the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Feb 2024
Diverging trends in aerosol sulfate and nitrate measured in the remote North Atlantic on Barbados are attributed to clean air policies, African smoke, and anthropogenic emissions
Abstract. Sulfate and nitrate aerosols degrade air quality, modulate radiative forcing and the hydrological cycle, and affect critical biogeochemical cycles, yet their global cycles are poorly understood. To address this issue, we examined trends in 21 years of aerosol measurements made at Ragged Point, Barbados—the easternmost promontory on the island located in the eastern Caribbean Basin. Though the site has historically been used to characterize African dust transport, here we focused on changes in nitrate and non-sea salt (nss) sulfate aerosol from 1990–2011. Nitrate aerosol concentrations are stable at 0.59 ug/m3 ± 0.04 ug/m3. Elevated nitrate concentrations in the spring of 2010 as well as during the summer and fall of 2008 are due to transported biomass burning emissions from both northern and southern Africa to our site. In contrast, nss-sulfate decreased 30 % at a rate of 0.02 ug/m3/yr in the 1990s, which we attribute to air quality policies enacted in the U.S. and Europe. Starting in 2000, sulfate began to increase to pre-1990s levels of 0.90 ug/m3. We used the Community Multiscale Air Quality (CMAQ) model simulations from the EPA’s Air QUAlity TimE Series (EQUATES) to better understand the changes in nss-sulfate after 2000. The model simulations estimate that increases in anthropogenic emissions, likely from Northern Africa, and increased oxidation efficiency of sulfur dioxide (SO2) explain the increase in nss-sulfate observed in Barbados. Our results serve as an incentive to better constrain emissions from developing countries and their impact on aerosol burdens in remote regions.
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RC1: 'Comment on egusphere-2024-11', Anonymous Referee #1, 01 Mar 2024
Review of Gaston et al., “Diverging trends in aerosol sulfate and nitrate measured in the remote North Atlantic on Barbados are attributed to clean air policies, African smoke, and anthropogenic emissions”
This manuscript describes trends in sulfate and nitrate concentrations measured from 1990-2011 at Barbados, as well as other species such as dust and ions. Nitrate was found to have changed little over the decades examined, while sulfate decreased and then started to increase around 2000. Modeled sulfate from the CMAQ-EQUATES model did not show similar trends, and a model-observation bias in sulfate was reported. Back trajectory analyses suggest that high concentrations of nitrate were associated with transport of biomass burning from Africa. The long record of measurements at Barbados are critical for understanding changes in global emissions of pollutants, and comparisons to model results are useful for understanding observations, as well as identifying potential biases within the model. This paper is fairly well written, however there are some potential issues and questions with some analyses which are described as part of the comments listed below. I recommend publication after addressing comments and updating the manuscript.
Line 18: Does this nitrate concentration reflect an average over this entire period? Does the 0.04 ug/m3 reflect a standard deviation or uncertainty?
Line 18 and onward: Please use the micrometer symbol for concentrations.
Line 21: It would help to list the years over which this reduction is true.
Line 22: Include end date here (e.g., from 2000 through 2011).
Line 23: The time frame here is 1990 and later? How do these levels correspond to pre-1990?
Line 51: This is unclear. Are the authors implying that emission inventories are lacking at observation sites?
Line 71: Both “U.S” and “US” are used- check text for consistency.
Line 80: Define “EU”
Line 89: What about fires in the U.S. and Canada, such as those that influenced the eastern U.S. in 2023?
Line 122: What is the size-cut of these measurements? How long are samples collected?
Line 135: Have any changes of these measurements occurred over this period that could influence trends? Why are data after 2011 not included?
Line 135: Include statements of how trends are calculated? Are they simple OLS regression? How is statistical significance determined?
Line 151: How are concentrations below minimum detection limits dealt with?
Line 178: I assume the ‘unexpectedly increased’ is referring to observations, not model?
Line 179: Line 166 says model is available from 2002 through 2019?
Line 184: Does the model predict ions or total sulfate, nitrate, calcium, etc.? What is the size range of the model output?
Line 184 and 189: Ions are written both as their chemical symbols and as words- check paper for consistency.
Line 194: Does the crustal abundance of Ca+2 refer to North Africa?
Line 208: What year does “goes back in time far enough” refer to?
Line 210: Examine trends in what?
Line 213: The section titles don’t need the results of the section included and can be simplified.
Line 226: What were trends in dust?
Line 233: Trends in what? Perhaps replace this with “concentration” since different years in each site are being considered, and so trends are difficult to compare.
Line 237: For additional comparisons/validations to the Barbados results, consider sulfate and nitrate data over the same periods from the IMPROVE site in Virgin Islands.
Line 249: Same comment as above: what are the trends in dust.
Line 250: What is the correlation coefficient between nss-sulfate and dust?
Line 315: Can the section title be generalized? It seems to include three very different topics.
Line 318: Line 320 suggests that that not all seasons show this behavior (not winter).
Line 352: The caption states that Figure 6 shows concentrations but the y-axis shows percentage change. I suspect this is done to plot emissions and concentrations on the same axis but perhaps have a secondary y-axis so actual concentration and emission values can be shown? Or change the caption and describe the % change in the text. How was % change calculated? Relative to what?
Line 361: Was only one model grid used to represent Ragged Point? Are the surrounding model grids consistent in concentration values?
Line 362: It seems like Figure S5 shows Na, not dust? Figure S6 shows dust.
Line 367: Typo: Dessert
Line 369: Can the authors provide more information on nss-sulfate estimates from the model? Were these determined from modeled sodium (I see this is referred to in the figure 8 caption- perhaps include in the text)? Figure S5 shows sodium concentrations that are 2-3 times higher than observations. How much uncertainty is in modeled nss-sulfate based on the difference in these measured and modeled sodium values?
Line 373: These percentages are the % relative to total? What years do these values refer to?
Line 387: Were daily data used to calculate performance evaluation statistics?
Line 389: Define SI.
Line 391: And 2005 (greater than 20%)
Line 394: What trend are the authors referring to?
Line 395: Typo: beings
Line 402-402: What are the implications for non-US emissions in CMAQ?
Line 406: What does “filter observations were limited” mean?
Line 408: include “model” here when referring to the high concentrations.
Line 416: What level of the model does SO2 refer to? What does “region near Barbados” mean (line 417).
Line 416: This analysis supposes that the sulfate oxidation is happening near the Barbados site, not necessarily that the sulfate has been transported. Is that a realistic assumption? Are there many sources of SO2 on Barbados? If not, it has to be assumed that the SO2 is transported to the site as well? The same assumptions are true for H2O2?
Line 425: Since the oxidation ratio is calculated with modeled SO2 and measured nss-SO4, can the authors describe how they accounted for the model bias in SO4 and its potential influence in the oxidation ratio? For example, without SO2 measurements, it is hard to say whether there is a bias in SO2 in the model, however, given the bias in nss-SO4, it’s possible and perhaps likely that there is also a bias in SO2. Doing a ballpark calculation based on the reported mean bias in Table S1 and the oxidation ratios shown in Figure 9, the correlation between the mean bias and the oxidation ratio is r = -0.7. Have the authors calculated the ratio with the modeled nss-SO4? Using eyeballed-values of the nss-sulfate concentrations from EQUATES in Figure S8 and the SO2 in Figure 9 to calculate the oxidation ratio, the correlation for the change in the modeled oxidation ratio over time is r=0.18. While it’s possible that the oxidation ratios in Figure 9 are real, the authors need to provide more evidence that the model bias is not influencing these estimates.
Line 439: Define RH
Line 444: The evidence related to the influence of anthropogenic emission impacts on trends could be strengthened. Without knowing the behavior in benzene or CO prior to 2002, it is inconclusive to state the role of the anthropogenic emissions influencing Barbados since it is unknown whether they were increasing before 2002?
Line 449-450: This statement seems contradictory to line 275-277 and 455.
Line 469-470: This statements has not been supported by strong evidence in the paper. Figure 5 shows one month for two separate years. Comparing back trajectories for all years before and after the shift in sulfate trends would support this statement. Or the model could show transport over time as well? Are there sources of African SO2 emissions that could help strengthen this statement? Does the EQUATES model show increased SO2 in the emission inventories in Africa?
Line 477: Over what time period are the highest increases in SO2 occurring?
Line 488-489: This may be true but it has not been shown in the paper (or did back trajectories suggest this?)
Line 511: Again, doesn’t the oxidation ratio correspond to the site location? This assumption is that the SO2 is not converted before it arrives? Others have shown the importance of heterogeneous reactions with dust particles and SO2 (e.g., Wang et al., 2018; Park et al., 2019), so how does that mechanism influence this argument?
Citation: https://doi.org/10.5194/egusphere-2024-11-RC1 -
RC2: 'Comment on egusphere-2024-11', Anonymous Referee #2, 03 Mar 2024
This study utilizes aerosol measurements from the Ragged Point site in Barbados, in conjunction with model simulations, to comprehend the effect of implementing air quality policies in the US and EU. This perspective is very interesting, given that the observation site is situated significantly distant from both the US and EU in a remote region. The implementation of air quality measures is expected to certainly influence local aerosol changes, but the effects on areas far away from the US and EU remain unclear. The long-term observational data is unique and intriguing. The data and the story have been presented clearly and meticulously. However, trace gases observation, such as NOx, SO2, and CO2 are absent in these studies, making the results less robust. The author has utilized simulated data to aid in understanding the information, which is helpful but also comes with high uncertainties. Furthermore, why is there no measurement data available after 2011? The current dataset is rather dated. I also have a concern regarding the absence of information from South America, a region geographically closer to Barbados. The local sources there may have less stringent air quality controls. In summary, addressing the following questions and comments would potentially lead to its acceptance.
Main comments:
- From the map, the observation site BACO is closer to South America, making its effect on BACO's aerosol more interesting to me. Do you know the ratio of wind from South America? The current study focuses on the effect of North America and Africa, which is good and interesting. However, how significant is the fraction they represent compared to the effect from South America?
- What is the aerosol and SO2 lifetime compared to the travel time of air masses from North America to the site?
- Check all figure number in the text.
- Anthropogenic emission is one of SO2 sources. However, in this study, when examining the air mass from the ocean, we cannot ignore the oceanic emission of SO2 from dimethyl sulfide (DMS), which may be affected by the ocean acidification and other parameters. In the section of discussion and conclusion, the authors exclude DMS by model simulation. However, the uncertainty of DMS simulations in the model are not well refined. It would be good to find some other possible evidence to support it, e.g., recent publication about DMS vs CO2. The contribution of local SO2 to aerosol and transported SO2 from US and EU need to be evaluated carefully.
Detailed comments:
Line 18-20, ‘Elevated …. in the spring of 2010 and summer and fall of…. biomass burning emissions to our site’, make the sentence more clear.
e.g., ‘as well as during the summer and fall of 2018’, ‘transported biomass burning emissions from both northern and southern Africa to our site.’
Line 25: change ‘predicts’ to ‘simulates’ and apply the same for the following instances.
Line 33: add ‘e.g.,’ to ‘(NOy)’.
Line 110: Information regarding the distance of the site from anthropogenic emissions is missing. Additionally, there is no information about the site's distance from various sources (line 245 mentioned multitude emission sources).
Line 125:126: Your pump was on when the wind blows from the ocean, thereby excluding emissions from the local islands. What is the ratio of the ocean wind to the wind from south America. However, does this also exclude anthropogenic emissions from the land direction, basically South America?
Line 224-226: What does it mean? Does it imply that these two references have already presented similar results and provided explanations? If that is the case, please specify. If not, kindly make a statement regarding the changes in NOx and SO2 during the same period as shown in Figure 1. The current information is unclear and difficult to comprehend.
Fig 2: why not add the data from Bermuda.
Line 233-245: The entire paragraph aims to indicate that Ragged Point is a more remote location and may be influenced by various emission sources. However, questions arise regarding the local measurements of SO2 and NOx—do they exhibit similar trends to aerosols? Additionally, an inquiry is posed regarding the multitude of emission sources and how they impact aerosols in Ragged Point.
In line 125-126 you already mentioned that your pump was on when the wind direction blows from the ocean. Then how about the data from Bermuda? Add more details for Bermuda.
Line 246-255: Why is there no correlation between dust and sulfate, but a modest correlation between nitrates and dust? Add more details for this.
Line 264: Seasonal trends?
Line 269-271: The days of back-trajectories for 2009 and 2010 MAM are different, does it make any difference for the analysis?
Figure 4: Mark the year for figure 4d.
Line 308: The impact of Amazon biomass burning on aerosol levels measured at BACO can only be observed when the air mass originates from the South American continent. Current evidence in this study is not strong enough to me.
Line 360-368: can be in the method.
Change the order of Fig 7 and Fig 8.
Line 412-417: can be in the method.
Line 436-437: How about the agreement of nss-K+ between observations and simulations?
Line 457-460: Compared to African wildfires, how about Amazon wildfires, which are much closer and larger.
Line 463-466: How fast the SO2 can be transported from US and EU to our observation site? Compared to SO2 lifetime?
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