Modeling urban pollutant transport at multiple resolutions: impacts of turbulent mixing
<p>Air pollution in cities impacts public health and climate. Turbulent mixing is crucial in pollutant formation and dissipation, yet current atmospheric models struggle to accurately represent it. Turbulent mixing intensity varies with model resolution, which has rarely been analyzed. To inve...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-08-01
|
| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/25/8831/2025/acp-25-8831-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849239841501347840 |
|---|---|
| author | Z. Yang Q. Du Q. Yang C. Zhao C. Zhao C. Zhao G. Li Z. Xia M. Xu R. Yuan Y. Li K. Xia J. Gu J. Feng |
| author_facet | Z. Yang Q. Du Q. Yang C. Zhao C. Zhao C. Zhao G. Li Z. Xia M. Xu R. Yuan Y. Li K. Xia J. Gu J. Feng |
| author_sort | Z. Yang |
| collection | DOAJ |
| description | <p>Air pollution in cities impacts public health and climate. Turbulent mixing is crucial in pollutant formation and dissipation, yet current atmospheric models struggle to accurately represent it. Turbulent mixing intensity varies with model resolution, which has rarely been analyzed. To investigate turbulent mixing variations at multiple resolutions and their implications for urban pollutant transport, we conducted experiments using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) at resolutions of 25, 5, and 1 km. The simulated meteorological fields and black carbon (BC) concentrations are compared with observations. Differences in turbulent mixing across multiple resolutions are more pronounced at night, resulting in noticeable variations in BC concentrations. BC surface concentrations decrease as resolution increases from 25 to 5 km and further to 1 km, but they are similar at 5 and 1 km resolutions. Enhanced planetary boundary layer (PBL) mixing coefficients and vertical wind flux at higher resolutions reduce BC surface concentration overestimations. The 1 km resolution parameterized lower mixing coefficients than 5 km but resolved more small-scale eddies, leading to similar near-surface turbulent mixing at both resolutions, while the intensity at higher altitudes was greater at 1 km. This caused BC to be transported higher and farther, increasing its atmospheric lifetime and column concentrations. Variations in mixing coefficients are partly attributed to differences in land use and terrain, with higher resolutions providing more detailed information that enhances PBL mixing coefficients, while grid size remains crucial in regions with more gradual terrain and land use changes. This study interprets how turbulent mixing affects simulated urban pollutant diffusion at multiple resolutions.</p> |
| format | Article |
| id | doaj-art-d07673a556ab4ffba168eced6ed7fb92 |
| institution | Kabale University |
| issn | 1680-7316 1680-7324 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj-art-d07673a556ab4ffba168eced6ed7fb922025-08-20T04:00:49ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242025-08-01258831885710.5194/acp-25-8831-2025Modeling urban pollutant transport at multiple resolutions: impacts of turbulent mixingZ. Yang0Q. Du1Q. Yang2C. Zhao3C. Zhao4C. Zhao5G. Li6Z. Xia7M. Xu8R. Yuan9Y. Li10K. Xia11J. Gu12J. Feng13Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, ChinaLaoshan Laboratory, Qingdao, ChinaCAS Center for Excellence in Comparative Planetology, University of Science and Technology of China, Hefei, ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, ChinaDeep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, China<p>Air pollution in cities impacts public health and climate. Turbulent mixing is crucial in pollutant formation and dissipation, yet current atmospheric models struggle to accurately represent it. Turbulent mixing intensity varies with model resolution, which has rarely been analyzed. To investigate turbulent mixing variations at multiple resolutions and their implications for urban pollutant transport, we conducted experiments using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) at resolutions of 25, 5, and 1 km. The simulated meteorological fields and black carbon (BC) concentrations are compared with observations. Differences in turbulent mixing across multiple resolutions are more pronounced at night, resulting in noticeable variations in BC concentrations. BC surface concentrations decrease as resolution increases from 25 to 5 km and further to 1 km, but they are similar at 5 and 1 km resolutions. Enhanced planetary boundary layer (PBL) mixing coefficients and vertical wind flux at higher resolutions reduce BC surface concentration overestimations. The 1 km resolution parameterized lower mixing coefficients than 5 km but resolved more small-scale eddies, leading to similar near-surface turbulent mixing at both resolutions, while the intensity at higher altitudes was greater at 1 km. This caused BC to be transported higher and farther, increasing its atmospheric lifetime and column concentrations. Variations in mixing coefficients are partly attributed to differences in land use and terrain, with higher resolutions providing more detailed information that enhances PBL mixing coefficients, while grid size remains crucial in regions with more gradual terrain and land use changes. This study interprets how turbulent mixing affects simulated urban pollutant diffusion at multiple resolutions.</p>https://acp.copernicus.org/articles/25/8831/2025/acp-25-8831-2025.pdf |
| spellingShingle | Z. Yang Q. Du Q. Yang C. Zhao C. Zhao C. Zhao G. Li Z. Xia M. Xu R. Yuan Y. Li K. Xia J. Gu J. Feng Modeling urban pollutant transport at multiple resolutions: impacts of turbulent mixing Atmospheric Chemistry and Physics |
| title | Modeling urban pollutant transport at multiple resolutions: impacts of turbulent mixing |
| title_full | Modeling urban pollutant transport at multiple resolutions: impacts of turbulent mixing |
| title_fullStr | Modeling urban pollutant transport at multiple resolutions: impacts of turbulent mixing |
| title_full_unstemmed | Modeling urban pollutant transport at multiple resolutions: impacts of turbulent mixing |
| title_short | Modeling urban pollutant transport at multiple resolutions: impacts of turbulent mixing |
| title_sort | modeling urban pollutant transport at multiple resolutions impacts of turbulent mixing |
| url | https://acp.copernicus.org/articles/25/8831/2025/acp-25-8831-2025.pdf |
| work_keys_str_mv | AT zyang modelingurbanpollutanttransportatmultipleresolutionsimpactsofturbulentmixing AT qdu modelingurbanpollutanttransportatmultipleresolutionsimpactsofturbulentmixing AT qyang modelingurbanpollutanttransportatmultipleresolutionsimpactsofturbulentmixing AT czhao modelingurbanpollutanttransportatmultipleresolutionsimpactsofturbulentmixing AT czhao modelingurbanpollutanttransportatmultipleresolutionsimpactsofturbulentmixing AT czhao modelingurbanpollutanttransportatmultipleresolutionsimpactsofturbulentmixing AT gli modelingurbanpollutanttransportatmultipleresolutionsimpactsofturbulentmixing AT zxia modelingurbanpollutanttransportatmultipleresolutionsimpactsofturbulentmixing AT mxu modelingurbanpollutanttransportatmultipleresolutionsimpactsofturbulentmixing AT ryuan modelingurbanpollutanttransportatmultipleresolutionsimpactsofturbulentmixing AT yli modelingurbanpollutanttransportatmultipleresolutionsimpactsofturbulentmixing AT kxia modelingurbanpollutanttransportatmultipleresolutionsimpactsofturbulentmixing AT jgu modelingurbanpollutanttransportatmultipleresolutionsimpactsofturbulentmixing AT jfeng modelingurbanpollutanttransportatmultipleresolutionsimpactsofturbulentmixing |