Quantification of urban heat islands using automatic weather station data and smart-city networks

With the advent of smart-city networks, new tools are now available to monitor urban heat islands (UHIs) and their driving factors. This study aims to analyze differences in the layer characteristics of existing Automatic Weather Station (AWS) data at the mesoscale and Smart Seoul Data of Things (S-DoT) data at the microscale, and to discuss the need for a multi-scale solution based on these differences. Data were collected from July 1, 2020 to September 30, 2020. The relationships of temperature with solar radiation (SR), green-area ratio (GAR), and altitude were evaluated. Kriging was used to interpolate the limited AWS data for S-DoT. The results showed that UHI characteristics measured using the two approaches were differnet. In daily distribution comparisons, both monitoring station types showed similar patterns for daily mean temperatures at all stations. However, the mean temperature of the S-DoT in summer was 26.51°C, while that of the AWS was 23.93°C. Furthermore, the AWS kriging results revealed AWS temperatures to be lower compared to their S-DoT counterparts. Potential reasons for this temperature difference were subsequently explored. It was determined that the S-DoT and AWS measure the temperature at the canopy layer and boundary layer, respectively. The SR effects differed depending on rainfall. The GAR showed a negative correlation with both S-DoT and AWS data, in which the temperature decreased as the GAR increased because of the heat-island reduction effect of green areas. Altitude showed large differences in influence related to differences in installation location. With regard to UHI policies, such mesoscale and microscale data should be used in a complementary manner to develop potential solutions at multiple scales.