the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessment of the impacts of cloud chemistry on surface SO2 and sulfate levels in typical regions of China
Jianyan Lu
Sunling Gong
Jian Zhang
Jianmin Chen
Lei Zhang
Chunhong Zhou
Abstract. A regional online chemical weather model WRF/ CUACE (China Meteorological Administration Unified Atmospheric Chemistry Environment) is used to assess the contributions of cloud chemistry to the SO2 and sulfate levels in typical regions in China. By comparing with several time series of in-situ cloud chemical observations on Mountain Tai in Shandong Province of China, the CUACE cloud chemistry scheme is found to well reproduce the cloud processing the consumption of H2O2, O3, SO2 and sulfate, and consequently is used in the regional assessment for a heavy pollution episode and monthly average in December 2016. During cloud availability in heavy pollution episode, the sulfate production increases 60–95 % and SO2 reduces over 80 %. And the cloud chemistry mainly affects the middle and lower troposphere below 5 km as well as within the boundary layer, and contributes significantly to SO2 reduction and sulfate increase in east-central China. Among the four typical contaminated regions in China, the Sichuan Basin (SCB) is mostly affected by the cloud chemistry, with the average SO2 abatement about 1–15 ppb and sulfate increase about 10–70 μg/m3, followed by Yangtze River Delta (YRD) where SO2 abatement is about 1–3 ppb and sulfate increase is about 10–30 μg/m3. However, the cloud chemistry contribution to Pearl River Delta (PRD) and North China Plain (NCP) are not significant and weaker than other two regions due to lighter pollution and less water vapor, respectively. In addition, the average contribution of cloud chemistry during the pollution period is distinctly greater than that for all December. This study provides a way to analyze the over-estimate phenomenon of SO2 in many chemical transport models.
Jianyan Lu et al.
Status: final response (author comments only)
-
RC1: 'Comment on egusphere-2023-521', Anonymous Referee #1, 12 Apr 2023
The manuscript accesses the contributions of cloud chemistry to the SO2 and sulfate levels by using WRF/CUACE. The model was used to simulate H2O2, O3, SO2, and sulfate on Mountain Tai and compared with the observations to verify the CUACE cloud chemistry. Then, the CUACE cloud chemistry was used in the regional assessment in December 2016 and a heavy pollution episode in four typical contaminated regions in China. The accessed cloud chemistry in model could well simulate the changes of SO2 and sulfate during the heavy pollution.
- As the authors stated, the most important question of this manuscript is the inappropriate Henry’s Law constant used in their model. They have used right Henry’s law and re-run the model for all the cases described in the paper. However, line 117-119: “The Henry’s law constants used in (6) to (8) are listed in table 1. The equilibrium constant KHS in Table 1 is set to be 1.23×10-3 M/atm in CUACE which is the same to that in Von et al (2000) but is 103 times lower than that in Leighton et al(1990).” While in the Table 1, Henry’s law constant was 1.23 M/atm. What exactly Henry’s Law constant used in the article?
- What’s the main differences between RTCLS and RT? I can’t understand these in the part “2.2 Assessment criteria”.
- Line 187: changed to “Although the R, RAD, and NMB of H2O2 in CP-2 is 0.06, 18%, and -19.6%, the simulated mean value of H2O2 is closer to the observed mean value than CP-1.”
- Line 189: “ For sulfate…” there are two data of R and NMB, these data belongs to what?
- Line 194-196: I can’t understand these discussions of SO2, O3 and H2O2 belongs to which periods.
- Line 197: “In addition, CP-2 shows the observed concentration of H2O2 is increased, compared to CP-1”. I can’t understand this sentence. What was used to compare?
- The paragraph from line 194-207 should be rewritten, the descriptions are very unclear, related to different substances, different periods, different years. The last two sentences are too similar.
- I can’t understand how to give the conclusion in line 209-210. The authors should explain the Figure 3.
- Line 229 and 230, one is “RMSEs”, the other is “RSME”. What’s the meaning of them?
- Line 354: delete “has been”
- There are still so many sentences are unclear and have typographical errors and redundant information. The authors need to improve the English of the whole manuscript.
Citation: https://doi.org/10.5194/egusphere-2023-521-RC1 -
CC1: 'Reply on RC1', Lu Jianyan, 02 May 2023
Dear Anonymous Referees,
Thank you very much for your thorough review of the manuscript. We have read the editor’s and the reviewer’s comments carefully, taking all of the reviewer’s comments into consideration and revised the manuscript accordingly. All the changes have been highlighted in the revised manuscript. Our detailed responses, including a point-by-point response to the reviews and a list of all relevant changes, are as follows:
Q1: As the authors stated, the most important question of this manuscript is the inappropriate Henry’s Law constant used in their model. They have used right Henry’s law and re-run the model for all the cases described in the paper. However, line 117-119: “The Henry’s law constants used in (6) to (8) are listed in table 1. The equilibrium constant KHS in Table 1 is set to be 1.23×10-3 M/atm in CUACE which is the same to that in Von et al (2000) but is 103 times lower than that in Leighton et al(1990).” While in the Table 1, Henry’s law constant was 1.23 M/atm. What exactly Henry’s Law constant used in the article?
A: Yes. According to Von et al (2000) and Chameides et al (1984), the equilibrium constant KHS is 1.23 M/atm. Therefore, we have used 1.23 M/atm as the equilibrium constant KHS in all cases of this paper. This Henry’s law constant KHS has been listed in Table 1, which has been corrected into line 130:
“The Henry’s law constants used in (6) to (8) are listed in Table 1. ”
Q2: What’s the main differences between RTCLD and RT? I can’t understand these in the part “2.2 Assessment criteria”.
A: RTCLD and RT both mean the ratio of a chemical species concentration, but they are defined at different stages of the model. RTCLD refers to the change ratio of substance i concentration before and after cloud chemical process within the model. RT represents the concentration ratio change of the substance i obtained with and without cloud chemistry, and is the ratio of the results of two model runs.
We have redescribed these two parameters into line 138-139:
“RTCLD refers to the concentration change ratio of substance i before and after the cloud chemical processes in a model run.”
In line 146-147:
“and the RT represents the concentration change ratio of the substance i with and without cloud chemistry in separate model runs:”
Q3：Line 187: changed to "Although the R, RAD, and NMB of H2O2 in CP-2 is 0.06, 18%, and -19.6%, the simulated mean value of H2O2 is closer to the observed mean value than CP-1."
A: Yes, you are right. We have changed this sentence in line 209-211:
“Although the R, RAD, and NMB of H2O2 in CP-2 is only 0.06, 18.0%, and -19.6%, the simulated mean value of H2O2 is closer to the observed mean value than that in CP-1 (RAD = 22.4%, NMB = -36.6%).”
Q4: Line 189: “For sulfate…” there are two data of R and NMB, these data belongs to what?
A: As listed in Table 3, the two Rs and NMBs are for Case CP-1 and CP-2, respectively.
We have revised this sentence in line 211-213:
“For sulfate, the simulated correlations are good with R of 0.32 and 0.54 for CP-1 and CP-2, respectively, but the model underestimates sulfate concentrations with NMB of -71.0% and -59.4% in CP-1 and CP-2.”
Q5: Line 194-196: 1 can't understand these discussions of SO2, O3 and H2O2 belongs to which periods.
Q6: Line 197: "ln addition, CP-2 shows the observed concentration of H2O2 is increased, compared to CP-1" I can't understand this sentence. What was used to compare?
Q7: The paragraph from line 194-207 should be rewritten, the descriptions are very unclear, related to different substances, different periods, different years. The last two sentences are too similar.
A: Yes, we have rewritten this paragraph in line 217-225:
“Another interesting point that is simulated correctly by the model is the increasing trend of H2O2 and the decreasing trend of SO2 from CP-1 to CP-2 (Table 3), representing year of 2015 and 2018, respectively. It was found that the observed and simulated mean values of H2O2 are 26.5 and 16.8 μM in CP-1, to 46.9 and 32.4 μM in CP-2, respectively. For SO2, the observed and simulated mean values are 2.2 and 2.3 μg/m3 in CP-1, to 0.6 and 0.6 μg/m3 in CP-2, respectively. The simulation results are consistent with the trends of other observational studies (Shen et al., 2012; Li et al., 2020b; Ren et al., 2009; Ye et al., 2021) The SO2 trends may be attributed to the relevant national emission control measures, but the increasing trend of H2O2 and O3, indicating an increasing oxidation ability of the atmosphere in the eastern part of China, needs further investigations.”
Q8: I can't understand how to give the conclusion in line 209-210. The authors should explain the Figure 3.
A: We have presented a more detailed description in line 226-231:
“To further evaluate the model performance, Figure 3 shows the satellite cloud maps, simulated column clouds, and simulated liquid water content at 8:00 LST on June 24, and 8:00 LST on June 25 in CP-1. At both times, the model's column clouds and liquid water distribution are consistent with the cloud distribution observed by the satellites. This indicates that the model's simulation of cloud distribution regions is realistic and the cloud chemistry initiation mechanism, cloud-water environment, is reasonably simulated.”
Q9: Line 229 and 230, one is “RMSEs”, the other is “RSME”. What’s the meaning of them?
A：All the “RMSEs” have been corrected into “RMSE”, which is showed in Table 4.
Q10: Line 354: delete "has been"
A: Yes, we have deleted it.
Q11: , There are still so many sentences are unclear and have typographical errors and redundant information. The authors need to improve the English of the whole manuscript.
A: Thanks for your suggestions. We have thoroughly revised the manuscript and highlighted the corrections in the revised manuscript.
The references newly added are listed as follows:
- Chameides, W. L.: The photochemistry of a remote marine stratiform cloud, J. Geophys. Res., 89, 4739-4756, https://doi.org/10.1029/JD089iD03p04739, 1984.
-
RC2: 'Comment on egusphere-2023-521', Anonymous Referee #2, 08 May 2023
The work by Lu et al. investigates the potential contribution of in-cloud processing to sulfate under highly polluted conditions in China. The WRF-CUACE model was used in this study. Model simulations were first compared to the observations on Mt. Tai, and then applied to other key regions in China where the model results are also compared to a large dataset of observations. The work finds high contributions of in-cloud oxidation to sulfate in some areas in December 2016 when the sulfur pollution level was high. While other studies suggest several pH-dependent aerosol pathways as the main contributors to sulfate in China, this study provides an evidence to highlight the role of in-cloud oxidation. Model simulations however may have large uncertainties from incomplete representations of chemistry and emissions, which needs to be justified more in the result discussion. A throughout English polishing is also necessary. I recommend it for publication in Atmospheric Chemistry and Physics after all the following comments being addressed.
Specific comments:
- Line 70-71: What kind of models here? Is this overestimation a common problem for all models, all seasons or years? Is it related to problems in inventory or chemistry? How different do these models treat the formation of sulfate? The authors conclude in the abstract that this study provides a way to analyze the overestimation. I don’t think this is clear yet.
- 2.3.1 and Sect. 3.1: For hourly model-observation comparisons, it is better to show them in time series instead of scatter plots in Fig. 2 so we can exam the model performance of catching cloud processing.
- Given the low R values of 0.06-0.4 and the clear difference in means (Table 3): the statements in Lines 185, 190, and 193 seemed inappropriate.
- Line 178-193: The model underpredicts the sulfate concentrations at Mt. Tai a lot (Table 3). The authors explain this as the incomplete model representation of other in-cloud pathways. What in-cloud pathways are missing in the model scheme? To what extent the underestimated O3 and H2O2 affect the in-cloud production of sulfate? More importantly, what are the aerosol history of the observations? Can aerosol pathways, e.g., the Mn-catalyzed oxidation [W Wang et al., 2021] or the H2O2 oxidation [Liu et al., 2020], be the main reason of the underestimation?
- Line 198-207: I am quite confused about what was stated here. This part needs to be rewritten. The increase in atmospheric oxidation and decrease of SO2 over years is not simulated by the model.
- Line 208-215: The analysis here is too brief. Please enrich to help readers understand. For the cloud liquid water, what are the observations and why the authors claim that the simulations are consistent with the observations? The simulations overestimate the cloud fraction, why and does it matter? Why do the cloud liquid water contents in Fig. 3 and 4 look different for 8:00 LST on the same dates? The sentence from Line 212-215 is long and grammatically unacceptable.
- Line 216-218: The statistical values shown in this section do not sufficiently support this summary. The authors can compare their model performance to other model studies with similar comparisons to prove the goodness of the simulations here. Observational uncertainties should also be considered.
- Line 225-227: I don’t observe this from Table 4. Maybe remove this sentence to avoid over-interpretation.
- Line 237-238: This is not true. After cloud evaporation, aerosol remains and can be reactivated again in the next cloud cycle. The authors need to consider the history of surface aerosol and the time scale of cloud processing.
- Line 249-252: How close? Please be specific. Comparing to other model studies for PM5, O3 and SO2 in those regions, is this model performance a good one (i.e., within a factor of two and similar means over the month)? It was concluded that the model captures well the variability of the pollutant concentrations. Do you mean spatial variability or temporal variability? Some of the R values in Table 5 are low.
- 3: Please provide the sample size for the four regions in Tables 4-8 in Sect. 2. For the whole-month comparisons of hourly SO2 and PM2.5 concentrations, I imagine some sites might not be represented well in the model. This should be discussed in Sect. 3 when presenting the modeled cloud contributions.
- 3.2.3 and Sect. 3.3: Are the simulations here consistent with other’s results? Comparisons to other studies should be added. For example, Aerosol surface pathways have been widely suggested in model studies for the sulfate formation [Li et al., 2018; T T Wang et al., 2022; and references therein]. Wang et al. showed in-cloud oxidation can only contribute a few percent of the surface sulfate mass in NCP [T T Wang et al., 2022; 2021]. Without implementing those mechanisms, the matches with the ground observations of the sulfate or PM2.5 mass in the model possibly means an overestimation of sulfate herein.
Also, the cloud-chemistry was evaluated for Mt. Tai for summer. When applying the model to regions other than Mt. Tai and to winter not summer, emission biases can be different. The model performance in different regions needs more careful analysis. Given that all the presented model results are associated with the model bias, model uncertainties should be discussed. It should be clear about how the potential model bias may affect some of the conclusions in Sect. 4.
- Line 263-265, 272-273, 282-292: The in-cloud contributions here are all simulated quantities, for which the authors need to bring up the comparisons to specific observations (not the whole region) to justify their conclusions. For example, in Line 282-284, the cloud processing can lead to up to 225 μg/m3 of sulfate, which seems extremely high. I am wondering for that specific time (21:00 LST on 20 December), what the observed PM5 concentrations are in SCB or Hangzhou Bay. If the model performance isn’t very good at that time, the conclusions might not be correct. I think the current manuscript was written in an over-quantitative way, which need to be revised with more careful analysis.
Technical remarks:
Line 50: “a Mount site” or a mountain site?
Line 76: Define “CMA” here not in Line 163.
Line 137-140: Awkward sentence. Please rewrite.
Line 141: Two “with”. Please rewrite.
Line 142: Units for 100×104 and 88×94?
Line 148-151: Awkward sentence. Please rewrite.
Line 159-161: Usually full terms go first with abbreviations in parentheses.
Line 164: I think you mean “air pollution” here.
Line 167-169: Are those cities? PRD, YRD, NCP, and SCB have been defined previously.
Line 169: “elements” should be “parameters”.
Line 174: “by five sectors of power…” should be “from power, industry, … and agriculture sectors”
Line 175: Why 2017?
Line 194-195: This is an incomplete sentence.
Line 228: Add a “,” after “wind speed”. Change “previous researches” to be “previous findings”.
Line 230: Delete “proposed by Emery et al.”
Line 232: What is very small? Wind speed?
Line 240-242: Awkward sentence. Please rewrite. Also, the following paragraph is redundant. That information can be merged into the analysis.
Sect. 3.2.1 and 3.2.2 can be combined. “Pollutants Evaluation” sounds strange.
Line 247: Delete “also”.
Line 248: Delete “figure omitted”.
Overall, Sect. 3 is poorly written and wordy. Please revise the whole section for English.
Line 340: Add the year and month to the dates.
Tables 3-8. I believe the results in the tables are mean concentrations or values. Please clarify.
The figure caption for Fig. 1 isn’t clear and has incorrect punctuation.
The color bars are missing in Fig. 2.
Please check the roles of the publisher and update the figures and captions accordingly (https://www.atmospheric-chemistry-and-physics.net/submission.html#figurestables). The terms of FY-2G cloud in Fig. 3 are redundant. Color bars can be combined for each of the two panels. The dates in the figure caption can be marked in the graph instead. Add descriptions about what the cloud image show (cloud fraction?) and what the triangle is. The font size in a3 and b3 is should be the same as others. Check the unit of liquid water content in Fig. 4. It is different from Figs. 3 and 5. It is confusing about the red triangle in a3 and b3 (real color in terms of simulated liquid water content?). Similar to Fig. 3, color bars in Figs. 4, 5, and 8 are repeated unnecessarily. The repeated legends in Figs. 10 and 11, the unnecessary frames in Figs. 6-8 and 10 make the graphs look ugly. The figure captions in Figs. 6-8, 10, and 11 and all table captions need to be revised for English. Please clarify that there are the mean values or concentrations listed in the tables not median or something else.
Table 8: “sellected” should be “selected”. It is better to not use abbreviation as “the whole Dec.”
[Reference]
Li, J., et al. (2018), Radiative and heterogeneous chemical effects of aerosols on ozone and inorganic aerosols over East Asia, Sci. Total Environ., 622, 1327-1342, doi:10.1016/j.scitotenv.2017.12.041.
Liu, T., S. L. Clegg, and J. P. D. Abbatt (2020), Fast oxidation of sulfur dioxide by hydrogen peroxide in deliquesced aerosol particles, Proc. Natl. Acad. Sci. U. S. A., 117(3), 1354-1359, doi:10.1073/pnas.1916401117.
Wang, T. T., et al. (2022), Sulfate Formation Apportionment during Winter Haze Events in North China, Environ. Sci. Technol., 56(12), 7771-7778, doi:10.1021/acs.est.2c02533.
Wang, W., et al. (2021), Sulfate formation is dominated by manganese-catalyzed oxidation of SO2 on aerosol surfaces during haze events, Nature Communications, 12(1), doi:10.1038/s41467-021-22091-6.
Citation: https://doi.org/10.5194/egusphere-2023-521-RC2
Jianyan Lu et al.
Jianyan Lu et al.
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 244 | 70 | 12 | 326 | 3 | 2 |
- HTML: 244
- PDF: 70
- XML: 12
- Total: 326
- BibTeX: 3
- EndNote: 2
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1