Lawal Abiola S, Skipper T Nash, Ivey Cesunica E, Goldberg Daniel L, Kaiser Jennifer, Russell Armistead G
School of Civil & Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Drive, Atlanta, Georgia 30332-0355, United States.
Department of Civil and Environmental Engineering, 760 Davis Hall, University of California, Berkeley, California 94720-1710, United States.
ACS EST Air. 2025 Apr 29;2(6):998-1008. doi: 10.1021/acsestair.4c00198. eCollection 2025 Jun 13.
Although Chemical Transport Models (CTMs) such as the Community Multiscale Air Quality Model (CMAQ) have been used in linking observations of trace gases to emissions and developing vertical column distributions, there remain consistent biases between CTM simulations and satellite retrievals. Simulated tropospheric NO vertical column densities (VCDs) are generally higher over areas with large NO sources when compared with retrievals, while an opposite bias is found over low NO regions. Artificial (i.e., numerical) dilution in the model, where emissions are mathematically dispersed uniformly within the originating CTM grid, can impact modeled NO:NO ratios, while lower CTM VCD levels often observed over rural areas can be attributed to missing emission sources of NO or flawed horizontal/vertical transport. Potential causes of both low and high biases are assessed in this study using CMAQ and Tropospheric Monitoring Instrument (TROPOMI) NO retrievals. It was found that more detailed modeling of NO plumes to assess the NO:NO ratio in two power plant plumes can mitigate the effect of artificial computational dilution, reducing the bias and overall differences in the observed vs modeled plumes (errors reduced by 30%). Adjustments of upper tropospheric NO led to overall improvements, with a reduction in CMAQ bias (-43% to -29%) and improved spatial correlation (0.81 to 0.86). This study highlights the importance of having accurate modeled NO:NO ratios when comparing models to retrievals and the impact of unintentional numerical dilution.
尽管诸如社区多尺度空气质量模型（CMAQ）之类的化学传输模型（CTM）已被用于将痕量气体的观测与排放联系起来并建立垂直柱分布，但CTM模拟与卫星反演之间仍存在一致的偏差。与反演结果相比，在NO源较大的区域，模拟的对流层NO垂直柱密度（VCD）通常更高，而在低NO区域则发现相反的偏差。模型中的人工（即数值）稀释，即排放物在原始CTM网格内进行数学上的均匀分散，会影响模拟的NO:NO比，而在农村地区经常观测到的较低CTM VCD水平可能归因于NO排放源的缺失或水平/垂直传输存在缺陷。本研究使用CMAQ和对流层监测仪器（TROPOMI）的NO反演数据评估了低偏差和高偏差的潜在原因。研究发现，对NO羽流进行更详细的建模以评估两个发电厂羽流中的NO:NO比，可以减轻人工计算稀释的影响，减少观测羽流与模拟羽流之间的偏差和总体差异（误差降低30%）。对流层上层NO的调整带来了整体改善，CMAQ偏差减小（从-43%降至-29%），空间相关性提高（从0.81提高到0.86）。本研究强调了在将模型与反演结果进行比较时，拥有准确的模拟NO:NO比的重要性以及无意的数值稀释的影响。
