An operational urban air quality model ENFUSER, based on dispersion modelling and data assimilation

被引:13
|
作者
Johansson, Lasse [1 ]
Karppinen, Ari [1 ]
Kurppa, Mona [1 ]
Kousa, Anu [2 ]
Niemi, Jarkko, V [2 ]
Kukkonen, Jaakko [1 ,3 ]
机构
[1] Finnish Meteorol Inst, Atmospher Composit Res, Helsinki, Finland
[2] Helsinki Reg Environm Serv Author HSY, Ilmalantori 1, Helsinki 00240, Finland
[3] Univ Hertfordshire, Ctr Atmospher & Climate Phys Res, Coll Lane, Hatfield AL10 9AB, Hertfordshire, England
关键词
Air quality; Dispersion modelling; Data assimilation; ROAD DUST; ATMOSPHERIC COMPOSITION; HIGH-RESOLUTION; STREET CANYON; LAND-USE; EMISSIONS; POLLUTION; SYSTEM; HELSINKI; SCHEMES;
D O I
10.1016/j.envsoft.2022.105460
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
An operational urban air quality modelling system ENFUSER is presented with an evaluation against measured data. ENFUSER combines several dispersion modelling approaches, uses data assimilation, and continuously extracts information from online, global open-access sources. The modelling area is described with a combination of geographic datasets. These GIS datasets are globally available with open access, and therefore the model can be applied worldwide. Urban scale dispersion is addressed with a combination of Gaussian puff and Gaussian plume modelling, and long-range transport of pollutants is accounted for via a separate regional model. The presented data assimilation method, which supports the use of AQ sensors and incorporates a longer-term learning mechanism, adjusts emission factors and the regional background values on an hourly basis. The model can be used with reasonable accuracy also in urban areas, for which detailed emissions inventories would not be available, due to the data assimilation capabilities.
引用
收藏
页数:19
相关论文
共 50 条
  • [1] Urban compaction or dispersion? An air quality modelling study
    Martins, Helena
    [J]. ATMOSPHERIC ENVIRONMENT, 2012, 54 : 60 - 72
  • [2] Data assimilation methods for urban air quality at the local scale
    Chi Vuong Nguyen
    Soulhac, Lionel
    [J]. ATMOSPHERIC ENVIRONMENT, 2021, 253
  • [3] Source Apportionment and Data Assimilation in Urban Air Quality Modelling for NO2: The Lyon Case Study
    Chi Vuong Nguyen
    Soulhac, Lionel
    Salizzoni, Pietro
    [J]. ATMOSPHERE, 2018, 9 (01):
  • [4] A problem-solving environment for data assimilation in air quality modelling
    van Velzen, N.
    Segers, A. J.
    [J]. ENVIRONMENTAL MODELLING & SOFTWARE, 2010, 25 (03) : 277 - 288
  • [5] Dynamic Neural Assimilation: a deep learning and data assimilation model for air quality predictions
    Tuckus, Nikodemas
    D'Elia, Ilaria
    Chinnici, Marta
    Arcucci, Rossella
    [J]. DISCOVER APPLIED SCIENCES, 2024, 6 (04)
  • [6] Dynamic Neural Assimilation: a deep learning and data assimilation model for air quality predictions
    Nikodemas Tučkus
    Ilaria D’Elia
    Marta Chinnici
    Rossella Arcucci
    [J]. Discover Applied Sciences, 6
  • [7] Lightweight Open Data Assimilation of Pan-European Urban Air Quality
    Miasayedava, Lizaveta
    Kaugerand, Jaanus
    Tuhtan, Jeffrey A.
    [J]. IEEE ACCESS, 2023, 11 : 84670 - 84688
  • [8] Data Assimilation and Air Quality Forecasting
    Eskes, Henk
    Timmermans, Renske
    Curier, Lyana
    de Wildt, Martijn de Ruyter
    Segers, Arjo
    Sauter, Ferd
    Schaap, Martijn
    [J]. AIR POLLUTION MODELING AND ITS APPLICATION XXII, 2014, : 189 - 192
  • [9] Modification of an operational dispersion model for urban applications
    de Haan, P
    Rotach, MW
    Werfeli, M
    [J]. JOURNAL OF APPLIED METEOROLOGY, 2001, 40 (05): : 864 - 879
  • [10] MODEL OF URBAN VISIBILITY BASED ON AIR QUALITY
    GEMMA, JL
    MILLER, DF
    [J]. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 1974, : 8 - 8