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Spatial and seasonal variation of particulate matter (PM10 and PM2.5) in Middle Eastern classrooms
Atmospheric Environment 80 (2013) 389e397
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Spatial and seasonal variation of particulate matter (PM10 and PM2.5) in Middle Eastern classrooms Maher Elbayoumi a, Nor Azam Ramli a, Noor Faizah Fitri Md Yusof a, *, Wesam Al Madhoun b a b
Clean Air Research Group, School of Civil Engineering, Universiti Sains Malaysia, Penang 14300, Malaysia Environment and Earth Science Department, The Islamic University at Gaza, Palestine
h i g h l i g h t s In classrooms, PM10 and PM2.5 concentrations significantly exceed the ambient concentrations. Classroom activities of the children led to re-suspension and increased levels of particles. Measurements of outdoor particles do not provide an accurate estimation of children’s exposure. I/O ratios of the PM concentrations of natural ventilated classrooms are more than 1.0. Spatial and seasonal variations of indoor and outdoor particulate concentrations noted.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 4 March 2013 Received in revised form 25 July 2013 Accepted 28 July 2013
Monitoring of PM10 and PM2.5 particularly in school microenvironments is extremely important due to their impact on the global burden of disease. PM10 and PM2.5 levels were monitored inside and outside the classrooms of twelve naturally ventilated schools located in Gaza strip, Palestine. The measurements were carried out using hand held particulate matter instrument during fall, winter and spring seasons from October 2011 to May 2012. The average concentration of indoor PM10 was 349.49 (196.57) mg m3 and for PM2.5 was 103.96 (84.96) mg m3. The indoor/outdoor ratios for PM10 and PM2.5 were found to be much greater than 1.00 for all case study schools due to resuspension of deposited particles from the floors. Furthermore, strong correlations were found between indooreoutdoor PM10 and PM2.5. The variations of PM10 and PM2.5 concentrations were significant for the three seasons. During winter, the mean indoor PM10 was 1.30 and 2.50 times higher than fall and spring concentrations respectively. Meanwhile, PM2.5 concentration in winter was 3.00 times higher than fall and spring concentrations. In relation to spatial variation, the concentration of PM10 in the lower storey level was significantly higher than the classrooms located in the higher storey level. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Indoor particulate matter School children Naturally ventilation Gaza
1. Introduction Indoor environmental quality in schools is a very important element in providing a healthy and comfortable learning environment where it has an effect on health, productivity, performance and comfort for students and teachers in a variety of ways (Daisey et al., 2003). A growing body of evidence has demonstrated that there are serious inadequate operation and maintenance of facilities inside schools buildings are seen as a result of chronic shortages of funding (Mendell and Heath, 2004). Moreover several studies * Corresponding author. Tel.: þ60 4 5996227; fax: þ60 4 5941009. E-mail address: [email protected] (N.F.F. Md Yusof). 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.07.067
showed the poor indoor environmental quality in schools due to insufficient ventilation especially in winter, infrequently and not thoroughly cleaned indoor surfaces, and a large number of students in relation to room area and volume (Almeida et al., 2011; Janssen et al., 1999). Particulate matter (PM2.5 and PM10) is considered one of the main pollutants that can exist inside schools buildings in way that can affect the student’s health. Several studies in natural ventilated building around the world indicate that the concentration of PM2.5 and PM10exceeding the limits recommended by World Health Organization (WHO) (Chithra and Shiva Nagendra, 2012; Diapouli et al., 2007; Habil and Taneja, 2011). The Middle East region is considered one of the most controversial regions for aerosol particle concentrations. The location of
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Fig. 1. Map of Gaza strip and the monitoring schools.
Middle East area at the intersection of air masses circulating among the three continents makes the aerosol concentrations governed by several important phenomena’s such as aerosol transportation and sea salt aerosol formation (Koçak et al., 2010). The seasonal variation in Middle East area is considered one of the main reasons that influence the raising concentration of PM2.5 and PM10. Seasonal dust storms that mainly formed in winter and spring seasons come from northern Africa and Arabian Peninsula desert inducing the transport of mineral dust to the region (Dayan and Levy, 2005). It has been estimated that 70 million tons of the Saharan dust transported every year and 20-30 million tons of this amount are deposited in the lower free troposphere of eastern Mediterranean area (Dayan et al., 1991; Koçak et al., 2010; Zereini and Wiseman, 2010). Furthermore, during warm seasons (summer, late spring and the first two months of fall season) the wind speed and direction are changed from east to west few hours after sunrise (Krom et al., 2004). This elevates the emission rate of sea salt aerosol such as ammonium bisulphate and nitrate. Moreover, The rate of reaction between these pollutants and local pollutants at coastal area is increased (Lelieveld et al., 2002). Several population based studies have established a strong correlation between exposures to seasonal fine particulate matter (PM) and increasing rates of mortality, morbidity, respiratory and
cardiovascular problems especially among children (Chen et al., 2012; Pope III et al., 2009; WHO, 2005). As reported by WHO, a short-term increase of 10 mg/m3 of PM for several days is associated with more coughing, lower respiratory symptoms, and hospital admissions increments due to respiratory problems (WHO, 2000). Therefore, the purpose of this study is to measure and compare the indoor concentration levels of particulate matter (PM2.5 and PM10) in order to find out the spatial and seasonal variations across Gaza strip schools. 2. Materials and methods 2.1. Description of study area Gaza strip (365 km2, 40 km long and between 6 and 12 km wide) is located on the eastern coast of the Mediterranean Sea. The area forms a transitional zone between the sub-humid coastal zone of Israel in the north, the semiarid plains of the northern Negev Desert in the east and the arid Sinai Desert of Egypt in the south (PMD, 2012). The climate in Gaza strip is characterized by mild and humid winter (DecembereMarch) which dominated by rainfall. The summer months (JuneeSeptember) are characterized by high humidity and the lack of wet precipitation. The spring season
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Table 1 Characteristics of monitoring schools. School name
Code
Number of students
Distance from main road (m)
Classroom cleaning activity
Location area
Nusirate prep boys A Nusirate prep boys D Elburaj prep girls B Dier Elbalah prep girls Bany Suhiela prep boys Bany Suhiela prep girls B Ahmad Abed Elaziz prep boys B Rafah prep girls B Elzaytoon prep girls B New Gaza prep boys A Beach prep girls B Salah Eldien prep boys
MCB MOB MCG MOG SOG SOB SCB SCG NOG NCB NCG NOB
733 712 903 1024 1132 1448 729 578 883 1066 1183 623
43 65 50 50 40 55 50 55 58 30 50 43
Daily Daily Daily Daily Daily Daily Daily Daily Daily Daily Daily Daily
Over populated Over populated Over populated Small town Urban area Urban area Urban area Over populated Urban area Urban area Over populated Urban area
(MarcheJune) is characterized by unsettled winter type weather for the first month, associated with North African cyclones; the rest of this period is very similar to that in the summer. Fall season (SeptembereDecember) usually characterized by an abrupt summer type weather in the first month; the rest of this period is characterized by the unsettled weather of winter (Koçak et al., 2010; PMD, 2012). 2.2. Sampling locations Sampling was done at Gaza strip in twelve naturally ventilated schools buildings that serve refugee students. The schools have three storeys and work in a double sessions (morning and afternoon). The locations were selected in order to obtain a realistic diagnosis of the temporal variation and spatial distribution of PM10 and PM2.5 in area as seen in Fig. 1 and Table 1. 2.3. Room’s selection In each selected school, three representative classrooms were selected for three sampling days. The initial inspection of wind direction was made in every school to identify the windward side of the building and one classroom was selected from each floor.
in in in in in in in in in in in in
the the the the the the the the the the the the
morning morning morning morning morning morning morning morning morning morning morning morning
and between school hours and between school hours
and and and and and
between between between between between
school school school school school
hours hours hours hours hours
camp camp camp
camp
camp
the blackboard at least 1 m from the wall and at least 1.5 m height from the floor (Blondeau et al., 2005; WHO, 2011). For outdoor sampling the samplers were placed at the front side of the building, usually near the playground area. Due to the lack of multiple samplers, indoor and outdoor measurements were taken alternately after each 15 min. In every selected classroom, 15 min grab sampling technique with 10 s sampling interval were used during the class time and followed by a 15 min period of outdoor concentration measurements (Habil and Taneja, 2011). Therefore, the individual 1620 indoor and outdoor measurements at each school were equally distributed over the monitoring duration (during different seasons) in order to cover meteorological conditions and pollutant concentrations as much as possible. The surface wind speed, ambient temperature and relative humidity in each site were simultaneously measured at the same time with particulate matter measurement. For a qualitative control of the measurements, a 5 min interval for the device stability was maintained after each 15 min measurement period. In addition, a protocol of information had to be filled out every day. The protocol included the time in which each measurement was taken, current weather conditions such as rain, wind, fog and dusty storm and other relevant observations.
2.4. Selection of monitoring instruments
2.6. Statistical analysis
The mass concentration of particles (PM2.5 and PM10) has been monitored using handheld optical particle counter (HAL-HPC300). The monitor performs particulate size measurements by using laser light scattering. Air with multiple particle sizes passes through a flat laser beam produced by an ultra-low maintenance laser diode. A 3-channel pulse height analyzer for size classification detects the scattering signals. The particle counter was factory calibrated, prior to the sampling campaign and the calibration was repeated every week using Zero-Count Filter (Hal, 2012). A Kanomax IAQ Monitor was used for temperature, relative humidity measurements and Smart Sensor Electronic Anemometer was used for wind speed.
The SPSS software package (version 20) was used for statistical analysis of data. Meanwhile, the indoor source generation (CS) is obtained by applying mass balance-based model (Chen and Zhao, 2011) to derive an equation connecting the ambient and indoor concentrations:
2.5. Sampling method A particulate monitoring program started from October 2011 to May 2012 at the twelve monitoring schools in order to cover fall, winter and spring seasons. The measurements were taken place in each site during schools hours for three consecutive days from 7:00 am to 12:00 pm in winter and spring seasons and from 12:00 pm to 5:00 pm in fall season. Sampling was conducted both inside and outside the selected classrooms during the studying activities. The samplers were placed inside the classroom opposite
v
dcin ¼ apvcout avcin kvcin þ s dt
(1)
where v is the volume of the room, t is time, a is the air exchange rate due to infiltration, p is the particle penetration factor, k is the particle deposition rate, and s is the indoor particle emission rate. Infiltration factor is defined based on the steady-state and zero indoor particle emission rate case of Eq. (2):
Fin ¼
ap aþk
(2)
When the natural ventilation system is used, the generalized definition of infiltration factor and indoor source generation (CS) can be expressed as:
Cin ¼ Fin Cout þ CS
(3)
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Fig. 3. Indoor/outdoor ratio for PM2.5. Fig. 2. Indoor/outdoor ratio for PM10.
where Cin is indoor concentration, Fin the infiltration factor, Cout is the outdoor concentration and CS is the indoor particle concentration which is contributed by indoor sources. The indoor particle concentration which is contributed by indoor source can be solved Eq. (3) from the regression of indoor concentration against the outdoor concentration for respective particle sizes. The slope of the regression estimates the Fin, and the intercept estimates the average concentration of indoor generated particles CS (Goyal and Khare, 2011). 3. Results and discussions 3.1. Descriptive data The average PM10 concentrations of indoor and outdoor for all the schools during the study period were 349.49 (196.57) mg m3 and 149.53 (98.35) mg m3 respectively. The indoor and outdoor concentration for PM2.5 were 103.96 (84.96) mg m3 and 60.49 (50.72) mg m3 respectively. The indoor and outdoor averages of PM10 and PM2.5 concentrations for most of the schools were higher than the recommended WHO 24-h limits (50 mg/mg3and 25 mg/mg3 for PM10 and PM2.5) in the three seasons. 3.2. Indoor and outdoor ratio (I/O) I/O ratio is an indicator for the strength of indoor sources, which could highly vary depending on the indoor source and outdoor concentration levels. Pollutants can migrate from outdoors to indoors and indoor air sources could exacerbate indoor air pollution. Indeed, several studies revealed that indoor air pollution concentrations can exceed outdoor air concentrations (Diette et al., 2007; EPA, 2012; Zock et al., 2002). Generally, the mean I/O ratios for PM10 andPM2.5werefound to be more than unity at all schools as presented in Figs. 2 and 3. The I/O ratio is range between (1.3e6.3) for PM10 and (1.3e3.8) for PM2.5. The PM10 I/O ratios in both fall and winter were greater than PM2.5 I/O ratio. Blondeau et al. (2005) observed that larger particles (>2 mm) are having higher I/O (up to 5 times) than the I/O of smaller particles in the presence of occupants activities. Moreover, the I/O for PM10 during fall was higher than spring and winter periods which is similar to the observations made by several studies (Goyal and Khare, 2009; Singer et al., 2004). This variation was due to the difference in ventilation rate. Habil and Taneja (2011) reported that the manual ventilation practices in natural ventilated buildings
(NVBs) such as the opening and closing of windows and doors affect the I/O of PM. During cold months such as winter, the early first month of spring and the late months of fall season especially in the morning, students tend to close windows and doors inside the classrooms to get warmer environment. This causes the accumulation of particulate matter in the indoor environment and decrease mixing and dilution of PM (Guo et al., 2008). Meanwhile, the I/O for PM2.5 during spring and fall were higher than winter periods. This higher variation may be due to the increase in penetration factor value for PM2.5 and decrease the rate of deposition when compared to PM10. Several studies revealed that the penetration factors are higher for NVBs than for mechanically-ventilated buildings because NVBs buildings have windows, doors, ventilators, cracks and leaks in the building envelope (Branis et al., 2005; Chen and Zhao, 2011). Furthermore, different parameters may directly influence and increase I/O such as differences in building envelope tightness and seasonal effects (Goyal and Khare, 2009; Poupard et al., 2005), pollutant differential penetration efficiency (Sarnat et al., 2006), building air exchange rates (Singer et al., 2004) and building design (Ashmore and Dimitroulopoulou, 2009). Moreover, human presence, occupancy rates and occupant activities such as walking and using chalk are other important factors determining indoor/out door pollution ratios (Branis and Safránek, 2011; Chithra and Shiva Nagendra, 2012; Diapouli et al., 2007; Goyal and Khare, 2009; Habil and Taneja, 2011; Majumdar et al., 2012). High I/O ratios for both PM10 and PM2.5, in all schools suggests that building envelope may not prevent the infiltration of particles indoor (Blondeau et al., 2005) and this high ratios were observed in different studies worldwide (Chithra and Shiva Nagendra, 2012; Diapouli et al., 2007; Habil and Taneja, 2011) and are in line with the observations from this study as presented in Table 2. 3.3. The calculated concentrations of indoor generated particles The result showed that indoor concentrations of PM10 and PM2.5 were greater than outdoor concentrations. The monitored schools are characterized by high population density, inadequate ventilation, and poor school facilities and conditions (Al-Khatib et al., 2003). Fromme et al. (2007) found the high coarse PM levels in schools were correlated with less frequent cleaning, which is not capable of removing deposited particles. As consequences, they can be resuspended again and again. Interestingly, Hunt et al. (2006) reported that deposited coarse dust is difficult to remove from the floor even if thorough cleaning is carried out. The indoor particle concentration which is contributed by indoor source can be solved from the regression of indoor
M. Elbayoumi et al. / Atmospheric Environment 80 (2013) 389e397 Table 2 Examples of I/O ratios for particulate matters in schools indoor places. Source Diapouli et al. (2007) Fromme et al. (2008) Stranger et al. (2008) Goyal and Khare (2009) Ismail et al. (2010) Habil and Taneja (2011) Branis and Safránek, 2011 Madureira et al. (2012) Chithra and Shiva Nagendra (2012) Buonanno et al. (2012) This study
# of schools
Location
I/O for PM10
7 1 27 1 3 4 3 11 1
Greece Germany Belgium India Malaysia India Czech Republic Porto, Portugal India
12 12
Italy Middle east
Table 4 Correlation coefficients between indoor and outdoor particulate matter. I/O for PM2.5
1.65 4.8 e 3.59 4.7 1.1 11.6 1.1 2.5
1.67 2.2 1.8 2.79 e 1.04 3.6 0.8 1.4
3 2.6
2.2 2.2
concentration against the outdoor concentration. Table 3 shows that the generation of indoor PM10 concentrations in all schools was high during winter season and even greater than WHO guideline. These high concentrations were mainly due to student’s activities as discussed earlier. Meanwhile, the indoor source of PM2.5 concentrations in most of schools was less than10 mg h1 in fall and spring seasons. However during winter season indoor source values was high which may be due to several potential factors. On the one hand, inside monitoring classrooms there are no indoor sources of PM2.5 (classrooms premises is non-smoking area and cooking is not permitted). However, smoking may be allowed in teachers rooms which in the same floor of the classrooms. Furthermore, cooking (hot drinks) during teachers break by using gas or kerosene stove can also be included as a significant indoor source. Moreover, the resuspension of dust due to student’s activities considered as a dominant indoor source of PM2.5 as reported by different studies (Fromme et al., 2008; Stranger et al., 2008; Yurtseven et al., 2012). On the other hand, indoor sources in two schools namely MCB and MOB had higher indoor generation of PM2.5 during winter season as shown in Table 3. The monitoring inside these two mentioned schools was done during the sand storm that hit the area during winter season. Therefore, the concentration of PM10 and PM2.5 indoor and outdoor were extremely high and increased the penetration rate into the classrooms. In addition to previous factors, the continually high CO2 values that observed in winter indicate an insufficient ventilation routine in schools. This lack of ventilation may inhibit the transport and removal of particles from room interiors to the outdoor.
Table 3 Indoor generation source term for PM10 and PM2.5. School
SCB NCG MCG SCG MCB NCB NOG MOG SOG SOB MOB NOB Minimum Maximum Mean Std. deviation
PM2.5 (mg h1)
393
PM10 (mg h1)
Fall
Winter
Spring
Fall
Winter
Spring
4 4.7 12.3 9.9 1 4.1 10.3 10.3 22.9 3.1 4.4 6.2 1.0 22.9 7.7 5.9
12.8 44.3 47.4 33.3 135.2 12.4 36.2 9.1 35.3 15.3 79.3 21.1 9.1 135.2 40.1 35.9
8 14.1 2 10.5 17.1 4.6 11.6 3.5 10.9 24.6 5.1 7.6 2.0 24.6 9.9 6.4
17.8 91.9 294 63.7 372 25.5 68.7 68.2 151.4 18.2 31.4 41.4 17.8 372.0 103.6 114.6
93.9 159.9 63.2 181.9 289.9 93.3 243.2 181 187.6 130.5 165.3 217 63.2 289.9 167.2 65.5
14.3 33.1 46.6 244 140.3 12.2 35.6 37.1 8.4 14.9 75.6 20.4 8.4 244.0 56.8 69.4
Fall Winter Spring a
Indoor PM2.5 vs. outdoor PM2.5
Indoor PM10 vs. outdoor PM10
Pearson coefficients
P-value
Pearson coefficients
P-value
0.79 0.78 0.78
<0.001a <0.001a <0.001a
0.63 0.73 0.71
<0.001a <0.001a <0.001a
p-value is significant at the 0.05 level.
3.4. Indoor and outdoor correlation The indooreoutdoor correlations for PM (PM10 and PM2.5) were carried out to view the dependency of indoor particles on their corresponding outdoor ones at all three seasons. Table 4 shows a strong correlation between indooreoutdoor levels for PM10 for both winter and spring seasons (r ¼ 0.73 for winter, r ¼ 0.71 for spring). However for fall season didn’t show the same relationship (r ¼ 0.63 which indicates another source inside the classrooms for PM10 such as re-suspension of deposited particles. Furthermore, a strong correlation was shown between indooreoutdoor levels for PM2.5 during the three seasons (r ¼ 0.78e0.79). Such a strong correlation indicates possibly similar sources of origin for both indoor and outdoor levels. 3.5. Seasonal variability Seasonal changes and meteorological parameters, such as temperature, relative humidity (RH), and wind speed significantly affect the concentration of PM in NVBs when compared to the mechanically ventilated buildings. Fig. 4 shows the daily 5-h average pattern (school hours) of indoor and outdoor PM10 and PM2.5 for the three seasons. The pattern of PM2.5 shows a clear seasonal influence. Although higher concentrations for PM10 and PM2.5 can be observed during winter, the daily pattern for PM10 is much higher than daily pattern for PM2.5. This is due to the shorter residence times of PM10 leaving PM2.5 suspended in the air. Moreover, the seasonal variability can be explained by the PM2.5/ PM10 ratios in Table 5. The mean ratios of PM2.5/PM10 were ranged from (0.16e0.43) and (0.20e0.55) for indoor and outdoor respectively. Furthermore, the lower PM2.5/PM10 indoor ratios were observed during fall season which is due to the re-suspension of road dust which increased during the warm and dry seasons. As the size distribution of PM10 shifts toward larger particles, the penetration factor decrease and the surface removal rate increase (Chen et al., 2012). Table 6 illustrates season/season ratios such as winter/fall ratio. The higher ratios of winter/fall and winter/spring obtained for both PM10 and PM2.5 may be due to the higher values of relative humidity and low temperature. Different studies show that average particle matter concentration tend to be higher in cold seasons, the seasons with the lowest ventilation capability, due to lower temperatures, high relative humidity, available organics emitted from vehicles and decreased atmospheric mixing height (Arkouli et al., 2010; Somuri, 2011). 3.5.1. Association of seasonal changes with meteorological parameters and PM concentrations The air quality varies at any place from season to season because the dynamics of the atmosphere and the meteorological conditions play a very important role in governing the fate of air pollutants. In this study, the relationship between ambient particulate matter data and meteorological factors, such as temperature, relative
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Fig. 4. Seasonal variation during school hours of PM10 and PM2.5.
humidity, and wind speed is statistically analysed. Table 7 depicts the descriptive statistics of meteorological factors during the three seasons. The associations of I/O ratio of PM concentrations and meteorological parameters were also observed as given in Table 8. As seen, I/O ratio for PM10 was negatively correlated significantly with indoor temperature during the spring season. The inverse relationship between temperature and I/O is due to the temperature differences between indoor and outdoor building. When the indoor temperature higher than the outdoor temperature this will cause air to be force out of the building and dilute the indoor concentration (Milner et al., 2004). Different studies report inverse relationship between ambient temperature and outdoor PM species concentration (Chan, 2002; Monn et al., 1995). Janssen et al. (2001) found that PM10 were lower in warm and humid regions of the US compared with milder climate areas. Previous studies failed to show significant correlation between indoor temperature and indoor particle concentration (Branis et al., 2005; Fromme et al., 2007). The relationship between ventilation rate and PM concentrations showed different trends during the three seasons. A negative but not significant correlation was shown between ventilation rate and PM10 and PM2.5 during fall and spring seasons. The increment of air exchange rates may increase the penetration of pollutants and increased the dilution process, the ex-filtration and the deposition rate. This finding is similar with several studies who investigated the influence of air change rate on the PM10 (I/O) ratios in different microenvironments (Alshitawi et al., 2009; Chao and Wong, 2002). On the contrary, a positive relationship between I/O ratio for both of PM10 and PM2.5 and ventilation rate existed during winter. Cold temperature in winter forced students to closed windows leading
Table 5 The ratio between PM10 and PM2.5.
Indoor PM2.5/PM10 Outdoor PM2.5/PM10
to reduction in ventilation rate and as consequence decreasing indoor concentrations of such pollutant. This finding is in accordance with Diapouli et al. (2007); Milner et al. (2004); and Riain et al. (2003). Significant positive Pearson-bivariate correlations were found between relative humidity and I/O ratio of PM10 during weekdays for winter and spring seasons. Chan (2002) found a strong correlation between outdoor humidity and rainfall and indoor concentrations, with I/O ratios generally increasing with increasing humidity, since increased outdoor humidity may wash out or absorb pollutants and lower the outdoor concentration. Meanwhile, negative and significant correlation was shown between relative humidity and I/O ratio of PM2.5 and PM10 during fall season. This variation of relative humidity effect was reported by different studies. Fromme et al. (2007) reported a significant increase in indoor PM2.5 by1.7 mg/m3 per increase in humidity by 10% in summer season. However reverse relation regarding humidity (a decrease by 6.4 mg/m3 per increase in 10% humidity) was shown during winter season. Furthermore, a negative (not significant) relationship was found between wind speed and PM10 and PM2.5 in the three seasons as depicted in Table 8. Because of the complexity of the influence of wind on indoor air quality not all studies have reached the same conclusions. A high wind speed enhances dispersion, decrease outdoor concentrations and increased penetration rate leading to increase indoor concentration. However low wind speeds favour accumulation of pollutants outdoor and consequently increase I/O ratios (Chithra and Shiva Nagendra, 2012). Chan (2002) study found a very low correlation between wind speed and indoor concentrations of particulate matter. On the contrary, Branis et al. (2005) detected significant inverse correlation between wind speed and indoor PM10 concentration levels. 3.6. Spatial variation of PM10 and PM2.5
Fall
Winter
Spring
0.16 0.29
0.43 0.55
0.31 0.2
The average concentration of PM10 and PM2.5 varied from school to school depending on outdoor source, indoor source and metrological parameters. Indeed, the spatial variability for PM depends
Table 6 Season to season ratio for PM10 and PM2.5. Pollutant
Winter
Fall
Spring
Winter/Fall
Winter/Spring
Indoor PM2.5 Indoor PM10
197.93 (84.93) 492.56 (209.49)
55.20 (29.18) 360.11 (133.74)
58.82 (25.05) 195.80 (100.52)
3.59 1.37
3.37 2.50
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Table 7 Descriptive statistics for meteorological parameters. Season Parameters
Minimum Maximum Mean Std. dev
Temperature C (Temp) Relative humidity (RH) Wind speed (WS) (m s1) Ventilation rate L s1.person Winter Temperature C (Temp) Relative humidity (RH) Wind speed (WS) (m s1) Ventilation rate L s1.person Spring Temperature C (Temp) Relative humidity (RH) Wind speed (WS) (m s1) Ventilation rate L s1.person
24.80 47.30 0.10 4.7 8.30 27.60 0.10 1.51 9.00 15.00 0.10 3.13
Fall
32.60 71.00 7.00 29.1 21.30 89.40 13.00 17.70 33.80 100.00 9.00 19.15
27.50 1.70 58.80 5.00 3.50 1.50 9.88 5.10 14.00 2.40 62.20 14.30 3.20 2.50 6.66 3.20 18.20 3.70 73.10 18.10 2.60 2.00 8.09 2.93
on the size fraction. For PM2.5, the spatial variability is generally small as shown in Fig. 5, whereas PM10 has a greater spatial variability as shown in Fig. 6. This finding is in accordance with Monn (2001) study who reported that the spatial variability for PM2.5 is generally small. Further, a high degree of spatial heterogeneity observed between several schools due to strong local source contributions. The average concentration of PM10 and PM2.5 was the highest during the winter season reflecting the high emission sources (e.g. dust storm, the high dispersion from sea wind and vehicular emission exhaust) around these sites. The highest mass concentration of PM10 was 782.2 mg/m3 and for PM2.5 was 317.4 mg/ m3 which observed at MCB site in winter. The reason for the increasing of particulate matter concentration inside schools is related to the increased of PM10 and PM2.5 concentrations in the ambient air due to sand storm that attacked the region during the sampling period. Besides the variation in intra-location spatial variability, a variation in PM10 and PM2.5 concentration showed in inter-site level as shows in Fig. 7a and b. The PM10 and PM2.5 levels are higher on lower floors than on higher ones. A one-way ANOVA was used to test for PM10 differences among three floor level. PM10 concentration for storey level differed significantly across the three sizes, F (2, 105) ¼ 3.82, p ¼ 0.03. Duncan post-hoc comparisons of the three floor level indicate that the ground level (Mean ¼ 406.21) had significantly higher concentration than the 2nd floor level (Mean ¼ 302.11). Comparisons between the 1st floor (Mean ¼ 340.21) and the other two groups were not statistically significant at p < 0.05. Further, a one-way ANOVA was used to test for PM2.5 differences concentrations among three floor levels. PM2.5 concentration for the three storey levels was not differed significantly, F (2, 105) ¼ 2.01, p ¼ 0.14. This finding is in accordance with Jung et al. (2011) study that reported a clear decreasing trend of PM with heights in urban areas due to strong sources emissions at ground level and weak vertical mixing conditions.
Fig. 5. The daily 5-h average of indoor concentration of PM10.
4. Conclusion It was demonstrated that the Gaza strip area is burdened by PM10 and PM2.5. The data from three seasons were analysed to investigate spatial and seasonal variation and correlation in order to gain more understanding on their variability and interrelations. Inside the schools buildings which have a higher occupant density, the indoor PM concentration exceed the WHO standard limits for all monitoring seasons. This is an alarming fact, especially in the case of the increasing rates of mortality, morbidity, respiratory and cardiovascular diseases in the area. The indoor PM10 average concentrations of winter, spring, and fall seasons were 492.5 mg/m3, 195.8 mg/m3 and 360.1 mg/m3, respectively. The indoor PM2.5 average concentrations at winter, spring, and fall seasons were 197.9 mg/m3, 58.8 mg/m3 and 55.2 mg/m3 respectively. As regards to spatial distribution, a different pattern among and within the schools was observed and the average daily patterns of PM10 and PM2.5 show a clear seasonal variation. Students may be exposed to different levels of particulate matter, depending on height of the building. The particulate matter concentrations in the ground level classrooms were greater than the first and second floor classrooms concentrations. The average indoor PM2.5/PM10 ratio was 0.16e0.55 in all seasons indicating that coarse particles
Table 8 Pearson correlation coefficient between (I/O) PM10 and PM2.5 and meteorological parameters. PM2.5 (I/O)
RH indoor RH outdoor Temp indoor Temp outdoor Ventilation rate WS a b
PM10 (I/O)
Fall
Winter
Spring
Fall
Winter
Spring
0.19a 0.20a 0.03 0.04 0.12 0.06
0.06 0.01 0.05 0.02 0.06 0.06
0.14b 0.13 0.13 0.11 0.01 0.04
0.36a 0.32a 0.06 0.04 0.12 0.06
0.23a 0.18a 0.11 0.12 0.06 0.13
0.20a 0.13 0.16b 0.12 0.10 0.02
p-value is significant at the 0.01 level. p-value is significant at the 0.05 level.
Fig. 6. The daily 5-h average of indoor concentration of PM2.5.
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Fig. 7. The spatial variation of indoor PM2.5 (a) and PM10 (b) concentration measured in different level.
are the dominating fraction in PM species. There are clear associations between indoor PM10 and PM2.5 data sets and outdoor measurements at all the measured sites. Furthermore, the indoor PM2.5/outdoor PM2.5 and indoor PM10/outdoor PM10 ratios are more than unity during the three seasons which indicates that the concentration of PM2.5 and PM10 is higher indoors than outdoors due to the reduction in ventilation rate, increasing the penetration rate and students activities. Acknowledgement The authors wish to acknowledge the Universiti Sains Malaysia (USM) for partly financing the study under RUI 814183 and RUI 811206. References Al-Khatib, I.A., Ju’ba, A., Kamal, N., Hamed, N., Hmeidan, N., Massad, S., 2003. Impact of housing conditions on the health of the people at al-Ama’ri refugee camp in the West Bank of Palestine. International Journal of Environmental Health Research 13, 315e326. Almeida, S.M., Canha, N., Silva, A., Freitas, M.C., Pegas, P., Alves, C., Evtyugina, M., Pio, C.A., 2011. Children exposure to atmospheric particles in indoor of Lisbon primary schools. Atmospheric Environment 45, 7594e7599. Alshitawi, M., Awbi, H., Mahyuddin, N., 2009. Particulate matter mass concentration (PM10) under different ventilation methods in classrooms. International Journal of Ventilation 8, 93e108. Arkouli, M., Ulke, A., Endlicher, W., Baumbach, G., Schultz, E., Vogt, U., Muller, M., Dawidowski, L., Faggi, A., Wolf-Benning, U., 2010. Distribution and temporal behavior of particulate matter over the urban area of Buenos Aires. Atmospheric Pollution Research 1, 1e8. Ashmore, M., Dimitroulopoulou, C., 2009. Personal exposure of children to air pollution. Atmospheric Environment 43, 128e141. Blondeau, P., Iordache, V., Poupard, O., Genin, D., Allard, F., 2005. Relationship between outdoor and indoor air quality in eight French schools. Indoor Air 15, 2e 12. Branis, M., Rezácová, P., Domasová, M., 2005. The effect of outdoor air and indoor human activity on mass concentrations of PM10, PM2.5, and PM1.0 in a classroom. Environmental Research 99, 143e149. Branis, M., Safránek, J., 2011. Characterization of coarse particulate matter in school gyms. Environmental Research 111, 485e491. Buonanno, G., Fuoco, F., Marini, S., Stabile, L., 2012. Particle resuspension in school gyms during Physical activities. Aerosol and Air Quality Research 12, 803e813. Chan, A.T., 2002. Indooreoutdoor relationships of particulate matter and nitrogen oxides under different outdoor meteorological conditions. Atmospheric Environment 36, 1543e1551. Chao, C.Y., Wong, K.K., 2002. Residential indoor PM10 and PM2.5 in Hong Kong and the elemental composition. Atmospheric Environment 36, 265e277. Chen, C., Zhao, B., 2011. Review of relationship between indoor and outdoor particles: I/O ratio, infiltration factor and penetration factor. Atmospheric Environment 45, 275e288. Chen, C., Zhao, B., Weschler, C.J., 2012. Indoor exposure to “Outdoor PM10”: assessing its influence on the relationship between PM10 and short-term mortality in US cities. Epidemiology 23, 870e878.
Chithra, V., Shiva Nagendra, S., 2012. Indoor air quality investigations in a naturally ventilated school building located close to an urban roadway in Chennai, India. Building and Environment 54, 159e167. Daisey, J.M., Angell, W.J., Apte, M.G., 2003. Indoor air quality, ventilation and health symptoms in schools: an analysis of existing information. Indoor Air 13, 53e64. Dayan, U., Heffter, J., Miller, J., Gutman, G., 1991. Dust intrusion events into the Mediterranean basin. Journal of Applied Meteorology 30, 1185e1199. Dayan, U., Levy, I., 2005. The influence of meteorological conditions and atmospheric circulation types on PM10 and visibility in Tel Aviv. Journal of Applied Meteorology 44, 606e619. Diapouli, E., Chaloulakou, A., Spyrellis, N., 2007. Indoor and outdoor particulate matter concentrations at schools in the Athens area. Indoor and Built Environment 16, 55e61. Diette, G.B., Hansel, N.N., Buckley, T.J., Curtin-Brosnan, J., Eggleston, P.A., Matsui, E.C., McCormack, M.C., Williams, D.A.L., Breysse, P.N., 2007. Home indoor pollutant exposures among inner-city children with and without asthma. Environmental Health Perspectives 115, 1665. EPA, 2012. Indoor Air Quality, Tools for Schools. Environmental Protection Agency (EPA). Fromme, H., Diemer, J., Dietrich, S., Cyrys, J., Heinrich, J., Lang, W., Kiranoglu, M., Twardella, D., 2008. Chemical and morphological properties of particulate matter (PM10, PM2.5) in school classrooms and outdoor air. Atmospheric Environment 42, 6597e6605. Fromme, H., Twardella, D., Dietrich, S., Heitmann, D., Schierl, R., Liebl, B., Rüden, H., 2007. Particulate matter in the indoor air of classroomsdexploratory results from Munich and surrounding area. Atmospheric Environment 41, 854e866. Goyal, R., Khare, M., 2009. Indooreoutdoor concentrations of RSPM in classroom of a naturally ventilated school building near an urban traffic roadway. Atmospheric Environment 43, 6026e6038. Goyal, R., Khare, M., 2011. Indoor air quality modeling for PM10, PM2.5, and PM1.0 in naturally ventilated classrooms of an urban Indian school building. Environmental Monitoring and Assessment 176, 501e516. Guo, H., Morawska, L., He, C., Gilbert, D., 2008. Impact of ventilation scenario on air exchange rates and on indoor particle number concentrations in an airconditioned classroom. Atmospheric Environment 42, 757e768. Habil, M., Taneja, A., 2011. Children’s exposure to indoor particulate matter in naturally ventilated schools in India. Indoor and Built Environment 20, 430e448. Hal, 2012. HAL-HPC300 User Manual. Hal Technology Company, USA. http://www. haltechnologies.com/Docs/HAL-HPC300%20User%20Manual.pdf. Hunt, A., Johnson, D.L., Griffith, D.A., 2006. Mass transfer of soil indoors by track-in on footwear. Science of the Total Environment 370, 360e371. Ismail, M., Sofian, M., Zafirah, N., Abdullah, A.M., 2010. Indoor air quality in selected samples of primary schools in Kuala Terengganu, Malaysia. Environment Asia 3, 103e108. Janssen, N., Hoek, G., Brunekreef, B., Harssema, H., 1999. Mass concentration and elemental composition of PM10 in classrooms. Occupational and Environmental Medicine 56, 482e487. Janssen, N.A.H., van Vliet, P.H.N., Aarts, F., Harssema, H., Brunekreef, B., 2001. Assessment of exposure to traffic related air pollution of children attending schools near motorways. Atmospheric Environment 35, 3875e3884. Jung, K.H., Bernabé, K., Moors, K., Yan, B., Chillrud, S.N., Whyatt, R., Camann, D., Kinney, P.L., Perera, F.P., Miller, R.L., 2011. Effects of floor level and building type on residential levels of outdoor and indoor polycyclic aromatic hydrocarbons, black carbon, and particulate matter in New York City. Atmosphere 2, 96e109. Koçak, M., Kubilay, N., Tugrul, S., Mihalopoulos, N., 2010. Atmospheric nutrient inputs to the northern levantine basin from a long-term observation: sources and comparison with riverine inputs. Biogeosciences 7, 4037e4050. Krom, M., Herut, B., Mantoura, R., 2004. Nutrient budget for the Eastern Mediterranean: implications for phosphorus limitation. Limnology and Oceanography 49, 1582e1592.
M. Elbayoumi et al. / Atmospheric Environment 80 (2013) 389e397 Lelieveld, J., Berresheim, H., Borrmann, S., Crutzen, P., Dentener, F., Fischer, H., Feichter, J., Flatau, P., Heland, J., Holzinger, R., 2002. Global air pollution crossroads over the Mediterranean. Science 298, 794e799. Madureira, J., Paciência, I., Fernandes, E.O., 2012. Levels and indooreoutdoor relationships of size-specific particulate matter in naturally ventilated Portuguese schools. Journal of Toxicology and Environmental Health, Part A 75, 1423e1436. Majumdar, D., Gajghate, D., Pipalatkar, P., Rao, C., 2012. Assessment of airborne fine particulate matter and particle size distribution in settled chalk dust during writing and dusting exercises in a classroom. Indoor and Built Environment 21, 541e551. Mendell, M.J., Heath, G.A., 2004. Do indoor pollutants and thermal conditions in schools influence student performance? A critical review of the literature. Indoor Air 15, 27e52. Milner, J., Dimitroulopoulou, C., ApSimon, H., 2004. Indoor Concentrations in Buildings from Sources Outdoors. UK Atmospheric Dispersion Modeling Liaison Committee. ADMLC/2004/2. Monn, C., 2001. Exposure assessment of air pollutants: a review on spatial heterogeneity and indoor/outdoor/personal exposure to suspended particulate matter, nitrogen dioxide and ozone. Atmospheric Environment 35, 1e32. Monn, C., Braendli, O., Schaeppi, G., Schindler, C., Ackermann-Liebrich, U., Leuenberger, P., 1995. Particulate matter 10 mm (PM10) and total suspended particulates (TSP) in urban, rural and alpine air in Switzerland. Atmospheric Environment 29, 2565e2573. PMD, 2012. Climate Bulletine. Palestinian Meteorological Department, Ramallah. http://www.pmd.ps/ar/mna5palestine.php. Pope III, C.A., Ezzati, M., Dockery, D.W., 2009. Fine-particulate air pollution and life expectancy in the United States. New England Journal of Medicine 360, 376e386. Poupard, O., Blondeau, P., Iordache, V., Allard, F., 2005. Statistical analysis of parameters influencing the relationship between outdoor and indoor air quality in schools. Atmospheric Environment 39, 2071e2080.
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Spatial and seasonal variation of particulate matter (PM10 and PM2.5) in Middle Eastern classrooms Maher Elbayoumi a, Nor Azam Ramli a, Noor Faizah Fitri Md Yusof a, *, Wesam Al Madhoun b a b
Clean Air Research Group, School of Civil Engineering, Universiti Sains Malaysia, Penang 14300, Malaysia Environment and Earth Science Department, The Islamic University at Gaza, Palestine
h i g h l i g h t s In classrooms, PM10 and PM2.5 concentrations significantly exceed the ambient concentrations. Classroom activities of the children led to re-suspension and increased levels of particles. Measurements of outdoor particles do not provide an accurate estimation of children’s exposure. I/O ratios of the PM concentrations of natural ventilated classrooms are more than 1.0. Spatial and seasonal variations of indoor and outdoor particulate concentrations noted.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 4 March 2013 Received in revised form 25 July 2013 Accepted 28 July 2013
Monitoring of PM10 and PM2.5 particularly in school microenvironments is extremely important due to their impact on the global burden of disease. PM10 and PM2.5 levels were monitored inside and outside the classrooms of twelve naturally ventilated schools located in Gaza strip, Palestine. The measurements were carried out using hand held particulate matter instrument during fall, winter and spring seasons from October 2011 to May 2012. The average concentration of indoor PM10 was 349.49 (196.57) mg m3 and for PM2.5 was 103.96 (84.96) mg m3. The indoor/outdoor ratios for PM10 and PM2.5 were found to be much greater than 1.00 for all case study schools due to resuspension of deposited particles from the floors. Furthermore, strong correlations were found between indooreoutdoor PM10 and PM2.5. The variations of PM10 and PM2.5 concentrations were significant for the three seasons. During winter, the mean indoor PM10 was 1.30 and 2.50 times higher than fall and spring concentrations respectively. Meanwhile, PM2.5 concentration in winter was 3.00 times higher than fall and spring concentrations. In relation to spatial variation, the concentration of PM10 in the lower storey level was significantly higher than the classrooms located in the higher storey level. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Indoor particulate matter School children Naturally ventilation Gaza
1. Introduction Indoor environmental quality in schools is a very important element in providing a healthy and comfortable learning environment where it has an effect on health, productivity, performance and comfort for students and teachers in a variety of ways (Daisey et al., 2003). A growing body of evidence has demonstrated that there are serious inadequate operation and maintenance of facilities inside schools buildings are seen as a result of chronic shortages of funding (Mendell and Heath, 2004). Moreover several studies * Corresponding author. Tel.: þ60 4 5996227; fax: þ60 4 5941009. E-mail address: [email protected] (N.F.F. Md Yusof). 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.07.067
showed the poor indoor environmental quality in schools due to insufficient ventilation especially in winter, infrequently and not thoroughly cleaned indoor surfaces, and a large number of students in relation to room area and volume (Almeida et al., 2011; Janssen et al., 1999). Particulate matter (PM2.5 and PM10) is considered one of the main pollutants that can exist inside schools buildings in way that can affect the student’s health. Several studies in natural ventilated building around the world indicate that the concentration of PM2.5 and PM10exceeding the limits recommended by World Health Organization (WHO) (Chithra and Shiva Nagendra, 2012; Diapouli et al., 2007; Habil and Taneja, 2011). The Middle East region is considered one of the most controversial regions for aerosol particle concentrations. The location of
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Fig. 1. Map of Gaza strip and the monitoring schools.
Middle East area at the intersection of air masses circulating among the three continents makes the aerosol concentrations governed by several important phenomena’s such as aerosol transportation and sea salt aerosol formation (Koçak et al., 2010). The seasonal variation in Middle East area is considered one of the main reasons that influence the raising concentration of PM2.5 and PM10. Seasonal dust storms that mainly formed in winter and spring seasons come from northern Africa and Arabian Peninsula desert inducing the transport of mineral dust to the region (Dayan and Levy, 2005). It has been estimated that 70 million tons of the Saharan dust transported every year and 20-30 million tons of this amount are deposited in the lower free troposphere of eastern Mediterranean area (Dayan et al., 1991; Koçak et al., 2010; Zereini and Wiseman, 2010). Furthermore, during warm seasons (summer, late spring and the first two months of fall season) the wind speed and direction are changed from east to west few hours after sunrise (Krom et al., 2004). This elevates the emission rate of sea salt aerosol such as ammonium bisulphate and nitrate. Moreover, The rate of reaction between these pollutants and local pollutants at coastal area is increased (Lelieveld et al., 2002). Several population based studies have established a strong correlation between exposures to seasonal fine particulate matter (PM) and increasing rates of mortality, morbidity, respiratory and
cardiovascular problems especially among children (Chen et al., 2012; Pope III et al., 2009; WHO, 2005). As reported by WHO, a short-term increase of 10 mg/m3 of PM for several days is associated with more coughing, lower respiratory symptoms, and hospital admissions increments due to respiratory problems (WHO, 2000). Therefore, the purpose of this study is to measure and compare the indoor concentration levels of particulate matter (PM2.5 and PM10) in order to find out the spatial and seasonal variations across Gaza strip schools. 2. Materials and methods 2.1. Description of study area Gaza strip (365 km2, 40 km long and between 6 and 12 km wide) is located on the eastern coast of the Mediterranean Sea. The area forms a transitional zone between the sub-humid coastal zone of Israel in the north, the semiarid plains of the northern Negev Desert in the east and the arid Sinai Desert of Egypt in the south (PMD, 2012). The climate in Gaza strip is characterized by mild and humid winter (DecembereMarch) which dominated by rainfall. The summer months (JuneeSeptember) are characterized by high humidity and the lack of wet precipitation. The spring season
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391
Table 1 Characteristics of monitoring schools. School name
Code
Number of students
Distance from main road (m)
Classroom cleaning activity
Location area
Nusirate prep boys A Nusirate prep boys D Elburaj prep girls B Dier Elbalah prep girls Bany Suhiela prep boys Bany Suhiela prep girls B Ahmad Abed Elaziz prep boys B Rafah prep girls B Elzaytoon prep girls B New Gaza prep boys A Beach prep girls B Salah Eldien prep boys
MCB MOB MCG MOG SOG SOB SCB SCG NOG NCB NCG NOB
733 712 903 1024 1132 1448 729 578 883 1066 1183 623
43 65 50 50 40 55 50 55 58 30 50 43
Daily Daily Daily Daily Daily Daily Daily Daily Daily Daily Daily Daily
Over populated Over populated Over populated Small town Urban area Urban area Urban area Over populated Urban area Urban area Over populated Urban area
(MarcheJune) is characterized by unsettled winter type weather for the first month, associated with North African cyclones; the rest of this period is very similar to that in the summer. Fall season (SeptembereDecember) usually characterized by an abrupt summer type weather in the first month; the rest of this period is characterized by the unsettled weather of winter (Koçak et al., 2010; PMD, 2012). 2.2. Sampling locations Sampling was done at Gaza strip in twelve naturally ventilated schools buildings that serve refugee students. The schools have three storeys and work in a double sessions (morning and afternoon). The locations were selected in order to obtain a realistic diagnosis of the temporal variation and spatial distribution of PM10 and PM2.5 in area as seen in Fig. 1 and Table 1. 2.3. Room’s selection In each selected school, three representative classrooms were selected for three sampling days. The initial inspection of wind direction was made in every school to identify the windward side of the building and one classroom was selected from each floor.
in in in in in in in in in in in in
the the the the the the the the the the the the
morning morning morning morning morning morning morning morning morning morning morning morning
and between school hours and between school hours
and and and and and
between between between between between
school school school school school
hours hours hours hours hours
camp camp camp
camp
camp
the blackboard at least 1 m from the wall and at least 1.5 m height from the floor (Blondeau et al., 2005; WHO, 2011). For outdoor sampling the samplers were placed at the front side of the building, usually near the playground area. Due to the lack of multiple samplers, indoor and outdoor measurements were taken alternately after each 15 min. In every selected classroom, 15 min grab sampling technique with 10 s sampling interval were used during the class time and followed by a 15 min period of outdoor concentration measurements (Habil and Taneja, 2011). Therefore, the individual 1620 indoor and outdoor measurements at each school were equally distributed over the monitoring duration (during different seasons) in order to cover meteorological conditions and pollutant concentrations as much as possible. The surface wind speed, ambient temperature and relative humidity in each site were simultaneously measured at the same time with particulate matter measurement. For a qualitative control of the measurements, a 5 min interval for the device stability was maintained after each 15 min measurement period. In addition, a protocol of information had to be filled out every day. The protocol included the time in which each measurement was taken, current weather conditions such as rain, wind, fog and dusty storm and other relevant observations.
2.4. Selection of monitoring instruments
2.6. Statistical analysis
The mass concentration of particles (PM2.5 and PM10) has been monitored using handheld optical particle counter (HAL-HPC300). The monitor performs particulate size measurements by using laser light scattering. Air with multiple particle sizes passes through a flat laser beam produced by an ultra-low maintenance laser diode. A 3-channel pulse height analyzer for size classification detects the scattering signals. The particle counter was factory calibrated, prior to the sampling campaign and the calibration was repeated every week using Zero-Count Filter (Hal, 2012). A Kanomax IAQ Monitor was used for temperature, relative humidity measurements and Smart Sensor Electronic Anemometer was used for wind speed.
The SPSS software package (version 20) was used for statistical analysis of data. Meanwhile, the indoor source generation (CS) is obtained by applying mass balance-based model (Chen and Zhao, 2011) to derive an equation connecting the ambient and indoor concentrations:
2.5. Sampling method A particulate monitoring program started from October 2011 to May 2012 at the twelve monitoring schools in order to cover fall, winter and spring seasons. The measurements were taken place in each site during schools hours for three consecutive days from 7:00 am to 12:00 pm in winter and spring seasons and from 12:00 pm to 5:00 pm in fall season. Sampling was conducted both inside and outside the selected classrooms during the studying activities. The samplers were placed inside the classroom opposite
v
dcin ¼ apvcout avcin kvcin þ s dt
(1)
where v is the volume of the room, t is time, a is the air exchange rate due to infiltration, p is the particle penetration factor, k is the particle deposition rate, and s is the indoor particle emission rate. Infiltration factor is defined based on the steady-state and zero indoor particle emission rate case of Eq. (2):
Fin ¼
ap aþk
(2)
When the natural ventilation system is used, the generalized definition of infiltration factor and indoor source generation (CS) can be expressed as:
Cin ¼ Fin Cout þ CS
(3)
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Fig. 3. Indoor/outdoor ratio for PM2.5. Fig. 2. Indoor/outdoor ratio for PM10.
where Cin is indoor concentration, Fin the infiltration factor, Cout is the outdoor concentration and CS is the indoor particle concentration which is contributed by indoor sources. The indoor particle concentration which is contributed by indoor source can be solved Eq. (3) from the regression of indoor concentration against the outdoor concentration for respective particle sizes. The slope of the regression estimates the Fin, and the intercept estimates the average concentration of indoor generated particles CS (Goyal and Khare, 2011). 3. Results and discussions 3.1. Descriptive data The average PM10 concentrations of indoor and outdoor for all the schools during the study period were 349.49 (196.57) mg m3 and 149.53 (98.35) mg m3 respectively. The indoor and outdoor concentration for PM2.5 were 103.96 (84.96) mg m3 and 60.49 (50.72) mg m3 respectively. The indoor and outdoor averages of PM10 and PM2.5 concentrations for most of the schools were higher than the recommended WHO 24-h limits (50 mg/mg3and 25 mg/mg3 for PM10 and PM2.5) in the three seasons. 3.2. Indoor and outdoor ratio (I/O) I/O ratio is an indicator for the strength of indoor sources, which could highly vary depending on the indoor source and outdoor concentration levels. Pollutants can migrate from outdoors to indoors and indoor air sources could exacerbate indoor air pollution. Indeed, several studies revealed that indoor air pollution concentrations can exceed outdoor air concentrations (Diette et al., 2007; EPA, 2012; Zock et al., 2002). Generally, the mean I/O ratios for PM10 andPM2.5werefound to be more than unity at all schools as presented in Figs. 2 and 3. The I/O ratio is range between (1.3e6.3) for PM10 and (1.3e3.8) for PM2.5. The PM10 I/O ratios in both fall and winter were greater than PM2.5 I/O ratio. Blondeau et al. (2005) observed that larger particles (>2 mm) are having higher I/O (up to 5 times) than the I/O of smaller particles in the presence of occupants activities. Moreover, the I/O for PM10 during fall was higher than spring and winter periods which is similar to the observations made by several studies (Goyal and Khare, 2009; Singer et al., 2004). This variation was due to the difference in ventilation rate. Habil and Taneja (2011) reported that the manual ventilation practices in natural ventilated buildings
(NVBs) such as the opening and closing of windows and doors affect the I/O of PM. During cold months such as winter, the early first month of spring and the late months of fall season especially in the morning, students tend to close windows and doors inside the classrooms to get warmer environment. This causes the accumulation of particulate matter in the indoor environment and decrease mixing and dilution of PM (Guo et al., 2008). Meanwhile, the I/O for PM2.5 during spring and fall were higher than winter periods. This higher variation may be due to the increase in penetration factor value for PM2.5 and decrease the rate of deposition when compared to PM10. Several studies revealed that the penetration factors are higher for NVBs than for mechanically-ventilated buildings because NVBs buildings have windows, doors, ventilators, cracks and leaks in the building envelope (Branis et al., 2005; Chen and Zhao, 2011). Furthermore, different parameters may directly influence and increase I/O such as differences in building envelope tightness and seasonal effects (Goyal and Khare, 2009; Poupard et al., 2005), pollutant differential penetration efficiency (Sarnat et al., 2006), building air exchange rates (Singer et al., 2004) and building design (Ashmore and Dimitroulopoulou, 2009). Moreover, human presence, occupancy rates and occupant activities such as walking and using chalk are other important factors determining indoor/out door pollution ratios (Branis and Safránek, 2011; Chithra and Shiva Nagendra, 2012; Diapouli et al., 2007; Goyal and Khare, 2009; Habil and Taneja, 2011; Majumdar et al., 2012). High I/O ratios for both PM10 and PM2.5, in all schools suggests that building envelope may not prevent the infiltration of particles indoor (Blondeau et al., 2005) and this high ratios were observed in different studies worldwide (Chithra and Shiva Nagendra, 2012; Diapouli et al., 2007; Habil and Taneja, 2011) and are in line with the observations from this study as presented in Table 2. 3.3. The calculated concentrations of indoor generated particles The result showed that indoor concentrations of PM10 and PM2.5 were greater than outdoor concentrations. The monitored schools are characterized by high population density, inadequate ventilation, and poor school facilities and conditions (Al-Khatib et al., 2003). Fromme et al. (2007) found the high coarse PM levels in schools were correlated with less frequent cleaning, which is not capable of removing deposited particles. As consequences, they can be resuspended again and again. Interestingly, Hunt et al. (2006) reported that deposited coarse dust is difficult to remove from the floor even if thorough cleaning is carried out. The indoor particle concentration which is contributed by indoor source can be solved from the regression of indoor
M. Elbayoumi et al. / Atmospheric Environment 80 (2013) 389e397 Table 2 Examples of I/O ratios for particulate matters in schools indoor places. Source Diapouli et al. (2007) Fromme et al. (2008) Stranger et al. (2008) Goyal and Khare (2009) Ismail et al. (2010) Habil and Taneja (2011) Branis and Safránek, 2011 Madureira et al. (2012) Chithra and Shiva Nagendra (2012) Buonanno et al. (2012) This study
# of schools
Location
I/O for PM10
7 1 27 1 3 4 3 11 1
Greece Germany Belgium India Malaysia India Czech Republic Porto, Portugal India
12 12
Italy Middle east
Table 4 Correlation coefficients between indoor and outdoor particulate matter. I/O for PM2.5
1.65 4.8 e 3.59 4.7 1.1 11.6 1.1 2.5
1.67 2.2 1.8 2.79 e 1.04 3.6 0.8 1.4
3 2.6
2.2 2.2
concentration against the outdoor concentration. Table 3 shows that the generation of indoor PM10 concentrations in all schools was high during winter season and even greater than WHO guideline. These high concentrations were mainly due to student’s activities as discussed earlier. Meanwhile, the indoor source of PM2.5 concentrations in most of schools was less than10 mg h1 in fall and spring seasons. However during winter season indoor source values was high which may be due to several potential factors. On the one hand, inside monitoring classrooms there are no indoor sources of PM2.5 (classrooms premises is non-smoking area and cooking is not permitted). However, smoking may be allowed in teachers rooms which in the same floor of the classrooms. Furthermore, cooking (hot drinks) during teachers break by using gas or kerosene stove can also be included as a significant indoor source. Moreover, the resuspension of dust due to student’s activities considered as a dominant indoor source of PM2.5 as reported by different studies (Fromme et al., 2008; Stranger et al., 2008; Yurtseven et al., 2012). On the other hand, indoor sources in two schools namely MCB and MOB had higher indoor generation of PM2.5 during winter season as shown in Table 3. The monitoring inside these two mentioned schools was done during the sand storm that hit the area during winter season. Therefore, the concentration of PM10 and PM2.5 indoor and outdoor were extremely high and increased the penetration rate into the classrooms. In addition to previous factors, the continually high CO2 values that observed in winter indicate an insufficient ventilation routine in schools. This lack of ventilation may inhibit the transport and removal of particles from room interiors to the outdoor.
Table 3 Indoor generation source term for PM10 and PM2.5. School
SCB NCG MCG SCG MCB NCB NOG MOG SOG SOB MOB NOB Minimum Maximum Mean Std. deviation
PM2.5 (mg h1)
393
PM10 (mg h1)
Fall
Winter
Spring
Fall
Winter
Spring
4 4.7 12.3 9.9 1 4.1 10.3 10.3 22.9 3.1 4.4 6.2 1.0 22.9 7.7 5.9
12.8 44.3 47.4 33.3 135.2 12.4 36.2 9.1 35.3 15.3 79.3 21.1 9.1 135.2 40.1 35.9
8 14.1 2 10.5 17.1 4.6 11.6 3.5 10.9 24.6 5.1 7.6 2.0 24.6 9.9 6.4
17.8 91.9 294 63.7 372 25.5 68.7 68.2 151.4 18.2 31.4 41.4 17.8 372.0 103.6 114.6
93.9 159.9 63.2 181.9 289.9 93.3 243.2 181 187.6 130.5 165.3 217 63.2 289.9 167.2 65.5
14.3 33.1 46.6 244 140.3 12.2 35.6 37.1 8.4 14.9 75.6 20.4 8.4 244.0 56.8 69.4
Fall Winter Spring a
Indoor PM2.5 vs. outdoor PM2.5
Indoor PM10 vs. outdoor PM10
Pearson coefficients
P-value
Pearson coefficients
P-value
0.79 0.78 0.78
<0.001a <0.001a <0.001a
0.63 0.73 0.71
<0.001a <0.001a <0.001a
p-value is significant at the 0.05 level.
3.4. Indoor and outdoor correlation The indooreoutdoor correlations for PM (PM10 and PM2.5) were carried out to view the dependency of indoor particles on their corresponding outdoor ones at all three seasons. Table 4 shows a strong correlation between indooreoutdoor levels for PM10 for both winter and spring seasons (r ¼ 0.73 for winter, r ¼ 0.71 for spring). However for fall season didn’t show the same relationship (r ¼ 0.63 which indicates another source inside the classrooms for PM10 such as re-suspension of deposited particles. Furthermore, a strong correlation was shown between indooreoutdoor levels for PM2.5 during the three seasons (r ¼ 0.78e0.79). Such a strong correlation indicates possibly similar sources of origin for both indoor and outdoor levels. 3.5. Seasonal variability Seasonal changes and meteorological parameters, such as temperature, relative humidity (RH), and wind speed significantly affect the concentration of PM in NVBs when compared to the mechanically ventilated buildings. Fig. 4 shows the daily 5-h average pattern (school hours) of indoor and outdoor PM10 and PM2.5 for the three seasons. The pattern of PM2.5 shows a clear seasonal influence. Although higher concentrations for PM10 and PM2.5 can be observed during winter, the daily pattern for PM10 is much higher than daily pattern for PM2.5. This is due to the shorter residence times of PM10 leaving PM2.5 suspended in the air. Moreover, the seasonal variability can be explained by the PM2.5/ PM10 ratios in Table 5. The mean ratios of PM2.5/PM10 were ranged from (0.16e0.43) and (0.20e0.55) for indoor and outdoor respectively. Furthermore, the lower PM2.5/PM10 indoor ratios were observed during fall season which is due to the re-suspension of road dust which increased during the warm and dry seasons. As the size distribution of PM10 shifts toward larger particles, the penetration factor decrease and the surface removal rate increase (Chen et al., 2012). Table 6 illustrates season/season ratios such as winter/fall ratio. The higher ratios of winter/fall and winter/spring obtained for both PM10 and PM2.5 may be due to the higher values of relative humidity and low temperature. Different studies show that average particle matter concentration tend to be higher in cold seasons, the seasons with the lowest ventilation capability, due to lower temperatures, high relative humidity, available organics emitted from vehicles and decreased atmospheric mixing height (Arkouli et al., 2010; Somuri, 2011). 3.5.1. Association of seasonal changes with meteorological parameters and PM concentrations The air quality varies at any place from season to season because the dynamics of the atmosphere and the meteorological conditions play a very important role in governing the fate of air pollutants. In this study, the relationship between ambient particulate matter data and meteorological factors, such as temperature, relative
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Fig. 4. Seasonal variation during school hours of PM10 and PM2.5.
humidity, and wind speed is statistically analysed. Table 7 depicts the descriptive statistics of meteorological factors during the three seasons. The associations of I/O ratio of PM concentrations and meteorological parameters were also observed as given in Table 8. As seen, I/O ratio for PM10 was negatively correlated significantly with indoor temperature during the spring season. The inverse relationship between temperature and I/O is due to the temperature differences between indoor and outdoor building. When the indoor temperature higher than the outdoor temperature this will cause air to be force out of the building and dilute the indoor concentration (Milner et al., 2004). Different studies report inverse relationship between ambient temperature and outdoor PM species concentration (Chan, 2002; Monn et al., 1995). Janssen et al. (2001) found that PM10 were lower in warm and humid regions of the US compared with milder climate areas. Previous studies failed to show significant correlation between indoor temperature and indoor particle concentration (Branis et al., 2005; Fromme et al., 2007). The relationship between ventilation rate and PM concentrations showed different trends during the three seasons. A negative but not significant correlation was shown between ventilation rate and PM10 and PM2.5 during fall and spring seasons. The increment of air exchange rates may increase the penetration of pollutants and increased the dilution process, the ex-filtration and the deposition rate. This finding is similar with several studies who investigated the influence of air change rate on the PM10 (I/O) ratios in different microenvironments (Alshitawi et al., 2009; Chao and Wong, 2002). On the contrary, a positive relationship between I/O ratio for both of PM10 and PM2.5 and ventilation rate existed during winter. Cold temperature in winter forced students to closed windows leading
Table 5 The ratio between PM10 and PM2.5.
Indoor PM2.5/PM10 Outdoor PM2.5/PM10
to reduction in ventilation rate and as consequence decreasing indoor concentrations of such pollutant. This finding is in accordance with Diapouli et al. (2007); Milner et al. (2004); and Riain et al. (2003). Significant positive Pearson-bivariate correlations were found between relative humidity and I/O ratio of PM10 during weekdays for winter and spring seasons. Chan (2002) found a strong correlation between outdoor humidity and rainfall and indoor concentrations, with I/O ratios generally increasing with increasing humidity, since increased outdoor humidity may wash out or absorb pollutants and lower the outdoor concentration. Meanwhile, negative and significant correlation was shown between relative humidity and I/O ratio of PM2.5 and PM10 during fall season. This variation of relative humidity effect was reported by different studies. Fromme et al. (2007) reported a significant increase in indoor PM2.5 by1.7 mg/m3 per increase in humidity by 10% in summer season. However reverse relation regarding humidity (a decrease by 6.4 mg/m3 per increase in 10% humidity) was shown during winter season. Furthermore, a negative (not significant) relationship was found between wind speed and PM10 and PM2.5 in the three seasons as depicted in Table 8. Because of the complexity of the influence of wind on indoor air quality not all studies have reached the same conclusions. A high wind speed enhances dispersion, decrease outdoor concentrations and increased penetration rate leading to increase indoor concentration. However low wind speeds favour accumulation of pollutants outdoor and consequently increase I/O ratios (Chithra and Shiva Nagendra, 2012). Chan (2002) study found a very low correlation between wind speed and indoor concentrations of particulate matter. On the contrary, Branis et al. (2005) detected significant inverse correlation between wind speed and indoor PM10 concentration levels. 3.6. Spatial variation of PM10 and PM2.5
Fall
Winter
Spring
0.16 0.29
0.43 0.55
0.31 0.2
The average concentration of PM10 and PM2.5 varied from school to school depending on outdoor source, indoor source and metrological parameters. Indeed, the spatial variability for PM depends
Table 6 Season to season ratio for PM10 and PM2.5. Pollutant
Winter
Fall
Spring
Winter/Fall
Winter/Spring
Indoor PM2.5 Indoor PM10
197.93 (84.93) 492.56 (209.49)
55.20 (29.18) 360.11 (133.74)
58.82 (25.05) 195.80 (100.52)
3.59 1.37
3.37 2.50
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395
Table 7 Descriptive statistics for meteorological parameters. Season Parameters
Minimum Maximum Mean Std. dev
Temperature C (Temp) Relative humidity (RH) Wind speed (WS) (m s1) Ventilation rate L s1.person Winter Temperature C (Temp) Relative humidity (RH) Wind speed (WS) (m s1) Ventilation rate L s1.person Spring Temperature C (Temp) Relative humidity (RH) Wind speed (WS) (m s1) Ventilation rate L s1.person
24.80 47.30 0.10 4.7 8.30 27.60 0.10 1.51 9.00 15.00 0.10 3.13
Fall
32.60 71.00 7.00 29.1 21.30 89.40 13.00 17.70 33.80 100.00 9.00 19.15
27.50 1.70 58.80 5.00 3.50 1.50 9.88 5.10 14.00 2.40 62.20 14.30 3.20 2.50 6.66 3.20 18.20 3.70 73.10 18.10 2.60 2.00 8.09 2.93
on the size fraction. For PM2.5, the spatial variability is generally small as shown in Fig. 5, whereas PM10 has a greater spatial variability as shown in Fig. 6. This finding is in accordance with Monn (2001) study who reported that the spatial variability for PM2.5 is generally small. Further, a high degree of spatial heterogeneity observed between several schools due to strong local source contributions. The average concentration of PM10 and PM2.5 was the highest during the winter season reflecting the high emission sources (e.g. dust storm, the high dispersion from sea wind and vehicular emission exhaust) around these sites. The highest mass concentration of PM10 was 782.2 mg/m3 and for PM2.5 was 317.4 mg/ m3 which observed at MCB site in winter. The reason for the increasing of particulate matter concentration inside schools is related to the increased of PM10 and PM2.5 concentrations in the ambient air due to sand storm that attacked the region during the sampling period. Besides the variation in intra-location spatial variability, a variation in PM10 and PM2.5 concentration showed in inter-site level as shows in Fig. 7a and b. The PM10 and PM2.5 levels are higher on lower floors than on higher ones. A one-way ANOVA was used to test for PM10 differences among three floor level. PM10 concentration for storey level differed significantly across the three sizes, F (2, 105) ¼ 3.82, p ¼ 0.03. Duncan post-hoc comparisons of the three floor level indicate that the ground level (Mean ¼ 406.21) had significantly higher concentration than the 2nd floor level (Mean ¼ 302.11). Comparisons between the 1st floor (Mean ¼ 340.21) and the other two groups were not statistically significant at p < 0.05. Further, a one-way ANOVA was used to test for PM2.5 differences concentrations among three floor levels. PM2.5 concentration for the three storey levels was not differed significantly, F (2, 105) ¼ 2.01, p ¼ 0.14. This finding is in accordance with Jung et al. (2011) study that reported a clear decreasing trend of PM with heights in urban areas due to strong sources emissions at ground level and weak vertical mixing conditions.
Fig. 5. The daily 5-h average of indoor concentration of PM10.
4. Conclusion It was demonstrated that the Gaza strip area is burdened by PM10 and PM2.5. The data from three seasons were analysed to investigate spatial and seasonal variation and correlation in order to gain more understanding on their variability and interrelations. Inside the schools buildings which have a higher occupant density, the indoor PM concentration exceed the WHO standard limits for all monitoring seasons. This is an alarming fact, especially in the case of the increasing rates of mortality, morbidity, respiratory and cardiovascular diseases in the area. The indoor PM10 average concentrations of winter, spring, and fall seasons were 492.5 mg/m3, 195.8 mg/m3 and 360.1 mg/m3, respectively. The indoor PM2.5 average concentrations at winter, spring, and fall seasons were 197.9 mg/m3, 58.8 mg/m3 and 55.2 mg/m3 respectively. As regards to spatial distribution, a different pattern among and within the schools was observed and the average daily patterns of PM10 and PM2.5 show a clear seasonal variation. Students may be exposed to different levels of particulate matter, depending on height of the building. The particulate matter concentrations in the ground level classrooms were greater than the first and second floor classrooms concentrations. The average indoor PM2.5/PM10 ratio was 0.16e0.55 in all seasons indicating that coarse particles
Table 8 Pearson correlation coefficient between (I/O) PM10 and PM2.5 and meteorological parameters. PM2.5 (I/O)
RH indoor RH outdoor Temp indoor Temp outdoor Ventilation rate WS a b
PM10 (I/O)
Fall
Winter
Spring
Fall
Winter
Spring
0.19a 0.20a 0.03 0.04 0.12 0.06
0.06 0.01 0.05 0.02 0.06 0.06
0.14b 0.13 0.13 0.11 0.01 0.04
0.36a 0.32a 0.06 0.04 0.12 0.06
0.23a 0.18a 0.11 0.12 0.06 0.13
0.20a 0.13 0.16b 0.12 0.10 0.02
p-value is significant at the 0.01 level. p-value is significant at the 0.05 level.
Fig. 6. The daily 5-h average of indoor concentration of PM2.5.
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Fig. 7. The spatial variation of indoor PM2.5 (a) and PM10 (b) concentration measured in different level.
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