the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Constraining the budget of NOx and VOCs at a remote Tropical island using multi-platform observations and WRF-Chem model simulations
Abstract. Volatile organic compounds (VOCs) act as precursors to ozone and secondary organic aerosols, which have significant health and environmental impacts, and they can reduce the atmospheric oxidative capacity. However, their budget remains poorly quantified, especially over remote areas such as the Tropical oceans. Here, we present high-resolution simulations of atmospheric composition over Réunion Island, in the Indian Ocean, using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The coexistence and spatial heterogeneity of anthropogenic and biogenic emission sources in this region present a valuable but challenging test of the model performance. The WRF-Chem model is evaluated against several observational datasets, including Proton Transfer Reaction Mass Spectrometry (PTR-MS) measurements of VOCs and oxygenated VOCs (OVOCs) at the Maïdo Observatory (2160 m a.s.l.) in January and July 2019. While the primary goal of our study is a better understanding of (O)VOC budget at remote Tropical latitudes, important model refinements are made to improve the model performance, including the implementation of high-resolution anthropogenic and biogenic isoprene emissions, updates to the chemical mechanism, and adjustments to the boundary conditions. These refinements are supported by comparisons with PTR-MS data as well as with meteorological measurements at Maïdo, in situ NOx and O3 measurements from the air quality Atmo-Réunion network, Fourier Transform Infrared Spectroscopy (FTIR) measurements of O3, CO, ethane and several OVOCs, also at Maïdo, and satellite retrievals from the TROPOspheric Monitoring Instrument (TROPOMI).
TROPOMI NO2 data suggests that anthropogenic emissions, particularly from power plants near Le Port, dominate NOx levels over the island. Both TROPOMI and in situ surface NO2 comparisons are used to adjust the power plant emissions at Le Port. Surface ozone concentrations are overestimated by ~6 ppbv on average, likely due to the neglect of halogen chemistry in the model. Whereas modelled NO2 over oceans is too low in summer when the lightning source is turned off, the inclusion of this source results in model overestimations corroborated by comparisons with upper tropospheric NO2 mixing ratios derived from TROPOMI using the cloud-slicing technique (Marais et al., 2021). The model generally succeeds in reproducing the PTR-MS isoprene and its oxidation products (Iox), except for a moderate underestimation (~30 %) of noontime isoprene concentration and for modelled concentration peaks near dawn and dusk, that are not seen in the observations. The ratio of Iox to isoprene (0.8 at noon in January) is fairly well reproduced by the model. The methanol and monoterpenes observations both suggest overestimations of their biogenic emissions, by factors of about 2 and 5, respectively. Acetaldehyde anthropogenic emissions are likely strongly overestimated, due to the lumping of higher aldehydes into this compound. Without this lumping, the modelled acetaldehyde would be underestimated by almost one order of magnitude, suggesting the existence of a large missing source, likely photochemical. The comparisons suggest the existence of a biogenic source of MEK equivalent to about 3 % of isoprene emissions, likely associated with the dry deposition and conversion to MEK of key isoprene oxidation products. A strong model underestimation of the PTR-MS signal at mass 61 is also found, by a factor of 3–5 during daytime, consistent with previously reported missing sources of acetic and peracetic acid.
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RC1: 'Comment on egusphere-2024-3555', I. Pérez, 21 Jan 2025
This is a quite complete paper about the atmospheric chemistry over the Reunion island. The focus is placed on NOx (NO+NO2) and VOC (volatile organic compounds). The period used extended during January and July 2019. Meteorological variables together with atmospheric concentrations were used. Moreover, the comparison between measured and calculated values is presented. A detailed description of input data is presented and results cover formaldehyde, methanol, isoprene, monoterpenes, acetaldehyde, acetone among other substances. Calculated series are presented, but also the daily cycle is highlighted. Finally, FTIR and TROPOMI data are compared with modelled results. Consequently, the paper presents a noticeable amount of information of atmospheric chemistry for this remote site and it merits to be published in Atmospheric Chemistry and Physics, although some minor changes should be introduced prior to its final acceptance.
Although the analysis depth is noticeable, the time extension is quite limited. Perhaps the authors could introduce some comments about the data representativeness, i.e., they could indicate if atmospheric conditions are steady over this site or if accused and frequent changes have been observed.
Similarly, since this is a quite limited site, the authors could inform about the possible extension of their results to other sites around the world. This information would be useful to increase the result applicability and the number of possible readers.
Most of the paper is focused on Reunion Island. However, section 3.5 includes Mauritius Island. Perhaps the authors could introduce a short comment for this change in the studied area. Moreover, they should indicate if input data belong to Reunion Island and if model results are extended to Mauritius Island.
Possible weaknesses or limitations of this study could be introduced.
Minor remarks.
Perhaps citations of figures and previous studies should not be introduced in the conclusion section.
Citation: https://doi.org/10.5194/egusphere-2024-3555-RC1 -
RC2: 'Comment on egusphere-2024-3555', Anonymous Referee #2, 04 Feb 2025
General Comments
The manuscript evaluates the WRF-Chem model's ability to simulate chemical species over Réunion Island, focusing on key species such as formaldehyde, methanol, isoprene, Iox (isoprene oxidation products), monoterpenes, acetone, NO₂, NOₓ, OH, and O₃. While the study offers valuable insights into VOC and NOₓ simulations in a tropical setting, significant improvements are needed, particularly in defining clear scientific objectives and providing a robust statistical evaluation of model performance for both meteorological and chemical species. Substantial revisions are required.
Major Comments:
- The manuscript provides excessive detail on data collection methods, such as PTR-MS measurements. Given the primary focus on model evaluation, these descriptions should be condensed, retaining only essential information relevant to data analysis and model validation.
- The study introduces updates to chemical mechanisms (Sections 2.2.3.1, 2.2.3.2, Table 2, and sensitivity run S4). If these updates represent novel developments, they should be explicitly highlighted in the abstract and conclusion. Otherwise, proper citations should be included.
- Despite significant differences in simulated precursor concentrations (Fig. 18), O₃ predictions remain relatively stable. The authors should provide a thorough explanation of this discrepancy.
- The use of sequential 2-day simulations (Lines 158–160) is unsuitable for evaluating monthly trends. It is recommended to conduct daily 48-hour simulations, starting at a consistent time (e.g., 00Z or 12Z UTC), and assess either Day 1 or Day 2 results to account for numerical model performance variations over different forecast hours.
- The description of biogenic emissions in Section 2.3.2 should be streamlined unless significant modifications were made to MEGAN 2.0.4.
- Sensitivity run S1: The over-prediction of NO₂ and NOₓ at the LP station (Lines 598–600) is attributed to surface-level emission injection. Instead of reducing NOₓ emissions by a factor of five, a more appropriate approach would be to inject emissions at the plume rise height.
- Figure 8: Evaluating meteorological simulations based on a single-site comparison is insufficient. Are additional meteorological observation sites available? Including statistical metrics (e.g., correlation coefficient, RMSE, mean bias) would strengthen the evaluation.
- Figure 10: Include a simulated vs. observed O₃ comparison at the Le Port station to assess the impact of NOₓ and VOC predictions on surface O₃ levels.
- Lines 368–369 mention 18 air quality monitoring stations. The evaluation should incorporate all available sites with statistical metrics such as correlation coefficient, root mean square error (RMSE), and index of agreement (IOA).
- Lines 33–34: The statement attributing a 6 ppbv O₃ overestimation to the exclusion of halogen chemistry is overly simplistic. Ozone overprediction can stem from multiple factors, including uncertainties in emissions, vertical mixing, model resolution, and atmospheric chemistry.
Minor Comments:
- Line 23: Define “a.s.l.”
- Line 12: Add Piton de la Fournaise’s location to Figure 3.
- Line 176: Define MEGAN and include references.
- Line 190: Spell out MVK and MACR.
- Line 230: Clarify the meaning of “higher resolution data (0.75° x 0.75°).”
- Line 233: Spell out “BIGALK.”
- Line 241: Define “EDGAR.”
- Line 242: Define “HTAP.”
- Line 354: Avoid duplicate definitions (e.g., ISOPOOH on Line 196).
- Line 560: Define NOₓ (=NO+NO₂) upon first mention.
- Lines 578–579: List stations categorized as “other stations.”
- Line 582: Correct “cyan” to “blue” (also check Line 588).
- Line 591: No green dots appear in Figure 2—did you mean red dots?
- Lines 613–614: The statement that NOₓ model agreement issues stem from O₃ overestimation is confused, as NOₓ is one of major precursors of O₃ formation rather than being driven by O₃ levels.
- Line 629: Should reference be “Fig. 14”?
Citation: https://doi.org/10.5194/egusphere-2024-3555-RC2 - AC1: 'Comment on egusphere-2024-3555', Catalina Poraicu, 12 Mar 2025
Status: closed
-
RC1: 'Comment on egusphere-2024-3555', I. Pérez, 21 Jan 2025
This is a quite complete paper about the atmospheric chemistry over the Reunion island. The focus is placed on NOx (NO+NO2) and VOC (volatile organic compounds). The period used extended during January and July 2019. Meteorological variables together with atmospheric concentrations were used. Moreover, the comparison between measured and calculated values is presented. A detailed description of input data is presented and results cover formaldehyde, methanol, isoprene, monoterpenes, acetaldehyde, acetone among other substances. Calculated series are presented, but also the daily cycle is highlighted. Finally, FTIR and TROPOMI data are compared with modelled results. Consequently, the paper presents a noticeable amount of information of atmospheric chemistry for this remote site and it merits to be published in Atmospheric Chemistry and Physics, although some minor changes should be introduced prior to its final acceptance.
Although the analysis depth is noticeable, the time extension is quite limited. Perhaps the authors could introduce some comments about the data representativeness, i.e., they could indicate if atmospheric conditions are steady over this site or if accused and frequent changes have been observed.
Similarly, since this is a quite limited site, the authors could inform about the possible extension of their results to other sites around the world. This information would be useful to increase the result applicability and the number of possible readers.
Most of the paper is focused on Reunion Island. However, section 3.5 includes Mauritius Island. Perhaps the authors could introduce a short comment for this change in the studied area. Moreover, they should indicate if input data belong to Reunion Island and if model results are extended to Mauritius Island.
Possible weaknesses or limitations of this study could be introduced.
Minor remarks.
Perhaps citations of figures and previous studies should not be introduced in the conclusion section.
Citation: https://doi.org/10.5194/egusphere-2024-3555-RC1 -
RC2: 'Comment on egusphere-2024-3555', Anonymous Referee #2, 04 Feb 2025
General Comments
The manuscript evaluates the WRF-Chem model's ability to simulate chemical species over Réunion Island, focusing on key species such as formaldehyde, methanol, isoprene, Iox (isoprene oxidation products), monoterpenes, acetone, NO₂, NOₓ, OH, and O₃. While the study offers valuable insights into VOC and NOₓ simulations in a tropical setting, significant improvements are needed, particularly in defining clear scientific objectives and providing a robust statistical evaluation of model performance for both meteorological and chemical species. Substantial revisions are required.
Major Comments:
- The manuscript provides excessive detail on data collection methods, such as PTR-MS measurements. Given the primary focus on model evaluation, these descriptions should be condensed, retaining only essential information relevant to data analysis and model validation.
- The study introduces updates to chemical mechanisms (Sections 2.2.3.1, 2.2.3.2, Table 2, and sensitivity run S4). If these updates represent novel developments, they should be explicitly highlighted in the abstract and conclusion. Otherwise, proper citations should be included.
- Despite significant differences in simulated precursor concentrations (Fig. 18), O₃ predictions remain relatively stable. The authors should provide a thorough explanation of this discrepancy.
- The use of sequential 2-day simulations (Lines 158–160) is unsuitable for evaluating monthly trends. It is recommended to conduct daily 48-hour simulations, starting at a consistent time (e.g., 00Z or 12Z UTC), and assess either Day 1 or Day 2 results to account for numerical model performance variations over different forecast hours.
- The description of biogenic emissions in Section 2.3.2 should be streamlined unless significant modifications were made to MEGAN 2.0.4.
- Sensitivity run S1: The over-prediction of NO₂ and NOₓ at the LP station (Lines 598–600) is attributed to surface-level emission injection. Instead of reducing NOₓ emissions by a factor of five, a more appropriate approach would be to inject emissions at the plume rise height.
- Figure 8: Evaluating meteorological simulations based on a single-site comparison is insufficient. Are additional meteorological observation sites available? Including statistical metrics (e.g., correlation coefficient, RMSE, mean bias) would strengthen the evaluation.
- Figure 10: Include a simulated vs. observed O₃ comparison at the Le Port station to assess the impact of NOₓ and VOC predictions on surface O₃ levels.
- Lines 368–369 mention 18 air quality monitoring stations. The evaluation should incorporate all available sites with statistical metrics such as correlation coefficient, root mean square error (RMSE), and index of agreement (IOA).
- Lines 33–34: The statement attributing a 6 ppbv O₃ overestimation to the exclusion of halogen chemistry is overly simplistic. Ozone overprediction can stem from multiple factors, including uncertainties in emissions, vertical mixing, model resolution, and atmospheric chemistry.
Minor Comments:
- Line 23: Define “a.s.l.”
- Line 12: Add Piton de la Fournaise’s location to Figure 3.
- Line 176: Define MEGAN and include references.
- Line 190: Spell out MVK and MACR.
- Line 230: Clarify the meaning of “higher resolution data (0.75° x 0.75°).”
- Line 233: Spell out “BIGALK.”
- Line 241: Define “EDGAR.”
- Line 242: Define “HTAP.”
- Line 354: Avoid duplicate definitions (e.g., ISOPOOH on Line 196).
- Line 560: Define NOₓ (=NO+NO₂) upon first mention.
- Lines 578–579: List stations categorized as “other stations.”
- Line 582: Correct “cyan” to “blue” (also check Line 588).
- Line 591: No green dots appear in Figure 2—did you mean red dots?
- Lines 613–614: The statement that NOₓ model agreement issues stem from O₃ overestimation is confused, as NOₓ is one of major precursors of O₃ formation rather than being driven by O₃ levels.
- Line 629: Should reference be “Fig. 14”?
Citation: https://doi.org/10.5194/egusphere-2024-3555-RC2 - AC1: 'Comment on egusphere-2024-3555', Catalina Poraicu, 12 Mar 2025
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