6PPD-quinone (6PPDQ) is a recently discovered chemical that is acutely toxic to coho salmon (Oncorhynchus kisutch) and can form via environmental exposure of 6PPD, a compound found extensively in tire wear particles (TWPs). TWPs deposited on roads are transported to aquatic ecosystems via stormwater, contributing to microplastic pollution and organic contaminant loads. However, little is known about the fate of TWPs and their leachable contaminants in these systems. We conducted three experiments at a high school in Tacoma, Washington, to quantify the treatment performance of permeable pavement (PP) formulations, a type of green stormwater infrastructure (GSI), for TWPs and ten tire-associated contaminants, including 6PPDQ. The PPs comprised concrete and asphalt, with and without cured carbon fibers, to improve the mechanical properties of PPs. Pavements were artificially dosed and had underdrains to capture effluent. Three experiments were conducted to evaluate PP mitigation of tire-associated pollution using cryomilled tire particles (cTPs). The 1st and 3rd experiments established a baseline for TWPs and contaminants and assessed the potential for continued pollutant release. During experiment 2, cTPs were applied to each pavement. Our results showed that the PPs attenuated >96 % of the deposited cTPs mass. An estimated 52-100 % of potentially leachable 6PPDQ was removed by the PP systems between the influent and effluent sampling stations. Background 6PPDQ concentrations in effluents ranged from 0 to 0.0029 mu g/L. Effluent 6PPDQ concentrations were not explained by effluent TWP concentrations in experiments 1 or 2 but were significantly correlated in experiment 3, suggesting that leaching of 6PPDQ from TWPs retained in the pavement was minimal during a subsequent storm. Our results suggest that PPs may be an effective form of GSI for mitigating tire-associated stormwater pollution. The improved strength offered by cured carbon fiber-amended pavements extends PP deployment on high-traffic roadways where tire-associated pollution poses the greatest environmental risk.

This study introduces a cutting-edge, high-resolution tool leveraging the predictive prowess of convolutional neural networks to advance the field of hazard assessment in urban pluvial flooding scenarios. The tool uniquely accounts for the high heterogeneity of urban space and the potential impact of complex climate scenarios, which are often underestimated by traditional data-reliant methods. Employing Shenzhen as a case study, the model showcased superior accuracy, resilience, and interpretability, illuminating potential flood hazards. The performance analysis shows that the model can accurately predict the vast majority of urban flood depths, but has errors in extreme flood predictions (depths greater than 35 cm). Findings underscore escalating flood impacts under enhanced scenario loads, with western and central Shenzhen-regions rife with construction-highlighted as particularly vulnerable. Under the most severe matrix scenario (Scenario 25), economic losses are estimated to be about $25,484 million. These commercial and residential hotspots are anticipated to suffer maximum economic loss, with these two areas accounting for 39.6% and 25.1% of the total losses, necessitating reinforced mitigation efforts, especially during extreme rainfall events and high soil saturation levels. In addition, the flooding control strategies should prioritize the reduction of flood inundation areas and integrate functionally oriented land use characteristics in their development. By aiding in the precise identification of flood-prone areas, this research expedites the development of efficient evacuation plans, bolsters urban sustainability, and augments climate resilience, ultimately mitigating flood-induced economic tolls.