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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 9 | Copyright
Atmos. Chem. Phys., 18, 6771-6783, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 15 May 2018

Research article | 15 May 2018

Self-organized classification of boundary layer meteorology and associated characteristics of air quality in Beijing

Zhiheng Liao1, Jiaren Sun1,2, Jialin Yao3, Li Liu1, Haowen Li1, Jian Liu1, Jielan Xie1, Dui Wu4,5, and Shaojia Fan1 Zhiheng Liao et al.
  • 1School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China
  • 2South China Institute of Environmental Sciences, Ministry of Environmental Protection of the People's Republic of China, Guangzhou, Guangdong, China
  • 3Weather Modification Office of Shanxi Province, Taiyuan, Shanxi, China
  • 4Institute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou, Guangdong, China
  • 5Guangdong Engineering Research Centre for Online Atmospheric Pollution Source Appointment Mass Spectrometry System, Jinan University, Guangzhou, Guangdong, China

Abstract. 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 PM2.5, NO2 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 SO2 pollution layers, the highest average SO2 concentrations are typically observed in Node 3 (a layer with strong stability in the upper ABL) rather than Node 9. In contrast, near-surface O3 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 PM2.5 loading for different years, the relative (absolute) contributions of the ABL anomaly to elevated PM2.5 levels are estimated to be 58.3% (44.4µgm−3) in January 2013, 46.4% (22.2µgm−3) in December 2015 and 73.3% (34.6µgm−3) in December 2016.

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This paper investigates the modulation effect of ABL meteorology on Beijing’s surface air quality based on self-organizing maps. The self-organized ABL types correspond to significantly distinct pollutant loadings and diurnal evolution, particularly in winter. Anomalous stable ABL conditions are estimated to contribute 58.3 %, 46.4 % and 73.3 % of the elevated PM2.5 concentrations in January 2013, December 2015 and December 2016.
This paper investigates the modulation effect of ABL meteorology on Beijing’s surface air...