JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2025, Vol. 60 ›› Issue (5): 133-142.doi: 10.6040/j.issn.1671-9352.0.2025.018

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Generalized additive models for characterizing atmospheric mercury mass concentration variations in the coastal area of Qingdao in summer

WANG Guojing1, LI Tao1,2*, ZHEN Jiebo1, NIE Xiaoling1,3, GONG Chaofan1, WANG Yan1   

  1. 1. School of Environmental Science and Engineering, Shandong University, Qingdao 266237, Shandong, China;
    2. Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Kowloon 99907, Hong Kong, China;
    3. School of Water Conservancy and Environment, University of Jinan, Jinan 250022, Shandong, China
  • Published:2025-05-19

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Abstract: To study the characteristics of atmospheric mercury pollution in coastal Qingdao and evaluate the influences of pollution sources and meteorological conditions, an online measurement of total gaseous mercury(TGM)was conducted on Xiaomai Island in Qingdao during the summer of 2023. A generalized additive model(GAM)was constructed to investigate the linear and nonlinear relationships between TGM and air pollutants as well as meteorological parameters. The results showed the mass concentration of TGM was(2.94±0.75)ng·m-3, within a range of 1.74-6.49 ng·m-3, indicating a moderate pollution level in China. TGM mass concentrations were significantly affected by anthropogenic emissions, meteorological conditions, and sea-land breezes. In the univariate GAM model results, CO concentration, relative humidity(RH), and sea-level pressure(P0)had substantial effects on TGM variations. Specifically, CO and NOx mass concentration exhibited increasing relationships with TGM, indicating that anthropogenic emissions significantly contribute to TGM levels. While RH and P0 showed decreasing relationships, indicating that favorable meteorological conditions contribute to the dilution or dispersion of TGM. Multivariate GAM model incorporating conventional pollutants and meteorological parameters further improved the fitting results, achieving an explanatory power of 84.6% for TGM variations. The interactions between atmospheric pollutants, WD, RH, and boundary layer height(BLH)significantly influenced TGM variability. This study quantitatively visualizes the complex impacts of various factors and their interactions on TGM variations, revealing the combined effects of pollution transport and meteorological conditions, which is critical for understanding the atmospheric mercury cycle and pollution control in coastal areas.

Key words: atmospheric mercury, generalized additive model, anthropogenic emissions, meteorological factors, interaction effects

CLC Number: 

  • X823
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