Variability of soil potential for biodegradation of petroleum hydrocarbons in a heterogeneous subsurface

Andreas H. Kristensen*, Tjalfe G. Poulsen, Lars Mortensen, Per Moldrup

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Quantifying the spatial variability of factors affecting natural attenuation of hydrocarbons in the unsaturated zone is important to (i) performing a reliable risk assessment and (ii) evaluating the possibility for bioremediation of petroleum-polluted sites. Most studies to date have focused on the shallow unsaturated zone. Based on a data set comprising analysis of about 100 soil samples taken in a 16m-deep unsaturated zone polluted with volatile petroleum compounds, we statistically and geostatistically analysed values of essential soil properties. The subsurface of the site was highly layered, resulting in an accumulation of pollution within coarse sandy lenses. Air-filled porosity, readily available phosphorous, and the first-order rate constant (k1) of benzene obtained from slurry biodegradation experiments were found to depend on geologic sample characterization (P<0.05), while inorganic nitrogen was homogenously distributed across the soil stratigraphy. Semivariogram analysis showed a spatial continuity of 4-8.6m in the vertical direction, while it was 2-5 times greater in the horizontal direction. Values of k1 displayed strong spatial autocorrelation. Even so, the soil potential for biodegradation was highly variable, which from autoregressive state-space modeling was partly explained by changes in soil air-filled porosity and gravimetric water content. The results suggest considering biological heterogeneity when evaluating the fate of contaminants in the subsurface.

Original languageEnglish
Pages (from-to)573-580
Number of pages8
JournalJournal of Hazardous Materials
Issue number1-3
StatePublished - Jul 2010
Externally publishedYes


  • Biological heterogeneity
  • Petroleum vapors
  • Semivariogram analysis
  • Spatial variability
  • State-space modeling


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