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Asian Journal of Atmospheric Environment - Vol. 16 , No. 2

[ Research Article ]
Asian Journal of Atmospheric Environment - Vol. 15, No. 3
Abbreviation: Asian J. Atmos. Environ
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 30 Sep 2021
Received 26 Apr 2021 Revised 17 Jun 2021 Accepted 30 Jul 2021

Real-time Monitoring of Bioaerosol in a Residential Property in Central Tokyo
Nobuyuki Tanaka*
Environmental Science Research Laboratory, Central Research Institute of Electric Power Industry (CRIEPI), 1646 Abiko, Abiko-shi, Chiba 270-1194, Japan

Correspondence to : *Tel: +81-4-7182-1181 E-mail:

Copyright © 2021 by Asian Association for Atmospheric Environment
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.


Real-time onsite monitoring of indoor airborne microbes in a residential property in central Tokyo was carried out in 2020 and 2021, following the onset of the COVID-19 pandemic. A microbial sensor utilizing fluorescence emitted by microorganisms was used to measure bioaerosol concentrations in the living room and children’s bedroom as well as on the balcony. Indoor PM2.5 was also monitored simultaneously at certain time points using a PM2.5 sensor. The behavior of the residents was also recorded during some monitoring periods. The average number concentration of microbes as fungi in the living room was 15,100, 58,800, and 10,600 counts m-3 in spring, summer, and winter, respectively, increasing in summer when the outside temperature was high. Microbial number concentrations were closely related to human behavior, increasing rapidly during periods of physical activity, but decreasing again within 20-30 min of the activity ending. There was no clear correlation between indoor microbial number concentrations and PM2.5 concentrations, suggesting that indoor microorganisms are concentrated in coarse particles, such as dust, which are quickly removed via gravitational settling. The concentration of indoor airborne microorganisms decreased significantly after ventilation, and although an occasional increase was observed immediately after ventilation, concentrations decreased again rapidly within 10-20 min. These results suggest that even a short period of ventilation can significantly reduce the indoor bioaerosol.

Keywords: Bioaerosol, Indoor air pollution, Human behavior, Real-time monitoring, Ventilation


Japanese spend more than 80% of their day indoors (Shiotsu et al., 1998), and the time spent at home is thought to have increased because of the COVID-19 pandemic, with the implementation of lockdown measures and increase in telecommuting. Maintaining a healthy indoor air environment has therefore become even more important.

Indoor air contains a number of chemical components, such as CO2, NOx, and volatile organic compounds (VOCs), as well as bioaerosols, such as fungi and bacteria (e.g., Salthammer, 2020; Salonen et al., 2019; Wolkoff, 2018; Wolkoff and Nielsen, 2017). Bioaerosols in indoor air are a major component of total indoor aerosols, in some case accounting for almost 50% (Jaenicke, 2005). A number of reports on bioaerosols in indoor air have already been published. For example, Hospodsky et al. (2012) studied the particle size distribution of bioaerosols in a university classroom and found that bacteria were more concentrated in PM10 than PM2.5. Meanwhile, Mirhoseini et al. (2016) reported a positive correlation between microbial and PM1 concentrations in indoor air in several different types of building, including offices, laboratories, and apartments. These findings suggest that microorganisms are distributed over a range of particle sizes.

Bioaerosols can enter the human body and cause infectious diseases, as well as increasing health risks by emitting microbial VOCs, a form of metabolite, into the room. Hospodsky et al. (2012) previously reported that airborne bacteria and fungi in indoor environments are linked to the development and exacerbation of chronic respiratory illnesses, such as asthma. Meanwhile, Yen et al. (2020) revealed a correlation between the amount of indoor fungi and endotoxin exposure and the prevalence of allergies and asthma in children. Understanding the status of indoor microbial contamination is therefore important in reducing the human health risks associated with exposure to microbes.

In most residential properties, a major source of microorganisms in indoor air is human activities (Mandal and Brandl, 2011; Stetzenbach, 1997). Talking, sneezing, coughing, walking, washing, and using the bathroom all generate bioaerosols (Mandal and Brandl, 2011), as do activities such as vacuuming and cleaning (Veillette et al., 2013). Indoor microbial concentrations therefore tend to be higher when people are present (e.g., Hospodsky et al., 2012), although food, pets, carpets, and indoor plants are also sources of bioaerosols (Mandal and Brandl, 2011; Kalogerakis et al., 2005; Cox and Wathes, 1995).

Outdoor air is also an important source of indoor microorganisms. Zhou et al. (2020) studied the sources of indoor airborne microorganisms in eight commercial buildings in Hong Kong, and found that outdoor air and occupants’ skin were the main contributors. Moreover, comparisons of indoor emissions (typically from occupancy and resuspension) and ventilation (outside air infiltration) as sources of airborne bacteria and fungi in a typical house in Oklahoma, USA, revealed that indoor emissions were the main source of particulate matter, bacteria, and fungi, accounting for about 94% (PM10), 97%, and 91% of indoor air levels, respectively (Kwan et al., 2020). Thus, the sources of indoor airborne microorganisms vary greatly depending on the location and the building type, with the concentration and composition affected by factors including indoor population, behavior, and ventilation. The above findings were mainly obtained by collecting indoor bioaerosols and identifying the species and amount of microorganisms through cultivation or polymerase chain reaction (PCR) measurement, and the temporal resolution was not high. If the indoor bioaerosols can be continuously monitored with high temporal resolution, further knowledge about indoor bioaerosols may be obtained.

Microbial sensors able to easily and automatically measure microbial concentrations in the air without pretreatment were recently developed (Weber et al., 2019; Tominaga et al., 2017). By utilizing fluorescence emitted by the microorganisms themselves, these sensors have potential in real-time onsite measurements of microorganism concentrations in the air at a temporal resolution of approximately 10 min. In this technique, microorganisms are detected by monitoring intracellular fluorescent molecules such as riboflavin, NADH, and tryptophan (Tominaga et al., 2017). Although these sensors cannot identify the species or whether they are alive, and have the possibility of detecting fluorescence of abiotic substances (e.g., polycyclic aromatic hydrocarbons: PAHs, soot; Huffman et al., 2010), the approach is considered valuable from the viewpoint of screening for microorganisms in the air. In this study, we therefore preliminarily monitored indoor bioaerosols continuously using a recently developed microbial sensor with high temporal resolution, and evaluated the effects of indoor population, behavior, and ventilation on indoor bioaerosol concentrations.


Indoor air monitoring was carried out in the living room and children’s bedroom in a single-family dwelling with four family members in central Tokyo. The outline of the rooms is shown in Fig. 1. The living room was located on the second floor of the three-story building, with the south side facing a balcony. Two ventilation ports (naturally aspirated) installed on the walls of the balcony side were open throughout. The children’s bedroom (bedroom number 1 in Fig. 1) was located to the east of the living room, with a window on the east side of the room. A ventilation port (naturally aspirated) installed on the east wall was kept open.

Fig. 1. 
Outline of the house and rooms used for monitoring. A, B and C represent the monitoring points.

Indoor air monitoring was carried out in spring (17-26 April) and summer (17-19 August) of 2020 and in winter (1-4 February) of 2021. These periods were after the COVID-19 had spread in Japan, and thus the number of people staying at home was deemed higher than normal owing to lockdown measures and government recommendations to work from home. A microbe fluorescence biosensor (BM-300C, Sharp) was installed on the south side of the living room (point A in Fig. 1) or in the children’s bedroom (point C in Fig. 1) at a height of 100 cm from the floor. The sensor selectively draws particles below 10 μm diameter by means of a cyclone. The sensor relies on the microbes emitting a characteristic fluorescence when illuminated by radiation at a certain frequency. However, this method is unable to identify the species or distinguish between fungi and bacteria. In addition, the method is not applicable to viruses, and it is not possible to ascertain whether the microbes are alive. The measurement value therefore represents the concentration of all microbes (fungi and bacteria). This instrument measures the fluorescent intensity, which is converted into fungal and bacterial equivalent number concentrations. In this study, we use the concentrations as fungi. The concentration as bacteria is calculated 2.75 times the concentration as fungi in this system. The sensor rapidly and automatically measures the amount of airborne microbes, including bacterial microbes and mold spores, in a fixed volume of air over a specified period of time. Hence, it is well suited to analyses of the temporal variation in airborne microbial concentrations in indoor air. The amount of microbial biomass in the air was monitored at 10-min intervals. During summer and winter monitoring, the PM2.5 concentration was also measured simultaneously at 1-min intervals using a PM2.5 sensor (P-sensor, Industrial Hygiene Device Calibration, Inc.) placed close to the microbe sensor. Temperature and relative humidity (RH) in the living room were also recorded by a household thermo-hygrometer at 1-hour intervals during the observation term of residents’ behavior in spring. In summer and winter, temperature and RH in the living room were monitored at 5-min intervals using a temperature and humidity sensor (2JCIE-BU01, OMRON).

During some observations, the behavior of the residents in each room, the usage of air conditioning equipment, and opening and closing of windows and external glass doors were also recorded. In addition, microbial number concentrations on the balcony (point B in Fig. 1) were also measured in each season at intervals of 10 min. Microbial number concentrations after opening and closing the windows and cleaning the children’s bedroom were also measured at 10-min intervals.

3. 1 Temperature and RH in the Living Room and Number Concentrations of Indoor Airborne Microbes

The average temperature in the living room in spring, summer and winter were 20(±1)°C, 25.4(±0.8)°C, and 20.5(±1.2)°C, respectively. The average RH in the living room in spring, summer and winter were 50(±3)%, 65(±3)%, and 53(±4)%, respectively. There was little variation in the room temperature and RH within the same season. Both temperature and RH were higher in summer, while they were about the same level in spring and winter.

Concentrations of bioaerosol (as fungi, same as below) in the living room in each season are shown in Table 1. The data were obtained during a period of continuous measurement for 12 h or more using the fixed microbial sensor. Data obtained during periods of time when the sensor was moved were excluded. During the data collection period, the air conditioner in the living room was always operated except late at night in summer and winter, while it was not operated in spring. The glass doors, windows and doors in the living room were always closed except for a very short time when people were entering or leaving, according to the behavioral records of the residents. The average concentrations of bioaerosol in the living room were 15,100, 58,800, and 10,600 counts m-3 in the spring, summer, and winter, respectively, and tended to be higher in the summer when inside and outside temperatures were also high. In fact, the temperature in the living room during the summer season was about 5°C higher than in the spring and winter seasons. This was consistent with microbial activity, which tended to increase in warmer weather. However, there was no significant difference in maximum concentrations between summer and winter. Microorganisms in indoor air are also generated by textiles such as rugs, duvets, and clothes (Mandal and Brandl, 2011), all of which are habitats for microorganisms; thus, even in winter, the concentration of indoor microorganisms can temporarily increase with increased movement of residents and air currents.

Table 1. 
Concentrations of airborne microbes as fungi in the living room in each season (counts m-3).
Spring Summer Winter
Total I1) II1) III1) Total I1) II1) III1) Total I1) II1) III1)
Average 15,100 26,600 11,900 5,330 90,300 160,900 54,700 17,900 10,600 30,600 2,030 994
Max 176,000 176,000 94,200 107,000 390,000 390,000 119,000 24,300 319,000 319,000 77,100 25,300
Min ND1) ND1) ND1) ND1) 13,400 48,300 22,000 13,400 ND1) ND1) ND1) ND1)
SD3) 17,700 22,700 12,900 9,820 78,400 69,400 28,600 2,790 35,200 57,300 8,540 3,880
1)Behavior categories: I, someone in the room and involved in a physical activity; II, someone in the room but not involved in physical activity; and III, no one in the room
2)Not detected
3)Standard deviation

Table 1 also shows movement of the residents within the living room and the effect of different behaviors on indoor air concentrations of bioaerosol. Behavior categories I, II, and III represent the following: someone in the room and involved in physical activity (e.g., cooking, cleaning, walking, or folding laundry), someone in the room but not involved in a physical activity (e.g., reading or resting on the couch), and no one in the room, respectively. Airborne microbial number concentrations in the living room were highest during category I in all seasons, followed by categories II and III. These findings suggest that indoor microbes increase following periods of physical activity. In addition, the microbial number concentration increased notably while tidying away futons and folding laundry, suggesting that microorganisms on bedding and clothing have a significant impact on indoor microbial number concentrations. These results are consistent with previous studies showing that indoor bioaerosols are generated from factors including human activities, food preparation, the presence of pets, and carpets (Hospodsky et al., 2012; Mandal and Brandl, 2011).

3. 2 Effect of Population on Concentrations of Airborne Microbial Biomass

Fig. 2 shows the relationship between the number of people in the living room and the concentration of bioaesorol across four days in spring 2020. A positive correlation was observed between the two factors (r2=0.30), with an increase in airborne microbes with an increasing number of people in the room. These findings confirm the aforementioned results whereby physical movement caused an increase in microbial number concentrations (Table 1). In other words, indoor microbial number concentrations can be roughly expressed as a function of the indoor population. Because of the COVID-19 pandemic at the time of this experiment, more time was being spent indoors than average, and therefore, the concentrations of airborne microorganisms observed were thought to have been higher than those before the pandemic.

Fig. 2. 
Relationship between the average number of people in the living room and the number concentration of airborne microbes.

3. 3 Effect of Human Behaviors on Number Concentrations of Airborne Microbes in the Living Room

Fig. 3 shows the relationship between airborne microbial number concentrations over time and behavior across four days in spring 2020. The microbial number concentration was low from midnight to dawn when the room was empty, and was intermittently high at all other times, in line with periods of activity. Behaviors corresponding to high microbial number concentrations included physical activities such as eating, taking in and folding laundry and futons, exercising on the rug, and cleaning (Category I, see Table 1 and section 3. 1). Although the microbial number concentration in the indoor air increased sharply with each of these behaviors, values tended to decrease sharply about 10 to 20 min after each activity ended. The indoor concentration of microbes therefore fluctuated rapidly within a short period of time depending on the behavior.

Fig. 3. 
Temporal changes in airborne microbes in the living room across four days in spring 2020. A key to each behavior type is shown below each figure.

Even with similar behavior, some variation in microbial number concentrations was observed. For example, the maximum microbial number concentration at breakfast was about 60,000 counts m-3 on April 22, 23, and 25, while the maximum was about 40,000 counts m-3 on April 24. The maximum microbial number concentration during lunch was about 60,000 counts m-3 on April 23, while the maximum concentration was about 40,000 counts m-3 on April 22, 24, and 25. Furthermore, microbial number concentrations was about 80,000-90,000 counts m-3 during dinner on April 22 and 23, 2020, but was about 20,000 counts m-3 during dinner on April 24. In contrast, all of the breakfasts on these four days were Japanese meals (rice, miso soup, and unheated side dishes such as natto and seaweed) with no grilled cooking. Lunch consisted mainly of noodles such as ramen and soba noodles, and no grilled cooking was also done. Dinner, on the other hand, consisted of grilled meat on April 22 and 23, and no grilled food on April 24 and 25.

Here, grilled cooking of meat can generate PAHs (e.g. Saito et al., 2014; Tanaka et al., 2012), which can affect the measurements of the microbial sensor because PAHs emit fluorescence (Huffman et al., 2010). As already mentioned, Fig. 3 shows that the microbial number concentration tended to increase during meals regardless of the cooking method. On the other hand, the microbial number concentration was slightly higher during the grilled cooking of meat (dinner on April 22 and 23) when PAHs tends to be generated. This suggested that PAHs emitted by cooking may affect the measured values of the microbial sensor, but the effect was not considered to be significant. In other words, the signal values of the microbial sensor in this study were considered to be mostly derived from the fluorescence of the microorganisms.

Moreover, the high concentrations of airborne microbes observed while handling laundry and bedding was likely due to the dispersal of microorganisms on each item, in addition to those originating from the residents themselves.

3. 4 Relationship between Airborne Microbes and PM2.5

Fig. 4 shows the results of simultaneous measurement of airborne microbes and PM2.5 concentrations in the living room during the summer. In general, the temporal changes in microbial number concentrations and PM2.5 concentrations tended to differ (Fig. 4A, C, and D); however, similar trends were observed during some time periods (Fig. 4B). The relationship between the microbial number concentration and PM2.5 concentration (10-min average) during the time periods corresponding to (A) through (D) in Fig. 4 is shown in Fig. 5. The correlation coefficient (r2) between the microbial number concentration and PM2.5 concentration during the time period shown in Fig. 4B was 0.77, while the r2 values during the other time periods ranged from 0.088 to 0.27. These results suggest that while airborne microorganisms tend not to be concentrated in small particles of less than 2.5 μm in size, it is possible. In line with this, Hospodsky et al. (2012) measured the concentration of microorganisms in PM2.5 and PM10 in a university classroom and found high concentrations in the latter. As shown in Fig. 3, the airborne microbial number concentration temporarily increased with physical activity of the residents, although levels tended to decrease rapidly within a short period of time of 10-20 min after the activity ended. This is consistent with the idea that microorganisms are concentrated in coarse particles, since they are removed rapidly from the air via gravitational settling. On the other hand, Mirhoseini et al. (2016) reported a modest positive correlation between PM1 concentrations and microbial concentrations in schools, offices, and residential properties. The reason for these differences is unknown, but in case most of the coarse particles in the indoor air are removed, the correlation between the concentration of PM2.5 and the concentration of microbial may be high. In fact, in Fig. 4B, where a high correlation between the microbial number concentration and the PM2.5 concentration was observed, the coarse particles may have been removed from the indoor air via gravitational settling because no one was in the living room after 12 : 30. As a result, most of the microbes may have been concentrated into PM2.5. In addition, they may be due to differences in the indoor particle concentration and size distribution. Frequent cleaning and the use of air purifiers are thought to remove most coarse particles, such as dust, from indoor air, while less frequent cleaning and the absence of air purifiers are thought to increase the presence of coarse particles. Thus, these differences may also affect the particle size distribution of microorganisms.

Fig. 4. 
Temporal variation in airborne microbes and PM2.5 concentrations in the living room.

Fig. 5. 
Relationship between microbial number concentration and PM2.5 in the living room. (A) to (D) correspond to (A) to (D) in Fig. 4, respectively.

3. 5 Effect of Ventilation on Microbial Number Concentrations in Indoor Air

Airborne microbial number concentrations in the living room (point A in Fig. 1) and on the balcony (point B in Fig. 1) in each season are shown in Table 2. Since only one microbe sensor was available, concentrations were measured alternately between the two locations at 10-min intervals. Microbial number concentrations in both the living room and balcony were highest in the summer and lowest in the winter. In spring and winter, the microbial number concentration was lower on the balcony than in the living room, while in summer, concentrations were slightly higher on the balcony than the living room. This result may reflect the fact that in spring and winter, the indoor temperature was higher than the outdoor temperature, thereby increasing the number of microorganisms. In contrast, in summer, the living room was air-conditioned and the temperature was lower than the outdoor temperature, thereby inhibiting microbial growth. Here, it is considered that the microbial sensor used in this study also can detect fluorescence emitted by pollen. Since the amount of pollen derived from flowers such as Japanese cedar and Japanese cypress is high in the spring in Japan, the signal value of the sensor may be high due to fluorescence by pollen. In general, pollen concentration is much higher outdoor than indoor (Hugg and Rantio-Lehtimäki, 2007; Ohashi et al., 2005; Kiyosawa and Yoshizawa, 2001), so if the microbial sensor detected fluorescence from pollen, the signal would be higher outdoor than indoor. However, as shown in Table 2, the microbial number concentration indoors was higher than that indoors during spring, indicating that the effect of pollen on the measured values of the microbial sensor is considered to be small. For seasons other than summer, the concentration of indoor microorganisms is thought to be reduced as a result of ventilation, since the concentration of microorganisms outside is lower than that inside.

Table 2. 
Concentration of airborne microbes in the living room and on the balcony (counts m-3).
Spring Summer Winter
Living room Balcony Living room Balcony Living room Balcony
Average 45,900 16,100 85,000 106,000 11,100 1,270
SD1) 19,700 6,620 33,300 17,100 31,800 2,700
1)Standard deviation

The effect of opening and closing the window and external glass doors on the indoor concentration of airborne microbial biomass in the children’s bedroom and living room is shown in Fig. 6. In the children’s bedroom, airborne microbial number concentrations tended to decrease after opening the window, especially in winter (Fig. 6A and B). These results suggest that ventilating the room by opening the window reduced the concentration of indoor airborne microorganisms. Meanwhile, in the living room, airborne microbial number concentrations increased for about 30 min after opening the external glass doors, before decreasing (Fig. 6C). The immediate increase in microbial number concentrations after opening the doors was thought to be due to the fact that microorganisms on the floor, for example on the rug, were disturbed by the influx of outside air. The living room data shown in Fig. 6C were all obtained in the morning, prior to which the room was empty for 8 h. Most of the microorganisms suspended in the air within the living room will therefore have been removed via gravitational settling. Thus, as shown above, when the windows and external glass doors are opened in a room that has been unoccupied for a long time, the microbial number concentration may temporarily increase. In contrast, in the children’s bedroom, the microbial number concentration did not increase after opening the window, possibly because the microorganisms on the floor and other surfaces had already been disturbed as a result of movement within the room, for example while making the beds. Overall, these results suggest that microbial number concentrations in indoor air can be reduced by opening windows or external doors for about 30 min.

Fig. 6. 
Effect of ventilation on the concentration of airborne microbes in the child’s room (A and B) and living room (C).


In order to clarify the factors affecting microbial concentrations in indoor air, we studied the microbial number concentrations and PM2.5 concentrations in indoor air using a microbial sensor and PM2.5 sensor. The behavior of occupants in the room was also recorded and the relationship with indoor microbial number concentrations was analyzed. Indoor microbial number concentrations tended to increase in summer and decrease in winter, in line with the fact that microbial activity was higher in the summer when temperatures were higher. Microbial number concentrations also tended to increase with physical activity of the occupants, and with an increase in the number of occupants in the room, although concentrations decreased again rapidly within 20-30 min of the activity ending. There was no clear correlation between microbial number concentrations and PM2.5 concentrations in the indoor air except for some time period, suggesting that indoor microorganisms tend to be concentrated in coarse particles, which are quickly removed from the air by gravitational settling, even when disturbed by human activities. The concentration of microorganisms in the room also decreased rapidly after 10-20 min of ventilation, suggesting that microorganisms can be removed by only a short period of ventilation.

1. Cox, C.S., Wathes, C.M. (1995) Bioaerosols handbook. New York: Lewis Publishers.
2. Fujioka, K., Yamada, S., Tominaga, T. (2015) Rapid measurement of airborne bacteria by using microbial sensor. Journal of Antibacterial and Antifungal Agents, 43, 555-562, (in Japanese).
3. Hospodsky, D., Qian, J., Nazaroff, W.W., Yamamoto, N., Bibby, K., Rismani-Yazdi, H., Peccia, J. (2012) Human Occupancy as a Source of Indoor Airborne Bacteria. PLos ONE, 7(4), e34867.
4. Huffman, J.A., Treutlein, B., Pöschl, U. (2010) Fluorescent biological aerosol particle concentrations and size distributions measured with an Ultraviolet Aerodynamic Particle Sizer (UV-APS) in Central Europe. Atmospheric Chemistry and Physics, 10, 3215-3233.
5. Hugg, T., Rantio-Lehtimäki, A. (2007) Indoor and outdoor pollen concentrations in private and public spaces during the Betula pollen season. Aerobiologia, 23, 119-129.
6. Jaenicke, R. (2005) Abundance of cellular material and proteins in the atmosphere. Science, 308, 73.
7. Kalogerakis, N., Paschali, D., Lekaditis, V., Pantidou, A., Eleftheriadis, K., Lazaridis, M. (2005) Indoor air quality - bioaerosol measurements in domestic and office premises. Journal of Aerosol Science, 36, 751-761.
8. Kiyosawa, H., Yoshizawa, S. (2001) Intrusion of airborne pollen into indoor environment and exposure dose. Journal of Architecture Planning and Environmental Engineering, AIJ, 548, 63-68, (in Japanese).
9. Kwan, S.E., Shaughnessy, R., Haverinen-Shaughnessy, U., Kwan, T.A., Peccia, J. (2020) The impact of ventilation rate on the fungal and bacterial ecology of home indoor air. Building and Environment, 177, 106800.
10. Mandal, J., Brandl, H. (2011) Bioaerosols in Indoor Environment - A Review with Special Reference to Residential and Occupational Locations. The Open Environmental & Biological Monitoring Journal, 4, 83-96.
11. Mirhoseini, S.H., Nikaeen, M., Satoh, K., Makimura, K. (2016) Assessment of Airborne Particles in Indoor Environments: Applicability of Particle Counting for Prediction of Bioaerosol Concentrations. Aerosol and Air Quality Research, 16, 1903-1910.
12. Ohashi, E., Yoshida, S., Ooka, R., Miyazawa, H. (2005) Indoor concentration of aiaborne ceder pollen and allergen (Cry j1). Journal of Environmental Engineering, AIJ, 594, 39-43, (in Japanese).
13. Saito, E., Tanaka, N., Miyazaki, A., Tsuzaki, M. (2014) Concentration and particle size distribution of polycyclic aromatic hydrocarbons formed by thermal cooking. Food Chemistry, 153, 285-291.
14. Salonen, H., Salthammer, T., Morawska, L. (2019) Human exposure to NO2 in school and office indoor environments. Environment International, 130, 104887.
15. Salthammer, T. (2020) Emerging indoor pollutants. International Journal of Hygiene and Environmental Health, 224, 113423.
16. Shiotsu, M., Yoshizawa, S., Ikeda, K., Nozaki, A. (1998) Survey on human activity patterns according to time and place - basic research on the exposure dose to indoor air pollutants Part 1-. Journal of Architecture Planning and Environmental Engineering, 511, 45-52.
17. Stetzenbach, L.D. (1997) Introduction to aerobiology. In Manual of Environmental Microbiology. Washington DC: ASM Press, pp. 619-628.
18. Tanaka, N., Ohtake, K., Tsuzaki, M., Miyazaki, A. (2012) Analysis of polycyclic aromatic hydrocarbons in oil-mist emitted from food grilling. Bunseki Kagaku, 61, 77-86, (in Japanese).
19. Tominaga, T., Fujioka, K., Hijikuro, M., Tada, T., Nishikawa, Y. (2017) Mechanism of increased microbial autofluorescence by heat treatment. Letters in Applied Microbiology, 66, 118-123.
20. Veillette, M., Knibbs, L.D., Pelletier, A., Charlebois, R., Lecours, P.B., He, C., Morawska, L., Duchainea, C. (2013) Microbial Contents of Vacuum Cleaner Bag Dust and Emitted Bioaerosols and Their Implications for Human Exposure Indoors. Applied and Environmental Microbiology, 79(20), 6331-6336.
21. Weber, J., Hauschild, J., Ijzerman-Boon, P., Forng, R., Horsch, J., Yan, L., Prasad, A., Henry, R., Claassen, M., Villari, P., Shereefa, S., Wyatt, J., Bolden, J.S., Pycke, J., Dassu, D. (2019) Continuous microbiological environmental monitoring for process understanding and reduced interventions in aseptic manufacturing. PDA Journal of Pharmaceutical Science and Technology, 73, 121-134.
22. Wolkoff, P., Nielsen, G.D. (2017) Effects by inhalation of abundant fragrances in indoor air - An overview. Environment International, 101, 96-107.
23. Wolkoff, P. (2018) Indoor air humidity, air quality, and health - An overview. International Journal of Hygiene and Environmental Health, 221, 376-390.
24. Yen, Y.-C., Yang, C.-Y., Wang, T.-N., Yen, P.-C., Ho, C.-K., Mena, K.D. (2020) Household airborne endotoxin associated with asthma and allergy in elementary school-age children: a case - control study in Kaohsiung, Taiwan. Environmental Science and Pollution Research, 27, 19502-19509.
25. Zhou, Y., Lai, Y., Tong, X., Leung, M.H.Y., Tong, J.C.K., Ridley, I.A., Lee, P.K.H. (2020) Airborne Bacteria in Outdoor Air and Air of Mechanically Ventilated Buildings at City Scale in Hong Kong across Seasons. Environmental Science and Technology, 54, 11732-11743.