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Air pollution abatement from Green-Blue-Grey infrastructure

  • Prashant Kumar
  • , Karina Corada
  • , Sisay E. Debele
  • , Ana Paula Mendes Emygdio
  • , K. V. Abhijith
  • , Hala Hassan
  • , Parya Broomandi
  • , Richard Baldauf
  • , Nerea Calvillo
  • , Shi Jie Cao
  • , Sylvane Desrivières
  • , Zhuangbo Feng
  • , John Gallagher
  • , Thomas Rodding Kjeldsen
  • , Anwar Ali Khan
  • , Mukesh Khare
  • , Sri Harsha Kota
  • , Baizhan Li
  • , Shelagh K. Malham
  • , Aonghus McNabola
  • Anil Namdeo, Arvind Kumar Nema, Stefan Reis, S. M.Shiva Nagendra, Abhishek Tiwary, Sotiris Vardoulakis, Jannis Wenk, Fang Wang, Junqi Wang, Darren Woolf, Runming Yao, Laurence Jones
  • University of Surrey
  • Trinity College Dublin
  • Southeast University
  • University of East London
  • Maynooth University
  • Nazarbayev University
  • United States Environmental Protection Agency
  • University of Warwick
  • King's College London
  • Delhi Pollution Control Committee
  • Indian Institute of Technology Kanpur
  • Joint International Research Laboratory of Green Buildings and Built Environments, MOE
  • Bangor University
  • Northumbria University
  • Centre For Ecology & Hydrology
  • De Montfort University
  • University of Canberra
  • Chinese Academy of Sciences
  • University of the Chinese Academy of Sciences
  • Wirth Research Ltd
  • University of Reading
  • Liverpool Hope University

Research output: Contribution to journalReview articlepeer-review

33   Link opens in a new tab Citations (SciVal)

Abstract

Green-blue-grey infrastructure (GBGI) offers environmental benefits in urban areas, yet its impact on air pollution is under-researched, and the literature fragmented. This review evaluates quantitative studies on GBGI's capability to mitigate air pollution, compares their specific pollutant removal processes, and identifies areas for further investigation. Of the 51 GBGI types reviewed, only 22 provided quantitative pollution reduction data. Street trees and mixed-GBGI are the most studied GBGIs, with efficacy influenced by wind, GBGI type vegetation characteristics, and urban morphology. Negative percentages denote worsening air quality, while positive reflect improvement. The 22 different GBGI grouped into eight main categories provide an average (± s.d.) reduction in air pollution of 16 ± 21%, with substantial reduction shown by linear features (23 ± 21%), parks (22 ± 34%), constructed GI (14 ± 25%), and other non-sealed urban areas (14 ± 20%). Other individual GBGI reducing air pollutants include woodlands (21 ± 38%), hedges (14 ± 25%), green walls (14 ± 27%), shrubland (12 ± 20%), green roofs (13 ± 23%), parks (9±36%), and mixed-GBGI (7 ± 23 %). On average, GBGI reduced PM1, PM2.5, PM10, UFP and BC by 13 ± 21%, 1 ± 25%, 7 ± 42%, 27 ± 27%, and 16 ± 41%, respectively. GBGI also lowered gaseous pollutants CO, O3 and NOx by 10 ± 21%, 7 ± 21%, and 12 ± 36%, on average, respectively. Linear (e.g., street trees and hedges) and constructed (e.g., green walls) features can impact local air quality, positively or negatively, based on the configuration and density of the built environment. Street trees generally showed adverse effects in street canyons and beneficial outcomes in open-road conditions. Climate change could worsen air pollution problems and impact GBGI effectiveness by shifting climate zones. In Europe and China, climate shifts are anticipated to affect 8 of the 22 GBGIs, with the rest expected to remain resilient. Despite GBGI's potential to enhance air quality, the meta-analysis highlights the need for a standardised reporting structure or to enable meaningful comparisons and effectively integrate findings into urban pollution and climate strategies.

Original languageEnglish
Article number100100
JournalInnovation Geoscience
Volume2
Issue number4
Early online date10 Dec 2024
DOIs
Publication statusPublished - 10 Dec 2024

Acknowledgements

The authors thank the team members of the GCARE (Sarkawt Hama, Leticia Figueiredo Candido, Patrick Connerton, Robiel E Manzueta Felix, Soheila Khalili) for helping with the data extraction and the participants of the RECLAIM Network Plus Horizon Scanning Workshop.

Funding

This work has been commissioned by the UKRI (EPSRC, NERC, AHRC) funded RECLAIM Network Plus project (EP/W034034/1; EP/W033984) under its ‘synthesis review’ series. The following authors acknowledge the funding received through their grants: PK and LJ (NE/X002799/1; NE/X002772/1); LJ (H2020 REGREEN, EU Grant agreement No. 821016); PB (Collaborative Research Project grant, Nazarbayev University, Project No. 211123CRP1604); AT (Research England, Grant No. 0070.02.19), SV (NHMRC Special Initiative in Human Health and Environmental Change, Grant No. 2008937).

Funders
Engineering and Physical Sciences Research Council

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities
  2. SDG 13 - Climate Action
    SDG 13 Climate Action

ASJC Scopus subject areas

  • Earth and Planetary Sciences (miscellaneous)
  • Environmental Science (miscellaneous)

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