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Lookup NU author(s): Dr Xiang XieORCiD, Professor Mohamad KassemORCiD, Dr Sheen Cabaneros
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Urban governance is hindered by the disconnect between qualitative knowledge embedded in textual sources and the predictive capabilities of quantitative models, making proactive policymaking challenging. This study proposes and validates a two-phase framework that automates the 'dialogue' between the evidence paradigms for urban air quality management. In the first phase, a Retrieval-Augmented Generation (RAG) pipeline synthesises extensive literature into a structured knowledge graph, generating evidence-based policy hypotheses. This approach navigates the challenge of evaluating overwhelming potential policy combinations. Following this, these qualitative hypotheses are automatically translated into simulation scenarios and subjected to rigorous, ex-ante quantitative testing, providing the rapid feedback necessary to overcome slow policy evaluation cycles. A case study in London, addressing post-2023 ULEZ expansion air quality challenges, demonstrates the framework's real-world utility. The RAG identified a portfolio of next-generation interventions targeting heating systems, construction machinery, and solvent emissions. Subsequent simulation with the SHERPA model produced spatial impact maps of pollution-level reductions and, critically, uncovered complex system dynamics. The framework's ability to efficiently screen a broad portfolio of options while revealing such complex consequences underscores its value for proactive governance. By integrating textual and simulation evidence into a scalable methodology, this research provides a robust tool to enable more effective, evidence-informed urban environmental policy.
Author(s): Xie X, Kassem M, Cabaneros S, Pan HX
Publication type: Article
Publication status: Published
Journal: Journal of Environmental Management
Year: 2026
Volume: 404
Print publication date: 15/04/2026
Online publication date: 25/03/2026
Acceptance date: 22/03/2026
Date deposited: 23/03/2026
ISSN (print): 0301-4797
ISSN (electronic): 1095-8630
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.jenvman.2026.129470
DOI: 10.1016/j.jenvman.2026.129470
Data Access Statement: The source code for constructing and retrieving the Urban Air Quality Knowledge Graph and simulating is open-source and publicly available on GitHub at https://github.com/XiangX91/urban-air-quality-kg and https://github.com/XiangX91/urban-air-mitigation-sim, respectively. The baseline datasets for the SHERPA model, including emission inventories and source–receptor models, are distributed with the tool and are derived from publicly maintained European environmental data repositories.
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