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<p>This study investigates the spatiotemporal distribution of Carbon Monoxide (CO) pollution and its relationship with vegetation health in Abuja Municipal Area Council, Nigeria, from mid-2018 to 2025. Using Sentinel-5P and Sentinel-2 data via Google Earth Engine, geospatial techniques (per-pixel linear regression, spatial visualization with ArcGIS Pro), statistical analyses (Pearson correlation, linear and polynomial regression), and machine learning models (Random Forest regression) were employed to examine CO concentration trends and vegetation dynamics via the Enhanced Vegetation Index (EVI). Urban hotspots (Garki 1, Kabusa, Karu, Gui) recorded peak CO levels of 0.073&#xa0;mol/m² in 2025, while suburban areas (Gwagwa, Karshi 1, Orozo) showed lower concentrations. CO levels were higher during dry seasons (0.067–0.074&#xa0;mol/m²) than wet seasons (0.046–0.053&#xa0;mol/m²), likely influenced by increased traffic, generator use, and biomass burning, as supported by seasonal trends. EVI revealed urban vegetation stress, particularly in Garki 1 and City Center 1, with a peak of 0.485 in 2025 and a low of -0.526 in 2019, indicating persistent degradation. A Pearson correlation coefficient of <i>r</i> = 0.695 (<i>p</i> = 0.055) suggests a strong but not statistically significant linear association between CO and EVI, while third-degree polynomial regression (R² = 0.5502) and Random Forest regression (R² = 0.7006) indicate a non-linear relationship, with CO and its polynomial terms being key predictors in the Random Forest model. This positive correlation reflects seasonal vegetation cycles and resilient urban species, not a causal benefit of CO. The study advances Sustainable Development Goals (SDGs) 11 (Sustainable Cities and Communities), 13 (Climate Action), and 15 (Life on Land) but is limited by unaccounted confounders like soil moisture. It recommends real-time air quality monitoring, urban greening and cross-regional studies to support sustainable urban planning and climate resilience.</p>

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Spatiotemporal insights on carbon monoxide pollution and vegetation health dynamics in abuja municipal area council, Nigeria

  • 期刊论文

  • 发表日期：

    2025年10月14日

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  • Jibril Olarotimi Salawu,
  • Appollonia Aimiosino Okhimamhe,
  • Mairo Muhammed

摘要

This study investigates the spatiotemporal distribution of Carbon Monoxide (CO) pollution and its relationship with vegetation health in Abuja Municipal Area Council, Nigeria, from mid-2018 to 2025. Using Sentinel-5P and Sentinel-2 data via Google Earth Engine, geospatial techniques (per-pixel linear regression, spatial visualization with ArcGIS Pro), statistical analyses (Pearson correlation, linear and polynomial regression), and machine learning models (Random Forest regression) were employed to examine CO concentration trends and vegetation dynamics via the Enhanced Vegetation Index (EVI). Urban hotspots (Garki 1, Kabusa, Karu, Gui) recorded peak CO levels of 0.073 mol/m² in 2025, while suburban areas (Gwagwa, Karshi 1, Orozo) showed lower concentrations. CO levels were higher during dry seasons (0.067–0.074 mol/m²) than wet seasons (0.046–0.053 mol/m²), likely influenced by increased traffic, generator use, and biomass burning, as supported by seasonal trends. EVI revealed urban vegetation stress, particularly in Garki 1 and City Center 1, with a peak of 0.485 in 2025 and a low of -0.526 in 2019, indicating persistent degradation. A Pearson correlation coefficient of r = 0.695 (p = 0.055) suggests a strong but not statistically significant linear association between CO and EVI, while third-degree polynomial regression (R² = 0.5502) and Random Forest regression (R² = 0.7006) indicate a non-linear relationship, with CO and its polynomial terms being key predictors in the Random Forest model. This positive correlation reflects seasonal vegetation cycles and resilient urban species, not a causal benefit of CO. The study advances Sustainable Development Goals (SDGs) 11 (Sustainable Cities and Communities), 13 (Climate Action), and 15 (Life on Land) but is limited by unaccounted confounders like soil moisture. It recommends real-time air quality monitoring, urban greening and cross-regional studies to support sustainable urban planning and climate resilience.