Self-organized classification of boundary layer meteorology and associated characteristics of air quality in Beijing
Self-organizing maps (SOMs; a feature-extracting technique based on an unsupervised machine learning algorithm) are used to classify atmospheric boundary layer (ABL) meteorology over Beijing through detecting topological relationships among the 5-year (2013–2017) radiosonde-based virtual potenti...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-05-01
|
| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/18/6771/2018/acp-18-6771-2018.pdf |
| _version_ | 1819030707008176128 |
|---|---|
| author | Z. Liao J. Sun J. Sun J. Yao L. Liu H. Li J. Liu J. Xie D. Wu D. Wu S. Fan |
| author_facet | Z. Liao J. Sun J. Sun J. Yao L. Liu H. Li J. Liu J. Xie D. Wu D. Wu S. Fan |
| author_sort | Z. Liao |
| collection | DOAJ |
| description | Self-organizing maps (SOMs; a feature-extracting technique based on an
unsupervised machine learning algorithm) are used to classify atmospheric
boundary layer (ABL) meteorology over Beijing through detecting topological
relationships among the 5-year (2013–2017) radiosonde-based virtual
potential temperature profiles. The classified ABL types are then examined
in relation to near-surface pollutant concentrations to understand the
modulation effects of the changing ABL meteorology on Beijing's air quality.
Nine ABL types (i.e., SOM nodes) are obtained through the SOM classification
technique, and each is characterized by distinct dynamic and thermodynamic
conditions. In general, the self-organized ABL types are able to distinguish
between high and low loadings of near-surface pollutants. The average
concentrations of PM<sub>2.5</sub>, NO<sub>2</sub> and CO dramatically increased from
the near neutral (i.e., Node 1) to strong stable conditions (i.e., Node 9)
during all seasons except for summer. Since extremely strong stability can
isolate the near-surface observations from the influence of elevated
SO<sub>2</sub> pollution layers, the highest average SO<sub>2</sub> concentrations are
typically observed in Node 3 (a layer with strong stability in the upper
ABL) rather than Node 9. In contrast, near-surface O<sub>3</sub> shows an opposite
dependence on atmospheric stability, with the lowest average concentration
in Node 9. Analysis of three typical pollution months (i.e., January 2013,
December 2015 and December 2016) suggests that the ABL types are the primary
drivers of day-to-day variations in Beijing's air quality. Assuming a fixed
relationship between ABL type and PM<sub>2.5</sub> loading for different years,
the relative (absolute) contributions of the ABL anomaly to elevated
PM<sub>2.5</sub> levels are estimated to be 58.3 % (44.4 µg m<sup>−3</sup>) in
January 2013, 46.4 % (22.2 µg m<sup>−3</sup>) in December 2015 and 73.3 % (34.6 µg m<sup>−3</sup>)
in December 2016. |
| first_indexed | 2024-12-21T06:34:26Z |
| format | Article |
| id | doaj.art-d3ac7b90c782429ba522901f8b230ee0 |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-12-21T06:34:26Z |
| publishDate | 2018-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-d3ac7b90c782429ba522901f8b230ee02022-12-21T19:12:55ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242018-05-01186771678310.5194/acp-18-6771-2018Self-organized classification of boundary layer meteorology and associated characteristics of air quality in BeijingZ. Liao0J. Sun1J. Sun2J. Yao3L. Liu4H. Li5J. Liu6J. Xie7D. Wu8D. Wu9S. Fan10School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, Guangdong, ChinaSchool of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, Guangdong, ChinaSouth China Institute of Environmental Sciences, Ministry of Environmental Protection of the People's Republic of China, Guangzhou, Guangdong, ChinaWeather Modification Office of Shanxi Province, Taiyuan, Shanxi, ChinaSchool of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, Guangdong, ChinaSchool of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, Guangdong, ChinaSchool of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, Guangdong, ChinaSchool of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, Guangdong, ChinaInstitute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou, Guangdong, ChinaGuangdong Engineering Research Centre for Online Atmospheric Pollution Source Appointment Mass Spectrometry System, Jinan University, Guangzhou, Guangdong, ChinaSchool of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, Guangdong, ChinaSelf-organizing maps (SOMs; a feature-extracting technique based on an unsupervised machine learning algorithm) are used to classify atmospheric boundary layer (ABL) meteorology over Beijing through detecting topological relationships among the 5-year (2013–2017) radiosonde-based virtual potential temperature profiles. The classified ABL types are then examined in relation to near-surface pollutant concentrations to understand the modulation effects of the changing ABL meteorology on Beijing's air quality. Nine ABL types (i.e., SOM nodes) are obtained through the SOM classification technique, and each is characterized by distinct dynamic and thermodynamic conditions. In general, the self-organized ABL types are able to distinguish between high and low loadings of near-surface pollutants. The average concentrations of PM<sub>2.5</sub>, NO<sub>2</sub> and CO dramatically increased from the near neutral (i.e., Node 1) to strong stable conditions (i.e., Node 9) during all seasons except for summer. Since extremely strong stability can isolate the near-surface observations from the influence of elevated SO<sub>2</sub> pollution layers, the highest average SO<sub>2</sub> concentrations are typically observed in Node 3 (a layer with strong stability in the upper ABL) rather than Node 9. In contrast, near-surface O<sub>3</sub> shows an opposite dependence on atmospheric stability, with the lowest average concentration in Node 9. Analysis of three typical pollution months (i.e., January 2013, December 2015 and December 2016) suggests that the ABL types are the primary drivers of day-to-day variations in Beijing's air quality. Assuming a fixed relationship between ABL type and PM<sub>2.5</sub> loading for different years, the relative (absolute) contributions of the ABL anomaly to elevated PM<sub>2.5</sub> levels are estimated to be 58.3 % (44.4 µg m<sup>−3</sup>) in January 2013, 46.4 % (22.2 µg m<sup>−3</sup>) in December 2015 and 73.3 % (34.6 µg m<sup>−3</sup>) in December 2016.https://www.atmos-chem-phys.net/18/6771/2018/acp-18-6771-2018.pdf |
| spellingShingle | Z. Liao J. Sun J. Sun J. Yao L. Liu H. Li J. Liu J. Xie D. Wu D. Wu S. Fan Self-organized classification of boundary layer meteorology and associated characteristics of air quality in Beijing Atmospheric Chemistry and Physics |
| title | Self-organized classification of boundary layer meteorology and associated characteristics of air quality in Beijing |
| title_full | Self-organized classification of boundary layer meteorology and associated characteristics of air quality in Beijing |
| title_fullStr | Self-organized classification of boundary layer meteorology and associated characteristics of air quality in Beijing |
| title_full_unstemmed | Self-organized classification of boundary layer meteorology and associated characteristics of air quality in Beijing |
| title_short | Self-organized classification of boundary layer meteorology and associated characteristics of air quality in Beijing |
| title_sort | self organized classification of boundary layer meteorology and associated characteristics of air quality in beijing |
| url | https://www.atmos-chem-phys.net/18/6771/2018/acp-18-6771-2018.pdf |
| work_keys_str_mv | AT zliao selforganizedclassificationofboundarylayermeteorologyandassociatedcharacteristicsofairqualityinbeijing AT jsun selforganizedclassificationofboundarylayermeteorologyandassociatedcharacteristicsofairqualityinbeijing AT jsun selforganizedclassificationofboundarylayermeteorologyandassociatedcharacteristicsofairqualityinbeijing AT jyao selforganizedclassificationofboundarylayermeteorologyandassociatedcharacteristicsofairqualityinbeijing AT lliu selforganizedclassificationofboundarylayermeteorologyandassociatedcharacteristicsofairqualityinbeijing AT hli selforganizedclassificationofboundarylayermeteorologyandassociatedcharacteristicsofairqualityinbeijing AT jliu selforganizedclassificationofboundarylayermeteorologyandassociatedcharacteristicsofairqualityinbeijing AT jxie selforganizedclassificationofboundarylayermeteorologyandassociatedcharacteristicsofairqualityinbeijing AT dwu selforganizedclassificationofboundarylayermeteorologyandassociatedcharacteristicsofairqualityinbeijing AT dwu selforganizedclassificationofboundarylayermeteorologyandassociatedcharacteristicsofairqualityinbeijing AT sfan selforganizedclassificationofboundarylayermeteorologyandassociatedcharacteristicsofairqualityinbeijing |