Formation of oil-particle aggregates with motor oil and kaolinite clay in cold and warm freshwater. Issue 3 (2nd February 2023)

• Record Type: Journal Article
• Authors: Fahmi Alali, Abdulrhman; Wang, Shu; Zhu, Zhenduo; Atkinson, Joseph
• Is Part Of: Environmental science. Volume 25:Issue 3(2023)
• Page Start: 566
• Page End: 576
• Publication Date: 2023-02-02
• Subjects: Environmental monitoring -- Periodicals; Biological monitoring -- Periodicals; Environmental chemistry -- Periodicals; 363.7363
• DOI: 10.1039/d2em00364c
• Languages: English
• ISSNs: 2050-7887

Abstract

Abstract : This study is the first of its kind to examine experimentally and computationally the formation of oil particle aggregates (OPAs) with motor oils as a function of oil viscosity, mixing intensity, and water temperature in freshwater. Abstract : Motor oil is one of the most common pollutants in stormwater runoff in freshwater ecosystems. It can form aggregates with solids (creating oil particle aggregates, OPAs) which complicates the understanding of the fate and transport of motor oil, particularly in cold freshwater, conditions that have not been studied extensively. Laboratory and numerical experiments were conducted with kaolinite clay and three types of motor oil in both cold and warm freshwater, in which: (1) the interaction of clay particles with motor oil was experimentally investigated, in response to changes in oil viscosity, water temperature, and mixing intensity; (2) variability in particle size distribution of the formed OPAs was measured; and (3) a mechanistic OPA formation model was applied and results were compared with experimental data. The results showed that kaolinite clay and motor oil formed mostly droplet-type OPAs, lower-viscosity oil tended to form a wider size range of OPAs, and higher mixing intensity and higher water temperature produced larger numbers of smaller OPAs. Although there was a reasonably good match between the experimental data and the modeling results, more research is needed to further improve the modeling framework.