“…This parameter combines air temperature and relative humidity and is more closely related to plant evaporation demands than air temperature alone. Since a water‐related effect on emission was suspected in the previous analysis of the 2000 field campaign, a parameterization of emission based on cuvette WVPD was attempted [ Núñez et al , 2002]. Figure 4 represents the 2000 and 2001 emission rates versus WVPD.…”
Section: Resultsmentioning
confidence: 99%
“…There are few experimental works concerning biogenic emissions from natural vegetation in the European Iberian Peninsula [ Pio et al , 1993; Peñuelas and Llusiá , 1999; Llusiá and Peñuelas , 2000; Sabillón and Cremades , 2001; Núñez et al , 2002]. Most of these studies have focused on both Mediterranean Spanish and Atlantic Portuguese coastal vegetation.…”
Section: Introductionmentioning
confidence: 99%
“…This work presents an experimental characterization of monoterpene emission from Q. ilex natural vegetation in the Madrid area during late summer to early autumn season using branch enclosure (2000 and 2001 field campaigns) and micrometeorological techniques (2001, 2002, and 2003 field campaigns). The 2000 field campaign included measurements over a pine species ( Pinus pinea ) and has been analyzed in detail in a previous paper [ Núñez et al , 2002]. A severe soil water deficit was considered to be responsible for the great reduction observed in emission at high temperature and low humidity.…”
[1] An experimental characterization of biogenic emission from Quercus ilex ssp. rotundifolia in a forest near Madrid, Spain, was carried out in the early autumn of the years [2000][2001][2002][2003]. A dynamic branch enclosure technique was implemented to determine the monoterpene emission rates of this evergreen oak species during the 2000 and 2001 campaigns. Major compounds emitted during both measurement periods were limonene, a-pinene, b-pinene, sabinene, and myrcene. In the 2000 field campaign the light-and temperature-dependent model of Guenther et al. [1993] did not fit the data due to drastic reductions of emission rates (and leaf gas exchange related parameters) observed at high air temperature and low air humidity (high water vapor pressure deficit). This plant physiological activity depletion and the subsequent emission reduction were attributed to severe water soil deficit conditions, as precipitation was very scarce during the growing season. In contrast, during the 2001 field campaign, neither emission nor physiological activity showed strong decreases in hot days. A good fit of experimental data to Guenther model was achieved in this field campaign (r 2 = 0.90), and linear regression gave a standard emission factor (E S ) of 14.0 mg gdw À1 h À1 (gdw is grams dry weight). Soil moisture was presumably higher than during the 2000 campaign due to recent rain events. With the purpose of documenting the drought stress effect at canopy level, monoterpene
“…This parameter combines air temperature and relative humidity and is more closely related to plant evaporation demands than air temperature alone. Since a water‐related effect on emission was suspected in the previous analysis of the 2000 field campaign, a parameterization of emission based on cuvette WVPD was attempted [ Núñez et al , 2002]. Figure 4 represents the 2000 and 2001 emission rates versus WVPD.…”
Section: Resultsmentioning
confidence: 99%
“…There are few experimental works concerning biogenic emissions from natural vegetation in the European Iberian Peninsula [ Pio et al , 1993; Peñuelas and Llusiá , 1999; Llusiá and Peñuelas , 2000; Sabillón and Cremades , 2001; Núñez et al , 2002]. Most of these studies have focused on both Mediterranean Spanish and Atlantic Portuguese coastal vegetation.…”
Section: Introductionmentioning
confidence: 99%
“…This work presents an experimental characterization of monoterpene emission from Q. ilex natural vegetation in the Madrid area during late summer to early autumn season using branch enclosure (2000 and 2001 field campaigns) and micrometeorological techniques (2001, 2002, and 2003 field campaigns). The 2000 field campaign included measurements over a pine species ( Pinus pinea ) and has been analyzed in detail in a previous paper [ Núñez et al , 2002]. A severe soil water deficit was considered to be responsible for the great reduction observed in emission at high temperature and low humidity.…”
[1] An experimental characterization of biogenic emission from Quercus ilex ssp. rotundifolia in a forest near Madrid, Spain, was carried out in the early autumn of the years [2000][2001][2002][2003]. A dynamic branch enclosure technique was implemented to determine the monoterpene emission rates of this evergreen oak species during the 2000 and 2001 campaigns. Major compounds emitted during both measurement periods were limonene, a-pinene, b-pinene, sabinene, and myrcene. In the 2000 field campaign the light-and temperature-dependent model of Guenther et al. [1993] did not fit the data due to drastic reductions of emission rates (and leaf gas exchange related parameters) observed at high air temperature and low air humidity (high water vapor pressure deficit). This plant physiological activity depletion and the subsequent emission reduction were attributed to severe water soil deficit conditions, as precipitation was very scarce during the growing season. In contrast, during the 2001 field campaign, neither emission nor physiological activity showed strong decreases in hot days. A good fit of experimental data to Guenther model was achieved in this field campaign (r 2 = 0.90), and linear regression gave a standard emission factor (E S ) of 14.0 mg gdw À1 h À1 (gdw is grams dry weight). Soil moisture was presumably higher than during the 2000 campaign due to recent rain events. With the purpose of documenting the drought stress effect at canopy level, monoterpene
“…On the other hand, some studies have reported exceptionally high emissions not substantiated by other investigations. For instance, Owen et al (2002) report significant isoprene emissions from Mediterranean Pinus pinea, in contrast to all other studies (Nuñez et al, 2002;Sabillón and Cremades, 2001;Staudt et al, 1997Staudt et al, , 2000. Additional errors were associated with emission rates assigned based on studies that employed semi-quantitative or non-quantitative techniques.…”
The capacity for volatile isoprenoid production under standardized environmental conditions (<i>E</i><sub>S</sub>), the emission factor) is a key characteristic in constructing isoprenoid emission inventories. However, there is large variation in published <i>E</i><sub>S</sub> estimates for any given species, and this variation leads to significant uncertainties in emission predictions. We review the sources of variation in <i>E</i><sub>S</sub> that are due to measurement and analytical techniques and calculation and averaging procedures. This review demonstrates that estimations of <i>E</i><sub>S</sub> critically depend on applied experimental protocols and on data processing and reporting. A great variety of experimental setups has been used in the past, contributing to study-to-study variations in <i>E</i><sub>S</sub> estimates. We suggest that past experimental data should be distributed into broad quality classes depending on whether the data can or cannot be considered quantitative based on rigorous experimental standards. Apart from analytical issues, the accuracy of <i>E</i><sub>S</sub> values is strongly driven by extrapolation and integration errors introduced during data processing. Additional sources of error, especially in meta-database construction, can further arise from inconsistent use of units and expression bases of <i>E</i><sub>S</sub>. We propose a standardized experimental protocol for BVOC estimations and highlight basic meta-information that we strongly recommend to report with any <i>E</i><sub>S</sub> measurement. We conclude that standardization of experimental and calculation protocols and critical examination of past reports is essential for development of accurate emission factor databases
“…Air humidity is known to be a controlling factor of stomatal conductance and transpiration. Some authors have argued and demonstrated that water vapour pressure (Núñez et al . 2002) and/or relative humidity (Llusià & Peñuelas 1999) could also be a driving factor for isoprene and monoterpene emission.…”
The ability to predict isoprene emissions from plants is important for predicting atmospheric chemistry. To improve the basis for prediction capability, data obtained from continuous field measurements of isoprene and monoterpene emissions from three Amazonian tree species were related to observed environmental and leaf physiological parameters using a new neural network approach. The environmental parameters included leaf temperature, light, relative humidity, water vapour pressure deficit, and the history of ambient temperature and ozone concentration, whereas the physiological parameters included stomatal conductance, assimilation and intercellular CO 2 concentration. The neural approach with 24 different combinations of these parameters was applied to predict the emission variability observed during short time periods (2-3 d) with individual tree branches and, on a longer-term scale, in aggregated data sets from different seasons, leaf developmental stage, and light environment. The results were compared to the quasi standard emission algorithm for isoprene. On the short-term scale, good agreement ( r 2 ª ª ª ª 0.9) was obtained between observations and predictions of the standard algorithm as well as predictions of the neural network using the same input parameters (leaf temperature and light). When these predictors were used to model the long-term emission variability, r 2 was reduced to < 0.5 for both approaches. Remarkably, for the neural technique, more than 50% of the unexplained variance could be explained by the mean temperature of the preceding 36 h. An even better network performance was obtained with physiological parameter combinations ( r 2 > 0.9) suggesting a strong and applicable link between isoprenoid emission and leaf primary metabolism.
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