# Field Measurement of the Dynamic Interaction between Urban Surfaces and Microclimates in Humid Subtropical Climates with Multiple Sensors

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Site Description

_{20}) of about 1.1 × 10

^{−1}cm/s (personal communication with Jinhuang Construction, Taipei City, Taiwan).

## 3. Methodology

#### 3.1. Effect of Surface Water Content

#### 3.2. Surface Temperature

#### 3.3. Near-Surface Microclimate Temperature

## 4. Data Analyses Results

#### 4.1. Data Summary

#### 4.2. Effect of Surface Water Content

#### 4.3. Surface Temperature

^{2}= 0.81) and Equation (5) (R

^{2}= 0.84), respectively. The p-value and 95% CI range of coefficients are reported in Table 7. The scatterplots between predicted and measured surface temperature for pervious and impervious pavers with the distribution of residual error are provided in Figure 5 and Figure 6, respectively.

#### 4.4. Near-Surface Microclimate Air Temperature

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**(

**a**) Aerial photo of the experimental sites with locations of underground thermometers (red circles, T1-T5), runoff gages (blue triangles, not used in this study), and the weather station (purple square) marked, (

**b**) scheme of surface and underground temperature monitoring before sensor instrument installation with the location of surface thermometers marked, and (

**c**) close-up view of PT-1000 sensor used for underground and surface temperature measurements (Adapted from Ramalingam et al. [29]).

**Figure 3.**Near-surface air temperature measurement sensor setup and locations: (

**a**) the apparatus of near-surface air temperature measurement (white heat shields visible); (

**b**) the vicinity of the measurement point at the pervious site; and (

**c**) the vicinity of the measurement point at the impervious site, where the dark covers in the center of (

**b**,

**c**) house underground thermometer shafts.

**Figure 4.**Summary of mean surface and near-surface air temperature at different altitudes of both paver types with a dashed line showing the skipped range of temperature.

**Figure 5.**Scatterplot of data points of predicted vs. measured surface temperature and residual error for pervious paver with R

^{2}= 0.81 (dash line is 1:1).

**Figure 6.**Scatterplot of data points of predicted vs. measured surface temperature and residual error for the impervious paver with R

^{2}= 0.84 (dash line is 1:1).

**Figure 7.**Scatterplots of data points of predicted vs. measured near-surface air temperature (dash lines are 1:1) for (

**a**) the pervious site at 0.05 m altitude, (

**b**) the pervious site at 3 m altitude, (

**c**) the impervious site at 0.05 m altitude, and (

**d**) the impervious site at 3 m altitude.

**Figure 8.**An example of the higher surface temperature of pervious paver with high solar irradiance and no surface moisture content.

**Figure 9.**Reduction of near-surface air temperature by pervious surface for different ambient air temperatures.

PT1000 Surface Temperature Sensor | ||

Temperature | Range | −50–110 °C |

Accuracy | ±1% | |

Sampling interval | 5 min | |

WSC-120 Weather Station | ||

Temperature | Range | 0–60 °C |

Accuracy | ±0.3 °C | |

Humidity | Range | 0–100% RH |

Accuracy | ±3% RH | |

Wind speed | Range | 0–60 m/s |

Accuracy | ± (0.3 + 0.03 $\times $ wind speed) m/s | |

Wind direction | Range | 0–360° |

Accuracy | ±2° | |

Rainfall | Accuracy | 1 mm |

Sampling interval | 5 min | |

JSQ-214 Pyranometer | ||

Field of view | 180° | |

Spectral range | 410–655 nm | |

Calibration uncertainty | ±5% | |

Sampling interval | 5 min | |

SP-214-SS Pyranometer | ||

Field of view | 180° | |

Spectral range | 360–1120 nm | |

Calibration uncertainty | ±3% | |

Sampling interval | 5 min | |

THD-8 Air Temperature and Humidity Sensor | ||

Temperature | Range | −20–80 °C |

Accuracy | ±0.3 °C | |

Humidity | Range | 0–100% RH |

Accuracy | ±2% RH | |

Sampling interval | 5 s |

**Table 2.**Initial predictive variables for input into the statistical models of pervious and impervious paver surface temperature.

Symbol | Description |
---|---|

$u$ | Wind speed (m/s) |

$T$ | Ambient air temperature (°C) |

$SR$ | Solar radiation (in irradiance, kW/m^{2}) |

${T}_{x-h}$ | x-hour antecedent mean ambient air temperature (°C) |

${R}_{x-h}$ | x-hour antecedent rainfall depth (mm) |

${T}_{x-h}\times {R}_{x-h}$ * | The interaction term of x-hour ambient air temperature and rainfall depth |

**Table 3.**Initial predictive variables for input into the statistical models of near-surface air temperature.

Symbol | Description |
---|---|

$u$ | Wind speed (m/s) |

$T$ | Ambient air temperature (°C) |

${T}_{surf}$ | Surface temperature |

$SR$ | Solar radiation (in irradiance, kW/m^{2}) |

${T}_{x-h}$ | x-hour antecedent mean ambient air temperature (°C) |

${R}_{x-h}$ | x-hour antecedent rainfall depth (mm) |

${T}_{x-h}\times {R}_{x-h}$ | The interaction term of x-hour air temperature and rainfall depth |

Wind Speed (m/s) | Air Temp. (°C) | Relative Humidity (%) | Solar Radiation (kW/m^{2}) | Surface Temp. (°C) | Air Temperature (°C) | |||||
---|---|---|---|---|---|---|---|---|---|---|

0.05 m | 0.5 m | 1 m | 2 m | 3 m | ||||||

Mean | 0.68 | 30.6 | 66 | 0.41 | 39.7 | 33.4 | 32.6 | 32.6 | 32.5 | 32.5 |

95% CI | (0, 9.25) | (24.5, 37.0) | (47, 85) | (0, 0.95) | (26.0, 52.6) | (24.7, 42.4) | (24.4, 41.3) | (24.2, 40.2) | (24.1, 40.5) | (24.2, 40.4) |

Wind Speed (m/s) | Air Temp. (°C) | Relative Humidity (%) | Solar Radiation (kW/m^{2}) | Surface Temp. (°C) | Air Temperature (°C) | |||||
---|---|---|---|---|---|---|---|---|---|---|

0.05 m | 0.5 m | 1 m | 2 m | 3 m | ||||||

Mean | 0.34 | 30.6 | 66 | 0.42 | 42.0 | 33.8 | 32.8 | 32.8 | 32.6 | 32.7 |

95% CI | (0, 6.38) | (24.2, 36.9 | (47, 84) | (0, 0.96) | (28.3, 56.4) | (24.8, 42.9) | (24.3, 40.6) | (24.1, 40.7) | (24.5, 40.3) | (24.9, 40.1) |

**Table 6.**Statistical modeling results for standard least-squares models of near-surface air temperature (${T}_{24h}$: 24-h antecedent mean ambient temperature, ${R}_{24h}$: 24-h antecedent rainfall depth).

Pervious Pavement Site | |||||||

Model Terms for 0.05 m Temp. | Coefficient | p-Value | 95% CI | Model Terms for 0.5-m Temp. | Coefficient | p-Value | 95% CI |

${T}_{24h}$ | 0.0044 | 0.95 | (−0.15, 0.16) | ${T}_{24h}$ | −0.00039 | 0.99 | (−0.12, 0.12) |

${R}_{24h}$ | −0.028 | 0.25 | (−0.076, 0.020) | ${R}_{24h}$ | −0.024 | 0.23 | (−0.063, 0.015) |

${T}_{24h}\times {R}_{24h}$ | −0.040 | 0.0044 * | (−0.067, −0.013) | ${T}_{24h}\times {R}_{24h}$ | −0.031 | 0.0062 * | (−0.053, −0.0090) |

Model Terms for 1 m Temp. | Coefficient | p-value | 95% CI | Model Terms for 1.8 m Temp. ^{+} | Coefficient | p-value | 95% CI |

${T}_{24h}$ | 0.0031 | 0.96 | (−0.12, 0.13) | ${T}_{24h}$ | 0.019 | 0.76 | (−0.10, 0.14) |

${R}_{24h}$ | −0.023 | 0.25 | (−0.063, 0.017) | ${R}_{24h}$ | −0.025 | 0.21 | (−0.064, 0.014) |

${T}_{24h}\times {R}_{24h}$ | −0.032 | 0.0063 * | (−0.054, −0.0092) | ${T}_{24h}\times {R}_{24h}$ | −0.023 | 0.041 * | (−0.045, −0.00098) |

Model Terms for 1.9 m Temp. ^{+} | Coefficient | p−value | 95% CI | Model Terms for 2 m Temp. | Coefficient | p−value | 95% CI |

${T}_{24h}$ | 0.021 | 0.74 | (−0.10, 0.15) | ${T}_{24h}$ | 0.023 | 0.71 | (−0.10, 0.15) |

${R}_{24h}$ | −0.025 | 0.21 | (−0.064, 0.014) | ${R}_{24h}$ | −0.025 | 0.20 | (−0.064, 0.014) |

${T}_{24h}\times {R}_{24h}$ | −0.022 | 0.052 | (−0.044, 0.00016) | ${T}_{24h}\times {R}_{24h}$ | −0.021 | 0.065 | (−0.043, 0.0013) |

Model Terms for 3 m Temp. | Coefficient | p-value | 95% CI | ||||

${T}_{24h}$ | 0.031 | 0.63 | (−0.094, 0.16) | ||||

${R}_{24h}$ | −0.027 | 0.18 | (−0.066, 0.013) | ||||

${T}_{24h}\times {R}_{24h}$ | −0.018 | 0.12 | (−0.040, 0.0044) | ||||

Impervious Pavement Site | |||||||

Model Terms for 0.05 m Temp. | Coefficient | p-value | 95% CI | Model Terms for 0.5 m Temp. | Coefficient | p-value | 95% CI |

${T}_{24h}$ | 0.065 | 0.40 | (−0.088, 0.22) | ${T}_{24h}$ | 0.043 | 0.47 | (−0.076, 0.16) |

${R}_{24h}$ | 0.026 | 0.38 | (−0.033, 0.086) | ${R}_{24h}$ | 0.023 | 0.32 | (−0.023, 0.069) |

${T}_{24h}\times {R}_{24h}$ | −0.033 | 0.028 * | (−0.063, 0.0037) | ${T}_{24h}\times {R}_{24h}$ | −0.025 | 0.030 * | (−0.048, −0.0025) |

Model Terms for 0.7 m Temp. ^{+} | Coefficient | p-value | 95% CI | Model Terms for 0.8 m Temp. ^{+} | Coefficient | p-value | 95% CI |

${T}_{24h}$ | 0.057 | 0.35 | (−0.062, 0.18) | ${T}_{24h}$ | 0.063 | 0.29 | (−0.056, 0.18) |

${R}_{24h}$ | 0.023 | 0.32 | (−0.023, 0.069) | ${R}_{24h}$ | 0.023 | 0.33 | (−0.023, 0.069) |

${T}_{24h}\times {R}_{24h}$ | −0.023 | 0.047 * | (−0.046, 0.00030) | ${T}_{24h}\times {R}_{24h}$ | −0.022 | 0.059 | (−0.045, 0.00089) |

Model Terms for 1 m Temp. | Coefficient | p-value | 95% CI | Model Terms for 2 m Temp. | Coefficient | p-value | 95% CI |

${T}_{24h}$ | 0.077 | 0.21 | (−0.044, 0.20) | ${T}_{24h}$ | 0.071 | 0.25 | (−0.049, 0.19) |

${R}_{24h}$ | 0.023 | 0.34 | (−0.024, 0.070) | ${R}_{24h}$ | 0.021 | 0.38 | (−0.026, 0.067) |

${T}_{24h}\times {R}_{24h}$ | −0.020 | 0.093 | (−0.043, 0.0034) | ${T}_{24h}\times {R}_{24h}$ | −0.017 | 0.17 | (−0.039, 0.0069) |

Model Terms for 3 m Temp. | Coefficient | p-value | 95% CI | ||||

${T}_{24h}$ | 0.064 | 0.29 | (−0.055, 0.18) | ||||

${R}_{24h}$ | 0.022 | 0.34 | −0.024, 0.068) | ||||

${T}_{24h}\times {R}_{24h}$ | −0.017 | 0.14 | (−0.040, 0.0058) |

^{+}Temperature interpolated.

Pervious Surface (R^{2} = 0.81) | Impervious Surface (R^{2} = 0.84) | |||||||
---|---|---|---|---|---|---|---|---|

Term | p-Value | Coefficient | 95% CI | VIF | p-Value | Coefficient | 95% CI | VIF |

$T$ | 0.0005 * | 1.22 | (0.55, 1.88) | 8.04 | <0.0001 * | 1.25 | (0.67, 1.83) | 8.42 |

$SR$ | <0.0001 * | 12.56 | (8.27, 16.86) | 2.59 | <0.0001 * | 11.37 | (7.76, 14.98) | 2.70 |

${T}_{24h}$ | 0.36 | −0.27 | (-0.85, 0.31) | 5.94 | 0.76 | −0.080 | (−0.61, 0.45) | 6.75 |

${R}_{24h}$ | 0.31 | −0.043 | (−0.13, 0.040) | 1.06 | 0.74 | 0.015 | (−0.072, 0.10) | 1.22 |

${T}_{24h}\times {R}_{24h}$ ^{+} | 0.0004 * | −0.086 | (−0.13, −0.040) | 1.19 | 0.0012 * | −0.071 | (−0.11, −0.029) | 1.30 |

^{+}In centered form in the final model.

**Table 8.**Statistical models for the near-surface air temperature at the pervious site at different altitudes.

Altitude | Term | Intercept | $u$ | $T$ | ${T}_{surf}$ | $SR$ | ${T}_{24h}$ |

0.05 m (R ^{2} = 0.95) | p-value | 0.076 | 0.090 | <0.0001 * | <0.0001 * | 0.11 | 0.058 |

Coeff. | −2.20 | −0.095 | 1.00 | 0.23 | 1.26 | −0.17 | |

95% CI | (−4.64, 0.24) | (−0.21, 0.015) | (0.79, 1.22) | (0.17, 0.30) | (−0.29, 2.81) | (−0.35, 0.0062) | |

VIF | - | 1.06 | 9.40 | 4.30 | 3.61 | 5.83 | |

0.5 m (R ^{2} = 0.95) | p-value | 0.13 | - | <0.0001 * | <0.0001 * | 0.11 | 0.085 |

Coeff. | −1.72 | - | 1.02 | 0.17 | 1.15 | −0.14 | |

95% CI | (−3.94, 0.50) | - | (0.82, 1.22) | (0.11, 0.23) | (−0.27, 2.58) | (−0.30, 0.020) | |

VIF | - | - | 9.19 | 4.30 | 3.60 | 5.74 | |

1 m (R ^{2} = 0.94) | p-value | 0.26 | - | <0.0001 * | <0.0001 * | 0.0018 * | - |

Coeff. | −1.42 | - | 0.89 | 0.15 | 2.20 | - | |

95% CI | (3.92, 1.07) | - | (0.78, 1.00) | (0.084, 0.21) | (0.85, 3.55) | - | |

VIF | - | - | 2.38 | 4.28 | 2.55 | - | |

2 m (R ^{2} = 0.95) | p-value | 0.084 | - | <0.0001 * | 0.0004 * | 0.018 * | 0.065 |

Coeff. | −2.03 | - | 1.10 | 0.11 | 1.81 | −0.16 | |

95% CI | (−4.35, 0.28) | - | (0.90, 1.31) | (0.053, 0.17) | (0.33, 3.29) | (−0.33, 0.0098) | |

VIF | - | - | 9.19 | 4.30 | 3.60 | 5.74 | |

3 m (R ^{2} = 0.95) | p-value | 0.086 | - | <0.0001 * | 0.0025 * | 0.0052 * | 0.064 |

Coeff. | −2.02 | - | 1.13 | 0.094 | 2.14 | −0.16 | |

95% CI | (−4.33, 0.29) | - | (0.92, 1.33) | (0.034, 0.15) | (0.66, 3.62) | (−0.33, 0.0093) | |

VIF | - | - | 9.19 | 4.30 | 3.60 | 5.74 |

**Table 9.**Statistical models for the near-surface air temperature at the impervious site at different altitudes.

Altitude | Term | Intercept | $T$ | ${T}_{surf}$ | $SR$ | ${T}_{24h}$ | ${R}_{24h}$ |

0.05 m (R ^{2} = 0.97) | p-value | <0.0001 * | <0.0001 * | <0.0001 * | 0.071 | 0.0008 * | 0.0045 * |

Coeff. | −4.16 | 1.16 | 0.23 | 1.22 | −0.27 | 0.036 | |

95% CI | (−6.16, −2.16) | (0.97, 1.35) | (0.16, 0.29) | (−0.10, 2.54) | (−0.43, −0.12) | (0.011, 0.060) | |

VIF | - | 9.73 | 5.58 | 4.05 | 6.41 | 1.08 | |

0.5 m (R ^{2} = 0.97) | p-value | 0.0007 * | <0.0001 * | <0.0001 * | 0.0046 * | 0.016 * | 0.0066 * |

Coeff. | −2.85 | 1.06 | 0.16 | 1.57 | −0.15 | 0.028 | |

95% CI | (−4.47, −1.23) | (0.91, 1.22) | (0.11, 0.21) | (0.50, 2.64) | (−0.28, −0.029) | (0.0080, 0.048) | |

VIF | - | 9.73 | 5.58 | 4.05 | 6.41 | 1.08 | |

1 m (R ^{2} = 0.97) | p-value | <0.0001 * | <0.0001 * | 0.0001 * | 0.0097 * | 0.026 * | 0.0089 * |

Coeff. | −3.92 | 1.16 | 0.12 | 1.67 | −0.17 | 0.031 | |

95% CI | (−5.81, −2.02) | (0.98, 1.34) | (0.063, 0.18) | (0.42, 2.93) | (−0.31, −0.020) | (0.0081, 0.054) | |

VIF | - | 9.73 | 5.58 | 4.05 | 6.41 | 1.08 | |

2 m (R ^{2} = 0.96) | p-value | 0.0004 * | <0.0001 * | 0.0001 * | 0.0306 * | 0.020 * | 0.011 * |

Coeff. | −3.65 | 1.16 | 0.13 | 1.44 | −0.18 | 0.032 | |

95% CI | (−5.62, −1.68) | (0.97, 1.34) | (0.064, 0.19) | (0.14, 2.75) | (−0.33, −0.030) | (0.0077, 0.056) | |

VIF | - | 9.73 | 5.58 | 4.05 | 6.41 | 1.08 | |

3 m (R ^{2} = 0.96) | p-value | 0.0019 * | <0.0001 * | <0.0001 * | 0.0213 * | 0.051 | 0.013 * |

Coeff. | −3.21 | 1.11 | 0.13 | 1.55 | −0.15 | 0.031 | |

95% CI | (−5.20, −1.23) | (0.92, 1.30) | (0.068, 0.19) | (0.24, 2.87) | (−0.31, 0.00074) | (0.0066, 0.055) | |

VIF | - | 9.73 | 5.58 | 4.05 | 6.41 | 1.08 |

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Tu, M.-c.; Chen, W.-j.
Field Measurement of the Dynamic Interaction between Urban Surfaces and Microclimates in Humid Subtropical Climates with Multiple Sensors. *Sensors* **2023**, *23*, 9835.
https://doi.org/10.3390/s23249835

**AMA Style**

Tu M-c, Chen W-j.
Field Measurement of the Dynamic Interaction between Urban Surfaces and Microclimates in Humid Subtropical Climates with Multiple Sensors. *Sensors*. 2023; 23(24):9835.
https://doi.org/10.3390/s23249835

**Chicago/Turabian Style**

Tu, Min-cheng, and Wei-jen Chen.
2023. "Field Measurement of the Dynamic Interaction between Urban Surfaces and Microclimates in Humid Subtropical Climates with Multiple Sensors" *Sensors* 23, no. 24: 9835.
https://doi.org/10.3390/s23249835