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Physicochemical factors and sources of PM10 at residential- urban environment in Kuala Lumpur a
ab
b
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Md. Firoz Khan , Mohd Talib Latif , Liew Juneng , Norhaniza Amil , Mohd Shahrul Mohd Nadzir
ab
b
& Hossain Mohammed Syedul Hoque
a
Centre for Tropical Climate Change System, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia b
School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia c
School of Industrial Technology (Environmental Division), Universiti Sains Malaysia, 11800 Penang, Malaysia. Accepted author version posted online: 01 Jun 2015.
To cite this article: Md. Firoz Khan, Mohd Talib Latif, Liew Juneng, Norhaniza Amil, Mohd Shahrul Mohd Nadzir & Hossain Mohammed Syedul Hoque (2015): Physicochemical factors and sources of PM10 at residential- urban environment in Kuala Lumpur, Journal of the Air & Waste Management Association, DOI: 10.1080/10962247.2015.1042094 To link to this article: http://dx.doi.org/10.1080/10962247.2015.1042094
Disclaimer: This is a version of an unedited manuscript that has been accepted for publication. As a service to authors and researchers we are providing this version of the accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proof will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to this version also.
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Physicochemical factors and sources of PM 10 at residential- urban environment in Kuala Lumpur
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Md. Firoz Khan1,*, Mohd Talib Latif1,2, Liew Juneng2, Norhaniza Amil2,3, Mohd Shahrul Mohd Nadzir1,2 and Hossain Mohammed Syedul Hoque2
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Centre for Tropical Climate Change System, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia 2
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School of Industrial Technology (Environmental Division), Universiti Sains Malaysia, 11800 Penang, Malaysia.
About authors
Md. Firoz Khan is a University Lecturer of Centre for Tropical Climate Change System,
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Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia Mohd Talib Latif is a Professor of Centre for Tropical Climate Change System, Institute of
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Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia and School
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of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
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School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
Liew Juneng and Mohd Shahrul Mohd Nazdir are Senior Lecturers and Hossain Mohammed
Syedul Hoque is a Ph.D. student of School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
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Norhaniza Amil is a Ph.D. student of School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia and School of Industrial Technology (Environmental Division), Universiti Sains
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Malaysia, 11800 Penang, Malaysia
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Long term measurements (2004-2011) of PM 10 and trace gases (CO, O 3 , NO, NO x , NO 2 , SO 2 ,
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CH 4, NmHC) have been conducted to study the effect of physicochemical factors on the PM 10 concentration. In addition, this study includes source apportionment of PM 10 in Kuala Lumpur urban environment. An advanced principal component analysis (PCA) technique coupled with absolute principal component scores (APCS) and multiple linear regression (MLR) has been
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applied. The average annual concentration of PM 10 for eight years is 51.3±25.8 μg m-3 which exceeds the Recommended Malaysian Air Quality Guideline (RMAQG) and international
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guideline values. Detail analysis shows the dependency of PM 10 on the linear changes of the
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motor vehicles in use and the amount of biomass burning, particularly from Sumatra, Indonesia during south-westerly monsoon. The main sources of PM 10 identified by PCA-APCS-MLR are traffic combustion (28%), ozone coupled with meteorological factors (20%), and windblown
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Abstract
particles (1%). However, the apportionment procedure left 28.0 μg m−3 i.e. 51% of PM 10 undetermined.
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IMPLICATIONS: Air quality is always a top concern around the globe. Especially in the South Asian regions, measures are not yet sufficient as revealed in our studies the concentrations of particulate matters
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exceed the tolerable limits. Long term data analysis and characterization of particular matters and their sources will aid the policy makers and the concerned authority to adapt measures and
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about future implications of air quality management.
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Keywords: Particulate matter; Urban air pollution; Back trajectory; Biomass burning; Source apportionment *
Corresponding author. Tel: +603-89213822, fax: +603-89253357
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E-mail: [email protected]; [email protected] (M. F. Khan)
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Introduction
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Atmospheric aerosols emitted from anthropogenic and natural sources play a vital role in the various atmospheric processes. Atmospheric aerosols affect the formation of cloud droplets, decrease visibility and influence climate change by scattering and absorbing radiation (Haywood
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policies according to the circumstances. Additionally, similar intensive studies will give insight
and Boucher, 2000; Ramanathan et al., 2001; Watson, 2002). Aerosol particles have potential detrimental effects on human health. Many studies on the health risks of atmospheric aerosols have established an implicit link between exposure to aerosols and increased rate of mortality
and morbidity (Pope III and Dockery, 2006; Wan Mahiyuddin et al. 2013; Pope et al., 2009). Lee
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et al. (2011) estimated the costs due to the inhalation of particulate matter in Seoul to be around $1057 million USD per year for acute exposure and $8972 million USD per year for chronic exposure.
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Emission from biomass burning in trans-boundary regions and development activities have
been identified as the main sources of PM 10 in Malaysia (Latif et al., 2011). Moreover, the
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(Dominick et al., 2012). Surface ozone and its precursors on the other hand affect PM 10 concentration indirectly. These have significant impact on the gas to particle phase
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transformation of aerosols (Chow et al., 1998). For example, NO x emitted from motor vehicles will influence the variability and formation of nitrate aerosols. Meteorological factors such as wind speed, wind direction and relative humidity have the greatest influence on aerosol source
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formation, transformation, transport deposition and scavenging processes. A study by Yadav et al. (2014) shows that the concentration of PM 10 increases with wind speed. Ambient temperature
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and relative humidity change the physical and chemical aspects of PM 10 (Hinds, 1999; Seinfeld and Pandis, 2006). The strong south-west monsoon and the location of the cyclonic system over
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the South China Sea and the Bay of Bengal influence the local PM 10 concentration (Juneng et al., 2011).
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concentration of PM 10 is influenced by anthropogenic gases, particularly carbon monoxide
Source apportionment techniques provide quantitative estimates of PM 10 sources. Multivariate
receptor models are widely accepted methods for the apportionment of PM 10 pollution sources. Principal component analysis (PCA) coupled with absolute principal component scores (APCS) have been applied as sound multivariate receptor models for this purpose (Friend et al., 2013;
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Harrison et al., 1996; Khan et al., 2010; Thurston and Spengler, 1985). However, source apportionment for aerosols using advanced and robust methods has yet to be systematically carried out for Malaysia. An extensive overview of the regional distribution of emissions,
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contributing sources, precursor gases, meteorology on a synoptic scale, as well as possible transport patterns of the aerosol mass are crucial in understanding the influence of physicochemical factors on the concentration of PM 10 at the local urban level. Therefore, the
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apportion the PM 10 sources at an urban hotspot of Kuala Lumpur, Malaysia.
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Materials and Method Sampling site
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The sampling site is situated at Cheras (N 03° 06.376', E 101° 43.072', 43.4 m above sea level)
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in Kuala Lumpur, Malaysia. The station is located in a densely populated residential area with classification as an urban background station by the Department of Environment (DoE),
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Malaysia. The sampling station location is shown in Figure 1. Measurements started early February of 2004 and continued until December 2011. PM 10 concentration, precursor gases, as well as parameters related to the local meteorology – carbon monoxide (CO), ozone (O 3 ), oxides
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aims of this paper are to address the physicochemical factors contributing to PM 10 as well as to
of nitrogen (NOx), nitrogen oxide (NO), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), methane
(CH 4 ), non-methane hydrocarbon (NmHC), wind speed (WS), wind direction (WDir), relative humidity (RH), and ambient temperature (T) - were measured. However, due to the
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unavailability of data for year 2011, CH 4 and NmHC were constructed for the period of 2004 – 2010 only.
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Measurement of PM 10 and atmospheric gases PM 10 was measured continuously with a Met-One Beta Attenuation Monitor (BAM 1020,
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BAM 1020 operation is based on beta-ray attenuation and provides real-time aerosol mass readings. The instrument has fairly high resolution of 0.1 μgm−3 at 16.7 Lmin-1 flow rates with
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lower detection limits of < 4.8 μgm−3 and < 1.0 μgm-3 for 1 h and 24 h, respectively (www.metone.com). Details of the measurements of the precursor gases are presented in Table 1.
Quality control and quality assurance (QC/QA) for PM 10 , trace
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gases and meteorological measurement
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Instruments used in in-situ monitoring were managed by a private company named Alam
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Sekitar Sdn. Bhd. (ASMA). An auto-calibration was scheduled for each instrument on a daily basis by which zero air calibration as well as standard cylinder gas was injected during the operation.
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USA) aerosol mass monitor, which is certified by the US Environmental Protection Agency. The
Multivariate and other statistical techniques Advanced principal component analysis (PCA) is a robust driving tool for sourceapportionment and quantitative estimation of PM 10 sources. PCA combined with APCS is used
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extensively for prediction and estimation of source strength in ambient aerosols (Thurston and Spengler, 1985; Harrison et al., 1996). A step-wise and detailed procedure for the PCA–APCS calculation has been explained in previous studies (Khan et al., 2010, 2012). As local
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meteorology governs the strength of the sources of PM 10 , we include the major meteorological factors in this apportionment, as in Juda-Rezler et al. (2011) and Viana et al. (2006). In order to achieve linearity of the data, wind vectors were resolved into their horizontal and vertical
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The measurements contain missing values. Imputation methods suggest various ways to
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replace the missing values with plausible values. Missing data has been replaced with the mean value of each variable. Mean substitution technique is recommended to use when the missing data is less than 10% of the measurement (Trikriktsis, 2005). The suitability and adequacy of the
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dataset for PCA was verified with the Kaiser-Meyer-Olkin (KMO) test. Taner et al. (2013) suggested that a sample dataset will be adequate and recognizable if the KMO value is greater
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than 0.5. Our KMO value was 0.8 which is consistent with Taner et.al (2013). The datasets of trace gases and meteorological parameters were normalized and subjected to PCA varimax
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rotation. The number of factors was optimized based on the Eigen value (>1). Additionally, PCA was used based on the number of factors and sensitivity of each variable to factor loading during the optimization process.
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components.
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Results and Discussion
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Time series of PM 10 and atmospheric trace gases
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Concentrations of PM 10 and trace gases are summarized in Table 2. The concentrations have
been divided into two categories: (i) weekdays and (ii) weekends. The overall mean
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average is 51.6±25.8 μgm−3. The annual average concentration exceeds the annual PM 10
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concentrations in the Recommended Malaysian Air Quality Guidelines (RMAQG) and in international standards, e.g., the World Health Organization (WHO) air quality guidelines, and the European Union (EU) air quality standards. The recommended air quality guideline set by the Malaysian government is 50 μgm−3 annually. The EU has set a lower value of 40 μgm−3 for
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yearly PM 10 mass (European Commission, 2013). The WHO on the other hand announced stricter recommendations as low as 20 μgm−3 for annual PM 10 to maintain better urban air
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quality (WHO, 2005).
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The overall average concentration of PM 10 on weekdays is higher (4%) than the concentration on weekends. This clearly shows the impact of vehicle numbers on PM 10 concentration. The concentration of ozone remained constant on weekdays and weekends. Ozone precursor
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concentration of the PM 10 for eight years of hourly data is 51.3±28.5 μgm−3 and the annual
concentration shows slight variation during the specified time periods. The degradation of trace gases in the atmosphere is dominated by their reaction with hydroxyl radical (OH). OH radicals are short-lived, and their formation and removal in the atmosphere is balanced. Reaction with ozone, nitric oxide and hydrogen peroxide is the primary pathway of OH production and is
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consumed by a multitude of reaction with trace gases (Hofzumahaus et al., 2009). OH can remove almost all the pollutants, including CO and VOCs. Reaction with CO and VOCs will produce hydroperoxy (HO 2 ) and organic peroxy (RO 2 ) radicals respectively. With NOx present
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in the air, RO 2 reacts with NO to produce HO 2 . OH radical is reformed by a reaction between HO 2 and NO with NO 2 as a by-product which degrades photochemically to produce ozone. Figure 2 depicts the time series of PM 10 concentration and the trace gases. Episodes of high
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concentration of PM 10 are seen at the end of 2005. However, PM 10 high concentration patterns
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do not match the ozone precursor concentration patterns. Ozone precursors are known to affect aerosol concentration indirectly. NO for example is scavenged photochemically, a few minutes after emission (Kemp and Palmgren, 1996). CH 4 and NmHC on the other hand show no clear seasonal variations. These two trace gases are believed to be the O 3 precursor gases (Duan et al.,
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2008) where NmHC participates in the formation of secondary organic aerosols (Cocker III et
PM 10 ).
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al., 2001). Thus, CH 4 and NmHC indirectly affect the variability of aerosol concentration (e.g.
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Local meteorology
As summarized in Table 2, the average temperature was 27.4°C, with a variation between
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concentration of CO are observed during the years 2004-2005. In contrast, episodes of high
20.9°C to 39.0°C. The minimum and maximum relative humidity were 29% and 100% with an average of 84%. Wind speed was on average of 4km/h. The synoptic wind field is shown in Figure 3. The wind over the Malaysian Peninsula generally blows from the northeasterly (NE) direction during November - March (Figure 3) and from the southwesterly (SW) direction June–
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September (Figure 3). The diurnal variations for T, RH, WS, and WDir are shown in Figure 4a. Temperature and relative humidity show an opposite diurnal cycle pattern. Temperature starts to increase from 8:00 local time (LT), and peaks at 14:00. On the other hand, the RH shows
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downward trend from 8:00 LT to 14:00 LT, reaches 90% at 24:00 LT, when the ambient temperature at the lowest. The air was dry and warm in the afternoon.
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The diurnal variations of PM 10 concentrations, the trace gases and meteorological parameters
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are shown in Figure 4a–4j. Initially, the entire database for each of the parameters was re-sorted based on an overall data, weekdays (WD), and weekend (WE), which then were summarized as the 24 h average. Their ranges per day were also extracted. Subgrouping of the database was considered to identify WE effect, if any. The diurnal concentration of PM 10 ranges from 45.7 to
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59.5 μgm−3 with an average mass concentration of 51.3 μgm−3. PM 10 concentration during the WD ranged between 45.6–60.8 μgm-3 with the diurnal average of 52.1 μgm−3. With small
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difference, PM 10 concentration during the WE ranged from 41.5–60.7 μgm−3 with an average of
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49.2 μgm−3 for the diurnal WE mass. The trend shows the highest in 2004, and then decreases gradually in the next eight years. PM 10 concentration shows significant variation during the eight years monitoring period as previously shown in Figure 2. However, the trend of diurnal
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Diurnal patterns of PM 10 and trace gases
distribution for all subgroups (WD, WE, and overall data) of PM 10 concentration shows similar behavior. The concentration of PM 10 from 09:00 to 24:00 LT on the WD is somewhat higher (5– 15%) as compared to the WE. Two distinct lowest hourly concentration were recorded at 07:00 LT and 14:00 LT; followed by a sharp peak at about 09:00 and 24:00 LT. The peak at 9:00 LT is
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caused by the traffic volume frequency during the morning rush hour while the 24:00 peak is due to the temperature inversion. Temperature inversion is a physical process that leads to the variability of PM 10 loading in the
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ambient air (Monks et al., 2009). The stagnant atmospheric conditions from 21:00 LT to 03:00 LT favors the accumulation of PM 10 . At the same time, a temperature inversion and stable
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process normally occurs during stable atmospheric conditions (Galindo et al., 2013). However, the wind speed in general increase substantially from 10:00–17:00 LT (Figure 4a), which
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disperses PM 10 . Similarly, a dilution effect could be observed in the deeper planetary boundary layer (PBL), where having a somewhat higher ambient and surface temperature, as well as a higher wind speed will considerably lower the concentration of aerosols (Watson, 2002;
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Barmpadimos et al., 2011; Chow et al., 2008; Pitz et al., 2003).
O 3 shows a unimodal diurnal pattern (Figure 4e). Around 14:00 LT, O 3 concentration reaches
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its peak, and soon thereafter starts to drop until 24:00 LT. The reactive oxygen (O) formed from
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reaction step 5 (R5) under an extended level of ultraviolet radiation leads to chemical conversion into O 3 in the presence of O 2 and bulk constituent (M) as in step 7 (R7). The reaction in steps 5 and 6 (R5 & R6) are also actively involved in the generation of surface level O 3 (Lin et al., 2011;
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conditions prevail, which eventually stabilize the air mass, and reduce the dispersion. This
Seinfeld and Pandis, 2006). This is known as a key heterogeneous reaction, which takes place in
the middle of the day. A similar change in O 3 with an afternoon maximum and a nighttime minimum were observed in another study in Malaysia (Toh et al., 2013). The diurnal plots of WD and WE coincide, suggesting that reaction step 5 (R5) (Figure 5) is primarily responsible for
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the generation of surface O 3. O 3 actively interferes in the formation of aerosol mass (R12). As seen in Figure 4b, the peak during the morning hours is the most significant, and comes from the rush hour traffic. CO and SO 2 have similar bimodal distributions.
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The mean NO concentration is 20.0 ppbv, with the lowest concentration at 2.9 ppbv, and the highest concentration at 50.3 ppbv. NO is released into the atmosphere from primary emission
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conversion of NO. In this study, the mean concentration of NO 2 is 21.5 ppbv, with lowest and highest concentrations being 12.3 and 32.9 ppbv, respectively. NO 2 increases up to maximum of
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96% during night due to the nighttime temperature inversion. Stable and stagnant conditions during night favor the formation of NH 4 NO 3 , when the relative humidity is high and the temperature is low (Stockwell et al., 2000). The stable forms of NO 3 - and NH 4 + (and in ambient
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aerosols; i.e. NH 4 NO 3 ) form the gaseous phase equilibrium of ammonia (NH 3 ) and nitric acid (HNO 3 ) via reaction step 12 (R12) (Figure 5) (Finlayson-Pitts and Pitts Jr., 2000). The
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conversion of NO 2 produces most particulate phase NO 3 − (Khoder, 2002) which contributes to the increased nighttime PM 10 level as explained by reaction step 2 (R2) (Figure 5).
The
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heterogeneous reaction steps (R2, R4, R5, and R6) in the above explanation are mainly responsible for this conversion (Figure 5). Therefore, the oxides of nitrogen are responsible for generating aerosol mass.
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sources. NO 2 on the other hand is generally formed in the atmosphere through photochemical
Irrespective of the season, the diurnal change in CH 4 concentration shows consistent decrease
from 08:00 LT until 14:00 LT. NmHC concentration shows downward trend after reaching the peak at 09. The lower level of CH 4 and NmHC during middle of the day is due to several
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recognized photochemical reactions. However, there is no clear evidence of the role of CH 4 and NmHC in the formation of PM 10 .
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Trajectory analysis and impact of regional emission The concentration of PM 10 during the SW monsoon is typically high. We constructed a 72-hr
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(Figure 6) using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Rolph, 2013). Moderate Resolution Imaging Spectroradiometer (MODIS) fire data
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representing biomass burning hotspots in the specific area of interest were downloaded from NASA LANCE FIRMS fire archive (http://firms.modaps.eosdis.nasa.-gov/download/) in the range of 8°S to 20°N, and 90°W to 120°E. This has been appended to the graphs of the backward trajectories. By considering the percentage of mean cluster trajectories, the air mass is found to
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originate from regional sources. Most of the trajectories originated from Sumatra, Indonesia. Biomass hot spots are also found densely distributed in the Indonesian region (Figure 6)
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particularly during the south-westerly monsoon. The synoptic wind field shows that wind blows
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from the same region during the south-westerly monsoon (Figure 3).
Source characterizations
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cluster monthly mean backward trajectories at 500 m above sea level and 00:00 hr (UTC)
By means of PCA–APCS, three source fingerprints has been identified when the Eigen value
is set to 1, as shown in Figure 7a. The principal components of the three fingerprints (PC1, PC2,
and PC3) accounted for 42%, 14%, and 8% of the total variance, respectively. In total, 64% of the variance can be explained in terms of the three PC factors identified. PC1 represents the
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major tracers CO, NOx, NO, NO 2 and NmHC. Transportation is the main source of CO emission in Malaysia (Streets et al., 2003; Zhang et al., 2009). Incomplete combustion of fossil fuels is the primary source of CO. The process includes several reactions in the atmospheric gas phase,
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which indirectly affects the formation of aerosol particles, as shown in Figure 5. Similarly, NOx is mainly emitted from combustion sources. Urban road traffic is believed to have contributed to the secondarily generated inorganic aerosol through the release of NOx (Kumar et al., 2008).
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Malaysia (Streets et al., 2003; Zhang et al., 2009). However, the forest and grassland fires also
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emit oxides of nitrogen (NO 2 ) (Carlo et al., 2015). Hence, PC1 can be identified as traffic emissions and contributes most significantly to PM 10 .
PC2 is associated with O 3 , WS, T and RH. Ozone is released into ambient air through
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photochemical reactions, mostly during the day. It is important to distinguish this principal component, as few of the potential meteorological parameters are grouped together. Wind speed,
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T, and RH are significant factors in a wider sense as they govern the transport as well as the transformation of the gas into aerosol particles. Wind speed is inversely proportional to PM 10
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mass, which suggests better ventilation or dispersion of aerosols (Petrakakis et al., 2006; Zoumakis, 1998). Van Poppel et al. (2012) found that the concentration of aerosol particles exponentially decreases with WS. Similarly, lower temperature and higher percentage of relative
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Mostly released from vehicles, NO is the second largest contributor to PM 10 concentration in
humidity affect the variability of PM 10 through the growth of aerosol particles (Hinds, 1999;
Jiun-Horng et al., 2011; Seinfeld and Pandis, 2006). Thus, PC2 is defined as ozone and meteorological factor.
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Wind vector resolved into horizontal and vertical components is the major contributor in PC3 (Figure 7a). Wind direction significantly affects the concentration of aerosol particles in Malaysian region, as the polluted air mass is mostly transported during the NE and SW
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monsoons (Fig 3). In agreement with this factor profile, the results of the mean cluster trajectories indicate that the air masses originate from the Sumatra, Indonesia (Fig 6). Allen et al. (2007) have concluded that the wind direction can be used to find pollutant source and strength.
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concentration of PM 10 . Consequently, PC3 is described as the windblown particulate matter.
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Multiple linear regression analysis (MLRA) was applied to estimate the mass of each PC source contributing to PM 10 expressed as mass over air volume (μgm−3). PM 10 was treated as the dependent variable, and APCS were used as independent variables. PCA scores were re-sampled to absolute scores using APCS analysis (Thurston and Spengler, 1985; Harrison et al., 1996).
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The MLRA shows that the coefficient of determination (R2) is 0.21 (p
Journal of the Air & Waste Management Association Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/uawm20
Click for updates
Physicochemical factors and sources of PM10 at residential- urban environment in Kuala Lumpur a
ab
b
bc
Md. Firoz Khan , Mohd Talib Latif , Liew Juneng , Norhaniza Amil , Mohd Shahrul Mohd Nadzir
ab
b
& Hossain Mohammed Syedul Hoque
a
Centre for Tropical Climate Change System, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia b
School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia c
School of Industrial Technology (Environmental Division), Universiti Sains Malaysia, 11800 Penang, Malaysia. Accepted author version posted online: 01 Jun 2015.
To cite this article: Md. Firoz Khan, Mohd Talib Latif, Liew Juneng, Norhaniza Amil, Mohd Shahrul Mohd Nadzir & Hossain Mohammed Syedul Hoque (2015): Physicochemical factors and sources of PM10 at residential- urban environment in Kuala Lumpur, Journal of the Air & Waste Management Association, DOI: 10.1080/10962247.2015.1042094 To link to this article: http://dx.doi.org/10.1080/10962247.2015.1042094
Disclaimer: This is a version of an unedited manuscript that has been accepted for publication. As a service to authors and researchers we are providing this version of the accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proof will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to this version also.
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions
Physicochemical factors and sources of PM 10 at residential- urban environment in Kuala Lumpur
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Md. Firoz Khan1,*, Mohd Talib Latif1,2, Liew Juneng2, Norhaniza Amil2,3, Mohd Shahrul Mohd Nadzir1,2 and Hossain Mohammed Syedul Hoque2
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1
Centre for Tropical Climate Change System, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia 2
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3
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School of Industrial Technology (Environmental Division), Universiti Sains Malaysia, 11800 Penang, Malaysia.
About authors
Md. Firoz Khan is a University Lecturer of Centre for Tropical Climate Change System,
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Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia Mohd Talib Latif is a Professor of Centre for Tropical Climate Change System, Institute of
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Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia and School
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of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
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School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
Liew Juneng and Mohd Shahrul Mohd Nazdir are Senior Lecturers and Hossain Mohammed
Syedul Hoque is a Ph.D. student of School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
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Norhaniza Amil is a Ph.D. student of School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia and School of Industrial Technology (Environmental Division), Universiti Sains
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Malaysia, 11800 Penang, Malaysia
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Long term measurements (2004-2011) of PM 10 and trace gases (CO, O 3 , NO, NO x , NO 2 , SO 2 ,
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CH 4, NmHC) have been conducted to study the effect of physicochemical factors on the PM 10 concentration. In addition, this study includes source apportionment of PM 10 in Kuala Lumpur urban environment. An advanced principal component analysis (PCA) technique coupled with absolute principal component scores (APCS) and multiple linear regression (MLR) has been
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applied. The average annual concentration of PM 10 for eight years is 51.3±25.8 μg m-3 which exceeds the Recommended Malaysian Air Quality Guideline (RMAQG) and international
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guideline values. Detail analysis shows the dependency of PM 10 on the linear changes of the
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motor vehicles in use and the amount of biomass burning, particularly from Sumatra, Indonesia during south-westerly monsoon. The main sources of PM 10 identified by PCA-APCS-MLR are traffic combustion (28%), ozone coupled with meteorological factors (20%), and windblown
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Abstract
particles (1%). However, the apportionment procedure left 28.0 μg m−3 i.e. 51% of PM 10 undetermined.
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IMPLICATIONS: Air quality is always a top concern around the globe. Especially in the South Asian regions, measures are not yet sufficient as revealed in our studies the concentrations of particulate matters
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exceed the tolerable limits. Long term data analysis and characterization of particular matters and their sources will aid the policy makers and the concerned authority to adapt measures and
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about future implications of air quality management.
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Keywords: Particulate matter; Urban air pollution; Back trajectory; Biomass burning; Source apportionment *
Corresponding author. Tel: +603-89213822, fax: +603-89253357
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E-mail: [email protected]; [email protected] (M. F. Khan)
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Introduction
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Atmospheric aerosols emitted from anthropogenic and natural sources play a vital role in the various atmospheric processes. Atmospheric aerosols affect the formation of cloud droplets, decrease visibility and influence climate change by scattering and absorbing radiation (Haywood
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policies according to the circumstances. Additionally, similar intensive studies will give insight
and Boucher, 2000; Ramanathan et al., 2001; Watson, 2002). Aerosol particles have potential detrimental effects on human health. Many studies on the health risks of atmospheric aerosols have established an implicit link between exposure to aerosols and increased rate of mortality
and morbidity (Pope III and Dockery, 2006; Wan Mahiyuddin et al. 2013; Pope et al., 2009). Lee
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et al. (2011) estimated the costs due to the inhalation of particulate matter in Seoul to be around $1057 million USD per year for acute exposure and $8972 million USD per year for chronic exposure.
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Emission from biomass burning in trans-boundary regions and development activities have
been identified as the main sources of PM 10 in Malaysia (Latif et al., 2011). Moreover, the
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(Dominick et al., 2012). Surface ozone and its precursors on the other hand affect PM 10 concentration indirectly. These have significant impact on the gas to particle phase
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transformation of aerosols (Chow et al., 1998). For example, NO x emitted from motor vehicles will influence the variability and formation of nitrate aerosols. Meteorological factors such as wind speed, wind direction and relative humidity have the greatest influence on aerosol source
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formation, transformation, transport deposition and scavenging processes. A study by Yadav et al. (2014) shows that the concentration of PM 10 increases with wind speed. Ambient temperature
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and relative humidity change the physical and chemical aspects of PM 10 (Hinds, 1999; Seinfeld and Pandis, 2006). The strong south-west monsoon and the location of the cyclonic system over
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the South China Sea and the Bay of Bengal influence the local PM 10 concentration (Juneng et al., 2011).
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concentration of PM 10 is influenced by anthropogenic gases, particularly carbon monoxide
Source apportionment techniques provide quantitative estimates of PM 10 sources. Multivariate
receptor models are widely accepted methods for the apportionment of PM 10 pollution sources. Principal component analysis (PCA) coupled with absolute principal component scores (APCS) have been applied as sound multivariate receptor models for this purpose (Friend et al., 2013;
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Harrison et al., 1996; Khan et al., 2010; Thurston and Spengler, 1985). However, source apportionment for aerosols using advanced and robust methods has yet to be systematically carried out for Malaysia. An extensive overview of the regional distribution of emissions,
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contributing sources, precursor gases, meteorology on a synoptic scale, as well as possible transport patterns of the aerosol mass are crucial in understanding the influence of physicochemical factors on the concentration of PM 10 at the local urban level. Therefore, the
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apportion the PM 10 sources at an urban hotspot of Kuala Lumpur, Malaysia.
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Materials and Method Sampling site
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The sampling site is situated at Cheras (N 03° 06.376', E 101° 43.072', 43.4 m above sea level)
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in Kuala Lumpur, Malaysia. The station is located in a densely populated residential area with classification as an urban background station by the Department of Environment (DoE),
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Malaysia. The sampling station location is shown in Figure 1. Measurements started early February of 2004 and continued until December 2011. PM 10 concentration, precursor gases, as well as parameters related to the local meteorology – carbon monoxide (CO), ozone (O 3 ), oxides
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aims of this paper are to address the physicochemical factors contributing to PM 10 as well as to
of nitrogen (NOx), nitrogen oxide (NO), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), methane
(CH 4 ), non-methane hydrocarbon (NmHC), wind speed (WS), wind direction (WDir), relative humidity (RH), and ambient temperature (T) - were measured. However, due to the
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unavailability of data for year 2011, CH 4 and NmHC were constructed for the period of 2004 – 2010 only.
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Measurement of PM 10 and atmospheric gases PM 10 was measured continuously with a Met-One Beta Attenuation Monitor (BAM 1020,
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BAM 1020 operation is based on beta-ray attenuation and provides real-time aerosol mass readings. The instrument has fairly high resolution of 0.1 μgm−3 at 16.7 Lmin-1 flow rates with
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lower detection limits of < 4.8 μgm−3 and < 1.0 μgm-3 for 1 h and 24 h, respectively (www.metone.com). Details of the measurements of the precursor gases are presented in Table 1.
Quality control and quality assurance (QC/QA) for PM 10 , trace
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gases and meteorological measurement
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Instruments used in in-situ monitoring were managed by a private company named Alam
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Sekitar Sdn. Bhd. (ASMA). An auto-calibration was scheduled for each instrument on a daily basis by which zero air calibration as well as standard cylinder gas was injected during the operation.
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USA) aerosol mass monitor, which is certified by the US Environmental Protection Agency. The
Multivariate and other statistical techniques Advanced principal component analysis (PCA) is a robust driving tool for sourceapportionment and quantitative estimation of PM 10 sources. PCA combined with APCS is used
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extensively for prediction and estimation of source strength in ambient aerosols (Thurston and Spengler, 1985; Harrison et al., 1996). A step-wise and detailed procedure for the PCA–APCS calculation has been explained in previous studies (Khan et al., 2010, 2012). As local
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meteorology governs the strength of the sources of PM 10 , we include the major meteorological factors in this apportionment, as in Juda-Rezler et al. (2011) and Viana et al. (2006). In order to achieve linearity of the data, wind vectors were resolved into their horizontal and vertical
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The measurements contain missing values. Imputation methods suggest various ways to
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replace the missing values with plausible values. Missing data has been replaced with the mean value of each variable. Mean substitution technique is recommended to use when the missing data is less than 10% of the measurement (Trikriktsis, 2005). The suitability and adequacy of the
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dataset for PCA was verified with the Kaiser-Meyer-Olkin (KMO) test. Taner et al. (2013) suggested that a sample dataset will be adequate and recognizable if the KMO value is greater
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than 0.5. Our KMO value was 0.8 which is consistent with Taner et.al (2013). The datasets of trace gases and meteorological parameters were normalized and subjected to PCA varimax
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rotation. The number of factors was optimized based on the Eigen value (>1). Additionally, PCA was used based on the number of factors and sensitivity of each variable to factor loading during the optimization process.
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components.
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Results and Discussion
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Time series of PM 10 and atmospheric trace gases
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Concentrations of PM 10 and trace gases are summarized in Table 2. The concentrations have
been divided into two categories: (i) weekdays and (ii) weekends. The overall mean
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average is 51.6±25.8 μgm−3. The annual average concentration exceeds the annual PM 10
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concentrations in the Recommended Malaysian Air Quality Guidelines (RMAQG) and in international standards, e.g., the World Health Organization (WHO) air quality guidelines, and the European Union (EU) air quality standards. The recommended air quality guideline set by the Malaysian government is 50 μgm−3 annually. The EU has set a lower value of 40 μgm−3 for
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yearly PM 10 mass (European Commission, 2013). The WHO on the other hand announced stricter recommendations as low as 20 μgm−3 for annual PM 10 to maintain better urban air
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quality (WHO, 2005).
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The overall average concentration of PM 10 on weekdays is higher (4%) than the concentration on weekends. This clearly shows the impact of vehicle numbers on PM 10 concentration. The concentration of ozone remained constant on weekdays and weekends. Ozone precursor
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concentration of the PM 10 for eight years of hourly data is 51.3±28.5 μgm−3 and the annual
concentration shows slight variation during the specified time periods. The degradation of trace gases in the atmosphere is dominated by their reaction with hydroxyl radical (OH). OH radicals are short-lived, and their formation and removal in the atmosphere is balanced. Reaction with ozone, nitric oxide and hydrogen peroxide is the primary pathway of OH production and is
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consumed by a multitude of reaction with trace gases (Hofzumahaus et al., 2009). OH can remove almost all the pollutants, including CO and VOCs. Reaction with CO and VOCs will produce hydroperoxy (HO 2 ) and organic peroxy (RO 2 ) radicals respectively. With NOx present
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in the air, RO 2 reacts with NO to produce HO 2 . OH radical is reformed by a reaction between HO 2 and NO with NO 2 as a by-product which degrades photochemically to produce ozone. Figure 2 depicts the time series of PM 10 concentration and the trace gases. Episodes of high
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concentration of PM 10 are seen at the end of 2005. However, PM 10 high concentration patterns
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do not match the ozone precursor concentration patterns. Ozone precursors are known to affect aerosol concentration indirectly. NO for example is scavenged photochemically, a few minutes after emission (Kemp and Palmgren, 1996). CH 4 and NmHC on the other hand show no clear seasonal variations. These two trace gases are believed to be the O 3 precursor gases (Duan et al.,
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2008) where NmHC participates in the formation of secondary organic aerosols (Cocker III et
PM 10 ).
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al., 2001). Thus, CH 4 and NmHC indirectly affect the variability of aerosol concentration (e.g.
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Local meteorology
As summarized in Table 2, the average temperature was 27.4°C, with a variation between
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concentration of CO are observed during the years 2004-2005. In contrast, episodes of high
20.9°C to 39.0°C. The minimum and maximum relative humidity were 29% and 100% with an average of 84%. Wind speed was on average of 4km/h. The synoptic wind field is shown in Figure 3. The wind over the Malaysian Peninsula generally blows from the northeasterly (NE) direction during November - March (Figure 3) and from the southwesterly (SW) direction June–
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September (Figure 3). The diurnal variations for T, RH, WS, and WDir are shown in Figure 4a. Temperature and relative humidity show an opposite diurnal cycle pattern. Temperature starts to increase from 8:00 local time (LT), and peaks at 14:00. On the other hand, the RH shows
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downward trend from 8:00 LT to 14:00 LT, reaches 90% at 24:00 LT, when the ambient temperature at the lowest. The air was dry and warm in the afternoon.
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The diurnal variations of PM 10 concentrations, the trace gases and meteorological parameters
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are shown in Figure 4a–4j. Initially, the entire database for each of the parameters was re-sorted based on an overall data, weekdays (WD), and weekend (WE), which then were summarized as the 24 h average. Their ranges per day were also extracted. Subgrouping of the database was considered to identify WE effect, if any. The diurnal concentration of PM 10 ranges from 45.7 to
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59.5 μgm−3 with an average mass concentration of 51.3 μgm−3. PM 10 concentration during the WD ranged between 45.6–60.8 μgm-3 with the diurnal average of 52.1 μgm−3. With small
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difference, PM 10 concentration during the WE ranged from 41.5–60.7 μgm−3 with an average of
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49.2 μgm−3 for the diurnal WE mass. The trend shows the highest in 2004, and then decreases gradually in the next eight years. PM 10 concentration shows significant variation during the eight years monitoring period as previously shown in Figure 2. However, the trend of diurnal
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Diurnal patterns of PM 10 and trace gases
distribution for all subgroups (WD, WE, and overall data) of PM 10 concentration shows similar behavior. The concentration of PM 10 from 09:00 to 24:00 LT on the WD is somewhat higher (5– 15%) as compared to the WE. Two distinct lowest hourly concentration were recorded at 07:00 LT and 14:00 LT; followed by a sharp peak at about 09:00 and 24:00 LT. The peak at 9:00 LT is
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caused by the traffic volume frequency during the morning rush hour while the 24:00 peak is due to the temperature inversion. Temperature inversion is a physical process that leads to the variability of PM 10 loading in the
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ambient air (Monks et al., 2009). The stagnant atmospheric conditions from 21:00 LT to 03:00 LT favors the accumulation of PM 10 . At the same time, a temperature inversion and stable
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process normally occurs during stable atmospheric conditions (Galindo et al., 2013). However, the wind speed in general increase substantially from 10:00–17:00 LT (Figure 4a), which
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disperses PM 10 . Similarly, a dilution effect could be observed in the deeper planetary boundary layer (PBL), where having a somewhat higher ambient and surface temperature, as well as a higher wind speed will considerably lower the concentration of aerosols (Watson, 2002;
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Barmpadimos et al., 2011; Chow et al., 2008; Pitz et al., 2003).
O 3 shows a unimodal diurnal pattern (Figure 4e). Around 14:00 LT, O 3 concentration reaches
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its peak, and soon thereafter starts to drop until 24:00 LT. The reactive oxygen (O) formed from
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reaction step 5 (R5) under an extended level of ultraviolet radiation leads to chemical conversion into O 3 in the presence of O 2 and bulk constituent (M) as in step 7 (R7). The reaction in steps 5 and 6 (R5 & R6) are also actively involved in the generation of surface level O 3 (Lin et al., 2011;
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conditions prevail, which eventually stabilize the air mass, and reduce the dispersion. This
Seinfeld and Pandis, 2006). This is known as a key heterogeneous reaction, which takes place in
the middle of the day. A similar change in O 3 with an afternoon maximum and a nighttime minimum were observed in another study in Malaysia (Toh et al., 2013). The diurnal plots of WD and WE coincide, suggesting that reaction step 5 (R5) (Figure 5) is primarily responsible for
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the generation of surface O 3. O 3 actively interferes in the formation of aerosol mass (R12). As seen in Figure 4b, the peak during the morning hours is the most significant, and comes from the rush hour traffic. CO and SO 2 have similar bimodal distributions.
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The mean NO concentration is 20.0 ppbv, with the lowest concentration at 2.9 ppbv, and the highest concentration at 50.3 ppbv. NO is released into the atmosphere from primary emission
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conversion of NO. In this study, the mean concentration of NO 2 is 21.5 ppbv, with lowest and highest concentrations being 12.3 and 32.9 ppbv, respectively. NO 2 increases up to maximum of
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96% during night due to the nighttime temperature inversion. Stable and stagnant conditions during night favor the formation of NH 4 NO 3 , when the relative humidity is high and the temperature is low (Stockwell et al., 2000). The stable forms of NO 3 - and NH 4 + (and in ambient
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aerosols; i.e. NH 4 NO 3 ) form the gaseous phase equilibrium of ammonia (NH 3 ) and nitric acid (HNO 3 ) via reaction step 12 (R12) (Figure 5) (Finlayson-Pitts and Pitts Jr., 2000). The
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conversion of NO 2 produces most particulate phase NO 3 − (Khoder, 2002) which contributes to the increased nighttime PM 10 level as explained by reaction step 2 (R2) (Figure 5).
The
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heterogeneous reaction steps (R2, R4, R5, and R6) in the above explanation are mainly responsible for this conversion (Figure 5). Therefore, the oxides of nitrogen are responsible for generating aerosol mass.
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sources. NO 2 on the other hand is generally formed in the atmosphere through photochemical
Irrespective of the season, the diurnal change in CH 4 concentration shows consistent decrease
from 08:00 LT until 14:00 LT. NmHC concentration shows downward trend after reaching the peak at 09. The lower level of CH 4 and NmHC during middle of the day is due to several
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recognized photochemical reactions. However, there is no clear evidence of the role of CH 4 and NmHC in the formation of PM 10 .
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Trajectory analysis and impact of regional emission The concentration of PM 10 during the SW monsoon is typically high. We constructed a 72-hr
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(Figure 6) using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Rolph, 2013). Moderate Resolution Imaging Spectroradiometer (MODIS) fire data
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representing biomass burning hotspots in the specific area of interest were downloaded from NASA LANCE FIRMS fire archive (http://firms.modaps.eosdis.nasa.-gov/download/) in the range of 8°S to 20°N, and 90°W to 120°E. This has been appended to the graphs of the backward trajectories. By considering the percentage of mean cluster trajectories, the air mass is found to
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originate from regional sources. Most of the trajectories originated from Sumatra, Indonesia. Biomass hot spots are also found densely distributed in the Indonesian region (Figure 6)
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particularly during the south-westerly monsoon. The synoptic wind field shows that wind blows
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from the same region during the south-westerly monsoon (Figure 3).
Source characterizations
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cluster monthly mean backward trajectories at 500 m above sea level and 00:00 hr (UTC)
By means of PCA–APCS, three source fingerprints has been identified when the Eigen value
is set to 1, as shown in Figure 7a. The principal components of the three fingerprints (PC1, PC2,
and PC3) accounted for 42%, 14%, and 8% of the total variance, respectively. In total, 64% of the variance can be explained in terms of the three PC factors identified. PC1 represents the
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major tracers CO, NOx, NO, NO 2 and NmHC. Transportation is the main source of CO emission in Malaysia (Streets et al., 2003; Zhang et al., 2009). Incomplete combustion of fossil fuels is the primary source of CO. The process includes several reactions in the atmospheric gas phase,
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which indirectly affects the formation of aerosol particles, as shown in Figure 5. Similarly, NOx is mainly emitted from combustion sources. Urban road traffic is believed to have contributed to the secondarily generated inorganic aerosol through the release of NOx (Kumar et al., 2008).
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Malaysia (Streets et al., 2003; Zhang et al., 2009). However, the forest and grassland fires also
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emit oxides of nitrogen (NO 2 ) (Carlo et al., 2015). Hence, PC1 can be identified as traffic emissions and contributes most significantly to PM 10 .
PC2 is associated with O 3 , WS, T and RH. Ozone is released into ambient air through
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photochemical reactions, mostly during the day. It is important to distinguish this principal component, as few of the potential meteorological parameters are grouped together. Wind speed,
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T, and RH are significant factors in a wider sense as they govern the transport as well as the transformation of the gas into aerosol particles. Wind speed is inversely proportional to PM 10
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mass, which suggests better ventilation or dispersion of aerosols (Petrakakis et al., 2006; Zoumakis, 1998). Van Poppel et al. (2012) found that the concentration of aerosol particles exponentially decreases with WS. Similarly, lower temperature and higher percentage of relative
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Mostly released from vehicles, NO is the second largest contributor to PM 10 concentration in
humidity affect the variability of PM 10 through the growth of aerosol particles (Hinds, 1999;
Jiun-Horng et al., 2011; Seinfeld and Pandis, 2006). Thus, PC2 is defined as ozone and meteorological factor.
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Wind vector resolved into horizontal and vertical components is the major contributor in PC3 (Figure 7a). Wind direction significantly affects the concentration of aerosol particles in Malaysian region, as the polluted air mass is mostly transported during the NE and SW
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monsoons (Fig 3). In agreement with this factor profile, the results of the mean cluster trajectories indicate that the air masses originate from the Sumatra, Indonesia (Fig 6). Allen et al. (2007) have concluded that the wind direction can be used to find pollutant source and strength.
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concentration of PM 10 . Consequently, PC3 is described as the windblown particulate matter.
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Multiple linear regression analysis (MLRA) was applied to estimate the mass of each PC source contributing to PM 10 expressed as mass over air volume (μgm−3). PM 10 was treated as the dependent variable, and APCS were used as independent variables. PCA scores were re-sampled to absolute scores using APCS analysis (Thurston and Spengler, 1985; Harrison et al., 1996).
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The MLRA shows that the coefficient of determination (R2) is 0.21 (p
