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<p>Wet Bulb Globe Temperature is a heat stress metric that accounts for air temperature, humidity, solar radiation, and wind speed. While methods of WBGT calculation from standard meteorological variables have been developed, these are often only used at coarse spatial scales or at specific points and overlook the underlying land use and land cover that is vitally important to microclimate development. This is particularly important in cities where the urban heat island has significant and spatially disparate impacts on local climate; some areas warm more than others. To address this gap, easily accessible and standard instrumentation was utilized to gather meteorological and environmental factors relevant to WBGT with common mobile measurement methods: walking, cycling, and driving transects. Machine learning methods are used to model and estimate each component of WBGT at a 10-m resolution using underlying land use and land cover data as inputs. The viability, challenges, accuracy, applications, and benefits of this novel mobile WBGT mapping method are assessed. Results indicate that for a sound UHI mapping campaign, a variety of transects should be used in a dense route network as allowed by the total number of citizen science volunteers and the size of the total study area.</p>

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A performance comparison of mobile transect methods for heat and humidity prediction in complex urban environments

  • 期刊论文

  • 发表日期：

    2026年1月22日

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  • Andrew Robinson,
  • Christopher Fuhrmann,
  • Charles Konrad,
  • Olivia M. Davis,
  • Eric A. Kirk,
  • David Parr

摘要

Wet Bulb Globe Temperature is a heat stress metric that accounts for air temperature, humidity, solar radiation, and wind speed. While methods of WBGT calculation from standard meteorological variables have been developed, these are often only used at coarse spatial scales or at specific points and overlook the underlying land use and land cover that is vitally important to microclimate development. This is particularly important in cities where the urban heat island has significant and spatially disparate impacts on local climate; some areas warm more than others. To address this gap, easily accessible and standard instrumentation was utilized to gather meteorological and environmental factors relevant to WBGT with common mobile measurement methods: walking, cycling, and driving transects. Machine learning methods are used to model and estimate each component of WBGT at a 10-m resolution using underlying land use and land cover data as inputs. The viability, challenges, accuracy, applications, and benefits of this novel mobile WBGT mapping method are assessed. Results indicate that for a sound UHI mapping campaign, a variety of transects should be used in a dense route network as allowed by the total number of citizen science volunteers and the size of the total study area.