Reassessing the Heterogeneous Effects of Greenspace on Air Pollution Conditioned on Seasons, Vegetation Types, and Street Structures
Year: 2026
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide 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 2.5 levels in spring (θ = −8.464) and autumn (θ = −6.506); trees increased PM 2.5 levels in summer (θ = 0.762); and mid-level vegetation showed opposite effects in spring (θ = −2.275) and winter (θ = 3.480). Mechanistically, these differences reflect the relative magnitudes of biogenic volatile organic compounds (BVOC) emissions, deposition processes, and aerodynamic effects. Moreover, the street height-to-width ratio significantly moderates greenspace’s effects due to the ventilation alteration within street canyons. These insights advance current knowledge and offer an evidence base for policy-making in urban greenspace construction and renewal and air pollution control.
Authors: Zhenchuan Yang, Mei-Po Kwan, Yan Zhang
Institution: Chinese University of Hong Kong
Language: English
Topic: Urban Green Space and Health
Subfield: Health, Toxicology and Mutagenesis
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
OpenAlex ID: W7162561184