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.
Long-term measurements (2004-2011) of PM10 (particulate matter with an aerodynamic diameter <10 μm) and trace gases (carbon monoxide [CO], ozone [O₃], nitrogen oxide [NO], oxides of nitrogen [NO(x)], nitrogen dioxide [NO₂], sulfur dioxide [SO₂], methane [CH₄], nonmethane hydrocarbon [NMHC]) have been conducted to study the effect of physicochemical factors on the PM10 concentration. In addition, this study includes source apportionment of PM10 in Kuala Lumpur urban environment. An advanced principal component analysis (PCA) technique coupled with absolute principal component scores (APCS) and multiple linear regression (MLR) has been applied. The average annual concentration of PM10 for 8 yr is 51.3 ± 25.8 μg m⁻³, which exceeds the Recommended Malaysian Air Quality Guideline (RMAQG) and international guideline values. Detail analysis shows the dependency of PM10 on the linear changes of the motor vehicles in use and the amount of biomass burning, particularly from Sumatra, Indonesia, during southwesterly monsoon. The main sources of PM10 identified by PCA-APCS-MLR are traffic combustion (28%), ozone coupled with meteorological factors (20%), and wind-blown particles (1%). However, the apportionment procedure left 28.0 μg m⁻³, that is, 51% of PM10 undetermined.
Principal component analysis (PCA) and correlation have been used to study the variability of particle mass and particle number concentrations (PNC) in a tropical semi-urban environment. PNC and mass concentration (diameter in the range of 0.25->32.0 μm) have been measured from 1 February to 26 February 2013 using an in situ Grimm aerosol sampler. We found that the 24-h average total suspended particulates (TSP), particulate matter ≤10 μm (PM10), particulate matter ≤2.5 μm (PM2.5) and particulate matter ≤1 μm (PM1) were 14.37 ± 4.43, 14.11 ± 4.39, 12.53 ± 4.13 and 10.53 ± 3.98 μg m(-3), respectively. PNC in the accumulation mode (<500 nm) was the most abundant (at about 99 %). Five principal components (PCs) resulted from the PCA analysis where PC1 (43.8 % variance) predominates with PNC in the fine and sub-microme tre range. PC2, PC3, PC4 and PC5 explain 16.5, 12.4, 6.0 and 5.6 % of the variance to address the coarse, coarser, accumulation and giant fraction of PNC, respectively. Our particle distribution results show good agreement with the moderate resolution imaging spectroradiometer (MODIS) distribution.
This study aims to determine the concentration of sterols used as biomarkers in the surface microlayer (SML) in estuarine areas of the Selangor River, Malaysia. Samples were collected during different seasons through the use of a rotation drum. The analysis of sterols was performed using gas chromatography equipped with a flame ionisation detector (GC-FID). The results showed that the concentrations of total sterols in the SML ranged from 107.06 to 505.55 ng L(-1). The total sterol concentration was found to be higher in the wet season. Cholesterol was found to be the most abundant sterols component in the SML. The diagnostic ratios of sterols show the influence of natural sources and waste on the contribution of sterols in the SML. Further analysis, using principal component analysis (PCA), showed distinct inputs of sterols derived from human activity (40.58%), terrigenous and plant inputs (22.59%) as well as phytoplankton and marine inputs (17.35%).
This study aimed to determine the spatial distribution of PM2.5 and PM10 collected in four regions (North, Central, South and East Coast) of Peninsular Malaysia during the southwest monsoon. Concurrent measurements of PM2.5 and PM10 were performed using a high volume sampler (HVS) for 24 h (August to September 2018) collecting a total of 104 samples. All samples were then analysed for water soluble inorganic ions (WSII) using ion chromatography, trace metals using inductively coupled plasma-mass spectroscopy (ICP-MS) and polycyclic aromatic hydrocarbon (PAHs) using gas chromatography-mass spectroscopy (GC-MS). The results showed that the highest average PM2.5 concentration during the sampling campaign was in the North region (33.2 ± 5.3 μg m-3) while for PM10 the highest was in the Central region (38.6 ± 7.70 μg m-3). WSII recorded contributions of 22% for PM2.5 and 20% for PM10 mass, with SO42- the most abundant species with average concentrations of 1.83 ± 0.42 μg m-3 (PM2.5) and 2.19 ± 0.27 μg m-3 (PM10). Using a Positive Matrix Factorization (PMF) model, soil fertilizer (23%) was identified as the major source of PM2.5 while industrial activity (25%) was identified as the major source of PM10. Overall, the studied metals had hazard quotients (HQ) value of <1 indicating a very low risk of non-carcinogenic elements while the highest excess lifetime cancer risk (ELCR) was recorded for Cr VI in the South region with values of 8.4E-06 (PM2.5) and 6.6E-05 (PM10). The incremental lifetime cancer risk (ILCR) calculated from the PAH concentrations was within the acceptable range for all regions.
Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O₃), nitrogen dioxide (NO₂), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O₃ measurements due to the lack of a reference instrument for CO and NO₂. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO₂) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.
This study aims to determine the composition of BTEX (benzene, toluene, ethylbenzene and xylene) and assess the risk to health at different sites in Malaysia. Continuous monitoring of BTEX in Kuala Lumpur City Centre, Kuala Terengganu, Kota Kinabalu and Fraser Hill were conducted using Online Gas Chromatograph. For comparison, BTEX at selected hotspot locations were determined by active sampling method using sorbent tubes and Thermal Desorption Gas Chromatography Mass Spectrometry. The hazard quotient (HQ) for non-carcinogenic and the life-time cancer risk (LTCR) of BTEX were calculated using the United States Environmental Protection Agency (USEPA) health risk assessment (HRA) methods. The results showed that the highest total BTEX concentrations using continuous monitoring were recorded in the Kuala Lumpur City Centre (49.56 ± 23.71 μg/m3). Toluene was the most dominant among the BTEX compounds. The average concentrations of benzene ranged from 0.69 ± 0.45 μg/m3 to 6.20 ± 3.51 μg/m3. Measurements using active sampling showed that BTEX concentrations dominated at the roadside (193.11 ± 114.57 μg/m3) in comparison to petrol station (73.08 ± 30.41 μg/m3), petrochemical industry (32.10 ± 13.13 μg/m3) and airport (25.30 ± 6.17 μg/m3). Strong correlations among BTEX compounds (p<0.01, r>0.7) at Kuala Lumpur City Centre showed that BTEX compounds originated from similar sources. The values of HQ at all stations were <1 indicating the non-carcinogenic risk are negligible and do not pose threats to human health. The LTCR value based on benzene inhalation (1.59 × 10-5) at Kuala Lumpur City Centre were between 1 × 10-4 and 1 × 10-5, representing a probable carcinogenic risk.
We present results from the OP3 campaign in Sabah during 2008 that allow us to study the impact of local emission changes over Borneo on atmospheric composition at the regional and wider scale. OP3 constituent data provide an important constraint on model performance. Treatment of boundary layer processes is highlighted as an important area of model uncertainty. Model studies of land-use change confirm earlier work, indicating that further changes to intensive oil palm agriculture in South East Asia, and the tropics in general, could have important impacts on air quality, with the biggest factor being the concomitant changes in NO(x) emissions. With the model scenarios used here, local increases in ozone of around 50 per cent could occur. We also report measurements of short-lived brominated compounds around Sabah suggesting that oceanic (and, especially, coastal) emission sources dominate locally. The concentration of bromine in short-lived halocarbons measured at the surface during OP3 amounted to about 7 ppt, setting an upper limit on the amount of these species that can reach the lower stratosphere.