ArticleEnvironmental science & technology2026
Reassessing the Heterogeneous Effects of Greenspace on Air Pollution Conditioned on Seasons, Vegetation Types, and Street Structures.
Article in Environmental science & technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Urban greenspace is generally assumed to mitigate air pollution. However, clear large-scale real-world evidence remains limited, particularly regarding how its effects vary with seasons, vegetation types, and street structures. To bridge this gap, this study integrates 4.254 million historical street view images, 1.894 million records of mobile monitoring air pollution data, and three-dimensional urban morphology data in Hong Kong, employing a double machine learning (DML) framework to disentangle the complex effects of urban greenspace. The results show that the DML framework provides more robustness and less biased estimates than conventional models. The estimated greenspace's effects vary substantially across vegetation types and seasons: grass reduced PM
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