Pdf/S the Klang Valley

A Long Term Study on Characterization and Source Apportionment of Particulate Pollution in Klang Valley, Kuala Lumpur

Shamsiah Abdul Rahman1*, Mohd Suhaimi Hamzah1, Md Suhaimi Elias1, Nazaratul Ashifa Abdullah Salim1, Azian Hashim1, Shakirah Shukor1, Wee Boon Siong2, Abdul Khalik Wood3

1 Waste and Environmental Technology Division, Malaysian Nuclear Agency, Bangi, 43000 Kajang, Selangor, Malaysia 2 Department of Chemistry, Unversity of Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia 3 Faculty of Applied Chemistry, Unversity of Technology MARA, 40450 Shah Alam, Selangor, Malaysia

ABSTRACT

Samples of airborne particulate matter, PM2.5 and PM10–2.5 were collected using a Gent stacked filter sampler at an urban site, Klang Valley, Kuala Lumpur between January 2002–December 2011. The samples were analyzed for their elemental composition and black carbon content by Particle Induced X-ray Emission (PIXE) and light absorption, respectively. The –3 annual average for PM2.5, PM10–2.5 and PM10 ranged from 21 to 35, 18 to 26 and 44 to 56 µg m , respectively. Factor analysis method and the Positive Matrix Factorisation (EPA PMF3) technique were also applied to the fine fraction data set in order to identify the possible sources of particulate matter and their contributions to the ambient particulate matter concentrations in the Klang Valley. A five factor PMF solution was found for PM2.5 particulate matter. The sources identified were; motor vehicles, industry, smoke/biomass burning, secondary sulphate and soil. It was found that the primary source of haze air particulate matter was locally generated mostly from vehicular emissions which contribute about 35% of the PM2.5 mass. The Hybrid Single Particle Lagrangian Intergrated Trajectory (HYSPLT) model was also used to explore possible long range transport of pollution. Smoke trans-boundary events were identified based on fine potassium from the data base in 2004, 2006 and 2008.

Keywords: Klang Valley; Elemental composition; Positive Matrix Factorization; Airborne particulate.

INTRODUCTION monsoon seasons, the winds are generally light and variable. There are many sources that contribute to the fine and coarse Klang Valley (Fig. 1) is a rapidly growing urban area particles in the area. Potential sources can originate from with the highest growth rate in Malaysia. The area comprises major highways that run throughout the Klang Valley, growth of Kuala Lumpur (lat 3°8′N; long 101°44′E), its suburbs, in population, unplanned and uncontrolled development of and adjoining cities and towns in the state of Selangor. The industrial premises that lead to higher emissions of organic weather is hot and humid with uniform temperatures and inorganic gases, chemicals and dust as well as noise throughout the year from 25°C to 35°C and the humidity is pollution and vibration disturbance. However the sources almost the same throughout the year 70%–80% during the of the pollutants and their contributions not only originate night-time and 50–60% during daytime. There are uniform from local activities to the local factors such as open burning, periodic changes in the wind flow patterns namely, the construction and increasing industrialization programs, but southwest monsoon, northeast monsoon and inter-monsoon also from the foreign activities such as forest fires and land seasons. The southwest monsoon season is usually established clearing in Sumatra and Kalimantan. Emissions from these in the latter half of May or early June and ends in September fires has caused trans-boundary haze pollution events that while the northeast monsoon season usually commences in have affected the entire Southeast Asian region. The haze early November and ends in March. During the two inter- episodes in Malaysia were reported as early as the 1980s followed by a number of haze episodes that were less intense and did not receive as much public attention. The first serious haze event in the country was reported in August * Corresponding author. 1991 followed by events in 1994, 1997, 1998, 2002, 2004, Tel.: +603-89112000; Fax: +603-89112179 2005, 2006 and 2009 (Keywood, 2003, Tangang et al., 2010). E-mail address: [email protected] These phenomenon have now become regular features in

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Fig. 1. Location of sampling site at the Klang Valley (Source: https://en.wikipedia.org/wiki/Klang_Valley).

Malaysia during the dry seasons in Feb–April and June– total fine mass, chemical composition, and contributions of September. different sources contributing towards the growing air For many years the International Atomic Energy Agency pollution problem in the Klang Valley are reported. The (IAEA) has been supporting IAEA/RCA project on air evidence that smoke from biomass burning being a significant pollution. The objective of the project was to demonstrate aerosol contributor during periods of excessive haze is also the applicability of nuclear and related analytical techniques discussed. (mainly NAA, XRF, PIXE and ICP-MS) in studies of pollution caused by APM (airborne particulate matter) which METHODS is now being recognized as a local, regional and global problem with a serious impact on human health, particularly Sampling for young and older people, on visibility, and on climate Air particulate samples were collected using a Gent stack change. The Malaysian Nuclear Agency has been involved sampler unit (Hopke et al., 1997) provided by the IAEA. in the project since 1998 in which the Klang Valley region The sampler was placed on a rooftop of a two story building has been selected as the study area. The project focused on at the Universiti Teknologi Malaysia, Kuala Lumpur the measurement of fine particle (PM2.5), coarse particle campus (3°10′30′′N, 101°43′24′′E) approximately 10 meters (PM10–2.5), black carbon (BC), major and trace elements, as in height. The campus lies between two highways to the north well as source quantification, and identification of long- and south, and is surrounded by busy roads closely linked range transport of fine airborne particulate matter. The to the Kuala Lumpur city center. Samples were collected study of fine particles for the samples collected during the between January 2002 and December 2011. The sampler period from 2000 to 2008 at Klang Valley identified five was programmed to run automatically at an air flow-rate of sources of aerosols in the Klang Valley (Rahman et al., 15 L min–1 to collect two fractions (< 2.5 µm and 2.5–10 2009a, b). These sources included sea spray, motor vehicle, µm aerodynamic diameter particles) of 24 hours duration smoke, industry, soil and unknown source. This paper is samples on 47mm diameter polycarbonate filters (with 0.4 reporting the results of a long term study of fine particles µm and 8 µm pore sizes respectively) at a frequency of a (PM2.5) for the samples collected during the 10 year study week or fortnight. Nearly 400 filters were collected during from 2002 to 2011 at Klang Valley, Kuala Lumpur. The the study period.

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Mass and Black Carbon Measurement starting times throughout the sampling interval. The total mass of each sample was determined by weighing the filter using a microbalance (METTLER Model MT5). RESULT AND DISCUSSION The balance was equipped with a Po-212 (alpha emitter) electrostatic charge eliminator (STATICMASTER) to Particulate Matter Mass Level eliminate the static charge accumulated on the filters before Table 1 below summarises the annual average each weighing. Black carbon concentration on a filter was concentration of PM2.5 and PM10–2.5 as well as PM10 for the measured by a Smoke Stain Reflectometer (EEL model study period of 2002–2011. A total of 382 sample pairs 43D). The method is based on the absorption of light, in (fine and coarse) were collected from the site. The average –3 which the amount of light absorption is proportional to the concentration of PM2.5 is 25 µg m with a maximum of 96 –3 –3 black carbon concentration on the filter. The black carbon µg m and minimum of 2.4 µg m . Estimation of PM10 concentration values were obtained according to the method was made by addition of fine fraction (2.5 µm) to the described by other workers (Edwards et al., 1983; Cohen coarse fraction (2.5–10 µm). There is no Malaysian guidelines et al., 2000; Biswas et al., 2003). or standard for the fine and coarse fractions, but the USEPA National Ambient Air Quality Standard (NAAQS) fine Elemental Analysis particle goals of 15 µg m–3 annual average with a maximum Elemental analysis was performed by Particle Induced X- of 35 µg m–3 averaged over 24 hour period was adopted for ray Emission (PIXE) (Trompetter et al., 2005) at the National reference. Fig. 2 shows the annual average concentrations of Isotope Centre, GNS Science, New Zealand. X-ray spectra PM2.5 and PM10–2.5 measured from the period of study 2002 to obtained from the PIXE measurements were analyzed with 2011 at Kuala Lumpur, Klang Valley. It was clearly seen that GUPIX software developed by Guelph University (Maxwell during the ten years of study the annual average of PM2.5 at et al., 1989, 1995). Concentration of the following twenty Klang Valley had consistently exceeded the annual standard elements were measured and analyzed for each sample: Al, of USEPA National Ambient Air Quality Standards –3 As, Br, Ca, Cl, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, Pb, S, Si, (NAAQS) of 15 µg m . The PM2.5 levels were also found to Ti, V and Zn. Calibration of the PIXE system was performed be slightly higher in 2002, 2005 and 2006. This could be due by irradiating suitable Micromatter thin target standards. to the haze pollution reported in those years (Malaysian haze, The receptor modeling using positive matrix factorization 2005; Southeast Asian haze, 2006; Haze, 2013) in the form of EPA PMF3.0 (US EPA, 2010) was then The trend of the annual average levels of PM10 applied to the data to confirm the possible sources of air concentration in the ambient air between 2002 and 2011 is pollution that contribute to the area of Klang Valley. These shown in Fig. 3 with an average of 48 µg m–3 which is just EPA–PMF processes and their applications have been below the Malaysian Ambient Air Quality Guidelines of discussed in detail elsewhere (Reff et al., 2007; Norris et 50 µg m–3. The values for 2005 and 2006 were found to al., 2008). In order to see the contributing location of distant exceed the Malaysian Guidelines, which could be also due sources (smoke) the Hybrid Single Particle Lagrangian to the serious haze events reported in those years. The plot Integrated Trajectory (HYSPLIT4) back–trajectories (Draxler of PM10 against PM2.5 in Fig. 4 shows that there is a good and Rolph, 2010) were used in this study to trace possible correlation between them, indicating that the air quality medium and long–range transport of pollution to the Klang problem related to air particulate matter in Klang Valley Valley area. Archived REANALYSIS meteorological data (regularly monitored through PM10) was mostly dominated were used as input. This model is accessible on-line at by the fine particle fraction. http://www.arl.noaa.gov/ready/hysplit4.html. The model is used to calculate the pathway of the air mass being sampled Black Carbon and Elemental Concentration backward in time from the receptor site from various Concentration of the following twenty elements (including

Table 1. Annual average of air particulate at Kuala Lumpur, Klang Valley, 2002–2011. –3 –3 –3 Year PM2.5 (µg m ) PM10–2.5 (µg m ) PM10 (µg m ) 2002 29.5 18.1 47.6 2003 25.4 20.9 46.3 2004 26.6 22.6 49.2 2005 31.4 23.3 54.7 2006 34.8 20.7 55.5 2007 23.7 21.8 45.5 2008 23.2 22.1 45.4 2009 21.4 23.0 44.4 2010 20.5 26.1 46.6 2011 24.0 24.9 48.9 Guidelines/standard US EPA NAAQS Malaysian AAQ 15 µg m–3 (annually) 50 µg m–3 (annually) 35 µg m–3 (24 hour) 150 µg m–3 (24 hour)

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–3 PM2.5 Average = 25 µg m

–3 Annual US EPA NAAQS Guidelines for PM2.5 = 15 µg m

Fig. 2. Annual average of PM2.5 and PM10–2.5 mass at Klang Valley, Kuala Lumpur 2002–2011.

–3 Malaysian Ambient Air Quality Guidelines for PM10 = 50 µg m

–3 PM10 Average = 48 µg m

Fig. 3. Annual average of PM10 mass, Klang Valley, Kuala Lumpur 2002–2011.

Fig. 4. Plot of PM10 versus PM2.5, Klang Valley, Kuala Lumpur 2002–2011.

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Table 2. Average of elemental concentration and statistics for PM2.5 at Klang Valley, Kuala Lumpur, 2002–2011. Mass/elements Average (ng m–3) Max (ng m–3) Median (ng m–3)SD (ng m–3) Percentage (%) Mass 25145 95750 232454 10760 100 BC 4500 13201 3948 1687 16.2 Na 556 4056 304 559 2.21 Mg 157 1144 102 185 0.62 Al 185 2560 108 258 0.74 Si 460 5696 259 541 1.83 P 50 728 21 99 0.20 S 2138 23830 1114 3121 8.50 Cl 150 1196 66 209 0.60 K 422 5064 259 556 1.68 Ca 127 2552 63 204 0.50 Ti 8.0 152 3.6 16 0.03 V 5.2 65 2.6 8.8 0.02 Cr 6.7 84 3.8 11 0.03 Mn 6.7 152 3.4 14 0.03 Fe 140 1552 79 195 0.55 Ni 3.6 44 2.2 5.8 0.01 Cu 23 380 8.1 47 0.09 Zn 47 980 25 83 0.19 As 5.7 133 0 15 0.02 Br 32 407 12 56 0.13 Pb 24 660 7.0 55 0.10 Sea salt 1411 10302 771 1419 5.50 Soil 2113 28026 1260 2609 8.50 Sulfate 8821 98299 4595 12875 36.5 Smoke 339 4488 205 464 1.50 RCM 17237 129846 12278 16670 70.0

BC) were analyzed for each sample: Al, As, Br, Ca, Cl, Cr, mass (RCM) is the estimated of total fine mass calculated Cu, Fe, K, Mg, Mn, Na, Ni, P, Pb, S, Si, Ti, V and Zn. from the sum of all the composite variables discussed above Table 2 below summarizes the mass and elemental including the black carbon (Malm et al. 1994). The average concentrations including max, median and standard deviation RCM calculated for the dataset was about 70% as relative (SD) values of PM2.5 as well as their percentage relative to to the gravimetric mass. This is considered quite good mass the gravimetric mass obtained for Klang Valley in 2002– closure for the datasets. The missing 30% of mass could be 2011. Black carbon (BC) and sulfur were found to be the organic matter (which was not measured in this study), major elemental components of the PM2.5 with 16% and nitrates and water vapour (Cohen et al., 1998). 8.5% respectively, relative to the fine particle mass. In addition the concentration of the aerosol components was Source Apportionment Using Positive Matrix also estimated using pseudo-elements analysis as described Factorization (EPA-PMF3) by Malm et al. (1994) and Cohen (2000). These variables are Identification and apportionment of pollutants to their combinations of the measured composition values that help to sources is very important in air quality management. Positive estimate the likely major source types. Soil with weight Matrix Factorization (PMF) (Noris et al., 2008) is an fraction of 8.4% was estimated by summing five elements advanced receptor model based on least-squares techniques Al, Ca, Fe, Si and Ti converted to their common oxides. that uses error estimates of the measured data to provide The estimated sulfate (36%) was calculated from sulfur weights in the fitting process. The method has been developed concentration assuming that sulfate was fully neutralized by Paatero and Tapper (1993, 1994) and has been successfully ammonium sulfate. Other components that represent aerosol applied to a number of data sets in air pollution studies components are sea salt and smoke with weight fractions of (Lee et al., 1999; Chueinta et al., 2000; Song et al., 2001; 5.6% and 1.4% respectively. The sea salt was calculated Begum et al., 2004). The method is based on solving the based on the measured sodium concentration assuming that factor analysis problem by least squares approach using a all sodium in the aerosol is associated with sea salt as sodium data point weighting method which decomposes a matrix of chloride. Smoke was calculated from non–soil potassium. data of dimension n rows and m columns into two matrixes, Fine potassium is an accepted indicator for smoke from G(nxp) and F(pxm), where n is the number of samples biomass burning, hence to obtain a reliable smoke indicator while m is the number of species that can be written as: from the measured potassium, potassium associated with soil is subtracted from the total fine potassium. Reconstructed X = GF + E (1)

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Briefly, a data matrix X of i by j dimensions, in which i ● The third factor has high loadings of BC, S and K which number of samples and j chemical species can be written as: represents a source from smoke/biomass burning (Alexander et al., 2001). The factor contributed about 9.3% p of the fine mass. Fine K is an accepted key element for xij gf ik kj e ij (2) smoke from biomass burning which is released especially k 1 under high temperatures in fires mostly as potassium

chloride and potassium sulfate (Khalil et al., 2003, Begum where p is the number of factors, f is species profile, g is et al., 2004, Baxla et al., 2009). The activities of open amount of mass and e is the residual for each sample. The ij burning have made smoke/biomass burning as one of task of PMF is to minimize the object function (Q), based the urban pollutants in the area. In addition the situation upon the uncertainties: was further aggravated by trans-boundary pollution of

Qe ()2 fine particles from external biomass burning activities.  ij/ sij (3) ● The fourth factor may be attributed to a mixture of industries as characterized by the high amounts of BC, where sij are the uncertainties in xij. The results are constrained Na, Al, S, K, Ca, Fe, Zn and Pb. The high loading of BC so that all species profiles (matrix F) are non-negative and suggesting mixing of smoke from motor vehicles during each sample has a non-negative source contribution (matrix transport. This source contributed about 47.8% to the G). Solution of Eq. (3) and the model are described in detail fine mass. There are significant numbers of industries elsewhere Paatero (Paatero and Tapper, 1994; Paatero, 1997). that are involved in metal works scattered around Klang In this study, elemental data with m = 21 and n = 382 Valley and the main industrial area is located about 15 were used to perform the PMF analysis. In order to get the to 20 km to the southern/southwestern side of the sampling average source finger print and their contribution to the fine site (Rahman et al., 2011). The industries include iron particle mass, 5 “bad” data were removed from the analysis. and steel industry, paint industry, aluminum fabrication, The elements As and Ni were categorized as “bad” species pharmaceutical, rubber glove production as well as food and excluded from the modeling as more than 50% of the data industries. was below the minimum detection limit. The best solution ● The last factor contributed about 4.5% and has been was found to be the five factor solution for the elemental identified as a secondary sulphate source due to the composition of the fine particulate matter in the Klang dominance of sulphur in the profile (Kim et al., 2004). Valley, Kuala Lumpur area. The value of true Q calculated As mentioned earlier in the first factor emissions from for p = 5 factors was 2980 which is about 86% of the diesel heavy trucks are the major sources of sulfur. The expected value Qexp of 3393. The result obtained is shown sampling site lies between two busy highways to the in Fig. 5. north and south. It is also surrounded by roads that are ● The first factor is dominated by BC, Si, S, Na and minor closely linked to the Kuala Lumpur city center and carrying quantities of metal species. These components were a significant amount of traffic. Another source of sulphur is associated with emissions from motor vehicles which from shipping activities at the Port Klang which is contribute about 35.3% of the fine mass. Emissions located about 40 km of southwest Kuala Lumpur. The from diesel heavy trucks are the major sources of BC presence of potassium loading in the factor could be and S. S is part of chemical make up for diesel (as well connected with the possible contribution of local wood as gasoline) fuels. Sulfuric acid is produced when sulfur smoke and possibly from other combustion related sources. combines with water vapor formed during the combustion Fig. 6 shows the annual trend source contribution in process, and some of this corrosive compound is emitted Klang Valley, Kuala Lumpur during 2002 to 2011. into the atmosphere through the exhaust. Ebihara et al. Approximately 35 % to 48 % of PM2.5 emitted in Klang (2008) reported that the city of Kuala Lumpur was among Valley, Kuala Lumpur within 2002–2011 came from the Asia cities that were heavily affected by vehicle motor vehicles and industry respectively. In 2002 and 2003 emissions, and a recent study on source apportionment PM2.5 mass was found to be dominated by motor vehicles (Rahman et al., 2011) in Kuala Lumpur, Klang Valley with contributions of 49.5% and 50.0% respectively. After showed that motor vehicles including two stroke were 2003 there was an increase of contributions from industry the main source for fine particles of Klang Valley, and has been the major source of PM2.5 mass since 2004. Kuala Lumpur with contribution of > 30% of the mass Contribution of natural sources; soil and biomass burning concentration. ranged from 2.1 to 5.7% and 4.0 to 15.6% respectively, ● The second factor is attributed to soil dust that contains whilst contributions from secondary sulphate ranged from characteristic elements of Al, Si, Mg, K and Ca (Watson 2.8 to 7.3% and Chow, 2001). This source contributed about 3.1% to the fine mass. The presence of other elements such as Trans-Boundary Pollution Fe, V, Cr, Cu and Zn indicated the influence of road Trans-boundary atmospheric pollution (the haze) in dust to the factor during transport. Fe for example is a Malaysia has been an issue of increasing importance over major component of vehicles and tyre wear has been the past few years. Haze occurs when there is sufficient reported to contribute significantly to the Zn load in smoke, dust, moisture and water vapor suspended in air to road dust (Thorpe et al., 2008). impair visibility. It is mainly caused by particulate matter

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Fig. 5. Source apportionment for the PM2.5 mass measured at Klang Valley, Kuala Lumpur, 2002–2011.

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Fig. 6. Annual trend in source contributions of the PMF- modeled five factors in Klang Valley, Kuala Lumpur. from various sources including smoke, road dust and noon on these dates are presented in Figs. 10–12. Evidence of particulate matter formed when gaseous pollutants react in all the events is identified and presented in the supporting the air (Haze, Begum et al., 2011). The composition of materials, Figs. SM1–SM6. smoke depends on the nature of burning fuels as well as There were two smoke events identified in 2004; Jun 21, the conditions of combustion. Smoke trans-boundry events 2004 and August 23, 2004 (Fig. 7) with concentrations of were identified based on fine potassium (K). Potassium is 646 ng m–3 and 721 ng m–3 respectively. Back trajectory released especially under high temperatures in fires mostly plots at different heights: 300 m, 500 m and 1000 m above as potassium chloride and potassium sulfate and has been ground level (AGL) were used to calculate the four day often used as indicator of biomass burning (Khalil et al., and five day backward trajectories respectively (Fig. 10). 2003; Begum et al., 2004; Baxla et al., 2009). In order to Both events were seen to be associated with international obtain a reliable smoke indicator from the fine potassium it transport of smoke from the area of Sumatra Island. The is necessary to subtract the fine potassium associated with smoke signature (300 m and 500 m) traveled about 315 km soil (Cohen et al., 2010). Hence, smoke can be obtained by (Riau) to 550 km (Jambi) in southwest direction from the following equation: source location to reach the receptor site in Kuala Lumpur. The two satellite images show the evidence of fires that Ksmoke = (Ktotal – 0.6 Fe) (4) were burning on the Indonesian island of Sumatra in June 2004 (June 17, 2004 and June 18 2004). When the Aqua Wind patterns play important roles in the distribution satellite passed over the region on those days, the Moderate and dispersion of pollutants in the atmosphere. During the Resolution Imaging Spectroradiometer (MODIS) captured southwest monsoon season which is usually established in this image along with numerous fire detections. Pixels in the later half of May or early June and ends in September, which fires were detected are marked in yellow and red hot, dry weather and winds blowing from the southwest help (see supporting material Fig. SM1). The MODIS on NASA’s fires explode in Indonesia forests for several weeks. Pollutants Aqua satellite had also detected actively burning fires emitted to the atmosphere through the forest fires can be (marked in red) on August 22, 2004 on the Indonesian island transported hundreds and even thousands of kilometers and of Sumatra and Borneo (Fig. SM2). Clouds and smoke have the capacity to reach the Peninsular Malaysia. were found to be swirling over the island of Borneo (right), In this study, smoke trans-boundary events (with evidence) Java (bottom), and Sumatra (left). were identified based on the pseudo–element K during the Fig. 11 shows the trajectory plots at different heights (300 southwest monsoon between May–September from the data m, 500 m, 1000 m) AGL calculated during the intermonsoon base 2004 and 2008; and during transition period October, period from October 10, 2006 to October 23, 2006. The 2006. In order to exclude low local source contributions, smoke signature on October 10, 2006 travels about 500 km only daily events with concentrations that are two standard from West Sumatra and passes through the area of Riau deviations above the mean value for a measured species (315 km) to reach the receptor site. The smoke concentration were considered (Figs. 7–9). The statistics and the peak at Kuala Lumpur was found to reach up to 1102 ng m–3 value concentration (ng m–3) of smoke for the selected dates which is more than two times the average concentration of during the study period are given in Table 3. Air parcel 477 ng m–3. From the NASA satellite image (Fig. SM3) back trajectories (Draxler and Rolph, 2010) beginning at there were haze events in several areas in the islands of

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Fig. 7. Time series plot of smoke at Klang Valley, Kuala Lumpur 2004.

Fig. 8. Time series plot of smoke at Klang Valley, Kuala Lumpur 2006.

Fig. 9. Time series plot of smoke at Klang Valley, Kuala Lumpur 2008.

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Table 3. Statistics and the smoke concentration for the selected dates in the studying period. Year Date Smoke (ng m–3) Mean (ng m–3) Median 2stdev 21/06/04 646 2004 335 316 337 23/08/04 721 10/10/06 1102 2006 477 336 662 23/10/06 789 30/07/08 1551 2008 684 517 1060 04/08/08 1995

Fig. 10. Four day (left) and five day (right) back trajectories calculated for Jun 21 and Aug 23, 2004.

Sumatra and Borneo on October 1 and October 4, 2006 due southeast direction, originating from the area of South to smoke from agricultural and forest fires burning in late Kalimantan. The event was found to be coincident with the September and early October 2006. It was also reported land and forest fires in Kalimantan, Indonesia as shown in that fires continued to burn throughout October 2006 and the supporting material, Fig. SM4. burnt large areas of Sumatra and Borneo. For back trajectories The backward trajectories of air parcels at different on October 23, 2006 the smoke signature reached Kuala starting heights of 300 m, 500 m and 1000 m on July 30, Lumpur by traveling about 1315 km to 1710 km from the 2008 (five days back) and August 4, 2008 (four days back)

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Fig. 11. Five day (left) and nine day (right) back trajectories calculated for Oct 10, 2006 and Oct 23, 2006. were also calculated (Fig. 12). The trajectories show that fires as shown in the Sumatra hotspot distribution map the air parcel on July 30 came from the southeast direction (Fig. SM6). This information is available free of charge via and traveled about 750 km from South Sumatra (province the Internet at http://modis.gsfc.nasa.gov/. of Palembang) to reach the receptor site in Kuala Lumpur in which the smoke concentration reach up to 1995 ng m–3. CONCLUSIONS In case of trajectory plots (300 m and 500 m) on August 4, the particles seem to have traveled from the southeast The yearly 24 hour average of PM2.5 concentrations direction from the same area of South Sumatra (Palembang) throughout the 10 year study period was consistently about then changed the direction to the southwest to reach the twice that of USEPA National Air Quality Standards, 15 –3 receptor site in Kuala Lumpur. From the NASA satellite µg m . Even the PM10 concentration that is mostly influenced image (see supporting material Fig. SM5) there were several by the PM2.5 concentration was also found to be at the higher fire locations detected by MODIS in the areas within the end of the Malaysian Air Quality Guidelines, 50 µg m–3. islands of Sumatra and Borneo for the week of August 4–11, Thus, significant increases of the air pollutants during dry 2008, as well as active fires on August 6, 2008. In addition a spells due to open burning or forest fires caused occurrence of number of hotspots were detected in the island of Sumatra haze episodes on one or two occasions of a year. It was in July 2008, indicating a high intensity of forest and land proven that the air pollutants resulted from the forest fire

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Fig. 12. Five day (left) and four day (right) back trajectories calculated for Jul 30 and Aug 04, 2008. in the neighboring country was transported trans-boundary analysis by PIXE. The authors gratefully acknowledge the and caused further deterioration of the already poor air NOAA Air Resources Laboratory (ARL) for the READY quality of the Klang Valley. Most of the haze-causing air website http://www.arl.noaa.gov/ready.php used to calculate pollutants were locally generated, mostly from vehicular the trajectories in this publication and Wikipedia, the free emissions. Therefore authorities should formulate a proper encyclopedia website, https://en.wikpedia.org.wiki/Klang_ plan and strategy to improve the air quality such as Valley for the article and map used in this paper. encouraging the usage of public transportation. The plan and strategy to improve the state of air quality should also SUPPORTING MATERIALS be handled at both local and regional level. NASA satellite images of peninsular Malaysia shrouded ACKNOWLEDGEMENTS in thick haze on June 17, 2004 (left) and June 18, 2004 (right) (Fig. SM1), NASA satellite image of fires burning This work was financially and technically supported by across Sumatra and Borneo Island on Aug 22, 2004 (Fig. IAEA/RCA Project No RAS/8/082 and RAS/7/013 and SM2), NASA satellite images of haze in the area of islands Malaysian Nuclear Agency. The authors would like to thanks of Sumatra and Borneo on October 1, 2006 (left) and October GNS Science, New Zealand for the assistance with sample 4, 2006 (right) (Fig. SM3), NASA satellite images of haze in

Rahman et al., Aerosol and Air Quality Research, 15: 2291–2304, 2015 2303 the area of islands of Sumatra and Borneo on October 8, 2006 Draxler, R.R. and Rolph, G.D. (2010). Hybrid Single-particle (left) and October 23, 2006 (right) (Fig. SM4). Fire locations Larangian Integrated Trajectory Model, (HYSPLIT), from MODIS for the week of August 4, 2008 in the area of http:/www.arl.noaa.gov/ready/hysplit4.html, last access: islands of Sumatra and Borneo (left) and active fires detected 10 April 2015 by MODIS on NASA’s Aqua satellite on August 6, 2008 Ebihara, M., Chung, Y.S., Dung, H.M., Moon, J.H., Ni, (right) (Fig. SM5), Sumatra hotspot distribution map, July B.F., Otoshi, T., Oura, Y., Santos, F.L., Sasajima, F., 2008 (Fig. SM6). This information is available free of Sutisna, Wee, B.S., Wimolwattanapun, W. and Wood, charge via the Internet at http://modis.gsfc.nasa.gov/. A.K. (2008). 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