Sources of persistent and mobile chemicals in municipal wastewater: a sewer perspective in Leipzig, Germany

Persistent and mobile (PM) chemicals spread in the water cycle and have been widely detected, yet information about their sources is still scarce. In this study, 67 PM chemicals were analyzed in 19 wastewater samples taken in the sewer system of the city of Leipzig, Germany, covering different industrial, clinical, and domestic discharges. A total of 37 of these analytes could be detected, with highly variable median concentrations between substances (median: 0.5-800 µg L-1) and for single substances between samples (e.g., 1,4-diazabicyclo[2.2.2]octane) by up to three orders of magnitude, with the highest single concentration exceeding 10 mg L-1 (p-cumenesulfonic acid). The emission of PM chemicals into the sewer system was classified as stemming from diffuse (14 analytes) or point sources (23 analytes), while 9 analytes fulfill both criteria. Many so-called industrial chemicals were also discharged from households (e.g., tris(2-chloroethyl) phosphate or 1H-benzotriazole). Examples for analytes showing specific sources are tetrafluoroborate (traffic-related industry and metal production and finishing), ε-caprolactam (large-scale laundry), or cyanuric acid (likely swimming pool). Furthermore, a correlation between 1-cyanoguanidine and guanylurea was observed for the traffic-related industry. This study outlines that sewer sampling can provide valuable information on the sources of PM chemicals. This knowledge is a prerequisite for their future emission control at source or substitution as an alternative to end-of-pipe treatment in municipal wastewater treatment plants.

Quality evaluation of Panax notoginseng (Burk.) F.H. Chen using supercritical fluid chromatography-mass spectrometry and chemical pattern recognition

An efficient supercritical fluid chromatography-mass spectrometry (SFC-MS) method was developed for the quality evaluation of Panax Notoginseng (Burk) F.H. Chen (P. notoginseng) by combination with chemical pattern recognition (CPR). Design of experiments (DoE) was applied to obtain optimal SFC-MS conditions. Several CPR methods including hierarchical cluster analysis (HCA), principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to establish a classification model based on the peak areas and contents of 12 components in P. notoginseng in order to evaluate the quality difference according to the collecting time (Chunqi and Dongqi) and medicinal parts (fibrous root, rhizome, branch root, and main root). PLS-DA has proved to be a satisfactory method with accurate discrimination of the selected samples. The characteristic variables based on the variable importance in projection (VIP) values were selected using PLS-DA. Three characteristic components (ginsenoside Rg₂, ginsenoside Rg₁, ginsenoside Rb₁) with higher VIP values (>1) were chosen to further build the CPR model. Subsequently, the model was verified by testing another set of samples and the results indicated that the established model was satisfactory. PLS-DA models based on the peak areas of the 12 selected analytes in 30 batches of P. notoginseng could give accurate classification. The obtained results demonstrate that the developed method using SFC-MS and PLS-DA has a great potential for the quality assessment of P. notoginseng.