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
  • [1] The Source Apportionment of Primary PM2.5 in an Aerosol Pollution Event over Beijing-Tianjin-Hebei Region using WRF-Chem, China
    Zhang, Yinglong
    Zhu, Bin
    Gao, Jinhui
    Kang, Hanqing
    Yang, Peng
    Wang, Lili
    Zhang, Junke
    AEROSOL AND AIR QUALITY RESEARCH, 2017, 17 (12) : 2966 - 2980
  • [2] Simulations of Summertime Ozone and PM2.5 Pollution in Fenwei Plain (FWP) Using the WRF-Chem Model
    Wang, Yuxi
    Cao, Le
    Zhang, Tong
    Kong, Haijiang
    ATMOSPHERE, 2023, 14 (02)
  • [3] Evaluation of WRF-Chem simulations on vertical profiles of PM2.5 with UAV observations during a haze pollution event
    Liu, Cheng
    Huang, Jianping
    Hu, Xiao-Ming
    Hu, Cheng
    Wang, Yongwei
    Fang, Xiaozhen
    Luo, Li
    Xiao, Hong-Wei
    Xiao, Hua-Yun
    ATMOSPHERIC ENVIRONMENT, 2021, 252
  • [4] Assessment of Transboundary PM2.5 from Biomass Burning in Northern Thailand Using the WRF-Chem Model
    Inlaung, Kevalin
    Chotamonsak, Chakrit
    Macatangay, Ronald
    Surapipith, Vanisa
    TOXICS, 2024, 12 (07)
  • [5] Meteorological characteristics within boundary layer and its influence on PM2.5 pollution in six cities of North China based on WRF-Chem
    Lv, Zhe
    Wei, Wei
    Cheng, Shuiyuan
    Han, Xiaoyan
    Wang, Xiaoqi
    ATMOSPHERIC ENVIRONMENT, 2020, 228 (228)
  • [6] Investigating impact of emission inventories on PM2.5 simulations over North China Plain by WRF-Chem
    Ma, Xiaoyan
    Sha, Tong
    Wang, Jianying
    Jia, Hailing
    Tian, Rong
    ATMOSPHERIC ENVIRONMENT, 2018, 195 : 125 - 140
  • [7] A hybrid model to improve WRF-Chem performance for crop burning emissions of PM2.5 and secondary aerosols in North India
    Nagar, Pavan K.
    Sharma, Mukesh
    URBAN CLIMATE, 2022, 41
  • [8] Assimilating Fengyun-4A observations to improve WRF-Chem PM2.5 predictions in China
    Hong, Jia
    Mao, Feiyue
    Gong, Wei
    Gan, Yuan
    Zang, Lin
    Quan, Jihong
    Chen, Jiangping
    ATMOSPHERIC RESEARCH, 2022, 265
  • [9] Utilisation FINN data version 2.5 for forecasting PM2.5 during forest fire 2019 in Sumatra by using WRF-Chem
    Kombara, Prawira Yudha
    Pratama, Alvin
    Cahyono, Waluyo Eko
    Setyawati, Wiwiek
    Adetya, Emmanuel
    Fitriana, Hana Listi
    JOURNAL OF SOUTHERN HEMISPHERE EARTH SYSTEMS SCIENCE, 2023, : 212 - 218
  • [10] Evaluating the sensitivity of fine particulate matter (PM2.5) simulations to chemical mechanism in WRF-Chem over Delhi
    Jat, Rajmal
    Jena, Chinmay
    Yadav, Prafull P.
    Govardhan, Gaurav
    Kalita, Gayatry
    Debnath, Sreyashi
    Gunwani, Preeti
    Acharja, Prodip
    Pawar, Pooja V.
    Sharma, Pratul
    Kulkarni, Santosh H.
    Kulkarni, Akshay
    Kaginalkar, Akshara
    Chate, Dilip M.
    Kumar, Rajesh
    Soni, Vijay Kumar
    Ghude, Sachin D.
    ATMOSPHERIC ENVIRONMENT, 2024, 323