Seasonal and diurnal variability of PM2.5 concentration along with the role of wind patterns over different locations of Delhi during the year 2018 to 2022

被引:0
作者
Vaishali, Rupesh M. [1 ,2 ]
Das, Rupesh M. [1 ,2 ]
机构
[1] CSIR Natl Phys Lab, Environm Sci & Biomed Metrol Div, Dr KS Krishnan Marg, New Delhi 110012, India
[2] Acad Sci & Innovat Res AcSIR, CSIR HRDC Campus,Postal Staff Coll Area,Sect 19, Ghaziabad 201002, Uttar Pradesh, India
关键词
Particulate matter (PM2.5); Meteorological Parameters; Air Quality; Seasonal Variation; Diurnal variation; Wind pattern; PARTICULATE MATTER; BLACK CARBON; POLLUTANTS; POLLUTION; AEROSOLS; REGION; RANGE; PM10;
D O I
10.1007/s10661-025-13761-9
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The present study assesses the seasonal and diurnal variability of PM2.5 concentrations across different locations in Delhi, emphasizing the role of wind speed and direction. PM2.5 concentrations were analyzed using descriptive and statistical techniques, including ANOVA, Mann-Kendall trend, and correlation analysis. Data from the CPCB CAAQMS network at five distinct locations-Industrial, Commercial, Residential, Traffic, and Green areas-were examined from 2018-2022. Seasonal variability analysis revealed that PM2.5 concentrations peaked at 300-350 mu g/m(3) during the post-monsoon and winter seasons, while lower levels (< 100 <mu>g/m(3)) were observed during the monsoon. Diurnal patterns exhibited a bimodal distribution, with peaks occurring during the morning (0800-1000 hour) and night (2000 to 2400 hour) time, driven by vehicular emissions, road dust, and wind-blown particles during the day and a stable boundary layer with reduced mixing height at night. Regions with significant industrial and traffic activities experienced 15-25% higher PM2.5 concentrations than commercial and green areas. The study identified a decreasing trend of approximately 15% in PM2.5 concentrations from the pre- to post-COVID period. Using correlational and t-test analysis along with the wind rose visualizations, it was revealed that meteorological parameters (wind speed and direction) significantly influence PM2.5 dispersion. A time lag of 2-4 hour was observed for pollutant transport, depending on the wind speed. Statistical analysis demonstrated a significant inverse correlation between wind speed and PM2.5 concentrations (p < 0.0001), highlighting the role of meteorological factors in pollutant dispersion. These findings provide actionable insights into air quality management and mitigation strategies for Delhi's diverse urban environments.
引用
收藏
页数:29
相关论文
共 46 条
  • [1] [Anonymous], 2009, National Ambient Air Quality Standards
  • [2] Bali K., 2019, Atmos. Chem. Phys. Discuss, V2019, P1, DOI [10.5194/acp-2019-731, DOI 10.5194/ACP-2019-731]
  • [3] Bardhan A., 2022, Stech Archives of Earth and Environment Sciences, V2, P103
  • [4] Local characteristics of and exposure to fine particulate matter (PM2.5) in four indian megacities
    Chen, Ying
    Wild, Oliver
    Conibear, Luke
    Ran, Liang
    He, Jianjun
    Wang, Lina
    Wang, Yu
    [J]. ATMOSPHERIC ENVIRONMENT-X, 2020, 5
  • [5] Trends and Variability of PM2.5 at Different Time Scales over Delhi: Long-term Analysis 2007-2021
    Chetna
    Dhaka, Surendra K.
    Longiany, Gagandeep
    Panwar, Vivek
    Kumar, Vinay
    Malik, Shristy
    Rao, A. S.
    Singh, Narendra
    Dimri, A. P.
    Matsumi, Yutaka
    Nakayama, Tomoki
    Hayashida, Sachiko
    [J]. AEROSOL AND AIR QUALITY RESEARCH, 2023, 23 (05)
  • [6] CPCB, 2011, Guidelines for the measurement of ambient air pollutants volume-ii central pollution control board guidelines for real-time sampling & analyses
  • [7] Examining the impact of lockdown (due to COVID-19) on ambient aerosols (PM2.5): A study on Indo-Gangetic Plain (IGP) Cities, India
    Das, Manob
    Das, Arijit
    Sarkar, Raju
    Saha, Sunil
    Mandal, Ashis
    [J]. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2021, 35 (06) : 1301 - 1317
  • [8] PM10 and PM2.5 in Indo-Gangetic Plain (IGP) of India: Chemical characterization, source analysis, and transport pathways
    Devi, Ningombam Linthoingambi
    Kumar, Amrendra
    Yadav, Ishwar Chandra
    [J]. URBAN CLIMATE, 2020, 33
  • [9] Tropospheric ozone variability in Delhi during pre & post monsoon periods: Decoding influence of seasonal variation, diurnal variation, short-range and long-range transport
    Dhawan, Sachin
    George, Mohan P.
    Jayachandran, K. S.
    Khare, Mukesh
    [J]. URBAN CLIMATE, 2023, 47
  • [10] Assessment of meteorological parameters on air pollution variability over Delhi
    Garsa, Kalpana
    Khan, Abul Amir
    Jindal, Prakhar
    Middey, Anirban
    Luqman, Nadeem
    Mohanty, Hitankshi
    Tiwari, Shubhansh
    [J]. ENVIRONMENTAL MONITORING AND ASSESSMENT, 2023, 195 (11)