The Experts below are selected from a list of 88323 Experts worldwide ranked by ideXlab platform
Savitri Garivait - One of the best experts on this subject based on the ideXlab platform.
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Air Pollutant emissions from rice straw open field burning in india thailand and the philippines
Environmental Pollution, 2009Co-Authors: Butchaiah Gadde, Sebastien Bonnet, Christoph Menke, Savitri GarivaitAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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potential assessment of the support vector machine method in forecasting ambient Air Pollutant trends
Chemosphere, 2005Co-Authors: Weizhen Lu, Wenjian WangAbstract: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.
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Air Pollutant parameter forecasting using support vector machines
Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290), 2002Co-Authors: Weizhen Lu, Wenjian Wang, A.y.t. Leung, Siu-ming Lo, R.k.k. Yuen, Zongben XuAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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Air Pollutant emissions from rice straw open field burning in india thailand and the philippines
Environmental Pollution, 2009Co-Authors: Butchaiah Gadde, Sebastien Bonnet, Christoph Menke, Savitri GarivaitAbstract: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.
Weizhen Lu - One of the best experts on this subject based on the ideXlab platform.
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potential assessment of the support vector machine method in forecasting ambient Air Pollutant trends
Chemosphere, 2005Co-Authors: Weizhen Lu, Wenjian WangAbstract: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.
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Air Pollutant parameter forecasting using support vector machines
Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290), 2002Co-Authors: Weizhen Lu, Wenjian Wang, A.y.t. Leung, Siu-ming Lo, R.k.k. Yuen, Zongben XuAbstract: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.
Leslie K Norford - One of the best experts on this subject based on the ideXlab platform.
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improving Air quality in high density cities by understanding the relationship between Air Pollutant dispersion and urban morphologies
Building and Environment, 2014Co-Authors: Chao Yuan, Edward Ng, Leslie K NorfordAbstract:Abstract In high-density megacities, Air pollution has a higher impact on public health than cities of lower population density. Apart from higher pollution emissions due to human activities in densely populated street canyons, stagnated Air flow due to closely packed tall buildings means lower dispersion potential. The coupled result leads to frequent reports of high Air pollution indexes at street-side stations in Hong Kong. High-density urban morphologies need to be carefully designed to lessen the ill effects of high density urban living. This study addresses the knowledge-gap between planning and design principles and Air pollution dispersion potentials in high density cities. The Air ventilation assessment for projects in high-density Hong Kong is advanced to include Air Pollutant dispersion issues. The methods in this study are CFD simulation and parametric study. The SST κ – ω model is adopted after balancing the accuracy and computational cost in the comparative study. Urban-scale parametric studies are conducted to clarify the effects of urban permeability and building geometries on Air pollution dispersion, for both the outdoor pedestrian environment and the indoor environment in the roadside buildings. Given the finite land resources in high-density cities and the numerous planning and design restrictions for development projects, the effectiveness of mitigation strategies is evaluated to optimize the benefits. A real urban case study is finally conducted to demonstrate that the suggested design principles from the parametric study are feasible in the practical high density urban design.
