Predict the particulate matter concentrations in 128 cities of China

被引:8
作者
Dun Meng [1 ]
Xu Zhicun [1 ]
Wu, Lifeng [1 ]
Yang, Yingjie [2 ]
机构
[1] Hebei Univ Engn, Coll Management Engn & Business, Handan 056038, Peoples R China
[2] De Montfort Univ, Ctr Computat Intelligence, Leicester LE1 9BH, Leics, England
基金
中国国家自然科学基金;
关键词
PM2; 5; PM10; Gray prediction model with fractional order accumulation; Buffer operators; Gray wolf optimizer; MODELS;
D O I
10.1007/s11869-020-00819-5
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
To predict the concentrations of PM2.5 and PM10 in the 128 cities of China, the discrete gray prediction model with fractional order accumulation (DFGM(1,1)) was used to predict the annual average PM2.5 and PM10 concentrations from 2019 to 2023. The result is as following: the annual average PM2.5 concentrations of Xi'an, Xuzhou, Ordos, Jingmen, Meizhou, Huizhou, Panzhihua, Kunming, Jixi, and Yichun are increasing from 2019 to 2023. The annual average PM2.5 concentrations in the 118 other cities are decreasing from 2019 to 2023. While the annual average PM10 concentrations of Taiyuan, Ordos, Dongguan, Karamay, Foshan, Yichun, Qitaihe, Jinzhou, and Heihe are increasing from 2019 to 2023, the annual average PM10 concentrations in the 119 other cities are decreasing from 2019 to 2023.
引用
收藏
页码:399 / 407
页数:9
相关论文
共 15 条
  • [1] PM2.5 concentration forecasting using ANFIS, EEMD-GRNN, MLP, and MLR models: a case study of Tehran, Iran
    Amanollahi, Jamil
    Ausati, Shadi
    [J]. AIR QUALITY ATMOSPHERE AND HEALTH, 2020, 13 (02) : 161 - 171
  • [2] SARIMA damp trend grey forecasting model for airline industry
    Bernardo Carmona-Benitez, Rafael
    Rosa Nieto, Maria
    [J]. JOURNAL OF AIR TRANSPORT MANAGEMENT, 2020, 82
  • [3] Estimation of electronic waste using optimized multivariate grey models
    Duman, Gazi Murat
    Kongar, Elif
    Gupta, Surendra M.
    [J]. WASTE MANAGEMENT, 2019, 95 : 241 - 249
  • [4] A method to predict PM2.5 resulting from compliance with national ambient air quality standards
    Kelly, James T.
    Reff, Adam
    Gantt, Brett
    [J]. ATMOSPHERIC ENVIRONMENT, 2017, 162 : 1 - 10
  • [5] Improvement of PM10 prediction in East Asia using inverse modeling
    Koo, Youn-Seo
    Choi, Dae-Ryun
    Kwon, Hi-Yong
    Jang, Young-Kee
    Han, Jin-Seok
    [J]. ATMOSPHERIC ENVIRONMENT, 2015, 106 : 318 - 328
  • [6] Grey Wolf Optimizer
    Mirjalili, Seyedali
    Mirjalili, Seyed Mohammad
    Lewis, Andrew
    [J]. ADVANCES IN ENGINEERING SOFTWARE, 2014, 69 : 46 - 61
  • [7] Short-term forecasting of renewable energy consumption: Augmentation of a modified grey model with a Kalman filter
    Moonchai, Sompop
    Chutsagulprom, Nawinda
    [J]. APPLIED SOFT COMPUTING, 2020, 87
  • [8] Predicting PK10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)
    Park, Sechan
    Kim, Minjeong
    Kim, Minhae
    Namgung, Hyeong-Gyu
    Kim, Ki-Tae
    Cho, Kyung Hwa
    Kwon, Soon-Bark
    [J]. JOURNAL OF HAZARDOUS MATERIALS, 2018, 341 : 75 - 82
  • [9] Impact of regional versus local resolution air quality modeling on particulate matter exposure health impact assessment
    Parvez, Fatema
    Wagstrom, Kristina
    [J]. AIR QUALITY ATMOSPHERE AND HEALTH, 2020, 13 (03) : 271 - 279
  • [10] Forecasting of Turkey's greenhouse gas emissions using linear and nonlinear rolling metabolic grey model based on optimization
    Sahin, Utkucan
    [J]. JOURNAL OF CLEANER PRODUCTION, 2019, 239