Influence of Local Circulation on Short-term Variations in Ground-level PM2.5 Concentrations

被引:1
|
作者
Lee, Su Jeong [1 ]
Lee, Sang-Hyun [1 ,2 ]
Choi, Hyung-Jin [3 ]
Kim, Joowan [1 ,2 ]
Kim, Maeng-Ki [1 ,2 ]
机构
[1] Kongju Natl Univ, Particle Pollut Res & Management Ctr, Gongju 32588, South Korea
[2] Kongju Natl Univ, Dept Atmospher Sci, Gongju 32588, South Korea
[3] Korea Mil Acad, Dept Civil Engn & Environm Sci, Seoul 01805, South Korea
关键词
Local circulation; Particulate matter; Coastal regions; K-means clustering; BEIJING-TIANJIN-HEBEI; METEOROLOGICAL CHARACTERISTICS; WEATHER PATTERNS; AIR-POLLUTION; EPISODES; QUALITY; HAZE;
D O I
10.4209/aaqr.240042
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Local air quality is greatly influenced by large- and small-scale weather systems through transport, deposition, and chemical transformation of emissions. Local circulation, in particular, can play a significant role under weak synoptic-scale forcing. To examine the influence of local circulation on daily ground-level PM2.5 concentrations, we utilized surface wind features observed at coastal stations in Midwest Korea, which hosts large industrial complexes and is located downwind of the Seoul Metropolitan area. Using K-means clustering, three circulation patterns were identified for the winter of 2021-2022, including one pattern under strong synoptic-scale forcing (Synoptic Cluster) and two local patterns (Sea Breeze Cluster and Stagnation Cluster). Each cluster is characterized by its unique wind patterns and different contributions to local air quality. The Stagnation Cluster, characterized by weak north-easterly winds with a comparatively short transport distance, was found to be most strongly linked to high PM(2.5 )levels, accounting for 57% of the high PM2.5 days (> 35 mu g m(-)(3)) during the 2021-2022 winter. Additionally, we discovered that the three most extreme PM2.5 events were all members of the Stagnation Cluster and that several consecutive stagnant days preceded each of these cases, facilitating local accumulations of nearby anthropogenic emissions. Overall, our findings emphasize that local air quality cannot be fully explained by synoptic-scale analysis, but can be better understood through the analysis of local circulation patterns. The study also highlights the importance of utilizing surface measurements and selecting features that can best describe the local circulation patterns in the region for the classification of local circulation, which contributes to better capturing both daily and hourly variability in PM2.5 concentrations under different weather regimes.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Short-term exposure to PM2.5 and risk of venous thromboembolism: A case-crossover study
    Renzi, Matteo
    Stafoggia, Massimo
    Michelozzi, Paola
    Davoli, Marina
    Forastiere, Francesco
    Solimini, Angelo G.
    THROMBOSIS RESEARCH, 2020, 190 : 52 - 57
  • [42] Short-term effect of PM2.5 on pediatric asthma incidence in Shanghai, China
    Ma, Yuxia
    Yu, Zhiang
    Jiao, Haoran
    Zhang, Yifan
    Ma, Bingji
    Wang, Fei
    Zhou, Ji
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2019, 26 (27) : 27832 - 27841
  • [43] Applying Artificial Neural Networks to Short-Term PM2.5 Forecasting Modeling
    Oprea, Mihaela
    Mihalache, Sanda Florentina
    Popescu, Marian
    ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2016, 2016, 475 : 204 - 211
  • [44] Short-term exposure to PM2.5 pollution in Iran and related burden diseases
    Baharvand, Parastoo
    Amoatey, Patrick
    Khaniabadi, Yusef Omidi
    Sicard, Pierre
    Raja Naqvi, Hasan
    Rashidi, Rajab
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH, 2025,
  • [45] Short-term effects of PM2.5 and its components exposure on endothelial function in Chinese elders
    Chen, Rukun
    Zhang, Kai
    Li, Xiaoguang
    Li, Jutang
    Jiang, Qixia
    SCIENCE OF THE TOTAL ENVIRONMENT, 2024, 907
  • [46] Short-Term Prediction of PM2.5 Using LSTM Deep Learning Methods
    Kristiani, Endah
    Lin, Hao
    Jwu-Rong Lin
    Yen-Hsun Chuang
    Chin-Yin Huang
    Chao-Tung Yang
    SUSTAINABILITY, 2022, 14 (04)
  • [47] Detection of PM2.5 plume movement from IoT ground level monitoring data
    Kanabkaew, Thongchai
    Mekbungwan, Preechai
    Raksakietisak, Sunee
    Kanchanasut, Kanchana
    ENVIRONMENTAL POLLUTION, 2019, 252 : 543 - 552
  • [48] Histological changes in smoking and COPD mice with short-term exposure of PM2.5
    Zhou, Tianyu
    Wang, Guangfa
    EUROPEAN RESPIRATORY JOURNAL, 2016, 48
  • [49] Short-term PM2.5 exposure induces transient lung injury and repair
    Li, Yu
    Lin, Bencheng
    Hao, De
    Du, Zhongchao
    Wang, Qi
    Song, Zhaoyu
    Li, Xue
    Li, Kuan
    Wang, Jianhai
    Zhang, Qiuyang
    Wu, Junping
    Xi, Zhuge
    Chen, Huaiyong
    JOURNAL OF HAZARDOUS MATERIALS, 2023, 459
  • [50] Can climate indices forecast daily variations of wintertime PM2.5 concentrations in East Asia?
    Jeong, Jaein I.
    Park, Rokjin J.
    SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 881