Evaluating the Urban Heat Mitigation Potential of a Living Wall in Milan: One Year of Microclimate Monitoring
"> Figure 1
<p>(<b>a</b>) Locations of the sites used in this case study (including The Westin Palace) in Milan. Yellow dot: ARPA weather station (Juvara); orange dot: Brera Astronomical Observatory weather station. (<b>b</b>) Zoomed-in aerial image of the studied building: The Westin Palace. (<b>c</b>) Photograph of the VGS facing northwest (Ogut, O., 20 March 2022).</p> "> Figure 2
<p>(<b>a</b>) Elevation of The Westin Palace hotel façade. (<b>b</b>) Zoom-in showing the details of the monitored part of the façade. (<b>c</b>,<b>d</b>) BlueSky™ Air Quality Monitors (Thermo-Systems Engineering Co., USA), (<b>c</b>) positioned on a bare surface (NV wall) and (<b>d</b>) positioned on the VGS surface (V wall). (<b>e</b>) Section of the LW system: the ±0.00 level is considered the entrance/ground-floor level. (<b>f</b>) Modular panel system with growing media.</p> "> Figure 3
<p>Datasets and analysis steps followed during the study. Data sources are highlighted with a colour code and a short ID in the legend.</p> "> Figure 4
<p>The whole calendar year dataset as originally measured (black line) for the non-vegetated wall (<b>top</b> panel) and the vegetated wall (<b>bottom</b> panel) and as reconstructed after comparison with the closest ARPA weather station (red line).</p> "> Figure 5
<p>(<b>a</b>) One-year monitored data (<b>top</b> panel) for the temperature differences of the NV and V walls with inter-weekly running averages, and the precipitation (<b>central</b> panel) and temperature (<b>bottom</b> panel) from the ARPA (AR subscript) weather station in Milan; (<b>b</b>) (from <b>top</b> to <b>bottom</b>) 1-year data for the temperature differences of the NV and V walls with weekly running averages, and the precipitation, and temperature in Milan. Green areas refer to optimal conditions, while red areas represent the risk of frost damage and/or low greenery growth due to cold-spell climate events. As introduced above, the monthly variability of the NV and V mean temperatures (<a href="#land-13-00794-f006" class="html-fig">Figure 6</a>b, thick black and green lines, respectively) and T<sub>Anomaly,VGS</sub> are described in <a href="#land-13-00794-f006" class="html-fig">Figure 6</a> together with the optimal GS. The zero value of T<sub>Anomaly,VGS</sub> in April–May (see the coincident peaks of the black and green lines in <a href="#land-13-00794-f006" class="html-fig">Figure 6</a>b) coincides with the beginning of the vegetation growth (i.e., the GS’s start time). The maximum impact of the vegetation occurs in the months from July to October, when the foliage is more luxuriant, contributing to an efficient cooling effect. Both <a href="#land-13-00794-f005" class="html-fig">Figure 5</a> and <a href="#land-13-00794-f006" class="html-fig">Figure 6</a> clearly show the identification of a period of stress on the vegetation and the optimal GS, and this phenological growing pattern is aligned with the thermal cooling impact.</p> "> Figure 6
<p>(<b>a</b>) Monthly anomalies and (<b>b</b>) monthly mean temperatures of the NV and V walls over the monitored calendar year. The green area highlights the GS period.</p> "> Figure 7
<p>(<b>a</b>) Rain and temperature indices for the NV (orange dots) and V (green dots) walls each month during the monitored period. (<b>b</b>) T<sub>Anomaly,VGS</sub> versus the difference in the number of heatwave events with respect to the NP period (ΔN<sub>T>25 °C</sub>) for the months with vegetative activity (i.e., May–September).</p> "> Figure 8
<p>Global warming (CC) effects, with reference to an EFP period, for the bare wall (white bars) and the vegetated wall (green bars). Grey area reports the error band.</p> "> Figure 9
<p>Map of case studies from the 13 selected papers in the literature, coloured in green.</p> "> Figure 10
<p>Present and future Köppen–Geiger classifications of the Alps, northern Italy, and central Italy: (<b>a</b>) shows the map close to the present (1980–2016), and (<b>b</b>) shows the far-future map (2071–2100) (produced after [<a href="#B31-land-13-00794" class="html-bibr">31</a>]).</p> ">
Abstract
:1. Introduction
1.1. Urban Heat Challenges and the Role of Vertical Green Structures
1.2. Aims and Objectives
2. Materials
2.1. Location and Climatic Conditions
2.2. Building Characteristics, VGS Characteristics, and Cultivated Species
Scientific Name | Geranium macrorrhizum | Achilea pitarmica | Heuchera micrantha | Trachelospermum jasminoedes |
---|---|---|---|---|
Picture | ||||
Common name(s) | Czakor, big-root cranesbill | Sneezewort, Perry’s White | Obsidian, crevice alumroot | Star jasmine |
Native area | SE Alps, Balkans | Europe, W Asia | W N America, British Columbia | E and SE Asia |
Water | X | XX | X | X |
Light | XXX | XX-XXX | XX | XX-XXX |
Blooming months | 6–10 | 6–9 | 6–7 | 6–8 |
Growth and form categories | H, D, E, T | H, D, E, T | H, D, T | W, E |
2.3. Monitoring Setup
3. Methodology
- −
- Through a dedicated monitoring campaign conducted by the authors during the 2021–2022 period (green dots: BlueSky datasets) [25];
- −
- Through retrieving data from the nearby weather station of v. Juvara (yellow dots: ARPA datasets) installed by ARPA (the Regional Agency for Protection and the Environment of the Lombardy region) [26];
- −
- Through downloading data from the HISTALP database (Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region) [27]. The HISTALP datasets refer to the long-term historical data collected at the weather station of the Brera Astronomical Observatory, a research centre of excellence of the National Institute of Astrophysics (INAF), located in Milan city centre (orange dots: HISTALP datasets—filled and void dots refer to HISTALP datasets for the near-past (NP, 1991–2020) and extremely far-past (EFP, 1763–1792) temporal ranges).
3.1. Data Reconstruction
3.2. Data Analysis
3.3. Errors
- −
- The accuracy of the NTC (Negative Temperature Coefficient) sensor in a BlueSky air monitor is ±0.2 °C, ±1.8% for temperature and relative humidity, while for the PM sensor, the accuracy is ±10% μg/m3 [25].
- −
3.4. Comparison with the Existing Literature
- −
- We selected papers using the same methodology, i.e., in situ monitoring;
- −
- We selected papers reporting VGS case studies conducted under a KG climate classification similar to that of Milan (i.e., Cfa).
- (1)
- (TNV-TV)day (°C): temperature difference between the NV and V walls during the daytime.
- (2)
- (TNV-TV)night (°C): temperature difference between the NV and V walls during the nighttime;
- (3)
- (TNV-TV)NRD (°C): temperature difference between NV and V during no-rain days (NRDs);
- (4)
- (TNV-TV)RD (°C): temperature difference between NV and V during rainy days (RDs).
4. Results and Discussion
4.1. Data Reconstruction and Analysis of the VGS’s Temperature Mitigation Capacity
4.1.1. Data Reconstruction and Uncertainty
4.1.2. Seasonal and Monthly Results
Season | (TNV-TV)day (°C) | (TNV-TV)night (°C) | (TNV-TV)RD (°C) | (TNV-TV)NRD (°C) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Min | Max | Mean | Min | Max | Mean | Min | Max | Mean | Min | Max | Mean | |
Summer | −6.8 | 7.4 | 2.1 | −6.8 | 5.8 | 1.1 | −2.6 | 6.1 | 1.6 | −6.8 | 7.4 | 2.1 |
Autumn | −7.2 | 8.7 | 2.8 | −2.4 | 7.6 | 2.2 | −2.9 | 8.7 | 1.9 | −7.2 | 7.9 | 2.9 |
Winter | −10.3 | 7.7 | 0.8 | −4.8 | 7.7 | 0.8 | −6.7 | 7.0 | 1.2 | −10.3 | 7.7 | 0.8 |
Spring | −8.7 | 8.1 | 0.8 | −5.9 | 4.9 | 0.5 | −2.1 | 2.3 | 0.3 | −8.7 | 8.1 | 0.9 |
4.2. Qualitative Comparison with the Existing Literature
Period | Reference | Orientation | Typology | Plants | Parameters |
---|---|---|---|---|---|
S | [33] | N | LW | W, E | (1) |
[38] | S | GF | W, D, E′, T | (1) (2) (3) | |
[34,35] | S | LW | H, D, E, T | (1) (2) (3) | |
[39] | E | GF | W, D, E′, T | (1) (3) | |
[40] | S, N | GF | H, E, T | (1) (2) (3) | |
[41] | W, SW | GF | W, D, E′, T | (1) | |
[42] | S | GF | H, W, D, E′, T | (1) | |
S–A | [36] | SW | LW | H, D, T > W, E | (1) (2) (3) |
[32] | W | LW | NS (6 species) | (1) | |
[43] | S, W | GF | H < E < W, D, T | (1) (3) (4) | |
W | [37] | S | LW | W, E | (2) (3) (4) |
ALL | [44] | W | GF | W, D, T | (1) |
4.3. Future Climate Change Scenarios and Consequences for VGSs
5. Conclusions and Further Research
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ARPA | Regional Agency for Protection and the Environment |
Cfa | temperate climate without dry seasons and with hot summers (T ≥ 22) |
EFP | extremely far past (1763–1792) |
NP | near past (1991–2020) |
GF | green façade |
GS | growing season |
H | herbaceous |
W | woody |
D | deciduous |
E | evergreen |
E’ | semi-evergreen |
T | tender |
HISTALP | Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region |
KG | Köppen–Geiger |
LW | living wall |
N | north |
W | west |
S | south |
E | east |
NRD | no rainy days |
NV | non-vegetated |
NW | northwest |
NE | northeast |
SW | southwest |
SE | southeast |
NTC | Negative Temperature Coefficient |
PM | particulate matter |
RDs | rainy days |
S | summer |
A | autumn |
W | winter |
SP | spring |
T | temperature |
V | vegetated |
VGS | vertical green structure |
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Ogut, O.; Tzortzi, J.N.; Cavazzani, S.; Bertolin, C. Evaluating the Urban Heat Mitigation Potential of a Living Wall in Milan: One Year of Microclimate Monitoring. Land 2024, 13, 794. https://doi.org/10.3390/land13060794
Ogut O, Tzortzi JN, Cavazzani S, Bertolin C. Evaluating the Urban Heat Mitigation Potential of a Living Wall in Milan: One Year of Microclimate Monitoring. Land. 2024; 13(6):794. https://doi.org/10.3390/land13060794
Chicago/Turabian StyleOgut, Ozge, Julia Nerantzia Tzortzi, Stefano Cavazzani, and Chiara Bertolin. 2024. "Evaluating the Urban Heat Mitigation Potential of a Living Wall in Milan: One Year of Microclimate Monitoring" Land 13, no. 6: 794. https://doi.org/10.3390/land13060794