**Microbiological Air Quality in a Highschool Gym Located in an Urban Area of Southern Poland—Preliminary Research**

#### **Ewa Br ˛agoszewska 1,\* , Izabela Biedro ´n <sup>2</sup> and Anna Mainka <sup>3</sup>**


Received: 27 June 2020; Accepted: 27 July 2020; Published: 29 July 2020

**Abstract:** The benefits of regular exercise include improved physical and mental health. The school gym is a particular micro-environment where students perform intensive physical training. The question is if there is an increased risk of microbiological contamination. This preliminary work studied the exposure of students to bacterial aerosol (BA) in a highschool gym located in an urban area of Southern Poland. A sampling of BA was undertaken with an Andersen six-stage impactor (ANDI). BA was identified using API (analytical profile index) tests. The BA concentrations were expressed as Colony Forming Units (CFU) per cubic metre of air. The results showed that before gym classes (BGC), the concentration of BA was 4.20 × 10<sup>2</sup> ± 49.19 CFU/m<sup>3</sup> , while during gym classes (DGC), the level of BA more than doubled (8.75 × 10<sup>2</sup> ± 121.39 CFU/m<sup>3</sup> ). There was also an increase in the respirable fraction of BA (particles less than 3.3 µm). Before the start of the sports activities, respirable fraction accounted for 30% of the BA, while during physical education classes, this share increased to over 80%. Identification of BA species showed that the dominant group of bacteria in the indoor air of the gym BGC was Gram-positive rods (61%) and for DGC it was Gram-positive cocci (81%). We detected that one bacteria strain (*Corynebacterium striatum*) was classified into risk group 2 (RG2) according to Directive 2000/54/EC. Additionally, multi-antibiotic resistance (MAR) showed that among the isolated airborne bacteria, the highest antibiotic resistance was demonstrated by *Staphylococcus epidermis* (isolated DGC) and *Pseudomonas* sp. (isolated BGC). The quantitative and qualitative information on microbiological air quality (MIAQ) in the school gym indicates that the actions to improve indoor physical activity spaces are recommended.

**Keywords:** microbiological indoor air quality (MIAQ); bacterial aerosol (BA); size distribution; gymnastic hall; multi-antibiotic resistance (MAR)

#### **1. Introduction**

According to available studies on indoor air quality (IAQ), it was found that both air pollution and physical conditions (including temperature, humidity and inefficient ventilation) have a negative impact on the health of building residents [1–9].

Previous studies conducted in educational buildings showed that poor microbiological indoor air quality (MIAQ) in classrooms exerts a negative effect on students' learning performance [10]. Poor air quality has been shown to increase absenteeism and increase the risk of asthma and other health-related issues [11,12]. In general, about 1% of school-age children in Poland suffer from chronic respiratory diseases [13]. There are 8000 secondary schools in Poland, with 13,360 students. The most popular

schools are post-gymnasium highschools, which enable students to obtain a certificate of maturity and continue to education in universities. These schools are attended by 86.8% of all post-gymnasium students. Among the educational facilities, 3818 include highschools, 424 of which are located in the cities of an urban area of Southern Poland (Silesia Province) [14].

Regulation from the Minister of National Education and Sport on safety and hygiene in public and non-public schools and institutions, and Framework Directive 89/391/EEC [15], oblige school principals to ensure safe and hygienic conditions for students and teachers [16,17]. This requirement is supported by the framework of school statutes as well as other general health and safety regulations.

Compared to other indoor spaces in educational buildings, it is interesting to note that school gyms offer a unique place to explore microbial diversity. Sports facilities exhibit exceptional conditions for bacterial aerosol (BA) proliferation, and the conditions in which students exercise are seldom examined. One of the parameters prevalent in this type of building is moisture emitted due to perspiration and water condensation as a result of the physical activity of occupants [18]. Although regular exercise improves overall well-being, decreases the prevalence of diseases and improves physical health, the public's passion for exercise has been discouraged by severe air pollution [19]. Increased physical activity in polluted areas consequently leads to elevated exposure of some pollutants producing adverse health effects [20–22]. During exercise, the air is generally inhaled through the mouth and at a higher-than-normal rate, so the intake of airborne contaminants increases, with increased penetration to the lower parts of the lungs [12].

Regardless of the existence of many reports on MIAQ in educational buildings [2,3,11,23–26], studies on potential risks of exposure to BA in gyms situated in academic environments during physical education or exercise lessons are very limited [18,27–29]. Additionally, there is still a shortage of information on the transmission of antimicrobial resistance through the air [30]. Multi-antibiotic resistance (MAR) is considered to be a rapidly progressing global public health issue with the potential of extensive environmental transmission. It is a widespread and alarming issue in global health, causing more than 700,000 deaths every year [31]. Including information about MIAQ in indoor spaces is recommended for health and well-being [32].

The objectives of this study are: (a) to assess bacterial aerosol (BA) air quality in the gymnastic hall of a highschool building located in the urban area of Southern Poland, (b) to determine the concentration and BA particle size distribution in the gymnastic hall before gym classes (BGC), during gym classes (DGC) and in the outdoor air (OUT), (c) to identify isolated strains of bacteria BGC and DGC and (d) to determine the antibiotic resistance of isolated strains of BA.

#### **2. Experiments**

#### *2.1. Sampling Sites*

The study was carried out in a highschool gym located in an urban area of Southern Poland (50◦15′44.121′′ N 19◦0 ′57.16′′ E). The research was conducted during the spring season. The spring season was selected for this study because recent research of MIAQ conducted in Southern Poland indicates that the highest BA concentration is consistently found in the spring [9,11,33]. Every measurement was conducted between 7:00 and 9:00 a.m. (BGC), and 10:00–12:00 a.m. (DGC, attended by 15–17 students).

In the analyzed school gym, MIAQ is primarily ensured by means of stack ventilation and airing through open and unsealed windows. The gym is aired before classes and during breaks when students leave the hall. Following Polish legislation, the ventilation system in each educational building is checked each year and should ensure three to five air changes per hour [12]. In the gymnastic hall, deep wet cleaning was carried out once a day after the occupancy period. Table 1 presents a description of the educational building. The device used for air temperature and humidity measurement was an portable Weather Station WMR 200 (Oregon Scientific, Portland, OR, USA).



#### *2.2. Sampling and Analytical Methods*

BA was collected using an Andersen six-stage impactor ANDI (Thermo Fisher Scientific, Waltham, MA, USA) with cut-off diameters of 7.0, 4.7, 3.3, 2.1, 1.1 and 0.65 µm, with constant flow rate (28.3 L/min). The sampling time was 10 min, following Nevalainen et al. [34]. The device was disinfected using 70% ethanol-immersed cotton balls between each sampling. Samples were collected on nutrient media. Tryptic soy agar (TSA, BioMaxima) was used for BA, with cycloheximide (500 mg/L, 95%, ACROS Organics™, USA) added to inhibit fungal growth.

All Petri dishes were incubated for 48 h at 36 ± 1 ◦C. The samples of BA were collected in the center of the gymnastic hall and taken at the height of the student's breathing zone, about 1.5 m from the ground. In total, we quantitatively analyzed 324 Petri dishes (18 measurement series for OUT, BGC and DGC) and qualitatively, we analyzed 216 Petri dishes (18 measurement series both for BGC and DGC) with biological material.

The testing of blank plates was performed per batch of prepared medium at the temperature used during the performed procedure. The blanks were not contaminated. The sampling equipment (ANDI) and laboratory equipment (laminar flow cabinet, autoclave, incubators and microscope) were regularly checked and had current certificates.

#### *2.3. Bacteria Identification*

BA strains obtained from each intake were isolated. In the first stage, colony morphology was macroscopically determined (shape, pigmentation, edges, etc.). The next stage was microscopic analysis (bacterial morphology, motility and reaction to Gram staining, etc.). The next steps focused on selecting a group of microorganisms present in each of the tested samples. For this purpose, a comparative analysis of the created feature matrix was used. A series of screening analyses led to the acquisition of strains of bacteria, present during each collection. The selected microorganisms were then identified, and their antibiotic resistance was determined.

Next, we gathered information about the main groups of bacteria present in the indoor air in the gymnastic hall. Isolated strains were cultivated on the agar medium with the addition of blood (trypticase-soy agar with 5% sheep blood). Selected strains were characterized in terms of their metabolic characteristics by using the biochemical test API (analytical profile index), which is supported by APIweb (bioMérieux, Marcy-l'Etoile, France). The following API systems were used: API 20E, API 20NE, API 50CH, API CORYNE, API STAPH and API STREP.

#### *2.4. Multi-Antibiotic Resistance Test (MAR)*

For antimicrobial susceptibility testing, the disc diffusion method was carried out according to the Kirby–Bauer Disk Diffusion Susceptibility Test Protocol [35]. Isolated strains of the BA were determined. During night cultures, the bacterial isolates were diluted to 1 McFarland (3 × 10<sup>8</sup> colony forming units/mL). 100 µL of bacterial inoculum was spread over the surface of a Mueller–Hinton agar plate (Oxoid, Columbia, MD, USA). Next, antimicrobial susceptibility testing discs (Oxoid, Columbia, MD, USA) were placed on inoculated Mueller–Hinton agar plates. 20 different antibiotics and their concentrations were chosen to take into consideration the common antibiotic resistance referred to in the literature on bacterial species. Three repetitions of each antibiotic were performed. Specific doses of the antibiotics are presented in Table 2.


**Table 2.** Antibiotics and their doses used in multi-antibiotic resistance test (MAR).

Petri dishes with bacteria were incubated at 37 ◦C for 24 h. After incubation, the areas of inhibition growth were measured. A three-stage scale was used in order to assess the bacteria resistance to antibiotics: diameter of growth inhibition < 15 mm—bacterial resistance to an antibiotic, diameter of growth inhibition between 16 and 25 mm—intermediate bacterial resistance to an antibiotic and diameter of growth inhibition > 25 mm—bacteria sensitive to the antibiotic.

#### *2.5. Statistical Analysis*

The R Studio 1.2.5042 was used to perform all statistical analyses, and the ggplot2 package was used to generate all plots [36]. The presence of significant differences was determined using one-way analysis of variance (ANOVA). Pairwise *t*-test with Bonferroni correction for multiple comparisons was performed if significant results were obtained in the ANOVA test (*p* < 0.05).

#### **3. Results and Discussion**

#### *3.1. Quantity of Bacterial Aerosol (BA) in a Highschool Gym: Before Gym Classes (BGC), during Gym*

The BA concentration of the highschool gym before gym classes (BGC), during gym classes (DGC) and the outdoor air (OUT) was measured. The highest mean value of the concentration of BA was DGC (8.75 × 10<sup>2</sup> ± 121.39 CFU/m<sup>3</sup> ), whilst the mean concentration of bacteria BGC was 4.20 × 10<sup>2</sup> ± 49.19 CFU/m<sup>3</sup> . The OUT concentration of BA was 2.34 × 10<sup>2</sup> ± 36.33 CFU/m<sup>3</sup> . The maximum value of BA concentration was obtained DGC (3.78 × 10<sup>2</sup> CFU/m<sup>3</sup> ), and the minimum was obtained OUT (4.0 CFU/m<sup>3</sup> ). The BA concentration indoors was nearly four times the concentration outdoors. These results indicate that the source of the increased concentration DGC is simply due to

the presence of students, because humans are the main source of bacterial emission indoors, and are confirmed with the data available in the literature [8,29,37]. BA contamination levels obtained in our study were lower than the threshold values of occupational exposure specified by the Expert Group on Biological Agents at the Polish Interdepartmental Commission for Maximum Admissible Concentrations and Intensities for Agents Harmful to Health in the Working Environment for public service buildings (5.0 × 10<sup>3</sup> CFU/m<sup>3</sup> for mesophilic bacteria) [38,39]. Comparing the values observed in our study with the limits suggested by the Commission of the European Communities (CEC) [40] indicates that the range of values obtained for DGC should be considered as moderate contamination (<2000 CFU/m<sup>3</sup> ), and also, for BGC and OUT, should be considered as average contamination (<500 CFU/m<sup>3</sup> ).

The ANOVA test was significant with F-value equal to 13.81. Based on these results, t-tests were used to determine for which variants there are significant differences. A significant difference was observed between BGC and DGC (*p* = 0.0015) and between DGC and OUT (*p* = 0.00086). No significant difference was found between OUT and BGC (*p* = 0.4577).

The Environmental Protection Agency (EPA) do not give any specific guidelines for bioaerosol concentration levels, so they are only as proposed by different governmental and private organizations [41]. Nevalainen, in 1989 [42], suggested an upper limit for bacterial aerosols ranging from 4500 to 10,000 CFU/m<sup>3</sup> . However, research conducted by the WHO expert group on the assessment of the health risk of biological agents in the indoor environment suggests that the total concentration of microorganisms should not exceed 1000 CFU/m<sup>3</sup> [43].

Similar studies carried out in Poland 16 years ago showed that the level of BA before gym classes was 5.50 × 10<sup>2</sup> CFU/m<sup>3</sup> . During the first lesson in the gym, the concentration of BA was 10 times higher (5.50 × 10<sup>3</sup> CFU/m<sup>3</sup> ) [44]. As it can be seen, the concentration of BA before gym classes is on the comparable level, but during gym classes, the study of Pastuszka et al. [44] reported significantly higher levels of BA. The probable reason was the higher occupancy in the gym.

Studies of MIAQ in fitness centers in Portugal reported that the average BA concentration was 5.56 × 10<sup>2</sup> CFU/m<sup>3</sup> and 8.24 × 10<sup>2</sup> CFU/m<sup>3</sup> in the studio and bodybuilding room, respectively. As it can be seen, the average level of BA during gym classes is in agreement with results from the bodybuilding room, while the BA concentration before gym classes is comparable to the levels in the gym studio [18]. The results of our measures BGC also agree with the studies of Dacarro et al. [27] and Grisoli et al. [28], who reported the levels of mesophilic bacteria in Italian gyms, which were 4.93 × 10<sup>2</sup> CFU/m<sup>3</sup> and 3.93 × 10<sup>2</sup> CFU/m<sup>3</sup> , respectively.

Besides, temperature and relative humidity are closely associated with bacterial growth and have significant effects on microbial diffusion in the air [28,45–47]. The ASHRAE's standard recommends that indoor temperatures in the winter and summer are between 20 and 23.8 ◦C and 22.7 and 26.1 ◦C, respectively [48]. The EPA recommends that relative humidity levels indoors should be between 30% and 50% [49]. Controlling these parameters, especially relative humidity, may be an effective strategy to prevent asthma and allergy symptoms among students and teachers [50].

#### *3.2. Particle Size Distribution of Bacterial Aerosol (BA) in a Highschool Gym: Before Gym Classes (BGC), during Gym Classes (DGC) and Outdoor Air (OUT)*

Figure 1 presents the analysis of the average concentration of BA collected from the different stages of the Andersen six-stage impactor (ANDI) in the indoor and outdoor air of the school building areas.

The highest average concentration of BA in the OUT was observed on the stage with the aerodynamic diameter ranging > 7.0 µm (Figure 1C). For BGC, the highest concentration of BA was observed for particles with an aerodynamic diameter ranging from 3.3 to 4.7 µm (Figure 1A). Stages with the aerodynamic diameter ranging from 0.65 to 3.3 µm had the highest concentration of BA from indoor samples DGC (Figure 1B).

**Figure 1.** Average concentration of bacterial colony-forming units (CFU) per cubic meter collected from the different stages of the Andersen six-stage impactor (ANDI): (**A**) before gym classes (BGC), (**B**) during gym classes (DGC) and (**C**) in the outdoor air (OUT).

As it can be seen, the size distribution of bacteria OUT and BGC were similar. Both distributions were characterized with a large share of coarse particles of BA. However, indoor bioaerosol collected BGC indicated a little higher contribution of smaller particles and lower contribution of particles, >7.0 µm, compared to outdoor BA. The sports activity significantly changed the patterns of this size distribution, shifting the peak into the smaller BA particles—in the range from 1.1 to 3.3 µm. The size distributions of BA collected DGC might indicate that the particles of BA were relatively fresh, and generally originating from exercising students. Some studies have found that the activities of humans increase the concentrations of aerosol diameter > 1 µm [51,52] in indoor environments. We hypothesize that human activity and human presence may affect the concentration of biological particles, as they do for ordinary aerosol particles.

The results suggest that during gym classes, students are at risk of being exposed to respirable particles (less than 3.3 µm) that can reach the trachea, bronchi and alveoli, and contribute to adverse respiratory symptoms. It can be seen that the concentration levels of BA obtained in our study are below these proposed standards. However, their long-term inhalation may cause adverse health effects, especially in students sensitive to this type of air pollution.

The ratio of respirable BA during gym classes was 80% compared to the reports [53,54] of a ratio of respirable BA inside public buildings ranging between 30% and 60%. Our result is likely to be an accurate level and may be evidence for the appearance of adverse respiratory symptoms, especially in students sensitive to this type of air pollution.

On the grounds of the statistical analysis, significant differences were found between differences between BA concentration in the OUT–BGC–DGC on some stages of the Andersen six-stage impactor (ANDI), with *p*-values < 0.05 (Table 3). The similar size distribution of BA we found at stage 1: between OUT and DGC, at stage 2: between OUT and BGC and BGC and DGC, at stage 3: between OUT and BGC and at stage 4, between OUT and DGC and BGC and DGC.


**Table 3.** Pairwise comparisons between bacterial aerosol (BA) concentrations on different stages of ANDI.

Italic entries indicate that the correlation is significant at the *p*-value less than 0.05; outdoor air (OUT), before gym classes (BGC) and during gym classes (DGC).

#### *3.3. Bacterial Diversity and Antibiotic Resistance of Bacterial Aerosol (BA) in a Highschool Gym before Gym Classes (BGC) and during Gym Classes (DGC)*

Detailed analysis of BA quality included 10 bacterial species (Figure 2). The statistical analysis of BA qualitative composition points to significant differences for all series between BGC and DGC for four analyzed groups of bacteria: Gram-positive cocci, non-sporing Gram-positive rods, sporing Gram-positive rods, family *Bacillaceae* and Gram-negative rods (*p*-values < 0.05).

**Figure 2.** The most frequent BA species identified in a highschool gym before gym classes (BGC) and during gym classes (DGC).

Gram-positive cocci of the genera *Micrococcus, Kocuria* and *Staphylococcus* were the main components of the bacteria DGC. When there were no students exercising at the gym, BGC Gram-positive cocci constituted 34% of the total BA. During the sporting activity, the percentage of these bacteria increased and constituted 81%. This group of bacteria commonly occur in natural environments and colonize the surface of human skin.

DGC, three *Staphylococcus* species were identified, with a high percentage of *S. lentus, S. epidermidis* and *S. chromogens.* This species composition is comparable to that identified in the indoor air of preschools, primary schools and highschools during the spring in 2016 and 2017 in Poland [11]. *Staphylococcus* was also the dominant bacteria group in sports facilities at the Centre of Physical Culture and Sport at the University in Northern Poland [12]. This bacteria genus may particularly affect the health of students [55].

The most frequently isolated group bacteria BGC was Gram-positive rods. Bacteria in this group are common in samples of food, water, soil and outdoor air [45]. Many of the Gram-positive rods are found in normal skin and mucous membrane flora of humans and various animals and can cause human infections [56].

According to the classifications in Directive 2000/54/EC [57], biological agents from risk group 2 (RG2) were also detected among isolated microorganisms. It was only one strain—*Corynebacterium striatum*, for which BGC constituted 14%, while DGC constituted 4% of total microbiota. Bacteria from RG2 are associated with human disease, which is rarely serious or for which preventive and therapeutic interventions are often available.

#### *3.4. Multi Antibiotic Resistance Test (MAR) of Bacterial Aerosol (BA) in a Highschool Gym before Gym Classes (BGC) and during Gym Classes (DGC)*

Antibiotic-resistant genes are rapidly evolving into a significant environmental problem and have become a global threat to public health [58]. Results of MAR (Figure 3) can be presented as the inhibition rate of growth diameter around antimicrobial susceptibility testing disks, mean values for each antibiotic and tested strain (in millimeters). The results of MAR demonstrated that the air environment DGC was highly impacted by antibiotic resistance bacteria, while in BGC, a smaller proportion of resistant microorganisms was observed.

**Figure 3.** Results of antibiotic resistance testing. Expressed in the values of the growth inhibition zone (mm).

The highest antibiotic resistance bacteria isolated DGC was *Staphylococcus epidermis. S. epidermidis* is from a group of mannitol-fermenting coagulase-negative staphylococci characterized by multiple antibiotic resistance [59,60].

*S. epidermidis* was one of the isolates showing the highest antibiotic resistance. Although it is an opportunistic pathogen and the most common skin commensal [61], it plays an important role in balancing the skin microflora and serves as a source of resistance genes [62]. Despite its widespread presence, we must not forget that infections are mainly caused by endogenous microflora [63]. In the case of exercises in the gym, we can expect the appearance of abrasions or scratches among students that are the result of physical activity. It is known that this species exhibits resistance to many antibiotics, and in addition, in recent years, methicillin-resistant *Staphyloccocus epidermidis* (MRSE) is the most common of the species in medical facilities [64]. Among strains isolated in hospitals, resistance to beta-lactam antibiotics—mainly penicillin and cephalosporins—was most commonly observed [63]. The isolated strain also had such a resistance pattern.

Our attention was drawn to the fact that in hospital cases, we often talk about postoperative infections (often associated with implant placement), where high antibiotic resistance is explained by the formation of biofilms by microorganisms [62,65]. In this case, a strain with a similar resistance pattern was isolated from the air sample.

The highest antibiotic resistance bacteria isolated BGC was *Pseudomonas* sp. *Pseudomonas* species are known to harbor multiple intrinsic and acquired resistance genes and host several mobile genetic elements [66]. *Pseudomonas* sp. includes species with both clinical and environmental implications. Important members of this genus include *P. aeruginosa, P. fluorescens* and *P. stutzeri*.

However, the presence of resistant *Pseudomonas* representatives, even if they do not belong to pathogenic species, confirms the results of Kittinger, in which it has been shown that these species can be a reservoir of antibiotic resistance and in favorable conditions pass it on to pathogenic species through horizontal gene transfer [66]. It was noted that *Pseudomonas* isolates from environmental samples (freshwater) show much lower antibiotic resistance than clinical isolates [67]. In our research, the bioaerosol isolate presents a resistance pattern more similar to clinical isolates. This confirms the assumption that the source of bioaerosol emissions in the gym is mainly human.

The most sensitive bacteria to antibiotics are *Corynebacterium striatum* and *Corynebacterium propinquum* (isolated in both BGC and DGC). *Corynebacterium* species are widely distributed in the environment and in the microbiota of humans and animals [68].

The most effective antibiotics were amikacin and doxycycline (aminoglycosides group), which are on the World Health Organization list of basic medicines (the safest and most effective medicines needed in the healthcare system). Aminoglycosides are natural or semisynthetic antibiotics derived from actinomycetes. They are potent, broad-spectrum antibiotics which act by inhibiting protein synthesis [69].

#### **4. Conclusions**

Although the presented research is the result of preliminary studies, the data cover one season and a limited number of repetitions, they allow the following conclusions to be drawn:


The results of this study indicate that the concentrations of bacterial aerosol (BA) in a highschool gym located in the naturally ventilated historic building are not particularly hazardous for the occupants, but the air in the gym is more contaminated than outdoor air, so we recommend to perform gym classes outside the building as often as possible.

**Author Contributions:** Conceptualization, E.B.; Data curation, E.B. and I.B.; Methodology, E.B. and I.B.; Supervision, E.B.; Visualization, I.B.; Writing—original draft, E.B., I.B. and A.M.; Writing—review and editing, E.B., I.B. and A.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Faculty of Energy and Environmental Engineering, Silesian University of Technology (statutory research).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Microclimate in Rooms Equipped with Decentralized Façade Ventilation Device**

#### **Ewa Zender-Swiercz ´**

Department of Building Physics and Renewable Energy, Faculty of Environmental Geomatic and Energy Engineering, Kielce University of Technology, 25-314 Kielce, Poland; ezender@tu.kielce.pl

Received: 29 June 2020; Accepted: 27 July 2020; Published: 29 July 2020

**Abstract:** Many building are characterized by insufficient air exchange, which may result in the symptoms of sick building syndrome (SBS). A large number of existing buildings are equipped with natural ventilation, whose work is disturbed by activities going to energy-saving. The thermomodernization activities are about mounting new sealed windows and laying thermal isolation, which reduces the amount of infiltrating/exfiltrating air. In many cases, the mechanical ventilation cannot be used due to a lack of a place in building or architectural and construction requirements. One of the solutions to improve the indoor microclimate is the decentralized façade ventilation. In the article, the internal air parameters in an office room equipped with decentralized façade ventilation device were analyzed. The room was equipped with a decentralized façade unit, which cyclically supplied and removed air from the room. The time of the supply/exhaust was changed to 2 min, 4 min, and 10 min. The temperature and the humidity of the indoor air and the outdoor air and the concentration of carbon dioxide inside the room were measured. The analysis showed that despite the lack of a heater in the device, the air temperature in the workplace and in the central point of the room was in the range of 20–22 ◦C. The air humidity was in the range of 27–43%.

**Keywords:** indoor microclimate; decentralized façade ventilation; air quality

#### **1. Introduction**

In order to live a healthy life and stay in good shape, people need air with adequate parameters that is free from any pollution. Air quality also affects the learning efficiency and labor productivity of people using rooms [1–10].

The general trend today is to make buildings energy-efficient, which is understood by the majority of building administrators as a reduction in the thermal losses and heating costs. As a result of this, actions are undertaken to seal and thermally insulate the building fabric and partitions. These procedures restrict air exchange in a room equipped with natural ventilation [11,12]. A reduced volume of air entering a building negatively affects the indoor air quality and induces a rise in the temperature, humidity, and pollution volume. This situation may result in the occurrence of mold on division walls, which in turn is destructive for the structure, and fungi spores may induce allergies and asthma. According to the analysis carried out by R. Górny [13], they are in fact biologically harmful due to immune reactivity, cytotoxicity, or the transport of mycotoxins.

Poor indoor air quality results in the occurrence of sick building syndrome (SBS) symptoms. Fisk et al. [14] analyzed the impact of the ventilation system capacity on the occurrence of the SBS symptoms. The average frequency of symptom occurrence increased by 23% with a ventilation system capacity drop from 36 to 18 m<sup>3</sup> /h·person, and it decreased by ca. 29% for air flow increase from 36 to 90 m<sup>3</sup> /h·person. Moreover, the researchers in [15–17] have proven the dependence between the occurrence of SBS and gender. In the same thermal environments and with the same type of performed work, women complained about their health problems more often. Females are more sensitive than

males to a deviation from an optimal temperature (thermal dissatisfaction ratio equals 1.74 [18,19]). For example, the adjusted odds ratio of a headache for females is 3.64 more often than for males [20].

Poor indoor air quality affects the disease incidence rate as well as work and learning efficiency [21]. The analysis conducted by Lan et al. [22] of the number of mistakes made during attention tests showed that the morning test had less mistakes than the last lesson test. Roelofsen [23] showed a productivity increase of 10 per cent when the indoor environment improved. Many researchers are interested in the analysis of dependencies between the capacities of ventilation systems and learning efficiency, for example, D. L. Johnson et al. [24], whose studies proved air quality disturbances in elementary schools and observed a carbon dioxide concentration equal to 4751 ppm. Vimalanathan and Babu [25] showed that temperature was the main factor affecting work efficiency and 21 ◦C was optimal, and the optimal illumination was 1000 lux.

Considering the current trend to isolate the building envelope, it is necessary to control the air humidity. The amount of moisture produced in a room (by breathing and evaporation of sweat) may even reach 100 kg per week [26]. The water vapor cannot diffuse in a sealed envelope and the air humidity increases. At the same time, the share of moisture volume in air (moisture penetrating through partitions by diffusion) in the humidity balance, determined according to the standard PN-EN 13788 [27], ranges from 1% to 3% of total moisture emission [28]. An intense removal of moisture from inside is required to maintain air humidity in a room at more or less constant level [29].

Analyses completed so far [12] have proven a growth in indoor air humidity after being thermally modernized in a naturally ventilated building. This means that while trying to reduce heat loss, the investors undertake actions affecting indoor air parameters such as humidity or pollution concentration. When any changes are to be introduced in a building, there is an unambiguous need to look globally at the building as a whole: the structure with building services. Without any extra ventilation holes provided, the indoor air humidity in rooms naturally ventilated with thermally insulated partitions increases as the moisture is not exhausted with the air to the outside, which proves an insufficient air exchange. The analysis showed that this parameter becomes higher in rooms where people are the only source of moisture, thus it can be concluded that higher humidity is not the effect of a specific office space use. It is also connected with mold species not appearing in kitchens, bathrooms, or social rooms. In buildings where thermal insulation was provided and the air supply method was altered, the air humidity remained at the same level.

Insufficient air exchange ratio in rooms increases both the humidity of the air and the carbon dioxide concentration [30]. However, greater exchange of air in naturally ventilated facilities is not an ideal solution, since it may result in an inside temperature drop [10].

Mechanical ventilation systems are most effective from the point of view of the volume of air exchanged in buildings. However, their use entails greater construction and operating costs. Heat recovery or mixing fresh air streams with exhaust air at a specific recirculation rate can be used to reduce costs, however, in each case, natural ventilation will be a cheaper solution. Researchers [31] have carried out an analysis of the natural ventilation in office buildings introduced as a solution to reduce energy consumption. The conclusion of their analysis was that sufficient air exchange in facilities equaled 1–6 h−<sup>1</sup> . However, there were certain conditions here: in the building, there were great heat gains and the building was designed in consideration of the relationship between architecture, shape of the building, its location, and the effectiveness of natural ventilation. Only these buildings will not be subjected to excessive chilling.

In the case of using mechanical exhaust ventilation, the risk of inside temperature drop is even greater due to larger volumes of inflowing air. Supply and supply–exhaust ventilation systems are the only ones that allow for the control of inflow air temperature. However, in these solutions, flowing air can be perceived as an unpleasant sensation of draught (DR: draught rating) [32]; whereas the discontent of people staying in rooms increases with growing air velocity and intensity of turbulence [33]. Analyses by Toftum et al. [34] indicated that air flow from the bottom and the front at an inside temperature

of 20 ◦C caused greater discomfort than air flow in the upper part of a room. At the same time, no discomfort was observed when the air temperature in a room was 26 ◦C.

Moreover, the problem of insufficient volume of air delivered to a room usually occurs in already existing facilities undergoing thermal modernization. In many cases, a mechanical ventilation system cannot be installed due to design constraints or insufficient space to fit air ducts. In this case, decentralized façade ventilation can help as it may contain units designed to alternately provide air supply and exhaust.

The literature provides analyses of decentralized façade ventilation units [35–37]; however, they usually concern the energy efficiency of a unit and its impact on building energy balance. However, it is necessary to carry out an analysis of the dependence between the inside and outside temperature in facilities equipped with decentralized façade ventilation systems.

Gruner M. and Haase M. [38] evaluated decentralized façade ventilation units with regard to their capacity to maintain thermal comfort. Temperature values measured by the authors ranged from 22 to 26 ◦C. However, the units analyzed by them were equipped with water heaters and heat recovery exchangers. Moreover, these units worked in pairs: one responsible for air supply and the other for exhaust.

The literature lacks analyses of solutions working not in pairs, but individually, as in some existing buildings, it is not possible to fit units working in pairs on the opposite external walls. Additionally, all of the units described in the literature have either been equipped with air heaters or a heat recovery system. Moreover, the researchers did not analyze the humidity changes in rooms equipped with decentralized façade ventilation units. The article presents an attempt to evaluate the microclimate parameters (indoor air temperature and humidity) in rooms provided with decentralized façade ventilation units.

#### **2. Experiments**

The analysis covered an office room (Figure 1) sized 2.97 m × 3.21 m × 3 m, designed for two people. The building was located in Poland in a moderate climate zone with low winter temperatures and high summer temperatures. The outdoor air temperatures characteristic for this location and at the season are within −20 to +10 ◦C. During the tests, the outdoor air temperature ranged from −9 to +10 ◦C. The heating system was deactivated in the analyzed room. Measuring equipment was located in the workplace and at the central point of the room. The room was equipped with a decentralized façade unit that was cyclically supplying and removing air from the room. During the supply cycle, air was drawn from the outside and delivered by the unit to the room, and then removed through a gap in the bottom part of the internal door. During the exhaust cycle, air was removed through the unit, and supplied via the gap in the internal door. A temperature of 21 ◦C was maintained in an adjacent room. The air supplied by the unit had the same temperature as the outside air, and the air flowing in through the door gap had the temperature of the air in the adjacent room.

The decentralized façade unit (Figure 2) was equipped with one fan (1) pumping air continuously in one direction, and the alternation of cycles was effected by dampers (3–6) opening and closing in pairs. Air flow route was dependent on the damper opening. Dampers 4 and 6 were open during the supply cycle, and dampers 3 and 5 were open during the exhaust cycle. During the supply cycle, the air flowed through sections 7 and 9, and during the exhaust cycle, through sections 8 and 10. Component no. 2 is an intake vent/exhaust vent, and no. 11 is the air-intake/air-exhaust.

Air temperature and humidity was measured in the room and outside. The research took 26 weeks during the fall–winter–spring seasons (from November to March). The period was divided into a two-week series, during which the measurement was performed continuously every 10 s. During the experiment, the unit setting was 2 min for eight weeks, 4 min also for eight weeks, and 10 min for 10 weeks. This research period was selected because the measurements were carried out in rooms used in real conditions (in summertime users often open windows, which considerably affects the results). Two air quality meters from Sensotron (Kozielska Street 63/5, Gliwice, Poland) were used for the tests

(Figure 3). Table 1 shows their measurement ranges and resolutions of indications. The instruments were set in the room user's workplace (on the desk) 0.8 m above the floor (point 1) and at central point of the room 1.5 m above the floor (point 2). In Figure 1, the green color indicates the location of the air supply/exhaust hole; orange indicates the locations of indoor air quality monitors; blue shows the locations of the microclimate meters; and purple represents the locations of the gap in the inner door. Figure 1 also shows the heights of the places of the measurements.

**Figure 1.** Layout of meters and the decentralized façade unit in the analyzed room.

**Figure 2.** Wall-mounted decentralized façade ventilation unit.

**Figure 3.** Indoor air quality monitor.

**Table 1.** Measurement ranges and resolution of indications. Indoor air quality monitor.


The air velocity in the room was measured using a microclimate meter equipped with three anemometers (Table 2). The measurement was carried out at three points: at the workplace, in the central point of the room, and 70 cm from the supply/exhaust grate. The air velocity was measured at three levels: feet, abdomen, and head.

**Table 2.** Microclimate meter specifications.


The values of the outside parameters (temperature and humidity of the external air) were recorded by a weather station located on the roof of the building. Table 3 presents the weather station specifications.



The amount of the supply/exhaust air and the supply/exhaust air velocity was measured using a balometer (Table 4).

The room ventilation unit was worked in three cycles with different air supply/exhaust duration: 2 min, 4 min, and 10 min. The time can be set in the range of 1 min to 10 min, thanks to the use of an actuator in the installation that closed and opened the dampers. The selected cycle lengths were dictated by the desire to show the differences in creating the microclimate at different settings of the device. One cycle consisted of successive air supply and exhaust. In the case of a 2 min cycle, the supply duration is 2 min. After this time, the actuator opens the closed dampers and closes the opened ones. The device switches to the exhaust air function, which also lasts 2 min. The same is applied for each time setting.


**Table 4.** The specifications of balometer station.

#### *Statistical Analysis*

The unit work was evaluated from the statistical point of view. The two-factor ANOVA with replication and the Tukey multiple comparison method were employed for this purpose. Determinants for the group of comparisons included setting (air supply/exhaust duration), measuring instrument location, outside temperature, and outside humidity.

#### **3. Results and Discussion**

#### *3.1. Experimental Studies*


**T (**

**oC)**

0:00

2:09

4:19

6:28

8:38

10:48

12:57

**t (h:min)** workplace central point external

15:07

17:16

19:26

21:36

23:45

The air temperature measured with a 10 s step allowed for the average value for each hour for a two week period to be calculated. The average air temperature values calculated for different points of the room proved to be insignificant fluctuations at different outside air temperatures.

Figure 4 shows the average air temperature values measured at two locations in the room during thirteen two-week periods. Each line corresponds to the daily changes in the average air temperature. Each point corresponds to the mean calculated for each hour of the day from the two-week measurement period. The trajectory of changes in the analyzed parameter in time show minor daily temperature fluctuations in each of the two measurement points. During periods 1–4, the duration of the air ventilation unit supply/exhaust cycle was 2 min (2 min for air supply, and the next 2 min for air exhaust); during periods 5–8, it was 4 min; and during periods 9–13, it was 10 min.

**Figure 4.** Average air temperature values at two locations in the room: (**a**) workplace, (**b**) central point of the room; Tint—inside temperature, ◦C; t—time, h:min.

19.0 23.0 19.0 23.0 An average of the values of measured temperature for a given hour in a day was calculated for each of the cycles, and inside air temperature values were compared to the outside air temperatures (Figure 5).

**T (**

**oC)**


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*Atmosphere* **2020**, *11*, 800

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**Figure 5.** The dependence between the average outside air temperature and average value of the parameter inside the room: (**a**) setting: 2 min, (**b**) setting: 4 min, (**c**) setting: 10 min; T—temperature, ◦C; t—time, h:min.

22.0 22.5 Temperature analysis proved that the values obtained for the workplace and central point of the room satisfied thermal comfort requirements according to the PN-EN 16798-1:2019-06 standard [39], despite the different values of outside air parameter. No influence of outside air temperature on room chilling was observed.

20.5 21.0 21.5 **Tint ( oC)** The next step involved the analysis of inside air temperature dependence on outside air temperature. Figure 6 demonstrates the obtained results and analyzed whether air supply/exhaust duration would affect the temperature value. The inside air temperature remained within the thermal comfort range throughout the measurement period. The average of the recorded temperature values ranged from 20.2 ◦C to 22.1 ◦C, which means that despite supplying low temperature air and regardless of air supply process duration (2 min, 4 min, 10 min), the temperature in the room was stable.

20.0 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 **Tex( <sup>o</sup>C)** 2 min 4 min 10 min Both in the shortest cycle of 2 min and the longest cycle of 10 min, the values of internal air temperature met the requirements of thermal comfort (Figures 7 and 8). The thermal comfort temperatures inside the room were maintained both at the outside air temperature of 4–5 ◦C and the temperature of −6 ◦C. At the same time, the temperature of the inside air was lower in the case of the negative values of the outside temperature, but also in this case the values met the comfort requirements.

Examples of days with similar external conditions (temperature −2 ± 4 ◦C and humidity 80–90%) were selected from the measurement data. Figure 9 shows the course of the temperature changes over time for two locations of the meters: the workplace and the central point of the room. In both cases,

−

the room met the requirements of thermal comfort regardless of the duration of the cycle. At the same time, the temperatures were lower for the longer cycle than for the shorter.

10:48

12:57

**t (h:min)** workplace central point external

15:07

17:16

19:26

21:36

23:45

Average air humidity values measured at different locations in the room proved to be minor fluctuations.

Figure 10 demonstrates the fluctuations of the average air humidity values over time.


**T (**

**oC)**

0:00

2:09

4:19

6:28

8:38

**Figure 6.** The dependence between inside air temperature and outside air temperature; Tint—inside temperature, ◦C; Tout—outside temperature, ◦C.

**Figure 7.** The course of changes in the indoor air temperature during the 10-min supply/exhaust cycle. (**a**) workplace; (**b**) central point.

1 4 7 10 13 16 19 22 25

1 4 7 10 13 16 19 22 25

No. of measurement 4˚C

No. of measurement 4˚C -6˚C

19.9 20.3 20.7 21.1 21.5 21.9 22.3

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Tint

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ºC)

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**Figure 8.** The course of changes in the indoor air temperature during the 2-min supply/exhaust cycle. (**a**) workplace; (**b**) central point.

**Figure 9.** The course of changes in the indoor air temperature for an example day. (**a**) workplace; (**b**) central point.

25

41

58

**RH (%)**

74

**Figure 10.** Average air humidity values at two locations in the room: (**a**) workplace, (**b**) central point of the room; RH—humidity, %; t—time, h:min.

90 90 Inside air humidity values were compared to outside air humidity in each of the thirteen measurement periods (Figure 11).

21:36

23:45

workplace central point external **Figure 11.** The dependence between the average outside air humidity and average value of the parameter inside the room: (**a**) setting: 2 min, (**b**) setting: 4 min, (**c**) setting: 10 min; RH—humidity, %; t—time, h:min.

RH (%)

Air humidity analysis proved that the values measured in the room did not always satisfy thermal comfort requirements according to the PN-EN 15251:2012 standard [40]. The decrease in the relative humidity in the room was observed when the outside temperature was low and the external relative humidity was high.

Figure 12 shows the relationship between the relative humidity of the indoor air and the temperature of the outdoor air. An increase in the indoor air humidity, along with an increase in outdoor temperature was demonstrated. Moreover, when the external temperature equaled −10 to −5 ◦C, the indoor air humidity did not meet the requirements of thermal comfort in accordance with PN-EN 15251:2012 [40]. At the same time, the difference between the relative humidity at the two measurement points was small. − − − −

**Figure 12.** The relationship of the relative humidity of indoor air on the outdoor air temperature. -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 Text (ºC) wp cp

− − − 60 70 80 90 100 2.5 3.6 The device effectiveness was assessed on the basis of measurements of the supply air velocity and the level of carbon dioxide concentration. The velocity and the supply air stream were measured for each of the analyzed cycles (Figure 13). The measured values made it possible to determine the air change rate, which was 2.3 h−<sup>1</sup> for the shortest cycle, and 2.7 h−<sup>1</sup> for the longest cycle. For comparison, devices with heat recovery exchangers and reversible fans [41] exchange the air with an air change rate of 0.18 h−<sup>1</sup> . This could be a sufficient value for living quarters, but for an office room, the number of air changes should be higher. − − −

50

**Figure 13.** The course of the air velocity and air volume changes in the supply/exhaust grate during the supply and exhaust cycle.

The literature [42] shows the influence of the pressure difference inside and outside the building on the speed of the supplied air, and thus the amount of air. In the case of the presented analysis, the focus was on measuring the air velocity and amount of air flowing into the room without analyzing the impact of wind conditions on the work of the device.

The air velocity was also measured within the room at three levels: the feet, abdomen, and head. The measurement was carried out in the workplace, at a central point, and at a distance of 70 cm from the supply/exhaust grate. The performed measurements made it possible to calculate the PMV index (predicted mean vote) in accordance with the PN-EN 7730 [43] standard (Figures 14 and 15).

**Figure 14.** The course of changes in the predicted mean vote (PMV) indicator during the 2-min cycle airflow. (**a**) workplace, (**b**) central point of the room, (**c**) 70 cm from the air supply grate; wp: workplace; cp: central point.

**Figure 15.** The course of changes in the PMV indicator during the 10-min cycle airflow. (**a**) Workplace, (**b**) central point of the room, (**c**) 70 cm from the air supply grate; wp: workplace; cp: central point.

On the basis of Figures 14 and 15, it can be seen that in the area of the head, abdomen, and feet, the workplace belongs to the category of room B, according to the classification of the standard PN EN 7730 [43]. The central point of the room was in category C at the end of the cycle (2 min). In the long cycle (10 min), it belonged to category C for almost the entire duration of the airflow (at all parts of the body). Additionally, the DR (draught rating) was calculated, which defines the percentage of people dissatisfied with the air movement. The analysis showed that in the case of the longest cycle (i.e., with a supplytime of 10 min), for half the time of supply (5 min), in the center of the room at the level of the abdomen, 13–20% of people were dissatisfied with the draught. However, users did not experience any draught in the area of the feet and head, so there will be no feeling of draught at every level of the body in the workplace location. However, at a distance of 70 cm from the supply/exhaust grate at the level of the abdomen, the air movement was strongly felt and the DR was from 37 to 64%. For this location, there was no feeling of draught at the levels of the feet and head. For a 2-min cycle, the index was 0 for all locations and all body parts, which means that there will be no dissatisfied people with the draft.

In the literature [44], there are efficiency analyses of decentralized devices equipped with two fans. For the efficiency assessment, we used the level of carbon dioxide concentration and radon concentration in the room. The analysis showed that the decentralized devices diluted the gaseous pollutants sufficiently. In the presented case, the measurement of carbon dioxide concentration also showed (Figure 16) that the façade device sufficiently exchanged the air for fresh air.

**Figure 16.** The course of changes in the concentration of carbon dioxide. Blue and purple colors are the 2 min cycle; yellow and green colors are the 10 min cycle; wp: workplace; cp: central.

The results are presented for an example day. There was a visible increase in the concentration of carbon dioxide upon entering the user's room. At the same time, with a longer supply/exhaust time (10 min), the maximum value of the carbon dioxide concentration was lower than for the short cycle (2 min). For each cycle length, throughout the entire period of measurements, the concentration of carbon dioxide did not exceed the value of 800 ppm, which means that the room met the ASHRAE [45] requirements for air quality in offices.

#### *3.2. Statistical Analysis*

Measured data were used to carry out the statistical analysis of the unit operation. The two-factor ANOVA was carried out for the *temperature* characteristic. The grouping variables were: *setting* with values of 2, 4, and 10 min, and *location* with values: wp (workplace) and cp (central point).

The zero hypotheses stating equality of the average values of the *temperature* characteristic was verified on the basis of all combinations of levels for both equivalent factors and the F statistic was used for this purpose (the ratio of intergroup variance to intragroup variance). Table 5 contains the results of completed calculations used to verify the hypothesis stating equality of the average values of the *temperature* characteristic in groups determined on the basis of both factors.


**Table 5.** Analysis of variance for the *temperature* characteristic.

A value *p* obtained for statistic F in a completed test of less than 0.0001 allows for the statement that there were at least two groups where the average values of the *temperature* characteristic differed.

Figure 17 demonstrates in box plots the significance of the effect of the interactions between the factors. The distribution of the *temperature* characteristic in groups defined by *setting* and *location* factors is illustrated in this way.

Figure 18 shows box plots illustrating the distribution of the *temperature* characteristic in groups defined by the *setting* factor levels.

**Figure 18.** Box plots illustrating the distribution of the *temperature* characteristic in groups defined by *setting* factor levels; Tint—temperature, ◦C.

*Atmosphere* **2020**, *11*, 800

Figure 19 shows box plots illustrating the distribution of the *temperature* characteristic in groups defined by *location* factor levels. A statistically significant main effect was observed both for *setting* and for *location*. Thus, it is well-grounded to apply the Tukey multiple comparison method.

**Figure 19.** Box plots illustrating the distribution of the *temperature* characteristic in groups defined by *location* factor levels; Tint—temperature, ◦C; wp: workplace; cp: central point.

Table 6 contains the calculation results for the *temperature* characteristic, carried out according to the Tukey method in groups matching the levels of 2, 4, and 10 min of the *setting* factor.

**Table 6.** The Tukey multiple comparison method tests for the *temperature* characteristic in groups defined on the basis of the *setting* factor levels. No. of Average Values: 3. Least significant difference: 0.12.


Table 6 shows that the highest average *temperature* value should be expected for the 2 min *setting* and the lowest for the 10 min setting.

The data in Table 7 confirm the conclusions derived from Table 6. None of the achieved 95-percent confidence intervals included zero, which means that the differences between average temperature values for each of the pairs were statistically significant. There is a possibility of the quantitative determination of the differences between average temperature values using 95-percent confidence intervals. For example, for the difference in average temperature values in groups matching the 4 min setting and 10 min setting, the extremes were 0.02 and 0.3. Each value within the interval with specified extremes was treated equally as a potential true value of the analyzed difference. Thus, it should be accepted that an average temperature for the 4 min setting may exceed the average temperature for the 2 min setting by either 0.02 or 0.3.

Table 8 contains the calculation results for the *temperature* characteristic, carried out according to the Tukey method in groups matching the following levels: wp (workplace) and cp (central point) of the *location* factor.


**Table 7.** Simultaneous 95-percent confidence intervals obtained using the Tukey method for the difference in avg. values of *temperature* in groups matching *setting* levels.

**Table 8.** The Tukey multiple comparison method tests for the *temperature* characteristic in groups defined on the basis of the *location* factor levels. No. of Average Values: 2. Least significant difference: 0.096.


Table 6 shows that the average values of the *temperature* characteristic in the group defined by workplace *location* were significantly higher than those corresponding to the central point location.

The data in Table 9 confirmed the conclusions derived from Table 8. None of the achieved 95-percent confidence intervals included zero, which means that the differences between the average temperature values for each of the pairs were statistically significant. The data allowed for the quantitative determination of the differences between the average temperature values by way of implementing 95-percent confidence intervals. For example, interval extremes for the difference in average temperature values in groups defined by workplace and central point location were 0.6 and 0.8, respectively. Each value within the interval with specified extremes was treated equally as a potential true value of the analyzed difference. Thus, it should be accepted that an average temperature for workplace location may exceed the average temperature for the central point location by either 0.6 or 0.8.

**Table 9.** Simultaneous 95-percent confidence intervals obtained using the Tukey method for difference in avg. values of *temperature* in groups matching *location* levels.


The next step involved carrying out the two-factor ANOVA for the *temperature* characteristic with the following grouping variables: *setting* with values of 2, 4, and 10 min and *outside temperature* with values of −7 ◦C and −3 ◦C.

The zero hypothesis stating equality of the average values of the *temperature* characteristic was verified on the basis of all combinations of levels for both equivalent factors. The F statistic was used for this purpose. Table 10 contains the results of the completed calculations used to verify the hypothesis stating the equality of average values of the *temperature* characteristic in groups determined on the basis of both factors.


**Table 10.** Analysis of variance for the *temperature* characteristic.

− −

A value *p* obtained for statistic F in the completed test of greater than 0.0001 allows to state that the average values of the *temperature* characteristic did not differ.

− − −

Figure 20 demonstrates in box plots the significance of the effect of the interactions between the factors. The distribution of the *temperature* characteristic in groups defined by *setting* and *outside temperature* factors is illustrated in this way.

**Figure 20.** Box plots illustrating the distribution of the *temperature* characteristic in groups defined by pairs of factors: *setting* and *outside temperature*; Tint—inside temperature, ◦C; Tout—outside temperature, ◦C.

Figure 21 shows box plots illustrating the distribution of the *temperature* characteristic in groups defined by the *outside temperature* factor levels.

**Figure 21.** Box plots illustrating the distribution of the *temperature* characteristic in groups defined by *outside temperature* factor levels; Tint—temperature, ◦C; Tout—outside temperature, ◦<sup>C</sup>

− − Table 11 contains the calculation results for the *temperature* characteristic, carried out according to the Tukey method in groups matching the levels of −7 ◦C and −3 ◦C of the *outside temperature* factor.

**Table 11.** The Tukey multiple comparison method tests for the *temperature* characteristic in groups defined on the basis of the *outside temperature* factor levels. No. of Average Values: 2. Least significant difference: 0.3.


\* Average values marked with the same letter do not differ significantly.

− − − − − − − −

Table 11 shows that the average temperature values did not differ significantly.

The two-factor ANOVA for the *humidity* characteristic was carried out in the same way. The grouping variables were: *outside temperature* with the values of −10.5, −10, −9.5, −9, −8.5, −8, −7.5, −7, −6.5, −6, −5.5, −5, −4.5, −4, −3.5, −3, −2.5, −2, −1.5, −1, −0.5, 0, 0.5, 1, 1.5, 2, 2.5, and 3 ◦C and *location* with the values wp (workplace) and cp (central point).

The zero hypothesis stating equality of the average values of the *humidity* characteristic was verified on the basis of all combinations of levels for both equivalent factors. The F statistic was used for this purpose (the ratio of intergroup variance to intragroup variance). Table 12 contains the results of the completed calculations used to verify the hypothesis stating equality of the average values of the *humidity* characteristic in groups determined on the basis of both factors.


**Table 12.** Analysis of variance for the humidity characteristic.

A value *p* obtained for statistic F in the completed test of less than 0.0001 allows for the statement that there were at least two groups where the average values of the *humidity* characteristic differed.

Figure 22 demonstrates in box plots the significance of the effect of the interactions between the factors. The distribution of the *humidity* characteristic in groups defined by *outside temperature* and *location* factors is illustrated in this way.

**Figure 22.** Box plots illustrating the distribution of the *humidity* characteristic in groups defined by pairs of factors: *outside temperature* and *location*; RHin—humidity, %; Tout—outside temperature, ◦C; wp: workplace; cp: central point.

Figure 23 shows box plots illustrating the distribution of the *humidity* characteristic in groups defined by the *outside temperature* factor levels.

− − − − − − − − − − − − − − − − − − − − − **Figure 23.** Box plots illustrating the distribution of the *humidity* characteristic in groups defined by *outside temperature* factor levels; RHin—humidity, %; Tout—outside temperature, ◦C.

−

−

Table 13 contains the values of the Least Significant Difference LSD and test statistic W for the *humidity* characteristic, carried out according to the Tukey method in groups matching the levels of −10.5, −10, −9.5, −9, −8.5, −8, −7.5, −7, −6.5, −6, −5.5, −5, −4.5, −4, −3.5, −3, −2.5, −2, −1.5, −1, −0.5, 0, 0.5, 1, 1.5, 2, 2.5, and 3 ◦C of the *outside temperature* factor.

**Table 13.** The Tukey multiple comparison method tests for the *humidity* characteristic in groups defined on the basis of the *outside temperature* factor levels.


The tests of multiple comparisons carried out using the Tukey method for the *humidity* characteristic in groups defined by the outside temperature showed that the highest average humidity value should be expected for the outside temperature of 3 ◦C, and the lowest for the outside temperature of −10.5 ◦C.

Figure 24 shows box plots illustrating the distribution of the *humidity* characteristic in groups defined by the *location* factor levels.

**Figure 24.** Box plots illustrating the distribution of the *humidity* characteristic in groups defined by the *location* factor levels; RHin—humidity, %; wp: workplace; cp: central point.

Table 14 contains the calculation results for the *humidity* characteristic, carried out according to the Tukey method in groups matching the workplace and central point levels of the *location* factor.

**Table 14.** The Tukey multiple comparison method tests for the *humidity* characteristic in groups defined on the basis of the *location* factor levels. No. of Average Values: 2. Least significant difference: 0.08.


Table 14 shows that the average values of the *humidity* characteristic in the group defined by the workplace *location* were significantly higher than those corresponding to the central point *location*.

#### **4. Conclusions**

The interior microclimate is extremely important for the health and well-being of people staying in rooms. Sick building syndrome is a current problem that many building administrators and users must cope with, and most often results from an insufficient air exchange, occurring as a consequence of the excessive sealing of buildings. By and large, a great majority of existing buildings are ventilated naturally. Thermal modernization works are usually limited to the thermal insulation of building cladding and providing airtight window joinery. In most cases, there is no possibility of installing mechanical ventilation systems. In these cases, decentralized façade units may be the right solution for occupants to improve the indoor microclimate. Completed analysis of the patented solution has proven that, in spite of the lack of heat recovery exchanger and air heater, the unit does not reduce the inside air temperature below the comfort level while replacing used air with fresh air. Throughout the period of measurements, temperature values ranged within 20–22 ◦C. Moreover, the PMV value calculated on the basis of measurements showed that in the workplace, category B was maintained in the area of the head, abdomen, and feet, and the central point of the room was in category C at the end of the cycle (2 min). In the long cycle (10 min), it belonged to category C for almost the entire duration of the airflow (at all parts of the body). However, it should be mentioned that there is a risk of a local sensation of discomfort (draught) in the case where a user stands in the axis of the air stream and the air supply/exhaust cycle is long. In this case, the DR index at a distance of 70 cm from the supply/exhaust grate at the level of abdomen may be as high as 64%. It is recommended to use heat recovery from exhaust air and possibly an electric heater to warm up the air in order to eliminate the risk of negative air movement impact and the sensation of discomfort. Further studies will focus on the search for an optimal way to recover heat for decentralized ventilation units.

Air humidity analysis has proven that the value of this parameter value was too low and ranged within 27 to 43%. This indicates the need to find a way to humidify air in decentralized façade units.

The analyses of both temperature and humidity have proven that the values of inside air temperature and humidity are not affected by the temperature and humidity of outside air. In this case, it is important that negative pressure generated during the exhaust cycle induces an inflow of warm and dry air from an adjacent room.

The impact of using the decentralized façade unit on inside air parameters was analyzed for each of the three durations of air supply/exhaust cycle (2 min, 4 min, 10 min). In each of these cases, no temperature drop in the room was observed, and the air humidity was too low. Research results obtained during the experiment were evaluated from the statistical point of view. Completed statistical analysis proved that the average temperature values did not differ significantly for the outside temperature factor. On the other hand, in the case of the air supply/exhaust cycle duration setting, the average inside temperature was the highest for the shortest cycle and the lowest for the longest cycle.

In conclusion, it is necessary to carry out further tests of the decentralized façade units that would be used as an efficient way to improve the interior microclimate. However, it is necessary to find methods for heat recovery and air humidification.

#### **5. Patents**

The article presents the test results of patent-protected equipment: Zender-Swiercz E., Piotrowski ´ J. *Room ventilation unit* (2017). Patent no. PL 228624 B1.

**Funding:** This research was funded by the program of the Minister of Science and Higher Education under the name: "Regional Initiative of Excellence" in 2019–2022 project number 025/RID/2018/19, financing amount PLN 12,000,000.

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**


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