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

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Abstract

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.

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Abbreviations

PAH:

Polycyclic aromatic hydrocarbon

Nap:

Naphthalene

Acy:

Acenaphthylene

Ace:

Acenaphthene

Fle:

Fluorine

Phe:

Phenanthrene

Ant:

Anthracene

Fla:

Fluoranthene

Pyr:

Pyrene

Baa:

Benzo(a)anthracene

Chr:

Chrysene

Bbf&Bkf:

Benzo(b,k)fluoranthene

Bap:

Benzo(a)pyrene

Daa:

Dibenzo(a,h)anthracene

Bgp:

Benzo(g,h,i)perylene

Inp:

Indeno(1,2,3-c,d)pyrene

PCA:

Principal component analysis

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Acknowledgments

This research was supported by the Environmental Protection Public Welfare Research Funds (grant no. 201009015) and Young Scientists Fund of NSFC (grant no. 40901249).

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Authors and Affiliations

  1. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Anwai Dayangfang 8, Bei**g, 100012, People’s Republic of China

    Geng Liu, Shijie Wang, Fasheng Li & Guanlin Guo

  2. School of Resources and Environment, Shanxi Agricultural University, Taigu, 030801, People’s Republic of China

    Geng Liu & Rutian Bi

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  1. Geng Liu
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  2. Rutian Bi
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  3. Shijie Wang
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  4. Fasheng Li
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  5. Guanlin Guo
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Correspondence to Guanlin Guo.

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Liu, G., Bi, R., Wang, S. et al. The use of spatial autocorrelation analysis to identify PAHs pollution hotspots at an industrially contaminated site. Environ Monit Assess 185, 9549–9558 (2013). https://doi.org/10.1007/s10661-013-3272-6

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  • DOI: https://doi.org/10.1007/s10661-013-3272-6

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