Preprints
https://doi.org/10.5194/acp-2021-957
https://doi.org/10.5194/acp-2021-957
 
11 Feb 2022
11 Feb 2022
Status: a revised version of this preprint is currently under review for the journal ACP.

Dramatic changes in atmospheric pollution source contributions for a coastal megacity in North China from 2011 to 2020

Baoshuang Liu1,2, Yanyang Wang1,2, He Meng3, Qili Dai1,2, Liuli Diao1,2, Jianhui Wu1,2, Laiyuan Shi3, Jing Wang3, Yufen Zhang1,2, and Yinchang Feng1,2 Baoshuang Liu et al.
  • 1State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
  • 2CMA NKU Cooperative Laboratory for Atmospheric Environment Health Research, Tianjin 300350, China
  • 3Qingdao Eco environment Monitoring Center of Shandong Province, Qingdao, 266003, China

Abstract. Understanding the effectiveness of long-term air pollution regulatory measures is important for control policy formulation. Efforts have been made using chemical transport modelling and statistical approaches to evaluate the efficacy of the Clean Air Action Plan (2013–2017, CAAP) and the Blue Sky Protection Campaign (2018–2020, BSPC) enacted in China. Changes in air quality due to reduction in emissions can be masked by meteorology, making it highly challenging to reveal the real effects of control measures. Knowledge gap still existed with respect to how sources changed before and after the CAAP and BSPC implemented, respectively, particularly in coastal area where anthropogenic emissions mixed with additional natural sources (e.g., marine aerosol). This work applied a machine learning-based meteorological normalization approach to decouple the meteorological effects from air quality trend in a coastal city in northern China (Qingdao). Secondly, the relative changes in source contributions to ambient PM2.5 with a ~10-year observation interval (2011–2012, 2016, and 2019) were also investigated. We discovered that the largest emission reduction section was likely from coal combustions, as the meteorologically normalized SO2 dropped by ~15.5 % per year and dispersion normalized SO42− decreased by ~41.5 % for annual average. Change in the meteorologically normalized NO2 was relatively stable (~1.0 % yr−1), and NO3 changed inappreciable in 2016–2019 but significantly higher than that prior to the CAAP. Crustal dust decreased remarkably after the CAAP began. Industrial emissions, for example, steel-related smelting, decreased after 2016 due to the relocation of steelmaking enterprises. Note that vehicle emissions were increased in importance, as opposed to the other primary sources. Similar to other mega cities, Qingdao also risks increased ozone pollution that in turns facilitate secondary particles formation in the future. The policy assessment approaches applied in this work also work for other places where air quality management is highly in demand to reduce air pollution.

Baoshuang Liu et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-957', Anonymous Referee #2, 19 Feb 2022
    • AC2: 'Reply on RC1', Yinchang Feng, 16 Apr 2022
  • RC2: 'Comment on acp-2021-957', Anonymous Referee #1, 04 Mar 2022
    • AC1: 'Reply on RC2', Yinchang Feng, 08 Mar 2022

Baoshuang Liu et al.

Baoshuang Liu et al.

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Short summary
Understanding effectiveness of air pollution regulatory measures is critical for control policy formulation. A machine learning and dispersion normalized approaches were applied to decouple meteorological deduced variations in Qingdao, China. Most pollutant concentrations decreased substantially after the Clean Air Action. Largest emission reduction was from coal combustions and steel-related smelting. Qingdao risks increased emissions from the increased vehicular population and ozone pollution.
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