Improving fine aerosol nitrate predictions using a Plume-in-Grid modeling approach
| Autor: | Maria Zakoura, Spyros N. Pandis |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Chemical transport model 010501 environmental sciences Particulates Atmospheric sciences 01 natural sciences Aerosol Plume Atmosphere chemistry.chemical_compound Nitrate chemistry Nitric acid NOx 0105 earth and related environmental sciences General Environmental Science |
| Zdroj: | Atmospheric Environment. 215:116887 |
| ISSN: | 1352-2310 |
| Popis: | Fine particulate nitrate concentrations are often overpredicted by chemical transport models (CTMs). We show that the production rate of nitric acid downwind of major sources of NOx is quite sensitive to the mixing of the NOx-rich plumes with the background atmosphere due to the nonlinearity of the corresponding chemistry especially during nighttime. Low model grid resolution causes artificial mixing of these plumes with the background contributing to these aerosol nitrate prediction errors. This study tests the hypothesis that Plume-in-Grid (PiG) modeling can improve the performance of CTMs for particulate nitrate with minimum computational cost. A PiG model was used in the CTM PMCAMx for major NOx sources in the United States during a summer month. The PM2.5 nitrate predictions at a coarse (36 × 36 km) grid resolution improved with the use of the PiG model (the bias decreased by 33% and the error by 18%) with just a 20% increase in computational cost. The use of the PiG model with 6 × 6 km grid decreased the nitrate bias by 60% and the error by 65% compared to the base case, but with an increase of 2.6 times in computational cost for its application in a limited subdomain (Pennsylvania). Similar results were obtained for a wintertime period. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect