Monitoring and modelling of indoor environmental quality to guide indoor greening applications

Indoor green infrastructures (iGI) can improve indoor environmental quality (IEQ), but inconsistent quantification methods undermine confidence in reported benefits and limit evidence-based design. This review synthesises monitoring and modelling methods to assess iGI impacts on IEQ, including outdoor-indoor interactions and climate change effects. Sixty studies were reviewed covering passive (potted plants, green walls, and indoor trees) and active (hydroponics, biofilters) systems. Both improve IEQ, with varying outcomes depending on configuration and environmental conditions. Passive systems, dominated by potted plants and green walls, demonstrate VOC reduction (typically 10%–30%) and help regulate temperature and humidity, while active systems achieve superior removal efficiencies for VOCs and particulate matter. The literature skews toward laboratory studies (59%) versus in-situ monitoring (33%). Many studies rely on short-duration chamber experiments (<1 m3; 6–48 h). Controlled settings report higher pollutant removal than real buildings, complicated by non-standardised iGI descriptors and ventilation data. Studies focus on a few plant taxa (Chlorophytum, Philodendron, Epipremnum, Spathiphyllum), measuring primarily temperature, humidity, and CO2. Modelling is context-specific, mainly targeting hygrothermal and CO2 processes in wall systems. Impacts depend on ventilation, lighting and greening intensity, with passive configurations reducing cooling demand by 22%–27% and low-light conditions weakening CO2 uptake [about 25 to 70 μmol/(m2∙s) PAR]. Outdoor–indoor interaction studies remain rare and poorly quantified. Few address future climate scenarios, creating uncertainty about benefits versus risks of elevated humidity, condensation/mould, and pollutant re-emission. Current practices inadequately attribute IEQ impacts to iGI interventions. Advancement requires standardised reporting protocols, integrated monitoring-modelling, explicit outdoor–indoor coupling, and climate-aware assessments with defined uncertainty boundaries.