Meter-scale pedestrian PET and cooling-lever attribution with an ai surrogate: a microclimate digital twin for a heat-resilient Barcelona superblock
Turning incoming weather into per-street heat decisions for urban climate resilience
DOI:
https://doi.org/10.66838/sauc.6427Keywords:
pedestrian thermal comfort, physiologically equivalent temperature, urban microclimate, street trees, machine-learning surrogate, heat-mitigation policy, BarcelonaAbstract
Pedestrian heat stress, expressed as physiologically equivalent temperature (PET), swings across dangerous thresholds within meters as sun, tree shade, and building shadow change, yet weather and satellite data are too coarse to resolve it, and city heat indicators do not map to what a pedestrian feels. For a superblock in Barcelona, we compute meter-scale diurnal pedestrian PET from measurement-driven unsteady CFD (with and without vegetation) and shade-aware radiation post-processing, and validate its air, humidity, and surface-temperature inputs against mobile transects, contact points, and a thermal camera. We decompose street-tree cooling into shade, evapotranspiration, and wind, compare heat measures on one PET metric, and train a machine-learning surrogate that rebuilds the block field in seconds. On a clear July afternoon, after correcting a validated warm-air bias, most of the block reaches strong heat stress; tree cooling is almost entirely shade and concentrates where the canopy shadow falls, while reflective pavement raises pedestrian PET even as it cools the road. The surrogate reproduces the sun/shade field at a thousandth of the cost, a same-day validated microclimate digital twin that maps pedestrian heat at street and hour scale and weighs the city's canopy target, reflective-pavement pilot, and shelter network on one measure.
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