Time series-based machine learning for forecasting multivariate water quality in full-scale drinking water treatment with various reagent dosages.

Accurately predicting drinking water quality is critical for intelligent water supply management and for maintaining the stability and efficiency of water treatment processes. This study presents an optimized time series machine learning approach for accurately predicting multivariate drinking water quality, explicitly considering the time-dependent effects of reagent dosing. By leveraging data from a full-scale treatment plant, we constructed feature-engineered time series datasets incorporating influent water quality parameters, reagent dosages and effluent water characteristics.

Demonstrating removal credits for contaminants of emerging concern in recycled water through a reverse osmosis barrier-A predictive framework.

The performance of individual reverse osmosis (RO) systems varies significantly with different contaminants of emerging concern (CECs). As such, log reduction values (LRVs) of the concentration of these chemicals cannot be arbitrarily credited in water treatment and water recycling. This study looks to present an approach to the management of chemical risks by providing a systematic validation of RO barrier performance with respect to LRV credits for various classes of CECs.

Sub-ppm determination of in solution by UV-vis high-performance liquid chromatography through solid phase extraction.

This study investigated a novel and sensitive analytical method based on a simple heat-based derivatization using 3-bromoacetyl coumarin as the reagent and analysis with a HPLC-UV system or just a UV-vis spectrometer to allow the sub-ppm determination of PFCAs in water solution with the potential for utilization in simple laboratories and field laboratory scenarios. A Strata-X-AW cartridge was used for the solid phase extraction (SPE) procedure and higher than 98% recoveries were obtained. The derivatization condition showed that a high efficiency of peak separation was obtained with obviously different retention time among various PFCAs derivatives using HPLC-UV analysis.

A chemical credit framework to predict the removal performance of organic chemicals of concern from water through an ozonation process.

Ozonation is an effective barrier for removing a wide spectrum of organic Chemicals of Concern (CoC) in a water treatment process. In this study, bench- and full-scale tests were conducted on a secondary treated effluent at the Eastern Treatment Plant (ETP) of Melbourne Water to probe the performance of ozonation in removing CoC in a wastewater discharge. From the secondary treated effluent as the feed to the ozone process, 58 organic chemicals were measured out of a possible 949 compounds by using the AIQS-DB analytical method.

Remediation of poly-and perfluoroalkyl substances (PFAS) contaminated soil using gas fractionation enhanced technology.

This study investigated a gas fractionation enhanced soil washing method for poly-and perfluoroalkyl substances (PFAS) removal from contaminated soil. With the assistance of gas fractionation, PFAS removal was increased by a factor of 9, compared to the conventional soil washing method. Pre-extraction (pre-treatment) of the soil with water before gas fractionation enhanced PFAS removal from soil.