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Emerging contaminants are found in all parts of our environment. Adsorption of these contaminants by activated carbon in water treatment plants is well-known; however, a problem resides in the handling of the spent adsorbents. As current regenerative technologies are expensive, the adsorbents are often destructed or landfilled. Here, we examine a novel regeneration method for the used adsorbents with subcritical water – i.e., hydrothermal treatment. The degradation of three well-known emerging contaminants – caffeine, trimethoprim and sulfamethoxazole – was studied with regard to processing temperature (160–280 °C), concentration (2 and 20 mg/L), and the impact of adsorbents. In addition to trimethoprim in the mix at 20 mg/L, the other contaminants were entirely degraded at 280 °C. To obtain insight into transformation products formed during hydrothermal regeneration, we performed non-target and targeted analyses with LC-MS-QTOF using two types of columns, C18 and ZIC-HILIC. This approach ensured a wide range of hydrophilicities. Results showed more transformation products for trimethoprim (20) compared to sulfamethoxazole and caffeine (4). To assess the regeneration efficiencies of the activated carbons, we conducted three cycles of regeneration at 280 °C and between 61 and 120 % degradation was achieved. Moreover, only two transformation products were detected and readsorbed on the adsorbent after regeneration. Hydrothermal regeneration efficiently degraded the target emerging contaminants, suggesting a potential approach for enabling alternative, sequential uses for regenerated activated carbon., Originally included in thesis in manuscript form.

On-site sewage treatment facilities (OSSFs) are widely used all over the world to treat wastewater when large-scale sewage treatment plants (STPs) are not economically feasible. Although there is great awareness that the release of untreated wastewater into the environment can lead to water-related diseases and eutrophication, little is known about organic contaminants and their removal by OSSFs, environmental load and fate. Thus, this PhD thesis aims to improve the knowledge about treatment efficiencies in current OSSFs, the environmental impact and fate of contaminants released from OSSFs, as well as how biochar fortification in sand filter (soil beds) OSSFs might increase removal of these contaminants. State-of-the-art analytical techniques for untargeted and targeted analyses were used and the results evaluated with univariate and multivariate statistics. Environmentally-relevant contaminants discharged from OSSFs were identified using untargeted analysis with two-dimensional gas chromatography mass spectrometry (GC×GC-MS) and a MS (NIST) library search in combination with a prioritization strategy based on environmental relevance. A method was successfully developed for the prioritized contaminants using solid phase extraction and GC×GC-MS, and the method was also applicable to untargeted analysis. This method was applied to several studies. The first study compared treatment efficiencies between STP and soil beds and showed that treatment efficiencies are similar or better in soil beds, but the removal among the same type of treatment facilities and contaminants varied considerably. Hydrophilic contaminants were generally inadequately removed in both types of treatment facilities and resulted in effluent levels in the nanogram per liter range. Additionally, several prioritized and sometimes badly removed compounds were found to be persistent, mobile, and bioavailable and two additional, untargeted contaminants identified by the NIST library search were potenti

Comprehensive two-dimensional (2D) gas chromatography (GC×GC) coupled to mass spectrometry (MS, GC×GC-MS), which enhances selectivity compared to GC-MS analysis, can be used for non-directed analysis (non-target screening) of environmental samples. Additional tools that aid in identifying unknown compounds are needed to handle the large amount of data generated. These tools include retention indices for characterizing relative retention of compounds and prediction of such. In this study, two quantitative structure–retention relationship (QSRR) approaches for prediction of retention times (1tR and 2tR) and indices (linear retention indices (LRIs) and a new polyethylene glycol–based retention index (PEG-2I)) in GC × GC were explored, and their predictive power compared. In the first method, molecular descriptors combined with partial least squares (PLS) analysis were used to predict times and indices. In the second method, the commercial software package ChromGenius (ACD/Labs), based on a “federation of local models,” was employed. Overall, the PLS approach exhibited better accuracy than the ChromGenius approach. Although average errors for the LRI prediction via ChromGenius were slightly lower, PLS was superior in all other cases. The average deviations between the predicted and the experimental value were 5% and 3% for the 1tR and LRI, and 5% and 12% for the 2tR and PEG-2I, respectively. These results are comparable to or better than those reported in previous studies. Finally, the developed model was successfully applied to an independent dataset and led to the discovery of 12 wrongly assigned compounds. The results of the present work represent the first-ever prediction of the PEG-2I.