A simplified non-linear chemistry transport model for analyzing NO<sub>2</sub> column observations: STILT–NO<sub><i>x</i></sub>
<p>Satellites monitoring air pollutants (e.g., nitrogen oxides; <span class="inline-formula">NO<sub><i>x</i></sub></span> <span class="inline-formula">=</span> <span class="inline-formula">NO</span> <s...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-11-01
|
| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/16/6161/2023/gmd-16-6161-2023.pdf |
| Summary: | <p>Satellites monitoring air pollutants (e.g., nitrogen oxides; <span class="inline-formula">NO<sub><i>x</i></sub></span> <span class="inline-formula">=</span> <span class="inline-formula">NO</span> <span class="inline-formula">+</span> <span class="inline-formula">NO<sub>2</sub></span>) or greenhouse gases (GHGs) are widely utilized to understand the spatiotemporal variability in and evolution of emission characteristics, chemical transformations, and atmospheric transport over anthropogenic hotspots. Recently, the joint use of space-based long-lived GHGs (e.g., carbon dioxide; <span class="inline-formula">CO<sub>2</sub></span>) and short-lived pollutants has made it possible to improve our understanding of emission characteristics. Some previous studies, however, lack consideration of the non-linear <span class="inline-formula">NO<sub><i>x</i></sub></span> chemistry or complex atmospheric transport. Considering the increase in satellite data volume and the demand for emission monitoring at higher spatiotemporal scales, it is crucial to construct a local-scale emission optimization system that can handle both long-lived GHGs and short-lived pollutants in a coupled and effective manner. This need motivates us to develop a Lagrangian chemical transport model that accounts for <span class="inline-formula">NO<sub><i>x</i></sub></span> chemistry and fine-scale atmospheric transport (STILT–<span class="inline-formula">NO<sub><i>x</i></sub></span>) and to investigate how physical and chemical processes, anthropogenic emissions, and background may affect the interpretation of tropospheric <span class="inline-formula">NO<sub>2</sub></span> columns (<span class="inline-formula">tNO<sub>2</sub></span>).</p>
<p>Interpreting emission signals from <span class="inline-formula">tNO<sub>2</sub></span> commonly involves either an efficient statistical model or a sophisticated chemical transport model. To balance computational expenses and chemical complexity, we describe a simplified representation of the <span class="inline-formula">NO<sub><i>x</i></sub></span> chemistry that bypasses an explicit solution of individual chemical reactions while preserving the essential non-linearity that links <span class="inline-formula">NO<sub><i>x</i></sub></span> emissions to its concentrations. This <span class="inline-formula">NO<sub><i>x</i></sub></span> chemical parameterization is then incorporated into an existing Lagrangian modeling framework that is widely applied in the GHG community. We further quantify uncertainties associated with the wind field and chemical parameterization and evaluate modeled columns against retrieved columns from the TROPOspheric Monitoring Instrument (TROPOMI v2.1). Specifically, simulations with alternative model configurations of emissions, meteorology, chemistry, and inter-parcel mixing are carried out over three United States (US) power plants and two urban areas across seasons. Using the U.S. Environmental Protection Agency (EPA)-reported emissions for power plants with non-linear <span class="inline-formula">NO<sub><i>x</i></sub></span> chemistry improves the model–data alignment in <span class="inline-formula">tNO<sub>2</sub></span> (a high bias of <span class="inline-formula">≤</span> 10 % on an annual basis), compared to simulations using either the Emissions Database for Global Atmospheric Research (EDGAR) model or without chemistry (bias approaching 100 %). The largest model–data mismatches are associated with substantial biases in wind directions or conditions of slower atmospheric mixing and photochemistry. More importantly, our model development illustrates (1) how <span class="inline-formula">NO<sub><i>x</i></sub></span> chemistry affects the relationship between <span class="inline-formula">NO<sub><i>x</i></sub></span> and <span class="inline-formula">CO<sub>2</sub></span> in terms of the spatial and seasonal variability and (2) how assimilating <span class="inline-formula">tNO<sub>2</sub></span> can quantify systematic biases in modeled wind directions and emission distribution in prior inventories of <span class="inline-formula">NO<sub><i>x</i></sub></span> and <span class="inline-formula">CO<sub>2</sub></span>, which laid a foundation for a local-scale multi-tracer emission optimization system.</p> |
|---|---|
| ISSN: | 1991-959X 1991-9603 |