Abstract
Climate change is a major contemporary phenomenon with multiple consequences. In urban areas, it exacerbates the urban heat island phenomenon. It impacts the health of the inhabitants and the sensation of thermal discomfort felt in urban areas. Thus, it is necessary to estimate as well as possible the air temperature at any point of a territory, in particular in view of the ongoing rationalization of the network of fixed meteorological stations of Météo-France. Understanding the air temperature is increasingly in demand to input quantitative models related to a wide range of fields, such as hydrology, ecology, or climate change studies. This study thus proposes to model air temperature, measured during four mobile campaigns carried out during the summer months, between 2016 and 2019, in Lyon (France), in clear sky weather, using regression models based on 33 explanatory variables from traditionally used data, data from remote sensing by LiDAR (Light Detection and Ranging), or Landsat 8 satellite acquisition. Three types of statistical regression were experimented: partial least square regression, multiple linear regression, and a machine learning method, the random forest regression. For example, for the day of 30 August 2016, multiple linear regression explained 89% of the variance for the study days, with a root mean square error (RMSE) of only 0.23 °C. Variables such as surface temperature, Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) have a strong impact on the estimation model. This study contributes to the emergence of urban cooling systems. The solutions available vary. For example, they may include increasing the proportion of vegetation on the ground, facades, or roofs, increasing the number of basins and water bodies to promote urban cooling, choosing water-retaining materials, humidifying the pavement, increasing the number of public fountains and foggers, or creating shade with stretched canvas.
Highlights
Climate change is a major current phenomenon with multiple environmental, social and economic consequences [1]
This study proposes to model air temperature, measured during four mobile campaigns carried out during the summer months, between 2016 and 2019, in Lyon (France), in clear sky weather, using regression models based on 33 explanatory variables from traditionally used data, data from remote sensing by LiDAR (Light Detection and Ranging), or Landsat 8 satellite acquisition
The measuring devices used in this study proved to be highly accurate since, after this comparison at theTMheétméoe-aFsruarnincge sditeeviocveesrutsheedsuinmtmhiesr s2t0u1d8ypperroiovded, thtoe abier theimghpleyraatcucrueractoerrseinlactei,onafotefrththesise two dcioffmerpeanrtisaocnquaitstihtieoMn ésotéuor-cFersanshceowsitseaovloewr tehset scuomrremlaetrio20n1c8opeeffiricoiden, tthoefa0i.r9t8e1mfpoerrtahtueraeircoterrmelpaetiroantuorfe and 0t.h97es7efotwr tohedirfefelaretinvteahcuqumisiditiiotynmsoeuarscuersedshboywLs OaGlo3w2e(sTtacbolrere2l)a.tion coefficient of 0.981 for the air temperature and 0.977 for the relative humidity measured by LOG 32 (Table 2)
Summary
Climate change is a major current phenomenon with multiple environmental, social and economic consequences [1] In urban areas, it exacerbates the urban heat island (UHI) phenomenon [2,3] which is characterized by a difference in temperature between an urban area and the surrounding rural areas. The roughness can cause a diminution of the wind speed and the SVF can reduce the release of heat during the night [2] Anthropogenic parameters such as industrial heat emissions, heating, transport, or air conditioning can contribute as well to heat intensification [17,18,19,20], the “cities consume 78 percent of the world’s energy and produce more than 60 percent of greenhouse gas emissions. They account for less than 2 percent of the Earth’s surface” [21]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
