The Multiple-Scale Nature of Urban Heat Island and Its Footprint on Air Quality in Real Urban Environment

atmosphere

Article The Multiple-Scale Nature of Urban Heat Island and Its Footprint on Air Quality in Real Urban Environment

Silvana Di Sabatino 1,*, Francesco Barbano 1 , Erika Brattich 1 and Beatrice Pulvirenti 2 1 Department of Physics and Astronomy DIFA, Alma Mater Studiorum University of Bologna, via Irnerio 46, 40126 Bologna (BO), Italy; [email protected] (F.B.); [email protected] (E.B.) 2 Department of Industrial Engineering, Alma Mater Studiorum University of Bologna, viale del Risorgimento, 2, 40136 Bologna (BO), Italy; [email protected] * Correspondence: [email protected]; Tel.: +39-051-2095215

Received: 14 October 2020; Accepted: 29 October 2020; Published: 2 November 2020

Abstract: The complex interaction between the Urban Heat Island (UHI), local circulation, and air quality requires new methods of analysis. To this end, this study investigates the multiple scale nature of the UHI and its relationship with flow and pollutant dispersion in urban street canyons with and without the presence of vegetation. Two field experimental campaigns, one in summer and one in winter, were carefully designed in two parallel urban street canyons in the city of Bologna (44◦290 N, 11◦200 E; Italy) characterized by a similar orientation with respect to the impinging background flow but with a different aspect ratio and a different presence of vegetation. In addition to standard meteorological variables, the dataset collected included high-resolution flow data at three levels and concentration data of several pollutants. The UHI has been evaluated by combining surface temperature of building facades and ground surfaces acquired during two intensive thermographic campaigns with air temperature from several stations in order to verify the presence of intra-city neighborhood scale UHIs additional to the more classical urban–rural temperature differences. The presence of trees together with the different morphologies was shown to mitigate the UHI intensity of around 40% by comparing its value in the center of the city free of vegetation and the residential area. To capture the multiple-scale nature of UHI development, a simple relationship for the UHI convergence velocity, used as a surrogate for UHI strength, is proposed and used to establish the relationship with pollutant concentrations. The reliability of the proposed relationship has been verified using a Computational Fluid Dynamics (CFD) approach. The existence of a robust relationship between UHI strength and pollutant concentration may indicate that the positive effect of mitigation solutions in improving urban thermal comfort likely will also positively impact on air pollution. These results may be useful for a quick assessment of the pollutant accumulation potential in urban street canyons.

Keywords: urban heat island; ventilation; urban street canyon; air pollution; vegetation

1. Introduction More than half of the world’s population live in urban areas, and especially in highly-dense cities, a proportion that is expected to increase to 68% by 2050 [1]. Long term projections indicate that urbanization together with the overall growth of the world’s population could add further 2.5 billion people to urban areas by 2050, with percentages reaching 90% in Asia and Africa [1]. Increasing urbanization and associated change in land surface physical properties such as roughness, thermal inertia, and albedo [2] and land surface processes can deeply aﬀect regional and local scale meteorology and air quality, through alteration of the surface–atmosphere coupling

Atmosphere 2020, 11, 1186; doi:10.3390/atmos11111186 www.mdpi.com/journal/atmosphere Atmosphere 2020, 11, 1186 2 of 32 such as exchange of momentum, water, and energy [3]. In particular, urbanization modifies local meteorological variables such as wind speed, air temperature, and also influences the planetary boundary layer (PBL) height, which in turn results in urban–rural air temperature and dispersal potential differences. Among these, the Urban Heat Island (UHI) effect, defined as the phenomenon of enhanced temperatures in urban areas compared to the surrounding countryside, is currently recognized as one of the most evident characteristics of urban climate [4]. UHI has been recognized as one of the most prominent issues of the 21st century in relation to urbanization and industrialization [5] and to global climate and environmental change [6], which is likely to exacerbate the frequency and duration of extreme heat events e.g., [7,8]. The UHI results from the reduction in vegetation and evapotranspiration, the prevalence of dark surfaces with reduced albedo, and increased anthropogenic heat production in cities [9], which together contribute to increase the temperatures of urban areas as compared to their surroundings. Besides triggering heat related diseases and premature deaths in cities [10,11], the elevated air and surface temperatures during UHI events increase the city-scale mean and peak cooling energy demand and associated greenhouse gas emissions [12] due to lower efficiency in Heating, Ventilation and Air Conditioning (HVAC systems and significant drops in urban thermal comfort [13]. Other less studied changes induced by urbanization are the variations in wind speed and direction caused by the spatial variations in surface roughness or by the modifications in land-sea temperature contrast [14]. In addition, changes in temperature and surface roughness can modify the PBL height during daytime and nighttime through modifications of the buoyancy production by sensible heat flux and variance in wind speed, ultimately changing the magnitude of turbulent kinetic energy [15]. All these changes in meteorological variables, modifying emissions, chemical reaction rates, gas-particle phase partitioning of semi-volatile species, pollutant dispersion, pollutant deposition, and vertical mixing can drive changes in the concentrations of airborne pollutants [16]. In addition, the rapid development of urbanization leads to increased emissions of air pollutants [17], which together with other factors including climate, meteorology, physiography, and social conditions lead to severe air quality problems [18]. Indeed, the urban atmosphere is generally characterized by higher levels of pollution particles with respect to the rural atmosphere, a phenomenon called the Urban Pollution Island (UPI) [19]. Altogether, therefore, although urbanization leads to a positive series of human welfare outcomes, its effects on human health are controversial [20]. While mitigation measures to counteract the UHI phenomenon, including the design of cool pavements by increasing the albedo of surfaces and making them more porous, permeable, and water retentive, the increased utilization of green spaces within the urban landscape e.g., [21], and the exploitation of the cooling effects of wind and water [22], are well studied and documented, less so is the superposition of other changes, such as the increased exposure to air pollutants, likely exacerbated by the simultaneous increased heat stress. Thus, the identification of mitigation measures capable simultaneously to improve thermal comfort and air quality is currently needed. In this framework, urban street trees have been recognized to provide many environmental, social, and economic benefits for our cities [23], among them the reduction of building energy use [24], reduction in high urban temperatures, mitigation of the UHI [25], mitigation of climate change [26], and improvement in thermal comfort [27] as linked to the enhanced shading and conversion of radiation into latent heat and evapotranspiration [28,29]. The intensification of trees and more in general of green infrastructure has also been considered as a solution to reduce air and noise pollution and enhance sustainability of cities [30] providing better storm-water management [31]. However, while the benefits derived from green infrastructure on thermal comfort and urban heat island mitigation are undoubted, the impacts on air quality at street levels, including both positive and negative effects depending on different factors such as meteorology, climatology, height, shape, and density of the tree species, are still a matter of investigation [32]. It is understood that an improved knowledge of the interaction between UHI and air quality will be useful to target mitigation actions. Atmosphere 2020 11 Atmosphere 2020, 11, 1186x FOR PEER REVIEW 3 of 32

As discussed above, several methods of investigations have been used in the literature to study As discussed above, several methods of investigations have been used in the literature to study the UHI, the UPI, and their inter-linkages with urban flow and dispersion. As far as the numerical the UHI, the UPI, and their inter-linkages with urban flow and dispersion. As far as the numerical approaches are concerned, methods rely on both atmospheric models such as WRF (Weather approaches are concerned, methods rely on both atmospheric models such as WRF (Weather Research and Forecasting), e.g., [33–35] and Computational Fluid Dynamics (CFD) approaches, e.g., Research and Forecasting), e.g., [33–35] and Computational Fluid Dynamics (CFD) approaches, [36–38]. While the first category is able to capture the large-scale interaction with the local features e.g., [36–38]. While the first category is able to capture the large-scale interaction with the local by solving the complete set of equations that capture the full relationship between surface and a features by solving the complete set of equations that capture the full relationship between surface and rotating atmosphere, the latter uses generic thermo-fluid dynamics equations having the advantage a rotating atmosphere, the latter uses generic thermo-fluid dynamics equations having the advantage of characterizing individual obstacles effects on flow and dispersion. Specifically, CFD approaches of characterizing individual obstacles effects on flow and dispersion. Specifically, CFD approaches have been used to evaluate the role of canyon related features, such as the effect of aspect ratio and have been used to evaluate the role of canyon related features, such as the effect of aspect ratio and wind/ventilation characteristics on the urban thermal microclimate, UHI, heat, and mass transport, wind/ventilation characteristics on the urban thermal microclimate, UHI, heat, and mass transport, e.g., [39–44]. Within the present study, CFD simulations are complementary to the data analyses and e.g., [39–44]. Within the present study, CFD simulations are complementary to the data analyses and used only for the purpose of assessing the robustness of a simple relationship between temperature used only for the purpose of assessing the robustness of a simple relationship between temperature gradients and flow and concentration field. To our knowledge this is the first study of this kind gradients and flow and concentration field. To our knowledge this is the first study of this kind relating relating the multiple scale-nature of the UHI with the flow dynamics, providing a relationship the multiple scale-nature of the UHI with the flow dynamics, providing a relationship between UHI and between UHI and UPI in urban street canyons, and using CFD simulations to strengthen the validity UPI in urban street canyons, and using CFD simulations to strengthen the validity of such a relationship. of such a relationship. Following the above, this paper investigates the link between UHI and Following the above, this paper investigates the link between UHI and pollutant concentration in pollutant concentration in urban street canyons, assessed with and without vegetation. To evaluate urban street canyons, assessed with and without vegetation. To evaluate how the two effects are how the two effects are linked, two experimental field campaigns were carried out in the city of linked, two experimental field campaigns were carried out in the city of Bologna (Figure1) in Italy Bologna (Figure 1) in Italy (44°29′ N, 11°20′ E) within the “iSCAPE” (“Improving the Smart Control (44 29 N, 11 20 E) within the “iSCAPE” (“Improving the Smart Control of Air Pollution in Europe”) of Air◦ 0Pollution◦ 0in Europe”) H2020 research and innovation project. Specifically, we try to respond H2020 research and innovation project. Specifically, we try to respond to the following questions: to the following questions: Could the UHI phenomenon be a multiple-scale phenomenon? What is the impact of the UHI at • Could the UHI phenomenon be a multiple-scale phenomenon? What is the impact of the UHI different scales on the flow dynamics? Are there any differences in behavior? at different scales on the flow dynamics? Are there any differences in behavior? • As UHI and UPI are commonly linked, is it possible to determine a relationship linking these two • As UHI and UPI are commonly linked, is it possible to determine a relationship linking these twoaspects aspects at a veryat a very local local scale? scale? How How the associated the associated circulation circulation would would develop develop and how and can how relate can to relatelocal pollutant to local pollutant concentrations? concentrations?

Figure 1. MapMap showing showing the the location location of of the the city of Bologn Bolognaa in Italy (red dot) (source: Esri, Esri, Garmin, Garmin, USGS -United States Geological Survey-, NPS-National Park Service-).

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With the intent to respond to the previous questions, the paper is structured as follows. After the Introduction section, Section2 describes the two field campaigns, detailing the characteristics of the two urban street canyons where the two campaigns were carried out and the instrumentation therein deployed. Section 3.1 focuses on the evaluation of the multiscale nature of the UHI effect at urban and neighborhood levels. Section 3.2 then derives a relationship between UHI and pollutant concentrations in urban street canyons, verified by means of high-resolution CFD simulations adequately setup. Finally, Section4 draws the main conclusions of this work.

2. Field Campaigns in Bologna

2.1. Rationale for the Study and Site Description Because of the inherent complexity and multiple interactions between physical processes at different spatial and temporal scales, the study of the urban environment requires a multiapproach investigation. Indeed, flow dynamics and turbulence structures are well known to depend on spatial and temporal scales within the city [45,46], time of the day and insolation [47], and even traffic regimes [48,49]. Atmospheric processes such as ventilation, diffusion or flow circulation can affect or be affected by vegetation and urban heat island effects. With the aim to disentangle the contribution of UHI and pollutant concentration, in this study the analysis of field data collected during two experimental field campaigns was complemented with numerical simulations. In particular, two intensive experimental field campaigns were carried out respectively during summer (10 August 2017–24 September 2017) and winter (16 January 2018–14 February 2018) to measure atmospheric flows and turbulence, air temperature and air quality within two different street canyons embedded in the city of Bologna. The city is located at the southern end of the Po Valley at the foothill of the Apennines chain, a region considered a hotspot for climate change and air pollution [50]. The city morphology can be considered as typical for European cities [51,52], characterized by a densely built historical center surrounded by residential suburbs and a small industrial area. The center is characterized by a dense network of narrow street canyons occasionally interrupted by small parks or squares, while the residential suburb streets retain the canyon-like shape but diminishing the building packaging. Two parallel street canyons running north to south, Marconi St. and Laura Bassi St., were chosen as experimental sites for the campaigns, being the first representative of the historical area, and the second of the suburbs (Figure2). Laura Bassi St. is a 700 m long street surrounded by both small private houses (2–3 floors) and higher buildings (4–5 floors), accounting for a mean building height H of 17 m. The street is composed of two lanes surrounded by an almost regular deployment of deciduous trees, for a mean street width W of 25 m. Marconi St., about 600 m long, is surrounded by buildings with at least 4–5 floors up to 9–10 floors (H = 33 m). The street is composed of four lanes, two for private and two reserved for public transport (W = 20 m). Along the carriageway, there are porticos, covered pathways with sideways. No vegetative element is planted along this road, except for the last 50 m approaching one street end, where there is a single lane of deciduous trees placed on one side of the road. Top views of both canyons are pictured in Figure2. The majority of the vegetative elements in Laura Bassi St. neighborhood belongs to the Platanus Acerifolia Mill family, a hybrid of Platanus Orientalis, and Platanus Occidentalis, widely spread in European urban habitats. The average height of the branch-free trunk is about 5 m and the crown extends up to 15 m in height. Along the street, tree trunks are regularly spaced at approximately 8 m distance, allowing different crowns superimposition. Atmosphere 2020, 11, 1186 5 of 32 Atmosphere 2020, 11, x FOR PEER REVIEW 5 of 32

Figure 2.FigureStreet 2. Street canyons canyons views views andand details (W (W = mean= mean street street width, width, H = mean H =streetmean height, street α = height, orientation angle of the street canyon with respect to the meteorological north, LTZ = Limited Traffic α = orientation angle of the street canyon with respect to the meteorological north, LTZ = Limited Traffic Zone) of the two street canyons in Bologna: (a) Marconi St.; (b) Laura Bassi St. (source: Google Earth). Zone) of the two street canyons in Bologna: (a) Marconi St.; (b) Laura Bassi St. (source: Google Earth). 2.2. Instrumentation Setup 2.2. Instrumentation Setup As previously described, the main scope of this work is to investigate the analysis of the UHI at Asdifferent previously spatial described, scales, and the to address main scope the interaction of this workbetween is toUHI investigate and UPI effects. the analysis As such, ofand the UHI at differentaccording spatial to scales,the indications and to of address [53] describing the interaction the need for between urban-specific UHI and measurement UPI effects. techniques As such, and accordingto provide to the indicationsguidance to policy of [53 and] describing decision makers the need for the for design urban-specific of sustainable measurement cities, we utilized techniques to provideobservations guidance from to policya network and of decision meteorological makers and for air thequality design monitoring of sustainable stations covering cities, we the utilized various spatial scales involved in the investigations (see Figure 3). observations from a network of meteorological and air quality monitoring stations covering the various spatialAtmosphere scales involved2020, 11, x FOR in thePEER investigations REVIEW (see Figure3). 6 of 32

FigureFigure 3. Monitoring 3. Monitoring sites sites network network for for the the Bologna Bologna experimentalexperimental campaigns campaigns (source: (source: Google Google Earth). Earth).

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As previously described in Section 2.1, the main investigation sites are the street canyons of Marconi St. and Laura Bassi St., where a full set of instrumentations was deployed to measure both meteorological and air quality quantities. Supporting measurements of ancillary variables are retrieved from the local environmental protection agency ARPAE (Agenzia Regionale per la Prevenzione, l’Ambiente e l’Energia dell’Emilia-Romagna) network of monitoring stations inside the city. In particular, Asinelli Tower (site AS, 44◦29039.2” N, 11◦20048.2” E) is a synoptic meteorological station (100 m AGL -above ground level-), while Silvani St. (site SI, 44◦30003.9” N, 11◦19042.6” E) is an urban meteorological station in the city center (35 m AGL), necessary to downscale the synoptic conditions to the urban background (city-scale) meteorological characteristics. Porta San Felice (site PSF, 44◦29056.8” N, 11◦19038.2” E) and Giardini Margherita (site GM, 44◦28057.6” N, 11◦21014.9” E) are two air quality monitoring stations, characterized as urban traffic and urban background respectively, which allow evaluation of traffic-related air pollutant characteristics at city and in-canyon scales. Finally, at Irnerio St., on the roof of the Department of Physics and Astronomy of the University of Bologna (site CL, 44◦29058.1” N, 11◦21014.4” E), additional supporting measurements of boundary layer height were carried out using a Vaisala CL31 ceilometer. In addition to the urban network, the rural meteorological station of Mezzolara (site MZ; 44◦350 N, 11◦330 E, within the Po Valley about 7 km to the north of Bologna) is used as a representative extra-urban site for the investigation. The main sites of the campaigns were extensively instrumented to cover a large variety of the local-scale dynamics and thermodynamics of vegetated and non-vegetated urban canopies, and to explore the links with air quality. Three sites were deployed at three different heights within and above each canyon, named ground, mid-canyon, and rooftop levels. The instrumentation deployed in each canyon is specular, but the installation heights are different due to the difference in the identified buildings where the instruments were deployed. In Marconi St., ground level (site MA1, 44◦29055.4” N, 11◦20018.0” E) instrumentations are set at 4 m (AGL), mid-canyon level (site MA2, 44◦30004.4” N, 11◦20022.2” E) is set at 7 m (AGL) and the rooftop (site MA3, 44◦30004.4” N, 11◦20022.2” E) is located at 33 m (AGL). In Laura Bassi St., the respective heights are 3 m, 9 m (AGL) inside the canyon (sites LB1 and LB2, 44◦29002.4” N, 11◦22005.3” E and 44◦28054.6” N, 11◦22000.4” E), while the rooftop-level (site LB3, 44◦28059.9” N, 11◦22001.2” E) height had to change from the 18 m of the summer campaign to the 15 m of the winter one according to site availability (the locations of each site is displayed in Figure4). Two mobile laboratories, i.e., vans equipped for air quality and meteorological measurements, were deployed by ARPAE at MA1 and LB1 locations. Mobile laboratories were equipped for continuous measurements of air quality pollutants such as nitrogen dioxides (NOx-NO2-NO), carbon monoxide (CO), sulphur dioxide (SO2), ozone (O3), and particulate matter (PM10 and PM2.5), together with cup anemometer and wind vane for wind speed and direction measurements and thermohygrometers for temperature and relative humidity measurements, all at a sampling rate of 1 min except for particulate matter (1 day sampling rate). High-frequency meteorological instrumentation was installed at each level in and above the canyons to enhance the temporal resolution of the meteorological data and to enable the investigation of turbulent processes. The 3D-wind field was sampled at a 20 Hz with GILL Windmaster sonic anemometers (Gill Instruments Limited, Hampshire, UK), while air temperature, relative humidity and pressure were gathered at sampling rate of 1 Hz respectively by HCS2S3 Rotronic thermohygrometers (Rotronic Instruments Ltd., Crawley, UK) and Vaisala PTB110 barometers (Vaisala, Helsinki, Finland). Additional measurements of the energy balance between short-wave and long-wave far-infrared radiation versus surface-reflected short-wave and outgoing long-wave radiation were performed at sampling rate of 1 min at both MA3 and LB3 locations with CNR4 radiometers (Kipp & Zonen B.V., Delft, The Netherlands). A summary of the experimental setup at each location site is presented in Table1. Atmosphere 2020, 11, x FOR PEER REVIEW 7 of 32 summer campaign to the 15 m of the winter one according to site availability (the locations of each Atmosphere 2020 11 site is displayed, , 1186in Figure 4). 7 of 32

Figure 4. Positions of the monitoring sites (blue dots) and of the locations chosen for the temperature Figure 4. Positions of the monitoring sites (blue dots) and of the locations chosen for the temperature measurements of the building façades and of the ground surface during the two intensive thermographic measurements of the building façades and of the ground surface during the two intensive campaigns in the two street canyons in Bologna (numbers within the red circles): (a) Marconi St. and thermographic campaigns in the two street canyons in Bologna (numbers within the red circles): (a) (b) Laura Bassi St. (source: OpenStreet Map). Marconi St. and (b) Laura Bassi St. (source: OpenStreet Map).

Two mobile laboratories, i.e., vans equipped for air quality and meteorological measurements, were deployed by ARPAE at MA1 and LB1 locations. Mobile laboratories were equipped for continuous measurements of air quality pollutants such as nitrogen dioxides (NOx-NO2-NO), carbon monoxide (CO), sulphur dioxide (SO2), ozone (O3), and particulate matter (PM10 and PM2.5), together with cup anemometer and wind vane for wind speed and direction measurements and thermohygrometers for temperature and relative humidity measurements, all at a sampling rate of 1 min except for particulate matter (1 day sampling rate). High-frequency meteorological instrumentation was installed at each level in and above the canyons to enhance the temporal resolution of the meteorological data and to enable the investigation of turbulent processes. The 3D- wind field was sampled at a 20 Hz with GILL Windmaster sonic anemometers (Gill Instruments Limited, Hampshire, UK), while air temperature, relative humidity and pressure were gathered at sampling rate of 1 Hz respectively by HCS2S3 Rotronic thermohygrometers (Rotronic Instruments Ltd., Crawley, UK) and Vaisala PTB110 barometers (Vaisala, Helsinki, Finland). Additional measurements of the energy balance between short-wave and long-wave far-infrared radiation versus surface-reflected short-wave and outgoing long-wave radiation were performed at sampling rate of 1 min at both MA3 and LB3 locations with CNR4 radiometers (Kipp & Zonen B.V., Delft, The Netherlands). A summary of the experimental setup at each location site is presented in Table 1.

Atmosphere 2020, 11, x FOR PEER REVIEW 8 of 32 Atmosphere 2020, 11, x FOR PEER REVIEW 8 of 32 Atmosphere 2020, 11, x FOR PEER REVIEW 8 of 32 Atmosphere 2020, 11, 1186 8 of 32 Table 1. Experimental setup and measurements. Table 1. Experimental setup and measurements. Site Site DescriptionTable 1. Experimental setup and measurements.Type Instrumentation Sample Rate Site Site DescriptionTable 1. Experimental setup and measurements.Type Instrumentation Sample Rate Marconi St. GroundSite level (MA1) Site Description Type SonicInstrumentation anemometer Sample20 Hz Rate Marconi St. GroundSite level (MA1) Site Description Type SonicInstrumentation anemometer Sample20 Hz Rate Marconi St. GroundSite level (MA1) Instrumented mobile Site Description van (measuring at TypeThermohygrometer Instrumentation 1 Hz Sample Rate In-canyon high- Sonic anemometer 20 Hz MarconiMarconi St. St. Ground Ground levellevel (MA1) (MA1) Instrumented4 m AGL) parked mobile sideline van (measuring below a at Thermohygrometer 1 Hz In-canyon high- BarometerSonic anemometer1 Hz 20 Hz Instrumented4 m AGL) parked mobile sideline van (measuring below a at frequency meteorology Thermohygrometer 1 Hz 4compact m AGL) mean-high parked sideline building below with a BarometerThermohygrometer 1 Hz 1 Hz Instrumented mobile van (measuring at 4 m AGL)frequencyIn-canyonand air meteorology quality high- Barometer 1 Hz 4compact m galleryAGL) mean-high parked and no sideline vegetation. building below with a In-canyon high-frequency Barometer 1 Hz parked sideline below a compact mean-high buildingfrequencyand air meteorology quality AirBarometer quality Lab 1 min/11 Hz d * compactgallery mean-high and no vegetation. building with meteorology and air quality 1 min/1 d * with gallery and no vegetation. and air quality Air quality Lab 1 min/1 d * gallery and no vegetation. Air quality Lab Marconi St. Mid-canyon level Air quality Lab 1 min/1 d * Sonic anemometer 20 Hz Marconi St. (MA2)Mid-canyon level Marconi St. Mid-canyon level Sonic anemometer 20 Hz Marconi St. (MA2)Mid-canyon level T-bone pole on the 2nd floor balcony (7 Sonic anemometerSonic anemometer 20 Hz 20 Hz (MA2)(MA2) T-bone pole on the 2nd floor balcony (7 Sonic anemometer 20 Hz 1 Hz (MA2) T-bonem AGL) pole stretching on the 2nd towards floor balconythe street (7 In-canyon high- T-bonem AGL) pole stretching on the 2nd towards ﬂoor balcony the street (7 m AGL) In-canyon high- fromm AGL) compact stretching high-rise towards building. the street Short frequencyIn-canyonIn-canyon meteorology high- high-frequency stretching towards the street from compact high-rise Thermohygrometer 1 Hz fromtree compact line on high-rise the opposite building. side. Short frequency meteorologymeteorology Thermohygrometer frombuilding. compact Short high-rise tree line onbuilding. the opposite Short side. frequency meteorology Thermohygrometer 1 Hz tree line on the opposite side. Thermohygrometer 1 Hz tree line on the opposite side.

Marconi St. Roof-top level (MA3) Sonic anemometer 20 Hz Marconi St. Roof-top level Marconi St. Roof-top level (MA3) Sonic anemometerSonic anemometer 20 Hz 20 Hz Marconi St. Roof-top(MA3) level (MA3) Thermohygrometer 1 Hz Vertical pole on the rooftop balcony of Thermohygrometer 1 Hz 1 min aVertical 33-m (AGL) pole onhigh the building. rooftop balconyLocated ofon Thermohygrometer 1 Hz Canyon rooftop Verticala 33-mcompact pole (AGL) onhigh-rise thehigh rooftop building. building balcony Located with of a no 33-m on (AGL) high building. Located on compact high-rise buildinginterfaceCanyon high-frequencyCanyon rooftop rooftop interface obstacles.compact There high-rise is no building vegetation with on no the with no obstacles. There is no vegetation on the rooftopinterfacemeteorology high-frequencyhigh-frequency meteorology rooftopobstacles. but There trees isat no the vegetation canyon opposite on the Radiometer but trees at the canyon opposite side (see MA2). meteorology Radiometer 1 min rooftop but trees at the canyon opposite rooftop but treesside (seeat the MA2). canyon opposite Radiometer 1 min side (see MA2).

Laura Bassi St. Ground level Laura Bassi St. Ground level Sonic anemometerSonic anemometer 20 Hz 20 Hz Laura Bassi(LB1) St. Ground level Sonic anemometer 20 Hz (LB1) Instrumented mobile van (measuring at Sonic anemometerThermohygrometer 20 Hz 1 Hz (LB1) Instrumented mobile van (measuring at Thermohygrometer 1 Hz (LB1) InstrumentedInstrumented3 m AGL) parked mobile mobile vansideline van (measuring (measuring below at the 3 mat AGL) In-canyon high- Barometer 1 Hz 3 m AGL) parked sideline below the In-canyon high- ThermohygrometerBarometer 1 Hz parkedtree3 m lineAGL) sideline and parked in below front sideline the of tree an lineisolatedbelow and the in 2- front offrequencyIn-canyonIn-canyon meteorology high- high-frequency 1 min/1 d * antree isolated line and 2-ﬂoor in front house. of Trees an isolated on both 2- sides offrequencymeteorology meteorology and air quality Barometer 1 Hz floortree linehouse. and Trees in front on bothof an sidesisolated of the 2- frequencyand air meteorology quality floor house. Treesthe on street. both sides of the and air quality Air quality Lab floor house. Treesstreet. on both sides of the and air quality Air quality Lab 1 min/1 d * street. Air quality Lab 1 min/1 d * street. Air quality Lab 1 min/1 d *

Atmosphere 2020, 11, 1186 9 of 32

Atmosphere 2020, 11, x FOR PEER REVIEW 9 of 32 Atmosphere 2020, 11, x FOR PEER REVIEW Table 1. Cont. 9 of 32 Laura Bassi St. Mid-canyon level (LB2)Site Site Description TypeSonic anemometer Instrumentation 20 Hz Sample Rate Laura Bassi St.(LB2) Mid-canyon level T-bone pole on the 3rd floor balcony (9 Laura Bassi St. Mid-canyon Sonic anemometer 20 Hz (LB2) m AGL) stretching towards the street In-canyon high- Sonic anemometer 20 Hz level (LB2) T-bonem AGL) pole stretching on the 3rd towards floor balconythe street (9 In-canyon high- 1 Hz T-bonemfrom AGL) isolated pole stretching on the3-floor 3rd towardsfloor house. balcony theTrees street (9 on m AGL)frequencyIn-canyonIn-canyon meteorology high- high-frequency stretching towards the street from isolated 3-floor house. meteorology Thermohygrometer 1 Hz from isolatedboth sides 3-floor of the house. street. Trees on frequency meteorology ThermohygrometerThermohygrometer 1 Hz Trees on both sides of the street. both sides of the street. Thermohygrometer 1 Hz Laura Bassi St. Rooftop level Laura Bassi St. Rooftop level Sonic anemometer 20 Hz Laura Bassi St. Rooftop level Sonic anemometer 20 Hz Laura Bassi(LB3) St. Rooftop level Sonic anemometer 20 Hz (LB3) Sonic anemometerThermohygrometer 20 Hz 1 Hz (LB3) Vertical pole on the rooftop of a 18/15 Thermohygrometer 1 Hz 1 min Vertical**-m (AGL) pole on height the rooftop semi-isolated of a 18/15 Thermohygrometer 1 Hz Verticalbuilding**-m pole (AGL) on of the 6 rooftopfloors height (the ofsemi-isolated a 18tallest/15 **-m of the (AGL) heightCanyon rooftop semi-isolated building of 6 floors (the tallest of the Canyon rooftop interface neighborhood).building of 6 floors No obstacles (the tallest from of the the interfaceCanyon high-frequency rooftop neighborhood).neighborhood). No No obstacles obstacles from from the neighboring the interface high-frequencyhigh-frequency meteorology neighboring buildings and no meteorology buildingsneighborhood).neighboring and no vegetation No buildings obstacles on theand from rooftop. no the Trees interface on meteorology high-frequency Radiometer vegetation on the rooftop. Trees on both vegetationneighboringboth on sidesthe rooftop.buildings of the street Trees and below. noon both meteorology Radiometer 1 min sides of the street below. vegetationsides on of the the rooftop. street below. Trees on both Radiometer 1 min sides of the street below.

Silvani St. (SI) Cup and vane SilvaniSilvani St. (SI) Cup and vane 30 min Cup and vaneanemometer 30 min Silvani St. (SI) WMO-(World Meteorological anemometer 30 min WMO-(World Meteorological Cupanemometer and vane 30 min WMO-(WorldOrganization) Meteorological standard meteorological Organization) standard 30 min meteorologicalOrganization)WMO-(World station standard at 35Meteorological/27 meteorological *** m AGL located on topUrban (city-scale) anemometer UrbanUrban (city-scale) (city-scale) meteorology of theOrganization)station highest at building 35/27 standard *** of m the AGL neighborhoodmeteorological located on (red dot on Urbanmeteorology (city-scale) stationtop of at the 35/27 highest ***left m ﬁgure).building AGL located of the on Thermohygrometer top of the highest building of the Thermohygrometer 30 min neighborhood (red dot on left figure). meteorology neighborhoodtop of the highest (red dot building on left of figure). the Thermohygrometer 30 min neighborhood (red dot on left figure). Asinelli Tower (AS) Cup and vane Asinelli Tower (AS) Cup and vane 30 min Asinelli Tower (AS) anemometer 30 min WMO-standard meteorological station Cupanemometer and vane 30 min WMO-standardWMO-standard meteorological meteorological station atstation 100 m AGL on 30 min topat of100 the m Asinelli AGL on Tower top of (red the dot Asinelli on left ﬁgure), Synoptic scale anemometer WMO-standardat 100 m AGL onmeteorological top of the Asinelli station SynopticSynoptic scale scale meteorology 70–80 m above the nearest buildings. Towerat 100 (red m AGL dot onon lefttop figure),of the Asinelli 70–80 m Synopticmeteorology scale Thermohygrometer Towerabove (red dotthe neareston left figure), buildings. 70–80 m meteorology Thermohygrometer 30 min above the nearest buildings. Thermohygrometer 30 min

WMO-standard urban traffic air quality Gas and particulate Porta San Felice (PSF) Urban traffic air quality 30 min WMO-standard2-m AGL station urban (red dot traffic on leftair qualityfigure) Gas andanalyzer particulate Porta San Felice (PSF) Urban traffic air quality 30 min 2-m AGL station (red dot on left figure) analyzer

Atmosphere 2020, 11, 1186 10 of 32

Table 1. Cont. Atmosphere 2020, 11, x FOR PEER REVIEW 10 of 32 Atmosphere 2020, 11, x FOR PEER REVIEW 10 of 32 Atmosphere 2020, 11, xSite FOR PEER REVIEW Site Description Type Instrumentation Sample10 of Rate 32 sideline to the urban ring-road next to a Porta San Felice (PSF) sideline to the urban ring-road next to a sideline tosmall the urban vegetated ring-road area. next to a WMO-standardsmall urbanvegetated traffi carea. air quality 2-m AGL 30 min Gas and particulate station (red dot on left figure) sideline to the urban Urban traffic air quality analyzer ring-road next to a small vegetated area.

Giardini Margherita (GM) Giardini Margherita (GM) GiardiniGiardini Margherita Margherita (GM)(GM) WMO-standard urban background 2-m WMO-standard urban background 2-m WMO-standardAGL air quality urban station background (red dot 2-m on AGL left air qualityUrban background air Gas and particulate 30 min AGL air quality station (red dot on left Urban background air Gas and particulateGas and particulate 30 min stationfigure)AGL (red air within dotquality on the left station largest ﬁgure) (redwithin urban dot the parkon largest left of urbanUrban backgroundUrbanquality background air air qualityGas andanalyzer particulate 30 min figure) within the largest urban park of quality analyzer analyzer 30 min figure) within theparkthe largest city. of the city.urban park of quality analyzer the city.

Irnerio St. (CL) IrnerioIrnerio St. (CL) Ceilometer located on the rooftop 16 s CeilometerCeilometer located located on the rooftop on the balconyrooftop (15 m AGL)Urban boundary-layer balcony (15 m AGL) within the city, Urban boundary-layerUrban boundary-layer heightCeilometer Ceilometer 16 s balconywithin the(15 city, m AGL) with an within open verticalthe city, path. Urban boundary-layerheight Ceilometer 16 s balconywith (15 an m open AGL) vertical within path. the city, height Ceilometer 16 s with an open vertical path. height with an open vertical path.

Mezzolara (MZ) Cup and vane 30 min Mezzolara (MZ) Cup and vaneCup and vane 30 min Mezzolara (MZ) WMO-standard meteorological station Cupanemometer and vane 30 min WMO-standard meteorological station Rural meteorology anemometeranemometer 30 min atWMO-standard 10/2 *** m AGL meteorological within the rural station area anemometer WMO-standardat 10/2 *** m meteorological AGL within stationthe rural at 10 area/2 *** m AGLRural meteorology atwithin 10/2 ***Mezzolara, m AGL within 7 km to the the rural north area of Rural meteorology 30 min withinwithin the Mezzolara, rural area within 7 km Mezzolara,to the north 7 kmof to theRural meteorology within Mezzolara,northBologna. 7 of km Bologna. to the north of ThermohygrometerThermohygrometer 30 min Bologna. Thermohygrometer 30 min Bologna.

* 1 min refers to the gaseous compounds, 1 day to the particulate matters. ** 18 m is the summer-campaign elevation, 15 m the winter-campaign elevation. *** The * 1 *min 1 min refers refers to to the the gaseous gaseous compounds,compounds, 1 day1 day to theto particulatethe particul matters.ate matters. ** 18 m ** is 18 the m summer-campaign is the summer-campaign elevation, 15 elevation, m the winter-campaign 15 m the winter-campaign elevation. *** The elevation. ﬁrst value refers*** The to the *first 1 anemometer,min value refers refers to the theto second the gaseous anemometer, to the compounds, thermohygrometer. the second 1 day to the thermohygrometer.particulate matters. ** 18 m is the summer-campaign elevation, 15 m the winter-campaign elevation. *** The first value refers to the anemometer, the second to the thermohygrometer. first value refers to the anemometer, the second to the thermohygrometer.

Atmosphere 2020, 11, 1186 11 of 32

Within the experimental campaigns, two intensive operational periods (one per campaign) were devoted to collecting extra measurements to investigate the UHI phenomenon. Specifically,the temperature distribution of building façades and ground surfaces in the two streets was analyzed collecting simultaneously images in the two canyons with two high-performance thermal cameras FLIR T620 ThermalCAMs (FLIR Systems Inc., Wilsonville, OR, USA) with uncooled microbolometer, 640 480 × pixels resolution and an image acquisition frequency of 50–60 Hz, selecting two appropriate periods characterized by clear sky and calm winds, i.e., during synoptic conditions particularly favorable to the onset of UHI, i.e., characterized by weak synoptic forcing [54] (22–23 August 2017 during the summer campaign, and 8–9 February 2018 during the winter campaign). Specifically, we selected 1 days for which the weather forecasts indicated wind speed below 5 m s− at 700 hPa and characterized by clear-sky conditions (see AppendixB). During the experiments, images were collected simultaneously by standing operators during a 24 h acquisition with regular intervals of 2 h (total of 12 acquisitions in 24 h at each site). This choice allowed the collection of images at 12:00 (close to the maximum surface temperature), 14:00, 16:00 (close to the maximum air temperature), 18:00, 20:00, 22:00 (close to maximum UHI intensity), 24:00, 02:00, 04:00, 06:00, 08:00, 10:00, and 12:00 (UTC + 2). Note that during the summer campaign a technical issue occurred to the thermal camera in Laura Bassi St., so measurements in that canyon from midnight to the end of the experiment were performed with a delay of 1 h with respect to Marconi St. Analyzed buildings were selected on the basis of the homogeneity of construction material and the absence of obstacles (balconies, eave, etc.), metal or glass. Several shots or portions of the same building façade at several different heights were taken in order to maintain a similar resolution for all images. Finally, measurements of ground surfaces were also collected. The precise positioning of the selected buildings is provided in Figure4. In particular, in Marconi St. the buildings 1, 2, and 8 are located on the east side, while buildings number 4, 5, and 6 are located on the west side; finally, the positions 3 and 7 indicate the sites where the temperature of the asphalt surface was measured. Similarly, in Laura Bassi St., buildings 5, 7, and 8 are located on the east side, while buildings number 1, 2, and 3 are located on the west side, and finally numbers 4 and 6 indicate the positions of the measurements of the ground surface.

3. Results and Discussion

3.1. Urban Heat Island To address the urban heat island development at neighborhood and city scale levels we used the data collected within the two intensive thermographic campaigns performed in the two street canyon areas described previously. Figure5 presents and compares the diurnal evolution of the air temperature measured within and above the two street canyons of Marconi and Laura Bassi (sites 1 and 2 respectively) and at the rural meteorological station of Mezzolara (MZ in Figure5), during the summer and winter experimental campaigns. Besides the evolution of air temperature measured with the thermohygrometers, the Figure also presents the pattern of temperature of the building façades, considering one reference building located on the west side and one located on the east side in both canyons (respectively, MA P1 E and MA P5W i.e., position 1 and 5 in Marconi St. in Figure4a, LB P1W and LB P8E, i.e., positions 1 and 8 in Laura Bassi St. in Figure4b). Atmosphere 2020, 11, 1186 12 of 32 Atmosphere 2020, 11, x FOR PEER REVIEW 12 of 32

FigureFigure 5.5. TemperatureTemperature evolutionevolution within the day of the summer (22–23 (22–23 August August 2017) 2017) (a (a) )and and winter winter (8–9(8–9 February February 2018 2018))( b()b) intensive intensive thermographicthermographic campaigncampaign in the two street street canyons canyons in in Bologna Bologna (Marconi(Marconi St.St. on on the the left left and and Laura Laura Bassi Bassi St. on St. the on right), the right), measured measured by the thermo-hygrometers by the thermo-hygrometers located locatedat ground at ground and mid-level and mid-level (respectively (respectively site 1 site an 1d and2), the 2), theAgenzia Agenzia Regionale Regionale per per la laPrevenzione, Prevenzione, l’Ambientel’Ambiente ee l’Energial’Energia dell’Emilia-Romagnadell’Emilia-Romagna (ARPAE (ARPAE)) instrumentation in in one one rural rural meteorological meteorological stationstation (MZ)(MZ) andand ofof buildingbuilding façadesfaçades of buildings loca locatedted on on the the west west and and east east side side of of the the two two street street canyonscanyons as as retrieved retrieved fromfrom thethe thermalthermal imagesimages acquiredacquired withwith the two thermal cameras.

FigureFigure5 5shows shows very very clearly clearly the the UHIUHI phenomenon,phenomenon, i.e.,i.e., thethe eeffectffect of of overheating overheating of of cities cities with with respectrespect toto thethe countrysidecountryside duringduring thethe summersummer season.season. The The effect effect is is reduced reduced at at Laura Laura Bassi Bassi St. St. with with respectrespect toto MarconiMarconi St.,St., bothboth duedue toto thethe presencepresence of vegetation as as well well as as being being located located further further from from thethe city city centercenter in a a residential residential area area in in the the outskirt outskirtss of Bologna of Bologna where where the presence the presence of small of small houses houses and andapartments apartments favor favor heat heat exchange exchange and and urban urban therma thermall comfort comfort with with respect respect to to the the highly highly packaged packaged buildingsbuildings typical typical of of the the historical historical city city center center where where Marconi Marconi St. St. lies. lies. This This also also indicates indicates the the positive positive role ofrole trees of intrees improving in improving thermal thermal comfort comfort and reducing and reducing the urban the heat urban island heat phenomenon. island phenomenon. In addition, In theaddition, plots also the show plots thatalso duringshow that the during night both the streetnight canyonsboth street are canyons isothermal, are isothermal, i.e., there is i.e., no temperature there is no ditemperaturefference between difference thebuilding between façadesthe building on the façade two sidess on ofthe the two streets sides norof the with streets the temperature nor with the of thetemperature air in the streetof the canyon,air in the while street during canyon, the while day, during depending the day, on the depending position on of thethe Sun,position one of side the is hotterSun, one than side the otheris hotter one: than this the effect other is visible one: this at both effect street is visible canyons at butboth is street more canyons evident atbut Marconi is more St. evident at Marconi St. During winter (Figure 5b), similar UHI effects are observed at both sites, with

Atmosphere 2020, 11, 1186 13 of 32 Atmosphere 2020, 11, x FOR PEER REVIEW 13 of 32

Duringurban–rural winter (Figure differences5b), similar of about UHI e6 ff°Cects but are with observed a nighttime at both sites,increase with in urban–rural both air and diff erencesbuildings’ of abouttemperatures 6 ◦C but observed with a nighttime during the increase night inof both8 February air and 2018. buildings’ This effect temperatures was partially observed attributed during to the the nightpresence of 8 of February a cloud 2018. cover This limiting effect the was nighttime partially attributedradiative tocooling, the presence as indicated of a cloud during cover night limiting both by thelocal nighttime observations radiative and cooling, by thermal as indicated composite during satellit nighte images. both by In local addition, observations the nighttime and by increase thermal can compositebe tentatively satellite attributed images. Into addition,the on and the off nighttime switching increase of residential can be heating: tentatively in this attributed case, the to difference the on andbetween off switching the patterns of residential observed heating: in the in two this street case, thecanyons difference might between be due theto the patterns different observed kinds of in thebuildings two street in the canyons two street might canyons, be due towhere the diMarconifferent kindsSt. is characterized of buildings inby the block two of street flats canyons,with central whereheating Marconi whereas St. is Laura characterized Bassi St. is by instead block characteri of flats withzed central by small heating residential whereas houses Laura with Bassi independent St. is insteadheating. characterized Considering by smallthe similar residential urban–rural houses with temperature independent differences heating. in Considering the two seasons the similar and the urban–ruralreduced differences temperature at di neighborhoodfferences in the scales two seasonsobserved and in the reducedwinter season, differences in addition at neighborhood to the limited scalesimpact observed of vegetation in the winter during season, this in season, addition on to th thee following limited impact we chose of vegetation to focus during just on this the season, summer on theobservations. following we chose to focus just on the summer observations.

3.1.1.3.1.1. City City (Urban–Rural) (Urban–Rural) and and Neighborhood Neighborhood (Canyon–Canyon) (Canyon–Canyon) Scales Scales TheThe UHI UHI is generallyis generally attributed attributed to to thethe didifferentialfferential heat heat release release in in the the atmosphere atmosphere between between urban urbanand andrural rural environment, environment, causing causing a horizontal a horizontal temperature temperature difference dibetweenfference them. between However, them. the However,heterogeneous the heterogeneous morphologymorphology of a city allows of a city the allows development the development of horizontal of horizontal temperature temperature differences differenceswithin the within urban the environment urban environment (for example (for example between betweentwo neighborhoods two neighborhoods characterized characterized by different by dimorphologyfferent morphology and vegetation and vegetation amounts). amounts). In the current In the study, current we study, will address we will the address classical the urban–rural classical urban–ruraltemperature temperature differencedi asfference a city-scale as a city-scaleUHI, while UHI, thewhile intra-city the intra-city(canyon–canyon) (canyon–canyon) temperature temperaturedifference di willfference be interpreted will be interpreted as neighborhood-sca as neighborhood-scalele UHI. UHI.Figure Figure 6a show6a showss the theevolution evolution of the + of thenormalized normalized UHI UHI intensities intensities ∆∆T satisfyingsatisfying this this classification. classification. By By “normalized” “normalized” we we intend intend the themeasured measured value value of ofthe the temperature temperature difference difference divided divided by its by maximum its maximum in the analyzed in the analyzed period. We period.use the We normalized use the normalizedvalues to enhance values tothe enhanceshape of the the shapequantity’s of the evolution, quantity’s highlighting evolution, the + highlightinganalogous the evolution analogous of evolutionthe ∆ signals of the ∆notwithstandingT signals notwithstanding the different thespatial different scales spatial involved scales in the involvedcomputation. in the computation. It is also worth It is alsonoticing worth that noticing while that an whileinversion an inversion in the UHI in the intensity UHI intensity is observed is observedcomputing computing the temperature the temperature difference difference with withabove-ca above-canopynopy urban urban temperatures, temperatures, this thisbehavior behavior almost almostdisappears disappears with with the the in-canyons in-canyons and and between-canyo between-canyonsns temperatures,temperatures, highlighting highlighting again again the the high high impactimpact of the of morphologythe morphology and and heat heat release release from from urban urban surfaces surfaces (buildings (buildings and and street). street).

Figure 6. (a) Normalized UHI intensity (solid lines) evolution within the day of the summer intensive Figure 6. (a) Normalized UHI intensity (solid lines) evolution within the day of the summer intensive thermographic campaign in Bologna (22–23 August 2017). The shadowed region deﬁnes the nocturnal thermographic campaign in Bologna (22–23 August 2017). The shadowed region defines the nocturnal period between sunset (2000 UTC) and sunrise (0630 UTC). (b) UHI intensity as a function of the wind period between sunset (2000 UTC) and sunrise (0630 UTC). (b) UHI intensity as a function of the wind speed. Diﬀerent locations are compared. City scale UHI is shown with full circles, neighborhood scale speed. Different locations are compared. City scale UHI is shown with full circles, neighborhood scale UHI with full triangles. UHI with full triangles.

Refs. [55–57] have derived and proven a simple relation between the urban wind speed and the UHI intensity directly from the temperature equation

Atmosphere 2020, 11, 1186 14 of 32

Refs. [55–57] have derived and proven a simple relation between the urban wind speed and the UHI intensity directly from the temperature equation

. ∂T Tu Tr H = U − + 0 . (1) ∂t − L ρcP

The advection term (first term of the right-hand side of the equation, hereafter RHS) is expressed with its scale values, where U is the urban wind speed, Tu is the urban temperature, Tr is the rural temperature and L = √4A/π = 13,400 m is the city diameter. This term is responsible for the dissipation of the UHI due to the temperature advection and so can be interpreted as a cooling factor. The last . term of the RHS of Equation (1) accounts for the divergence of the heat flux H0, and sustains the UHI slowing the cooling. As this last term is not always measured, ref. [56] parametrized its contribution in a simple attenuation factor K of the cooling term. Therefore, integrating Equation (1) considering only the first term of the RHS multiplied by K and using ∆T = Tu Tr, we get − U ∆T(t) = ∆T exp K t (2) 0 − L where ∆T0 is the initial temperature difference before the onset of the cooling, i.e., is the maximum temperature difference. The factor K ranges between 0 and 1. When K = 1 we are in the limiting . case of H0 = 0, while for K < 1 the diffusive heat flux retarding the night cooling process is taken into account. Figure6b shows the dependency of the city and neighborhood-scales UHI intensity on 1 velocity, with the solid lines retrieved using Equation (2). ∆T0 and K are extrapolated at U = 0.5 ms− (activation threshold for cup and vane anemometers) fitting Equation (2) on the measured data for a fixed time t f = 6 h identified as the time of cooling between sunset and the nocturnal ∆T equilibrium (see Table2).

Table 2. Fit characteristics and likelihood parameters (coeﬃcient of determination R2 and root mean square error RMSE) between data in Figure6b and Equation (2) for the di ﬀerent cases.

2 ∆T0 [◦C] K R RMSE SI-MZ 5.8 0.7 0.78 0.97 AS-MZ 7.9 0.8 0.76 1.43 MA1-MZ 8.1 0.6 0.76 1.30 LB1-MZ 6.1 0.6 0.72 1.14 MA1-LB1 2.3 0.8 0.60 0.43

Figure6b clearly shows the UHI-intensity dependency on the urban wind speed, as suggested by Equation (2). Moreover, it highlights the similar behavior of the city and neighborhood-scale UHI. The different degree of dependency observed between the two scales of UHI can attributed to the smaller initial (and maximum) temperature difference ∆T0 measured at the neighborhood scale. Indeed, this evidence is quite expected as the temperature gradients within the same city should be constrained by the temperature diffusion within the urban environment. Conversely, the largely different land use of the countryside is expected to enlarge the temperature difference with the city, explaining the larger initial temperature observed in Figure6b. The values of K also highlight the larger contribution of the heat flux divergence in the canyon-rural temperature cooling process, as the canopy environment tends to preserve the diurnal temperature more easily than the atmosphere above (leading to a smaller contribution of the heat flux divergence in the urban-rural temperature cooling process) due to the vicinity of multiple heat sources. The presence of vegetation in Laura Bassi St. does not impact on the heat fluxes contribution, but together with the larger aspect ratio of the canyon (with respect to Marconi St.), contributes to reduce the UHI intensity. This difference between the business-center canyon of Marconi St. and the vegetated-residential canyon of Laura Bassi St. is certified by the presence of a small UHI phenomenon between the two neighborhoods, Atmosphere 2020, 11, 1186 15 of 32 due to the different cooling efficacy. The impact of the heat flux divergence is instead small as already observed for the city-scale UHI involving the canyons.

3.1.2. CanyonAtmosphere (Facade-Facade) 2020, 11, x FOR PEER Scale: REVIEW The In-Canyon Thermal Circulation 15 of 32

As3.1.2. by the Canyon mere (Facade-Facade) definition of temperature Scale: The In-Canyon difference Thermal between Circulation two locations, a very local UHI effect can be observed also within an urban canopy, where a horizontal temperature gradient can As by the mere definition of temperature difference between two locations, a very local UHI establisheffect between can be the observed opposite also sides within of the an canyonurban canopy, (building where façade a horizontal to building temperature façade). Moregradient precisely, can this temperatureestablish between gradient the acts opposite as the sides primary of the forcing canyon of (building the in-canyon façade thermalto building circulation. façade). More As for the largeprecisely, scale UHI this phenomenon temperature previouslygradient acts discussed, as the primary this localforcing phenomenon of the in-canyon establishes thermal a circulation. relationship betweenAs the for in-canyon the large scale wind UHI speed phenomenon and the building previously façade discussed, temperature this local di phenomenonfference (evaluated establishes using a the eastrelationship and west canyon-side between the building in-canyon façades wind shown speed inand Figure the 5building). Figure façade7a shows temperature the similar difference behavior of these(evaluated two quantities, using the highlighting east and west that canyon-side a direct proportionalitybuilding façades show relatesn in the Figure temperature 5). Figure 7a di showsfference and thethe wind similar speed behavior (while of it wasthese inverse two quantities, at larger highlighting scales). While that ata largerdirect proportionality scales the wind relates speed the was temperature difference and the wind speed (while it was inverse at larger scales). While at larger a cooling factor, within the canopy the temperature difference forces the circulation. The following scales the wind speed was a cooling factor, within the canopy the temperature difference forces the quadratic fit suits this in-canyon dependency between the building façade temperature difference from circulation. The following quadratic fit suits this in-canyon dependency between the building façade the thermographictemperature datadifference and thefrom in-canyon the thermographic wind speed data measuredand the in-canyon at the ground wind speed level measured of each canyonat the ground level of each canyon ∆T(t) = aU2 + b (3) |∆()| = + (3) as displayedas displayed in Figure in Figure7b. Fit 7b. parameters Fit parameters are are reported reported in in TableTable 33.. Despite Despite the the clear clear dependency dependency of the of the windwind speed speed on on the the temperature temperature di difference,fference, andand herebyhereby thethe importance of of the the thermal thermal forcing forcing in in drivingdriving the in-canyon the in-canyon circulation, circulation, the presencethe presence of of a non-negligiblea non-negligible ambientambient wind wind speed speed U measureda measured above theabove canopy the (ascanopy observed (as observed at the site at SIthe and site AS, SI and Figure AS,6 b)Figure can be 6b) responsible can be responsible to drive anto additionaldrive an inertial componentadditional inertial within component the canyon. within In this the scenario,canyon. In the this measured scenario, the wind measured speed wouldwind speed be the would sum of be the sum of a thermally driven and an inertially driven component. While the inertial a thermally driven Ur and an inertially driven UI component. While the inertial component is typically component is typically a fraction of the wind speed above the canopy, the thermal one requires a a fraction of the wind speed above the canopy, the thermal one requires a different parametrization. different parametrization.

Figure 7.Figure(a) Urban 7. (a) Urban Heat IslandHeat Island (UHI) (UHI) intensity intensity from from building building façade façade temperature temperature difference difference (solid (solid lines) andlines) in-canyon and in-canyon wind wind speed speed (dashed (dashed lines) lines) evolution evolution within within the the dayday ofof thethe summer intensive intensive thermographicthermographic campaign campaign in Bologna in Bologna (22–23 (22–23 August August 2017). 2017). The The shadowed shadowed regionregion defines defines the the nocturnal nocturnal period betweenperiod between sunset sunset (2000 (2000 UTC) UTC) and and sunrise sunrise (0630 (0630 UTC). UTC). (b ()b UHI) UHI intensity intensity from building building façade façade temperaturetemperature difference difference as a function as a function of the of in-canyonthe in-canyo windn wind speed speed measured measured at the ground ground levels levels of of each canyon.each canyon.

Table 3.TableFit characteristics 3. Fit characteristics and likelihoodand likelihood parameters parameters (coe (coefficientﬃcient of of determinationdetermination R2 andand root root mean mean square errorsquare (RMSE)) error (RMSE)) between between data indata Figure in Figure7b and 7b and Equation Equation (3) (3) for for the the di differentﬀerent cases.cases.

[°C m−2 s2]2 2 b 2 RMSE a [◦C m− s ] b R RMSE MA 0.55 0.28 0.87 0.73 MA 0.55 0.28 0.87 0.73 LB 6.39 −0.51 0.87 0.65 LB 6.39 0.51 0.87 0.65 −

Atmosphere 2020, 11, 1186 16 of 32

AtmosphereAs the 2020 thermally, 11, x FOR driven PEER REVIEW component must be a quadratic function of the temperature difference16 of 32 within the canyon (we must satisfy Equation (3) and the inertial component cannot by definition), we adoptAs the thermally UHI convergence driven component velocity [58 must,59] defined be a qu asadratic function of the temperature difference within the canyon (we must satisfy Equation (3) and the inertial component cannot by definition), we 1/2 adopt the UHI convergence velocity [58,59]Ur = definedC(gαL ∆ asT ) (4) | | =(|∆|)/ (4) where g is the gravitational acceleration, α is the thermal expansion coefficient, L is a length scale set equalwhere to the is canyon the gravitational width, and acceleration,C = 0.08 is a constant is the thermal computed expansion by [58] fromcoefficient, laboratory is experimentsa length scale andset theequal similarity to the theory.canyon Specifically, width, andC is=0.08 given by is the a independencyconstant computed of Ur ofby the [58] Reynolds from laboratory number Reexperiments= UW/ν (where and theU in-canyonsimilarity theory. ground-level Specifically, wind speed, is givenW is theby the mean independency canyon width, of and ofν isthe theReynolds kinematic number viscosity of = the / air) (where evaluated comparingin-canyon Equationground-level (4) withwindRe speed,, as done in is Figure the mean8a. Incanyon our case, width, scaling and the measured is the kinematic wind speed viscosity and evaluatingof the air) evaluated its independency comparing on the Equation Reynolds (4)number, with , weas obtain done in di Figurefferent values8a. In our of Ccase,, named scalingC1, the equal measured to 0.13 andwind 0.32 speed for Lauraand evaluating Bassi St. andits independency Marconi St., respectively.on the Reynolds This discrepancynumber, we inobtain the constant different between values of the laboratory, named and, equal the to current 0.13 and investigation 0.32 for Laura is associatedBassi St. and with Marconi the inertial St., respectively. component of This the discre windpancy speed, in not the accounted constant between by Equation the laboratory (4). To verify and thisthe hypothesis, current investigation we compute is associated the total parametrized with the inertial velocity componentUs as of the wind speed, not accounted by Equation (4). To verify this hypothesis, we compute the total parametrized velocity as = ( )1/2 + Us 0.08 gαW ∆T /C1Ua (5) =0.08(| |∆| |) + (5) where Ua is the wind speed measured in Silvani Street. Equation (5) provides a good approximation of where is the wind speed measured in Silvani Street. Equation (5) provides a good approximation theof in-canyonthe in-canyon measured measured velocity, velocity, as shown as shown in Figure in8 a.Figure The clear8a. The di ff clearerence difference between thebetween two canyons the two unveilscanyons the unveils total absence the total of absence a diurnal of evolutiona diurnal ofevolution the wind of speedthe wind within speed Laura within Bassi Laura St., while Bassi inSt., Marconiwhile in St. Marconi the behavior St. the approaches behavior approaches the ambient the wind ambient dynamics. wind It dynamics. is suggested It is that suggested the presence that ofthe treespresence is such of that trees the is wind such inthat the the surface wind layer in the of thesurface canopy layer (below of the the canopy tree crown) (below is the forced tree to crown) remain is smallforced during to remain the whole small day, during causing the a decreasewhole day, in thecausing circulation a decrease intensity. in the Moreover, circulation the presenceintensity. ofMoreover, trees is a larger the presence reduction of factortrees is for a thelarger inertial reduct wind-speedion factor componentfor the inertial than wind-speed the disadvantageous component aspectthan the ratio disadvantageous of Marconi St. aspect ratio of Marconi St.

FigureFigure 8. 8. ((aa)) NormalizedNormalized UHI UHI convergence convergence velocity velocity as as a afunction function of ofthe the Reynolds Reynolds number. number. (b) (b) Comparison of the total parametrized velocity Us (dashed lines with diamonds) with the measured Comparison of the total parametrized velocity (dashed lines with diamonds) with the measured windwind speed speedU (dotted (dotted lines lines with with pentagrams) pentagrams) withinwithin thethe canyons. The The shadowed shadowed region region defines defines the thenocturnal nocturnal period period between between sunset sunset (2000 (2000 UTC) UTC) and and sunrise (0630 UTC).UTC). The different behavior of UHIs depending on the horizontal scale suggests a complex interaction The different behavior of UHIs depending on the horizontal scale suggests a complex interaction between different areas of the same city, where different internal boundary layers (IBLs) may develop between different areas of the same city, where different internal boundary layers (IBLs) may develop and interact with each other. Specifically, [60] proposed a conceptual model where the presence of and interact with each other. Specifically, [60] proposed a conceptual model where the presence of “overlapping IBLs” is such that nearer the urban canopy there are spatially localized patches of flow “overlapping IBLs” is such that nearer the urban canopy there are spatially localized patches of flow in local equilibrium with the surface. This model suggested that atmospheric phenomena may be in local equilibrium with the surface. This model suggested that atmospheric phenomena may be influenced by being confined in the same IBL or developed between different IBLs. Homogeneous influenced by being confined in the same IBL or developed between different IBLs. Homogeneous conditions (as an isothermal atmosphere within the canopy, see Figure5) defines a single IBL, which may conditions (as an isothermal atmosphere within the canopy, see Figure 5) defines a single IBL, which may develop gradients with the neighboring IBLs, leading to spatial heterogeneities. For this reason, the city and neighborhood scale UHIs are expected to behave similarly as they involve multiple IBLs (in the case of the city-scale UHI, the whole urban and rural boundary layers). Conversely, the façade-

Atmosphere 2020, 11, 1186 17 of 32 develop gradients with the neighboring IBLs, leading to spatial heterogeneities. For this reason, the city and neighborhood scale UHIs are expected to behave similarly as they involve multiple IBLs (in the case of the city-scale UHI, the whole urban and rural boundary layers). Conversely, the façade-to-façade temperature difference occurs within a single IBL and, as such, it presents a different and unique interaction with the local flow field.

3.2. Canyon-Scale UHI and UPI Interaction It has been documented that UHI and UPI, two major problems of the urban environment which are becoming more serious with rapid urbanization, can interact with each other, through their effect on the intensity of turbulence mixing and on radiation transfer processes [16]. This makes it necessary to study both effects concurrently. To establish a link between the thermal circulation and the pollutant concentrations measured in the Marconi St., we derived a parameterization for both quantities. In particular, the UHI convergence velocity Ur toward an urban area was parameterized following the previously introduced Equation (4) using the value of 0.08 for the empirical constant C = 0.08 previously derived by [58] in laboratory experiments analyzing the horizontal radial convergence flow velocity in the presence of heated elements in a fluid. Implementing this parameterization for a street canyon, we considered the characteristic length scale of the UHI equal to the mean street canyon width, while the temperature difference causing the thermal circulation was considered that between the west and east building façades. In the case of pollutant concentrations, we derived a normalization considering all the processes impacting on pollutant concentrations in a canyon, clearly depending first of all on the emission source rate but impacted by other atmospheric local processes. To derive a normalization for pollutant concentrations in an urban street canyon, we selected CO as a passive pollutant emitted by vehicle exhausts. As previously presented in [61], we then identified the key influencing factors on pollutant concentrations in an urban street canyon: (1) the source strength; (2) the canyon geometry, related to the dispersion volume and affecting the mass exchange between the canyon and the upper atmosphere; (3) the background wind velocity impinging on the canyon. A detailed explanation of the algorithm will be presented in [62], but briefly in analogy with [63], measured CO concentrations were normalized with a reference concentration CO* defined as

Qe CO∗ = (6) HWUb

1 where Qe is the pollutant source rate [g s− ], Ub is the bulk velocity measured above the canyon, H is the mean building height, and W is the mean street canyon width. In particular, bulk velocity was derived from the measurements of the sonic anemometers placed on the rooftops in the two street canyons, while the mean building height and street canyon width were estimated from detailed digital maps of the two areas. An algorithm was instead developed to estimate the pollutant source rate. To this end, the traffic counts available as number of vehicles travelling in Marconi St. with a 5 min time resolution from inducive loops technology were classified upon fleet composition, i.e., fuel type (gasoline, diesel, LPG -Liquid Petroleum Gas-, CNG -Compressed Natural Gas-), vehicle type (motorcycle, light vehicles, heavy vehicles, buses) and EURO technology. The number of buses passing through Marconi St. was calculated from the bus time schedules in Bologna (Regional transport company TPER (Trasporto Passeggeri Emilia Romagna) [64]). The local fleet composition was then extracted from the regional inventory of circulating vehicles (Italian Car Club company (ACI, Automobile Club d’Italia) [65]) and was used to disaggregate the difference between the total traffic counts and the number of buses derived from bus schedules into traffic counts per vehicle type. Pollutant emission rates [g/km] were then finally estimated using the joint EMEP/EEA (European Monitoring and Evaluation Programme/European Environment Agency) air pollutant emission inventory guidebook for each vehicle category [66]. Finally, the pollutant source rate Qe was estimated 1 from pollutant emissions using a representative vehicle urban speed of 19 km h− [66]. Atmosphere 2020, 11, 1186 18 of 32

Figure9 presents the diurnal patterns of the thermal velocity and of normalized CO concentrations Atmospherein Marconi 2020, 11 and, x FOR Laura PEER Bassi REVIEW St. during the summer UHI experiment. 18 of 32

FigureFigure 9. Diurnal 9. Diurnal pattern pattern of ofthe the thermal thermal circulation circulation velocityvelocity and and of of normalized normalized CO CO concentrations concentrations in in (a) Marconi(a) Marconi St. St.and and in in(b ()b Laura) Laura Bassi Bassi St. St. during during thethe summersummer intensive intensive thermographic thermographic campaign. campaign. Figure9 shows very clearly the existence of a relationship between the UHI velocity and the pattern Figure 9 shows very clearly the existence of a relationship between the UHI velocity and the of normalized concentrations in Marconi St., whereas in Laura Bassi the thermal circulation is not patterndirectly of normalized related with concentrations the pollutant distribution,in Marconi St., which whereas is driven in Laura by other Bassi mechanisms the thermal owing circulation to is notthe directly presence related of trees with and the to thepollutant different distribution, morphology which of the neighborhood.is driven by other It should mechanisms be noted owing that to the presencealthough theof trees good and relationship to the different between Urmorpholoand thegy normalized of the neighborhood. concentration mayIt should appear be counter noted that althoughintuitive, the it good is a consequence relationship of the between fact that Ur the and presence the ofnormalized a temperature concentration gradient between may two appear opposite counter intuitive,façades it mayis a leadconsequence to the establishment of the fact of that an opposite the presence vortex [of67 ],a whichtemperature confines gradient the primary between vortex two oppositeclosest façades to the ground may lead and to diminish the establishment the vertical exchange. of an opposite The establishment vortex [67], of which this new confines vortex depends the primary vortexon closest the strength to the of ground the temperature and diminish gradient. the Ifvertic not soal strongexchange. and ofThe the establishment order of 10 ◦C of as this in our new case, vortex dependswe will on see the that strength the effect of ofthe the temp temperatureerature gradientgradient. will If benot to so deform strong the and vortex of the and order finally of diminish 10 °C as in its ability to remove pollutants from the near surface. Simulations performed in the following section our case, we will see that the effect of the temperature gradient will be to deform the vortex and will be used to prove this argument. The rise in normalized concentration observed at Laura Bassi finally diminish its ability to remove pollutants from the near surface. Simulations performed in the St. at 3:00 (local time) is due to the normalization procedure that uses a reference concentration CO* following section will be used to prove this argument. The rise in normalized concentration observed (Equation (6)). Further, while traffic pollutant concentrations (CO, but also NOx) show generally lower at Lauranighttime Bassi values St. at and 3:00 a first (local peak time) during is the due morning to the rush normalization hours, the normalization procedure procedurethat uses involves a reference concentrationcalculating CO* the ratio (Equation with the (6)). reference Further, CO* while estimated traffic with pollutant the pollutant concentrations source rate (CO, and but the also bulk NOx) showvelocity generally (the otherlower values nighttime in the equationvalues and are linkeda first to peak geometric during factors the and morning as such rush are constant hours, the normalizationvalues). Average procedure conditions involves at 3:00 calculating were characterized the ratio bywith high the bulk reference velocities CO* and estimated low pollutant with the pollutantsource source rates (as rate connected and the with bulk the velocity reduced (the transit other of vehicles values duringin the nighttime),equation are altogether linked resultingto geometric factors and as such are constant values). Average conditions at 3:00 were characterized by high bulk velocities and low pollutant source rates (as connected with the reduced transit of vehicles during nighttime), altogether resulting in a very low CO* value and therefore a sharp increase of the normalized CO/CO* concentration. The scatterplot (Figure 10) of normalized CO concentrations vs. UHI velocity in Marconi St. confirms the presence of a linear relationship between the two quantities, suggesting that the thermal circulation inside the street canyon may increase the pollutant concentrations at pedestrian level, a result in agreement with previous modeling studies by [68,69] and with the nighttime negative correlations between UPI and UHI obtained by [16]. Previous literature [70] showed the reverse effect, i.e., that haze pollution may contribute further to the UHI,

Atmosphere 2020, 11, 1186 19 of 32 in a very low CO* value and therefore a sharp increase of the normalized CO/CO* concentration. The scatterplot (Figure 10) of normalized CO concentrations vs. UHI velocity in Marconi St. confirms the presence of a linear relationship between the two quantities, suggesting that the thermal circulation insideAtmosphere the 2020 street, 11, canyonx FOR PEER may REVIEW increase the pollutant concentrations at pedestrian level, a result19 of in 32 agreement with previous modeling studies by [68,69] and with the nighttime negative correlations betweenconfirming UPI again and UHI the obtainedpresence byof an [16 interaction]. Previous between literature air [70 pollution] showed and the reverse the UHI eff phenomenon.ect, i.e., that haze The pollutionreduced maymagnitude contribute of normalized further to theconcentrations UHI, confirming in Marconi again theSt. presenceduring the of summer an interaction thermographic between aircampaign pollution is anddue theto the UHI fact phenomenon. that our normalization The reduced procedure magnitude successfully of normalized removes concentrations all other factors in Marconidriving pollutant St. during concentrations the summer thermographic in the street cany campaignon, i.e., the is due geometry to the factof the that canyon, our normalization the pollutant procedureemissions successfullyand the bulk removes wind speed all other above factors the canyon driving. Therefore, pollutant concentrations our results show inthe the street existence canyon, of a i.e.,relation the geometry between ofthe the thermal canyon, forcing the pollutant exerted by emissions UHI and and pollutant the bulk concentrations wind speed abovein a street the canyon.canyon. Therefore,In the following our results section,show the existencescaling for of aheat relation and betweenconcentration the thermal and the forcing existence exerted of bya linear UHI andrelationship pollutant between concentrations concentrations in a street and canyon. the therma In thel circulation following is section, verified the with scaling CFD for simulations heat and concentrationadequately set and up. the existence of a linear relationship between concentrations and the thermal circulation is verified with CFD simulations adequately set up.

Figure 10. Scatterplot of normalized concentrations vs. thermal circulation velocity in Marconi St. duringFigure the10. summerScatterplot intensive of normalized thermographic concentrations campaign. vs. thermal circulation velocity in Marconi St. during the summer intensive thermographic campaign. Verification with CFD Simulations VerificationIn order with to examine CFD Simulations the results presented in the previous section, high resolution numerical CFD (ComputationalIn order to examine Fluid the Dynamics) results presented were carried in the outprevious on an section, idealized high street resolution canyon numerical with the same CFD geometry(Computational as Marconi Fluid St. Dynamics) and considering were the carried effect out of temperature on an idealized difference street between canyon building with the facades same ongeometry the two sidesas Marconi of the streetSt. and canyon. considering the effect of temperature difference between building facadesThe on CFD the codetwo sides CD-adapco of the street STAR-CCM canyon.+ 19.2 has been employed to solve the steady-state Reynolds-averagedThe CFD code Navier–StokesCD-adapco STAR-CCM+ (RANS) equations 19.2 has withbeen aemployed realizable to k- solveε turbulence the steady-state model, similarReynolds-averaged to [71]. The Navier–Stokes buoyancy thermal (RANS) eff ectsequations have with been a considered realizable k- inε turbulence this work, model, as generated similar to [71]. The buoyancy thermal effects have been considered in this work, as generated by the temperature differences between the building facades and air temperature. The effects related to radiation and material properties have been included in the temperature boundary conditions, that in our case were known from measurements. A detailed description of the model is available in the Appendix A. The inflow wind profile has been calculated solving a two equations system using data from the two previously cited meteorological stations (Asinelli and Silvani), in order to obtain the friction

Atmosphere 2020, 11, 1186 20 of 32 by the temperature differences between the building facades and air temperature. The effects related to radiation and material properties have been included in the temperature boundary conditions, that in our case were known from measurements. A detailed description of the model is available in the AppendixA. The inflow wind profile has been calculated solving a two equations system using data from the two previously cited meteorological stations (Asinelli and Silvani), in order to obtain the friction velocity u and the roughness length z0 for each wind condition. The displacement height d has been ∗ set equal to 1/3 of the street canyon medium height, i.e., 11 m for Marconi St. This approach highlights the relationship between wind direction and urban geometry. The temperature boundary conditions were instead set using the street canyon façade temperatures measured during the summer thermographic campaigns, thoroughly described in the previous section. In particular, the differences between the following two cases are considered:

1. 22/08/2018 12:00 (UTC+2) 2. 22/08/2018 16:00 (UTC+2)

The velocity and temperature boundary conditions for these two cases are reported in Table4.

Table 4. Wind and temperature data used to set the boundary conditions in the idealized street canyon in the two cases analyzed.

1 1 Wind Speed (m s− ) Wind Direction (◦) u* (m s− ) z0 (m) T East (K) T West (K)

Case 1 2.69 127◦ 0.4419 1.3067 300 307 Case 2 2.61 91◦ 0.1473 0.0003 312 302

The two cases shown in Table4 are particularly interesting since the wind velocity magnitude is about the same, the wind direction is perpendicular to the canyon (coming from east) in both cases, but the temperature difference presents the opposite sign. Therefore, these two cases represent two opposite scenarios in terms of thermal boundary conditions. The numerical simulations are aimed to support the analysis and the results about the interaction between the UHI and the UPI obtained at street-canyon from the observations collected in the experimental campaign in Marconi St., presented in the previous section. For this reason, since the simulations refer to Marconi street where no tree is present, the role of trees is not considered in the setup of CFD simulations. Figures 11 and 12 show the velocity vectors and CO concentration obtained in three street canyons with different aspect ratios, with case 1 and case 2 boundary conditions. Considering a canyon with AR = 1 and comparing the two cases with opposite building temperature differences (Figures 11 and 12a), we observe that the location of the vortex center slightly moves lower on the right. This explains the reduced CO concentration differences obtained in the two scenarios, for both the leeward side and the windward side. Considering a canyon with AR equal to 1.65 and comparing the results obtained in the two cases with opposite temperature difference (Figures 11 and 12b), we observe that the buoyancy-induced flow breaks the strong recirculating vortex. The wind flow structure within the canyon is very peculiar, still resulting in only one primary vortex but located far away from the ground. Indeed, an upward buoyancy flux opposes the downward airflow along the wall. As a result, the CO pollutant plume increases significantly its shape, above all in lower areas and especially along the windward side: overall, the pollutant dispersion is greatly reduced. Atmosphere 2020, 11, 1186 21 of 32 Atmosphere 2020, 11, x FOR PEER REVIEW 21 of 32

FigureFigure 11.11. Velocity vectorsvectors obtainedobtained inin thethe twotwo casescases withinwithin a streetstreet canyoncanyon with aspect ratio 1 (a),), 1.651.65 ((bb)) andand 22 ((cc).). CaseCase 11 ((toptop)) andand CaseCase 22 ((bottombottom).).

Atmosphere 2020, 11, 1186 22 of 32 Atmosphere 2020, 11, x FOR PEER REVIEW 22 of 32

FigureFigure 12.12. CO concentration proﬁlesprofiles within a street canyon with AR equal to 1 ( a), 1.65 ( b) and 2 ( c). CaseCase 11 (red(red line)line) andand Case Case 2 2 (blue (blue line). line). Vertical Vertical proﬁles profiles are are shown shown by by probe probe lines, lines, one one located located at 0.5at 0.5 m fromm from the the leeward leeward side side (panels (panels on on the the left) left) and and one one on theon the windward windwa siderd side (panels (panels on theon the right). right).

Finally,Considering considering a canyon a canyon with withAR = AR 1 and equal comparing to 2 and observing the two thecases results with obtained opposite in building the two casestemperature with opposite differences temperature (Figures di 11fference and 12a), (Figures we observ 11 ande that 12c), the we location can remark of the that vortex the combination center slightly of thermalmoves lower effects on and the buildings’ right. This narrow explains shape the creates reduced a multiple CO concentration vortex structure. differences When obtained the windward in the walltwo scenarios, and the ground for both are the warmer leeward than side the and air andthe windward the leeward-side, side. a weaker ground-level secondary recirculatingConsidering vortex a canyon is easily with recognizable AR equal to near 1.65 and the windwardcomparing side,the results right obtained below the in primarythe two cases one. Indeed,with opposite the upward temperature buoyancy difference flux strongly (Figures contrasts 11 and 12b), with we the observe downward that airflowthe buoyancy-induced along the wall, dividingflow breaks the windthe strongstructure recirculating into two counter-rotatingvortex. The wind vortices, flow structure and in particularwithin the acanyon clockwise is very top vortexpeculiar, and still a reverseresulting secondary in only one vortex. primary In thisvortex scenario, but located the pollutant far away dispersionfrom the ground. is enhanced Indeed, with an respectupward to buoyancy the preceding flux opposes scenario the considering downward AR airflow equal along to 1.65, the butwall. still As significantly a result, the CO reduced pollutant and worryingplume increases if compared significantly to a regular its shape, canyon above as in all the in first lower scenario, areas especiallyand especially close along to the the ground windward along pedestrians’side: overall, walkways. the pollutant dispersion is greatly reduced. ByFinally, considering considering the whole a canyon diurnal with cycle AR equal for the to cases 2 and AR observing equal to the 1 and results 2, respectively, obtained in a the similar two relationcases with between oppositeUr temperatureand CO/CO* difference as that derived (Figures in 11 the and previous 12c), we section can remark for AR that equal the combination to 1.65 and illustratedof thermal by effects Figures and9 and buildings’ 10 is obtained. narrow Figure shape 13 shows creates the a pattern multiple of normalized vortex structure. CO concentrations When the obtainedwindward in wall the CFD and simulations,the ground are as comparedwarmer than to the the observed air and the normalized leeward-side, concentrations. a weaker ground-level Also shown aresecondary the plots recirculating for the vortices vortex for is five easily times. recognizab As such,le Equation near the windward (4) provides side, a simple right below model the to describeprimary howone. theIndeed, temperature the upward difference buoyancy between flux buildingstrongly co facadesntrasts within with the downward street canyon airflow can drive along the the airflow wall, dynamicsdividing the and wind the shape structure of the into vortices two withincounter-rotating the canyon vortices, and therefore and in the particular pollutant a concentration.clockwise top vortex and a reverse secondary vortex. In this scenario, the pollutant dispersion is enhanced with respect to the preceding scenario considering AR equal to 1.65, but still significantly reduced and worrying if compared to a regular canyon as in the first scenario, especially close to the ground along pedestrians’ walkways. By considering the whole diurnal cycle for the cases AR equal to 1 and 2, respectively, a similar relation between Ur and CO/CO* as that derived in the previous section for AR equal to 1.65 and illustrated by Figures 9 and 10 is obtained. Figure 13 shows the pattern of normalized CO

Atmosphere 2020, 11, x FOR PEER REVIEW 23 of 32 concentrations obtained in the CFD simulations, as compared to the observed normalized concentrations. Also shown are the plots for the vortices for five times. As such, Equation (4) provides a simple model to describe how the temperature difference between building facades within the street canyonAtmosphere can2020 drive, 11, the 1186 airflow dynamics and the shape of the vortices within the canyon and therefore23 of 32 the pollutant concentration.

Figure 13. Pattern of observed and simulated normalized CO concentration within a street canyon with FigureAR equal 13. Pattern to 1.65 inof Computationalobserved and simulated Fluid Dynamics normalized (CFD) CO simulations. concentration Also within shown a arestreet the canyon vortices withobserved AR equal in the to CFD 1.65 simulations. in Computational Fluid Dynamics (CFD) simulations. Also shown are the vortices observed in the CFD simulations. 4. Conclusions 4. ConclusionsIn this paper, we have investigated the multiple scale nature of the UHI phenomenon and its impactIn this on flowpaper, dynamics, we have and investigated have derived the amultip relationshiple scale betweennature of UHI the and UHI pollutant phenomenon concentrations and its impactin a real on urban flow dynamics, environment. and Tohave this derived aim, two a rela intensivetionship experimental between UHI field and campaigns pollutant wereconcentrations carried out inin a tworeal urbanurban streetenvironment. canyons inTo the this city aim, of Bolognatwo intensive characterized experimental by a difieldfferent campaigns presence were of vegetation, carried outi.e., in one two being urban a tree-free street canyons street canyon in the and city one of characterizedBologna characterized by the presence by a ofdifferent a tree line presence along bothof vegetation,sides of the i.e., road. one being a tree-free street canyon and one characterized by the presence of a tree line alongThe comparisonboth sides of of the the road. air temperatures measured in the city within two intensive thermographic campaignsThe comparison with those measuredof the air in thetemperatures countryside me revealedasured a UHIin the eff ectcity of roughlywithin 6–7two◦ C;intensive however, thermographicthe comparison campaigns between the with temperatures those measured measured in th ine thecountryside two canyons revealed indicated a UHI on averageeffect of 2 roughly◦C higher 6–7temperatures °C; however, in thethe tree-freecomparison street between canyon the with temp respecteratures to the measured vegetated in canyon the two located canyons in a indicated residential onarea average away from2 °C thehigher city temperatures center, as an indication in the tree-f of theree presencestreet canyon of local with UHIs respect inside to the the urban vegetated texture canyonowing located to the di inff erenta residential morphologies area away and from presence the city of vegetationcenter, as an of indication the neighborhoods. of the presence Different of local UHI UHIsscales inside have the been urban identified texture and owing their to impactthe different on the morphologies flow velocity and is quantified. presence of Cityvegetation (urban–rural) of the neighborhoods.and neighborhood Different (canyon–canyon) UHI scales have scales been share identified the same and exponential their impact decrease on the of theflow UHI velocity intensity is quantified.as a function City of (urban–rural) wind speed, and confirming neighborhood the existence (canyon–canyon) of an internal scales UHI share induced the same by exponential morphology decreasedifferences of the and UHI vegetation intensity as presence a function between of wind di speed,fferent confirming areas of the the city. existence A third of an local internal UHI UHI effect induced(induced by by morphology opposite façade differences temperature and vegetation difference presence within the between same canyon) different is areas instead of identifiedthe city. A as thirddirect local forcing UHI of effect a local (induced thermal by circulation, opposite faça quantifiedde temperature as the UHI difference convergence within velocity. the same The canyon) different isbehaviors instead identified of UHIs as depending direct forcing on the of horizontala local thermal scale circulation, is hypothetically quantified attributed as the toUHI the convergence development velocity.of overlapping The different IBLs within behaviors the urban of UHIs one. depending As soon as on the the UHI horizontal intensity isscale associated is hypothetically to different attributedIBLs, their to the behavior development will be of similar overlapping to the IBLs urban–rural within the UHI urban where one. the As “IBLs”soon as arethe UHI the urban intensity and isrural. associated Conversely, to different within IBLs, the their same behavior IBL, temperature will be similar differences to the willurban–rural most likely UHI be where a forcing the “IBLs” for local thermal circulations. Further, in order to examine the concurrent UHI and UPI phenomena, we derived the UHI convergence velocity resulting from the temperature difference between the west and east building façades in the non-vegetated canyon and a normalization for pollutant concentrations taking into Atmosphere 2020, 11, 1186 24 of 32 account all key influencing factors, i.e., the emission source rate, canyon geometry, and background wind velocity. The analysis indicated the presence of a relationship between UHI and normalized concentrations, suggesting indeed an interaction between UHI and UPI phenomena, and in particular that the thermal circulation inside street canyons may be responsible of increasing the pollutant concentrations at pedestrian level. The scaling adopted for the UHI convergence velocity was then verified by high resolution CFD simulations in an idealized street canyon considering two cases characterized by similar wind direction and intensity but opposite building temperature differences. CFD simulations demonstrated that the temperature difference between the building façades on the two sides of the canyons drives the airflow dynamics determining the position and shape of the vortices, definitely governing the pollutant concentrations in the street canyon. Overall, the results of this work indicate that the positive role of mitigation solutions in reducing UHI effect may further extend to air pollution and therefore may be seen as an effective solution in mitigating two of the more pressing modern issues with important consequences on human health, therefore potentially greatly enhancing the sustainability of modern cities.

Author Contributions: Conceptualization, S.D.S.; methodology, S.D.S., F.B., E.B., B.P.; software, B.P.; validation, E.B., F.B., B.P.; formal analysis, F.B., E.B., B.P., S.D.S.; investigation, S.D.S., F.B., E.B., B.P.; resources, S.D.S.; data curation, F.B., E.B.; writing—original draft preparation, S.D.S., F.B., E.B., B.P.; writing—review and editing, S.D.S., F.B., E.B., B.P.; visualization, F.B., E.B.; supervision, S.D.S.; project administration, S.D.S.; funding acquisition, S.D.S. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded from the iSCAPE project by the European Union’s H2020 Research and Innovation program (H2020-SC5-04-2015), grant agreement No. 689954. Acknowledgments: The authors would like to acknowledge Luca Torreggiani, Enrico Minguzzi, and Carla Barbieri from the ARPAE Environmental Protection Agency, Marianna Nardino from IBIMET-CNR, Marco Deserti from Regione Emilia Romagna, and Massimo Bacchetti and Francesca Di Nicola from the Department of Physics and Astronomy of the University of Bologna for their support in the realization of the Bologna experimental field campaigns; the Municipality of Bologna for granting permissions to conduct measurements in public spaces and for the traffic counts in the two urban street canyons; the CGIL (Confederazione Generale Italiana del Lavoro), Thomas Marcacci, Etienne Caldironi, and Vittorio Pulga for their kind license of the sites for the installation of the instrumental setup; TPER (Trasporto Passeggeri Emilia Romagna) and ACI (Automobile Club d’Italia) for data on buses transit and fleet composition available through their websites; and OpenStreet Map and contributors for providing the maps. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Appendix A Here we provide the details of the setup of CFD simulations adopted in the present study to verify the scaling derived for the UHI convergence velocity. The transport equations for kinetic energy k and the turbulent dissipation rate ε are: " ! # ∂ µt (ρk) + (ρku) = µ + k + Pk ρ(ε ε0) + Sk (A1) ∂t ∇ · ∇ · σk ∇ − −

and ! ∂ µt 1 ε ε0 (ρε) + (ρεu) = µ + ε + Cε1Pε Cε2 f2ρ + Sε (A2) ∂t ∇ · ∇ · σε ∇ Tε − Tε − T0 where u is the average velocity, µ is air dynamic viscosity, σk, σε,Cε1, and Cε2 are model coefficients, Pk and Pε are production terms, whose formulation depend on the k-ε model variant, f2 is a damping function that mimics the decrease of turbulent mixing near the walls, enforcing realizability, Sk and Sε are user specific source terms, ε0 is the ambient turbulent dissipation rate value in the source terms, Tε is the large-eddy time scale, and T0 the specific time-scale related to ambient turbulent source term. Mean flow, turbulence, energy, and dispersion equations were discretized using a second order Atmosphere 2020, 11, 1186 25 of 32 scheme and the Semi-Implicit Method for Pressure Linked Equation (SIMPLE) scheme was used for pressure–velocity coupling. The buoyancy forces have been considered under the Oberbeck–Boussinesq approximation, i.e., in the Navier–Stokes equation the mass density is constant in all the terms with the exception of the gravitational body force term. The local momentum balance equation then gives: ! ∂ui ∂ui ∂p ∂τij ρ + uj = ρgi + (A3) ∂t ∂xj − ∂xi ∂xj where the density ρ is assumed to be a function of temperature and pressure in accordance with the ideal gas law: p ρ(T, p) = (A4) RT

R0 1 where R is the speciﬁc gas constant, R = M , with R0 = 8314.4621 (J kmol K− ) and M is the gas molecular weight. In this work, CO has been used as tracer, where the mass diﬀusion is computed as:

µt J = ρD + c (A5) − Sct ∇

2 Cµk where D is the molecular diffusion coefficient for the pollutant in the mixture, µt = ρ ε is the turbulent viscosity, Y is the mass fraction of the pollutant, and ρ is the mixture density. µ Sc = t = 0.7 is the turbulent Schmidt number, where D is the turbulent diffusivity. t ρDt t The pollutant sources have been simulated by separating volumes of section 0.5 m 0.5 m. × Four linear sources have been created in Marconi St.: two simulating the car traffic and two simulating the public bus traffic. In the main lateral crosspiece of Marconi St., Riva di Reno St., two linear sources for each canyon have been modelled to represent the car traffic emissions. The emissions rate (ER) was set using emissions previously derived with the methodology outlined in the previous section and by splitting the values equally into the linear sources. Meteorological observations to set boundary conditions were obtained from two ARPAE meteorological stations on top of Asinelli’s tower and on the roof of ARPAE’s headquarter (Viale Silvani 6) (Figure4 in the main text). At the inflow boundary, vertical profiles for mean velocity U, turbulence kinetic energy k and turbulence dissipation rate ε of the neutrally stratified atmospheric boundary layer have been imposed, according to [72]: ! u z d U(z) = ∗ ln − (A6) k z0 u2 z k(z) = p ∗ 1 (A7) Cµ − δ u3 z ε(z) = ∗ 1 (A8) k(z + z0) − δ where z is the vertical position above the ground [m], z0 the roughness length representative for 1 the terrain windward the computational domain [m], u* the friction velocity [m s− ], k = 0.42 the van Karman constant, Cµ = 0.09, and δ is the height of the computational domain. A set of preliminary sensitivity tests have been performed, both for choosing the dimensions of the boxes used for refinements and for choosing the dimensions of the elements near the building walls. As shown in Figure A1, the mesh is built by three zones, each of which characterized by structured elements with homogeneous dimensions. The first zone is the external domain, with the coarsest elements, Atmosphere 2020, 11, 1186 26 of 32 Atmosphere 2020, 11, x FOR PEER REVIEW 26 of 32 containingcontaining a a box box with with elements elements with with a medium a medium size si andze and another another box surroundingbox surrounding the area the and area the and street the canyons,street canyons, containing containing the finest the elements. finest elements.

FigureFigure A1. A1.Example Example ofof meshmesh zoneszones (a(a)) and and grid grid refinement refinement near near walls walls ( b(b).). A geometry with a canyon having the same dimensions of canyon A has been used as a base A geometry with a canyon having the same dimensions of canyon A has been used as a base geometry for the first part of the sensitivity tests. Table A1 shows the dimensions of the box containing geometry for the first part of the sensitivity tests. Table A1 shows the dimensions of the box the finest elements, the size of the elements, and the total number of N_cells in the computational containing the finest elements, the size of the elements, and the total number of N_cells in the domain. The numerical results have been shown to verify and to support the analysis and the results computational domain. The numerical results have been shown to verify and to support the analysis about the interaction between UHI and UPI at street canyon scale, obtained from the observations and the results about the interaction between UHI and UPI at street canyon scale, obtained from the collected in the experimental field campaign previously presented. Since the CFD simulations refer to observations collected in the experimental field campaign previously presented. Since the CFD a street canyon where no tree is present (Marconi St.), the setup does not consider the effect of trees. simulations refer to a street canyon where no tree is present (Marconi St.), the setup does not consider the effect of trees. Table A1. Comparison between temperatures measured in Marconi street (Texp) and those obtained by the Computational Fluid Dynamics (CFD) simulations (Tnum). Table A1. Comparison between temperatures measured in Marconi street (Texp) and those obtained

by the ComputationalTime Fluid (UTC Dynamics+2) T (CFD)exp [◦C] simulationsTnum [◦ (TC]num). Deviation (%) 22/08—12.00 27.0 25.7 5 Time (UTC +2) [°C] [°C] Deviation (%) 22/08—14.00 27.6 26.9 3 22/08—12.00 27.0 25.7 5 22/08—16.00 27.8 28.1 1 22/08—14.0022/08—18.00 27.6 26.5 26.926.9 2 3 22/08—16.0022/08—20.00 27.8 24.6 24.628.1 0 1 22/08—18.0022/08—22.00 26.5 23.2 23.126.9 0 2 22/08—20.0022/08—24.00 24.6 22.1 21.724.6 2 0 23/08—02.00 21.6 21.1 2 22/08—22.0023/08—04.00 23.2 21.3 20.823.1 2 0 22/08—24.0023/08—06.00 22.1 20.9 19.821.7 5 2 23/08—02.0023/08—08.00 21.6 22.6 22.221.1 2 2 23/08—04.0023/08—10.00 21.3 26.9 25.620.8 5 2 23/08—06.00 20.9 19.8 5 Five refinements23/08—08.00 have been compared,22.6 in the range N_cells22.2 = [0.250–6.181] 2 millions of elements. Figure A2 shows23/08—10.00 the normalized root26.9 mean square25.6 deviation obtained from 5 a vertical line in the middle of the canyon, defined as Five refinements have been compared, in the range N_cells = [0.250–6.181] millions of elements. v u 2 Figure A2 shows the normalized root meantu squarexg deviationp x obtained from a vertical line in the P 2, − g1,p middle of the canyon, defined as p xg1 NRMSE = (A9) g N p , −, ∑ (A9) where x is the variable used for the comparison (velocity, CO concentration, or temperature), g1 and g p= 2 are the grid number of two refinements, is the point along a probe line where the comparison is made, and Np is the number of points in the probe line. where x is the variable used for the comparison (velocity, CO concentration, or temperature), g1 and g2 are the grid number of two refinements, p is the point along a probe line where the comparison is made, and Np is the number of points in the probe line.

AtmosphereAtmosphere 2020, 112020, x, 11FOR, 1186 PEER REVIEW 27 of 32 27 of 32

FigureFigure A2.A2.Convergence Convergence tests tests results. results. Figure A2 shows that the convergence is achieved for velocity, CO concentration, Figureand temperature A2 shows results. that the convergence is achieved for velocity, CO concentration, and temperatureAseries results. of validation test has been performed by comparing the numerical results with experiments Aperformed series of in validation Marconi street. test Table has A1 been shows performed the comparison by between comparin experimentalg the numerical measurements results with experimentsand numerical performed results. in Marconi street. Table A1 shows the comparison between experimental The l deviation between the numerical results and measurements is then 1.4 ◦C, while the l2 measurements ∞and numerical results. norm is 2.5 ◦C and the standard deviation is 0.7 ◦C. The deviation between the numerical results and measurements is then 1.4 °C, while the norm isAppendix 2.5 °C and B the standard deviation is 0.7 °C. This appendix provides a brief overview on the criteria we adopted to identify the atmospheric Appendixconditions B suitable for the two intensive thermographic campaigns. After the identification of suitable periods from the regional synoptic charts provided from regional weather forecast service Thisof the appendix Regional provides Environmental a brief Protection overview Agency on the (ARPAE), criteria we we analyzed adopted the to data identify recorded the from atmospheric conditionsthe atmospheric suitable soundingsfor the launchedtwo intensive at San Pietro thermographic in Capofiume, locatedcampaigns. 20 km to After the north-north-east the identification of suitableof periods Bologna, from to verify the theregional absence synoptic of strong charts synoptic provided forcing and from ensure regional the minimum weather criteria forecast for service of the Regionalthe onset Environmental of the UHI with minimum Protection synoptic Agency perturbations. (ARPAE), As we from analyzed the soundings the data reported recorded below, from the we were able to verify and check the fulfilment of our criteria in two consecutive days during both atmospheric soundings launched at San Pietro in Capofiume, located 20 km to the north-north-east1 the summer and winter experimental campaign: wind speed at 700 hPa lower than 5 m s− and of Bologna,clear-sky to verify conditions. the absence The first soundingof strong was synopt meantic toforcing identify and the ensure day, while the theminimum second one criteria to for the onset ofcheck the theUHI persistence with minimum of the required synoptic conditions perturbati withinons. the As experiment. from the Figures soundings A3 and reported A4 show below, we were ablethe soundings to verify for and the check summer the and fulfilment winter UHI of experiment, our criteria respectively. in two consecutive days during both the summer and winter experimental campaign: wind speed at 700 hPa lower than 5 m s−1 and clear-sky conditions. The first sounding was meant to identify the day, while the second one to check the persistence of the required conditions within the experiment. Figures A3 and A4 show the soundings for the summer and winter UHI experiment, respectively.

Atmosphere 2020, 11, 1186 28 of 32 Atmosphere 2020, 11, x FOR PEER REVIEW 28 of 32 Atmosphere 2020, 11, x FOR PEER REVIEW 28 of 32

Figure A3. Atmospheric soundings on skew-T diagram obtained at the 0000 UTC of 22 (a) and 23 (b) FigureFigure A3. A3. AtmosphericAtmospheric soundings soundings on onskew-T skew-T diagram diagram obtained obtained at the at 0000 the 0000 UTC UTC of 22 of (a 22) and (a) 23 and (b 23) August 2017 at San Pietro in Capofiume, 20 km north-north-east from Bologna. Within each plot, the August(b) August 2017 2017at San at Pietro San Pietro in Capofiume, in Capoﬁume, 20 km 20 north- km north-north-eastnorth-east from fromBologna. Bologna. Within Within each plot, each the plot, leftish black line represents the dew point, the rightish black line is the temperature while the arrows leftishthe leftish black black line represents line represents the de thew dew point, point, the therightish rightish black black line line is the is the temperature temperature while while the the arrows arrows are the wind speed in knots following the Beaufort scale (Source: areare thethe wind wind speed in knotsspeed followingin knots the Beaufort following scale (Source: the http:Beaufort//weather.uwyo.edu scale /(Source:upperair / http://weather.uwyo.edu/upperair/sounding.html). http://weather.uwyo.edu/upsounding.html). perair/sounding.html).

Figure A4. Atmospheric soundings on skew-T diagram obtained at the 0000 UTC of 8 (a) and 9 (b) Figure A4. Atmospheric soundings on skew-T diagram obtained at the 0000 UTC of 8 (a) and 9 (b) FigureFebruary A4. 2018Atmospheric at San Pietro soundings in Capofiume, on skew-T 20 diagram km north-north-east obtained at the from 0000 Bologna. UTC of Within 8 (a) and each 9 ( plot,b) February 2018 at San Pietro in Capofiume, 20 km north-north-east from Bologna. Within each plot, Februarythe leftish 2018 black at lineSan representsPietro in Capofiume, the dew point, 20 thekm rightish north-north-east black line isfrom the temperatureBologna. Within while each the arrowsplot, the leftish black line represents the dew point, the rightish black line is the temperature while the theare leftish the wind black speed line inrepresents knots following the dew the point, Beaufort the rightish scale (Source: black linehttp: is// weather.uwyo.eduthe temperature while/upperair the / arrows are the wind speed in knots following the Beaufort scale (Source: arrowssounding.html are ).the wind speed in knots following the Beaufort scale (Source: http://weather.uwyo.edu/upperair/sounding.html). http://weather.uwyo.edu/upperair/sounding.html). Within both periods, clear-sky conditions are observed through the absence of convective available Within both periods, clear-sky conditions are observed through the absence of convective potentialWithin energy both periods, and small clear-sky amounts conditions of condense are and observed gaseous through water, ensuringthe absence the absenceof convective of both available potential energy and small amounts of condense and gaseous water, ensuring the absence availableconvective potential and stratus energy clouds and as small well amounts as precipitation. of condense Only duringand gaseous the second water, night ensuring of the winterthe absence period, of both convective and stratus clouds as well as precipitation. Only during the second night of the ofthe both convective convective available and stratus potential clouds energy as well was as not precipitation. zero but small Only enough during to ensurethe second that nonight convective of the winter period, the convective available potential energy was not zero but small enough to ensure that winterevents period, would the have convective developed availa duringble thepotential following energy day. was Wind not speedzero but in thesmall lower enough troposphere to ensure (below that no convective events would have developed1 during the following day. Wind speed in the lower no700 convective hPa) was alwaysevents would weak, reachinghave developed the 5 m sduring− only the during following the first day. night Wind of the speed summer in the period. lower troposphere (below 700 hPa) was always weak, reaching the 5 m s−1 only during the first night of the troposphereTherefore, (below both 700 periods hPa) was satisfied always theweak, criteria reaching of weakthe 5 m synoptic s−1 only forcingduring the suitable first night for the of the best summer period. summercharacterization period. of the UHI within the summer and winter intensive thermographic campaigns. Therefore, both periods satisfied the criteria of weak synoptic forcing suitable for the best Therefore, both periods satisfied the criteria of weak synoptic forcing suitable for the best characterization of the UHI within the summer and winter intensive thermographic campaigns. characterization of the UHI within the summer and winter intensive thermographic campaigns.

Atmosphere 2020, 11, 1186 29 of 32

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