Retrieval of Atmospheric Visibility and Its Driving Factors in Shanghai, China

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Manuscript ID: atmosphere-3896284-R1. Title: Retrieval of Atmospheric Visibility and Its Driving Factors in Shanghai. 

The combined effects of meteorological factors and aerosol chemical compositions on atmospheric visibility in Shanghai were investigated in this study based on the observed hourly dataset during 2022-2024. Correlation analysis and random forest modeling are employed to quantify the relative contributions of these factors. 

Comments:

1. Please include the country of the city of study in the title. This is despite its worldwide recognition.
2. I suggest removing the highlights from this article. They currently do not contribute significantly.
3. In the abstract, authors should include quantitative information to support the findings presented. Please include.
4. In the introduction, authors should explore the following aspects in depth. 1) How do weather conditions such as temperature inversion, wind, and vertical mixing influence visibility?
5. 2) How do extreme conditions of pollution and humidity influence visibility?
6. 3) What effects do daytime/nighttime and seasonal periods have on visibility?
7. 4) Explore the influence of specific chemical species of aerosols with optical properties that affect visibility.
8. In Chapter 2, authors should include a figure that shows the location of the study worldwide and also shows the characteristics of the monitoring station used (location, equipment, etc.). All of this is to support the information included in section 2.1.
9. In section 2.1, authors should include the measurement principles and standards considered, on which the measurement of the variables used in this study was based.
10. In Chapter 2, the authors should include references to support all techniques and methods used in this study. This chapter needs significant improvement.
11. In Chapter 2, authors should better visualize the following sections: 1) Description of the study site, 2) information collection system, and 3) information analysis. This comment is mandatory.
12. In the data and methods chapter, the authors should significantly improve the following aspects (detailed technical description). 1) Temporal and spatial resolution of the data? Temporal coverage: long periods, seasonal, day/night cycles? visibility measurement methods: calibration of the instrument used, possible errors and biases?; chemical aerosol data: which species (sulfate, nitrate, ammonium, black carbon, etc.)?; how are they sampled (instruments, location)?; frequency and quantitative accuracy?
13. 2) Filtering of erratic values: detection of outliers, invalid instruments?; treatment of missing data: how are they interpolated or discarded?; correction of temporal biases (e.g., instruments, location, seasonal changes)?; normalization or transformation of variables if necessary (logarithms, scales, etc.) for linear relationships or models? Please provide details on these aspects.
14. 3) Descriptive statistics: distributions (skewness, kurtosis), diurnal/nocturnal trends, seasonal trends. Simple/partial correlations between visibility and independent variables to understand basic relationships. Verify non-linearities, interactions between variables (e.g., humidity that enhances hygroscopic growth). Identification of different regimes (e.g., “high visibility” vs. “low visibility”) to compare conditions.
15. 4) In Chapter 2, authors should include a separate section to show the details of the model considered in this study. Sensitivity analysis and validation?
16. Please detail the following statistical aspects in Chapter 2. Verification of linear correlation assumptions when using correlations: is there heteroscedasticity, non-linearity, temporal dependence (autocorrelation)?; possible problems of multicollinearity between independent variables?; use of methods to capture interactions: tree models, ensembles, or adding interactive terms?; robust modeling technique against outliers?
17. Authors should separate the discussion and conclusion chapters. In the discussion chapter, authors should compare their findings with other reference studies. This commentary is mandatory.
18. In the discussion section, authors should consider the following aspects. 1) Optical instrument, extinction estimates, meteorological observations? Quality, calibration, possible biases?
19. 2) Uncertainty: even if the model gives high R² values, what are the absolute errors? How well does it predict extreme conditions?
20. 3) External validation: do they compare the model with independent stations, with satellite, with another method?
21. 4) Specific physical interactions: although they mention composition and humidity, details of the mechanism (how humidity enhances the growth of hygroscopic particles, dielectric effects, refraction, etc.).
22. 5) Generalization: Are these relationships specific to Shanghai due to its emission sources, local meteorology, geography? Please discuss.
23. Please draw relevant conclusions regarding the aspects indicated in points 18-22.
24. Please clearly indicate the main limitations detected during the development of this study and future lines of research.

Author Response

Reviewer 1:

Comment 1: Please include the country of the city of study in the title. This is despite its worldwide recognition.

Response 1: Thanks for your suggestion. We have revised the title to 'Retrieval of Atmospheric Visibility and Its Driving Factors in Shanghai, China'.

 

Comment 2: I suggest removing the highlights from this article. They currently do not contribute significantly.

Response 2: We removed the highlights section in the revised version.

 

Comment 3: In the abstract, authors should include quantitative information to support the findings presented. Please include.

Response 3: Thanks for your suggestion. We included some quantitative information description in the abstract section as follows:

Line 19: The results reveal significant negative correlations between visibility and both PM₂.₅ concentration and relative humidity, with partial correlation coefficient of -0.62 and -0.61.

Lines 22-25: Under 30 km high-visibility conditions, PM₂.₅ is the dominant predictor (39%) of atmospheric visibility variation, followed by relative humidity (35%). In contrast, during low-visibility conditions (lower than 7.5 km), relative humidity becomes the primary contributor (30%), the influence of PM₂.₅ weakens (18%), and aerosol chemical components account for a larger share (30%).

 

Comment 4: In the introduction, authors should explore the following aspects in depth. 1) How do weather conditions such as temperature inversion, wind, and vertical mixing influence visibility? 2) How do extreme conditions of pollution and humidity influence visibility? 3) What effects do daytime/nighttime and seasonal periods have on visibility? 4) Explore the influence of specific chemical species of aerosols with optical properties that affect visibility.

Response 4: Thanks for your suggestion. We have added a review of previous studies on the impact of meteorological conditions, chemical properties of particulate matter, and other factors on visibility in the introduction section. The content is as follows:

Lines 48-49: High concentrations of PM2.5 directly scatter and absorb sunlight, reducing visibility [2].

Lines 51-53: This complexity arises because larger and more light-scattering particles (e.g., sulfates, nitrates) disproportionately reduce visibility. Internally mixed particles with absorbing components (e.g., black carbon) exhibit compounding optical effects [5].

Lines 56-63: For example, the secondary formation of aerosols is highly sensitive to ambient temperature, humidity, and solar radiation. High humidity can promote new particle formation and gas-to-particle conversion [7,8]. Strong winds facilitate the horizontal transport of pollutants, whereas stagnant weather conditions and vertical temperature inversions contribute to the local accumulation of pollutants, exacerbating particulate pollution and thereby reducing visibility [9,10]. Hygroscopic aerosols such as sulfates and nitrates enhance aerosol growth by taking up water vapor, causing the particles to swell in size and form hygroscopic haze droplets, which reduces visibility even when the total PM mass remains constant [11,12].

Reference:

[2] Hu, S.; Zhao, G.; Tan, T.; Li, C.; Zong, T.; Xu, N.; Zhu, W.; Hu, M. Current challenges of improving visibility due to increasing nitrate fraction in PM2. 5 during the haze days in Beijing, China. Environmental Pollution 2021, 290, 118032.

[5] Won, W.-S.; Oh, R.; Lee, W.; Ku, S.; Su, P.-C.; Yoon, Y.-J. Hygroscopic properties of particulate matter and effects of their interactions with weather on visibility. Scientific reports 2021, 11, 16401.

[7] Wang, X.; Zhang, R. How did air pollution change during the COVID-19 outbreak in China? Bulletin of the American Meteorological Society 2020, 101, E1645-E1652.

[8] Wang, X.; Dickinson, R.E.; Su, L.; Zhou, C.; Wang, K. PM 2.5 pollution in China and how it has been exacerbated by terrain and meteorological conditions. Bulletin of the American Meteorological Society 2018, 99, 105-119.

[9] Sun, Y.; Wang, X. Meteorological factor contributions to the seesaw concentration pattern between PM2. 5 and O3 in Shanghai. Frontiers in Environmental Science 2022, 10, 1015723.

[10] Wang, X.; Zhang, R.; Tan, Y.; Yu, W. Dominant synoptic patterns associated with the decay process of PM 2.5 pollution episodes around Beijing. Atmospheric Chemistry and Physics Discussions 2020, 2020, 1-35.

[11] Zhang, X.; Ding, X.; Talifu, D.; Wang, X.; Abulizi, A.; Maihemuti, M.; Rekefu, S. Humidity and PM2. 5 composition determine atmospheric light extinction in the arid region of northwest China. Journal of environmental sciences 2021, 100, 279-286.

[12] Wang, X.; Wang, K.; Su, L. Contribution of atmospheric diffusion conditions to the recent improvement in air quality in China. Scientific reports 2016, 6, 36404.

 

Comment 5: In Chapter 2, authors should include a figure that shows the location of the study worldwide and also shows the characteristics of the monitoring station used (location, equipment, etc.). All of this is to support the information included in section 2.1.

Response 5: Thanks for your suggestion. We have added figure 1 to show the location of the study area. 

Figure 1. Spatial distribution of observational data sites. Blue markers represent the two PM2.5 monitoring sites, while the red five-pointed star indicates the location of Fudan University, which provided visibility, meteorological, and chemical composition observation data for this study.

 

Comment 6: In section 2.1, authors should include the measurement principles and standards considered, on which the measurement of the variables used in this study was based.

Response 6: The measurement principles of the instruments used for ​​visibility, humidity, and aerosol chemical components​​ have been supplemented in the revised version as follows:

Lines 143-147: The DNQ1 typically employs the ​​forward scattering method​​ to measure visibility, converting the ​​extinction coefficient​​ into the ​​meteorological optical range​​, which is commonly referred to as the ​​visibility value​​. The DHC2 uses a ​​capacitive humidity sensor​​, in which a specially designed ​​polymer thin-film capacitor​​ changes its ​​dielectric constant​​ in response to the ​​water vapor content in the surrounding air.

Lines 164-169: The AE-33 calculates BC mass concentration based on the ​​attenuation of light intensity​​ caused by the absorption of light by ​​black carbon particles deposited on the filter​​ as air passes through it. Before the enhanced observations, the ACSM and AE-33 had been calibrated. The chemical component data (measured at 15-minute intervals) and black carbon data (measured at 5-minute intervals) were averaged to hourly averages to match the temporal resolution of the meteorological observation data.

 

Comment 7: In Chapter 2, the authors should include references to support all techniques and methods used in this study. This chapter needs significant improvement.

Response 7: Thanks for your suggestion. In the revised version, we have incorporated relevant literature on meteorological instrument data validation and added citations for machine learning algorithms.

Lines 140-143: The hourly meteorological data used in this study including visibility, relative humidity, and precipitation, which were obtained from instruments produced by Jiangsu Radio Scientific Institute Co., Ltd., with the DNQ1 visibility sensor [35,36], DHC2 humidity sensor [37], and DSG1 present weather detector, respectively.

Lines 162-164: In addition, black carbon (BC) mass concentrations were measured using a seven-wavelength aethalometer (AE-33, Magee Scientific), equipped with a PM2.5 cyclone inlet [39,40].

Lines 175-178: The random forest regression algorithm is an ensemble learning method that constructs multiple decision trees to perform classification or prediction [41,42]. It is capable of handling high dimensional features and is robust to multicollinearity, while also providing automatic evaluation of feature importance and exhibiting strong resistance to outliers and noise [43,44].

Reference:

35. Yu, Y.; Ren, Z.; Meng, X. Reconstruction of daily haze data across China between 1961 and 2020. International Journal of Climatology 2022, 42, 5629-5643.
36. Li, X.; Tang, G.; Li, L.; Quan, W.; Wang, Y.; Zhao, Z.; Liu, N.; Hong, Y.; Ma, Y. Multilevel air quality evolution in Shenyang: Impact of elevated point emission reduction. Journal of Environmental Sciences 2022, 113, 300-310.
37. Weiwei, L.; Xiaohua, L.; Zuoyang, T.; Xiaohua, X.; Yong, X. Fault Analysis and Maintenance of DZZ Series of Automatic Weather Stations. Meteorological and Environmental Research 2018, 9, 35-37.
38. Dong, H.; Zou, Y.; Yan, M.; Sun, H.; Chen, J.; Yan, Y.; Zhu, C.; Hao, C.; Chen, Z. Epidemiological characteristics of RSV in pediatric inpatients with lower respiratory tract infections in Suzhou and their correlation with meteorology and atmospheric pollutants. BMC Infectious Diseases 2025, 25, 662.
39. Chen, W.; Cao, X.; Ran, H.; Chen, T.; Yang, B.; Zheng, X. Concentration and source allocation of black carbon by AE-33 model in urban area of Shenzhen, southern China. Journal of Environmental Health Science and Engineering 2022, 20, 469-483.
40. Singh, S.; Kumar, M.; Verma, B.K.; Kumar, S. Optimizing air pollution prediction with random forest algorithm. Aerosol Science and Engineering 2025, 1-14.
41. Ding, W.; Qie, X. Prediction of air pollutant concentrations via RANDOM forest regressor coupled with uncertainty analysis—A case study in Ningxia. Atmosphere 2022, 13, 960.
42. Gocheva-Ilieva, S.G.; Voynikova, D.S.; Stoimenova, M.P.; Ivanov, A.V.; Iliev, I.P. Regression trees modeling of time series for air pollution analysis and forecasting. Neural Computing and Applications 2019, 31, 9023-9039.
43. Sun, H.; Gui, D.; Yan, B.; Liu, Y.; Liao, W.; Zhu, Y.; Lu, C.; Zhao, N. Assessing the potential of random forest method for estimating solar radiation using air pollution index. Energy Conversion and Management 2016, 119, 121-129.

 

Comment 7: In Chapter 2, authors should better visualize the following sections: 1) Description of the study site, 2) information collection system, and 3) information analysis. This comment is mandatory.

Response 7: We have supplemented the station data acquisition information and underlying surface information in the new version.

Lines 130-136: The Fudan University Meteorological Observation Station, established in 2019, is a facility integrating teaching and research. Its construction complies with the regulations of the World Meteorological Organization and is equipped with standard instruments for conventional meteorological variables, atmospheric composition, and boundary layer turbulence structures. All observational data from the station are uniformly collected by the DZZ4 data acquisition system into the Ground-based Integrated Observation Business Software (Ver 3.0.2.615) of the China Meteorological Administration's Meteorological Observation Center.

Lines 139-142: The station features a grassland surface type, with urban land cover located approximately 1 km away. The hourly meteorological data used in this study including visibility, relative humidity, and precipitation, which were obtained from instruments produced by Jiangsu Radio Scientific Institute Co., Ltd., with the DNQ1 visibility sensor [35,36].

 

Comment 8：In the data and methods chapter, the authors should significantly improve the following aspects (detailed technical description). 1) Temporal and spatial resolution of the data? Temporal coverage: long periods, seasonal, day/night cycles? visibility measurement methods: calibration of the instrument used, possible errors and biases?; chemical aerosol data: which species (sulfate, nitrate, ammonium, black carbon, etc.)?; how are they sampled (instruments, location)?; frequency and quantitative accuracy? 2) Filtering of erratic values: detection of outliers, invalid instruments?; treatment of missing data: how are they interpolated or discarded?; correction of temporal biases (e.g., instruments, location, seasonal changes)?; normalization or transformation of variables if necessary (logarithms, scales, etc.) for linear relationships or models? Please provide details on these aspects. 3) Descriptive statistics: distributions (skewness, kurtosis), diurnal/nocturnal trends, seasonal trends. Simple/partial correlations between visibility and independent variables to understand basic relationships. Verify non-linearities, interactions between variables (e.g., humidity that enhances hygroscopic growth). Identification of different regimes (e.g., “high visibility” vs. “low visibility”) to compare conditions. 4) In Chapter 2, authors should include a separate section to show the details of the model considered in this study. Sensitivity analysis and validation?

Response 8: Thank you very much for your suggestions. In the revised version, we have supplemented the calibration and quality control information of the station observation data, added statistical characteristic analysis of the visibility data, re-evaluated the impact of PM2.5 and relative humidity on visibility from the perspective of partial correlations, and provided detailed considerations for the machine learning modeling approach. Please review the specific modifications below.

Lines 147-153: The observation site is equipped with two parallel precipitation measurement systems: a tipping-bucket rain gauge and a siphon rain gauge. A daily precipitation of 1 mm is defined as effective precipitation. Precipitation is considered to have occurred if either rain gauge records effective precipitation. When precipitation is detected, if the relative humidity is below 75%, the data quality is deemed problematic, and all data from that time period are excluded. All instruments at the automatic weather station undergo regular annual calibration.​​

Lines 213-226: To evaluate the model's sensitivity to input variables and verify its robustness, a comprehensive sensitivity analysis was conducted, which encompassed feature importance assessment, feature perturbation analysis, as well as evaluations of multicollinearity and outlier robustness. Specifically, the intrinsic feature importance evaluation method based on impurity reduction in Random Forest was employed to quantify the contribution of each input variable to visibility prediction, calculating the mean decrease in impurity (e.g., Gini impurity) caused by each feature across all decision trees and then normalizing the results to derive the relative importance of each feature; the sensitivity of individual features to model predictions was further validated by randomly shuffling the values of single features (Permutation Importance) and observing the corresponding changes in model performance (R²); Variance Inflation Factor (VIF) analysis was performed to detect potential multicollinearity among meteorological variables; moreover, the model's resilience to outliers and data noise was jointly verified through model retraining after introducing controlled random noise to a defined proportion of the training set and preprocessing extreme values using the Median Absolute Deviation method during the data preparation stage.

Lines 234-237: The visibility data distribution exhibits significant left-skewness (negative skew) and leptokurtosis (peakedness), with a skewness value below zero and kurtosis above zero. The skewness reaches its maximum in summer (-0.34), likely due to frequent low-visibility events caused by higher relative humidity during this season.

Lines 257-263: The correlation coefficients between visibility and PM2.5, as well as relative humidity, showed significant improvement after controlling for the other variable, from-0.41 to -0.62 and -0.37 to -0.61, indicating a strong mutual influence between relative humidity and PM2.5. The partial correlation coefficient of visibility-PM2.5 and visibility-RH exhibited consistent diurnal variation patterns, reaching their maximum absolute values during 09:00-12:00 (i.e., -0.70 and -0.65) and minimum values during 17:00-19:00 (i.e., -0.53 and -0.53).

Table 1 Sample Size Statistics of Hourly-Scale Effective Visibility Data (2022–2024) and Statistical Characteristics of Hourly Visibility Distribution

Season	Total Samples (hours)	30 km Visibility (hours)	Proportion of 30 km Samples (%)	Skewness*	Kurtosis*
Spring	4979	2101	58.4%	-0.25 (-1.37)	1.96 (3.56)
Summer	5270	3077	80.5%	-0.34 (-2.71)	2.09 (9.53)
Autumn	4861	3912	69.6%	-0.03 (-1.70)	1.86 (4.52)
Winter	4624	3217	42.2%	-0.05 (-0.79)	1.88 (2.20)
Total	19734	12307	62.4 %	-0.09 (-1.41)	1.89 (3.61)

^*Skewness (>0: right-skewed; <0: left-skewed; =0: symmetric). Kurtosis (>0: more peaked; <0: flatter; =0: normal). Main values exclude visibility=30km; parenthetical values include all visibility samples.

Figure 3. Diurnal Variation of the Correlation Coefficients Between Visibility and PM_2.5 and Between Visibility and Relative Humidity in Shanghai, Excluding Data with Visibility of 30 km. Dashed lines represent the ​​Pearson correlation coefficients​​ between the two variables, while solid lines indicate the ​​partial correlation coefficients​​ controlling for the third variable.

 

Comment 9: Please detail the following statistical aspects in Chapter 2. Verification of linear correlation assumptions when using correlations: is there heteroscedasticity, non-linearity, temporal dependence (autocorrelation)?; possible problems of multicollinearity between independent variables?; use of methods to capture interactions: tree models, ensembles, or adding interactive terms?; robust modeling technique against outliers?

Response 9: We have provided detailed supplementary explanations and calculations regarding the model's ​​nonlinearity​​, ​​multicollinearity​​, and ​​heteroscedasticity​​.

Lines 178-181: The primary reason for selecting this model in our study is its ability to effectively capture complex nonlinear relationships among variables without requiring predefined functional forms—a capability that has been validated through preliminary scatter plots and locally weighted regression (LOESS) fitting.

Lines 188-192: The model was further subjected to ​​5-fold cross-validation​​ for internal validation. Additionally, to ensure the reliability of statistical inferences, an in-depth analysis of the model residuals was conducted. Specifically, the presence of ​​heteroscedasticity​​ was examined through the ​​Breusch-Pagan test​​, and the presence of ​​autocorrelation​​ was verified via the ​​Durbin-Watson test​​.

Lines 372-382: To verify heteroscedasticity and autocorrelation in the two models, Table 3 presents their Durbin-Watson test statistics: Model 1 and Model 2 yielded values of 2.3 and 2.1 respectively, both falling between 2 and 2.5, indicating no significant autocorrelation in either model. Meanwhile, the Durbin-Watson statistics for heteroscedasticity evaluation were 0.16 for Model 1 and 0.13 for Model 2 - both exceeding 0.05, which suggests the presence of heteroscedasticity in both models. However, the Random Forest model is inherently less sensitive to heteroscedasticity. The presence of multicollinearity among selected meteorological variables was assessed through Variance Inflation Factor (VIF) calculations. The results in Table 4 indicate that some meteorological features in Model 2 (i.e., NO₃ and NH₄) exhibit VIF values exceeding 10, suggesting moderate multicollinearity. However, the Bootstrap sampling mechanism inherent in the Random Forest model can effectively mitigate the impact of such multicollinearity on model stability.

Table 3 The Statistical Characteristics of the Two Random Forest Models

	R2	RMSE (m)	MAE (m)	MAPE	Durbin-Watson*	Breusch-Pagan*
Model 1	0.83	2369.3	1471.31	12.3%	2.2	0.16
Model 2	0.93	1652.9	1020.9	9.8%	2.1	0.13

^*If the ​​Durbin-Watson statistic​​ is close to ​​2​​, it indicates ​​no significant autocorrelation​​; if it ​​significantly deviates from 2​​, it suggests the ​​presence of autocorrelation​​. For the ​​Breusch-Pagan test​​, a ​​p-value < 0.05​​ indicates ​​significant heteroscedasticity​​, whereas a ​​p-value ≥ 0.05​​ suggests ​​no significant heteroscedasticity​​.

Table 4 The Variance Inflation Factors (VIFs) of the Variables in the Two Models

Variables	PM2.5	RH	DOY	hour	prec	Org	NO3	SO4	NH4	Chl	BC
Model 1	4.6	6.2	3.5	2.8	4.0
Model 2	3.8	5.3	1.1	1.0	2.8	3.8	16.3	6.2	12.6	3.3	3.7

 

 

Comment 10: Authors should separate the discussion and conclusion chapters. In the discussion chapter, authors should compare their findings with other reference studies. This commentary is mandatory.

Response 10: Thanks for your suggestion. In the new version, we compared the results of this study with existing research, elaborating on multiple aspects including the relative contributions of relative humidity and particulate matter concentration to visibility, as well as the contributions of aerosol chemical components.

Lines 409-417: The results in this study are consistent with previous studies, in which visibility degradation was found to be jointly controlled by PM₂.₅ concentration and relative humidity[45]. Stronger negative correlations during nighttime were attributed to enhanced aerosol hygroscopic growth, as similarly reported in Beijing and Korea[17,46,47]. The greater influence of sulfate, nitrate, ammonium, and black carbon under polluted or humid conditions was also observed, in line with findings that secondary inorganic aerosols and absorbing species contribute to nonlinear visibility loss[48,49]. Importantly, the shift in dominance from PM₂.₅ under clean conditions to humidity and aerosol composition under haze was quantified, and a predictive framework was provided through the random forest model.

Reference

[17] Wang, X.; Zhang, R.; Yu, W. The effects of PM2. 5 concentrations and relative humidity on atmospheric visibility in Beijing. Journal of Geophysical Research: Atmospheres 2019, 124, 2235-2259.

[45] Cheng, Y.; Zheng, G.; Wei, C.; Mu, Q.; Zheng, B.; Wang, Z.; Gao, M.; Zhang, Q.; He, K.; Carmichael, G. Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China. Science advances 2016, 2, e1601530.

[46] Joo, S.; Shin, J.; Tesche, M.; Dehkhoda, N.; Kim, T.; Noh, Y. Increased number concentrations of small particles explain perceived stagnation in air quality over Korea. Atmospheric Chemistry and Physics 2025, 25, 1023-1036.

[47] Wen, W.; A, N.T.W.; Liu, L.; Ma, X.; Shen, L.; Deng, Z. Comparative analysis of the impact of PM2. 5 components on visibility, extinction and oxidative potential in beijing, China. Air Quality, Atmosphere & Health 2025, 1-14.

[48] Shang, X.; Zhang, K.; Meng, F.; Wang, S.; Lee, M.; Suh, I.; Kim, D.; Jeon, K.; Park, H.; Wang, X. Characteristics and source apportionment of fine haze aerosol in Beijing during the winter of 2013. Atmospheric Chemistry and Physics 2018, 18, 2573-2584.

[49] Wu, C.; Wang, G.; Wang, J.; Li, J.; Ren, Y.; Zhang, L.; Cao, C.; Li, J.; Ge, S.; Xie, Y. Chemical characteristics of haze particles in Xi'an during Chinese Spring Festival: Impact of fireworks burning. Journal of Environmental Sciences 2018, 71, 179-187.

Comment 11: In the discussion section, authors should consider the following aspects. 1) Optical instrument, extinction estimates, meteorological observations? Quality, calibration, possible biases? 2) Uncertainty: even if the model gives high R² values, what are the absolute errors? How well does it predict extreme conditions? 3) External validation: do they compare the model with independent stations, with satellite, with another method? 4) Specific physical interactions: although they mention composition and humidity, details of the mechanism (how humidity enhances the growth of hygroscopic particles, dielectric effects, refraction, etc.). 5) Generalization: Are these relationships specific to Shanghai due to its emission sources, local meteorology, geography? Please discuss. Please draw relevant conclusions regarding the aspects indicated in points 18-22. Please clearly indicate the main limitations detected during the development of this study and future lines of research.

Response 11: Thank you very much for your suggestions. Following your advice, we have added a discussion section at the end of the paper, addressing the uncertainties in data and methods, the generalizability of the conclusions, and the key scientific issues that may need attention in future research.

Lines 418-425: Uncertainty remains in both data and methodology. Although the high-resolution visibility retrievals and random forest model achieved high R² values, prediction errors persisted under extreme polluted or clean-air conditions, suggesting limited robustness at boundary cases. The reliance on ground-based optical and meteorological measurements also introduces potential biases due to calibration accuracy and instrument stability, which may affect the reliability of the outcomes. While the results confirm the significant roles of PM₂.₅ and relative humidity in visibility degradation, the numerical findings should be interpreted with caution. Additional independent validation and more comprehensive observations are required to strengthen confidence in the conclusions.

Lines 426-434: The strong influence of sulfate, nitrate, ammonium, and black carbon on visibility has been widely reported, yet the relative contributions of PM₂.₅ and humidity are likely shaped by Shanghai’s specific emission mix, meteorological conditions, and geographic setting. This limits the generalizability of the conclusions to other regions. Future research should incorporate multi-city comparisons and multi-platform observations, including satellite and network data, to assess broader applicability. Moreover, the current study provides only a partial treatment of aerosol–radiation interactions and hygroscopic processes. More detailed mechanistic studies combining observational and modeling approaches are necessary to improve physical understanding and enhance predictive capacity across diverse environments.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have presented a study on the relationship between visibility and PM2.5, relative humidity and composition of PM2.5 in Shanghai using hourly data.

The authors used correlation method of visibility and PM2.5 as well as between visibility and relative humidity modulating by composition of PM2.5 (organic aerosols, sulfate, nitrate, ammonium and BC or black carbon). Seasonal and diurnal analysis of these correlations is also performed under low visibility and high visibility conditions. The results show expected understanding of the roles of PM2.5 and humidity in visibility. Under high visibility cases, PM2.5 plays a main contribution to visibility but in low visibility conditions humidity is more important. The authors have shown that the role of composition PM2.5 such as sulfate, nitrate and ammonium in PM2.5 in modulating the visibility relationship with PM2.5 and humidity and their contribution is also important. 

Rather than individual correlation regression analysis, the authors could use the multiple Ridge regression of visibility versus PM2.5 and humidity. This can be useful in explaining the contribution of each to visibility. Do other meteorological data such as wind speed and temperature can influence the results ? 

The authors have also conducted ML random forest method to understand the complex non-linear relationship of visibility and PM2.5, humidity and composition PM2.5. Models with and without composition PM2.5 have confirmed the regression analysis results but these models also can allow the prediction of visibility. RF model with composition PM2.5 increase the accuracy of the model as compared with model not using composition PM2.5.

Overall, the methods used in the manuscript is robust and the results as informative which allow for the prediction of visibility using monitoring data. I recommend the manuscript to have minor revision before being accepted for publication.

Author Response

Reviewer 2:

General comment: The authors used correlation method of visibility and PM2.5 as well as between visibility and relative humidity modulating by composition of PM2.5 (organic aerosols, sulfate, nitrate, ammonium and BC or black carbon). Seasonal and diurnal analysis of these correlations is also performed under low visibility and high visibility conditions. The results show expected understanding of the roles of PM2.5 and humidity in visibility. Under high visibility cases, PM2.5 plays a main contribution to visibility but in low visibility conditions humidity is more important. The authors have shown that the role of composition PM2.5 such as sulfate, nitrate and ammonium in PM2.5 in modulating the visibility relationship with PM2.5 and humidity and their contribution is also important. 

Response: Thank you very much for your positive evaluation of our work. Please kindly review our point-by-point responses below.

 

Comment 1: Rather than individual correlation regression analysis, the authors could use the multiple Ridge regression of visibility versus PM2.5 and humidity. This can be useful in explaining the contribution of each to visibility.

Response 1: Thank you very much for your valuable suggestions. In the revised version, in addition to considering the Poisson correlation coefficients between visibility, relative humidity, and PM, we have also incorporated partial correlation coefficients as well as the results of ridge regression. Please kindly review the following content.

Lines 256-260: Both variables show negative correlations with visibility, with stronger correlations observed during nighttime hours. The correlation coefficients between visibility and PM2.5, as well as relative humidity, showed significant improvement after controlling for the other variable, from-0.41 to -0.62 and -0.37 to -0.61, indicating a strong mutual influence between relative humidity and PM2.5.

Lines 305-313: To address the potential multicollinearity between relative humidity and PM₂.₅ in their influence on visibility, Table 2 reports the ridge regression coefficients derived from models incorporating both variables. When all samples are considered, the coefficient for PM₂.₅ exceeds that of relative humidity, indicating that particulate matter concentration is the primary driver of visibility variation. However, stratified analysis reveals contrasting patterns: under low-visibility conditions, the coefficient for relative humidity substantially surpasses that of PM₂.₅, highlighting the dominant role of atmospheric moisture in governing visibility degradation. Conversely, under high-visibility conditions, the coefficient associated with PM₂.₅ is greater, underscoring the leading contribution of particulate matter to visibility variability in relatively clean environments.

Table 2 Regression coefficients of the visibility ridge regression model based on relative humidity and PM (ridge parameter = 1)

	All case	Vis<7500	Vis>7500
Intercept	46102	16284	43405
PM2.5	-232.12	-13.55	-223.65
RH	-202.76	-114.51	-160.45

 

Comment 2: Do other meteorological data such as wind speed and temperature can influence the results? 

Response 2: Thank you for your valuable suggestion. As you correctly noted, meteorological factors such as wind speed and temperature can indeed influence the relationship between visibility, particulate matter, and humidity. The underlying mechanism, however, lies in the way meteorological conditions, including wind speed, temperature, and large-scale circulation, affect the secondary formation of pollutants, their local accumulation, and regional transport. These processes alter pollutant concentrations, microphysical properties, and chemical compositions. Variations in chemical composition and physical characteristics subsequently modify hygroscopicity, scattering ability, and other optical properties, ultimately impacting visibility. Since aerosol chemical composition differs substantially across regions, studies focusing solely on the effects of temperature and wind speed on visibility often lack general applicability. Therefore, this study did not pursue an in-depth investigation of these factors. Nevertheless, to provide a more comprehensive context, we have expanded the Introduction to include a review of previous studies addressing the influence of meteorological variables on visibility.

Lines 53-63: In addition, meteorological conditions influence visibility by affecting the formation of secondary aerosols, regional transport, and local accumulation of pollutants, all of which impact the physicochemical properties of local aerosols [6]. For example, the secondary formation of aerosols is highly sensitive to ambient temperature, humidity, and solar radiation. High humidity can promote new particle formation and gas-to-particle conversion [7,8]. Strong winds facilitate the horizontal transport of pollutants, whereas stagnant weather conditions and vertical temperature inversions contribute to the local accumulation of pollutants, exacerbating particulate pollution and thereby reducing visibility [9,10]. Hygroscopic aerosols such as sulfates and nitrates enhance aerosol growth by taking up water vapor, causing the particles to swell in size and form hygroscopic haze droplets, which reduces visibility even when the total PM mass remains constant [11,12].

Reference

[6] Kim, B.-Y.; Cha, J.W.; Chang, K.-H.; Lee, C. Estimation of the Visibility in Seoul, South Korea, Based on Particulate Matter and Weather Data, Using Machine-learning Algorithm. Aerosol and Air Quality Research 2022, 22, 220125, doi:10.4209/aaqr.220125.

[7] Wang, X.; Zhang, R. How did air pollution change during the COVID-19 outbreak in China? Bulletin of the American Meteorological Society 2020, 101, E1645-E1652.

[8] Wang, X.; Dickinson, R.E.; Su, L.; Zhou, C.; Wang, K. PM 2.5 pollution in China and how it has been exacerbated by terrain and meteorological conditions. Bulletin of the American Meteorological Society 2018, 99, 105-119.

[9] Sun, Y.; Wang, X. Meteorological factor contributions to the seesaw concentration pattern between PM2. 5 and O3 in Shanghai. Frontiers in Environmental Science 2022, 10, 1015723.

[10] Wang, X.; Zhang, R.; Tan, Y.; Yu, W. Dominant synoptic patterns associated with the decay process of PM 2.5 pollution episodes around Beijing. Atmospheric Chemistry and Physics Discussions 2020, 2020, 1-35.

[11] Zhang, X.; Ding, X.; Talifu, D.; Wang, X.; Abulizi, A.; Maihemuti, M.; Rekefu, S. Humidity and PM2. 5 composition determine atmospheric light extinction in the arid region of northwest China. Journal of environmental sciences 2021, 100, 279-286.

[12] Wang, X.; Wang, K.; Su, L. Contribution of atmospheric diffusion conditions to the recent improvement in air quality in China. Scientific reports 2016, 6, 36404.

 

General Comment: The authors have also conducted ML random forest method to understand the complex non-linear relationship of visibility and PM2.5, humidity and composition PM2.5. Models with and without composition PM2.5 have confirmed the regression analysis results but these models also can allow the prediction of visibility. RF model with composition PM2.5 increase the accuracy of the model as compared with model not using composition PM2.5. Overall, the methods used in the manuscript is robust and the results as informative which allow for the prediction of visibility using monitoring data. I recommend the manuscript to have minor revision before being accepted for publication.

Response: We sincerely thank you for your recognition of our work. As you pointed out, the main goal of this study is to perform diagnostic analyses to explore potential factors affecting visibility and to develop an inversion model using machine learning. Because chemical composition data are not routinely observed and are difficult to obtain, we initially constructed the visibility inversion model based solely on conventional meteorological variables, achieving an R2 of 0.83. By further incorporating chemical composition data, which provide richer information, the model bias was substantially reduced and the R2 improved to 0.93. This demonstrates that, even without chemical composition data, conventional meteorological variables can capture the majority of visibility variability, providing useful guidance for future studies.

 

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Manuscript ID: atmosphere-3896284-R2. Title: Retrieval of Atmospheric Visibility and Its Driving Factors in Shanghai. 

The combined effects of meteorological factors and aerosol chemical compositions on atmospheric visibility in Shanghai were investigated in this study based on the observed hourly dataset during 2022-2024. Correlation analysis and random forest modeling are employed to quantify the relative contributions of these factors. 

Comments:

1. Please check the numbering of all figures throughout the article.
2. It appears that the authors failed to respond to the last comments (points 23 and 24). Please review. Provide a response for each point.
3. In this new version, the authors have adequately addressed each of the 24 comments made in the previous review round. Therefore, I continue to accept the article with minor comments.

Author Response

Comment 1: Please check the numbering of all figures throughout the article.

Response 1: Thank you for your reminder. We have rechecked all figure numbers. In the latest revision, we added Figure 1 and optimized Figure 3. The numbering of all figures in the text and captions has been verified for accuracy.

 

Comment 2: It appears that the authors failed to respond to the last comments (points 23 and 24). Please review. Provide a response for each point.

Response 2: We apologize for the oversight in the first-round response to reviewers. In our initial reply, we inadvertently consolidated reviewer comments 23 and 24 into response 22. In this revised version, we have provided additional elaboration specifically addressing these two comments. Please refer to the corresponding modifications for details.

ROUND1 Comment 23: Please draw relevant conclusions regarding the aspects indicated in points 18-22.

Response 23: Lines 426-436: The strong influence of sulfate, nitrate, ammonium, and black carbon on visibility has been widely reported, yet the relative contributions of PM₂.₅ and humidity are likely shaped by Shanghai’s specific emission mix, meteorological conditions, and geographic setting. This limits the generalizability of the conclusions to other regions. Future research should incorporate multi-city comparisons and multi-platform observations, including satellite and network data, to assess broader applicability. Although this study is based solely on observational data from a single case in Shanghai, the diversified comprehensive observational data confirm that even without detailed chemical composition information, conventional meteorological observations (such as relative humidity and PM₂.₅ concentration) can effectively capture the main variability characteristics of visibility, providing important reference for subsequent studies.

ROUND1 Comment 24: Please clearly indicate the main limitations detected during the development of this study and future lines of research.

Response 24: Lines 437-444: Moreover, the current study provides only a partial treatment of aerosol–radiation interactions and hygroscopic processes. The study highlights the dominant role of hygroscopic growth and secondary aerosol components, it lacks a mechanistic investigation into the coupled feedbacks between aerosol radiative forcing (e.g., black carbon heating altering boundary layer stability) and humidity dynamics. This simplification restricts a deeper physical understanding of the observed diurnal asymmetries and nonlinear visibility responses, highlighting the need for integrated observational-modeling approaches to unravel these complex processes. More detailed mechanistic studies combining observational and modeling approaches are necessary to improve physical understanding and enhance predictive capacity across diverse environments.

 

Comment 3: In this new version, the authors have adequately addressed each of the 24 comments made in the previous review round. Therefore, I continue to accept the article with minor comments.

Response 3: We sincerely appreciate the reviewers' insightful comments over the two rounds of revisions. Your constructive suggestions have significantly enhanced the clarity of our paper's structure and the rigor of its content. We have carefully considered each comment and made systematic improvements to the manuscript, striving to present our research findings to a higher standard. Once again, please accept our heartfelt gratitude.

Author Response File: Author Response.pdf