Evaluation of Nontargeted Mass Spectral Data Acquisition Strategies for Water Analysis and Toxicity-Based Feature Prioritization by MS2Tox.

The machine-learning tool MS2Tox can prioritize hazardous nontargeted molecular features in environmental waters, by predicting acute fish lethality of unknown molecules based on their MS2 spectra, prior to structural annotation. It has yet to be investigated how the extent of molecular coverage, MS2 spectra quality, and toxicity prediction confidence depend on sample complexity and MS2 data acquisition strategies. We compared two common nontargeted MS2 acquisition strategies with liquid chromatography high-resolution mass spectrometry for structural annotation accuracy by SIRIUS+CSI:FingerID and MS2Tox toxicity prediction of 191 reference chemicals spiked to LC-MS water, groundwater, surface water, and wastewater. Data-dependent acquisition (DDA) resulted in higher rates (19-62%) of correct structural annotations among reference chemicals in all matrices except wastewaters, compared to data-independent acquisition (DIA, 19-50%). However, DIA resulted in higher MS2 detection rates (59-84% DIA, 37-82% DDA), leading to higher true positive rates for spectral library matching, 40-73% compared to 34-72%. DDA resulted in higher MS2Tox toxicity prediction accuracy than DIA, with root-mean-square errors of 0.62 and 0.71 log-mM, respectively. Given the importance of MS2 spectral quality, we introduce a "CombinedConfidence" score to convey relative confidence in MS2Tox predictions and apply this approach to prioritize potentially ecotoxic nontargeted features in environmental waters.

Nontarget Analysis of Polluted Surface Waters in Bangladesh Using Open Science Workflows.

Nontarget mass spectrometry has great potential to reveal patterns of water contamination globally through community science, but few studies are conducted in low-income countries, nor with open-source workflows, and few datasets are FAIR (Findable, Accessible, Interoperable, Reusable). Water was collected from urban and rural rivers around Dhaka, Bangladesh, and analyzed by liquid chromatography high-resolution mass spectrometry in four ionization modes (electrospray ionization ±, atmospheric pressure chemical ionization ±) with data-independent MS2 acquisition. The acquisition strategy was complementary: 19,427 and 7365 features were unique to ESI and APCI, respectively. The complexity of water pollution was revealed by >26,000 unique molecular features resolved by MS-DIAL, among which >20,000 correlated with urban sources in Dhaka. A major wastewater treatment plant was not a dominant pollution source, consistent with major contributions from uncontrolled urban drainage, a result that encourages development of further wastewater infrastructures. Matching of deconvoluted MS2 spectra to public libraries resulted in 62 confident annotations (i.e., Level 1-2a) and allowed semiquantification of 42 analytes including pharmaceuticals, pesticides, and personal care products. In silico structure prediction for the top 100 unknown molecular features associated with an urban source allowed 15 additional chemicals of anthropogenic origin to be annotated (i.e., Level 3). The authentic MS2 spectra were uploaded to MassBank Europe, mass spectral data were openly shared on the MassIVE repository, a tool (i.e., MASST) that could be used for community science environmental surveillance was demonstrated, and current limitations were discussed.

Phthalate metabolites in harbor porpoises (Phocoena phocoena) from Norwegian coastal waters.

The exposure of marine mammals to phthalates has received considerable attention due to the ubiquitous occurrence of these pollutants in the marine environment and their potential adverse health effects. The occurrence of phthalate metabolites is well established in human populations, but data is scarce for marine mammals. In this study, concentrations of 17 phthalate metabolites were determined in liver samples collected from one hundred (n = 100) by-caught harbor porpoises (Phocoena phocoena) along the coast of Norway. Overall, thirteen phthalate metabolites were detected in the samples. Monoethyl phthalate (mEP), mono-iso-butyl phthalate (mIBP), mono-n-butyl phthalate (mBP) and phthalic acid (PA) were the most abundant metabolites, accounting for detection rates ≥ 85%. The highest median concentrations were found for mIBP (30.6 ng/g wet weight [w.w.]) and mBP (25.2 ng/g w.w.) followed by PA (7.75 ng/g w.w.) and mEP (5.67 ng/g w.w.). The sum of the median phthalate metabolites concentrations that were found in the majority of samples (detection rates > 50%) indicated that concentrations were lower for porpoises collected along the coastal area of Bodø (Nordland), Lebesby (Finnmark) and Varangerfjord (as compared to other coastal areas); these areas are among the least populated coastal areas but also the most distant (>700 km) from offshore active oil and gas fields. The monomethyl phthalate metabolite (mMP) was detected in 69% of the samples, and to our knowledge, alongside with PA, this is the first report of their occurrence in marine mammals. PA, as the non-specific marker of phthalate exposures, showed a statistically significant negative association with the body mass and length of the harbor porpoises. Among the phthalate metabolites, statistically significant positive associations were found between mBP and mIBP, mMP and mEP, PA and mEP, mIBP and mono(2-ethyl-5-oxohexyl) phthalate (mEOHP), mIBP and mono(2-ethyl-5-hydroxyhexyl) phthalate (mEHHP), mBP and mEHHP, mono-n-nonyl phthalate (mNP) and PA, and between monobenzyl phthalate (mBzP) and mNP. To our knowledge, this is the first study on the biomonitoring of 17 phthalate metabolites in harbor porpoises.