Framework for nontargeted investigation of contaminants released by wildfires into stormwater runoff: Case study in the northern San Francisco Bay area

Comparison of Trace Organic Chemical Removal Efficiencies between Aerobic and Anaerobic Membrane Bioreactors Treating Municipal Wastewater

Evaluating persistent trace organic chemicals (TOrCs) and transformation products (TPs) in membrane bioreactors (MBRs) is essential, given that MBRs are now widely implemented for wastewater treatment and water reuse. This research applied comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC/TOF-MS)-based nontargeted analysis to compare the effectiveness of parallel aerobic and anaerobic MBRs (AeMBRs and AnMBRs, respectively), treating the same municipal wastewater. The average total chromatographic feature peak area abundances were significantly reduced by 84% and 72% from influent to membrane permeate in both the AeMBR and AnMBR (p < 0.05), respectively. However, the reduction of the average number of chromatographic features was significant for only AeMBR treatment (p = 0.006). A similar number of TPs were generated during both AeMBR and AnMBR treatments (165 vs 171 compounds, respectively). The overall results suggest that the AeMBR was more effective for reducing the diversity of TOrCs than the AnMBR, but both aerobic and anaerobic processes had a similar reduction of TOrC abundance. Suspect screening analysis using GC×GC/TOF-MS, which resulted in the tentative identification of 351 TOrCs, proved to be a powerful approach for uncovering compounds previously unreported in wastewater, including many fragrances and personal care products.

Novel chemical contaminants associated with thirdhand smoke in settled house dust

Thirdhand smoke (THS) is the persistent and toxic residue from tobacco smoke in indoor environments. A comprehensive understanding of the chemical constituents of THS is necessary to assess the risks of long-term exposure and to establish reliable THS tracers. The objective of this study was to investigate compounds associated with THS through nontargeted analysis (NTA) of settled house dust samples from smokers' and non-smokers' homes, using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC/TOF-MS). Compounds that were either only present in dust from smokers' homes or that had significantly larger abundance than in non-smokers' homes were termed qualified compounds. We identified 140 qualified compounds, and of these, 42 compounds were tentatively identified by searching matching mass spectra in NIST electron impact (EI) mass spectral library including 20 compounds confirmed with their authentic standards. Among the 42 compounds, 26 compounds were statistically more abundant (p < 0.10) in dust from homes of smokers; seven were tobacco-specific compounds, two of which (nornicotyrine, 3-ethenylpyridine) have not been reported before in house dust. Two compounds, tris (2-chloroethyl) phosphate (a toxic compound used as a flame retardant and reported in tobacco) and propanoic acid, 2-methyl-, 1-(1,1-dimethylethyl)-2-methyl-1,3-propanediyl ester (highly abundant and reported in exhaled air of smokers), were found in dust from all smokers' homes and in zero non-smokers' homes, making these potential THS tracers, possibly associated with recent smoking. Benzyl methyl ketone was significantly higher in dust in smokers' homes, and was previously reported not as a product of tobacco but rather as a form of methamphetamine. This compound was recently reported in mainstream tobacco smoke condensate through NTA as well. These identified potential tracers and chemical components of THS in this study can be further investigated for use in developing THS contamination and exposure assessments.

Exploring applications of non-targeted analysis in the characterization of the prenatal exposome

Capturing the breadth of chemical exposures in utero is critical in understanding their long-term health effects for mother and child. We explored methodological adaptations in a Non-Targeted Analysis (NTA) pipeline and evaluated the effects on chemical annotation and discovery for maternal and infant exposure. We focus on lesser-known/underreported chemicals in maternal and umbilical cord serum analyzed with liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS). The samples were collected from a demographically diverse cohort of 296 maternal-cord pairs (n = 592) recruited in San Francisco Bay area. We developed and evaluated two data processing pipelines, primarily differing by detection frequency cut-off, to extract chemical features from non-targeted analysis (NTA). We annotated the detected chemical features by matching with EPA CompTox Chemicals Dashboard (n = 860,000 chemicals) and Human Metabolome Database (n = 3140 chemicals) and applied a Kendrick Mass Defect filter to detect homologous series. We collected fragmentation spectra (MS/MS) on a subset of serum samples and matched to an experimental MS/MS database within the MS-Dial website and other experimental MS/MS spectra collected from standards in our lab. We annotated ~72 % of the features (total features = 32,197, levels 1-4). We confirmed 22 compounds with analytical standards, tentatively identified 88 compounds with MS/MS spectra, and annotated 4862 exogenous chemicals with an in-house developed annotation algorithm. We detected 36 chemicals that appear to not have been previously reported in human blood and 9 chemicals that were reported in less than five studies. Our findings underline the importance of NTA in the discovery of lesser-known/unreported chemicals important to characterize human exposures.

Leached Compounds from Smoked Cigarettes and Their Potential for Bioaccumulation in Rainbow Trout (Oncorhynchus mykiss)

Cigarette butts are one of the most prevalent forms of litter worldwide and may leach toxic compounds when deposited in aquatic environments. Previous studies demonstrated that smoked cigarette leachate is toxic toward aquatic organisms. However, the specific bioavailable chemicals from the leachate and the potential for human and wildlife exposure through the food chain were unknown. Using a nontargeted analytical approach based on GC×GC/TOF-MS, 43 compounds were confirmed to leach from smoked cigarettes when exposed to a water source. Additionally, the bioaccumulation potential of organic contaminants in an edible fish, rainbow trout (Oncorhynchus mykiss), was assessed through direct exposure to the leachate of smoked cigarettes at 0.5 CB/L for 28 days. There was a significant reduction in fish mass among the exposed rainbow trout vs the control group (χ2 (1) = 5.3, p = 0.021). Both nontargeted and targeted chemical analysis of representative fish tissue identified four tobacco alkaloids, nicotine, nicotyrine, myosmine, and 2,2'-bipyridine. Their average tissue concentrations were 466, 55.4, 94.1, and 70.8 ng/g, respectively. This study identifies leached compounds from smoked cigarettes and demonstrates the uptake of specific chemicals in rainbow trout, thus suggesting a potential for accumulation in food webs, resulting in human and wildlife exposure.

Chemical tracers for Wildfires-Analysis of runoff surface Water by LC/Q-TOF-MS

A quantitative methodology using high resolution mass spectrometry was developed for the identification of organic compounds derived from wildfires in surface water samples. The methodology involves the use of solid-phase extraction (SPE) followed by detection using liquid chromatography-quadrupole time of flight-mass spectrometry (LC/Q-TOF-MS) for a group of fourteen chemical compounds (pyridine, benzene, naphthalene and biphenyl polycarboxylic acids). All compounds were successfully separated chromatographically using a reversed phase column and they were identified by accurate mass using the deprotonated species and their main fragment ions. The method produced excellent accuracies (>95%) and precisions (3-10%) for all the compounds studied. This methodology was successfully applied to the identification of fourteen compounds in runoff surface waters impacted by wildfires in Colorado in 2020. Concentrations of individual compounds ranging from 0.1 to 59.5 μg/L were found in wildfire impacted waters, with totals of ∼200 μg/L, thus showing these compounds as chemical tracers of wildfire events at significantly high concentrations. In addition, non-target analysis using chromatography patterns and mass spectrometry identification by MS-MS revealed other polycarboxylic acid isomers were also present in runoff surface water samples.

Micron-size tire tread particles leach organic compounds at higher rates than centimeter-size particles: Compound identification and profile comparison

Tire tread particles (TTP) are environmentally prevalent microplastics and generate toxic aqueous leachate. We determined the total carbon and nitrogen leachate concentrations and chemical profiles from micron (∼32 μm) and centimeter (∼1 cm) TTP leachate over 12 days. Dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) were used to measure the concentration of leached compounds. Nontargeted chemical analysis by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC/TOF-MS) was used to compare the chemical profiles of leachates. After leaching for 12 days, DOC was 4.0 times higher in the micron TTP leachate than in the centimeter TTP leachate, and TDN was 2.6 times higher. The total GC×GC/TOF-MS chromatographic feature peak area was 2.9 times greater in the micron TTP leachate than the centimeter TTP leachate, and similarly, the total relative abundance of 54 tentatively identified compounds was 3.3 times greater. We identified frequently measured tire-related chemicals, such as 6PPD, N-cyclohexyl-N'-phenylurea (CPU), and hexa(methoxymethyl)melamine (HMMM), but nearly 50% of detected chemicals were not previously reported in tire literature or lacked toxicity information. Overall, the results demonstrate that smaller TTP have a greater potential to leach chemicals into aquatic systems, but a significant portion of these chemicals are not well-studied and require further risk assessment.

Suspect Screening and Chemical Profile Analysis of Storm-Water Runoff Following 2017 Wildfires in Northern California

The combustion of structures and household materials as well as firefighting during wildfires lead to releases of potentially hazardous chemicals directly into the landscape. Subsequent storm-water runoff events can transport wildfire-related contaminants to downstream receiving waters, where they may pose water quality concerns. To evaluate the environmental hazards of northern California fires on the types of contaminants in storm water discharging to San Francisco Bay and the coastal marine environment, we analyzed storm water collected after the northern California wildfires (October 2017) using a nontargeted analytical (NTA) approach. Liquid chromatography quadrupole time-of-flight mass spectrometric analysis was completed on storm-water samples (n = 20) collected from Napa County (impacted by the Atlas and Nuns fires), the city of Santa Rosa, and Sonoma County (Nuns and Tubbs fires) during storm events that occurred in November 2017 and January 2018. The NTA approach enabled us to establish profiles of contaminants based on peak intensities and chemical categories found in the storm-water samples and to prioritize significant chemicals within these profiles possibly attributed to the wildfire. The results demonstrated the presence of a wide range of contaminants in the storm water, including surfactants, per- and polyfluoroalkyl substances, and chemicals from consumer and personal care products. Homologs of polyethylene glycol were found to be the major contributor to the contaminants, followed by other widely used surfactants. Nonylphenol ethoxylates, typically used as surfactants, were detected and were much higher in samples collected after Storm Event 1 relative to Storm Event 2. The present study provides a comprehensive approach for examining wildfire-impacted storm-water contamination of related contaminants, of which we found many with potential ecological risk. Environ Toxicol Chem 2022;41:1824-1837. © 2022 SETAC.

Automated high confidence compound identification of electron ionization mass spectra for nontargeted analysis

Nontargeted analysis based on mass spectrometry is a rising practice in environmental monitoring for identifying contaminants of emerging concern. Nontargeted analysis performed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC/TOF-MS) generates large numbers of possible analytes. Moreover, the default spectral library similarity score-based search algorithm used by LECO® ChromaTOF® does not ensure that high similarity scores result in correct library matches. Therefore, an additional manual screening is necessary, but leads to human errors especially when dealing with large amounts of data. To improve the speed and accuracy of the chemical identification, we developed CINeMA.py (Classification Is Never Manual Again). This programming suite automates GC×GC/TOF-MS data interpretation by determining the confidence of a match between the observed analyte mass spectrum and the LECO® ChromaTOF® software generated library hit from the NIST Electron Ionization Mass Spectral (NIST EI-MS) library. Our script allows the user to evaluate the confidence of the match using an algorithmic method that mimics the manual curation process and two different machine learning approaches (neural networks and random forest). The script allows the user to adjust various parameters (e.g., similarity threshold) and study their effects on prediction accuracy. To test CINeMA.py, we used data from two different environmental contaminant studies: an EPA study on household dust and a study on stormwater runoff. Using a reference set based on the analysis performed by highly trained users of the ChromaTOF and GC×GC/TOF-MS systems, the random forest model had the highest prediction accuracies of 86% and 83% on the EPA and Stormwater data sets, respectively. The algorithmic approach had the second-best prediction accuracy (82% and 79%), while the neural network accuracy had the lowest (63% and 67%). All the approaches required less than 1 min to classify 986 observed analytes, whereas manual data analysis required hours or days to complete. Our methods were also able to detect high confidence matches missed during the manual review. Overall, CINeMA.py provides users with a powerful suite of tools that should significantly speed-up data analysis while reducing the possibilities of manual errors and discrepancies among users, and can be applicable to other GC/EI-MS instrument based nontargeted analysis.