Measurement and prediction of ozone levels around a heavily industrialized area: a neural network approach

被引:80
|
作者
Elkamel, A
Abdul-Wahab, S
Bouhamra, W
Alper, E
机构
[1] Kuwait Univ, Dept Chem Engn, Safat 13060, Kuwait
[2] Sultan Qaboos Univ, Coll Engn, Dept Mech & Ind Engn, Muscat, Oman
来源
ADVANCES IN ENVIRONMENTAL RESEARCH | 2001年 / 5卷 / 01期
关键词
emission estimation; ozone; meteorological factors; neural networks; regression models;
D O I
10.1016/S1093-0191(00)00042-3
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper presents an artificial neural network model that is able to predict stone concentrations as a function of meteorological conditions and precursor concentrations. The network was trained using data collected during a period of 60 days near an industrial area in Kuwait. A mobile monitoring station was used for data collection. The data were collected at the same site as the ozone measurements. The data fed to the neural network were divided into two sets: a training set and a testing set. Various architectures were tried during the training process. A network of one hidden layer of 25 neurons was found to give good predictions for both the training and testing data set. In addition, the predictions of the network were compared to measurements taken during other times of the year. The inputs to the neural network were meteorological conditions (wind speed and direction, relative humidity, temperature, and solar intensity) and the concentration of primary pollutants (methane, carbon monoxide, carbon dioxide, nitrogen oxide, nitrogen dioxide, sulfur dioxide, non-methane hydrocarbons, and dust). A backpropagation algorithm with momentum was used to prepare the neural network. A partitioning method of the connection weights of the network was used to study the relative % contribution of each of the input variables. It was found that the precursors carbon monoxide, carbon dioxide, nitrogen oxide, nitrogen dioxide, and sulfur dioxide had the most effect on the predicted ozone concentration. In addition, temperature played an important role. The performance of the neural network model was compared against linear and non-linear regression models that were prepared based on the present collected data. It was found that the neural network model consistently gives superior predictions. Based on the results of this study, artificial neural network modeling appears to be a promising technique for the prediction of pollutant concentrations. (C) 2001 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:47 / 59
页数:13
相关论文
共 50 条
  • [1] A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area
    Yi, JS
    Prybutok, VR
    [J]. ENVIRONMENTAL POLLUTION, 1996, 92 (03) : 349 - 357
  • [2] Ozone Pollution Prediction around Industrial Areas Using Fuzzy Neural Network Approach
    Zahedi, Gholamreza
    Saba, Sahar
    Elkamel, Ali
    Bahadori, Alireza
    [J]. CLEAN-SOIL AIR WATER, 2014, 42 (07) : 871 - 879
  • [3] Atmospheric depositions around a heavily industrialized area in a seasonally dry tropical environment of India
    Singh, RK
    Agrawal, M
    [J]. ENVIRONMENTAL POLLUTION, 2005, 138 (01) : 142 - 152
  • [4] Photochemical model evaluation of the surface ozone impact of a power plant in a heavily industrialized area of southwestern Spain
    Castell, N.
    Mantilla, E.
    Salvador, R.
    Stein, A. F.
    Millan, M.
    [J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2010, 91 (03) : 662 - 676
  • [5] Prediction of ozone formation based on neural network
    Sohn, SH
    Oh, SC
    Jo, BW
    Yeo, YK
    [J]. JOURNAL OF ENVIRONMENTAL ENGINEERING-ASCE, 2000, 126 (08): : 688 - 696
  • [6] The neural network prediction dynamics of the ozone layer
    Sakash, Irina Yu.
    Lankin, Jully P.
    [J]. FOURTEENTH INTERNATIONAL SYMPOSIUM ON ATMOSPHERIC AND OCEAN OPTICS/ATMOSPHERIC PHYSICS, 2008, 6936
  • [7] Impacts of land-surface forcing on local meteorology and ozone concentrations in a heavily industrialized coastal urban area
    Chang, Jackson Hian-Wui
    Griffith, Stephen M.
    Lin, Neng-Huei
    [J]. URBAN CLIMATE, 2022, 45
  • [8] Neural network approach to prediction of temperatures around groundwater heat pump systems
    Lo Russo, Stefano
    Taddia, Glenda
    Gnavi, Loretta
    Verda, Vittorio
    [J]. HYDROGEOLOGY JOURNAL, 2014, 22 (01) : 205 - 216
  • [9] A neural network model for three-hours-ahead prediction of ozone concentration in the urban area of Palermo
    Brunelli, U.
    Piazza, V.
    Pignato, L.
    [J]. AIR POLLUTION XIV, 2006, 86 : 133 - +
  • [10] Soil concentrations and source apportionment of polybrominated diphenyl ethers (PBDEs) and trace elements around a heavily industrialized area in Kocaeli, Turkey
    Banu Cetin
    [J]. Environmental Science and Pollution Research, 2014, 21 : 8284 - 8293