the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Role of atmospheric aerosols in severe winter fog over Indo Gangetic Plains of India: a case study
Abstract. Winter fog and severe aerosol loading in the boundary layer over north India, especially in the Indo-Gangetic Plain (IGP), cause disruption in the daily lives of millions of people in the region. To understand better the role of atmospheric aerosols on the occurrence, spatial extent, and persistence of winter fog in the IGP, several model simulations have been performed using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem). Results from WRF-Chem represented relative humidity (RH) and fog formation in agreement with observations when using the ERA-Interim reanalysis data as meteorological initial/boundary conditions and soil nudging were applied. WRF-Chem successfully simulates the spatial distribution and magnitude of PM2.5 when evaluated with observations from the Central Pollution Control Board of India (CPCB) monitoring network. However, the aerosol composition predicted by WRF-Chem was quite different from measurements obtained during the Winter Fog Experiment (WiFEX) in Delhi, with chloride aerosol fraction being strongly underpredicted (~66.6 %). By investigating a fog event on December 23–24, 2017 over central IGP, we found that the aerosol-radiation feedback weakens turbulence, lowers the boundary layer height, and increases PM2.5 concentrations and RH within the boundary layer. The increase in RH is found to be important for fog formation and it promoted the growth of aerosol size and increased aerosol activation in the polluted environment over IGP. Loss of aerosols through deposition of cloud droplets is found to be a significant aerosol loss process during fog. The internal mixing of absorbing aerosols and hygroscopic growth reduces the single scattering albedo impacting aerosol-radiation feedbacks. Aqueous-phase chemistry increases the PM2.5 concentrations during fog events which subsequently participates in aerosol-radiation feedback. With aerosol-radiation interaction and aqueous phase chemistry, fog formation began 1–2 hours earlier and caused a longer fog duration than when these processes were not included in the WRF-Chem simulation. These processes were also found to increase RH, stabilize the boundary layer, increase PM2.5 promoting aerosol activation, and thus increasing the fog water content over IGP. This suggests that the aerosol-radiation feedback and secondary aerosols play an important role in the air quality and in the intensity and lifetime of fog over IGP.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-1686', Anonymous Referee #1, 16 Oct 2023
- AC1: 'Reply on RC1', Chandrakala Bharali, 19 Jan 2024
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RC2: 'Comment on egusphere-2023-1686', Anonymous Referee #2, 17 Nov 2023
The authors simulated a fog event with high aerosol loading over the Indo Gangetic Plains of India, and estimated the role of atmospheric aerosols in severe winter fog through aerosol-radiation interaction and aqueous phase chemistry. Overall, this is well-executed study and the topic is very interesting. The authors made great efforts in model evaluation and sensitivity simulation. However, several conclusions in the main text should be further explained and the presentation quality can be improved. I suggest a major revision before it can be accepted.
Major comments:
I am very curious about if the fog event is due to radiation or advection. If this is a radiation fog, then the role of AR feedback could be a major reason. However, if this is an advection fog, the authors may need to pay attention on wind changes.
Simulated PM2.5. In L363, the authors mentioned that the observed high PM2.5 can be predicted by model but not for PM2.5 composition (Fig.7). I am very confused about this. The model strongly underestimated observed inorganic PM2.5 composition on 24th Dec; then why was total PM2.5 mass concentration well simulated?
Decreased PM2.5 in CIGP due to AR feedback. The authors mentioned this issue in L427-436 but didn’t explain the reason clearly. How about the changes in winds due to AR, which may be a reason.
High particulate Cl. The model can’t reproduce the observed high Cl that could come from trash-burning and industrial emissions. I am wondering if trash-burning emissions from Chaudhary et al. (2021) can represent Cl emissions at a city scale.
Fog duration. In Fig.15, I find the results from three simulations are quite similar and I suggest the authors to clearly identify their difference.
Minor comments:
L18-20: the model tends to strongly underestimate observed PM2.5.
L33: “These processes” refers to aerosol-radiation interaction and aqueous phase chemistry? The latter can’t change PBL meteorology.
L42: “NOx” refers to emission or concentration?
L47&L55: duplication for NAAQS
L123-125: I suggest to move this description to Section 2.2 Observations
L183: please rephrase “aromatic compounds HONO”
L270-272: again, this information should be given in the Observations part if not.
L300: in Fig.2, I find most of the correlation coefficient (r) for RH is below 0.87. so I am not convinced by the “r2>0.75”.
L308: please change “are” to “is”.
L351-352: I suggest the authors to show the correlation coefficient using daily data to justify this argument.
Section4: It looks the authors failed to discuss about the uncertainties in chemistry scheme used in the model, which is also an importance source of model biases.
L532: In fact, the model can’t reproduce the observed aerosol composition. The following discussions on previous studies couldn’t be helpful.
Citation: https://doi.org/10.5194/egusphere-2023-1686-RC2 - AC2: 'Reply on RC2', Chandrakala Bharali, 19 Jan 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1686', Anonymous Referee #1, 16 Oct 2023
- AC1: 'Reply on RC1', Chandrakala Bharali, 19 Jan 2024
-
RC2: 'Comment on egusphere-2023-1686', Anonymous Referee #2, 17 Nov 2023
The authors simulated a fog event with high aerosol loading over the Indo Gangetic Plains of India, and estimated the role of atmospheric aerosols in severe winter fog through aerosol-radiation interaction and aqueous phase chemistry. Overall, this is well-executed study and the topic is very interesting. The authors made great efforts in model evaluation and sensitivity simulation. However, several conclusions in the main text should be further explained and the presentation quality can be improved. I suggest a major revision before it can be accepted.
Major comments:
I am very curious about if the fog event is due to radiation or advection. If this is a radiation fog, then the role of AR feedback could be a major reason. However, if this is an advection fog, the authors may need to pay attention on wind changes.
Simulated PM2.5. In L363, the authors mentioned that the observed high PM2.5 can be predicted by model but not for PM2.5 composition (Fig.7). I am very confused about this. The model strongly underestimated observed inorganic PM2.5 composition on 24th Dec; then why was total PM2.5 mass concentration well simulated?
Decreased PM2.5 in CIGP due to AR feedback. The authors mentioned this issue in L427-436 but didn’t explain the reason clearly. How about the changes in winds due to AR, which may be a reason.
High particulate Cl. The model can’t reproduce the observed high Cl that could come from trash-burning and industrial emissions. I am wondering if trash-burning emissions from Chaudhary et al. (2021) can represent Cl emissions at a city scale.
Fog duration. In Fig.15, I find the results from three simulations are quite similar and I suggest the authors to clearly identify their difference.
Minor comments:
L18-20: the model tends to strongly underestimate observed PM2.5.
L33: “These processes” refers to aerosol-radiation interaction and aqueous phase chemistry? The latter can’t change PBL meteorology.
L42: “NOx” refers to emission or concentration?
L47&L55: duplication for NAAQS
L123-125: I suggest to move this description to Section 2.2 Observations
L183: please rephrase “aromatic compounds HONO”
L270-272: again, this information should be given in the Observations part if not.
L300: in Fig.2, I find most of the correlation coefficient (r) for RH is below 0.87. so I am not convinced by the “r2>0.75”.
L308: please change “are” to “is”.
L351-352: I suggest the authors to show the correlation coefficient using daily data to justify this argument.
Section4: It looks the authors failed to discuss about the uncertainties in chemistry scheme used in the model, which is also an importance source of model biases.
L532: In fact, the model can’t reproduce the observed aerosol composition. The following discussions on previous studies couldn’t be helpful.
Citation: https://doi.org/10.5194/egusphere-2023-1686-RC2 - AC2: 'Reply on RC2', Chandrakala Bharali, 19 Jan 2024
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Chandrakala Bharali
Mary Barth
Rajesh Kumar
Sachin D. Ghude
Vinayak Sinha
Baerbel Sinha
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3331 KB) - Metadata XML
-
Supplement
(1330 KB) - BibTeX
- EndNote
- Final revised paper