Multiscale Analysis of Green Infrastructure Impacts on PM2.5 and PM10 Pollution in Delhi, India - Kumar, Atul; Mukherjee , Mahua; Goswami, Ajanta; Saxena, Nishant; Rahul, Aditya (Geographica Pannonica, 2025)
Urban air pollution, particularly from fine particulate matter (PM2.5 and PM10), poses critical environmental and public health challenges in rapidly urbanizing regions. This study presents a multiscale, seasonal analysis of the relationship between Green Infrastructure (GI) landscape characteristics and PM concentrations in Delhi, India. Using high-resolution Sentinel-2 imagery (2019–2021) and air quality data from 39 Central Pollution Control Board (CPCB) monitoring stations, we quantified 15 GI characteristics across five spatial scales (0.5–2.5 km) using NDVI. Empirical Bayesian Kriging was applied for spatial interpolation of PM values, and Otsu’s thresholding was used to delineate vegetated areas. Principal Component Analysis (PCA) and regression models revealed that compositional metrics—such as Class Area (CA) and Percentage of Landscape (PLAND)—showed consistent negative correlations with PM2.5 and PM10 levels across all scales and seasons. Configuration metrics, including Largest Patch Index (LPI), Edge Density (ED), and Aggregation Index (AI), exhibited scale- and season-specific influences, with stronger effects observed at broader spatial scales during winter and autumn. The findings suggest that both the quantity and spatial arrangement of urban vegetation significantly affect local air quality. The study underscores the need for scale-aware, evidence-based GI planning as a nature-based solution, supporting India’s airshed-level approach to urban pollution management. These insights offer practical guidance for urban policymakers and planners aiming to enhance air quality through strategic green infrastructure design.