Assessment of the spatial variation and source apportionment of air pollution based on chemometric techniques: a case study in the peninsular malaysia
20 Oct 2015-Vol. 77, Iss: 1, pp 33-34
TL;DR: In this paper, the authors investigate the spatial variation in the source of air pollution, identify the percentage contribution of each pollutant and apportion the mass contribution of source categories using chemometric techniques.
Abstract: This study aims to investigate the spatial variation in the source of air pollution, identify the percentage contribution of each pollutant and apportion the mass contribution of each source category using chemometric techniques. Hierarchical agglomerative cluster analysis (HACA) successfully grouped the five air monitoring sites into three groups (cluster 1, 2 and 3). Principal component analysis (PCA) was used to spot out the sources of air pollution which are attributed to anthropogenic activities. Multiple linear regression (MLR) was used to develop an equation model that explains the contribution of pollutants in each cluster. However, it was observed that particulate matter (PM 10 ) and Ozone (O 3 ) are the most significant pollutants influencing the value of air pollutant index (API). Meanwhile, the source apportionment indicates that cluster 1 is influenced by gas and non-gas pollutants to a degree of 84%, weather condition 15% and 1% by gas and secondary pollutants. Cluster 2 is affected by gas and secondary pollutants to a tune of 87% and 13% by weather condition while cluster 3 is apportioned with 98% secondary gas and non-gas pollutants and 2% weather condition. This study reveals the usefulness of chemometric technique in modeling and reducing the cost and time of monitoring redundant stations and parameters.
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TL;DR: It is suggested that ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model as compared to the other modelling methods.
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TL;DR: A comprehensive review of relevant scientific journals concerning on the major environmental issues in Malaysia, published between 2013 and 2017, suggested that chemometrics techniques have a greater accuracy, flexibility and efficiency to be applied in environmental modelling.
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TL;DR: In this article, the spatial-temporal relationship of particulate matter (PM10), to determine the characteristic of each location and to classify hierarchical of the location in relation to their impact on PM10 concentration in Klang Valley.
Abstract: The urbanization in Klang Valley, Peninsular Malaysia over the last decades has induce the atmospheric pollution’s risk resulted to negative impact on the environment. The aims of this paper are to identify the spatial-temporal relationship of particulate matter (PM10), to determine the characteristic of each location and to classify hierarchical of the location in relation to their impact on PM10 concentration in Klang Valley. The Spearman correlation test indicate that there was strong significant relationship between all the locations (> 0.7; p < 0.001) and moderate relationship between Petaling Jaya-Kajang and Kajang-Shah Alam (< 0.7; p < 0.001). The principal component analysis (PCA) identifies all four locations have been affected by PM10 which were determined as one of the pollutant that deteriorated the air quality. Cluster analysis (CA) has classified the PM10 pattern into three (3) different classes; Class 1 (Klang), Class 2 (Petaling Jaya and Kajang) and Class 3 (Shah Alam) based on location. Further analysis of CA would be able to classify the PM10 classes into groups depending on their dissimilarities characteristic. Thus, possible period of extreme air quality degradation could be identified. Therefore, statistical and envirometric techniques have proved the impact of the various location on increasing concentration of PM10.
4 citations
Cites background or methods from "Assessment of the spatial variation..."
...XLSTAT software has been used as a tool because of its flexibility, multidimensionality and ability to synthesize complex data sets [15]....
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...KMO test shows the multicolinearity or in simple words, identification of similarity in correlation value exists between two or more items [15]....
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TL;DR: In this paper, the authors presented the application of selected environmetric in the Perlis River Basin, which showed that PCA extracted nine principal components with eigenvalues greater than one, which equates to about 77.15% of the total variance in the water quality data set.
Abstract: This study presents the application of selected environmetric in the Perlis River Basin. The results show PCA extracted nine principal components (PCs) with eigenvalues greater than one, which equates to about 77.15% of the total variance in the water-quality data set. The absolute principal component scores (APCS)-MLR model discovered BOD and COD as the main parameters, which indicates the measure of the agricultural pollution in the Perlis River Basin, the hierarchical agglomerative cluster analysis (HACA) shows 11 monitoring stations assembled into two clusters in accordance with similarities in the concentration of BOD and COD, which are grouped in P4. The X control chart shows that the mean concentration of BOD and COD in P4 is in the control process. The capability ratio (Cp) was applied to measure the risk of the concentration in terms of the river pollution in a subsequent period of time using the limit NWQS.
3 citations
TL;DR: The spatially classify the variation of Melaleuca cajuputi essential oil fingerprint based on different sampling location using chemometric technique along Terengganu coastal area is performed.
Abstract: Cajuputi essential oil is extracted from the leaves of Melaleuca cajuputi Powell. This study is performed to spatially classify the variation of Melaleuca cajuputi essential oil fingerprint based on different sampling location using chemometric technique along Terengganu coastal area. Discriminant Analysis (DA) successfully discriminate 10 fingerprint of essential oil into three different groups with three significant peaks in FTIR analysis. Hierarchical agglomerative cluster analysis (HACA) successfully grouped the 10 sampling stations into three groups (cluster A, B and C).Classification criteria is based on the intensity movement of functional group either bending or stretching of the essential oil compound Multiple linear regression (MLR) was used to develop an equation model that explains the prediction of species fingerprint in each cluster by different locations.
2 citations
Cites background from "Assessment of the spatial variation..."
...Through FTIR interpretation, spectroscopic range number from 3000-2840 cm-1 represent bending of methylene group and significant peak number at 2981 cm-1 assigned that there is medium intensity bending of methylene group inside the essential oil compound [43]....
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References
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TL;DR: Simple linear regression Multiple linear regression Regression Diagnostics: Detection of Model Violations Qualitative Variables as Predictors Transformation of Variables Weighted Least Squares The Problem of Correlated Errors Analysis of Collinear Data Biased Estimation of Regression Coefficients Variable Selection Procedures Logistic Regression Appendix References as discussed by the authors
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TL;DR: The purpose of this statement is to provide healthcare professionals and regulatory agencies with a comprehensive review of the literature on air pollution and cardiovascular disease and practical recommendations for healthcare providers and their patients are outlined.
Abstract: Air pollution is a heterogeneous, complex mixture of gases, liquids, and particulate matter. Epidemiological studies have demonstrated a consistent increased risk for cardiovascular events in relation to both short- and long-term exposure to present-day concentrations of ambient particulate matter. Several plausible mechanistic pathways have been described, including enhanced coagulation/thrombosis, a propensity for arrhythmias, acute arterial vasoconstriction, systemic inflammatory responses, and the chronic promotion of atherosclerosis. The purpose of this statement is to provide healthcare professionals and regulatory agencies with a comprehensive review of the literature on air pollution and cardiovascular disease. In addition, the implications of these findings in relation to public health and regulatory policies are addressed. Practical recommendations for healthcare providers and their patients are outlined. In the final section, suggestions for future research are made to address a number of remaining scientific questions.
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TL;DR: This study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in waterquality for effective river water quality management.
Abstract: Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were applied for the evaluation of temporal/spatial variations and the interpretation of a large complex water quality data set of the Fuji river basin, generated during 8 years (1995–2002) monitoring of 12 parameters at 13 different sites (14 976 observations). Hierarchical cluster analysis grouped 13 sampling sites into three clusters, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites, based on the similarity of water quality characteristics. Factor analysis/principal component analysis, applied to the data sets of the three different groups obtained from cluster analysis, resulted in five, five and three latent factors explaining 73.18, 77.61 and 65.39% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to discharge and temperature (natural), organic pollution (point source: domestic wastewater) in relatively less polluted areas; organic pollution (point source: domestic wastewater) and nutrients (non-point sources: agriculture and orchard plantations) in medium polluted areas; and organic pollution and nutrients (point sources: domestic wastewater, wastewater treatment plants and industries) in highly polluted areas in the basin. Discriminant analysis gave the best results for both spatial and temporal analysis. It provided an important data reduction as it uses only six parameters (discharge, temperature, dissolved oxygen, biochemical oxygen demand, electrical conductivity and nitrate nitrogen), affording more than 85% correct assignations in temporal analysis, and seven parameters (discharge, temperature, biochemical oxygen demand, pH, electrical conductivity, nitrate nitrogen and ammonical nitrogen), affording more than 81% correct assignations in spatial analysis, of three different sampling sites of the basin. Therefore, DA allowed a reduction in the dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management.
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TL;DR: The over-extraction of groundwater is the major cause of groundwater salinization and arsenic pollution in the coastal area of Yun-Lin, Taiwan and this model explains over 77.8% of the total groundwater quality variation.
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