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

Abstract

Concentrations of nine inorganic elements (Na, Zn, Ca, Fe, Ni, Mn, Cu, Cd and Al) in particulate matter (PM 10 ) 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 PM 10 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) PM 10 -Ni, (2) Zn-Na, (3) Fe-Cu-Ca-Cd, (4) Mn and (5) Al for summer; and (1) PM 10 , (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 PM 10 .

Source Identification of Heavy Metals in Particulate Matter (PM10) in a Malaysian Traffic Area Using Multivariate Techniques

“…The results also exposed significant differences among sites (p<0.05) for pM 10 . The results were consistent with the findings of [9,13], where pM 10 concentrations were higher during the southwest monsoon. This is because high density has a higher traffic load and is characterized by movement of heavy-duty vehicles where great number of particulates are produced and pollute the atmosphere.…”

“…commonly increased the concentrations of heavy metals during the southwest monsoon [13] . Multivariate statistical analysis was applied for eight heavy metals for different traffic densities (high, medium, and low) utilizing pCA and CA to evaluate potential contributing sources of heavy metals in the study area which are considered of the most prevalent and useful statistical methods for uncovering the potential structure of a set of variables [12].…”

“…The aim of CA is to determine a systematic organization of the variables based on shared common characteristics of groups/clusters. In this way it cognitively facilitates the prediction of common properties on the basis of overall group membership [13]. Cluster analysis was applied to heavy metals in pM 10 from an air sampling standardized matrix of samples by means of the complete linkage method, and the outcomes were recorded as a dendrogram.…”

“…The concentration of Zn in pM 10 was once reported as the highest compared to Cr and Mn in urban areas in Malaysia, which indicated an association between Zn and emissions from tire use, car brakes, and different types of petrol [12][13]. However, recent studies on heavy metals in pM 10 have shown that Fe had the highest levels, indicating that vehicular emissions were the main source of pM 10 around a very busy highway [14].…”

Statistical Evaluation of Landfill Leachate System and its Impact on Groundwater and Surface Water in Malaysia

“…The relationship between different parameters was studied by computing Pearson correlation coefficient between all possible pairs of parameters (Alkarkhi et al 2008). The similarity between the three sampling sites was determined using cluster analysis (CA) which can summarize the results in a pictorial form or a dendrogram as done by Yusup and Alkarkhi (2011). The analyses were performed using the statistical software R.…”