Estimating the removal of atmospheric particulate pollution by the urban tree canopy of London under current and future environments
Estimating the removal of atmospheric particulate pollution by the urban tree canopy of London under current and future environments
Urban green space and particularly the tree canopy have been highlighted as offering a mitigation potential against atmospheric particulate pollution. In this paper current and future particulate (PM10) deposition to the urban tree canopy of the Greater London Authority (GLA) was estimated. A modelling approach was used based on the Urban Forest Effects Model (UFORE) and a modified version. Here we give evidence showing that these deposition models can be adapted to run from annual mean meteorological and PM10 concentration data, thus providing a methodology to examine future scenarios.
Depending on the modelling approach, the urban canopy of the GLA is currently estimated to remove between 852 and 2121 tonnes of PM10 annually; representing between 0.7% and 1.4% of PM10 from the urban boundary layer. Estimates of PM10 removal which take into account a planned increased in tree cover, from the current 20% to 30% of the GLA land area, suggest deposition of 1109–2379 tonnes (1.1–2.6% removal) by the year 2050. The evidence provided here suggests that the targeting of tree planting in the most polluted areas of the GLA and particularly the use of street trees which have the greatest exposure to PM10, would have the greatest benefit to future air quality. The increased deposition would be greatest if a larger proportion of coniferous to broadleaved trees were used at such sites.
climate change, greater london authority, pm10, ufore, air quality
Tallis, Matthew
25cbd143-1f8c-491b-9eac-f1c632dfcfda
Sinnett, Danielle
bcb4a895-45e2-4393-82ea-46855c65abf9
Freer-Smith, Peter
c4299981-dbfe-46b3-b8fe-c4b9d8803393
1 September 2011
Tallis, Matthew
25cbd143-1f8c-491b-9eac-f1c632dfcfda
Sinnett, Danielle
bcb4a895-45e2-4393-82ea-46855c65abf9
Freer-Smith, Peter
c4299981-dbfe-46b3-b8fe-c4b9d8803393
Tallis, Matthew, Taylor, Gail, Sinnett, Danielle and Freer-Smith, Peter
(2011)
Estimating the removal of atmospheric particulate pollution by the urban tree canopy of London under current and future environments.
Landscape and Urban Planning.
(doi:10.1016/j.landurbplan.2011.07.003).
Abstract
Urban green space and particularly the tree canopy have been highlighted as offering a mitigation potential against atmospheric particulate pollution. In this paper current and future particulate (PM10) deposition to the urban tree canopy of the Greater London Authority (GLA) was estimated. A modelling approach was used based on the Urban Forest Effects Model (UFORE) and a modified version. Here we give evidence showing that these deposition models can be adapted to run from annual mean meteorological and PM10 concentration data, thus providing a methodology to examine future scenarios.
Depending on the modelling approach, the urban canopy of the GLA is currently estimated to remove between 852 and 2121 tonnes of PM10 annually; representing between 0.7% and 1.4% of PM10 from the urban boundary layer. Estimates of PM10 removal which take into account a planned increased in tree cover, from the current 20% to 30% of the GLA land area, suggest deposition of 1109–2379 tonnes (1.1–2.6% removal) by the year 2050. The evidence provided here suggests that the targeting of tree planting in the most polluted areas of the GLA and particularly the use of street trees which have the greatest exposure to PM10, would have the greatest benefit to future air quality. The increased deposition would be greatest if a larger proportion of coniferous to broadleaved trees were used at such sites.
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Published date: 1 September 2011
Keywords:
climate change, greater london authority, pm10, ufore, air quality
Organisations:
Centre for Biological Sciences
Identifiers
Local EPrints ID: 196443
URI: http://eprints.soton.ac.uk/id/eprint/196443
ISSN: 0169-2046
PURE UUID: 3a5495f8-9f7e-4977-993c-9d6ff48d75d5
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Date deposited: 07 Sep 2011 10:00
Last modified: 14 Mar 2024 04:07
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Author:
Matthew Tallis
Author:
Gail Taylor
Author:
Danielle Sinnett
Author:
Peter Freer-Smith
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