title: Co-occurrence correlations of heavy metals in sediments revealed using network analysis.
abstract: In this study, the correlation-based study was used to identify the co-occurrence correlations among metals in marine sediment of Hong Kong, based on the long-term (from 1991 to 2011) temporal and spatial monitoring data. 14 stations out of the total 45 marine sediment monitoring stations were selected from three representative areas, including Deep Bay, Victoria Harbour and Mirs Bay. Firstly, Spearman's rank correlation-based network analysis was conducted as the first step to identify the co-occurrence correlations of metals from raw metadata, and then for further analysis using the normalized metadata. The correlations patterns obtained by network were consistent with those obtained by the other statistic normalization methods, including annual ratios, R-squared coefficient and Pearson correlation coefficient. Both Deep Bay and Victoria Harbour have been polluted by heavy metals, especially for Pb and Cu, which showed strong co-occurrence with other heavy metals (e.g. Cr, Ni, Zn and etc.) and little correlations with the reference parameters (Fe or Al). For Mirs Bay, which has better marine sediment quality compared with Deep Bay and Victoria Harbour, the co-occurrence patterns revealed by network analysis indicated that the metals in sediment dominantly followed the natural geography process. Besides the wide applications in biology, sociology and informatics, it is the first time to apply network analysis in the researches of environment pollutions. This study demonstrated its powerful application for revealing the co-occurrence correlations among heavy metals in marine sediments, which could be further applied for other pollutants in various environment systems.
authors.affiliation: Environmental Biotechnology Lab, The University of Hong Kong, Hong Kong, China; State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, East China University of Science and Technology, 200237 Shanghai, China. Electronic address: lilyliu@ecust.edu.cn.
authors.forename: Lili
authors.initials: L
authors.lastname: Liu
authors.affiliation: Environmental Biotechnology Lab, The University of Hong Kong, Hong Kong, China; School of Environment Science and Technology, Shanghai Jiao Tong University, 200240 Shanghai, China. Electronic address: wangzply@sjtu.edu.cn.
authors.forename: Zhiping
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Environmental Biotechnology Lab, The University of Hong Kong, Hong Kong, China. Electronic address: RichieJu520@gmail.com.
authors.forename: Feng
authors.initials: F
authors.lastname: Ju
authors.affiliation: Environmental Biotechnology Lab, The University of Hong Kong, Hong Kong, China. Electronic address: zhangt@hku.hk.
authors.forename: Tong
authors.initials: T
authors.lastname: Zhang
mesh_terms: D004784:Environmental Monitoring / Q000379:methods*; D005843:Geography; D019015:Geologic Sediments / Q000032:analysis*; D006723:Hong Kong; D019216:Metals, Heavy / Q000032:analysis*; D014874:Water Pollutants, Chemical / Q000032:analysis*
chemical_list: D019216:Metals, Heavy; D014874:Water Pollutants, Chemical
keywords: Co-occurrence correlations; Heavy metals; Network; Normalization; Sediment