the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of improved representation of VOC emissions and production of NOx reservoirs on modeled urban ozone production
Abstract. The fraction of urban volatile organic compounds (VOC) emissions attributable to fossil fuel combustion has been declining in many parts of the world, resulting in a need to better constrain other anthropogenic sources of these emissions. During the National Institute of Environmental Research (NIER) and National Aeronautics and Space Administration (NASA) Korea-United States Air Quality (KORUS-AQ) field study in Seoul, South Korea during May–June 2016, air quality models underestimated ozone, formaldehyde, and peroxyacetyl nitrate (PAN) indicating an underestimate of VOCs in the emissions inventory. Here, we use aircraft observations interpreted with the GEOS-Chem chemical transport model to assess the need for increases in VOC emissions. We find that the largest increases are attributable to compounds associated with volatile chemical products, liquefied petroleum gas (LPG) and natural gas emissions, and long-range transport. Revising model chemistry to better match observed VOC speciation together with increasing model emissions of underestimated VOC species increased calculated OH reactivity by +2 s-1 and ozone production by 2 ppb hr-1. Ozone increased by 6 ppb below 2 km and 9 ppb at the surface, and formaldehyde and acetaldehyde increased by 30 % and 120 % aloft, respectively, all in better agreement with observations. The larger increase in acetaldehyde was attributed to ethanol emissions which we found to be as important for ozone production as isoprene or alkenes. The increased acetaldehyde largely resolved the model PAN bias. The need for additional unmeasured VOCs however was indicated by a remaining model bias of -1 ppb in formaldehyde and 57 % and 52 % underestimate in higher peroxynitrates (PNs) and alkyl nitrates (ANs), respectively. We added additional chemistry to the model to represent an additional six PNs from observed VOCs but were unable to account for the majority of missing PNs. However, four of these PNs were modeled at concentrations similar to other commonly measured PNs (>2 % of PAN) indicating that these should be measured in future campaigns. We hypothesize that emissions of oxygenated VOCs (OVOCs) such as >C5 aldehydes from cooking and/or alkenes associated with volatile chemical products could produce both PNs and ANs and improve remaining model biases. Emerging research on the emissions and chemistry of these species will soon allow for modeling of their impact on local and regional photochemistry.
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Status: open (until 20 May 2024)
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RC1: 'Comment on egusphere-2024-951', Anonymous Referee #1, 24 Apr 2024
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This study aims to address the underestimation of volatile organic compounds (VOCs) in emission inventories by adjusting scaling factors, optimizing photochemical reactions, and revising the schemes for generating additional polycyclic aromatic hydrocarbons (PAHs). They utilized both ground-based and airborne observation data to evaluate the extent of the impact of improved VOCs emissions on the Seoul Metropolitan Area. This contributes to the enhancement of the model's ability to simulate urban air quality. This paper is exceptionally well-crafted, presenting intriguing findings. Consequently, I suggest it should be published following appropriate revisions.
1. To improve the alignment between observed VOC speciation and model predictions, the chemical mechanism has been revised and underestimated VOCs species has been increased. These revisions are designed to improve the understanding of VOC species in the atmosphere and to enhance the simulation capability of the model. I would like to know if these improvements are suitable for other regions than the SMA and the period other than May 1 to June 10, 2016.
2. Line 158, Are you referring to the process of individually adjusting the scaling factors for VOCs species and comparing them with observed values to determine the optimal scaling factors? And the determination of the best scaling factor typically involves assessing how well the adjusted model outputs align with actual measurements. What metrics were used in this study to quantify the differences between predicted and observed values?
3. Line 219, The observations clearly show a shift from increasing to decreasing POx with increasing NOx at approximately 6 ppb. However, the model did not capture this feature at all. What could be the reason for this? In addition, many discrepancies between simulations and observations in this study have been explained by insufficient model resolution. Could this be further addressed through the use of high-resolution models?
4. After scaling VOCs, the model simulations of NOx concentrations tend to be underestimated during the day and overestimated at night in Fig. 4b. Could you provide a more detailed explanation for this phenomenon?
5. The model does not fully capture the vertical profiles of PNs in the observation in Fig. 6. Is this due to an underestimation in the VOC emissions?
6. Line 370, Please verify the absence of Figure 2d in the article.
7. Line 230 The word "tha" appears to be a spelling error.
8. Figure 1 lacks a serial number indicating its order.
Citation: https://doi.org/10.5194/egusphere-2024-951-RC1
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