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<p>Rapid urban growth poses significant risks to environmental quality and public health, particularly through particulate matter (PM) pollution, yet its assessment in many cities remains constrained by sparse monitoring networks. Land Use Regression (LUR) models offer a cost-effective alternative, but their performance is often limited by restricted use of spatially explicit predictors. Remote sensing indices such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) remain underutilized, despite their potential to capture land cover and land-use dynamics influencing PM₂.₅ and PM₁₀ distributions. This study applies an LUR framework in Abuja, Nigeria, integrating ground-based PM₂.₅ and PM₁₀ measurements from nine monitoring sites over 28 sampling days in each of the dry and wet seasons with satellite-derived NDVI and NDBI to explore seasonal pollution dynamics. Results indicate pronounced seasonal variability, with PM concentrations peaking during the dry season. The highest mean PM₂.₅ (80.6 ± 14.8&#xa0;µg/m³) and PM₁₀ (89.1 ± 15.4&#xa0;µg/m³) concentrations were observed at SS5, a high commercial and traffic-intensive location, while SS2, characterized by extensive vegetation cover and low traffic, recorded the lowest levels. Seasonal greening was evident as NDVI increased from 0.0571 to 0.4125 in the dry season to 0.0109–0.7861 in the wet season, whereas NDBI values were higher in the dry season (up to 0.3487), reflecting greater built-up exposure. NDVI (negative) and NDBI (positive) jointly accounted for approximately 54% of the observed spatial variability in PM concentrations in the dry season and about 28% in the wet season; however, the associated regression coefficients were not statistically significant. All observed mean PM concentrations exceeded World Health Organization guideline limits. Overall, the findings suggest that vegetation cover and built-up intensity may contribute to PM spatial variability, but the relationships identified should be interpreted as exploratory rather than statistically robust.</p>

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Remote sensing–enhanced land use regression modelling for predicting urban particulate matter concentrations

  • 期刊论文

  • 发表日期：

    2026年3月26日

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  • Sani Abubakar Mashi,
  • Daniel Oluwatobi Ojo,
  • Sunday Ishaya

摘要

Rapid urban growth poses significant risks to environmental quality and public health, particularly through particulate matter (PM) pollution, yet its assessment in many cities remains constrained by sparse monitoring networks. Land Use Regression (LUR) models offer a cost-effective alternative, but their performance is often limited by restricted use of spatially explicit predictors. Remote sensing indices such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) remain underutilized, despite their potential to capture land cover and land-use dynamics influencing PM₂.₅ and PM₁₀ distributions. This study applies an LUR framework in Abuja, Nigeria, integrating ground-based PM₂.₅ and PM₁₀ measurements from nine monitoring sites over 28 sampling days in each of the dry and wet seasons with satellite-derived NDVI and NDBI to explore seasonal pollution dynamics. Results indicate pronounced seasonal variability, with PM concentrations peaking during the dry season. The highest mean PM₂.₅ (80.6 ± 14.8 µg/m³) and PM₁₀ (89.1 ± 15.4 µg/m³) concentrations were observed at SS5, a high commercial and traffic-intensive location, while SS2, characterized by extensive vegetation cover and low traffic, recorded the lowest levels. Seasonal greening was evident as NDVI increased from 0.0571 to 0.4125 in the dry season to 0.0109–0.7861 in the wet season, whereas NDBI values were higher in the dry season (up to 0.3487), reflecting greater built-up exposure. NDVI (negative) and NDBI (positive) jointly accounted for approximately 54% of the observed spatial variability in PM concentrations in the dry season and about 28% in the wet season; however, the associated regression coefficients were not statistically significant. All observed mean PM concentrations exceeded World Health Organization guideline limits. Overall, the findings suggest that vegetation cover and built-up intensity may contribute to PM spatial variability, but the relationships identified should be interpreted as exploratory rather than statistically robust.