Physicochemical factors and sources of particulate matter at residential urban environment in Kuala Lumpur

Khan, Firoz; Latif, Mohd Talib; Juneng, Liew; Amil, Norhaniza; Nadzir, Mohd Shahrul Mohd; Hoque, Hossain Mohammed Syedul

doi:10.1080/10962247.2015.1042094

Cited by 32 publications

(24 citation statements)

References 46 publications

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“…A combination of PMF version 5.0 (PMF 5.0) and MLR analysis was employed to determine source apportionment where results of the MLR were used to apportion the PM 2.5 chemical compositions in order to quantify sources. Details of the PMF procedure used in this study are similar to our previous work as discussed in Khan et al (2015b). In brief, two data files were used as an input, i.e.…”

Section: Source Apportionmentmentioning

confidence: 99%

“…During the peak hour of 08:00 to 09:00, 24 016 vehicles were recorded on this road. Previous studies have shown that road traffic can make substantial contributions to particulate mass concentrations in the Klang Valley area (Awang et al, 2000;Afroz et al, 2003;Rahman et al, 2011;Abdullah et al, 2012;Wahid et al, 2013;Ee-Ling et al, 2015;Khan et al, 2015b). NO − 3 and NO − 2 could also possibly come from the secondary aerosol of ammonium nitrate from anthropogenic activities in the surrounding area such as motor vehicle exhaust, industries (petrochemical industry, iron/steel plant, etc.)…”

Section: Factor 4: Mixed Traffic and Industrial (No −mentioning

confidence: 99%

“…A study by Rahman et al (2011) also used similar chemical compositions for the SA analysis of PM 2.5 samples from the Klang Valley, which resulted in five sources: two stroke engine emissions, motor vehicle emissions, smoke/biomass burning, soil and industry. PMF was also effectively applied by Khan et al (2015b) to their polycyclic aromatic hydrocarbon (PAH) data set to characterise the PM 2.5 for the semi-urban area of Bangi, Malaysia. The study revealed three main sources: gasoline combustion, diesel and heavy oil combustion, and natural gas and coal burning.…”

Section: Introductionmentioning

confidence: 99%

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Seasonal variability of PM<sub>2.5</sub> composition and sources in the Klang Valley urban-industrial environment

et al. 2016

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Abstract. This study investigates the fine particulate matter (PM2.5) variability in the Klang Valley urban-industrial environment. In total, 94 daily PM2.5 samples were collected during a 1-year campaign from August 2011 to July 2012. This is the first paper on PM2.5 mass, chemical composition and sources in the tropical environment of Southeast Asia, covering all four seasons (distinguished by the wind flow patterns) including haze events. The samples were analysed for various inorganic components and black carbon (BC). The chemical compositions were statistically analysed and the temporal aerosol pattern (seasonal) was characterised using descriptive analysis, correlation matrices, enrichment factor (EF), stoichiometric analysis and chemical mass closure (CMC). For source apportionment purposes, a combination of positive matrix factorisation (PMF) and multi-linear regression (MLR) was employed. Further, meteorological–gaseous parameters were incorporated into each analysis for improved assessment. In addition, secondary data of total suspended particulate (TSP) and coarse particulate matter (PM10) sampled at the same location and time with this study (collected by Malaysian Meteorological Department) were used for PM ratio assessment. The results showed that PM2.5 mass averaged at 28 ± 18 µg m−3, 2.8-fold higher than the World Health Organisation (WHO) annual guideline. On a daily basis, the PM2.5 mass ranged between 6 and 118 µg m−3 with the daily WHO guideline exceeded 43 % of the time. The north-east (NE) monsoon was the only season with less than 50 % sample exceedance of the daily WHO guideline. On an annual scale, PM2.5 mass correlated positively with temperature (T) and wind speed (WS) but negatively with relative humidity (RH). With the exception of NOx, the gases analysed (CO, NO2, NO and SO2) were found to significantly influence the PM2.5 mass. Seasonal variability unexpectedly showed that rainfall, WS and wind direction (WD) did not significantly correlate with PM2.5 mass. Further analysis on the PM2.5 ∕ PM10, PM2.5 ∕ TSP and PM10 ∕ TSP ratios reveal that meteorological parameters only greatly influenced the coarse particles (particles with an aerodynamic diameter of greater than 2.5 µm) and less so the fine particles at the site. Chemical composition showed that both primary and secondary pollutants of PM2.5 are equally important, albeit with seasonal variability. The CMC components identified were in the decreasing order of (mass contribution) BC > secondary inorganic aerosols (SIA) > dust > trace elements > sea salt > K+. The EF analysis distinguished two groups of trace elements: those with anthropogenic sources (Pb, Se, Zn, Cd, As, Bi, Ba, Cu, Rb, V and Ni) and those with a crustal source (Sr, Mn, Co and Li). The five identified factors resulting from PMF 5.0 were (1) combustion of engine oil, (2) mineral dust, (3) mixed SIA and biomass burning, (4) mixed traffic and industrial and (5) sea salt. Each of these sources had an annual mean contribution of 17, 14, 42, 10 and 17 % respectively. The dominance of each identified source largely varied with changing season and a few factors were in agreement with the CMC, EF and stoichiometric analysis, accordingly. In relation to meteorological–gaseous parameters, PM2.5 sources were influenced by different parameters during different seasons. In addition, two air pollution episodes (HAZE) revealed the influence of local and/or regional sources. Overall, our study clearly suggests that the chemical constituents and sources of PM2.5 were greatly influenced and characterised by meteorological and gaseous parameters which vary greatly with season.

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Section: Source Apportionmentmentioning

confidence: 99%

Section: Factor 4: Mixed Traffic and Industrial (No −mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Seasonal variability of PM<sub>2.5</sub> composition and sources in the Klang Valley urban-industrial environment

et al. 2016

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“…During the southwest monsoon, which is usually associated with the dry season, a high density of biomass fire or wildfires hotspots were reported in the upwind of the sampling location [ Khan et al ., , , , ; Sahani et al ., ]. The dominant boundary‐transported air was from the South Sumatra region.…”

Section: Resultsmentioning

confidence: 99%

Comprehensive assessment of PM_2.5 physicochemical properties during the Southeast Asia dry season (southwest monsoon)

et al. 2016

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A comprehensive assessment of fine particulate matter (PM2.5) compositions during the Southeast Asia dry season is presented. Samples of PM2.5 were collected between 24 June and 14 September 2014 using a high‐volume sampler. Water‐soluble ions, trace species, rare earth elements, and a range of elemental carbon (EC) and organic carbon were analyzed. The characterization and source apportionment of PM2.5 were investigated. The results showed that the 24 h PM2.5 concentration ranged from 6.64 to 68.2 µg m−3. Meteorological driving factors strongly governed the diurnal concentration of aerosol, while the traffic in the morning and evening rush hours coincided with higher levels of CO and NO2. The correlation analysis for non sea‐salt K+‐EC showed that EC is potentially associated with biomass burning events, while the formation of secondary organic carbon had a moderate association with motor vehicle emissions. Positive matrix factorization (PMF) version 5.0 identified the sources of PM2.5: (i) biomass burning coupled with sea salt [I] (7%), (ii) aged sea salt and mixed industrial emissions (5%), (iii) road dust and fuel oil combustion (7%), (iv) coal‐fired combustion (25%), (v) mineral dust (8%), (vi) secondary inorganic aerosol (SIA) coupled with F− (15%), and (vii) motor vehicle emissions coupled with sea salt [II] (24%). Motor vehicle emissions, SIA, and coal‐fired power plant are the predominant sources contributing to PM2.5. The response of the potential source contribution function and Hybrid Single‐Particle Lagrangian Integrated Trajectory backward trajectory model suggest that the outline of source regions were consistent to the sources by PMF 5.0.

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“…For example, emissions from deforestation and land clearing fires in the Sumatra and Kalimantan of Indonesia, triggered a record-breaking smoke-haze episode in Singapore during June 2013 (Velasco and Rastan, 2015). The temporal trend of PM 10 in Kuala Lumpur, Malaysia, is dependent on the amount of biomass burning from Sumatra, Indonesia, during south-westerly monsoons (Khan et al, 2015). In Indochina (the continental portion of SEA), a trajectory analysis showed that the tropospheric ozone column in northern Thailand during dry season is enhanced by biomass burning in surrounding regions, including Myanmar, Laos and India (Sonkaew and Macatangay, 2015).…”

Section: Introductionmentioning

confidence: 99%

Evaluating Spatial and Temporal Variations of Aerosol Optical Depth and Biomass Burning over Southeast Asia Based on Satellite Data Products

Chang

Hsiao

Hwang

2015

Aerosol Air Qual. Res.

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Many severe air quality problems in the major cities of Southeast Asia (SEA) are related to atmospheric aerosols, and these are mainly caused by smoke haze from biomass burning. To better understand the cause and effect relationships for the tempo-spatial distributions of atmospheric aerosols in SEA, a variety of satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) products of aerosol optical depth (AOD), precipitation, burned area (BA) and fire pixel counts (FC, derived from the active fire product) were collected and processed to evaluate the spatial and temporal variations among atmospheric aerosol, climate factors, and biomass burning in SEA during 2002-2011. High AOD zones (HAZs) located in the continental and maritime portion of SEA are identified through hotspot analysis of AOD maps. The peak AOD, BA and FC months are concentrated in the dry seasons of each HAZ. Although BA and FC are mostly identified in Indochina, the HAZ located in maritime SEA has a comparable level of AOD which may be contributed by the fire-related emissions from peatland in Indonesia. Compared to the commonly used fire-effected area dataset (MCD45 product), BA derived from a hybrid approach (MCD64 product) that considers both active fire (AF) and land change information has higher correlation coefficients with AOD in both HAZs. Linear regression models are then developed for the Indochina and the maritime HAZs, to estimate the level of AOD from the MODIS monthly fire datasets. In general the empirical models can better explain the temporal trends of AOD in HAZs by using AF-based products. The links between regional aerosol and local burning in Indochina SEA are relatively complex due to the cross-boundary transport of aerosol from Southern China.

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Physicochemical factors and sources of particulate matter at residential urban environment in Kuala Lumpur

Cited by 32 publications

References 46 publications

Seasonal variability of PM<sub>2.5</sub> composition and sources in the Klang Valley urban-industrial environment

Seasonal variability of PM<sub>2.5</sub> composition and sources in the Klang Valley urban-industrial environment

Comprehensive assessment of PM_2.5 physicochemical properties during the Southeast Asia dry season (southwest monsoon)

Evaluating Spatial and Temporal Variations of Aerosol Optical Depth and Biomass Burning over Southeast Asia Based on Satellite Data Products

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Physicochemical factors and sources of particulate matter at residential urban environment in Kuala Lumpur

Cited by 32 publications

References 46 publications

Seasonal variability of PM&lt;sub&gt;2.5&lt;/sub&gt; composition and sources in the Klang Valley urban-industrial environment

Seasonal variability of PM&lt;sub&gt;2.5&lt;/sub&gt; composition and sources in the Klang Valley urban-industrial environment

Comprehensive assessment of PM2.5 physicochemical properties during the Southeast Asia dry season (southwest monsoon)

Evaluating Spatial and Temporal Variations of Aerosol Optical Depth and Biomass Burning over Southeast Asia Based on Satellite Data Products

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Seasonal variability of PM<sub>2.5</sub> composition and sources in the Klang Valley urban-industrial environment

Seasonal variability of PM<sub>2.5</sub> composition and sources in the Klang Valley urban-industrial environment

Comprehensive assessment of PM_2.5 physicochemical properties during the Southeast Asia dry season (southwest monsoon)