the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating nitrogen and sulfur deposition across China during 2005–2020 based on multiple statistical models
Kaiyue Zhou
Lin Zhang
Mingrui Ma
Xuejun Liu
Yu Zhao
Abstract. Due to the rapid development of industrialization and substantial economy, China has become one of the global hotspots of nitrogen (N) and sulfur (S) deposition following Europe and the USA. Here, we developed a dataset with full coverage of N and S deposition from 2005 to 2020, with multiple statistical models that combine ground-level observations, chemistry transport simulations, satellite-derived vertical columns, and meteorological and geographic variables. Based on the newly developed random forest method, the multi-year averages of dry deposition of OXN, RDN and S in China were estimated at 10.4, 14.4 and 16.7 kg N/S ha−1 yr−1, and the analogous numbers for total deposition were respectively 15.2, 20.2 and 25.9 kg N/S ha−1 yr−1 when wet deposition estimated previously with a GAM model was included. The Rdry/wet of N stabilized in earlier years and then gradually increased especially for RDN, while that of S declined for over ten years and then slightly increased. RRDN/OXN was estimated to be larger than 1 for the whole research period and clearly larger than that of the USA and Europe, with a continuous decline from 2005 to 2011 and a more prominent rebound afterwards. Compared with the USA and Europe, a more prominent lagging response of OXN and S deposition to precursor emission abatement was found in China. The OXN dry deposition presented a descending gradient from east to west, while the S dry deposition a descending gradient from north to south. After 2012, the OXN and S deposition in eastern China declined faster than the west, attributable to stricter emission controls. Positive correlation was found between regional deposition and emissions, while smaller deposition to emission ratios (D/E) existed in developed eastern China with more intensive human activities.
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Kaiyue Zhou et al.
Status: final response (author comments only)
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RC1: 'Comment on egusphere-2023-620', Anonymous Referee #1, 28 Apr 2023
This study aims to develop a machine learning framework for estimating spatial distribution and long-term trend of N and S deposition across China. Estimated dataset during the period from 2005 to 2020 is valuable to understand effects of emission reductions on deposition and N and S input to ecosystems in China. On the other hand, the dataset has considerable uncertainties (as the authors mentioned in section 3.4). The authors should also take the uncertainties into account in other sections. In addition, there are some parts where discussion is insufficient. Specific comments are shown as follows:
[Major comments]
To estimate dry deposition flux, deposition velocity (Vd) was calculated by CTM (GEOS-Chem). Current Vd models (resistant models) have large uncertainties, especially for gaseous and particulate Nr components. Therefore, the authors should open Vd calculation in detail. Although the authors indicate gaseous Vd parameterization in L255 (Wesely, 1989) used in this study, aerosol Vd parameterization should be indicated too. General aerosol models output Vd by size. On the other hand, monitored particulate NH4, NO3 do not have detailed size information (only the information of cutoff size: PM2.5, PM10, or TPM). It is necessary to explain how to treat the aerosol size to calculate the dry deposition based on equation (1). Moreover, calculated Vd values should be indicated. For example, average values of Vd for each land use are very informative for relevant researchers. This will be important information when comparing the dataset with the results of other studies.
This study uses wet deposition of SO4 (EANET) and wet or bulk deposition of NO3, NH4 (NNDMN). There is a need to discuss which regions the overestimation of NO3, NH4 by bulk sampling may affect in “3 Results and discussion”.
[Minor comments]
- L180: Does “chemical transport mode (CTM) results” means emission inventory? If so, “emission inventory” should be used as shown in Fig. 2. “mode” may be mistake for “model”.
- L184: “dry deposition rate” means dry deposition amount. “dry deposition velocity” is correct.
- L215: Regarding particulate NH4, NO3, particle size information should be indicated (TPM, PM10, PM2.5 etc.). In the case of PM2.5, dry deposition of NO3 in coarse aerosols, which contributes considerable part of total NO3 dry deposition, is ignored. If so, it should be discussed in section 3.4.
- L406-410: Trend analysis (e.g. Mann-Kendall test) is effective to mention statistically that Rdry/wet is stable for N, decline for S before 2015. It is also available for the increases after 2015.
- L420-423: In Figure S3, the range of temperature variation during the period is within 1 K. I think it is too small to enhance dry deposition by stomatal uptake. Moreover, dry deposition of these N and S component (aerosols and reactive gases) to stomata is small compared to deposition to cuticle.
- L440-445: Please check the amount of N deposition in USA and Europe in Table S4. They are too small compared with the global distribution of total N deposition (Fig. 4.8a) by Vet et al (2014) in L883. It shows the range of 1-20 kg N and 2-40 kg N in USA and Europe, respectively. Schwede and Lear (2014) also shows same range of N deposition in USA. (http://dx.doi.org/10.1016/j.atmosenv.2014.04.008)
- L471-485: The authors should discuss why the short-term emission reduction was not well reflected in the deposition. For example, Yamaga et al. (2021) in L935 mentioned that recent decrease of total S deposition in Japan was associated with recent reduction in SO2 in China. Therefore, the short-term emission reduction might be reflected in decrease of transboundary air pollution at first, because the reduction started from the east side in China.
- L600-607: The authors are requested to discuss why the larger N deposition was found in summer.
Citation: https://doi.org/10.5194/egusphere-2023-620-RC1 -
CC1: 'Comment on egusphere-2023-620', Lei Duan, 01 May 2023
It is of great interest and importance to modeling the historical S/N deposition in China, one of the hotspots in the world, for supporting policy-making. Based on various databases and applying machine learning method, the authors estimate the deposition of different species of N and S at relatively high resolution during 2005-2020. A delayed response of N/S deposition to NOx/SO2 emission abatement was found in China. In general, the manuscript was well written, and worth publishing after some minor revision. The reason of delay need be further discussion, e.g., the transport of N/S out of land of China and the changes of atmospheric oxidizing capacity. Some detailed comments are:
Line 30: The full name of OXN or RDN need be given when first occurs.
Line 34 & 35: Same as above for Rdry/wet and RRDN/OXN.
Line 68: to reduce acid rain and later improve …
Line 72: Total emission control of NOx was carried out in the 12th FYP.
Line 77: Which years?
Line 138: (GAM)
Line 223: The transformation of SO2 to sulfate depends on atmospheric oxidizing capacity, thus on NOx concentration (or emission). The discussion on the uncertainty need added.
Line 257: What were the modeled years? How was the performance of the model for China.
Line 299: Although there is reference, the brief introduction on the method is needed. What was the performance of the model?
Line 345: Were these studies carried out for the whole country or just at several sites? Since the monitoring sites are concentrated in the more developed east part of China, how did you consider the uncertainty caused by the bias?
Line 353: in its high stage?
Line 378: Limited before 2010. The total emission control of NOx was carried out in the 12th FYP (2011-2015).
Line 379: The total emission control of NOx in the 12th FYP required the installation of SCR since 2011, although more and more SCR installation had been finished after 2013.
Line 418: Another reason that increasing NOx led to high atmospheric oxidizing capacity, and thus promoting sulfate formation for wet deposition?
Line 421: How about the decrease in atmospheric oxidizing capacity?
Line 453: Replacing continents by regions? Same for the whole text.
Line 463: Delete ‘in’. Need more recent literature on the trends.
Line 469: 2005 was only for SO2. NOx had later year.
Line 485: What were the reasons for the delay? How about the contribution of natural sources for NOx, or the transboundary transport of S/N?
Line 506: What are the countries for example?
Line 508: although not as strong as for NOx.
Line 538: Delete ‘the pollution was mainly transported by atmospheric turbulence and’.
Line 546: Not dominate, although share more.
Line 551: Did the results of satellite-derived VCDs show so sharp reduction of vertical column densities? How about the possibility of overestimation of the emission reduction?
Line 555: I guess higher ratio of emission from the east than west was transported out of land and deposit on the sea. What is the effect on the effectiveness?
Line 577: The D/E ratio for the whole China need be added in the figure.
Line 592: Emphasis need be taken to the region under the line, which might have transboundary deposition.
Line 633: The effects of atmospheric oxidizing capacity on sulfate formation was not considered.
Line 993: The full name of CNEMC and NNDMN need be added.
Line 1018: How about the data sources of deposition in USA and EU?
Line 1044: Same as above.
Line 1066: The ratio of N and S deposition need be added in Figure e?
Line 1072: I suggest to use the same scale (in the legend) for dry, wet and total deposition.
Citation: https://doi.org/10.5194/egusphere-2023-620-CC1 -
RC2: 'Comment on egusphere-2023-620', Lei Duan, 02 May 2023
It is of great interest and importance to modeling the historical S/N deposition in China, one of the hotspots in the world, for supporting policy-making. Based on various databases and applying machine learning method, the authors estimate the deposition of different species of N and S at relatively high resolution during 2005-2020. A delayed response of N/S deposition to NOx/SO2 emission abatement was found in China. In general, the manuscript was well written, and worth publishing after some minor revision. The reason of delay need be further discussion, e.g., the transport of N/S out of land of China and the changes of atmospheric oxidizing capacity. Some detailed comments are:
Line 30: The full name of OXN or RDN need be given when first occurs.
Line 34 & 35: Same as above for Rdry/wet and RRDN/OXN.
Line 68: to reduce acid rain and later improve …
Line 72: Total emission control of NOx was carried out in the 12th FYP.
Line 77: Which years?
Line 138: (GAM)
Line 223: The transformation of SO2 to sulfate depends on atmospheric oxidizing capacity, thus on NOx concentration (or emission). The discussion on the uncertainty need added.
Line 257: What were the modeled years? How was the performance of the model for China.
Line 299: Although there is reference, the brief introduction on the method is needed. What was the performance of the model?
Line 345: Were these studies carried out for the whole country or just at several sites? Since the monitoring sites are concentrated in the more developed east part of China, how did you consider the uncertainty caused by the bias?
Line 353: in its high stage?
Line 378: Limited before 2010. The total emission control of NOx was carried out in the 12th FYP (2011-2015).
Line 379: The total emission control of NOx in the 12th FYP required the installation of SCR since 2011, although more and more SCR installation had been finished after 2013.
Line 418: Another reason that increasing NOx led to high atmospheric oxidizing capacity, and thus promoting sulfate formation for wet deposition?
Line 421: How about the decrease in atmospheric oxidizing capacity?
Line 453: Replacing continents by regions? Same for the whole text.
Line 463: Delete ‘in’. Need more recent literature on the trends.
Line 469: 2005 was only for SO2. NOx had later year.
Line 485: What were the reasons for the delay? How about the contribution of natural sources for NOx, or the transboundary transport of S/N?
Line 506: What are the countries for example?
Line 508: although not as strong as for NOx.
Line 538: Delete ‘the pollution was mainly transported by atmospheric turbulence and’.
Line 546: Not dominate, although share more.
Line 551: Did the results of satellite-derived VCDs show so sharp reduction of vertical column densities? How about the possibility of overestimation of the emission reduction?
Line 555: I guess higher ratio of emission from the east than west was transported out of land and deposit on the sea. What is the effect on the effectiveness?
Line 577: The D/E ratio for the whole China need be added in the figure.
Line 592: Emphasis need be taken to the region under the line, which might have transboundary deposition.
Line 633: The effects of atmospheric oxidizing capacity on sulfate formation was not considered.
Line 993: The full name of CNEMC and NNDMN need be added.
Line 1018: How about the data sources of deposition in USA and EU?
Line 1044: Same as above.
Line 1066: The ratio of N and S deposition need be added in Figure e?
Line 1072: I suggest to use the same scale (in the legend) for dry, wet and total deposition.
Citation: https://doi.org/10.5194/egusphere-2023-620-RC2 -
RC3: 'Comment on egusphere-2023-620', Anonymous Referee #3, 09 May 2023
Review of Zhou et al.
This study estimated nitrogen and sulfur deposition across China during 2005-2020 based on multiple statistical models and satellite observations. Their estimates were separated by the dry and wet contributions, based on satellite observations. The authors also present a long-term record of speciated nitrogen deposition. The authors motivate their study by (1) indicating the need for long-term measurements of dry deposition (2) calling for more spatially-resolved estimates of nitrogen and sulfur deposition (beyond what surface networks can provide) and (3) the large uncertainty of previous estimates for China.
This study is not suitable for publication because several aspects of their approach appear poorly justified. It claims to do far more than it does. This starts from the title/abstract: the authors use three satellite observations (NO2/SO2 column concentrations and NH3 column concentrations) from which they extract both dry and wet deposition of 7 species (NO3-, HNO3, NO2, NH4+, NH3, SO2, and SO42-). These transformations rely heavily on a CTM model (GEOS-Chem) and surface observations in a procedure that is not described sufficiently in the main text or Methods, and even not well described in the Supporting Information.
This approach seems to contradict the motivations for the study: their estimates have large uncertainties in estimating dry HNO3, NO3-, SO4 due to the lack of the satellite data of HNO3, NO3-, SO4; wet deposition in fact was the bulk deposition estimated. Dry deposition accounted for around 20% of the bulk deposition based on observation at three rural stations on the North China Plain, and this contribution could reach 39% in urban areas. Thus, the total nitrogen and sulfur could be largely overestimated.
I cannot see any improvements compared to previous studies regarding our understanding of China’s atmospheric deposition. One acceptable approach is to use the data assimilation methods rather than the rough approach conducted here. The authors can use the satellite NO2/SO2 column concentrations and NH3 column concentrations to better constrain the estimates of CTM modelling.
The study fails to present any uncertainty analysis. Given the error on the satellite retrievals, and the numerous transformations required to estimate the deposition, these uncertainties are likely extremely large and thus a discussion of uncertainties should be central to this analysis.
In terms of novelty, multiple studies have reported similar results using remote sensing of SO2, NO2 and NH3 to estimate deposition:
Yu, G., Jia, Y., He, N., Zhu, J., Chen, Z., Wang, Q., Piao, S., Liu, X., He, H., Guo, X., 940 Wen, Z., Li, P., Ding, G., and Goulding, K.: Stabilization of atmospheric nitrogen deposition in China over the past decade, Nature Geosci., 12, 424-431, https://doi.org/10.1038/s41561-019-0352-4, 2019
Zhao, Y., Xi, M., Zhang, Q., Dong, Z., Ma, M., Zhou, K., Xu, W., Xing, J., Zheng, B., Wen, Z., Liu, X., Nielsen, C. P., Liu, Y., Pan, Y., and Zhang, L.: Decline in bulk deposition of air pollutants in China lags behind reductions in emissions, Nature Geosci., 15, 190-195, https://doi.org/10.1038/s41561-022-00899-1, 2022.
In addition to these major flaws, there are a number of additional deficiencies described below.
Lines 218-216: This overview of methodology is completely unclear; more information is needed here and in Methods on how f(so2, so4) are used (over what time horizon) and how they are analysed.
Line 324-368: Unclear why the deposition/concentrations numbers are not compared to GEOS-Chem with no further context (it would also be useful to compare with the GEOS-Chem values since that model is used to translate columns to surface concentrations). If comparison with a model is featured, the authors should evaluate the model precipitation and concentrations to establish whether the model is biased in terms of concentrations or the parameterization of the deposition process (assumed dep velocity, precipitation, etc.).
Figure 2: this is not a particularly useful figure; a more specific graphic showing how the authors went from column concentrations of 3 species to estimate the 7 species of speciated wet & dry deposition would be more helpful to orient the reader.
Lines 249: how do the deposition velocities used here compare to those assumed in GEOS-Chem or WRF? Why did the authors choose to use another model (WRF) here rather than GEOS-Chem (which they used to translate from column to surface concentrations/deposition)?
Lines 370-513: how do estimated trends compare to previous analyses of satellite observations and ground observations in China?
Citation: https://doi.org/10.5194/egusphere-2023-620-RC3
Kaiyue Zhou et al.
Kaiyue Zhou et al.
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