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

by C Boissard, F Chervier, A L Dutot
ATMOSPHERIC CHEMISTRY AND PHYSICS ()
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Abstract

Using a statistical approach based on artificial neural networks, an\nemission algorithm (ISO-LF) accounting for high to low frequency\nvariations was developed for isoprene emission rates. ISO-LF was\noptimised using a data base (ISO-DB) specifically designed for this\nwork, which consists of 1321 emission rates collected in the literature\nand 34 environmental variables, measured or assessed using National\nClimatic Data Center or National Centers for Environmental Predictions\nmeteorological databases. ISO-DB covers a large variety of emitters (25\nspecies) and environmental conditions (10 degrees S to 60 degrees N).\nWhen only instantaneous environmental regressors (instantaneous air\ntemperature T0 and photosynthetic photon flux density L0) were used, a\nmaximum of 60% of the overall isoprene variability was assessed with\nthe highesTemissions being strongly underestimated. ISO-LF includes a\ntotal of 9 high (instantaneous) to low (up to 3 weeks) frequency\nregressors and accounts for up to 91% of the isoprene emission\nvariability, whatever the emission range, species or climate\ninvestigated. ISO-LF was found to be mainly sensitive to air temperature\ncumulated over 3 weeks (T21) and to L0 and T0 variations. T21, T0 and L0\nonly accounts for 76% of the overall variability.

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