the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Measurement report: Assessment of Asian emissions of ethane, propane, carbon monoxide, and NOx based on observations from the island of Hateruma
Adedayo Rasak Adedeji
Stephen Joseph Andrews
Matthew Joseph Rowlinson
Mathew Joseph Evans
Alastair Charles Lewis
Shigeru Hashimoto
Hitoshi Mukai
Hiroshi Tanimoto
Yasunori Tohjima
Takuya Saito
Abstract. The island of Hateruma is the southernmost inhabited island of Japan. Here we interpret observations of carbon monoxide (CO), ethane (C2H6), propane (C3H8), nitrogen oxides (NOx and NOy) and ozone (O3) made from the island in 2018 with the GEOS-Chem atmospheric chemistry transport model. We simulated the concentrations of these species within a nested grid centered over the site, with a model resolution of 0.5°×0.625°. We use the Community Emissions Data System (CEDS) emissions dataset for anthropogenic emissions and add a geological source of C2H6 and C3H8. The model captured the seasonality of primary pollutants (CO, C2H6, C3H8) at the site - high concentrations in the winter months when oxidation rates are low and flow is from the north, and low concentrations in the summer months when oxidation rates are higher and flow is from the south. It also simulates many of the synoptic scale events with Pearson’s correlation coefficients (r) of 0.74, 0.88 and 0.89 for CO, C2H6 and C3H8, respectively. Concentrations of CO are well simulated by the model (with a gradient of best fit between model and measurements of 0.91) but simulated concentrations of C2H6 and C3H8 are significantly lower than the observations (gradients of best fit between model and measurement of 0.57 and 0.41, respectively), most noticeably in the winter months. Simulated NOx concentrations were underestimated but NOy appear to be overestimated. The concentration of O3 is moderately well simulated (gradient of best fit line of 0.76, with an r of 0.87) but there is a tendency to underestimate concentrations in the winter months. By switching off the model’s biomass burning emissions we show that during winter biomass burning has limited influence on the concentration of compounds in the winter but can represent a sizeable fraction in the summer. We also show that increasing the anthropogenic emissions of C2H6 and C3H8 in Asia by factors of 2.22 and 3.17, respectively, significantly increases the model’s ability to simulate these species in the winter months, consistent with previous studies.
Adedayo Rasak Adedeji et al.
Status: final response (author comments only)
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RC1: 'Comment on acp-2022-703', Anonymous Referee #1, 23 Dec 2022
This manuscript presents the simulation of meteorological parameters and pollutant species over the small island of Hateruma. The manuscript also investigates the impact of biomass burning emissions and close-by anthropogenic emissions for ethane and propane on this island. I do think that the study is aligned well to a Measurement Report and does provide new results. However, I have indicated major revisions as there are quite a few areas where I recommend further motivation and explanation, as well as more precise discussion of the results. These gaps do impact on the current scientific quality and presentation quality, which is why I have indicated that they are "major".
I have made detailed comments on the attached that I recommend are considered. -
RC2: 'Comment on acp-2022-703', Anonymous Referee #2, 09 Feb 2023
Adedeji et al. (2023), Measurement Report: Assessment of Asian emissions of ethane, propane, carbon monoxide, and NOx based on observations from the island of Hateruma. This paper presents a long-term dataset collected at a southern Japanese island ideally situated as a receptor location for East Asian airmasses. The dataset is used in an assessment of the GEOS-Chem model, with evaluations of both meteorological and chemical parameters. The authors investigate the influence of biomass burning and scale the VOC emissions to achieve better magnitude agreements between the observations and the model.
This paper shows the utility and importance of these long-term remote datasets in environmental monitoring, and the dataset presented is well suited to the publication. However, some structural issues need to be addressed in the paper, and the analysis is lacking some detail and focus. I have indicated major revisions based on these assessments, but ultimately want to see this work in the publication record. I look forward to seeing a revision of this paper.
Major Comments:
- The number of figures in the paper is excessive for the analysis presented. Section 3 is the area that needs the most work; moving the time series to the appendix, consolidating the scatter plots into a single figure, and summarizing the quartile comparisons between the model and observations as a figure will focus the discussion considerably. I would also encourage a seasonal analysis of the data presented in the model/observation comparisons, which is interesting scientifically and will better motivate your use of a seasonal split in your later analysis.
- Figure 2 and the back trajectory calculations are presented in the introduction but should be moved to the results. As the paper currently stands this figure is only used to motivate analyzing the winter data separately from the summer data in the VOC scaling simulations. However, leveraging these back trajectory groupings to get at source signatures can be very interesting for this type of receptor site analysis. For instance, when you get airflow from the oceans (North and South Pacific), does the model capture the background ozone adequately? Do you see ratio changes between ethane and pentane based on air mass source?
- The Biomass burning analysis is interesting but feels incomplete. Is there a reason that there wasn’t a run with no biomass burning at all? It seems like it would have helped clarify the behavior in Figure 12. This section is also very focused on the ethane disagreement, but biomass burning sources will influence all your chemical variables; does the biomass burning have any influence on the NOy modeling issues? Aging of smoke will also change the chemical characteristics of the measured air masses, and you have a dataset that allows some investigation on the impact of aging during transport. Motivating this section more thoroughly in the introduction will help structure the analysis here.
- I struggled with section 5 (scaling the anthropogenic emissions) and would like to see this section clarified. This is another area where leveraging the back trajectory groupings would have been useful in the calculations (i.e. defining the background). While the overall outcome of scaling up the emissions seems to have solved some of the magnitude issues in the model comparison, there’s quite a bit more here that can be probed with respect to sources. This is an area where trace gas ratios coupled with back trajectories can clarify some of weirdness in the model/observation mismatches and help really get at where the model is struggling. I was also curious if the % changes in OH and Ozone you show in Figure 16 match observations.
- In general I encourage the authors to look at the use of trace gas ratios in their analysis. The ethane/pentane ratio is a published metric, and even ratios to CO can be very helpful for understanding transport and aging. If CO2 is available for this time period, CO to CO2 ratios can be extremely useful in receptor site analysis and has been used previously in East Asia air quality research.
Technical Comments:
- In general, I would like to see more details in the experimental section. You need more information in your measurement table (frequency of measurement, uncertainties, LOD), and I would have liked to have seen a summary table of the simulations run. For instance, I had missed that there was a no shipping traffic run.
- Check the layer order on figures; in the time series figures you occasionally swap which line is on top.
- There are superfluous figures in this paper; I recommend moving all of the time series comparisons to the appendixes and consolidating scatter plots into single figures (ie, section 3). I would also recommend consolidating your two map figures into a single map or moving the biomass burning domains to the appendix.
- The paper needs a brief editing pass for grammar and spelling.
- The scatter plots need to have labels for the 1:1 lines.
- I would also recommend adding subfigure lettering, following the journal guidelines.
- In Figure 12 I recommend adjusting the legend labels for clarity.
- Please be explicit about your time period of study.
- One of the points made in the introduction is the use of geologic sources ethane and propane; was this a novel addition to the model? It isn’t clear from the text, but this seems like a neat addition that wasn’t probed further in the modeling.
Citation: https://doi.org/10.5194/acp-2022-703-RC2 -
RC3: 'Comment on acp-2022-703', Anonymous Referee #3, 12 Feb 2023
Adedeji et al. present a comparison of observations of atmospheric composition over a regional background station (Hateruma island) with the GEOS-Chem model. The impacts of anthropogenic, biomass-burning, and ship emissions were discussed by conducting sensitivity simulations and classification of different air masses using back trajectories. I have the following comments/clarifications for the authors to consider during the revision.
- How do the observed levels and seasonality at Hateruma compare with other background sites in Asia (oceanic regions or high-altitude mountains)?
- Air mass trajectories and observations should be analyzed more deeply to identify regions where emissions are underestimated, instead of considering a general increase across Asia.
- How well the atmospheric dynamics/transport has been captured by the model (e.g., in comparison to ERA5 reanalysis). Describe any nudging that has been considered to limit the errors.
- Section 3.1: l.129: does the high-resolution simulation tend to better capture the local environmental effect!
- Figures 4-5-6 can be combined into a single figure with 3 rows, similarly figures 7-8-9.
- Manuscript needs a language check/improvement; e.g., replace “NOy observations are in overestimate by model” by “NOy is overestimated by the model”
- How much the correlations between the model and observations (r values) change when emissions are tuned?
- NOx/NOy bias seems the major limitation of the model at Hateruma island; is this also the case with other stations in Asia!
Citation: https://doi.org/10.5194/acp-2022-703-RC3
Adedayo Rasak Adedeji et al.
Data sets
Assessment of Asian emissions of ethane, propane, carbon monoxide, and NOx based on observations from the island of Hateruma Adedayo R. Adedeji, Stephen J. Andrews, Matthew J. Rowlinson, Mathew J. Evans, Alastair C. Lewis, Shigeru Hashimoto, Hitoshi Mukai, Hiroshi Tanimoto, Yasunori Tohjima, and Takuya Saito https://drive.google.com/drive/folders/1SRQ6eqqBrKN3xBI-4_-SMDGpdNDI0qPj?usp=sharing
Adedayo Rasak Adedeji et al.
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