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Air Pollutant

The Experts below are selected from a list of 88323 Experts worldwide ranked by ideXlab platform

Savitri Garivait – 1st expert on this subject based on the ideXlab platform

  • Air Pollutant emissions from rice straw open field burning in india thailand and the philippines
    Environmental Pollution, 2009
    Co-Authors: Butchaiah Gadde, Sebastien Bonnet, Christoph Menke, Savitri Garivait

    Abstract:

    Abstract Rice is a widely grown crop in Asia. China (30%) and India (21%) contribute to about half of the world’s total rice production. In this study, three major rice-producing countries in Asia are considered, India, Thailand and the Philippines (the later two contributing 4% and 2% of the world’s rice production). Rice straw is one of the main field based residues produced along with this commodity and its applications vary widely in the region. Although rice production practises vary from one country to another, open burning of straw is a common practice in these countries. In this study, an approach was followed aiming at (a) determining the quantity of rice straw being subject to open field burning in those countries, (b) congregating Pollutant specific emissions factors for rice straw burning, and (c) quantifying the resulting Air Pollutant emissions. Uncertainties in the results obtained as compared to a global approach are also discussed.

Wenjian Wang – 2nd expert on this subject based on the ideXlab platform

  • potential assessment of the support vector machine method in forecasting ambient Air Pollutant trends
    Chemosphere, 2005
    Co-Authors: Weizhen Lu, Wenjian Wang

    Abstract:

    Monitoring and forecasting of Air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to Air Pollutants existing in urban Air. The accurate models for Air Pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict Air Pollutant levels in advancing time series based on the monitored Air Pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting Air quality parameters with different time series and of better generalization performance than the RBF model.

  • Air Pollutant parameter forecasting using support vector machines
    Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290), 2002
    Co-Authors: Weizhen Lu, Wenjian Wang, A.y.t. Leung, Siu-ming Lo, R.k.k. Yuen, Zongben Xu

    Abstract:

    Forecasting of Air quality parameters is an important topic of atmospheric and environmental research today due to the health impact caused by Airborne Pollutants existing in urban areas. The support vector machine (SVM), as a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and has been reported to perform well with some promising results. The work presented examines the feasibility of applying SVM to predict Pollutant concentrations. The functional characteristics of the SVM are also investigated. The experimental comparison between the SVM and the classical radial basis function (RBF) network demonstrates that the SVM is superior to conventional RBF in predicting Air quality parameters with different time series.

Butchaiah Gadde – 3rd expert on this subject based on the ideXlab platform

  • Air Pollutant emissions from rice straw open field burning in india thailand and the philippines
    Environmental Pollution, 2009
    Co-Authors: Butchaiah Gadde, Sebastien Bonnet, Christoph Menke, Savitri Garivait

    Abstract:

    Abstract Rice is a widely grown crop in Asia. China (30%) and India (21%) contribute to about half of the world’s total rice production. In this study, three major rice-producing countries in Asia are considered, India, Thailand and the Philippines (the later two contributing 4% and 2% of the world’s rice production). Rice straw is one of the main field based residues produced along with this commodity and its applications vary widely in the region. Although rice production practises vary from one country to another, open burning of straw is a common practice in these countries. In this study, an approach was followed aiming at (a) determining the quantity of rice straw being subject to open field burning in those countries, (b) congregating Pollutant specific emissions factors for rice straw burning, and (c) quantifying the resulting Air Pollutant emissions. Uncertainties in the results obtained as compared to a global approach are also discussed.