Environmental Data, Modeling and Digital Simulation for the Evaluation of Climate Adaptation and Mitigation Strategies in the Urban Environment

Abstract

The worsening effects of climate change in urban settings imply the application of effective measures for climate adaptation and mitigation actions on the building–open space system. It means the development of innovative climate-resilient design approaches through the elaboration of knowledge processes and methodological workflows supported by key enabling technologies (KETs). This paper presents the middle results of the research project PRIN (Progetti di Rilevante Interesse Nazionale) 2017 “TECH-START—key enabling TECHnologies and Smart environmenT in the Age of gReen economy. Convergent innovations in the open space/building system for climaTe mitigation”. The goal of this paper is to show a methodological workflow and an operational protocol for digital modeling and simulation for the evaluation of climate adaptation and mitigation strategies in urban settings, applied to the case of the former Centro Polifunzionale Marianella in the northern area of Naples. The results of the experimental application demonstrate the effectiveness of the meta-design proposals, with particular reference to the reduction of vulnerability to the heat wave phenomenon. The experimentation expresses the consistency of the methodological workflow. The results obtained demonstrate that the methodological approach based on KETs is effective in the evaluation of climate-resilient design actions through the definition of a set of indicators whose values return performance and characteristics of the design solutions.

1. Introduction

The relationship between the effects of atmospheric CO₂ emissions and the increase in global warming leads to critical scenarios of rising temperatures. According to the assessments of the Intergovernmental Panel on Climate Change developed in 2022, an increase in average global temperatures of about 1 °C above pre-industrial levels, with a trend of +0.2 °C every 10 years, would result in the threshold of +1.5 °C being reached by 2040 [1,2].

To tackle the effects of climate change’s hazards, integrated climate mitigation and adaptation measures are necessary [3]. However, according to the UNEP Adaptation Gap Report 2023, progress in the implementation of climate adaptation measures is still far from what is needed [4], and the Climate Adaptation Strategy has identified smarter, more systemic, and swifter adaptation measures, needed shortly, to shape new viable solutions [5]. Within the new climate regime, hazards due to climate change may cause significant impacts in the areas of greatest vulnerability, such as urban systems [6]. Such hazards [7,8,9,10] represent “the potential occurrence of a natural or human-induced physical event […] that may cause […] impacts” [1], including damage to infrastructure, livelihoods, and service provision.

In this scenario, climate-resilient design fits in as an approach to environmentally oriented and climate-responsive design, based on the inclusion of sustainable design principles such as “resilience, comfort, resource efficiency, and biotic support” [11]. Climate-resilient redevelopment of the built environment has been the subject of recent international and national technical policies such as the European Green Deal 2019 [12], as well as funding programs, such as the Next Generation EU strategy [13], aimed at green and energy transition through actions on the built environment. The Renovation Wave for Europe program sets priority intervention criteria in the field of building renovation, identifying them in energy efficiency actions, circular use of resources, making energy-efficient buildings affordable, and respecting architectural quality [14]. Mission 2 of the PNRR (Piano Nazionale di Ripresa e Resilienza) Nextgeneration Italia—as Italy’s post-pandemic recovery plan under the Next Generation EU (NGEU) funding program—takes on climate topics. The PNRR, through the Piani Urbani Integrati (PUI), addresses the peripheries of the metropolitan cities as vulnerable territories to transform into smart and sustainable cities, focusing on the building–outdoor space system as the most significant field of intervention [13].

The scientific and technical literature focuses on the urgency of applying climate adaptation and mitigation measures to the built environment. However, several practical questions arise when dealing with measuring the efficiency of climate resilient design:

• It is important to verify the project ex ante and ex post;
• It is key to identify a set of indicators to measure climate adaptation actions;
• It is crucial to model and simulate the response of the built environment to climate stimuli.

This is the thematic framework of the research project PRIN (Progetti di Rilevante Interesse Nazionale) 2017 “TECH-START—key enabling TECHnologies and Smart environmenT in the Age of gReen economy. convergent innovations in the open space/building system for climaTe mitigation”. Key enabling technologies (KETs) play a key role in developing innovative approaches to climate-resilient design through the elaboration of knowledge processes and methodological workflows supported by ICT tools [15]. Enabling technologies, such as the Internet of things (IoT), big data analytics, open data, additive manufacturing, BIM, GIS, and simulation and modeling tools, are an innovation factor for the construction of innovative urban habitats, by evaluating and testing the effectiveness of design choices. This paper discusses the following research question: how can the climate resilience of the building–outdoor space system be increased and the efficiency of climate resilient solutions for the project be measured? According to this question, the hypotheses of the present work are the following:

• Climate-resilient solutions are known to improve the response of buildings and outdoor spaces to climatic stimuli;
• It is possible to measure this response with KET tools, capable of simulating, measuring, and verifying this performance;
• However, a methodological workflow is needed to set up the simulations, with respect to the environmental context in which the project is located;
• An environmental analysis methodology in the context of the project is needed first;
• Such a methodology allows modeling and simulation to be applied in a site-appropriate and effective manner with respect to the objective of increasing climate resilience.

The proposed objective of this paper is a methodological workflow to identify the urban areas most critical to climate impacts and select the appropriate climate resilient design solutions by modeling and digital simulation using different types of KETs, such as GIS tools, ENVI-met, Dragonfly, and Grasshopper. This workflow is applied and validated in the case of the former Centro Polifunzionale Marianella, in the northern area of Naples. This paper presents the middle results of the PRIN 2017 “TECH-START” research project, developed also with the contribution of the Programma PON R&I 2014-2020—Asse IV “Istruzione e ricerca per il recupero—REACT-EU” (Codice Unico di Progetto—CUP: 65F21003090003). Section 2 describes the proposed methodology, which is divided into two phases: the GIS-based environmental analysis of the urban settlements, and the operative protocol for modeling and simulation of the climate-adaptive design solutions for the buildings and the outdoor spaces. Section 3 is devoted to outlining the experimental results of the modeling and simulation for the case study area, which are further discussed in Section 4. The conclusions are reported in Section 5.

2. Materials and Methods

This article deals with the construction of a methodology, based on a knowledge approach, to evaluate the capacity of adaptation and mitigation of the building–outdoor space system to climate change through the measurement of the performances of climate-resilient meta-design proposals. The field of experimentation is the urban district of Piscinola-Miano in the city of Naples.

2.1. Summary of the Methodological Approach

As part of the PRIN 2017 research project, a methodological approach has been developed to evaluate the capacity of adaptation and mitigation to climate change of the building–outdoor space system through the implementation of climate-resilient solutions. It is based on the digitization of information and data processing through the use of digital tools such as GIS-based tools (urban scale), ENVImet, and Grasshopper with Dragonfly (building–outdoor space system) for modeling and simulation. The methodological workflow (Figure 1) has two different phases: (1) knowledge of the environmental system in urban settlements through the construction of a GIS-based database; and (2) the operative protocol based on evidence-based methods for modeling and simulation of the building–outdoor space system to choose the most appropriate design solutions for climate adaptation and climate mitigation.

2.2. Study Area: The Former Centro Polifunzionale Marianella in the Northern Area of Naples

The study area is located in the Piscinola district in the northen area of Naples. This area is characterized by a relevant green system of parks (the Scampia Park and the Capodimonte Park), the natural system of Vallone di San Rocco, and rural green areas. The building–outdoor space system to be tested in the second phase of the study is identified in the former Centro Polifunzionale Marianella (Figure 2), characterized by an uneven building fabric with low thermal performances and many open spaces, but lacking in outdoor comfort conditions, and by a green system that is poorly interconnected and ineffective with regard to climate-proofing. The building, designed in the 1980s by the architect Gerardo Mazziotti, has several characteristics of authorship as well as architectural quality, due to the original design, as it was inspired by the Corbusian principles of the modulor, the golden section, and the roof garden. Today, the building shows technical and functional deterioration and energy performance decay.

2.3. Knowledge-Based Approach to Environmental Systems in Urban Settlements

Knowledge of environmental systems in urban settlements is a complex challenge, as it involves several elements, including morphological and spatial interactions. To address this complexity, it is essential to use predictive models of knowledge to develop strategies and decision support tools aimed at identifying technical and design solutions that can improve the adaptation and mitigation capabilities of urban settlements in the face of climate change [16]. In the context of this research, a knowledge-based approach was implemented to address the knowledge of the environmental system in urban settlements. This approach aims to promote site-specific design experiments for the urban districts, to develop innovative solutions for adapting to climate impacts and mitigating GHGs emissions. The knowledge-based approach is based on the collection and analysis of detailed data on the study area, including environmental, morphological, and spatial characteristics, as well as information on social and economic dynamics. This knowledge provides a solid basis for the design and implementation of project interventions that take into account the main characteristics of the urban environment. The goal is to contribute to tackling the impacts of climate change while also acting on GHG emission reduction at the building and urban scale, through design solutions that take into account the specific needs of the area.

The approach is based on the National Plan for Adaptation to Climate Change (NPACC) of the Ministry of Environment and Security [17], which identifies strategies and actions (“soft” actions—non-infrastructural; “grey” actions—infrastructural; “green” actions”—infrastructural linked to ecological aspects) aimed at mitigating climate risks and enhancing the resilience of environmental systems. The plan provides important guidance on how to read environmental systems to define appropriate lines of development to tackle climate change. To this end, the knowledge model tested in the study area proposes an integration of the green and grey levels to describe urban conditions and the interaction between the natural and anthropic environment in a systemic manner. The adopted approach analyzes the environmental system by structuring it into five sectoral areas and layers [18]: non-structural aspects (SOFT layer); infrastructural aspects related to transport and technological networks (GREY layer); aspects related to the green system (GREEN layer); aspects related to the water system (BLUE layer); and built-up system aspects (RED layer) (Figure 3).

Construction of the GIS-Based Database

The described knowledge model was implemented through the construction of a GIS-based database. The opensource software QGIS 3.28.15 “Firenze” was used to construct the database. In the first step, all the relevant vectorial layers were imported into the GIS environment, each with specific features representing the different morphological, environmental, and functional-spatial characteristics of the study area, to support the analysis of the environmental system according to the different sectoral categories (see Section 2.4.1). For each sectoral area of analysis, a corresponding thematic area was developed. For example, for the green system, all the polygons representing the different types of green areas in the area under examination were identified and mapped. Subsequently, each polygon was associated with the category of green area (such as natural, rural, infrastructural, urban, and pertaining green areas), and distinct symbols were assigned to each category. In addition, a category classification was carried out to make the differences between different types of green areas clearer. Finally, using the ‘field calculator’ and the ‘calculate geometry’ command, the area in square meters of each polygon was calculated to quantify the percentage incidence of each category identified in the study area. This type of analysis, based on satellite data (Google, ESRI) and official databases from the administrations (Topographical Database and CTR), made it possible to obtain information on the spatial and quantitative distribution of natural capital in the study area. The same thematic process was applied to the other physical and structural sector areas (green, grey, red, blue).

Moreover, the database has been implemented with the addition of features related to a set of environmental indicators (albedo and surface runoff coefficient). Using the same cartographic base, in a GIS environment, the polygons representing the building–open space system were associated with the corresponding values of the identified indicators. This integration is aimed at assessing the problems of the area concerning climate impacts.

2.4. The Operative Protocol to Test Climate Adaptation and Mitigation Solutions’ Effectiveness

The operative protocol represents the second phase of the proposed methodological workflow to identify the most critical urban areas to climate impacts and select the appropriate climate resilient design solutions in urban settlements.

In the first step, the climate-resilient design solutions are chosen in accordance with the environmental system analyses carried out in the first phase of the methodological workflow. Afterwards, an indicators and indexes system is defined for measurement of the microclimatic behavior of the building–outdoor space system. The next steps are modeling, simulation, and data extraction. Both modeling and simulation processes for the analysis of the microclimatic and performance behavior of the building–outdoor space system, after the implementation of the categories of intervention, are based on an operative workflow for data exchange using different ICT tools [19]. The operative protocol proposed in this paper (Figure 4) is based on the use of Grasshopper’s Virtual Programming Language (VPL) platform and some of its add-ons, such as Ladybug and Dragonfly. The use of Grasshopper’s VPL allowed the modeling capabilities of the three-dimensional modeling software Rhinoceros 6 to be combined with the analytical potential of the microclimate modeling software ENVI-met 5. In this way, it is possible to set a process for testing the environmental performance of the design proposal to assess the improvement of the capacity to adapt to climate impacts and reduce the CO₂ concentration.

The geometric modeling process was conducted through Rhinoceros 6 software. Meanwhile, the microclimatic simulation process was conducted through ENVI-met 5. The use of parametric design tools such as Grasshopper, Dragonfly, and df_envimet allowed the association of the simulation of the physical behavior of the elements within the urban area with the three-dimensional model, thus making it possible to assess the interactions between these elements and the surrounding environmental components.

2.4.1. Identification of Climate-Resilient Technical Solutions

To provide the responses to climate adaptation and mitigation goals in the study case, it was necessary to identify adequate climate-resilient technical solutions at different scales. Based on the analyses at the urban scale carried out in the first phase, the following strategies have been developed at the district scale (Piscinola-Miano and Scampia) (Figure 5):

• The introduction of ecological mobility through new cycle paths and the implementation of existing pedestrian paths; energy self-generation with the inclusion of plants for the production of energy from renewable sources;
• The increase in urban greening by redeveloping the spaces and by implementing links between different green areas within the study area; improvement of the water management system of the area through the insertion of systems for the collection and reuse of rainwater and reduction of the degree of waterproofing of the area;
• The improvement of the quality of existing building stock, mainly through the upgrading of public housing buildings.

To obtain measurable results, it was necessary to make a shift from the district and strategic scale to the more operative one of the building–system outdoor space, where these strategic lines have been detailed focusing on three main aspects: water-sensitive management, sustainable management of green areas, and self-production of energy from renewable sources (Figure 5).

The technical solutions to improve the adaptation and climate mitigation of the building–outdoor space system derives from the study and analysis of similar experiences at the national and international levels, such as the Urban Adaptation Support Tool [20], developed within the European Platform for Climate Adaptation Adapt [21], and the Urban Green-Blue grids for resilient cities [22]. The solutions identified by the analysis of the catalogues are included in broader categories of intervention for climate-resilient design, focusing on climate phenomena linked to the increase in temperatures and rainfall.

Regarding outdoor space, the categories of climate-resilient intervention that have been used within the experimental application presented in this work are the following:

• Greening—the inclusion of elements such as trees, rain gardens, or small green areas; this type of solution helps to reduce the concentration of CO₂ in urban areas while improving, at the same time, the conditions of outdoor thermo-hygrometric comfort, by creating shady areas and activating evapotranspiration phenomena, with positive effects on the reduction of urban temperatures and the impacts of heat waves and the urban heat island effect [23]; in addition, solutions such as wetlands, bioshields, buffer zones, green roofing, tree pits, and street side swales contribute to the reduction of the impacts of urban flooding by controlling the surface runoff;
• De-paving—reducing the level of waterproofing of horizontal urban surfaces by introducing permeable pavements, with adequate thermal and physical capacities that allow urban surfaces to not reach high temperatures and to contribute to the surface outflow of rainwater, thanks to the permeability of the materials concerning the underlying layers [24];
• Cool materials, characterized by high solar reflectance—the use of this type of materials within urban contexts promotes the reduction of surface temperatures and contributes to a reduction of the urban heat island effect [25,26].

Because of the lack of data and information on the building, which is not accessible, and due to the authorship of architect Gerardo Mazziotti, which imposes several constraints, a smaller number of design interventions have been proposed:

• Extensive green roof to limit heat loss, generating a positive impact on the energy needs of the building—in addition, the presence of vegetation on the roof promotes the absorption of CO₂ from the surrounding environment and increases the coverage’s ability to reflect solar radiation [27];
• Replacement of fixtures with high-performance fixtures to reduce the thermal dispersion of the building—this leads to a significant reduction in energy consumption and, consequently, of the GHGs into the atmosphere;
• Photovoltaic system—a system that uses solar energy to produce electricity without CO₂ emissions or other pollutants, thus contributing significantly to mitigating the environmental impact of the building through the activation of a system for the self-production of energy from renewable sources.

The proposed interventions for the building focused mainly on the envelope and the control of heat loss, allowing for optimization of the energy efficiency of the complex and consequently reducing CO₂ emissions [27].

2.4.2. Definition of a System of Indices and Indicators for the Analysis of the Microclimatic Behavior of the Building–Outdoor Space System

To evaluate the adaptation capacity to extreme climatic events, such as heat waves in urban areas, and the ability to decrease GHG emissions, a set of indices and indicators was established. This set, derived from the scientific literature and the current state of the art, was integrated in the operative workflow. In this way, it was possible to estimate the performances of proposed climate-proof design categories (see Section 2.4.1) by using computational tools to evaluate the microclimate behavior of the case study area.

The indices and indicators chosen are as follows:

• PMV—predicted mean vote;
• Tmrt—mean radiant temperature (measured in °C);
• PoT—potential air temperature (measured in °C);
• TSur—surface temperature (measured in °C);
• CO₂ concentration (measured in ppm—parts per million).

The PMV is an index to assess the thermal comfort perceived by users. This index takes into account both environmental and subjective variables. The result of the index is a numerical value on a scale that typically ranges from -3 (very cold) to +3 (very hot), with 0 representing the state of thermal comfort. However, since it is generally an index derived from a mathematical function for the restitution of indoor thermal comfort, the lower and upper limits of the experimental values provided by the Fanger scale are not necessarily exhaustive for outdoor conditions and may reach values even outside the proposed standard scale [28,29].

In setting the parameters for calculating the PMV, those relating to the characteristics of the subject with respect to whom the calculation is made play an important role [28,29]. The types of subjects taken into account in the experimental application for the former Centro Polifunzionale Marianella are:

• Adult (man, height 175 cm, weight 75 kg, 35 years of age, clothing value 0.70);
• Senior, representative of the weak groups that will occupy the building according to the project hypothesis (man, height 165 cm, weight 65 kg, 75 years of age, clothing value 0.70).

Mean radiant temperature is a synthetic indicator useful for assessing the impact of heat on the human body and thermo-hygrometric outdoor comfort [30,31,32] and is considered one of the most suitable indicators for assessing the impact of extreme heat events on humans due to its close relationship with urban morphology and vegetation characteristics.

Potential air temperature is the temperature an air mass would have if it were brought to a standard pressure. Surface temperature, on the other hand, gives the temperature of the air near the earth’s surface. Both are expressed in °C.

These four parameters are used to evaluate the adaptation capacity to climate impacts of the complex ex-ante and ex-post the proposed project intervention. On the other hand, regarding the evaluation of climate mitigation capacity, it was chosen to measure and evaluate the CO₂ concentration values that climate-responsive interventions will be able to guarantee in absolute terms [33]. The value returned by the ENVI-met 5 software is in parts per million (ppm), which indicates that for every million parts of air, one part is carbon dioxide.

2.4.3. Modeling Phase

The first stage of the modeling phase involved the three-dimensional geometric modeling of the study area under analysis. This process was conducted using the 3D modeling software Rhinoceros 6. The process involves modeling the area, the object of analysis, from two-dimensional graphs (plans and sections). There are, therefore, some necessary details to be taken into account to carry out the next microclimate simulation phases. Firstly, when developing the model, it is necessary to ensure that all the geometries formed are closed surfaces and solids that are subsequently read by Grasshopper as “closed brep”. This is necessary in order to avoid reading and transfer problems from the three-dimensional Rhinoceros environment to the parametric Grasshopper environment.

As part of the experimentation, the three-dimensional geometric model of the study area was simplified down to the restitution of just the volume as far as the building is concerned. This decision is due to the desire to speed up the following simulation process, simplifying the restitution of the real condition in the digital environment as much as possible. Moreover, as the area is located in the plain north of Naples with minimal differences in elevation, it was decided to represent all the surfaces of the outdoor space on a single plane, providing the 0.0 elevation of the elaborated model.

In the geometric modeling phase, however, it is necessary to make an initial comparison with the possibilities of the microclimate modeling software ENVI-met 5. As part of the developed workflow, it was decided to use the open-source version of the software, which imposes limits on the size of the area that can be simulated from time to time [34]. This restriction requires that the model of the area whose microclimatic behavior is to be modeled and simulated be inscribed in a parallelepiped of dimensions 50 × 50 × 40 cells. For this purpose, a correction had to be made to the modeling phase. This correction consists of developing two different models with two different degrees of resolution, which will subsequently also lead to a double data collection campaign in the simulation phase.

The first model that represents the entire study area is simplified not only in terms of digital transfer of the building but also of outdoor space. This is because, in order to fit the entire area into the maximum simulable model size provided by the software, it is necessary to apply a data resolution of 10:1 type (which means that the information of 10 cells is synthesized in a single cell). This resolution entails the necessary simplification or elimination of all those elements whose size is too small to be read by the software and which would therefore complicate the transition from the three-dimensional model to the microclimate model.

The second model, on the other hand, retains all the complexity of the outdoor space while remaining at the volumetric level as far as the building is concerned. To do this, it was necessary to divide the study area into 27 quadrants of dimensions 50 × 50 m so that they would fall within the permissible dimensions provided by ENVI-met (50 × 50 × 40). In this case, it was decided to simulate six of the identified quadrants with a data resolution of type 1:1 (each cell contains the information of only one cell).

Once the three-dimensional geometric modeling phase was completed in Rhinoceros, it was necessary to transfer this model to the parametric Grasshopper environment by associating each closed geometry in Rhinoceros with a corresponding “Closed Brep” in Grasshopper (no further add-ons are required to carry out this operation).

The modeling phase is completed with the ‘transformation’ of the 3D model into a model that can be read for microclimate simulation. This is accomplished by using the tools provided by Grasshopper plug-ins: Dragonfly and df_envimet (2022 version) [35]. These software plug-ins allow the association of the physical, thermal, and environmental characteristics required to simulate the microclimatic behavior of the area to the previously realized geometries. In this step, each geometry is associated with a material from the materials database provided by ENVI-met. Once this association is completed, a model is obtained and ready to be read by the ENVI-met Suite program Spaces. The modeling phase (Figure 6) was conducted to reconstruct the building–outdoor space complex in its pre-intervention state and then, ex-post, the application of the climate-responsive design hypothesis.

2.4.4. Simulation Phase

The first step in the simulation phase concerns loading the climate data for the study area. To do this, it is necessary to fit the Ladybug plug-in into the generative algorithm—developed in Grasshopper—which allows the climate data contained in the .epw files to be imported into the process. EnergyPlus Weather (EPW) is a file format developed by EnergyPlus, which has been adopted as a standard file format for climate data by numerous building simulation tools [36]. The file records climate data (weather data) recorded by weather stations throughout the year. In the study case, data from the Naples-Capodichino weather station are imported into the generative algorithm and relate to the year 2005, the latest available data recorded to date. It was decided not to further project the climate data in order not to affect the accuracy of the simulation output. The .epw files correspond to the .stat files (expanded EnergyPlus weather statistics), which report statistics on the climate data collected in the EPW that are useful for setting the basic conditions for the simulation.

In fact, through Ladybug, a series of basic climatic conditions (such as wind direction, humidity, and temperature) and the geographical coordinates are directly imported from the .epw file. Nevertheless, through the .stat file associated with the .epw file, the specific day on which the simulation is to be carried out is directly imported. In this case, it was chosen to finalize the day on which the worst conditions occur during Extreme Hot Week.

After setting the necessary climatic information, it is associated with the previously realized model, complete with all its physical, thermal, and environmental characteristics. The following step is to choose the period over which to carry out the simulation (24 h) and then start the simulation using the ‘Simple Forcing’ option for temperature and humidity, as also suggested in the literature on the subject to resolve the problems relating to lateral boundaries recorded in previous versions of the software. The simulation process starts directly from Grasshopper’s parametric environment using, however, Envi-met Core, from the ENVI-met suite.

As with the modeling phase, the simulation phase was carried out ex-ante and ex-post for the application of the climate-proof intervention categories for the former Centro Polifunzionale Marianella.

2.4.5. Software Interoperability

One significant challenge in the workflow revolves around the interoperability process, which is heavily reliant on employing suitable IT tools. By considering the interoperability of their outputs, these results become easily interpretable and translatable across various software platforms (Figure 7). This ensures seamless monitoring across different software environments, including geometric modeling with Rhinoceros, parametric design utilizing Grasshopper and its add-ons, and microclimate simulation using ENVI-met 5.

Each piece of software requires the joint use of different tools, with the need to export and import different files, repeating the process several times, until the final definition of the definitive operational process is obtained.

Interoperability was possible above all by the use of Grasshopper’s VPL, which made it possible, through the development of a generative algorithm, bringing together different basic components of Grasshopper, Ladybug, Dragonfly, and df_envimet, to pass data from the basic geometric model created in Rhinoceros and the different ENVI-met 5 suites for microclimate simulation.

2.4.6. Simulation and Data Extraction

After the simulation phase is complete, the ENVI-met 5 software produces a folder containing the outputs of the microclimate simulations for each model processed. Since no specific time intervals (time steps) were set, the simulation process generated hourly outputs. To evaluate the effectiveness of the climate adaptation measures, an analysis of the outputs was carried out at 12 a.m.

To analyze the data produced by the simulation phase, ‘Leonardo’, an additional application of the ENVI-met 5 software, was used. This suite provides a basic visualization in the form of a map. The data are processed by the software and, based on the previously created model, returns a two-dimensional representation of the study area. Each cell on the map is associated with a color representing the value of a specific parameter being analyzed. In addition, the data can be exported in .csv format to allow further statistical analysis using data visualization and analysis software such as Excel (Office version 365).

In the specific case study, it was decided to use both methods of collecting and representing the data resulting from the simulations, both for the ‘synthetic’ model covering the entire study area and for the detailed models of individual quadrants. For each environmental parameter analyzed (such as PMV, mean radiant temperature, potential air temperature, surface temperature, and CO₂ concentration), a color scale was defined in which each color corresponds to a specific parameter value (Figure 8). As a result, the cartographic representations of the outputs are clear and concise tools, facilitating decision making in urban design phases.

3. Results

This section presents the results of proposed workflow. The first subsection focuses on the outcomes of the knowledge phase at the urban scale and the second one reports the outputs of the performed simulations of the building–outdoor space system.

3.1. Results of the Knowledge Phase

The use of GIS tools for analysis at the urban scale enabled the measurement of some of the features of the study area at the urban scale. For the study area, in the Piscinola-Miano district, it was possible to observe a presence of green areas equal to 39%, encompassing all permeable surfaces. The remaining 61% comprises impermeable horizontal expanses, with 13% attributed to buildings and the remaining 48% to artificial surfaces. Furthermore, the analysis of the green systems underscores a limited presence of natural areas, concentrated predominantly in the Vallone San Rocco area. The peri-urban and agricultural character of the case study area is unmistakable, as validated by historical documentation and maps. Moreover, the analyses—carried out according to the proposed knowledge approach—delineate a context characterized by pronounced urbanization and a substantial residential density within the examined area. The existing buildings were primarily constructed between 1946 and 1980 using reinforced concrete construction techniques. Currently, the lack of maintenance has resulted in a general deterioration of the buildings. Specifically, the buildings’ thermal performance, integral to the building envelope, has experienced discernible degradation. Lastly, the analysis of the transport infrastructure sector showed that the area features historically significant axes and metropolitan railway lines. However, the existing routes are primarily designated for road transport, with a conspicuous absence of tracks designated for sustainable mobility.

3.2. Experimental Results of the Operative Protocol for the Measurement of Microclimatic Behaviour of Outdoor Spaces

The values of the indices and indicators were processed using ENVI-met 5 software. From the simulation process, it was possible to analyze and evaluate the behavior of the outdoor space in response to climatic impacts on the study area following the proposed climate-proofing intervention. For each environmental parameter (listed in Section 2.4.2), data were extracted for both the current state (CS) and the design proposal (DP) and processed using Excel (

Table 1. Data collection and analysis of selected environmental indicators PMV (adults), PMV (seniors), and POT_10 at CS (current state) and DP (design proposal).

Area		PMV 1 Adults			PMV 1 Seniors			POT 2
		CS 3	DP 4	Δ% 5	CS 3	DP 4	Δ%	CS 3	DP 4	Δ%
3 × 1	Average	3.68	2.96	−0.20	3.77	3.12	−0.17	31.84	30.91	−0.03
	Minimum	2.52	2.18	−0.13	2.52	2.13	−0.16	30.90	29.96	−0.03
	Maximum	4.15	3.66	−0.12	4.27	4.04	−0.05	32.53	31.85	−0.02
3 × 2	Average	3.59	3.66	0.02	3.67	3.75	0.02	31.38	31.05	0.00
	Minimum	2.48	2.66	0.07	2.49	2.68	0.08	30.56	30.24	−0.01
	Maximum	4.25	4.11	−0.03	4.37	4.23	−0.03	32.54	31.91	−0.02
3 × 3	Average	3.85	3.43	−0.11	3.95	3.51	−0.11	31.50	31.23	−0.01
	Minimum	2.62	2.46	−0.06	2.63	2.47	−0.06	30.84	30.38	−0.01
	Maximum	4.34	4.08	−0.06	4.47	4.19	−0.06	32.31	32.04	−0.01
4 × 1	Average	3.44	3.94	0.15	3.51	4.05	0.15	31.80	31.40	−0.01
	Minimum	2.63	2.90	0.10	2.65	2.93	0.11	30.82	30.66	−0.01
	Maximum	3.82	4.23	0.11	3.93	4.35	0.11	32.51	31.97	−0.02
4 × 2	Average	3.28	3.69	0.13	3.34	3.78	0.13	31.27	31.18	0.00
	Minimum	2.31	2.64	0.14	2.31	2.66	0.15	30.51	30.51	0.00
	Maximum	3.92	4.10	0.04	4.04	4.21	0.04	32.48	31.79	−0.02
4 × 3	Average	4.33	3.43	−0.21	4.47	3.50	−0.22	33.13	31.38	−0.05
	Minimum	2.75	2.45	−0.11	2.78	2.45	−0.12	29.42	30.16	0.03
	Maximum	4.76	4.07	−0.15	4.93	4.19	−0.15	35.05	32.36	−0.08
General	Average	3.58	3.47	−0.03	3.66	3.54	−0.03	30.68	30.51	−0.01
	Minimum	2.54	2.28	−0.10	2.55	2.27	−0.11	29.76	29.47	−0.01
	Maximum	4.08	3.98	−0.03	4.19	4.08	−0.03	31.84	31.84	0.00

¹ PMV: predicted mean vote, ² POT: potential air temperature, ³ CS: current state, ⁴ DP: design proposal, and ⁵ Δ%: percentage variation between current state values and design proposal values.

and

Table 2. Data collection and analysis of selected environmental indicators TSUR, CO₂ concentration, and MRT at CS (current state) and DP (design proposal).

Area		TSUR 1			CO2 (ppm) 2			Tmrt 3
		CS 4	DP 5	Δ% 6	CS 4	DP 5	Δ% 6	CS 4	DP 5	Δ% 6
3 × 1	Average	33.30	29.40	−0.12	415.35	412.76	-0.01	54.82	46.76	−0.15
	Minimum	19.85	19.85	0.00	412.71	407.94	-0.01	34.58	35.77	0.03
	Maximum	45.02	44.11	−0.02	416.70	415.78	0.00	59.04	56.08	−0.05
3 × 2	Average	29.24	29.01	−0.01	414.89	414.54	0.00	54.52	55.13	0.01
	Minimum	19.85	19.85	0.00	413.35	413.20	0.00	42.57	43.25	0.02
	Maximum	51.98	50.38	−0.03	416.73	415.90	0.00	59.00	59.31	0.01
3 × 3	Average	37.46	33.44	−0.11	415.07	414.45	0.00	58.01	52.13	−0.10
	Minimum	19.85	19.85	0.00	414.18	412.29	0.00	45.85	41.43	−0.10
	Maximum	53.48	48.67	−0.09	416.29	415.86	0.00	61.72	58.86	−0.05
4 × 1	Average	32.19	33.36	0.04	415.45	414.79	0.00	54.62	57.80	0.06
	Minimum	19.85	19.85	0.00	413.71	412.20	0.00	42.82	44.52	0.04
	Maximum	45.04	46.02	0.02	416.67	415.95	0.00	60.09	60.57	0.01
4 × 2	Average	26.59	26.73	0.01	414.78	414.76	0.00	54.62	55.51	0.02
	Minimum	19.85	19.85	0.00	413.49	413.72	0.00	42.82	43.55	0.02
	Maximum	43.99	44.63	0.01	416.65	415.77	0.00	60.09	59.48	−0.01
4 × 3	Average	37.56	34.11	−0.09	417.53	414.68	−0.01	58.70	51.86	−0.12
	Minimum	19.85	19.85	0.00	412.55	412.12	0.00	46.17	41.35	−0.10
	Maximum	47.21	48.95	0.04	420.01	416.22	−0.01	62.19	58.70	−0.06
General	Average	34.54	33.23	−0.04	413.83	413.47	0.00	57.64	56.01	−0.03
	Minimum	19.85	19.85	0.00	412.06	410.19	0.00	43.12	41.27	−0.04
	Maximum	47.59	49.11	0.03	415.51	415.50	0.00	60.97	60.61	−0.01

¹ TSUR: surface temperature, ² CO₂ [ppm]: CO₂ concentration values measured in ppm, ³ Tmrt: mean radiant temperature, ⁴ CS: current state, ⁵ DP: design proposal, and ⁶ Δ%: percentage variation between current state values and design proposal values.

). For both models, it was decided to obtain an immediate measure of the difference in behavior between the current state and the design proposal by calculating the percentage variation of the mean value, maximum value, and minimum value of each chosen environmental parameter. The results of the microclimate simulation for the time of 12 a.m. on 10 August are listed in Table 1 and Table 2. Table 1 displays the PMV values for both adults and ageing people, along with the values of potential air temperature. In Table 2, values for surface temperature, mean radiant temperature, and CO₂ concentration are compiled.

The average percentage variation was calculated based on the final mean of the results obtained for each quadrant. The process was repeated for each selected environmental parameter. The microclimatic simulations showed (Figure 8), for both models, an average 8% reduction in PMV values for adult and an average 7% reduction for ageing people. Similar results can be observed for the mean radiant temperature values, with a 6% reduction for the design proposal scenario. These values demonstrate an overall improvement in outdoor thermo-hygrometric comfort conditions. This is further confirmed by the 6% reduction (corresponding to about 2 °C) of the potential air temperature and surface temperature values. The design solutions for climate mitigation are less effective, resulting in an average reduction of only 1% between the actual state and the proposed project.

4. Discussion

The analyses of the environmental system of the study area show a territory characterized by high soil impermeability, fostering intense runoff phenomena during extreme rainfall events. Furthermore, examinations of albedo coefficients on horizontal surfaces reveal a consistently low mean albedo coefficient. This is particularly evident in more urbanized areas, indicating a potentially higher vulnerability to heatwaves and the urban heat island effect. The thematic maps resulting from this process have become a crucial tool for the identification of environmental problems within the study area and the necessary climate adaptation and mitigation measures.

The acquired results of the operative protocol, shown in Table 1 and Table 2, reveal the impact of the design proposal with a particular focus on the outdoor space system enhancements, such as de-impermeabilization and heightened urban greening. These interventions, while not consistently significant across all selected environmental parameters, collectively engender an overall improvement in the environmental performance of the study area. The design proposal, especially concerning the outdoor space system, contributes meaningfully to mitigating environmental stressors. The values observed from the simulation demonstrate the effectiveness of the proposed design interventions in improving the adaptation capacity of the case study area to reduce the impacts associated with extreme temperature phenomena, such as heatwaves. As for climate mitigation, on the contrary, the results are not particularly positive. However, it should be noted that in the current versions of ENVI-met, a starting value of 400 ppm of CO₂ concentration is configured, based on global trends showing an average annual concentration of carbon dioxide of 407.4 ppm in 2018 [33,37]. According to the software manufacturers, this base value has to be unchanged to ensure accurate results. In the results obtained, therefore, it is necessary to observe the absolute increase and reduction concerning the default value of 400 ppm. In the analysis of the specific quadrants, it can also be seen that, although the percentage of change is decreasing in most quadrants (Figure 8), there is a greater impact in those where the addition of vegetation has been high. This is particularly evident in the thematic maps depicting surface temperature values. Furthermore, these results demonstrate that the inclusion of vegetation elements contributes significantly to the improvement of outdoor comfort through the generation of brought shadows that reduce the thermal load on the ground, the reduction of non-natural surfaces, and triggering of the evapotranspiration phenomena, affecting the PMV and the Tmrt index in a positive way.

The six categories of climate-proof interventions—deduced from the literature and previous studies [19,20,21,22,23,24,25,26,27,37,38,39,40]—have proven effective to counter the rise in temperatures and the phenomenon of urban heat waves and, at the same time, to foreshadow possible scenarios of adaptation and climate mitigation for the case study area. Based on the obtained results, the comparison of before and after the design proposal confirms the effectiveness of the proposed climate-proof solutions in reducing the effects of heat waves and increasing outdoor comfort. The use of nature-based elements plays a crucial role in enhancing outdoor comfort. The shade created by strategically placed tree-lined rows helps ease the heat on the ground. Additionally, the establishment of small green spaces like rain gardens improves comfort by cutting down on unnatural surfaces and starting evapotranspiration. This multifaceted approach, involving the combination of tree-lined structure and compact green areas, significantly improves outdoor comfort levels and thus the adaptation capacity of the area [38]. Moreover, from the data derived from the simulations, the use of cool materials contributes to a significant reduction in potential air temperature and surface temperature (Figure 8e,f). On the other hand, although depaving intervention have been introduced in the design proposal to enhance the permeability of the horizontal surfaces, no simulative data quantify the effectiveness of the intervention on the building–outdoor space system with regard to the reduction of runoff or the effects of heavy rains.

The results showed that the methodological approach is effective in the evaluation of climate-responsive design solutions for climate change mitigation and adaptation actions through the definition of a set of indicators. In this framework, the use of digital tools and an approach focused on data-driven design to model and simulate the microclimatic behavior of the building–outdoor space system played a crucial role in the definition of the methodological workflow discussed in this contribution [41,42]. The methodological workflow establishes an operational modeling and simulation protocol that includes some innovative factors: the construction of knowledge of the urban system according to downscaling processes (from the inter-district to the building scale), the double-level simulation, and the definition of appropriate geometric modeling criteria.

5. Conclusions

The methodological workflow and the simulation and modeling protocol have theoretical and operational implications for the design process. The operational implications consist of the possibility of prefiguring improved design scenarios as the project solutions change, setting the input data through the degree of detail, precision, and specificity of the model. Thematic maps, diagrams, and graphs of the simulation results (Figure 8) allow the communication of the results to stakeholders and non-expert users. The reading of the output data made it possible to identify specific critical issues and the application of targeted design strategies for climate adaptation and mitigation. The open-source versions of the digital tools and databases used allow for effective replicability of the process.

However, a further deepening of the climate mitigation aspects represented by the amount of CO₂ sequestered, avoided, and stored would be necessary through the integration of dedicated tools, such as I-Tree Eco V6.0, since the CO₂ results obtained in the simulation workflow are to be considered reliable but site-specific. Therefore, the next research steps include further developments aimed at the implementation of the process for the quantification of ecosystem services for microclimate regulation and climate mitigation. Moreover, simulating the actual state in comparison with the intervention in the medium to long term (2050, 2100) to assess the response of the building–outdoor space system to future climate impacts would represent a further advance.