Micropollutants are emerging as a new challenge to the scientific community. This review provides a summary of the recent occurrence of micropollutants in the aquatic environment including sewage, surface water, groundwater and drinking water. The discharge of treated effluent from WWTPs is a major pathway for the introduction of micropollutants to surface water. WWTPs act as primary barriers against the spread of micropollutants. WWTP removal efficiency of the selected micropollutants in 14 countries/regions depicts compound-specific variation in removal, ranging from 12.5 to 100%. Advanced treatment processes, such as activated carbon adsorption, advanced oxidation processes, nanofiltration, reverse osmosis, and membrane bioreactors can achieve higher and more consistent micropollutant removal. However, regardless of what technology is employed, the removal of micropollutants depends on physico-chemical properties of micropollutants and treatment conditions. The evaluation of micropollutant removal from municipal wastewater should cover a series of aspects from sources to end uses. After the release of micropollutants, a better understanding and modeling of their fate in surface water is essential for effectively predicting their impacts on the receiving environment.

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This study investigated the removal of micropollutants using polyurethane sponge as attached-growth carrier. Batch experiments demonstrated that micropollutants could adsorb to non-acclimatized sponge cubes to varying extents. Acclimatized sponge showed significantly enhanced removal of some less hydrophobic compounds (logD < 2.5), such as ibuprofen, acetaminophen, naproxen, and estriol, as compared with nonacclimatized sponge. The results for bench-scale sponge-based moving bed bioreactor (MBBR) system elucidated compound-specific variation in removal, ranging from 25.9% (carbamazepine) to 96.8% (bEstradiol 17-acetate) on average. In the MBBR system, biodegradation served as a major removal pathway for most compounds. However, sorption to sludge phase was also a notable removal mechanism of some persistent micropollutants. Particularly, carbamazepine, ketoprofen and pentachlorophenol were found at high concentrations (7.87, 6.05 and 5.55 lg/g, respectively) on suspended biosolids. As a whole, the effectiveness of MBBR for micropollutant removal was comparable with those of activated sludge processes and MBRs. AbstractThis study investigated the removal of micropollutants using polyurethane sponge as attached-growth carrier. Batch experiments demonstrated that micropollutants could adsorb to non-acclimatized sponge cubes to varying extents. Acclimatized sponge showed significantly enhanced removal of some less hydrophobic compounds (log D < 2.5), such as ibuprofen, acetaminophen, naproxen, and estriol, as compared with nonacclimatized sponge. The results for bench-scale sponge-based moving bed bioreactor (MBBR) system elucidated compound-specific variation in removal, ranging from 25.9%(carbamazepine) to 96.8% (β-Estradiol 17-acetate) on average. In the MBBR system, biodegradation served as a major removal pathway for most compounds. However, sorption to sludge phase was also a notable removal mechanism of some persistent micropollutants. Particularly, carbamazepine, ketoprofen and pentachlorophenol were 2 found at high concentrations (7.87, 6.05 and 5.55 µg/g, respectively) on suspended biosolids. As a whole, the effectiveness of MBBR for micropollutant removal was comparable with those of activated sludge processes and MBRs.

As emerging contaminants, microplastics are challenging to characterize, particularly when their size is at the nanoscale. While imaging technology has received increasing attention recently, such as Raman imaging, decoding the scanning spectrum matrix can be difficult to achieve result digitally and automatically via software and usually requires the involvement of personal experience and expertise. Herewith, we show a dual-principal component analysis (PCA) approach, where (i) the first round of PCA analysis focuses on the raw spectrum data from the Raman scanning matrix and generates two new matrices, with one containing the spectrum profile to yield the PCA spectrum and the other containing the PCA intensity to be mapped as an image; (ii) the second round of PCA analysis merges the spectrum from the first round of PCA with the standard spectra of eight common plastics, to generate a correlation matrix. From the correlation value, we can digitally assign the principal components from the first round of PCA analysis to the plastics toward imaging, akin to dataset indexing. We also demonstrate the effect of the data pretreatment and the wavenumber variations. Overall, this dual-PCA approach paves the way for machine learning to analyze microplastics and particularly nanoplastics.