Journal article

Assessment of high (diurnal) to low (seasonal) frequency variations of isoprene emission rates using a neural network approach

Boissard C, Chervier F, Dutot A ...see all

Atmospheric Chemistry and Physics, vol. 8, issue 7 (2008) pp. 2089-2101

  • 8


    Mendeley users who have this article in their library.
  • 5


    Citations of this article.
Sign in to save reference


Using a statistical approach based on artificial neural networks, an
emission algorithm (ISO-LF) accounting for high to low frequency
variations was developed for isoprene emission rates. ISO-LF was
optimised using a data base (ISO-DB) specifically designed for this
work, which consists of 1321 emission rates collected in the literature
and 34 environmental variables, measured or assessed using National
Climatic Data Center or National Centers for Environmental Predictions
meteorological databases. ISO-DB covers a large variety of emitters (25
species) and environmental conditions (10 degrees S to 60 degrees N).
When only instantaneous environmental regressors (instantaneous air
temperature T0 and photosynthetic photon flux density L0) were used, a
maximum of 60% of the overall isoprene variability was assessed with
the highesTemissions being strongly underestimated. ISO-LF includes a
total of 9 high (instantaneous) to low (up to 3 weeks) frequency
regressors and accounts for up to 91% of the isoprene emission
variability, whatever the emission range, species or climate
investigated. ISO-LF was found to be mainly sensitive to air temperature
cumulated over 3 weeks (T21) and to L0 and T0 variations. T21, T0 and L0
only accounts for 76% of the overall variability.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text


  • C Boissard

  • F Chervier

  • A L Dutot

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free