Assessing Emissions from Pharmaceutical Manufacturing Based on Temporal High-Resolution Mass Spectrometry Data

Abstract

This study presents a nontarget approach to detect discharges from pharmaceutical production in municipal wastewater treatment plant (WWTP) effluents and to estimate their relevance on the total emissions. Daily composite samples were collected for 3 months at two WWTPs in Switzerland, measured using liquid chromatography high-resolution mass spectrometry, and time series were generated for all features detected. The extent of intensity variation in the time series was used to differentiate relatively constant domestic inputs from highly fluctuating industrial emissions. We show that an intensity variation threshold of 10 correctly classifies compounds of known origin and reveals clear differences between the two WWTPs. At the WWTP receiving wastewater from a pharmaceutical manufacturing site, (i) 10 times as many potential industrial emissions were detected as compared to the WWTP receiving purely domestic wastewater; (ii) for 11 pharmaceuticals peak concentrations, >10 g/L and up to 214 g/L were quantified, which are clearly above typical municipal wastewater concentrations; and (iii) a pharmaceutical not authorized in Switzerland was identified. Signatures of potential industrial emissions were even traceable at the downstream Rhine monitoring station at a >4000-fold dilution. Several of them occurred repeatedly, suggesting that they were linked to regular production, not to accidents. Our results demonstrate that small wastewater volumes from a single industry not only left a clear signature in the effluents of the respective WWTP but also influenced the water quality of one of Europe's most important river systems. Overall, these findings indicate that pharmaceutical production is a relevant emission source even in highly developed countries with a strong focus on water quality, such as Switzerland.

Occurrence and Distribution of Pharmaceuticals and their Transformation Products in Luxembourgish Surface Waters

“…34 The second suspect list consists of 82 pharmaceutical TPs. These TPs were derived from two sources: PubChem 28 , and a recent study by Anliker et al 18 . From PubChem, TPs were obtained from the Transformations table of a given compound (where available) using R scripts 35 written to programmatically download transformation product information 36 .…”

“…28,30 Sixty-seven TPs were extracted from PubChem in this way (coming from a total of 53 parents -44 parents were on the original CNS list of 816 parent compounds, while the remaining 9 parents are actually themselves TPs with reciprocal transformations). The remaining 15 TPs were obtained from Anliker et al 18 . Curation of the final suspect list involved deduplication and multiple steps of interconversion between chemical identifiers (e.g., CAS to PubChem CID, InChIKey to CID) using PubChem's Identifier Exchange Service 37 to facilitate compound comparisons and ensure that the final list of 82 TPs was unique.…”

“…NTA and suspect screening are effective techniques for the monitoring and discovery of xenobiotics in the aquatic environment. [17][18][19][20] Nevertheless, the interpretation of HRMS data presents challenges that highlight the need for computational tools to enable the proper identification and annotation of the chemical components in environmental matrices. 21 MetFrag (https://ipb-halle.github.io/MetFrag/) 22 is an open source tool for compound identification, including in silico fragmentation, mass spectral matching, and metadata functions.…”

Retrospective non-target analysis to support regulatory water monitoring: from masses of interest to recommendations via in silico workflows

“…not paired with any compound database [9,40,49]. Many examples use only the Frag-menterScore to rank candidates retrieved from Chem-Spider alone [3,31,35], PubChem alone [29,61,64], or a combination of either or both with other databases [8,25,45,47] like KEGG [22], FOR-IDENT [30] and MassBank [36]. Several studies have begun to use one or more of MetFrag's post-relaunch capabilities such as data source, patent, and/or reference counts for the respective compound database used [4,5,11,39,41,42,63], spectral library similarity [4,5,11,21,63], and presence in suspect lists [5,28,41].…”

Retrospective Non-target Analysis to Support Regulatory Water Monitoring: From Masses of Interest to Recommendations via in silico workflows

“…m/z 152.0198 Scenario 3: High Spectral Scores but low Metadata Scores (Moderate MetFrag Score;[3][4][5][6] …”