Street-scale modelling, measurements, and participatory tools for climate-resilient urban greening

Urban streets are critical micro-environments where people experience disproportionately high exposure to air pollution and heat stress due to dense traffic, limited ventilation, and extensive surface sealing. Despite their importance for daily exposure and wellbeing, streets remain among the most challenging urban spaces for implementing effective climate adaptation and air-quality mitigation strategies at scales relevant to households and communities. This study is motivated by the need to translate evidence from street-scale environmental assessment into practical, inclusive, and actionable urban greening solutions. The primary objectives are threefold, first, we evaluates a set of street-level case studies to assess different combinations of green infrastructure (GI), including street trees, hedges, green walls, and pocket green spaces. Second, it integrates high-resolution street-scale modelling with in-situ measurements to quantify GI impacts, capture spatial variability, and identify context-specific trade-offs across contrasting street typologies. Third, the project translates scientific evidence into practice through the development of a decision-support framework and DIY Greening Cards, enabling residents, communities, and local authorities to select feasible, evidence-led greening interventions tailored to local constraints. To achieve these objectives, GP4Streets employs an integrated modelling–measurement framework. High-resolution street-scale dispersion and microclimate models are used to simulate changes in pollutant concentrations (e.g. PM_2.5 and NO₂) and thermal conditions arising from alternative GI scenarios. These simulations are complemented by in-situ measurements from fixed sensor networks deployed across streets with varying traffic intensity, and land-use characteristics, capturing real-world variability in air quality and thermal comfort. Model outputs and observations are jointly analysed to evaluate average effects, spatial heterogeneity, and the sensitivity of outcomes to street form, local emissions, vegetation characteristics, and meteorological conditions. Preliminary findings indicate that the effectiveness of GI at the street scale is highly context-dependent, with benefits strongly influenced by street configuration, vegetation type, and placement. While some GI combinations deliver measurable reductions in pollutant exposure and thermal stress, others introduce trade-offs related to airflow restriction or uneven distribution of benefits across the street canyon. Measurement results are further used to evaluate the model outcomes. By embedding scientific evidence within accessible DIY Greening cards and a decision-support tool, present work demonstrates how street-scale GI can be operationalised to support inclusive, scalable, and socially grounded approaches to urban climate adaptation and air-quality mitigation.