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the Creative Commons Attribution 4.0 License.
| 22 Apr 2022
Long-term variation study of fine-mode particle size and regional characteristics using AERONET data
Abstract. To identify the long-term trend of particle size variation, we analyzed aerosol optical depth (AOD, τ) separated as dust (τD) and coarse-(τPC) and fine-pollution particles (τPF) depending on emission sources and size. Ångström Exponent values are also identified separately as total and fine-mode particles (αT and αPF). We checked these trends in various ways; 1) first-order linear regression analysis of the annual average values, 2) percent variation using the slope of linear regression method, and 3) a reliability analysis using the Mann-Kendall (MK) test. We selected 17 AERONET sun/sky radiometer sites classified into six regions, i.e., Europe, North Africa, the Middle East, India, Southeast Asia, and Northeast Asia. τ decreased in Europe and Asian regions and increased in the Middle East, India, and North Africa. Values of τPC and τPF, show that aerosol loading caused by non-dust aerosols decreased in Europe and Asia and increased in India. In particular, τPF considerably decreased in Europe and Northeast Asia (95 % confidential levels in MK-test), and τPC decreased in Northeast Asia (Z-values for Seoul and Osaka are -2.95 and -2.31, respectively). The change in τPC reflects the reduction of primary emissions from plants and other anthropogenic sources as the result of regulations by air policies. Values of αT decreased by -3.3 to -30.5 % in Europe, North Africa, and the Middle East, which means the mean size of aerosol particles increased. Particle size on average became smaller over India and Asian regions considered in our study. We find that αT increased by 1.3 to 13.1 %. In particular, αPF increased in most areas., showing the probability that the average particle size of fine-mode aerosols became smaller in recent years.
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Interactive discussion
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RC1: 'Comment on acp-2022-219', Anonymous Referee #1, 18 May 2022
General comments:
This paper describes the determination of long-term trends of aerosol optical depths (AODs) at 17 AERONET stations in different regions of the world. The AODs are separated into dust- and pollution related fractions, and the pollution-related AOD is further divided into fractions associated with fine- and coarse mode aerosol components. Like the AODs, the total and fine-mode Angstrom exponents were analysed in terms of possible trends.
The topic is suitable for ACP, even though the main result, a reduction of pollution related AODs at many urban sites during the last 20 years, is not surprising. This work is based on experimental and theoretical work of many research groups contributing to AERONET. Using AERONET data for innovative research by other groups is of course in order and welcome but should meet certain standards to value the AERONET project. In my view the current paper is not suitable for publication and does not meet these standards. The numerical data analysis is questionable, formulas and results are partly inconsistent. Moreover, some important aspects are not considered, e.g. whether or not yearly averages of the various variables are suitable to represent conditions at a site (considering the typical high variability of aerosol loads, compositions, data gaps, seasonal effects). Instead, the yearly averages are treated like ordinary measurement values that, apart from a linear trend, scatter statistically. An analysis of uncertainties is missing completely. The years 2019, 2020 and 2021 were excluded from the analysis. It would have been interesting to see how significant short-term effects of lockdown measures were compared to any long-term trends.
Specific comments:
Title: The title is misleading. Fine-mode particle size is studied here rather indirectly. The title should reflect what was done in the study.
Line 28: “τ decreased in Europe and Asian regions and increased in the Middle East, India, and North Africa.” After reading the paper, it turns out that these statements are partly incorrect by the authors’ own standards of significance. τ decreased significantly in 2/3 of stations in Europe and 2/6 stations in Asia, it increased at 1/2 stations in India and at none in the Middle East and Africa.
Line 31: The abstract should be comprehensible without the remainder of the text. The meaning of “Z-values for Seoul and Osaka are -2.95 and -2.31, respectively” is unclear, a three-digit precision of these parameters is misleading.
Line 32: “…reduction of primary emissions from plants and other anthropogenic sources…” Unclear.
Line 33: “Values of αT decreased by -3.3 to - 30.5% in Europe, North Africa, and the Middle East, which means ….” These decreases are all classified as not-significant (Tab. 4). Why is this highlighted as a result in the abstract? Again, the number of digits pretends a high precision that is not justified.
Line 35: “We find that αT increased by 1.3 to 13.1%.” Same problem again. The increase is significant only at 2/8 stations in India and Asia.
Line 35: “In particular, αPF increased in most areas.” αPF significantly increased at merely 5/17 stations.
Line 72: “We also analyzed …. (Dubovik et al., 2002,….)” The use of the citations is unclear. What was the aim of this study, what is new and what was done before and requires a citation?
Line 79: “Second, the depolarization ratio is essential to calculate dust ratio, so we have limitations in using data in American and European sites.” This statement is unclear. What are these limitations? And what is different at the three European sites that were selected. What excludes all American sites?
Line 81: “Third, the selected observation sites need to be representative of regional characteristics” Unclear. What are the criteria? Why are, for example, three sites directly at the Mediterranean representative for Europe? Are urban sites representative at all for a region?
Line 107, Tab. S1: The number of observation days is quite small, e.g. around 30 or less per year for the European sites. A quick check for Thessaloniki shows many more days marked as measurement days. What is limiting the observation days in this study?
Line 112: “…we divided aerosol types as dust, fine- and coarse-mode particles depending on depolarization ratio and particle size data”. The definition of fine- and coarse mode should be clarified here. According to the AERONET documentary there is a flexible limit in a range between 0.44 µm and 0.99 µm that is determined separately for each distribution. Does this affect the current study?
Line 131: Remove “of particles”. This is not the fraction of particles but the fraction of AOD. Is the CMF at 1020 nm an AERONET product? On the webpage I only see FMF (1-CMF) for 500 nm.
Line 132: “Thus, RD represents the proportion of AOD for pure dust particles in a mixed aerosol plume”. If Eq. 3 is correct, this statement in incorrect. The dust ratio apparently describes the dust contribution to (lidar) backscatter coefficient, not AOD.
Line 140: “CMF is higher than RD in most cases, which implies that these coarse-mode particles include dust and pollution particles”. Again, if Eq. (3) is correct, the lidar ratios must be applied here too for direct comparison.
Line 143: “The ratio of coarse-mode particles (CMP) denotes the proportion (number concentration) of coarse-mode pollution particles to total particles”. So far, the discussion was on AOD and contributions to AOD. Now it’s number concentrations?
Eq. (2): I think the lidar ratios must be applied here too. Moreover, the underlying assumption is that all dust particles belong to the coarse mode fraction. Therefore, CMP will be a lower limit and the FMF (pollution) an upper limit because the fine mode is then considered pollution only?
Eq. (3): The lidar ratios were not considered before, as already mentioned. I think the equation requires an explanation and/or citation.
Line 150: “The parameter S is the lidar ratio of the aerosol mixture. S can be calculated from the AERONET data products.” Did you calculate this ratio, or is it part of the AERONET data products? It would be interesting to know how strongly the S can deviate from the Sd.
Line 154: “The AOD of coarse-mode particles (τPC)…” Shouldn’t that be coarse-mode pollution particles?
Line 158: “The term αPC is the Ångström exponent of coarse-mode pollution. We used the value of 0.16 and 0.14 for Asian and Saharan dust, respectively (Shin et al., 2018).” Why do you use a dust-value for coarse-mode pollution particles? The Angstrom exponent is not only a function of size but also of aerosol type. I would expect much greater values.
Eq. (6): τD,λ is probably calculated as in Eq. (5)? Anyway, this equation needs more explanation. I understand the approach if you would only have CMF1020. But as far as I can see AERONET provides FMF500 and Angstrom exponents for the fine-mode and the total AOD. So there seem to be other possibilities or options to check your calculated τPF,λ. Please clarify which AERONET quantities were used and which quantities were derived from these in detail.
Line 165/166: What is the difference between “First,…” and “Second,…”?
Eq. (7)/line 170: N should be the time span in years rather than the “number of available data” because in some of your time records there are gaps. If you define the percentage change this way the data in the tables are not comparable.
Line 172: “The p-value (probability value) is a scalar that describes how likely it is that the data occurred by random chance. The p-value should be small enough, depending on the level of confidence.” How were the p-values calculated and how “small” should they be?
Line 172: “We calculated all data and found a statistically significant trend”. Certainly not for all sites and quantities.
Line 177: “The magnitude of the trend can be explained by Sen’s slope test, which is a powerful method for investigating linear trends.” What was calculated and what does it mean? The “powerful method” is not used at all in the following.
Line 178: How were the Z-values calculated? As for the p-values and the S, the details should be given somewhere, at least in the Supplement (if you really need them all). Otherwise, no one can reproduce these numbers.
Line 197: “…based on a threshold value, denoted as RD, which…” RD was introduced before as dust ratio not as a threshold.
Line 200: “In this study, we used seven aerosol types based on the values based on the values of δp, respectively.” It’s unclear if a new classification is introduced here or if the method by Shin et al., 2019 was adopted. What means “…, respectively”?
Line 205: “The results of our aerosol classification reflect the characteristics of the six regions well.” What does “well” imply? Well, in terms what is expected anyway? Well, in terms that the stations are representative for a region?
Line 241: “…we compared trends in the average values of τT for each region and site during 2001–2018 (supplementary Figure S2). The average value of τT at 440 nm was highest (1.22 ± 0.76) in Beijing….” Fig. S2 clearly shows annual averages but these data do not fit to the results in Tab. 1. For example, the values for Beijing range from about 1.05 to 1.45 in the figure while in Tab. 1 a mean of 1.22 is listed (reasonable) with a standard deviation of 0.76 (unexplained).
Table 1: A look at Tab. 1 shows that the simple budget equation (6) is not fulfilled, especially at the sites with high dust contributions, i.e. τD+τPC+τPF > τT I wonder if this has to do with missing lidar factors in Eq. 2 or problems with Eq. 5. Moreover, I cannot reproduce the FMF from FMF = τPF/τT or FMF = τPF/(τD+τPC+τPF). What does all that mean for the trend analysis?
Line 247: “We find an average value of -0.0138 τTð¦ð−1” This rate of change corresponds to the slope of the red dashed line in Fig. S2 (f). From that a total change of 0.22 in 16 years can be calculated. Divided by the mean of 1.22 gives an -18% change according to Eq. 7. However, in Tab. 2 a change of -33% is listed.
Line 249: “The Chiang Mai and Bangkok sites in Southeast Asia showed a decrease of τT, too.” This statement is not justified. Looking at Fig. S2 (e) there is clearly no trend at both sites. For Chiang Mai this is confirmed by the small Z-value in Tab. 3 while for Bangkok a Z-value of about 2 is reported which indicates a significant (negative) trend. The slope listed in Tab. 2 is merely -0.0012, hardly visible in the figure and certainly not significant given the scatter of the yearly data.
Table 2: The percent variations given in brackets are not very helpful. First, they are not comparable because they refer to different time spans for different sites. Second large numbers do not automatically mean that the trends are significant.
In Figs. 3 and 4 slopes corresponding to significant trends could be colour coded. This would give a quick overview without having to consult Tab. 3 and 4 for each site.
In Figs. S2-S7 standard deviations of the yearly averages should be added. I assume this will reveal how variable the different quantities really are. Something that is not discussed in the trend analysis.
In Table 3 and 4 dutifully all statistical results are listed but what do they mean? Are the results using Z, p and S as indicators consistent? Can anything be learned from using the different approaches? What does a dash mean? Why do the Z-values have no signs consistently? Moreover, significant digits should be considered. If your criterion for significance is whether Z is greater or lower than 1.7, why report 2.3062? Some of the Z-values are numerically identical which indicates what a crude number that really is for a limited number of data points.
Line 250-383: The remainder of this section is very confusing because of the great number of sites, slopes and potential trends discussed. It is difficult to see what is relevant here and what not. I didn’t check everything in detail. The authors must make sure their statements are correct and in fact backed by the data in figures and tables. I suggest to only discuss those trends that were found significant. Example from line 297: “… but the percent variation was as high as 112.29% (Thessaloniki)”. This appears to be a large, high precision increase but it’s not significant (Tab. 3).
Technical corrections
Line 36: “… showing the probability that …” Rephrase.
Eq. (4) should probably read τPC,1020 = τ1020 × CMP1020
Line 121: “…show a comparably high correlation”. What is comparably high?
Line 133: Remove “particles” and “particle” to clarify.
Line 150: “Here, 1020 denotes the wavelength at 1020 nm.” That should be clear enough.
Line 160: “equation (5)”. Should read “equation (6)”, I assume.
Line 166/167 and 169/170: You should consistently assign slope and intercept with a and b.
Line 192: “Results” and Discussion
Citation: https://doi.org/10.5194/acp-2022-219-RC1 -
RC2: 'Comment on acp-2022-219', Anonymous Referee #2, 18 Jun 2022
The paper claims to fulfill a long-term aerosol properties study using AERONET data. The authors present the analyses separating between dust and fine mode particles. Further analyses try to separate between different aerosol components including also the Angström parameter. The different multi-year trends are analyzed and discussed. Therefore, the topic is of interest for Atmospheric Chemistry and Physics.
However, I agree with all the comments raised by referee 1. AERONET project includes extraordinary efforts of many research groups worldwide and homogenize data quality check and criteria. Data use in the paper does not meet AERONET standard quality and the methodology proposed by the authors is questionable. I would like to add some more points that make me not to recommend the paper for publication, and I recommend the authors to re-submit the paper after addressing the main comments and those by referee 1:
- Author’s methodology is based in depolarization ratios and lidar ratio. To obtain these data it is necessary the retrieval of aerosol size distribution and optical properties from AERONET inversion that combine sun and sky radiances and solve and ill-posed problem. This is good for obtaining depolarization ratios to compare with lidar systems or to compute lidar ratios for elastic lidar. But for doing climatology studies it has no sense because the inversion by itself provides aerosol characteristics. Obviously, the inversion is sensitive to noise in the input measurements and has uncertainties, and by these reasons AERONET establish strict criteria for the retrieval. The most important is geometry that limit retrievals early in the morning and in the late afternoon, and that AOD must be greater than 0.4. For all these reasons I believe that the methodology proposed by the authors provided do not add value to AERONET inversions.
- The limitations in the use of AERONET inversions mentioned above make 7-8 inversions maximum per-day in the best conditions. But the strong limitation in AOD (>0.4) make the analyses is biases for studying AOD statistics. However, AOD from direct solar measurements are less restrictive and AERONET present much more data. So I believe these are the data that must be used. AERONET also provides fine and coarse mode AOD by the Spectral Deconvolution Algorithm that can be used. All of this is supported by the low number of measurements that authors present in Table S1.
- The authors claim a statistics of Angström parameter for different aerosol types and relate directly Angström parameter with size distribution. The authors must be careful because Angström parameter is an intensive parameter and only tells about the predominance of one mode over other. I found many statements in the text that try to relate Angström parameter with number of particles. That must be corrected. Some critical examples are in Lines 342-343, Line 352, Line 354, Lines 368-369 or Line 395.
- The effort of analyzing 17 AERONET stations is very valuable. But currently AERONET directly provided in its web page a statistics for all AERONET´s stations. Therefore, the added value must be clear. There are more than 400 stations and the analysis must cover all regions covered by AERONET. For example, there are more stations in Europe and South Asia than those presented by the authors. Other regions are excluded with no reasons – e.g. North America
- For climatological studies it is usually required a period of 30 years, so I do understand why to establish a period of 9 years. Obviously there are very few stations with 30 years measurements, but there are AERONET stations since 1995 continuously acquiring. What is clear that the number stations with more than 10 years of continuous data is considerable higher than the number used by the authors.
- There mistakes in the organization of the paper. There are many discussions based on the supplementary material. Figures S1-S7 must be included in the paper. There are many other Figures and Tables not even introduced in the paper, especially Figure 2 and Tables 3 and 4.
Other comments:
I agree with Referee 1 that title is misleading and the abstract does not reflect the main points of the paper.
Lines 44-49: Why to focus on health issues? AERONET sun photometry main use is for climatic reasons.
Line 53: There is no way to measure mass concentration with AERONET sun photometry. Same critic for the statement in Line 68.
Lines 69-70: Dubovik et al., 2002 did not use linear depolarization ratio. The other studies focused more on lidar measurements.
Line 80: ‘Depolarization ratio is essential to calculate ratio’. This is only true for lidar data. AERONET can calculate dust ratio using the AERONET inversion code
Line 116: Details of AERONET inversion code are not in Noh et al., 2017. Please correct.
Line 163: The big challenge is how to intercompare database that can depend on the variability of meteorological and local conditions.
Line 198: The mention to SSA is confused. It is obvious that aerosol types depend on SSA
Line 207: ‘In Europe, Southeast Asia, and Northeast Asia, pollution particles are the main type of aerosols ’. This is only related to the specific sites selected. There remote sites where this statement is not true.
Citation: https://doi.org/10.5194/acp-2022-219-RC2
Interactive discussion
Status: closed
-
RC1: 'Comment on acp-2022-219', Anonymous Referee #1, 18 May 2022
General comments:
This paper describes the determination of long-term trends of aerosol optical depths (AODs) at 17 AERONET stations in different regions of the world. The AODs are separated into dust- and pollution related fractions, and the pollution-related AOD is further divided into fractions associated with fine- and coarse mode aerosol components. Like the AODs, the total and fine-mode Angstrom exponents were analysed in terms of possible trends.
The topic is suitable for ACP, even though the main result, a reduction of pollution related AODs at many urban sites during the last 20 years, is not surprising. This work is based on experimental and theoretical work of many research groups contributing to AERONET. Using AERONET data for innovative research by other groups is of course in order and welcome but should meet certain standards to value the AERONET project. In my view the current paper is not suitable for publication and does not meet these standards. The numerical data analysis is questionable, formulas and results are partly inconsistent. Moreover, some important aspects are not considered, e.g. whether or not yearly averages of the various variables are suitable to represent conditions at a site (considering the typical high variability of aerosol loads, compositions, data gaps, seasonal effects). Instead, the yearly averages are treated like ordinary measurement values that, apart from a linear trend, scatter statistically. An analysis of uncertainties is missing completely. The years 2019, 2020 and 2021 were excluded from the analysis. It would have been interesting to see how significant short-term effects of lockdown measures were compared to any long-term trends.
Specific comments:
Title: The title is misleading. Fine-mode particle size is studied here rather indirectly. The title should reflect what was done in the study.
Line 28: “τ decreased in Europe and Asian regions and increased in the Middle East, India, and North Africa.” After reading the paper, it turns out that these statements are partly incorrect by the authors’ own standards of significance. τ decreased significantly in 2/3 of stations in Europe and 2/6 stations in Asia, it increased at 1/2 stations in India and at none in the Middle East and Africa.
Line 31: The abstract should be comprehensible without the remainder of the text. The meaning of “Z-values for Seoul and Osaka are -2.95 and -2.31, respectively” is unclear, a three-digit precision of these parameters is misleading.
Line 32: “…reduction of primary emissions from plants and other anthropogenic sources…” Unclear.
Line 33: “Values of αT decreased by -3.3 to - 30.5% in Europe, North Africa, and the Middle East, which means ….” These decreases are all classified as not-significant (Tab. 4). Why is this highlighted as a result in the abstract? Again, the number of digits pretends a high precision that is not justified.
Line 35: “We find that αT increased by 1.3 to 13.1%.” Same problem again. The increase is significant only at 2/8 stations in India and Asia.
Line 35: “In particular, αPF increased in most areas.” αPF significantly increased at merely 5/17 stations.
Line 72: “We also analyzed …. (Dubovik et al., 2002,….)” The use of the citations is unclear. What was the aim of this study, what is new and what was done before and requires a citation?
Line 79: “Second, the depolarization ratio is essential to calculate dust ratio, so we have limitations in using data in American and European sites.” This statement is unclear. What are these limitations? And what is different at the three European sites that were selected. What excludes all American sites?
Line 81: “Third, the selected observation sites need to be representative of regional characteristics” Unclear. What are the criteria? Why are, for example, three sites directly at the Mediterranean representative for Europe? Are urban sites representative at all for a region?
Line 107, Tab. S1: The number of observation days is quite small, e.g. around 30 or less per year for the European sites. A quick check for Thessaloniki shows many more days marked as measurement days. What is limiting the observation days in this study?
Line 112: “…we divided aerosol types as dust, fine- and coarse-mode particles depending on depolarization ratio and particle size data”. The definition of fine- and coarse mode should be clarified here. According to the AERONET documentary there is a flexible limit in a range between 0.44 µm and 0.99 µm that is determined separately for each distribution. Does this affect the current study?
Line 131: Remove “of particles”. This is not the fraction of particles but the fraction of AOD. Is the CMF at 1020 nm an AERONET product? On the webpage I only see FMF (1-CMF) for 500 nm.
Line 132: “Thus, RD represents the proportion of AOD for pure dust particles in a mixed aerosol plume”. If Eq. 3 is correct, this statement in incorrect. The dust ratio apparently describes the dust contribution to (lidar) backscatter coefficient, not AOD.
Line 140: “CMF is higher than RD in most cases, which implies that these coarse-mode particles include dust and pollution particles”. Again, if Eq. (3) is correct, the lidar ratios must be applied here too for direct comparison.
Line 143: “The ratio of coarse-mode particles (CMP) denotes the proportion (number concentration) of coarse-mode pollution particles to total particles”. So far, the discussion was on AOD and contributions to AOD. Now it’s number concentrations?
Eq. (2): I think the lidar ratios must be applied here too. Moreover, the underlying assumption is that all dust particles belong to the coarse mode fraction. Therefore, CMP will be a lower limit and the FMF (pollution) an upper limit because the fine mode is then considered pollution only?
Eq. (3): The lidar ratios were not considered before, as already mentioned. I think the equation requires an explanation and/or citation.
Line 150: “The parameter S is the lidar ratio of the aerosol mixture. S can be calculated from the AERONET data products.” Did you calculate this ratio, or is it part of the AERONET data products? It would be interesting to know how strongly the S can deviate from the Sd.
Line 154: “The AOD of coarse-mode particles (τPC)…” Shouldn’t that be coarse-mode pollution particles?
Line 158: “The term αPC is the Ångström exponent of coarse-mode pollution. We used the value of 0.16 and 0.14 for Asian and Saharan dust, respectively (Shin et al., 2018).” Why do you use a dust-value for coarse-mode pollution particles? The Angstrom exponent is not only a function of size but also of aerosol type. I would expect much greater values.
Eq. (6): τD,λ is probably calculated as in Eq. (5)? Anyway, this equation needs more explanation. I understand the approach if you would only have CMF1020. But as far as I can see AERONET provides FMF500 and Angstrom exponents for the fine-mode and the total AOD. So there seem to be other possibilities or options to check your calculated τPF,λ. Please clarify which AERONET quantities were used and which quantities were derived from these in detail.
Line 165/166: What is the difference between “First,…” and “Second,…”?
Eq. (7)/line 170: N should be the time span in years rather than the “number of available data” because in some of your time records there are gaps. If you define the percentage change this way the data in the tables are not comparable.
Line 172: “The p-value (probability value) is a scalar that describes how likely it is that the data occurred by random chance. The p-value should be small enough, depending on the level of confidence.” How were the p-values calculated and how “small” should they be?
Line 172: “We calculated all data and found a statistically significant trend”. Certainly not for all sites and quantities.
Line 177: “The magnitude of the trend can be explained by Sen’s slope test, which is a powerful method for investigating linear trends.” What was calculated and what does it mean? The “powerful method” is not used at all in the following.
Line 178: How were the Z-values calculated? As for the p-values and the S, the details should be given somewhere, at least in the Supplement (if you really need them all). Otherwise, no one can reproduce these numbers.
Line 197: “…based on a threshold value, denoted as RD, which…” RD was introduced before as dust ratio not as a threshold.
Line 200: “In this study, we used seven aerosol types based on the values based on the values of δp, respectively.” It’s unclear if a new classification is introduced here or if the method by Shin et al., 2019 was adopted. What means “…, respectively”?
Line 205: “The results of our aerosol classification reflect the characteristics of the six regions well.” What does “well” imply? Well, in terms what is expected anyway? Well, in terms that the stations are representative for a region?
Line 241: “…we compared trends in the average values of τT for each region and site during 2001–2018 (supplementary Figure S2). The average value of τT at 440 nm was highest (1.22 ± 0.76) in Beijing….” Fig. S2 clearly shows annual averages but these data do not fit to the results in Tab. 1. For example, the values for Beijing range from about 1.05 to 1.45 in the figure while in Tab. 1 a mean of 1.22 is listed (reasonable) with a standard deviation of 0.76 (unexplained).
Table 1: A look at Tab. 1 shows that the simple budget equation (6) is not fulfilled, especially at the sites with high dust contributions, i.e. τD+τPC+τPF > τT I wonder if this has to do with missing lidar factors in Eq. 2 or problems with Eq. 5. Moreover, I cannot reproduce the FMF from FMF = τPF/τT or FMF = τPF/(τD+τPC+τPF). What does all that mean for the trend analysis?
Line 247: “We find an average value of -0.0138 τTð¦ð−1” This rate of change corresponds to the slope of the red dashed line in Fig. S2 (f). From that a total change of 0.22 in 16 years can be calculated. Divided by the mean of 1.22 gives an -18% change according to Eq. 7. However, in Tab. 2 a change of -33% is listed.
Line 249: “The Chiang Mai and Bangkok sites in Southeast Asia showed a decrease of τT, too.” This statement is not justified. Looking at Fig. S2 (e) there is clearly no trend at both sites. For Chiang Mai this is confirmed by the small Z-value in Tab. 3 while for Bangkok a Z-value of about 2 is reported which indicates a significant (negative) trend. The slope listed in Tab. 2 is merely -0.0012, hardly visible in the figure and certainly not significant given the scatter of the yearly data.
Table 2: The percent variations given in brackets are not very helpful. First, they are not comparable because they refer to different time spans for different sites. Second large numbers do not automatically mean that the trends are significant.
In Figs. 3 and 4 slopes corresponding to significant trends could be colour coded. This would give a quick overview without having to consult Tab. 3 and 4 for each site.
In Figs. S2-S7 standard deviations of the yearly averages should be added. I assume this will reveal how variable the different quantities really are. Something that is not discussed in the trend analysis.
In Table 3 and 4 dutifully all statistical results are listed but what do they mean? Are the results using Z, p and S as indicators consistent? Can anything be learned from using the different approaches? What does a dash mean? Why do the Z-values have no signs consistently? Moreover, significant digits should be considered. If your criterion for significance is whether Z is greater or lower than 1.7, why report 2.3062? Some of the Z-values are numerically identical which indicates what a crude number that really is for a limited number of data points.
Line 250-383: The remainder of this section is very confusing because of the great number of sites, slopes and potential trends discussed. It is difficult to see what is relevant here and what not. I didn’t check everything in detail. The authors must make sure their statements are correct and in fact backed by the data in figures and tables. I suggest to only discuss those trends that were found significant. Example from line 297: “… but the percent variation was as high as 112.29% (Thessaloniki)”. This appears to be a large, high precision increase but it’s not significant (Tab. 3).
Technical corrections
Line 36: “… showing the probability that …” Rephrase.
Eq. (4) should probably read τPC,1020 = τ1020 × CMP1020
Line 121: “…show a comparably high correlation”. What is comparably high?
Line 133: Remove “particles” and “particle” to clarify.
Line 150: “Here, 1020 denotes the wavelength at 1020 nm.” That should be clear enough.
Line 160: “equation (5)”. Should read “equation (6)”, I assume.
Line 166/167 and 169/170: You should consistently assign slope and intercept with a and b.
Line 192: “Results” and Discussion
Citation: https://doi.org/10.5194/acp-2022-219-RC1 -
RC2: 'Comment on acp-2022-219', Anonymous Referee #2, 18 Jun 2022
The paper claims to fulfill a long-term aerosol properties study using AERONET data. The authors present the analyses separating between dust and fine mode particles. Further analyses try to separate between different aerosol components including also the Angström parameter. The different multi-year trends are analyzed and discussed. Therefore, the topic is of interest for Atmospheric Chemistry and Physics.
However, I agree with all the comments raised by referee 1. AERONET project includes extraordinary efforts of many research groups worldwide and homogenize data quality check and criteria. Data use in the paper does not meet AERONET standard quality and the methodology proposed by the authors is questionable. I would like to add some more points that make me not to recommend the paper for publication, and I recommend the authors to re-submit the paper after addressing the main comments and those by referee 1:
- Author’s methodology is based in depolarization ratios and lidar ratio. To obtain these data it is necessary the retrieval of aerosol size distribution and optical properties from AERONET inversion that combine sun and sky radiances and solve and ill-posed problem. This is good for obtaining depolarization ratios to compare with lidar systems or to compute lidar ratios for elastic lidar. But for doing climatology studies it has no sense because the inversion by itself provides aerosol characteristics. Obviously, the inversion is sensitive to noise in the input measurements and has uncertainties, and by these reasons AERONET establish strict criteria for the retrieval. The most important is geometry that limit retrievals early in the morning and in the late afternoon, and that AOD must be greater than 0.4. For all these reasons I believe that the methodology proposed by the authors provided do not add value to AERONET inversions.
- The limitations in the use of AERONET inversions mentioned above make 7-8 inversions maximum per-day in the best conditions. But the strong limitation in AOD (>0.4) make the analyses is biases for studying AOD statistics. However, AOD from direct solar measurements are less restrictive and AERONET present much more data. So I believe these are the data that must be used. AERONET also provides fine and coarse mode AOD by the Spectral Deconvolution Algorithm that can be used. All of this is supported by the low number of measurements that authors present in Table S1.
- The authors claim a statistics of Angström parameter for different aerosol types and relate directly Angström parameter with size distribution. The authors must be careful because Angström parameter is an intensive parameter and only tells about the predominance of one mode over other. I found many statements in the text that try to relate Angström parameter with number of particles. That must be corrected. Some critical examples are in Lines 342-343, Line 352, Line 354, Lines 368-369 or Line 395.
- The effort of analyzing 17 AERONET stations is very valuable. But currently AERONET directly provided in its web page a statistics for all AERONET´s stations. Therefore, the added value must be clear. There are more than 400 stations and the analysis must cover all regions covered by AERONET. For example, there are more stations in Europe and South Asia than those presented by the authors. Other regions are excluded with no reasons – e.g. North America
- For climatological studies it is usually required a period of 30 years, so I do understand why to establish a period of 9 years. Obviously there are very few stations with 30 years measurements, but there are AERONET stations since 1995 continuously acquiring. What is clear that the number stations with more than 10 years of continuous data is considerable higher than the number used by the authors.
- There mistakes in the organization of the paper. There are many discussions based on the supplementary material. Figures S1-S7 must be included in the paper. There are many other Figures and Tables not even introduced in the paper, especially Figure 2 and Tables 3 and 4.
Other comments:
I agree with Referee 1 that title is misleading and the abstract does not reflect the main points of the paper.
Lines 44-49: Why to focus on health issues? AERONET sun photometry main use is for climatic reasons.
Line 53: There is no way to measure mass concentration with AERONET sun photometry. Same critic for the statement in Line 68.
Lines 69-70: Dubovik et al., 2002 did not use linear depolarization ratio. The other studies focused more on lidar measurements.
Line 80: ‘Depolarization ratio is essential to calculate ratio’. This is only true for lidar data. AERONET can calculate dust ratio using the AERONET inversion code
Line 116: Details of AERONET inversion code are not in Noh et al., 2017. Please correct.
Line 163: The big challenge is how to intercompare database that can depend on the variability of meteorological and local conditions.
Line 198: The mention to SSA is confused. It is obvious that aerosol types depend on SSA
Line 207: ‘In Europe, Southeast Asia, and Northeast Asia, pollution particles are the main type of aerosols ’. This is only related to the specific sites selected. There remote sites where this statement is not true.
Citation: https://doi.org/10.5194/acp-2022-219-RC2
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Cited
Juseon Shin
Juhyeon Sim
Naghmeh Dehkhoda
Sohee Joo
Taekyung Kim
Gahyung Kim
Detlef Müller
Matthias Tesche
Sungkyun Shin
Dongho Shin
Youngmin Noh
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