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

Abstract

Forcing pathways between urban surfaces (impervious and pervious pavers) and near-surface air temperature were measured and investigated with a network of multiple sensors. Utilizing field data measured between April 2021 and May 2022, and assuming that the influential variables follow the basic heat-transfer energy-balance equations, multiple regression-based statistical models were built to predict the surface temperature and near-surface air temperature (0.05 m, 0.5 m, 1 m, 2 m, and 3 m) of one impervious paver site and one pervious paver site in Taipei City, Taiwan. Evaporative cooling was found to be more influential on the pervious paver with a statistically significant influence on the microclimate up to 1.8 m (and up to 0.7 m for the impervious paver), using in situ data with an ambient air temperature higher than 24 °C. The surface temperature is mainly affected by solar shortwave radiation and ambient air temperature. As for near-surface air temperature, ambient air temperature is the most influential factor, followed by surface temperature. The importance of surface temperature indicates the influence of upwelling longwave radiation on the microclimate. The predictive equations show that pervious surfaces can help cities with hot and humid climates fight the changing climate in the future.

Keywords: field measurements; impervious; microclimate; multiple regression; permeable; pervious; urban heat island.

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 2

Close-up view of the weather station sensor set.

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 R² = 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 R² = 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.