the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Mar 2024
Impact of meteorological conditions on BVOC emission rate from Eastern Mediterranean vegetation under drought
Abstract. A comprehensive characterization of drought’s impact on biogenic volatile organic compounds (BVOC) emissions is essential for understanding atmospheric chemistry under global climate change, with implications for both air quality and climate model simulation. Currently, the effects of drought on BVOC emissions are not well characterized. Our study aims to test: i) whether instantaneous changes in meteorological conditions can serve as a better proxy for drought-related changes in BVOC emission compared to the absolute values of the meteorological parameters, as indicated in a companion article based on BVOC mixing-ratio measurements; ii) the impact of a plant under drought stress receiving a small amount of precipitation on BVOC emission rate, and on the manner in which the emission rate is influenced by meteorological parameters. To address these objectives, we conducted our study during the warm and dry summer conditions of the Eastern Mediterranean region, focusing on the impact of drought on BVOC emissions from natural vegetation. Specifically, we conducted branch-enclosure sampling measurements in Ramat Hanadiv Nature Park, both under natural drought and after irrigation (equivalent to 5.5–7 mm precipitation), for six selected branches of Phillyrea latifolia, the highest BVOC emitter in this park, in September–October 2020. The samplings were followed by gas chromatography-mass spectrometry analysis for BVOCs identification and flux quantification. The results corroborate the finding that instantaneous changes in meteorological parameters, particularly relative humidity (RH), offer the most accurate proxy for BVOC emission rates under drought, compared to the absolute values of either temperature (T) or RH. However, after irrigation, the correlation of the detected BVOC emission rate with the instantaneous changes in RH became significantly more moderate, or even reversed. Our findings highlight that under drought, the instantaneous changes in RH, and to a lesser extent in T, are the best proxy for the emission rate of monoterpenes (MTs) and sesquiterpenes (SQTs), whereas under moderate drought conditions, T or RH serves as the best proxy for MT and SQT emission rate, respectively. In addition, the detected emission rates of MTs and SQTs increased by 150 % and 545 %, respectively, after a small amount of irrigation.
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RC1: 'Comment on egusphere-2024-529', Anonymous Referee #1, 17 Apr 2024
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Li et al., in their article "Impact of meteorological conditions on BVOC emission rate from Eastern Mediterranean vegetation under drought", report the results from a field experiment conducted in Ramat Hanadiv Nature Park (Israel) to investigate the effect of drought on BVOC emissions from vegetation. They selected the species Phillyrea latifolia for their study. BVOC emissions from six branches were analysed between September and October 2020. After collecting BVOC emissions under natural drought conditions for a day, the plants were irrigated with a moderate amount of water, corresponding to about 5.5–7 mm of precipitation and BVOC emissions were again sampled on the following day.
BVOC emissions of Phillyrea latifolia were dominated by monoterpenes (MTs) and sesquiterpenes (SQTs) were also detected. The authors found that during the natural drought period, BVOC emissions were better correlated with changes in environmental parameters (especially relative humidity, RH), rather than with their absolute values.The manuscript is well-structured and follows mostly a logical presentation. At times, some clarification would be needed. The results, even though derived only from one species, offer a framework that is potentially important to modelling of BVOC emissions under drought conditions. It is expected that similar experiments will be performed in the future with other species. I only have minor comments and a few technical comments (see below), which are mostly to clarify a few aspects of the method as well as to add to the discussion of the results. Therefore, I recommend accepting the manuscript with minor corrections to address the comments.
Minor comments:
- l. 156: The authors write that the adsorbent tube have been 'precoated', however, I assume that the tubes are filled with adsorbent materials and not only 'coated'. Is that right?
- l. 185: The authors write that 'the calibration analytes were injected [...] onto clean sorbent tubes [...] at a nitrogen flow of 80 mL min-1'. Can the authors add for how long the sorbent tubes were flushed with nitrogen after injection? And maybe the injected volume (even though it's mentioned in the supplementary material, it might be worth mentioning it here).
- ll. 208-210: The way the reference samplings were performed is unclear. The authors write that 'Prior to the reference sampling, the system and branches were given at least 60 min to adapt'. Do they mean 'after the reference sample and prior to the first sampling from the branch'? Also, they write 'After the 10th sampling on the second measurement day [...], the sampled branch was cut [...]'. Is there a reason that this was not done after the 9th sampling and before the last reference sample? Has the same branch been taken out of the Tedlar bag before the irrigation and allowed again to adapt after the first reference sampling of the second day? The schematic in Fig. 2 helps, but the authors could be more explicit about the reference samples and taking the branch in and out of the bag.
- ll. 211-216: The authors write 'Leaf net dry weight and area were evaluated within 24h', is that simply the net weight, as the drying occurs later, for 72h at 60°C?
- ll. 218-221: The authors describe which five compounds were chosen for quantification by GC-MS, stating that they are the ones with 'the highest sampled mass'. Could the author state if that is true for all individual branches or from combining all the results from all the branches? From Figs. 4 and 5, 'other' MTs and SQTs appear (see further comment below), meaning that they have also been quantified. Can the authors explain how they quantified the other compounds?
- Sect. 2.4.3: Here I have been wondering if I understand the approach correctly. Is it so that the time of the last step (n) is the time when the BVOC sampling ends? So there are three 'steps' that are happening during the sampling, but the BVOC emission rate is an average over these 30 minutes. Have the authors considered using the time in the middle of the sampling period and have 30 minutes steps? Would these lead to similar results, but with n=3? This section might need some clarification regarding the different time steps and the assumptions made.
- l. 281 (Eq. 6): Also here is it a bit unclear what is 'n' if i=1 indicates the daily minimum. Is AET measured for each emission rate following the minimum? Should AET then by definition be always positive? Or it does not have to do with the minimum around noon-time? Or is this only valid for the drought period and for the irrigation experiment it is simply the first sample after noon? Please clarify.
- Sect. 3.3: This section would benefit from reorganization. My understanding is that PCA analysis has been done for each branch individually (Fig. 6), while the Pearson's values have been averaged? Is there a specific reason why PCA could not be done on the entire dataset? Also between lines 402 and 405, the authors mention average Person's values for MTs and SQTs with respect to δRH and δT as well as for RH and T. Are the two respective values in bracket for MTs with respect to both variables and then SQTs with respect to these same two variables? This could be clarified. Also, why have the authors decided to report selected Pearson's values for the PCA and not all? They don't seem to be easily derived from Fig. 6.
- Fig. 6: In this figure, is it assumed that the x-axis is the first factor and the y-axis the second factor of the PCA analysis?
- ll. 462-465: How do the authors reconcile that while δRH is a better proxy (for drought conditions), the correlations are too weak to predict emissions. What about multiple regressions? Could that be an option rather than use δRH only with additional studies?
- ll. 496-499: Here the authors contradict themselves, calling the correlations 'strong' while previously they wrote that they were too weak to predict BVOC emissions (for drought conditions).
Technical comments:
- l. 148: CO, HC, CO2, and H2O have not been defined previously.
- Fig. 4: The legend shows 'Other monoterpene'. Is that only one other monoterpenes or various MTs? Also, the legend shows 'Medium' instead of 'Median'. Also, this figure does not include the shading in the legend, unlike Fig. 5. This could be harmonized.
- Fig. 5: The legend in this figure also has 'Medium' instead of 'Median'.
- ll. 640-642: I assume that this manuscript was submitted first, and the companion paper cited has been submitted later, as it seems that the title has changed and the year should be 2024 for the preprint. This should be changed in the final manuscript.
- Figs. S1 to S5: It should be explained somewhere what the blue and pink arrows represent.
- Section S2's title: There seems to be too many parentheses in this title.
Citation: https://doi.org/10.5194/egusphere-2024-529-RC1 -
RC2: 'Comment on egusphere-2024-529', Anonymous Referee #2, 20 Apr 2024
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Li et al present experimental findings from branch enclosure measurements of BVOC emissions from Phillyrea latifolia under drought and irrigation conditions. The precise effects of meteorological conditions, under existing drought conditions, on BVOC emissions have not been well studied and this paper makes a timely contribution. The paper is clear, thorough, well-written and within the scope of Biogeosciences. I recommend publication following clarification on the below (minor) points:
Line 132 – 133: is there any data you can cite to support the idea that Phillyrea latifolia is the greatest BVOC-contributing plant species in the park? Or is this based on MEGAN? You mention later that the species does not emit much isoprene, so presumably this is based on MT and SQT emissions?
Line 290 – 291: the soil moisture is described as “around” and “~” but then two ranges of values to 1 decimal place are given. Could this be rephrased as “soil moisture ranged between X and Y % before irrigation, and X and Y % after irrigation”.
Figure 4: On Figure 5 it’s useful to have the yellow and blue shading explained in the legend, could you add that here too?
Section 3.2.2 (Line 356): It is interesting to see from Figure 5 that post-irrigation, as well as an increase the amounts of SQT emitted, there are also some changes to the composition of compounds emitted – could you add some discussion around this?
Line 425 – 428: Is this is reason you don’t present r values for the whole drought and whole irrigated sets of data that are discussed on Lines 401 – 413? If so, you could move this explanation earlier in the text to justify that.
Line 442: Please add clarification in the caption for Table 1 that these are the average Pearson coefficients from multiple individual branch values.
Citation: https://doi.org/10.5194/egusphere-2024-529-RC2
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