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Statistical analysis and environmental impact of pre-existing particle growth events in a Northern Chinese coastal megacity: A 725-day study in 2010-2018.
Wei, Xing; Zhu, Yujiao; Gao, Yang; Gao, Huiwang; Yao, Xiaohong.
Affiliation
  • Wei X; Key Laboratory of Marine Environment and Ecology (MoE), Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Sanya Oceanographic Institution, Ocean University of China, Qingdao, China.
  • Zhu Y; Environment Research Institute, Shandong University, Qingdao 266237, China.
  • Gao Y; Key Laboratory of Marine Environment and Ecology (MoE), Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Sanya Oceanographic Institution, Ocean University of China, Qingdao, China; Laboratory for Marine Ecology and Environmental Sciences, Laoshan Laboratory, Qingdao, China.
  • Gao H; Key Laboratory of Marine Environment and Ecology (MoE), Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Sanya Oceanographic Institution, Ocean University of China, Qingdao, China; Laboratory for Marine Ecology and Environmental Sciences, Laoshan Laboratory, Qingdao, China.
  • Yao X; Key Laboratory of Marine Environment and Ecology (MoE), Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Sanya Oceanographic Institution, Ocean University of China, Qingdao, China; Laboratory for Marine Ecology and Environmental Sciences, Laoshan Laboratory, Qingdao, China. Electronic address:
Sci Total Environ ; 933: 173227, 2024 Jul 10.
Article in En | MEDLINE | ID: mdl-38750744
ABSTRACT
Pre-existing particles usually constitute the major fraction of atmospheric particles, except during some episodes in the presence of strong emissions and/or secondary generation of fresh particles. Previous case studies have investigated the growth of pre-existing particles and their potential environmental and climate impacts. However, there is limited knowledge about the statistical characteristics of these growth events and related effects. In this study, we examine pre-existing particle growth events using a large dataset (725 days from 2010 to 2018) collected at a coastal megacity in northern China. The occurrence frequency of pre-existing particle growth events was 12.4 % (90 out of 725 days). When these events were related to measured criteria air pollutants, no significant differences were found in PM2.5, SO2, NO2 and NO2 + O3 concentrations between periods with and without pre-existing particle growth events. These 90-day events can be further classified into two categories, i.e., Category 1, with 68 % of events representing the growth of pre-existing particles alone, and Category 2, with 32 % of events representing the simultaneous growth of pre-existing and newly formed particles. In Category 2, the growth rates of pre-existing particles and newly formed particles were close in 21 % of the cases, while pre-existing particles exhibited significantly larger growth rates in 69 % of the cases. Conversely, in 10 % of the cases, the growth rates of newly formed particles were larger. The different growth rate mechanisms were discussed in terms of the volatility of atmospheric condensation vapors. In addition, we present case studies on the impact of pre-existing particle growth on cloud condensation nuclei simultaneously measured, specifically considering the chemistry of condensation vapors and pre-existing particles.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Sci Total Environ Year: 2024 Document type: Article Affiliation country: China Country of publication: Netherlands

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Sci Total Environ Year: 2024 Document type: Article Affiliation country: China Country of publication: Netherlands