Study on microclimate observation network for urban unit: A case study in a campus of Shenzhen, China

Abstract

Urban units are city components that have relatively pure functions while carry a large number of urban residents. The quality of an urban unit's microclimate environment is closely related to the health and comfort of its population. In this study, we develop a technology on microclimate observation for urban units, which is experimentally applied in a campus in Shenzhen, China. We have arrived at the following conclusions: (1) The application of precision integration technology and small sensors greatly reduces the physical size of the microclimate observation instrument. With the help of advanced algorithms for data quality control, the cost of detection and measuring instruments can be greatly reduced even when accuracy is maintained so that the instruments can be densely deployed in urban units. (2) The urban unit microclimate observation network, as a supplement to the traditional meteorological observation system, makes up for the deficiencies of coarse resolution and limited capability of the traditional observation system on describing urban heterogeneity, and can be used to more precisely depict the heterogeneity of the microclimate within the urban units. Utilizing the technology of Internet of things, it provides data support for people, who work, live and study in the urban units, to take appropriate measures to regulate microclimate environment, and also provides external parameters for automatic adjustment and control of smart air conditioners and dehumidifiers. (3) Temperature monitoring data show that the detected daily temperature ranges of the indoor stations within urban unit are relatively small, and the daily average temperatures observed by the indoor stations are quite different from those observed by the automatic weather stations located in open spaces, indicating that conventional meteorological observation has little reference value to the indoor environment of urban units. At the same time, the observed data of the different indoor monitoring points are quite different from each other, which are closely related to their specific environments. (4) Analysis of relative humidity data shows that the rules of variation of indoor relative humidity are quite different from those of open space, and indoor crowd and monitoring point environment may affect the indoor relative humidity.