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The monitoring of occupational exposure to chemicals is essential for assessing the workplace. In the case of hazardous and carcinogenic chemicals, such as benzene, occupational monitoring becomes even more crucial. Trans,trans-muconic acid (t,t-MA) is one of the benzene urinary metabolites. Pretreatment methods for t,t-MA generally include liquid–liquid extraction and solid–phase extraction. Using dispersive liquid–liquid microextraction (DLLME) during sample preparation and extraction can reduce extraction costs and environmental impacts. Furthermore, the process is cost-effective and easy to operate. This study is aimed to develop, optimize, and validate an analytical method for measuring t,t-MA concentration in urine matrix through DLLME combined with high-performance liquid chromatography. In this method, five variables including pH, the volume of the extractant and the disperser, salt content and the time of centrifugation were optimized using the response surface methodology with a central composite design approach and experimental data. The proposed DLLME was successfully applied to real samples of exposed workers to benzene with extraction efficiencies from 95.8 to 102.4 %. The optimum conditions were pH 8, extractant solvent, 300 μL, disperser solvent, 300 μL, salt, 3.4 % and centrifuge, 3 min. According to the result of this study, the proposed DLLME approach can be effectively applied to the biomonitoring of individuals exposed to benzene.

Investigations have shown the traces of antibiotics in surface water, groundwater, wastewater treatment plants, and drinking water. However, conventional wastewater treatment is not entirely effective for vancomycin degradation. Advanced oxidation is one of the most widespread methods of antibiotic degradation in aqueous media. Vancomycin was quantified by high-performance liquid chromatography. The Response Surface Methodology (RSM) based on Central Composite Design (CCD) was used to explore and optimise the effect of the independent variables on vancomycin degradation. Independent variables were as follows: pH (3–11), vancomycin concentration (15–75 mg/L), TiO2–clinoptilolite (25–125 mg in 250 mL reactor volume), the temperature (25–45°C), and the reaction time (15–75 min). The validity and adequacy of the model were confirmed by the corresponding statistics (F–value = 111.5, correlation coefficient R2 = 0.98, adjusted R2 = 0.97, and prediction R2 = 0.95). The vancomycin degradation efficiency was 97% under optimal conditions (pH = 5, vancomycin concentration = 30 mg/L, TiO2-clinoptilolite content = 50.2 mg in a 250 mL reactor volume, temperature = 32.24°C, and reaction time = 50.9 min). This process followed the zero-order reaction kinetics model (R2 = 0.98), and the removal rate of Total Organic Carbon (TOC) under optimal conditions was 56%. The results indicated that supporting TiO2 on clinoptilolite particles could increase the photocatalytic properties. [ABSTRACT FROM AUTHOR]

The present study was performed to identify possible factors affecting the biological monitoring of benzene, toluene, and xylene (BTX) metabolites. Fifty persons with occupational exposure to BTX and 28 administrative employees were evaluated. Urinary trans, trans-muconic acid (t,t-MA), Hippuric Acid (HA) and Methyl Hippuric Acid (MHA) were determined as the biomarkers of exposure. The mean levels of t,t-MA, HA, and MHA in the exposed group were 541.90 (µg‧g creatinine−1), 0.0199, and 0.00205 (g‧g creatinine−1), respectively, which were higher than the non-exposed group (significant difference, Pvalue <0.001). In the regression model obtained from the amount of urinary t,t-MA, level of education, work experience, residence, length of work per shift, use of personal protective equipment (PPE), and habit of consuming industrial food or fruit juice had a significant effect. HA levels were affected by variables including education level, residence, consumption of industrial food or fruit juice in the past three days, and use of masks, gloves, and PPE. Due to the small amount of MHA in urine, there was a weak correlation between it and independent variables. Based on results, it was concluded that the health effects of exposure to BTX were significantly different between the two groups. [ABSTRACT FROM AUTHOR]

Due to the importance of amino acids (AAs) as the basic bricks of proteins and their application in the drug and food industries, there is great interest in their separation and identification using simple and inexpensive approaches. Application of predictive models for the determination of the behavior of AAs can reduce trial-and-error experiments. Herein, the retardation factor (RF) of 21 protein AAs were studied using the quantitative structureretardation factor (QSRR) model. The RF values of the AAs in ethanol–sodium azide solution as the mobile phase of reversed phase thin layer chromatography (RP-TLC) were correlated with the structural properties of the AAs. The suggested QSRR indicated excellent fitting and prediction ability (R2train = 0.95 and R2test = 0.94). Furthermore, other statistical tests, such as y-scrambling, cross validation and the Williams plot confirmed the stability, absence of chance and the suitable applicability domain, respectively. It was shown that the sum of geometrical distances between oxygen and nitrogen atoms in an AA molecule is an important factor for the RF values of the AAs in the ethanol– sodium azide.