# Developing Urban Heat Mitigation Strategies for a Historic Area Using a High-Fidelity Parametric Numerical Simulation: A Case Study in Singapore

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Simulation Scenarios Description

#### 2.2. CFD Simulation Setup

#### 2.2.1. Computational Domain and Meshing

^{+}of targeted buildings is around 250, which is well within the logarithm region.

#### 2.2.2. Boundary Conditions

^{2}for office buildings, 120–260 W/m

^{2}for hotels, and 250–350 W/m

^{2}for retail buildings, according to the cooling load data obtained from the energy audit results during the operating hours, complied by the Building Construction Authority (BCA) and National Environment Agency (NEA). The dataset on the measured cooling load showed that the mean cooling load per air-conditioned floor area of office buildings (from 58 projects), hotels (from 32 projects), and retail buildings (from 28 projects), ranged from 54–100 W/m

^{2}, 40–98 W/m

^{2}, and 93–195 W/m

^{2}, respectively with mean values of 74 W/m

^{2}, 61 W/m

^{2}, and 130 W/m

^{2}, respectively [40]. Therefore, taking office building as an example, the average anthropogenic heat emission can be calculated as follows:

^{2}, with the largest mean hourly flux of 113 W/m

^{2}.

_{2}) Singapore [42] and were calculated based on the total air-conditioned area [22]. Therefore, we can produce the result that ${\mathrm{Q}}_{\mathrm{A}}$ which represents anthropogenic heat from of each unit was 3.25 kW. Therefore, the total anthropogenic heat ($\mathrm{Q})$ was calculated using equation as:

^{2}which is the typical area of one apartment unit in Singapore; and ${R}_{p}$ is the public area ratio which was assumed to be 20%. We calculated the gross floor area (${A}_{GF}$) as:

#### 2.3. Data Analysis

## 3. Results and Analysis

#### 3.1. Comparison of Wind Speed between Cases A and B

#### 3.2. Comparison of Air Temperature Increments among Cases C, D, and E

#### 3.3. Urban Heat Mitigation Strategies Evaluation among Case F, G, and H

## 4. Discussion

#### 4.1. Effect of Anthropogenic Heat and Urban Morphology on the Urban Heat Environment in the Singapore Chinatown Area

#### 4.2. Practical Implications for Proposed Mitigation Strategies in Historic Areas

## 5. Conclusions

- The average wind speed decreases of 43%, from 1.11 m/s in Case A to 0.63 m/s in Case B. This indicates the great impact of new development, i.e., high-rise buildings, on pedestrian-level air flow of the historic area.
- The mean air temperature increased by 0.16 °C for Case C, 0.52 °C for Case D and 0.87 °C for Case E, respectively. This indicates that the anthropogenic heat emission from surrounding high-rise buildings had less effect than that from historic shophouses in Chinatown.
- The integration of open spaces and building porosity, which create wind corridors together, can promote outdoor natural ventilation and heat dispersion at the study area. Compared with Case E, the three mitigation cases improve outdoor thermal environment, with mean temperature reduction of 33%, 25%, and 21%, respectively.
- To retain the urban texture of the area, the locations and number of removed shophouses should include either one shophouse closer to the wind inlet and perpendicular to the wind direction as a priority, or a number of shophouses at the end or in the middle of the row where heat emission accumulates, in some cases.

## 6. Limitations and Future works

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Nomenclature

Abbreviations | |

ABL | Atmospheric boundary layer |

AC | Air-conditioning |

BCA | Building and construction authority |

CBD | Central Business District |

CFD | Computational fluid dynamics |

COP | Coefficient of performance |

NE | Northeast |

NEA | National environment agency |

UHI | Urban heat island |

Symbols | |

$A$ | Gross floor area of buildings (m^{2}) |

${A}_{h}$ | Typical area of one unit of residential buildings (m^{2}) |

${A}_{GF}$ | Gross floor area (m^{2}) |

${\mathrm{c}}_{\mathrm{a}}$ | Specific heat of air (J·kg^{−1}·K^{−1}) |

${\mathrm{C}}_{\mu}$ | Model constant |

${\mathrm{E}}_{\mathrm{c}}$ | Cooling energy (kw) |

E_{2} | Energy Efficient Singapore |

${\mathrm{m}}_{\mathrm{a}}$ | Input mass flow rate of heat emissions (kg·s^{−2}) |

$N$ | Number of units in residential buildings |

${N}_{F}$ | Floor number of residential buildings |

$\mathrm{Q}$ | Total anthropogenic heat (kw) |

${\mathrm{Q}}_{\mathrm{A}}$ | Anthropogenic heat from each unit of residential buildings (kw) |

${\mathrm{Q}}_{\mathrm{c}}$ | Cooling loads generated in the indoor spaces (kw) |

${R}_{p}$ | Public area ratio |

${\mathrm{u}}_{\mathrm{ABL}}^{\ast}$ | Atmospheric boundary layer friction velocity (m/s) |

$\mathrm{z}$ | Hight above ground (m) |

${\mathrm{z}}_{0}$ | Aerodynamic roughness length (m) |

$\mathsf{\omega}$ | Specific dissipation (s^{−1}) |

$\mathsf{\kappa}$ | Turbulent kinetic energy (m^{2}·s^{−2}) |

$\mathsf{\epsilon}$ | TKE dissipation rate (m^{2}·s^{−3}) |

## Appendix A

**Table A1.**Equations to calculate ${\mathrm{m}}_{\mathrm{a}}$ for buildings with different AC types and functions.

AC Type | Building Function | Equation |
---|---|---|

Air cooling | Shophouses (retail) | ${\mathrm{m}}_{\mathrm{a}}=\frac{130\times \left(\frac{4.2+1}{4.2}\right)\mathrm{A}}{{\mathrm{c}}_{\mathrm{a}}\mathsf{\Delta}\mathrm{T}}$ |

Residential buildings | ${\mathrm{m}}_{\mathrm{a}}=\frac{1000\times 2.5\times \left(\frac{3.34+1}{3.34}\right)\times \frac{\left(1-{\mathrm{R}}_{\mathrm{p}}\right){\mathrm{A}}_{\mathrm{GF}}}{{\mathrm{A}}_{\mathrm{h}}}}{{\mathrm{c}}_{\mathrm{a}}\mathsf{\Delta}\mathrm{T}}$ | |

Water cooling | Retail | ${\mathrm{m}}_{\mathrm{a}}=\frac{0.25\times 130\times \left(\frac{4.2+1}{4.2}\right)\mathrm{A}}{{\mathrm{c}}_{\mathrm{a}}\mathsf{\Delta}\mathrm{T}}$ |

Office | ${\mathrm{m}}_{\mathrm{a}}=\frac{0.25\times 74\times \left(\frac{4.2+1}{4.2}\right)\mathrm{A}}{{\mathrm{c}}_{\mathrm{a}}\mathsf{\Delta}\mathrm{T}}$ | |

Hotel | ${\mathrm{m}}_{\mathrm{a}}=\frac{0.25\times 61\times \left(\frac{4.2+1}{4.2}\right)\mathrm{A}}{{\mathrm{c}}_{\mathrm{a}}\mathsf{\Delta}\mathrm{T}}$ |

## Appendix B

RTL1 | RTL2 | RTL3 | RTL4 | RTL5 | RTL6 | RTL7 | RTL8 | RTL9 | RTL10 | RTL11 | RTL12 | RTL13 | FTL1 | FTL2 | FTL3 | FTL4 | FTL5 | Ave | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Case A | 0.73 | 1.06 | 1 | 0.96 | 1.07 | 1.8 | 0.79 | 0.46 | 1.01 | 0.96 | 2.23 | 1.51 | 0.94 | 1.67 | 1.67 | 0.62 | 0.58 | 0.93 | 1.11 |

Case B | 1.01 | 0.54 | 0.61 | 0.76 | 0.8 | 0.76 | 0.45 | 0.36 | 0.34 | 0.51 | 0.45 | 0.64 | 1.15 | 0.54 | 0.36 | 0.32 | 1.07 | 0.64 | 0.63 |

Case B-A | 38% | −49% | −39% | −21% | −25% | −58% | −43% | −22% | −66% | −47% | −80% | −58% | 22% | −68% | −78% | −48% | 84% | −31% | −43% |

Case C | 1.39 | 0.92 | 1.7 | 2.26 | 1.75 | 0.69 | 0.47 | 0.52 | 0.84 | 1.92 | 1.16 | 1.16 | 2.34 | 1.21 | 1.13 | 1.94 | 2.23 | 0.9 | 1.36 |

Case D | 1.39 | 1.47 | 0.99 | 1.43 | 1 | 0.91 | 2.82 | 0.57 | 0.88 | 1.1 | 1.08 | 0.69 | 1.57 | 0.43 | 1.36 | 0.81 | 0.96 | 1.51 | 1.17 |

Case E | 1.62 | 1.62 | 1.52 | 2.38 | 1.44 | 1.19 | 0.39 | 0.33 | 1.52 | 1.86 | 1.27 | 1.31 | 3.04 | 1.36 | 1.17 | 0.62 | 2.09 | 2.25 | 1.47 |

Case C-E | −14% | −21% | 12% | −5% | 22% | 42% | 21% | 58% | 45% | 3% | −9% | −12% | −23% | −11% | −3% | 213% | 7% | −60% | −8% |

Case D-E | −14% | 27% | 35% | 40% | −31% | −24% | 623% | 73% | −42% | −41% | −15% | −47% | −48% | −68% | 16% | 31% | −54% | −33% | −21% |

Case F | 1.9 | 1.8 | 2.08 | 2.49 | 1.64 | 1.07 | 0.58 | 0.42 | 1.13 | 1.81 | 2.35 | 2.23 | 2.21 | 2.28 | 1.39 | 0.47 | 2.44 | 0.76 | 1.61 |

Case G | 1.61 | 1.38 | 1.77 | 1.89 | 2.25 | 2.06 | 0.68 | 0.82 | 1.24 | 1.69 | 1.96 | 1.5 | 3.84 | 1.86 | 0.77 | 0.89 | 1.06 | 1.31 | 1.59 |

Case H | 1.22 | 1.62 | 1.16 | 1.62 | 0.79 | 1.98 | 0.77 | 1.18 | 1.23 | 1.46 | 2.32 | 1.88 | 1.74 | 2.39 | 0.97 | 0.79 | 1.26 | 1.29 | 1.43 |

Case F-E | 17% | 55% | 37% | 5% | 14% | −10% | 49% | 27% | −26% | −3% | 85% | 70% | −27% | 68% | 19% | −24% | 17% | −66% | 10% |

Case G-E | −1% | 19% | 16% | −21% | 56% | 73% | 74% | 148% | −18% | −9% | 54% | 15% | 26% | 37% | −34% | 44% | −49% | −42% | 8% |

Case H-E | −25% | 40% | −24% | −32% | −45% | 66% | 97% | 258% | −19% | −22% | 83% | 44% | 43% | 76% | −17% | 27% | −40% | −43% | −3% |

RTL1 | RTL2 | RTL3 | RTL4 | RTL5 | RTL6 | RTL7 | RTL8 | RTL9 | RTL10 | RTL11 | RTL12 | RTL13 | FTL1 | FTL2 | FTL3 | FTL4 | FTL5 | Ave | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Case C | 0.22 | 0.19 | 0.15 | 0.12 | 0.16 | 0.25 | 0.2 | 0.25 | 0.08 | 0.06 | 0.21 | 0.11 | 0.13 | 0.12 | 0.12 | 0.11 | 0.24 | 0.14 | 0.16 |

Case D | 0.46 | 0.49 | 0.78 | 1.05 | 0.3 | 0.43 | 0.24 | 0.57 | 0.39 | 1.14 | 0.04 | 0.54 | 0.09 | 0.77 | 0.95 | 0.64 | 0.35 | 0.07 | 0.52 |

Case E | 1.3 | 1.26 | 1.42 | 0.92 | 1.16 | 1.03 | 1.43 | 1.08 | 0.37 | 0.31 | 0.48 | 0.67 | 0.13 | 1.45 | 0.94 | 0.89 | 0.49 | 0.35 | 0.87 |

Case C-E | −83% | −85% | −89% | −87% | −86% | −77% | −86% | −77% | −78% | −81% | −56% | −84% | 0 | −92% | −87% | −88% | −51% | −60% | −82% |

Case D-E | −65% | −61% | −45% | 14% | −74% | −58% | −83% | −47% | 5% | 268% | −92% | −19% | −31% | −47% | 1% | −28% | −29% | −80% | −41% |

Case F | 0.71 | 0.32 | 0.97 | 0.95 | 0.78 | 0.73 | 1.18 | 0.62 | 0.51 | 0.51 | 0.28 | 0.28 | 0.23 | 0.23 | 0.59 | 0.89 | 0.26 | 0.46 | 0.58 |

Case G | 1.05 | 0.64 | 1.41 | 1.35 | 0.39 | 0.75 | 0.34 | 0.98 | 0.3 | 0.29 | 0.29 | 0.39 | 0.01 | 0.2 | 1.09 | 1.07 | 0.91 | 0.23 | 0.65 |

Case H | 0.86 | 0.69 | 0.89 | 1.43 | 1.12 | 0.6 | 1.02 | 0.68 | 0.46 | 0.69 | 0.22 | 0.37 | 0.88 | 0.23 | 0.42 | 0.9 | 0.69 | 0.2 | 0.69 |

Case F-E | −45% | −75% | −32% | 3% | −33% | −29% | −18% | −43% | 38% | 65% | −42% | −58% | 77% | −84% | −37% | 0 | −47% | 31% | −33% |

Case G-E | −19% | −49% | −1% | 47% | −66% | −27% | −76% | −9% | −19% | −6% | −40% | −42% | −92% | −86% | 16% | 20% | 86% | −34% | −25% |

Case H-E | −34% | −45% | −37% | 55% | −3% | −42% | −29% | −37% | 24% | 123% | −54% | −45% | 577% | −84% | −55% | 1% | 41% | −43% | −21% |

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**Figure 2.**(

**a**) Simulation scenarios of Case A and B to represent the urban geometry in the past (1960s) and the current (2020s). (

**b**) (

**Left**) Simulation scenarios of Case C–E to represent the impact of anthropogenic heat emitted from high-rise buildings, historic shophouses, and both. (

**c**) (

**Right**) Simulation scenarios of Case F–H to represent the developed mitigation strategies focusing on the historic shophouses, high-rise buildings, and both.

**Figure 3.**Horizontal and vertical cross-sections of the computational domain of Case B as an example to present domain size, meshing, and input wind direction.

**Figure 4.**Meshing for Case B as an example (Buildings highlighted as red are the historic shophouses conserved at the Chinatown area).

**Figure 5.**Validation results [22].

**Figure 6.**Examples of locations and geometries of heat sources regarding different AC types and building functions (AC—air conditioning).

**Figure 7.**Locations of test lines for data analysis (RTL represents the test line on streets with both roadway and footway and FTL represents the test line on streets with only footway.).

**Figure 8.**Wind speed contours of Case A (

**left**) and Case B (

**right**) at the study area (buildings highlighted as red are the historic shophouses conserved at the Chinatown area).

**Figure 9.**(

**a**) (

**Left**). Air Temperature increment contours of Case C–E. (

**b**) (

**Right**) Air Temperature increment contours of Case F–H.

**Figure 10.**Wind speed of Case A & B and the variance of Case (B−A) % of each test line and the average value. Variance of Case (B−A) was calculated with the equation $\mathrm{Case}\left(\mathrm{B}-\mathrm{A}\right)=\frac{{v}_{\mathrm{Case}\mathrm{B}}-{v}_{\mathrm{Case}\mathrm{A}}}{{v}_{\mathrm{Case}\mathrm{A}}}\%$ (${v}_{\mathrm{Case}\mathrm{A}}$ is the wind velocity of each test line in Case A and ${v}_{\mathrm{Case}\mathrm{B}}$ is the wind velocity of each test line in Case B).

**Figure 11.**(

**a**). Temperature increment of Case C & E and the variance of Case (C–E) % of each test line and the average value, Variance of Case (C–E) was calculated with the equation $\mathrm{Case}\left(\mathrm{C}-\mathrm{E}\right)=\frac{{T}_{\mathrm{Case}\mathrm{C}}-{T}_{\mathrm{Case}\mathrm{E}}}{{T}_{\mathrm{Case}\mathrm{E}}}\%$ (${T}_{\mathrm{Case}\mathrm{C}}$ is the temperature increment of each test line in Case C and ${T}_{\mathrm{Case}\mathbf{E}}$ is the temperature increment of each test line in Case E). (

**b**). Temperature increment of Case D & E and the variance of Case (D–E) % of each test line and the average value, Variance of Case (D–E) was calculated with the equation $\mathrm{Case}\left(\mathrm{D}-\mathrm{E}\right)=\frac{{T}_{\mathrm{Case}\mathrm{D}}-{T}_{\mathrm{Case}\mathrm{E}}}{{T}_{\mathrm{Case}\mathrm{E}}}\%$ (${T}_{\mathrm{Case}\mathrm{D}}$ is the temperature increment of each test line in Case D and ${T}_{\mathrm{Case}\mathrm{E}}$ is the temperature increment of each test line in Case E).

**Figure 12.**(

**a**). Mitigation strategies description for historic shophouses in Chinatown. (

**b**). Mitigation strategies description for surrounding high-rise buildings.

**Figure 13.**(

**a**). Temperature increment of Case F & E and the variance of Case (F–E) % of each test line and the average value, Variance of Case (F–E) was calculated with the equation $\mathrm{Case}\left(\mathrm{F}-\mathrm{E}\right)=\frac{{T}_{\mathrm{Case}\mathrm{F}}-{T}_{\mathrm{Case}\mathrm{E}}}{{T}_{\mathrm{Case}\mathrm{E}}}\%$ (${T}_{\mathrm{Case}\mathrm{F}}$ is the temperature increment of each test line in Case D and ${T}_{\mathrm{Case}\mathrm{E}}$ is the temperature increment of each test line in Case E). (

**b**). Temperature increment of Case G & E and the variance of Case (G–E) % of each test line and the average value, Variance of Case (G–E) was calculated with the equation $\mathrm{Case}\left(\mathrm{G}-\mathrm{E}\right)=\frac{{T}_{\mathrm{Case}\mathrm{G}}-{T}_{\mathrm{Case}\mathrm{E}}}{{T}_{\mathrm{Case}\mathrm{G}}}\%$ (${T}_{\mathrm{Case}\mathrm{G}}$ is the temperature increment of each test line in Case D and ${T}_{\mathrm{Case}\mathrm{E}}$ is the temperature increment of each test line in Case E). (

**c**). Temperature increment of Case H & E and the variance of Case (H–E) % of each test line and the average value, Variance of Case (H–E) was calculated with the equation $\mathrm{Case}\left(\mathrm{H}-\mathrm{E}\right)=\frac{{T}_{\mathrm{Case}\mathrm{H}}-{T}_{\mathrm{Case}\mathrm{E}}}{{T}_{\mathrm{Case}\mathrm{E}}}\%$ (${T}_{\mathrm{Case}\mathrm{H}}$ is the temperature increment of each test line in Case D and ${T}_{\mathrm{Case}\mathrm{E}}$ is the temperature increment of each test line in Case E).

Cases | Description | Aim |
---|---|---|

Case A | Represents urban geometry of the study area in 1960s | Step 1: To clarity the impact of new development on pedestrian-level air flow |

Case B | Represents urban geometry of the study area in 2020s | |

Case C | The urban geometry of 2020s and anthropogenic heat from the surrounding high-rise buildings are applied | Step 2: To assess the air temperature increment caused by anthropogenic heat emitted |

Case D | The urban geometry of 2020s and anthropogenic heat from the historic shophouses are applied | |

Case E | The urban geometry of 2020s and anthropogenic heat from both high-rise buildings and the historic shophouses are applied | |

Case F | The mitigation strategy is to create horizontal porosity and open spaces by removing some shophouses | Step 3: To develop the mitigation strategies based on the assessment from previous two steps |

Case G | The mitigation strategy is to create both vertical and horizontal porosity by modifying the geometry of the surrounding high-rise buildings | |

Case H | The mitigation strategies in Case F and G are integrated in this case |

Turbulence Model | SST k-ω |
---|---|

Computational grid type | Unstructured poly-hexcore meshes |

Blockage ratio | <5% |

Grid expansion ratio | 1.2 |

Density | Boussinesq |

Solving algorithms | SIMPLE |

Input wind profile | Power law equation |

Inflow boundary condition | Operating temperature: 27 °C |

Incoming wind speed | Power-law profile with the reference wind speed of 7.6 m/s at 300 m above the ground |

Incoming wind directions | northeast (the prevailing wind direction of Singapore) |

Heat flow specification method | Mass flow rate inlet: normal to boundary direction (Heat emission temperature: 40 °C) |

Other boundary conditions |
Outflow: pressure outlet Bottom and buildings: Wall Top: Symmetry |

Convergence criteria | 1E-6 for all variables |

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## Share and Cite

**MDPI and ACS Style**

Zhu, W.; Zhang, L.; Mei, S.-J.; Yuan, C.
Developing Urban Heat Mitigation Strategies for a Historic Area Using a High-Fidelity Parametric Numerical Simulation: A Case Study in Singapore. *Buildings* **2022**, *12*, 1311.
https://doi.org/10.3390/buildings12091311

**AMA Style**

Zhu W, Zhang L, Mei S-J, Yuan C.
Developing Urban Heat Mitigation Strategies for a Historic Area Using a High-Fidelity Parametric Numerical Simulation: A Case Study in Singapore. *Buildings*. 2022; 12(9):1311.
https://doi.org/10.3390/buildings12091311

**Chicago/Turabian Style**

Zhu, Wei, Liqing Zhang, Shuo-Jun Mei, and Chao Yuan.
2022. "Developing Urban Heat Mitigation Strategies for a Historic Area Using a High-Fidelity Parametric Numerical Simulation: A Case Study in Singapore" *Buildings* 12, no. 9: 1311.
https://doi.org/10.3390/buildings12091311