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Article

Microbiological Contamination Assessment in Higher Education Institutes

by
Carla Viegas
1,2,3,*,
Raquel Pimenta
1,
Marta Dias
1,
Bianca Gomes
1,
Miguel Brito
1,
Liliana Aranha Caetano
1,4,
Elisabete Carolino
1 and
Anita Quintal Gomes
1,5
1
H&TRC-Health & Technology Research Center, ESTeSL-Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, 1990-096 Lisboa, Portugal
2
Public Health Research Centre, NOVA National School of Public Health, Universidade NOVA de Lisboa, 1099-085 Lisboa, Portugal
3
Comprehensive Health Research Center (CHRC), NOVA Medical School, Universidade NOVA de Lisboa, 1169-056 Lisboa, Portugal
4
Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, University of Lisbon, 1649-003 Lisboa, Portugal
5
Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
Atmosphere 2021, 12(8), 1079; https://doi.org/10.3390/atmos12081079
Submission received: 25 July 2021 / Revised: 17 August 2021 / Accepted: 19 August 2021 / Published: 23 August 2021
(This article belongs to the Special Issue Indoor Air Quality—What Is Known and What Needs to Be Done)
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Versions Notes

Abstract

:
The higher education sector represents a unique environment and it acts as a work environment, a learning environment for students, and frequently, also a home environment. The aim of this study was to determine the microbial contamination (SARS-CoV-2, fungi, and bacteria) in Higher Education Facilities (HEI) by using active and passive sampling methods and combining culture-based methods with molecular tools targeting Aspergillus section Fumigati. In addition, the resistance to azole profile was also assessed. Surface samples showed a range of total bacterial contamination between 1 × 103 to 3.1 × 106 CFU·m−2, while Gram-negative bacteria ranged from 0 to 1.9 × 104 CFU·m−2. Fungal contamination ranged from 2 × 103 to 1.8 × 105 CFU·m−2 on MEA, and from 5 × 103 to 1.7 × 105 CFU·m−2 on DG18. The most prevalent species found on both media was Cladosporium sp. (47.36% MEA; 32.33% DG18). Aspergillus genera was observed on MEA (3.21%) and DG18 (14.66%), but not in the supplemented media used for the azole screening. Aspergillus section Fumigati was detected in 2 air samples (2.22%, 2 out of 90 samples) by qPCR. When testing for SARS-CoV-2 all results were negative. The present study showed that although cleaning and disinfection procedures are done regularly due to the COVID-19 pandemic, being effective in eliminating SARS-CoV-2, surfaces were often contaminated with microorganisms other than SARS-CoV-2. This can be a result of increasing resistance to biocides, and to the wide range of environmental factors that can contribute to the dissemination of microbial contamination indoors.

1. Introduction

In the last few decades, several studies were conducted to understand the impact of indoor air quality (IAQ) in public health in different environments, including residential building, shopping malls, schools, health care centers, offices, museums, libraries, temples, and churches, among others [1,2,3,4,5,6,7]. It was indicated that decreased IAQ can negatively affect human health as most people spend around 90% of their time indoors, mainly at home or in the workplace [8]. Due to this, IAQ has emerged and received increasing attention from international scientific community, political institutions, and environmental governances [2,8,9]. The indoor air pollution (IAP), that refers to the existence of pollutants, such as volatile organic compounds (VOCs), particulate matter (PM), inorganic compounds, physical chemicals, and microbiological agents, can influence negatively the IAQ and are harmful to the human health [8,9].
Health effects from indoor air pollutants may be experienced soon after exposure (immediately) or, possibly, years later (long-term) [10]. The respiratory system is often the primary to be affected by the IAP effects [8]. Exposure to microorganisms, in particular, is associated with irritating effects (eyes, nose, skin), allergic reactions (asthma, rhinitis), digestive problems, infectious diseases (pneumonia, tuberculosis, Legionnaire’s disease, severe acute respiratory syndrome), and toxic reactions due to the presence of mycotoxins [8,11,12]. Furthermore, it is important to take into account that immunocompromised individuals are more susceptible to acquiring infections caused by microorganisms, which can sometimes be fatal [11,13,14,15]. A common example is that of Aspergillus conidia, which can be ubiquitous and are easily dispersed in the air [16]. From the various Aspergillus sections, Fumigati is the genus most frequently associated with respiratory symptoms [17]. Another major concern regarding microbial contamination is the emergence and spread of drug-resistant pathogens that have acquired new resistance mechanisms, leading to antimicrobial resistance, making it difficult or impossible to treat common infections [18]. In fact, the development of resistance to antifungal drugs, particularly in section Fumigati, is a phenomenon with growing prevalence in Europe that has been associated with therapeutic failure and high mortality rates [19]. Furthermore, the co-prevalence of SARS-CoV-2 in these environments can pose a serious threat to the health of all occupants [20]. In this context, assessing the microbial contamination is a useful and important tool of prevention to microbial exposure and to guarantee IAQ [21].
The higher education sector represents a unique environment acting as a work environment for faculty members, a learning environment for students, and frequently, a home environment for students [22]. Thus, faculties comprise a high population density that spend more than eight hours per day, five days a week inside their buildings [22,23,24]. In addition, the higher education buildings have a very wide usage as they include lecture halls, gyms, dormitories, day care, restaurants, garden, and laboratories [23,24]. Since many outbreaks of epidemic diseases are correlated with the presence of microorganisms in the air (for example, influenza A pandemic—H1N1 2009—was frequently reported in universities in 2009), the assessment of the level of microbiological contamination, especially in crowed places at universities, should be performed regularly [25].
Assessment of microbiological contamination in HEI has previously been performed in several facilities, such as classrooms, sports hall, laboratories, rooms, entrances, libraries, cafeterias, and restaurants [21,23,24,26,27,28,29,30,31,32,33,34]. However, each of the above-mentioned studies at universities focused mainly on a single type of indoor environment [35]. Importantly, SARS-CoV-2 detection has been carried out in a very limited number of HEI [36], being very relevant to assess it in additional HEI and compare its levels with those of other microorganisms that might be present indoors.
The aim of this study was to determine the microbial contamination (SARS-CoV-2, fungi, and bacteria) in HEI environment by using active and passive sampling methods and combining culture-based methods and molecular tools targeting Aspergillus section Fumigati. In addition, the resistance to azole profile was also assessed in the HEI analyzed.

2. Materials and Methods

2.1. Facilities Assessed and Sampling Sites

This study was performed between July and December 2020 in ten selected facilities of Portuguese HEI, including the presidency building (PB), social services (SS) and eight faculties (HEI 1 to HEI 8). It was part of an enlarged cross-sectional study with financial support to implement an integrated approach on Occupational Health to tackle the COVID-19 pandemic and microbiological contamination assessment aiming to ensure safety in academic recovery during the pandemic crises [37].
The ten facilities under study are located in the Lisbon district and all presented different core activities depending of their purpose and graduation courses held (Table 1).
The sampling sites were chosen based on previous selection by areas of facility by the Occupational Health Services, in the scope of SARS-CoV-2 surveillance held during the 2nd pandemic wave in Portugal and before starting the 2nd semester of the academic year 2020/2021. A walkthrough survey and checklist were applied in order to prioritize the most critical workplaces/areas in relation to SARS-CoV-2 contamination. In summary, sampling sites of each facility were selected according to the following criteria: 50–80% workplaces/facilities occupation, activities performed that can lead to higher number of workers per workplace and/or without a mask (cantine) or based on workers positive serologic surveillance results (positive result for IgM+ or IgG+) [37]. All the facilities implemented a contingency plan that included working, whenever possible, in home office even concerning theoretical lectures, wearing a mask indoor and outdoor (when 2 meters distance was not possible to ensure) and workers should remain at home (quarantine) when COVID-19 symptoms arise until further diagnose.
Environmental samples (air and surface samples) were performed in each area and 6 to 25 samples were collected at each location (Table 1). Most of the sampling sites were common among all the facilities, such as offices (including human and financial resources, academic services, logistics, accounting, and acquisition department), attendance room, reception rooms, auditoriums, meals spaces, bathrooms and libraries. Although there are classrooms in almost every location, the type of classroom analyzed varied widely in all locations, including rooms for music, dance, choir, theatre and multimedia, laboratories, and gyms. In addition, in some locations, there were samples collected from student’s social rooms, workshops, changing rooms and professors’ room (Table 1).
The samples were collected mainly in the morning and during normal activities, except for HEI 1, where the samples were collected in two days, one of them in the afternoon. In all facilities, the cleaning method applied was based on cleaning and disinfection recurring to bactericide and virucide, bleach and multipurpose detergent. Most of the sampling sites (56.8%) registered between 1 to 9 workers to follow the contingency plans. However, the accurate number of workers was not possible to obtain due to workers quarantine in the same day of the assessments to comply with contingency plans.

2.2. Samples Collected and Assays Performed

Air samples of 600 L were collected using the impinger Coriolis μ air sampler (Bertin Technologies, Montigny-le-Bretonneux, France) with a flow rate of 300 L/min collected into a conical vial containing 5 mL Buffer NVL (NZY Viral RNA Isolation kit (MB40701) component) (Figure 1). Surface samples were collected by swabbing the areas of each sampling site, using sterile cotton swabs moistened in Buffer NVL (SARS-CoV-2 assessment) or sterilized water (fungi and bacteria assessment). A 10 cm × 10 cm square stencil, disinfected between samplings with a 70% alcohol solution was used (ISO 18593: 2004) to allow quantification. On some surfaces with common characteristics, such as surfaces material and cleaning procedures, composite samples were performed (swabbing different surfaces with the same swab) [38] (Figure 1).
Culture based methods were applied only in surface samples. Every swab was later extracted with 1 ml of 0.1% Tween™ 80 saline solution (NaCl 0.9%) for 30 min at 250 rpm on an orbital laboratory shaker (Edmund Bühler SM-30, Hechingen, Germany) and plated onto the selected media. Four different culture media were used in order to enhance the selectivity for bacterial and fungal growth, as follows: 2% malt extract agar (MEA) with 0.05 g L−1 chloramphenicol media, and dichloran glycerol (DG18) agar based media, for fungal characterization; Tryptic Soy Agar (TSA) with 0.2% nystatin, for total bacteria assessment; and Violet Red Bile Agar (VRBA), for Gram-negative bacteria.
Antifungal resistance was also screened by inoculating 150 µL of the samples on Sabouraud dextrose agar (SDA) supplemented with 4 mg/L itraconazole (ITR), 2 mg/L voriconazole (VOR), 0.5 mg/L Posaconazole (POS), or SDA alone (as control) (adapted from the EUCAST guidelines [39]).
All the inoculated plates were incubated at 27 °C for five days for fungal growth (four days regarding azole resistance screening) or for seven days at 30 °C and 37 °C for bacterial growth and for Gram-negative bacterial growth, respectively. After the incubation period, quantitative (colony-forming units—CFU·m−2) results for fungi and bacteria were obtained. When colony overgrowth was observed due to fungi with fast growing rates (Mucorales, Chrysonilia sitophila and Trichoderma sp.), making it impossible to count colonies, the median of all colony values obtained in all locations of the same facility was assumed. Fungal species were also identified microscopically using lactophenol cotton blue mount procedures. Morphological identification was achieved through macro and microscopic characteristics [40].
Air samples obtained by impinger were also analyzed by molecular detection of Aspergillus section Fumigati. Procedures and reactions were performed as previously reported [41].
Concerning SARS-CoV-2 assessment on air and surfaces, the procedures used for sample inactivation, RNA extraction and detection were as previously submitted [37]. In each analysis, a positive (a SARS-CoV-2 positive sample) and a negative (water) sample were included. Moreover, in order to detect possible PCR inhibitors, an internal control was added to each PCR reaction (TATAA Universal RNA Spike I).

2.3. Statistical Analysis

Data were analyzed using SPSS statistical software, V26.0 for windows. The results were considered significant at the 5% significance level. To test the normality of the data, the Kolmogorov–Smirnov test was used. To characterize the sample, frequency analysis (n, %) was used for qualitative data. To study the relationship between bacterial and fungal counts and resistance to azoles and Aspergillus section, Spearman’s correlation coefficient was used, since the assumption of normality was not verified.

3. Results

3.1. Viable Microbial Contamination

Surface samples showed a range of total bacterial contamination between 1 × 103 (classroom and attendance room) and 3.1 × 106 CFU·m−2 (offices). The highest median value (or total values in the case of classroom, attendance room, computer room, changing and dressing room, gym, study room and multimedia) was 5 × 105 CFU·m−2 (study room) and the lowest was 1 × 103 CFU·m−2 (classroom and attendance room). Gram-negative bacteria in surface samples ranged from 0 to 1.9 × 104 CFU·m−2 (laboratory) with a highest median value of 9.5 × 103 CFU·m−2 in the laboratory (Figure 2 and Figure 3).
Total bacterial contamination found in different facilities ranged from 3.8 × 104 (HEI 2) to 4 × 106 CFU·m−2 (HEI 7), with the highest median value found in HEI 7 (5 × 105 CFU·m−2) and the lowest median value found in Social Services (2 × 103 CFU·m−2). Gram-negative bacteria ranged between 0 to 3 × 104 CFU·m−2 (HEI 3), with a highest median value of 4 × 103 CFU·m−2 (HEI 3) (Figure 2 and Figure 3).
Fungal contamination on surfaces ranged from 0 to 1.5 × 105 CFU·m−2 (offices) on MEA, and from 0 to 1.8 × 105 CFU·m−2 (professors room) on DG18. The median values (or total values in the case of classroom, attendance room, computer room, changing and dressing room, gym, study room and multimedia) ranged from 0 to 5.9 × 104 CFU·m−2 (changing and dressing rooms) in MEA, and from 0 to 9.1 × 104 CFU·m−2 (professors room) in DG18 (Figure 4 and Figure 5).
Fungal contamination found in different facilities ranged from 2 × 103 (HEI 4) to 1.8 × 105 CFU·m−2 (HEI 6) on MEA, and from 5 × 103 (HEI 4) to 1.7 × 105 CFU·m−2 (HEI 2) on DG18. The median values on MEA ranged between 0 (HEI 4) to 3.1 × 104 CFU·m−2 (HEI 6), and from 0 (HEI 4) to 8 × 103 CFU·m−2 (HEI 6) on DG18 (Figure 4 and Figure 5).
Regarding fungal distribution 10 and 9 different fungal species were observed, on MEA and DG18, respectively. The most prevalent genera found on both media was Cladosporium sp. (47.36% MEA; 32.33% DG18), followed by Penicillium sp. (40.94%) and Aspergillus sp. (3.21%) on MEA, whereas on DG18 were Aureobasidium sp. (28.97%) and Penicillium sp. (20.67%).
Aspergillus genera was observed on MEA (3.21%) and DG18 (14.66%) (Table 2). There were found 5 different Aspergillus sections on MEA, being Candidi the most prevalent (6 × 103 CFU·m−2; 35.29%), followed by Fumigati (5 × 103 CFU·m−2; 29.41%), Nigri (3 × 103 CFU·m−2; 17.65%), Nidulantes (2 × 103 CFU·m−2; 11.76%) and Aspergilli (1 × 103 CFU·m−2; 5.88%). On DG18 there were found 3 different Aspergillus sections, being Nidulantes the most identified (7.7 × 104 CFU·m−2; 92.77%) followed by Candidi (5 × 103 CFU·m−2; 6.02%) and Circumdati (1 × 103 CFU·m−2; 1.20%) (Figure 6).

3.2. Azole Resistance Screening

Positive fungal growth on at least one azole supplemented media was observed for 10 fungal species. Cladosporium sp. was the most abundant species in all three azoles (64.71% on ITR; 65.49% on VOR; 53.33% on POS). Penicillium sp. was also found in all three azoles, being the second most prevalent species in two of three azoles (14.79% on VOR; 22.22% on POS). Chrysosporium sp., Chrysonilia sitophila and Mucor sp. were also presented in all three azoles. Alternaria sp. and Aureobasidium sp. were only observed on ITR and VOR. Acremonium sp. and Rhizopus sp. were only detected on VOR and POS, respectively (Table 3).

3.3. Molecular Detection

Regarding SARS-CoV-2, all the environmental samples preformed presented negative results. Aspergillus section Fumigati was detected in 2 samples (2.22%, 2 out of 90 samples) (Table 4).

3.4. Correlation Analysis

Regarding bacterial counts in TSA, significant correlations were detected with counts in VRBA (rS = 0.252, p = 0.015), in SAB (rS = 0.354, p = 0.001), in VOR (rS = 0.235, p = 0.033) and in POS (rS = 0.343, p = 0.001) and with the number of workers (rS = 0.287, p = 0.009). These results indicate that higher bacterial counts in TSA are related to higher bacterial counts in VRBA, higher azole resistance counts (either in SAB, VOR or POS) and higher number of workers (Table 5).
Considering bacterial counts in VRBA, only significant correlation was found with fungal counts in DG18 (rS = 0.235, p = 0.024), revealing that higher bacterial counts in VRBA are related to higher fungal counts in DG18 (Table 5).
With regard to fungal counts in MEA, significant correlations were detected with fungal counts in DG18 (rS = 0.586, p = 0.000), in SAB (rS = 0.494, p = 0.000), in ITR (rS = 0.362, p = 0.001) and in VOR (rS = 0.485, p = 0.000), with Aspergillus sp. counts, fungi in MEA (rS = 0.265, p = 0.011) and with number of workers (rS = 0.226 p = 0.043). These results reveal that higher fungal counts in MEA are related to higher fungal counts in DG18, higher azole resistance (either in SAB, ITR or VOR), higher values of Aspergillus sp. counts in MEA and higher number of workers (Table 5).
With respect to fungal counts in DG18, significant correlations were detected with azole resistance in SAB (rS = 0.562, p =0.000), in ITR (rS = 0.479, p = 0.000), in VOR (rS = 0.572, p = 0.000) and in POS (rS = 0.314, p = 0.004) and with Aspergillus sp. counts in DG18 (rS= 0.321, p = 0.002). These results indicate that higher fungal counts in DG18 are related to higher azole resistance (either in SAB, ITR, VOR or POS) and higher values of Aspergillus sp. counts in DG18 (Table 5).
Regarding azole resistance in SAB, significant correlations were detected with azole resistance in ITR (rS = 0.478, p = 0.000), in VOR (rS = 0.638, p = 0.000) and in POS (rS= 0.289, p = 0.008) and with Aspergillus sp. counts—azole resistance in SAB (rS = 0.388, p = 0.000), showing that higher counts in SAB are related to greater resistance to azoles in other media, including in the Aspergillus sp. counts (Table 5).
Concerning azole resistance in ITR, significant correlations were detected with azole resistance in VOR (rS = 0.472, p = 0.000) and in POS (rS = 0.360, p = 0.001), revealing that higher azole resistance in ITR is related with higher azole resistance in VOR and POS (Table 5).
With regard to azole resistance in VOR, significant correlations were detected with azole resistance in POS (rS = 0.308, p = 0.005) and with Aspergillus sp. counts in MEA (rS= 0.243, p = 0.027) and Aspergillus sp. counts in SAB (rS = 0.375, p = 0.000), revealing that higher azole resistance in VOR is related with higher azole resistance in POS, higher values of Aspergillus sp. counts in MEA and higher and Aspergillus sp. counts in SAB (Table 5).
Regarding Aspergillus section, the following significant correlations were found: (i) fungi in MEA and azole resistance in VOR (rS = 0.360, p = 0.001), which indicates that higher values in MEA are related to higher resistance to azoles in VOR; (ii) higher counts in SAB and VOR (rS = 0.294, p = 0.007), which reveals that higher counts in SAB is related to greater azole resistance in VOR (Table 5).

4. Discussion

The IAQ in HEI is of great importance due to the impact it has on the health and performance of students, professors and staff [22,23,24]. Furthermore, microbiological contamination assessment is one of the main parameters that affect IAQ, since potentially pathogenic microorganisms can be disseminated as bioaerosols and via contact with contaminated surfaces [8,9,42] or through resuspension from air to surfaces [42].
It has already been previously reported that the presence of fungi and bacteria in indoor air is influenced by a wide range of factors, such as human occupancy and their activities, humidity levels, ventilation, environmental characteristics, water infiltrations, construction and decoration materials and outdoor air [11,43,44]. Due to the extensive list of factors that influence IAQ, exposure assessment to microorganisms remains a challenge to every exposure assessor/industrial hygienist [45]. In fact, as in other risk factors exposure assessment studies, the sampling approach is of critical importance to achieve an accurate risk characterization regarding microbiological agents [46]. Active methods, based on air sampling, rely within the most common methods used for samples collection. However, they may not represent the real scenario regarding the inhalation exposure, since they can only reflect the load from a short period of time (mostly minutes), corresponding to the sampling duration, thus representing only a small fraction of the microbiological contamination exposure [47,48,49]. Despite these concerns, the impingement method, also based on air sampling, has been the most used for the SARS-CoV-2 assessment in indoor environments [37], since it allows for longer active sampling times, thus ensuring collection of sufficient airborne viruses for detection by molecular tools [50].
In the case of passive methods, such as the surface swabs used in this study, they allow to characterize the contamination over a longer period of time (after the last cleaning procedure), thus providing a more comprehensive picture of the real exposure [46,47,48,49]. Indeed, they have previously been used in several indoor environments [46,51,52,53,54,55,56,57,58,59,60,61] generally providing more detailed and complete information regarding fungal species distribution. The use of both sampling methods allows to overcome each method limitation, ensuring a more precise exposure assessment [44]. This is further reinforced with the use of culture based-methods and molecular tools. Indeed, although most of the studies performed in HEI are focused on air quality screenings [28,30,32,33,62,63], surface analyses have also been shown to be relevant, as they may also reflect the contamination in the air by resuspension depending of the activities developed indoors, thus possibly leading to increased levels in airborne concentration [57,64,65].
Previous studies have shown that, besides the sampling approach, culture media applied also influence the results obtained for fungi and bacteria detection in environments [46,56]. Regarding bacterial contamination, culture media allows for the discrimination between total bacteria and Gram-negative bacteria [56]. As expected, in our study, the contamination of Gram-negative bacteria was lower than that of total bacteria, as expected, since the latter represents the number of Gram-positive bacteria and Gram-negative bacteria. The presence of bacteria on surfaces is a common situation, especially in the most frequently touched surfaces, as their main contamination sources are the occupants and their activities [66,67,68]. Thus, it was not surprising that the highest total bacterial counts were found in the offices (31.8%), followed by the auditorium (15.7%) and the meals space (14.4%), which are the places with higher occupancy. In fact, the positive correlation found between higher bacterial counts in TSA and higher number of workers emphasizes this contribution.
Although bacteria are ubiquitous and generally of human origin (from skin and mucous membranes) and not harmful for health, the presence of Gram-negative bacteria is a special concern, as they may have natural resistance to antibiotics and can also produce endotoxins, which can cause respiratory symptoms [11,43,57,68].
Regarding the fungal contamination assessment, besides the use of MEA, as suggested in the Portuguese guidance for IAQ assessment [11], DG18 was also selected to be used, since this media constitutes a better alternative for colony counting, also allowing to obtain higher diversity of genera [47,69]. Contrary to these expectations in this study we have obtained more diversification of fungal species in MEA (10 different species) than in DG18 (9 species) with the same trend being observed for Aspergillus sections (5 on MEA; 3 on DG18).
Interestingly, in the present study, the concentration of fungi on the surfaces was lower than that of bacteria, similarly to a study on surface swabs in university facilities [23]. The most prevalent fungi found in our study were Cladosporium sp., Penicillium sp. and Aspergillus sp., which is in accordance with other studies based on the use of surfaces swabs as sampling approach [64,70,71,72].
While offices (28.5%), meals space (13.4%) and theatre and choir room (12.8%) were the areas most contaminated by fungi on MEA, on DG18 we detected higher fungal loads in professors’ rooms (32.0%), offices (24.0%) and changing and dressing room (11.8%). A possible reason for the higher counts of fungi in these areas could be the fact that, due to the COVID-19 pandemic, all occupants were encouraged to open windows to prevent COVID-19 infection [20]. Indeed, evidence clearly indicated that opening windows increases the levels of fungi and other microorganisms in the air and on the surfaces as a result of the passage of outdoor air into indoors [33,42,67,73].
HEI 6 and HEI 2 have the highest counts of total fungal contamination on MEA and DG18 media, respectively. HEI 2 samples were analyzed in DG18 and are among the areas with higher contamination values, possessing a considerable concentration of fungi. However, in this building only professor’s rooms were analyzed by this method. Fungal growth can be promoted in the presence of moisture, and many fungi grow easily on any surface that becomes wet or moistened, such as faucets, which are present, for example, in meals space and changing and dressing room [67,74,75].
Aspergillus sections, being most of them reported as mycotoxins producers (Circumdati, Nigri, Fumigati) [76], some with clinical relevance (Fumigati, Nigri and Aspergilli) [44,47,77], presented higher diversity on MEA media. Of note, while these sections were only identified in MEA, Aspergillus section Circumdati was only isolated on DG18 media, as previously reported [46]. These Aspergillus sections are considered indicators of harmful fungal contamination [11,77,78,79] and measurements should be applied to avoid their presence indoors. Furthermore, the detection of Aspergillus sections Candidi and Nidulantes should be emphasized due to their toxigenic potential [47,76,78].
The emergence of pathogenic fungi resistant to antifungal agents widely used in the treatment of fungal infections, which can cause therapeutic failure, has been notorious in recent years [80,81]. In this study, the screening of fungal resistance to three medical azoles was conducted. Cladosporium sp. was the fungal species mostly present in all three azoles (64.71% on ITR; 65.49% on VOR; 53.33% on POS), followed by Penicillium sp. Regarding Mucor sp. and Rhizopus sp., they are intrinsically resistant to voriconazole, with itraconazole and posaconazole as first-line therapy [44,82]. However, in this study, we have observed the growth of these fungal species in the presence of itraconazole and posaconazole thus indicating the need to further characterize fungal resistance of those isolates [44,82].
The identification of a fungal species in more than one azole suggests a multi-drug resistance phenotype that must be further evaluated through antifungal susceptibility testing [82] by the reference microdilution method (EUCAST) [83]. A confirmed resistance phenotype would indicate the presence of azole-resistant fungal species in these settings, thus constituting a higher exposure risk, especially for immunocompromised occupants [57]. One limitation to this characterization is that reference values are defined only for Aspergillus sp. and Candida sp. [84].
Emergent antifungal resistance in Aspergillus fumigatus is the main cause of invasive fungal infections [77,85]. In this study, Aspergillus section Nigri was identified in one azole media (0.70% on VOR), whereas Aspergillus sections Candidi, Fumigati, Nidulantes, Nigri, and Circumdati were identified in control Sabouraud, in MEA and/or DG18. These results are in line with previous data from dwellings and hospital environment, where no Aspergillus species were able to grow on azole-media, despite being observed in Sabouraud, MEA or DG18 [48,54,56,57]. Of note, cryptic Aspergillus species might be underestimated in azole-media due to the presence of fast-growing species, such as Chrysonilia sitophila and Mucorales group [44,56,57].
Our culture-based methods allowed the identification of the Aspergillus section Fumigati in a wide number of samples, with molecular tools also detecting this section in different and in a smaller number of samples. Despite this discrepancy, it is of relevance to use both methods, as they provide complimentary information and answer different questions. Indeed molecular tools allow precise, fast, specific and sensitive detection of microorganisms. Importantly, they also can identify dead or dormant microorganisms and can discriminate toxigenic strains from regular strains within some fungal species [86]. Although culture based-methods are selective, revealing only microorganisms able to grow on a particular growth media, therefore, underestimating the total number of microorganisms in samples, these methods are crucial since the viability of bioburden is of critical importance to estimate health risks, as it affects biological mechanisms, such as inflammatory and cytotoxic responses [82,83,87]. This reinforces the idea of combining both molecular and culture-based methods [44].
As previous suggested [88] the sampling approach to assess SARS-CoV-2 included passive and active sampling methods, swabs being the most common found in the literature [88] and with increased detection when compared with other sampling methods [88,89]. Although the sampling volume from the active sampling was the one recommended [90] and the detection technique was the one widely used for SARS-CoV-2 detection [88], all the results were negative indicating the efficacy of the present measures in place on the assessed facilities. Further studies, should include a different sampling approach by using glass-fiber and PTFE filters to be employed in low and high-volume air samplers and applying samples pretreatments allowing obtain an increased virus concentrations [91].

5. Conclusions

The present study showed that although the regular cleaning and disinfection procedures effectively removed SARS-CoV-2 from surfaces, these remained contaminated with other microorganisms besides SARS-CoV-2. This can be a result of an increased resistance to biocides, and of the wide range of environmental factors that can contribute to the dissemination of microbial contamination indoors.
Therefore, we recommended that corrective measures should be implemented to reduce bacterial and fungal presence in surfaces to avoid contamination in the air due to resuspension. Additional studies aiming at correlating air and surfaces microorganisms’ burden can be a valuable tool in finding the contamination sources.

Author Contributions

Conceptualization, C.V.; methodology, C.V.; formal analysis, C.V., R.P., M.D., B.G., L.A.C., M.B., E.C. and A.Q.G.; investigation, C.V.; resources, C.V.; writing—original draft preparation, C.V., R.P., M.D. and L.A.C.; writing—review and editing, C.V., M.B. and A.Q.G.; supervision, C.V.; project administration, C.V.; funding acquisition, C.V. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Instituto Politécnico de Lisboa, Lisbon, Portugal for funding the Project “IPL MOMENTO ZERO” (ESTeSL/IPL/2020). H&TRC authors gratefully acknowledge the FCT/MCTES national support through the UIDB/05608/2020 and UIDP/05608/2020.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest. I have full control of all primary data and permission is given to the journal to review the data if requested.

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Figure 1. Sampling strategy adopted and assays applied. * Lack of extracts quantities in two samples to perform the assay.
Figure 1. Sampling strategy adopted and assays applied. * Lack of extracts quantities in two samples to perform the assay.
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Figure 2. Total bacteria in surface swabs.
Figure 2. Total bacteria in surface swabs.
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Figure 3. Gram-negative bacteria in surface swabs.
Figure 3. Gram-negative bacteria in surface swabs.
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Figure 4. Fungal contamination in surface swabs on MEA.
Figure 4. Fungal contamination in surface swabs on MEA.
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Figure 5. Fungal contamination in surface swabs on DG18.
Figure 5. Fungal contamination in surface swabs on DG18.
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Figure 6. Aspergillus sections distribution in surface swabs after inoculation onto (a) MEA and (b) DG18.
Figure 6. Aspergillus sections distribution in surface swabs after inoculation onto (a) MEA and (b) DG18.
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Table
Table 1. Developed activities, areas sampled and samples number in each facility.
Table 1. Developed activities, areas sampled and samples number in each facility.
Study SiteDevelped Activities/Graduation Courses HeldAssessed AreasSurface Swabs (for SARS-CoV-2)Surface Swabs (for Fungi and Bacteria)Air Samples
PBPresidency services that support the HEI in the activities common to the institutionCommunication Office (Doorknob)110
Communication Office (Ventilation grids)11
Drivers Reception (Counter)11
Entrance (Touch screen and money)11
Entrance (Buttons)1
Lunchroom (Microwave and Coffee machine)10
Lunchroom (Refrigerator)10
Lunchroom (Ventilation grids)11
Human Resources (Coffee machines)10
Human Resources (Access card)10
Human Resources (Staplers and stamps)10
Human Resources (Ventilation grids)11
Financial Resources (Staplers and stamps)11
Human and Financial Resources (Windows)10
Human and Financial Resources (Doorknob)10
Financial Resources (Ventilation grids in operation)11
Reception 1 (Printer)10
Reception 2 (Table)10
Acquisition Department (1st floor) (Windows)10
Acquisition Department (1st floor) (Keys and card)10
Acquisition Department (1st floor) (Ventilation grids in operation)11
Male Bathroom (1st floor) (Doorknob and Faucets)10
Female Bathroom (1st floor) (Doorknob and Faucets)10
Corridor (1st floor) (Printer)10
Academics, Quality and Planning Department (Mobile air conditioning equipment)10
SSSocial services that implement the school social action policy and the provision of support and benefits to students who attend the institutionLunchroom (Table)011
Lunchroom (Microwave, Refrigerator)11
Lunchroom (Door)11
Waiting Room/Classroom (Coffee Machine)101
Waiting Room/Classroom (Coffee Machine, PC and eraser)01
Waiting Room/Classroom (Table)11
Reception (Counter)111
Reception (Door and Water Machine)11
Bathroom (Doors and Faucets)111
Bathroom (Floor)01
Attendance Room (Table and Acrylic Protection)111
Accounting (Printer)111
Accounting (Doors)10
Accounting (Floor)01
HEI 1Health sciences and technologies for the exercise of skills in the field of disease prevention and health promotion, diagnosis and therapeutic intervention and rehabilitationLogistics (Door and Window)111
Accounting/Provisioning (Printer, Door and Stapler)111
Academic Services (Printer SA and Printer corridor)111
Reception (Door, Handrail and Balcony)110
Bar (Chairs and Door)111
Library (Printer and Computer)111
Lunchroom (1st floor) (Fridge and microwave)111
Lunchroom (2nd floor) (Fridge and microwave)111
Corridor (1st floor) (Printer)110
Bathroom (1st floor) (Doorknobs, Door and Flush toilet)110
Office (Mouse, Keyboard, Light switch, Door and Doorknob)110
Corridor (2nd floor) (Printer)110
Bathroom (2nd floor) (Doorknobs, Door and Flush toilet)110
HEI 2Accounting and business technicians to perform administrative and financial accounting activities of national organizationsFinancial Services (Printers and Stapler)111
Professors Room 1 (Printers and Computers)111
Professors Room 2 (Printers, Computers and TV control)111
Cafeteria/Bar (Counter and Tables)111
Auditorium (Handrail, Light switch and Door)111
Student’s Social Room (Door, Fridge, Microwave, Faucet, Coffee and Machine)111
Library (Printer and Computers)111
Human Resources (Printer, Door, Acrylics, Counter and Light switch)111
HEI 3Classic engineering areas and in the area of renewable energies, acoustics, environment, hygiene and safety, urban rehabilitation, biomedical engineering and managementLibrary (Computer, Printer and Doorknob)111
Lunchroom 1 (Faucet, Doorknob, Chair, Table, Microwave, Coffee machine and Faucet)111
Lunchroom 2 (Microwave, Toaster, Kettle, Doorknob, Door, Table and Chair)111
Bar 1 (Microwave, Refrigerator, Handles, Cash register and ATM)111
Workshop (Machine, Doorknob and Door)111
Bar 2 (Cash register, Control, Fridge and Microwave)111
Organic Chemistry Lab 1 (Scale and Faucets)111
Organic Chemistry Lab 2 (Equipments, Door and Doorknob)111
Inorganic Chemistry Lab (Computer, Doorknobs, Equipments, Micropipettes and Refrigerator)111
Canteen (Faucets, Counter, Acrylic and Cash Register)111
Auditorium (Doorknobs, Door, Table, Chair and Eraser)111
HEI 4Theater and cinema, including conducting research activities, experimentation and artistic productionLibrary (PC, Windows and Tables)111
Canteen (Chairs, Table, PC and Doorknob)111
Bar (Counter, Napkin Holder, PC, Coffee Machine and Table)111
Grand Auditorium (Doorknob, PC and Chairs)111
Small Auditorium (Scenic Interpretation Studio) (Door, Table, Doorknob and Chairs)111
Computer Room (Switch, Mouse, PC, Keyboard, Door and Doorknob)111
Workshop (Doorknob, Tables, Lockers and Equipment)111
HEI 5Teachers, and other educational agents with a high level of cultural, scientific, technical, and professional preparationBar/Cafeteria (Cash Register and Counters)111
Science Laboratory (Physic-Chemical) (Chairs, Scales, Faucets and Handle)111
Student’s Social Room (Microwave, Faucet, Windows and Chairs)111
Theatre and Choir Room (Switch, Door, Doorknob, Windows, Tables and Chairs)111
Changing room/Dressing room (Doors, Doorknobs, Faucets)111
Gym (Doors, Doorknobs, Table, Chair, Equipment)111
Ceramic Workshop (Tables, Chairs, Faucets, Eraser, Doorknob, Door)111
Dance Room (Doorknobs, Doors, Eraser)111
Music Room (Switch, Piano, Doorknob, Window and Chairs)111
HEI 6Artistic, technical, technological, and scientific training for music professionalsGrand Auditorium (Chairs, Tables, Piano and Tripods)110
Small Auditorium (Chairs, Tables, Piano and Tripods)11
Library (Computers and Tables)11
Academic Services (Printer, Doorknob and Cabinets)11
Corridor (Printer)11
Academic Services (Customer Service) (Acrylic, Pens, Coffee Machine and Tables)11
Lunchroom (Microwave, Tables, Chairs, Refrigerator, Doorknobs and Switch)11
Choir Room (Chairs, Tables, Piano, Eraser, Switch, Door and Doorknobs)11
Music Room (Eraser, Tripods, Acrylic, Door, Doorknob, Switch and Piano)11
HEI 7Social communication, including the areas of audiovisual and multimedia, journalism, advertising and marketing, and public relations and business communicationBar (Chairs, Tables, Cash Register, Acrylic, Counter and Calculator)110
Study Room (Food and coffee machines, Chairs and Tables)11
Corridor (Printer)11
“Home Food” Space (Microwave, Water machine, Tables and Chairs)11
Auditorium (Door, Doorknob and Chairs)11
Academic Services (Tables, Switch, Chairs, Acrylic, Printer, Stapler, Hole Puncher, Door and Doorknob)11
Library (Tables, Chairs and Acrylic)11
Multimedia Warehouse (Printer, Computers, Keyboards, Mouse, Barcode reader, Microwave, TV and Chairs)11
HEI 8DanceAcademic Services (Printer, Doorknob, Acrylic and Table)110
Dance Reception (Doorknob, Computer, Telephone and Keys)11
Professors Room (Doorknob, Printer, Switch, Flush toilet, Table and Chairs)11
Atrium Dance Studio (Bar and Sound System)11
Dance Studio 1 (Bar and Sound System)11
Dance Studio 2 (Bar and Sound System)11
Total 1069248
Table
Table 2. Fungal species distribution in surface swabs after inoculation onto MEA and DG18 media.
Table 2. Fungal species distribution in surface swabs after inoculation onto MEA and DG18 media.
MEADG18
Fungin (CFU·m−2); %Fungin (CFU·m−2); %
Cladosporium sp.2.51 × 105; 47.36Cladosporium sp.1.83 × 105; 32.33
Penicillium sp.2.17 × 105; 40.94Aureobasidium sp.1.64 × 105; 28.98
Aspergillus sp.1.70 × 104; 3.21Penicillium sp.1.17 × 105; 20.67
Rhizopus sp.1.40 × 104; 2.64Aspergillus sp.8.30 × 104; 14.66
Aureobasidium sp.1.20 × 104; 2.26Chrysosporium sp.1.10 × 104; 1.94
Alternaria sp.8.00 × 103; 1.51Fusarium sp.3.00 × 103; 0.53
Chrysosporium sp.5.00 × 103; 0.94Chrysonilia sp.2.00 × 103; 0.35
Phoma sp.3.00 × 103; 0.57Mucor sp.2.00 × 103; 0.35
Acremonium sp.2.00 × 103; 0.38Acremonium sp.1.00 × 103; 0.18
Chrysonilia sitophila1.00 × 103; 0.19
Total5.30 × 105; 100 5.66 × 105; 100
Table
Table 3. Fungal distribution in azole-supplemented SAB media from surface swab samples.
Table 3. Fungal distribution in azole-supplemented SAB media from surface swab samples.
SABITRVORPOS
Fungin (CFU·m−2); %n (CFU·m−2); %n (CFU·m−2); %n (CFU·m−2); %
Acremonium sp.1.10 × 104; 3.440.001.00 × 103; 0.700.00
Alternaria sp.1.00 × 103; 0.312.00 × 103; 3.921.00 × 103; 0.700.00
Aspergillus section Candidi2.00 × 103; 0.630.000.000.00
Aspergillus section Fumigati3.00 × 103; 0.940.000.000.00
Aspergillus section Nidulantes5.00 × 103; 1.560.000.000.00
Aspergillus section Nigri8.00 × 103; 2.500.001.00 × 103; 0.700.00
Bipolaris sp.3.00 × 103; 0.940.000.000.00
Aspergillus section Circumdati1.00 × 103; 0.310.000.000.00
Aureobasidium sp.9.00 × 103; 2.810.006.00 × 103; 4.230.00
Chrysonilia sitophila1.00 × 103; 0.313.00 × 103; 5.884.00 × 103; 2.821.00 × 103; 2.22
Chrysosporium sp.2.20 × 104; 6.889.00 × 103; 17.651.30 × 104; 9.158.00 × 103; 17.78
Cladosporium sp.1.93 × 105; 60.313.30 × 104; 64.719.30 × 104; 65.492.40 × 104; 53.33
Mucor sp.1.30 × 104; 4.062.00 × 103; 3.922.00 × 103; 1.411.00 × 103; 2.22
Penicillium sp.4.40 × 104; 13.752.00 × 103; 3.922.10 × 104; 14.791.00 × 104; 22.22
Rhizopus sp.3.00 × 103; 0.940.000.001.00 × 103; 2.22
Ulocladium sp.1.00 × 103; 0.310.000.000.00
Total3.20 × 105; 1005.10 × 104; 1001.42 × 105; 1004.50 × 104; 100
Table
Table 4. Molecular detection of Aspergillus section Fumigati.
Table 4. Molecular detection of Aspergillus section Fumigati.
Sample TypeCFU·m−2 (in MEA/DG18)Cq
Swab0/031.56
0/028.32
Table
Table 5. Study of the relationship between bacterial and fungal counts, azole resistance (SAB, ITR, VOR and POS) and Aspergillus sections (MEA, DG18) and azole resistance screening (SAB, VOR). Spearman correlation coefficient results.
Table 5. Study of the relationship between bacterial and fungal counts, azole resistance (SAB, ITR, VOR and POS) and Aspergillus sections (MEA, DG18) and azole resistance screening (SAB, VOR). Spearman correlation coefficient results.
Bacterial Counts (CFU·m−2)Fungal Counts (CFU·m−2)Azole Resistance (CFU·m−2)Aspergillus sp.Number of Workers
FungiAzole Resistance
VRBAMEADG18SABITRVORPOSMEADG18SABVOR
Bacterial countsTSA0.252 *0.1490.1410.354 **0.1300.235 *0.343 **−0.,1150.0430.061−0.0090.287 **
VRBA −0.0080.235 *0.0590.1190.1080.064−0.098−0.022−0.061−0.061−0.082
Fungal countsMEA 0.586 **0.494 **0.362 **0.485 **0.1230.265 *0.1690.1290.1100.226 *
DG18 0.562 **0.479 **0.572 **0.314 **0.0010.321 **0.1400.0020.084
Azole resistanceSAB 0.478 **0.638 **0.289 **0.079−0.0230.388 **0.0700.182
ITR 0.472 **0.360 **0.0210.0410.162−0.058−0.112
VOR 0.308 **0.243 *0.0900.375 **0.1330.171
POS −0.1820.1630.079−0.062−0.132
Aspergillus sp.FungiMEA 0.0680.0100.360 **0.189
DG18 −0.083−0.025−0.027
Azole resistanceSAB 0.29 4 **−0.072
VOR 0.190
* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).
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Viegas, C.; Pimenta, R.; Dias, M.; Gomes, B.; Brito, M.; Aranha Caetano, L.; Carolino, E.; Gomes, A.Q. Microbiological Contamination Assessment in Higher Education Institutes. Atmosphere 2021, 12, 1079. https://doi.org/10.3390/atmos12081079

AMA Style

Viegas C, Pimenta R, Dias M, Gomes B, Brito M, Aranha Caetano L, Carolino E, Gomes AQ. Microbiological Contamination Assessment in Higher Education Institutes. Atmosphere. 2021; 12(8):1079. https://doi.org/10.3390/atmos12081079

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Viegas, Carla, Raquel Pimenta, Marta Dias, Bianca Gomes, Miguel Brito, Liliana Aranha Caetano, Elisabete Carolino, and Anita Quintal Gomes. 2021. "Microbiological Contamination Assessment in Higher Education Institutes" Atmosphere 12, no. 8: 1079. https://doi.org/10.3390/atmos12081079

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