Attention: From Tires to Organisms – NationalTransport, Exposure, and Eco-Health Risks of6PPD/6PPD-Q in Terrestrial-Aquatic Systems
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Authors
College of Environment and Climate, Jinan University, 511443 China
College of Environment and Climate, Jinan University, 511443 China
College of Environment and Climate, Jinan University, 511443 China
Institute of Malaysia and International Studies (IKMAS), Universiti Kebangsaan Malaysia (The National University of Malaysia), Malaysia
Abstract
Urbanization and global transportation expansion have led to widespread release of tire-derived contaminants N-(1,3-dimethylbutyl)-N’-phenyl-p-phenylenediamine (6PPD) and its quinone derivative (6PPD-Q), posing critical environmental and public healthrisks. As a key tire anti-ozonant, 6PPD rapidly oxidizes to 6PPD-Q, a highly toxic compound linked to aquatic organism mor-tality. This review synthesizes their "tires-to-organisms" pollution continuum across terrestrial-aquatic systems. Focusing onChina (the world’s largest tire producer/consumer), 6PPD-Q emissions surged 97.5% (68.2–134.7 tons) from 2013–2023, withsoil as the primary environmental sink (55.67%). 6PPD/6PPD-Q exhibit dual mobility, pervading air, water, and soil, andinduce species-specific toxicity—especially in aquatic early life stages—via conserved pathways (oxidative stress, mitochondrialdysfunction). Ubiquitous in human urine, serum, and cerebrospinal fluid, they pose multi-organ risks, with children, pregnantwomen, and occupational workers most vulnerable, linked to non-alcoholic fatty liver disease, colorectal cancer, and neurologicaldysfunction. Critical research gaps include EV-related tire wear impacts, terrestrial non-model organism toxicity, longitudinalhealth studies, and mitigation validation. This work provides a comprehensive framework to inform evidence-based policiesand safeguard ecosystems and human health.
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References
Chen, Z., et al. (2022). Dynamicvit: Efficient vision transformers with dynamic token sparsification. Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 13907–13916.
Fedus, W., et al. (2021). Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. Adv. Neural Inf. Process. Syst., 34, 1037–1051.
He, Y., et al. (2021). Layer-adaptive structured pruning guided by latency. Adv. Neural Inf. Process. Syst., 34, 12497–12509.
Lepikhin, D., et al. (2022). Gshard: Scaling giant models with conditional computation and automatic sharding. Proc. Int. Conf. Mach. Learn., 12965–12977.
Li, B., Xu, C., Zhang, D., Wang, S., Xu, J., Xiao, B., Feng, Y., Fu, H. Y., Chen, X., & Zhang, Z. (2025). Combined analysis of network toxicology and multiomics revealed the potential mechanism of 6PPDQ-induced hepatotoxicity in mice. Environmental Science & Technology, 59, 10204–10214. doi:10.1021/acs.est.5c03906.
Li, Y., et al. (2021). Dyhead: Unifying object detection heads with attentions. Adv. Neural Inf. Process. Syst., 34, 22661–22672.
Liang, Y., Zhu, F., Li, J., Wan, X., Ge, Y., Liang, G., & Zhou, Y. (2024). P-phenylenediamine antioxidants and their quinone derivatives: A review of their environmental occurrence, accessibility, potential toxicity, and human exposure. Science of the Total Environment, 948, 174449. doi:10.1016/j.scitotenv.2024.174449.
Lillicrap, T., et al. (2015). Continuous control with deep reinforcement learning. Proc. Int. Conf. Mach. Learn., 1871–1880.
Lin, T. Y., et al. (2014). Microsoft COCO: Common objects in context. Proc. Eur. Conf. Comput. Vis., 740–755.
OpenAI (2023). GPT-3.5 technical report. OpenAI Res.
Qian, C., Yang, Z., Cheng, Y., Zheng, Y., Wang, X., & Wang, H. (2025). 6-PPD triggered lipid metabolism disorder and inflammatory response in larval zebrafish (Danio rerio) by regulating PPARγ/NF-κB. Environmental Pollution, 368, 125785. doi:10.1016/j.envpol.2025.125785.
Raffel, C., et al. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21, 1–67.
Ramezany, S., Martinez, G., Mugnai, A., Houle, D., & Bellenger, J. P. (2025). Feasibility of using Pleurozium schreberi as a biomonitor to study antiozonant dispersion: A case study in southern Quebec. Science of the Total Environment, 994, 180047. doi:10.1016/j.scitotenv.2025.180047.
Somepalli, K., & Andaluri, G. (2025). Transformation pathways, detection, removal, and sustainable alternatives of 6PPD and its quinone derivative (6PPDQ): A comprehensive review. Emerging Contaminants, 11, 100547. doi:10.1016/j.emcon.2025.100547.
Tang, L., Li, X., Zhu, S. Y., Wan, Y. K., Zhang, S. K., Ma, J. Z., Yang, Y., Chen, A. W., Luo, L., & Cao, L. Y. (2025). 6PPD and 6PPD quinone induce endometrial cell dysfunction via activating ERα and GPER at human-relevant levels. Environmental Science & Technology, 59, 9918–9929. doi:10.1021/acs.est.4c13961.
Tian, Z., Zhao, H., Peter, K. T., Gonzalez, M., Wetzel, J., Wu, C., Hu, X., Prat, J., Mudrock, E., & Kolodziej, E. P. (2020). A ubiquitous tire rubber-derived chemical induces acute mortality in coho salmon. Science, 371, 185–189. doi:10.1126/science.abd6951.
Wang, A., et al. (2019). GLUE: A multi-task benchmark and analysis platform for natural language understanding. Proc. Int. Conf. Learn. Represent.
Wang, B., Sun, W., Ye, X., Liu, Z., & Zhang, H. (2025a). Occurrence, analytical methods, and ecotoxicological effects of 6PPD-Quinone in aquatic environments: A review. TrAC Trends in Analytical Chemistry, 153, 118449. doi:10.1016/j.trac.2025.118449.
Wang, W., Li, Y., & Wang, D. (2025b). Exposure to 6-PPD Quinone disrupts adsorption and catabolism of leucine and causes mitochondrial dysfunction in Caenorhabditis elegans. Toxics, 13, 544. doi:10.3390/toxics13070544.
Xu, F., Su, M., & Tang, S. (2025a). Spatiotemporal distribution of 6PPD-Q in China revealed by a national-scale quantification framework. Environmental Science & Technology. doi:10.1021/acs.est.5c12242.
Xu, K., Mittal, K., & Basu, N. (2025b). Transcriptomic points of departure for 6PPD-Quinone derived from human Caco-2 and HepG2 cells. Toxicology in Vitro, 109, 106129. doi:10.1016/j.tiv.2025.106129.
Zhang, S., Tang, J., Qiu, Z., Huo, X., Liu, D., & Zeng, X. (2025). Environmental and human health risks of 6PPD and 6PPDQ: Assessment and implications. Toxics, 13, 873. doi:10.3390/toxics13100873.
Zhang, Y., & Li, M. (2025a). A control-theoretic MCP framework for MLLMs’ efficiency and interpretability. Preprints. doi:10.20944/preprints202509.0093.v2.
Zhang, Y., & Li, M. (2025b). Text mining and Machine Learning-based data extraction for environmental pollutants (PPDs and PPDs-Q): Machine Learning-enabled global aqueous concentration compilation and potential biotic risk implications. Preprints. doi:10.20944/preprints202508.1957.v1.
Zhou, D., et al. (2022). Automatic chain of thought prompting in large language models. Adv. Neural Inf. Process. Syst., 35, 1788–1801.
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All data used in this study were obtained from publicly accessible databases (Web of Science, Scopus, PubMed, and U.S. EPA ECOTOX). The literature mining pipeline and related scripts are available in a public GitHub repository as specified in the Methods section. Processed datasets supporting the conclusions are provided within the article and Supplementary Materials. No new experimental datasets were generated.