GIS-based approach and multivariate statistical analysis for identifying sources of heavy metals in marine sediments from the coast of Hong Kong.

Abstract

Hong Kong is an urbanized coastal city which experiences substantially different metal loads from anthropogenic activities. This study was aimed at analyzing the spatial distribution and pollution evaluation of ten selected heavy metals (As, Cd, Cr, Cu, Pb, Hg, Ni, Zn, Fe, V) in the coastal sediments of Hong Kong. The distribution of heavy metal pollution in sediments has been analyzed using the geographic information system (GIS) technique, and their pollution degrees, corresponding potential ecological risks and source identifications, have been studied by applying the enrichment factor (EF) analysis, contamination factor (CF) analysis, potential ecological risk index (PEI), and integrated multivariate statistical methods, respectively. Firstly, the GIS technique was used to access the spatial distribution of the heavy metals; the result revealed that pollution trend of these metals was decreased from the inner to the outer coast sites of the studied area. Secondly, combining the EF analysis and CF analysis, we found that the pollution degree of heavy metals followed the order of Cu > Cr > Cd > Zn > Pb > Hg > Ni > Fe > As > V. Thirdly, the PERI calculations showed that Cd, Hg, and Cu were the most potential ecological risk factors compared to other metals. Finally, cluster analysis combined with principal component analysis showed that Cr, Cu, Hg, and Ni might originate from the industrial discharges and shipping activities. V, As, and Fe were mainly derived from the natural origin, whereas Cd, Pb, and Zn were identified from the municipal discharges and industrial wastewater. In conclusion, this work should be helpful in the establishment of strategies for contamination control and optimization of industrial structures in Hong Kong.