Source Apportionment of Air Pollution: A Case Study In Malaysia
29 Dec 2014-Vol. 72, Iss: 1, pp 83-88
TL;DR: In this article, the Principal Component Analysis (PCA) method from chemometric technique was applied to identify the source identification of pollution around the study area, which has identified methane (CH 4 ), non-methane hydrocarbon (NmHC), total hydrocarbons (THC), ozone (O 3 ), and particulate matter under 10 microns (PM 10 ) are the most significant parameters around the area.
Abstract: Air pollution is becoming a major environmental issue in Malaysia. This study focused on the identification of potential sources of variations in air quality around the study area based on the data obtained from the Malaysian Department of Environment (DOE). Eight air quality parameters in ten monitoring stations for seven years (2006 – 2012) were gathered. The Principal Component Analysis (PCA) method from chemometric technique was applied to identify the source identification of pollution around the study area. The PCA method has identified methane (CH 4 ), non-methane hydrocarbon (NmHC), total hydrocarbon (THC), ozone (O 3 ) and particulate matter under 10 microns (PM 10 ) are the most significant parameters around the study area. From the study, it can be concluded that the application of the PCA method in chemometric techniques can be applied for the source apportionment purpose. Hence, this study indicated that for the future and effective management of the Malaysian air quality, an effort should be placed as a priority in controlling point and non-point pollution sources.
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TL;DR: In this paper, the authors assessed the heavy metal (Pb, Zn, Ni, Fe, Mn, Cr, Cd, As, Co and Cu) concentrations in PM10 in Agra, India.
Abstract: This study assesses the heavy metal (Pb, Zn, Ni, Fe, Mn, Cr, Cd, As, Co and Cu) concentrations in PM10 in Agra, India. The analysis of heavy metals was carried out by Inductively Coupled Plasma-Mass Spectrometer (ICP-MS) after digestion of quartz fiber filter paper. The annual mean concentration for PM10 was 214.61 μg m−3 and higher than WHO and NAAQS limit. The results showed mean value of heavy metals followed the order: Fe > Zn > Cr > Mn > Pb > Ni > Cu > Co > As>Cd. The carcinogenic risks of As, Cd, Co, Cr, Pb and Ni for both children and adults via dermal contact and ingestion exposure were within the acceptable level (
46 citations
TL;DR: In this paper, a study of air pollution trend analysis in Malaysia from 2010 to 2015 was performed with the objective of determining the API trend in Malaysia, where 19,872 datasets for all Malaysian air quality monitoring stations that had API value greater than 100 and a total of 52,584 datasets for Muar District in Johor were used.
Abstract: Air pollution index (API) is used in Malaysia to determine the level of air quality. API is based on the calculation consist of pollutants PM10, O3, CO2, SO2, and NO2. Unhealthy air quality can harm human health and the environment as well as property. In view of this fact, a study of air pollution trend analysis in Malaysia from 2010 to 2015 was performed with the objective of determining the API trend in Malaysia from 2010 to 2015. A dataset of API value was obtained from the Air Quality Division, Department of Environment Malaysia (DOE). In this study, 19,872 datasets for all Malaysian air quality monitoring stations that had API value greater than 100 and a total of 52,584 datasets for Muar District in Johor were used. XLSTAT add-in 2014 was used to analyze the API hourly reading. Analysis shows that the air monitoring station at Sekolah Menengah Teknik Muar in Johor shows the highest value of API reading with 663 on 23 June 2013 (emergency level), where on that day Malaysia faced its worst air quality due to haze episodes. Other locations also show the worst air quality with API registering at unhealthy, very unhealthy, and hazardous levels.
33 citations
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...Besides, the study done by [14] shows that parameters such as O3 and PM10 are responsible for air quality variations....
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TL;DR: In this paper, the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA), and multiple linear regressions (MLR) for assessing the air quality data and air pollution sources pattern recognition were applied.
Abstract: This study intends to show the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA) and multiple linear regressions (MLR) for assessing the air quality data and air pollution sources pattern recognition. The data sets of air quality for 12 months (January–December) in 2007, consisting of 14 stations around Peninsular Malaysia with 14 parameters (168 datasets) were applied. Three significant clusters - low pollution source (LPS) region, moderate pollution source (MPS) region, and slightly high pollution source (SHPS) region were generated via HACA. Forward stepwise of DA managed to discriminate 8 variables, whereas backward stepwise of DA managed to discriminate 9 out of 14 variables. The method of PCA and FA has identified 8 pollutants in LPS and SHPS respectively, as well as 11 pollutants in MPS region, where most of the pollutants are expected derived from industrial activities, transportation and agriculture systems. Four MLR models show that PM10 categorize as the primary pollutant in Malaysia. From the study, it can be stipulated that the application of chemometric techniques can disclose meaningful information on the spatial variability of a large and complex air quality data. A clearer review about the air quality and a novel design of air quality monitoring network for better management of air pollution can be achieved.
32 citations
TL;DR: In this article, the theory of particle number concentration (PNC) with PM2.5, also the health effects and origins of the emissions were investigated, and the results indicated that metals in PM 2.5 emission are frequently Pb, Se, Zn, Cd, As, Bi, Ba, Cu, Rb, V, Ni, Fe, Ca, Mn, Cr, Al, Si and K.
Abstract: Air pollution is a worldwide issue that is mainly caused from excessive inhalation of hazardous PM2.5 pollutant that is emitted into the air. The objective of this study is to assess the fundamental knowledge revolving PM2.5 (particles aerodynamic diameter of lower than or equal to 2.5 μm) and its inorganic composition in ambient air of urban areas, mainly in Malaysia in comparison to other Southeast Asia countries. This research also investigates the theory of particle number concentration (PNC) with PM2.5, also the health effects and origins of the emissions. The factors affecting the PM2.5 mass include the local emission, El Nino phenomenon, land, meteorological effects, monsoons, rainfall events, sea breeze, transboundary pollution and seasonal changes. 24 h mean PM2.5 mass concentration for metropolitan regions in the SEA is in the range of 11 μgm−3 and 72.3 μgm−3, while between 5.30 μgm−3 and 55.89 μgm−3 for semi-urban zones. For rural area, the 24 h mean PM2.5 value is about 30 μgm−3. The findings indicate that metals in PM2.5 emission are frequently Pb, Se, Zn, Cd, As, Bi, Ba, Cu, Rb, V, Ni, Fe, Ca, Mn, Cr, Al, Si and K, where Zn has the uppermost range of 133.50 to 419.30 ngm−3 while the major water-soluble ions exist are NH4+, K+, Ca2+, Na+, in which Na+, NH4+ and Cl- are present in aged sea salt and mixed industrial, Ca2+ and Mg2+ present in mineral dust, NH4+, K+ and SO42− present in mixture of SIA and biomass burning. There is a high correlation between the particle mass concentration and PNC level, especially the ones in accumulation mode (PNC0.1–1.0) which are mostly originated from the emission of heavy traffic streets.
22 citations
Cites background from "Source Apportionment of Air Polluti..."
...pollution control is important to prevent the hazard to continuously happening and worsening the ambient air in the long run (Azid et al. 2015)....
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TL;DR: In this paper, an overview of the quality of water in the Gomti River through water quality index (WQI) and multivariate statistical techniques to check if WQI is enough for a nutrient-polluted river in the urban stretch.
Abstract: Most of the tropical rivers of the world are being affected by multiple sources of pollution. The intensity of pollution is much bigger in the urban stretches due to discharge of untreated or partially treated sewage. A rapid and cost-effective tool is required for identification of water quality problems and their spatial variation for determining the main pollution sources and to detect relationships between various parameters. For this study, Gomti River, a major tributary of River Ganges, India, was considered which has gained substantial attention because of increasing anthropogenic pollution loads that has badly affected its water quality and ecosystem functions. The urban segment is polluted with organic substances, nutrients and heavy metals. The study provides an overview of the quality of water in the Gomti River through water quality index (WQI) and multivariate statistical techniques to check if WQI is enough for a nutrient-polluted river in the urban stretch. The study suggests that periodic monitoring and the water quality index development are not enough as it does not incorporate all the aspect of a rivers water quality. The separate assessment of nitrogenous biochemical oxygen demand, carbonaceous biochemical oxygen demand, sediment oxygen demand and the nitrification inhibition aspects are required to be integrated when developing a WQI. Present study illustrates that water quality of Gomti River has gradually worsened from upstream and downstream to middle stretch. The middle stretch was found to be most polluted as the major drains are concentrated within this stretch. Principal component analysis/factor analysis (PCA/FA) helped in obtaining and recognizing the factors/sources accountable for river water quality differences in the study area. The findings are useful for the decisions regarding water quality management and this can also be applied for speedy and low-cost assessment of water quality of the polluted urban stretch of other tropical rivers for better environmental management and planning perspective.
20 citations
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