Assessing green space exposure: From traditional metrics to the Green Exposure Index (GEI) with application to a Northern Italy residential dataset

Urban green areas contribute to healthier cities by improving air quality, promoting physical activity and social cohesion, and mitigating the urban heat island effect. However, assessing exposure to green spaces remains a key methodological challenge in epidemiologic research. In this study, we compared traditional green space indices and developed a composite Green Exposure Index (GEI) integrating vegetation cover, density and accessibility to improve exposure assessment. We applied this new index in a population based amyotrophic lateral sclerosis (ALS) case-control dataset from a Northern Italy community. The GEI consists of three components: NDVI, the Green Coverage Ratio and an accessibility index defined for this application. We computed these components for all residential locations across an 8400 km2 domain from 1985 to 2020. Seasonal NDVI better captured vegetation patterns than annual values, and spatial aggregation restricted to vegetated areas reduced the overestimation associated with circular buffers. The GEI was evaluated under three illustrative weighting scenarios, which produced substantial differences in exposure classification and confirmed that metric choice strongly influences results. In our case study, the equally weighted GEI3 placed 79.7% of the population in the intermediate Mildly Exposed and Exposed categories, resulting in a balanced distribution better suited for epidemiologic analysis. Analysis of GEI time series revealed green space exposure changes from 1985 to 2020, identifying areas characterized by urbanization or green redevelopment. Findings from this case study show the added value of composite indices like the GEI for characterizing green space exposure and capturing long-term dynamics in vegetation and land use, with applications in epidemiology and urban planning.