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IndoorairpollutionlevelsinpublicbuildingsinThailandandexposureassessment

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Environ Monit Assess (2009) 156:581–594DOI 10.1007/s10661-008-0507-z

Indoor air pollution levels in public buildingsin Thailand and exposure assessment

Aungsiri Klinmalee · Kasama Srimongkol ·Nguyen Thi Kim Oanh

Received: 25 March 2008 / Accepted: 24 July 2008 / Published online: 19 August 2008© Springer Science + Business Media B.V. 2008

Abstract Levels of pollutants including PM2.5and PM2.5 composition (black carbon and watersoluble ions), SO2, NO2, CO, CO2, and BTEX(benzene, toluene, ethylbenzene, xylene) weremonitored for indoor and outdoor air at a univer-sity campus and a shopping center, both locatedin the Northern suburb of Bangkok. Samplingwas done during December 2005–February 2006on both weekdays and weekends. At the univer-

Practical implications. In Asian tropical region,where homes are normally well naturally ventilatedwith open doors all year around, the most non-cookingrelated indoor air pollution is expected to occur in airconditioned public buildings. Penetration of outdoorcontaminated air to the indoor environment,bioeffluents from occupants, pollutant resuspension,and other indoor sources may contribute to thehigh indoor air pollution in these buildings but fewcomprehensive studies have been reported. Theinformation on the indoor air pollution would beimportant for the public and building owners so thatappropriate measures can be taken to reduce thepersonal exposure and the health risk.

A. Klinmalee · K. Srimongkol · N. T. Kim Oanh (B)Environmental Engineering and Technology,School of Environment Resources and Development,Asian Institute of Technology,Phatumthani 12120, Thailande-mail: kimoanh@ait.ac.th

Present Address:K. SrimongkolMinistry of Labor, Bangkok 10400, Thailand

sity, indoor monitoring was done in two differentair conditioned classrooms which shows the I/Oratios for all pollutants to be below 0.5–0.8 duringthe weekends. However, on weekdays the ratiosfor CO2 and most detected BTEX were above1.0. The concept of classroom occupancy wasdefined using a function of the student number ina lecture hour and the number of lecture hoursper day. Classroom 2, which had a higher occu-pancy than classroom 1, was characterized byhigher concentrations of most pollutants. PM2.5was an exception and was higher in classroom 1(37 μg/m3, weekdays) as compared to classroom 2(26 μg/m3, weekdays) which was likely linked tothe dust resuspension from the carpeted floor inthe former. Monitoring was also done in the shop-ping mall at three different sites. Indoor pollutantslevels and the I/O ratios at the shopping mallwere higher than at the university. Levels of allpollutants measured at the car park, except fortoluene and CO2, were the highest. I/O ratios ofthe pollutants at the mall were above 1.0, whichindicates the relatively higher influence of theindoor sources. However, the black carbon con-tent in PM2.5 outdoor is higher than indoor, whichsuggest the important contribution from outdoorcombustion sources such as the traffic. Majorsources of outdoor air pollution in the areas werebriefly discussed. Exposure modeling was appliedusing the time activity and measured pollutantconcentrations to assess the exposure of different

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groups of people in the study areas. High exposureto PM2.5, especially for the people working in themall, should be of health effect concern.

Keywords Indoor air quality ·PM2.5 composition · I/O ratio ·Exposure assessment · Shopping mall ·University classroom · Bangkok

Introduction

Indoor levels of many pollutants are often higherthan those typically encountered outside, whichwould cause significant harmful health effects dueto a long time period that people staying indoor.Klepeis et al. (2001) reported that people (in USA)may spend an average of 87% of their time inenclosed buildings and approximately 6% of theirtime in enclosed vehicles. Tightly sealed buildingsare an additional concern for the health of thosewho live and work inside. Skolnick (1989) reportedthat a population living in the tight energy-efficient buildings contracted upper respiratorydiseases at rates 46 to 50% higher than a com-pared group living in better ventilated houses.

Indoor air pollution concentrations depend ona large number of factors such as indoor sourcesand the emission rates, air exchange rate, the pen-etration of outdoor pollutants into the indoorenvironment, and the pollutant sink or removalrate on indoor surfaces (Kamens et al. 1999;Thatcher and Layton 1995; Beak et al. 1997).Understanding the relationship between indoorand outdoor pollution levels, in particular theindoor to outdoor ratios (I/O), would help to iden-tify the main causes for high indoor air pollutionand hence to develop effective strategies to reducehealth risks resulted from indoor air pollution.I/O ratio >1 means that the major sources of airpollutant are indoor sources, and I/O ratio <1implies the predominance of outdoor sources. I/Oratio was used to identify the source of exposureto pollutants in primary school children in Athens(Diapouli et al. 2007). Crist et al. (2008) used I/Oratio to indicate that personal PM2.5 exposureswere significantly affected by indoor PM2.5 thanby ambient PM2.5 in Ohio for urban and suburbanarea. In Asian tropical region, where homes are

normally well naturally ventilated with open doorsall year around, the high non-cooking relatedindoor air pollution is expected to occur in airconditioned public buildings. However, the airpollution levels in these buildings remain largelyunknown. This study, therefore, has been de-signed to provide the information through moni-toring for indoor and outdoor air pollution in auniversity campus and a shopping center in thesuburb of Bangkok.

Materials and methods

Study areas and sampling sites

Both the university campus and the shopping cen-ter are located in the Northern skirts of Bangkok,which is upwind from Bangkok during the dryseason when the northeast monsoon is prevalent.The distance from the Bangkok city center tothe shopping center is about 30 km and to theuniversity, Asian Institute of Technology (AIT),is about 40 km. All samples were collected duringDec 2005–Feb 2006, which is the dry season andwith higher ambient air pollution levels in the area(Kim Oanh et al. 2006).

University campus

Two classrooms at AIT were monitored, eachis equipped with standard school furniture madeof wooden clipboard and metal that fixed to thefloor, a blackboard (for chalk), an overhead andan LCD projector. The wooden furniture wasmade of particle board with over 5 years old. Eachclassroom has the capacity of about 100 persons.Both of them are located on the second floorwith dimensions Length × Width × Height ofaround 6 m × 6 m × 4.5 m. Classroom 1 hasa carpeted floor and is ventilated by the centralair conditioning system (AC). Classroom 2 has awooden floor and is ventilated by a standaloneAC. Each classroom has 2 doors and no windows.Classroom 1 has both doors leading to a closecorridor while classroom 2 has one door leadingto the same corridor and the other door leadingto an open corridor looking to a green garden.During the sampling all doors were close. Thereis a filter pad in the ventilation system in each

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classroom and the accumulated particles would beregularly removed. Two classrooms are occupiedfrom Monday through Friday, usually from 9:00 to16:00. The number of students presented during ateaching hour (50 min) varies from a few to thenearly full classroom capacity. During the week-ends, when the sampling was conducted, therewas no student in the classroom. Sampling onweekdays, therefore, represents high-occupancyperiod while sampling on weekends representsthe non-occupancy period. In order to quantifyboth number of students and the time period theclassroom is occupied we here use the occupancyconcept calculated using Eq. 1.

Occupancy =n∑

i=1

Si × hi (1)

Where, the Si is the number of students attendinga lecture hour hi. The summation is taken for thewhole sampling period (9:00–15:00) for n lecturehours. The data for Si and hi were obtained fromquestionnaires.

Several sampling devices were deployed, whichwere packed in a bag (backpack). In the class-room, the bag was placed on a student table in themiddle of the room, about 120 cm above ground,which is the sitting breathing zone. Outdoor sam-ples were taken in a garden, about 20 m away fromthe classroom building and 1.5 m above groundusing an identical backpack of samplers. Detaildescription of samplers and analytical methods foreach pollutant will be presented later.

Shopping mall

The Future Park Rangsit is a big shopping com-plex covering an area of about 517,000 m2. It facesa heavily traveled highway (Phaholyothin) at adistance of around 100 m and is surrounded byother busy urban roadways. There are houses andfarmlands and small industries surrounding thecomplex. A backpack of samplers was placed at1.5 m height at the front ground (about 15 m fromthe store front wall) and at the parking (around10 m from the store back wall). The parkinggarage is multiple-storey attached to the back sideof the store. Inside the store, each backpack with

samplers was carried by a subject while wander-ing through it. The shopping mall was observedto be much more crowded during the weekendsthan the weekdays though no concrete data onnumber of shoppers is available. The sampling wasconducted from the opening to closing time of themall, i.e. from around 11:00 to 21:00.

In addition to the above two sampling sites,samples for VOC and PM2.5 were also taken at anautomatic air monitoring station of the PollutionControl Department (PCD) located in the BangkokUniversity. The data of ambient air pollution(CO, NO2, SO2) and meteorology recorded at thisstation during the monitoring period were alsocollected. The station is located around 800 m awayfrom the highway and approximately midwaybetween AIT and the shopping mall.

At AIT, sampling at each classroom was con-ducted for 7 days including five weekdays and twoweekend days. At the shopping mall the samplingwas also done on both weekdays and weekendsfor between 2–4 days at different sites. Detail onsampling schedule and pollutants monitored ateach location are presented in Table 1.

Sampling and analytical methods

Fine particle mass and composition

Personal Environmental Monitor Model 200(PEMTM, MSP), which is a single-stage impactor,was used to collect particles with dynamic diame-ter ≤2.5 μm (PM2.5) at a pumping rate of 4 L/min.Quartz filters were used which were pre-firedat 550◦C for at least 6 h to remove organiccompounds. Prior to pre-sampling weighing andpost-sampling weighing by a microbalance, thefilters were desiccated for 24 h at temperature(20 ± 5◦C) and relative humidity (40 ± 10%).The collected PM samples were analyzed for mass,water soluble ions by ion chromatography (Chow1995), and black carbon (BC) by a smokestain re-flectometer (EEL, MD43). Thirteen ions in PM2.5were analyzed including lithium, sodium, ammo-nium, potassium, magnesium, calcium, fluoride,chloride, bromide, nitrite, nitrate, phosphate andsulfate. Detail on the ion and BC analytical meth-ods is discussed in Kim Oanh et al. (2006).

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Table 1 Sampling schedule and monitored pollutants

Site Day n, days Date Parameters

University campus (9:00–15:00)CR1 WDa 5 Jan 29–31; Feb 10 and 14 PM2.5, SO2, NO2, BTEX, CO, CO2

WEb 2 Jan 28 PM2.5,NO2, BTEX, CO, CO2

Feb 18 PM2.5, CO, CO2

CR2 WD 5 Jan 25–27, Feb 9 PM2.5, SO2, NO2, BTEX, CO, CO2

Feb 11 PM2.5, NO2, BTEX, CO, CO2

WE 2 Jan 24 PM2.5, SO2, NO2, BTEX, CO, CO2

Feb 18 PM2.5, CO, CO2

Outdoor WD 5 Jan 29–31, Feb 10 PM2.5, SO2, NO2, BTEX, CO, CO2

Feb 14 PM2.5, NO2, BTEX, CO, CO2

WE 2 Jan 28 PM2.5, SO2, NO2, BTEX, CO, CO2

Dec 23 COShopping mall (11:00–21:00)

Inside WD 4 Jan 3 and 5 PM2.5, SO2, NO2, BTEXJan 9 CO, CO2

Jan 10 PM2.5, SO2, NO2, BTEX, CO, CO2

WE 3 Dec 30; Jan 8 and 14 PM2.5, SO2, NO2, BTEX, CO, CO2

In the parking WD 2 Jan 5 PM2.5, SO2, NO2, BTEX, CO, CO2

Jan 9 COWE 3 Dec 30 PM2.5, SO2, NO2, BTEX, CO, CO2

Jan 1 and 8 COAt front ground WD 2 Jan 3 PM2.5, SO2, NO2, BTEX

Jan 9 CO, CO2

WE 2 Jan 1 CO, CO2

Jan 8 PM2.5, SO2, NO2, BTEX, CO, CO2

Bangkok University WD 1 Jan 10 and 14 PM2.5, BTEX

n number of sampling days, CR classroomaWD weekdaybWE weekend

VOCs

Benzene, ethylbenzene, toluene and xylenes, col-lectively known as BTEX, were sampled usingSKC—coconut shell charcoal tubes (Ø6 mm ×70 mm length), each packed with two charcoaladsorbent sections, front and back, with mesh of20 and 40 and mass of 50 mg and 100 mg, respec-tively. The sampled air was driven through thecharcoal tube using a portable personal pump ata flow rate of 0.2 L/min for the whole samplingperiod at each site.

BTEX were analyzed using NIOSH method1501 (NIOSH 1994). Accordingly, the collectedsamples were desorbed in 1 mL carbon disulfideand allowed to stand with occasional shaking for30 min. A 14B gas chromatograph (GC) equippedwith a flame ionization detector (GC-FID) wasused for BTEX quantification. A 1 μL extract

was used in each injection. The column used wasCapillary CCP-SIL-8CB for pesticide-chrompak50 m × ID 0.25 mm × film thickness 0.12 μm.The oven temperature was set initially at 40◦C for2 min and was then raised to 100◦C at a rate of10◦C/min. Both the auxiliary and detector tem-peratures were at 210◦C.

Sulfur dioxide

SO2 samples were collected using SKC-Anasorb747 (impregnated activated beaded carbon) ad-sorbent contained in a glass tube (Ø6 mm ×70 mm length), which was also packed with twoadsorbent sections similarly to the VOC samplingtube described above. The sampling set-up wassimilar to that of VOC but the pumping ratewas set at 0.1 L/min. The samples were analyzedfollowing the OSHA Method ID 200, i.e. SO2 was

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desorbed using a solution containing 0.3 N (≈1%)H2O2 in 15 mM sodium hydroxide (NaOH)and the resultant SO2−

4 was analyzed by IC(OSHA 1992).

Nitrogen dioxide

NO2 was sampled using tubes of SKC-Molecularsieve (Triethanolamine-impregnated molecularsieve) Ø7 mm × 110 mm length, each alsopacked with two adsorbent sections but with themass 200 mg and 400 mg, respectively. A similarsampling set-up as for VOC was used but thepumping rate was 0.05 L/min. The samples wereanalyzed according to OSHA Method ID 182(OSHA 1991), i.e. the collected NO2was desorbedusing a 1.5% Triethanolamine (TEA) solutionand the resultant nitrite (NO−

2 ) was quantified byion chromatography.

Carbon monoxide and carbon dioxide

CO and CO2 were measured using Indoor AirQuality Meters (IAQ-CALCTM) Model 8760/876.The instrument is the real time monitoring deviceconsisting of a dual wavelength non-dispersiveinfrared sensor (NDIR) for CO2 and Electro-chemical sensor for CO. The measurement rangeof CO2 is 0–5000 ppm and for CO is 0–500 ppm.The measurements were taken as 5 s averageand were subsequently averaged for the wholemonitoring period. The meters were calibratedbefore use. In addition, temperature and relativehumidity were also monitored by this instrument.

QA/QC

All pumps were calibrated before and after eachsampling day. Samples were stored in an ice boxduring the transport. At AIT all the samples wererefrigerated before analysis. The storage periodwas maximum 2–3 days. A preliminary samplingwas done to check for the breakthrough, accord-ingly the front and back adsorbents were sepa-rately extracted and analyzed in four sampledtubes (two for the parking and two for the insidedepartment store). Based on the criteria given byNIOSH (2003), that a breakthrough is consideredto have occurred if Wb > 0.1Wf (Wf and Wb are

the weights of the analytes found in the front andback sorbents, respectively), no breakthrough hasoccurred in the samples in our study. Benzenecontaminant of the carbon disulfide solvent wasremoved before it was used for extraction by usingconcentrated sulfuric acid and concentrated nitricacid (OSHA 1980). The method minimum detec-tion quantity for BTEX was 0.2–0.3 ng.

It is noted that in this study we collected bothindoor and outdoor samples using the same typesof the equipment so as the results are readily com-parable and I/O ratios can be reliably estimated.These sampling methods with low flow rates maynot be able to collect enough pollutants to bedetected by the analytical methods. However, asseen later in the results the method can detectmost pollutants except for xylenes.

Noted that I/O ratio was calculated for eachsample pairs and subsequently the average wastaken and presented in this paper.

Results and discussions

Air pollution levels at University campus

In each classroom, indoor air pollution levels weregenerally fluctuating from day to day dependingon the classroom occupancy (Fig. 1). On week-ends when no student was in the classrooms thepollutant levels were lower than weekdays. Theaverage daily occupancy of classroom 1,133 ± 35student/h, was lower than classroom 2,159 ± 19student/h. Except for PM2.5, the pollutant levelsin classroom 2 were generally higher than class-room 1 during weekdays (Table 2 and Fig. 1).

PM2.5 was the most significant pollutant in theclassrooms though the levels were lower than out-door. In each classroom, PM2.5 levels on week-days were higher than on weekends showing thedependence on the presence of people. However,average PM2.5 in classroom 1 on both weekdaysand weekends (37 and 31 μg/m3, respectively)was higher than classroom 2 (26 and 19 μg/m3,respectively), although the occupancy on week-days of classroom 1 was lower than classroom 2.Thus, other factors may cause the elevated PM2.5.Indoor PM2.5 directly linked to the human activ-ities such as walking on carpet or dry dusting and

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Fig. 1 Pollutant levelsvs. occupancy forclassroom 1 (a) andclassroom 2 (b) onweekdays (WD) andweekend (Wk)

a) CR1

-5

15

35

55

75

95

29-Jan-06(WD)

30-Jan-06(WD)

31-Jan-06(WD)

10-Feb-06(WD)

12-Feb-06(WD)

28-Jan-06(Wk)

18-Jan-06(Wk)

Con

cent

ratio

n0

30

60

90

120

150

180

210

Occ

upan

cy, S

tude

nt.h

b) CR2

-5

15

35

55

75

95

24-Jan-06(WD)

25-Jan-06(WD)

26-Jan-06(WD)

27-Jan-06(WD)

9-Feb-06 (WD)

11-Feb-06(Wk)

18-Feb-06(Wk)

Con

cent

ratio

n

0

30

60

90

120

150

180

210

Occ

upan

cy, S

tude

nt.h

PM2.5 (µg/m3) CO (ppm) CO2 /50(ppm) B (µg/m3) T (µg/m3)

E (µg/m3) m-X (µg/m3) SO2 (ppb) NO2 (ppb) Occupancy

vacuuming the room which could resuspend theparticles (Ferro et al. 2004; Kopperud and Ferro2004; Cao et al. 2005). In this study, resuspensionof dust from the carpet in classroom 1, whendisturbed by students, may be a reason for itshigh PM2.5 levels. It is noted that a substantialamount of large particles may also be resuspendedindoor (Blondeau et al. 2005) however the coarsefraction was not monitored in our study. Cao et al.

(2005) reported the PM2.5 levels in rural homes,averaged at 26 μg/m3, that are in the same rangeof our results for classrooms. The levels recordedby Molnar et al. (2007) in high school classroomsin Stockholm, Sweden (8 μg/m3) and those re-ported for a home in a US study, 7.7–14.2 μg/m3,are lower than the levels found in our study.Generally high outdoor PM2.5 in the study areamay be the reason for the high indoor pollution.

Table 2 Concentrationand I/O ratios of targetpollutants at university onweekdays

CR classroomaMean ± S.D. are givenbI/O is between classroom1 and AIT outdoor

Parameter Concentrationa I/O ratiob

CR1 (carpet) CR2 (wooden) Outdoor (for CR1)

PM2.5, μg/m3 37 ± 6 26 ± 6 60 ± 16 0.6CO, ppm 0.4 ± 0.2 1.0 ± 0.1 1.2 ± 0.4 0.3CO2, ppm 711 ± 272 1,332 ± 609 392 ± 61 1.8Benzene, μg/m3 3.8 ± 0.5 5.6 ± 3.9 3.6 ± 1.2 1.1Toluene, μg/m3 6.0 ± 2.2 9.5 ± 3.7 10.6 ± 6.0 0.6Ethyl benzene, μg/m3 3.0 ± 0.7 3.1 ± 0.3 2.5 ± 0.2 1.2m-xylene, μg/m3 1.2 ± 1.3 3.1 ± 0.6 1.1 ± 0.9 1.1SO2, ppb 1.5 ± 1.2 1.3 ± 0.8 1.7 ± 1.2 0.9NO2, ppb 3.6 ± 1.9 7.4 ± 3.2 7.2 ± 4.6 0.5Occupancy, student/h 135 ± 35 159 ± 19 – –

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Several important outdoor PM2.5 sources may bepresent in the study area. A multi-year receptormodeling study for PM2.5 at the university siteshow the average contribution of diesel vehicles of40% (mainly from the nearby highway), biomassburning of 30% (rice straw burning in the field sur-rounding), secondary sulfate and nitrate of 20%,and 10% from other sources (Kim Oanh 2004).At AIT during weekdays there were additionalsources related to the staff transport to work,especially the diesel buses used for the purpose.Other sources may not vary much during a weeksuch as open biomass burning. The classroom hadthe doors close which would minimize the naturalventilation and reduce the levels of PM in theclassrooms. The filter pad present in the venti-lation system can retain a part of large PM butwould not be efficient to remove fine particles.No information on air exchange rate is availablewhich would be required for a better explanationof the exchange of PM2.5 and other pollutantsbetween indoor and outdoor environment.

Both indoor and outdoor PM2.5 levels werelower on weekends than weekdays (Table 3) re-sulting in almost similar I/O ratios between week-end and weekdays. Mean I/O ratios of PM2.5for the two classrooms are 0.5–0.6 indicatingan important contribution of outdoor sources toindoor PM2.5. On weekends, all pollutants pre-sented in Table 3 show I/O ratios less than 0.8. Onweekdays higher I/O ratios were recorded for thepollutants with the ratios reaching above 1.0 forCO2, benzene, ethylbenzene, and m-xylene, whichmay be related to bioeffluents. Note that, the I/O

ratios were only calculated for classroom 1 forwhich simultaneous indoor and outdoor data wereavailable.

CO levels were found low both indoor andoutdoor, and the I/O ratios were substantiallylower than 1.0. This indicates the predominantoutdoor sources of CO as expected since therewas no combustion in the classrooms. The 5 minaverage CO data also show the highest concentra-tions in the morning on weekdays correspondingto a higher traffic flow during this time of theday. CO2, an indicator of bioeffluents, was higherindoors than the outdoor and also higher in class-room 2 where higher occupancy was observed.The CO2 levels found in the classrooms are withinthe typical range of indoor CO2 concentration of700–2,000 ppm cited by Jones (1999). The I/Oratio on weekdays was as high as 1.8. Similarly,concentrations of BTEX in classroom 2, wherehigher occupancy was observed, were higher thanclassroom 1. This indicates the influence of thehuman bioeffluents, which are known to consist ofa wide range of inorganic and organic compounds(Bienfait et al. 1992). The levels found at AITclassrooms are somewhat higher than the geomet-ric mean for 12 office buildings reported by Daiseyet al. (1994) but still in the same range if thestandard deviation is also considered. Some VOCmay be emitting from paintings and furniture (USEPA 1998) but this contribution is expected to besmall in the study classrooms due to aging effects.BTEX levels at AIT, however, fell in the rangesreported for 56 office buildings by Godish (2000)with the median values for benzene, toluene,

Table 3 Concentrationand I/O ratios of targetpollutants at university onweekends

The range is shown inparentheses for pollutantswith two data points, andsingle values shown forthose with one data pointNA no data,CR classroomaBetween classroom 1and outdoor

Parameter (unit) Concentration I/O ratioa

CR1 (carpet) CR2 (wooden) Outdoor (for CR1)

PM2.5, μg/m3 31 (29–32) 19 (15–22) 56 0.5CO, ppm 0.6 (0.4–0.9) 0.1 0.9 0.7CO2, ppm 348 (298–398) NA NA –Benzene, μg/m3 3.7 3.9 6.8 0.5Toluene, μg/m3 5.8 8.2 9.2 0.6Ethyl benzene, μg/m3 2.4 2.8 2.9 0.8p-xylene, μg/m3 ND ND ND –m-xylene, μg/m3 2.0 3.0 2.4 0.8o-xylene, μg/m3 ND ND ND –SO2, ppb NA NA 4.5 –NO2, ppb NA NA 7.7 –

Environ Monit Assess

m,p-xylene of 3.7, 9.0 and 5.2 μg/m3, respectively.Quite low levels of SO2 and NO2 were detectedboth indoor and outdoor in the campus. The I/Oratios of both pollutants were <1.0, indicating thepredominance of outdoor sources.

Pollution levels at shopping mall

The monitoring period at different sites in theshopping mall started from the open time to theclosing time, 11:00–20:00. The resulting concentra-tions at different sites and I/O ratios (departmentstore levels/front ground levels) are presented inTable 4 (weekdays) and Table 5 (weekends).

Levels of all indoor pollutants at the shoppingmall were higher during the weekends than theweekdays, which are linked to the more crowdedconditions on weekends. These levels were alsosignificantly higher than those at the universitycampus. The lowest levels were recorded at thefront ground, immediately outdoor of the mall,though was measured quite close (100 m) to thebusy Paholyothin highway. The I/O ratios werefound to be well above 1.0 (1.5–14.6) for mostpollutants, except SO2 and NO2, on both week-days and weekends. In particular, I/O ratios forPM2.5 were high on both weekends (2.0) andweekdays (1.5). This implies a relatively higherinfluence of indoor sources on the PM2.5 levelsin the mall in spite of the busy traffic around

the mall. Note that the daily average of trafficdensity on this Paholyothin highway is around60,000 vehicles with the rush hour density of 6,600vehicles/h (Kim Oanh et al. 2000). The 24 h PM2.5levels at different sites along the highway are quitehigh, i.e. at Bangkok University of 42–61 μg/m3

(Tables 4, 5) and the dry season average at AIT of36 μg/m3 (Kim Oanh et al. 2006). This finding issimilar to that of Jones et al. (2000) who reportedthe indoor sources to dominate the PM2.5 levelsat a roadside home in the city of Birmingham, UKwith the I/O ratio of 1.0 ± 1.3.

PM2.5 on weekdays at the parking was 55 μg/m3,which is in the same range of the levels inside thestore (50 ± 14 μg/m3). Higher levels were foundon weekends: 78 μg/m3 at the parking, and 83 ±27 μg/m3 inside the store. The levels of PM2.5found in the store are lower than those reportedby Tsai et al. (2000) for the shops around theOdean Circle in the center of Bangkok with heavytraffic (above 120 μg/m3).

The levels of most monitored pollutants, pre-sented in Tables 4 and 5, were the highest at theparking on both weekdays and weekends. Theaverage CO at the parking (10–14 ppm), over the9–10 h sampling period, was the highest, whichis well above the levels inside department storeand front ground (1–3 ppm). Emission from cars,pickup and vans (no entrance allowed for trucksor buses) should be the main reason for the high

Table 4 Pollutant concentration and I/O ratios for shopping mall on weekdays

Parameters (unit) Concentration I/O ratioa

Department store Parking Front ground Bangkok U.

PM2.5, μg/m3 50 ± 14 55 34 42 1.5CO, ppm 2 (2.2–2.7) 10 (5–15) 1 NA 1.6CO2, ppm 869 (833–905) 579 (576–582) 499 NA 1.7Benzene, μg/m3 34 ± 8 78 NA 6 NAToluene, μg/m3 338 ± 156 149 NA 18 NAEthyl benzene, μg/m3 11 ± 6 15 NA ND NAp-xylene, μg/m3 7 ± 2 15 NA ND NAm-xylene, μg/m3 20 ± 6 34 NA ND NAo-xylene, μg/m3 8 ± 2 27 NA ND NASO2, ppb 5 ± 5 36 1 NA –NO2, ppb 40 ± 8 110 36 NA –

Mean ± S.D. for the pollutants with above three data points. Range is given for two data points. The rest is for those withone data pointND not detected (minimum detectable quantity is 0.2–0.3 ng), NA no dataaI/O ratios between department store and front ground

Environ Monit Assess

Table 5 Pollutant concentration and I/O ratios shopping mall on weekends

Parameters (unit) Concentration I/O ratioa

Department store Parking Front ground Bangkok U.

PM2.5 (μg/m3) 83 ± 27 78 41 61 2.0CO (ppm) 3 ± 0.03 14 ± 4 2 (1.5–2.3) NA 1.6CO2(ppm) 1,113 ± 260 765 ± 83 382 (373–391) NA 2.9Benzene (μg/m3) 29 ± 25 81 9 (7–11) 6 3.2Toluene (μg/m3) 273 ± 235 114 19 (16–21) 24 14.6Ethylbenzene (μg/m3) 4 ± 6 10 1 (0.8–1.8) 3 3.3p-xylene (μg/m3) 3 ± 5 11 1 (0–1.6) ND 3.4m-xylene (μg/m3) 8 ± 13 26 3 (2.7–3.9) 2 2.5o-xylene (μg/m3) 3 ± 6 14 1 (0.8–2.2) 2 2.4SO2 (ppb) 2 ± 2 39 2 NA –NO2 (ppb) 27 ± 24 213 26 NA –

Mean ± S.D. for the pollutants with above three data points. Range is given for two data points. The rest is for those withone data pointND not detected, NA no dataaI/O ratios between department store and front ground

pollutants levels in the parking, whereas the highlevels inside the store may be from both indoorand outdoor sources. It was possible that air pol-lutants from the parking intruded to the insidedepartment store though the connecting doorswhich were opened frequently. Note that nosmoking is allowed inside the store (as well as inthe university classrooms). The human bioefflu-ents from the crowd and cooking in restaurants inthe store may contribute substantially to the highlevels there.

The highest CO2 levels inside the store, espe-cially over weekends (1,113 ± 260 ppm) are linkedto the more crowded condition and indicates theneed for better ventilation. High CO2 levels areoften found in the populated places, e.g. the levelsin an office (open plan office, reception desk, con-ference room, single room office) in Hong Kongwere reported to be 821–1,012 ppm (Wong and

Mui 2007), which is close to our results. Toluenelevels were high both inside the store and at theparking, which may be from both bioeffluents andvehicles. The levels of BTEX inside the depart-ment store were much higher than the values foroffice buildings reported by Godish (2000) andDaisey et al. (1994) quoted earlier.

PM2.5 composition

Black carbon

Average concentration and range of black carbon(BC) from all sites in both university campus andthe shopping mall are presented in Table 6. BCis an indicator of the unburned element carbon,which is a product of incomplete combustion.The highest BC concentration was found at theparking, which was probably associated with the

Table 6 BC in PM2.5 indifferentmicroenvironments

Range is given for twodata pointsaAverage and 1 STD, inparentheses are thenumber of samples

Sampling sites (n) BCa, μg/m3 BC/PM2.5 Average I/O

UniversityClassroom 1 (7) 11 ± 1 0.3 ± 0.1 0.9Classroom 2 (7) 8 ± 2 0.4 ± 0.1 –Outdoor (6) 12 ± 1 0.2 ± 0.1 –

Shopping mallDepartment store (6) 6 ± 2 0.1 ± 0.1 0.67Car park (2) 28 (27–29) 0.4 (0.3–0.5) –Front ground (2) 8 (7–8) 0.2 (0.2–0.2) –Bangkok University (2) 9 (9–10) 0.2 (0.2–0.2) –

Environ Monit Assess

vehicle emission. The BC levels indoor were lowerthan outdoor in both locations, with I/O ratiosless than 1.0. BC is a measure for soot particlesthat are toxic and also can adsorb a range oftoxic organic compounds including the genotoxicpolycyclic aromatic hydrocarbons (Seinfeld andPandis 1998). High BC concentration thus wouldincrease the health hazard associated with theparticles. It is noted that, the BC levels in theclassrooms and inside the department store ap-peared to be in the same range with slightly higherlevels found in the classrooms. The opposite wasobtained for NO2 which was significantly higherinside the shopping store than the classroom. Thismay be linked to the relative intensity of nearbyoutdoor sources at each site. More contributionof biomass burning to the pollution levels wouldbe expected at the university campus sites whichwould contribute less NO2 than the diesel vehicleemission dominant at the shopping mall. Furtherstudy on the contributing sources in the indoorand outdoor air pollution levels at each site shouldbe conducted which is not within the scope of thispaper.

Ionic composition

Out of 13 ionic species analyzed only ten weredetected and results are presented in Fig. 2a(university) and Fig. 2b (shopping mall). At theuniversity, the most abundant species was ammo-nium followed by sulfate and nitrate, which weresignificantly higher than other detected species.High ionic species levels in the classrooms shouldbe associated with the outdoor PM2.5 that pene-trated inside. Specifically, high concentration ofsulfate is the marker for contribution of outdoorfine particles to indoor (Jones et al. 2000). Inaddition, for ammonium, bioeffluents from theoccupants may also be significant.

At the shopping mall, the levels of detectedionic species were higher than those at the uni-versity. The highest sulfate and nitrate levels werefound at the parking while ammonium and cal-cium levels were higher at the front ground. Thelowest ionic concentrations in PM2.5 were foundfor samples collected inside the store. Thus, out-door sources of ionic species appeared to be more

influential. Note that high sulfate concentrationsin PM2.5 at both locations may be significant forhealth effects as sulfate particles are known to behighly toxic (WHO 2000).

Exposure assessment

In this study the 24 h time weighted average per-sonal exposure to pollution level was calculatedusing Eq. 2.

Eij =m∑

j=i

fij Cij (2)

Where, Eij is the exposure of individual ‘i’ inmicroenvironment j (μg/m3), fij is the fraction oftime spent by individual ‘i’ in microenvironment‘ j ’ during the 24-h period (i.e. tij/24), Cij is averagepollutant concentration (μg/m3) in microenviron-ment ‘ j ’ when individual ‘i’ is present, and ‘m’ isthe number of microenvironments considered inthe model (Chow et al. 2002).

a) Ionic Species in PM2.5 at University Campus

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Con

cent

ratio

n (µ

g/m

3 )

Classroom 1 Classroom 2 Outdoor

a) Ionic Species in PM2.5 at University Campus

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Na+ NH4+ K+ Mg 2+Mg 2+ Ca 2+Ca 2+ F-F- Cl-Cl- NO2

-NO2- NO3

-NO3- SO4

2-SO42-

Na+ NH4+ K+ Mg 2+ Ca 2+ F- Cl- NO2

- NO3- SO4

2-Na+ NH4+ K+ Mg Ca 2+ F- Cl- NO2

- NO3- SO4

2-

Con

cent

ratio

n (µ

g/m

3 )

Classroom 1 Classroom 2 Outdoor

b) Ionic species in PM2.5 at Shopping Mall

0.0

1.0

2.0

3.0

4.0

5.0

Car park Department store Front ground

b) Ionic species in PM2.5 at Shopping Mall

0.0

1.0

2.0

3.0

4.0

5.0

Car park Department store Front ground

Fig. 2 Ionic species in PM2.5: a at university campus andb at shopping mall (Concentrations are in μg/m3 at 25◦C,1 atm)

Environ Monit Assess

Table 7 Daily 24-h personal exposure at the university campus and working staff in shopping mall on weekdays andweekends

Parameters University campus Shopping center staff Guideline

Weekday Weekend Salesperson Guard in parking lots standards

Weekday Weekend Weekday Weekend(μg/m3)

PM2.5, μg/m3 49 ± 11 50 45 ± 5 71 ± 9 46 69 25 (24-h)a

CO, ppm 0.4 ± 0.2 0.6 (0.4–0.9) 1.8 (1.7–1.9) 2.3 ± 0.01 4.4 5.9 8 ppm (8-h)b

CO2, ppm 711 ± 272 348 (298–398) 622 (610–634) 626 ± 78 526 510 1,000 ppmc

Benzene, μg/m3 8.5 ± 3.2 8.4 124.2 ± 52.1 107.2 ± 78.2 61.5 54.1 –Toluene, μg/m3 2.7 ± 0.4 2.8 3.7 ± 1.9 3.9 (2.4–5.3) 5.0 5.3 260 (1 week)d

Ethylbenzene, μg/m3 ND ND 2.3 ± 0.6 2.7 4.8 3.5 870 (1 year)e

p-xylene, μg/m3 1.2 ± 0.9 2.3 8.1 ± 2.0 6.3(2.7–10.0) 12.9 10.8 870 (1 year)e

m-xylene, μg/m3 ND ND 2.8 ± 0.6 4.7 9.1 5.9 870 (1 year)e

SO2, μg/m3 5.2 ± 2.2 NA 6.7 ± 4.2 7.4 ± 1.6 33.4 40.1 20 (24-h)a

NO2, μg/m3 10.6 ± 3.9 15 52.6 ± 5.2 38.0 ± 15.3 96.4 154.6 200 (1-h)a

The average and 1 STD are presented for the pollutants with above three data points; range is given for those with two datapoint, and a single value given for those with one data pointND not detected, NA no dataaWHO (2005)bBienfait et al. (1992)cASHRAE (1999)dWHO (2000),eWHO (1997)

In the university campus, the average frac-tion of time that students spend in classroom,library, outdoor, and dormitory was obtainedfrom the time-activity surveys. On average, onweekdays each student spends 11 h/day in class-room and library, and 13 h/day in dormitoryand outdoor in the campus. On weekends, eachstudent spends about 6 h/day in classroom andlibrary, and 18 h/day in dormitory and outdoor.

It is assumed that the pollutant concentrationsin the standard dormitories were the same as thecampus outdoor air. This assumption is supportedby the facts that dormitories are mostly well venti-lated naturally or mechanically without AC, andno cooking is allowed indoor. Further, it is alsoconsidered that the pollutant concentrations in thelibrary were the same as in the classroom whichwas assumed to be the average levels of the twoclassrooms.

The time fraction that a salesperson (inside thestore) or a guard (in the parking) spent at theshopping mall was estimated based on their aver-age working period of 8 h/day on both weekdaysand weekends. After the working hours, it wasassumed that they spend the rest time of a day(16 h) in the environment where the air pollu-

tion levels equal the average levels measured atthe nearby PCD monitoring station (BangkokUniversity), outdoor AIT campus and the frontground of the mall.

Table 8 Paired sample T-test results for indoor (I) andoutdoor (O) concentration at university (AIT) and shop-ping mall

Parameter, unit Significant difference

University (AIT) Shopping mall

PM2.5, μg/m3 Yes NoCO, ppm Yes YesCO2,ppm No YesBenzene, μg/m3 No NoToluene, μg/m3 No NoEthyl benzene, μg/m3 No Nop-xylene, μg/m3 ND Nom-xylene, μg/m3 No Noo-xylene, μg/m3 ND NoSO2, ppb No NoNO2, ppb No NoBC, μg/m3 Yes No

Confidence interval = 98% (α = 0.02). For university,indoor is classroom 1 and outdoor is AIT outdoor. Forshopping mall indoor is in department store and outdooris at front groundND not detected

Environ Monit Assess

The daily PM2.5 exposure of all consideredgroups (Table 7) well exceeded the 24-h US EPAambient air quality standard of 35 μg/m3 and, ofcourse, also the WHO guidelines of 25 μg/m3.For other pollutants the 24 h personal expo-sure was rather low though some BTEX maybe genotoxic (US EPA 1997) hence the levelsshould be as low as possible. Fine particles areknown to be the most health hazard (Dockery andPope 1994; Donaldson et al. 2001) hence the highexposure levels to PM2.5 may cause significanteffects. Source mitigation measures includingsource removal and air cleaners, especially forindoor air of the department store, and betterventilation should be applied to provide betterindoor air quality. The paired sample T-test wasdone in order to test the statistical significance ofthe difference between indoor and outdoor con-centrations in both areas (Table 8). Only CO lev-els were significantly different between the indoorand outdoor environments in both areas. PM2.5and BC levels seem to be significantly differentbetween indoor and outdoor air at the universityonly while CO2 levels are different for the shop-ping mall only.

Conclusions

Indoor levels of PM2.5 and other gaseous pollu-tants in the classrooms are a function of occu-pancy with higher levels found on weekdays thanweekends. A carpeted floor of the classroom maybe an important source of PM2.5 resuspension.I/O ratios for most pollutants on weekends wereless than 0.8 suggesting the influence of outdoorsources. On weekdays, the I/O ratios were higherfor all pollutants and for some pollutants the I/Oratios were even above 1.0 indicating the con-tribution of indoor sources related to classroomactivities. The indoor levels in the shopping mallwere higher than the university especially forPM2.5. I/O ratios for all pollutants at the shoppingmall were well above 1.0 implying a significantcontribution sources including the penetration ofair pollution from the parking. High black car-bon (BC) fraction in PM2.5 found both indoorand outdoor at the university and shopping mall

suggests that a high portion of PM2.5 in the airwas originated from combustion. The BC I/Oratio less than 1.0, however, indicates that thepollution mainly originated from outdoor sources.Ammonium and sulfate were the most abundantionic species in PM2.5 though at the shoppingmall high calcium was also found that may comefrom road dust. The 24-h exposure to PM2.5 posesthe largest health threat (based on comparisonwith WHO guidelines) among all considered pol-lutants, which was >45 μg/m3 for all consideredpeople groups. The highest exposure is for theguards in the car park and the salespersons dur-ing weekends (around 70 μg/m3) and the lowestwas for the students. Better ventilation should beprovided for the public buildings and air cleanersshould be used, specifically for the departmentstore, to reduce the indoor PM2.5 levels. Thisstudy focuses only on the monitoring mechanicallyventilated buildings. Monitoring of indoor air pol-lutions for other types of houses including the nat-urally ventilated, which are popular in Thailand,should be conducted to gain better understandingof overall picture of air quality in the region.

Acknowledgements The authors would thank the AirPollution Team at AIT for their assistance and cooperationin sampling and samples analysis. The Swedish Interna-tional Development Agency (Sida) is acknowledged forthe partial financial support through the project “Regionalair pollution research network for improving air qualityin Asia” coordinated by AIT. The scholarship grant pro-vided by the Royal Thai Government (RTG) is specificallyacknowledged.

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