Wintertime source apportionment of PM2.5 pollution in million plus population cities of India using WRF-Chem simulation

被引:0
|
作者
Jat, Rajmal [1 ,2 ]
Gurjar, Bhola Ram [2 ]
Ghude, Sachin D. [1 ]
Yadav, Prafull P. [1 ,3 ]
机构
[1] Minist Earth Sci, Indian Inst Trop Meteorol, Pune, Maharashtra, India
[2] Indian Inst Technol Roorkee, Dept Civil Engn, Roorkee, Uttarakhand, India
[3] Savitribai Phule Pune Univ, Dept Atmospher & Space Sci, Pune 411007, Maharashtra, India
关键词
Million-plus population cities; Wintertime; PM2.5; pollution; Emission sources; WRF-Chem; TECHNOLOGY-LINKED INVENTORY; AIR-QUALITY; PARTICULATE MATTER; PM1; AEROSOLS; EMISSIONS; MODEL; TRENDS; IMPACTS; URBANIZATION; TRANSPORT;
D O I
10.1007/s40808-024-02119-8
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Several major Indian cities experience elevated PM2.5 concentrations, particularly during the winter season. Effective air quality management in these densely populated urban areas necessitates a comprehensive understanding of the diverse emission sources contributing to air pollution. This study investigates PM2.5 pollution in 53 million-plus population cities (MPPC's) across India during the winter of 2015-2016 utilizing the Weather Research and Forecasting model coupled with chemistry (WRF-Chem). Multiple model simulations were employed to study the impact of various source sectors on local PM2.5 pollution and their emissions in these cities. The findings indicate significant contributions to local PM2.5 pollution from major emission source sectors in MPPCs. The influence of PM2.5 pollution plumes originating from these cities on regional PM2.5 pollution in India is evident across all sectors. In MPPCs situated in the east, north, and central regions of India, the primary contributors to local PM2.5 pollution include residential and transportation sectors, alongside energy sectors in specific cities marked by elevated emissions from power plants. In the MPPCs of western India, the industrial and energy sectors are identified as the primary contributors to local PM2.5 pollution. Meanwhile, in the MPPCs of south India, the major contributors are identified as industrial and residential sectors. In a comprehensive overview encompassing 53 MPPCs, the primary contributors to local PM2.5 pollution are identified as follows: the energy sector in 7 cities, the industrial sector in 8 cities, the residential sector in 29 cities, and the transportation sector in 9 cities. The correlation between PM2.5 pollution loadings and meteorological parameters reveals that PM2.5 pollution levels in MPPCs are influenced by both local emissions and meteorological factors. Specifically, wind speed and boundary layer height play critical roles in regulating the dispersion of pollution. Consequently, regulating emissions from these cities effectively requires consideration of both the primary emission source sectors and the prevailing meteorological conditions specific to each city's geographical location.
引用
收藏
页码:6065 / 6082
页数:18
相关论文
共 50 条
  • [41] Source apportionment of PM2.5 pollution in the central six districts of Beijing, China
    Zhang, Yuepeng
    Li, Xuan
    Nie, Teng
    Qi, Jun
    Chen, Jing
    Wu, Qiong
    JOURNAL OF CLEANER PRODUCTION, 2018, 174 : 661 - 669
  • [42] Seasonal variation of chemical composition and source apportionment of PM2.5 in Pune, India
    Gawhane, Ranjeeta D.
    Rao, Pasumarthi Surya Prakasa
    Budhavant, Krishnakant B.
    Waghmare, Vinayak
    Meshram, Dhananjay C.
    Safai, Pramod D.
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2017, 24 (26) : 21065 - 21072
  • [43] Seasonal variation of chemical composition and source apportionment of PM2.5 in Pune, India
    Ranjeeta D. Gawhane
    Pasumarthi Surya Prakasa Rao
    Krishnakant B. Budhavant
    Vinayak Waghmare
    Dhananjay C. Meshram
    Pramod D. Safai
    Environmental Science and Pollution Research, 2017, 24 : 21065 - 21072
  • [44] Assimilating a blended dataset of satellite-based estimations and in situ observations to improve WRF-Chem PM2.5 prediction
    Ma, Xingxing
    Liu, Hongnian
    Peng, Zhen
    ATMOSPHERIC ENVIRONMENT, 2024, 319
  • [45] Assimilation of PM2.5 ground base observations to two chemical schemes in WRF-Chem - The results for the winter and summer period
    Werner, Malgorzata
    Kryza, Maciej
    Pagowski, Mariusz
    Guzikowski, Jakub
    ATMOSPHERIC ENVIRONMENT, 2019, 200 : 178 - 189
  • [46] Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing
    Cheng, Xinghong
    Liu, Yuelin
    Xu, Xiangde
    You, Wei
    Zang, Zengliang
    Gao, Lina
    Chen, Yubao
    Su, Debin
    Yan, Peng
    SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 682 : 541 - 552
  • [47] Importance of regional PM2.5 transport and precipitation washout in heavy air pollution in the Twain-Hu Basin over Central China: Observational analysis and WRF-Chem simulation
    Hu, Weiyang
    Zhao, Tianliang
    Bai, Yongqing
    Kong, Shaofei
    Xiong, Jie
    Sun, Xiaoyun
    Yang, Qingjian
    Gu, Yao
    Lu, Huicheng
    SCIENCE OF THE TOTAL ENVIRONMENT, 2021, 758
  • [48] Improved PM2.5 predictions of WRF-Chem via the integration of Himawari-8 satellite data and ground observations
    Hong, Jia
    Mao, Feiyue
    Min, Qilong
    Pan, Zengxin
    Wang, Wei
    Zhang, Tianhao
    Gong, Wei
    ENVIRONMENTAL POLLUTION, 2020, 263
  • [49] 基于WRF-Chem模式的华东区域PM2.5预报及偏差原因
    周广强
    谢英
    吴剑斌
    余钟奇
    常炉予
    高伟
    中国环境科学, 2016, 36 (08) : 2251 - 2259
  • [50] Response of PM2.5 pollution to meteorological and anthropogenic emissions changes during COVID-19 lockdown in Hunan Province based on WRF-Chem model
    Dai, Simin
    Chen, Xuwu
    Liang, Jie
    Li, Xin
    Li, Shuai
    Chen, Gaojie
    Chen, Zuo
    Bin, Juan
    Tang, Yifan
    Li, Xiaodong
    ENVIRONMENTAL POLLUTION, 2023, 331