Next Article in Journal
Helicopter Pilots Encountering Fog: An Analysis of 109 Accidents from 1992 to 2016
Next Article in Special Issue
Development of a Wide-Range Non-Dispersive Infrared Analyzer for the Continuous Measurement of CO2 in Indoor Environments
Previous Article in Journal
Modeling Ozone Source Apportionment and Performing Sensitivity Analysis in Summer on the North China Plain
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Assessment of Children’s Potential Exposure to Bioburden in Indoor Environments

H&TRC-Health & Technology Research Center, ESTeSL-Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, 1990-096 Lisbon, Portugal
Centro de Investigação em Saúde Pública, Universidade NOVA de Lisboa, 1600-560 Lisbon, Portugal
Comprehensive Health Research Center (CHRC), 1169-056 Lisbon, Portugal
Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, University of Lisbon, 1649-003 Lisbon, Portugal
Faculty of Medicine, University of Lisbon Institute of Molecular Medicine, 1649-028 Lisbon, Portugal
Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, 2695-066 Bobadela, Portugal
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(9), 993;
Received: 3 September 2020 / Revised: 13 September 2020 / Accepted: 15 September 2020 / Published: 17 September 2020
(This article belongs to the Special Issue Indoor Air Quality—What Is Known and What Needs to Be Done)


The exposure to particles and bioaerosols has been associated with the increase in health effects in children. The objective of this study was to assess the indoor exposure to bioburden in the indoor microenvironments more frequented by children. Air particulate matter (PM) and settled dust were sampled in 33 dwellings and four schools with a medium volume sampler and with a passive method using electrostatic dust collectors (EDC), respectively. Settled dust collected by EDC was analyzed by culture-based methods (including azole resistance profile) and using qPCR. Results showed that the PM2.5 and PM10 concentrations in classrooms (31.15 μg/m3 and 57.83 μg/m3, respectively) were higher than in homes (15.26 μg/m3 and 18.95 μg/m3, respectively) and highly exceeded the limit values established by the Portuguese legislation for indoor air quality. The fungal species most commonly found in bedrooms was Penicillium sp. (91.79%), whereas, in living rooms, it was Rhizopus sp. (37.95%). Aspergillus sections with toxigenic potential were found in bedrooms and living rooms and were able to grow on VOR. Although not correlated with PM, EDC provided information regarding the bioburden. Future studies, applying EDC coupled with PM assessment, should be implemented to allow for a long-term integrated sample of organic dust.

1. Introduction

Children are more susceptible to air pollutants compared to adults since they breathe more air relative to their body weight, their immune system is still in development and they have a lower ability to deal with the toxicity due to their undeveloped airways [1,2]. Children spend more than 85% of their time in indoor environments, mainly at home and school [3] and therefore it is essential to assess the indoor air quality (IAQ) in these microenvironments to estimate their integrated exposure to air pollutants.
Pollutants such as particulate matter (PM) are linked to an increase in morbidity and mortality [4,5]. PM is a complex mixture of small-diameter particles with different physical and chemical characteristics. PM is classified according to their diameter (e.g., PM2.5 and PM10, which are particles with an aerodynamic diameter smaller than 2.5 and 10 μm, respectively), because this physical characteristic highly affects the penetration into the respiratory tract [6,7]. PM2.5 or fine particles reach the lower respiratory tract, while the PM2.5–10 or coarse particles can reach the upper respiratory tract. In addition, the health impact of the PM depends on its composition, which is highly determined by the emission sources.
Bioaerosols are usually defined as PM with biological origins such as microorganisms, pollen and plant fibers. The exposure to biological agents can lead to a wide range of adverse health effects, including allergies, infection diseases, breathing problems and cancer [4].
Previous studies reported a wide range of environmental factors that influence bioburden (covering bacteria and fungi) indoors, such as the occupancy of the spaces [8,9], building layout, ventilation [10] and cleaning procedures including the type of products applied [4]. Furthermore, poor maintenance of heating, ventilation and air conditioning systems can also enhance the hazardous effects of many biological and nonbiological pollutants [11]. Due to the influence of these multiple environmental variables, sampling bioburden should be performed by passive methods, together with more conventional air sampling [12,13,14,15]. Indeed, passive methods allow defining the contamination of a larger period of time (ranging from weeks to several months), whereas air samples can only replicate the load from a shorter period of time (mostly minutes) [16].
The electrostatic dust collector (EDC) is a passive collection device easy-to-use that comprises an electrostatic polypropylene cloth [17]. The use of this device is gradually increasing since it is low-cost and effective for the collection of dust [16,18,19], and it has already been applied for the bioburden assessment in several indoor environments [16,19,20,21,22,23,24,25,26,27].
The emergence worldwide of drug-resistant human pathogenic fungal species, such as Candida sp. and Aspergillus fumigatus, and the increasing reports of therapeutic failure against fungal infections caused by environmental resistant strains [28,29,30], has revealed the need of surveillance of fungal resistance in the indoor and outdoor environments, which is mostly described for Aspergillus section Fumigati [31,32,33,34,35,36].
In this study, the exposure to PM and bioburden in the indoor microenvironments frequented by children was assessed by particle measurement and by the use of EDCs. This work also explored the suitability of EDCs for identifying critical control points of indoor exposure to PM, and for characterizing the bioburden present indoors. The fungal burden was also characterized through molecular detection of the species with toxigenic potential and also via analysis of antifungal resistance profile.

2. Materials and Methods

2.1. Location of the Studied Schools and Dwellings

This work was developed in the framework of the LIFE Index Air. Available online: ( (accessed on 14-09-2020) and was conducted in 33 dwellings (D1–D33) and 4 schools (S1–S4) located in the city of Lisbon, Portugal from September 2017 to October 2018. Figure 1 shows the location of the studied schools and homes.

2.2. Air Particulate Matter and Settled Dust Sampling

PM2.5 and PM2.5–10 was sampled with a medium volume sampler (MVS6, Leckel, Sven Leckel, Germany), which was installed in the living room of the dwellings and in a classroom of the schools, as described by Faria et al. (2020). Filters were analyzed by gravimetry before and after sampling with a microbalance (Sartorius R160P, Greifensee, Switzerland) and PM mass concentration was determined by dividing the filter loads by the volume of filtered air. All microenvironments were monitored for 5 days during the occupied period, summing a total of 330 sampled filters.
Dust was collected through a passive method using an electrostatic dust collector (EDC), which comprises an electrostatic polypropylene cloth [17]. Dust was collected from 30 to 44 days in an EDC with a surface exposure area of 0.00636 m2. In the dwellings, the EDCs were exposed in the living room (a total of 33) and in the children’s bedroom (a total of 31) and in schools, the EDCs were placed in the classrooms (a total of 4). The EDC was then used for the bioburden assessment.

2.3. Electrostatic Dust Cloth Extraction and Bioburden Characterization

In order to determine the mass of the collected dust, each EDC was weighted after sampling and subtracted to the mean of 10 EDCs weighted before sampling. Settled dust collected by the EDC was analyzed by culture-based methods and using real-time PCR (qPCR), targeting 4 different Aspergillus sections (Flavi, Fumigati, Circumdati and Nidulantes). The target fungi were selected based on the classification as indicators of harmful fungal contamination [37].
EDC samples were subject to extraction and bioburden characterized by culture-based methods as previously described [16,19,22,26,27]. EDC were washed and 0.15 mL seeded onto 2% malt extract agar (MEA) with 0.05 g/L chloramphenicol media; dichloran glycerol (DG18) agar-based media; tryptic soy agar (TSA) with 0.2% nystatin for total bacteria assessment; violet red bile agar (VRBA) for Gram-negative bacteria.
Samples were also spread (0.15 mL) onto Sabouraud dextrose agar (SDA) media supplemented with 4 mg/L itraconazole (ITR), 1 mg/L voriconazole (VOR) or 0.5 mg/L posaconazole (POS, protocol adapted from the EUCAST 2017 guidelines) [38] for the screening of antifungal resistance [19].
Incubation of MEA, DG18 and azole screening plates at 27 °C for 5 to 7 days and TSA and VRBA plates at 30 and 35 °C for 7 days, respectively, was performed.
Molecular identification of the different fungal species/strains was achieved by qPCR using the CFX-Connect PCR System (Bio-Rad, Hercules, CA, USA) on EDC collected (bedrooms n = 31; living rooms n = 33; classrooms = 4). Reactions included 1× iQ Supermix (Bio-Rad), 0.5 μM of each primer (Table 1), and 0.375 μM of TaqMan probe in a total volume of 20 μL. Amplification followed a three-step PCR: 50 cycles with denaturation at 95 °C for 30 s, annealing at 52 °C for 30 s and extension at 72 °C for 30 s (Table 1). Nontemplate control was used in every PCR reaction. For each gene that was amplified, a nontemplate control and positive control were used, consisting of DNA obtained from a reference that belonged to the culture collection of the Reference Unit for Parasitic and Fungal Infections, Department of Infectious Diseases of the National Institute of Health, from Dr. Ricardo Jorge. These strains have been sequenced for ITS B-tubulin and Calmodulin.

2.4. Statistical Analysis

The statistical software SPSS V24.0 for Windows® was used for data analysis. The results were considered significant at a 5% significance level. The frequency analysis (n, %) was applied for the qualitative data, and the minimum, maximum, median and interquartile range were calculated for the quantitative data. The median and the interquartile range were used, since outliers were detected and the mean and standard deviation were influenced by these values. The Shapiro-Wilk test was applied to test data normality, and Spearman’s correlation coefficient to study the relationship between two quantitative variables. Kruskal–Wallis test was used to compare EDC weight, fungal counts on MEA and DG18 and bacteria counts on TSA and VRB among the different sampling locations, since the assumption of normality was not verified. When statistically significant differences were detected, the Kruskal–Wallis multiple comparisons test was the analyses selected. For the comparison of the concentration of the particles between the two sampling locations (classroom and living room) the Mann–Whitney test was used, since the assumption of normality was not verified.

3. Results

3.1. Particulate Matter Assessment

The PM2.5 and PM10 average concentrations in the classrooms were 31.15 and 57.83 µg/m3, respectively, with a range between 19.47 and 52.91 µg/m3 for PM2.5 and between 32.72 and 109.02 µg/m3 for PM10. Table 2 shows that in dwellings, the concentrations ranged between 6.05 and 67.96 µg/m3 for PM2.5 and between 9.14 and 72.95 µg/m3 for PM10, with an average concentration of 15.26 µg/m3 and 18.95 µg/m3, respectively. The PM2.5 concentrations exceeded the 8-hr limit value established by the Portuguese legislation for indoor air quality (Portaria 353-A/2013, 25 μg/m3) in 50% of the schools and in 12% of the dwellings and the PM10 limit value (50 μg/m3) was exceeded in 50% of the schools and in 3% of the dwellings.
Regarding the settled dust collected by the EDC, the schools presented an average level of 1.42 g/m2/d with a range between 1.28 and 1.57 g/m2/d and the dwellings registered an average of 3.36 g/m2/d with a range between 1.27 and 11.16 g/m2/d. In dwellings, the living room presented an average amount of 3.6 g/m2/d and the bedroom of 3.11 g/m2/d (Table 2).

3.2. Bacterial Contamination Assessment

From the 31 samples collected in the bedrooms, the total bacteria contamination ranged from below the detection limit to 1.42 × 103 CFU/m2/d, with the Gram-negative bacteria contamination, ranging from below the detection limit to 3.15 × 101 CFU/m2/d.
Total bacteria contamination in the 33 EDC collected in living rooms ranged from below the detection limit to 3.42 × 103 CFU/m2/d, with the Gram-negative bacteria contamination, ranging from below the detection limit to 4.60 × 101 CFU/m2/d.
In the 4 EDC samples collected in the classrooms, the total bacteria contamination ranged from below the detection limit to 6.2 × 101 CFU/m2/d, while there was no contamination by Gram-negative bacteria (Table 3).

3.3. Fungal Contamination Assessment

A total of 31 EDC were collected from bedrooms. The fungal contamination in these samples ranged from lower the detection limit to 2.00 × 103 CFU/m2/d (D30) in MEA, and from lower the detection limit to 2.81 × 103 CFU/m2/d (D32) in DG18. The most commonly found fungal species in MEA was Penicillium sp. (2.00 × 103 CFU/m2/d; 89.43%), followed by Cladosporium sp. (1.59 × 102 CFU/m2/d; 7.10%) and Chrysosporium sp. (2.56 × 101 CFU/m2/d; 1.14%; Table 4). In DG18, the most prevalent species were Cladosporium sp. (2.81 × 103 CFU/m2/d; 90.44%), Penicillium sp. (2.07 × 102 CFU/m2/d; 6.67%) and Aspergillus sp. (1.05 × 102 CFU/m2/d; 1.23%; Table 4). Four different Aspergillus sections were identified in the EDC samples from the bedrooms, two found in MEA (Nigri and Fumigati; 1.05 × 101 CFU/m2/d), and two in DG18 (Candidi and Circumdati; 3.81 × 101 CFU/m2/d; Figure 2).
In the 33 EDC collected from the living rooms, the fungal contamination ranged from lower the detection limit to 5.24 × 103 CFU/m2/d (D3, D6 and D28) in MEA, and from lower the detection limit to 2.62 × 103 CFU/m2/d (D32). In MEA, the most common was Rhizopus sp. (5.24 × 103 CFU/m2/d; 38.11%), followed by Chrysonilia sp. (5.24 × 103 CFU/m2/d; 38.11%) and Chrysosporium sp. (2.64 × 103 CFU/m2/d; 19.19%); in DG18, Chrysonilia sp. (2.62 × 103 CFU/m2/d; 76.55%), followed by Penicillium sp. (3.54 × 102 CFU/m2/d; 10.33%) and Cladosporium sp. (1.7 × 102 CFU/m2/d; 4.96%) were the most prevalent (Table 4). A total of eight Aspergillus sections were identified in the samples from the living room. Five different sections were found in MEA, including Aspergillus section Fumigati (6.18 × 101 CFU/m2/d), Flavi and Nigri (2.62 × 101 CFU/m2/d; Figure 2). In DG18, six Aspergillus sections were identified, with the most prevalent being Nidulantes (7.89 × 101 CFU/m2/d), followed by Fumigati (3.67 × 101 CFU/m2/d) and Clavati (1.57 × 101 CFU/m2/d; Figure 2).
Four EDC were recovered from classrooms. The fungal contamination in the MEA samples ranged from the lower detection limit (S1) to 1.76 × 101 CFU/m2/d (in the three remaining samples), and in DG18 from the lower detection limit (S1 and S3) to 1.02 × 101 CFU/m2/d (in S4). Three different fungal species were identified in the MEA samples: Penicillium sp. (1.76 × 101 CFU/m2/d; 64.21%), Chrysonilia sp. and Cladosporium sp. (4.91 × 101 CFU/m2/d; 17.90%; Table 4). Four fungal species were found in DG18: Chrysosporium sp. (1.02 × 101 CFU/m2/d; 40.79%), Aspergillus section Nidulantes, Chrysonilia sp. and Cladosporium sp. (1.02 × 101 CFU/m2/d; 19.74%; Table 4).

3.4. Azole-Resistance Screening

Seventeen different fungal species were detected on azole-resistance screening in 61 EDC samples, of which 11 were able to grow in at least one azole among the tested conditions. Noteworthy, Aspergillus sections Candidi and Nigri were able to grow on VOR in two distinct samples. Reduced susceptibility to multiazoles (i.e., fungal ability to grow in more than one azole) was observed in 14 EDC samples, for five different fungal species, including Penicillium sp. (VOR+POS in three samples), Chrysosporium sp. (VOR+POS in one sample, ITR+VOR in one sample) or Cladosporium sp. (ITR+VOR in two samples, VOR+POS in three samples, ITR+VOR+POS in one sample; Table 5). Similar to the results obtained with MEA in dwellings (Table 4), some of the most frequent fungal species were C. sitophila (83.05% SAB, 11.17% POS, 1.68 VOR), Cladosporium sp. (40.44% ITR, 38.33% VOR, 37.03% POS, 13.22% SAB) and Penicillium sp. (45.60% VOR, 27.21% ITR, 21.65% POS, 2.29% SAB; Table 5).

3.5. Molecular Assessment

None of the Aspergillus sections targeted (Circumdati, Flavi, Fumigati and Nidulantes) on the EDC were amplified by RT-PCR.

3.6. Correlation Analysis

Regarding the EDC weight, significant correlations, with moderate or low intensity, were detected with particles PM2.5 (rS = −0.395, p = 0.015), particles PM10 (rS = −0.486, p = 0.002), bacterial contamination on TSA (rS = −0.252, p = 0.042) and with Aspergillus prevalence on MEA (Rs = 0.555, p = 0.049). These results show that higher EDC weights are related to lower concentrations of particles (PM2.5 and PM10), lower bacterial contamination on TSA and higher Aspergillus prevalence on MEA (Table 6).
Considering the concentration of PM, only a significant positive correlation was detected, with a strong intensity, between the PM2.5 and PM10 (rS = 0.957, p < 0.0001), which means that higher concentrations of particles PM2.5 are related to higher concentrations of PM10 (Table 6).
Regarding fungal contamination on MEA, significant positive and moderate correlations were detected with (i) fungal contamination on DG18 (rS = 0.457, p < 0.0001), (ii) fungal presence on VOR (rS = 0.281, p = 0.020) and (iii) fungal detection on POS (rS = 0.280, p = 0.021), indicating that higher fungal contamination on MEA is related with higher fungal contamination on DG18 and with fungal counts on VOR and on POS (Table 6).
Regarding the fungal contamination on DG18, significant correlations of weak intensity and positive direction were detected with the fungal presence on VOR (rS = 0.262, p = 0.031) and on POS (rS = 0.276, p = 0.023), and with Aspergillus prevalence on DG18 (rS = 0.459, p = 0.042), revealing that higher fungal contamination on DG18 is related with the higher fungal counts on VOR and POS and Aspergillus prevalence on DG18 (Table 6).
Finally, a significant correlation, of weak intensity and in a positive direction, between fungal presence on VOR and POS (rS = 0.250, p = 0.039), which indicates that higher fungal counts on VOR are related with higher fungal counts on POS (Table 6).

3.7. Comparison between Sampling Locations

From the comparison between sampling locations, only significant differences were detected in: (i) the EDC weight (χ_(K-W)^2 (2) = 6.74, p = 0.046), showing that in the classroom the EDC had less weight; (ii) the concentration of PM2.5 (U = 15.000, p = 0.013) and PM10 (U = 8.000, p = 0.005) particles presented the classroom as the sampling location with the highest concentrations (Table 7).

4. Discussion

To contribute to the assessment of children’s exposure to particles and bioburden, EDC was exposed for an extended period to collect dust in two home locations and at schools [27] (Figure 1). Although with some downsides, that rely mainly on the fact that bioaerosols are highly dynamic, thus difficult to collect in a representative way [43], settled dust is considered to be a long-term integrated sample of particles that have been airborne. As such this method is more reliable to sample bioaerosols [44]. Indeed, settled dust evidences a composite view of bioaerosols in the indoor environment that is being assessed [19,22,27]. Therefore, EDC permits consistent estimation of exposure, since a single EDC analysis is equal to the sum of several air-impaction measurements [45]. Furthermore, EDC allows for an exclusive identification of some fungal species and higher fungal diversity, when compared to air samples obtained by impaction or even with other passive methods [27]. The coupling of this sampling method with particle measurement allowed a more complete analysis of children’s exposure in their daily lives.
Indoor particle exposure constitutes a significant percentage of overall exposure, as children spend the majority of the time indoors [3]. In our study, both fractions (PM2.5 and PM10) had higher concentrations in schools than in dwellings, which is related to children’s activity during classes, resuspension of PM and inadequate ventilation [3]. Studies carried out in European cities showed similar concentrations in schools [46,47] and in dwellings [48,49,50].
The settled dust presented a different pattern, characterized by higher levels in the dwellings. This difference between the PM and the collected dust by the EDC behavior has already been found in other studies, which indicated that settled dust is less influenced by the short-term variability of the indoor activities and ventilation [51,52]. Particle deposition depends on the size of the particles, their sedimentation processes (diffusion in the case of very small particles or gravity in the case of larger particles) [52], the amount of furniture in the spaces [53], the type of ventilation and air turbulence [54].
The importance of using different culture media was validated and followed the same tendency as previously reported in studies performed in different indoor environments [16,19,26,27,41,55]. Regarding bacteria detection, no contamination by Gram-negative bacteria was detected in classrooms, which can partially be explained by less tolerance to the environmental conditions of these species [56]. In what refers to fungal contamination, it was possible to detect different species in both culture media applied (MEA and DG18), with higher diversity of Aspergillus sections on living rooms as observed on DG18. Indeed, the exclusive identification by DG18 of Aspergillus sections Circumdati and Nidulantes, both with toxigenic potential [57], on living rooms should be highlighted. Another concern regarding the toxigenic potential of the fungal species was the detection of Aspergillus section Flavi on the living rooms and of Aspergillus section Fumigati present in both sampling locations. Additionally, Aspergillus sections Circumdati, Flavi, Fumigati and Nidulantes identification should be emphasized since all the four Aspergillus sections are considered as indicators of harmful fungal contamination and, although our study has not detected these toxigenic species, their analysis should be performed in order to better contribute to the implementation of corrective measures [37]. Indeed, these species can produce mycotoxins that can become airborne on conidia or smaller fragments suggesting a potential inhalation or ingestion by indoor occupants [58]. Mycotoxins are known to have a wide array of adverse health effects or being carcinogenic to humans [59].
Culture-based methods were able to provide positive results within Aspergillus genera, whereas the Aspergillus sections were not detected with molecular tools. Despite these observations, molecular tools are generally a suitable solution to overcome the nonviable/nonculturable limits of the commonly used culture-based methods as they might also provide a more exhaustive diversity profile (e.g., high throughput sequencing), unlike culture methods that might reveal less abundant taxa in an environment. However, culture-independent molecular methods often only identify most of the organisms until taxonomic levels [60,61] and this level of identification is insufficient for exposure assessment. Furthermore, it has already been reported that the viability of microorganisms can affect their inflammatory and/or cytotoxic potential and only viable microorganisms can cause infections, justifying the preference of culture-based methods [62,63,64].
As fungal resistance to available azole drugs is an emergent global health problem [65], especially with Aspergillus fumigatus [29,66,67], an exploratory screening of the frequency of fungal reduced susceptibility to azoles in dwellings and schools was conducted in this study. Some nonpathogenic species exhibited reduced susceptibility to one or more azoles, including Aspergillus sections Nigri and Candidi. In order to confirm the resistance phenotype of these species, further susceptibility tests and/or molecular detection of resistance mutations must be performed. So far, azole-resistant isolates with identical genetic profiles were found to be globally distributed and sourced from both clinical and environmental locations, thus, reinforcing azole resistance as an international public health concern [67]. In Portugal, some resistant Aspergillus sp. have already been found in the environment (data not published), but never in this context. If the resistance phenotype is confirmed, it will be a novelty as it has never been described in these environments.
The statistical analysis revealed some positive correlations that suggest (more evident on MEA than on DG18) that fungal reduced susceptibility to azole drugs, such as voriconazole and posaconazole, might be developed when higher fungal contamination is present in those environments. Moreover, it seems that reduced susceptibility to voriconazole and posaconazole are also related among these two azoles. This can be important (if azole resistance is confirmed) to understand the development of resistance, since voriconazole and posaconazole, though belonging to the same azole class, differ in their molecular structure: voriconazole is a short-tailed triazole (similar to triazole fungicides used in agriculture), whereas posaconazole (such as itraconazole) is a long-tailed triazole [68]. Understanding how fungal mutations affect drug affinity is necessary for the design of improved azoles that might overcome fungal resistance [69].

5. Conclusions

The indoor exposure to PM and bioburden at children’s dwellings and schools was assessed by particle measurement and by using EDC. Results showed that the PM concentrations in classrooms highly exceeded the limit values established by the Portuguese legislation for indoor air quality. Although not correlated with PM, EDC provided information regarding the bioburden present indoors unveiling the presence of fungal species with toxigenic potential and nonpathogenic species exhibited reduced susceptibility to one or more azoles, including Aspergillus sections Nigri and Candidi.
Future studies at a larger scale, applying the same sampling approach—EDC coupled with particulate matter assessment—should be implemented to allow for a long-term integrated sample of organic dust.

Author Contributions

Conceptualization, C.V.; formal analysis, C.V., B.A., M.D., L.A.C., E.C., A.Q.G., T.F. and V.M.; funding acquisition, C.V. and S.M.A.; investigation, C.V., T.F. and S.M.A.; methodology, C.V., T.F. and S.M.A.; Supervision, S.M.A.; writing—original draft preparation, C.V., L.A.C., T.F., M.D. and S.M.A.; writing—review and editing, C.V. and S.M.A. All authors have read and agreed to the published version of the manuscript.


This work was supported by LIFE Index-Air project (LIFE15 ENV/PT/000674). This work reflects only the authors’ view and EASME is not responsible for any use that may be made of the information it contains. Authors also gratefully acknowledge the FCT support through the UID/Multi/04349/2019 project and the PhD grant SFRH/BD/129149/2017.


H&TRC authors gratefully acknowledge the FCT/MCTES national support through the UIDB/05608/2020 and UIDP/05608/2020.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Selgrade, M.K.; Plopper, C.G.; Gilmour, M.I.; Conolly, R.B.; Foos, B.S.P. Assessing the health effects and risks associated with children’s inhalation exposures—Asthma and allergy. J. Toxicol. Environ. Health Part A 2008, 71, 196–207. [Google Scholar] [CrossRef] [PubMed]
  2. WHO. Effects of Air Pollution on Children’s Health and Development–A Review of the Evidence; WHO Regional Office for Europe, WHO Press: Copenhagen, Denmark, 2005. [Google Scholar]
  3. Faria, T.; Martins, V.; Correia, C.; Canha, N.; Diapouli, E.; Manousakas, M.; Eleftheriadis, K.; Almeida, S.M. Children’s exposure and dose assessment to particulate matter in Lisbon. Build. Environ. 2020, 171, 106666. [Google Scholar] [CrossRef]
  4. Douwes, J.; Thorne, P.; Pearce, N.; Heederik, D. Bioaerosol health effects and exposure assessment: Progress and prospects. Ann. Occup. Hyg. 2003, 47, 187–200. [Google Scholar] [PubMed][Green Version]
  5. Martinelli, N.; Olivieri, O.; Girelli, D. European Journal of Internal Medicine Air particulate matter and cardiovascular disease: A narrative review. Eur. J. Intern. Med. 2013, 24, 295–302. [Google Scholar] [CrossRef] [PubMed]
  6. Calvo, A.I.; Alves, C.; Castro, A.; Pont, V.; Vicente, A.M.; Fraile, R. Research on aerosol sources and chemical composition: Past, current and emerging issues. Atmos. Res. 2013, 120–121, 1–28. [Google Scholar] [CrossRef]
  7. Seinfeld, J.H.; Pandis, S.N. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change; John Wiley and Sons: Incorporated, NY, USA, 1998. [Google Scholar]
  8. Ekhaise, F.O.; Ogboghodo, B.I. Microbiological indoor and outdoor air quality of two major hospitals in Benin City, Nigeria. Sierra Leone J. Biomed. Res. 2011, 3, 169–174. [Google Scholar]
  9. Sudharsanam, S.; Swaminathan, S.; Ramalingam, A.; Thangavel, G.; Annamalai, R.; Steinberg, R.; Balakrishnan, K.; Srikanth, P. Characterization of indoor bioaerosols from a hospital ward in a tropical setting. Afr. Health Sci. 2012, 12, 217–225. [Google Scholar] [CrossRef]
  10. Ekhaise, F.O.; Isitor, E.E.; Idehen, O.; Emoghene, A.O. Airborne Microflora in the Atmosphere of an Hospital Environment of University of Benin Teaching Hospital (UBTH), Benin City, Nigeria. World J. Agric. Sci. 2010, 6, 166–170. [Google Scholar]
  11. Salama, K.F.; Berekaa, M.M. Assessment of air quality in Dammam slaughter houses, Saudi Arabia. Int. J. Med. Sci. Public Health Online 2015, 5, 287–291. [Google Scholar] [CrossRef]
  12. Klánova, K.; Hollerová, J. Hospital indoor environment: Screening for microorganisms and particulate matter. Indoor Built Environ. 2003, 12, 61–67. [Google Scholar] [CrossRef]
  13. Park, D.U.; Yeom, J.K.; Lee, W.J.; Lee, K.M. Assessment of the levels of airborne bacteria, gram-negative bacteria, and fungi in hospital lobbies. Int. J. Environ. Res. Public Health 2013, 10, 541–555. [Google Scholar] [CrossRef] [PubMed]
  14. Tang, C.S.; Wan, G.H. Air quality monitoring of the post-operative recovery room and locations surrounding operating theatres in a medical center in Taiwan. PLoS ONE 2013, 8, 61093. [Google Scholar]
  15. Cabo Verde, S.; Almeida, S.M.; Matos, J.; Guerreiro, D.; Meneses, M.; Faria, T.; Botelho, D.; Santos, M.; Viegas, C. Microbiological assessment of indoor air quality at different hospital sites. Res. Microbiol. 2015, 166, 557–563. [Google Scholar] [CrossRef]
  16. Viegas, C.; Coggins, A.M.; Faria, T.; Caetano, L.A.; Gomes, A.Q.; Sabino, R.; Fleming, G.T. Fungal burden exposure assessment in podiatry clinics. Int. J. Environ. Health Res. 2018, 28, 167–177. [Google Scholar] [CrossRef] [PubMed]
  17. American Conference of Governmental Industrial Hygienists (ACGIH). Threshold Limit Values for Chemical Substances and Physical Agents and Biological Exposure Indices; ACGIH: Cincinnati, OH, USA, 2009. [Google Scholar]
  18. Kilburg-Basnyat, B.; Metwali, N.; Thorne, P.S. Performance of electrostatic dust collectors (EDCs) for endotoxin assessment in homes: Effect of mailing, placement, heating and electrostatic charge. J. Occup. Environ. Hyg. 2016, 13, 85–93. [Google Scholar] [CrossRef][Green Version]
  19. Viegas, C.; Santos, P.; Almeida, B.; Monteiro, A.; Carolino, E.; Quintal Gomes, A.; Viegas, S. Electrostatic dust collector: A passive screening method to assess occupational exposure to organic dust in primary health care centers. Air Qual. Atmos. Health 2019, 12, 573–583. [Google Scholar] [CrossRef]
  20. Cozen, W.; Avol, E.; Diaz-Sanchez, D.; McConnell, R.; Gauderman, W.J.; Cockburn, M.G.; Mack, T.M. Use of an electrostatic dust cloth for self-administered home allergen collection. Twin Res. Hum. Genet. 2008, 11, 150–155. [Google Scholar] [CrossRef]
  21. Normand, A.C.; Vacheyrou, M.; Sudre, B.; Heederik, D.J.J.; Piarroux, R. Assessment of dust sampling methods for the study of cultivable-microorganism exposure in stables. Appl. Environ. Microbiol. 2009, 75, 7617–7623. [Google Scholar] [CrossRef][Green Version]
  22. Madsen, A.M.; Matthiesen, C.B.; Frederiksen, M.W.; Frederiksen, M.; Frankel, M.; Spilak, M.; Timm, M. Sampling, extraction and measurement of bacteria, endotoxin, fungi and inflammatory potential of settling indoor dust. J. Environ. Monit. 2012, 14, 3230–3239. [Google Scholar] [CrossRef]
  23. Dorado-Garcia, A.; Bos, M.E.; Graveland, H.; Van Cleef, B.A.; Verstappen, K.M.; Kluytmans, J.A.; Wagenaar, J.A.; Heederik, D.J. Risk factors for persistence of livestock-associated MRSA and environmental 506 exposure in veal calf farmers and their family members: An observational longitudinal study. BMJ 2013, 3, e003272. [Google Scholar]
  24. Kilburg-Basnyat, B.; Metwali, N.; Thorne, P.S. Effect of deployment time on endotoxin and allergen exposure assessment using electrostatic dust collectors. Annals Occup. Hyg. 2014, 59, 104–115. [Google Scholar]
  25. Feld, L.; Bay, H.; Angen, Ø.; Larsen, A.R.; Madsen, A.M. Survival of LA-MRSA in Dust from Swine Farms. Ann. Work Expo. Health 2018, 62, 147–156. [Google Scholar] [CrossRef] [PubMed][Green Version]
  26. Viegas, C.; Monteiro, A.; Aranha Caetano, L.; Faria, T.; Carolino, E.; Viegas, S. Electrostatic Dust Cloth: A Passive Screening Method to Assess Occupational Exposure to Organic Dust in Bakeries. Atmosphere 2018, 9, 64. [Google Scholar] [CrossRef][Green Version]
  27. Viegas, C.; Twarużek, M.; Lourenço, R.; Dias, M.; Almeida, B.; Caetano, L.A.; Carolino, E.; Gomes, A.Q.; Kosicki, R.; Soszczyńska, E.; et al. Bioburden Assessment by Passive Methods on a Clinical Pathology Service in One Central Hospital from Lisbon: What Can it Tell Us Regarding Patients and Staff Exposure? Atmosphere 2020, 11, 351. [Google Scholar] [CrossRef][Green Version]
  28. Snelders, E.; Melchers, W.J.; Verweij, P.E. Azole resistance in Aspergillus fumigatus: A new challenge in the management of invasive aspergillosis? Future Microbiol. 2011, 6, 335–347. [Google Scholar] [CrossRef] [PubMed]
  29. Verweij, P.E.; Chowdhary, A.; Melchers, W.J.G.; Meis, J.F. Azole resistance in Aspergillus fumigatus: Can we retain the clinical use of mold-active antifungal azoles? Clin. Infect. Dis. 2016, 62, 362–368. [Google Scholar] [CrossRef][Green Version]
  30. Fisher, M.C.; Hawkins, N.J.; Sanglard, D.; Gurr, S.J. Worldwide emergence of resistance to antifungal drugs challenges human health and food security. Science 2018, 18, 739–742. [Google Scholar] [CrossRef][Green Version]
  31. Snelders, E.; Huis in ’t Veld, R.A.G.; Rijs, A.J.M.M.; Kema, G.H.J.; Melchers, W.J.G.; Verweij, P.E. Possible Environmental Origin of Resistance of Aspergillus fumigatus to Medical Triazoles. Appl. Environ. Microbiol. 2009, 75, 4053–4057. [Google Scholar] [CrossRef][Green Version]
  32. Ahmad, S.; Khan, Z.; Hagen, F.; Meis, J.F. Occurrence of triazole-resistant Aspergillus fumigatus with TR34/L98H mutations in outdoor and hospital environment in Kuwait. Environ. Res. 2014, 133, 20–26. [Google Scholar] [CrossRef]
  33. Loeffert, S.T.; Hénaff, L.; Dupont, D.; Bienvenu, A.L.; Dananché, C.; Cassier, P.; Bénet, T.; Wallon, M.; Gustin, M.P.; Vanhems, P. Prospective survey of azole drug resistance among environmental and clinical isolates of Aspergillus fumigatus in a French University hospital during major demolition works. J. Mycol. Méd. 2018, 28, 469–472. [Google Scholar] [CrossRef]
  34. Chen, Y.; Kuo, S.; Wang, H.; Wu, C.; Lin, Y.; Li, W.; Lee, C. Azole resistance in Aspergillus species in Southern Taiwan: An epidemiological surveillance study. Mycoses 2019, 62, 1174–1181. [Google Scholar] [CrossRef] [PubMed]
  35. Cho, S.Y.; Lee, D.G.; Kim, W.B.; Chun, H.S.; Park, C.; Myong, J.P.; Park, Y.J.; Choi, J.K.; Lee, H.J.; Kim, S.H.; et al. Epidemiology and Antifungal Susceptibility Profile of Aspergillus Species: Comparison between Environmental and Clinical Isolates from Patients with Hematologic Malignancies. J. Clin. Microbiol. 2019, 57, e02023-18. [Google Scholar] [CrossRef] [PubMed][Green Version]
  36. Monteiro, C.; Pinheiro, D.; Maia, M.; Faria, M.A.; Lameiras, C.; Pinto, E. Aspergillus species collected from environmental air samples in Portugal-molecular identification, antifungal susceptibility and sequencing of cyp51A gene on A. fumigatus sensu stricto itraconazole resistant. J. Appl. Microbiol. 2019, 126, 1140–1148. [Google Scholar] [CrossRef] [PubMed]
  37. American Industrial Hygiene Association. Field Guide for the Determination of Biological Contaminants in Environmental Samples, 2nd ed.; AIHA Biosafety Committee: Falls Church, VA, USA, 1996. [Google Scholar]
  38. Arendrup, M.C.; Sulim, S.; Holm, A.; Nielsen, L.; Nielsen, S.D.; Knudsen, J.D.; Drenck, N.E.; Christensen, J.J.; Johansen, H.K. Diagnostic issues, clinical characteristics, and outcomes for patients with fungemia. J. Clin. Microbiol. 2011, 49, 3300–3308. [Google Scholar] [CrossRef][Green Version]
  39. Mayer, Z.; Bagnara, A.; FaÅNrber, P.; Geisen, R. Quantification of the copy number of nor-1, a gene of the aflatoxin biosynthetic pathway by real-time PCR, and its correlation to the cfu of Aspergillus flavus in foods. Int. J. Food Microbiol. 2003, 82, 143–151. [Google Scholar] [CrossRef]
  40. Cruz-Perez, P.; Buttner, M.P.; Stetzenbach, L.D. Detection and quantitation of Aspergillus fumigatus in pure culture using polymerase chain reaction. Mol. Cell. Probes 2001, 15, 81–88. [Google Scholar] [CrossRef]
  41. Viegas, C.; Faria, T.; Aranha, L.; Carolino, E.; Quintal Gomes, A.; Viegas, S. Aspergillus prevalence in different occupational settings. J. Occup. Environ. Hyg. 2017, 14, 771–785. [Google Scholar] [CrossRef]
  42. EPA, United States Environmental Protection Agency. About the National Exposure Research Laboratory (NERL). 2017. Available online: (accessed on 19 June 2017).
  43. Vissers, M.; Doekes, G.; Heederik, D. Exposure to wheat allergen and fungal α-amylase in the homes of bakers. Clin. Exp. Allergy 2001, 31, 1577–1582. [Google Scholar] [CrossRef]
  44. Noss, I.; Wouters, I.M.; Visser, M.; Heederik, D.J.J.; Thorne, P.S.; Brunekreef, B.; Doekes, G. Evaluation of a Low-Cost Electrostatic Dust Fall Collector for Indoor Air Endotoxin Exposure Assessment. Appl. Environ. Microbiol. 2008, 74, 5621–5627. [Google Scholar] [CrossRef][Green Version]
  45. Institute of Medicine. Damp Indoor Spaces and Health; The National Academies Press: Washington, DC, USA, 2004. [Google Scholar]
  46. Rivas, I.; Viana, M.; Moreno, T.; Pandolfi, M.; Amato, F.; Reche, C.; Bouso, L.; Àlvarez-Pedrerol, M.; Alastuey, A.; Sunyer, J.; et al. Child exposure to indoor and outdoor air pollutants in schools in Barcelona, Spain. Environ. Int. 2014, 69, 200–212. [Google Scholar] [CrossRef][Green Version]
  47. Rovelli, S.; Cattaneo, A.; Nuzzi, C.P.; Spinazzè, A.; Piazza, S.; Carrer, P.; Cavallo, D.M. Airborne particulate matter in school classrooms of northern Italy. Int. J. Environ. Res. Public Health 2014, 11, 1398–1421. [Google Scholar] [CrossRef] [PubMed][Green Version]
  48. Hänninen, O.O.; Lebret, E.; Ilacqua, V.; Katsouyanni, K.; Künzli, N.; Srám, R.J.; Jantunen, M. Infiltration of ambient PM 2.5 and levels of indoor generated non-ETS PM 2.5 in residences of four European cities. Atmos. Environ. 2004, 38, 6411–6423. [Google Scholar] [CrossRef]
  49. Langer, S.; Ramalho, O.; Derbez, M.; Ribéron, J.; Kirchner, S.; Mandin, C. Indoor environmental quality in French dwellings and building characteristics. Atmos. Environ. 2016, 128, 82–91. [Google Scholar] [CrossRef]
  50. Stranger, M.; Potgieter-vermaak, S.S.; Grieken, R.V. Comparative overview of indoor air quality in Antwerp, Belgium. Environ. Int. 2007, 33, 789–797. [Google Scholar] [CrossRef] [PubMed]
  51. Cox, J.; Indugula, R.; Vesper, S.; Zhu, Z.; Jandarov, R.; Reponen, T. Comparison of indoor air sampling and dust collection methods for fungal exposure assessment using quantitative PCR. Environ. Sci. Process. Impacts 2017, 1–18. [Google Scholar] [CrossRef]
  52. Estokova, A.; Stevulova, N. Investigation of Suspended and Settled Particulate Matter in Indoor Air. In Atmospheric Aerosols—Regional Characteristics—Chemistry and Physics; Abdul-Razzak, H., Ed.; IntechOpen: London, UK, 2012; pp. 455–480. [Google Scholar] [CrossRef][Green Version]
  53. Thatcher, T.L.; Lai, A.C.K.; Moreno-Jackson, R.; Sextro, R.G.; Nazaroff, W.W. Effects of room furnishings and air speed on particle deposition rates indoors. Atmos. Environ. 2002, 36, 1811–1819. [Google Scholar] [CrossRef][Green Version]
  54. Thatcher, T.L.; Layton, D.W. Deposition, resuspension, and penetration of particles within a residence. Atmos. Environ. 1995, 29, 1487–1497. [Google Scholar] [CrossRef]
  55. Viegas, C.; Faria, T.; Cebola de Oliveira, A.; Aranha Caetano, L.; Carolino, E.; Quintal-Gomes, A.; Twarużek, M.; Kosicki, R.; Soszczyńska, E.; Viegas, S. A new approach to assess fungal contamination and mycotoxins occupational exposure in forklifts drivers from waste sorting. Mycotoxin Res. 2017, 33, 285–295. [Google Scholar] [CrossRef]
  56. Adhikari, A.; Kettleson, E.M.; Vesper, S.; Kumar, S.; Popham, D.L.; Schaffer, C.; Indugula, R.; Chatterjee, K.; Allam, K.K.; Grinshpun, S.A.; et al. Dustborne and airborne Gram-positive and Gram-negative bacteria in high versus low ERMI homes. Sci. Total Environ. 2014, 482–483, 92–99. [Google Scholar] [CrossRef][Green Version]
  57. Varga, J.; Baranyi, N.; Chandrasekaran, M.; Vágvölgyi, C.; Kocsubé, S. Mycotoxin producers in the Aspergillus genus: An update. Acta Biol. Szeged. 2015, 59, 151–167. [Google Scholar]
  58. Brochers, A.T.; Chang, C.; Gershwin, E. Mold and human health: A reality check. Clin. Rev. Allergy Immunol. 2017, 52, 305–322. [Google Scholar] [CrossRef] [PubMed]
  59. Bennett, J.W.; Klich, M. Mycotoxins. Clin. Microbiol. Rev. 2013, 16, 497–516. [Google Scholar] [CrossRef] [PubMed][Green Version]
  60. Mbareche, H.; Veillette, M.; Bilodeau, G.J.; Duchaine, C. Fungal aerosols at dairy farms using molecular and culture techniques. Sci. Total Environ. 2019, 653, 253–263. [Google Scholar] [CrossRef]
  61. Madsen, A.M.; Frederiksen, M.W.; Jacobsen, M.H.; Tendal, K. Towards a risk evaluation of workers’ exposure to handborne and airborne microbial species as exempli fi ed with waste collection workers. Environ. Res. 2020, 183, 109177. [Google Scholar] [CrossRef] [PubMed]
  62. Cooley, J.; Wong, W.; Jumper, C.; Hutson, J.; Williams, H.; Schwab, C.; Straus, D. An animal model for allergic penicilliosis induced by the intranasal instillation of viable Penicillium chrysogenum conidia. Thorax 2000, 55, 489–496. [Google Scholar] [CrossRef][Green Version]
  63. Huttunen, K.; Hyvarinen, A.; Nevalainen, A.; Komulainen, H.; Hirvonen, M.R. Production of proinflammatory mediators by indoor air bacteria and fungal spores in mouse and human cell lines. Environ. Health Perspect. 2003, 111, 85–92. [Google Scholar] [CrossRef][Green Version]
  64. Croston, T.L.; Nayak, A.P.; Lemons, A.R.; Goldsmith, W.; Gu, J.K.; Germolec, D.R.; Beezhold, D.H.; Green, B.J. Influence of Aspergillus fumigatus conidia viability on murine pulmonary micro RNA and m RNA expression following subchronic inhalation exposure. Clin. Exp. Allergy 2016, 46, 1315–1327. [Google Scholar] [CrossRef][Green Version]
  65. Microbiology, N. Stop neglecting fungi. Nat. Microbiol. 2017, 2, 17120. [Google Scholar] [CrossRef][Green Version]
  66. Chowdhary, A.; Meis, J.F. Emergence of azole resistant Aspergillus fumigatus and One Health: Time to implement environmental stewardship. Environ. Microbiol. 2018, 20, 1299–1301. [Google Scholar] [CrossRef][Green Version]
  67. Sewell, T.R.; Zhu, J.; Rhodes, J.; Hagen, F.; Meis, J.F.; Fisher, M.C.; Jombart, T. Nonrandom Distribution of Azole Resistance across the Global Population of Aspergillus fumigatus. mBio 2019, 10, e00392-19. [Google Scholar] [CrossRef][Green Version]
  68. Caramalho, R.; Tyndall, J.D.A.; Monk, B.C.; Larentis, T.; Lass-Flörl, C.; Lackner, M. Intrinsic short-tailed azole resistance in mucormycetes is due to an evolutionary conserved aminoacid substitution of the lanosterol 14α-demethylase. Sci. Rep. 2017, 7, 15898. [Google Scholar] [CrossRef] [PubMed]
  69. Sagatova, A.A.; Keniya, M.V.; Wilson, R.K.; Sabherwal, M.; Tyndall, J.D.; Monk, B.C. Triazole resistance mediated by mutations of a conserved active site tyrosine in fungal lanosterol 14α-demethylase. Sci. Rep. 2016, 6, 26213. [Google Scholar] [CrossRef] [PubMed][Green Version]
Figure 1. Location of the studied schools (blue) and dwellings (green) in Lisbon, Portugal.
Figure 1. Location of the studied schools (blue) and dwellings (green) in Lisbon, Portugal.
Atmosphere 11 00993 g001
Figure 2. Aspergillus sections identified in the electrostatic dust collectors (EDC) samples from the bedrooms and the living rooms.
Figure 2. Aspergillus sections identified in the electrostatic dust collectors (EDC) samples from the bedrooms and the living rooms.
Atmosphere 11 00993 g002
Table 1. Sequence of primers and TaqMan probes used for real-time PCR.
Table 1. Sequence of primers and TaqMan probes used for real-time PCR.
Aspergillus Sections TargetedSequencesReference
Flavi (Strains with toxigenic potential)
Forward Primer
Reverse Primer5′-TCGTGCATGTTGGTGATGGT-3′[39]
Forward Primer5′-CGCGTCCGGTCCTCG-3′
Reverse Primer5′-CGGGCACCAATCCTTTCA-3′[41]
Forward Primer5′–CGGCGGGGAGCCCT-3′
Table 2. Settled dust (g/m2/d) and PM2.5 and PM10 concentrations (µg/m3) measured in dwellings and schools.
Table 2. Settled dust (g/m2/d) and PM2.5 and PM10 concentrations (µg/m3) measured in dwellings and schools.
Settled Dust (g/m2/d)PM2.5 (µg/m3)PM10 (µg/m3)
Range (min–max)1.28–1.5719.47–52.9132.72–109.02
Range (min–max)1.27–11.16--
Living RoomsAverage3.6015.2618.95
Range (min–max)1.28–11.166.05–67.969.14–72.95
Range (min–max)1.27–10.74--
Table 3. Bacteria contamination (CFU/m2/d) in each studied location.
Table 3. Bacteria contamination (CFU/m2/d) in each studied location.
Location Total BacteriaGram-Negative Bacteria
BedroomsRange (min–max)31*−1.42 × 103*−3.15 × 101
Living RoomsRange (min–max)33*−3.42 × 103*−4.60 × 101
ClassroomsRange (min–max)4*−6.2 × 101-
N—Number of samples collected. *−Below the detection limit.
Table 4. Fungal species found in each studied location.
Table 4. Fungal species found in each studied location.
BedroomsAlternaria sp.21.05 × 1010.4711.05 × 1010.34
Aureobasidium sp.15.24 × 1000.2315.24 × 1000.17
Chrysosporium sp.32.56 × 1011.1429.49 × 1000.31
Cladosporium sp.81.59 × 1027.10142.81 × 10390.44
Geotrichum sp.14.14 × 1000.1815.24 × 1000.17
Penicillium sp.172.00 × 10389.43122.07 × 1026.67
Aspergillus sp.21.05 × 1010.4723.81 × 1011.23
Fusarium sp.22.18 × 1010.970**
Crysonilia sitophila0**22.10 × 1010.68
Living roomsAlternaria sp.15.24 × 1000.040**
Aspergillus sp.21.33 × 1020.9721.68 × 1024.91
Aureobasidium sp.14.91 × 1000.040**
Chrysonilia sp.25.24 × 10338.1112.62 × 10376.55
Chrysosporium sp.42.64 × 10319.1986.68 × 1011.95
Cladosporium sp.132.22 × 1021.61121.7 × 1024.96
Fusarium sp.0**12.46 × 1010.72
Geotrichum sp.0**21.48 × 1010.43
Penicillium sp.142.65 × 1021.93163.54 × 10210.33
Rhizopus sp.25.24 × 10338.110**
Ulocladium sp.0**15.24 × 1000.15
ClassroomsPenicillium sp.21.76 × 10164.210**
Chrysonilia sp.14.91 × 10017.9014.91 × 10019.74
Cladosporium sp.14.91 × 10017.9014.91 × 10019.74
Aspergillus sp.0**14.91 × 10019.74
Chrysosporium sp.0**11.02 × 10140.79
N—Number of isolates observed. *—Lower the detection limit.
Table 5. Fungal species found on azole-screening media.
Table 5. Fungal species found on azole-screening media.
Alternaria sp.84.91 × 1010.220*015.24 × 1010.940*0
Aspergillus section Aspergilli15.24 × 1000.020*00*00*0
Aspergillus section Candidi0*00*015.24 × 1000.940*0
Aspergillus section Fumigati29.38 × 1000.040*00*00*0
Aspergillus section Nigri146.91 × 1010.310*029.38 × 1001.680*0
Aspergillus section Nidulantes219.02 × 1010.410*00*00*0
Aureobasidium sp.0*021.05 × 10116.1852.40 × 1014.3014.14 × 1014.41
Crysonilia sitophila30001.84 × 10⁴83.050*029.38 × 1001.6821.05 × 10111.17
Chrysosporium sp.245.24 × 1010.2415.24 × 1008.0931.54 × 1012.7632.41 × 10125.73
Cladosporium sp.5612.92 × 10313.2262.62 × 10140.44552.14 × 10238.3373.47 × 10137.03
Fusarium incarnatum-equiseti species complex21.05 × 1010.050*00*00*0
Fusarium oxysporum species complex0*015.24 × 1008.090*00*0
Geotrichum sp.0*00*021.05 × 1011.880*0
Litchemia sp.21.05 × 1010.050*00*00*0
Penicillium sp.1625.07 × 1022.2931.76 × 10127.21532.54 × 10245.6052.03 × 10121.65
Syncephalastrum racemosum14.91 × 1000.020*00*00*0
Paecilomyces sp.11.57 × 1010.070*00*00*0
Ulocladium sp.0*00*021.05 × 1011.880*0
*—Lower the detection limit.
Table 6. Study of the relationship between the weight of EDCs, particulate matter (PM2.5 and PM10), bacterial (TSA and VRBA) and fungal (MEA and DG18) contamination, fungi in azole-screening media (ITR, VOR and POS) and Aspergillus prevalence (MEA and DG18).
Table 6. Study of the relationship between the weight of EDCs, particulate matter (PM2.5 and PM10), bacterial (TSA and VRBA) and fungal (MEA and DG18) contamination, fungi in azole-screening media (ITR, VOR and POS) and Aspergillus prevalence (MEA and DG18).
VariablesParticles (µg/m3)Bacteria (CFU/m2/d)Fungi (CFU/m2/d)Fungi in Azole-Screening MediaAspergillus Prevalence
EDC Weight (g/m2/d)−0.395 *−0.486 **−0.252 *0.1180.0380.2100.0300.0830.0000.555 *0.253
PM2.5 0.957 **0.2400.123−0.084−0.0130.164−0.084−0.014−0.386−0.392
PM10 0.2200.114−0.0660.0230.153−0.0640.027−0.426−0.447
TSA 0.1460.0050.0580.0530.0340.119−0.379−0.203
RB 0.0690.1410.0550.033−0.0090.019−0.106
MEA 0.457 **−0.0970.281 *0.280 *0.4200.196
DG18 −0.0190.262 *0.276 *0.1070.459 *
Fungi in azole-screening mediaITR 0.0780.026−0.3240.029
VOR 0.250 *0.3350.287
POS −0.032−0.155
Aspergillus prevalence
MEA 0.355
*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).
Table 7. Comparison of EDC weight, particulate matter concentration, fungal and bacterial contamination, fungal presence in azole-screening media and Aspergillus prevalence between sampling locations (Kruskal–Wallis test or Mann–Whitney test).
Table 7. Comparison of EDC weight, particulate matter concentration, fungal and bacterial contamination, fungal presence in azole-screening media and Aspergillus prevalence between sampling locations (Kruskal–Wallis test or Mann–Whitney test).
VariablesRanksTest Statistics
LocationNMean Rank χ2 Kruskal–Wallis or Mann–Whitney UDfp
EDC Weight (g)Classroom411.756.174 *20.046 ***
Living room3337.68
ParticlesPM2.5 (µg/m3)Classroom431.7515.000 ** 0.013 ***
Living room3317.45
PM10 (µg/m3)Classroom433.508.000 ** 0.005 ***
Living room3317.24
Bacterial contaminationTSA (CFU/m2/d)Classroom425.880.774 *20.679
Living room3233.25
RB (CFU/m2/d)Classroom431.000.491 *20.782
Living room3334.08
Fungal contaminationMEA (CFU/m2/d)Classroom425.383.228 *20.199
Living room3338.65
DG18 (CFU/m2/d)Classroom421.753.306 *20.192
Living room3338.18
Fungal presence in AzolesITRClassroom446.005.049 *20.080
Living room3335.12
VORClassroom426.505.273 *20.072
Living room3339.77
POSClassroom438.005.920 *20.052
Living room3338.18
Aspergillus prevalenceMEAClassroom44.503.338 *20.188
Living room58.60
Living room813.25
* Kruskal–Wallis test. ** Mann–Whitney test. *** Statistically significant differences at a 5% significance level.

Share and Cite

MDPI and ACS Style

Viegas, C.; Almeida, B.; Dias, M.; Caetano, L.A.; Carolino, E.; Gomes, A.Q.; Faria, T.; Martins, V.; Marta Almeida, S. Assessment of Children’s Potential Exposure to Bioburden in Indoor Environments. Atmosphere 2020, 11, 993.

AMA Style

Viegas C, Almeida B, Dias M, Caetano LA, Carolino E, Gomes AQ, Faria T, Martins V, Marta Almeida S. Assessment of Children’s Potential Exposure to Bioburden in Indoor Environments. Atmosphere. 2020; 11(9):993.

Chicago/Turabian Style

Viegas, Carla, Beatriz Almeida, Marta Dias, Liliana Aranha Caetano, Elisabete Carolino, Anita Quintal Gomes, Tiago Faria, Vânia Martins, and Susana Marta Almeida. 2020. "Assessment of Children’s Potential Exposure to Bioburden in Indoor Environments" Atmosphere 11, no. 9: 993.

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop