Real-time air quality forecasting, part I: History, techniques, and current status

被引:321
|
作者
Zhang, Yang [1 ,2 ]
Bocquet, Marc [3 ,4 ,5 ]
Mallet, Vivien [3 ,4 ,5 ]
Seigneur, Christian [4 ,5 ]
Baklanov, Alexander [6 ]
机构
[1] N Carolina State Univ, Dept Marine Earth & Atmospher Sci, Raleigh, NC 27695 USA
[2] Tsinghua Univ, Sch Environm, Beijing 100084, Peoples R China
[3] Paris Rocquencourt Res Ctr, INRIA, Paris, France
[4] Univ Paris Est, CEREA Atmospher Environm Ctr, Joint Lab Ecole Ponts ParisTech, F-77955 Marne La Vallee, France
[5] Univ Paris Est, EDF R&D, F-77955 Marne La Vallee, France
[6] DMI, Res Dept, DK-2100 Copenhagen, Denmark
关键词
Air quality forecasting; Historic milestone; Techniques and tools; Evaluation methods; COMPREHENSIVE PERFORMANCE EVALUATION; GENERAL-CIRCULATION MODEL; 1999 SOUTHERN OXIDANTS; PARTICULATE MATTER; REGRESSION-MODELS; TRANSPORT MODEL; NEURAL-NETWORKS; TECHNICAL NOTE; KALMAN-FILTER; URBAN METEOROLOGY;
D O I
10.1016/j.atmosenv.2012.06.031
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Real-time air quality forecasting (RT-AQF), a new discipline of the atmospheric sciences, represents one of the most far-reaching development and practical applications of science and engineering, poses unprecedented scientific, technical, and computational challenges, and generates significant opportunities for science dissemination and community participations. This two-part review provides a comprehensive assessment of the history, current status, major research and outreach challenges, and future directions of RT-AQF, with a focus on the application and improvement of three-dimensional (3-D) deterministic RT-AQF models. In Part I, major milestones in the history of RT-AQF are reviewed. The fundamentals of RT-AQF are introduced. Various RT-AQF techniques with varying degrees of sophistication and skills are described comparatively. Among all techniques, 3-D RT-AQF models with online-coupled meteorology chemistry and their transitions from mesoscale to unified model systems across scales represent a significant advancement and would greatly enhance understanding of the underlying complex interplay of meteorology, emission, and chemistry from global to urban scales in the real atmosphere. Current major 3-D global and regional RT-AQF models in the world are reviewed in terms of model systems, component models, application scales, model inputs, forecast products, horizontal grid resolutions, and model treatments of chemistry and aerosol processes. An important trend of such models is their coupling with an urban model or a computational fluid dynamic model for urban/local scale applications at 1 km or less and with an exposure model to provide real-time public health assessment and exposure predictions. Evaluation protocols are described along with examinations of current forecasting skills and areas with large biases of major RT-AQF models. (c) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:632 / 655
页数:24
相关论文
共 50 条
  • [31] A Scalable and Reliable Model for Real-time Air Quality Prediction
    Li, Liying
    Li, Zhi
    Reichmann, Lara G.
    Woodbridge, Diane Myung-kyung
    2019 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI 2019), 2019, : 51 - 57
  • [32] Electronic System for Real-Time Indoor Air Quality Monitoring
    Adochiei, Felix-Constantin
    Teodor-Nicolescu, Serban
    Adochiei, Ioana-Raluca
    Seritan, George-Calin
    Enache, Bogdan-Adrian
    Argatu, Florin-Ciprian
    Costin, Diana
    2020 INTERNATIONAL CONFERENCE ON E-HEALTH AND BIOENGINEERING (EHB), 2020,
  • [33] LoRaWAN Based Real-Time Air Quality Monitoring System
    Filip, Bogdan
    Simo, Attila
    Vatau, Doru
    Frigura-Iliasa, Flavin M.
    Musuroi, Sorin
    Andea, Petru
    2020 IEEE 18TH WORLD SYMPOSIUM ON APPLIED MACHINE INTELLIGENCE AND INFORMATICS (SAMI 2020), 2020, : 69 - 72
  • [34] Internet questionnaire and real-time indoor air quality monitoring
    Kähkönen, E
    Zitting, A
    Airo, E
    Valkonen, J
    Leikas, M
    INDOOR AND BUILT ENVIRONMENT, 1997, 6 (06) : 331 - 336
  • [35] Experimental Study of Real-Time Comprehensive Indoor Air Quality
    Hwang, Kwang-Il
    Park, Seung-Kyu
    ADVANCED MULTIMEDIA AND UBIQUITOUS ENGINEERING: FUTURETECH & MUE, 2016, 393 : 151 - 155
  • [36] Flexible dynamic regression models for real-time forecasting of air pollutant concentration
    Mantovan, P
    Pastore, A
    ADVANCES IN MULTIVARIATE DATA ANALYSIS, 2004, : 265 - 276
  • [37] Technical Solution for a Real-Time Air Quality Monitoring System
    Simo, A.
    Dzitac, S.
    Frigura-Iliasa, F. M.
    Musuroi, S.
    Andea, P.
    Meianu, D.
    INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, 2020, 15 (04)
  • [38] Real-time optimal techniques for unmanned air vehicles fuel saving
    Akhtar, N.
    Whidborne, J. F.
    Cooke, A. K.
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING, 2012, 226 (G10) : 1315 - 1328
  • [39] OPERATIONAL FORECASTING WITH REAL-TIME DATABASES
    BAE, DH
    GEORGAKAKOS, KP
    NANDA, SK
    JOURNAL OF HYDRAULIC ENGINEERING-ASCE, 1995, 121 (01): : 49 - 60
  • [40] Real-Time river flow forecasting
    Shamseldin, AY
    RIVER BASIN MODELLING FOR FLOOD RISK MITIGATION, 2006, : 181 - 195