| Popis: |
Exposure and vulnerabilities to heat stress are concentrated in cities, yet exhibit large intra-urban variability. However, existing Weather Sensor Networks (WSNs) that monitor relevant meteorological conditions are typically installed at a much coarser resolution and generally do not cover canopy-layer conditions in cities. There are few examples of fine urban-scale massive sensor networks at street-level, however, they rarely provide any data beyond air temperature and humidity needed to assess, map and calculate thermal comfort, and many street-level networks often lack the real-time data transmission and quality control procedures necessary for real-time communication.Here, we present a customizable two-tiered WSN setup, coupled with a quality and data processing chain, to quantify, map and communicate heat exposure data and resolve intra-urban variabilities in real-time. The hierarchical urban canopy-layer network developed for long-term monitoring of thermal comfort conditions (and also heavy precipitation and wind storm impacts) in the city of Freiburg, Germany, consists of two different station systems that are integrated into public street lights at a uniform height of 3 m a.g.l. Thirteen “tier I stations“ are strategically placed in representative built-up and rural areas. They are equipped with a ClimaVUE 50 all-in-one weather sensor (precipitation, wind, radiation, temperature, humidity, pressure) and a Black Globe Sensor (both from Campbell Scientific, Inc.) which enables real-time thermal comfort calculations such as the Physiologically Equivalent Temperature (PET) or the Universal Thermal Climate Index (UTCI). Tier I stations feature a custom-built multi-purpose logger which is controlled by a Raspberry Pi Zero running a custom remote control software and GSM data transmission. This allows for a highly flexible setup that can easily be expanded to include additional sensors (e.g. air quality) in the future. In addition, 35 commercial “tier II stations“ (LoRAIN, Pessl Instruments GmbH) measure air temperature, humidity and precipitation and transmit data over NB-IoT. These tier II stations significantly increase the spatial density of the WSN at a lower cost per site. In addition to urban street-light mounted locations, an additional eight sites in non-built-up locations capture areas with predominantly rural and natural land cover, with selected stations specifically measuring cold-air drainage channels into the city.With measuring and transmission intervals of one and five minutes, respectively, one major purpose of this WSN is to develop machine learning routines for data quality control and quality assessment in real-time and downscaling thermal comfort data from tier II to tier I stations and areas not covered by stations. Moreover, the WSN will provide input and validation data for numerical high-resolution modelling of urban heat exposure. Real-time visualizations inform researchers, city officials and the general public with instantaneous and historical data at neighborhood-scale. |
