Impact of green infrastructures on urban microclimates: A critical review of data collection methods

Temperature is the most relevant factor affecting the urban microclimate. Increased urban density, hard surfaces with inappropriate thermal property and lack of vegetation have resulted in the increase of temperatures in urban areas and high pollution levels. In return, greening is known as the best solution for mitigating high temperatures in urban context, thus has recently become the focus of extensive research. In this paper, a systematic literature review is performed to investigate the effect of vegetation on microclimate in urban context focusing on air temperature, humidity and wind speed. Parks and urban green spaces are the features studied with respect to two different methodologies used for data collection: on-site measurements and numerical calculation or modelling. The aim of this study is to compare results of the two methodological approaches used by researchers in order to investigate their similarities and differences. The comparison shows that both methods can be applied for studies at various scales, from circumscribed areas such as parks and their surroundings (micro scale) to investigation including the whole city (mesoscale). However, due to costs and time constrains, studies using field measurements are mostly limited to one season thus lacking comparative analysis of effects of vegetation over longer periods. Instead, the use of simulations and modelling can help including different temporal scenarios and a variety of parameters in the study, even though in a more simplified form than in real case studies. According to the review while empirical data collection was more popular in the past, simulations and modelling have been used more frequently in recent years due to the advantages mentioned above. It also shows that best results can be achieved integrating the two approaches, where field measurements can either provide realistic data for simulation input or represent a means for validation of modelling outputs