Doreena Dominick

Long term assessment of air quality from a background station on the Malaysian Peninsula

Rural background stations provide insight into seasonal variations in pollutant concentrations and allow for comparisons to be made with stations closer to anthropogenic emissions. In Malaysia, the designated background station is located in Jerantut, Pahang. A fifteen-year data set focusing on ten major air pollutants and four meteorological variables from this station were analysed. Diurnal, monthly and yearly pollutant concentrations were derived from hourly continuous monitoring data. Statistical methods employed included principal component regression (PCR) and sensitivity analysis. Although only one of the yearly concentrations of the pollutants studied exceeded national and World Health Organisation (WHO) guideline standards, namely PM10, seven of the pollutants (NO, NO2, NOx, O3, PM10, THC and CH4) showed a positive upward trend over the 15-year period. High concentrations of PM10 were recorded during severe haze episodes in this region. Whilst, monthly concentrations of most air pollutants, such as: PM10, O3, NOx, NO2, CO and NmHC were recorded at higher concentrations between June and September, during the southwest monsoon. Such results correspond with the mid-range transport of pollutants from more urbanised and industrial areas. Diurnal patterns, rationed between major air pollutants and sensitivity analysis, indicate the influence of local traffic emissions on air quality at the Jerantut background station. Although the pollutant concentrations have not shown a rapid increase, an alternative background station will need to be assigned within the next decade if development projects in the surrounding area are not halted.

Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia

The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.

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-, SO4(2-), 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). SO4(2-), 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 earths crust (28%), sea spray (16%) and a mixture of soil dust and vehicle emissions (14%).

Characterization of rainwater chemical composition after a Southeast Asia haze event: insight of transboundary pollutant transport during the northeast monsoon

Open biomass burning in Peninsula Malaysia, Sumatra, and parts of the Indochinese region is a major source of transboundary haze pollution in the Southeast Asia. To study the influence of haze on rainwater chemistry, a short-term investigation was carried out during the occurrence of a severe haze episode from March to April 2014. Rainwater samples were collected after a prolonged drought and analyzed for heavy metals and major ion concentrations using inductively coupled plasma mass spectroscopy (ICP-MS) and ion chromatography (IC), respectively. The chemical composition and morphology of the solid particulates suspended in rainwater were examined using a scanning electron microscope coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). The dataset was further interpreted using enrichment factors (EF), statistical analysis, and a back trajectory (BT) model to find the possible sources of the particulates and pollutants. The results show a drop in rainwater pH from near neutral (pH 6.54) to acidic (<pH 4.00) during the haze to non-haze transitional period, suggesting that the deposition rate of sulfate and nitrate in the atmosphere via the precipitation process was relatively lower compared to the mineral components. Zinc, nitrate, and calcium, which were the predominant elements in the first rainwater samples. Besides, the results of the SEM-EDS indicated that the zinc was possibly originated from anthropogenic activities which are consistent with the results obtained from EF. The BT model showed that pollutants transported from the mainland of Indo-China and the marine region in the South China Sea were responsible for the high pollution event in the study area. These findings can be useful in identifying contributions of pollutants from single or multiple sources in rainwater samples during haze episodes.

Source Apportionment of Particulate Matter (PM10) and Indoor Dust in a University Building

A study on source apportionment of indoor dust and particulate matter (PM10) composition was conducted in a university building by using chemometrics. The objective of this study was to investigate the potential sources of selected heavy metals and ionic species in PM10 and indoor dust. PM10 samples were collected using a low-volume sampler (LVS) and indoor dust was collected using a soft brush. Inductively coupled plasma spectrometry (ICP-MS) was used to determine the concentration of heavy metals, while the concentration of cations and anions was determined by atomic absorption spectrometer (AAS) and ion chromatography (IC), respectively. The concentration of PM10 recorded in the building throughout the sampling period ranged from 20 ± 10 μgm−3 to 80 ± 33 μgm−3. The composition of heavy metals in PM10 and indoor dust were dominated by zinc (Zn), followed by lead (Pb), copper (Cu), and cadmium (Cd). Principle component analysis (PCA) and multiple linear regression (MLR) showed that the main sources of pollutants in PM10 came from indoor renovations (73.83%), vehicle emissions (16.38%), earth crust sources (9.68%), and other outdoor sources (0.11%). For indoor dust, the pollutant source was mainly earth crust. This study suggests that chemometrics can be used for forensic investigation to determine the possible sources of indoor contaminants within a public building.

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 100ppbv. The hourly average and maximum O3 concentrations of 31 and 92ppbv 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.