Inferring the source strength of isoprene from ambient concentrations
Inferring the source strength of isoprene from ambient concentrations
This paper reports on the application of an inverse Lagrangian technique that uses localized near-field (LNF) theory to calculate the source strength profile of isoprene from deciduous forest canopies. The basic tenet considered in this study is that the prevailing ambient isoprene concentrations observed over forests represent the source strength of the underlying surface as the scalar is transported from the sites of biosynthesis to the measurement point above forest canopies. Using information on the distribution of active isoprene biomass and the plant canopy environment, a two-storey canopy model was developed and applied to estimate isoprene emission rate profiles for a monoculture aspen forest whose isoprene source is homogeneously distributed throughout the landscape. Modelled results show that isoprene emission rates strongly vary with canopy depth, with maximum values coinciding with canopy layers with largest amount of active biomass. The model also captures the strong diurnal patterns of isoprene emissions from the forest canopy. We conclude that the present modelling system provides a practical method for estimating isoprene emission rate profiles based on the knowledge of atmospheric turbulence and ambient concentrations.
isoprene, source strength, lnf, inverse lagrangian model, emission rate, secondary organic aerosols
1281-1293
Tiwary, Abhishek
f948aa5f-5c42-42b0-8bcb-d8ea5e0d58f8
Fuentes, José D.
ec375b2b-4004-4fcc-8fc4-ceb9e6908e0e
Barr, Jordan G.
7c88abc1-af28-4f47-9424-9392fb7645c5
Wang, Daniel
80693fe4-649c-4cda-9ed1-9659488cc7d3
Colls, Jeremy J.
db25acee-6679-413a-b47d-93f361fc0591
September 2007
Tiwary, Abhishek
f948aa5f-5c42-42b0-8bcb-d8ea5e0d58f8
Fuentes, José D.
ec375b2b-4004-4fcc-8fc4-ceb9e6908e0e
Barr, Jordan G.
7c88abc1-af28-4f47-9424-9392fb7645c5
Wang, Daniel
80693fe4-649c-4cda-9ed1-9659488cc7d3
Colls, Jeremy J.
db25acee-6679-413a-b47d-93f361fc0591
Tiwary, Abhishek, Fuentes, José D., Barr, Jordan G., Wang, Daniel and Colls, Jeremy J.
(2007)
Inferring the source strength of isoprene from ambient concentrations.
Environmental Modelling & Software, 22 (9), .
(doi:10.1016/j.envsoft.2006.09.002).
Abstract
This paper reports on the application of an inverse Lagrangian technique that uses localized near-field (LNF) theory to calculate the source strength profile of isoprene from deciduous forest canopies. The basic tenet considered in this study is that the prevailing ambient isoprene concentrations observed over forests represent the source strength of the underlying surface as the scalar is transported from the sites of biosynthesis to the measurement point above forest canopies. Using information on the distribution of active isoprene biomass and the plant canopy environment, a two-storey canopy model was developed and applied to estimate isoprene emission rate profiles for a monoculture aspen forest whose isoprene source is homogeneously distributed throughout the landscape. Modelled results show that isoprene emission rates strongly vary with canopy depth, with maximum values coinciding with canopy layers with largest amount of active biomass. The model also captures the strong diurnal patterns of isoprene emissions from the forest canopy. We conclude that the present modelling system provides a practical method for estimating isoprene emission rate profiles based on the knowledge of atmospheric turbulence and ambient concentrations.
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e-pub ahead of print date: 22 January 2007
Published date: September 2007
Keywords:
isoprene, source strength, lnf, inverse lagrangian model, emission rate, secondary organic aerosols
Organisations:
Civil Maritime & Env. Eng & Sci Unit
Identifiers
Local EPrints ID: 354507
URI: http://eprints.soton.ac.uk/id/eprint/354507
ISSN: 1364-8152
PURE UUID: db9980de-d31a-458e-acff-da895ba1784c
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Date deposited: 02 Aug 2013 09:25
Last modified: 14 Mar 2024 14:19
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Contributors
Author:
Abhishek Tiwary
Author:
José D. Fuentes
Author:
Jordan G. Barr
Author:
Daniel Wang
Author:
Jeremy J. Colls
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