the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Satellite Detection of NO2 Distributions and Comparison with Ground-Based Concentrations
Abstract. In this study we assess the capability of current-generation satellites to capture the variability of near-surface nitrogen dioxide (NO2) monitoring data, with the goal of supporting health and regulatory applications. We consider NO2 vertical column densities (VCD) over the United States from two satellite instruments, the Tropospheric Monitoring Instrument (TROPOMI), and Tropospheric Emissions: Monitoring of Pollution (TEMPO), and compare with ground-based concentrations as measured by the EPA’s Air Quality System (AQS) monitors. While TROPOMI provides a longer-term record of assessment (2019–2023), TEMPO informs diurnal patterns relevant to evaluating peak NO2. We analyze frequency distributions and quantify their similarity using the Jensen-Shannon Divergence (JSD), where smaller values indicate better agreement. Satellite and ground monitor NO2 distributions are most similar away from major roads, as indicated by the JSD of 0.008 calculated for TROPOMI and ground monitors at non-roadways, compared with a JSD near interstates of 0.158 and a JSD near highways of 0.095. Seasonal analysis shows the most similarity in distributions in winter, with a JSD of 0.010, and the most difference in summer, with a JSD of 0.035. Across seasons and monitor locations, TEMPO consistently has a lower or similar JSD as TROPOMI, with TEMPO JSDs ranging from 0.005 to 0.151 and TROPOMI JSDs ranging from 0.012 to 0.265. TEMPO’s agreement with monitors in both December 2023 and July 2024 is found to be best around midday, with non-road monitors’ JSD in July as low as 0.008 at 16 UTC (~11 am LT).
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RC1: 'Comment on egusphere-2025-226', Anonymous Referee #1, 13 Feb 2025
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Overall, this is an interesting area of study and the manuscript could become stronger if my concerns are addressed.
Abstract: The authors should consult the “guidelines for authors”: https://www.atmospheric-chemistry-and-physics.net/policies/guidelines_for_authors.html. For instance, the title is not specific and the abstract lacks specific conclusions on the important implications of the work (i.e., just JSD values are given).
Introduction: The introduction is fairly well written, but verbose. Please consider condensing text given that the audience for this journal article will already be familiar with much of the information provided. The paragraphs on advanced methods, frequency distributions and JSD could be moved to “data and methods”.
Section 2: How are the data averages binned in time? Seasons? Months?
Sections 2.2-2.3: It would be worth discussing the number of satellite pixels that are required for comparison to your distance definitions in Section 2.4. That is, do most of the distances fall within one relative-coarse satellite pixel? Two pixels? Is this the reason there is little variation in the satellite results presented in Figure 1? Could you address this issue with oversampling or some other similar technique?
Line 214: mixing meters and miles. Is that how distances are reported for the AQS monitors? Why not meters and kilometers or feet and miles?
Section 2.4: Concerning the way that you classified the monitors, would it make sense to create categories such as “rural interstates”, “urban interstates”, “industrial interstates”, etc. The location of nearby non-interstate roads and industry will certainly affect your results, especially in the pixel size of a satellite instrument. Development typically occurs along major roadways. You could create such bins based on population density or some other metric that indicates how developed an area is.
Figure 1: The time period isn’t specified. A summer month? An annual average? Could you show these relationships for four seasons? Regions?
Figure 3: Are the TEMPO observations filtered for the same time of day as the TROPOMI overpass to make this more of an apples-to-apples comparison? I see now in the paragraph beginning on line 451 that the TEMPO data are for all hours.
Section 3: You present a lot of statistics, which can be overwhelming to a reader. Could you begin each Section 3 subsection with a statement like “In this subsection, we show that our data analysis indicates [insert high level conclusion here].” That is, tell the reader up front what the take-away messages are.
Section 4 last paragraph: Can you elaborate on this with an example or two of how your study may help? I’m worried that people involved in these activities may not know how to translate your results into action.
Citation: https://doi.org/10.5194/egusphere-2025-226-RC1
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