The in situ oil sands production method called steam-assisted gravity drainage (SAGD) reuses process wastewater following treatment. However, the treatment and reuse processes concentrate contaminants in the process water. To determine the concentration and dynamics of inorganic and organic contaminants, makeup water and process water from six process steps were sampled at a facility employing the SAGD process in Alberta, Canada. In the groundwater used for the makeup water, the total dissolved organic carbon (DOC) content was 4 mg/L. This significantly increased to 508 mg/L in the produced water, followed by a gradual increase with successive steps in subsequent water treatments. The concentrations and dynamics of DOC constituents in the process water determined by gas chromatography-mass spectrometry showed that in the produced water, volatile organic compounds (VOCs) such as acetone (33.1 mg/L) and 2-butanone (13.4 mg/L) predominated, and there were significant amounts of phenolic compounds (total 9.8 mg/L) and organic acids including naphthenic acids (NAs) corresponding to the formula C(n)H(2n+Z)O(X) for combinations of n = 4 to 18, Z = 0 and -2, and X = 2 to 4 (53 mg/L) with trace amounts of polycyclic aromatic hydrocarbons (PAHs) such as naphthalene and phenanthrene. No organic contaminants, except for saturated fatty acids, were detected in the groundwater. The concentration of DOC in the recycled water was 4.4-fold higher than that in the produced water. Likewise, the total concentrations of phenols and organic acids in the recycled water were 1.7- and 4.5-fold higher than in the produced water, whereas the total concentrations of VOCs and PAHs in the recycled water were reduced by over 80%, suggesting that phenols and organic acids are selectively concentrated in the process water treatment. This comprehensive chemical analysis thus identified organic constituents that were concentrated in the process water and which interfere with subsequent water treatments in the SAGD process.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.watres.2012.07.036 | DOI Listing |
J Chem Theory Comput
January 2025
Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum, Bochum 44780, Germany.
Training accurate machine learning potentials requires electronic structure data comprehensively covering the configurational space of the system of interest. As the construction of this data is computationally demanding, many schemes for identifying the most important structures have been proposed. Here, we compare the performance of high-dimensional neural network potentials (HDNNPs) for quantum liquid water at ambient conditions trained to data sets constructed using random sampling as well as various flavors of active learning based on query by committee.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Science and Technology Branch, Pacific Environmental Science Centre, Environment and Climate Change Canada, Pacific and Yukon Laboratory for Environmental Testing, North Vancouver, BC, Canada.
Spilled plant-based oils behave very differently in comparison to petroleum oils and require different clean-up measures. They do not evaporate, disperse, dissolve, or emulsify to a significant degree but can polymerize and form an impermeable cap on sediment, smothering benthic media and resulting in an immediate impact on the wildlife community. The current study explored the application of rapid up-to-date direct analysis in real time (DART) with high-resolution mass spectrometry for plant-based oil typing.
View Article and Find Full Text PDFEnviron Manage
January 2025
Department of Engineering, Reykjavik University, Reykjavík, Iceland.
This research assesses heavy metal contamination within the riparian zone of the Danro River, a tributary of the Ganges River basin in India, particularly impacted by sand mining activities. The study conducted analyses on major and trace elements in soil samples, focusing on those identified as ecologically hazardous by the Water Framework Directive of India. Utilizing a combination of indices (Enrichment Factor, Pollution Load Index, and Index of geo-accumulation) and statistical techniques such as Principal Component Analysis (PCA), the investigation aimed to evaluate contamination severity, ecological risks, and pollution sources.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Department of Environmental Management, Graduate School of Agriculture, Kindai University, Nara, Japan.
Efficient agricultural management often relies on farmers' experiential knowledge and demands considerable labor, particularly in regions with challenging terrains. To reduce these burdens, the adoption of smart technologies has garnered increasing attention. This study proposes a convolutional neural network (CNN)-based model as a decision-support tool for smart irrigation in orchard systems, focusing on persimmon cultivation in mountainous regions.
View Article and Find Full Text PDFJ Phys Chem B
January 2025
Department of Engineering Mechanics, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden.
We here explore confinement-induced assembly of whey protein nanofibrils (PNFs) into microscale fibers using microfocused synchrotron X-ray scattering. Solvent evaporation aligns the PNFs into anisotropic fibers, and the process is followed in situ by scattering experiments within a droplet of PNF dispersion. We find an optimal temperature at which the order parameter of the protein fiber is maximized, suggesting that the degree of order results from a balance between the time scales of the forced alignment and the rotational diffusion of the fibrils.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!