Loss of micropollutants on syringe filters during sample filtration: Machine learning approach for selecting appropriate filters.

Prefiltration before chromatographic analysis is critical in the monitoring of environmental micropollutants (MPs). However, in an aqueous matrix, such monitoring often leads to out-of-specification results owing to the loss of MPs on syringe filters. Therefore, this study investigated the loss of seventy MPs on eight different syringe filters by employing Random Forest, a machine learning algorithm. The results indicate that the loss of MPs during filtration is filter specific, with glass microfiber and polytetrafluoroethylene filters being the most effective (<20%) compared with nylon (>90%) and others (regenerated-cellulose, polyethersulfone, polyvinylidene difluoride, cellulose acetate, and polypropylene). The Random Forest classifier showed outstanding performance (accuracy range 0.81-0.95) for determining whether the loss of MPs on filters exceeded 20%. Important factors in this classification were analyzed using the SHapley Additive exPlanation value and Kruskal-Wallis test. The results show that the physicochemical properties (LogKow/LogD, pKa, functional groups, and charges) of MPs are more important than the operational parameters (sample volume, filter pore size, diameter, and flow rate) in determining the loss of most MPs on syringe filters. However, other important factors such as the implications of the roles of pH for nylon and pre-rinsing for PTFE syringe filters should not be ignored. Overall, this study provides a systematic framework for understanding the behavior of various MP classes and their potential losses on syringe filters.

Predicting micropollutant fate during wastewater treatment using refined classical kinetic model based on quantitative monitoring in multi-metropolitan regions of South Korea.

This study aimed to implement an extensive prediction model for the fate of micropollutants (MPs) in wastewater treatment plants (WWTPs). Five WWTPs equipped with seven different biological treatment processes were monitored from 2020 to 2022 with three to four sampling events in each year, and 27 datasets for 20 MPs were collected. Among these datasets, 12 were used to investigate the behavior and fate of MPs in WWTPs in South Korea. Metformin, acetaminophen, caffeine, naproxen, and ibuprofen were the MPs with the highest influent concentrations (ranging from 3,933.3-187,637.0 ng L-1) at all WWTPs. More than 90% of MPs were removed by biological treatment processes in all WWTPs. The Kruskal-Wallis test verified that their efficacy did not differ statistically (p-value > 0.05). Meanwhile, to refine the performance of the prediction model, this study optimized the biodegradation rate constants (kbio) of each MP according to the variation of seasonal water temperature. As a result, compared to the original prediction model, the mean difference between the actual data and predicted results (MEAN) decreased by 6.77%, while the Nash-Sutcliffe efficiency (NSE) increased by 0.226. The final MEAN and NSE for the refined prediction model were calculated to be 5.09% and 0.964, respectively. The prediction model made accurate predictions, even for MPs exhibiting behaviors different from other cases, such as estriol and atrazine. Consequently, the optimization strategy proposed in this study was determined to be effective because the overall removal efficiencies of MPs were successfully predicted even with limited reference datasets.

Understanding and improving the reusability of phosphate adsorbents for wastewater effluent polishing.

Phosphate is a vital nutrient for life but its discharge from wastewater effluents can lead to eutrophication. Adsorption can be used as effluent polishing step to reduce phosphate to very low concentrations. Adsorbent reusability is an important parameter to make the adsorption process economically feasible. This implies that the adsorbent can be regenerated and used over several cycles without appreciable performance decline. In the current study, we have studied the phosphate adsorption and reusability of commercial iron oxide based adsorbents for wastewater effluent. Effects of adsorbent properties like particle size, surface area, type of iron oxide, and effects of some competing ions were determined. Moreover the effects of regeneration methods, which include an alkaline desorption step and an acid wash step, were studied. It was found that reducing the adsorbent particle size increased the phosphate adsorption of porous adsorbents significantly. Amongst all the other parameters, calcium had the greatest influence on phosphate adsorption and adsorbent reusability. Phosphate adsorption was enhanced by co-adsorption of calcium, but calcium formed surface precipitates such as calcium carbonate. These surface precipitates affected the adsorbent reusability and needed to be removed by implementing an acid wash step. The insights from this study are useful in designing optimal regeneration procedures and improving the lifetime of phosphate adsorbents used for wastewater effluent polishing.