Occurrence, removal, and prioritization of organic micropollutants in four full-scale wastewater treatment plants in Korea.

This study investigated the occurrence, removal rate, and potential risks of 43 organic micropollutants (OMPs) in four municipal wastewater treatment plants (WWTPs) in Korea. Results from two-year intensive monitoring confirmed the presence of various OMPs in the influents, including pharmaceuticals such as acetaminophen (pain relief), caffeine (stimulants), cimetidine (H2-blockers), ibuprofen (non-steroidal anti-inflammatory drugs- NSAIDs), metformin (antidiabetics), and naproxen (NSAIDs) with median concentrations of >1 µg/L. Some pharmaceuticals (carbamazepine-anticonvulsants, diclofenac-NSAIDs, propranolol-ß-blockers), corrosion inhibitors (1H-benzotriazole-BTR, 4-methyl-1H-benzotriazole-4-TTR), and perfluorinated compounds (PFCs) were negligibly removed during WWTP treatment. The OMP concentrations in the influents and effluents were mostly lower in August than those of other months (p-value <0.05) possibly due to wastewater dilution by high precipitation or enhanced biodegradation under high-temperature conditions. The anaerobic-anoxic-oxic process (A2O) with a membrane bioreactor exhibited higher OMP removal than other processes, such as A2O with sedimentation or the conventional activated sludge process (p-value <0.05). Pesticides (DEET and atrazine), corrosion inhibitors (4-TTR and BTR), and metformin were selected as priority OMPs in toxicity-driven prioritization, whereas PFCs were determined as priority OMPs given their persistence and bioaccumulation properties. Overall, our results contribute to an important database on the occurrence, removal, and potential risks of OMPs in Korean WWTPs.

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.

Multitemporal analysis of land subsidence induced by open-pit mining activity using improved combined scatterer interferometry with deep learning algorithm optimization.

Mine operational safety is an important aspect of maintaining the operational continuity of a mining area. In this study, we used the InSAR time series to analyze land surface changes using the ICOPS (improved combined scatterers with optimized point scatters) method. This ICOPS method combines persistent scatterers (PS) with distributed scatterers (DS) to increase surface deformation analysis's spatial coverage and quality. One of the improvements of this study is the use of machine learning in postprocessing, based on convolutional neural networks, to increase the reliability of results. This study used data from the Sentinel-1 SAR C-band satellite during the 2016-2022 observation period at the Musan mine, North Korea. In the InSAR surface deformation time analysis, the maximum average rate of land subsidence was approximately > 15.00 cm per year, with total surface deformation of 170 cm and 70 cm for the eastern dumping area and the western dumping area, respectively. Analyzing the mechanism of land surface changes also involved evaluating the geological conditions in the Musan mining area. Our research findings show that combining machine learning and statistical methods has great potential to enhance the understanding of mine surface deformation.

Adsorption of uranyl ion on hexagonal boron nitride for remediation of real U-contaminated soil and its interpretation using random forest.

Acid leaching has been widely applied to treat contaminated soil, however, it contains several inorganic pollutants. The decommissioning of nuclear power plants introduces radioactive and soluble U(VI), a substance posing chemical toxicity to humans. Our investigation sought to ascertain the efficacy of hexagonal boron nitride (h-BN), an highly efficient adsorbent, in treating U(VI) in wastewater. The adsorption equilibrium of U(VI) by h-BN reached saturation within a mere 2 h. The adsorption of U(VI) by h-BN appears to be facilitated through electrostatic attraction, as evidenced by the observed impact of pH variations, acidic agents (i.e., HCl or H2SO4), and the presence of background ions on the adsorption performance. A reusability test demonstrated the successful completion of five cycles of adsorption/desorption, relying on the surface characteristics of h-BN as influenced by solution pH. Based on the experimental variables of initial U(VI) concentration, exposure time, temperature, pH, and the presence of background ions/organic matter, a feature importance analysis using random forest (RF) was carried out to evaluate the correlation between performances and conditions. To the best of our knowledge, this study is the first attempt to conduct the adsorption of U(VI) generated from real contaminated soil by h-BN, followed by interpretation of the correlation between performance and conditions using RF. Lastly, a. plausible adsorption mechanism between U(VI) and h-BN was explained based on the experimental results, characterizations, and a. comparison with previous adsorption studies on the removal of heavy metals by h-BN.

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.

Theoretical Analysis of a Mathematical Relation between Driving Pressures in Membrane-Based Desalting Processes.

Osmotic and hydraulic pressures are both indispensable for operating membrane-based desalting processes, such as forward osmosis (FO), pressure-retarded osmosis (PRO), and reverse osmosis (RO). However, a clear relation between these driving pressures has not thus far been identified; hence, the effect of change in driving pressures on systems has not yet been sufficiently analyzed. In this context, this study formulates an actual mathematical relation between the driving pressures of membrane-based desalting processes by taking into consideration the presence of energy loss in each driving pressure. To do so, this study defines the pseudo-driving pressures representing the water transport direction of a system and the similarity coefficients that quantify the energy conservation rule. Consequently, this study finds three other theoretical constraints that are required to operate membrane-based desalting processes. Furthermore, along with the features of the similarity coefficients, this study diagnoses the commercial advantage of RO over FO/PRO and suggests desirable optimization sequences applicable to each process. Since this study provides researchers with guidelines regarding optimization sequences between membrane parameters and operational parameters for membrane-based desalting processes, it is expected that detailed optimization strategies for the processes could be established.

Two-Stage Interference Cancellation for Device-to-Device Caching Networks.

Wireless device-to-device (D2D) caching networks are studied, in which n nodes are distributed uniformly at random over the network area. Each node caches M files from the library of size m ≥ M and independently requires a file from the library. Each request will be served by cooperative D2D transmission from other nodes having the requested file in their cache memories. In many practical sensor or Internet of things (IoT) networks, there may exist simple sensor or IoT devices that are not able to perform real-time rate and power control based on the reported channel quality information (CQI). Hence, it is assumed that each node transmits a file with a fixed rate and power so that an outage is inevitable. To improve the outage-based throughput, a cache-enabled interference cancellation (IC) technique is proposed for cooperative D2D file delivery which first performs IC, utilizing cached files at each node as side information, and then performs successive IC of strongly interfering files. Numerical simulations demonstrate that the proposed scheme significantly improves the overall throughput and, furthermore, such gain is universally achievable for various caching placement strategies such as random caching and probabilistic caching.