Site- and house-specific and meteorological factors influencing exchange of particles between outdoor and indoor domestic environments

Building and Environment 160 (2019) 106181

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Site- and house-specific and meteorological factors influencing exchange of particles between outdoor and indoor domestic environments

T

Maria Chiesa, Rossella Urgnani, Riccardo Marzuoli, Angelo Finco, Giacomo Gerosa* Department of Mathematics and Physics, Università Cattolica Del Sacro Cuore, Via Musei 41, 25121, Brescia, Italy

ARTICLE INFO

ABSTRACT

Keywords: Particles number Particle mass Domestic environments ANOVA I/O ratio House-specific factors

The aim of this study was to investigate the influence of site- and house-specific as well as meteorological factors on indoor and outdoor particle concentrations. Particle number (PN) and particle mass (PM) concentrations were monitored in 62 houses in Brescia, Northern Italy, during two winter monitoring periods. Measurements were conducted in houses of non-smokers and without the activation of any internal aerosol sources from domestic activities such as cooking and cleaning. Indoor and outdoor particle concentrations were measured for 30 min in each house. A wide-range aerosol spectrometer (MINIWRAS 1371, GRIMM) was used to detect 41 classes of particles from 10 nm to 35 μm. The effects of natural ventilation on indoor particle concentrations were also considered by measuring PM and PN indoor/outdoor ratios before and after a window was opened for a period of 15 min. An ANOVA analysis was performed on a sub-dataset of 35 houses to evaluate the dependence of PN and PM concentrations on site- and house-specific as well as meteorological factors. The house energy class was significantly related to indoor particle concentrations, and window dimensions seemed to influence the concentrations both before and after the ventilation period. We found that some factors, e.g., the floor level, need further investigation.

1. Introduction Many scientific studies have found a relationship between air pollution and health [1–5], especially in terms of an increasing occurrence of respiratory and cardiovascular diseases associated with high exposures to high PM concentrations. People spend most of their time in indoor environments [6–9], and therefore indoor air quality (IAQ) measurements are extremely important. In the last few decades, investigating IAQ has become a significant issue due to increasing attention from political institutions that aim at improving the comfort, health and wellbeing of building occupants [10]. The health risks from exposure to indoor pollution are higher for vulnerable groups such as the elderly and infants, who spend most of their time at home. Hence, much research is focused on indoor air quality in houses and schools [11–15] and the chemical composition and toxicity of indoor pollutants [16–21]. Indoor particulate matter has three main origins [22] which are 1) generation from internal sources and/or activities, such as smoking, cleaning or cooking [12,14,18,19,23–28], 2) penetration from the outdoor environment during the natural ventilation period or through walls and fixtures leakages [29–37], especially if no air filtration systems are present [37–39] and 3) generation from indoor chemical reactions such as secondary organic aerosol (SOA). Ji and Zhao *

(2015) [22] studied the apportionment of these three indoor PM2.5 sources and found outdoor and indoor contributions of 54%–96% and of 4–46%, respectively and a small (up to 3%) SOA presence in indoor PM2.5 concentrations. In the absence of active internal sources, indoor particles are mainly of external origin [22,40] and many researchers have focused their studies on the link between indoor and outdoor air quality [22,40–42]. Many of them however, when measuring indoor PM concentrations, did not consider the outdoor contribution because their tests were performed while indoor activities responsible of PM generation were present. Questionnaires or diaries were often completed by residents to keep track of those activities and to investigate occupants’ habits [43,44]. When active indoor PM sources are not present during the monitoring period, the calculation of PM indoor/ outdoor ratios (I/O ratios) is representative of the actual level of particles penetration from outdoor into indoor environments [45]. The I/O ratio is a very useful indicator for evaluating indoor air quality with regards to outdoor pollutants concentrations [41,46]. It represents the ratio of PN or PM concentrations measured indoor and outdoor respectively, highlighting a different size resolved exposure to PN or PM in ambient or domestic environments. Usually the I/O ratio is smaller than 1, but when indoor sources are active or outdoor particulate concentrations drop considerably it can reach greater values [46].

Corresponding author. E-mail address: [email protected] (G. Gerosa).

https://doi.org/10.1016/j.buildenv.2019.106181 Received 7 March 2019; Received in revised form 7 May 2019; Accepted 4 June 2019 Available online 12 June 2019 0360-1323/ © 2019 Elsevier Ltd. All rights reserved.

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Many studies have attempted to find a correlation between the I/O ratio and meteorological factors or human activities using Principal Component Analysis [11] or statistical regression techniques [14,47]. However, such studies have never been conducted in Italy and in particular in the city of Brescia, a highly polluted area of the Po Valley [48] in the North-East of Italy. Past research, focused on the relationship between IAQ and the energy class of buildings, has revealed some conflicts between the comfort and well-being perceived by residents of dwellings classed as high energy and the actual IAQ monitored with experimental tests, even when considering the influence of ventilation systems [49]. Although the Directive 2010/31/EU on the energy performance of buildings [50] states that all new constructions should be nearly-zero energy by 31 December 2020 it remains unclear whether these types of buildings will provide a better IAQ compared to standard buildings [51]. Despite IAQ being taken into account in the certification schemes (e.g., BREEAM, LEED, HK BEAM, [52]) it is often addressed by well-being feelings of the residents derived from questionnaires and not by effectively measuring indoor pollutants concentrations. Some studies have evaluated the IAQ in low-energy buildings [42,51,53,54]. Kaulenienè et al. (2016) [53] studied IAQ in 11 low energy buildings in Lithuania and asserted that it was generally good assuming there was adequate ventilation. Derbez et al. (2018) [54] measured 19 organic compounds and aldehydes, PM2.5, radon, NO2, T and RH in 72 energy efficient buildings and found statistical evidence of higher indoor concentrations of hexaldehyde, alpha-pinene and limonene compared to previous studies on conventional new built and retrofitted houses in France [55,56]. The present research focuses on particulate matter and aims to evaluate its penetration into domestic environments as a function of some site, house-specific and meteorological factors. The temporary absence of indoor sources imposed by our monitoring protocol was aimed at investigating background concentrations of indoor PM in the monitored dwellings and their relationship with outdoor concentrations. In particular, the main focus was to test whether PN, PM and I/O ratio values significantly depend on:

• •

2. Materials and methods The research comprised an experimental phase, followed by the statistical analysis of qualitative and quantitative data collected during the monitoring periods. All measurements were conducted during the winter period. The first monitoring period commenced on the 21st of December 2016 and ended on the 13th of March 2017, while the second commenced on the 22nd of December 2017 and ended on the 28th of February 2018. The PM outdoor concentrations are usually very high in winter because meteorological conditions are very unfavourable to the dispersion of pollutants. Winter is therefore the season with the highest PM10 (daily limit value: 50 μg m−3) and PM2.5 (annual health protection value: 25 μg m-3) concentrations. Houses in Brescia are represented by multifamily buildings, whose dwellings are mainly represented by apartments and single-family buildings that are mainly detached houses and some converted farm outbuildings. Sixty-two houses were randomly selected from the houses of Brescia city. A subsample of 35 dwellings was then selected by eliminating the houses where the PM I/O ratios were greater than 3 (among the 62 where monitoring activities took place) because these values were only acquired during extreme situations characterised by strong winds and heavy rain events. This reduction was necessary for data homogenisation because the former database comprised data collected on two different monitoring periods. The final sample was a good representation of the variability of the house types, building energy classes, single/multi-family buildings, and site-specific characteristics of the city (CENED database, 2019 [60]). Table 1 describes the dwelling characteristics of the final subsample of 35 case studies and the residents’ type. Since the monitoring protocol did not allow the activation

1. Building type (e.g., energy class, window dimensions and fixtures), 2. Meteorological factors (e.g., wind speed, precipitations) and, 3. Site-specific factors (e.g., proximity to busy roads). Previous studies have attempted to find a link between IAQ and building characteristics [57,58] such as construction year, dwelling location and type, ventilation system, and building material. Langer and Bekö (2013) [57] found that these parameters seemed to have almost no effect on indoor PM2.5 and PM10 levels. That is why some other building characteristics were added to the present investigation, for example fixture materials, window dimensions and floor levels, while other characteristics such as construction year and building material were indirectly considered under the energy class parameter (“Casa Clima” certification scheme [59]). This is the first study to explore a relationship between indoor PM concentrations and buildings energy class in Italy. With regards to previous studies in this field, the novel aspects of the present research are the following:

Table 1 Dwellings characteristics and residents' type. Dwellings characteristics of the 35 case studies considered for the statistical analysis and type of residents. Habits related to usual residents’ activities in the houses are not reported because the monitoring protocol did not allow measurements during the activation of PM indoor sources.

• the high number of houses investigated (62), within a very critical • • •

of PM and PN I/O ratios on site- and house-specific factors as well as meteorological data (e.g., temperature, precipitations and wind speed); the investigation of a potential significant dependence of the PM and PN I/O ratios on house-specific factors (e.g., building energy class and window fixtures) and, the integration of field measurements of atmospheric particle concentrations in ultrafine, fine and coarse ranges with meteorological parameters and house-specific data collected during the monitoring period via direct interviews of house residents.

Dwelling characteristics/ resident type

Categories

Percentage (%)

House type

Apartment Detached house Semidetached house Converted farm outbuildings High (A-B classes) Medium (C-E classes) Low (F-G classes) Window airing Mechanical ventilation system Single family Multi-family Working couples with small children Working couples with adult student children Retired singles/couples Working singles

65.71 11.43 17.15 5.71 14.28 22.86 62.86 94.29 5.71 17.14 82.86 28.57 20.00 40.00 11.43

Energy class

urban area in Brescia, Po Valley, Northern Italy, in terms of PM concentrations; the selection of non-smokers’ houses, thus avoiding smoke influence on indoor concentrations; the adopted measurement protocol (in particular, the absence of active indoor PM sources during the monitoring periods and at least a few hours before) that allowed a more representative calculation of PM I/O ratios showing the actual level of particles penetration from outdoor into indoor environments; the adoption of the one-way ANOVA to find an eventual dependence

Ventilation system Buildings Type of residents

2

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Table 2 ANOVA predictive parameters considered in the research. Details concerning the type of factor (meteorological, house-specific, temporal), the categories considered for each predictive factor as well as the source of data collected for the statistical analysis are reported. Parameters

Factor

Categories

Source of data

Meteorological

Temperature (°C) Wind speed (m s−1) Precipitations (mm) Floor Energy class No. of residents Proximity to busy roads Windows in the room: Perimeter Surface of those opened Fixtures material Time

T < 5 °C; T ≥ 5 °C ws < 1 m s−1; ws ≥ 1 m s−1 precipitations < 1 mm; precipitations ≥1 mm 0; 1; 2; 3+ High (A,B); medium-low (C, D, E,F,G) 1; 2–3; 4+ Close; Far

Regional Environmental Agency (ARPA) [52]

House-specific

Temporal

2p < 10 m (small); 2p ≥ 10 m (big) S < 3 m2 (low); S ≥ 3 m2 (medium) Wood; Aluminium; PVC Morning (9:00–11:00); afternoon (14:00–16:00)

Personal Interview

Instrument

• indoor monitoring of PN and PM concentrations for 30 min; • outdoor monitoring of PN and PM concentrations for 30 min; • natural ventilation by window opening (WO) in the monitored room during the last 15 min of the outdoor measurements and, • closure of windows and repetition of the measurements for indoor

of indoor PM sources, the habits related to common activities during the measurements inside the house were not important for our study. The infiltration rates of the houses in the sample were calculated according to the UNI EN 12831–1:2018 standard, and ranged from 0.08 h−1 to 0.75 h−1, with an average value of 0.16 h−1 for the high energy class buildings and 0.21 h−1 and 0.38 h−1 for medium and low energy class buildings, respectively. Measurements were carried out in the morning and in the afternoon in order to maximise the number of monitored houses and to meet the residents’ needs.

aerosol concentrations in the same room previously monitored for 15 min.

The measurement protocol comprised three phases in a temporal series (indoor, outdoor and indoor after being aired from open windows), since only one instrument was available so that measurements could not be conducted indoors and outdoors at the same time like Frank et al. (2003) did [40]. It was therefore decided to conduct continuous measurements for 75 min, which consisted of 30 min indoors and outdoors, followed by other 15 min of indoor monitoring after being aired from open windows, to maintain fairly stable environmental conditions during the entire monitoring period. Measurements were performed in the absence of active aerosol internal sources in order to evaluate the influence of particles originating outdoors on indoor air quality (Franck et al., 2003) [40]. Using the indoor, outdoor and indoor post-ventilation data, two different PM and PN I/O ratios were calculated being pre-and post-WO. In this way the impact of outdoor PM concentrations on indoor PM concentrations and the effect of opening windows for an airing period of 15 min on indoor air quality were evaluated. The air in the ventilated room was changed from 1 to 5 times due to an open window airing period of 15 min. While the measurements were being conducted, the house residents were interviewed to collect information about some house characteristics (Table 2).

2.1. PN and PM concentrations monitoring A wide-range aerosol spectrometer (MINI WRAS 1371, GRIMM, DE) was used to measure the PN and PM concentrations. This spectrometer can count particles with diameters between 10 nm and 35 μm, dividing them in 41 size intervals. The instrument uses an optical method to detect particles with an optical diameter greater than 250 nm and a unipolar corona charger coupled with a cup electrometer to count particles with d < 250 nm. The instrument indirectly calculates PM10, PM2.5 and PM1 with the usual approximations of particle sphericity and applies specific corrective factors to take into account the average density of different particle granulometries. The ALVEOLIC mass concentration fraction is also provided by the instrument according to the UNI EN 481 standard. The PN and PM concentration values were recorded once a minute. In this study particles were grouped according to different size ranges: PN0.1/ultrafine (10–100 nm), PN1, PN2.5, PN10, fine (100 nm-1 μm) and coarse (1–10 μm). The concentration of particles with d > 10 μm was very low, and because their health impact is negligible, particles with optical diameters over 10 μm were not considered.

2.3. Statistical analysis

2.2. Sampling protocol

PN and PM concentrations monitored indoors, outdoors and indoors post WO, the I/O PM and PN ratios pre and post WO, the meteorological and house-specific factors (reported in Table 2) were used as a database for the analysis of variance. The PN, PM concentrations and I/ O ratio values were considered as dependent variables, while meteorological and house-specific factors were chosen as independent (or predictive) variables. The data for the ANOVA comprised 35 houses, and in order to guarantee homogenous data derived from the two monitoring periods, houses with PM outdoor < 20 μg m−3 and I/O > 3 were excluded. As illustrated in Table 2, each independent variable was divided into two or more categories, as required by ANOVA. For the meteorological factors, the mean time of the total measurement periods were calculated for temperature and wind speed. Rain can reduce particulate concentrations for some days, and therefore the amount of precipitation in the 24 h previous to the measurement time was considered. One-way

A protocol was established to compare the different house-specific results, avoiding potential interfering factors. In particular, residents were asked not to activate fine and ultrafine aerosol sources during the previous evening (in case of morning measurements) or in the hours immediately before the monitoring period (in case of afternoon measurements). They were also requested not to open the windows from the evening before the monitoring day to avoid contamination of the indoor environment by external air. The temporary absence of indoor sources was necessary in order to avoid the interference of residents’ activities on indoor PN and PM monitoring and thus finding a relationship between background indoor concentrations and outdoor concentrations. The following protocol was followed for the measurements:

• identification of a monitoring room in the house (with direct access to the outside);

3

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Fig. 1. Meteorological characterization of the two monitoring periods. Meteorological characterisation of the first (a) and the second (b) monitoring periods. Average daily data were calculated from continuous data monitored at the air quality stations of Villaggio Sereno and Brescia Broletto in Brescia (Source of data: ARPA Lombardia [61,62]).

22.2 μg m−3, respectively. During the second monitoring period, significant precipitation (> 1 mm) occurred on the 26th and the 27th of December 2017, on the 1st and the 9th of January 2018, from the 1st to the 2nd of February 2018, on the 6th, 9th, 12th and 18th of February 2018 and from the 22nd to the 23rd of February 2018. The minimum daily temperatures were between −8.6 °C and 9.1 °C, while the maximum daily temperatures were between 16.8 °C and 24 °C. The PM10 average value for the entire period was 47.3 μg m−3, with a standard deviation of 18.7 μg m−3. For PM2.5, the average value and the standard deviation were 38.3 μg m−3 and 16.1 μg m−3, respectively. During the second winter campaign 43% of the houses were monitored during rain events or immediately after high rain events, while 78% of the houses were monitored during days characterised by high wind speeds (greater than or equal to 1 m s−1). This affected the registered PM values and mean values were lower in the second monitoring period than in the first. Fig. 2 shows the maximum, minimum, median, 1st and 3rd quartile values of PM and PN I/O ratios for the 62 monitored houses, indicating the high variability depending on meteorological conditions. Generally, during the winter period without active PM indoor sources, the I/O ratio is lower than 1 indicating a better air quality indoors than outdoors. However, these measurements have revealed that with high wind speeds and/or intense precipitation the I/O ratios assumed values much greater than 1. In the case of extreme weather conditions, the I/O ratios assumed values greater than 3 (excluded from the statistical analysis). As shown in Fig. 2, the I/O ratio assumed values well above 1 on four occasions, but on average the 75th percentile was lower than 2. The average particle density distributions as a function of the

ANOVA calculates PN, PM concentrations and I/O means and variances between and within the different categories, comparing their ratios to a critical value in order to find a significant relationship between a certain dependent variable and a predictive factor. The following ANOVA assumptions were checked before performing the test:

• the independence of measurements (guaranteed because different houses were monitored); • data is normally distributed (checked with the Shapiro-Wilk test); • homoscedasticity (verified with the Levene test); For each test, including ANOVA, the value 0.05 was assumed as the reference p-value. 3. Results Fig. 1 (a, b) shows precipitation, wind speed and PM10 and PM2.5 average daily values monitored by a local air quality station of the Environmental Protection Agency [61,62] during both monitoring periods. During the first monitoring period, the minimum daily temperatures were between −6 °C and 7 °C, while the maximum daily temperatures were between 0 °C and 15 °C. Precipitation above 1 mm occurred from the 12th to the 13th of January 2017, from the 2nd to the 6th of February 2017, on the 28th of February 2017 and on the 4th of March 2017. The PM10 average value for the entire period was approximately 55.6 μg m−3, with a standard deviation of 27.7 μg m−3. For PM2.5, the average value and the standard deviation were 44.4 μg m−3 and 4

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Fig. 2. PN and PM I/O ratios for the two monitoring periods. I\O differences, with box plots representing the I\O ratios for PM10, PM2.5, PM1, ULTRAFINE, FINE and COARSE particles before (PRE, a) and after (POST, b) window opening (WO). Minimum, maximum, median values and first (Q1) and third (Q3) quartiles were calculated considering all the 62 monitored houses. The * in figure a stands for the value 26.47. ULTRAFINE, FINE and COARSE are particle numbers related to 10 nm–100 nm, 100 nm- 1 μm and 1 μm–10 μm dimensional ranges, respectively.

indoor distribution and 2700 cm−3 for the outdoor distribution. Concentrations also remained high in the fine region (100 nm–1000 nm) assuming values from 1100 cm−3, gradually dropping to 0.28 cm−3 and from 1800 cm−3 to 0.56 cm−3 for indoor and outdoor

particle diameter are shown in Fig. 3. The distributions maxima are in the accumulation mode fraction (geometric mean diameter around 60 nm) for both indoor and outdoor particles. The PN average maximum values reached 1800 cm−3 for the

Fig. 3. Average indoor and outdoor PN distributions. Indoor and outdoor average particle number distributions considering the 35 houses selected for statistical analysis. Note the logarithmic scale on both the x-axis and y-axis. The error bars represent the standard error of the mean (SEM). 5

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represent a sufficiently large sample compared to the datasets used in previous studies [11,13–15,34]. The size-resolved distributions of PN concentrations emerging from the two monitoring periods indicate that, with no active internal sources, the indoor and outdoor PN distribution trends were quite similar, suggesting that indoor particles were mainly of outdoor origin. This concords with findings by Franck et al. (2003) [40]. The temporary absence of indoor sources imposed by our monitoring protocol was focused on investigating background concentrations of indoor PM in the monitored dwellings and their relationship with outdoor concentrations. We did not perform tests on the indoor PM sources so were not able to correctly associate the different PM fractions to specific PM sources. Nevertheless, when considering similar studies related to the activation of PM indoor sources (e.g., Ji and Zhao, 2015) and the results presented in Fig. 3 we suggest that:

Table 3 Anova test results related to PN and PM concentrations. ANOVA test results with p < 0.05 (*), p < 0.01 (**) or p < 0.001 (***) for indoor, outdoor and indoor post-window opening (post WO) monitoring periods. Grey boxes represent insignificant results (p > 0.05). Only the factors with some significant p-values are shown. FINE and COARSE represent particle numbers related to 100 nm–1 μm and 1–10 μm dimensional ranges, respectively. ALVEOLIC is the mass concentration of particles evaluated according to the UNI EN 481 standard. INDOOR Energy class

Window perimeter

OUTDOOR

INDOOR POST WO

Wind speed

Proximity to busy roads

Wind speed

PN0.1 PN1 PN2.5 PN10

** ** ** **

*** *** *** ***

*** *** *** ***

FINE COARSE

** ***

PM1 PM2.5 PM10

* ** **

* * *

* * *

ALVEOLIC

**

*

**

**

*

*

1) ultrafine and fine PM fractions presented higher outdoor concentrations than indoors, suggesting that their emissions were mainly due to outdoor sources and, 2) the difference between indoor and outdoor coarse particle concentrations was negligible and therefore it may be more difficult to remove coarse indoor particles (alternatively, indoor sources can be considered mainly responsible for coarse particles generation).

**

The maximum values of indoor and outdoor distributions presented by particles in the accumulation range were also found in other studies [40,63,64]. Franck et al. (2003) [40] also observed lower particle concentrations indoor than outdoor, concluding that the indoor environment is partially insulated from the outside. This study revealed that under specific meteorological conditions such as heavy rain episodes and strong wind events, indoor PN and PM concentrations could be higher than outdoor concentrations (i.e. PM and PN I/O ratio values could be greater than one) even with no active indoor sources. When considering our results, case studies associated with weather events such as heavy rain or strong wind (that led to I/O ratio values greater than 3) have been excluded in order to create homogeneous data from both monitoring campaigns, thus some limitations arose in finding meaningful relationships between meteorological parameters and outdoor PM and PN concentrations. Conversely, this study found that the energy class had a strong influence on indoor PM and PN concentrations (Fig. 4 a-d). Thomas et al. (2019) [34] studied indoor particle concentrations as a function of insulation in dwellings. They reported that the concentration of particles with aerodynamic diameters lower than 300 nm was higher for missing insulation on exterior walls over 5%. As no smoking or burning activities occurred in the dwellings, they concluded that those particles were coming from the outdoor environment. In this work, average concentrations in high energy class buildings (A or B) assumed halved values compared to those registered in medium-low energy class dwellings (Fig. 4d). Considering the low energy and high energy class buildings monitored in the present study under the “low insulated group” and “high insulated group” of Thomas et al. (2019) [34] we can state that high energy class buildings hinder the penetration of particles in the house due to better insulation. On the contrary, our study found that the energy class had a strong influence on indoor PM and PN concentrations affecting the infiltration of both ultrafine and coarse particles (Fig. 4 a-d). The dependence of the infiltration rates on the energy class of dwellings has been investigated in Cheng and Li, 2018 [65] where the distribution of infiltration values calculated for low and high energy class buildings presented values ranging from 0.05 to 1.32 h−1 and an average value of 0.4 h−1. These reported values agree with those calculated in the present work. Considering the sitespecific factors, our measurements have demonstrated that local traffic negatively affects outdoor PN concentrations, increasing the indoor density of fine particles in the house after the natural ventilation period (Fig. 5). In particular, when indoor values were measured after the windows

Table 4 Anova test results related to I/O ratios. ANOVA test results with p < 0.05 (*), p < 0.01 (**) or p < 0.001 (***) for I\O ratios pre and post window opening (WO). Grey boxes represent insignificant results (p > 0.05). Only the factors with some significant p-values are shown. ULTRAFINE, FINE and COARSE represent particle numbers related to 10–100 nm, 100 nm–1 μm, and 1–10 μm dimensional ranges, respectively. I/O PRE WO Energy class PM10 PM2.5 PM1

* *

ULTRAFINE FINE COARSE

* **

I/O POST WO Window perimeter

Window surface

* *

Wind speed

*

measurements, respectively. The distribution of indoor particles showed lower concentrations in the ultrafine and fine regions compared to outdoor measurements, while for particles greater than 2.5 μm the two distributions firstly overlapped, while particle diameters between 3 and 9 μm for outdoor concentrations presented lower values than indoor concentrations. The statistical analysis, in particular the ANOVA, indicated that indoor PN and PM concentrations vary significantly depending on the building energy class, while the window perimeter of the room monitored is statistically related only to indoor coarse particle concentration (Tables 3 and 4). For outdoor concentrations, a significant dependence on wind speed was confirmed. For indoor particle concentrations monitored after the window airing period (post WO) a significant dependence on the proximity to busy roads was evidenced, as well as a dependence on wind speed that affects particle concentrations of outdoor air entering the room during the ventilation period. The significant difference among PN, PM or I/O ratios average values associated to different levels of the same predictive factor are demonstrated in Figs. 4–8. 4. Discussion The 35 houses in the database used to perform the ANOVA test 6

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Fig. 4. Influence of building energy class on PN and PM indoor concentrations and I/O ratios. Average PN (a, b), PM (c) concentrations and I/O ratios (d) pre windows opening (WO) for high (A, B) or medium-low (C, D, E, F, G) energy classes. The error bars represent the standard error of the mean (SEM). ULTRAFINE, FINE and COARSE stand for 10 nm–100 nm, 100 nm- 1 μm and 1 μm–10 μm particle number ranges, respectively. ALVEOLIC is the mass concentration of particles evaluated according to UNI EN 481 standard.

indoors, causing a 32% increment of their concentrations if the perimeter is lower than 10 m. Another window characteristic that influences indoor air quality is the surface of the windows opened during the ventilation period (Fig. 7). The I/O ratio measured indoors after the natural ventilation of the room (window opening) was almost 8% higher for a wide window surface (S ≥ 3 m2). If the opened windows were small there was a lower penetration of particles indoors and the outdoor air would not worsen the indoor air quality. From our results it emerged that the window perimeter is linked to the capacity of fixtures to retain particles inside the house, while the window surface is strictly related to the natural ventilation effect on improving or worsening indoor air quality. Other studies found a correlation between particle concentrations or I/O ratios and meteorological parameters [14,44]. In particular, Chan (2002) [44], using statistical regression techniques, pointed out the influence of outdoor temperature on the I/O ratio for respirable suspended particulate (RSP), measuring greater I/O ratios for higher T, while Lee et al. (2016) [14] found that T was one of the predictors of their IOR (I/ O ratio) model, performing a Pearson correlation analysis followed by a multi-step multivariate linear regression. In our case, ANOVA did not find any relationship between temperature and PM, PN or I/O ratios. Chan (2002) [44] investigated the influence of wind speed on RSP (Respirable Suspended Particles) I/O ratio, but obtained a poor correlation. In contrast, our ANOVA revealed that wind speed is linked not only to outdoor, but also to indoor post WO, PM, and PN concentrations. When wind speed exceeded 1 m s−1 outdoor PN concentrations reduced by up to 40% (Fig. 8a) and PM by up to 41% (Fig. 8b), while the indoor post WO, PN, and PM concentrations were 42% lower (Fig. 8a and b). The quality of indoor air improved if the windows were opened in a windy day, because the I/O ratio associated to fine particles after the ventilation period was 6% lower if the wind speed exceeded 1 m s−1

Fig. 5. Influence of proximity to busy roads on indoor particles after windows airing. Average fine indoor post windows opening (WO) particle concentrations with respect to traffic. CLOSE means that the monitored room faced a busy road, while FAR means it did not. The error bars represent the standard error of the mean (SEM). FINE is the particle number related to the dimensional range 100 nm- 1 μm.

were opened on a busy road, the concentration of indoor fine particles was 38% higher than indoor fine particle concentrations in low traffic areas (Fig. 5). No significant relationships emerged from the ANOVA between the proximity to busy roads and indoor concentrations before the window airing period. From the results it also emerged that the windows perimeter in the monitoring room influenced coarse particle concentration (Fig. 6 a-b). A low window perimeter is related to a greater density of coarse particles with respect to a perimeter higher than 10 m. This could be due to a building “trapping” effect, whereby the coarse particles produced by internal sources cannot easily pass outside and remain 7

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Fig. 6. Influence of windows perimeter of the room monitored on coarse indoor particles and I/O ratio before windows airing. Average coarse indoor concentrations and I/O ratios pre windows opening(WO) for coarse particles for a small (2p < 10 m) or a big (2p ≥ 10 m) windows perimeter in the monitored indoor room. The error bars represent the standard error of the mean (SEM). COARSE is the particle number related to the dimensional range 1 μm–10 μm.

Fig. 8. Influence of wind speed on PN and PM concentrations and fine particles I/O ratio after windows airing. Average PN (a), PM (b) concentrations and I/O ratio after windows opening (WO) (c) for wind velocity higher or lower than 1 m s−1. The error bars represent the standard error of the mean (SEM). FINE is the particle number related to the dimensional range 100 nm- 1 μm. ALVEOLIC is the mass concentration of particles evaluated according to UNI EN 481 standard. Fig. 7. Influence of surface of windows opened on the I/O ratio of ultrafine particles after windows airing. Average I/O ratio after windows opening (WO) for a low (S < 3 m2) or a medium (S ≥ 3 m2) surface of windows opened during the ventilation period. The error bars represent the standard error of the mean (SEM). ULTRAFINE is the particle number related to the dimensional range 10 nm–100 nm.

considering closed windows and no window airing. In particular, outdoor PM2.5 concentrations were reduced by 41% and 76% with outdoor wind speeds between 1 m s−1 and 3 m s−1 and greater than 5 m s−1 respectively, compared to speed concentrations lower than 1 m s−1. This significant decrease in outdoor PM2.5 concentrations, that concords with our findings that average outdoor PM2.5 concentrations reduced by 41% for wind speeds higher than 1 m s−1 and led to an increase of the I/O ratio with values of 0.6 and 0.7 with outdoor wind speeds between 1 m s−1 and 3 m s−1 and greater than 5 m s−1, respectively compared to an average value of 0.45 for low wind speeds (< 1 m s−1). In this study, the ANOVA results showed a statistically significant decrease in the I/O ratio associated with fine particles and outdoor wind speeds greater than 1 m s−1, but after the window airing period our results are well aligned with other findings [67]. Lee et al. (2016) [14] also considered the floor level as a predictor for their IOR (INDOOR OUTDOOR RATIO) model. The floor level

(Fig. 8c). Our results are supported by previous studies [66,67] that aimed to investigate the correlations between indoor and outdoor PM, as well as the PM I/O ratio with wind speed. Lang et al. (2016) showed that diffusion dominates when outdoor wind speed is lower than 6 m s−1 and outdoor pollutants concentrations are negatively correlated with wind speed values. Wan et al. (2015) showed a statistically significant increase of PM2.5 I/O ratios for outdoor wind speeds higher than 1 m s−1 when 8

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influence on PN, PM concentrations and I/O ratio was also investigated in our study, but no significant correlations were found. This result is most probably because measurements were carried out in different houses and days, so the relationship between floor level and PM, PN concentrations and I/O ratio values was altered by the different meteorological and territorial conditions associated with the specific measurements.

Declaration of interest None. Acknowledgments The research leading to these results was co-funded by Università Cattolica del Sacro Cuore under the framework of the ANAPNOI Project (call D3.2 of year 2016). We also thank the citizens who actively participated in the study.

5. Limitations of the study Some limitations of the present study are, firstly, the measurement of simultaneous indoor/outdoor PN and PM concentrations that was not possible because only one aerosol spectrometer was available. It was therefore not possible to simultaneously investigate PM concentrations at different heights with respect to ground level, so the parameter “floor level” will require further investigation. Secondly, concentrations of gaseous compounds such as CO2, formaldehyde and VOCs were not monitored and therefore, this study addressed IAQ in residential buildings with a specific focus on indoor PN and PM concentrations.

References [1] K.H. Kima, E. Kabir, S. Kabir, A review on the human health impact of airborne particulate matter, Environ. Int. 74 (2015) 136–143. [2] Y. Guo, Y. Jia, X. Pan, L. Liu, H.E. Wichmann, The association between fine particulate air pollution and hospital emergency room visits for cardiovascular diseases in Beijing, China, Sci. Total Environ. 407 (2009) 4826–4830. [3] L.V. Veremchuk, V.I. Yankova, T.I. Vitkina, A.V. Nazarenko, K. Sergeevich Golokhvast, Urban air pollution, climate and its impact on asthma morbidity, Asian Pac J Trop Biomed 6 (2016) 76–79. [4] G. Polichetti, S. Coccoa, A. Spinalia, V. Trimarcoa, A. Nunziata, Effects of particulate matter (PM10, PM2.5 and PM1) on the cardiovascular system, Toxicology 261 (2009) 1–8. [5] M. Szyszkowicz, B.H. Rowe, R.D. Brook, Even low levels of ambient air pollutants are associated with increased emergency department visits for hypertension, Can. J. Cardiol. 28 (3) (2012) 360–366. [6] P.L. Jenkins, T.J. Phillips, E.J. Mulberg, S.P. Hui, Activity patterns of Californians: use of and proximity to indoor pollution sources, Atmos. Environ. 26 (1992) 2141–2148. [7] N.E. Klepeis, W.C. Nelson, W.R. Ott, J.P. Robinson, A.M. Tsang, P. Switzer, J.V. Behar, S.C. Hern, W.H. Gelmann, The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants, J. Expo. Anal. Environ. Epidemiol. 11 (3) (2001) 231–252. [8] A. Farrow, H. Taylor, J. Golding, Time spent in the home by different family members, Environ. Technol. 18 (1997) 605–614. [9] S. Brasche, W. Bischof, Daily time spent indoors in German homes – baseline data for the assessment of indoor exposure of German occupants, Int. J. Hyg Environ. Health 208 (4) (2005) 247–253. [10] A. Cincinelli, T. Martellini, Indoor air quality and health, Int. J. Environ. Res. Public Health 14 (11) (2017) 1286. [11] P. Blondeau, V. Iordache, O. Poupard, D. Genin, F. Allard, Relationship between outdoor and indoor air quality in eight French schools, Indoor Air 15 (1) (2005) 2–12. [12] I. Colbeck, Z.A. Nasir, Z. Ali, Characteristics of indoor/outdoor particulate pollution in urban and rural residential environment of Pakistan, Indoor Air 20 (1) (2010) 40–51. [13] N.C. Jones, C.A. Thornton, D. Mark, R.M. Harrison, Indoor/outdoor relationships of particulate matter in domestic homes with roadside, urban and rural locations, Atmos. Environ. 34 (2000) 2603–2612. [14] J.Y. Lee, S.H. Ryu, G. Lee, G. Bae, Indoor-to-outdoor particle concentration ratio model for human exposure analysis, Atmos. Environ. 127 (2016) 100–106. [15] L. Morawska, C. He, J. Hitchins, D. Gilbert, S. Parappukkaran, The relationship between indoor and outdoor airborne particles in the residential environment, Atmos. Environ. 35 (2001) 3463–3473. [16] S. Oeder, S. Dietrich, I. Weichenmeier, W. Schober, G. Pusch, R.A. Jçrres, R. Schierl, D. Nowak, H. Fromme, H. Behrendt, J.T.M. Buters, Toxicity and elemental composition of particulate matter from outdoor and indoor air of elementary schools in Munich, Germany, Indoor Air 22 (2012) 148–158. [17] B. Alharbi, M.M. Shareef, T. Husain, Study of chemical characteristics of particulate matter concentrations in Riyadh, Saudi Arabia, Atmos.Pollut. Res 6 (2015) 88–98. [18] S.W. See, R. Balasubramanian, Chemical characteristics of fine particles emitted from different gas cooking methods, Atmos. Environ. 42 (2008) 8852–8862. [19] D.A. Missia, E. Demetriou, N. Michael, E.I. Tolis, J.G. Bartzis, Indoor exposure from building materials: a field study, Atmos. Environ. 44 (2010) 4388–4395. [20] J. Herrera Murillo, S. Rodriguez Roman, J.F. Rojas Marin, A. Campos Ramos, S. Blanco Jimenez, B. Cardenas Gonzalez, D.G. Baumgardner, Chemical characterization and source apportionment of PM10 and PM2.5 in the metropolitan area of Costa Rica, Central America, Atmos.Pollut. Res 4 (2013) 181–190. [21] F.J. Kelly, J.C. Fussell, Size, source and chemical composition as determinants of toxicity attributable to ambient particulate matter, Atmos. Environ. 60 (2012) 504–526. [22] W. Ji, B. Zhao, Contribution of outdoor-originating particles, indoor-emitted particles and indoor secondary organic aerosol (SOA) to residential indoor PM2.5 concentration: a model-based estimation, Build. Environ. 90 (2015) 196–205. [23] E. Abt, H.H. Suh, P. Catalano, P. Koutrakis, Relative contribution of outdoor and indoor particle sources to indoor concentrations, Environ. Sci. Technol. 34 (2000) 3579–3587. [24] T. Hussein, T. Glytsos, J. Ondracek, V. Zdimal, Lazaridis M. Hameri, J. Smolik, M. Kulmala, Particle size characterization and emission rates during indoor activities in a house, Atmos. Environ. 40 (2006) 4285–4307. [25] N. Serfozo, S.E. Chatoutsidou, M. Lazaridis, The effect of particle resuspension

6. Conclusions This study conducted two winter monitoring campaigns of indoor and outdoor PN and PM concentrations in order to understand the impact of outdoor concentrations on indoor air quality in domestic environments. The experimental measurements were carried out in 62 houses located in the city of Brescia in Northern Italy, with no active indoor PM sources, and a subsample of 35 houses was used to perform the statistical analysis. The average distribution of indoor and outdoor particles showed that, with no active indoor sources, indoor concentrations were lower than outdoor concentrations but followed the same trend. Both distributions presented a maximum in the accumulation mode fraction (Dp around 60 nm), followed by a gradual decrease to negligible concentrations for particles with d > 10 μm. Weather conditions such as heavy rain or high wind speeds caused a significant reduction in PN and PM outdoor concentrations and thus monitoring higher indoor concentrations, even with no active indoor PM sources led to I/O ratios well above 1. From the interviews of the residents of the houses (mainly housespecific factors) and the ANOVA analysis, it was found that the building energy class, the proximity to busy roads, wind speed, and the perimeter and surface of windows could affect PN and PM concentrations as well as the I/O ratio. From our results, some strategies to improve indoor air quality in a city such as Brescia can be suggested. For example, because a high wind speed is able to abate outdoor PM and PN concentrations, opening windows during a windy day can be advised. If high surface windows are opened to naturally ventilate a house, a high number of outdoor particles will enter the house, negatively affecting indoor air quality. Even a low perimeter of fixtures of the monitored room (e.g., window perimeter) limits the potential for coarse indoor particles to move outdoors and remain trapped in the house, leading to higher concentrations. Furthermore, indoor concentrations of houses located on busy roads are negatively affected after open windows that face those streets. Other factors investigated, in particular window fixtures and floor levels, did not influence indoor or outdoor air quality, but this could be due to protocol or statistical analysis. For example, to fully understand the floor influence on outdoor and indoor PN and PM concentrations, more instruments are needed to monitor different heights at the same time. In order to understand the influence of windows and fixture materials on indoor PN and PM concentrations, performing a large number of measurements presenting a variability of fixtures materials for each energy class would be necessary. The low, medium or high energy class houses in this study mainly had wooden window fixtures and a statistical analysis was not possible. 9

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[26] [27] [28] [29] [30] [31] [32] [33] [34]

[35] [36] [37] [38] [39] [40]

[41] [42] [43] [44]

[45]

[46]

during walking activity to PM10 mass and number concentrations in an indoor microenvironment, Build. Environ. 82 (2014) 180–189. Y.C. Li, M. Shua, S.S.H. Ho, C. Wang, J.J. Cao, G.H. Wang, X.X. Wang, K. Wang, X.Q. Zhao, Characteristics of PM2.5 emitted from different cooking activities in China, Atmos. Res. 166 (2015) 83–91. E. Géhin, O. Ramalho, S. Kirchner, Size distribution and emission rate measurement of fine and ultrafine particle from indoor human activities, Atmos. Environ. 42 (2008) 8341–8352. T. Glytsos, J. Ondráček, L. Džumbová, I. Kopanakis, M. Lazaridis, Characterization of particulate matter concentrations during controlled indoor activities, Atmos. Environ. 44 (2010) 1539–1549. C. Chen, B. Zhao, Review of relationship between indoor and outdoor particles: I/O ratio, infiltration factor and penetration factor, Atmos. Environ. 45 (2011) 275–288. Z. Chen, C. Chen, S. Wei, Y. Wu, Y. Wang, Y. Wan, Impact of the external window crack structure on indoor PM2.5 mass concentration, Build. Environ. 108 (2016) 240–251. D.L. Liu, W.W. Nazaroff, Modeling pollutant penetration across building envelopes, Atmos. Environ. 35 (2001) 4451–4462. D.L. Liu, W.W. Nazaroff, Particle penetration through building cracks, Aerosol Sci. Technol. 37 (2003) 565–573. C.J. Jeng, W.B. Kindzierski, D.W. Smith, Modeling entry of micron-sized and submicron-sized particles into the indoor environment, Aerosol Sci. Technol. 37 (2003) 753–769. N.M. Thomas, L. Calderón, J. Senick, M. Sorensen-Allacci, D. Plotnik, M. Guo, Y. Yu, G. Gong, C.J. Andrews, G. Mainelis, Investigation of indoor air quality determinants in a field study using three different data streams, Build. Environ. 154 (2019) 281–295. A. Li, T. Ren, C. Yang, W. Lv, F. Zhang, Study on particle penetration through straight, L, Z and wedge-shaped cracks in buildings, Build. Environ. 114 (2017) 333–343. Y. Lv, H. Wang, S. Wei, T. Wu, T. Liu, B. Chen, The experimental study on indoor and outdoor penetration coefficient of atmospheric fine particles, Build. Environ. 132 (2018) 70–82. t Zhang, z Su, j Wang, s Wang, Ventilation, indoor particle filtration, and energy consumption of an apartment in northern China, Build. Environ. 143 (2018) 280–292. M. Kulmala, A. Asmi, L. Pirjola, Indoor air aerosol model: the effect of outdoor air, filtration and ventilation on indoor concentrations, Atmos. Environ. 33 (1999) 2133–2144. F. Wang, D. Meng, X. Li, J. Tan, Indoor-outdoor relationships of PM2.5 in four residential dwellings in winter in the Yangtze River Delta, China, Environ. Pollut. 215 (2016) 280–289. U. Franck, O. Herbarth, B. Wehner, A. Wiedensohler, M. Manjarrez, How do the indoor size distributions of airborne submicron and ultrafine particles in the absence of significant indoor sources depend on outdoor distributions? Indoor Air 13 (2003) 174–181. Z. Shao, J. Bi, Z. Ma, J. Wang, Seasonal trends of indoor fine particulate matter and its determinants in urban residences in Nanjing, China, Build. Environ. 125 (2017) 319–325. S. Paraschiv, L.S. Paraschiv, A review on interactions between energy performance of the buildings, outdoor air pollution and the indoor air quality, Energy Procedia 128 (2017) 179–186. I. Odeh, T. Hussein, Activity pattern of urban adult students in an eastern mediterranean society, Int. J. Environ. Res. Public Health 13 (2016) 960. C. Schweizer, R.D. Edwards, L. Bayer-Oglesb, W.J. Gauderman, V. Ilacqua, M.J. Jantunen, H.K. Lai, M. Nieuwenhuijsen, Indoor time-microenvironment-activity patterns in seven regions of Europe, J. Expo. Sci. Environ. Epidemiol. 17 (2007) 170–181. U. Franck, O. Herbarth, B. Wehner, A. Wiedensohler, M. Manjarrez, How do the indoor size distributions of airborne submicron and ultrafine particles in the absence of significant indoor sources depend on outdoor distributions? Indoor Air 13 (2003) 174–181. D. Massey, A. Kulshrestha, J. Masih, A. Taneja, Seasonal trends of PM10, PM5.0,

[47] [48] [49] [50]

[51] [52] [53] [54]

[55] [56] [57] [58] [59] [60] [61] [62] [63] [64]

[65] [66] [67]

10

PM2.5 & PM1.0 in indoor and outdoor environments of residential homes located in North-Central India, Build. Environ. 47 (2012) 223–231. A.T. Chan, Indoor-outdoor relationships of particulate matter and nitrogen oxides under different outdoor meteorological conditions, Atmos. Environ. 36 (2002) 1543–1551. D. Pernigotti, E. Georgieva, P. Thunis, B. Bessagnet, Impact of meteorological modelling on air quality: summer and winter episodes in the Po valley (Northern Italy), Int. J. Environ. Pollut. 50 (2012) 111–119. P. Wallner, P. Tappler, U. Munoz, B. Damberger, A. Wanka, M. Kundi, H. Hutter, Health and wellbeing of occupants in highly energy efficient buildings: a field study, Int. J. Environ. Res. Public Health 14 (2017) 314. Directive 2010/31/EU of the european parliament and of the council of 19 may 2010 on the energy performance of buildings, https://eur-lex.europa.eu/ LexUriServ/LexUriServ.do?uri=OJ:L:2010:153:0013:0035:en:PDF , Accessed date: 26 April 2019. A. Steinemann, P. Wargocki, B. Rismanchi, Ten questions concerning green buildings and indoor air quality, Build. Environ. 112 (2017) 351–358. W.L. Lee, J. Burnett, Benchmarking energy use assessment of HK-BEAM, BREEAM and LEED, Build. Environ. 43 (11) (2008) 1882–1891. M. Derbez, G. Wyart, E. Le Ponner, O. Ramalho, J. Ribéron, C. Mandin, Indoor air quality in energy‐efficient dwellings: levels and sources of pollutants, Indoor Air 28 (2018) 318–338. S. Kirchner, M. Derbez, C. Duboudin, et al., Indoor Air Qualcity in French Dwellings. Report N° AIVC – CR12. AIVC, 21 pp http://www.aivc.org/sites/ default/files/members_area/medias/pdf/CR/CR12%20IAQ%20French%20Survey. pdf, (2009) Accessed May 01, 2019. S. Kirchner, C. Mandin, M. Derbez, et al., Qualité d’air intérieur, qualité de vie. 10 ans de recherche pour mieux respirer, CSTB. Editions, Paris, France, 2011. V. Kaunelienė, T. Prasauskas, E. Krugly, I. Stasiulaitienė, D. Čiužas, L. Šeduikytė, D. Martuzevičius, Indoor air quality in low energy residential buildings in Lithuania, Build. Environ. 108 (2016) 63–72. S. Langer, G. Bekö, Indoor air quality in the Swedish housing stock and its dependence on building characteristics, Build. Environ. 69 (2013) 44–54. S. Langer, O. Ramalho, M. Derbez, J. Ribéron, S. Kirchner, C. Mandin, Indoor environmental quality in French dwellings and building characteristics, Atmos. Environ. 128 (2016) 82–91. Il Vostro Certificato CasaClima, http://www.agenziacasaclima.it/smartedit/ documents/inhalte/_Inhalte_Publikationen/_published/Il%20vostro%20certificato %20CasaClima.pdf , Accessed date: 26 April 2019. Energy Certification of Buildings (CENED) Database (Buildings Energy Class and Dwellings Characteristics Data) of the Lombardy Region, http://www.cened.it , Accessed date: 6 May 2019. ARPA Lombardia (Meteorological Data), http://www.arpalombardia.it/siti/ arpalombardia/meteo/richiesta-dati-misurati/Pagine/RichiestaDatiMisurati.aspx , Accessed date: 10 January 2019. ARPA Lombardia (Air Quality Data), http://www.arpalombardia.it/sites/qaria/_ layouts/15/qaria/ricercaidati.aspx , Accessed date: 10 January 2019. U. Franck, T. Tuch, M. Manjarrez, A. Wiedensohler, O. Herbarth, Indoor and outdoor submicrometer particles: exposure and epidemiologic relevance ("the 3 indoor Ls"), Environ. Toxicol. 21 (6) (2006) 606–613. J. Zhao, K. Weinhold, M. Merkel, A. Schmidt, S. Schlecht, T. Tuch, B. Wehner, W. Birmili, A. Wiedensohler, Concept of high quality simultaneous measurements of the indoor and outdoor aerosol to determine the exposure to fine and ultrafine particles in private homes, Gefahrst. Reinhalt. Luft 3 (2018) 73–78. P.L. Cheng, X. Li, Air infiltration rates in the bedrooms of 202 residences and estimated parametric infiltration rate distribution in Guangzhou, China, Energy Build. 164 (2018) 219–225. F.L. Lang, W.Q. Yan, Q. Zhang, J. Cao, Size distribution of atmospheric particle number in Beijing and association with meteorologicalconditions, China Environ. Sci. 33 (2013) 1153–1159. Y. Wan, C. Chen, P. Wang, Y. Wang, Z. Chen, L. Zhao, Infiltration characteristic of outdoor fine particulate matter (PM2.5) for the window gaps, Procedia.Eng. 121 (2015) 191–198.