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Assessment and prediction of land surface temperature effects on human thermal comfort in the city of Oran, Algeria
Urban expansion has made thermal conditions a significant concern in the city of Oran. The daily dynamics of transportation and industrial activities can result in high temperatures, which can cause stress for residents, particularly during the summer. In this study, Landsat 8 data were used to extr...
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Published in: | Theoretical and applied climatology 2024-08, Vol.155 (8), p.7737-7755 |
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description | Urban expansion has made thermal conditions a significant concern in the city of Oran. The daily dynamics of transportation and industrial activities can result in high temperatures, which can cause stress for residents, particularly during the summer. In this study, Landsat 8 data were used to extract Land Surface Temperature (LST) for July 18, 2015, and July 15, 2020. Anthropogenic, microclimatic, and atmospheric pollutant variables and a Random Forest (RF) model were employed to predict temperatures for 2025. The results revealed that 26% of the study area is characterized by low temperatures that do not exceed 33 °C; this area consists mainly of forests and water surfaces. 25% exhibit extreme temperatures exceeding 42 °C, with the industrial zone and port of Oran being the main heat sources. Additionally, with 48% of the study area, built-up areas and bare land are characterized by mean temperatures ranging between 33.87 °C and 42.28 °C. With a mean temperature of 37.27 °C, the simulation for 2025 shows that temperatures are expected to decrease by 0.53 °C, with forests and water surfaces being the main classes. Our findings provide valuable information on the future thermal balance of cities and can assist planners in designing more effective medium and long-term policies from both environmental and tourism perspectives. |
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The daily dynamics of transportation and industrial activities can result in high temperatures, which can cause stress for residents, particularly during the summer. In this study, Landsat 8 data were used to extract Land Surface Temperature (LST) for July 18, 2015, and July 15, 2020. Anthropogenic, microclimatic, and atmospheric pollutant variables and a Random Forest (RF) model were employed to predict temperatures for 2025. The results revealed that 26% of the study area is characterized by low temperatures that do not exceed 33 °C; this area consists mainly of forests and water surfaces. 25% exhibit extreme temperatures exceeding 42 °C, with the industrial zone and port of Oran being the main heat sources. Additionally, with 48% of the study area, built-up areas and bare land are characterized by mean temperatures ranging between 33.87 °C and 42.28 °C. With a mean temperature of 37.27 °C, the simulation for 2025 shows that temperatures are expected to decrease by 0.53 °C, with forests and water surfaces being the main classes. 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The daily dynamics of transportation and industrial activities can result in high temperatures, which can cause stress for residents, particularly during the summer. In this study, Landsat 8 data were used to extract Land Surface Temperature (LST) for July 18, 2015, and July 15, 2020. Anthropogenic, microclimatic, and atmospheric pollutant variables and a Random Forest (RF) model were employed to predict temperatures for 2025. The results revealed that 26% of the study area is characterized by low temperatures that do not exceed 33 °C; this area consists mainly of forests and water surfaces. 25% exhibit extreme temperatures exceeding 42 °C, with the industrial zone and port of Oran being the main heat sources. Additionally, with 48% of the study area, built-up areas and bare land are characterized by mean temperatures ranging between 33.87 °C and 42.28 °C. With a mean temperature of 37.27 °C, the simulation for 2025 shows that temperatures are expected to decrease by 0.53 °C, with forests and water surfaces being the main classes. 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The daily dynamics of transportation and industrial activities can result in high temperatures, which can cause stress for residents, particularly during the summer. In this study, Landsat 8 data were used to extract Land Surface Temperature (LST) for July 18, 2015, and July 15, 2020. Anthropogenic, microclimatic, and atmospheric pollutant variables and a Random Forest (RF) model were employed to predict temperatures for 2025. The results revealed that 26% of the study area is characterized by low temperatures that do not exceed 33 °C; this area consists mainly of forests and water surfaces. 25% exhibit extreme temperatures exceeding 42 °C, with the industrial zone and port of Oran being the main heat sources. Additionally, with 48% of the study area, built-up areas and bare land are characterized by mean temperatures ranging between 33.87 °C and 42.28 °C. 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subjects | Air pollution Algeria Anthropogenic factors Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences Cities Climatology Earth and Environmental Science Earth Sciences heat Heat sources High temperature humans Industrial areas Land surface temperature Landsat Low temperature Mean temperatures microclimate pollutants prediction Random variables Remote sensing summer Surface temperature Temperature effects Temperature extremes Thermal comfort Tourism transportation Urban sprawl urbanization Waste Water Technology Water Management Water Pollution Control |
title | Assessment and prediction of land surface temperature effects on human thermal comfort in the city of Oran, Algeria |
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