Natural Attenuation of Nonionic Surfactants Used in Hydraulic Fracturing Fluids: Degradation Rates, Pathways, and Mechanisms.
Katie M HeyobJens BlotevogelMichael BrookerMorgan V EvansJohn J LenhartJustin WrightRegina LamendellaThomas BorchPaula J MouserPublished in: Environmental science & technology (2017)
Hydraulic fracturing fluids are injected into shales to extend fracture networks that enhance oil and natural gas production from unconventional reservoirs. Here we evaluated the biodegradability of three widely used nonionic polyglycol ether surfactants (alkyl ethoxylates (AEOs), nonylphenol ethoxylates (NPEOs), and polypropylene glycols (PPGs)) that function as weatherizers, emulsifiers, wetting agents, and corrosion inhibitors in injected fluids. Under anaerobic conditions, we observed complete removal of AEOs and NPEOs from solution within 3 weeks regardless of whether surfactants were part of a chemical mixture or amended as individual additives. Microbial enzymatic chain shortening was responsible for a shift in ethoxymer molecular weight distributions and the accumulation of the metabolite acetate. PPGs bioattenuated the slowest, producing sizable concentrations of acetone, an isomer of propionaldehyde. Surfactant chain shortening was coupled to an increased abundance of the diol dehydratase gene cluster (pduCDE) in Firmicutes metagenomes predicted from the 16S rRNA gene. The pduCDE enzymes are responsible for cleaving ethoxylate chain units into aldehydes before their fermentation into alcohols and carboxylic acids. These data provide new mechanistic insight into the environmental fate of hydraulic fracturing surfactants after accidental release through chain shortening and biotransformation, emphasizing the importance of compound structure disclosure for predicting biodegradation products.
Keyphrases
- microbial community
- genome wide
- electronic health record
- genome wide identification
- sewage sludge
- hydrogen peroxide
- big data
- fatty acid
- machine learning
- gene expression
- nitric oxide
- risk assessment
- deep learning
- artificial intelligence
- antibiotic resistance genes
- preterm birth
- saccharomyces cerevisiae
- solid state
- anaerobic digestion