Children's Exposure to Indoor Particulate Matter in ... - SAGE Journals

Original Paper

Indoor and Built Environment

Indoor Built Environ 2011;20;4:430–448

Accepted: March 29, 2011

Children’s Exposure to Indoor Particulate Matter in Naturally Ventilated Schools in India Mahima Habila

Ajay Tanejaa,b

a

Department of Chemistry, School of Chemical Sciences, St. John’s College, Agra, India Department of Chemistry, Dr. B.R. Ambedkar University, Khandari Campus, Agra, India

b

Key Words Indoor particulate matter E School children E Seasonal variation E Health effects

Abstract The high levels of indoor particulate matter in developing countries and the apparent scale of its impact on the global burden of disease underline the importance of particulate as an environmental health risk and the consequent need for monitoring them particularly in indoor school microenvironments. PM10, PM2.5 and PM1.0 levels were monitored inside and outside the classrooms of four naturally ventilated schools during winter (December 2007 to January 2008) and summer (April 2008 to May 2008) as seasonal campaigns using Grimm 1.109 along with CO2, temperature, humidity and ventilation rate. Additionally, data on classroom condition, school building, surrounding area and prevalence of health symptoms were collected with the help of a questionnaire. During winter, mean indoor PM10, PM2.5 and PM1.0 concentrations ranged up to 497, 220 and 135 mg m3, which lowered to 3, 3 and 2 times, respectively, in summer as compared to winter. The average indoor/outdoor ratios were found to be 41 at ß The Author(s), 2011. Reprints and permissions: http://www.sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1420326X11409455 Accessible online at http://ibe.sagepub.com Figures 1–7 appear in colour online

almost all the sites. Furthermore, indoor–outdoor correlations were carried out, indicating poor correlations at all the sites except at one school located in residential area. Inter-particulate ratios in the indoor environments were also obtained, indicating that strong correlations exist in both the campaigns, which were significant at p50.01.

Introduction Even though indoor air quality (IAQ) is perceived to be important for the learning ability of school pupils [1], there have been few good scientific, statistically sound studies of IAQ in school and its impact on the learning ability of children. It has become increasingly clear that exposure to contaminated indoor air may not only be unpleasant, but can have serious adverse health effects [2–6]. Therefore, there is a growing concern about school environments and the possible impact on health particularly of children. Since children are the most sensitive subgroup of the total population, schools are among the important places in health work where children spend about half of their wakening hours. From an educational point of view, the

Ajay Taneja, Department of Chemistry, Dr. B.R. Ambedkar University, Khandari Campus, Agra, India, Tel. 91 9897476288. E-Mail [email protected]

IAQ, ventilation of school buildings, ambient air quality around schools and exhaust quality of air emission sources situated near school are some major factors that affect the health of children and indirectly the learning and productivity performances [7,8]. Studies indicating outdoor pollution as a significant contributor to indoor levels have been reported specially from schools built near highways central or industrial areas [9–11]. IAQ experiences adverse effects mainly when school buildings are naturally ventilated, providing way for outdoor pollutants to infiltrate or enter indoors where the closing and opening of inlets (doors and windows) are regulated according to occupants’ thermal comfort and seasonal variation. Further, there are studies that have found indoor pollutant levels greater than outdoor levels, as the apparent effects of outdoor particles probably occur due to exposure indoors [12,13]. Long-term indoor exposure to particulate accounts for an average value three times higher than that indicated by outdoor measurements alone, resulting in increased absenteeism [14]. However, both indoor and outdoor particulate matter (PM) levels are known to exhibit significant short-term variability showing excursions of ambient PM on excess mortality and morbidity [15]. Thus, it is very important to include short-term variations of indoor and outdoor concentrations in exposure studies. Sometimes classrooms are also notoriously populated having high-risk subjects engaged with dust-generating activities when compared with the healthy elderly subjects, experiencing high CO2 and inadequate ventilation [16]. In India, mostly, schools are naturally ventilated, where air moves through opened doors and windows. Moreover, large windows can increase the outdoor contamination during warmer parts of the year due to opened windows. Particles emitted from different sources have different compositions and toxicities, giving rise to different health effects [17]. Such effects caused by indoor air pollutants are more widespread than those caused by outdoor air pollutants because the exposed occupants are in close proximity to the source of indoor air pollutants. As asserted by the rule of 1000, released indoor pollutants is 1000 times more likely to reach lungs than related outdoor pollutants [18]. In a study done by Costa and Schelege in 1999, symptoms like nose and throat irritation were inferred to be caused by PM that got deposited in the nasal passages and upper airways and stimulated sensory nerve reflexes, which could also cause inflammation, mucus production, coughing and sneezing in an effort to clear the lung particles [19].

However, the smaller the particles, the more evident the health effects [20]. Moreover, diverse sources of PM would lead to a wide range of particle sizes. PM is also emitted from vehicle exhaust, the mechanical wear of tyres and breaks and the ejection of particles from the pavement and unpaved road shoulders by resuspension processes [21–24]. Such emissions are more likely to have occurred due to vehicles gathering around school area for picking and dropping of children. It should be remarked that regarding the adverse health risks, the World Health Organization (WHO) concluded that health risks are present at any levels of airborne particles [18]. The health and well-being of children are a fundamental issue in education. Furthermore, unavailability to provide good air quality in schools can have consequences such as increasing the potential for long-term and short-term health effects for the pupils and staff, affecting children’s learning environment, comfort and attendance reducing productivity of teachers and staff due to discomfort, sickness absenteeism, strained relationships between school administration, and parents and staff effectiveness. During the past years, several studies have dealt with the influence of outdoor pollution on IAQ. Many of them were based on simultaneous measurements of outdoor and indoor contaminant concentrations in naturally ventilated or air-conditioned buildings [25,26], compilation of the obtained PM levels with the standards [16], children exposure and its relationship with asthma and allergies [27], particle size problems and its influence on the relative indoor–outdoor concentration levels [28], and particle size and its chemical composition [29–32]. Such factors could provide great information on indoor concentration levels; the problems of estimating total human exposure, however, would remain a challenge, considering the number of species and indoor environments to consider. More experimental data are necessary, but it also seems essential that a synthesis effort be undertaken either to propose typical indoor/outdoor (I/O) ratios for target contaminants and buildings, or to identify the parameters influencing the ratios. The study presented in this paper fits into this scheme. The paper presents the measurements performed in four schools of Agra, India, located by the roadside and in residential areas and a detailed analysis of the results obtained. The paper emphasises the comparison of the present computed indoor concentrations with previously published data and also with the WHO guidelines to determine the likely underlying health effects. Evidence of the parameters clearly influencing contaminants concentration levels, indoors, is also presented,

Indoor Particulate Matter in Schools

Indoor Built Environ 2011;20:430–448

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although more complete results were obtained from Pearson’s correlation coefficient. Being a developing country, India aims to impart quality education to its pupils and aims to educate as many children as possible for a better and improved future. A bulk of school buildings can be found near each roadside and in residential localities. The city experiences hot dry wind during summers (April–September) and cold and chilly wind during morning hours at peak winters (December–January). Being a tourist spot, large development in the economy is seen, daily in and out, giving rise to highly polluted roads caused by means of transportation, as 60% pollution in this city is due to vehicles [33]. High intensity of traffic on these main roads was usually between 7 am in morning and 3 pm in the evening during a school day during the period 2007–2008. Air pollutants (PM10, PM2.5 and PM1.0) and environmental parameters (CO2, temperature, relative humidity (RH) and ventilation rates) were determined and measured simultaneously in four schools for short term in two campaigns held in winter and summer. The objectives of the study were to characterise/compare air pollution levels in schools located at roadside and in residential areas in both seasons in order to observe the seasonal variation and short-term exposure effects to compare the mean concentrations with the established standards and suggest useful and affordable measures which can reduce pollutant levels and improve air quality in classrooms. This study presents the first investigation on the exposure of PM to school children in Agra, India.

Materials and Methods Site Descriptions Sampling was done in Agra, the city of Taj Mahal, (278100 N, 788020 E) located in central northern India. Four schools (S1, S2, S3 and S4) were selected for monitoring IAQ in two campaigns held in winter season (December 2007 to January 2008) and summer (April 2008 to May 2008) season, respectively, as shown in Figure 1. A total of 80 samples were collected during the campaigns I (n ¼ 40) and II (n ¼ 40) from indoor and outdoor locations of all the schools located at roadsides and in urban areas of the city. Two classrooms were monitored at each school (S1, S2, S3 and S4) for indoor and outdoor concentrations in both the campaigns. Selection of schools was based on the location of schools located in different microenvironments

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(roadside and residential areas), so as to obtain a general view of the exposure in schools located in the different microenvironments of the city. Roadside schools, S1 is located near road crossing (with heavy traffic) and S2 near main roads (with less traffic). Schools S3 and S4 are located in residential areas, surrounded by streets having continuous and occasional traffic related to locality activities. So in order to compare the IAQ in different environments, schools at both roadside (S1 and S2) and in residentially (S3 and S4) located areas were averaged together. Details of each school and its surrounding area are presented in Table 1. Moreover, particular attention was paid while selecting classrooms showing similar geometrical features like all the classrooms selected for indoor air measurement were rectangular or squared showing one external wall, one side in contact with a corridor and the other two sides in contact with the adjacent classrooms. All schools were naturally ventilated. Air could enter and escape continually from all rooms through doors, windows, cracks and other openings. The schools varied with respect to factors such as age, construction and size. In addition, the teachers were asked about the predominant classroom window position (open or closed) during monitoring hours of the school. However, none of the sampled schools had previously reported complaints with respect to IAQ.

Sampling Methods Sampling was conducted both inside and outside the classrooms. Measurements were taken during complete school hours (5–6 h), along with 30 min before (in morning) and after the school (in afternoon). This feature was practised to evaluate PM levels when classrooms were occupied and unoccupied, as the sampling duration was restricted within the teaching hours only. The sampling duration taken in winter season was different from that in summer because of different school timings in cold and hot seasons (08:30 am to 02:30 pm in winter and 07:00 am to 01:00 pm in summer, respectively). School activity usually starts in the morning with scheduled teaching hours along with few free intervals of 10–15 min. The afternoon hours are reserved for activities like studying, sports and games for the pupils. The sampling unit was placed inside the classroom, opposite to the blackboard, about 1 m above the floor level, the level at which the children would normally inhale and away from windows and doors. Outdoor measurements were taken outside the sampled classroom in the open at a distance of 2 m. Due to a lack of

Habil and Taneja

Fig. 1. Agra city map showing sampling sites. Note: S1 ¼ roadside school (heavy-trafficked road crossing), S2 ¼ roadside school (less trafficked main road), S3 ¼ residential area school (residential area with street experiencing continuous traffic), S4 ¼ Residential area school (residential area with street experiencing traffic occasionally – mainly at school time).

multiple samplers, indoor and outdoor measurements were taken alternately after each 30 min, by placing the sampler at any one location (I/O) for the first 30 min and for the other location (I/O) for the next 30 min till the stated sampling duration. In order to obtain continuous data of PM concentrations indoors and outdoors, the instrument was placed next day to monitor those locations lacking intervals of the previous day in the same order. Then, these measured values were merged together to obtain the fullday data variation in indoor and outdoor environments of the schools. A variation of 4–12% was observed in the indoor and outdoor mass concentrations of PM when sampled continuously in a given order for one successive week before the start of the sampling within similar schools and seasons. Similar methodology was followed by [23] in residential home environments to compare the indoor and outdoor data.

Instrumentation Indoor and outdoor PM levels (PM10, PM2.5 and PM1.0) levels were monitored using Grimm aerosol dust monitor model 1.109, which works on the principle of light scattering by constantly drawing the air via volume controlled pump through a flat beam of laser light. All scattered signals generated while particles cross this beam are detected with a high-speed photo diode, analysed by an integrated pulse height analyser and counted at 908 to give real-time measurements [34]. Moreover, the total particles can be collected on 47 mm PTFE filter paper for chemical analysis. Its real-time measuring range is from 0.25 to 32 mm in 31 channel sizes, each unit is with National Institute of Standards and Technology certified, monodisperse latex on the size of channels calibrated [22]. The instrument works with a flow rate of 1.2 L min1 5%, consistent with the controller for continuous measurement.



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Table 1. Detailed site description of schools monitored in two seasonal campaigns I and II Site Location Surrounding details

Constructional activity Distance from main road/street (m) Distance from playground (m) Distance from parking area (m) Number of windows Number of doors Number of ceiling fans Ventilation status Occupancy rate (%) (strength of students in each classroom ¼ 50) Classroom area (m2) Classroom condition (damage walls, ceiling and furniture) Classroom cleaning Adjacent corridor cleaning

S1

S2

S3

S4

Roadside with heavy traffic Road crossing, commercial, dusty area

Roadside with less traffic

Residential area with continuous traffic Densely populated, commercial area, green Yes 10 10 10 3 2 4 Windows opened 97

Residential area with occasional traffic Moderately populated, less commercial, green

No 15 5 8 1 2 2 Windows closed 93

Welding and repair shops, dusty areas (muddy playground) No 25 20 12 4 2 4 Windows closed 83

120 Dusty floor

120 Dusty floor and windows bases

100 Less dusty floor

100 Less dusty floor and windows bases

Twice a week Daily after school

Daily after school Daily after school

Daily after school Daily between school hours

Daily after school Daily between school hours

The instrument was set to average the data over 15 min to reduce the response time and to enable the identification of individual sources. This dust monitor was placed indoors and outdoors, with a due consideration of children’s safety and possible vandalism. Environmental parameters like CO2 (ppm), temperature (8C), RH (%) and ventilation m3 h1 were monitored simultaneously using Young Environmental Systems, Canada (YES 205 and YES 206) multi-gas monitors and wind speed measured by Wind Monitor WM 251 Envirotech.

Questionnaire Survey A total of 350 questionnaires were distributed to the pupils being monitored in classrooms at both roadside and residential schools (175 and 175, respectively), where 300 questionnaires were returned from all schools. However, one questionnaire was completed by one pupil such that the response rate of the reported symptoms is the number of received questionnaire with that symptom, divided by the number of distributed questionnaires. This consisted of three parts: questions regarding the school area, classroom condition and health effects or symptoms experienced by the children due to the school environment. The questionnaire was also used to record occurrences of acute respiratory symptoms, which would provide a powerful method for assessing the impact of short-term changes in

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Yes 12 8 15 3 2 4 Windows opened 94


human health due to environment [35]. Symptoms included were: difficulty in concentration, dry throat, back pain, dizziness, dry flaking skin, itching, sneezing, high stress, eye irritation, shortness of breath, headache, drowsiness, cold and flu and allergies. Moreover, symptomatic children were judged to be those who had reported an increase in these symptoms due to their past health records after coming to school. Furthermore, occupants were also requested for comments and suggestive ways to improve air quality in classrooms of the school area according to their views. About 50% of the children reported factors like poor ventilation, cleanliness and crowded classrooms as some of the main causatives of bad air quality in classrooms and schools.

Statistical Methods The continuous particle concentration data and other environmental parameters were first investigated by descriptive statistics (mean, minimum, maximum and standard deviation), which varied at a significance level of ( p50.001) between indoors and outdoors, throughout the study at campaigns I and II. In addition, these average particulate concentrations over the study period were determined by taking the arithmetic mean of data from all study days (using MS Excel). The correlations matrices were evaluated using the Univiariate Pearson’s correlation

Habil and Taneja

Table 2. Guidelines and standards of pollutants and parameters Pollutants and parameters

Threshold limits

PM10 (mg m3) PM2.5 (mg m3) PM1.0 (mg m3) CO2 (ppm)

50 (24 h average) 25 (24 h average) NA 1800

Temperature (8C) Humidity (%) Ventilation rates (m3h1)

19–26 30–70 528.8 m3h1 per person

Standards WHO 2005 WHO 2005 NA WHO 1995 and ASHRAE 62-1989 ASHRAE 55-1992 ASHRAE 55-1992 ASHRAE 62-1989

NA: not available.

Table 3. Mean concentrations of measured parameters in four classrooms in both campaigns I and II Parameters

CO2 (ppm) Temperature (8C) Humidity (%) Ventilation rates (m3h1) Wind speed (km h1) Occupancy (%)

Winter

Summer

Minimum

Maximum

Mean

SD

Minimum

Maximum

Mean

SD

703.84 17.84 46.00 33.62 5.28 91.00

1041.84 21.97 57.00 62.24 6.45 99.00

834.05 19.47 53.34 47.84 5.86 96.00

220.23 2.87 7.67 20.84 0.77 5.56

466.23 25.52 62.00 126.00 9.33 70.00

667.92 34.5 80.00 236.08 10.26 96.00

529.94 29.43 72.25 151.27 9.79 87.75

141.55 6.22 12.61 77.50 0.54 18.31

coefficient (two tailed) to determine the degree of intercorrelation between indoor–outdoor PM, indoor– indoor PM, indoor ventilation rates with indoor CO2 and PM concentrations in both campaigns that could suggest confounding effects. Differences between the results obtained for health effects of children at the sampled roadside and residential area schools were also calculated during both campaigns and the independent t-test was used to determine statistical significance (using the SPSS statistical package).

wind speed 5–6 km h1. In campaign II (summer season), the mean temperature recorded was 29.438C, RH 72.25%, ventilation rate 42.02 m3 h1, CO2 level 529.94 ppm, and wind speed 9–10 km h1. The detailed fluctuations of the above parameters and the guidelines used are presented in Tables 2 and 3, respectively.

Classrooms were monitored during the period of occupancy (5 h daily) for 67% of days during campaign I in winter season (December 2007 to January 2008) and 70% of the days during campaign II in summer (April 2008 to May 2008). The attendance (number of students) in winter did, virtually, not differ from attendance in summer, but by only 8%, as given in Table 1. As usual for school buildings in India, none of them had an airconditioning system but were all naturally ventilated by opening doors and windows. During the winter, the mean inside classroom temperature was 19.478C, RH 53.34%, ventilation rates 13.29 m3 h1, CO2 level 834.05 ppm, and

PM Concentrations Seasonal Variation Descriptive statistics of PM concentrations measured at the four schools influenced by traffic emissions and the surrounding localities are presented in Table 4. From the results obtained, it is noticeable that the mean indoor PM concentrations in winter were higher than those monitored in summer at all schools except S3 and S4. In winter, the mean PM10 was 524.76 mg m3, SD ¼ 169.36; PM2.5 240.95 mg m3, SD ¼ 152.79; and PM1.0 259.51 mg m3, SD ¼ 55.62 at both roadside schools S1 and S2. However, these concentrations fell to a mean value of 153.37 mg m3, SD ¼ 53.47 (PM10); 60.61 mg m3, 3 SD ¼ 17.43 (PM2.5); and 38.39 mg m , SD ¼ 12.32 (PM1.0) in summer season for the same sites (S1 and S2). Moreover, the residential area schools S3 and S4 in summer also indicated a higher mean PM concentration with negligible difference between indoor and outdoor concentrations compared with other sites (S1 and S2).



Results

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Table 4. Descriptive statistics of indoor and outdoor PM concentrations at each site (S1, S2, S3 and S4) during school hours in campaigns I and II (n ¼ 40 each) PM size fraction

Indoor Minimum (mg m3)

Winter (campaign I) S1 PM10 S1 PM2.5 S1 PM1.0 S2 PM10 S2 PM2.5 S2 PM1.0 S3 PM10 S3 PM2.5 S3 PM1.0 S4 PM10 S4 PM2.5 S4 PM1.0 Summer (campaign II) S1 PM10 S1 PM2.5 S1 PM1.0 S2 PM10 S2 PM2.5 S2 PM1.0 S3 PM10 S3 PM2.5 S3 PM1.0 S4 PM10 S4 PM2.5 S4 PM1.0

Maximum (mg m3)

Outdoor Mean (SD) (mg m3)

Minimum (mg m3)

Maximum (mg m3)

Mean (SD) (mg m3)

CV

236.55 100.00 72.42 333.00 90.00 44.72 150.00 58.00 37.70 225.00 38.47 16.76

677.00 475.86 322.00 1200.00 585.00 310.00 675.96 550.00 293.60 888.00 301.87 135.00

395.66 285.91 209.02 653.86 196.08 110.33 387.09 270.96 162.74 551.64 128.32 60.15

(116.50) (90.19) (56.77) (222.22) (107.75) (54.47) (141.85) (150.07) (87.61) (193.54) (50.59) (29.38)

0.29 0.33 0.27 0.33 0.54 0.49 0.36 0.55 0.53 0.35 0.39 0.48

218.00 110.00 91.00 220.00 97.00 33.00 97.00 55.76 35.70 83.56 21.67 13.60

542.00 421.00 311.00 1010.00 555.00 225.00 711.00 534.67 281.00 867.00 256.00 99.00

298.41 229.16 174.62 618.44 244.96 108.68 397.41 281.57 163.38 548.19 120.57 58.20

(70.48) (61.40) (60.06) (235.11) (148.86) (59.63) (171.28) (162.11) (86.35) (299.53) (61.65) (28.98)

0.23 0.38 0.34 0.38 0.60 0.54 0.43 0.57 0.52 0.54 0.51 0.49

120.00 40.70 21.90 55.56 18.60 9.80 27.00 12.00 12.47 75.00 36.00 30.00

368.80 116.80 78.00 280.50 95.00 39.00 270.00 98.00 55.00 450.55 255.00 230.00

193.88 82.24 54.09 112.86 38.98 22.70 109.13 44.17 29.94 206.48 140.80 104.99

(60.28) (17.90) (17.50) (46.67) (16.97) (7.14) (49.83) (18.43) (10.86) (85.86) (73.06) (51.38)

0.31 0.21 0.32 0.41 0.15 0.31 0.45 0.41 0.36 0.41 0.51 0.48

75.00 24.00 12.00 35.00 20.00 10.00 26.00 23.00 14.00 61.00 52.00 26.00

260.00 98.00 69.00 256.00 85.00 29.00 260.00 108.70 187.00 401.00 236.00 190.00

199.38 68.47 43.18 85.90 33.72 18.44 113.53 51.62 34.61 200.06 133.09 99.56

(61.10) (61.01) (13.55) (42.65) (14.43) (5.55) (51.47) (17.46) (27.95) (87.54) (49.02) (40.12)

0.30 0.89 0.31 0.49 0.42 0.30 0.45 0.33 0.80 0.43 0.36 0.40

Therefore, the means recorded were 157.80 mg m3, SD ¼ 67.84, for PM10; 92.48 mg m3, SD ¼ 45.74, for PM2.5; and 67.46 mg m3, SD ¼ 31.12 for PM1.0. Further, on comparing, it was observed that concentrations of indoor PM10, PM2.5 and PM1.0 levels were significantly higher for the roadside schools (S1 and S2) than the residential schools (S3 and S4), in both campaigns I and II. The concentrations were found in the campaign I, to be 1.11, 1.20 and 1.29 times higher; and in the campaign II, 0.97, 0.65 and 0.56 times higher for the above respective PM sizes. When comparing the indoor PM10 and PM2.5 to the 24 h average National Ambient Air Quality Standard (NAAQS) as specified by Central Pollution Control Board, India, the PM10 concentrations found in the schools were 3–5 times and PM2.5 were 2–4 times exceeded the NAAQS (100 mg m3 for PM10, 60 mg m3 for PM2.5). The measurement data were also compared with the WHO air-quality guidelines [36] as given in Table 2, (50 and 25 mg m3, 24 h average for PM10 and PM2.5, respectively), which also exceeded by 7.74–13.07 times for PM10 and 5.13–11.43 for PM2.5, though, ‘‘the average data of the total period used for the calculation may not reflect the

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CV


short term changes in concentration due to some short term activity’’. Thus, the averaged data calculated revealed that PM concentrations are dependent not only on the seasons but also on the presence of pupil (activity patterns) as reported by [37].

PM Diurnal Variations Figure 2 shows the mean association of PM size fractions (PM10, PM2.5 and PM1.0) at monitored roadside schools which varied highly in winters, with PM10 levels being the maximum (1200 mg m3) at site S2, i.e. during morning hours along with some elevations. However, at S1, PM10 levels started rising steeply with broad peaks (at 11:45 am and 13:45 pm), along with some small peaks during morning and afternoon (09:45 am and 12:45, respectively) in recess and break periods. A similar pattern of rise and fall of PM2.5 and PM1.0 levels at the same time was observed at S1 whereas at S2, sharp peaks around 11:00 and 13:30 were observed when the school ends, which may be attributed to children messing and rushing out of the classrooms.

Habil and Taneja

Fig. 2. Indoor and outdoor PM variations during school hours at sites S1 and S2 in campaign I (winter season).

Fig. 3. Indoor and outdoor PM variations during school hours at sites S3 and S4 in campaign I (winter season).

Moreover, some stability was also seen during the teaching hours, but declined at few intervals (08:45 at S1 and 09:30 at S2), when classrooms were unoccupied and declined less around 12:30 am at S2, which might be due to less occupied classrooms. Relative to such conditions, few indoor levels remained elevated after the school ends at 13:45 pm onwards at S2, which might be due to the cleaning activities (when classrooms were swept). Similar but multiple elevated peaks for PM10 levels were observed at S4 (relative to S2), starting from the morning to afternoon, at 08:15, 09:15, 11:15 and 13:15, as shown in Figure 3. This might be due to the devotional activity (prayer, exercises and doing action songs) within the classroom when the school starts, small breaks between classes, recess (break for eating and drinking) and

discussions periods, when only indoor levels were higher than outdoors. Both S3 and S4 were observed to have regular high variation of PM levels, especially at S3, which showed similar pattern of PM10, PM2.5 and PM1.0 levels like S1. This suggests that when the levels were higher, the proportion of fine particles PM2.5 and PM1.0 would be greater [38]. Such fluctuations may arise due to constant activity like continuous opening and closing of doors for entering and leaving the classrooms. These levels further showed some irregular decline around 12:30 pm, which might have occurred due to school activities done in free periods. High PM2.5 morning peak reflected traffic emissions which penetrate inside the school building from outdoor idling of vehicles which drop children in



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Fig. 4. Indoor and outdoor PM variations during school hours at sites S1 and S2 in campaign II (summer season).

school, as seen at S4. However, in contrast with PM2.5 and PM1.0, PM10 concentrations increased considerably with several peaks in morning hours (10:30 to 11:30), with a steep decline around 12:00 to 13:00 pm breaks but again elevation showing sharp peak during the time the school ends, due to rushing out from classroom. As observed during winters, the PM levels during summers (campaign II) markedly showed lower levels of concentrations with sharp peaks. This is clearly seen at roadside especially at S1, where morning hours were fully stable (due to the strict class schedules) followed by two peaks, one highest during the recess time (10:00 to 10:30) and the other one during the time school ends (11:45 to 12:07) at S1. However, few small elevations at 07:30 and 12:00 were also observed at site S2 (Figure 4). Also, some low-broad peaks during morning to afternoon hours were observed (when the pupils are free from lessons and when classes end). Similar to winter, S3 showed continuous small fluctuations for indoor levels with only one high peak for outdoors, indicating a higher outdoor concentration than indoors during the recess around 10:00 to 11:00, when the classroom was unoccupied, as shown in Figure 5. Further, when the PM10 levels reduced, the association between PM2.5 and PM1.0 was shown to have increased, and when PM10 rose due to increased activity of the pupils, PM2.5 and PM1.0 concentrations dropped accordingly. Moreover, high elevations were observed at S4 due to particles brought inside by shoes from outside through intense activities of the children and due to the effect of a nearby construction site. However, in contrast with teaching hours, non-teaching hours showed a steep decline for all PM (PM10, PM2.5 and PM1.0) concentrations, mainly when classrooms were unoccupied before the start of school (08:30 and 07:00 in campaigns I and II) and after

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the ending of school (13:30 and 12:00 in campaigns I and II), which were found to be 2–3 times lower than those observed during the teaching hours (from 08:30 to 13:30 in campaign I and from 07:30 to 12:00 in campaign II). Thus, it shows that the levels of PM could vary according to the school hours with different school activity patterns. This can be further examined of the indoor– outdoor ratios and correlations of different size fractions (PM10, PM2.5 and PM1.0).

Spatial Variation Figure 6 shows the box plots representing the spatial distribution with values of the respective indoor:outdoor relationships as I/O ratios. As I/O ratios are the indicators for the strength of indoor sources, which could highly vary depending on the indoor activities and outdoor concentration levels. Generally, during both campaigns, the mean I/ O ratios for PM10, PM2.5 and PM1.0 were found to be 1 and greater than 1 at all sites except at S3 and S2. Schools S1 and S2 were found to have higher I/O ratios with an average of 1.21 (PM10), 1.01 (PM2.5) and 1.08 (PM1.0) in campaign I and 1.15 (PM10), 1.23 (PM2.5) and 1.27 (PM1.0) during campaign II than urban residential area schools, i.e. 0.98 (PM10), 0.99 (PM2.5) and 1.01 (PM1.0) in campaign I and 0.99 (PM10), 0.95 (PM2.5) and 0.95 (PM1.0) in campaign II. Thus, the findings have indicated that a resuspension of dust in the schools should be investigated as the primarily issue of concern in classrooms with less cleanliness. The measured indoor–outdoor concentrations were found higher than the other studies done internationally. On comparing with the concentrations measured by [16], PM10 levels were found to be 3–5 times higher in classrooms and about 3–4 times outside the classroom [39].

Habil and Taneja

Fig. 5. Indoor and outdoor PM variations during school hours at sites S3 and S4 in campaign II (summer season).

Fig. 6. I/O ratios of PM concentrations at: (a) campaign I and (b) campaign II.



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Moreover, on comparing with other studies of schools in Athens area, PM10 was 2 times and PM2.5 4 times higher indoors [22]. In a study done at a school near motorways [40] PM2.5 was 7–16 times higher indoors [40]. Indoor–Outdoor and Indoor–Indoor PM Correlations Correlation of indoor and outdoor levels can imply a source relationship between indoor–outdoor environments [41]. Therefore, the indoor–outdoor correlations for PM (PM10, PM2.5 and PM1.0) at all sites during both the campaigns were carried out to view the dependency of indoor particles on their corresponding outdoor ones, as shown in Figure 7(a–d). Site S3 showed a very strong correlation between indoor–outdoor levels (r ¼ 0.75 for PM10, r ¼ 0.85 for PM2.5 and r ¼ 0.87 for PM1.0) in campaign I (winter) only. Such a strong correlation indicates possibly similar sources of origin for both indoor and outdoor levels. The schools S1, S2 and S4 showed poor correlations in both the campaigns. Indoor–outdoor correlations ranged between r ¼ 0.03 and 0.46 for PM10, r ¼ 0.13 and 0.40 for PM2.5 and r ¼ 0.16 and 0.21 PM1.0 in campaign I and between r ¼ 0.03 and 0.16, r ¼ 0.05 and 0.42 and r ¼ 0.03 and 0.31 for PM10, PM2.5 and PM1.0, respectively, in campaign II, thus indicating that different indoor sources existed compared to the outdoor air pollutants which contributed to the particulates found indoors and their concentrations were only partly influenced by the outdoor air. However, all the variations in indoor PM concentrations cannot be explained as functions of outdoor PM concentrations. Therefore, in addition to the above indoor–outdoor correlations, further inter-particulate correlations in the indoor environment were obtained at all sites to explore their sources and variations. This resulted in strong correlations which were significant at p50.01 during both the campaigns. Similar to indoor–outdoor correlations site, S3 showed highest indoor–indoor correlation during campaign I between PM10 and PM2.5 (r ¼ 0.91), between PM10 and PM1.0 (r ¼ 0.86) and between PM2.5 and PM1.0 (r ¼ 0.94), as given in Table 5, which was reduced to r ¼ 0.71, r ¼ 0.64 between PM10 and PM2.5 and PM10 and PM1.0, respectively, and raised to r ¼ 0.99 between PM2.5 and PM1.0 at campaign II, as given in Table 5. The variation indicates resuspension of the particulates in air which could be the most likely cause of elevated PM10 concentrations, which was mainly composed of 86– 91% of PM2.5 and PM1.0 in winters (with closed windows), and 49–84% (with opened windows) in summers in all the

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sites. These dust particles are brought in by shoes, bags and combustion products, which accumulated due to closed windows and therefore less ventilation, with 83– 97% of total number of strength of pupils strength presented in the sampled classrooms (Table 1). Association of Ventilation Rate with CO2 and PM Concentrations In addition to above correlations, associations of PM concentrations (PM10, PM2.5 and PM1.0) and CO2 were also observed with regard to ventilation rates as given in Table 6. Significant Pearson bivariate correlations were seen during both seasons. PM10 showed highest correlation with low ventilation rates, significant at p ¼ 0.00 ( p50.01) in campaign I (winter), indicating poor air quality inside classrooms, whereas, PM2.5 and PM1.0 showed high significance ( p ¼ 0.005 and p ¼ 0.016) in campaign II (summer). CO2 was positively associated with ventilation with higher p-value 0.009 in campaign II and 0.018 in campaign I, respectively. Lower ventilation rates ranged between 33.62 m3 h1 and 64.04 m3 h1 (mean 3 1 47.84 m h ) in winter and 126.03 and 236.08 m3 h1 (mean ¼ 151.27 m3 h1 in summer. Strong correlation explains the decay of CO2 levels when exchange takes place through open windows and also when classrooms are unoccupied, indicating the ability to building ventilation. Further, the values of CO2 obtained in the study were found higher than those in other studies, like in Uppasala schools, which exceeded CO2 comfort value in 74% classrooms with natural ventilation [42]. A project of EPA/National Association of Energy Service Companies in the schools of five cities of USA found correlation of elevated CO2 levels with inadequate ventilation rates of 528.80 m3 h1 per person, as recommended by ASHRAE in 1992 [43,44]. Other studies published [45–54] also showed high CO2 levels, sometimes extremely high indoor concentrations which often exceeded the comfort value, indicating inadequate ventilation. Short-term Health Implications In order to have some idea of the possible effect of the adverse IAQ on the pupils’ health, a relationship between the past health records and present increase in symptoms was investigated by analysing the returned 300 questionnaires, as given in Table 7. Significant difference (p50.001) was observed when applying independent sample t-test to the symptoms reported by the children at the roadside and urban (residential area) schools.

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Fig. 7. Indoor–outdoor correlations of PM10, PM2.5 and PM1.0 at sites: (a) S1and S2 in campaign I (winter season); (b) S3 and S4 in campaign I (winter season); (c) S1 and S2 in campaign II (summer season); and (d) S3 and S4 in campaign II (summer season).

Symptoms reported at the roadside schools were 1–5 times higher than those located in residential areas in campaign I and 1–7 times for the same in campaign II. This increased difference supports the fact that outdoor as well as indoor polluted air could influence the air quality indoors.

Moreover, symptoms indicating high significant results between the roadside and residential area schools in campaign I were: dry flaking skin, 5.6 times and dizziness 5.3 times and in campaign II: Dizziness 7 and Dry flaking skin 6.6 times higher roadside to residential area schools.



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Fig. 7. Continued.

Discussion The measurements revealed considerable variations between indoor and outdoor PM10, PM2.5 and PM1.0 levels, which were carried out for comparison purpose and their impact on children’s health. The data analysed show lower indoor PM concentrations at S3 and S4 in campaign I, which could be attributed to the intense construction activities taking place in the school premises. There were vehicular

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emissions nearby, due to heavy and low traffics and a huge parking slot built in the school. However, in campaign II (summer), the levels varied largely at S1 and S2 and this was due to the influence of different ventilation practices like opening and closing of doors and windows in naturally ventilated schools in different seasons (winters and summers). Such a practice have contributed to the mobility of dust particles being transferred from outdoor polluted air, muddy playgrounds in school campus (like that in S2, as given in Table 1) to indoors by means of

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Table 5. Indoor–indoor PM correlations during campaigns I and II PM10 (IN) Campaign I S1 PM10 (IN) PM2.5 (IN) PM1.0 (IN) S2 PM10 (IN) PM2.5 (IN) PM1.0 (IN) S3 (i) PM10 (IN) PM2.5 (IN) PM1.0 (IN) S4 PM10 (IN) PM2.5 (IN) PM1.0 (IN) Campaign II S1 PM10 (IN) PM2.5 (IN) PM1.0 (IN) S2 PM10 (IN) PM2.5 (IN) PM1.0 (IN) S3 PM10 (IN) PM2.5 (IN) PM1.0 (IN) S4 PM10 (IN) PM2.5 (IN) PM1.0 (IN)

PM2.5 (IN)

PM1.0 (IN)

1.00 – –

0.87** 1.00 –

0.78** 0.91** 1.00

1.00 – –

0.39** 1.00 –

0.33* 0.71** 1.00

1.00 – –

0.91** 1.00 –

0.86** 0.94** 1.00

1.00 – –

0.72** 1.00 –

0.56** 0.74** 1.00

1.00 – –

0.71** 1.00 –

0.49** 0.68** 1.00

1.00 – –

0.48** 1.00 –

0.01 0.81** 1.00

1.00 – –

0.71** 1.00 –

0.64** 0.99** 1.00

1.00 – –

0.84** 1.00 –

0.75** 0.88** 1.00

The values are correlation coefficients (Pearson – two tailed). **p50.01, *p50.05 (n ¼ 20 each).

Table 6. Association of indoor ventilation rates with CO2 and PM concentrations during campaigns I and II (n ¼ 20 each) Ventilation rate

PM10

Campaign I (winter) Pearson’s r 0.96** p-Value 0.00 Campaign II (summer) Pearson’s r 0.96** p-value 0.00

PM2.5

PM1.0

CO2

0.81* 0.01

0.82* 0.01

0.88** 0.00

0.86** 0.00

0.72* 0.04

0.91** 0.00

Pearson’s r, Pearson correlation coefficient; p-value, significance value. **Correlation is significant at the 0.01 level (two tailed). *Correlation is significant at the 0.05 level (two tailed).


shoes, school bags, clothes having mud [55] or by crushing of chalk dust while writing on the blackboard and intense activities of the pupils especially during the summer season, when the windows are kept open for ventilation purposes. Similar variation was also observed in the residential area schools which might be due to the same activity of construction being carried out in the school area or due to unpaved area to the nearby classrooms, where dust was carried indoors by the summer winds. The time-series data shows a clear evidence of resuspension of coarse particles (PM10) in indoor air during certain time intervals like morning hours (around 08:30, 10:00, 12:00 and 13:30 in winter and around 07:00, 10:00 and 12:00 in summer), when the pupils were allowed free time for their curricular activities, time for playing, eating and drinking and some in afternoon hours when the children leave the classroom with much messed activities and deposition of the fractions PM2.5 and PM1.0 [56]. Other peaks of PM10 levels were also observed due to the rise in the children’s movement occurring like S3 (during both seasons) during small breaks (08:00 to 10:00) and continuous activity of leaving and entering the classroom. When the school was busy during the class hours and when the lessons ended, indoor activities would cease and a decline of PM10 was observed, whereas suspended particles PM2.5 and PM1.0 would increase. This also supports the findings of [14,57] that also discovered that coarse particles could be suspended when activity occurs and when pupils were present indoors. Also a study done by [58] stated that even ‘‘light’’ activities by one or more occupants could cause a 2–4 times increases in the concentrations of PM 4 5510. This may also have contributed to the outdoor elevations near classrooms when the pupils were rushing here and there in the corridors in free time. Further on comparing the teaching hours (from 08:30 to 13:30 in campaign I and from 07:30 to 12:00 in campaign II) with the non-teaching hours (before 08:30 and 07:00 in campaigns I and II and after the school hours (13:30 and 12:00 in campaigns I and II), a steep decline in concentrations of PM was observed. This may be due to less occupancy of classrooms when the school ends, whereas, when the school starts, concentrations would increase with the increasing occupancy rate in the classroom, as shown in Figures 2–5. Thus, the presence of pupils in school building could result in resuspension of coarse particles in air and therefore higher indoor levels, which can lead to adverse health effects compared to outdoor levels [14]. I/O concentrations could vary largely due to a larger number of factors (including locations within school premises and outside the school, ventilation


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Table 7. Increase in symptoms of students in the schools based on questionnaire and their health records Symptoms

Winter (campaign I)% Past health records

Difficulty in concentration Dry throat Back pain Dizziness Dry flaking Itching Sneezing High stress Eye irritation Shortness of breath Headache Drowsiness Cold and Flu Allergies a

Summer (campaign II)%

Increase in Past health Increase Past health Increase in Past health Increase in symptoms records in symptoms records symptoms records symptoms after coming after coming after coming after coming to school to school to school to school

Roadside

Roadside

Residential

Residential

Roadside

Roadside

Residential

Residential

22 16 10 42a 20 24 32 16 12 21 25 11 36 08

36 30 40 53a 45 48 38 42 40 28 46 33 47 35

10 08 12 04 05 10 21 05 09 03 10 06 24a 16

20 18 22 10 08 13 27 15 21 09 24 14 32a 25

21 22 10 33a 12 23 14 26 28 20 27 14 04 10

27 28 30 42a 33 38 26 35 31 26 36 20 18 25

05 12 04 05 03 06 10 05 10 03 17a 10 04 10

17 14 13 06 05 08 22 12 18 07 23a 12 22 19

Maximum percentage.

system and different occupants’ activities) like I/O ratios [59] less than 1 at S2 and S3 may be due to high intensive construction activities being carried out nearby to the school, which increased the settling rate of particles to the nearby surfaces of the classroom entrances [60,28]). The larger the particle, the greater the deposit would be on surfaces on (floors and furnishings [48], whereas I/O ratios greater than 1, as observed at S1 and S2, could be associated with particle generation and resuspension of previously deposited particles in densely populated classrooms, especially when influenced by vehicular emission and roadside dust at the roadside located schools. Thus, the occupants could influence IAQ in different ways, as human activities could result also in particle generation or resuspension of previously deposited particles [61]. Moreover, S3 showed strong correlations between indoor and outdoor PM concentrations, which might be again attributed to the resuspension of the deposited dust particles originating from the construction activities taking place during the winter season, and also the traffic emissions from a close vicinity of major streets with heavy traffic during school days that get congested during morning and afternoon and this could be the main pollution source in urbanised areas [57]. Nevertheless, this comparison provided sufficient support to the hypothesis by [57] about the significant influences of ambient airborne particles on the indoor microenvironments. However, resuspension was also mentioned as the

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dominant underlying physical process basically in schools [45]. In addition, the wind speed could also influence both indoor and outdoor particle concentrations [57]. On the other hand, most of the classrooms today are less frequently and less thoroughly cleaned (once or twice weekly wipe over the floor) so that the sediment dust particles are only partly removed from indoor spaces. This in turn could result in a continued resuspension of particle on the room surfaces correlating with large number of occupants with high activities in small room size [51]. This high correlation factor would be associated with cases when indoor and outdoor pollutants are emitted from the same source, corresponding with that either locations are without indoor sources or with indoor sources that might be under control, whereas indication of different indoor and outdoor sources at other schools could originate from generation by occupants themselves, generation resulting from school activities like damaged walls, floor, ceiling, windows, chalk dust, dirty dusting material, storing broken and unused furniture or resuspension of previously deposited particles due to improper cleaning facilities, as cleaning of classrooms would be carried out usually after children have left the school. From the inspection of weak indoor and outdoor correlations, it is observed that indoor–indoor correlations showed significantly strong correlations between PM size fractions (PM10 and PM2.5 (r ¼ 0.91), PM10 and PM1.0 (r ¼ 0.86) and PM2.5 and PM1.0 (r ¼ 0.94) at campaign I,

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the highest between PM2.5 and PM1.0 at campaign II (r ¼ 0.99), inside classrooms only. These also showed seasonal variation, suggesting particle resuspension in air with high intensive activities, like open discussions carried out in crowded classrooms with low ventilation rates due to closed doors and windows in winter which was reduced to some extent during summers due to infiltration with higher air exchange rates. Regarding internal sources, usage rates of chalk in the classrooms were not monitored and are difficult to assess in practice. The only information available is that the four classrooms investigated were equipped with blackboards. However, other lower correlations would indicate less association of indoor particles, which could be highly dependent on outdoor sources like traffic emission from nearby minor roads, particles emerging from welding and repair shops and those from muddy playground in school premise with good ventilation rates in summer where larger particles are blown by wind rather than smaller ones. Poor IAQ in classrooms with low ventilation rates and high CO2 levels were observed significantly in both seasons. This also suggests that reduced ventilation rates (and higher indoor pollution) could be associated with a decreased ability to concentrate along with increased adverse health symptoms [62]. However, PM10, PM2.5 and PM1.0 all correlated highly with proper ventilation rates, which could influence a better air quality, due to open doors and windows. The main source of carbon dioxide in building is exhaled breath. Carbon dioxide itself is not a health threat at levels typically found indoors, but when outdoor air ventilation rates are low, CO2 levels and other pollutant levels are not diluted as much and therefore also tend to be high. According to ASHRAE Standard 62-1989, indoor CO2 levels should not exceed 1000 ppm. The values exceeding this threshold indicate insufficient fresh air and are associated with a higher frequency of health complaints [43]. A recent study [63] indicated the normal range of CO2 to be 600–1000 ppm and recommended that it should not exceed the upper value. As carbon dioxide levels above 600 ppm present in the air would indicate a problem, high CO2 levels observed particularly in winter in different time intervals exceeded the recommended lower value of 600 ppm and reached over 1300 ppm due to presence of closed doors and windows. This indicated insufficient ventilation routine in schools and therefore insufficient removal of indoor pollutants, as outdoor air that flows through a building usually dilute and removes indoor air contaminants [52]. Crowded classrooms could also be the reason of high CO2. Like CO2, ventilation also dilutes and removes air contaminants and removes moisture for

further condensation risks. Ventilation rates in naturally ventilated school buildings are usually higher in summer and could carry dust particles compared to winter, due to hot and dry blowing winds in the summer compared to the low and chilly air in winter. Reported increased health symptoms can be associated with outdoor locations in highly polluted areas with high diesel emissions, mechanical repair and commercial shops, and work activities around school like construction could affects air quality in classrooms of schools located near major roads, road crossings and densely populated areas with less greenery like roadside schools (S1 and S2) [25,26]. But schools located in residential locality are much more beneficial in this way as they would be far away from the main road and covered with green surrounding areas (S3 and S4). Other studies also pointed out the health relevance of the proximity of schools to motorways and/ or busy streets, concluding that a large number of children could be regularly exposed to elevated levels of traffic emissions in schools [40,64–66]. Moreover, a study done by [67] stated that when individuals experience symptoms (e.g. dry eyes or watery eyes, dry throat, lethargy, headache, chest tightness), they begin to perceive a reduction in their own performances. Such a perception increases as the number of symptoms increases, averaging a 3% loss with three symptoms, and an 8% loss with five symptoms. However, respiratory symptoms are closely associated with increased absenteeism, which is one of the leading causes of school absenteeism, accounting for 14 million missed school days per year [68]. Thus adverse air quality conditions would not only cause health problems, but could trigger existing conditions.



Conclusions The measurements revealed considerable differences and relationships not only between indoor and outdoor air qualities but also between indoor and indoor PM levels in schools in Agra. We came to a conclusion that a sharp increase in the levels of PM (with regard to time slots when class was occupied) was always observed during times of high physical activity, such as several minutes before the school and after the school and also during breaks. This could lead to deposit particles by children entering the classroom or resuspension of previously deposition of particles due to the children’s activities during breaks. However, the difference in PM concentrations in different seasons is most likely due to the different ventilation

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practices in summer and winter seasons. Due to increased ventilation in the summer, the indoor PM levels could be strongly dependent on outdoor levels while in the winter, the classroom PM could be more strongly influenced by indoor activities. In spite of outdoor contamination, indoor sources could play a major role in affecting the IAQ in naturally ventilated classrooms. I/O concentration ratios were found close to, or above 1.00 at all sites, indicating a strong dependence on particle size, the pupils being the pathways sources. I/O ratios were found to decrease with increasing particle size mainly during winter, when doors and windows are closed and opened only for entering and leaving of occupants, thus carrying outdoor particles indoors, accumulating deposits on surfaces due to improper cleaning procedures. However, the results could be explained due to the equal contribution of both phenomena (resuspension and generation of particles). Moreover, high PM levels indoors were associated with low and high ventilation rates, continuous generation of particles in damaged school buildings, resuspension due to intense activity of occupants, high penetrations of finer particles from leakages in the building envelope, low or no cleanliness in classrooms, large number of occupants in small size classrooms, frequent rubbing of chalk dust on blackboards, and accumulation of unused and broken furniture indoors. The study also showed that high levels of CO2 in classrooms were directly related to lowventilation rates in densely populated classrooms with no space to move around. Therefore, it is important to keep CO2 levels below the current standard (1000 ppm). Reduction in bad air quality in schools can be achieved by reducing the number of pupils in a classroom, increasing the length of breaks between classes and by increasing ventilation flow by increasing the number of ceiling fans and exhaust fans, thus further increasing the indoor air exchange with outdoor air. To reduce the pollutant levels, maintenance and renovation must be carried out in summer vacations when the schools are

closed for a long time. As activities in highly polluted localities, such as alongside busy roadways, could increase the overall intensity, duration and frequency of exposure, all of then are relevant to the evaluation of an individual’s risk profile for disease. Therefore, there is possible loss in learning ability because the children’s health suffers, which causes increase in the symptoms after students come to school; hence the study of poor air quality in classrooms should deserve more attention. A major strength of our investigation was the restriction of measurements to actual teaching hours of a school day. Our investigation, which covered teaching hours only, will thus give a more realistic estimation of the PM concentrations of pupils being exposed to at schools. Therefore, by applying simple useful measure of cleanliness, less and comfortable occupancy and building schools in areas with low pollutants and high greenery levels in future will surely help to reduce the contaminant levels indoors. This illustrates that short-term PM exposure levels could exhibit important variability which would contribute greatly to the children’s total exposure, emphasising its importance in children exposure studies.

Acknowledgments The study was financially supported by the Department of Science and Technology (DST) New Delhi (project no. SR/S4/ AS-262/05). The authors thank Dr F.M Prasad, Principal, St. John’s College, Agra and Dr Ashok Kumar, Head, Department of Chemistry, St. John’s College, Agra, for providing us with the necessary facilities. They also thank Prof. V.D Thomas, Head, Department of English, St. John’s college, Agra, for his valuable comments to improve the English language. Special thanks are extended to Dr Aditi Kulshrestha and Mr David D. Massey for their kind support and guidance. They also thank the school authorities, staff and students of the schools studied for their cooperation.

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