• Synthesises 56 empirical studies on summertime residential window operation. • Identifies strong room-specific and climate-dependent behavioural patterns. • Reveals urban noise, pollution, and security as major inhibitors of ventilation. • Reviews and compares modelling approaches from logistic to hybrid data-driven frameworks. • Outlines pathways for adaptive design and context-sensitive ventilation standards. Maintaining summertime Indoor Environmental Quality (IEQ) in dwellings is critical for energy efficiency, health, and occupant comfort, especially in dense cities facing rising overheating risks. Window opening remains one of the most accessible and impactful adaptive behaviours for improving Natural Ventilation (NV) and regulating Indoor Air Quality (IAQ), temperature, and humidity. However, evidence on window opening behaviours remains fragmented across room types, climates, and neighbourhood contexts. This systematic review synthesises 56 empirical studies of summertime residential window operation. Evidence mapping shows a literature bias toward temperate, urban settings, often based on small, monitored cohorts and binary state logs. Clear room-specific patterns emerge: bedrooms exhibit nocturnal and early-morning purge signatures, whereas living rooms display daytime, activity-driven use. Behaviour is influenced by climate and context: urban noise, pollution, and privacy concerns suppress overnight window opening, while high humidity and rainfall in hot-humid regions shorten openings and trigger earlier mechanical Air Conditioning (AC). Time of day and temperature are the most consistent window opening behavioural drivers, while IAQ indicators such as CO₂ vary more by room type. Evidence remains limited regarding the roles of pollution, noise, air-conditioning presence, window-to-room ratios, façade-level wind effects, and the geographic transferability of models. Current window opening behaviour modelling and validation practices remain limited, and most national guidelines lack standardised window-use assumptions. Future research should integrate real-time behavioural and contextual data, adopt mixed-effects and non-linear models with event-based validation, and develop climate- and room-specific behavioural profiles to support adaptive, energy-efficient, and occupant-responsive design.
Fallahpour et al. (Thu,) studied this question.
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