Ozone stress as a driving force of sesquiterpene emissions: a suggested parameterisation

Abstract

Abstract. Sesquiterpenes (C15H24) are semi-volatile organic compounds emitted by vegetation and are of interest in atmospheric research because they influence the oxidative capacity of the atmosphere and contribute to the formation of secondary organic aerosols. However, little is known about their emission pattern and no established parameterisation is available for global emission models. The aim of this study is to investigate a Central European spruce forest and its emission response to meteorological and environmental parameters, looking for a parameterisation that incorporates heat and oxidative stress as the main driving forces of the induced emissions. Therefore, a healthy ca. 80 yr old Norway spruce (Picea abies) tree was selected and a dynamical vegetation enclosure technique was applied from April to November 2011. The emissions clearly responded to temperature changes with small variations in the β-factor along the year (βspring = 0.09 ± 0.01, βsummer = 0.12 ± 0.02, βautumn = 0.11 ± 0.02). However, daily calculated values revealed a vast amount of variability in temperature dependencies ((0.02 ± 0.002) < β < (0.27 ± 0.04)) with no distinct seasonality. By separating the complete dataset in 10 different ozone regimes, we found that in moderately or less polluted atmospheric conditions the main driving force of sesquiterpene emissions is the temperature, but when ambient ozone mixing ratios exceed a critical threshold of (36.6 ± 3.9) ppbv, the emissions become primarily correlated with ozone. Considering the complete dataset, cross correlation analysis resulted in highest correlation with ambient ozone mixing ratios (CCO3 = 0.63 ± 0.01; CCT = 0.47 ± 0.02 at t = 0 h for temperature) with a time shift 2–4 h prior to the emissions. An only temperature dependent algorithm was found to substantially underestimate the induced emissions (20% of the measured; R2 = 0.31). However, the addition of an ozone dependent term improved substantially the fitting between measured and modelled emissions (81% of the modelled emissions could be explained by the measurements; R2 = 0.63), providing confidence about the reliability of the suggested parameterisation for the spruce forest site investigated.