تحلیل عدالت فضایی در توزیع فضای سبز و همپوشانی آن با آلایندگی مونوکسید کربن در محلات کلانشهر تبریز

Extended Abstract

Introduction

Urban environmental justice has emerged as a critical concern in rapidly developing cities, especially in contexts where spatial inequality intersects with ecological vulnerability. In Iranian metropolises such as Tabriz, the uneven distribution of urban green space and disproportionate exposure to air pollutants like carbon monoxide (CO) reflect deep-rooted disparities in urban planning and resource allocation. Green spaces are not merely aesthetic or recreational assets; they serve as vital ecological infrastructures that regulate microclimates, absorb pollutants, and promote physical and mental well-being. However, when access to these spaces is skewed across neighborhoods, it raises pressing questions about spatial justice and the right to a healthy urban environment. This study investigates the spatial distribution of urban green space in Tabriz and examines its correlation with CO pollution levels across neighborhoods. By integrating remote sensing data, spatial statistical techniques, and inequality metrics, the research aims to identify patterns of environmental injustice and propose a replicable framework for urban equity analysis.

Data and Methods

The study adopts a multi-layered analytical approach combining geospatial analysis, statistical modeling, and environmental assessment. Data sources include: Population data from the latest national census, aggregated at the neighborhood level. Land-use maps identifying urban parks and green infrastructure. Sentinel-2 satellite imagery for calculating the Normalized Difference Vegetation Index (NDVI), a proxy for green space density. Sentinel-5P satellite data for measuring carbon monoxide (CO) concentrations as an indicator of urban air pollution.

To assess inequality in green space distribution, the Lorenz curve and Gini coefficient were employed. These metrics quantify the degree of concentration and disparity in green space allocation relative to population distribution. Spatial autocorrelation was analyzed using Global Moran’s I and Local Moran’s I, which detect clustering patterns and spatial outliers. Additionally, Lee’s L-index was used to examine the spatial association between NDVI and CO levels, offering insight into the co-location of vegetation and pollution. All spatial analyses were conducted in ArcGIS Pro, supplemented by statistical validation in R and Python environments to ensure reproducibility and robustness.

Findings

The results reveal stark disparities in the distribution of green space across Tabriz neighborhoods. Specifically: Only 10% of neighborhoods account for over 50% of the city’s total green space, indicating a highly concentrated allocation. The Gini coefficient for green space was calculated at 0.7066, signifying severe inequality. In contrast, the Gini coefficient for population was 0.1965, suggesting a relatively balanced demographic spread. NDVI analysis showed that vegetation cover is clustered and uneven, with significant variation between peripheral and central districts. CO pollution data also exhibited spatial clustering, with elevated concentrations in densely populated central areas and industrial zones. Spatial correlation analysis using Lee’s L-index identified distinct zones of environmental injustice: HH zones (High NDVI, High CO) were found in the northwestern neighborhoods and areas near the airport, indicating green space presence but high pollution exposure—possibly due to traffic and industrial activity. LH zones (Low NDVI, High CO) were concentrated in central urban districts, where population density is high, vegetation is scarce, and pollution levels are elevated. These areas represent critical hotspots of spatial and environmental vulnerability.

The juxtaposition of green space scarcity and pollution exposure in central neighborhoods underscores a compounded form of environmental injustice. Residents in these areas not only lack access to ecological benefits but are also disproportionately burdened by health risks associated with air pollution. The findings challenge conventional urban planning paradigms that prioritize aesthetic greening in affluent districts while neglecting the ecological needs of marginalized communities. Moreover, the significant spatial association between NDVI and CO levels suggests that vegetation cover may play a role in modulating pollution concentrations. However, the presence of HH zones indicates that green space alone is insufficient to mitigate pollution in areas with high vehicular or industrial emissions. This calls for integrated urban strategies that combine ecological restoration with emission control and equitable resource distribution. The methodological framework—combining remote sensing, spatial statistics, and inequality metrics—demonstrates a scalable and adaptable approach for assessing spatial justice in other urban contexts. It also highlights the importance of using context-sensitive indicators that reflect local climatic, cultural, and infrastructural realities rather than relying solely on global benchmarks.

Conclusion

This study provides a comprehensive analysis of spatial justice in urban green space distribution and pollution exposure in Tabriz. The findings reveal a dual burden of ecological deprivation and environmental risk in specific neighborhoods, emphasizing the need for data-driven, equity-oriented urban planning. Policymakers should prioritize interventions in LH zones, where residents face the greatest environmental and health challenges. By integrating satellite data, spatial analysis, and inequality assessment, the research offers a replicable model for diagnosing urban environmental injustice. Future studies should expand this framework to include other pollutants, socio-economic indicators, and longitudinal data to capture temporal dynamics of urban inequality. Ultimately, achieving spatial justice requires not only technical precision but also a commitment to inclusive and sustainable urban governance.