Linear alkylbenzenesulfonates (LAS) are anionic surfactants that are used in large quantities in industrial and consumer products. They enter the environment primarily through wastewater and sludge. In this study, the sorption of LAS to the surfaces of sediment particles was investigated as a function of LAS homologue, H + concentration in solution, Ca 2+ concentration in solution, sediment properties, and concentration of solids. Evidence for both hydrophobic and specific or electrostatic interactions was seen. Isotherms were generally nonlinear and were represented well by the Freundlich and the virial (electrostatic) equations. Comparisons of apparent distribution ratios, D (L/kg), for linear portions of the isotherms showed ∆ log D/∆ log n CH2 ≈ 0.4, ∆ log D/∆ log [H + ] ≈ 0.17, and ∆ log D/∆ log [Ca 2+ ] ≈ 0.23. The value of D for different sediments seemed to correlate most closely with the organic carbon content (f oc ) of the sediments. The value of the distribution ratio increased with the concentration of solids in the system; this effect could be explained partially by the concomitant increase in Ca 2+ concentration in solution.

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Chronic kidney disease (CKD) has emerged as a major public health problems worldwide. It frequently progresses to end-stage renal disease which is related to very high cost and mortality. Novel biomarkers can provide insight into the novel mechanism, facilitate early detection and monitor progression of CKD and its response to therapeutic interventions. To identify potential biomarkers, we applied an UPLC-HDMS together with univariate and multivariate statistical analyses using plasma samples from patients with CKD of diverse etiologies (100 sera in discovery set and 120 sera in validation set) and two different rat models of CKD. Using comprehensive screening and validation workflow, we identified a panel of seven metabolites which were shared by all patients and animals regardless of the underlying cause of CKD. These included ricinoleic acid, stearic acid, cytosine, LPA(16:0), LPA(18:2), 3-methylhistidine, and argininic acid. The combination of these seven biomarkers enabled the discrimination of patients with CKD from healthy subjects with a sensitivity of 83.3% and a specificity of 96.7%. In addition, these biomarkers accurately reflected improvements in renal function in response to the therapeutic interventions. Our results indicated that the identified biomarkers may improve the diagnosis of CKD and provide a novel tool for monitoring of the progression of disease and response to treatment in CKD patients.