Universal Indicators for Oil and Gas Prospecting Based on Bacterial Communities Shaped by Light-Hydrocarbon Microseepage in China.

Light hydrocarbons accumulated in subsurface soil by long-term microseepage could favor the anomalous growth of indigenous hydrocarbon-oxidizing microorganisms, which could be crucial indicators of underlying petroleum reservoirs. Here, Illumina MiSeq sequencing of the 16S rRNA gene was conducted to determine the bacterial community structures in soil samples collected from three typical oil and gas fields at different locations in China. Incubation with n-butane at the laboratory scale was performed to confirm the presence of "universal microbes" in light-hydrocarbon microseepage ecosystems. The results indicated significantly higher bacterial diversity in next-to-well samples compared with background samples at two of the three sites, which were notably different to oil-contaminated environments. Variation partitioning analysis showed that the bacterial community structures above the oil and gas fields at the scale of the present study were shaped mainly by environmental parameters, and geographic location was able to explain only 7.05% of the variation independently. The linear discriminant analysis effect size method revealed that the oil and gas fields significantly favored the growth of Mycobacterium, Flavobacterium, and Pseudomonas, as well as other related bacteria. The relative abundance of Mycobacterium and Pseudomonas increased notably after n-butane cultivation, which highlighted their potential as biomarkers of underlying oil deposits. This work contributes to a broader perspective on the bacterial community structures shaped by long-term light-hydrocarbon microseepage and proposes relatively universal indicators, providing an additional resource for the improvement of microbial prospecting of oil and gas.

A proposal of "core enzyme" bioindicator in long-term Pb-Zn ore pollution areas based on topsoil property analysis.

To study the effects of long-term mining activities on the agricultural soil quality of Mengnuo town in Yunnan province, China, the heavy metal and soil enzyme activities of soil samples from 47 sites were examined. The results showed that long-term mining processes led to point source heavy metal pollution and Pb, Cd, Zn and As were the primary metal pollutants. Polyphenoloxidase was found the most sensitive soil enzyme activity and significantly correlated with almost all the metals (P < 0.05). Amylase (for C cycling), acid phosphatase (for P cycling) and catalase (for redox reaction) activities showed significantly positive correlations (P < 0.05) with Pb, Cd, Zn and As contents. The correlations between soil enzymes activities and Cd, Pb and Zn contents were verified in microcosm experiments, it was found that catalase activity had significant correlations (P < 0.05) with these three metals in short-term experiments using different soils under different conditions. Based on both field investigation and microcosm simulation analysis, oxidoreductases activities (rather than a specific enzyme activity) were suggested to be used as "core enzyme", which could simply and universally indicate the heavy metal pollution degrees of different environments. And hydrolases (for C, N, P and S recycling) could be used as a supplement to improve correlation accuracy for heavy metal indication in various polluted environments.