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

Characterisation of particle mass and number concentration on the east coast of the Malaysian Peninsula during the northeast monsoon

Particle mass concentrations (PM10, PM2.5 and PM1) and particle number concentration ((PNC); 0.27 μm ≤ Dp ≤ 34.00 μm) were measured in the tropical coastal environment of Bachok, Kelantan on the Malaysian Peninsula bordering the southern edge of the South China Sea. Statistical methods were applied on a three-month hourly data set (9th January to 24th March 2014) to study the influence of north-easterly winds on the patterns of particle mass and PNC size distributions. The 24-h concentrations of particle mass obtained in this study were below the standard values detailed by the Recommended Malaysian Air Quality Guideline (RMAQG), United States Environmental Protection Agency (US EPA) and European Union (EU) except for PM2.5, which recorded a 24-h average of 30 ± 18 μg m-3 and exceeded the World Health Organisation (WHO) threshold value (25 μg m-3). Principal component analysis (PCA) revealed that PNC with smaller diameter sizes (0.27-4.50 μm) showed a stronger influence, accounting for 57.6% of the variability in PNC data set. Concentrations of both particle mass and PNC increased steadily in the morning with a distinct peak observed at around 8.00 h, related to a combination of dispersion of accumulated particles overnight and local traffic. In addition to local anthropogenic, agricultural burning and forest fire activities, long-range transport also affects the study area. Hotspot and backward wind trajectory observations illustrated that the biomass burning episode (around February-March) significantly influenced PNC. Meteorological parameters influenced smaller size particles (i.e. PM1 and Dp (0.27-0.43 μm)) the most

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

Water quality of prawn pond effluent and heavy metals in prawn (Penaeus vannamei)

Water quality and concentration of heavy metals (Pb, Cu, Zn and Cd) were determined in sample of water and prawn parts (head, meat and shell) of (Penaeus vannamei) from three ponds located at Bako, Asajaya and Sempadi. Water from all three locations were high in concentrations of P04 3- and NH3-N. Meanwhile, water from Bako and Sempadi were high in concentrations of NO-2-N. Water from Sempadi have highest concentration of Pb and Cu and lowest concentration of Zn. Water from Asajaya has the lowest concentration of Pb (0.10 mg/L) and Cu (0.02mg/L). Water from Bako has higher concentration of Zn (0.21 mg/L) and Cd (0.10mg/L). P. vannamei grown in Bako have the highest concentration of Cu in the head part (7.47 mg/kg of fresh weight) and the lowest concentration of Cu in meat part (2.45 mg/kg of fresh weight). The highest concentration of Pb in the shell (7.11 mg/kg of fresh weight) and the lowest concentration of Cu in the meat (0.75 mg/kg of fresh weight) were observed from Asajaya farm. Prawns from Sempadi have the highest concentration of Cu in the head (7.8 mg/kg of fresh weight) and the lowest concentration of Cu in the meat (0.95 mg/kg of fresh weight). Level of Pb in prawn are exceeded the value given by Malaysian Food Act this 2.0 mg/kg. There are no significant positive correlations between the metals level in water and prawn part

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

The long-term assessment of air quality on an island in Malaysia

This study aims to evaluate the air quality on Langkawi Island, a famous tourist destination in Malaysia, using 13 years of data (1999-2011) recorded by the Malaysian Department of Environment. Variations of seven air pollutants (O3, CO, NO, NO2, NOx, SO2 and PM10) and three meteorological factors (temperature, humidity and wind speed) were analysed. Statistical methods used to analyse the data included principal component regression (PCR) and sensitivity analysis. The results showed PM10 was the dominant air pollutant in Langkawi and values ranged between 5.0 μg m−3 and 183.2 μg m−3. The patterns of monthly values showed that the concentrations of measured air pollutants on Langkawi were higher during the south-west monsoon (June-September) due to seasonal biomass burning activities. High CO/NOx ratio values (between 28.3 and 43.6), low SO2/NOx ratio values (between 0.04 and 0.12) and NO/NO2 ratio values exceeding 2.2 indicate the source of air pollutants in this area was motor vehicles. PCR analysis grouped the seven variables into two factor components: the F1 component consisted of SO2, NO and NOx and the F2 component consisted of PM10. The F1 component (R2 = 0.931) indicated a stronger standardized coefficient value for meteorological variables compared to the F2 component (R2 = 0.059). The meteorological variables were statistically significant (p \u3c 0.05) in influencing the distribution of the air pollutants. The status of air quality on the island could be improved through control on motor vehicle emissions as well as collaborative efforts to reduce regional air pollution, especially from biomass burning