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
  • [31] Enhanced urban PM2.5 prediction: Applying quadtree division and time-series transformer with WRF-chem
    Zhang, Shiyan
    Yu, Manzhu
    ATMOSPHERIC ENVIRONMENT, 2024, 337
  • [32] Source apportionment studies on particulate matter (PM10 and PM2.5) in ambient air of urban Mangalore, India
    Kalaiarasan, Gopinath
    Balakrishnan, Raj Mohan
    Sethunath, Neethu Anitha
    Manoharan, Sivamoorthy
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2018, 217 : 815 - 824
  • [33] Comparison of PM2.5 Chemical Components over East Asia Simulated by the WRF-Chem and WRF/CMAQ Models: On the Models' Prediction Inconsistency
    Choi, Min-Woo
    Lee, Jae-Hyeong
    Woo, Ju-Wan
    Kim, Cheol-Hee
    Lee, Sang-Hyun
    ATMOSPHERE, 2019, 10 (10)
  • [34] Estimation of particulate matter pollution using WRF-Chem during dust storm event over India
    Soni, Manish
    Verma, Sunita
    Mishra, Manoj K.
    Mall, R. K.
    Payra, Swagata
    URBAN CLIMATE, 2022, 44
  • [35] Anthropogenic emissions estimated using surface observations and their impacts on PM2.5 source apportionment over the Yangtze River Delta, China
    Feng, Shuzhuang
    Jiang, Fei
    Wang, Hengmao
    Shen, Yang
    Zheng, Yanhua
    Zhang, Lingyu
    Lou, Chenxi
    Ju, Weimin
    SCIENCE OF THE TOTAL ENVIRONMENT, 2022, 828
  • [36] 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
  • [37] Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States
    Goldberg, Daniel L.
    Gupta, Pawan
    Wang, Kai
    Jena, Chinmay
    Zhang, Yang
    Lu, Zifeng
    Streets, David G.
    ATMOSPHERIC ENVIRONMENT, 2019, 199 : 443 - 452
  • [38] Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland
    Werner, Malgorzata
    Kryza, Maciej
    Guzikowski, Jakub
    REMOTE SENSING, 2019, 11 (20)
  • [39] 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
  • [40] 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