Multivariate analysis of heavy metals concentrations in river estuary

Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data obtained from two rivers in the Penang State of Malaysia for the concentration of heavy metal ions (As, Cr, Cd, Zn, Cu, Pb, and Hg) using a flame atomic absorption spectrometry (F-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometry (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). MANOVA showed a strong significant difference between the two rivers in terms of heavy metal concentrations in water samples. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used four parameters (Zn, Pb, Cd and Cr) affording 100% correct assignations. Results indicated that the two rivers were different in terms of heavy metals concentrations in water, and the major difference was due to the contribution of Zn. A negative correlation was found between discriminate functions (DF) and Cr and As, whereas positive correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metal concentrations. Correlation matrix between the parameters exhibited a strong evidence of mutual dependence of these metals.     

Cluster analysis of inorganic elements in particulate matter in the air environment of an equatorial urban coastal location

Concentrations of nine inorganic elements (Na, Zn, Ca, Fe, Ni, Mn, Cu, Cd and Al) in particulate matter (PM₁₀) in the air of an equatorial urban coastal location during 2009 were studied during summer and winter monsoon seasons using high-volume sampling techniques. Atomic absorption spectrophotometry was used to analyse the samples. The concentrations of most inorganic elements were higher during summer than winter, except for Cu and Zn. The main inorganic elements in PM₁₀ are Na, Zn and Ca. High concentrations of Na and Ca are due to marine aerosols. Analysis of enrichment factors showed that inorganic elements are from non-crustal sources. Cluster analysis identified five clusters in the summer and six in the winter: (1) PM₁₀–Ni, (2) Zn–Na, (3) Fe–Cu–Ca–Cd, (4) Mn and (5) Al for summer; and (1) PM₁₀, (2) Zn, (3) Fe–Ni, (4) Cu–Ca–Na–Cd, (5) Mn and (6) Al for winter. Combining both correlation and cluster analysis, it was found that Fe–Cu–Cd was from industry/vehicle emissions, Zn was from resuspended soil, Mn was from metallurgical processes, Ni was from a nearby power plant and Al was from crustal sources. Inorganic element concentrations could be a good indicator of local sources of PM₁₀.     

Optimal conditions of Al and Fe extraction from laterite soil using D-optimal design

This paper reports pioneering work in identifying an alternative coagulation agent of wastewater treatment, given the availability of commonly used agents are of a higher cost relative to more natural sources, such as soil. The alternative proposed is laterite soil from northern Malaysia because it contains high amounts of Al and Fe, which are well-known coagulants. The soil was grinded and sieved to obtain uniform particle sizes of <250???m. Al and Fe were extracted from the soil. Extraction agents: (1) HCl, (2) NaOH, and (3) HCl?+?NaCl were chosen. It was found that the most effective agent to extract Fe was 5?N HCl while to extract Al was HCl?+?NaCl, 2 and 4?N, respectively. D-optimal design observed that extraction time t, temperature T, and ratio of amount of laterite soil to amount of extractants r, showed a significant effect on Al extraction. In contrast, the combination of factors t and r exhibited insignificant effect on Fe extraction while other factors were significant. The optimum conditions for extraction of both Al and Fe were 90?°C, 40?min, for r?=?1:15, which gave [Fe]?=?1,870?mg/l and [Al]?=?0.17?mg/l and 90?°C, 90?min, for r?=?1:10, which gave [Fe]?=?2,900?mg/l and [Al]?=?0.130?mg/l. Since concentration of Fe extracted from laterite soil was high, it was concluded that laterite soil can be considered as an alternative and novel source of coagulant applicable in a wastewater treatment coagulation process.     

Analysis of heavy metal concentrations in sediments of selected estuaries of Malaysia—a statistical assessment

Statistical analysis of heavy metal concentrations in sediment was studied to understand the interrelationship between different parameters and also to identify probable source component in order to explain the pollution status of selected estuaries. Concentrations of heavy metals (Cu, Zn, Cd, Fe, Pb, Cr, Hg and Mn) were analyzed in sediments from Juru and Jejawi Estuaries in Malaysia with ten sampling points of each estuary. The results of multivariate statistical techniques showed that the two regions have different characteristics in terms of heavy metals selected and indicates that each region receives pollution from different sources. The results also showed that Fe, Mn, Cd, Hg, and Cu are responsible for large spatial variations explaining 51.15% of the total variance, whilst Zn and Pb explain only 18.93 of the total variance. This study illustrates the usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets to get better information about the heavy metal concentrations and design of monitoring network.     

Assessment of surface water quality of selected estuaries of Malaysia: multivariate statistical techniques

Multivariate statistical techniques such as cluster analysis (CA), factor analysis (FA) were used for the evaluation of spatial variations and the interpretation of a large complex water quality data set of two selected estuaries of Malaysia. The two locations of interest with 10 sites in each location were Kuala Juru (Juru estuary) and Bukit Tambun (Jejawi estuary). Cluster analysis showed that some sites in both locations have similar sources of pollution from point or non-point sources whereas FA yielded four factors which are responsible for water quality variations explaining more than 80% of the total variance of the data set and allowed to group the selected water quality. Correlation analysis of the data showed that some parameters have strong association with other parameters and they share a common origin source. This study illustrates the usefulness of multivariate statistical analysis for evaluation and interpretation of complex data sets to get better information about the pollution sources/factors and understanding the behavior of the parameters in water quality for effective river water quality management.