Characterization of Indoor PM 10,PM 2.5,and Ultrafine Particles in Elementary School Classrooms: A Review

Abstract

The significance of particulate matter (PM) in elementary schools is underscored by the presence of a large number of young children who are more susceptible to indoor air pollution. For the first time, the state of knowledge all over the world regarding PM₁₀, PM_2.5, and ultrafine particles in classrooms of elementary schools with students whose age ranged from 5 to 13 years is reviewed in this article, with an emphasis on indoor classroom concentrations of PM₁₀ and PM_2.5, the sources, compositions, and health effects of PM₁₀ and PM_2.5, and the influencing factors of classroom PM₁₀ and PM_2.5 (building age, seasons, meteorology, ventilation rates, and indoor activities) and a discussion of ultrafine particles. In the summary, the unique characteristics of indoor particle pollution in elementary schools are synthesized, followed by strategies to minimize exposure of children to PM at schools. Finally, the improvements in future research designs on PM in elementary schools are recommended.

Introduction

In recent years, people have paid more attention to indoor air quality in workplace and residential environments. People spend >90% of their time indoors (Jenkins et al., 1992); therefore, the quality of indoor air is crucial to us. Many factors can cause the deterioration of indoor air quality, such as building materials (Lin et al., 2009), architectural coatings applied to building materials (Lin and Corsi, 2007; Corsi and Lin, 2009), heating, ventilation, and air conditioning systems (Zhao et al., 2007), consumer products (Nazaroff and Weschler, 2004), and various indoor activities (Weschler, 2009). Moreover, air pollutants generated from outdoor sources can affect indoor air quality, as well as our health.

Several research studies have reported significant health risks associated with exposure to particulate matter (PM) (Pope and Dockery, 2006). Most studies on PM exposure consider particle mass, particularly particles smaller than 10 or 2.5 μm (PM₁₀ and PM_2.5, respectively). Lately, research interest has been directed toward ultrafine particles (UFPs) as well. UFPs, that is, particles with aerodynamic diameter smaller than 0.1 μm, contribute very little to the overall particle mass but are dominating if number concentration is considered (Keywood et al., 1999; Woo et al., 2001). There is mounting evidence of the toxicological effects of UFPs on human health concluded by the World Health Organization (WHO, 2005). In urban environments, where vehicular emissions constitute the main source of fine particles, >80% of particles in the atmosphere are smaller than 0.1 μm (Jamriska et al., 1999; Hitchins et al., 2000). Indoor processes leading to secondary organic aerosol (SOA) formation can also contribute to indoor UFP concentrations (Morawska et al., 2008).

Schools have complex indoor environment influenced by many factors such as number and age of occupants, their activities, building design, sources of pollution inside the building, outdoor pollutant concentrations, and ventilation conditions (Dockery and Spengler, 1981; Lee and Chang, 2000; Daisey et al., 2003). In classrooms, PM₁₀ concentrations during school hours has been shown to be two to five times higher than outdoor concentrations and twice as high as the 24 h classroom average (Roorda-Knape et al., 1998; Janssen et al., 1999). In particular, urban schools are located in close proximity to high-density roadways. Indoor concentrations of PM_2.5 significantly increased with increasing truck traffic density and significantly decreased with increasing distance (Janssen et al., 2001; Kingham et al., 2008). Most recently, investigations showed that there were many occasions in classrooms where indoor UFP (<0.1 μm) concentrations increased significantly compared with outdoor levels because of the fact that most elementary schools use liquid materials for art classes, and all schools use detergents for cleaning (Morawska et al., 2009).

Preadolescent children constitute one of the population subgroups most sensitive to air pollution because their physiological and immunological systems are still in the process of development (Farhat et al., 2005). It has been also suggested that they may receive a higher dose of PM to their lungs compared with adults. This may be due to a greater fractional deposition with each breath and/or larger minute ventilation relative to lung size (Bennett and Zeman, 1998). In effect, a 10-year longitudinal study in southern California found the deficits in the growth of children's lung function to be associated with higher ambient concentrations of PM (Gauderman et al., 2000). School children spend at least one third of their total time inside school buildings (ISIAQ, 2000). Therefore, air quality in classrooms is of special concern as indoor air problems do not always produce easily recognizable impacts on health and welfare and can be subtle. Research showed that pollution of both the classroom (high concentrations of PM and the presence of dust sources) and dwellings (domestic mold growth and dampness) seem to be the major factors affecting pupils' health and indicated that indoor air quality in schools is poor and both the school and the home environment need to be improved to achieve a healthy and comfortable environment for children (Van Dijken et al., 2006).

The significance of PM in elementary schools is underscored by the presence of a large number of young children who are more susceptible to indoor air pollution. However, current state of knowledge all over the world regarding PM₁₀, PM_2.5, and UFPs in classrooms of elementary schools with students whose age ranged from 5 to 13 years has never been reviewed. In this article, past experimental research is described, with an emphasis on measurements of PM₁₀ and PM_2.5 in elementary school classrooms with students whose age ranged from 5 to 13 years, the sources, compositions, and health effects of PM₁₀ and PM_2.5, and the influencing factors of PM₁₀ and PM_2.5 (building age, seasons, meteorology, ventilation rates and indoor activities) and a discussion of UFPs. Finally, the unique characteristics of indoor particle pollution in elementary schools is concluded, followed by strategies to minimize exposure of children to PM at schools, as well as improvements in future research designs on children's exposure to PM in elementary schools.

Summary of Past Research

Concentrations of PM₁₀ and PM_2.5

In the recent decade, there has been plenty of information available on the concentration levels of airborne PM in school classrooms, specifically PM₁₀ and PM_2.5, in the published literature. Data extracted from 15 original source reports are summarized in Table 1. A number of studies examining PM levels in classrooms have reported high levels of PM₁₀ and PM_2.5 (Janssen et al., 1997; Roorda-Knape et al., 1998; Fromme et al., 2005, 2007; Heudorf, 2007; Stranger et al., 2007, 2008; Goyal and Khare, 2009; Yang et al., 2009), with a wide range varying from 13 to 1181.1 μg m⁻³ and 13.5 to 359.9 μg m⁻³, respectively. Two studies in the United States (Keeler et al., 2002; John et al., 2007) and one study in Sweden (Molnár et al., 2007) reported lower levels of PM₁₀ and PM_2.5 than other published literatures (Table 1). Another study in India observed extremely high levels of PM₁₀ and PM_2.5 in winter, with concentrations of 1181.1 and 359.9 μg m⁻³ during school time (Goyal and Khare, 2009). The wide range of PM₁₀ and PM_2.5 concentrations indicate the large potential for reduction and the need for identification of factors responsible for this variability.

Table 1.

Average Concentrations (Range) (in μg m⁻³) of Particulate Matter Concentrations

PM₁₀	PM_2.5	Country	Sampling integrals	Sources ^a
72.2–119.9		The Netherlands	School time for 3 weeks	1
	23	The Netherlands	5–7 h during school time	2
80.8–157.0		The Netherlands	8 h during school time	3
45.9–74.4		The Netherlands	24 h	3
	24.03	Czech Republic	7–10 days long	4
	3.3–19	Sweden	2 weeks	5
	101.25–115.25	South Korea	5–7 h during school time	6
	50–88	Germany	school time for 3 weeks	7
13–128		Germany	5 h during school time	8
71.7–105	13.5–23	Germany	5 h during school time	9^b
118.2^c	37.4^c	Germany	5 h during school time	10
8.7–31.6	8.0–16.4	Michigan, USA	8 h during school time	11
	15.56–17.30	Ohio, USA	7 h during school time	12
	61	Belgium	12 h day time during weekdays	13
	54–72	Belgium	12 h day time during weekdays	14
410.6–1181.1	71–359.9	India	6 h during school time	15^d

Sources 1–15: Roorda-Knape et al. (1998); Janssen et al. (2001); Janssen et al. (1997); Branis et al. (2009); Molnár et al. (2007); Yang et al. (2009); Heudorf (2007); Fromme et al. (2005, 2007, 2008); Keeler et al. (2002); John et al. (2007); Stranger et al. (2007, 2008); Goyal and Khare (2009).

Laser aerosol spectrometer.

Median concentrations.

Precalibrated GRIMM-make environmental dust monitor.

One way to understand how high the reported values of PM₁₀ and PM_2.5 are in classrooms is to compare them with current standards indoors. Present worldwide indoor guideline values of PM_2.5 and PM₁₀ mass concentrations are listed in Table 2. The reported values of PM_2.5 and PM₁₀ in almost all (12) studies in Table 1 are above the guidelines of Belgium, Norway, and Canada. Four studies reported higher values than the guidelines of the United States. Values of PM_2.5 and PM₁₀ observed in the studies in South Korea and India exceed the guideline of Taiwan. Tremendously large concentrations of PM₁₀ and PM_2.5 in the study in India even exceeded the guideline of China, which is the least stringent one as far as we know.

Table 2.

Worldwide Indoor Guideline Values of PM_2.5 and PM₁₀ Mass Concentrations (in μg m⁻³)

Indoor	Belgium	Norway	Canada	United States ^a	Taiwan	China
PM_2.5	15 (y)	20 (4 h)	40 (h)	65 (24 h)	100 (24 h)	150
PM₁₀	40 (24 h)			150 (24 h)	150 (24 h)	180 (8 h)

Illinois Department of Public Health Guidelines.

h, hourly value; y, annual value.

The high levels of PM₁₀ and PM_2.5 may be partly due to the measurement methods used by Goyal and Khare (2009), although other reasons may exist. Almost all reports in Table 1 used gravimetric methods to measure mass concentrations of PM₁₀ and PM_2.5 except for the study by Fromme et al. (2007), which utilized laser aerosol spectrometer, and the study by Goyal and Khare (2009), in which precalibrated GRIMM-make environmental dust monitor, similar to photometers, was employed. It has been pointed out several times that the readings of photometers may exaggerate the real mass concentrations measured by reference gravimetrical methods (Gorner et al., 1995; Ramachandran et al., 2000; Branis et al., 2005). Sampling integrals seem to have little influence on measurements of PM₁₀ and PM_2.5 based on the review of listed literatures (Table 1). Measured 5-h average concentrations of PM₁₀ (Fromme et al., 2007) were comparable to measured 3-week average concentrations of PM₁₀ (Roorda-Knape et al., 1998).

Sources, compositions, and health effects of PM₁₀ and PM_2.5

Although the school environment in developed countries normally lacks typical indoor PM sources such as smoking and cooking, a great number of children are present in a limited space over several hours. For schools near major roadways, fine particles are more correlated with outdoor particle pollution related to traffic (Janssen et al., 2001; Fromme et al., 2007; John et al., 2007; Molnár et al., 2007; Zöllner et al., 2007; Branis et al., 2009; McConnell et al., 2010), whereas coarse particles are mostly affected by human activity (Fromme et al., 2007; Branis et al., 2009). In this review, it appears that Janssen et al. (2001) was the first to assess the traffic impact on indoor classroom PM. Janssen et al. (2001) measured traffic-related air pollution (PM_2.5, NO₂, and benzene) in and outside 24 schools located within 400 m of motorways in The Netherlands. Reflectance of PM_2.5 filters was measured as a proxy for elemental carbon (EC). The relationship between this proxy and measurements of EC was studied in a subsample and a high correlation was established. In both indoor and outdoor air, concentrations of PM_2.5 and “soot” significantly increased with increasing truck traffic density and significantly decreased with increasing distance. The percentage of time that the school was downwind of the motorway during the measurements was significantly associated with soot and NO₂, but not with PM_2.5 (Janssen et al., 2001). This study has shown that concentrations of PM_2.5 in and outside schools near motorways are significantly associated with distance from roadways, traffic density, and traffic composition.

John et al. (2007) monitored indoor and outdoor PM_2.5 concentrations and trace elements in three elementary schools in Central and Southeast Ohio during the period of February 1, 1999, through August 31, 2000. The locations included a rural elementary school in Athens, Ohio, and two urban schools within Columbus, OH. Potential indoor source contribution function analysis showed that PM_2.5 levels at the monitoring sites were affected by transport from adjoining urban areas and industrial complexes located along the Ohio River Valley. The trace metal and ionic concentrations in the collected samples were analyzed using an X-ray fluorescence spectrophotometer and ion chromatography unit, respectively. Sulfate ion was found to be the largest component present in the samples at all three sites. Other abundant components included nitrate, chloride, ammonium, and sodium ions, as well as calcium, silicon, and iron. PM_2.5 and its major component, sulfate ion, showed strong seasonal variations with maximum concentrations observed during the summer at all three sites. A preliminary outdoor source apportionment using the principal component analysis (PCA) technique was performed. The results from the PCA suggest that the study region was primarily impacted by industrial, fossil fuel combustion, and geological sources. The 2002 emissions inventory data for PM_2.5 compiled by the Ohio Environmental Protection Agency also showed impacts of similar source types, and this was used to validate the PCA (John et al., 2007). This study indicated that in all seasons the indoor PM_2.5 levels may strongly depend on outdoor levels.

Molnár et al. (2007) sampled PM_2.5 indoors and outdoors at 40 sampling sites in three communities in Stockholm, Sweden: in 10 classrooms in 5 schools, at 10 preschools, and 20 nonsmoking homes, during nine 2-week periods. Each sampling site was sampled twice, once during winter and once during spring. The samples were analyzed for elemental concentrations using X-ray fluorescence spectroscopy. In all locations, significantly higher outdoor concentrations were found for elements that are related to long-range transported air masses (S, Ni, Br, and Pb), whereas only Ti was higher indoors in all locations. Similar differences for S, Br, and Pb were found in both seasons for homes and schools. In preschools, different seasonal patterns were seen for the long-range transported elements S, Br, and Pb and the crustal elements Ti, Mn, and Fe. The indoor/outdoor (I/O) ratios for S and Pb suggest an outdoor PM_2.5 particle net infiltration of about 0.6 in these buildings. The community located 25 km from the city center had significantly lower outdoor concentrations of elements of crustal or traffic origin compared with the two central communities, but had similar levels of long-range transported elements. Significant correlations were found between PM_2.5 and most elements outdoors (R² = 0.45–0.90) (Molnár et al., 2007).

Fromme et al. (2008) characterized the chemical and morphological properties of PM (PM₁₀ and PM_2.5) in two classrooms during school hours. The following components of PM were quantified: water-soluble ions, EC, organic carbon, and the absorption coefficient of the filters. Studies have shown high concentrations of PM in schools. Using the measured sulfate content on PM filters as an indicator for ambient PM sources, they estimated that 43% of PM_2.5 and 24% of PM₁₀ were of ambient origin. The composition of the classrooms' PM (e.g., high calcium concentrations) and the findings from combined analysis by scanning electron microscopy and energy dispersive X-ray spectrometry suggested that the indoor PM consists mainly of earth crustal materials, deterioration of the building materials, and chalks. Physical activity of the pupils leads to resuspension of mainly indoor coarse particles and greatly contributes to increasing PM₁₀ in classrooms (Fromme et al., 2008). The concentration of fine particles caused by combustion processes is comparable indoors and outdoors. It was concluded that PM measured in classrooms (Fromme et al., 2008) has major sources other than outdoor particles.

Branis et al. (2009) measured the size-segregated mass concentration of PM in a naturally ventilated elementary school gym during eight campaigns, 7–10 days long, from November 2005 through August 2006 in a central part of Prague (Czech Republic). The air was sampled using a five-stage cascade impactor. The indoor concentrations of PM_2.5 recorded in the gym exceeded the WHO recommended 24-h limit of 25 μg m⁻³ in 50% of the days measured. The average 24-h concentrations of PM_2.5 (24.03 μg m⁻³) in the studied school room did not differ much from those obtained from the nearest fixed site monitor (25.47 μg m⁻³) and the indoor and ambient concentrations were closely correlated (correlation coefficient: 0.91), suggesting a high outdoor-to-indoor penetration rate. The coarse indoor fraction concentration (PM_2.5–10) was associated with the number of exercising pupils (correlation coefficient: 0.77), indicating that human activity is its main source (Branis et al., 2009).

However, in some cases, for schools without ambient heavy traffic, fine particle can come from both indoors and outdoors (Stranger et al., 2007; Fromme et al., 2008) and, sometimes, can even be indoor dominated (Stranger et al., 2008). In Belgium, the first comprehensive study of indoor air quality in primary school indoor environments was performed by Stranger et al. (2007), in which concentrations of PM, as well as its elemental composition and water-soluble ionic content, were quantified. This baseline study showed that PM_2.5 I/O ratios exceeded unity in specific schools, because of resuspension of dust, and is therefore indicative of indoor sources of PM_2.5, which is supported by another study by Stranger et al. (2008), who assessed the indoor air quality of 27 primary schools located in the city center and suburbs of Antwerp, Belgium. The I/O ratios and the building and classroom characteristics of each school were investigated (Stranger et al., 2008). Stranger et al. (2008) presented results on indoor and local outdoor PM_2.5 mass concentrations, its elemental composition in terms of K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, Pb, Al, Si, S, and Cl, and its black smoke content. No linear correlation was established between indoor and outdoor levels for PM_2.5 mass concentrations. The author believed that this was an indication of the considerable influence of time-dependant indoor circumstances of the classrooms, like ventilation habits, which was not quantified well in the study. The report also perceived that PM_2.5 I/O ratios exceeded unity. The resuspension of dust due to room occupation is probably the main contributor for the >1 I/O ratios reported for elements typically constituting dust particles. The elements S, Si, Fe, Ca, and Al had the highest contributions to local outdoor PM and Ca, S, Si, and Al to indoor PM in schools. None or very weak correlations were found between the indoor and outdoor elemental composition of the particles (Stranger et al., 2008).

Although there have not been many studies investigating the effects of indoor particle exposure on health, there have been numerous studies generally investigating the effect of particle exposure on health. As a significant fraction of indoor particle concentrations originates from outdoors, PM in classrooms are not that different from those originating from ambient sources, except that indoor PM may adsorb some chemicals that exist only in classrooms. The key question in terms of health effects of PM is “what is the influence of chemical compositions and particle size on health?” Through a comprehensive review on current review articles, metals are important PM components for the development of both pulmonary and cardiovascular disease. Metals may also be involved in PM-induced allergic sensitization, but the epidemiological evidence for this is scarce. Soluble organic compounds appear to be implicated in PM-induced allergy and cancer, but the data from epidemiological studies are insufficient for any conclusions (Harrison and Yin, 2000; Schwarze et al., 2006). In terms of the influence of particle size on health, Harrison and Yin (2000) reported that concentrations of PM_2.5 and PM₁₀ are very highly correlated (APEG, 1999), making the task of distinguishing between the two metrics in epidemiological studies almost impossible because both do not behave as independent variables. However, studies from other parts of the world do provide limited evidence that fine particles PM_2.5 are more toxic than coarse particles (Lippmann, 1989). A comprehensive evaluation of the literature provides compelling evidence that continued reductions in exposure to combustion-related fine PM, especially PM_2.5, will result in improvements in cardiopulmonary health (Pope and Dockery, 2006). However, coarse fraction of PM_2.5–10 has a health effect that should not be neglected according to Schwarze et al. (2006), which may have been the only group to comprehensively review indoor PM properties and health effects.

In a short summary, for schools near major roadways, fine particles are more correlated with outdoor particle pollution related to traffic, whereas coarse particles are mostly affected by human activity. However, in some cases, for schools without ambient heavy traffic, fine particles can come from both indoors and outdoors and, sometimes, can even be indoor dominated. The major components of indoor PM in schools are sulfate, nitrate, chloride, ammonium, and sodium ions, calcium, silicon, iron, crustal elements Ti and Mn, as well as EC and organic carbon. Metals may be involved in PM-induced allergic sensitization, and soluble organic compounds appear to be implicated in cancer, but the data from epidemiological studies are insufficient for any conclusions. Enough evidence shown in health studies of ambient PM demonstrates that fine particles PM_2.5 are more toxic than coarse particles, although the health effect of coarse fraction of PM_2.5–10 should not be neglected.

Influencing factors of PM₁₀ and PM_2.5

On the basis of a review of existing literature, reported impact factors of indoor PM₁₀ and PM_2.5 in classrooms include building age, seasons, meteorology, ventilation rates, and indoor activities.

Yang et al. (2009) obtained indoor and outdoor air samples of PM₁₀ during summer, autumn, and winter from three sites: a classroom, a laboratory, and a computer classroom, at 55 different schools that include 5 kindergartens, 20 elementary schools, 15 middle schools, and 15 high schools. The selection of the schools was based on the number of years since the schools had been constructed. No significant influence of both seasons and building ages on indoor levels of PM₁₀ was observed. Average concentrations of PM₁₀ measured in classrooms at elementary schools were 103.5, 115.25, and 101.25 μg m⁻³ in summer, autumn, and winter, and 84.63, 83.58, 84.42, and 83.39 μg m⁻³ in buildings with age of <1, 1–3, 3–5, and >10, respectively. The author concluded that the problems causing indoor air pollution at the schools were chemicals emitted by building materials or furnishings and insufficient ventilation rates (Yang et al., 2009). This is the only literature that compared the indoor levels of PM₁₀ in school classrooms according to the age of school buildings (Yang et al., 2009), although no significant influence of either season or building age on indoor levels of PM₁₀ was observed.

A few other studies observed strong influence of seasons (Fromme et al., 2007; John et al., 2007; Branis et al., 2009; Goyal and Khare, 2009). Fromme et al. (2007) evaluated indoor PM₁₀ and PM_2.5 in 64 schools in the city of Munich and a neighboring district outside the city boundary in winter 2004–2005 in 92 classrooms and in summer 2005 in 75 classrooms. The size of the classrooms ranged between 47 and 98 m² (median: 68 m²) and the volume between 160 and 437 m³ (median: 222 m³). During the period of occupancy (∼5 school hours daily), the classrooms were occupied by 9–35 subjects (median: 24). The attendance (number of pupils) in winter did not virtually differ from attendance in summer. It was observed that most classrooms have limited indoor space relative to number of occupants, because in winter, in 60% of classes the daily median CO₂ concentration exceeded 1,500 ppm (Fromme et al., 2007). Higher average concentrations of indoor classroom PM₁₀ (105.0 μg m⁻³) and PM_2.5 (38.9 μg m⁻³) were reported to be increased in winter than indoor classroom PM₁₀ (71.7 μg m⁻³) and PM_2.5 (22.1 μg m⁻³) in summer in Germany. The author speculated that the difference is most likely due to the different ventilation practices in summer and winter. Because of increased ventilation in spring, summer, and autumn, indoor PM levels may strongly depend on outdoor levels, whereas in winter the classroom PM may be more strongly influenced by indoor activities (Fromme et al., 2007). A similar influence of seasons on indoor classroom PM was also reported in India by Goyal and Khare (2009) and in Czech Republic by Branis et al. (2009). Goyal and Khare (2009) completed a 1-year study of indoor and outdoor respirable suspended PM₁₀, PM_2.5, and PM_1.0 mass concentration at a classroom of a naturally ventilated school building located near an urban roadway in Delhi City. The monitoring started from August 2006 till August 2007, including weekdays (Monday, Wednesday, and Friday) and weekends (Saturday and Sunday) from 8:00 a.m. to 2:00 p.m. Levels of indoor PM₁₀ and PM_2.5 ranged from 162.4 (nonwinter) to 531.1 μg m⁻³ (winter) and 37.7 (nonwinter) to 244.7 μg m⁻³ (winter) during weekends, whereas extremely high levels of indoor PM₁₀ and PM_2.5 were detected, ranging from 410.6 (nonwinter) to 1181.1 μg m⁻³ (winter) and 71 (nonwinter) to 359.9 μg m⁻³ (winter) during weekday school time throughout the year (Goyal and Khare, 2009). This is also comparable to the observation made by Branis et al. (2009) that the highest concentrations of particles were found in the winter during weekdays and the lowest in the summer during the weekends in an elementary school gym (Branis et al., 2009). However, a different influence of seasons on indoor classroom PM_2.5 was observed by John et al. (2007) that the indoor environment was found to be more contaminated during the spring months (March through May) at New Albany (a suburb of Columbus, OH) and East Athens (rural Ohio area). The difference of the influence of seasons is probably due to different geology and meteorology patterns among these studies.

PM concentrations in classrooms are dependent not only on the seasons but also on meteorology. Goyal and Khare (2009) reported a significant influence of meteorological parameters. They observed that indoor PM_2.5 concentrations decrease with increase in ambient temperature and wind speed and with decrease in relative humidity. This is contrary to what was observed by Fromme et al. (2007), who reported a significant increase of PM_2.5 by 1.7 μg m⁻³ per increase in humidity by 10% in an explorative analysis. Similarly, Branis et al. (2005) completed the measurement of 12-h mass concentration of PM₁₀, PM_2.5, and PM₁ in a university lecture room that has similarities with a classroom in an elementary school because of similar high occupant density and similar modes of ventilation. A high positive association was recorded between PM concentrations and indoor relative humidity, and a strong negative association was recorded between PM concentrations and wind speed, but no statistically significant correlation was found between PM and temperature (Branis et al., 2005). In addition, Goyal and Khare (2009) further observed that indoor occupant activities strongly influence indoor PM₁₀ concentrations (Goyal and Khare, 2009), consistent with the reports of Fromme et al. (2008) and Branis et al. (2009). Specifically, I/O for all sizes of particulates was >1, which implies that building envelope does not provide protection from outdoor pollutants (Goyal and Khare, 2009). Higher I/O for PM₁₀ indicates the presence of its indoor sources in classroom. Only in the absence of occupants' activities during weekends do indoor PM₁₀ concentrations follow the linear trends with temperature, relative humidity, and wind speed. PM₁₀ concentrations reduce by 67% during weekends of nonwinters and 46% during winters, suggesting that the indoor concentrations are strongly influenced by activities of occupants during weekdays (Goyal and Khare, 2009).

Ventilation rate also has influence on indoor classroom PM, particularly PM_2.5 (Heudorf, 2007; Goyal and Khare, 2009). According to Goyal and Khare (2009), in nonwinters, the maximum average value of ventilation rate is 102.98 cfm person⁻¹ when all the windows are open and fans are running, and in winters, the minimum mean ventilation rate is 26.9 cfm person⁻¹ when windows are closed and fans are off. It is observed that indoor PM₁₀ and PM_2.5 concentrations decrease with increase in ventilation rate. However, PM₁₀ concentrations indoors do not follow it at certain time intervals, specifically during 10:30 a.m. to 12:00 pm and during 1:30 to 2:00 p.m. (Goyal and Khare, 2009). The author speculated that the reason might come from the effect of intense occupants' activities/movements on indoor PM₁₀ and PM_2.5 concentration. Heudorf (2007) pointed out the infrequent cleaning in most schools and investigated the impact of cleaning and ventilation on PM in classrooms in Germany by measuring PM₁₀ concentrations for 3 weeks; first week: “normal” cleaning (twice a week) and ventilation; second week: intensified cleaning and ventilation (five times a week); third week: intensified cleaning and intensified ventilation (Heudorf, 2007). The results showed that levels of PM₁₀ in the classrooms during the 3 weeks were 69 ± 19 μg m⁻³ and they were dominated by occupancy and the persons' activity, which is again consistent with Fromme et al. (2008) and Branis et al. (2009). Intensified cleaning showed a significant decrease in all classrooms' PM₁₀ concentrations (79 ± 22 to 64 ± 15 μg m⁻³). The effect of ventilation on levels of PM₁₀ was inconsistent (Heudorf, 2007).

In brief, the influence of building age has not been found because of limited data. Highest concentrations of particles are generally found in the winter and lowest in the summer. Indoor concentrations of PM decrease with increase in ambient temperature and wind speed and with decrease in relative humidity and decrease with increase in ventilation rate in the absence of occupants' activities. Strong occupant's activities increase indoor PM concentrations, especially PM₁₀.

Discussion of UFPs

UFPs seem to have enhanced toxicity per unit mass and their toxicity increases as particle size decreases (Donaldson and MacNee, 1998). Seaton et al. (1995) have hypothesized that it is the ability of UFPs to penetrate the lung wall, inducing inflammation in the pulmonary interstitial, which in turn stimulates the production of clotting factors in the blood, which is responsible for the recognized ability of airborne particles to exacerbate ischemic heart disease, a health outcome that had been previously extremely hard to explain on mechanistic grounds.

Although there has been an increased interest in the sources, concentration levels, and human exposure to UFPs (<0.1 μm) measured in terms of number, there are only a few studies regarding indoor UFP concentrations in school classrooms, in relation to either outdoor traffic or indoor processes in the published literature (Diapouli et al., 2007; Guo et al., 2008; Weichenthal et al., 2008; Morawska et al., 2009). Diapouli et al. (2007) completed an examination of indoor and outdoor UFPs (0.01 to >1 μm) concentration levels in the area of Athens during cold seasons of 2003 and 2004. Seven primary schools, located in areas with different characteristics of urbanization and traffic density, as well as a typical suburban residence, were monitored. UFPs number concentration was monitored by a condensation particle counter (model CPC 3007), with a logging time interval of 1 min. Classroom 8-h mean concentrations were in the range of 2.2 to 25.3 × 10³ particles cm⁻³. The highest values (mean value equal to 22.6 × 10³ particles cm⁻³) were observed at Missouri, a site in close vicinity with a major motorway (Diapouli et al., 2007). The lowest concentration levels (mean value equal to 7.0 × 10³ particles cm⁻³) were measured inside a classroom in a rural area (RU). In general, classroom UFP concentrations decreased with reduced degree of traffic density and urbanization, indicating that, in the absence of significant indoor sources, vehicular emission influenced greatly the indoor UFP concentration levels (Diapouli et al., 2007). Weichenthal et al. (2008) characterized UFP counts in classrooms during the winter months. Between January and March 2007, schools A and B were each visited for three 1-week periods and two different classrooms were monitored in the same school on each sampling day. All UFPs were collected in occupied classrooms for ∼7 h between 8:30 a.m. and 3:30 p.m., and all instruments were placed ∼1 m above classroom floors away from windows and doors. UFP counts were recorded in classrooms at 10-s sampling intervals using TSI P-TRAK 8525 Ultrafine Particle Counters (TSI, Shoreview, MN). These instruments are CPCs with continuous data-logging capabilities and are capable of detecting particles between ∼0.02 and 1 μm. In total, UFP count data were collected on 60 occasions in 37 occupied classrooms at one elementary school and one secondary school in Pembroke, Ontario. On average, classroom UFP counts were similar at both schools, with a combined average of 5,017 particle cm⁻³ (95% confidence interval: 4,300, 5,734). The UFP levels are at the same magnitude as the low levels in the study by Diapouli et al. (2007); however, a little higher than what Guo et al. (2008) observed.

In Australia, Guo et al. (2008) conducted a 2-week intensive measurement campaign of indoor and outdoor air pollution in a primary school in September 2006, to investigate indoor and outdoor correlations of particle number (PN) concentrations and the impact of air exchange rate (AER) on the indoor PN concentration. The subject school is located in a small village surrounded by local residences and roads carrying a low level of local traffic. The outdoor sampling site was located at the school's oval, whereas the indoor sampling site was in an air-conditioned classroom. The classroom is usually occupied by students during school hours during weekdays. PN concentration and size distribution in the size range from 0.014 to 0.800 μm were simultaneously measured indoors and outdoors using two scanning mobility particle sizers, each comprising an electrostatic classifier (TSI, model 3071A) and CPC (TSI, models 3010 and 3025). Average PN concentration was 2.11 × 10³ particle cm⁻³ (Guo et al., 2008). It was suggested by authors that the penetration efficiency decreased with increasing outdoor PN concentration. This finding is thought to have occurred because the observed increases in outdoor PN resulted from an increase in the smallest particles, which have lower penetration efficiency than larger particles. The study also showed a significant effect of AER on indoor PN concentrations under stable outdoor PN concentrations. In general, the higher the AER was, the lower the indoor PN concentration (Guo et al., 2008).

In addition to outdoor ambient sources for indoor classroom UFPs, the study by Morawska et al. (2009) was the first to investigate UFPs (<0.1 μm) in primary school classrooms, in relation to the indoor classroom activities. The investigations were conducted in three classrooms during two measuring campaigns, which together encompassed a period of 60 days (Morawska et al., 2009), in Australia. Two TSI model 3934 scanning mobility particle sizers (TSI, St. Paul, MN) were used to measure school indoor and outdoor particle size distributions in the range from 0.015 to 0.737 μm (PN: 0.015–0.7). TSI CPCs model 3022 and 3025A were used to measure total outdoor and indoor PN concentrations in size ranges from 0.007 to 3 μm (PN: 0.007) and 0.005–3 μm (PN: 0.005), respectively. To investigate indoor volatile organic compound (VOC) concentrations, samples were collected by trapping tubes packed with Tenax-TA. The samples were analyzed using gas chromatography–mass spectrometry. Initial investigations showed that under normal operating conditions of the school there were many occasions in all three classrooms where indoor particle concentrations increased significantly compared with outdoor levels. By far the highest increases in the classroom resulted from art activities (painting, gluing, and drawing), at times reaching over 1.4 × 10⁵ particle cm⁻³, which is 1 to 2 orders of magnitude larger than the reported levels above (Diapouli et al., 2007; Guo et al., 2008; Weichenthal et al., 2008). The indoor particle concentrations exceeded outdoor concentrations by ∼1 order of magnitude, with account median diameter ranging from 20 to 50 nm (Morawska et al., 2009). Significant increases also occurred during cleaning activities, when detergents were used. Controlled experiments showed that this monoterpene, emitted from the detergent, reacted with O₃ (at outdoor ambient concentrations ranging from 0.06 to 0.08 ppm) and formed SOA (Morawska et al., 2009).

In short, the main source of UFPs in school classrooms is outdoor combustion processes, particularly vehicle emissions in urban environments and indoor processes or activities (i.e., cleaning with detergent and art activities in classrooms) leading to SOA formation can also contribute to indoor UFP concentrations in classrooms.

Conclusions and Future Research Needs

Generally speaking, elementary schools are not that different from other public space buildings. However, what is unique to schools includes (1) insufficient ventilation in schools (especially in winter), (2) relatively small room sizes, (3) a large number of young pupils who are more susceptible to air pollutants, (4) infrequent cleaning, (5) active human activities with constant resuspension of particles from room surfaces, (6) urban elementary schools being usually surrounded by major traffic lanes, and (7) various indoor activities, such as cleaning by detergents, painting, and gluing in art class. These problems also provide future strategies to improve indoor air quality or guide future study of indoor air quality in schools. For example, schools with all windows and doors open, intensified cleaning without detergent or with detergent with less ozone-reactive organic compounds (i.e., five times a week), large classroom size (10 m² per pupil or more), limited indoor activities (such as less exercise, and chemical used in classrooms), and with greater distance away from traffic and industry (100 m or more) may have better indoor air quality.

There are only a few studies on indoor UFP concentrations in school classrooms, in relation to either outdoor traffic or indoor processes in the published literature. Further studies are recommended to better understand this and to minimize exposure of school children to UFPs from these indoor sources. Further, there is a clear need for improvements in research designs. In particular, there is a need for better health exposure assessments, including metals and soluble organic compounds in PM₁₀, PM_2.5, and ultrafine PM in epidemiological investigations. Metals may be involved in PM-induced allergic sensitization, and soluble organic compounds appear to be implicated in cancer, but the data from epidemiological studies are insufficient for any conclusions so far. However, if UFP number or mass is the critical factor on children's health, much more data are needed in the future studies.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

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Characterization of Indoor PM 10,PM 2.5,and Ultrafine Particles in Elementary School Classrooms: A Review

Abstract

Abstract

Introduction

Summary of Past Research

Concentrations of PM10 and PM2.5

Sources, compositions, and health effects of PM10 and PM2.5

Influencing factors of PM10 and PM2.5

Discussion of UFPs

Conclusions and Future Research Needs

Footnotes

Author Disclosure Statement

References

Concentrations of PM₁₀ and PM_2.5

Sources, compositions, and health effects of PM₁₀ and PM_2.5

Influencing factors of PM₁₀ and PM_2.5