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.

Persistence and removal of trace organic compounds in centralized and decentralized wastewater treatment systems

The persistence of trace organic chemicals in treated effluent derived from both centralized wastewater treatment plants (WWTPs) and decentralized wastewater treatment systems (DEWATS) is of concern due to their potential impacts on human and ecosystem health. Here, we utilize non-targeted analysis (NTA) with comprehensive two-dimensional gas chromatography coupled with time of flight mass spectrometry (GC × GC/TOF-MS) to conduct an evaluation of the common persistent and removed compounds found in two centralized WWTPs in the USA and South Africa and one DEWATS in South Africa. Overall, removal efficiencies of chemicals were similar between the treatment plants when they were compared according to the number of chemical features detected in the influents and effluents of each treatment plant. However, the DEWATS treatment train, which has longer solids retention and hydraulic residence times than both of the centralized WWTPs and utilizes primarily anaerobic treatment processes, was able to remove 13 additional compounds and showed a greater decrease in normalized peak areas compared to the centralized WWTPs. Of the 111 common compounds tentatively identified in all three influents, 11 compounds were persistent in all replicates, including 5 compounds not previously reported in effluents of WWTPs or water reuse systems. There were no significant differences among the physico-chemical properties of persistent and removed compounds, but significant differences were observed among some of the molecular descriptors. These results have important implications for the treatment of trace organic chemicals in centralized and decentralized WWTPs and the monitoring of new compounds in WWTP effluent.

Organic contaminants in human breast milk identified by non-targeted analysis

Understanding the health implications of human exposure to mixtures of chemical contaminants is aided by analytical methods that can screen for a broad range of both expected and unexpected compounds. We performed a proof-of-concept analysis combining human breast milk, a biomonitoring matrix for determining contaminant exposure to mothers and infants, with a non-targeted method based on comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC/TOF-MS). A total of 172 presumably anthropogenic halogenated compounds and non-halogenated cyclic and aromatic compounds were tentatively identified in breast milk from San Diego, California through mass spectral database searches. Forty of the compounds were prioritized for confirmation based on halogenation or 100% frequency of detection, and the identities of 30 were verified using authentic standards. Thirty-four (85%) of the prioritized contaminants are not typically monitored in breast milk surveys, and 31 (77%) are regulated in at least one market worldwide, indicating breast milk may be a useful biomonitoring matrix for non-targeted analysis and the assessment of human exposure to future emerging or undiscovered contaminants.

A Two-stage Peak Alignment Algorithm for Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry-Based Metabolomics

Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC/TOF-MS) has been applied to metabolomics analyses recently. However, retention time shifts in the two-dimensional gas chromatography will introduce difficulty to compare compound profiles obtained from multiple samples. In this work, a novel two-stage peak alignment algorithm has been developed for data analysis of GC×GC/TOF-MS. In the first stage, our algorithm detects and merges multiple peak entries of the same metabolite into one peak entry. After a z-score transformation of metabolite retention times, landmark peaks will be selected from all samples based on both two-dimensional retention times and mass spectrum similarity of fragment ions measured by Pearson's correlation coefficient. In the second stage, the original two-dimensional retention time shift will be corrected using a local linear fitting method. A progressive retention time map searching method is used to align peaks in all samples together based on the parameters optimized in the first stage. Our algorithm can avoid defining a threshold of retention time window and spectrum similarity, which is very difficult for the users since the experimental condition is always changed in different experimental runs, even for the repeat experiments. The experimental results show that our algorithm can work well in peak alignment from real biological samples, which is very important for the further analysis.