Modeling the isoprene emission rate from leaves

Contents

	Summary	541
I.	Introduction	542
II.	The biochemical control over isoprene emission rate	542
III.	General forms of the models used to predict the leaf isoprene emission rate	543
IV.	Modeling the short-term responses to photon flux density	545
V.	Resolving problems with the current Guenther algorithm covering the PPFD-dependence of Ei	546
VI.	The temperature dependence of isoprene emission rate	547
VII.	Clarifying issues with the current Guenther algorithm covering the temperature-dependence of Ei	549
VIII.	The CO2 dependence of the isoprene emission rate	549
IX.	Modeling the relation between isoprene emission and leaf conductance	551
X.	Modeling the longer-term processes that control isoprene emission rate	552
XI.	Conclusions	556
	References	556

Summary

The leaves of many plants emit isoprene (2-methyl-1,3-butadiene) to the atmosphere, a process which has important ramifications for global and regional atmospheric chemistry. Quantitation of leaf isoprene emission and its response to environmental variation are described by empirically derived equations that replicate observed patterns, but have been linked only in some cases to known biochemical and physiological processes. Furthermore, models have been proposed from several independent laboratories, providing multiple approaches for prediction of emissions, but with little detail provided as to how contrasting models are related. In this review we provide an analysis as to how the most commonly used models have been validated, or not, on the basis of known biochemical and physiological processes. We also discuss the multiple approaches that have been used for modeling isoprene emission rate with an emphasis on identifying commonalities and contrasts among models, we correct some mathematical errors that have been propagated through the models, and we note previously unrecognized covariances within processes of the models. We come to the conclusion that the state of isoprene emission modeling remains highly empirical. Where possible, we identify gaps in our knowledge that have prevented us from achieving a greater mechanistic foundation for the models, and we discuss the insight and data that must be gained to fill those gaps.