Identification and characterization of foehn events in Beijing and their impact on air pollution episodes
<p>This study proposes a method for identifying foehn events in Beijing using automatic weather station (AWS) data, considering upper-air wind direction, topography, meteorological changes, and foehn propagation. Analysis of AWS data from 2015 to 2020 revealed an annual average of 56.5 foehn d...
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/25/8683/2025/acp-25-8683-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849390025489252352 |
|---|---|
| author | J. Li J. Li J. Li J. Li J. Zhang J. Zhang M. Bai J. Su J. Su Q. Li Q. Li X. Jia X. Jia |
| author_facet | J. Li J. Li J. Li J. Li J. Zhang J. Zhang M. Bai J. Su J. Su Q. Li Q. Li X. Jia X. Jia |
| author_sort | J. Li |
| collection | DOAJ |
| description | <p>This study proposes a method for identifying foehn events in Beijing using automatic weather station (AWS) data, considering upper-air wind direction, topography, meteorological changes, and foehn propagation. Analysis of AWS data from 2015 to 2020 revealed an annual average of 56.5 foehn days, with these days occurring most frequently in winter and least frequently in summer. High-frequency foehn areas exhibit a band-like distribution from the northwestern mountainous region to the southeastern plains, while low-frequency areas are primarily concentrated in the northeastern plains. The horizontal extent of the foehn influence is maximal in spring and minimal in summer. Foehn-induced hourly temperature increases can exceed 11 <span class="inline-formula">°C</span>, peaking from night to early morning. Approximately 67 % of pollution episodes are accompanied by foehn events, with foehn duration negatively correlated to pollution episode duration. 60.4 % of foehn events coincide with decreasing concentrations of particulate matter of 2.5 <span class="inline-formula">µm</span> diameter (PM<span class="inline-formula"><sub>2.5</sub></span>), while 39.6 % show increases. Rapid PM<span class="inline-formula"><sub>2.5</sub></span> concentration increases (<span class="inline-formula">>50</span> <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M6" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><mi mathvariant="normal">µ</mi><mi mathvariant="normal">g</mi><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">m</mi><mrow><mo>-</mo><mn mathvariant="normal">3</mn></mrow></msup><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">h</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="53pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="505d76e8b5ded133fe6eaf5d88b4e40d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-25-8683-2025-ie00001.svg" width="53pt" height="15pt" src="acp-25-8683-2025-ie00001.png"/></svg:svg></span></span>) primarily correspond to weak foehn events (temperature increase <span class="inline-formula"><2</span> <span class="inline-formula">°C</span>). Foehn winds influence pollution through direct and indirect effects. The direct effect, associated with strong northwesterly pressure gradients, can rapidly decrease pollutant concentrations. The indirect effect, linked to weak pressure gradients, alters the boundary-layer structure, causing rapid increases in pollutant concentrations following the termination of foehn. This foehn identification method, applicable to long-term historical surface observations, not only facilitates a deeper understanding of how foehn phenomena evolve and contribute to temperature increases under global warming, but also advances an in-depth exploration of the relationships between foehn events and high-impact weather phenomena.</p> |
| format | Article |
| id | doaj-art-19ba87e490e64e268a3ee676328d36de |
| 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-19ba87e490e64e268a3ee676328d36de2025-08-20T03:41:47ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242025-08-01258683870010.5194/acp-25-8683-2025Identification and characterization of foehn events in Beijing and their impact on air pollution episodesJ. Li0J. Li1J. Li2J. Li3J. Zhang4J. Zhang5M. Bai6J. Su7J. Su8Q. Li9Q. Li10X. Jia11X. Jia12Institute of Urban Meteorology, China Meteorological Administration, Beijing, 100089, ChinaBeijing Research Center for Urban Meteorological Engineering and Technology, Beijing, 100089, ChinaKey Laboratory of Urban Meteorology, China Meteorological Administration, Beijing, 100089, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, ChinaInstitute of Urban Meteorology, China Meteorological Administration, Beijing, 100089, ChinaBeijing Research Center for Urban Meteorological Engineering and Technology, Beijing, 100089, ChinaBeijing Municipal Climate Center, Beijing, 100089, ChinaInstitute of Urban Meteorology, China Meteorological Administration, Beijing, 100089, ChinaBeijing Research Center for Urban Meteorological Engineering and Technology, Beijing, 100089, ChinaInstitute of Urban Meteorology, China Meteorological Administration, Beijing, 100089, ChinaBeijing Research Center for Urban Meteorological Engineering and Technology, Beijing, 100089, ChinaInstitute of Urban Meteorology, China Meteorological Administration, Beijing, 100089, ChinaBeijing Research Center for Urban Meteorological Engineering and Technology, Beijing, 100089, China<p>This study proposes a method for identifying foehn events in Beijing using automatic weather station (AWS) data, considering upper-air wind direction, topography, meteorological changes, and foehn propagation. Analysis of AWS data from 2015 to 2020 revealed an annual average of 56.5 foehn days, with these days occurring most frequently in winter and least frequently in summer. High-frequency foehn areas exhibit a band-like distribution from the northwestern mountainous region to the southeastern plains, while low-frequency areas are primarily concentrated in the northeastern plains. The horizontal extent of the foehn influence is maximal in spring and minimal in summer. Foehn-induced hourly temperature increases can exceed 11 <span class="inline-formula">°C</span>, peaking from night to early morning. Approximately 67 % of pollution episodes are accompanied by foehn events, with foehn duration negatively correlated to pollution episode duration. 60.4 % of foehn events coincide with decreasing concentrations of particulate matter of 2.5 <span class="inline-formula">µm</span> diameter (PM<span class="inline-formula"><sub>2.5</sub></span>), while 39.6 % show increases. Rapid PM<span class="inline-formula"><sub>2.5</sub></span> concentration increases (<span class="inline-formula">>50</span> <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M6" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><mi mathvariant="normal">µ</mi><mi mathvariant="normal">g</mi><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">m</mi><mrow><mo>-</mo><mn mathvariant="normal">3</mn></mrow></msup><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">h</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="53pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="505d76e8b5ded133fe6eaf5d88b4e40d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-25-8683-2025-ie00001.svg" width="53pt" height="15pt" src="acp-25-8683-2025-ie00001.png"/></svg:svg></span></span>) primarily correspond to weak foehn events (temperature increase <span class="inline-formula"><2</span> <span class="inline-formula">°C</span>). Foehn winds influence pollution through direct and indirect effects. The direct effect, associated with strong northwesterly pressure gradients, can rapidly decrease pollutant concentrations. The indirect effect, linked to weak pressure gradients, alters the boundary-layer structure, causing rapid increases in pollutant concentrations following the termination of foehn. This foehn identification method, applicable to long-term historical surface observations, not only facilitates a deeper understanding of how foehn phenomena evolve and contribute to temperature increases under global warming, but also advances an in-depth exploration of the relationships between foehn events and high-impact weather phenomena.</p>https://acp.copernicus.org/articles/25/8683/2025/acp-25-8683-2025.pdf |
| spellingShingle | J. Li J. Li J. Li J. Li J. Zhang J. Zhang M. Bai J. Su J. Su Q. Li Q. Li X. Jia X. Jia Identification and characterization of foehn events in Beijing and their impact on air pollution episodes Atmospheric Chemistry and Physics |
| title | Identification and characterization of foehn events in Beijing and their impact on air pollution episodes |
| title_full | Identification and characterization of foehn events in Beijing and their impact on air pollution episodes |
| title_fullStr | Identification and characterization of foehn events in Beijing and their impact on air pollution episodes |
| title_full_unstemmed | Identification and characterization of foehn events in Beijing and their impact on air pollution episodes |
| title_short | Identification and characterization of foehn events in Beijing and their impact on air pollution episodes |
| title_sort | identification and characterization of foehn events in beijing and their impact on air pollution episodes |
| url | https://acp.copernicus.org/articles/25/8683/2025/acp-25-8683-2025.pdf |
| work_keys_str_mv | AT jli identificationandcharacterizationoffoehneventsinbeijingandtheirimpactonairpollutionepisodes AT jli identificationandcharacterizationoffoehneventsinbeijingandtheirimpactonairpollutionepisodes AT jli identificationandcharacterizationoffoehneventsinbeijingandtheirimpactonairpollutionepisodes AT jli identificationandcharacterizationoffoehneventsinbeijingandtheirimpactonairpollutionepisodes AT jzhang identificationandcharacterizationoffoehneventsinbeijingandtheirimpactonairpollutionepisodes AT jzhang identificationandcharacterizationoffoehneventsinbeijingandtheirimpactonairpollutionepisodes AT mbai identificationandcharacterizationoffoehneventsinbeijingandtheirimpactonairpollutionepisodes AT jsu identificationandcharacterizationoffoehneventsinbeijingandtheirimpactonairpollutionepisodes AT jsu identificationandcharacterizationoffoehneventsinbeijingandtheirimpactonairpollutionepisodes AT qli identificationandcharacterizationoffoehneventsinbeijingandtheirimpactonairpollutionepisodes AT qli identificationandcharacterizationoffoehneventsinbeijingandtheirimpactonairpollutionepisodes AT xjia identificationandcharacterizationoffoehneventsinbeijingandtheirimpactonairpollutionepisodes AT xjia identificationandcharacterizationoffoehneventsinbeijingandtheirimpactonairpollutionepisodes |