The use of spatial autocorrelation analysis to identify PAHs pollution hotspots at an industrially contaminated site

The identification of contamination hotspots are an important indicator of the degree of contamination in localized areas, which can contribute towards the re-sampling and remedial strategies used in the seriously contaminated areas. Accordingly, 114 surface samples, collected from an industrially contaminated site in northern China, were assessed for 16 polycyclic aromatic hydrocarbons (PAHs) and were analyzed using multivariate statistical and spatial autocorrelation techniques. The results showed that the PCA leads to a reduction in the initial dimension of the dataset to two components, dominated by Chr, Bbf&Bkf, Inp, Daa, Bgp, and Nap were good representations of the 16 original PAHs; Global Moran's I statistics indicated that the significant autocorrelations were detected and the autocorrelation distances of six indicator PAHs were 750, 850, 1,200, 850, 750, and 1,200 m, respectively; there were visible high-high values (hotspots) clustered in the mid-bottom part of the site through the Local Moran's I index analysis. Hotspot identification and spatial distribution results can play a key role in contaminated site investigation and management.