Non-targeted screening and photolysis transformation of tire-related compounds in roadway runoff.

Roadway runoff serves as a crucial pathway for transporting contaminants of emerging concern (CECs) from urban environments to receiving water bodies. Tire-related compounds originating from tire wear particles (TWPs) have been frequently detected, posing a potential ecological threat. Yet, the photolysis of tire-related compounds within roadway runoff remains inadequately acknowledged. Addressing this deficit, our study utilized high-resolution mass spectrometry (HRMS) to characterize the chemical profile of roadway runoff across eight strategically selected sites in Guangzhou, China. 219 chemicals were identified or detected within different confidence levels. Among them, 29 tire-related contaminants were validated with reference standards, including hexa(methoxymethyl)melamine (HMMM), 1,3-diphenylguanidine (DPG), dicyclohexylurea (DCU), and N-cyclohexyl-2-benzothiazol-amine (DCMA). HMMM exhibited with the abundance ranging from 2.30 × 104-3.10 × 106, followed by DPG, 1.69 × 104-8.34 × 106. Runoff sample were exposed to irradiation of 500 W mercury lamp for photodegradation experiment. Photolysis results indicated that tire-related compounds with a low photolysis rate, notably DCU, DCMA, and DPG, are more likely to persist within the runoff. The photolytic rates were significantly correlated with the spatial distribution patterns of these contaminants. Our findings underscore TWPs as a significant source of pollution in water bodies, emphasizing the need for enhanced environmental monitoring and assessment strategies.

Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction.

The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database matching in non-targeted screening (NTS) analysis. In this study, we developed a machine learning (ML) model to predict RTs of CECs in NTS analysis. Using 1051 CEC standards, we evaluated Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN) with molecular fingerprints and chemical descriptors to establish an optimal model. The SVR model utilizing chemical descriptors resulted in good predictive capacity with R2ext = 0.850 and r2 = 0.925. The model was further validated through laboratory NTS compound characterization. When applied to examine CEC occurrence in a large wastewater treatment plant, we identified 40 level S1 CECs (confirmed structure by reference standard) and 234 level S2 compounds (probable structure by library spectrum match). The model predicted RTs for level S2 compounds, leading to the classification of 153 level S2 compounds with high confidence (ΔRT <2 min). The model served as a robust filtering mechanism within the analytical framework. This study emphasizes the importance of predicted RTs in NTS analysis and highlights the potential of prediction models. Our research introduces a workflow that enhances NTS analysis by utilizing RT prediction models to determine compound confidence levels.

Oxidation of 1,3-diphenylguanidine (DPG) by goethite activated persulfate: Mechanisms, products identification and reaction sites prediction.

As emerging pollutants continue to be discovered, studies on the degradation behavior of emerging pollutants have proliferated, but few studies have focused on the reactivity of the new pollutants themselves. The work investigated the oxidation of a representative roadway runoff-derived organic contaminant, 1,3-diphenylguanidine (DPG) by goethite activated persulfate (PS). DPG exhibited the highest degradation rate (kd = 0.42 h-1) with present of PS and goethite at pH 5.0, then started to decrease with increasing pH. Chloride ion inhibited DPG degradation by scavenging HO·. Both HO· and SO4-· were generated in goethite activated PS system. Competitive kinetic experiments and flash photolysis experiments were conducted to investigate free radical reaction rate. The second-order reaction rate constants for DPG reacting with HO· and SO4-· were quantified (kDPG + HO·,kDPG + SO4-·), which both reached above 109 M-1 s-1. Chemical structures of five products were identified, four of them were previously detected in DPG photodegradation, bromination and chlorination processes. By density functional theory (DFT) calculations, ortho- and para- C were more easily attacked by both HO· and SO4-·. Abstraction of H on N by HO· and SO4-· were the favorable pathways, and the product TP-210 might be generated by cyclization of DPG radical from abstraction of H on N (3). The results of this study help us to better understand the reactivity of DPG with SO4-· and HO·.