the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Jan 2024
On the uncertainty of anthropogenic aromatic VOC emissions: evaluation and sensitivity analysis
Abstract. Volatile organic compounds (VOCs) significantly impact air quality and atmospheric chemistry, influencing ozone formation and secondary organic aerosol production. Despite their importance, the uncertainties associated with representing VOCs in atmospheric emission inventories are considerable. This work presents a spatiotemporal assessment and evaluation of benzene, toluene, and xylene (BTX) emissions and concentrations in Spain by combining bottom-up emissions, air quality modelling techniques and ground-based observations. The emissions produced by HERMESv3 were used as input to the MONARCH model to simulate surface concentrations across Spain. Comparing modelled and observed levels revealed uncertainty in the anthropogenic emissions, which were further explored through sensitivity tests. The largest levels of observed benzene and xylene were found in industrial sites near coke ovens, refineries and car manufacturing facilities, where the modelling results show large underestimations. Official emissions reported for these facilities were replaced by alternative estimates, allowing to heterogeneously improve the model's performance, highlighting that uncertainties representing industrial emission processes remain. For toluene, consistent overestimations in background stations were mainly related to uncertainties in the spatial disaggregation of emissions from industrial use solvent activities, mainly wood paint applications. Observed benzene levels in Barcelona's urban traffic areas were five times larger than the ones observed in Madrid. MONARCH failed to reproduce the observed gradient between the two cities due to uncertainties in estimating emissions from motorcycles and mopeds. Our results are constrained by the spatial and temporal coverage of available BTX observations, posing a key challenge in evaluating the spatial distribution of modelled levels and associated emissions.
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RC1: 'Comment on egusphere-2023-3145', Anonymous Referee #1, 23 Feb 2024
Oliveria et al. utilize HERMES, an emissions model that calculates an anthropogenic inventory and outputs gridded output, and MONARCH, a Chemical Transport Model, to simulate ambient benzene, toluene, and xylene concentrations. These modeled concentrations are then compared to ground-level observations in many locations throughout Spain. The modeling system is then perturbed to generate BTX concentrations for a few sector-specific sensitivity tests.
Overall, I think it deserves publication in EGUsphere if my comments below are addressed. In particular, I do not think the authors consider other variables that can lead to model bias beyond incorrect emissions to a large enough degree. There is no discussion related to potential biases in modeled meteorology, which can have a large impact on simulated concentrations of atmospheric constituents. Below, I list all my comments and attempt to provide guidance, where possible.
Major Comments:
- 5-7, 8-10, and 11-13 feature a lot of repetition in what is being presented. I feel like both spatial plots in each set can be removed and are largely (and more easily digestible) represented by what is shown in Figs. 7/10/13. Or generate a scatter plot (obs/model) with traffic/industrial/background each represented by a different marker. That would be much easier to follow and extract information from. For example, it isn’t clear to me how good the agreement is between model and observations, as alluded to on Line 334.
- Line 476: I am extremely confused by this comparison. From the proceeding paragraph and based on reading the figure, it looks like 12 facilities from the LPS database and 2 facilities from PRTR are shown. So, where are LPS and PRTS being compared in the figure?
Minor Comments:
- “Heterogeneously” in line 10 seems awkward. I’m not sure what the author’s mean when they use that word.
- Line 74: “model chemical transport model”. Remove the first “model” in the sentence.
- Line 136: Feed à fed
- 3: Please include “road” in “road transport” on the figure legend.
- Line 362: The model appears low biased in the afternoon, and I would not dismiss an issue with the temporal profiles as quickly as the author’s seem to.
- Broadly, when describing the observation stations using city names, I’m largely unsure what part of the spatial figures the authors are discussing (I’m an American with limited geographical knowledge of Spain).
- Line 405: The authors attribute bias from “traffic stations” to issues related to cold-start emissions. While possible, near-road stations are not only influenced by mobile emissions. In addition, there are other mobile source processes beyond cold-start emissions that could affect wintertime model performance. I wouldn’t jump to such definitive conclusions.
- Line 409: Again, I don’t think you can conclude this issue is due to model chemistry and not attributable to (at least partially) temporal allocation of emissions. Also, what about meteorology? Perhaps the meteorological data has a bias in something like PBL, which can influence the diurnal pattern of modeled concentrations.
- Line 523: Again, I don’t think you can conclude the issue is missing emissions without considering meteorological effects or grid scale of the base (10km x 10km) model. In fact, the modest improvements following huge increases in emissions in the alternative scenario, to me, indicate some of these other factors (e.g., PBL) likely have a stronger influence on modeled concentrations than emission perturbations. In addition, perhaps the speciation of NMVOC is what is incorrect. A speciation profile can have considerable uncertainty.
Citation: https://doi.org/10.5194/egusphere-2023-3145-RC1 -
RC2: 'Comment on egusphere-2023-3145', Anonymous Referee #2, 24 Feb 2024
Major comments:
Oliveria et al. utilize the CTM model to simulate BTX concentrations and compare them with observational data. However, several critical points are not clearly presented, especially regarding the VOC chemical reactions and the CTM model itself. If the paper clarifies these issues in the discussion, this paper deserves to be published.
BTX react with the OH radical, ozone, and the NO3 radical, with both OH and NO3 being challenging to measure in ambient conditions. Therefore, the model's performance in simulating ozone is crucial to demonstrate that the CTM model functions well based on the emission and meteorological inputs. I suggest that the author should include an evaluation of ozone, like NO2 provided in Appendix E.
Furthermore, there is a critical issue in the chemical process within the model simulation part: The CB05 reactions R128 and R138 use model lumped species XYL and TOL, but the author has added A2 and A3 reactions in this study and uses the same species names (XYL and TOL) for explicit xylene and toluene. There is no clear method to distinguish between the lumped and explicit species that share the same species name. As a result, the model results for XYL and TOL represent lumped species and not the explicit species (xylene and toluene). The GC-MS/FID observed data for xylene and toluene are for explicit species. If so, the comparison does not match like with like. Please address this issue.
Specific comments:
- Line 103: “The VOC speciation mapping disaggregates total VOCs ……” explains the emission and VOC speciation mapping for the CTM model. In this study, are the BTX emission data for explicit Toluene and Xylene, or are they for the model's lumped species TOL and XYL? The lumped species TOL and XYL are used to represent similar species like Ethylbenzene, Styrene, Indene, etc. The lumped XYL can reflects more than 80 species, and TOL can represents more than 50 species.
- Line 122: "Notably, the mechanism also accounts for explicit species, namely...", In the CB05 document (https://www.camx.com/Files/CB05_Final_Report_120805.pdf) , the XYL and TOL are "xylene and other polyalkyl aromatics" and "Toluene and other monoalkyl aromatics". They are lumped species.
- In Appendix A, what is the reference for the A1 reaction rate constant (k) (2.47E-12 e^(-206/T)) and the products (OH + 0.764*BENZRO2)? The rate constant (k) for Benzene in gas-phase chemistry mechanisms, CB6r2 and MCM3.3, is both 2.3E-12 e^(-190/T) (source: https://mcm.york.ac.uk/MCM/species/BENZENE). The inclusion of the OH radical as a product (OH + 0.764*BENZRO2) in the appears to be incorrect. If this additional OH radical product in A1 is considered in the CTM model, it will impact the OH concentration, VOCs concentration and reactions.
- Figure5: symbol for Max Observed Value (blue star) not in the figure.
Citation: https://doi.org/10.5194/egusphere-2023-3145-RC2 -
RC3: 'Comment on egusphere-2023-3145', Anonymous Referee #3, 24 Feb 2024
Oliveria et al. employ HERMES and MONARCH to simulate ambient concentrations of BTX. The authors conducted a rigorous evaluation of BTX emissions from different sectors, temporal variation including weekday-weekend differences to an extent. The simulated concentrations are subsequently compared to ground-level observations in Spain. Further, the biases between observed and modeled data were explored through sensitivity tests. I appreciate the authors highlighting the need for extensive BTX measurements and regulations. This work is justifiable for publication in ACP. However, there are some shortcomings of the study that need to be addressed:
- The title needs to highlight “model-evaluation”
- Although sensitivity tests were performed for some source aspects that are driving the uncertainty, the authors need to acknowledge that ambient VOC levels are not only driven by emission sources but also by the atmospheric chemical mechanisms, meteorology, and transported pollutants from upwind regions. Please clarify uncertainties that these aspects may lead to.
- The actual ambient measurements of NMVOCs itself have uncertainty depending on the measurement method. This uncertainty is exacerbated when estimates or calculations are made without direct measurements. In Section 3.3.1, authors should address the biases inherent in reported NMVOC emissions derived from PRTR estimation/calculations. It is imperative to elucidate how this impacts the findings of the study. Additionally, the paper lacks an examination of previous research that delves into these aspects.
- The paper's structure resembles more of a "report" than a "manuscript," making it challenging to navigate the results. Certain sections could be consolidated to improve clarity. For instance, the technical comments provided below could serve as an illustration.
Technical comments:
- To enhance reader comprehension and facilitate result comparison, consider consolidating standalone figures and their respective discussions. For instance, merge figures 5, 8, and 11; figures 7, 10, and 13; figures 6, 9, and 12, along with Tables 2, 3, and 4.
- Line 456: “To assess this, …...” this information e.g., the use of LPS and PRTR in this paper has to be clarified in the method section.
- While the conclusion section exceeds traditional length norms, it effectively summarizes all aspects of the paper. I would suggest adding a separate section after the conclusion to specifically address the atmospheric implications of this research.
Editorial comments:
- Abstract:
- Abbreviate HERMESv3 and MONARCH
- L9-11: reads odd, please rephrase for better readability.
- Introduction:
- L22: biomass burning can be from natural activity as well.
- L24: rewrite for better readability.
- L26: clarify what you mean by human-induced atmospheric changes.
- L33: confusing as two different statistics for benzene are presented. Rewrite for clarity.
- L37: clarify if Huang et a. (2014) is a global estimate or site-specific.
- L45: The change in paragraph reads odd. Mention why EI is important, and give reasons and references.
- L51: mention that MITERD (2023) is EU-based and may be different for other regions of the world.
- L56: Clarify what you mean by overlapping uncertainty sources.
- L59: remove “in the literature”
- L60-63: rewrite the sentence for better readability.
- Data and Method:
- Clarify why was the year 2019 chosen in this paper.
- L110: clarify what you mean by state-of-the-art works.
- L126: (“i.e. cloud,….”) rewrite for clarity.
- L134: remove “for”
- L142: add citation/web link associated with Spanish ministry data.
- Table 1: In the caption, add in Spain.
- L 157-165: redundant information. Remove redundant lines that are evident in Table 1. Some additional info in this para can be added to the Table 1 caption.
- Results:
- L209: mention “figure not included” for clarity.
- L195: use the Arabic numbering system to separate different groups for better readability.
- L206: replace weekend effect with “weekday-weekend effect”
- L363: clarify what these chemical processes affecting VOCs are.
- Conclusion:
- No need to abbreviate VOC or SOA.
Citation: https://doi.org/10.5194/egusphere-2023-3145-RC3 -
RC4: 'Comment on egusphere-2023-3145', Anonymous Referee #4, 08 Mar 2024
The study by Oliveria et al. focuses on the uncertainties associated with the representation of VOCs in atmospheric emission inventories. They tested the spatiotemporal distribution of benzene, toluene, and xylene (BTX) as predicted by an atmospheric model (MONARCH), which utilized emissions produced by the High-Elective Resolution Modelling Emission System (HERMESv3) as input. The spatiotemporal distribution of BTX was compared with ground observations in Spain. The analysis considered different station classifications according to the measurement stations location, with respect to nearby emission sources (e.g., traffic, industrial, rural traffic, rural background etc.). The manuscript addresses an important, yet understudied topic, while the approach used is generally suitable.
However, although the methods apply an atmospheric chemistry model (MONARCH), the authors did not consider the effects of atmospheric dynamics and chemistry on the biases between simulations and observations. In several cases, the explanations given for such discrepancies are speculative, without adequately exploring causal effect. More information and/or analysis should be provided to support the suitability of MONARCH to adequately represent the spatial distribution of benzene, toluene, and xylene by ensuring the appropriate representation of their oxidation rates and their explicit inclusion in the model.
Specific comments:
Line 5 – “HERMESv3” – Brief information should be provided so that the reader knows that this is an emission model.
Line 6-“ MONARCH” – provide brief information to inform the reader about the type of model it is.
Lines 26-27 - The sentence should be clearer regarding the contribution of VOCs to SOA via those oxidants.
Line 43 – “They are continuously measured” - The meaning here is not clear to me - when or where are they measured?
Line 48 – “highest uncertainty” - In spatial/temporal distribution? concentrations? Sector wise?
Lines 58-70 – Not clear to me which information given here specifically refers to the UK.
Lines 65-66 – “Third, the availability and quality of observational data for VOCs are often limited in scope” - Can you briefly state in what ways? Currently, it is not clear to me how it differs from the second point you raise.
Lines 80-85: The manuscript could be made more concise by excluding this section.
Line 96 – Can you specify the spatial resolution applied?
Line 113- “sectional-bulk” - not clear to me what is meant by this.
Line 135 – “CAMS” – define at first appearance.
Line 249 – “Urban and suburban industrial stations were also aggregated” – what is the rational for this aggregation?
Line 277 – “from -0.21 to 0.92 µg.m-3” – Can you specify the bias in percentage?
Lines 278 – 279 – “This is mainly attributed to underestimations of VOCs” – VOCs emission?
Lie 281 – “NO2” should be written with subscripted “2”.
Lines 312-313 – “Notably, underestimations are more pronounced during winter, suggesting a potential underestimation of road traffic cold start emissions” - For traffic? I don’t see that this winter trend is significant when looking at Fig. 7 and Table 2
Figure 7 - Specify H, M and DOW in the figure caption.
Figure 9 - What is the difference between "Hourly, Daily" and "Daily"- what is the meaning of "Hourly, Daily"?
Line 363 – “chemical processes affecting VOCs” - This is not clear to me. Can you specify what kind of chemical reaction could lead to an earlier VOCs morning build-up compared to the measurements? Do you imply that benzene and toluene are formed by chemical reactions which occur in the morning? Could meteorological effects/stratification of the atmosphere could play a role here too?
Line 363 – “What do you mean by "temporal profiles"? Can you specify which physical or chemical processes could lead to the trend you refer to?
Lines 405-407 - This is not clear to me - Can you explain why you believe that the large underestimation of traffic emissions in winter is related to cold-start emissions?
Line 409 – “Indicating an issue related to VOC chemistry in MONARCH’ – Can you explain why you necessarily attribute this issue to VOC chemistry? The same comment is relevant for toluene and benzene.
Lines 553 – multiple occurrences of “the’
Line 601 – “the impact on air quality results” - What do you mean by this?
Lines 607-611 – There are several other reasons for differences in addition to the apparent not well enough representation of emissions from mopeds and motorcycles. Can you provide concrete evidence that these factors impose a dominant impact on differences between modeling and observations?
Lines 612-613 – “suggesting that some sources are either not accurately represented in our model or are unaccounted for” - What about atmospheric chemistry effects and/or meteorological effects?
Lines 613-614 –please rephrase.
Table 7 – Specify more clearly what do you mean by “Scenario”.
Line 622 – “evaluation studies” - Which evaluation studies?
Conclusions section –This section should be termed differently, possibly Summary and Conclusions”. For instance, lines 619-629 do not contain conclusions.
Line 631 – “Annual emissions of benzene, toluene and xylene over Spain reach 11 kt, 36 kt, and 25 kt, respectively” - For what years? Is this maximum or average values?
Lines 652-655 – Please rephrase for clarity.
Lines 661-663 – This does not seem like a conclusion to me.
Lines 687-688 – It is speculative while included in Conclusion section.
Line 703- “improve the performance of air quality models” - Can you elaborate on in what ways this improvement can be achieved?
Line 707 – “focusing on assessing the impact on PM2.5” - If you add this here, explain briefly why assessing the impact of PM2.5 is important and better connect it to the context of your study.
Citation: https://doi.org/10.5194/egusphere-2023-3145-RC4 -
AC1: 'Comment on egusphere-2023-3145', Kevin Oliveira, 10 Apr 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3145/egusphere-2023-3145-AC1-supplement.pdf
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-3145', Anonymous Referee #1, 23 Feb 2024
Oliveria et al. utilize HERMES, an emissions model that calculates an anthropogenic inventory and outputs gridded output, and MONARCH, a Chemical Transport Model, to simulate ambient benzene, toluene, and xylene concentrations. These modeled concentrations are then compared to ground-level observations in many locations throughout Spain. The modeling system is then perturbed to generate BTX concentrations for a few sector-specific sensitivity tests.
Overall, I think it deserves publication in EGUsphere if my comments below are addressed. In particular, I do not think the authors consider other variables that can lead to model bias beyond incorrect emissions to a large enough degree. There is no discussion related to potential biases in modeled meteorology, which can have a large impact on simulated concentrations of atmospheric constituents. Below, I list all my comments and attempt to provide guidance, where possible.
Major Comments:
- 5-7, 8-10, and 11-13 feature a lot of repetition in what is being presented. I feel like both spatial plots in each set can be removed and are largely (and more easily digestible) represented by what is shown in Figs. 7/10/13. Or generate a scatter plot (obs/model) with traffic/industrial/background each represented by a different marker. That would be much easier to follow and extract information from. For example, it isn’t clear to me how good the agreement is between model and observations, as alluded to on Line 334.
- Line 476: I am extremely confused by this comparison. From the proceeding paragraph and based on reading the figure, it looks like 12 facilities from the LPS database and 2 facilities from PRTR are shown. So, where are LPS and PRTS being compared in the figure?
Minor Comments:
- “Heterogeneously” in line 10 seems awkward. I’m not sure what the author’s mean when they use that word.
- Line 74: “model chemical transport model”. Remove the first “model” in the sentence.
- Line 136: Feed à fed
- 3: Please include “road” in “road transport” on the figure legend.
- Line 362: The model appears low biased in the afternoon, and I would not dismiss an issue with the temporal profiles as quickly as the author’s seem to.
- Broadly, when describing the observation stations using city names, I’m largely unsure what part of the spatial figures the authors are discussing (I’m an American with limited geographical knowledge of Spain).
- Line 405: The authors attribute bias from “traffic stations” to issues related to cold-start emissions. While possible, near-road stations are not only influenced by mobile emissions. In addition, there are other mobile source processes beyond cold-start emissions that could affect wintertime model performance. I wouldn’t jump to such definitive conclusions.
- Line 409: Again, I don’t think you can conclude this issue is due to model chemistry and not attributable to (at least partially) temporal allocation of emissions. Also, what about meteorology? Perhaps the meteorological data has a bias in something like PBL, which can influence the diurnal pattern of modeled concentrations.
- Line 523: Again, I don’t think you can conclude the issue is missing emissions without considering meteorological effects or grid scale of the base (10km x 10km) model. In fact, the modest improvements following huge increases in emissions in the alternative scenario, to me, indicate some of these other factors (e.g., PBL) likely have a stronger influence on modeled concentrations than emission perturbations. In addition, perhaps the speciation of NMVOC is what is incorrect. A speciation profile can have considerable uncertainty.
Citation: https://doi.org/10.5194/egusphere-2023-3145-RC1 -
RC2: 'Comment on egusphere-2023-3145', Anonymous Referee #2, 24 Feb 2024
Major comments:
Oliveria et al. utilize the CTM model to simulate BTX concentrations and compare them with observational data. However, several critical points are not clearly presented, especially regarding the VOC chemical reactions and the CTM model itself. If the paper clarifies these issues in the discussion, this paper deserves to be published.
BTX react with the OH radical, ozone, and the NO3 radical, with both OH and NO3 being challenging to measure in ambient conditions. Therefore, the model's performance in simulating ozone is crucial to demonstrate that the CTM model functions well based on the emission and meteorological inputs. I suggest that the author should include an evaluation of ozone, like NO2 provided in Appendix E.
Furthermore, there is a critical issue in the chemical process within the model simulation part: The CB05 reactions R128 and R138 use model lumped species XYL and TOL, but the author has added A2 and A3 reactions in this study and uses the same species names (XYL and TOL) for explicit xylene and toluene. There is no clear method to distinguish between the lumped and explicit species that share the same species name. As a result, the model results for XYL and TOL represent lumped species and not the explicit species (xylene and toluene). The GC-MS/FID observed data for xylene and toluene are for explicit species. If so, the comparison does not match like with like. Please address this issue.
Specific comments:
- Line 103: “The VOC speciation mapping disaggregates total VOCs ……” explains the emission and VOC speciation mapping for the CTM model. In this study, are the BTX emission data for explicit Toluene and Xylene, or are they for the model's lumped species TOL and XYL? The lumped species TOL and XYL are used to represent similar species like Ethylbenzene, Styrene, Indene, etc. The lumped XYL can reflects more than 80 species, and TOL can represents more than 50 species.
- Line 122: "Notably, the mechanism also accounts for explicit species, namely...", In the CB05 document (https://www.camx.com/Files/CB05_Final_Report_120805.pdf) , the XYL and TOL are "xylene and other polyalkyl aromatics" and "Toluene and other monoalkyl aromatics". They are lumped species.
- In Appendix A, what is the reference for the A1 reaction rate constant (k) (2.47E-12 e^(-206/T)) and the products (OH + 0.764*BENZRO2)? The rate constant (k) for Benzene in gas-phase chemistry mechanisms, CB6r2 and MCM3.3, is both 2.3E-12 e^(-190/T) (source: https://mcm.york.ac.uk/MCM/species/BENZENE). The inclusion of the OH radical as a product (OH + 0.764*BENZRO2) in the appears to be incorrect. If this additional OH radical product in A1 is considered in the CTM model, it will impact the OH concentration, VOCs concentration and reactions.
- Figure5: symbol for Max Observed Value (blue star) not in the figure.
Citation: https://doi.org/10.5194/egusphere-2023-3145-RC2 -
RC3: 'Comment on egusphere-2023-3145', Anonymous Referee #3, 24 Feb 2024
Oliveria et al. employ HERMES and MONARCH to simulate ambient concentrations of BTX. The authors conducted a rigorous evaluation of BTX emissions from different sectors, temporal variation including weekday-weekend differences to an extent. The simulated concentrations are subsequently compared to ground-level observations in Spain. Further, the biases between observed and modeled data were explored through sensitivity tests. I appreciate the authors highlighting the need for extensive BTX measurements and regulations. This work is justifiable for publication in ACP. However, there are some shortcomings of the study that need to be addressed:
- The title needs to highlight “model-evaluation”
- Although sensitivity tests were performed for some source aspects that are driving the uncertainty, the authors need to acknowledge that ambient VOC levels are not only driven by emission sources but also by the atmospheric chemical mechanisms, meteorology, and transported pollutants from upwind regions. Please clarify uncertainties that these aspects may lead to.
- The actual ambient measurements of NMVOCs itself have uncertainty depending on the measurement method. This uncertainty is exacerbated when estimates or calculations are made without direct measurements. In Section 3.3.1, authors should address the biases inherent in reported NMVOC emissions derived from PRTR estimation/calculations. It is imperative to elucidate how this impacts the findings of the study. Additionally, the paper lacks an examination of previous research that delves into these aspects.
- The paper's structure resembles more of a "report" than a "manuscript," making it challenging to navigate the results. Certain sections could be consolidated to improve clarity. For instance, the technical comments provided below could serve as an illustration.
Technical comments:
- To enhance reader comprehension and facilitate result comparison, consider consolidating standalone figures and their respective discussions. For instance, merge figures 5, 8, and 11; figures 7, 10, and 13; figures 6, 9, and 12, along with Tables 2, 3, and 4.
- Line 456: “To assess this, …...” this information e.g., the use of LPS and PRTR in this paper has to be clarified in the method section.
- While the conclusion section exceeds traditional length norms, it effectively summarizes all aspects of the paper. I would suggest adding a separate section after the conclusion to specifically address the atmospheric implications of this research.
Editorial comments:
- Abstract:
- Abbreviate HERMESv3 and MONARCH
- L9-11: reads odd, please rephrase for better readability.
- Introduction:
- L22: biomass burning can be from natural activity as well.
- L24: rewrite for better readability.
- L26: clarify what you mean by human-induced atmospheric changes.
- L33: confusing as two different statistics for benzene are presented. Rewrite for clarity.
- L37: clarify if Huang et a. (2014) is a global estimate or site-specific.
- L45: The change in paragraph reads odd. Mention why EI is important, and give reasons and references.
- L51: mention that MITERD (2023) is EU-based and may be different for other regions of the world.
- L56: Clarify what you mean by overlapping uncertainty sources.
- L59: remove “in the literature”
- L60-63: rewrite the sentence for better readability.
- Data and Method:
- Clarify why was the year 2019 chosen in this paper.
- L110: clarify what you mean by state-of-the-art works.
- L126: (“i.e. cloud,….”) rewrite for clarity.
- L134: remove “for”
- L142: add citation/web link associated with Spanish ministry data.
- Table 1: In the caption, add in Spain.
- L 157-165: redundant information. Remove redundant lines that are evident in Table 1. Some additional info in this para can be added to the Table 1 caption.
- Results:
- L209: mention “figure not included” for clarity.
- L195: use the Arabic numbering system to separate different groups for better readability.
- L206: replace weekend effect with “weekday-weekend effect”
- L363: clarify what these chemical processes affecting VOCs are.
- Conclusion:
- No need to abbreviate VOC or SOA.
Citation: https://doi.org/10.5194/egusphere-2023-3145-RC3 -
RC4: 'Comment on egusphere-2023-3145', Anonymous Referee #4, 08 Mar 2024
The study by Oliveria et al. focuses on the uncertainties associated with the representation of VOCs in atmospheric emission inventories. They tested the spatiotemporal distribution of benzene, toluene, and xylene (BTX) as predicted by an atmospheric model (MONARCH), which utilized emissions produced by the High-Elective Resolution Modelling Emission System (HERMESv3) as input. The spatiotemporal distribution of BTX was compared with ground observations in Spain. The analysis considered different station classifications according to the measurement stations location, with respect to nearby emission sources (e.g., traffic, industrial, rural traffic, rural background etc.). The manuscript addresses an important, yet understudied topic, while the approach used is generally suitable.
However, although the methods apply an atmospheric chemistry model (MONARCH), the authors did not consider the effects of atmospheric dynamics and chemistry on the biases between simulations and observations. In several cases, the explanations given for such discrepancies are speculative, without adequately exploring causal effect. More information and/or analysis should be provided to support the suitability of MONARCH to adequately represent the spatial distribution of benzene, toluene, and xylene by ensuring the appropriate representation of their oxidation rates and their explicit inclusion in the model.
Specific comments:
Line 5 – “HERMESv3” – Brief information should be provided so that the reader knows that this is an emission model.
Line 6-“ MONARCH” – provide brief information to inform the reader about the type of model it is.
Lines 26-27 - The sentence should be clearer regarding the contribution of VOCs to SOA via those oxidants.
Line 43 – “They are continuously measured” - The meaning here is not clear to me - when or where are they measured?
Line 48 – “highest uncertainty” - In spatial/temporal distribution? concentrations? Sector wise?
Lines 58-70 – Not clear to me which information given here specifically refers to the UK.
Lines 65-66 – “Third, the availability and quality of observational data for VOCs are often limited in scope” - Can you briefly state in what ways? Currently, it is not clear to me how it differs from the second point you raise.
Lines 80-85: The manuscript could be made more concise by excluding this section.
Line 96 – Can you specify the spatial resolution applied?
Line 113- “sectional-bulk” - not clear to me what is meant by this.
Line 135 – “CAMS” – define at first appearance.
Line 249 – “Urban and suburban industrial stations were also aggregated” – what is the rational for this aggregation?
Line 277 – “from -0.21 to 0.92 µg.m-3” – Can you specify the bias in percentage?
Lines 278 – 279 – “This is mainly attributed to underestimations of VOCs” – VOCs emission?
Lie 281 – “NO2” should be written with subscripted “2”.
Lines 312-313 – “Notably, underestimations are more pronounced during winter, suggesting a potential underestimation of road traffic cold start emissions” - For traffic? I don’t see that this winter trend is significant when looking at Fig. 7 and Table 2
Figure 7 - Specify H, M and DOW in the figure caption.
Figure 9 - What is the difference between "Hourly, Daily" and "Daily"- what is the meaning of "Hourly, Daily"?
Line 363 – “chemical processes affecting VOCs” - This is not clear to me. Can you specify what kind of chemical reaction could lead to an earlier VOCs morning build-up compared to the measurements? Do you imply that benzene and toluene are formed by chemical reactions which occur in the morning? Could meteorological effects/stratification of the atmosphere could play a role here too?
Line 363 – “What do you mean by "temporal profiles"? Can you specify which physical or chemical processes could lead to the trend you refer to?
Lines 405-407 - This is not clear to me - Can you explain why you believe that the large underestimation of traffic emissions in winter is related to cold-start emissions?
Line 409 – “Indicating an issue related to VOC chemistry in MONARCH’ – Can you explain why you necessarily attribute this issue to VOC chemistry? The same comment is relevant for toluene and benzene.
Lines 553 – multiple occurrences of “the’
Line 601 – “the impact on air quality results” - What do you mean by this?
Lines 607-611 – There are several other reasons for differences in addition to the apparent not well enough representation of emissions from mopeds and motorcycles. Can you provide concrete evidence that these factors impose a dominant impact on differences between modeling and observations?
Lines 612-613 – “suggesting that some sources are either not accurately represented in our model or are unaccounted for” - What about atmospheric chemistry effects and/or meteorological effects?
Lines 613-614 –please rephrase.
Table 7 – Specify more clearly what do you mean by “Scenario”.
Line 622 – “evaluation studies” - Which evaluation studies?
Conclusions section –This section should be termed differently, possibly Summary and Conclusions”. For instance, lines 619-629 do not contain conclusions.
Line 631 – “Annual emissions of benzene, toluene and xylene over Spain reach 11 kt, 36 kt, and 25 kt, respectively” - For what years? Is this maximum or average values?
Lines 652-655 – Please rephrase for clarity.
Lines 661-663 – This does not seem like a conclusion to me.
Lines 687-688 – It is speculative while included in Conclusion section.
Line 703- “improve the performance of air quality models” - Can you elaborate on in what ways this improvement can be achieved?
Line 707 – “focusing on assessing the impact on PM2.5” - If you add this here, explain briefly why assessing the impact of PM2.5 is important and better connect it to the context of your study.
Citation: https://doi.org/10.5194/egusphere-2023-3145-RC4 -
AC1: 'Comment on egusphere-2023-3145', Kevin Oliveira, 10 Apr 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3145/egusphere-2023-3145-AC1-supplement.pdf
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