it passes through a volume of air containing particles. The intensity of the scattered light is directly related to the concentration and size distribution of the particles in the air. The sensor uses a laser or LED light source, and as particles scatter the light, the sensor measures the scattered signal. This information is then processed to determine the concentration of PM in the air, providing accurate and real-time readings of particulate matter levels.
To assess the influence of outdoor air pollution on particulate matter (PM) concentration levels, an external sensor was continuously operated throughout the indoor measurement periods. This enabled a comparative analysis to determine whether outdoor PM concentrations affected indoor levels, given that the rooms are naturally ventilated through open windows. The sensor used for outdoor PM concentration measurements is of a DLS-type, manufactured by Airly Sp. Z o.o. (Kraków, Poland).
All sensors were calibrated and deployed in the university environments to provide continuous measurements of CO2 and PM concentrations, offering insight into air quality variations during the measurement episodes.

3. Results and Interpretations

Results and interpretations are split into two subchapters representing CO2 and OM concentrations found during the different measurement episodes and university areas.

3.1. CO2 Concentrations

According to air quality directives of the European Union, the maximum allowed concentration limit of CO2 is 1000 ppm [42].
CO2 concentration measurements for each experimental condition presented in the previous chapter are represented below (AMPH-1 Figure 1; AMPH-2 Figure 2; TOOL-1 Figure 3; CLASS-1 Figure 4; LAB-1 Figure 5), including the window configurations, opened or closed partially, for a specific amount of time, or during the entire episode.
During the AMPH-1 measurement episode, windows were open during a study break time of 10 min between 11:50 and 12:00 h.
During the AMPH-2 measurement episode, the windows were opened at the start of the monitoring period and remained open for the duration of the episode.
During the TOOL-1 measurement episode, windows constantly open.
During the CLASS-1 measurement episode, windows were opened only during the break for 10 min between 18:30 and 18:40 h.
During the LAB-1 measurement episode, windows were opened only during the break for 10 min between 11:00 and 11:10 h.
Minimum and maximum reached CO2 concentrations are represented in Table 3 (red colored text represents CO2 values exceeding the allowed limit of 1000 ppm).
Several statistical analyses were conducted, including one-sample or paired t-tests (depending on whether only open-window data or both open and closed conditions were available), along with calculations of Cohen’s d for effect size and Wilcoxon signed-rank tests for non-parametric comparison.
CO2 concentration patterns and air quality varied markedly across the experimental conditions due to differences in ventilation quality, occupancy, and spatial configuration. In AMPH-1, a slight dip in CO2 after a break was followed by a steady increase, suggesting ineffective ventilation. While this change was statistically significant (p = 4.6 × 10−10), the small effect size (Cohen’s d = 0.26) confirms only a minor practical improvement when windows were opened, likely due to suboptimal window positioning and inadequate airflow capacity. In AMPH-2, CO2 concentrations consistently decreased over time yet remained substantially above the 1000 ppm threshold (mean = 1906 ppm, p = 1.6 × 10−29, Cohen’s d = 1.45), indicating persistent air quality issues. The improvement likely resulted from better window placement and partial ventilation, but high initial values—likely from prior occupancy and poor pre-ventilation—tempered the overall effectiveness.
In contrast, TOOL-1 displayed excellent air quality. CO2 levels averaged 668 ppm (p < 0.0001, Cohen’s d = −6.08), well below recommended limits. This strong result is likely due to favorable room orientation (facing a vegetated courtyard), which reduced external pollution and enhanced passive ventilation. Temperature and humidity were also significantly lower than baselines, reinforcing the room’s conducive environmental characteristics.
CLASS-1 showed the most effective dynamic response to ventilation, with CO2 dropping from 1708 ppm to 1436 ppm after window opening (p = 4.7 × 10−69, Cohen’s d = 0.82). This condition benefited from a corner layout and cross-ventilation via windows on two perpendicular walls. Although occupancy was moderately high (70%), the spatial configuration enabled substantial improvement. A minor post-break CO2 rise reflected normal occupant activity. LAB-1 shared a similar trend but with a higher occupancy (93%), leading to faster CO2 accumulation post-break. Still, window opening yielded a meaningful drop from 2049 ppm to 1744 ppm (p = 2.5 × 10−47, Cohen’s d = 0.65). Notably, humidity dropped significantly (Cohen’s d = 1.2), underscoring the strong drying effect of ventilation.

3.2. Indoor PM Concentrations

As a general statement, which follows all recorded results, PM2.5 and PM10 are regulated by either European Union Directives (PM2.5) or by the Romanian Law (PM10), with PM2.5 having an annual average limit of 20 µg/Nm3 [43], and PM10 having a daily limit of 50 µg/Nm3 [44]. Neither the European Union nor the Romanian Law regulates PM1 limits.
PM concentration measurements for each experimental condition presented in the previous chapter are represented below (AMPH-1 Figure 6; AMPH-2 Figure 7; TOOL-1 Figure 8; CLASS-1 Figure 9; LAB-1 Figure 10), including the window configurations, opened or closed partially, for a specific amount of time, or during the entire episode.
During the AMPH-1 measurement episode, windows were open during a study break time of 10 min between 11:50 and 12:00 h.
During the AMPH-2 measurement episode, the windows were opened at the start of the monitoring period and remained open for the duration of the episode.
During the TOOL-1 measurement episode, windows were constantly open.
During the CLASS-1 measurement episode, windows were opened only during the break for 10 min between 18:30 and 18:40 h.
During the LAB-1 measurement episode, windows were opened only during the break for 10 min between 11:00 and 11:10 h. Two sets of measurements are shown next to each other due to a power outage during the measurement episode.
Minimum and maximum reached PM concentrations indoors are represented in Table 4 (red colored text represents PM10 values exceeding the allowed daily limit of 50 µg/Nm3; PM2.5 has an annual limit, therefore inconclusive for only a daily measurement episode, and PM1 is unregulated). Outdoor PM concentration levels are also included in the table, representing only the maximum values recorded during the measurement episode. These values are indicated by the label “Max(out)”.
The same kinds of statistical analyses as for CO2 were used for PM.
PM concentration trends across experimental conditions varied due to room use, ventilation, occupancy, and surface materials. In AMPH-1, PM levels remained mostly within permissible limits, with occasional PM10 exceedances. Statistically significant reductions were observed when windows were opened—PM10 dropped from 34.94 to 27.75 µg/m3 (p = 1.8 × 10−6, d = 0.51), PM2.5 from 15.78 to 14.61 µg/m3 (p = 1.0 × 10−5, d = 0.47), and PM1 from 12.24 to 11.45 µg/m3 (p = 2.0 × 10−11, d = 0.76)—reflecting moderate ventilation impact despite spatial limitations. In AMPH-2, although PM showed a decreasing trend, values were significantly above limits: medians reached 176.5 µg/m3 for PM10 (p = 2.3 × 10−168), 35.2 for PM2.5 (p = 4.5 × 10−142), and 13.6 for PM1 (p = 3.4 × 10−156). The extreme pollution levels and the pronounced difference between PM10 and smaller particle sizes are likely due to dust-trapping carpet over wood flooring, which released coarse particles during occupant movement [45,46,47], overwhelming the benefits of good window positioning.
TOOL-1 showed persistently high PM values despite open windows. PM10 averaged 58.15 µg/m3 (p = 6.2 × 10−50, d = 0.46), PM2.5 was 28.24 (p = 4.7 × 10−221, d = 1.2), and PM1 reached 24.79 (p < 1 × 10−300, d = 2.08), all far above standard thresholds. These elevations are strongly linked to indoor metalworking activity [48] and possible infiltration from polluted outdoor air. In CLASS-1, PM levels were low and stable. PM10 dropped significantly from 36.44 to 30.79 µg/m3 with window opening (p = 6.3 × 10−4, d = 0.35), while PM2.5 and PM1 changes were minimal and of limited practical significance (PM2.5: p = 0.26, d = 0.11; PM1: p = 0.032, d = 0.22). Tiled flooring and minimal external PM infiltration contributed to the favorable indoor environment.
LAB-1 exhibited higher PM levels than CLASS-1, driven by increased occupancy and an incidental chalk dust release. PM10 averaged 53.83 µg/m3 (p = 1.71 × 10−4, d = 0.17), slightly above the 50 µg/m3 baseline. PM2.5 showed a marginal increase (20.52 µg/m3; p = 2.89 × 10−3, d = 0.13), but PM1 was substantially elevated at 15.83 µg/m3 (p = 2.98 × 10−179, d = 1.97), indicating strong impact from fine particles, likely exacerbated by both occupant activity [49] and outdoor PM infiltration during the open-window period.

4. Conclusions

This investigation revealed that indoor air quality across university environments is highly dependent on room characteristics, ventilation, and human activity. CO2 levels exceeded the 1000 ppm threshold in four out of five environments, with AMPH-1 recording a maximum of 3018 ppm and LAB-1 reaching 2251 ppm. Although TOOL-1 demonstrated ideal CO2 conditions (mean = 668 ppm), its PM10 levels were substantially elevated (max = 140.3 µg/m3), likely due to metalworking and dust infiltration.
Amphitheater AMPH-2 exhibited the most severe PM10 pollution (median = 176.5 µg/m3, max = 541.5 µg/m3), exacerbated by carpet flooring that resuspended coarse particles. LAB-1 and CLASS-1, both facing internal courtyards, showed better ventilation outcomes. For instance, window opening in LAB-1 reduced PM10 by 21.9% (from 57.3 to 44.8 µg/m3) and CO2 by 305 ppm (from 2049 to 1744 ppm, p = 2.5 × 10−47). CLASS-1 benefited from cross-ventilation, achieving a 15.9% reduction in CO2 (from 1708 to 1436 ppm, p = 4.7 × 10−69).
Importantly, surface materials and occupant motion emerged as stronger predictors of indoor PM levels than the type of activity performed. AMPH-2, despite low-impact activities, showed more PM pollution than TOOL-1 due to its carpeting. These results call for targeted interventions: entrance matting systems, minimized textile surfaces, occupancy scheduling, and break-time ventilation. For heritage buildings with natural ventilation constraints, such pragmatic strategies are essential for mitigating health risks related to PM and CO2 exposure.

Author Contributions

Conceptualization, D.-M.M.; methodology, D.B. and R.-M.B.; software, D.-M.M. and A.A.; validation, I.I. and D.B.; formal analysis, D.-M.M. and D.B.; investigation, D.-M.M.; resources, I.I.; writing—original draft preparation, D.-M.M.; writing—review and editing, D.-M.M.; supervision, I.I. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article as variable values and graphical representations.

Acknowledgments

The authors would like to acknowledge Nicolae Muntean for having the initiative for performing such real-time measurements in university settings.

Conflicts of Interest

The authors declare no conflicts of interest.

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Atmosphere 16 00972 g001
Figure 1. Concentrations of CO2 during the measurement episode inside the amphitheater under experimental conditions described for AMPH-1.
Figure 1. Concentrations of CO2 during the measurement episode inside the amphitheater under experimental conditions described for AMPH-1.
Atmosphere 16 00972 g001
Atmosphere 16 00972 g002
Figure 2. Concentrations of CO2 during the 2 h measurement episode inside the second amphitheater under experimental conditions described for AMPH-2.
Figure 2. Concentrations of CO2 during the 2 h measurement episode inside the second amphitheater under experimental conditions described for AMPH-2.
Atmosphere 16 00972 g002
Atmosphere 16 00972 g003
Figure 3. Concentrations of CO2 during the 2 h measurement episode inside the tool workshop under experimental conditions described for TOOL-1.
Figure 3. Concentrations of CO2 during the 2 h measurement episode inside the tool workshop under experimental conditions described for TOOL-1.
Atmosphere 16 00972 g003
Atmosphere 16 00972 g004
Figure 4. Concentrations of CO2 during the 2 h measurement episode inside the classroom under experimental conditions described for CLASS-1.
Figure 4. Concentrations of CO2 during the 2 h measurement episode inside the classroom under experimental conditions described for CLASS-1.
Atmosphere 16 00972 g004
Atmosphere 16 00972 g005
Figure 5. Concentrations of CO2 during the 2 h measurement episode inside the laboratory under experimental conditions described for LAB-1.
Figure 5. Concentrations of CO2 during the 2 h measurement episode inside the laboratory under experimental conditions described for LAB-1.
Atmosphere 16 00972 g005
Atmosphere 16 00972 g006
Figure 6. Concentrations of PM during the measurement episode inside the amphitheater under experimental conditions described for AMPH-1.
Figure 6. Concentrations of PM during the measurement episode inside the amphitheater under experimental conditions described for AMPH-1.
Atmosphere 16 00972 g006
Atmosphere 16 00972 g007
Figure 7. Concentrations of PM during the measurement episode inside the second amphitheater under experimental conditions described for AMPH-2.
Figure 7. Concentrations of PM during the measurement episode inside the second amphitheater under experimental conditions described for AMPH-2.
Atmosphere 16 00972 g007
Atmosphere 16 00972 g008
Figure 8. Concentrations of PM during the measurement episode inside the tool workshop under experimental conditions described for TOOL-1.
Figure 8. Concentrations of PM during the measurement episode inside the tool workshop under experimental conditions described for TOOL-1.
Atmosphere 16 00972 g008
Atmosphere 16 00972 g009
Figure 9. Concentrations of PM during the measurement episode inside the classroom under experimental conditions described for CLASS-1.
Figure 9. Concentrations of PM during the measurement episode inside the classroom under experimental conditions described for CLASS-1.
Atmosphere 16 00972 g009
Atmosphere 16 00972 g010
Figure 10. Concentrations of PM during the measurement episode inside the laboratory under experimental conditions described for LAB-1.
Figure 10. Concentrations of PM during the measurement episode inside the laboratory under experimental conditions described for LAB-1.
Atmosphere 16 00972 g010
Table 1. Areas where measurements were conducted and their characteristics.
Table 1. Areas where measurements were conducted and their characteristics.
AreaIDVentilationMaximum Space CapacityReal
Occupancy		Occupancy LevelDate
AmphitheaterAMPH-1Natural20013869%10 April 2024
AmphitheaterAMPH-2Natural18010558%27 March 2025
WorkshopTOOL-1Natural201785%9 April 2024
ClassroomCLASS-1Natural301860%2 April 2025
LaboratoryLAB-1Natural151493%10 April 2025
Table 2. Spatial positioning of the rooms.
Table 2. Spatial positioning of the rooms.
IDWindow Direction
AMPH-1Facing the main road
AMPH-2Facing the main road
TOOL-1Facing the inner courtyard
CLASS-1Facing the inner courtyard
LAB-1Facing the inner courtyard
Table 3. Minimum and maximum concentrations of CO2 recorded during each measurement episode under the respective experimental conditions in the different areas.
Table 3. Minimum and maximum concentrations of CO2 recorded during each measurement episode under the respective experimental conditions in the different areas.
CO2 [ppm]
Exp. Cond.MinMax
AMPH-116633018
AMPH-210602715
TOOL-1582794
CLASS-17421755
LAB-19902251
Table 4. Minimum and maximum concentrations of PM recorded during each measurement episode under the respective experimental conditions in the different areas.
Table 4. Minimum and maximum concentrations of PM recorded during each measurement episode under the respective experimental conditions in the different areas.
PM1 [µg/Nm3]PM2.5 [µg/Nm3]PM10 [µg/Nm3]
Exp. Cond.MinMaxMax(out)MinMaxMax(out)MinMaxMax(out)
AMPH-18.119.512.119.628.118.749.736.822.02
AMPH-28.719.96.4111.579.210.0844.8541.511.91
TOOL-114.957.317.021662.525.2922.2140.332.65
CLASS-15.58.57.015.818.39.996.1116.911.52
LAB-112.428.823.0513.939.337.0623.1370.349.04
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Mustață, D.-M.; Bisorca, D.; Ionel, I.; Adjal, A.; Balogh, R.-M. Real-Time Insights into Indoor Air Quality in University Environments: PM and CO2 Monitoring. Atmosphere 2025, 16, 972. https://doi.org/10.3390/atmos16080972

AMA Style

Mustață D-M, Bisorca D, Ionel I, Adjal A, Balogh R-M. Real-Time Insights into Indoor Air Quality in University Environments: PM and CO2 Monitoring. Atmosphere. 2025; 16(8):972. https://doi.org/10.3390/atmos16080972

Chicago/Turabian Style

Mustață, Dan-Marius, Daniel Bisorca, Ioana Ionel, Ahmed Adjal, and Ramon-Mihai Balogh. 2025. "Real-Time Insights into Indoor Air Quality in University Environments: PM and CO2 Monitoring" Atmosphere 16, no. 8: 972. https://doi.org/10.3390/atmos16080972

APA Style

Mustață, D.-M., Bisorca, D., Ionel, I., Adjal, A., & Balogh, R.-M. (2025). Real-Time Insights into Indoor Air Quality in University Environments: PM and CO2 Monitoring. Atmosphere, 16(8), 972. https://doi.org/10.3390/atmos16080972

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