Spatial patterns of microbial diversity and activity in an aged creosote contaminated site. Bacterial communities in an aged Creosote contaminated soil

The design of successful bioremediation demands a thorough knowledge of the indigenous microbial populations in the polluted site. High spatial heterogeneity of pollution levels and soil microbial communities present a complication in implementing bioremediation. Although spatial variation in abundance and diversity of soil organisms has long been considered ‘‘noise’’ in microbiological studies, it has in several studies been shown that various free-living microbial populations display clear spatial patterns. The spatial patterns of microbial distribution primarily arise from their dependence on spatially variable soil geochemical parameters. The objectives of the present study were to characterize the spatial heterogeneity of the soil microbial community at a former wood-treatment site contaminated with creosote. A grid design sampling was performed on the site and 16S rRNA pyrosequencing was used to assess the bacterial community structure. Basal respiration assay and FDA hydrolysis assay for enzymatic activity were carried out as a measure of microbial activity. Geostatistical analysis revealed the spatial relationship between the phenotypic community composition, microbial activity and geochemical soil variables, such as PAH concentrations, soil solution pH, electric conductivity, total organic carbon (TOC), and particle aggregate distribution. We noticed a strong correlation of the spatial distribution patterns of different phyla with the geochemical parameters. Alphaproteobacterial abundance was higher in the hotspots of PAH pollution whereas Actinobacterial abundance followed the opposite trend. A pH gradient was found on the site which was shown to define the spatial distribution of Acidobacteria. The microbial activity varied spatially with the distribution of pollutants and TOC. The use of pyrosequencing and soil microbial activity combined with chemical/physical data revealed highly relevant information for implementing bioremediation on the site.