the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Mar 2023
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.
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Jianyan Lu et al.
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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.
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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 -
AC1: 'Reply on RC2', Sunling Gong, 30 May 2023
Dear Anonymous Referee,
Thank you for your thorough review of the manuscript. We have taken all of the your 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 review and a list of all relevant changes, are as follows:
Q1: 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.
A: Yes, the overestimation was a common problem. We described it in detail in line 77-84:
“Some models have reported that they failed to reproduce SO2 and sulfate, particularly underestimating sulfate and overestimating SO2 over China (Buchard et al., 2014; Hong et al., 2017a; Wei et al., 2019; Cheng et al., 2016). These are mainly caused by the uncertainties in meteorological conditions (Sun et al., 2016) and emission inventories (Ma et al., 2018; Hong et al., 2017b; Sha et al., 2019a;), as well as unclear and/or inaccurate physical and chemical mechanisms associated with air pollutants (He and Zhang, 2014; He et al., 2015; Georgiou et al., 2018; Sha et al., 2019b). The inadequate inclusion or lack of cloud chemistry of SO2 consumption simulations was one of the main causes (Ge et al., 2021; Cheng et al., 2016). ”
Q2: 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.
A: A time series might be clear than the scattered plots. We choose to use the scatter plots due to the factor that there are a lot of missing H2O2 and sulfate observations at Mount Tai. Those selected from the observations have to meet two conditions: 1) there are clouds over the Mount Tai from satellite image, 2) there are observations.
However, we have added the time series plots of O3 and SO2 both for simulation and observation for 2018 as an example (Fig. S1).
Figure S1. Time series of the simulated and observed O3 and SO2
Q3: 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.
A: We have deleted the sentence in line 185 in the revised manuscript, and rewritten these sentences in line 217-229:
“Some reasons might contribute to the underestimations. Firstly, the latitude of the observed site at Mount Tai is 1483 meters, which may be in the boundary layer during the day time and in the free atmosphere during the night time in summer (Zhu et al., 2022). Therefore, the diurnal variation of the boundary layer affects the three-dimensional concentration distribution of oxidants and aerosols (Zhao et al., 2013; Peng et al., 2021), and influences the development of cloud formation. Secondly, there exists model bias due to the difficulties to represent the complex topography of Mount Tai and the cloud physics. Thirdly, the cloud chemistry in CUACE lacks the pathway for TMI-catalyzed oxidation and NO2-catalyzed oxidation as well as some other newly discovered oxidation mechanisms, which can lead to the bias in SO2 and sulfate. Fourthly, typical measurement systems for ambient aerosols easily misinterpret organosulfur (mainly in the presence of hydroxy-methane sulfonate (HMS)) as inorganic sulfate, thus leading to a positive observational bias, e.g., mean bias during winter haze in Beijing is 20% (Moch et al., 2018; Song et al., 2019).”
Q4: 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?
A: Yes. The cloud chemistry mechanism in CUACE has the pathways for the oxidation of SO2 by H2O2 and O3, but lacks the TMI-catalyzed mechanism and NO2-catalyzed mechanism as well as other newly discovered oxidation mechanisms. The result shows H2O2 is the main oxidant for the conversion of SO2 to sulfate. Meanwhile, we have rewritten the reasons for the underestimation of sulfate in line 214-226 as described in Q3.
Q5: 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.
A: The trend is simulated by the model, and we have rewritten this paragraph in line 230-238:
“Another interesting point that is simulated correctly by the model is the increasing trend of H2O2 andthe decreasing trend of SO2 from 2015 to 2018. The observed and simulated mean values of H2O2 are 26.5 and 16.8 μM in CP-1 in 2015, to 46.9 and 32.4 μM in CP-2 in 2018, respectively. For SO2, the observed and simulated mean values are 2.2 and 2.3 μg/m3 in CP-1 in 2015, to 0.6 and 0.6 μg/m3 in CP-2 in 2018, respectively in Table 3. Both the observations and simulations show clearly the increasing trend of H2O2 andthe decreasing trend of SO2 from 2015 to 2018. This conclusion is 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 decreasing and H2O2 and O3 increasinghave been tightly attributed to the national SO2 and particulate emission control measures since 2013 (Lu et al., 2020; Fan et al., 2021)”
Q6: 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.
A: Yes, we have rewritten this paragraph in line 239-245:
“Figure 4 shows the RTCLD of SO2 and simulated liquid water contents at 2:00 and 8:00 LST on both June 24 and June 25 in CP-1 at Mount Tai. The column cloud and the liquid water contents which are consistent with the cloud images indicate that there is cloud with sufficient water vapor in and around the vicinity of Mount Tai (Fig. 3). The SO2 consumption rate (RTCLD(SO2)) distribution is consistent with the liquid water distribution at all four times (Fig. 4). The SO2 depletion rate is above 80% at Mount Tai which is compatible to the observation (Li et al., 2020). All of these indicate that the model can capture the SO2 consumption in the cloudy environment.”
Q7: 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.
A: Yes, you are right. We have rewritten this paragraph in line 246-250:
“In summary, SO2, H2O2, O3 and sulfate concentration are in the same order of the observations, and the mean values of SO2 are close to the observed in the cloud chemistry comparison. WRF/CUACE is also able to simulate the decreasing trend of SO2 and the increasing trends of O3 and H2O2 with year. Therefore, the cloud chemistry mechanism in WRF/CUACE is relatively reasonable to reproduce the cloud chemistry for the gaseous pollutant SO2, sulfate and the important oxidants of H2O2 and O3.”
In terms of model performance, the answer to Q10 has included the other model studies with similar comparisons.
Q8: Line 225-227: I don’t observe this from Table 4. Maybe remove this sentence to avoid over-interpretation.
A: We have deleted this sentence.
Q9: 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.
A: We have deleted this sentence, and rewritten this paragraph in line 265-277:
“Figure 5 shows the satellite cloud images, the column cloud and the liquid water content simulated for the maturation and dissipation stages (19-22 Dec.) of the HPE. The satellite image shows that the cloud coverage region is mainly in the southwest of China besides SCB on the 19th, covering most of eastern China including NCP, YRD, PRD and SCB on the 20th and the 21st, and then moving eastward outside of China on the 22nd (Fig. 4 a1-d1). The cloud distribution fits well with the satellite images (Fig. 4 a2-d2). The column liquid water distribution also moves from west to east as the episode developed (Fig. 5 a3-d3), but is located more southern part of eastern China than that of the clouds. In SCB and YRD, the liquid water content is more abundant, reaching over 100.0 g/m2, than that in PRD, only up to 10.0 g/m2. NCP has the least liquid water content in the four regions, especially in Beijing, Tianjin and northwestern part of Hebei Province ranged 0.001~0.01 g/m2, mostly due to the dry environment and partly due to the overestimated temperature and underestimated humidity in Table 4. Above all, CUACE not only effectively simulates pollution but also provides a relatively reasonable meteorological background basis for cloud chemistry in the heavy pollution periods.”
Q10: 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.
A: Yes, we have rewritten this paragraph in line 280-303:
“The hourly PM2.5, O3 and SO2 concentrations simulated in four regions are compared with the observations (Table 5). Most of the simulations are within a factor of two of the observations (figure omitted), and the mean values of the three pollutants in the four regions are close to the observations in DEC and HPE-2. It is indicated that the model captures the variability of PM2.5, O3 and SO2 concentrations for both DEC and HPE in NCP, YRD, PRD and SCB. During HPE-2, the difference of mean values of SO2 ranged from -7.6 to 10.4 μg/m3, of O3 ranged from -22 to 23.3 μg/m3, and of PM2.5 ranged from -156.5 to 48.8 μg/m3. During DEC, the difference of mean values of SO2 ranged from -21.5 to -1.2 μg/m3, of O3 ranged from 1.1 to 7.7 μg/m3, and of PM2.5 ranged from -71.3 to 1.3 μg/m3. For PM2.5, In the NCP, the R of HPE is 0.84, which is higher than the 0.39 of DEC in PRD. In the NCP, the R of DEC is 0.62, which is higher than the 0.30 of HPE. The R is high for both DEC and HPE in YRD, with the value of 0.73 and 0.70. The differences of R between DEC and HPE are small in YRD and SCB. For SO2, the model simulations are better for HPE in the three regions of NCP, YRD and SCB, than that for DEC. The Rs of HPE and DEC are 0.60 and 0.48 in NCP, 0.61 and 0.45 in YRD, and 0.49 and 0.19 in SCB, respectively. The correlations between observations and simulations for HPE and DEC in PRD are not significantly different, with R of 0.32 and 0.39, respectively.
The ability of CUACE to simulate SO2, O3 and sulfate concentrations have been validated in many previous research applications (Zhou et al., 2021; Zhang et al., 2021). Compared with the PM2.5 concentrations simulated by WRF-CUACE used by Ke et al. (2020), the correlation is 0.41~0.85 in NCP, and 0.64~0.74 in YRD. The ability of other atmospheric models in China has the same performance such as NACRMS, and the correlation is about 0.68 for fine particulate matter in NCP during haze period (Wang et al., 2014).
The overall performance of the pollutants that can be routinely observed from the surface network have been evaluated. Then, the following part of this paper will focus on assessing the effects of cloud chemical processes.”
Q11: 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.
A: Yes. The surface observations used for the analysis in Sect. 3 are all hourly data from 55 city sites from the China National Environmental Monitoring Centre. They are mostly located in the urban area. Usually, there are several observation sites for a city. We then average the data from all the sites by excluding some obviously abnormal at one time and use the averaged data to presently the city.
For the whole-month comparisons of hourly SO2 and PM2.5 concentrations, some sites are not represented well in the model. Therefore, we have added discussion of that in Sect. 3 in line 401-405:
“The statistical metrics of SO2 and PM2.5 hourly concentrations in 55 representative cities with and without cloud chemistry in the model were analyzed. The results indicate that most of the sites are improved with cloud chemistry in the SO2 concentration simulation and 42 of the 55 cities are with the increasing R. In the PM2.5 simulation, the correlations also are improved in some cities after the presence of cloud chemistry.
Q12: 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.
A: Yes. We compared with other studies, and have added in manuscript.
In line 309-319:
“Figure 6 is the mean sulfate concentration for DEC and HPE-2 for SO2 and sulfate. The high and low centers of monthly mean SO2 and sulfate concentrations of CUACE in December 2016 are coincided with the yearly average of the same year by Gao et al. (2021), in the SCB and NCP. For NCP, the mean sulfate concentration in Figure 6b is comparable to that by Wang et al. (2021) and Wang et al. (2022) in December 2016 as both of which increase from northwest to southeast almost in the same magnitude. The sulfate concentrations are low on a monthly basis and high at the pollution maturity stage compared to the average of several pollution processes studied by Wang et al. (2022) in December 2016. The simulation of sulfate concentration is relatively reasonable in NCP. For SCB, sulfate concentrations are compatible to the observed in winter time in 2015 by Kong et al. (2020). The sulfate concentration in Guangzhou is almost twice of the observations formed from aqueous-phase reactions in Zhou et al. (2020) and Guo et al. (2020).”
In line 324-329:
“Ge et al. (2021) have evaluated the effects of in-cloud aqueous-phase chemistry on SO2 oxidation in the Community Earth System Model version 2 (CESM2). They found the results incorporating detailed cloud aqueous-phase chemistry greatly reduced SO2 overestimation, which reduced by 0.1-10 ppb in China, and more than 10 ppb in some regions in Winter. This finding is consistent with the results demonstrated in Figure 7 of this study, where SO2 concentrations are depleted by 0.1-10 ppb in China.”
In line 344-349:
“Sulfate formation rates by H2O2 oxidation under winter haze conditions range from 10 to 1000 μg/m3/s (Fig. S2), which is close to the range of 10 to 100 μg/m3/s tested by Wang et al. (2022) in several pollution episodes in December 2016. The heaviest and longest duration pollution episode that had a large number of clouds and high liquid water content (Fig. 5) on December 19-21, 2016, which are very favorable for the occurrence of in-cloud oxidation processes. Therefore, the in-cloud oxidation in this study is relatively reasonable.”
Figure 8. The mean sulfate concentration for DEC (a, c) and HPE-2 (b, d) for SO2 (c, e) and sulfate (a, b).
Figure S2. Sulfate formation rates by H2O2 oxidation in cloud in HPE-2.
Q13: 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.
A: We agree that 225 μg/m3 sulfate production by cloud chemistry was too high. We have deleted the high ranges of sulfate production 150-225 μg/m3, which was estimated from the plot scale bar. Actual increase by cloud chemistry was not that large (Fig. 9). However, we do find that the observed PM2.5 concentrations up to 350 μg/m3 at 14:00 on the 20th and 236 μg/m3 at 21:00 on the 20th in Chengdu in SCB, up to 76 μg/m3 at 14:00 on the 20th and 77 μg/m3 at 21:00 on the 20th in Hangzhou in YRD (Fig.S3), were very high, partially supporting the cloud production of sulfate production at these specific times. At the same time, our model results showed that sulfate increases by cloud chemistry during these time periods were 10-20μg/m3 and 20-30 μg/m3 14:00 and 21:00 on 20th at Chengdu, 20-60 μg/m3 and 30-60 μg/m3 at Hangzhou.
Unfortunately, we cannot find enough observations related to cloud chemistry in all four regions. For this reason, we can only compare SO2, O3 and PM2.5 with routine observations. As for sulfate, we can only compare it at Mount Tai and with other studies as described in Q12 and Ge et al. (2021).
Figure S3. time series of PM2.5 concentrations in Chengdu and Hangzhou in SCB. S0-S8 represent several observation sites in a city
Figure 9. The differences in surface sulfate concentrations between with and without cloud chemistry at 21:00 LST on 20 Dec. (a), at 17:00 LST on 21 Dec. (b), and at 12:00 LST on 22 Dec. (c) (Units: μg/m3).
Technical remarks:
1). Line 50: “a Mount site” or a mountain site?
A: Yes. We have corrected a mountain site in line 53.
2). Line 76: Define “CMA” here not in Line 163.
A: Yes. We have deleted.
3). Line 137-140: Awkward sentence. Please rewrite.
A: Yes. We have rewritten this sentence in line 155-157:
“Mount Tai with an altitude of 1483 meter, located in central Shandong Province, is the highest point of the North China Plain. It is an ideal observation site for cloud chemistry observation (Li et al., 2017; Li et al., 2020a; Li et al., 2020b).”
4). Line 141: Two “with”. Please rewrite.
A: Yes. We have rewritten this sentence in line 161-162:
“WRF/CUACE is set up with two-domain nesting for the evaluation, and the Riguan Peak is the central point (Fig. 1a).”
5). Line 142: Units for 100×104 and 88×94?
A: Units for 100×104 and 88×94 are grids numbers.
Line 148-151: Awkward sentence. Please rewrite.
A: Yes. We have rewritten this sentence in line 166-168:
“The time period of December 2016 was selected to assess the regional contribution of cloud chemistry to SO2 and sulfate in CUACE as a typical heavy pollution episode occurred from 16 to 21, covering most part of east China with the highest hourly PM2.5 concentration exceeding 1100 μg m-3,”
6). Line 159-161: Usually full terms go first with abbreviations in parentheses.
A: Yes. We have corrected in line 179-181.
7). Line 164: I think you mean “air pollution” here.
A: Yes. We have corrected in line 184.
8). Line 167-169: Are those cities? PRD, YRD, NCP, and SCB have been defined previously.
A: Yes. Those are cities.We have deleted in line 186.
9). Line 169: “elements” should be “parameters”.
A: Yes. We have corrected in line 186.
10). Line 174: “by five sectors of power…” should be “from power, industry, … and agriculture sectors”
A: Yes. We have corrected in line 191.
11). Line 175: Why 2017?
A: The most recent emissions source we have were for the year of 2017.
12). Line 194-195: This is an incomplete sentence.
A: Yes. We have rewritten the paragraph and this sentence has been removed.
13). Line 228: Add a “,” after “wind speed”. Change “previous researches” to be “previous findings”.
A: Yes. We have corrected in line 266-267.
14). Line 230: Delete “proposed by Emery et al.”
A: Yes. We have deleted.
15). Line 232: What is very small? Wind speed?
A: Yes. We have rewritten this sentence in line 262-264.
“The RSME of wind speed and the wind speed for HPE is smaller than that of DEC, which indicates that the model can relatively reasonably capture the static condition.”
16). Line 240-242: Awkward sentence. Please rewrite. Also, the following paragraph is redundant. That information can be merged into the analysis.
A: Yes. We have rewritten this part in line 270-277.
“The column liquid water distribution also moves from west to east as the episode developed (Fig. 5 a3-d3), but is located more southern part of eastern China than that of the clouds. In SCB and YRD, the liquid water content is more abundant, reaching over 100.0 g/m2, than that in PRD, only up to 10.0 g/m2. NCP has the least liquid water content in the four regions, especially in Beijing, Tianjin and northwestern part of Hebei Province ranged 0.001-0.01 g/m2, mostly due to the dry environment and partly due to the overestimated temperature and underestimated humidity in Table 4. Above all, CUACE not only effectively simulates pollution but also provides a relatively reasonable meteorological background basis for cloud chemistry in the heavy pollution periods.”
17). Sect. 3.2.1 and 3.2.2 can be combined. “Pollutants Evaluation” sounds strange.
A: Yes. We have changed Pollutants Evaluation to Chemical evaluation.
18). Line 247: Delete “also”.
A: Yes. We have deleted.
19). Line 248: Delete “figure omitted”.
A: Yes. We have deleted.
20). Overall, Sect. 3 is poorly written and wordy. Please revise the whole section for English.
A: Yes. We have rewritten Sect. 3, and all the changes have been highlighted in the revised manuscript.
21). Line 340: Add the year and month to the dates.
A: Yes. We have defined the days of the pollution stages in line 305-308:
“The regional impacts of cloud chemical processes on surface SO2 and sulfate are analyzed for DEC and for HPE. The pollution episode (HPE) is investigated with respect to the developing stage HPE-1 (Dec. 16-18, 2016), the maturity stage HPE-2 (Dec. 19-21, 2016) and to the dissipation stage HPE-3 (Dec. 22, 2016) for the four pollution regions of NCP, YRD, PRD and SCB.”
22). Tables 3-8. I believe the results in the tables are mean concentrations or values. Please clarify.
A：Yes. We have clarified the observed mean and simulated mean.
23). The figure caption for Fig. 1 isn’t clear and has incorrect punctuation.
A: Yes. We have redrawn the diagram and corrected the punctuation.
24). The color bars are missing in Fig. 2.
A: Yes. The color of the dots in Fig. 2 represents the density, and the red color is the high density area. We have rewritten the figure caption for Fig. 2.
25). 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.
A: Yes. We have added descriptions about the triangle, removed unnecessary color bars, and marked the dates in figure 3 and 4. We have adjusted the font name and font size in figure 3, 5, and others. We removed the unnecessary frames in figure 6-8 and 10. We have clarified the mean values in some tables. We have checked all captions and adjusted.
26). Table 8: “sellected” should be “selected”. It is better to not use abbreviation as “the whole Dec.”
Yes. We have changed the table 8 to figure 11, and we didn’t use the whole Dec..
The references newly added are listed as follows:
- Fan, D., Ye. Y., and Wang, W.: Air Pollution Control and Public Health:Evidence from “Air Pollution Prevention and Control Action Plan” in China, Statistical Research, 38(9), 60-74, https://doi.org/10.19343/j.cnki.11-1302/c.2021.09.005, 2021.
- He, J. and Zhang, Y.: Improvement and further development in CESM/CAM5: gas-phase chemistry and inorganic aerosol treatments, Atmos. Chem. Phys., 14, 917-9200, https://doi.org/10.5194/acp-14-9171-2014, 2014.
- Hong, C., Zhang, Q., Zhang, Y., Tang, Y., Tong, D., and He, K.: Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects, Geosci. Model Dev., 10, 2447-2470, https://doi.org/10.5194/gmd-10-2447-2017, 2017a.
- Hong, C., Zhang, Q., He, K., Guan, D., Li, M., Liu, F., and Zheng, B.: Variations of China's emission estimates: response to uncertainties in energy statistics, Atmos. Chem. Phys., 17, 1227-1239, https://doi.org/10.5194/acp-17-1227-2017, 2017b.
- Ke, H., Gong, S., He, J., Zhou, C., Zhang, L., Zhou, Y.: Assessment of Open Biomass Burning Impacts on Surface PM5 Concentration, Chinese Academy of Meteorological Sciences, 31, 105-106, https://doi.org/10.11898/1001-7313.20200110, 2020.
- Kong, L., Tan, Q., Feng, M., Qu, Y., An, J., Liu, X., Cheng, N., Deng, Y., Zhai, R., and Wang, Z.: Investigating the characteristics and source analyses of PM2.5 seasonal variations in Chengdu, Southwest China, Chemosphere, 243, 125267, https://doi.org/10.1016/j.chemosphere.2019.125267,
- Lu, X., Zhang, S., Xing, J., Wang, Y., Chen, W., Ding, D., Wu, Y., Wang, S., Duan, L., and Hao, J.: Progress of Air Pollution Control in China and Its Challenges and Opportunities in the Ecological Civilization Era, Engineering., 6 (12), 1423-1431, https://doi.org/1016/j.eng.2020.03.014, 2020.
- Peng, J., Hu, M., Shang, D. J., Wu, Z., Du, Z., Tan, T., Wang, Y., Zhang, F., and Zhang, R.: Explosive secondary aerosol formation during severe haze in the North China Plain, Environ Sci Technol 55(4), 2189–2207, https://doi.org/10.1021/acs.est.0c07204,
- Sun, K., Liu, H., Ding, A., and Wang, X.: WRF-Chem Simulation of a Severe Haze Episode in the Yangtze River Delta, China, Aerosol Air Qual. Res., 16, 1268-1283, https://doi.org/10.4209/aaqr.2015.04.0248, 2016.
- Sha, T., Ma, X. Y., Jia, H. L., van der A, R. J., Ding, J. Y., Zhang, Y. L., and Chang, Y. H.: Exploring the influence of two inventories on simulated air pollutants during winter over the Yangtze River Delta, Atmos. Environ., 206, 170-182, https://doi.org/10.1016/j.atmosenv.2019.03.006, 2019a.
- Sha, T., Ma, X., Jia, H., Tian, R., Chang, Y., Cao, F., and Zhang, Y.: Aerosol chemical component: Simulations with WRF-Chem and comparison with observations in Nanjing, Atmospheric Environment, 218, https://doi.org/10.1016/j.atmosenv.2019.116982, 2019b.
- Wang, T., Liu, M., Liu, M., Song, Y., Xu, Z., Shang, F., Huang, X., Liao, W., Wang, W., Ge, M., Cao, J., Hu, J., Tang, G., Pan, Y., Hu, M., and Zhu, T.: Sulfate Formation Apportionment during Winter Haze Events in North China, Environ. Sci. Technol., 56(12), 7771-7778, https://doi.org/1021/acs.est.2c02533, 2022.
- Wang, W., Liu, M., Wang, T., Song, Y., Zhou, L., Cao, J., Hu, J., Tang, G., Chen, Z., Li, Z., Xu, Z., Peng, C., Lian, C., Chen, Y., Pan, Y., Zhang, Y., Sun, Y., Li, W., Zhu, T., Tian, H., and Ge, M.: Sulfate formation is dominated by manganese-catalyzed oxidation of SO2 on aerosol surfaces during haze events, Nature Communications, 12(1), https://doi.org/1038/s41467-021-22091-6, 2021.
- Wang, Z., Wang, Z., Li, J., Zheng H., Yan P., Li, J.: Development of a Meteorology-Chemistry Two-Way Coupled Numerical Model (WRF-NAQPMS) and Its Application in a Severe Autumn Haze Simulation over the Beijing-Tianjin-Hebei Area, China, Climatic and Environmental Research, 19(2), 153-163, https://doi.org/10.3878/j.issn.1006-9585.2014.13231, 2014.
- Zhou, S., Wu, L., Guo, J., Chen, W., Wang, X., Zhao, J., Cheng, Y., Huang, Z., Zhang, J., Sun, Y., Fu, P., Jia, S., Tao, J., Chen, Y., and Kuang, J.: Measurement report: Vertical distribution of atmospheric particulate matter within the urban boundary layer in southern China – size-segregated chemical composition and secondary formation through cloud processing and heterogeneous reactions, Atmos. Chem. Phys., 20, 6435-6453, https://doi.org/10.5194/acp-20-6435-2020, 2020.
- Zhu, Y., Yang, L., Chen, J., Kawamura, K., Sato, M., Tilgner, A., van Pinxteren, D., Chen, Y., Xue, L., Wang, X., Simpson, I. J., Herrmann, H., Blake, D. R., and Wang, W.: Molecular distributions of dicarboxylic acids, oxocarboxylic acids and α-dicarbonyls in PM5 collected at the top of Mt. Tai, North China, during the wheat burning season of 2014, Atmos. Chem. Phys., 18, 10741-10758, https://doi.org/10.5194/acp-18-10741-2018, 2018.
Citation: https://doi.org/10.5194/egusphere-2023-521-AC1
Interactive discussion
Status: closed
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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.
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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 -
AC1: 'Reply on RC2', Sunling Gong, 30 May 2023
Dear Anonymous Referee,
Thank you for your thorough review of the manuscript. We have taken all of the your 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 review and a list of all relevant changes, are as follows:
Q1: 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.
A: Yes, the overestimation was a common problem. We described it in detail in line 77-84:
“Some models have reported that they failed to reproduce SO2 and sulfate, particularly underestimating sulfate and overestimating SO2 over China (Buchard et al., 2014; Hong et al., 2017a; Wei et al., 2019; Cheng et al., 2016). These are mainly caused by the uncertainties in meteorological conditions (Sun et al., 2016) and emission inventories (Ma et al., 2018; Hong et al., 2017b; Sha et al., 2019a;), as well as unclear and/or inaccurate physical and chemical mechanisms associated with air pollutants (He and Zhang, 2014; He et al., 2015; Georgiou et al., 2018; Sha et al., 2019b). The inadequate inclusion or lack of cloud chemistry of SO2 consumption simulations was one of the main causes (Ge et al., 2021; Cheng et al., 2016). ”
Q2: 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.
A: A time series might be clear than the scattered plots. We choose to use the scatter plots due to the factor that there are a lot of missing H2O2 and sulfate observations at Mount Tai. Those selected from the observations have to meet two conditions: 1) there are clouds over the Mount Tai from satellite image, 2) there are observations.
However, we have added the time series plots of O3 and SO2 both for simulation and observation for 2018 as an example (Fig. S1).
Figure S1. Time series of the simulated and observed O3 and SO2
Q3: 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.
A: We have deleted the sentence in line 185 in the revised manuscript, and rewritten these sentences in line 217-229:
“Some reasons might contribute to the underestimations. Firstly, the latitude of the observed site at Mount Tai is 1483 meters, which may be in the boundary layer during the day time and in the free atmosphere during the night time in summer (Zhu et al., 2022). Therefore, the diurnal variation of the boundary layer affects the three-dimensional concentration distribution of oxidants and aerosols (Zhao et al., 2013; Peng et al., 2021), and influences the development of cloud formation. Secondly, there exists model bias due to the difficulties to represent the complex topography of Mount Tai and the cloud physics. Thirdly, the cloud chemistry in CUACE lacks the pathway for TMI-catalyzed oxidation and NO2-catalyzed oxidation as well as some other newly discovered oxidation mechanisms, which can lead to the bias in SO2 and sulfate. Fourthly, typical measurement systems for ambient aerosols easily misinterpret organosulfur (mainly in the presence of hydroxy-methane sulfonate (HMS)) as inorganic sulfate, thus leading to a positive observational bias, e.g., mean bias during winter haze in Beijing is 20% (Moch et al., 2018; Song et al., 2019).”
Q4: 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?
A: Yes. The cloud chemistry mechanism in CUACE has the pathways for the oxidation of SO2 by H2O2 and O3, but lacks the TMI-catalyzed mechanism and NO2-catalyzed mechanism as well as other newly discovered oxidation mechanisms. The result shows H2O2 is the main oxidant for the conversion of SO2 to sulfate. Meanwhile, we have rewritten the reasons for the underestimation of sulfate in line 214-226 as described in Q3.
Q5: 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.
A: The trend is simulated by the model, and we have rewritten this paragraph in line 230-238:
“Another interesting point that is simulated correctly by the model is the increasing trend of H2O2 andthe decreasing trend of SO2 from 2015 to 2018. The observed and simulated mean values of H2O2 are 26.5 and 16.8 μM in CP-1 in 2015, to 46.9 and 32.4 μM in CP-2 in 2018, respectively. For SO2, the observed and simulated mean values are 2.2 and 2.3 μg/m3 in CP-1 in 2015, to 0.6 and 0.6 μg/m3 in CP-2 in 2018, respectively in Table 3. Both the observations and simulations show clearly the increasing trend of H2O2 andthe decreasing trend of SO2 from 2015 to 2018. This conclusion is 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 decreasing and H2O2 and O3 increasinghave been tightly attributed to the national SO2 and particulate emission control measures since 2013 (Lu et al., 2020; Fan et al., 2021)”
Q6: 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.
A: Yes, we have rewritten this paragraph in line 239-245:
“Figure 4 shows the RTCLD of SO2 and simulated liquid water contents at 2:00 and 8:00 LST on both June 24 and June 25 in CP-1 at Mount Tai. The column cloud and the liquid water contents which are consistent with the cloud images indicate that there is cloud with sufficient water vapor in and around the vicinity of Mount Tai (Fig. 3). The SO2 consumption rate (RTCLD(SO2)) distribution is consistent with the liquid water distribution at all four times (Fig. 4). The SO2 depletion rate is above 80% at Mount Tai which is compatible to the observation (Li et al., 2020). All of these indicate that the model can capture the SO2 consumption in the cloudy environment.”
Q7: 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.
A: Yes, you are right. We have rewritten this paragraph in line 246-250:
“In summary, SO2, H2O2, O3 and sulfate concentration are in the same order of the observations, and the mean values of SO2 are close to the observed in the cloud chemistry comparison. WRF/CUACE is also able to simulate the decreasing trend of SO2 and the increasing trends of O3 and H2O2 with year. Therefore, the cloud chemistry mechanism in WRF/CUACE is relatively reasonable to reproduce the cloud chemistry for the gaseous pollutant SO2, sulfate and the important oxidants of H2O2 and O3.”
In terms of model performance, the answer to Q10 has included the other model studies with similar comparisons.
Q8: Line 225-227: I don’t observe this from Table 4. Maybe remove this sentence to avoid over-interpretation.
A: We have deleted this sentence.
Q9: 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.
A: We have deleted this sentence, and rewritten this paragraph in line 265-277:
“Figure 5 shows the satellite cloud images, the column cloud and the liquid water content simulated for the maturation and dissipation stages (19-22 Dec.) of the HPE. The satellite image shows that the cloud coverage region is mainly in the southwest of China besides SCB on the 19th, covering most of eastern China including NCP, YRD, PRD and SCB on the 20th and the 21st, and then moving eastward outside of China on the 22nd (Fig. 4 a1-d1). The cloud distribution fits well with the satellite images (Fig. 4 a2-d2). The column liquid water distribution also moves from west to east as the episode developed (Fig. 5 a3-d3), but is located more southern part of eastern China than that of the clouds. In SCB and YRD, the liquid water content is more abundant, reaching over 100.0 g/m2, than that in PRD, only up to 10.0 g/m2. NCP has the least liquid water content in the four regions, especially in Beijing, Tianjin and northwestern part of Hebei Province ranged 0.001~0.01 g/m2, mostly due to the dry environment and partly due to the overestimated temperature and underestimated humidity in Table 4. Above all, CUACE not only effectively simulates pollution but also provides a relatively reasonable meteorological background basis for cloud chemistry in the heavy pollution periods.”
Q10: 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.
A: Yes, we have rewritten this paragraph in line 280-303:
“The hourly PM2.5, O3 and SO2 concentrations simulated in four regions are compared with the observations (Table 5). Most of the simulations are within a factor of two of the observations (figure omitted), and the mean values of the three pollutants in the four regions are close to the observations in DEC and HPE-2. It is indicated that the model captures the variability of PM2.5, O3 and SO2 concentrations for both DEC and HPE in NCP, YRD, PRD and SCB. During HPE-2, the difference of mean values of SO2 ranged from -7.6 to 10.4 μg/m3, of O3 ranged from -22 to 23.3 μg/m3, and of PM2.5 ranged from -156.5 to 48.8 μg/m3. During DEC, the difference of mean values of SO2 ranged from -21.5 to -1.2 μg/m3, of O3 ranged from 1.1 to 7.7 μg/m3, and of PM2.5 ranged from -71.3 to 1.3 μg/m3. For PM2.5, In the NCP, the R of HPE is 0.84, which is higher than the 0.39 of DEC in PRD. In the NCP, the R of DEC is 0.62, which is higher than the 0.30 of HPE. The R is high for both DEC and HPE in YRD, with the value of 0.73 and 0.70. The differences of R between DEC and HPE are small in YRD and SCB. For SO2, the model simulations are better for HPE in the three regions of NCP, YRD and SCB, than that for DEC. The Rs of HPE and DEC are 0.60 and 0.48 in NCP, 0.61 and 0.45 in YRD, and 0.49 and 0.19 in SCB, respectively. The correlations between observations and simulations for HPE and DEC in PRD are not significantly different, with R of 0.32 and 0.39, respectively.
The ability of CUACE to simulate SO2, O3 and sulfate concentrations have been validated in many previous research applications (Zhou et al., 2021; Zhang et al., 2021). Compared with the PM2.5 concentrations simulated by WRF-CUACE used by Ke et al. (2020), the correlation is 0.41~0.85 in NCP, and 0.64~0.74 in YRD. The ability of other atmospheric models in China has the same performance such as NACRMS, and the correlation is about 0.68 for fine particulate matter in NCP during haze period (Wang et al., 2014).
The overall performance of the pollutants that can be routinely observed from the surface network have been evaluated. Then, the following part of this paper will focus on assessing the effects of cloud chemical processes.”
Q11: 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.
A: Yes. The surface observations used for the analysis in Sect. 3 are all hourly data from 55 city sites from the China National Environmental Monitoring Centre. They are mostly located in the urban area. Usually, there are several observation sites for a city. We then average the data from all the sites by excluding some obviously abnormal at one time and use the averaged data to presently the city.
For the whole-month comparisons of hourly SO2 and PM2.5 concentrations, some sites are not represented well in the model. Therefore, we have added discussion of that in Sect. 3 in line 401-405:
“The statistical metrics of SO2 and PM2.5 hourly concentrations in 55 representative cities with and without cloud chemistry in the model were analyzed. The results indicate that most of the sites are improved with cloud chemistry in the SO2 concentration simulation and 42 of the 55 cities are with the increasing R. In the PM2.5 simulation, the correlations also are improved in some cities after the presence of cloud chemistry.
Q12: 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.
A: Yes. We compared with other studies, and have added in manuscript.
In line 309-319:
“Figure 6 is the mean sulfate concentration for DEC and HPE-2 for SO2 and sulfate. The high and low centers of monthly mean SO2 and sulfate concentrations of CUACE in December 2016 are coincided with the yearly average of the same year by Gao et al. (2021), in the SCB and NCP. For NCP, the mean sulfate concentration in Figure 6b is comparable to that by Wang et al. (2021) and Wang et al. (2022) in December 2016 as both of which increase from northwest to southeast almost in the same magnitude. The sulfate concentrations are low on a monthly basis and high at the pollution maturity stage compared to the average of several pollution processes studied by Wang et al. (2022) in December 2016. The simulation of sulfate concentration is relatively reasonable in NCP. For SCB, sulfate concentrations are compatible to the observed in winter time in 2015 by Kong et al. (2020). The sulfate concentration in Guangzhou is almost twice of the observations formed from aqueous-phase reactions in Zhou et al. (2020) and Guo et al. (2020).”
In line 324-329:
“Ge et al. (2021) have evaluated the effects of in-cloud aqueous-phase chemistry on SO2 oxidation in the Community Earth System Model version 2 (CESM2). They found the results incorporating detailed cloud aqueous-phase chemistry greatly reduced SO2 overestimation, which reduced by 0.1-10 ppb in China, and more than 10 ppb in some regions in Winter. This finding is consistent with the results demonstrated in Figure 7 of this study, where SO2 concentrations are depleted by 0.1-10 ppb in China.”
In line 344-349:
“Sulfate formation rates by H2O2 oxidation under winter haze conditions range from 10 to 1000 μg/m3/s (Fig. S2), which is close to the range of 10 to 100 μg/m3/s tested by Wang et al. (2022) in several pollution episodes in December 2016. The heaviest and longest duration pollution episode that had a large number of clouds and high liquid water content (Fig. 5) on December 19-21, 2016, which are very favorable for the occurrence of in-cloud oxidation processes. Therefore, the in-cloud oxidation in this study is relatively reasonable.”
Figure 8. The mean sulfate concentration for DEC (a, c) and HPE-2 (b, d) for SO2 (c, e) and sulfate (a, b).
Figure S2. Sulfate formation rates by H2O2 oxidation in cloud in HPE-2.
Q13: 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.
A: We agree that 225 μg/m3 sulfate production by cloud chemistry was too high. We have deleted the high ranges of sulfate production 150-225 μg/m3, which was estimated from the plot scale bar. Actual increase by cloud chemistry was not that large (Fig. 9). However, we do find that the observed PM2.5 concentrations up to 350 μg/m3 at 14:00 on the 20th and 236 μg/m3 at 21:00 on the 20th in Chengdu in SCB, up to 76 μg/m3 at 14:00 on the 20th and 77 μg/m3 at 21:00 on the 20th in Hangzhou in YRD (Fig.S3), were very high, partially supporting the cloud production of sulfate production at these specific times. At the same time, our model results showed that sulfate increases by cloud chemistry during these time periods were 10-20μg/m3 and 20-30 μg/m3 14:00 and 21:00 on 20th at Chengdu, 20-60 μg/m3 and 30-60 μg/m3 at Hangzhou.
Unfortunately, we cannot find enough observations related to cloud chemistry in all four regions. For this reason, we can only compare SO2, O3 and PM2.5 with routine observations. As for sulfate, we can only compare it at Mount Tai and with other studies as described in Q12 and Ge et al. (2021).
Figure S3. time series of PM2.5 concentrations in Chengdu and Hangzhou in SCB. S0-S8 represent several observation sites in a city
Figure 9. The differences in surface sulfate concentrations between with and without cloud chemistry at 21:00 LST on 20 Dec. (a), at 17:00 LST on 21 Dec. (b), and at 12:00 LST on 22 Dec. (c) (Units: μg/m3).
Technical remarks:
1). Line 50: “a Mount site” or a mountain site?
A: Yes. We have corrected a mountain site in line 53.
2). Line 76: Define “CMA” here not in Line 163.
A: Yes. We have deleted.
3). Line 137-140: Awkward sentence. Please rewrite.
A: Yes. We have rewritten this sentence in line 155-157:
“Mount Tai with an altitude of 1483 meter, located in central Shandong Province, is the highest point of the North China Plain. It is an ideal observation site for cloud chemistry observation (Li et al., 2017; Li et al., 2020a; Li et al., 2020b).”
4). Line 141: Two “with”. Please rewrite.
A: Yes. We have rewritten this sentence in line 161-162:
“WRF/CUACE is set up with two-domain nesting for the evaluation, and the Riguan Peak is the central point (Fig. 1a).”
5). Line 142: Units for 100×104 and 88×94?
A: Units for 100×104 and 88×94 are grids numbers.
Line 148-151: Awkward sentence. Please rewrite.
A: Yes. We have rewritten this sentence in line 166-168:
“The time period of December 2016 was selected to assess the regional contribution of cloud chemistry to SO2 and sulfate in CUACE as a typical heavy pollution episode occurred from 16 to 21, covering most part of east China with the highest hourly PM2.5 concentration exceeding 1100 μg m-3,”
6). Line 159-161: Usually full terms go first with abbreviations in parentheses.
A: Yes. We have corrected in line 179-181.
7). Line 164: I think you mean “air pollution” here.
A: Yes. We have corrected in line 184.
8). Line 167-169: Are those cities? PRD, YRD, NCP, and SCB have been defined previously.
A: Yes. Those are cities.We have deleted in line 186.
9). Line 169: “elements” should be “parameters”.
A: Yes. We have corrected in line 186.
10). Line 174: “by five sectors of power…” should be “from power, industry, … and agriculture sectors”
A: Yes. We have corrected in line 191.
11). Line 175: Why 2017?
A: The most recent emissions source we have were for the year of 2017.
12). Line 194-195: This is an incomplete sentence.
A: Yes. We have rewritten the paragraph and this sentence has been removed.
13). Line 228: Add a “,” after “wind speed”. Change “previous researches” to be “previous findings”.
A: Yes. We have corrected in line 266-267.
14). Line 230: Delete “proposed by Emery et al.”
A: Yes. We have deleted.
15). Line 232: What is very small? Wind speed?
A: Yes. We have rewritten this sentence in line 262-264.
“The RSME of wind speed and the wind speed for HPE is smaller than that of DEC, which indicates that the model can relatively reasonably capture the static condition.”
16). Line 240-242: Awkward sentence. Please rewrite. Also, the following paragraph is redundant. That information can be merged into the analysis.
A: Yes. We have rewritten this part in line 270-277.
“The column liquid water distribution also moves from west to east as the episode developed (Fig. 5 a3-d3), but is located more southern part of eastern China than that of the clouds. In SCB and YRD, the liquid water content is more abundant, reaching over 100.0 g/m2, than that in PRD, only up to 10.0 g/m2. NCP has the least liquid water content in the four regions, especially in Beijing, Tianjin and northwestern part of Hebei Province ranged 0.001-0.01 g/m2, mostly due to the dry environment and partly due to the overestimated temperature and underestimated humidity in Table 4. Above all, CUACE not only effectively simulates pollution but also provides a relatively reasonable meteorological background basis for cloud chemistry in the heavy pollution periods.”
17). Sect. 3.2.1 and 3.2.2 can be combined. “Pollutants Evaluation” sounds strange.
A: Yes. We have changed Pollutants Evaluation to Chemical evaluation.
18). Line 247: Delete “also”.
A: Yes. We have deleted.
19). Line 248: Delete “figure omitted”.
A: Yes. We have deleted.
20). Overall, Sect. 3 is poorly written and wordy. Please revise the whole section for English.
A: Yes. We have rewritten Sect. 3, and all the changes have been highlighted in the revised manuscript.
21). Line 340: Add the year and month to the dates.
A: Yes. We have defined the days of the pollution stages in line 305-308:
“The regional impacts of cloud chemical processes on surface SO2 and sulfate are analyzed for DEC and for HPE. The pollution episode (HPE) is investigated with respect to the developing stage HPE-1 (Dec. 16-18, 2016), the maturity stage HPE-2 (Dec. 19-21, 2016) and to the dissipation stage HPE-3 (Dec. 22, 2016) for the four pollution regions of NCP, YRD, PRD and SCB.”
22). Tables 3-8. I believe the results in the tables are mean concentrations or values. Please clarify.
A：Yes. We have clarified the observed mean and simulated mean.
23). The figure caption for Fig. 1 isn’t clear and has incorrect punctuation.
A: Yes. We have redrawn the diagram and corrected the punctuation.
24). The color bars are missing in Fig. 2.
A: Yes. The color of the dots in Fig. 2 represents the density, and the red color is the high density area. We have rewritten the figure caption for Fig. 2.
25). 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.
A: Yes. We have added descriptions about the triangle, removed unnecessary color bars, and marked the dates in figure 3 and 4. We have adjusted the font name and font size in figure 3, 5, and others. We removed the unnecessary frames in figure 6-8 and 10. We have clarified the mean values in some tables. We have checked all captions and adjusted.
26). Table 8: “sellected” should be “selected”. It is better to not use abbreviation as “the whole Dec.”
Yes. We have changed the table 8 to figure 11, and we didn’t use the whole Dec..
The references newly added are listed as follows:
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- Hong, C., Zhang, Q., He, K., Guan, D., Li, M., Liu, F., and Zheng, B.: Variations of China's emission estimates: response to uncertainties in energy statistics, Atmos. Chem. Phys., 17, 1227-1239, https://doi.org/10.5194/acp-17-1227-2017, 2017b.
- Ke, H., Gong, S., He, J., Zhou, C., Zhang, L., Zhou, Y.: Assessment of Open Biomass Burning Impacts on Surface PM5 Concentration, Chinese Academy of Meteorological Sciences, 31, 105-106, https://doi.org/10.11898/1001-7313.20200110, 2020.
- Kong, L., Tan, Q., Feng, M., Qu, Y., An, J., Liu, X., Cheng, N., Deng, Y., Zhai, R., and Wang, Z.: Investigating the characteristics and source analyses of PM2.5 seasonal variations in Chengdu, Southwest China, Chemosphere, 243, 125267, https://doi.org/10.1016/j.chemosphere.2019.125267,
- Lu, X., Zhang, S., Xing, J., Wang, Y., Chen, W., Ding, D., Wu, Y., Wang, S., Duan, L., and Hao, J.: Progress of Air Pollution Control in China and Its Challenges and Opportunities in the Ecological Civilization Era, Engineering., 6 (12), 1423-1431, https://doi.org/1016/j.eng.2020.03.014, 2020.
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- Sun, K., Liu, H., Ding, A., and Wang, X.: WRF-Chem Simulation of a Severe Haze Episode in the Yangtze River Delta, China, Aerosol Air Qual. Res., 16, 1268-1283, https://doi.org/10.4209/aaqr.2015.04.0248, 2016.
- Sha, T., Ma, X. Y., Jia, H. L., van der A, R. J., Ding, J. Y., Zhang, Y. L., and Chang, Y. H.: Exploring the influence of two inventories on simulated air pollutants during winter over the Yangtze River Delta, Atmos. Environ., 206, 170-182, https://doi.org/10.1016/j.atmosenv.2019.03.006, 2019a.
- Sha, T., Ma, X., Jia, H., Tian, R., Chang, Y., Cao, F., and Zhang, Y.: Aerosol chemical component: Simulations with WRF-Chem and comparison with observations in Nanjing, Atmospheric Environment, 218, https://doi.org/10.1016/j.atmosenv.2019.116982, 2019b.
- Wang, T., Liu, M., Liu, M., Song, Y., Xu, Z., Shang, F., Huang, X., Liao, W., Wang, W., Ge, M., Cao, J., Hu, J., Tang, G., Pan, Y., Hu, M., and Zhu, T.: Sulfate Formation Apportionment during Winter Haze Events in North China, Environ. Sci. Technol., 56(12), 7771-7778, https://doi.org/1021/acs.est.2c02533, 2022.
- Wang, W., Liu, M., Wang, T., Song, Y., Zhou, L., Cao, J., Hu, J., Tang, G., Chen, Z., Li, Z., Xu, Z., Peng, C., Lian, C., Chen, Y., Pan, Y., Zhang, Y., Sun, Y., Li, W., Zhu, T., Tian, H., and Ge, M.: Sulfate formation is dominated by manganese-catalyzed oxidation of SO2 on aerosol surfaces during haze events, Nature Communications, 12(1), https://doi.org/1038/s41467-021-22091-6, 2021.
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- Zhou, S., Wu, L., Guo, J., Chen, W., Wang, X., Zhao, J., Cheng, Y., Huang, Z., Zhang, J., Sun, Y., Fu, P., Jia, S., Tao, J., Chen, Y., and Kuang, J.: Measurement report: Vertical distribution of atmospheric particulate matter within the urban boundary layer in southern China – size-segregated chemical composition and secondary formation through cloud processing and heterogeneous reactions, Atmos. Chem. Phys., 20, 6435-6453, https://doi.org/10.5194/acp-20-6435-2020, 2020.
- Zhu, Y., Yang, L., Chen, J., Kawamura, K., Sato, M., Tilgner, A., van Pinxteren, D., Chen, Y., Xue, L., Wang, X., Simpson, I. J., Herrmann, H., Blake, D. R., and Wang, W.: Molecular distributions of dicarboxylic acids, oxocarboxylic acids and α-dicarbonyls in PM5 collected at the top of Mt. Tai, North China, during the wheat burning season of 2014, Atmos. Chem. Phys., 18, 10741-10758, https://doi.org/10.5194/acp-18-10741-2018, 2018.
Citation: https://doi.org/10.5194/egusphere-2023-521-AC1
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