The health implications of PM_2.5 in the tropical region of Southeast Asia (SEA) are significant as PM_2.5 can pose serious health concerns. PM_2.5 concentration and sources here are strongly influenced by changes in the monsoon regime from the south-west quadrant to the north-east quadrant in the region. In this work, PM_2.5 samples were collected at a semi-urban area using a high-volume air sampler at different seasons on 24 h basis. Analysis of trace elements and water-soluble ions was performed using inductively coupled plasma mass spectroscopy (ICP-MS) and ion chromatography (IC), respectively. Apportionment analysis of PM_2.5 was carried out using the United States Environmental Protection Agency (US EPA) positive matrix factorization (PMF) 5.0 and a mass closure model. We quantitatively characterized the health risks posed to human populations through the inhalation of selected heavy metals in PM_2.5. 48 % of the samples collected exceeded the World Health Organization (WHO) 24 h PM_2.5 guideline but only 19 % of the samples exceeded 24 h US EPA National Ambient Air Quality Standard (NAAQS). The PM_2.5 concentration was slightly higher during the north-east monsoon compared to south-west monsoon. The main trace metals identified were As, Pb, Cd, Ni, Mn, V, and Cr while the main ions were SO₄²⁻, NO₃⁻, NH₄⁺, and Na. The mass closure model identified four major sources of PM_2.5 that account for 55 % of total mass balance. The four sources are mineral matter (MIN) (35 %), secondary inorganic aerosol (SIA) (11 %), sea salt (SS) (7 %), and trace elements (TE) (2 %). PMF 5.0 elucidated five potential sources: motor vehicle emissions coupled with biomass burning (31 %) were the most dominant, followed by marine/sulfate aerosol (20 %), coal burning (19 %), nitrate aerosol (17 %), and mineral/road dust (13 %). The hazard quotient (HQ) for four selected metals (Pb, As, Cd, and Ni) in PM_2.5 mass was highest in PM_2.5 mass from the coal burning source and least in PM_2.5 mass originating from the mineral/road dust source. The main carcinogenic heavy metal of concern to health at the current location was As; the other heavy metals (Ni, Pb, and Cd) did not pose a significant cancer risk in PM_2.5 mass concentration. Overall, the associated lifetime cancer risk posed by the exposure of hazardous metals in PM_2.5 is 3–4 per 1 000 000 people at this location.

This study investigates the fine particulate matter (PM_2.5) variability in the Klang Valley urban-industrial environment. In total, 94 daily PM_2.5 samples were collected during a 1-year campaign from August 2011 to July 2012. This is the first paper on PM_2.5 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 PM_2.5 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 PM_2.5 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, PM_2.5 mass correlated positively with temperature (T) and wind speed (WS) but negatively with relative humidity (RH). With the exception of NO_x, the gases analysed (CO, NO₂, NO and SO₂) were found to significantly influence the PM_2.5 mass. Seasonal variability unexpectedly showed that rainfall, WS and wind direction (WD) did not significantly correlate with PM_2.5 mass. Further analysis on the PM_2.5 ∕ PM₁₀, PM_2.5 ∕ 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 PM_2.5 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, PM_2.5 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 PM_2.5 were greatly influenced and characterised by meteorological and gaseous parameters which vary greatly with season.

In this study, we quantified carbonaceous PM_2.5 in Malaysia through annual observations of PM_2.5, focusing on organic compounds derived from biomass burning. We determined organic carbon (OC), elemental carbon and concentrations of solvent-extractable organic compounds (biomarkers derived from biomass burning sources and n-alkanes). We observed seasonal variations in the concentrations of pyrolyzed OC (OP), levoglucosan (LG), mannosan (MN), galactosan, syringaldehyde, vanillic acid (VA) and cholesterol. The average concentrations of OP, LG, MN, galactosan, VA and cholesterol were higher during the southwestern monsoon season (June–September) than during the northeastern monsoon season (December–March), and these differences were statistically significant. Conversely, the syringaldehyde concentration during the southwestern monsoon season was lower. The PM_2.5 OP / OC4 mass ratio allowed distinguishing the seven samples, which have been affected by the Indonesian peatland fires (IPFs). In addition, we observed significant differences in the concentrations between the Indonesian peatland fire (IPF) and other samples of many chemical species. Thus, the chemical characteristics of PM_2.5 in Malaysia appeared to be significantly influenced by IPFs during the southwestern monsoon season. Furthermore, we evaluated two indicators, the vanillic acid / syringic acid (VA / SA) and LG / MN mass ratios, which have been suggested as indicators of IPFs. The LG / MN mass ratio ranged from 14 to 22 in the IPF samples and from 11 to 31 in the other samples. Thus, the respective variation ranges partially overlapped. Consequently, this ratio did not satisfactorily reflect the effects of IPFs in Malaysia. In contrast, the VA / SA mass ratio may serve as a good indicator, since it significantly differed between the IPF and other samples. However, the OP / OC4 mass ratio provided more remarkable differences than the VA / SA mass ratio, offering an even better indicator. Finally, we extracted biomass burning emissions' sources such as IPF, softwood/hardwood burning and meat cooking through varimax-rotated principal component analysis.