the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Remotely Sensed and Surface Measurement Derived Mass-Conserving Inversion of Daily High-Resolution NOx Emissions and Inferred Combustion Technologies in Energy Rich Northern China
Xiaolu Li
Hong Geng
Liling Wu
Xiaohui Wu
Chengli Yang
Rui Zhang
Liqin Zhang
Abstract. This work presents a new model free inversion estimation framework using daily TROPOMI NO2 columns and observed fluxes from the continuous emissions monitoring systems (CEMS) to quantify three years of daily-scale emissions of NOx at 0.05°×0.05° over Shanxi Province, a major world-wide energy producing and consuming region. The NOx emissions, day-to-day variability, and uncertainty on a climatological basis are computed to be 1.83, 1.01, and 1.06 Tg per year respectively. The highest emissions are concentrated in the lower Fen River valley, which accounts for 25 % of the area, 52 % of the NOx emissions, and 72 % of CEMS sources. Two major forcing factors (10th to 90th percentile) are horizontal transport distance per day (66–666 km) and lifetime of NOx (6.7–18.4 h). Both of these values are consistent with NOx emissions to both the surface layer and the free troposphere. The third forcing factor, the ratio of NOx / NO2, on a pixel-by-pixel basis is demonstrated to have a significant correlation with the combustion temperature and energy efficiency of large energy consuming sources. Specifically, thermal power plants, cement, and iron and steel companies have a relatively high NOx / NO2 ratio, while coking, industrial boilers, and aluminium oxide show relatively low ratio. Variance maximization is applied to daily TROPOMI NO2 columns identifies three significant modes, and successfully attributes them both spatially and temporally to (a) this work’s computed emissions, (b) remotely sensed TROPOMI UVAI, and (c) computed transport based on TROPOMI NO2.
Xiaolu Li et al.
Status: open (until 23 Mar 2023)
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RC1: 'Comment on egusphere-2023-2', Anonymous Referee #2, 15 Feb 2023
reply
This paper uses a mass balance approach to interpret observations of TROPOMI NO2 columns and CEMS NOx emission flux, enabling the propagation of measured flux at in situ sites to build seamless monthly NOx emission estimates across Shanxi Province, China. It also further interprets the variability of derived model parameters in their framework that represent NOx/NO2 emission ratio (alpha1), NOx lifetime (alpha2) and horizontal advection rate (alpha3). To my knowledge, this is a pioneering study that evaluates and interprets an established space-borne emission estimation method, which has rarely been validated using densely distributed flux observations. The paper is overall well-written with sound methods and results. At the same time, some critical details are missing, and structural changes of the contents are needed. I support the publication of this manuscript, provided that the following comments can be addressed.
Major comments:
1) The MFIEF approach is largely originated from similar box-modeling ideas in previous studies (Beirle et al., 10.1126/sciadv.aax98, 2019; Kong et al., 10.5194/acp-19-12835-2019, 2019), while differs to some extent in details about assumptions on each source/sink process. Line 84-86 presents these studies in the overall "previous study" category, which seem to diminish this connection. I suggest the authors to introduce these approaches in the end of this paragraph, and acknowledge the similarity of idea used in this paper. Also, certain discussions about the uniqueness and capabilities of MFIEF (e.g., using measured emissions, fitting variable parameters that were fixed in previous approaches, etc.) relative to these previous methods should also be added in the Introduction and/or Discussion Section.
2) The UVAI is used as a proxy of OH and NOx lifetime to interpret EOF2. However, UVAI is a measure of aerosol absorption, while the actual radiation flux reach surface is also sensitive to aerosol scattering, cloud extinction, and solar angles. I did not find existing literature reporting strong correlation between UVAI and OH or NOx lifetime, so it is not convincing for me to justify the interpretation related with Figure 11. Please provide stronger evidence to justify the use of UVAI, or switch to use other parameters (e.g., alpha2?).
3) Section 3.3: besides introducing the total uncertainties, contributions from each factor should also be included. One particularly important source is the performance of the fitting and the consequent errors in each parameter. This is the most fundamental information to evaluate the fidelity of the MFIEF framework. How much variability of observed VNO2 and ENOx can be explained by Eq. (3)? As the fitting is performed including all observations, is it unbiased for all months and grids?
4) Section 3.4: The derived emissions are representative of ambient fluxes (instead of initial emissions from the furnace), so the rapid NO-NO2 conversion and consequently the NOx/NO2 ratio is dependent on not only the combustion environment factors discussed in the manuscript, but also ambient chemistry (e.g., photolysis rate and ozone concentration). I assume the latter factor might be more important in driving the seasonal variations in Figs. 12 and 14. Due to the lack of full consideration of all driving factors as well as the lack of outstanding hotspot of alpha1 from certain month or factory, the current discussion of alpha1 in this section is relatively more conjectural than the other part of the paper. My overall suggestion is to greatly reduce the amount of discussion and focus on 1-2 most convincing observations that can be concluded from existing data, with acknowledgement of various factors driving alpha1 variability and precluding a full explanation of all revealed variabilities.
Specific comments:
1) Line 22-23: As outlined before, the statement of "significant correlation with combustion temperature and energy efficiency" might be too strong here.
2) Line 39: delete "are more serious".
3) Line 48: delete "(2015, 2020)".
4) Citations in the Introduction Section:
Line 42: should also cite Zhang et al., 10.1073/pnas.1907956116, 2020; Wang et al., 10.1073/pnas.2007513117, 2020; Li et al., 10.1016/j.scitotenv.2021.150011, 2022; Wei et al., 10.5194/acp-23-1511-2023, 2023.
Line 54: (Beirle et al., 2011) is not a paper studying NOx forming aerosol.
Line 56: Besides China, some other regional inventories (e.g., McDonald et al., 10.1021/es401034z, 2013; Xing et al., 10.5194/acp-13-7531-2013, 2013) might be worth citing.
Line 92: can cite (Zheng et al., 10.5194/acp-18-14095-2018, 2018) for MEIC.
5) Line 85-86: As outlined before, should clarify that your approach improves these assumptions to some extent.
6) Line 91-92: These two are bottom-up inventories, so should follow after Line 73? I do not see clear connection of this sentence with the previous text.
7) Line 98: What idea from bottom-up inventory is used in your approach?
8) Line 107: As outlined before, using UVAI as a proxy of UV radiation seems not appropriate.
9) Fig. 3c and 4c: set log-scale for x-axis might increase the readability of the figure. Also, 28% of days are absent in 2019 so would that affect the sampling and representativeness of data in Fig. 3?
10) Line 211: this is true for daytime and locations with strong NOx emissions only. See (Kenagy et al., 10.1029/2018JD028736, 2018) for nighttime sinks, and (Romer Present et al., 10.5194/acp-20-267-2020, 2020) for possible significant daytime sink via reactions with RO2. As Eq. 3 relates VNO2 at afternoon overpass to 24-h mean emissions, the lifetime should also reflect all hours during the day.
11) Equation 3: Since VNO2 is a snapshot of afternoon overpass while ENOx is 24-h average, so alpha1-alpha3 all contain the conversion from overpass time to 24-h mean. Should acknowledge this fact.
12) Fig. 5: As outlined before, alpha1 is not just determined by type of source.
13) Fig. 6: a scatter plot of Fig. 6a vs. Fig. 3a will provide an insight about how representative Eq. 3 is. Certain locations with strong emissions while unmeasured by Fig. 3a should also be discussed (e.g., are these exactly locations of missed stationary sources?).
14) Fig. 8: What spatial extent is used to calculate the city-mean emissions? If the range is too small, the difference between MEIC and CEMS could be dominated by dilution by the large grid cell.
15) Fig. 9: Are the spatial distribution of EOF1 correlating with that of alpha1? How about EOF2 vs. alpha2? EOF3 vs. divergence?
16) Sections 3.1 and 3.2: Alpha1-alpha3 all exhibit certain spatial and temporal variabilities. What are the implications on previous methods that have simpler (e.g., fixed) assumptions?
17) Line 462: should mention possible benefits from Geostationary instruments that can be promising to resolve the expectedly strong diurnal variability of alpha1-alpha3.
Citation: https://doi.org/10.5194/egusphere-2023-2-RC1
Xiaolu Li et al.
Xiaolu Li et al.
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