Personal exposure to indoor aerosols as actual concern: Perceived indoor and outdoor air quality, and health performances

Building and Environment 165 (2019) 106403

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Personal exposure to indoor aerosols as actual concern: Perceived indoor and outdoor air quality, and health performances

T

Hyeon-Ju Oha,b,1,∗, Na-Na Jeonga, Jong-Ryeul Sohna, Jongbok Kimb,∗∗ a b

Department of Public Health Sciences, Korea University, Seoul, 02841, South Korea Department of Materials Science and Engineering, Kumoh National Institute of Technology, 61 Daehak-ro (yangho-dong), Gumi, Gyeongbuk, 39177, South Korea

A R T I C LE I N FO

A B S T R A C T

Keywords: Indoor environmental quality Human perception Thermal comfort PM concentration Potential source Health performance

Indoor environmental quality (IEQ) is known to affect personal health performances. A respiratory illness symptom can be an indicator of health performance that is related to the personal exposure concern and the IEQ. However, little is known about the effect of personal exposure to either indoor or outdoor sources on the human perception of the IEQ. Here, we conducted a study to evaluate the association of human perception and health performance to exposure assessments by analyzing questionnaire survey obtained from 396 students and 64 parents. To test exposure conditions at different scenarios, schools and homes located in rural and urban areas were selected. We assessed the IEQ by measuring temperature, relative humidity, particulate matters, and CO2 level. Also, the effect of potential pollutant factors on the IEQ was evaluated at three different cases: vacuum cleaning, cooking, and air purifier operation. From the IEQ measurement, there was no difference in PM10 and airborne bacteria concentrations between urban and rural areas of both schools and homes. But, PM2.5 showed significant difference between the areas. CO2 levels in schools were correlated to the number of students. This study shows that health performance was strongly associated with people's perception of outdoor environmental quality. We found that perception can be considered a predictor of health performance as a health-related environmental marker. This study suggests the importance of reviewing public regulations regarding control of potential indoor pollutant sources, use of air purifiers and aspects indicative of satisfaction with indoor environmental exposure.

1. Introduction Specific aspects of people's perceptions have emerged as key elements related to personal exposure to Indoor Environmental Quality (IEQ), which encompasses many potential pollutant factors of indoor and outdoor environments [1–5]. IEQ has a significant impact on public health, comfort and well-being [6–10] and can affect human general sensation [9–13]. Indoor environmental quality (IEQ) is determined by evaluating numerous pollutants from a wide spectrum of pollution sources [6,14–17]. Indoor air pollutants can cause or contribute to short- and long-term health problems [18–21]. Because exposure to indoor pollutants has the potential to affect our health, air quality monitoring and AQI (Air Quality Index) correspond to concentrations of PM10 and PM2.5. However, there is a lack of personal exposure describing real quantified exposure assessments of indoor and outdoor potential

sources [22]. Marina Vance [23] investigated indoor air quality as HOME-chem, during a “worst-case scenario” of Thanksgiving dinner, four weeks of cooking and cleaning, by measuring emissions using high-tech instrumentation in a ranch house on the engineering campus of the University of Texas at Austin. Her studies showed that toasters emitted particles in concentrations higher than outdoors of fine particulate matter small enough to reach deep inside our lungs. Further, carbonbased chemicals evaporate at room temperature and encompass a huge variety of molecules that are emitted by both plants and human activities. The main factors that determine indoor air quality are chemicals, suspended particles, microbes, and humidity, ventilation and temperature [24,25]. Particles from outdoor air may contribute to indoor air particle loads [26], but there are also indoor sources such as combustion (Lam et al., 2006), cooking, cleaning, other human activities and particles

∗

Corresponding author. Department of Public Health Sciences, Korea University, Seoul, 02841, South Korea. Corresponding author. E-mail addresses: [email protected] (H.-J. Oh), [email protected] (J. Kim). 1 Current affiliation, Department of Environmental Sciences, Rutgers, The State University of New Jersey, 14 College Farm Road, New Brunswick, NJ 08901, USA. ∗∗

https://doi.org/10.1016/j.buildenv.2019.106403 Received 29 July 2019; Received in revised form 30 August 2019; Accepted 5 September 2019 Available online 06 September 2019 0360-1323/ © 2019 Published by Elsevier Ltd.

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1.109 Optical Particle Counter, Grimm Technologies Inc., Douglasville, GA). Also, for homes, two real-time aerosol monitors (model 1.108 and model 1.109 Optical Particle Counter, Grimm Technologies Inc., Douglasville, GA) were used for PM measurements in inside and outside, respectively. The inlets of the Grimm OPC devices were located approximately 1.2–1.5 m above ground level. During the sampling period, temperature, relative humidity and CO2 levels were recorded hourly using an indoor digital thermo-hygrometer (W. W. Grainger, Inc., Illinois, USA) and CO2 sensor (Carbon Dioxide Meter PCE-WMM 50, PCE Instruments, Alicante, Spain). Moreover, on selected days of cooking and cleaning events, an additional Grimm device was used for sampling. For the bioaerosol collection, Anderson samplers with 400(N)0.25 mm holes (MAS-100 Eco, MERCK, USA) were placed 1.2–1.5 m above ground level and equipped with a suction pump sucking at a flow rate of 100 L/min for 2 min (Buck, A.P. Buck, Inc., USA). The measurement procedure followed the Anderson principle (Solomon, 2003), and Tryptic Soy Agar (Hanil Komed CO., LTD. KOREA) and SDAC (Sabouraud Dextrose Agar + Chloram, DIFCO., USA) were used for airborne bacteria and fungi, respectively. During the investigation periods (9:00–16:00), three samples were taken from each location at 20 min intervals in the morning and afternoon. The inside of the collider was disinfected with 70% alcohol before and after sampling, the medium was sterilized, and samples were carried in an ice box to prevent contamination. Samples were transferred to the laboratory and bacteria and fungi were incubated at 35 °C for 48 h and 72 h, respectively, before bacterial and fungal colonies were counted. One sample per day was used as a blank to exclude environmental factors that may affect bacteria and fungi, such as temperature and humidity, as well as to compensate for measurement errors during sampling. For analysis of biological microbial populations of airborne bacteria, the air sampler was placed 1.5 m above ground level in the indoor and outdoor sampling sites. A mixed cellulose ester membrane filter (4.7 mm diameter, 0.45 μm pore size, Millipore) was installed in the sampler cartridge with a pump (24 L/min for 24 h) to filter airborne aerosols, allowing biological air contaminants to adhere to the filter. All cartridges and membrane filters were sterilized before sampling to prevent contamination and membrane filters were placed in sterilized tubes and stored frozen. Aerosol samples were analyzed using the metagenome analysis [43,44], which is a method of extracting DNA from a sampled to reveal the community structure through the nucleotide sequence [43,44]. The metagenome analysis was performed to collectively view the sample composition by extracting DNA directly from the filter and performing polymerase chain reaction (PCR). Finally, the product of PCR was purified, and pyrosequencing analysis was conducted, and resulting sequence data were processed using bioinformatic analysis to identify microbial communities [43,44].

formed by reactions between indoor pollutants [27]. Some studies suggest that reducing and/or removing pollutant sources and ventilating with clean outdoor air are the most effective ways to improve indoor air quality [28,29]. However, air quality should be assessed by humans in spaces polluted with different types of indoor pollution sources [28,30]. Because man-made nanoparticles are increasingly multiplying, respirable-sized airborne particles (aerodynamic diameters ≤ 4 μm, PM4) including nanoparticles [31] were associated with incident wheezing, current asthma, and asthma-related emergency department visits among children [24,32,33]. Children are more susceptible to indoor pollutants [24,34,35]. Their exposure levels deserve public policy attention as there are social costs associated with illness related to indoor environments such as homes and schools [36]. These pollutants affect vulnerable population groups in their homes and schools [37]; some dangerous indoor pollutants have yet to be discovered [38]. Exposure assessment for pollutants is made difficult by the fact that pollutants are present as mixtures, therefore exposure assessment must rely on measurements of pollutant markers [39–41]. Examples of these markers, also called indicators, include NO2, O3 and airborne particle in the various pollutant sources identified in the questionnaires (e.g. contact to sources) [40]. Human perception of IEQ in indoor and outdoor environments is influenced by respiratory symptoms, leading people to associate exposure to a specific pollutant source with a given activity. Thus, it has been found that the perception of wellbeing is influenced both by perception of the environment and by the presence of positive and negative respiratory symptoms [42]. In this study, exposure assessment in schools and homes were conducted by measuring the IEQ parameters: particulate matter, bioaerosols, temperature, relative humidity and carbon dioxide. In particular, the IEQ results were evaluated in association to perceived IEQ and health performances. Additionally, for a controlled study to relate IEQ induced by individual activity and/or lifestyle issues to its perceptional quality, cooking and cleaning scenarios were set as potential pollutant sources in homes. To our knowledge, no prior studies have addressed the relation of IEQ and human perception and health performance. Therefore, our study aims to 1) investigate exposure to indoor and outdoor pollutants in schools and homes 2) evaluate the effects of indoor and outdoor potential sources and 3) perform the questionnaire survey on perceived indoor and outdoor quality and health performance. 2. Materials and methods The study was conducted in two parts: 1) personal exposure assessment, with distributions of indoor and outdoor aerosols and potential causal factors, and 2) evaluation of a questionnaire survey to evaluate people's perception of exposure effects and health performance.

2.1.1. Effects of the potential indoor pollutant factors Cooking and vacuuming were determined potential pollutant factors for indoor environments. Prior to each cooking or vacuuming event, three measurements of PM and CO2 were taken in natural ventilation at 20 min intervals to check background concentrations. Cooking and vacuuming were separately conducted every two weeks not to effect on another factor.

2.1. Personal exposure assessment We identified 19 schools and 12 homes, located in urban and rural areas, as two types of locations. Fig. 1-a shows sampling sites investigated during four seasons (3/10/2014–2/20/2016): urban elementary school (EU), an middle school MU), and homes (HU1 and HU2) in the urban areas and rural elementary school (ER), middle school (MR), and homes (HR1 and HR2) in the rural areas. The field measurements were determined to be three classrooms per school. All schools were naturally ventilated during mid-seasons (Spring and Autumn) and cooled and heated by a central HVAC system during the summer and winter seasons. Particulate matter concentrations were measured at each point located inside (two classrooms) and outside (playground and outdoor) of schools by four real-time aerosol monitors (model 1.108 and model

2.2. Questionnaire survey: perceived indoor and outdoor quality and health performances Table 1 provides schools information about year of construction, surrounding environment, facilities, and the ambient temperature and humidity. Before taking IEQ measurements, weeklong indoor environmental surveys were conducted at each sampling site in the spring, summer, autumn and winter from March 2014 to March 2016. The questionnaire consisted of five sections with 17 questions related to: (1) 2

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Fig. 1. Distribution of selected sampling points (19 schools and 12 homes) for investigation IEQ in schools (EU, ER, MU and MR) and homes (HU and HR) with population density (a) and the number of cars as the traffic volume (b) in Seoul, Korea. The field measurements were determined to be three classrooms per school.

school classrooms and playgrounds showed that the MU group had the highest concentrations in classrooms, while the EU group had the highest concentration (of 89.3 μg/m3 (SD 12.33 μg/m3) in outdoor areas. PM10 concentrations in homes were highest in HU1 place. No significant difference was observed in school concentrations between the urban and rural groups except for in the classrooms. The highest PM2.5 concentrations were found in the classroom and playground of the MR group, while the highest average concentration of PM2.5 outdoor areas was found in the MU group (28.56 μg/m3). There was a significant difference between classroom and playground in the ER group. Fig. 3 shows morning and afternoon concentrations of airborne bacteria (Fig. 3a) and fungi (Fig. 3b) in school classrooms and homes for each of the four seasons measured. Genus levels for both bacteria and fungi from all sample locations were analyzed along with the distribution of bio-aerosol concentrations. The concentrations of airborne bacteria in schools ranged from 543.2 CFU/m2 (SD 49.67 CFU/m2) to 678.4 CFU/m2 (SD 87.34 CFU/m2) and 458.3 CFU/m2 (SD 85.47 CFU/ m2) to 664.3 CFU/m2 (SD 78.9 CFU/m2) during summer and winter, respectively. Concentrations of airborne bacteria in homes (HR1 and HR2) had similar concentrations, ranging from 556.8 CFU/m2 (SD 55.67 CFU/m2) to 734.2 CFU/m2 (SD 45.67 CFU/m2) and 554.4 CFU/ m2 (SD 34.2 CFU/m2) to 733.4 CFU/m2 (SD 23.6 CFU/m2) during summer and winter, respectively. In rural areas, airborne fungi concentrations were significantly different between middle school and elementary school during summer and winter seasons (Fig. 3). The bioaerosol concentration, especially fungi, was reported to be different based on the characteristics of school and home, rather than regional characteristics of urban and rural areas [46,47]. The results in this study indicated that natural ventilation, which was a characteristic ventilation method in Korea, was more dominant than the operation of centralized HVAC system. Some middle schools and homes operated individual mechanical air conditioners, which resulted in poor centralized control of indoor temperature and humidity.

perceived indoor and outdoor air quality (satisfaction) (2) thermal comfort and/or willingness to accept indoor temperature, humidity and insulation from cold; (3) health performance (symptoms of respiratory illness symptoms and the number of hospital visits). Participants were asked to return completed questionnaires in person or via email. Table 2 shows the spearman correlation between selected IEQ parameters measured at schools in 2014–2016. During cooling (summer), mid (spring and autumn) and heating seasons (winter), health performance was defined as the number of hospital visits and allergy symptoms were surveyed as well. TSV was investigated for temperature, relative humidity and clo sensation using the ISO 5-level scale (−2 to +2) (cool, slightly cool, neutral, slightly warm, warm) [45], and changes among results were used to indicate perception of satisfaction with IEQ. 2.3. Statistical analysis PM concentrations were compared as functions of location and season using a paired-sample t-test (Sigmaplot 2011, Version 12.3, Systat Software Inc., San Jose, CA). When significant effects were observed for a given variable, differences between individual pairs of variables were examined. Significance was determined at p < 0.05. Linear and/or nonlinear regression analysis was performed to evaluate the relationships between indoor and outdoor PM concentrations and CO2 concentrations and number of people. To analyze the relationship between IEQ and potential health performance, the Spearman correlation of non-normally distributed variables was performed using the Sigma plot program. 3. Results 3.1. Exposure assessment 3.1.1. Quantification of aerosol exposure Fig. 2 shows quantified aerosol exposure based on measurements taken from schools (classroom, playground, outdoor areas) and homes (living room and outdoor areas) in the urban (EU, MU and HU) and rural (ER, MR and UR) groups over four seasons (3/10/2014–2/20/ 2016). Average indoor PM10 values for classrooms ranged from 56.9 μg/m3 (SD 3.23 μg/m3) to 85.9 μg/m3 (SD 9.43 μg/m3), from 67.22 μg/m3 (SD 8.22 μg/m3). In the EU group, indoor and outdoor concentrations were significantly different. Further, comparison of concentrations from

3.1.2. Bioaerosol genus and species Fig. 4 presents the genus levels of samples. The genus Micrococcus comprised 2.3% (SD 2.2%) to 18.9% (SD 3.7%) of airborne bacteria and was most dominant in the MR group. A high proportion of this genus was found in the school classroom during the winter season, while a somewhat higher proportion was found in homes during the summer season. In outdoor samples, less than 5% of genera identified in school 3

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> 40 24 24 34.2 (13.2) Off

– Off

Off

Off

Off

ON

343

356

332

340

526

522

Home

During summer and winter seasons, temperature investigated in outdoors ranged from 28.3 °C to 36.3 °C and from 6.3 °C to 14.2 °C, respectively. a Temperature during spring and autumn seasons.

24.2 (2.0) 23 2 (1) 6 (3)

24.2 (2.0) 25 2 (1) 6 (3)

25.2 (2.0) 26 2 (2) 10 (2)

26.2 (2.2) 17 2 (1)

Cooking Cleaning Cooking Cleaning

4 (2)

24.1 (1.2) 16 106 (8) 69 (12)

24.2 (1.2) 19 148 (12) 83 (9)

22.3 (2.1) 22 112 (9) 72 (13) ON 523

classrooms were Streptomyces, Pseudonocardia and Nocardiopsis, while these genera comprised less than 3% of airborne bacteria in homes. Within the genus Micrococcus, the most frequently identified species was M. luteus, which seemed dominate human and roadside environments. The genus Cladosporium was predominant in both classroom and home, followed by Hyphodontia, Aspergillus and Thanatephous. The proportion of Cladosporium was similar in schools (23.2–39.1%) and homes (23.3–33.4%). In the schools, the average values of morning and afternoon samples from all three locations ranged from 2.2–23.4% of the genus Apsergillus, 2.1–4.2% of the genus Perenniporia, and 2–11.0% of the genus Irpex. Since culture-based methods have several limitations such as lack of reproducibility, selection effect, and limitation of sampling time, total bioaerosol number could be underestimated [48]. Therefore, identification of bioaerosol genus and species were required in addition to their concentrations in the exposure assessment. Moreover, the associated occupant questionnaire responses on health symptoms/effects may provide insight into the significance of these findings [49,50]. In this study, the two major fungal groups identified both schools and homes were Cladosporium and Aspergillus (Fig. 4) and some outdoor fungi (not shown in this paper) concentrations were found to be higher than those indoors indicating the presence of fungi sources in outdoors. Higher concentrations of airborne fungi were found outdoors and this implies that the ambient air was the major source for airborne fungi in these predominantly mechanically-ventilated and/or natural ventilated buildings [50,51]. Past study showed that four most commonly found fungal groups (Cladosporium, Penicillium, Yeast and Aspergillus) were the same for both indoor and outdoor samples [49].

35.3 (1.2)

> 30 24 24 33.2 (12.7) 36.4 (1.3)

> 30 16 24 32.3 (12.3) 36.7 (1.2)

> 30 16 28 33.2 (18.2) 38.1 (1.3)

14 180 180 34.22 (17.2) 36.2 (2.2)

14 180 180 33.2 (11.2) 35.8 (5.2)

11 150 180 35.3 (17.2) 36.7 (7.2)

11 150 180 33.4 (12.2) 36.2 (3.6)

24.4 (1.4) 27.9 (2.1) 24.23 (1.3) 23.3 (1.4) 25.4 (2.1) 24.6 (3.2) 25.2 (2.1) 23.5 (2.2) 23.6 (2.6) 28 127 (12) 73 (11) Air purifier On/Off ON

45 (10) 50 (5) 50 (5) 66 (4) 50 (4) 66 (3) 50 (3) 66 (2) EU a, b, c, d, e ER a, b, c, d, e MU A, b, c, d, e MR a, b, c, d HU1 a, b HU2 a, b, c, d, e HR1 a, b HR2 a, b, c School

511

Age Female Male Outdoor Indoor Outdoora Indoor Adults (teachers, parents) Students

Number of people, mean (SD) for questionnaire survey Event Air purifier Area (m2), Mean (SD) Number of samples Sampling site Type

Table 1 Characteristics for sampling sites in schools (elementary and middle) and homes.

Building age, mean Adults (teachers, parents)

Temp (°C), mean (SD)

Relative Humidity (%), Mean (SD)

Number of people participated in questionnaire

H.-J. Oh, et al.

3.1.3. Relationship between indoor and outdoor PM Fig. 5 shows a scatter diagram of PM measurements taken from 16 indoor and outdoor sampling sites from the urban area (EU, MU and HU) and an area near a roadway (ER, MR and HR). Relationships between indoor and outdoor PM were evaluated in schools and homes with natural ventilation from opening windows during mid seasons (spring and autumn). The nine roadway sampling sites (five elementary schools, three middle schools and one home) were all situated near a heavily trafficked road (Table 1). For PM10, a linear relationship with coefficient of determination (R2) of 0.23–0.38 and 0.12–0.63 was found for the urban and roadway groups, respectively (Fig. 5a, b and c). For PM2.5, R2 ranged from 0.22 to 0.40 and from 0.10 to 0.36, respectively for the urban and roadway groups (Fig. 5d, e, and f). 3.2. Variations of IEQ (PM and CO2) depend on potential factors 3.2.1. Event 1: PM and CO2 variations from cooking and vacuuming in home Cooking and vacuuming episodes were defined as factors affecting indoor pollution. Fig. 8 shows a linear regression of CO2 concentrations and the number of students during summer and winter seasons. During summer, the linear regression equations were: y = 31.72 x −348.93 for EU, y = 31.50 x + 376.02 for ER, y = 24.64 x + 301.60 for MU and y = 23.15 x + 423.04 for MR. The R2 coefficients of determination were 0.71, 0.75, 0.85 and 0.71 for EU, ER, MU and MR, respectively. On the other hand, CO2 concentrations ranged from 657 ppm to 997 ppm for 14–29 students during winter season. The CO2 concentration values were 902 ppm (SD 34 ppm) - 989 ppm (SD 45 ppm) and 1344 ppm (SD 23 ppm) - 1356 ppm during cooking for episode 1 (10 min, EP 1) and episode 2 (EP2), respectively. Fried rice and soup were cooked in EP1, while fried rice, soup and bacon were prepared in EP2. Slight increases in CO2 concentrations after 10 min of vacuuming were observed in episodes 3 (EP 3) and 4 (EP 4). As presented in Fig. 8-d, these CO2 values ranged from 894 ppm (SD 66 ppm) 934 ppm (SD 42 ppm) and 734 ppm (SD 43 ppm) - 892 ppm (SD 4

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Table 2 Spearman correlation between selected IEQ parameters measured at schools in 2014–2016. 1 1 2 3 4 5 6 7 8 9 10 11 12 a b

Outdoor

Indoor

Air purifier ON

Air purifier OFF

PM10 PM2.5 Airborne bacteria Airborne fungi PM10 PM2.5 Airborne bacteria Airborne fungi CO2 PM10 PM2.5 CO2

1.000 .564b .234a -.125 .324a .232a .221a -.221 -.256 .744b .655b .452a

2

3 b

.564 1.000 .112 -.247 .543a .644b .122 -.111 .323 .443a .644b .423a

4 a

.234 .112 1.000 .112 .212 .113 .458 -.223 .112 .332a .434a .110

5

-.12 -.24 .112 1.000 -.112 -.322 -.222 -.122 -.219 -.122 -.277 -.412

6 a

.324 .543a .212 -.112 1.000 .667b .459a -.234 .335 .532b .522b .236a

7 a

.232 .644b .113 -.322 .667b 1.000 .122 -.354 .111 .432a .334 .189

8 a

.221 .122 .458 -.222 .459a .122 1.000 -.228 .199 .132 .021 .214

-.221 -.111 -.223 -.122 -.234 -.354 -.228 1.000 -.212 -.033 -.312 -.213

9 -.256 .323 .112 -.219 .335 .111 .199 -.212 1.000 .233 .321 .519b

10

11 b

.744 .443a .332a -.122 .532b .432a .132 -.033 .233 1.000 .766b .654b

12 b

.655 .644b .434a -.277 .522b .334 .021 -.312 .221a .766b 1.000 .329a

.452a .423a .110 -.412 .236a .189 .214 -.213 .519b .654b .329a 1.000

Correlation is significant at > 0.05 as 2-tailed. Correlation is significant at > 0.01 as 2-tailed.

Fig. 2. Concentrations of particulate matter (a: PM10 and b: PM2.5) investigated using filter-based gravimetric method in classrooms, playground and outdoors at schools (EU, ER, MU and MR) and at homes (HU 1, 2 and HR 1, 2). The data are based on averages of five repeats, and error bars are one standard deviation. *p < 0.05 and p values are based on pairwise comparison using Holm-Sidak method.

Fig. 3. Concentrations of airborne bacteria (a) and airborne fungi (b) at schools (EU, ER, MU and MR) and homes (HR1 and HR2) during summer and winter seasons in 2016. The data are based on averages of three repeats, and error bars are one standard deviation. *p < 0.05, **p < 0.001, and p values are based on pairwise comparison using Holm-Sidak method.

24 ppm), respectively. The PM concentrations ranged from 56.9 μg/m3 (SD 5.5 μg/m3) 66.3 μg/m3 (SD 2.8 μg/m3) and 78.9 μg/m3 (SD 7.8 μg/m3) - 88.2 μg/ m3 (3.6 μg/m3) during cooking (10 min) in episodes 1 (EP 1) and 2 (EP2), respectively: On the other hand, CO2 concentrations increased slightly during vacuuming (10 min) in episodes 3 (EP 3) and 4 (EP 4). As presented in Fig. 7-e, these CO2 concentrations 33.9 μg/m3 (SD 7.6 μg/m3) - 41.2 μg/m3 (SD 3.9 μg/m3) in EP 3 and 45.8 μg/m3 (SD 5.7 μg/m3) - 59.9 μg/m3 (SD 4.1 μg/m3), in EP 4.

3.2.2. Event 2: PM variations by traffic volume and effect of air-purifiers in schools Concentrations of PM and CO2 were measured hourly in indoor urban areas with natural ventilation and outdoor areas where traffic volume was determined by number of cars entering the area. For PM (PM10 and PM2.5) concentrations, mass of concentrations and dependence of removal efficiencies on air-purifier operation were investigated (Fig. 6). The standard deviation is only provided for months where weekly sampling was performed for both EU and MU. For the EU location, hourly average indoor PM values ranged from 5

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Fig. 4. Genius species and level for airborne bacteria (a) and airborne fungi (b) observed in schools (EU, ER, MU and MR) and homes (HU and HR) during summer and winter seasons in 2016. The data are based on averages of three repeats, and error bars are one standard deviation.

77.2 μg/m3 (SD 5.5 μg/m3) to 104.5 μg/m3 (SD 3.8 μg/m3) and 32.5 μg/ m3 (SD 5.6 μg/m3) to 49.4 μg/m3 (SD 4.9 μg/m3) for PM10 and PM2.5, respectively; these concentrations are slightly higher than the PM10 concentrations measured with a filter-based gravimetric method at the same location. For the MU location, hourly average indoor PM values ranged from 75.4 μg/m3 (SD 8.9 μg/m3) to 92.3 μg/m3 (SD 4.5 μg/m3) and 32.5 μg/m3 (SD 7.4 μg/m3) to 49.4 μg/m3 (SD 6.7 μg/m3) for PM10 and PM2.5, respectively; these concentrations are similar to the PM10 concentrations measured with the filter-based gravimetric method.

The results obtained for PM10 and PM2.5 aerosols in the EU location indicate that PM removal efficiency in the urban area varied from 15.6% (SD 3.6%) to 39.5% (SD 3.8%) for PM10 and from 3.2% (SD 1.1%) to 20.6% (SD 2.3%) for PM2.5. For the MU location, removal efficiencies ranged from 15.1% (SD 2.6%) to 29.2% (SD 2.2%) for PM10 and from 6.5% (SD 2.1%) to 21.7% (SD 3.2%) for PM2.5. These results are shown in Fig. 6.

6

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Fig. 4. (continued)

humidity (RH). For temperature, 2–5% of respondents were dissatisfied, 23–85% were neutral, 10–72% were satisfied and 5–30% were very satisfied. For RH, 0–5% were very dissatisfied, 5–20% were dissatisfied, 5–85% were neutral, 5–25% were satisfied and 2–15% were very satisfied with RH. On the other hand, respondents’ ratings for indoor and outdoor quality differed between the urban and rural groups. For indoor air quality, satisfaction rates in the urban group ranged from 0% (MU) to 18% (EU), 62% (ER) to 85% (HU2) and from 5% (HR1) to 25% (MU) for very dissatisfied, neutral and very satisfied, respectively. For

3.3. Association of human perception and health performance to exposure assessments A total of 501 people was interviewed with a loss of approximately 8% when sample size was analyzed. Regarding health performance and perception of satisfaction and thermal comfort, 98% of interviewees were teachers, students and parents. Average student age was 11 in elementary schools and 13 in middle schools. In urban and rural schools and homes, 85% of respondents were asked to rate their satisfaction with temperature (Temp.) and relative 7

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Fig. 5. The relationship between indoor and outdoor PM10 (a, b and c) and PM2.5 (d, e and f) investigated using Grimm OPC 1.109 and their linear regression equations during natural ventilation in autumn season in 2016.

correlations between negative satisfaction/comfort ratings and PM, airborne bacteria, and CO2 concentrations. Particularly, the perception of satisfaction with thermal comfort was negatively correlated with indoor and outdoor IEQ parameters. The rate of satisfaction with indoor air quality, thermal comfort and allergy symptoms seriously affected their IEQ exposure assessment results (Table 3). Moreover, these were

outdoor air quality, satisfaction rates ranged from ranged from 7% (ER) to 45% (HU2), from 5% (EU) to 82% (ER) and from 5% (HU1 and HU2) to 49% (MU) for dissatisfied, neutral and satisfied, respectively. Table 3 shows Spearman correlations between exposure assessment and perception and between exposure assessment and health performance (number of hospital visits) in 2014–2016. There are significant 8

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Fig. 6. The concentrations of PM10 using Grimm OPC 1.109 and CO2 concentrations (a, d), PM2.5 using Grimm OPC 1.109 and traffic volume investigated during 10 min (b, e) and the PM removal efficiency during air purifier operation in EU(a, b, c) and MU (d, e, f) of urban areas. The data are based on averages of three repeats, and error bars are one standard deviation.

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Fig. 6. (continued)

4. Discussion

all significantly associated with increased hospital visits and the survey results showed significant correlations between outdoor environmental quality parameters and hospital visits due to respiratory illness.

Indoor and outdoor pollutant exposure in the urban and rural groups showed no significant differences for PM concentrations, as shown in the comparison of schools and homes indoors and (with the 10

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Fig. 7. Correlation between number of people and CO2 concentrations in schools in summer (a) and winter (b) seasons during ventilation operation in schools, CO2 concentrations and particulate matter at homes (HU1 and HU2) as a function of the potential events of cooking (c and e) and vacuuming (d and f). The data are based on averages of three repeats, and error bars are one standard deviation.

vacuuming events. Because these activities cause higher exposure to pollutants, they may cause health problems if the IEQ is not improved. Furthermore, because of natural ventilation, students may be exposed to traffic-related pollutants [52–55] for over 8 h (9 a.m.–5 p.m.) in the classroom. When indoor pollutants are also present in homes, students’ real exposure may have more adverse health effects. Investigation of slight increases in indoor fungi concentrations in schools and homes indicated that long-term exposure to bioaerosol caused

exceptions of EU for PM10 and MR for PM2.5). These results show that residents were clearly affected by PM released from transport combustion and/or outdoor sources in outdoors and indicate that exposure to respirable PM, which penetrates the alveoli where gas is exchanged, can cause long-term impacts and poor school performance as demonstrated by the survey results. The results of the study on indoor events highlight the importance of exposure assessment for potential indoor pollutant factors, namely the high PM associated with cooking and 11

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Fig. 8. Distribution of IEQ satisfaction votes across all people. The data are based on the questionnaire survey obtained from 396 students and 64 parents in schools (EU, ER, MU, MR) and homes (HU1, HU2, HR1 and HR2) in 2014–2016.

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Table 3 Spearman correlation between selected IEQ parameters measured at schools in 2014–2016.

1 2 3 4 5 6 7 8 9 10 11 12 a b

Outdoor

Indoor

Air purifier ON

Air purifier OFF

PM10 PM2.5 Airborne Airborne PM10 PM2.5 Airborne Airborne CO2 PM10 PM2.5 CO2

bacteria fungi

bacteria fungi

% Satisfactory Indoor air quality

% Satisfactory Thermal comfort

Symptom allergy

Visit to hospital due to respiratory illness

-.533b -.563b -.223a -.121 -.688b -.643b -.232a .121 -.765b -.676b -.766b -.687b

-.433a -.354a -.211a -.122 -.756b -.553b -.765b -.221b -.758b -.566b -.454b -.665b

.421a .355a .124 -.112 .323a .331a .120 .088 .756b .455a .438a .644b

.322a .237a .211a -.129 .343a .112 .211a .113 .199 .191 .212a .434a

Correlation is significant at > 0.05 as 2-tailed. Correlation is significant at > 0.01 as 2-tailed.

When the windows opening area was smaller than 4.9 m2 for schools and 2.5 m2 for homes, corresponding to less than 0.05 m3/s natural ventilation rate, the CO2 concentrations ranged from 500 to 1000 ppm. When the area exceeds 4.9 m2, corresponding to larger than 0.07 m3/s natural ventilation rate, the indoor temperature and humidity changed significantly: 2–3 °C for temperature and around 15% for humidity after 3 h. However, the mechanical ventilation rates ranged from 3.0 l/min to 3.5 l/min due to the operation of air conditioners and/or individual mechanical ventilation. Actually, residential and school buildings are ventilated naturally and mechanically. Both ventilation methods provide a healthier indoor environment by introducing external fresh air into the indoor air [63]. However, natural ventilation still lacks a controlled, mechanical process to ensure that a home or building is consistently ventilated [69]. Natural ventilation can occur through air infiltration or open doors and windows. The practice of opening doors and windows typically provides more adequate ventilation than infiltration leakage, but still falls short when it comes to optimal indoor air quality [70]. On the other hand, average I/O ratio of CO2 ranged from 1.1 to 3.1 (Fig. 6). Recently concentration of greenhouse gases has increased, and the observed concentrations of atmospheric CO2 have steadily increased 300–390 ppm based on IPCC report 2014. In addition, the observed atmospheric CO2 level increased at a rate of 1.9–2.1 ppm per year and locally elevated CO2 levels could occur due to CO2 emissions from transportation in urban group. Ventilation could be controlled based on the assumed outdoor CO2 level of 400 ppm [71]. Ventilation guideline recommended indoor CO2 levels not to exceed the surrounding outdoor concentration by 600 ppm. In this study, CO2 level observed in indoors exceeds by above 1000 ppm, which indicated poor ventilation systems result in high CO2 concentrations, and the number of people also effects on CO2 concentration. For the questionnaire on IEQ, TSV and health performance, we determined as five-point scale (Fig. 8 and Appendix). Because the survey involved children, parents and teachers in charge explained the concept of thermal sensations to children first, followed by the survey. To increase the accuracy of the survey, questions about temperature in schools were also asked by teachers, and those children whose responses contained unusual responses were asked again or excluded from the analysis. The Appendix provides the questionnaire. Our results show that reported health performances and thermal comfort-related perception were associated with exposure assessment. The results of the Spearman correlation suggest that, the primary factors affecting perception of satisfaction with IEQ were: living in an area with high traffic-related pollution (exposed areas), perception of road traffic pollutants at home, evaluating IEQ for indoor potential pollutant sources, and reports of health-performances related to symptoms of respiratory illness [72].

allergy symptoms and continuous adverse effects for students. Past studies modified the IEQ index and demonstrated that exposures differ by individual characteristics and that affluence may be associated with higher levels of exposure [56]. In this case, it is important to evaluate as many potential exposures in indoor environments as possible by evaluating not only pollutants but human activities as well [1,57]. Because of poor air quality in cities, sales of consumer air cleaning systems will nearly triple by 2019 [58]. This trend is also driven by publicity related to poor air quality and increasing awareness health effects caused by air pollution. Our IEQ monitoring index identified numerous potential exposure situations and is capable of evaluating the relationship between affluence and the ability to improve comfort (e.g. using air purifiers and dehumidifiers) [58–61]. Mechanical ventilation systems have simultaneous positive effects on different investigated pollutants and especially, lower indoor-tooutdoor concentration ratios were detected simultaneously for not only PM10 but also CO2 concentrations. Therefore, the indoor air quality is affected by several pollutants and whose behavior cannot be predicted by the CO2, but most of the studies performed a general evaluation of the indoor air quality just using CO2 as a comprehensive indicator [62]. In addition, CO2 concentration (as a tracer gas) is used to assess ventilation adequacy with respect to IEQ, and outdoor ventilation is conducted to control exposures to pollutants. It is one of the methods for preventing health problems that are caused by poor IEQ [63]. In this study, CO2 concentration selected as an index for IEQ was needed to evaluate in the exposure assessment associated with health effects in indoors [29,64,65]. For airborne bacteria and fungi, I/O ratios of the observed sites varied from 0.7 to 1.3 and from 0.7 to 1.2, respectively. In some schools, the average I/O ratio of airborne fungi was higher than that of airborne bacteria, indicating higher penetration of airborne fungi than airborne bacteria from outdoors, and stronger indoor sources of airborne fungi than airborne bacteria, considering the characteristics of indoor environment. Temperature measured in this study was 21–25 °C and did not exceed 26 °C. However, some levels reach above a pleasant room temperature range (21–22 °C) [66] due to the natural ventilation and irregular ventilation operating. Windows should be able to be opened and the rooms should have sufficient sun protection but, ventilation systems should be able to be adjusted from every indoor place [66]. With the control of temperature, an appropriate humidity is essential for the healthy and comfortable indoor air. Too much humidity can cause mold, increase allergens, and lead to potential illness. Moreover, mold can potentially cause structural damage to the home [67]. If the humidity rises above the EPA's recommended range of 30–60% [68], dehumidifier and/or air conditioner should be utilized to maintain an indoor air quality. 13

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5. Conclusions

Regarding environmental exposure, the present study has identified the importance of using perceptions, such as feelings of thermal comfort and satisfaction with indoor and outdoor air quality, as indicators [73]. Information gained from the surveys can therefore be used to inform future decisions about characterizing environmental quality. Awareness of the risks of air pollution has increased during the last years [32,74]. Air pollution is mainly related with health effects, especially respiratory diseases [75,76]. Past studies on air pollution perception were performed with the quantitative methods (questionnaires and opinion polls) focusing in measuring human attitudes and cognitive processes related to air pollution [77]. Attitudes and perceptions are not static and also depend on a multiplicity of factors. In other words, both attitudes and beliefs are not driven only by the object of the attitude (e.g. the environmental hazard). According to the trends data from Google, people first searched for weather updates when they logged on to the internet and air pollution is also linked inextricably to climate change. Attitudes and perceptions towards an environmental hazard were developed with the generation of anxiety and concern. Especially, PM2.5 is one of nationwide issues affecting both cities and villages because it can travel far from its source. The methodological approach used in this study emphasizes the quantified exposure and health effect due to the differences in perception among individuals and social groups with many factors related to the risk source. It can serve as a model for future quantitative inhalation exposure assessments associated with human perception. Such assessments will be required to obtain quantitative exposure data for a wide variety of the individual (from personality to lifestyle issues) and the context in which perception are necessary for the ongoing development of safety guidelines and potential regulations. For the exposure to pollutants, we performed the association between IEQ real field study and human perception, and health performances were examined as well. The inhalable aerosols depending on the potential pollutants’ sources were quantified for the exposure assessment in homes and schools. However, there is a concern regarding potential exposures to indoor and outdoor pollutants, especially those that have been shown to cause health risk [78–81], potential inhalation exposures [82,83], including respirable particles, i.e., particles that can penetrate deep into the gas-exchange regions of the lung [84,85]. Therefore, for all inhalable aerosol in investigated indoors, 89–95% of aerosol deposition occurred in the head airways, while < 7% deposited in the alveolar and < 4% in the tracheobronchial regions [83]. In addition, we calculated the lifetime average daily doses (LADDs) (μg/ kg−1day) and total estimated LADDs for human considering the information of living schedule (inhalation rate, body weight and exposure duration based on US EPA and 2 h exposure time as potential sources) [86] for resident ranged from approximately 1.35 × 10−4 to 1.46 × 10−4 depending on a potential pollutant sources, indicating possible health effects. Air pollution in worldwide is a major public health risk, ranking alongside cancer, heart disease and obesity [87,88]. Poor air quality affects our health and has cost for our society, our economy and society and, especially it shortens lives and damages quality of life for many people [77,87]. The Environmental Protection Agency (EPA) sets a three-year daily average of 12 μg of PM 2.5 and 15 μg of PM 10 as the limit for protection of public health. Especially, in the United States, there was a daily mortality increase of 0.79% for each 10 μg/m3 increase in PM 10 and a 1.58% increase for each 10 μg increase in PM 2.5. This would translate to an extra 178 deaths on a day when levels of these pollutants increased by these amounts [89]. Most of all, since many sources of air pollution can affect air quality, emission could be existed into the air and effect on human health in indoors, so adults and children with lung or heart problems are greater risk of symptoms. Therefore, health advice is needed for people at-risk when air quality poor because of rising levels of pollution worldwide.

This study shows that perception can be used as an indicator of pollutants and suggests a need for periodic surveys. Perception based on the IEQ satisfaction and exposure assessment for pollutants depends on indoor and outdoor potential sources and can be used as a basic indicator to evaluate personal exposure in IEQ research. Additionally, it was verified that indoor air quality was not improved through use of an air purifier when outdoor air pollution levels were higher, and there was a significant difference between air quality awareness and health performance in questionnaire participants. Residents perceived indoor air quality to be less pleasant when the dwellings were located in high traffic areas. We observed strong correlation between IEQ perception and environmental exposure, as well as between IEQ perception and satisfaction with outdoor air quality. We must remember that a regular survey assessing exposure and IEQ will help people estimate health performance in the future. Acknowledgements This work was funded by the Korean Government through the National Research Foundation of Korea Grant (NRF2018R1A6A1A03025761, NRF- 2018R1A6A3A11048705). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.buildenv.2019.106403. References [1] S.J. Keller-Olaman, J.D. Eyles, S.J. Elliott, K. Wilson, N. Dostrovsky, M. Jerrett, Individual and neighborhood characteristics associated with environmental exposure, Environ. Behav. 37 (4) (2016) 441–464. [2] T.C.d. Silva, L.Z.d.O. Campos, W. Balée, M.F.T. Medeiros, N. Peroni, U.P. Albuquerque, Human impact on the abundance of useful species in a protected area of the Brazilian Cerrado by people perception and biological data, Landsc. Res. 44 (1) (2017) 75–87. [3] M. Gallastegi, A. Jimenez-Zabala, A. Molinuevo, J.J. Aurrekoetxea, L. Santa-Marina, L. Vozmediano, J. Ibarluzea, Exposure and health risks perception of extremely low frequency and radiofrequency electromagnetic fields and the effect of providing information, Environ. Res. 169 (2019) 501–509. [4] R. Ramirez-Vazquez, J. Gonzalez-Rubio, E. Arribas, A. Najera, Characterisation of personal exposure to environmental radiofrequency electromagnetic fields in Albacete (Spain) and assessment of risk perception, Environ. Res. 172 (2019) 109–116. [5] G.V. Pham, M. Shancer, M.R. Nelson, Only other people post food photos on Facebook: third-person perception of social media behavior and effects, Comput. Hum. Behav. 93 (2019) 129–140. [6] F. Wu, D. Jacobs, C. Mitchell, D. Miller, M.H. Karol, Improving indoor environmental quality for public health: impediments and policy recommendations, Environ. Health Perspect. 115 (6) (2007) 953–957. [7] M. Xu, B. Hong, R. Jiang, L. An, T. Zhang, Outdoor thermal comfort of shaded spaces in an urban park in the cold region of China, Build. Environ. 155 (2019) 408–420. [8] S. Selinheimo, A. Vuokko, C. Hublin, H. Jarnefelt, K. Karvala, M. Sainio, H. Suojalehto, J. Suvisaari, T. Paunio, Health-related quality among life of employees with persistent nonspecific indoor-air-associated health complaints, J. Psychosom. Res. 122 (2019) 112–120. [9] Y. Hou, J. Liu, J. Li, Investigation of indoor air quality in primary school classrooms, Procedia Engineering 121 (2015) 830–837. [10] C. Huizenga, S. Abbaszadeh, L. Zagreus, Air quality and thermal comfort in office buildings: results of a large indoor environmental quality survey, Proceeding of Healthy Building 3 (2006) 393–397. [11] D. Krawczyk, B. Wadolowaka, Analysis of indoor air parameters in an education building, Energy procedia 147 (2018) 96–103. [12] A. Stamatelopoulou, D.N. Asimakopoulos, T. Maggos, Effects of PM, TVOCs and comfort parameters on indoor air quality of residences with young children, Build. Environ. 150 (2019) 233–244. [13] S. Kim, J.A. Senick, G. Mainelis, Sensing the invisible: understanding the perception of indoor air quality among children in low-income families, INt. J. ChildComputer. Interact. 19 (2019) 79–88. [14] Y. Sun, J. Hou, R. Cheng, Y. Sheng, X. Zhang, J. Sundell, Indoor air quality, ventilation and their associations with sick building syndrome in Chinese homes, Energy Build. 197 (2019) 112–119. [15] J. Madureira, I. Paciência, J. Rufo, E. Ramos, H. Barros, J.P. Teixeira, E. de Oliveira

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