envair360: Physical Intelligence to Design, Operate, and Demonstrate the Impact of Urban Mobility—A Real-World Experience in Cartagena

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a framework for integrating multi-scale models and data governance in urban air quality and mobility research.

🏢実務担当者:Offers a blueprint for cities to design, deploy, and verify LEZ policies with transparent evidence chains.

🏛政策担当者:Demonstrates a methodology for evidence-gated policy implementation, useful for urban climate and air quality regulation.

Low-emission zones (LEZs) require cities to define policy rules, predict effects before deployment, and verify outcomes afterwards, yet mobility, emissions, meteorology, exposure, data governance, and public communication are commonly handled in separate systems. This paper presents envair360, a Physical Intelligence architecture and a four-stage, evidence-gated LEZ methodology connecting project definition, baseline feasibility, digital-twin design, deployment, and verified impact closure. A design-science method is combined with an operational case study of Cartagena, Spain, because the research object is both a socio-technical artefact and a context-dependent municipal deployment. The technology chain is selected to bridge complementary scales and functions: SUMO for link- and vehicle-level traffic, WRF and CHIMERE for meteorology and regional chemistry, MUNICH and street-canyon parameterisation for computationally tractable street resolution, model-output calibration anchored to measurements, and FIWARE/NGSI-LD for governed context exchange. The manuscript distinguishes city observations, peer-reviewed component validation, demonstrated platform capabilities, and policy or engineering targets. A Murcia component study reports lower hourly than daily agreement after deep-learning calibration (NO2: r=0.79 hourly and 0.94 daily; O3: r=0.85 hourly and 0.97 daily), illustrating the importance of temporal aggregation and transfer limits. Digitisation of the prior Madrid ozone-density figure indicates modal shifts of approximately +32.0 and +27.8 source-axis units at two stations; the supplied source does not permit a numerical NOx bias estimate. A separate six-city export audit covers 24,384 records and 4064 street segments and demonstrates a common model-output schema, not predictive validation. In Cartagena, project documentation reports elevated PM10/PM2.5, urban heat and solar-radiation stress, and a plausible role for dry-climate dust resuspension, supporting a superblock-oriented LEZ proposal with a long-term 30% vehicular CO2 reduction target. The paper’s specific contribution is the governed orchestration, evidence taxonomy, quality gates, reproducible lineage, explicit policy-scenario representation, and portable city-onboarding protocol; it does not claim that the individual scientific models, the Cartagena deployment, or the cited project targets originated in this manuscript.

• semanticscholar https://doi.org/10.3390/su18158017first seen 2026-08-09 05:29:50