the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Aug 2025
High resolution Air Quality simulation in the Himalayan valleys, a case study in Bhutan
Abstract. Our study focuses on Bhutan, a highly mountainous country where governmental authorities are increasingly monitoring air pollution. To support further analysis and the monitoring strategy, we present the first high-resolution air quality simulations with the chemistry transport model WRF-CHIMERE over the western region of Bhutan at a spatial resolution of roughly 1 km. Increasing the horizontal resolution of the model improve the performances, decreases potential errors due to too important spatial average of meteorological and emissions data having an high spatial variability. However, the air pollutant emissions must be improved at fine scale with better proxy, particularly for industries where improvement are still required. For the first time, we propose high resolution maps of air pollution (concentrations and deposition fields). Our simulations confirm that Bhutan valleys also suffer from air pollution mainly due to PM2.5 (concentrations exceeding 20 µg m−3) dominated by carbonaceous species, largely above the World Health Organization guidelines. Wildfires and anthropogenic activities release large amount of carbonaceous species and can also impact the glaciers by atmospheric fallout. Wildfires can locally contribute to 20 % of the total PM2.5 concentrations over a 15 days period, and theoretically, black carbon can be transported up to the highest peaks. Ecosystems are at risks with deposition fluxes of sulfur and nitrogen species comparable with other locations at risk in the world.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-3641', Anonymous Referee #1, 23 Sep 2025
- AC1: 'Reply on RC1', Bertrand Bessagnet, 19 Nov 2025
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RC2: 'Comment on egusphere-2025-3641', Anonymous Referee #2, 16 Nov 2025
This manuscript presents the first high-resolution (≈1 km) regional air-quality simulation over western Bhutan using the WRF–CHIMERE modelling system. The authors develop a new fine-scale emission inventory—downscaled from EDGAR—and simulate PM₂.₅, O₃, and other pollutant fields for February–March 2025. Model outputs are evaluated against sparse observations (the Thimphu reference station and two low-cost sensors in the Haa valley), and spatial patterns of air pollution and deposition are examined. Key findings include persistently elevated PM₂.₅ levels that exceed WHO guidelines, with carbonaceous aerosols dominating the load; substantial contributions from biomass burning (wildfires); and non-negligible long-range transport of BC and dust affecting high-altitude Himalayan glaciers. The study further quantifies nitrogen, sulfur, carbon, and dust deposition, showing that fluxes—especially for nitrogen species—are comparable to values observed in high-risk ecosystems globally. While increased spatial resolution generally improves model performance (notably for temperature and near-surface gradients), several biases remain. Overall, this work provides valuable high-resolution pollution maps and new insights into the drivers of air quality in Bhutan, with implications for regional environmental management. The novelty lies less in the modelling methodology itself than in the application to a region that has been seldom studied and is characterized by complex orography and diverse emission sources.
Major Comments
- Conducting air-quality simulations at 1 km resolution over Bhutan’s highly complex terrain is both novel and useful. As the authors note, no comparable fine-scale modelling has been performed in this region. The study addresses relevant questions on local versus transported pollution, the role of wildfires, and deposition processes. However, the analysis is limited to a ~1.5-month late-winter to early-spring period. The manuscript would benefit from a brief discussion of how representative these months are of typical annual conditions, or from an explicit acknowledgement of the limitations of this single-season case study.
- The custom high-resolution inventory is a strength of the study. The proxy-based downscaling of EDGAR emissions is clearly described, and the dominance of residential combustion aligns with known local practices (e.g., wood-burning stoves). Nevertheless, this approach inevitably overlooks some local sources and spatial heterogeneity. Industrial emissions, in particular, remain highly uncertain. The manuscript would be strengthened by an explicit discussion or estimation of the uncertainties associated with these proxies and by a qualitative assessment of the sensitivity of the results to downscaling assumptions.
- The nested WRF–CHIMERE setup (0.25°, 0.05°, 0.01°) is well described, and the use of 46 vertical levels with nudging in the outer domain is appropriate. The choice of the simulation period is justified by available observations. For completeness and reproducibility, the authors should consider adding a succinct table or flow chart summarizing key model configuration aspects—e.g., boundary conditions, chemical mechanisms, emission species. In addition, the manuscript should further clarify how the chosen vertical resolution improves representation of valley inversion layers and cold-pool dynamics.
- The analysis of fire impacts is compelling. The comparison between CTRL and NOFI configurations shows that local fires in March increased PM₂.₅ by ~20 % in affected districts, with hourly peaks exceeding 70 %, while fires in Myanmar and India contribute ~20 % near Bhutan’s southeastern border. These results are policy-relevant. However, the treatment of fire emissions should be described more precisely: Were CAMS GFAS emissions used exclusively? Were local hotspots incorporated? It would also be useful to comment on the degree to which the model reproduces the observed spatial and temporal patterns of the wildfire events.
- Mapping deposition fluxes is highly relevant for evaluating glacier darkening and ecosystem vulnerability. The manuscript convincingly shows that nitrogen and organic-carbon deposition reaches levels comparable to sensitive regions worldwide. To contextualize these results, the authors could briefly compare their values with known ecosystem critical loads or with findings from similar Himalayan studies.
- The study clearly highlights that PM₂.₅ levels in valleys and urban centers routinely exceed health guidelines, informing mitigation priorities such as cleaner residential fuels and wildfire management. As a second step, the authors are encouraged to implement ensemble modelling approaches—through multi-physics, multi-parameter, or multi-model perturbations—to quantify structural and parametric uncertainties inherent in high-resolution atmospheric simulations. Ensemble frameworks provide probabilistic ranges instead of single deterministic outputs, thereby reinforcing the robustness of the findings and greatly enhancing their utility for evidence-based policy and decision-making.
Minor Comments
- The manuscript is generally well structured and the figures/tables are clear
Wording issue : “an quality simulation” should be corrected to “an air-quality simulation.”
- In the emission inventory section, clarify that “primary OM” refers to organic matter, as this may not be familiar to all readers, particularly those less accustomed to EDGAR conventions.
Citation: https://doi.org/10.5194/egusphere-2025-3641-RC2 - AC2: 'Reply on RC2', Bertrand Bessagnet, 19 Nov 2025
Video supplement
Impact of wildfires on Bhutan environment Bertrand Bessagnet https://doi.org/10.5281/zenodo.16526751
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General comments
The paper High resolution Air Quality simulation in the Himalayan valleys, a case study in Bhutan presents an application of the CHIMERE chemistry transport model over the west Bhutan. Though the modelling application is quite standard the area of study is particularly challenging due to the scarcity of data and available literature.
The authors provided a thorough description of the modelling design and implementation as well as of the evaluation of the obtained results, though limited by the availability of observed meteorological and air quality data.
A second interesting aspect of this paper is the evaluation and discussion of different issues related to the effect of air pollution in remote areas, such as deposition over glaciers and role of wildfires.
Therefore, the paper fits the scope of ACP. The paper is also well written, with concise and clear statements, and it does not require any substantial review of syntax and language.
The paper could be published considering just a general review of the section on the evaluation of the model performance that is sometimes unclear and partially confusing.
To this aim, additional details are available in the following section.
Specific comments and Technical corrections
P4 – R97-98 – Sentence is not clear
P8 – R183 – Are observed data discussed in this subsection presented in Figure S2?
P10 – R2025 - Are observed data discussed in this subsection presented in any figure?
P10 – R219 – Does Figure S5 refer to all available data?
P11 – R229 – A relation exists “between the observed PM coarse fraction” and what?
P11 – r230-235 – This section is not very clear. This paragraph should be focused on model performance evaluation, but here the discussion seems on observed data, which are also compared to literature data
P11 - R238-241 – Discrepancies in Haa stations for PM2.5 during March seem more related to a difficulty of the model in capturing the two episodes (Meteorology? Emissions?) than to spatial resolution
P11 – R250 – “overestimates”
P11 – R253-255 – Is BC time series shown in Figure 5?
P12 – Figure4 – Is this Figure mentioned in the text?
P12 – R258-262 – Are the industrial sources considered as point sources or ground level emissions? Could Also this aspect influence the performance?
P12 – R262-266 – Why do the analysis of meteorological performance is not placed before air quality?
P14 – R294 – contour lines in Figure 8 are visible only over white areas
P15 – R318 – “1 or 2 mg/m2”?
P21 – R380 – How were wildfire emissions estimated and modulated?