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Algorithmic optimization of climbing plant positioning and CNC fabrication for urban microclimate enhancement

2026/04/15 by Dominik Sędzicki, Lucyna Nyka, Jan Cudzik
Agricultural and Biological Sciences · Engineering · Environmental Science · #Architecture and Computational Design #Greenhouse Technology and Climate Control #Urban Heat Island Mitigation

paper · pdf · doi:10.1080/09613218.2026.2656666

openalex publication_date 2026/04/15 · openalex created_date 2026/04/17 · openalex updated_date 2026/07/13

Abstract

This research examines the integration of CNC-fabricated plywood installations with algorithmically positioned climbing plants to address localized microclimatic challenges in confined urban spaces. Conducted in a poorly ventilated courtyard in Gdańsk, Poland, the study investigates air temperature and relative humidity from June to September, comparing conditions within the installation with those in adjacent unshaded areas and at a municipal weather station. The algorithmic method integrates site-specific environmental parameters, such as solar exposure, temperature gradients and soil moisture, with species-specific ecological requirements to optimize plant positioning, with plant health and long-term viability as the primary objectives and microclimatic modification through shading and evapotranspiration as secondary outcomes. Measurements indicate consistent increases in relative humidity and modest, seasonally dependent reductions in air temperature near the installation, alongside substantial day-to-day variability typical of urban microclimates. As these effects are site-specific and derived from a single, short-term study without extended pre-installation baselines or comprehensive meteorological variables, the findings are interpreted as evidence of local microclimatic moderation rather than urban heat-island mitigation. The research contributes to urban climate adaptation by demonstrating the integration of computational design, digital fabrication and ecological principles, and by offering a replicable framework for data-driven, small-scale green infrastructure design in space-constrained environments.

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