the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of the first year of Pandora NO2 measurements over Beijing and application to satellite validation
Ouyang Liu
Zhengqiang Li
Yangyan Lin
Cheng Fan
Ying Zhang
Kaitao Li
Peng Zhang
Yuanyuan Wei
Tianzeng Chen
Jiantao Dong
Gerrit de Leeuw
Abstract. Nitrogen dioxide (NO2) is a highly photochemically reactive gas, has a lifetime of only a few hours, and at high concentrations it is harmful to human beings. Therefore, it is important to monitor NO2 with high-precision, time-resolved instruments. To this end, a Pandora spectrometer has been installed on the roof of the laboratory building of the Aerospace Information Research Institute of the Chinese Academy of Sciences in the Olympic Park, Beijing, China. The concentrations of trace gases (including NO2, HCHO, O3) measured with Pandora are made available through the open-access Pandora data base (https://data.pandonia-global-network.org/Beijing-RADI/Pandora171s1/). In this paper, an overview is presented of the Pandora NO2 data collected during the first year of operation, i.e., from August, 2021, to July, 2022. The data show that NO2 concentrations were high in the winter and low in the summer, with diurnal cycle where the concentrations reach a minimum during day time. The concentrations were significantly lower during the 2022 Winter Olympics in Beijing, showing the effectiveness of the emission control measures during that period. The Pandora observations show that during northerly winds clean air is transported to Beijing with low NO2 concentrations, whereas during southerly winds pollution from surrounding areas is transported to Beijing and NO2 concentrations are high. The contribution of tropospheric NO2 to the total NO2 VCD varies significantly on daily to seasonal time scales, i.e., close to 50 % in autumn and winter, and close to 70 % in spring and autumn. The comparison of Pandora-measured surface concentrations with collocated in situ measurements using a Thermo Scientific 42i-TL Analyzer shows that the Pandora data are low and that the relationship between Pandora-derived surface concentrations and in situ measurements are different for low and high NO2 concentrations. Explanations for these differences are offered in terms of measurement techniques and physical (transport) phenomena. The use of Pandora total and tropospheric NO2 vertical column densities (VCDs) for validation of collocated TROPOMI data, resampled to 100×100 m2, shows that although on average the TROPOMI VCDs are slightly lower, they are well within the expected error for TROPOMI of 0.5 Pmolec ⋅ cm−2 + 0.2 to 0.5 ⋅ VCDtrop The location of the Pandora instrument within a sub-orbital TROPOMI pixel of 3.5×5.5 km2 may result in an error in the TROPOMI-derived tropospheric NO2 VCD between 0.223 and 0.282 Pmolec.cm-2, i.e., between 1.7 % and 2 %. In addition, the data also show that the Pandora observations at the Beijing-RADI site are representative for an area with a radius of 10 km.
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Ouyang Liu et al.
Status: open (until 10 Oct 2023)
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RC1: 'Comment on amt-2023-177', Anonymous Referee #1, 06 Sep 2023
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The authors report the NO2 observations in Beijing China from first Pandora instrument, show the local temporal variation of NO2 and reveal the spatial and temporal representativeness of atmospheric column NO2 concentrations obtained from ground-based remote sensing. The manuscript is well structured and logic, gives some observational facts and valuable conclusions, and deserves to be published in the AMT journal. However, I still have a few comments as below I hope authors will clarify before publication.
- In section 2.2, two subheadings 2.2.3 appear. Also the reanalysis data are not instrumental and should not be presented in this section; the authors are requested to adjust them.
- This manuscript focuses on the analyses and comparisons of pandora instrumental observations and is not solely a measure of the differences between TROPOMI and pandora observations; The paper shows that the pandora observations are also compared to ground-based observations at least and that the differences are measured. It is therefore recommended that the methods section be revised and improved by correcting the description of the paragraph below line 225 and adding the description of the methods in the other sections, if any.
- In line 252 for the ratio of DQ2 data to total data, 2176 divided by 80,153 does not equal 28.2%. Please check and revise.
- In Figure 2 we can see that the number of observations changes from month to month. How is this variation taken into account in the statistical process, e.g., by calculating the median, mean, etc.? Do you average all the observations within a time period in a month or divide the data into per days first and then take the mean?
- In section 3.2, the authors may have missed a phenomenon. There are still several red dots distributed in the north-west around the interval 270° to 320° in Fig 4. However, the author states that clean air is transmitted from the northwest. I think this may not be a coincidence and would appreciate an explanation.
- In section 3.6, why the spatial representation of Pandora is 10km instead of 20km, I noticed that the Df mentioned in this manuscript is very close between 10km and 20km, with a difference of only 0.002. What is the significance of the author's introduction of Df if it is not to be used as a metric for evaluation? I would be grateful if this was clarified.
Citation: https://doi.org/10.5194/amt-2023-177-RC1 -
RC2: 'Comment on amt-2023-177', Anonymous Referee #2, 25 Sep 2023
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General Comments:
This paper presents the NO2 observations from the Pandora spectrometer in Beijing from August 2021 to July 2022. The authors quantitively discuss the temporal variations of NO2 observations on different time scales, and analyze the influences of the wind on NO2 VCDs using reanalysis data. The Pandora NO2 measurements are compared with ground-based in-situ measurements, and the reasons behind their differences are further explained. Finally, the authors use the Pandora NO2 data to validate TROPOMI v1.4 tropospheric NO2 VCDs, and give an estimation of the spatial representativeness of Pandora NO2 measurements.
Overall, I think this paper is clear and well structured. I recommend it be published after addressing the comments listed below.
Specific Comments:
Line 237-239: Why do you use the defined “standard deviation” instead of covariance, which seems more appropriate, to evaluate TROPOMI and Pandora data sets?
Line 294: The number of days for December is reduced to 12 or 9? The “Number of days with high quality data” for December in Table 1 is 9.
Line 323-324: “Because of the large diurnal variation of the NO2 VCDs, only data have been selected at the TROPOMI overpass time at 13:00 BJT”. The causal relationship here is unreasonable. If you select the TROPOMI overpass time, it is expected that you also analyze the TROPOMI NO2 VCDs for comparison. Please consider adding the comparison results with TROPOMI observations, or explaining reasonably your thoughts about the time selection.
Line 348-350: what are the reasons for the diurnal variations of the tropospheric / total ratio? Are they related to the diurnal variations of NOx emissions, photochemistry or stratospheric NO2? Please specify.
Line 362-363: please specify the reasons why enhanced solar shortwave radiation (more active photochemical reactions) can result in higher ratio in the spring.
Line 395-399: How do you know the variations of the tropospheric NO2 VCDs from Figure 7? Only total VCDs are shown in this figure.
Section 3.6: The quantification of the spatial representativeness of the Pandora observations at the Beijing-RADI site is based on TROPOMI v1.4 tropospheric NO2 VCDs. However, it has been well known that TROPOMI v1.x data are significantly underestimated, especially for polluted regions. Please use updated TROPOMI PAL v2.3.1 or reprocessed TROPOMI v2.4.0 tropospheric NO2 VCDs to validate the robustness of your conclusion.
Technical comments:
Figure 2 and Figure 5: I find these two figures are nearly impossible to read the characteristics of diurnal variations. If the discussion of diurnal variations is important, please add additional figures clearly showing the details.
Line 35: add a period after “VCDtrop”.
Line 123: change “capitol” to “capital”.
Line 312: change “NO2” to “NO2” in the subheading.
Line 361: change “ration” to “ratio”
Citation: https://doi.org/10.5194/amt-2023-177-RC2
Ouyang Liu et al.
Ouyang Liu et al.
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