Keywords

1 Introduction

Dense cities expose people to different kinds of climate vulnerabilities: extreme events, concentration of inhabitants in risk-prone areas, inadequate buildings and many others.

This is why research attention on spatial configuration and physical features of cities has recently risen in various disciplines, including geography, urban ecology, urban and environmental design, building design, urban climatology and building physics (Erell 2012). As a reaction to the demanding need to mitigate climate change and the ecological impact of the built environment, in order to adapt to inevitable consequences and promote health and well-being, research efforts have focused on the unintended interaction among cities’ physical characteristics, microclimate and energy balance with a diagnostic or design perspective (Lenzholer 2015; Stewart and Oke 2012). This led to a number of relevant changes in the design discourse and structure—both in term of process and method—and contributed to highlight the key elements for the decisions about the main enforcement actions to be included in urban regeneration design process (Morganti and Rogora 2021).

However, this subject is still fragmented: studies hardly reach comprehensive outcomes due to the complexity of the above-mentioned interaction in the built environment and to the lack of skilled scholars and professionals. By consequence, practical application in urban regeneration process remains limited.

The present study proposes and discusses a novel digital design workflow (Fig. 69.1) that integrates urban climate quality and associated indicators in urban and architectural design, adopting a cross-scale approach. The novelty of the study lies in permitting architects to analyse environmental performance and to take evidence-based urban and building design choices. The main goal is to help architects and urban designers to easily control climate and energy parameters, impacts and associated urban vulnerability trough well-known digital tools.

Fig. 69.1
An illustration indicates the objectives as social well-being and climatic well-being. It indicates the workflow and interactions between the city scale, district scale, and building scale, along with the analytical level and design level.

Visual representation of the workflow

Full size image

2 Materials and Method

2.1 Analytical and Design Approach

The design process has been supported by the workflow in Fig. 69.2. It is articulated by running both the analytical process of environmental simulations and the design process. The analyses were executed through digital tools. The main objective was to validate the current state of the art about the impact of certain specific parameters on the urban microclimate performance, crossing different scales: the neighbourhood, the district, the island and the building. The impact of urban morphology at neighbourhood level on urban microclimate and UHI was investigated, by focusing on the influence of building typology and form to heating and cooling demand in the Mediterranean climate.

Fig. 69.2
A set of 2 site plans. The plan at the bottom illustrates the constructed buildings around the region.

Current state (top) and masterplan (bottom) of the project area

Full size image

2.2 Case Study

A regeneration project of an urban area of 22@ Innovation district of Poblenou in Barcelona has been used as case study to test the novel digital design workflow (Fig. 69.2). The project area is about 8 hectares and currently characterized by a limited number of low-energy efficiency housings, scattered across industrial buildings. The reference scenario is tested by exploiting the research-by-design transformations of the above-mentioned project area. Four different scenarios are presented to compare in detail at both neighbourhood and building scale.

2.3 Climate and Microclimate Evaluation

These sections describe how climate and microclimate were studied and the metrics used to compare different scenarios. The study mainly covers the summer period, during which the urban fabric’s morphological characteristics influence the urban heat island most significantly. A preliminary analysis was carried out on the Mediterranean climate of Barcelona, characterised by hot, dry summers (Csa Koppen-Geiger climate classification), shown in Fig. 69.3. The analysed climate data from Barcelona El Prat airport will later be considered as rural station data for the urban heat island analysis. The typical climate presents relatively high average outdoor temperatures throughout the year (average annual temperature 16°), and a rather high average annual relative humidity of 73%, due to the proximity of the sea.

Fig. 69.3
2 graphs. A double bar graph on the left exhibits the data on average monthly air temperature and relative humidity. The dry bulb temperatures peak in the seventh and eighth months and then descend. A line graph on the right plots monthly solar radiation over the year.

Climatic conditions of Barcelona (based on Barcelona 081,810 IWEC EPW file). Average monthly air temperature and relative humidity values (left); monthly average daily Solar Radiation values (right)

Full size image

2.4 UHI District Scale

The effect that the regenerative design of the case study’s urban area has on the urban heat island was assessed using Urban Weather Generator (Bueno et al. 2012). The UWG algorithm evaluates the difference in temperatures between a rural context and the urban canopy layer; the UWG calculation tool has already been validated for Barcelona’s climate in previous researches (Salvati et al., 2019). The ‘rural’ EPW climate weather file, which provides the meteorological inputs, was taken from EnergyPlus weather data (Barcelona 081810 IWEC), and includes climate data from Barcelona El Prat airport (Elev. 4 m, 41.33 °N, 2.1 °E). The 3D model of the project was used to provide the morphological input parameters (Fig. 69.4). Table 69.1 lists the input variables that were customised, while others, such as the traffic-sensitive heat flux, kept their default values.

Fig. 69.4
A site plan highlights an area of 10970899 square meters.

Analysed scenario—district scale

Full size image
Table 69.1 Customized input UWG parameters
Full size table

2.5 UHI Island Scale—Comparative Analysis

The UHI effect has been evaluated to compare four different scenarios of an island in the project area. The three alternative scenarios, against which the SA_Courtyard has been cross-referenced, were created, by varying the morphology of the urban fabric, and by fixing the built-up cubature at about 112.000 m3 (as in the reference block), as shown in Fig. 69.5. The single island was assumed to repeat homogeneously over an area of 1 km × 1 km, and we focused on the impact that the variation of morphological parameters alone (Site coverage, Façade to site ratio, Average building height) can have on the UHI in the Mediterranean context. (Salvati et al., 2019) This comparison was central to validate how different urban forms display different climatic performances, and to prove that some morphological parameters have a more negative effect on the UHI than others.

Fig. 69.5
A chart of 4 sets of 2 D and 3 D plans of a city block. The plans denote scenarios a through d. A table at the bottom exhibits the details of morphological parameters, S A courtyard, S B slab, S C block, and S D tower.

Framework of the city block (top left); visual representation of four different alternative urban fabric (top right); values of the morphological parameters used for calculating the UHI (bottom)

Full size image

2.6 Energy Demand—Building Scale

Moving to the building scale, a courtyard residential building from the reference model was studied and designed in further detail. A comparative analysis of the energy demand for heating and cooling was carried out using EnergyPlus software. Three alternative building types for the courtyard one, based on existing building plans (linear block, low-rise block, high-rise tower) has been computed (Fig. 69.6), highlighting the interdependence of different design scales and addressing optimal building performances. The typologies have been put in their urban context of the four scenarios used for the UHI assessment, by inserting, as input climate data for each scenario modified running UWG from the rural one of Barcelona El Prat airport. The study has a comparative purpose, and it does not constitute an absolute assessment of energy demand, since some factors that have been kept by default or not taken into account. Table 69.2 shows the input values: in modelling the scenarios, the shape (compactness) and window-to-wall ratio parameters were varied, while keeping constant the envelope performance, the activity and the HVAC templates set to the standard values for residential buildings by the Código Técnico de la Edificación (Ministerio de Fomento 2017).

Fig. 69.6
An illustration represents a group of 3 D structures. The structures are grouped into 4 parts, which denote the S A courtyard, S C linear block, S C block, and S D tower. The buildings are marked as cubic blocks.

Visual representation of the four scenarios modelled in DesignBuilder

Full size image
Table 69.2 Building scale analysis input parameter
Full size table

3 Results and Discussion

The new urban layout has been planned with mixed uses, large amounts of public open spaces and permeable surfaces. This ensures outdoor and indoor comfort and the availability of spaces that inhabitants can use as climate shelters to cope with the increasingly uncomfortable and risky conditions caused by climate change (Taleghani, 2018). The block becomes very permeable to pedestrians and large bicycles. Besides, pedestrian paths are designed to encourage active mobility and the strategic location of services, so as to have attractive, safe and always active streets in the neighbourhood.

3.1 UHI—District Scale

The average values of summer and winter UHI, for the transformed project area, are quite low, respectively, 0.9 for the average summer UHI (month of July) and 1.0 average winter UHI (month of January). The summer UHI has been further investigated, since for the Mediterranean climate the UHI in the warm months, as demonstrated in other studies (Natanian and Auer 2020), has more evident effects, including on the energy performance of the building. In July, the hottest month in the city of Barcelona, a maximum average UHI value of 1.4 is reached at midnight. These values were benchmarked by running UWG with input parameters given by the average values of 10 existing urban fabrics taken from the study by (Salvati et al., 2019). The baseline model has well higher average summer and winter UHI values than the new urban settlement, 2.2 summer UHI and 1.5 winter UHI, respectively, as shown in Fig. 69.7.

Fig. 69.7
A set of 4 line graphs arranged in 2 rows. The graphs in the upper row denote the plots of rural versus urban temperature at the district scale and existing fabrics. The graphs in the bottom row denote the plots for the average U H I at the district scale and existing fabrics.

UWG output graphs July UHI analysis: transformed urban area at district scale (top) and baseline model of existing urban fabrics (bottom)

Full size image

3.2 UHI—Island Scale

With the input parameters of Fig. 69.5, we obtain the summer and winter average UHI values reported in Table 69.3. The values refer to low ranges of UHI having all low compactness index fabrics, it is noticeable how, in particular for summer, UHI confirms what has been already proved in other studies: the increase of vertical density (more façade surfaces) in Mediterranean climate contexts, due to the phenomenon of multiple reflections of radiation between surfaces in Urban Canyons, impacts on UHI via the causation of temperatures higher than those in other scenarios, where vertical density is lower.

Table 69.3 Average UHI values in the four different scenarios
Full size table

To verify this trend, the UHI values of the four scenarios were also compared to those provided by the graphical tools created for different mean heights in the study by Salvati et al. 2019. In these diagrams, the UHI values are only reported based on the variation of the morphological facings (ρurb, VHurb, Hbld). In the UHI values provided by the diagrams, there is a more direct and linear correspondence between the increase of vertical density and summer UHI, and the same output as in the UWG simulation is not available, as the diagrams were created neglecting the parameters of vegetation and tree coverage, albedo of the surfaces. In both evaluations, the less dense urban plot with tower buildings is the one with the highest UHI values, as it has more façade area. On the other side, the project case study with courtyard building typology has the lowest values in both seasons.

3.3 Energy Demand

Figure 69.8 shows the results of the analysis of the heating and cooling demand of the buildings of the various case studies, compared with the respective compactness coefficients calculated as

$${\text{Rc}} = {\text{Se}}\frac{{{\text{Se}}}}{{{\text{Sg}}}}4836\frac{{{\text{Vt}}^{{\frac{{2}}{{3}}}} }}{{{\text{Sg}}}}$$
(69.1)
Fig. 69.8
A stacked bar graph plots the cooling and heating demands for the S A courtyard, S B slabs, S C blocks, and S C tower. S C tower denotes the highest cooling demand, whereas the S C blocks denote the highest heating value. Each element indicates the values of r subscript c and w percentage at the top.

Annual heating and cooling demand (kW/m2), compactness index, window-to-wall ratio (%), and the average values of summer and winter UHI for the four different scenarios

Full size image

which refers to the equivalent surface of a sphere with the same volume as the building (Serra and Coch 1995) and the window-to-wall ratio. Since the intention was to focus mainly on the effect of building morphology urban fabric on the building’s energy performance, the presence of any shading devices on the windows was not taken into account, as this would have considerably modified the results. The courtyard type clearly has the lowest overall annual demand, which result is obtained despite the high values of heating demand with respect to the other buildings, thanks to the lower cooling demand in the summer months. It is evident that there is a more direct relationship between energy performance and the amount of glazed surfaces than between the former and of the compactness index, as already shown by Premrov et al. 2016. The tower typology has the highest cooling demand due to the heat entering from the large number of windows, considered without shading, which in return in the winter months provide high solar gains and therefore a lower heating demand.

3.4 Rural and Urban Energy Demand

By running the four simulations with the climate data of Barcelona airport as well, it is possible to see in the graph in Fig. 69.9 the gap between the rural and the urban context which is influenced by the UHI. It can be seen that the benefits in winter due to the higher external temperatures of the rural contexts are in any case lower than the energy surplus required in the summer months for the cooling system.

Fig. 69.9
A stacked double bar graph plots the energy demand in the rural and urban environments and urban canopy layer cooling and heating demands for S A courtyard, S B slab, S C block, and S D tower. The colors of the bars denote the heating and cooling layers in the urban canopy and the heating and cooling of rural environments.

Comparison between annual cooling and heating demand of the four scenarios with and without the effect of the UHI

Full size image

4 Conclusions

Through the workflow described above, an urban regeneration project was developed in the Mediterranean urban climatic context, which, in parallel, acted as a case study for several climatic and microclimatic analyses, focusing in particular on the effects of the urban heat island phenomenon, calculated with the validated UWG tool. By carrying out comparative analyses with other scenarios, different in morphology from the reference one, the influence trend of some morphological variables on the built environment’s energy performance has been verified at district, island and building scale. The results suggested that, during the summer period, urban layout with courtyard buildings of low average height is to be preferred over other types of fabrics, which have higher ‘vertical density’ and contribute to the increase of temperatures in cities, particularly at night. Turning to the building scale, it has been further verified that regenerative design must have a holistic and cross-scale approach given the interdependence of the effects that these different levels have on climatic well-being.

The importance of this type of study is related to the fact that in the preliminary stages of planning and design, the choices that most affect the quality of the space and environmental and the energy performances of buildings are concentrated. For instance, those about the shape of buildings and of urban layout are increasingly difficult to modify as design advances. The study contributes to foster the integration of scientific knowledge on urban climatology and sustainability of urban systems into the planning and design practices for densification and/or regeneration of existing urban areas. Through the use of available digital tools for climate and microclimate assessments, it allows for an integrated, cross-scale control of the design process. This workflow can be considered reliable for a pre-design phase, while for more detailed analyses, it needs further integrations and other tools. A limitation also lies in the reliance on different digital tools that require parallel 3D model creation on different software, without being able to use a single digital graphic interface during the overall analysis development.