the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Constraints on NOx emission in Thailand using GEMS satellite data
Abstract. Nitrogen oxides (NOx = NO + NO2) are key pollutants that contribute to ozone and secondary aerosol formation, posing environmental and health risks. Accurate simulation and forecasting of NOx pollution is essential for developing mitigation strategies. Local inventories in Thailand are infrequently updated, leading researchers to use global inventories such as CAMS-GLOB-ANT for simulation. Global inventories carry uncertainties due to assumptions in emission factors, outdated activity data, and coarse temporal resolution. To address these limitations, this study applies a top-down approach to update NOx emissions in Thailand using the iterative finite difference mass balance (IFDMB) method. Tropospheric NO2 vertical column densities (VCDs) from the GEMS are integrated with the WRF-Chem to refine CAMS-GLOB-ANT emissions for September 2023. The simulations with posterior emissions are evaluated against TROPOMI NO2 VCDs and surface NOx concentration. Results show that the baseline simulation overestimates NO2 VCDs across Thailand compared with GEMS, except in North Thailand. Consequently, IFDMB reduces NOx emissions across most regions but increases in the North. These adjustments improve model bias and error relative to GEMS. However, when evaluated against TROPOMI, we find an increase in the bias for North Thailand, likely due to discrepancies between GEMS and TROPOMI retrievals. Discrepancies between GEMS and TROPOMI highlight the importance of future calibration across satellite products. Comparisons to surface observations indicate that IFDMB shifts the NOx peak to later than observations. This is because observations are strongly influenced by local transportation sources, which are hard to observe and simulate by GEMS and the model, respectively.
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Status: open (until 09 Jul 2026)
- RC1: 'Comment on egusphere-2026-2309', Anonymous Referee #1, 22 Jun 2026 reply
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RC2: 'Comment on egusphere-2026-2309', Anonymous Referee #2, 24 Jun 2026
reply
The study by Thongsame uses a chemistry model and new geostationary satellite data to estimate nitrogen oxide emissions in Thailand. Overall, it is an interesting study using established methods to estimate these emissions. Secondary comparisons with TROPOMI and surface sites provide additional validation of the work. The discussion on the discrepancies between TROPOMI and GEMS are an interesting and valuable addition to the literature. The analysis is detailed, which suites the scope of ACP and suitable of publication subject to some minor corrections:
- In the abstract, GEMS does not “simulate” NO2 (Line 29).
- The authors state that GEO has higher spatial resolution than LEO. If oversampling is used, the LEO datasets can be higher resolutions (i.e. < 1 km) (Line 90).
- The discussion on the averaging kernels is unclear. Please add the equations used to apply the AKs and clear description. It would also be good to add a figure (e.g. in supporting information) comparing the GEMS and TropOMI AKs.
- Why is 230 hPa used for the tropopause (Line 167)? It will also shift in the day, which can be important. Please justify the reasoning behind this.
- Please expand on how the RMSE is calculated as the error term for GEMS data (Line 226)? Would the RMSE be based on the comparison of GEMS with a secondary data set?
- In Section 2.7, how are the metrics (NMB) calculated? Is this done using the observational error? I ask as the MB is used for the surface sites and no error term is available, so I assume the satellite metrics are normalised via the error term. Please make this clear what is used.
- In Section 3.1 and discussion of Figure 5, please add some more quantitative discussion as scientifically weaker as it is.
- Figure 6, would you not expect more iterations before the IFDMB converges. Iterations of 2-4 seems low or very quick to converge on a solution?
- A more detailed discussion on the differences between TropOMI and GEMS. Why can you not directly compare the two products? Would that not provide a systematic bias between the two instruments which other studies could use.
- For Section 3.5, could you not replace e.g. the GEMS apriori with that of TropOMI and recalculate the trop col NO2 around the TropOMI overpass time and GEMS hourly measurements. That would allow more direct and useful comparisons.
Citation: https://doi.org/10.5194/egusphere-2026-2309-RC2
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