A simple, sensitive and reliable HPLC-FLD method for the routine determination of 4-nonylphenol, 4-NP and 4-tert-octylphenol, 4-t-OP content in water samples was developed. The method consists in a liquid-liquid extraction of the target analytes with dichloromethane at pH 3.0-3.5 followed by the HPLC-FLD analysis of the organic extract using a Zorbax Eclipse XDB C8 column, isocratic elution with a mixed solvent acetonitrile/water 65:35, at a flow rate of 1.0 mL/min and applying a column temperature of 40°C. The method was validated and then applied with good results for the determination of 4-NP and 4-t-OP in Ialomiţa River water samples collected each month during 2006. The concentration levels of 4-NP and 4-t-OP vary between 0.08-0.17 μg/L with higher values of 0.24-0.37 μg/L in the summer months for 4-NP, and frequently <0.05 μg/L but also between 0.06-0.09 μg/L with higher values of 0.12-0.16 μg/L in July and August for 4-t-OP and were strongly influenced by sesonial and anthropic factors. The method was also applied on samples collected over 2 years 2007 and 2008 from urban wastewaters discharged into sewage or directly into the rivers by economic agents located in 30 Romanian towns. Good results were obtained when the method was used for analysis of effluents discharged into surface waters by 16 municipal wastewater treatment plants, during the year 2008.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s10661-011-2151-2 | DOI Listing |
J Eval Clin Pract
February 2025
Faculty of Health Sciences, Department of Nursing, Division of Public Health Nursing, Bandırma Onyedi Eylül University, Balıkesir, Turkey.
Aim: This study aimed to translate the Environmental Health Literacy Scale (EHLS) into Turkish and assess its construct validity and internal consistency.
Methods: This research employs a methodological design. The research was conducted during the 2022-2023 academic year with a sample of 500 students from the Faculty of Health Sciences.
Sci Rep
December 2024
Department of Nano-Chemical Engineering, Faculty of Advanced Technologies, Shiraz University, Shiraz, Iran.
MXene-based (nano)materials have recently emerged as promising solutions for antibiotic photodegradation from aquatic environments, yet they are limited by scalability, stability, and selectivity challenges in practical settings. We formulated FeO-SiO/MXene ternary nano-photocomposites via coupled wet impregnation and sonochemistry approach for optimised tetracycline (TC) removal (the second most used antibiotic worldwide) from water using response surface methodology-central composite design (RSM-CCD). The photocatalysts containing various loading of FeO/SiO (5-45 wt%) on the MXene with a range of calcination temperatures (300-600 °C) via RSM optimisation were synthesised, characterised regarding crystallinity properties, surface morphology, binding energy, and light absorption capability, and analysed for TC degradation efficiency.
View Article and Find Full Text PDFSci Rep
December 2024
School of Public Administration, Guangzhou University, Guangzhou, 510006, China.
With the accelerated urbanization and economic development in Northwest China, the efficiency of urban wastewater treatment and the importance of water quality management have become increasingly significant. This work aims to explore urban wastewater treatment and carbon reduction mechanisms in Northwest China to alleviate water resource pressure. By utilizing online monitoring data from pilot systems, it conducts an in-depth analysis of the impacts of different wastewater treatment processes on water quality parameters.
View Article and Find Full Text PDFReduced bacteria concentrations in wastewater is a key indicator of the efficacy of water resource recovery facilities (WRRFs). However, monitoring the presence of bacterial concentrations in real time at each stage of the WRRF is challenging as it requires taking and processing water samples offline. Although few studies have been proposed to predict bacterial concentrations using data-driven models, generalizing these models to unseen data from different WRRFs remains challenging.
View Article and Find Full Text PDFSci Rep
December 2024
Advanced Research Institute for Digital-Twin Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Traditional hydraulic structures rely on manual visual inspection for apparent integrity, which is not only time-consuming and labour-intensive but also inefficient. The efficacy of deep learning models is frequently constrained by the size of available data, resulting in limited scalability and flexibility. Furthermore, the paucity of data diversity leads to a singular function of the model that cannot provide comprehensive decision support for improving maintenance measures.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!