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<p>This study provides a detailed spatiotemporal assessment of atmospheric pollutants and land surface temperature (LST) in Riyadh, Saudi Arabia, from 2019 to 2024, utilizing Google Earth Engine (GEE) to process Sentinel-5P TROPOMI and MODIS MOD11A2 satellite datasets. The analyzed pollutants include sulfur dioxide (SO₂), ultraviolet aerosol index (UVAI), methane (CH₄), carbon monoxide (CO), formaldehyde (HCHO), and nitrogen dioxide (NO₂). Over the study period, SO₂ concentrations decreased from 0.000136&#xa0;mol/m² in 2019 to 0.000078&#xa0;mol/m² in 2024, while NO₂ declined from 0.00028&#xa0;mol/m² to 0.00019&#xa0;mol/m². UVAI reached a maximum of 1.6 in 2021, reflecting heightened dust activity, before stabilizing at 0.7. CO levels slightly decreased from 0.041&#xa0;mol/m² to 0.037&#xa0;mol/m², whereas CH₄ showed a small increase from 1843 ppb to 1855 ppb. O₃ remained relatively stable with an average of 0.17&#xa0;mol/m², and HCHO exhibited minor variability, peaking at 0.000041&#xa0;mol/m² in 2021. The mean LST rose from 42.45&#xa0;°C to 44.31&#xa0;°C, indicating an intensification of the urban heat effect. Correlation analysis revealed a very strong association between LST and CH₄ (<i>r</i> = 0.881), as well as strong correlations with HCHO (<i>r</i> = 0.709). Moderate correlations were observed with CO (<i>r</i> = 0.602) and NO₂ (<i>r</i> = 0.558). UVAI and SO₂ showed weaker relationships (<i>r</i> = 0.402 and 0.321, respectively). Geographically Weighted Regression (GWR) revealed significant spatial heterogeneity, with local R² values reaching up to 0.7728 for HCHO–LST relationships in densely built-up areas, and 0.725 for CO–LST in industrial zones, indicating pollutant–temperature linkages are strongest in urbanized and high-emission districts. They present the inter-related dynamics of the thermal and atmospheric conditions of an arid megacity and provide the required data for air quality policy-making and sustainable urbanization planning.</p>

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The impact of atmospheric pollutants on thermal conditions in Riyadh, saudi arabia based on google earth engine and geographically weighted regression model (GWR)

  • 期刊论文

  • 发表日期：

    2026年4月30日

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  • Saad Jubran S. Aljarbouai,
  • Fawzi Zarzoura,
  • Mahmoud El-Mewafi,
  • Sara Sameh

摘要

This study provides a detailed spatiotemporal assessment of atmospheric pollutants and land surface temperature (LST) in Riyadh, Saudi Arabia, from 2019 to 2024, utilizing Google Earth Engine (GEE) to process Sentinel-5P TROPOMI and MODIS MOD11A2 satellite datasets. The analyzed pollutants include sulfur dioxide (SO₂), ultraviolet aerosol index (UVAI), methane (CH₄), carbon monoxide (CO), formaldehyde (HCHO), and nitrogen dioxide (NO₂). Over the study period, SO₂ concentrations decreased from 0.000136 mol/m² in 2019 to 0.000078 mol/m² in 2024, while NO₂ declined from 0.00028 mol/m² to 0.00019 mol/m². UVAI reached a maximum of 1.6 in 2021, reflecting heightened dust activity, before stabilizing at 0.7. CO levels slightly decreased from 0.041 mol/m² to 0.037 mol/m², whereas CH₄ showed a small increase from 1843 ppb to 1855 ppb. O₃ remained relatively stable with an average of 0.17 mol/m², and HCHO exhibited minor variability, peaking at 0.000041 mol/m² in 2021. The mean LST rose from 42.45 °C to 44.31 °C, indicating an intensification of the urban heat effect. Correlation analysis revealed a very strong association between LST and CH₄ (r = 0.881), as well as strong correlations with HCHO (r = 0.709). Moderate correlations were observed with CO (r = 0.602) and NO₂ (r = 0.558). UVAI and SO₂ showed weaker relationships (r = 0.402 and 0.321, respectively). Geographically Weighted Regression (GWR) revealed significant spatial heterogeneity, with local R² values reaching up to 0.7728 for HCHO–LST relationships in densely built-up areas, and 0.725 for CO–LST in industrial zones, indicating pollutant–temperature linkages are strongest in urbanized and high-emission districts. They present the inter-related dynamics of the thermal and atmospheric conditions of an arid megacity and provide the required data for air quality policy-making and sustainable urbanization planning.