Abiola S Lawal, T Nash Skipper, Cesunica E Ivey, Daniel L Goldberg, Jennifer Kaiser, Armistead G Russell
{"title":"Potential Errors in CMAQ NO:NO<sub>2</sub> Ratios and Upper Tropospheric NO<sub>2</sub> Impacting the Interpretation of TROPOMI Retrievals.","authors":"Abiola S Lawal, T Nash Skipper, Cesunica E Ivey, Daniel L Goldberg, Jennifer Kaiser, Armistead G Russell","doi":"10.1021/acsestair.4c00198","DOIUrl":null,"url":null,"abstract":"<p><p>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<sub>2</sub> vertical column densities (VCDs) are generally higher over areas with large NO <sub><i>x</i></sub> sources when compared with retrievals, while an opposite bias is found over low NO <sub><i>x</i></sub> 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<sub>2</sub> ratios, while lower CTM VCD levels often observed over rural areas can be attributed to missing emission sources of NO <sub><i>x</i></sub> 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<sub>2</sub> retrievals. It was found that more detailed modeling of NO <sub><i>x</i></sub> plumes to assess the NO:NO<sub>2</sub> 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<sub>2</sub> 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<sub>2</sub> ratios when comparing models to retrievals and the impact of unintentional numerical dilution.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 6","pages":"998-1008"},"PeriodicalIF":0.0000,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12172011/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS ES&T Air","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1021/acsestair.4c00198","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/6/13 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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 NO2 vertical column densities (VCDs) are generally higher over areas with large NO x sources when compared with retrievals, while an opposite bias is found over low NO x regions. Artificial (i.e., numerical) dilution in the model, where emissions are mathematically dispersed uniformly within the originating CTM grid, can impact modeled NO:NO2 ratios, while lower CTM VCD levels often observed over rural areas can be attributed to missing emission sources of NO x 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) NO2 retrievals. It was found that more detailed modeling of NO x plumes to assess the NO:NO2 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 NO2 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:NO2 ratios when comparing models to retrievals and the impact of unintentional numerical dilution.