Integrated Analysis of Indoor Air Quality and Fungal Microbiota in Educational Heritage Buildings: Implications for Health and Sustainability

Abstract

Indoor air quality is paramount for the health and well-being of individuals, especially in enclosed spaces like office buildings, schools, hospitals, and homes where people spend a significant amount of time. Ensuring good indoor air quality is not only essential for reducing symptoms such as headaches, eye and respiratory irritation, fatigue, and difficulty in concentration, but it is also a key component of sustainable building practices aimed at promoting long-term health and environmental balance. This study aims to explore the impact of the microclimate and fungal microbiota on the health and cognitive performance of occupants in a university classroom, which is part of a cultural heritage building. The research delves into various microclimatic parameters, including temperature, relative humidity, CO₂, volatile organic compounds, O₂, and particulate matters (PM_2.5 and PM₁₀), to understand their influence on the development of microbiota and the manifestation of symptoms associated with Sick Building Syndrome. Over the course of a year-long investigation, microbiological samples were collected, revealing the presence of 19 fungal species, with Cladosporium, Alternaria, and Aureobasidium being the most prevalent genera. These species were found to thrive in an environment characterized by inadequate ventilation, posing potential health risks to occupants, such as allergic reactions and respiratory infections. Microclimatic parameter values such as mean temperature of 22.9 °C and mean relative humidity of 38.5% indicated moderate conditions for fungal proliferation, but occasional high levels of PM_2.5 and CO₂ indicated periods of poor indoor air quality, negatively influencing the comfort and health of the occupants. The questionnaires completed by 190 students showed that 51.5% reported headaches, 44.2% frequent sneezing, and 43.7% severe fatigue, linking these symptoms to increased levels of CO₂ and PM_2.5. The novelty of the study lies in the integrated approach to indoor air quality assessment in a heritage educational building, highlighting the need for improved ventilation and air management to enhance health and cognitive performance, while emphasizing sustainable indoor environment management that balances occupant well-being with the preservation of cultural heritage.

1. Introduction

People who work or live in buildings may experience discomfort and various health problems. These issues can be influenced by the characteristics and condition of the building, internal and external environmental factors, and the individual characteristics of the occupants [1,2,3,4]. This often leads to what is known as Sick Building Syndrome (SBS), which is a term used to describe a range of non-specific health symptoms experienced by occupants of buildings. These symptoms may include headaches, eye and respiratory tract irritation, fatigue, difficulty in concentration, and allergic reactions [5,6,7,8]. The condition is caused by a number of factors, such as indoor air quality (IAQ), expressed by non-compliant values of temperature and relative humidity (RH), insufficient ventilation, particulate matter (PM) or chemicals (volatile organic compounds, CO, and CO₂) and biological pollutants (fungi and bacteria) or other environmental agents that contribute to the deterioration of occupants’ health and comfort [9,10,11,12,13,14]. At the same time, since 1986, the World Health Organization has included the biological component in addressing the problem of SBS, which is one of the main factors threatening human health. These biological components can be represented by fungi, viruses, bacteria, pollen, etc. In this regard, inadequate air-conditioning systems have the potential to recirculate pathogens, which can lead to the spread of disease throughout an entire room or building [15,16].

Most studies have predominantly focused on viruses [17,18], bacteria [19,20,21], and fungi [22,23,24,25], reflecting their significant research priority in comparison to other factors. In terms of viruses, usually, influenza, severe acute respiratory syndrome virus (SARS), norovirus, smallpox virus, and rhinovirus are airborne. They could disperse rapidly through heating, ventilation, and air conditioning systems (HVAC), contributing to SBS in correlation with their virulence [26,27]. The reported bacterial diversity associated with SBS has been wider than the viral one, with high-throughput amplicon sequencing studies targeting the 16S rRNA gene showing that 326 bacterial genera could be implicated at a single analyzed location [28,29].

Fungi mainly contribute to SBS [30] via fungal particles (spores, mycotoxins, and cell fragments) that interact with humans through inhalation, ingestion, or skin contact. The most significant induced symptoms include respiratory and nervous problems, along with skin problems such as throat irritation, shortness of breath, irritation of the nasal mucosa, allergy symptoms such as sneezing, dry and itchy rashes, headache, dizziness, forgetfulness, fatigue, nausea, fever, and even chills [31]. One of the major challenges is to accurately identify pollution sources and quantify their impact on human health. In people with chronic conditions such as asthma, it has been significant to note that SBS could worsen their symptoms [32,33].

The large number of hours spent by occupants in some spaces (e.g., offices, hospitals, schools, homes, etc.) where IAQ is not adequate can affect their intellectual and work performance, such as cognitive performance, satisfaction, well-being, and productivity. Some of the factors have been widely studied, such as visual quality, but not the effects of indoor climate and indoor environment [34,35]. Thus, the literature individualizes the need for experimental studies to examine the relationship between specific indoor environmental factors and the performance of the occupants of the respective space [36,37]. The critical effect of indoor air pollution on cognitive function in the working-age population has been emphasized over time by different researchers [38,39,40].

Regarding the specialized literature, in the analysis carried out by Lipsa et al. [41] regarding fungal spores with allergenic effects in Romanian schools, parameters related to occupational health and SBS indicators were identified. Indoor air monitoring in schools contributes to the effective management of the environment and the reduction in health risks for students and staff [42]. The study by Bikaki et al. [43] highlights that IAQ in schools in Greece is influenced by the number of students, classroom equipment, ventilation rate, and pollutants from nearby traffic or construction. Effective monitoring systems can reduce health risks and improve academic performance [44,45]. Vasile et al. [46] and Sun et al. [47] emphasize the importance of adequate HVAC systems, and Catalina et al. [48] recommend mechanical ventilation for classrooms. Peyang et al. [49] propose a prototype air filter that combines filtration with UV light to reduce microorganisms and improve IAQ.

The aim of this study is to assess the impact of IAQ and fungal microbiota on the health and cognitive performance of the occupants of a classroom in an educational building, listed as a historical monument. The fact that this building is a heritage building significantly influences IAQ and fungal microbiota research, given that traditional materials and techniques used in construction can affect microclimate characteristics and biological pollutant dynamics. The lack of modern HVAC amplifies the accumulation of pollutants, while strict heritage conservation regulations limit upgrades and require the use of non-invasive methods and adapted solutions. The analysis includes the monitoring of IAQ parameters such as temperature, RH, CO₂, PM_2.5, and PM₁₀ to determine their contribution to the development of the fungal microbiota and the onset of symptoms associated with SBS. The study takes an integrated and innovative approach, analyzing in real time an extensive set of environmental parameters and investigating the correlations between them and the effects on the health and cognitive functions of the occupants. Complementary to the objective data, the research includes a subjective dimension, through the use of questionnaires applied to students, which provide valuable insights into air quality perception and felt symptoms. The integrated approach contributes to an in-depth understanding of how internal environmental factors influence health and productivity, highlighting the specific vulnerability of heritage buildings. At the same time, the article highlights the importance of long-term monitoring of environmental variables, identifies rare fungal species, and proposes effective measures to control and improve indoor air quality.

2. Materials and Methods

2.1. Case Study

The analyzed classroom is located in a building (Block C) within Campus 1 of the University of Oradea, belonging to the Faculty of Geography, Tourism and Sport. The campus is located on Universității Street, no. 1, Oradea, 410087, Bihor County, Romania (Figure 1). This building is inscribed in the list of historical monuments of Bihor County (Romania), being an integral part of an urbanistic complex influenced by the geometric vision promoted by the Viennese School of Architecture. Designed by the architect Vágó József, the building was erected between 1912 and 1914. The three-story building was converted into a teaching and administrative unit and currently houses three faculties alongside their classrooms, offices, and laboratories.

The room analyzed is a dedicated IT laboratory, with a volume of 441 m³, equipped with 16 computers, and with the capacity to accommodate 20 students and one teacher simultaneously. During the academic year, this space is intensively used from 08:00 to 20:00, Monday to Friday. The time spent by students in this room is discontinuous and limited, varying between 2 and 4 h per week, while teachers work between 2 and 10 h per week in the same environment [50]. As IT laboratories are often used by large numbers of students for extended periods, the ventilation requirements are higher than in a standard classroom. CO₂ and other contaminants can accumulate more quickly, affecting the perception of IAQ. At the same time, electronic devices can also emit volatile organic compounds (VOCs), which contribute to indoor air pollution and can amplify symptoms of discomfort. The additional ventilation induced by electronic devices can contribute to raising a larger volume of PM into the air. However, the analyzed laboratory does not have a modern HVAC system. Mainly, during the winter, temperature is moderated with the help of a classic heating system, which uses radiators; and during the summer, ventilation is conducted, especially by opening the windows.

This room was the subject of previous scientific research [50], which aimed to monitor twenty indicators of the internal microclimate from 1 September 2022 to 31 August 2023. The main objective of that study was to assess IAQ based on the values of chemical pollutants inside, and analyze its impact on human health, as well as on the level of concentration and academic performance of students.

The heritage status of the analyzed building can play a significant role in influencing indoor air quality parameters. Traditional building materials, commonly used in heritage buildings, can have hygroscopic characteristics, and contribute to variations in relative humidity, favoring the proliferation of fungal microbiota. Also, limitations imposed by conservation regulations can reduce the possibility of implementing modern mechanical ventilation systems, which can lead to the accumulation of air pollutants such as PM and VOCs. These aspects may negatively influence occupant health by increasing the risk of SBS-related symptoms [3,28]. Thus, the integrated analysis of microclimate parameters and fungal contamination in heritage buildings is essential for understanding the complex interactions between architectural features, human health, and the sustainable conservation of these valuable structures.

2.2. The Determination of the Internal Microclimate Parameters and Fungal Microbiota

For the determination of the interior microclimate, the pollutant levels were monitored continuously over a year, from 1 September 2022 to 31 August 2023. Twenty indicators were tracked, including temperature, RH, CO₂, CO, HCHO, VOC, H₂S, SO₂, O₂, O₃, NO, NO₂, CH₄, pressure (P), PM_2.5, PM₅, PM₁₀ and concentrations of positive ions (I+) and negative ions (I-) [50]. Most parameters were recorded using datalogger sensors that captured data every minute, which were then averaged hourly (

Table 1. The characteristics of the sensors used to determine the pollution indicators of the indoor environment in the classroom.

Sensor Model	Determined Indicators	Manufacturer	Accuracy
Extech SD800 devices	temperature, RH, CO2	Extech Instruments, Nashua, NH, USA	±0.8 °C (temperature), ±4% (RH), ±40 ppm (CO2)
Evikontroll Gas detection and control system devices	temperature, RH, CO2, VOC, O2	Evikontroll Gas, Tartu, Estonia	±0.5 °C (temperature), ±5% (RH), ±50 ppm (CO2), ±0.01 ppm (VOC), ±0.01% (O2)
BLATN BR-smart-123s device	VOC	BLATN Science and Technology, Beijing, China	±5% (VOC)
PCE-PCO 2	PM2.5, PM10	PCE Instruments UK, Southampton, UK	up to ±5% (PM2.5, PM10)
GrayWolf PC-3016	PM2.5, PM10	GrayWolf Sensing Solutions, Shelton, CT, USA	up to ±5% (PM2.5, PM10)

). Sensors were strategically placed throughout the room for optimal coverage, and they were selected based on the highest possible accuracy. Measurements were taken at heights of 1.2 to 1.3 m, reflecting the breathing zone of seated individuals [50] (Figure 2 and Figure 3a). The breathing zone is the space where people most frequently inhale and exhale air, and measurements at this height are more relevant to assessing the quality of breathing air for individuals in a sitting position. This is essential for studies related to health, comfort, or exposure to airborne contaminants, as pollutant concentrations can vary with height. Thus, placing the sensors in the breathing area maximizes the relevance of the data obtained. Among these indicators, the present study focused specifically on those pollutants with a high potential to influence the growth and development of fungal microbiota, but which, at the same time, have a great damaging potential for the health of students and teaching staff. Consequently, particular emphasis was placed on interpreting temperature, RH, CO₂, VOC, O₂, PM_2.5, and PM₁₀ (Figure 2). These parameters are significant for this scientific endeavor due to their direct impact on the indoor microenvironment and their potential to foster conditions conducive to microbial proliferation, as well as their effect on human health.

In addition to evaluating the specific indicators of the indoor microclimate, the study also aimed to determine the degree of fungal contamination of the air and the surfaces inside the analyzed space. The air fungal contamination was assessed using the Koch sedimentation method [51]. The Koch method was chosen because it is simple, economical, and easy to implement, being ideal for indoor air contamination studies. It collects spores and particles that settle naturally, reflecting the real conditions in the rooms. Widely used and well documented, the method allows for the isolation of viable fungal colonies, facilitating species identification and analysis of their health impact. Three Petri plates were placed at three distinct points in the investigated room—central table, smart board, and sensors—and left open for 30 min. For the Koch method, Sabouraud agar plates were prepared with a composition of 40 g/L glucose, 10 g/L peptone, 15 g/L agar, and 50 µg/mL chloramphenicol, adjusted to a pH of 5.6 [52]. The plates used in the Koch method were incubated at 20 °C for 10 days. The Omelianski formula for estimating fungal colony-forming units (CFUs) in classroom air is given as:

$$\mathrm{C}\mathrm{F}\mathrm{U}/{\mathrm{m}}^3={\displaystyle \frac{n\times \mathrm{10,000}}{S\times k}}$$

where, n = number of colonies on the plate; S = surface area of the Petri dish (for ∅90 mm, S = 63.62 cm²); k = air exposure time coefficient (k = 1 for 5 min, k = 2 for 10 min, k = 3 for 15 min, etc.) [53].

The sterile swab method was used to evaluate fungal contamination on surfaces. Those were employed to collect samples for surfaces that the teachers and students utilizing the classroom would regularly touch. The surfaces from which swab samples were taken were the central table, smartboard, power button computer case, computer keyboard, window handle, door handle, and paper magazines. The swabs were rinsed in 1 mL of saline solution, and 150 µL of the resuspended cells were plated on Sabouraud agar. Triplicate inoculations were performed for each swab, and the plates were incubated at 20 °C for 10 days. The temperature chosen was a moderate one, suitable for the growth of most species of fungi, such as those of the genera Cladosporium, Alternaria, and Aureobasidium, which were predominant in this study. The 10-day period was long enough to allow fungal colonies to grow and sporulate, facilitating their identification and analysis. These two characteristics are standard for such studies, as they allow for obtaining visible and well-developed colonies for further characterization and analysis.

In addition to the advantages they have, the swab and sedimentation methods also have some limitations. The sedimentation method captures only particles that settle naturally under the action of gravity, underestimating fungal spores and bioaerosols that remain suspended in the air. Similarly, the swab method only reflects the contamination of surfaces without providing information about the airborne microbiota in real time. The results of these methods can be influenced by factors such as ventilation, air currents, and human activity, which can lead, in some cases, to a distorted representation of airborne contaminants.

Figure 3a and

Table 2. Description of the samples collected from the interior of the analyzed classroom.

Sample Code	Collection Point	Height Above Ground	Sampling
S1	Central table	~1 m	Koch method	Swab
S2	Smartboard	~2 m	Koch method	Swab
S3	Sensors	~1.5 m	Koch method
S4	Power button computer case			Swab
S5	Computer keyboard			Swab
S6	Window handle			Swab
S7	Door handle			Swab
S8	Paper magazines			Swab

summarize the sampling points for both air and surface samples, the distribution at the classroom level, the height from the ground, and the sampling method.

All the collected samples were sealed with parafilm and further investigated following standard procedures for microbiological sample handling [54]. For each independent colony grown for 10 days on the plates, DNA extraction was performed using the Animal and Fungi DNA Preparation Kit^® (Jena Bioscience, Jena, Germany) according to the protocol provided by the manufacturer. The polymerase chain reaction (PCR) amplification reaction was performed in a total volume of 25 µL, containing a 1× MyTaq Reaction Buffer (Meridian Bioscience^®, London, UK), 0.5 mM of each primer (Macrogen Inc., Seoul, Republic of Korea), 1.25 U of MyTaq Red DNA Polymerase (Meridian Bioscience^®, London, UK) and 50–100 ng of template DNA. PCR conditions were as follows: initial denaturation at 95 °C for 5 min, followed by 35 cycles of denaturation at 95 °C for 30 s, hybridization at 56 °C for 30 s, and elongation at 72 °C for 30 s, with a final elongation step at 72 °C for 5 min. Fungal identification was achieved by targeting the ITS region using primers ITS 1 (5′-TCCGTAGGTGAACCTGCGG-3′) and ITS 4 (5′-TCCTCCGCTTATTGATATGC-3′). PCR-generated DNA fragments were purified from agarose gels using the NucleoSpin™ Gel and the PCR Clean-up kit (Macherey-Nagel, Ping-Tung, Taiwan), and sequencing was performed by a commercial service at Macrogen (MacrogenEurope, Amsterdam, The Netherlands) [53]. The choice of this kit was based on the fact that it is specifically designed for the extraction of DNA from fungi, which is essential for obtaining high-quality DNA from complex samples such as fungal spores. The kit is known for providing pure and well-representative DNA suitable for molecular applications such as PCR and sequencing. At the same time, it includes standardized reagents and protocols, reducing the risk of DNA contamination or degradation. The retrieved DNA sequences were submitted to GenBank under accession numbers PQ471461-PQ471480.

2.3. The Evaluation of Students’ Perception of IAQ

Regarding the evaluation methodology of students’ perception regarding IAQ and its effects on health and cognitive performance, a questionnaire containing 13 items was distributed. In addition to the sections dedicated to the collection of sociodemographic data, the questionnaire includes both dichotomous questions (with a yes/no answer) and questions with a frequency scale, as well as questions related to the perception and previous experiences related to IAQ, health symptoms, and personal health factors. Respondents were invited to rate both the frequency with which they experience IAQ-related problems such as extreme temperature, dry air, increased RH, unpleasant odors, PM or noise, and the occurrence of associated health symptoms such as headaches, nausea, frequent cough, eye or skin irritation, these aspects being analyzed according to their frequency of occurrence. The questionnaire was applied to a target group comprising 190 students (both from the bachelor’s cycle and from the master’s cycle) who carried out their activity inside the classroom.

Data analysis and interpretation of the results were carried out using RStudio (R version 4.3.1) and an Asus Rog graphics station with i9 processor and 64 GB RAM (Asus, Beitou, Taipei, Taiwan). We used frequencies and percentages to express categorical variables and mean ± standard deviation (SD) to present numerical variables. We implemented a scoring system based on the recoding of Likert-based items in the following domains: air quality concerns (n = 12) and symptoms (n = 12). The scores were assigned as follows: sometimes (1), often (2), and very frequent (3), whereas other responses (never or not the case) were assigned zeros. In this way, the scores of air quality concerns and symptoms ranged between 0 and 32, and higher scores indicated higher concerns about the air quality and higher frequencies of the reported symptoms, respectively. The scores were further used as dependent variables in univariable generalized linear regression analysis to assess factors associated with concerns and symptoms. The analysis included variables such as age, gender, smoking status, contact lens use, medication use, diagnosed chronic conditions, number of hours spent in space, and subjective assessment of air quality. These variables were tested individually to determine their association with IAQ concerns and symptoms. The significantly associated variables from the univariable analyses were subsequently used as independent variables in multivariable linear models. The results were expressed as beta coefficients and 95% confidence intervals (95% CIs). Statistical significance was deemed at p < 0.05. In this case, variables such as gender, smoking status, contact lens use, medication administration, and computer use were not significant predictors in the univariate analysis and were, therefore, not included in the multivariate models.

3. Results

3.1. The Analysis of Internal Microclimate Data and Its Influence on the Growth of Fungal Microbiota

The values obtained for the internal microclimate indicators in the analyzed classroom provide significant insights into the environmental conditions that can influence microbial growth and occupants’ health. The mean temperature observed was 22.9 °C, with a maximum of 27.1 °C and a minimum of 18.1 °C, accompanied by an SD of 1.1 °C. Temperature is a critical factor for microbial proliferation, as many bacteria and fungi thrive within the range of 20 °C to 30 °C. The average temperature falls within this optimal range, suggesting favorable conditions for microbial activity. However, the occasional higher temperature might inhibit some microorganisms, while the lower temperature could still support microbial growth, albeit at reduced rates [55]. RH measurements revealed a mean value of 38.5%, a maximum of 60.6%, and a minimum of 17.6%, with a SD of 9.2%. Most bacteria and fungi prefer RH levels above 60% for optimal growth. The relatively low mean RH in the classroom may limit microbial proliferation, although the peak recorded indicates periods when conditions were more conducive to microbial development [56] (Figure 4).

The analysis of the distribution of CO₂ levels in the monitored room revealed a high variability, characterized by a positive asymmetry (skewness = 4.20) and a high kurtosis (27.26), indicating an increased frequency of extreme values. CO₂ levels had a mean of 633.8 ppm, a maximum of 2744.4 ppm, and a minimum of 450 ppm, with an SD of 176.4 ppm (Figure 4). The median of 585.48 ppm provides a robust measure of central tendency, the interquartile range (IQR) of 103.02 ppm, bounded by the 25th percentile (543.90 ppm) and the 75th percentile (646.92 ppm), reflecting typical CO₂ concentrations in a room with normal activity. In general, concentrations remained below 650 ppm, suggesting adequate ventilation, but punctual episodes of high accumulation indicate moments of insufficient ventilation, potentially allowing the accumulation of bioaerosols and other contaminants that can serve as nutrient sources for microorganisms [57].

VOC is another critical class of pollutants for the IAQ. The mean VOC level was 1.43 mg/m³, with a maximum of 4.8 mg/m³, a minimum of 0.0 mg/m³, and an SD of 0.65 mg/m³. VOCs can act as carbon sources for certain microbial species, enhancing their growth and metabolic activities. The observed mean VOC level is moderate, but the maximum values indicate periods of high VOC concentration, which could significantly promote microbial proliferation [58]. O₂ levels, essential for aerobic microbial metabolism, had a mean of 21.67%, a maximum of 22.74%, and a minimum of 20.39%, with a SD of 0.50% (Figure 4). The recorded O₂ levels fall within the normal atmospheric range, providing sufficient O2 for aerobic microorganisms, thereby supporting their metabolic processes and growth [59].

PM_2.5 and PM₁₀ measurements indicated a mean PM_2.5 level of 10.1 µg/m³, with a maximum of 33.1 µg/m³, a minimum of 0.4 µg/m³, and a SD of 4.6 µg/m³. For PM₁₀, the mean level was 69.5 µg/m³, with a maximum of 131.2 µg/m³, a minimum of 29.5 µg/m³, and an SD of 13.5 µg/m³ (Figure 4). PM can harbor microorganisms and provide surfaces for microbial attachment, enhancing their spread and colonization. The high mean levels of PM_2.5 suggest a significant presence of fine PM, which can support microbial growth and dissemination within the classroom environment [60].

The fungal CFU values and degrees of contamination in the air samples were estimated using quality assessment standards for indoor air (

Table 3. Fungal loads in indoor air. Values of the fungal CFU/m³ represent the average values obtained for the used triplicates and the SD values.

Sample	Number of Fungal CFU/m3 of Air	Degree of Contamination
S1	79 ± 1	Low
S2	93 ± 1.5	Low
S3	159 ± 2.6	Medium

) [61]. The standards categorize the fungal loads as low for CFU values between 50 and 100 CFU/m³ in the cases of S1 and S2 samples and as medium for CFU values between 100 and 500 CFU/m³ in the cases corresponding to S3.

The obtained CFU values for fungi were comparable or lower to the ones previously published in SBS studies, respective 50 CFU/m³ [62], 65–2061 CFU/m³ [63], 50 CFU/m³ [30], 135 CFU/m³ [64], or 300–4150 CFU/m³ [65].

Fifty-three fungal isolates were analyzed and attributed to 19 distinct species and one uncultured compost fungus (Figure 5). The species belonged to twelve genera: Phialocephala, Cladosporium, Alternaria, Hansfordia, Epicoccum, Peniophora, Naganishia, Aureobasidium, Penicillium, Stereum, Fusarium, and Stemphylium. The most abundant genera were Cladosporium (26.92%), Alternaria (21.15%), Aureobasidium (15.38%), and Naganishia (13.46%). Regarding the phylum-level representation, the recovered fungi belonged to Ascomycota (81.13%) and Basidiomycota (18.87%). Regarding the fungal distribution in the analyzed room, 69.81% of the isolated fungi were identified in the air samples (28.3%—S1, 16.9%—S2, and 24.5%—S3). In comparison, the fungi retrieved from the most frequently touched surfaces were 11.32% on the door handle (S7), 7.45% on the power-bottom computer case (S4), 5.6% on the paper magazines (S8), 3.7% on the window handle (S6), and 1.8% on the computer keyboard (S5). Indoor fungal spores or other cell fragments are usually found as aerosols [66], while their sedimentation is determined by inertial impaction and gravity [67]. The species identified on the paper magazines (S8), Cladosporium cladosporioides, Alternaria alternata, and Aureobasidium pullulans, have the enzymatic apparatus to degrade cellulose and may use the paper as a feeding source in time [68,69,70].

The genera Cladosporium, Alternaria, Aureobasidium, and Epiccocum were frequently reported as fungal pollutants in indoor spaces [33] and correlated to SBS [22,26,63,65]. Their distribution in the study room was ubiquitous, as the genera were present in all the sample collection points. Cladosporium, Alternaria, Aureobasidium, and Epiccocum were primarily classified as endophytes, plant pathogens, or plant decomposers, and their presence indoors was due to indoor-outdoor air exchange. The other identified spices Fusarium subglutinans, Stemphylium vesicarium, Penicillium oxalicum, Peniophora laeta, Phialocephala fortinii, Hansfordia pulvinata, and Stereum hirsutum were characterized as endophytes and plant pathogens and their presence in indoor environments was extremely rare, as highlighted [71,72,73,74,75,76,77]. The species were most frequently isolated from the window handle (S6), which reiterated that the fungi indoors originated from the outdoor air. In the premises of the building containing the studied room, some present trees and plants could harbor the mentioned species, showing that their dispersal was usually limited to short distances [77]. Naganishia liquefaciens and the uncultured compost fungus isolate, AM711465.1, might originate from nearby soils as the fungi were not previously described as indoor inhabitants [78,79]. To the authors’ knowledge, all the plant- and soil-associated fungi from the present study were never reported in SBS studies.

3.2. The Data Analysis Regarding Students’ Perception of IAQ

In the current study, we analyzed the responses of 190 students regarding the perception of IAQ in the analyzed classroom. More than half of them were males (54.7%), and the mean ± age of them was 20.3 ± 2.4 years. Smokers represented 37.4%, and 6.3% of the sample were wearing contact lenses. Chronic diseases were prevalent among 12.1%, and 14.2% of students were receiving medications. The majority of participants were working on the PC (76.8%) with a mean working time of 3.4 ± 1.6 h. In general, participants spent 1.4 ± 0.6 days per week and 3.3 ± 1.6 h per day in the space (

Table 4. Demographic characteristics of the target group and their lifestyle.

Characteristic	N = 190 1
Gender
Female	86 (45.3%)
Male	104 (54.7%)
Age	20.3 ± 2.4
Occupation
Student	190 (100.0%)
Smoker
No	119 (62.6%)
Yes	71 (37.4%)
Wearing contact lenses
No	178 (93.7%)
Yes	12 (6.3%)
Taking any medication (daily/weekly/monthly)
No	163 (85.8%)
Yes	27 (14.2%)
Currently diagnosed with health problems (chronic diseases, allergies, etc.)
No	167 (87.9%)
Yes	23 (12.1%)
Pregnant (for Females)
No	86 (100.0%)
Yes	0 (0.0%)
Working on PC
No	44 (23.2%)
Yes	146 (76.8%)
PC working hours *	3.4 ± 1.6
Days spent in the space per week	1.4 ± 0.6
Hours spent in the space per day	3.3 ± 1.6

¹ n (%); Mean ± SD. * Descriptive data are based on 146 participants who were working on the PC.

).

In addition to this sanitized data, the primary air quality concerns reported the most by respondents—categorized as “sometimes”, “often,” or “very often”—were generally associated with insufficient ventilation and PM pollution (Table 4 and Figure 6). With the former most commonly reported—75.3% of respondents saw unventilated or closed air conditions as an issue in their area. Dust accumulation followed as a close second and was reported by 72.1% of respondents. Concerns over pollution were also high, with 63.2% reporting often suffering negative effects from it indoors. Finally, 62.7% of those surveyed indicated external noise as the leading distraction.

Mold was among the least frequently reported air quality problems, with only 6.8% of artifacts mentioning it as a problem in the study sample. High RH levels were also another cited factor (though less frequent at 41.1%), along with ventilation or dust. The patterns seen in the analyses presented in Figure 6 clearly indicate that considering ventilation as well as dust control and noise pollution are the most promising strategies to enhance perceived IAQ, whereas less frequently reported issues (such as molds or RH) may have a local relevance susceptible of improving under specific circumstances since all signs point out for a context-dependent improvement.

A significant majority of respondents, representing more than two-thirds (68.9%), rated the IAQ in their environment as “good” or “very good” (Figure 7). This positive assessment reflects a generally favorable perception of IAQ among participants. However, among the remaining 31.1% who rated IAQ as “poor” or “very poor”, significant variation was observed in the timing of IAQ problems. More precisely, 27.1% of this group indicated that IAQ problems were more significant at the beginning of the activity. Conversely, 35.6% experienced a degradation of IAQ during the activity, suggesting a progressive deterioration with its development. Also, 13.6% reported that the air became problematic only at the end of the activity, while a notable percentage of 20.3% experienced poor IAQ throughout the duration of the activity (Figure 7).

These results highlight the variability in how and when IAQ problems occur for different people, suggesting that air management strategies should be adaptive and consider the specific timing of activities to mitigate these problems effectively. The data also highlight the importance of continuous monitoring and appropriate interventions, especially in environments where IAQ tends to degrade over time.

The most frequently reported symptoms by participants were headaches/migraines, experienced by 51.5% of respondents, followed by repeated sneezing in 44.2% and severe fatigue in 43.7% of them. These symptoms appear to be the most prevalent among participants, suggesting a possible link between environmental conditions and health impact. Conversely, the least reported symptoms were nausea/vomiting, mentioned by only 4.7% of participants, and skin irritations, reported by 7.4% of respondents (Figure 8).

These results indicate a higher prevalence of symptoms related to the respiratory system and general states of discomfort, compared to symptoms of a gastrointestinal or skin nature, which appear to be much less common. This distribution of symptomatology can provide important information for the identification and management of risk factors in that environment.

Univariate and multivariate analyses were performed to identify factors and predictors associated with IAQ concerns among participants. In the univariate analysis, age was identified as a significant predictor (beta = −0.37, 95% CI −0.71 to −0.02, p = 0.038), suggesting that older participants were less likely to report IAQ concerns. Self-rated air quality also showed significant associations in all categories, with participants rating IAQ as “Good” (beta = −4.0, 95% CI −6.9 to −1.1, p = 0.007) or “Very good” (beta = −7.9, 95% CI −11 to −4.3, p < 0.001) reporting significantly fewer pre-occupations related to it compared to those who rated it as “Very low” (

Table 5. Factors and predictors of IAQ among the respondents.

	Univariable			Multivariable
Characteristic	Beta	95% CI	p-Value	Beta	95% CI	p-Value
Gender
Female	Reference	Reference
Male	−1.2	−2.9, 0.50	0.172
Age	−0.37	−0.71, −0.02	0.038	−0.31	−0.60, −0.02	0.039
Smoker
No	Reference	Reference
Yes	−0.96	−2.7, 0.78	0.280
Wearing contact lenses
No	Reference	Reference
Yes	0.38	−3.1, 3.8	0.828
Taking any medication (daily/weekly/monthly)
No	Reference	Reference
Yes	1.3	−1.1, 3.7	0.285
Currently diagnosed with health problems (chronic diseases, allergies, etc.)
No	Reference	Reference
Yes	1.5	−1.1, 4.1	0.259
Working on PC
No	Reference	Reference
Yes	0.24	−1.8, 2.2	0.813
Days spent in the space per week	−0.85	−2.2, 0.45	0.202
Hours spent in the space per day	0.28	−0.24, 0.80	0.287
Self-rating of the air quality in the space
Very low	Reference	Reference		Reference	Reference
Low	2.4	−0.68, 5.5	0.128	2.29	−0.76, 5.35	0.143
Good	−4.0	−6.9, −1.1	0.007	−3.98	−6.83, −1.14	0.007
Very good	−7.9	−11, −4.3	<0.001	−7.98	−11.6, −4.39	<0.001

CI = confidence interval.

).

In the multivariate analysis, both age (beta = −0.31, 95% CI −0.60 to −0.02, p = 0.039) and self-rated IAQ (beta = −3.98, 95% CI −6.83 to −1.14, p = 0.007 for “Good” air quality and beta = −7.98, 95% CI −11.6 to −4.39, p < 0.001 for air quality “Very good”) remained statistically significant, confirming their role as predictors of a reduced number of IAQ concerns. However, variables such as gender, smoking status, contact lens use, medication use, computer work, number of days spent in that space per week, and hours spent in that space daily did not show significant associations with concerns about IAQ (Table 5).

This review highlights that both age and subjective perception of air quality are important factors influencing reported concerns about IAQ. On the other hand, demographic and behavioral factors such as gender and smoking habits do not seem to have a significant impact in this specific context. These results suggest that improving the perception of IAQ could help reduce concerns about ambient air, especially among younger participants.

Univariate and multivariate analyses were performed to identify factors and predictors associated with symptom reporting in participant-rated space. On univariate analysis, age emerged as a significant negative predictor (beta = −0.22, 95% CI −0.43 to 0.00, p = 0.047), suggesting that older participants were less likely to report symptoms. The likelihood of reporting symptoms was also reduced among participants who worked at a computer (beta = −1.2, 95% CI −2.4 to −0.01, p = 0.049), as well as among those who rated IAQ as “good” (beta = −2.0, 95% CI −3.9 to −0.18, p = 0.032) or “very good” (beta = −2.8, 95% CI −5.1 to −0.48, p = 0.019).

On the other hand, participants who were taking medication (beta = 2.1, 95% CI 0.57 to 3.5, p = 0.007), those diagnosed with health problems (chronic diseases, allergies, etc.) (beta = 1.7, 95% CI 0.15 to 3.3, p = 0.033) and number of hours spent in space (beta = 0.52, 95% CI 0.21 to 0.84, p = 0.001) showed significant associations with symptom reporting. Also, the IAQ concerns score was positively associated with symptom reporting (beta = 0.38, 95% CI 0.31 to 0.45, p < 0.001), suggesting that as the IAQ concerns score increases, participants are more likely to report symptoms.

In multivariate analysis, only two independent predictors of symptoms remained significant: the number of hours spent in space per day (beta = 0.39, 95% CI 0.14 to 0.64, p = 0.002) and the preoccupation score of IAQ (beta = 0.30, 95% CI 0.22 to 0.37, p < 0.001). These factors indicate that as the worry score increases, participants are more likely to report symptoms (

Table 6. Factors and predictors of reporting symptoms by the respondents.

	Univariable			Multivariable
Characteristic	Beta	95% CI	p-Value	Beta	95% CI	p-Value
Gender
Female	Reference	Reference
Male	0.00	−1.0, 1.0	0.994
Age	−0.22	−0.43, 0.00	0.047	−0.03	−0.20, 0.13	0.694
Smoker
No	Reference	Reference
Yes	0.22	−0.86, 1.3	0.692
Wearing contact lenses
No	Reference	Reference
Yes	−0.27	−2.4, 1.9	0.803
Taking any medication (daily/weekly/monthly)
No	Reference	Reference		Reference	Reference
Yes	2.1	0.57, 3.5	0.007	0.80	−0.45, 2.05	0.212
Currently diagnosed with health problems (chronic diseases, allergies, etc.)
No	Reference	Reference		Reference	Reference
Yes	1.7	0.15, 3.3	0.033	0.63	−0.66, 1.93	0.339
Working on PC
No	Reference	Reference		Reference	Reference
Yes	−1.2	−2.4, −0.01	0.049	−0.71	−1.69, 0.27	0.157
Days spent in the space per week	0.23	−0.57, 1.0	0.571
Hours spent in the space per day	0.52	0.21, 0.84	0.001	0.39	0.14, 0.64	0.002
Self-rating of the air quality in the space
Very low	Reference	Reference		Reference	Reference
Low	1.9	−0.08, 3.9	0.061	0.91	−0.76, 2.58	0.287
Good	−2.0	−3.9, −0.18	0.032	−0.94	−2.53, 0.64	0.243
Very good	−2.8	−5.1, −0.48	0.019	−1.07	−3.13, 0.99	0.308
Air quality concerns score	0.38	0.31, 0.45	<0.001	0.30	0.22, 0.37	<0.001

CI = confidence interval.

).

4. Discussions

Maintaining a clean indoor environment and ensuring good air quality are essential factors in the spaces intended for teaching–learning activities, being critical for optimizing cognitive functions and protecting human health. Regarding the main indicators of indoor microclimate, recent research [80,81] shows that temperature and RH directly affect PM concentrations and respiratory health, contributing to SBS. Studies indicate that RH, in collaboration with temperature, has the ability to increase the amount of PM_2.5 and PM₁₀ in indoor air, which in turn are associated with a wide range of respiratory and cardiovascular problems [82]. In particular, PM_2.5 can enter the lungs, exacerbating conditions such as asthma and lung inflammation. Some studies show that PM10 and PM_2.5 can significantly increase the risk of hospitalizations for respiratory and circulatory diseases, with significantly stronger effects in the cold months [83]. Also, long-term exposure to PM₁₀ is associated with the occurrence of lung cancer and cardiovascular diseases [84]. At the same time, increased RH can facilitate the accumulation of PM and increase the risk of mold development, which affects respiratory health [50,85,86]. Punctual episodes of high CO₂ concentrations, driven by insufficient ventilation, have been correlated with decreased cognitive function and increased symptoms of fatigue and headaches [87]. In a study conducted in a university library, CO₂ concentrations exceeded 1000 ppm, which indicated inadequate ventilation, with the potential to affect health and productivity [88]. Also, NO₂ and O₃ are responsible for severe respiratory irritation, especially in people with pre-existing conditions, such as asthma [89]. Studies show that O₃ and NO₂ are the most harmful pollutants for cardiovascular and respiratory health in polluted urban environments, increasing the risk of hospitalizations, especially in the cold months of the year [90,91].

In indoor spaces, high temperature and RH, in particular, favor the growth and development of certain species of fungi and bacteria. These parameters significantly influence the microbiota in the indoor air, stimulating the development of several types of fungi. Cladosporium and Alternaria are the most common fungal species in indoor and outdoor air, particularly thriving in moderate RH and temperature conditions. They are prevalent in spring and summer, and high RH accelerates their proliferation. Studies show that Cladosporium can tolerate variations in RH, but grows better at high RH (above 75%), and Alternaria grows optimally at RH above 97% [92,93]. Penicillium and Aspergillus are well known for their adaptability to high RH and variable temperature environments. They are commonly found in homes and are associated with growth on damp materials or in poorly ventilated spaces. Penicillium grows at a lower RH of up to 80% and is predominant in cold periods [94,95]. Epicoccum, Fusarium, Aureobasidium, and Phialocephala develop in moderate RH conditions and are frequently found in the air and on interior surfaces, having the ability to colonize different surfaces [96,97].

High levels of CO₂ can inhibit the growth of certain species of fungi, but other species can adapt and even thrive in high CO₂ environments. For example, fungi such as Fusarium oxysporum, and Penicillium were able to grow even in atmospheres with 20–40% CO₂, but with a significant reduction in mycotoxin and ergosterol production [98]. Also, exposure to increased CO₂ can change the composition of the cell membrane, leading to its fluidization and affecting membrane functions [99]. Field studies [100,101,102] indicate that some species of fungi are favored by the presence of NO₂, especially in combination with other atmospheric pollutants, growing thus the colonization rate on interior surfaces and in environments with reduced ventilation; these include Cladosporium, Aureobasidium, Alternaria, Fusarium, Penicillium, and Epicoccum. In contrast, O₃ can act as a pollutant with negative effects on fungal growth by damaging cells and inhibiting essential enzyme activities. Studies show that O₃ can reduce fungal enzyme activity and biomass, depending on the species [103]. Cladosporium, Aspergillus, and Penicillium are fungal species that dominate indoor bioaerosols, and their concentrations increase significantly in the presence of PM₁₀ and PM_2.5. Studies in various indoor environments (laboratories, classrooms, and residences) have shown that fungi contribute significantly to the mass fraction of PM₁₀, which can increase the risk of allergies and respiratory problems [104,105,106].

Fungi are usually not harmful to many human individuals, but they could lead to various health issues. The global rates of fungal infections are ascending, with new fungal diseases emerging annually. The rise could be associated with changes in an individual’s state of health correlated with longer life spans, more immuno-compromised patients, uncontrolled underlying conditions undermining health (like diabetes), and misuse of antifungals [107].

The cultured-based method for fungal identification allowed for the isolation of the species with viable spores or mycelia fragments, which could be actively involved as indoor biological pollutants. The potential effects on human health were grouped into eight categories: systemic infections, superficial skin infections, neuropsychiatric problems, allergies, rheumatologic/other immune diseases, respiratory infections, hypersensitivity syndromes, and respiratory symptoms. Most frequently, the spores, hyphal fragments, VOCs, and toxins are the fungal elements affecting human health [108]. The impact of the isolated fungi on human health was plotted hierarchically, starting with the species causing multiple health-related issues (Figure 9).

Of all the genera of fungi identified, Cladosporium was the most abundant in the analyzed samples and might impact human health. The associated species like C. cladosporioides, C. pseudocladosporioides, C. colocasiae, C. peran-gustum, and C. allicinum could cause superficial skin infections, allergies, and respiratory infections, with C. cladosporioides being the most frequently encountered in comparison to C. allicinum which was rarer. Cladosporium is one of the most significant respiratory allergenic fungi, seconding Alternaria [109].

Alternaria was the second most abundant genus and one of the most impactful genera on human health, represented in this study by three species. Alternaria infectoria and A. alternata most intensely cause superficial skin infections and allergies due to produced spores. They could also impact a series of respiratory conditions like respiratory infections (like rhinosinusitis), hypersensitivity syndromes (hypersensitivity pneumonitis), and respiratory symptoms (bronchial asthma) [110]. On the other hand, A. tenuissima was much more rarely associated with superficial skin infections in immunocompromised patients and allergic reactions [111]. The Alternaria genus represented a vast source of allergens because the fungi contained various molecules with diverse chemical and biological characteristics. At least 17 IgE-reacting proteins were endogenous to A. alternata and officially became A. alternata allergens [112].

The following genus, Aureobasidium, represented by A. pullulans and A. melanogenum, might cause opportunistic skin and pulmonary infections, meningitis, splenic abscesses, and peritonitis [113]. On the other hand, the genus Naganishia was extremely rare and described as a human pathogen. Even more rarely, Naganishia liquefaciens (previously known as Cryptococcus liquefaciens) affected humans, as the species was identified as a colonizer of human skin [114]. Only a fatal situation concerning Naganishia liquefaciens was described in the case of multi-microbial meningitis in an immunocompromised patient [115].

The less abundant identified species were Epicoccum nigrum, Fusarium subglutinans, Penicillium oxalicum, and Peniophora laeta. Epicoccum nigrum might have similar effects on human health as Alternaria. They could trigger allergic reactions, respiratory infections (sinusitis), and hypersensitivity pneumonitis, and rarely, they might be involved in localized infections of the kidneys, muscles, or brain [116]. Fusarium subglutinans and many other species of the genus Fusarium could lead to various infections in humans like superficial skin infections (keratitis and onychomycosis), systemic infections (fungemia), or respiratory infections (pneumonia) [117]. Penicillium species (Penicillium oxalicum including) generally did not cause infections, even in immunocompromised patients. Extremely rare Penicillium oxalicum was responsible for invasive mycosis with voriconazole resistance in immunocompromised individuals with acute myloid leukemia, diabetes mellitus, and chronic obstructive pulmonary disease [118]. To the authors’ knowledge, Peniophora laeta was never reported as a human pathogen, but other species from the genus were documented in two cases as responsible for respiratory infections [119]. Also, Phialocephala fortinii, Hansfordia pulvinata, Stereum hirsutum, and uncultured compost fungus, AM711465.1, were never reported as human pathogens. Plant- and soil-associated fungi might not have the same pathogenic impact on humans, as their infection strategies developed in connection to plant cells and not to animal cells. The mechanisms regarding the infection strategies in animals are almost unknown [120], as fungi do not exhibit pathogenic traits [121]. All the identified fungi might not cause neuro-psychiatric problems or rheumatologic immune diseases.

The genera Cladosporium and Alternaria were frequently highlighted as exponent fungi in SBS [65]. Sporulating fungi majorly impacted SBS as spores were widely recognized as a common factor in allergic illnesses and were known as one of the primary indoor allergens. Any indoor fungus might cause allergic reactions, which could vary depending on the exposed person, the level of exposure, and the amount of ingested/inspired spores [28].

The results of the questionnaire applied to 190 students who attended the classroom show that the most common symptoms reported were headaches/migraines, followed by repeated sneezing and severe fatigue. These symptoms are consistent with literature data, suggesting that inadequate indoor air may contribute to the specific manifestations of SBS [122]. This is an important observation, considering that inadequate ventilation and the accumulation of pollutants such as PM_2.5 and PM₁₀ and high CO₂ levels can amplify this type of symptom [123]. According to studies in the field, SBS-type symptoms are frequently associated with poor IAQ, especially in educational spaces. Various studies suggest that exposure to high levels of CO₂ and PM can cause respiratory symptoms and impaired cognitive function [124]. This is also supported by the data from the applied questionnaire, where respondents frequently mentioned fatigue and cognitive difficulties, which may indicate a correlation between IAQ and reported symptoms.

From the perspective of possible interventions, the improvement of HVAC systems and the constant monitoring of microclimatic parameters (temperature, RH, CO₂, and others) are essential for reducing the symptoms of SBS. Also, the implementation of air filtration systems can reduce concentrations of pollutants such as PM and VOCs, which can significantly contribute to the discomfort reported by students [86,125].

5. Conclusions

The present study highlighted the crucial importance of IAQ in maintaining the health and well-being of the occupants of a classroom in a national heritage building with an educational function. By monitoring the internal microclimate and microbiota, but also by evaluating the subjective perception of the students, several important conclusions were obtained. The range of temperature recorded in the classroom, with an average of 22.9 °C and a maximum of 27.1 °C, was conducive to microbial proliferation; this range is optimal for the development of fungi and bacteria. However, the average RH of 38.5% was below the optimal level of 60% required for intensive microbiota development, indicating that microbial proliferation was limited, except when RH reached higher values, favoring its development. Some high CO₂ levels, with a maximum of 2744.4 ppm, suggest suboptimal ventilation in some situations, which may contribute to the accumulation of bioaerosols and contaminants. Also, increased levels of PM_2.5 and PM₁₀, with averages of 10.1 µg/m³ and 69.5 µg/m³, were associated with increased risk of respiratory impairment and microbial proliferation, having a significant impact on IAQ. They are recognized as a determinant of symptoms such as headaches, fatigue, and respiratory problems. Inadequate ventilation and the accumulation of pollutants negatively affect IAQ, increasing the risk of SBS. Air and surface analysis identified 19 species of fungi, predominantly the genera Cladosporium, Alternaria, and Aureobasidium, frequently associated with SBS. They are recognized for their impact on respiratory health, being correlated with allergies, skin infections, and respiratory problems in exposed persons. At the same time, identified fungi can indirectly influence cognitive performance by reducing the ability to concentrate, increasing fatigue, and inducing general discomfort. Genera such as Aureobasidium and Epicoccum, although less common, can trigger severe allergic reactions and even respiratory infections in some cases, which can affect brain health by reducing adequate O₂ intake due to airway inflammation.

The questionnaires applied to the 190 students and teachers revealed that the most frequently reported symptoms were headaches/migraines (51.5%), repeated sneezing (44.2%), and severe fatigue (43.7%). Other symptoms reported included eye irritation (36.8%), frequent cough (32.6%), and difficulty concentrating (28.9%). Rarer symptoms, such as nausea or abdominal discomfort, were mentioned by a smaller percentage, namely 4.7% of respondents. Perceptions of indoor air quality (IAQ) varied, with 68.9% of participants rating IAQ as ‘good’ or ‘very good’, while 31.1% rated it as ‘poor’ or ‘very poor’. IAQ problems were reported to be more pronounced at the beginning of the activity by 27.1% of respondents, while 35.6% noted a progressive deterioration during the activity. For 13.6% of participants, the air became problematic only toward the end of the activity, and 20.3% perceived IAQ as poor throughout the activity. These symptoms are consistent with exposure to a poor-quality indoor environment, confirming the correlation between air pollution and perceived discomfort.

Implementing an efficient HVAC system and using air filters to reduce pollutant particles and bioaerosols is recommended to improve IAQ and reduce symptoms associated with SBS. At the same time, the continuous monitoring of microclimatic parameters, such as temperature, RH, and chemical pollutants, is essential for maintaining a healthy environment conducive to educational and research activities.

Although IAQ-related symptoms were reported, the study did not include objective assessments of student or faculty cognitive performance. These measurements could have provided concrete data on the impact of air quality on cognitive functions such as attention, memory, and concentration. Future studies aim to take into account these aspects, as well to carry out complete and complex research on the impact of IAQ on the subjects’ performances in the context of SBS.

6. Limitations

The limitations of the microbiological approach of the present study refer to the focus solely on the fungal community (without bacteria and virus analysis) and the culture-based method used. This method’s limitations include the lack of reproducibility, the effect of the sampling points selection, the sampling time, the underestimation of the total number of fungal propagules, and the characterization of the unculturable fungi. The method might be complemented with the Next Generation Sequencing techniques for the fungal community analysis and supplementary bacterial and virus community analysis.

Regarding the statistical analyses implemented, such as multivariate regression analysis, we acknowledge their limitations, including the possibility of unobserved confounding variables and dependence on model assumptions such as linearity and multicollinearity. Although these factors may influence the robustness of the results, we believe that the methodology used adequately captures the relationships between the analyzed variables and the observed results. In addition, we acknowledge the possibility of subtle or complex effects, which can be explored in future studies using advanced methods.