Yu Liu1,2, Jiankai Dong2, Hongqiang Ma This email address is being protected from spambots. You need JavaScript enabled to view it.1, Yiqiang Jiang This email address is being protected from spambots. You need JavaScript enabled to view it.2, Wenke Zheng2, Xinmei Luo1 

1 School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang 330013, China
2 School of Architecture, Harbin Institute of Technology; Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150090, China

Received: May 1, 2022
Revised: July 22, 2022
Accepted: August 3, 2022

 Copyright The Author's institutions. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. 

Download Citation: ||https://doi.org/10.4209/aaqr.220174  

Cite this article:

Liu, Y., Dong, J., Ma, H., Jiang, Y., Zheng, W., Luo, X. (2022). An Overview: PM2.5 Concentration Levels in Urban Residential Buildings during the Past Two Decades. Aerosol Air Qual. Res. 22, 220174. https://doi.org/10.4209/aaqr.220174


  • Household PM2.5 concentrations across different countries were collected.
  • Influence factors of indoor PM2.5 concentrations were explicated.
  • Mean daily indoor PM2.5 concentrations ranged from 6.6 µg m3 to 493.0 µg m3.
  • Indoor PM2.5 concentrations in developing countries were much higher.


The public has become increasingly aware of the critical effect of fine particle matter (PM2.5) on indoor air quality. Urban residents spend more than half of their time at home. Therefore, monitoring PM2.5 concentrations in residential settings is critical. This paper presents a review of studies on PM2.5 concentrations in the living rooms of urban residential buildings. We included studies measuring indoor PM2.5 concentrations across different regions worldwide and then summarized the measured concentrations. Factors contributing to differences in indoor concentrations were identified and explained. The review results revealed that most of the included studies were conducted in Asia and in Europe, and some were conducted in North America and Africa. Moreover, the mean daily PM2.5 concentration ranged from 17.3 µg m–3 in North America to 68.6 µg m–3 in Asia. Factors influencing PM2.5 concentrations were as follows: indoor activities, ventilation type and air cleaner (AC) use, building type and performance, ambient environment and season. Smoking and cooking considerably increased PM2.5 concentrations in the living rooms, even in measurements conducted over a short time. The use of an AC could reduce indoor PM2.5 concentration in an average of 60%. Regarding building type, PM2.5 concentration in multifamily apartment buildings had higher PM2.5 concentrations than did single-room residences. Moreover, severe outdoor particle pollution increased indoor PM2.5 concentrations by up to 142% in low-energy residential buildings.

Keywords: PM2.5, Concentration, Residential building, Indoor activity, Ventilation, Ambient environment


Urban residents spend most of their time in indoor environments. Investigations of people’s activity patterns have indicated that more than half of the time in a day is spent in residential buildings (Kornartit et al., 2010; de Kluizenaar et al., 2017). The National Human Activity Pattern Survey of the United States reported that on average, people spend 16.6 h per day in average in residential buildings (Klepeis et al., 2001). Baxter et al. (2013) observed that people spend 16.98 to 18.05 h per day in residential buildings in New Jersey, the United States. The Canadian Human Activity Pattern Survey indicated that adult Canadians spend 15.83 to 16.0 h per day in homes (Leech et al., 2002; Matz et al., 2014, 2015). According to one exposure assessment study, children spend 16.1 to 17.35 h per day at home in Windsor, Ontario (Van Ryswyk et al., 2014). Moreover, reports indicate that on average, people spend 13.95 h per day at home in seven European cities (Schweizer et al., 2007). In Germany, Belgium and Denmark, people spend approximately 15.7 (Brasche and Bischof, 2005), 15.84 (Dons et al., 2011) and 17.3 h (Bekö et al., 2015) per day at home, respectively. These figures clearly indicate that people spend considerable time in residential buildings. Therefore, special attention should be paid to residential indoor environments.

Air pollution is a key factor influencing residential environments. Among all forms of air pollutants, particle matter (PM) has received the most extensive research attention because of its adverse effects on health (Stranger et al., 2007; Han et al., 2010; Wu et al., 2018; Zhu et al., 2018). In particular, fine particle matter (PM2.5, aerodynamic diameter less than 2.5 µm) causes severe morbidity and mortality because it is easily absorbed by the lungs and distributed throughout the human body (Hofmann, 2011; Achilleos et al., 2017; Li et al., 2017). Toxicological and epidemiological studies have indicated that PM2.5 concentrations in various environments are strongly associated with several adverse health outcomes such as respiratory and cardiovascular diseases (Wallace et al., 2003; Anderson et al., 2012; Wyzga and Rohr, 2015; Sun et al., 2019). Regarding urban outdoor environments, Cakmak et al. (2018) analysed the association between exposure to ambient PM2.5 and disease-related mortality across Canada; their research indicated that an increase of 10 µg m–3 in long-term PM2.5 exposure resulted in a hazard ratio of 1.26 for lung cancer mortality. You et al. (2017) evaluated PM exposure and element deposition distributions in the human respiratory system by using data collected alongside a highway in Singapore. Perrone et al. (2013) assessed the chemical composition of PM by using data collected from Italian urban sites. Furthermore, Zwozdziak et al. (2017) estimated the inhaled dose of ambient PM in an urban area of southern Poland, and they reported the PM deposition fractions. Martuzevicius et al. (2008) estimated traffic-induced PM2.5 concentrations near major highways in Cincinnati, the United States. Chen et al. (2017) determined the chemical components of regional PM2.5 and the corresponding source contributors in Guangzhou, China. Regarding urban indoor environments, Bai et al. (2020) assessed the health risks of polycyclic aromatic hydrocarbons (PAHs) attached in PM2.5 in an office environment. Chen et al. (2018) also investigated the associations between PM2.5 and asthmatic or allergic diseases in Chinese preschool children. In South Asia, exposure to indoor PM emissions from anthropogenic activities engenders considerable health risks (Junaid et al., 2018). Zhao et al. (2019) collected samples of PAHs from cooking emissions for health risk assessment in residential settings. Although numerous studies have reported the effects of outdoor PM, indoor particles have only recently started attracting research attention (Han et al., 2015, 2016; Butler and Madhavan, 2017; Tang and Wang, 2018; Huang et al., 2018). Therefore, addressing the problem of indoor PM2.5 is crucial.

Building envelopes separate residents from the outdoor environment, leading to an accumulation of indoor particles. Previous reviews have demonstrated that PM2.5 pervades in indoor environments. Diaz Lozano Patino and Siegel (2018) conducted a review of indoor environment air quality in European and American buildings, and observed that smoking led to high concentrations of PM2.5. Morawska et al. (2017) reviewed the exposure pathways of airborne particles of outdoor origin in home, school, office and aged care facility environments. Other scholars have also conducted reviews of air pollutants in office environments (Wolkoff, 2013; Al Horr et al., 2016; Nezis et al., 2019). However, these reviews have neither summarized PM2.5 concentrations nor identified the influential factors in urban residences. Additionally, people spend considerable time in residential buildings, especially in the living room. Hence, a thorough investigation of PM2.5 concentrations in living rooms is necessary for effective indoor air quality control. Accordingly, we present a review of PM2.5 concentrations in urban residential buildings, particularly studies predominantly focusing on the living room. Detailed analyses of the factors influencing PM2.5 concentrations are also presented.


2.1 Search Strategy

In this review, we included studies on PM2.5 concentrations in the living rooms of urban home environments over the past two decades. The following search terms were used: “indoor environment” OR “indoor air quality”, “indoor PM2.5” OR “indoor fine particulate matter” OR “indoor particles”, and “urban dwellings” OR “urban residential buildings” OR “urban homes”. The inclusion criteria were as follows: 1) being searchable on the Web of Science Core Collection, 2) being published up to 2020, 3) being written in English, and 4) reporting on PM2.5 concentrations in the living rooms of urban homes. Studies reporting on specific health outcomes, source apportionment, or the chemical species or composition of particles were excluded.

2.2 Literature Collection

Data from the selected studies were organized into tables and figures presenting PM2.5 concentrations. Fig. 1 illustrates the number of studies selected in this review. In the graph showing the number of publications each year, an increase in the volume of research on this topic can be observed.

Fig. 1. Reference amount in this review.Fig. 1. Reference amount in this review.


3.1 Indoor PM2.5 Measurements

We found that indoor PM2.5 monitoring is predominantly performed with mobile sampling instruments. Professional devices and low cost sensors are widely used in indoor environment measurements. Professional and accurate instruments are available for real-time monitoring. Particle number concentrations are measured using optical particle counter. Particle size distribution can be monitored by scanning mobility particle sizers. Particle mass concentrations are monitored by tapered element oscillating microbalance, continuous aerosol mass monitor, nephelometer and nanoparticle surface monitor. Moreover, due to the advantage of small dimension and low power demand, use of low cost sensors in indoor PM2.5 monitoring is continuously increasing. The accuracy of low cost sensors can be improved by proper calibration techniques (Gozzi et al., 2016).

3.2 PM2.5 Concentrations

We collected PM2.5 concentrations from published articles to provide a review of PM2.5 concentrations in households. As listed in Table S1, most studies reporting household PM2.5 concentrations were conducted in Asia and in Europe, and some studies were conducted in North America and in Africa. The earliest studies that reported PM2.5 measurements in living environments were conducted in North America and in Europe. Later studies were conducted in Asia, and few were conducted in Africa. Factors influencing PM2.5 concentrations were as follows: 1) continent and country, 2) indoor activities, 3) ventilation type and air cleaner (AC) use, 4) building type and performance, 5) and ambient environment and season.

3.2.1 Mean PM2.5 concentration

We reviewed the mean daily household PM2.5 concentrations measured by the included studies in different regions. Fig. 2 illustrates box plots of the mean daily PM2.5 concentrations worldwide. In Europe, the mean daily PM2.5 concentrations ranged from 6.0 µg m–3 in Colchester, the United Kingdom (Nasir and Colbeck, 2013), to 46.0 µg m–3 in Rome, Italy (Romagnoli et al., 2016). 

Fig. 2. Box plot of mean daily PM2.5 concentration worldwide.Fig. 2. Box plot of mean daily PM2.5 concentration worldwide. 

In North America, indoor PM2.5 concentrations varied from 6.5 µg m–3 in Edmonton, Canada (Kearney et al., 2014) to 45.4 µg m–3 in California, the United States (Sawant et al., 2004). In Asia, indoor PM2.5 concentrations ranged from 11.7 µg m–3 in Harbin, China (Liu et al., 2020b) to 207.3 µg m–3 in Wuhan, China (Yin et al., 2017). In Africa, indoor PM2.5 concentrations ranged from 45.0 µg m–3 in Alexandria, Egypt (Abdel-Salam, 2015) to 74.0 µg m–3 in Accra, Ghana (Zhou et al., 2014). Typically, the mean daily PM2.5 concentrations measured in Asia were higher than those measured in Europe and North America. Similar concentrations were observed among European and North American countries. The mean daily PM2.5 concentrations measured in Europe, North America, Asia and Africa were 18.7, 17.3, 68.6 and 60.1 µg m–3, respectively.

We also reviewed the mean PM2.5 concentrations measured using different monitoring intervals, namely 1-, 3-, 5-, 6-, 10-, 15- and 30-min intervals. The mean PM2.5 concentrations measured using 30-min intervals ranged from 10.0 µg m–3 in Bologna, Italy (Zauli Sajani et al., 2015) to 64.9 µg m–3 in the Yangtze River Delta of China (Wang et al., 2016). The mean PM2.5 concentrations measured using 10-min intervals were 8.7 µg m–3 in Switzerland (Meier et al., 2015) up to 107.7 µg m–3 along a roadside in Nablus, Pakistan (Jodeh et al., 2017). Regarding shorter monitoring intervals, the mean PM2.5 concentrations measured using 1-min intervals were 8.4 µg m–3 in Edinburgh and Lothian, the United Kingdom (Steinle et al., 2015); 28.6 µg m–3 in winter in Seoul, Korea (Hwang and Lee, 2018); and 69.7 µg m–3 along a roadside in Hong Kong, China (Cao et al., 2005). In addition, mean PM2.5 concentrations measured using short intervals were considerably higher in some Asian countries, particularly in environments involving smokers and during heating season. Remarkably, the highest mean PM2.5 concentration measured in 1-min sampling intervals was 572.0 µg m–3 under conditions of severe outdoor air pollution in Tianjin, China (Zhou et al., 2016).

3.2.2 Maximum PM2.5 concentration

We reviewed the maximum daily PM2.5 concentrations measured by the included studies in different regions worldwide, with our review revealing remarkable differences in the maximum daily PM2.5 concentrations among different sampling sites. In North America, the maximum daily PM2.5 concentration was 74.9 µg m–3 in Boston, the United States (Baxter et al., 2007). In Europe, the maximum daily PM2.5 concentration was 109.9 µg m–3 in Colchester, the United Kingdom (Nasir and Colbeck, 2013). However, the maximum daily PM2.5 concentrations measured in Africa and Asia were considerably higher; the concentration was 218.0 µg m–3 in Durban, South Africa (Gumede and Savage, 2017) and 368.0 µg m–3 in Lanzhou, China (Li et al., 2016). Higher PM2.5 concentrations were observed in some Asian and African countries under conditions of severe ambient air pollution (Zhou et al., 2016; Li et al., 2016; Gumede and Savage, 2017). This observation was likely influenced by high PM2.5 concentrations in the outdoor environment. As displayed in Fig. 3, the maximum daily PM2.5 concentrations were higher in Asia and Africa than in Europe and North America.

Fig. 3. Box plot of maximum daily PM2.5 concentration worldwide.Fig. 3. Box plot of maximum daily PM2.5 concentration worldwide.

Notably, a monitoring campaign conducted in France reported a maximum PM2.5 concentration of 568.0 µg m–3 (Langer et al., 2016). This high concentration was attributed to indoor smoking. In most regions, relatively high maximum PM2.5 concentrations were observed in the vicinity of particle emission sources despite the use of short sampling intervals. For example, in Prague, Czech Republic, the maximum PM2.5 concentration measured using a 5-min interval reached 2,282.0 µg m–3 (Braniš and Kolomazníková, 2010). In Tianjin, China, the maximum PM2.5 concentration measured using a 1-min interval was 1,370.0 µg m–3 (Zhou et al., 2016); this was measured in an environment involving group smoking. This extremely high PM2.5 concentration was primarily caused by considerable indoor activities over the short sampling period.

3.3 Factors Influencing PM2.5 Concentrations

The PM2.5 concentrations measured by the included studies are listed in Table S1. As mentioned, factors influencing PM2.5 concentrations were indoor activities, ventilation type and AC use, household type, ambient environment, and season. The effects of these factors on PM2.5 concentrations are discussed in the following sections.

3.3.1 Indoor activities

Because tobacco smoking often occurs in the living room, it is an indoor activity that considerably elevates PM2.5 concentrations (Janssen et al., 2000; Lai et al., 2004; Minguillón et al., 2012; Canha et al., 2019). PM2.5 concentrations measured in smoking households were compared with those measured in non-smoking ones, as illustrated in Fig. 4. In general, the mean daily PM2.5 concentration observed in smoking households (36.7 µg m–3) was higher than that observed in non-smoking ones (15.0 µg m–3) by 2.5-fold. In smoking households, the daily PM2.5 concentrations ranged from 16.0 to 109.0 µg m–3 (Nasir and Colbeck, 2013). In Helsinki, Finland, the mean PM2.5 concentrations in households of active smokers (31.1 µg m–3) were higher than those in households of non-smokers (9.9 µg m–3) by more than threefold (Koistinen et al., 2001). The mean PM2.5 concentrations observed in some Asian countries were considerably higher than those observed in other countries. In a Chinese household, the mean PM2.5 concentration measured within 1 min was 549.0 µg m–3; when the household involved smoking, the maximum PM2.5 concentration reached 1,317.0 µg m–3 (Zhou et al., 2016). These results indicate that during this period, the maximum PM2.5 concentration was higher than the mean PM2.5 concentration by more than twofold. In conclusion, cigarette smoking causes a significant increase in PM2.5 concentrations in the living room, particularly peak PM2.5 concentrations measured using a short sampling interval.

Fig. 4. Mean daily PM2.5 concentration in non-smoking and smoking households.Fig. 4. Mean daily PM2.5 concentration in non-smoking and smoking households.

Domestic cooking is another major source of particles in residential environments. Our included studies extensively reported high concentrations of cooking-generated PM2.5 in Europe (Abdullahi et al., 2013; Kosonen et al., 2006), North America (Olson and Burke, 2006) and Asia (Cao et al., 2017; Wang et al., 2020; Liu et al., 2020a). These studies indicated that the influence of cooking-generated particles was not limited to the kitchen but instead spread to other sections of the living environment; this is because opening kitchen and interior doors leads to PM2.5 diffusion throughout the living environment. A study performed a field measurement of PM2.5 concentration during cooking in an apartment in Korea and indicated that the PM2.5 concentration in the living room was higher than that in background areas by more than 12-fold (Kim et al., 2018). In addition, a monitoring campaign conducted in a Chinese style residence reported that cooking led to a peak PM2.5 concentration of 365.0 µg m–3 in the living room, whereas the PM2.5 concentration in background areas was 15.0 µg m–3 (Liu et al., 2020b). In a Korean residence with an open kitchen, the PM2.5 concentration in the living room could reach up to 1,000.0 µg m–3 (Kang et al., 2019). Furthermore, Zhao and Zhao (2020) conducted PM2.5 measurements in a household with an open kitchen design and reported that cooking in the kitchen led to PM2.5 concentrations ranging between 282.0 µg m–3 and 1,187.0 µg m–3 in the living room. These findings demonstrate that cooking in a household’s kitchen elevates PM2.5 concentrations in the living room, especially in households with open kitchens.

3.3.2 Ventilation type and AC use

The most common types of ventilation in residential buildings are natural/normal ventilation (NV) with or without an AC and mechanical ventilation (MV) with an air filtration unit. We reviewed PM2.5 concentrations measured in Chinese households with these types of ventilation, as presented in Fig. 5. In Harbin, China, the mean daily and maximum daily PM2.5 concentrations measured in households with NV in winter were 88.0 and 261.0 µg m–3, respectively. However, the mean daily and maximum daily PM2.5 concentrations measured in households with an AC in winter were 51.0 and 131.0 µg m–3, respectively (Xue et al., 2020). Therefore, the use of an AC reduced the mean daily and maximum daily PM2.5 concentrations in this region by 42.0% and 49.8%, respectively. In addition, in Beijing, China, the use of an AC reduced the mean daily PM2.5 concentration from 87.0 to 63.0 µg m–3 and reduced the maximum daily PM2.5 concentration from 223.0 to 116.0 µg m–3 (Deng et al., 2017). Therefore, the mean and maximum PM2.5 concentrations were reduced by 27.6% and 48.0%, respectively. Moreover, studies conducting measurements in the Yangtze River Delta region by using a 30-min interval revealed that the mean and maximum PM2.5 concentrations were 26.0 and 63.0 µg m–3, respectively, in households using both MV and an AC. Nevertheless, in households using only NV, the mean and maximum PM2.5 concentrations were up to 64.9 and 165.0 µg m–3, respectively (Wang et al., 2016). Therefore, seethe use of both MV and an AC reduced the mean and maximum PM2.5 concentrations by 60.0% and 61.8%, respectively.

Fig. 5. PM2.5 concentration in Chinese households with natural/normal ventilation (NV), mechanical ventilation (MV) and use of AC.Fig. 5. PM2.5 concentration in Chinese households with natural/normal ventilation (NV), mechanical ventilation (MV) and use of AC.

3.3.3 Building type and performance

We reviewed the mean PM2.5 concentrations measured in different building types and building performance levels, as displayed in Fig. 6. For different building types in European countries, single-room residences in Colchester, the United Kingdom, had the lowest mean daily PM2.5 concentration (6.0 µg m–3) (Nasir and Colbeck, 2013), followed by single-family houses in France (8.7 µg m–3) (Derbez et al., 2018), multifamily apartment buildings in Finland (9.0 µg m–3) (Du et al., 2015), and retrofitted multifamily apartment buildings in France (14.6 µg m–3) (Derbez et al., 2014a, 2014b). Multifamily houses in France had a higher mean daily PM2.5 concentration (32.0 µg m–3) than did other building types (Derbez et al., 2018). These results thus demonstrate that multifamily apartment buildings had higher mean PM2.5 concentrations than did single room residences. Notably, higher indoor PM2.5 concentrations were reported in winter than that in summer for most regions. One possible reason is that higher ambient PM2.5 concentrations in winter contributed to higher indoor concentrations (Jung et al., 2011).

Fig. 6. PM2.5 concentration in different building types and performance.Fig. 6. PM2.5 concentration in different building types and performance.

We noted remarkable differences in PM2.5 concentration between Chinese households. Specifically, measurement conducted during heating season in Harbin revealed that the mean daily PM2.5 concentration was 51.0 µg m–3 in conventional households and 38.0 µg m–3 in passive households (Xue et al., 2020). A measurement campaign of PM2.5 concentration in different building types revealed that the concentration in conventional households was higher than those observed in passive households by 1.79-folds (Wang et al., 2018). Furthermore, the mean PM2.5 concentrations in energy-saving and passive residential buildings were lower than those in conventional households. The low concentration was predominately attributed to the good building performance.

3.3.4 Ambient environment and season

Ambient environmental factors that affect PM2.5 concentrations include roadside conditions outside the household (proximity to a roadside with vehicular traffic, proximity to an urban area with no vehicular traffic, or proximity to industrial facilities) (Meng et al., 2005; Molnar et al., 2007; Lim et al., 2012; Schembari et al., 2013; Huang et al., 2015; Ji et al., 2018; Xiao et al., 2018; Zhou et al., 2018; Jeong et al., 2019; Oliveira et al., 2019; Ścibor et al., 2019), weather conditions (clear or hazy conditions), and climate zone and season (Chao and Wong, 2002; Cao et al., 2012; Garcia et al., 2016; Fang et al., 2020). Household mean PM2.5 concentrations under different ambient conditions are illustrated in Fig. 7. In typical residences without substantial outdoor pollution, the mean daily household PM2.5 concentration was 56.2 µg m–3. However, in residences located along roadsides with high traffic volumes or located in proximity to industrial facilities, the mean daily PM2.5 concentrations were 73.5 and 73.4 µg m–3, respectively (Huang et al., 2007). These results thus indicate that the mean daily PM2.5 concentration in residences close to vehicular traffic and industrial facilities increased by 30.6%. In Nablus, Pakistan, the mean daily PM2.5 concentration was 84.2 µg m–3 in households located in urban areas and 107.7 µg m–3 in households located along roadsides (Jodeh et al., 2017). These results demonstrate that compared with the mean PM2.5 concentration in households located in urban areas, the mean PM2.5 concentration in households located along roadsides increased by 27.9%. A similar trend was also observed in Hong Kong, China (Cao et al., 2005) and Agra, India (Massey et al., 2012), where the mean PM2.5 concentration was higher in households located along roadsides than in those located in urban areas with no vehicular traffic. The higher household PM2.5 concentrations were attributed to the elevated concentrations in the ambient environment.

 Fig. 7. Household mean PM2.5 concentration under different ambient conditions.Fig. 7. Household mean PM2.5 concentration under different ambient conditions.

Weather conditions also considerably influence household PM2.5 concentrations. During days involving severe haze pollution, high indoor PM2.5 concentrations can be explained by the severe outdoor PM2.5 pollution. Zhou et al. (2016) conducted indoor and outdoor measurements in households with slightly open windows during periods of severe particle pollution in the ambient environment by using a sampling time of 1 min; they observed that the mean indoor and outdoor PM2.5 concentrations were 572.0 and 723.0 µg m–3, respectively. When windows and doors were adequately sealed, the mean indoor PM2.5 concentration could still reach up to 246.0 µg m–3,

with the mean outdoor PM2.5 concentration ranging from 254.0 to 403.0 µg m–3. In addition, they reported that under conditions of low wind speed, the mean indoor and outdoor PM2.5 concentrations were 290.0 and 580.0 µg m–3, respectively; however, under conditions of high wind speed, the mean indoor and outdoor PM2.5 concentrations were 65.0 and 33.0 µg m–3, respectively.

Concentrations varied with seasons. The mean daily PM2.5 concentrations in different seasons in the various regions are presented in Fig. 8. In Bologna, Italy, the mean daily household PM2.5 concentrations in spring, summer, and winter were 6.6, 8.4, and 15.1 µg m–3, respectively, and the mean daily outdoor PM2.5 concentrations were 10.8, 13.7, and 31 µg m–3, respectively (Zauli Sajani et al., 2015). In Rome, Italy, the mean daily PM2.5 concentrations in indoor and outdoor environments were 27.3 and 12.8 µg m–3, respectively, in summer and 31.4 and 32.8 µg m–3, respectively, in winter (Perrino et al., 2016). By contrast, higher PM2.5 concentrations were reported in some Asian countries, particularly in winter. Specifically, in Lanzhou, China, the mean daily PM2.5 concentrations in indoor and outdoor environments were 119.0 and 328.0 µg m–3, respectively, in winter and 80.0 and 80.0 µg m–3, respectively, in summer (Li et al., 2016). The remarkable increases in both indoor and outdoor PM2.5 concentrations in winter were attributed to heating. In residences along roadsides in Agra, India, the peak mean PM2.5 concentration in winter was 207.0 µg m–3 in indoor environments and 212.0 µg m–3 in outdoor environments. However, in summer, the mean PM2.5 concentration in household was 119.0 µg m–3 (Massey et al., 2012). These results demonstrate that in most households in Asian countries, the mean indoor PM2.5 concentration in winter was higher than that in summer by approximately twofold. The increase in PM2.5 concentration in winter can be attributed to indoor space heating and increased ambient concentrations. Except for the studies by Kearney et al. (2014) and Pekey et al. (2010), the other studies included in this review reported consistently higher PM2.5 concentrations in winter than in summer in the various regions.

 Fig. 8. Household mean PM2.5 concentration in various seasons.
Fig. 8.
 Household mean PM2.5 concentration in various seasons.

As illustrated in Fig. 8, the mean indoor and outdoor PM2.5 concentrations in Shanghai, China, were 69.9 and 77.1 µg m–3 in the cold season, respectively, 46.1 and 39.8 µg m–3 in the transitional season, respectively, 32.3 and 31.6 µg m–3 in the hot season, respectively. Similar household indoor and outdoor PM2.5 concentrations were observed during different seasons in the hot summer climate zone. The mean PM2.5 concentrations measured by Dai et al. (2018) in households in different climate zones in China are indicated by the box plot in Fig. 9. The PM2.5 concentrations differed considerably among the seasons across the climate zones. Specifically, the mean PM2.5 concentration in winter was considerably higher than those in the other seasons. The household PM2.5 concentrations ranged from 75.0 µg m–3 in the mild zone to 163.0 µg m–3 in the cold zone in winter. By contrast, the mean PM2.5 concentrations ranged from 32 µg m–3 in the mild zone to 56 µg m–3 in the cold zone (Dai et al., 2018). Notably, higher PM2.5 concentrations were observed in the cold climate zone across the various seasons. The mean PM2.5 concentration was 24.8 µg m–3 in the severe cold climate zone, 43.0 µg m–3 in the cold climate zone, 35.5 µg m–3 in the hot-summer and cold-winter climate zone, 13.5 µg m–3 in the mild zone and 27.0 µg m–3 in the hot-summer and warm-winter climate zone. The highest mean PM2.5 concentration was observed in the cold climate zone, whereas the lowest was observed in the mild climate zone.

Fig. 9. Box plot of household daily PM2.5 concentration in various climate zones in China (Box from up to down refers to maximum, mean and minimum value).Fig. 9. Box plot of household daily PM2.5 concentration in various climate zones in China (Box from up to down refers to maximum, mean and minimum value).


We present a review of studies on PM2.5 concentrations in urban residential buildings. We reviewed PM2.5 concentrations that were measured using 1- to 30-min intervals in various regions worldwide. The findings indicate that PM2.5 concentrations across the world varied considerably. The mean daily PM2.5 concentrations in Asia and Africa were higher than those in Europe and North America by more than threefold. Similar concentrations were observed between European and North American countries. In some Asian countries, the PM2.5 concentrations measured using intervals of several minutes were up to > 1,000 µg m–3. In regions with considerable particle pollution, the use of different sampling intervals is likely to engender variations in the measured concentrations. For example, the use of long sampling intervals during periods of high concentration could lead to biased measurements of average concentrations. Therefore, we recommend the use of uniform sampling intervals across different monitoring campaigns to facilitate comparisons future studies.

On the basis of the findings of the included studies, we can conclude that different indoor activities, ventilation type and AC use, building type and building performance, ambient environment, and seasons can explain the large discrepancies in indoor PM2.5 concentrations between countries and regions. The main indoor particle sources that lead to large PM2.5 concentration variations in residential environments are smoking and cooking. The mean daily PM2.5 concentration in smoking households was higher than that in non-smoking households by 2.5-fold. Smoking led to a significant PM2.5 concentration increase in the living rooms within short periods. Regarding measurements conducted for Chinese households with different ventilation types, the mean PM2.5 concentrations were much lower in households with MV and ACs than in those with NV. The use of ACs could reduce the mean daily PM2.5 concentration and mean maximum PM2.5 concentration by 27.6%–60.0% and by 48.0%–61.8%, respectively.

We also observed differences in PM2.5 concentrations among different building types and building performance levels. The mean PM2.5 concentration in multifamily apartment buildings was higher than that in single-room residences in Europe. In China, the mean PM2.5 concentration in passive households was lower than that in conventional residential buildings. Severe outdoor particle pollution could increase indoor PM2.5 concentrations. The mean indoor PM2.5 concentration in winter was higher than that in summer by approximately twofold for most households in Asian and African countries. Regarding the measurements made in the different climate zones of China, nearly identical indoor and outdoor household PM2.5 concentrations were reported for different seasons in the hot summer climate zone. The mean household PM2.5 concentration in the cold zone was higher than that in the mild zone by more than threefold.


This work was funded by the National Natural Science Foundation of China (No. 51978193, 51808275 and 52068021), the Jiangxi Provincial Natural Science Foundation (No. 20202BABL204060) and the Scientific Research Project of Education Department of Jiangxi Province (No. GJJ190301). This manuscript was edited by Wallace Academic Editing.


  1. Abdel-Salam, M.M. (2015). Investigation of PM2.5 and carbon dioxide levels in urban homes. J. Air Waste Manage Assoc. 65, 930–936. https://10.1080/10962247.2015.1040138

  2. Abdullahi, K., Delgado-Saborit, J., Harrison, R. (2013). Emissions and indoor concentrations of particulate matter and its specific chemical components from cooking: A review. Atmos. Environ. 71, 260–294. https://10.1016/j.atmosenv.2013.01.061

  3. Achilleos, S., Kioumourtzoglou, M.A., Wu, C.D., Schwartz, J., Koutrakisa, P., Papatheodorou, S. (2017). Acute effects of fine particulate matter constituents on mortality: A systematic review and meta-regression analysis. Environ. Int. 109, 89–100. https://10.1016/j.envint.2017.09.010

  4. Al Horr, Y., Arif, M., Kaushik, A., Mazroei, A., Katafygiotou, M., Elsarrag, E. (2016). Occupant productivity and office indoor environment quality: A review of the literature. Build. Environ. 105, 369–389. https://10.1016/j.buildenv.2016.06.001

  5. Anderson, J., Thundiyil, J., Stolbach, A. (2012). Clearing the air: A review of the effects of particulate matter air pollution on human health. J. Med. Toxicol. 8, 166–175. https://10.1007/​s13181-011-0203-1

  6. Bai, L., Chen, W., He, Z., Sun, S., Qin, J. (2020). Pollution characteristics, sources and health risk assessment of polycyclic aromatic hydrocarbons in PM2.5 in an office building in northern areas, China. Sustain. Cities Soc. 53, 101891. https://10.1016/j.scs.2019.101891

  7. Baxter, L.K., Clougherty, J.E., Laden, F., Levy, J.I. (2007). Predictors of concentrations of nitrogen dioxide, fine particulate matter, and particle constituents inside of lower socioeconomic status urban homes. J. Exposure Sci. Environ. Epidemiol. 17, 433–444. https://10.1038/sj.jes.7500532

  8. Baxter, L.K., Burke, J., Lunden, M., Turpin, B.J., Rich, D.Q., Thevenet-Morrison, K., Hodas, N., Okaynak, H. (2013). Influence of human activity patterns, particle composition, and residential air exchange rates on modeled distributions of PM2.5 exposure compared with central-site monitoring data. J. Exposure Sci. Environ. Epidemiol. 23, 241–247. https://10.1038/jes.2012.​118

  9. Bekö, G., Kjeldsen, B.U., Olsen, Y., Schipperijn, J., Wierzbicka, A., Karottki, D.G., Toftum, J., Loft, S., Clausen, G. (2015). Contribution of various microenvironments to the daily personal exposure to ultrafine particles: Personal monitoring coupled with GPS tracking. Atmos. Environ. 110, 122–129. https://10.1016/j.atmosenv.2015.03.053

  10. Braniš, M., Kolomazníková, J. (2010). Year-long continuous personal exposure to PM2.5 recorded by a fast responding portable nephelometer. Atmos. Environ. 44, 2865–2872. https://10.1016/​j.atmosenv.2010.04.050

  11. Brasche, S., Bischof, W. (2005). Daily time spent indoors in German homes – Baseline data for the assessment of indoor exposure of German occupants. Int. J. Hyg. Environ. Health 208, 247–253. https://10.1016/j.ijheh.2005.03.003

  12. Butler, D.A., Madhavan, G. (2017). Communicating the health effects of indoor exposure to particulate matter. Indoor Air 27, 503–505. https://10.1111/ina.12373

  13. Cakmak, S., Hebbern, C., Pinault, L., Lavigne, E., Vanos, J., Crouse, D.L., Tjepkema, M. (2018). Associations between long-term PM2.5 and ozone exposure and mortality in the Canadian Census Health and Environment Cohort (CANCHEC), by spatial synoptic classification zone. Environ. Int. 111, 200–211. https://10.1016/j.envint.2017.11.030

  14. Canha, N., Lage, J., Coutinho, J.T., Alves, C., Almeida, S.M. (2019). Comparison of indoor air quality during sleep in smokers and non-smokers' bedrooms: A preliminary study. Environ. Pollut. 249, 248–256. https://10.1016/j.envpol.2019.03.021

  15. Cao, C., Gao, J., Wu, L., Ding, X., Zhang, X. (2017). Ventilation improvement for reducing individual exposure to cooking-generated particles in Chinese residential kitchen. Indoor Built Environ. 26, 226–237. https://10.1177/1420326x16673215

  16. Cao, J.J., Lee, S.C., Chow, J.C., Cheng, Y., Ho, K.F., Fung. K,, Liu, S.X., Watson, J.G. (2005). Indoor/outdoor relationships for PM2.5 and associated carbonaceous pollutants at residential homes in Hong Kong - Case study. Indoor Air. 15, 197–204. https://10.1111/j.1600-0668.​2005.00336.x

  17. Cao, J.J., Huang, H., Lee, S.C., Chow, J.C., Zou, C.W., Ho, K.F., Watson, J.G. (2012). Indoor/outdoor relationships for organic and elemental carbon in PM2.5 at residential homes in Guangzhou, China. Aerosol Air Qual. Res. 12, 902–910. https://10.4209/aaqr.2012.02.0026

  18. Chao, C.Y., Wong, K.K. (2002). Residential indoor PM10 and PM2.5 in Hong Kong and the elemental composition. Atmos. Environ. 36, 265–277. https://10.1016/S1352-2310(01)00411-3

  19. Chen, F., Lin, Z., Chen, R., Norback, D., Liu, C., Kan, H., Deng, Q., Huang, C., Hu, Y., Zou, Z., Liu, W., Wang, J., Lu, C., Qian, H., Yang, X., Zhang, X., Qu, F., Sundell, J., Zhang, Y., Li, B., Sun, Y., Zhao, Z. (2018). The effects of PM2.5 on asthmatic and allergic diseases or symptoms in preschool children of six Chinese cities, based on China, Children, Homes and Health (CCHH) project. Environ. Pollut. 232, 329–337. https://10.1016/j.envpol.2017.08.072

  20. Chen, X.C., Jahn, H.J., Engling, G., Ward, T.J., Kraemer, A., Ho, K.F., Yim, S.H.L., Chan, C.Y. (2017). Chemical characterization and sources of personal exposure to fine particulate matter (PM2.5) in the megacity of Guangzhou, China. Environ. Pollut. 231, 871–881. https://10.1016/j.envpol.​2017.08.062

  21. Dai, X., Liu, J., Li, X., Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Build. Environ. 144, 238–247. https://10.1016/j.buildenv.2018.08.019

  22. de Kluizenaar, Y., Kuijpers, E., Eekhout, I., Voogt, M., Vermeulen, R., Hoek, G., Sterkenburg, R., Pierik, F., Duyzer, J., Meijer, E., Pronk, A. (2017). Personal exposure to UFP in different micro-environments and time of day. Build. Environ. 122, 237–246. https://10.1016/j.buildenv.2017.​06.022

  23. Deng, G., Li, Z., Wang, Z., Gao, J., Xu, Z., Li, J., Wang, Z. (2017). Indoor/outdoor relationship of PM2.5 concentration in typical buildings with and without air cleaning in Beijing. Indoor Built Environ. 26, 60–68. https://10.1177/1420326x15604349

  24. Derbez, M., Berthineau, B., Cochet, V., Lethrosne, M., Pignon, C., Riberon, J., Kirchner, S. (2014a). Indoor air quality and comfort in seven newly built, energy-efficient houses in France. Build. Environ. 72, 173–187. https://10.1016/j.buildenv.2013.10.017

  25. Derbez, M., Berthineau, B., Cochet, V., Pignon, C., Ribéron, J., Wyart, G., Mandin, C., Kirchner, S. (2014b). A 3-year follow-up of indoor air quality and comfort in two energy-efficient houses. Build Environ. 82, 288–299. https://10.1016/j.buildenv.2014.08.028

  26. Derbez, M., Wyart, G., Le Ponner, E., Ramalho, O., Riberon, J., Mandin, C. (2018). Indoor air quality in energy-efficient dwellings: Levels and sources of pollutants. Indoor Air 28, 318–338. https://10.1111/ina.12431

  27. Diaz Lozano Patino, E., Siegel, J.A. (2018). Indoor environmental quality in social housing: A literature review. Build. Environ. 131, 231–241. https://10.1016/j.buildenv.2018.01.013

  28. Dons, E., Int Panis, L., Van Poppel, M., Theunis, J., Willems, H., Torfs, R., Wets, G. (2011). Impact of time–activity patterns on personal exposure to black carbon. Atmos. Environ. 45, 3594–3602. https://10.1016/j.atmosenv.2011.03.064

  29. Du, L., Prasauskas, T., Leivo, V., Turunen, M., Pekkonen, M., Kiviste, M., Aaltonen, A., Martuzevicius, D., Haverinen-Shaughnessy, U. (2015). Assessment of indoor environmental quality in existing multi-family buildings in North-East Europe. Environ. Int. 79. 74–84. https://10.1016/j.envint.​2015.03.001

  30. Fang, W., Song, W., Liu, L., Chen, G., Ma, L., Liang, Y., Xu, Y., Wang, X., Ji, Y., Zhuang, Y., Boubacar, A.H., Li, Y. (2020). Characteristics of indoor and outdoor fine particles in heating period at urban, suburban, and rural sites in Harbin, China. Environ. Sci. Pollut. Res. 27, 1825–1834. https://10.1007/s11356-019-06640-7

  31. Garcia, F., Shendell, D.G., Madrigano, J. (2016). Relationship among environmental quality variables, housing variables, and residential needs: A secondary analysis of the relationship among indoor, outdoor, and personal air (RIOPA) concentrations database. Int. J. Biometeorol. 61, 513–525. https://10.1007/s00484-016-1229-5

  32. Gozzi, F., Della Ventura, G., Marcelli, A. (2016). Mobile monitoring of particulate matter: State of art and perspectives. Atmos. Pollut. Res. 7, 228–234. https://doi.org/10.1016/j.apr.2015.09.007

  33. Gumede, P.R., Savage, M.J. (2017). Respiratory health effects associated with indoor particulate matter (PM2.5) in children residing near a landfill site in Durban, South Africa. Air Qual. Atmos. Health 10, 853–860. https://10.1007/s11869-017-0475-y

  34. Han, B., Kong, S., Bai, Z., Du, G., Bi, T., Li, X., Shi, G., Hu, Y. (2010). Characterization of elemental species in PM2.5 samples collected in four cities of northeast China. Water Air Soil Pollut. 209, 15–28. https://10.1007/s11270-009-0176-8

  35. Han, Y., Qi, M., Chen, Y., Shen, H., Liu, J., Huang, Y., Chen, H., Liu, W., Wang, X., Liu, J., Xing, B., Tao, S. (2015). Influences of ambient air PM2.5 concentration and meteorological condition on the indoor PM2.5 concentrations in a residential apartment in Beijing using a new approach. Environ. Pollut. 205, 307–314. https://10.1016/j.envpol.2015.04.026

  36. Han, Y., Li, X., Zhu, T., Lv, D., Chen, Y., Hou, L., Zhang, Y., Ren, M. (2016). Characteristics and relationships between indoor and outdoor PM2.5 in Beijing: A residential apartment case study. Aerosol Air Qual. Res. 16, 2386–2395. https://10.4209/aaqr.2015.12.0682

  37. Hofmann, W. (2011). Modelling inhaled particle deposition in the human lung—A review. J Aerosol Sci. 42, 693–724. https://10.1016/j.jaerosci.2011.05.007

  38. Huang, H., Cao, J.J., Lee, S.C., Zou, C.W., Chen, X.G., Fan, S.J. (2007). Spatial variation and relationship of indoor/outdoor PM2.5 at residential homes in Guangzhou City, China. Aerosol Air Qual. Res. 7, 518–530. https://10.4209/aaqr.2007.03.0018

  39. Huang, K., Song, J., Feng, G., Chang, Q., Jiang, B., Wang, J., Sun, W., Li, H., Wang, J., Fang, X. (2018). Indoor air quality analysis of residential buildings in northeast China based on field measurements and longtime monitoring. Build. Environ. 144, 171–183. https://10.1016/j.buildenv.2018.08.022

  40. Huang, L., Pu, Z., Li, M., Sundell, J. (2015). Characterizing the indoor-outdoor relationship of fine particulate matter in non-heating season for urban residences in Beijing. PLoS One 10, e0138559. https://10.1371/journal.pone.0138559

  41. Hwang, Y., Lee, K. (2018). Contribution of microenvironments to personal exposures to PM10 and PM2.5 in summer and winter. Atmos. Environ. 175, 192–198. https://10.1016/j.atmosenv.2017.​12.009

  42. Janssen, N., de Hartog, J, Hoek, G., Brunekreef, B., Lanki, T., Timonen, K., Pekkanen, J. (2000). Personal exposure to fine particulate matter in elderly subjects: Relation between personal, indoor, and outdoor concentrations. J. Air Waste Manage. Assoc. 50, 1133–1143. https://10.1080/10473289.2000.10464159

  43. Jeong, C.H., Salehi, S., Wu, J., North, M.L., Kim, J.S., Chow, C.W., Evans, G.J. (2019). Indoor measurements of air pollutants in residential houses in urban and suburban areas: Indoor versus ambient concentrations. Sci. Total Environ. 693, 133446. https://10.1016/j.scitotenv.​2019.07.252

  44. Ji, W., Li, H., Zhao, B., Deng, F. (2018). Tracer element for indoor PM2.5 in China migrated from outdoor. Atmos. Environ. 176, 171–178. https://10.1016/j.atmosenv.2017.12.034

  45. Jodeh, S., Hasan, A. R., Amarah, J., Judeh, F., Salghi, R., Lgaz, H., Jodeh, W. (2017). Indoor and outdoor air quality analysis for the city of Nablus in Palestine: seasonal trends of PM10, PM5.0, PM2.5, and PM1.0 of residential homes. Air Qual. Atmos. Health 11, 229–237. https://10.1007/​s11869-017-0533-5

  46. Junaid, M., Syed, J.H., Abbasi, N.A., Hashmi, M.Z., Malik, R.N., Pei, D.S. (2018). Status of indoor air pollution (IAP) through particulate matter (PM) emissions and associated health concerns in South Asia. Chemosphere 191, 651–663. https://10.1016/j.chemosphere.2017.10.097

  47. Jung, K.H., Bernabe, K., Moors, K., Yan, B., Chillrud, S.N., Whyatt, R., Camann, D., Kinney, P.L., Perera, F.P., Miller, R.L. (2011). Effects of floor level and building type on residential levels of outdoor and indoor polycyclic aromatic hydrocarbons, black carbon, and particulate matter in New York City. Atmosphere 2, 96–109. https://10.3390/atmos2020096

  48. Kang, K., Kim, H., Kim, D.D., Lee, Y.G., Kim, T. (2019). Characteristics of cooking-generated PM10 and PM2.5 in residential buildings with different cooking and ventilation types. Sci. Total Environ. 668, 56–66. https://10.1016/j.scitotenv.2019.02.316

  49. Kearney, J., Wallace, L., MacNeill, M., Héroux, M.E., Kindzierski, W., Wheeler, A. (2014). Residential infiltration of fine and ultrafine particles in Edmonton. Atmos. Environ. 94. 793–805. https://10.1016/j.atmosenv.2014.05.020

  50. Kim, H., Kang, K., Kim, T. (2018). Measurement of particulate matter (PM2.5) and health risk assessment of cooking-generated particles in the kitchen and living rooms of apartment houses. Sustainability 10, 843. https://10.3390/su10030843

  51. Klepeis, N.E., Nelson, W.C., Ott, W.R., Robinson, J.P., Tsang, A.M., Switzer, P., Behar, J.V., Hern, S.C., Engelmann, W.H. (2001). The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants. J. Exposure Anal. Environ. Epidemiol. 11, 231–252. https://doi.org/10.1038/sj.jea.7500165

  52. Koistinen, K.J., Hänninen, O., Rotko, T., Edwards, R.D., Moschandreas, D., Jantunen, M.J. (2001). Behavioral and environmental determinants of personal exposures to PM2.5 in EXPOLIS – Helsinki, Finland. Atmos. Environ. 35, 2473–2481. https://doi.org/10.1016/S1352-2310(00)​00446-5

  53. Kornartit, C., Sokhi, R., Burton, M., Ravindra, K. (2010). Activity pattern and personal exposure to nitrogen dioxide in indoor and outdoor microenvironments. Environ. Int. 36, 36–45. https://10.1016/j.envint.2009.09.004

  54. Kosonen, R., Koskela, H., Saarinen, P. (2006). Thermal plumes of kitchen appliances: Cooking mode. Energy Build. 38, 1141–1148. https://10.1016/j.enbuild.2006.01.003

  55. Lai, H.K., Kendall, M., Ferrier, H., Lindup, I., Alm, S., Hänninen, O., Jantunen, M., Mathys, P., Colvile, R., Ashmore, M.R., Cullinan, P., Nieuwenhuijsen, M.J. (2004). Personal exposures and microenvironment concentrations of PM2.5, VOC, NO2 and CO in Oxford, UK. Atmos. Environ. 38, 6399–6410. https://10.1016/j.atmosenv.2004.07.013

  56. Langer, S., Ramalho, O., Derbez, M., Ribéron, J., Kirchner, S., Mandin, C. (2016). Indoor environmental quality in French dwellings and building characteristics. Atmos. Environ. 128, 82–91. https://10.1016/j.atmosenv.2015.12.060

  57. Leech, J., Nelson, W., Brunett, R., Aaron, S., Raizenne, M. (2002). It's about time: A comparison of Canadian and American time–activity patterns. J. Exposure Anal. Environ. Epidemiol. 12, 427–432. https://10.1038/sj.jea.7500244

  58. Li, T., Cao, S., Fan, D., Zhang, Y., Wang, B., Zhao, X., Leaderer, B., Shen, G., Zhang, Y., Duan, X. (2016). Household concentrations and personal exposure of PM2.5 among urban residents using different cooking fuels. Sci. Total Environ. 548–549, 6–12. https://10.1016/j.scitotenv.2016.​01.038

  59. Li, Z., Wen, Q., Zhang, R. (2017). Sources, health effects and control strategies of indoor fine particulate matter (PM2.5): A review. Sci. Total Environ. 586, 610–622. https://10.1016/j.​scitotenv.2017.02.029

  60. Lim, S., Kim, J., Kim, T., Lee, K., Yang, W., Jun, S., Yu, S. (2012). Personal exposures to PM2.5 and their relationships with microenvironmental concentrations. Atmos. Environ. 47, 407–412. https://10.1016/j.atmosenv.2011.10.043

  61. Liu, S., Dong, J., Cao, Q., Zhou, X., Li, J., Lin, X., Qing, K., Zhang, W., Chen, Q. (2020a). Indoor thermal environment and air quality in Chinese-style residential kitchens. Indoor Air 30, 198–212. https://10.1111/ina.12631

  62. Liu, Y., Dong, J., Xu, X., Jiang, Y. (2020b). PM2.5 mass concentration variation in urban residential buildings during heating season in severe cold region of China: A case study in Harbin. Sci. Total Environ. 722, 137945. https://10.1016/j.scitotenv.2020.137945

  63. Martuzevicius, D., Grinshpun, S.A., Lee, T., Hu, S., Biswas, P., Reponen, T., LeMasters, G. (2008). Traffic-related PM2.5 aerosol in residential houses located near major highways: Indoor versus outdoor concentrations. Atmos. Environ. 42, 6575–6585. https://10.1016/j.atmosenv.2008.​05.009

  64. Massey, D., Kulshrestha, A., Masih, J., Taneja, A. (2012). Seasonal trends of PM10, PM5.0, PM2.5 & PM1.0 in indoor and outdoor environments of residential homes located in North-Central India. Build. Environ. 47, 223–231. https://10.1016/j.buildenv.2011.07.018

  65. Matz, C., Stieb, D., Davis, K., Egyed, M., Rose, A., Chou, B., Brion, O. (2014). Effects of age, season, gender and urban-rural status on time-activity: Canadian Human Activity Pattern Survey 2 (CHAPS 2). Int. J. Environ. Res. Public Health 11, 2108–2124. https://10.3390/ijerph110202108

  66. Matz, C., Stieb, D., Brion, O. (2015). Urban-rural differences in daily time-activity patterns, occupational activity and housing characteristics. Environ. Health 14, 88. https://10.1186/​s12940-015-0075-y

  67. Meier, R., Eeftens, M., Phuleria, H.C., Ineichen, A., Corradi, E., Davey, M., Fierz, M., Ducret-Stich, R.E., Aguilera, I., Schindler, C., Rochat, T., Probst-Hensch, N., Tsai, M. Y., Kunzli, N. (2015). Differences in indoor versus outdoor concentrations of ultrafine particles, PM2.5, PMabsorbance and NO2 in Swiss homes. J. Exposure Sci. Environ. Epidemiol. 25, 499–505. https://10.1038/​jes.2015.3

  68. Meng, Q.Y., Turpin, B.J., Korn, L., Weisel, C.P., Morandi, M., Colome, S., Zhang, J.J., Stock, T., Spektor, D., Winer, A., Zhang, L., Lee, J.H., Giovanetti, R., Cui, W., Kwon, J., Alimokhtari, S., Shendell, D., Jones, J., Farrar, C., Maberti, S. (2005). Influence of ambient (outdoor) sources on residential indoor and personal PM2.5 concentrations: Analyses of RIOPA data. J. Exposure Anal. Environ. Epidemiol. 15, 17–28. https://10.1038/sj.jea.7500378

  69. Minguillón, M.C., Schembari, A., Triguero-Mas, M., de Nazelle, A., Dadvand, P., Figueras, F., Salvado, J.A., Grimalt, J.O., Nieuwenhuijsen, M., Querol, X. (2012). Source apportionment of indoor, outdoor and personal PM2.5 exposure of pregnant women in Barcelona, Spain. Atmos. Environ. 59, 426–436. https://10.1016/j.atmosenv.2012.04.052

  70. Molnar, P., Bellander, T., Sallsten, G., Boman, J. (2007). Indoor and outdoor concentrations of PM2.5 trace elements at homes, preschools and schools in Stockholm, Sweden. J. Environ. Monitor. 9, 348–357. https://10.1039/b616858b

  71. Morawska, L., Ayoko, G.A., Bae, G.N., Buonanno, G., Chao, C. Y.H., Clifford, S., Fu, S.C., Hanninen, O., He, C., Isaxon, C., Mazaheri, M., Salthammer, T., Waring, M.S., Wierzbicka, A. (2017). Airborne particles in indoor environment of homes, schools, offices and aged care facilities: The main routes of exposure. Environ. Int. 108, 75–83. https://10.1016/j.envint.2017.07.025

  72. Nasir, Z., Colbeck, I. (2013). Particulate pollution in different housing types in a UK suburban location. Sci. Total Environ. 445–446, 165–176. https://10.1016/j.scitotenv.2012.12.042

  73. Nezis, I., Biskos, G., Eleftheriadis, K., Kalantzi, O.I. (2019). Particulate matter and health effects in offices - A review. Build Environ. 156, 62–73. https://10.1016/j.buildenv.2019.03.042

  74. Oliveira, M., Slezakova, K., Delerue-Matos, C., Pereira, M.C., Morais, S. (2019). Children environmental exposure to particulate matter and polycyclic aromatic hydrocarbons and biomonitoring in school environments: A review on indoor and outdoor exposure levels, major sources and health impacts. Environ. Int. 124, 180–204. https://10.1016/j.envint.2018.12.052

  75. Olson, D.A., Burke, J.M. (2006). Distributions of PM2.5 source strengths for cooking from the research triangle park particulate matter panel study. Environ. Sci. Technol. 40, 163–169. https://10.1021/es050359t

  76. Pekey, B., Bozkurt, Z.B., Pekey, H., Dogan, G., Zararsiz, A., Efe, N., Tuncel, G. (2010). Indoor/outdoor concentrations and elemental composition of PM10/PM2.5 in urban/industrial areas of Kocaeli City, Turkey. Indoor Air 20, 112–125. https://10.1111/j.1600-0668.2009.00628.x

  77. Perrino, C., Tofful, L., Canepari, S. (2016). Chemical characterization of indoor and outdoor fine particulate matter in an occupied apartment in Rome, Italy. Indoor Air 26, 558–570. https://10.1111/ina.12235

  78. Perrone, M.G., Gualtieri, M., Consonni, V., Ferrero, L., Sangiorgi, G., Longhin, E., Ballabio, D., Bolzacchini, E., Camatini, M. (2013). Particle size, chemical composition, seasons of the year and urban, rural or remote site origins as determinants of biological effects of particulate matter on pulmonary cells. Environ. Pollut. 176, 215–227. https://10.1016/j.envpol.2013.01.012

  79. Romagnoli, P., Balducci, C., Perilli, M., Vichi, F., Imperiali, A., Cecinato, A. (2016). Indoor air quality at life and work environments in Rome, Italy. Environ. Sci. Pollut. Res. Int. 23, 3503–3516. https://10.1007/s11356-015-5558-4

  80. Sawant, A.A., Na, K., Zhu, X., Cocker, K., Butt, S., Song, C., Cocker, D.R. (2004). Characterization of PM2.5 and selected gas-phase compounds at multiple indoor and outdoor sites in Mira Loma, California. Atmos. Environ. 38, 6269–6278. https://10.1016/j.atmosenv.2004.08.043

  81. Schembari, A., Triguero-Mas, M., de Nazelle, A., Dadvand, P., Vrijheid, M., Cirach, M., Martinez, D., Figueras, F., Querol, X., Basagaña, X., Eeftens, M., Meliefste, K., Nieuwenhuijsen, M. (2013). Personal, indoor and outdoor air pollution levels among pregnant women. Atmos. Environ. 64, 287–295. https://10.1016/j.atmosenv.2012.09.053

  82. Schweizer, C., Edwards, R., Bayer-Oglesby, L., Gauderman, W., Ilacqua, V., Jantunen, M., Lai, H., Nieuwenhuijsen, M., Künzli, N. (2007). Indoor time-microenvironment-activity patterns in seven regions of Europe. J. Exposure Sci. Environ. Epidemiol. 17, 170–181. https://10.1038/sj.​jes.7500490

  83. Ścibor, M., Balcerzak, B., Galbarczyk, A., Targosz, N., Jasienska, G. (2019). Are we safe inside? Indoor air quality in relation to outdoor concentration of PM10 and PM2.5 and to characteristics of homes. Sustain. Cities Soc. 48, 101537. https://10.1016/j.scs.2019.101537

  84. Steinle, S., Reis, S., Sabel, C., Semple, S., Twigg, M., Braban, C., Leeson, S., Heal, M., Harrison, D., Wu, H. (2015). Personal exposure monitoring of PM2.5 in indoor and outdoor microenvironments. Sci. Total Environ. 508, 383–394. https://10.1016/j.scitotenv.2014.12.003

  85. Stranger, M., Potgieter-Vermaak, S.S., Van Grieken, R. (2007). Comparative overview of indoor air quality in Antwerp, Belgium. Environ. Int. 33, 789–797. https://10.1016/j.envint.2007.02.014

  86. Sun, Y., Hou, J., Cheng, R., Sheng, Y., Zhang, X., Sundell, J. (2019). Indoor air quality, ventilation and their associations with sick building syndrome in Chinese homes. Energy Build. 197, 112–119. https://10.1016/j.enbuild.2019.05.046

  87. Tang, R., Wang, Z. (2018). Field study on indoor air quality of urban apartments in severe cold region in China. Atmos. Pollut. Res. 9, 552–560. https://10.1016/j.apr.2017.12.004

  88. Van Ryswyk, K., Wheeler, A., Wallace, L., Kearney, J., You, H., Kulka, R., Xu, X. (2014). Impact of microenvironments and personal activities on personal PM2.5 exposures among asthmatic children. J. Exposure Sci. Environ. Epidemiol. 24, 260–268. https://10.1038/jes.2013.20

  89. Wallace, L.A., Mitchell, H., O'Connor, G.T., Neas, L., Lippmann, M., Kattan, M., Koenig, J., Stout, J.W., Vaughn, B.J., Wallace, D., Walter, M., Adams, K., Liu, L.J., Inner-City Asthma, S. (2003). Particle concentrations in inner-city homes of children with asthma: The effect of smoking, cooking, and outdoor pollution. Environ. Health Perspect. 111, 1265–1272. https://10.1289/​ehp.6135

  90. Wang, F., Meng, D., Li, X., Tan, J. (2016). Indoor-outdoor relationships of PM2.5 in four residential dwellings in winter in the Yangtze River Delta, China. Environ. Pollut. 215, 280–289. https://10.1016/j.envpol.2016.05.023

  91. Wang, Y., Li, H., Feng, G. (2020). Simulating the influence of exhaust hood position on ultrafine particles during a cooking process in the residential kitchen. Build. Simul. 13, 1339–1352. https://10.1007/s12273-020-0640-3

  92. Wang, Z., Xue, Q., Ji, Y., Yu, Z. (2018). Indoor environment quality in a low-energy residential building in winter in Harbin. Build. Environ. 135, 194–201. https://10.1016/j.buildenv.2018.​03.012

  93. Wolkoff, P. (2013). Indoor air pollutants in office environments: Assessment of comfort, health, and performance. Int. J. Hyg. Environ. Health 216, 371–394. https://10.1016/j.ijheh.2012.08.001

  94. Wu, X., Vu, T.V., Shi, Z., Harrison, R.M., Liu, D., Cen, K. (2018). Characterization and source apportionment of carbonaceous PM2.5 particles in China - A review. Atmos. Environ. 189, 187–212. https://10.1016/j.atmosenv.2018.06.025

  95. Wyzga, R., Rohr, A. (2015). Long-term particulate matter exposure: Attributing health effects to individual PM components. J. Air Waste Manage. Assoc. 65, 523–543. https://10.1080/​10962247.2015.1020396

  96. Xiao, Y., Wang, L., Yu, M., Shui, T., Liu, L., Liu, J. (2018). Characteristics of indoor/outdoor PM2.5 and related carbonaceous species in a typical severely cold city in China during heating season. Build. Environ. 129, 54–64. https://10.1016/j.buildenv.2017.12.007

  97. Xue, Q., Wang, Z., Liu, J., Dong, J. (2020). Indoor PM2.5 concentrations during winter in a severe cold region of China: A comparison of passive and conventional residential buildings. Build. Environ. 180, 106857. https://10.1016/j.buildenv.2020.106857

  98. Yin, W., Hou, J., Xu, T., Cheng, J., Wang, X., Jiao, S., Wang, L., Huang, C., Zhang, Y., Yuan, J. (2017). Association of individual-level concentrations and human respiratory tract deposited doses of fine particulate matter with alternation in blood pressure. Environ. Pollut. 230, 621–631. https://10.1016/j.envpol.2017.07.006

  99. You, S., Yao, Z., Dai, Y., Wang, C.H. (2017). A comparison of PM exposure related to emission hotspots in a hot and humid urban environment: Concentrations, compositions, respiratory deposition, and potential health risks. Sci. Total Environ. 599–600, 464–473. https://10.1016/​j.scitotenv.2017.04.217

  100. Zauli Sajani, S., Ricciardelli, I., Trentini, A., Bacco, D., Maccone, C., Castellazzi, S., Lauriola, P., Poluzzi, V., Harrison, R. (2015). Spatial and indoor/outdoor gradients in urban concentrations of ultrafine particles and PM2.5 mass and chemical components. Atmos. Environ. 103, 307–320. https://10.1016/j.atmosenv.2014.12.064

  101. Zhao, Y., Chen, C., Zhao, B. (2019). Emission characteristics of PM2.5-bound chemicals from residential Chinese cooking. Build. Environ. 149, 623–629. https://10.1016/j.buildenv.2018.​12.060

  102. Zhao, Y., Zhao, B. (2020). Reducing human exposure to PM2.5 generated while cooking typical Chinese cuisine. Build Environ. 168, 106522. https://10.1016/j.buildenv.2019.106522

  103. Zhou, X., Cai, J., Zhao, Y., Chen, R., Wang, C., Zhao, A., Yang, C., Li, H., Liu, S., Cao, J., Kan, H., Xu, H. (2018). Estimation of residential fine particulate matter infiltration in Shanghai, China. Environ. Pollut. 233, 494–500. https://10.1016/j.envpol.2017.10.054

  104. Zhou, Z., Dionisio, K.L., Verissimo, T.G., Kerr, A.S., Coull, B., Howie, S., Arku, R.E., Koutrakis, P., Spengler, J.D., Fornace, K., Hughes, A.F., Vallarino, J., Agyei-Mensah, S., Ezzati, M. (2014). Chemical characterization and source apportionment of household fine particulate matter in rural, peri-urban, and urban West Africa. Environ. Sci. Technol. 48, 1343–1351. https://10.1021/​es404185m

  105. Zhou, Z., Liu, Y., Yuan, J., Zuo, J., Chen, G., Xu, L., Rameezdeen, R. (2016). Indoor PM2.5 concentrations in residential buildings during a severely polluted winter: A case study in Tianjin, China. Renewable Sustainable Energy Rev. 64, 372–381. https://10.1016/j.rser.2016.06.018

  106. Zhu, Y., Huang, L., Li, J., Ying, Q., Zhang, H., Liu, X., Liao, H., Li, N., Liu, Z., Mao, Y., Fang, H., Hu, J. (2018). Sources of particulate matter in China: Insights from source apportionment studies published in 1987-2017. Environ. Int. 115, 343–357. https://10.1016/j.envint.2018.03.037

  107. Zwozdziak, A., Gini, M. I., Samek, L., Rogula-Kozlowska, W., Sowka, I., Eleftheriadis, K. (2017). Implications of the aerosol size distribution modal structure of trace and major elements on human exposure, inhaled dose and relevance to the PM2.5 and PM10 metrics in a European pollution hotspot urban area. J. Aerosol Sci. 103, 38–52. https://10.1016/j.jaerosci.2016.10.004 

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