Spatial Heterogeneity in the Vegetation Cover–Air Quality Relationship: Evidence from a Zone-Wise Assessment of Ahmedabad, India

Abstract

Air pollution continues to pose a substantial threat to environmental sustainability and public health in rapid urbanizing cities, highlighting the need for evidence-based and nature-based mitigation strategies. Although urban vegetation has been widely recognized for its capacity to improve air quality, limited research has explored how the influence of vegetation varies across different urban zones or assessed the potential benefits of targeted greening interventions. This study addresses this gap by investigating the relationship between vegetation condition and air quality across the seven administrative zones of Ahmedabad Municipal Corporation (AMC), India. Monthly Normalized Difference Vegetation Index (NDVI) values were derived from Sentinel-2 satellite imagery using Google Earth Engine, while monthly Air Quality Index (AQI) data were obtained from the National Air Quality Index (NAQI) monitoring network for the period 2021–2025. Correlation analysis was employed to examine the association between NDVI and AQI, followed by zone-wise linear regression modelling to quantify the influence of vegetation condition on air quality. Furthermore, scenario-based sensitivity analysis was conducted to evaluate the potential impact of hypothetical changes in vegetation condition across different administrative zones. The findings demonstrate a consistent inverse relationship between NDVI and AQI across Ahmedabad, indicating that healthier and denser vegetation is generally associated with improved air quality. However, the magnitude of this relationship varies considerably among zones, revealing significant spatial heterogeneity in vegetation–air quality interactions. The regression models confirmed that the influence of vegetation on air quality is location-dependent, while the sensitivity analysis identified priority zones where improvements in vegetation condition are expected to yield comparatively greater reductions in air pollution. These findings suggest that uniform city-wide greening programmes may not produce optimal environmental outcomes and that spatially targeted interventions are likely to be more effective. By combining satellite-derived vegetation monitoring, official air-quality observations, zone-wise regression modelling, and sensitivity analysis, this study proposes a practical decision-support framework for prioritizing urban greening initiatives. The proposed methodology provides a scientifically robust and transferable approach for supporting evidence-based urban planning, sustainable environmental management, and nature-based air pollution mitigation in rapidly growing cities.