Seasonal and long term variations of surface ozone concentrations in Malaysian Borneo

Malaysian Borneo has a lower population density and is an area known for its lush rainforests. However, changes in pollutant profiles are expected due to increasing urbanisation and commercial-industrial activities. This study aims to determine the variation of surface {O3} concentration recorded at seven selected stations in Malaysian Borneo. Hourly surface {O3} data covering the period 2002 to 2013, obtained from the Malaysian Department of Environment (DOE), were analysed using statistical methods. The results show that the concentrations of {O3} recorded in Malaysian Borneo during the study period were below the maximum Malaysian Air Quality Standard of 100 ppbv. The hourly average and maximum {O3} concentrations of 31 and 92 ppbv reported at Bintulu (S3) respectively were the highest among the {O3} concentrations recorded at the sampling stations. Further investigation on {O3} precursors show that sampling sites located near to local petrochemical industrial activities, such as Bintulu (S3) and Miri (S4), have higher NO2/NO ratios (between 3.21 and 5.67) compared to other stations. The normalised {O3} values recorded at all stations were higher during the weekend compared to weekdays (unlike its precursors) which suggests the influence of {O3} titration by {NO} during weekdays. The results also show that there are distinct seasonal variations in {O3} across Borneo. High surface {O3} concentrations were usually observed between August and September at all stations with the exception of station {S7} on the east coast. Majority of the stations (except {S1} and S6) have recorded increasing averaged maximum concentrations of surface {O3} over the analysed years. Increasing trends of {NO2} and decreasing trends of {NO} influence the yearly averaged maximum of {O3} especially at S3. This study also shows that variations of meteorological factors such as wind speed and direction, humidity and temperature influence the concentration of surface O3

Characteristics of Aerosol Particles: Concentration, Particle Size and Formation Mechanisms in Urban-Marine Environments

Atmospheric particle properties were measured in the South Eastern coastal city of Wollongong, Australia, during an intensive field campaign known as Measurement of Urban, Marine and Biogenic Air (MUMBA), between 15th January and 16th February 2013. A scanning mobility particle sizer (SMPS) was operated to measure particle number size distributions ranging from 14 nm to 660 nm in diameter. Principal component analysis was applied to the entire data measured by SMPS and, based on strong component loadings (value \u3e 0.75), three size fractions (i) Small (NS) :15 nm \u3c Dp \u3c 50 nm, (ii) Medium (NM) :60 nm \u3c Dp \u3c 150 nm and (iii) Large (NL) :210 nm \u3c Dp \u3c 450 nm were revealed. The three size fractions described 89% of the dataset cumulative variance. The daily pattern of particle number size distribution revealed morning, afternoon and night peaks. Traffic emissions and marine aerosols were the major contributors of particles observed in the morning, when the NS fraction dominated. A mixture of marine aerosols and secondary aerosols from photochemical oxidation was the main contributor during the afternoon. The Port Kembla Steel Works and the urban areas were the major contributors of particles at night. Secondary organic aerosols were identfied by a mass ratio of organic carbon to elemental carbon (OC/EC) of greater than 1, and this was commonly observed. A weak correlation (R2 = 0.3) between OC and EC indicated that there were multiple sources of both OC and EC

Spatial distribution and source apportionment of air pollution in Malaysia through environmetric techniques

This research involves the analyses of secondary air quality data collected at twelve monitoring stations in Malaysia between 2001 and 2009. Several environmetric techniques were applied on this nine-year daily average database. The environmetric techniques incorporated discriminant analysis (DA) to investigate the significant discriminating air quality variables, hierarchical agglomerative cluster analysis (HACA) to access the spatial air quality patterns and principal component analysis (PCA) to determine the probable sources of air pollutants. The artificial neural network (ANN) analysis used to determine the air quality model structure as well as the most significant variable that influenced the Air Pollutant Index (API). A combined receptor model (PCA/MLR) was then used is to assess the source apportionment of the significant variables. The DA computed nine significant variables to discriminate the five levels of air quality. HACA grouped the twelve air monitoring stations into three different clusters. The PCA results showed that the probable sources of air pollution within the study areas were combustion of fuels in all modes of transportation, offshore oil installation, agriculture operations, combustion of wood and industrial activities. For the overall air quality spatial assessment, ANN produced the best fit model with high R2 values (0.803 ≤ R2 ≤0.807, p<0.05). It also revealed that more than 80% of the air quality variability is explained by the nine significant variables (CO, O3, PM10, NO2, SO2, temperature,humidity and wind speed). Further, the ANN analysis showed that among the nine significant variables, PM10 was the most important variable that influenced the API value variation. In addition, the combined receptor model (PCA/MLR) showed that in all three clusters, more than 70% of the API values were influenced by ozone, O3 (secondary gas pollutant) and particulate matter with diameter of less than 10 micrometers, PM10 (non-gas air pollutants). The research verifies that environmetric techniques are highly viable and effective for analyzing large amounts of complex data to glean vital knowledge about air quality, especially the behavior characteristics of specific air pollutants and air pollution patterns. This knowledge can be employed as decision tools for policy makers in planning for more effective air quality monitoring programs

Composition and source apportionment of dust fall around a natural lake

The aim of this study was to determine the source apportionment of dust fall around Lake Chini, Malaysia. Samples were collected monthly between December 2012 and March 2013 at seven sampling stations located around Lake Chini. The samples were filtered to separate the dissolved and undissolved solids. The ionic compositions (NO3−, SO42 −, Cl− and NH4+) were determined using ion chromatography (IC) while major elements (K, Na, Ca and Mg) and trace metals (Zn, Fe, Al, Ni, Mn, Cr, Pb and Cd) were determined using inductively coupled plasma mass spectrometry (ICP-MS). The results showed that the average concentration of total solids around Lake Chini was 93.49 ± 16.16 mg/(m2·day). SO42 −, Na and Zn dominated the dissolved portion of the dust fall. The enrichment factors (EF) revealed that the source of the trace metals and major elements in the rain water was anthropogenic, except for Fe. Hierarchical agglomerative cluster analysis (HACA) classified the seven monitoring stations and 16 variables into five groups and three groups respectively. A coupled receptor model, principal component analysis multiple linear regression (PCA-MLR), revealed that the sources of dust fall in Lake Chini were dominated by agricultural and biomass burning (42%), followed by the earth\u27s crust (28%), sea spray (16%) and a mixture of soil dust and vehicle emissions (14%)

Characteristics of airborne particle number size distributions in a coastal-urban environment

Particle number size distributions are among the most important parameters in trying to understand the characteristics of particle population. Atmospheric particles were measured in an interaction of mixed environments in the Southeastern coastal city of Wollongong, Australia, during a comprehensive field campaign known as Measurements of Urban, Marine and Biogenic Air (MUMBA). MUMBA ran in summer season between 21 st December 2012 and 15 th February 2013. Particle number concentrations measured during this campaign were indicative of the interplay between marine environments and urban air which met the objective of this campaign. Particle number size distributions ranging from 14 nm to 660 nm in diameter, as measured by Scanning Mobility Particle Sizer (SMPS) in this study, were grouped using Principal Component Analysis. Based on strong component loadings (value ≥ 0.75), three different factors were identified (i) Small Factor (N S ): 15 nm \u3c Dp \u3c 50 nm, (ii) Medium Factor (N M ): 60 nm \u3c Dp \u3c 150 nm and (iii) Large Factor (N L ): 210 nm \u3c Dp \u3c 450 nm. The three factors describe 89% of the dataset cumulative variance. Particles in this region are dependent upon the interaction between the sources, and cannot be viewed as a simple mixture of biogenic and anthropogenic sources associated with various mechanical processes. The particles observed in the morning were found to be influenced by combustion emissions, presumably primarily from traffic, which is most obvious in N L . The particle population during the day was found to be influenced by a mixture of marine sources and secondary aerosols production initiated by photochemical oxidation. The local steel works and the urban environment were the major contributors of particles at night. Secondary organic aerosols were identified in this study by the mass ratio of organic carbon to elemental carbon (OC/EC). Biogenic sources influenced secondary organic aerosols formation as a moderate correlation (R 2 = 0.6) was observed between secondary organic aerosols mass and biogenic isoprene. The processes described in this paper are likely repeated at other coastal urban environments worldwide