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Diane S. Wirtz Ahmed G. El-Din Ahmed Idriss 《Journal of the Air & Waste Management Association (1995)》2013,63(12):1847-1857
Abstract Ground-level ozone is a secondary pollutant that has recently gained notoriety for its detrimental effects on human and vegetation health. In this paper, a systematic approach is applied to develop artificial neural network (ANN) models for ground-level ozone (O3) prediction in Edmonton, Alberta, Canada, using ambient monitoring data for input. The intent of these models is to provide regulatory agencies with a tool for addressing data gaps in ambient monitoring information and predicting O3 events. The models are used to determine the meteorological conditions and precursors that most affect O3 concentrations. O3 time-series effects and the efficacy of the systematic approach are also assessed. The developed models showed good predictive success, with coefficient of multiple determination values ranging from 0.75 to 0.94 for forecasts up to 2 hr in advance. The inputs most important for O3 prediction were temperature and concentrations of nitric oxide, total hydrocarbons, sulfur dioxide, and nitrogen dioxide. 相似文献
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Wirtz DS El-Din MG El-Din AG Idriss A 《Journal of the Air & Waste Management Association (1995)》2005,55(12):1847-1857
Ground-level ozone is a secondary pollutant that has recently gained notoriety for its detrimental effects on human and vegetation health. In this paper, a systematic approach is applied to develop artificial neural network (ANN) models for ground-level ozone (O3) prediction in Edmonton, Alberta, Canada, using ambient monitoring data for input. The intent of these models is to provide regulatory agencies with a tool for addressing data gaps in ambient monitoring information and predicting O3 events. The models are used to determine the meteorological conditions and precursors that most affect O3 concentrations. O3 time-series effects and the efficacy of the systematic approach are also assessed. The developed models showed good predictive success, with coefficient of multiple determination values ranging from 0.75 to 0.94 for forecasts up to 2 hr in advance. The inputs most important for O3 prediction were temperature and concentrations of nitric oxide, total hydrocarbons, sulfur dioxide, and nitrogen dioxide. 相似文献
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Farhat Ameny Elleuch Jihen Ben Amor Faten Barkallah Mohamed Smith Kirsty F. Ben Neila Idriss Abdelkafi Slim Fendri Imen 《Environmental science and pollution research international》2022,29(59):88699-88709
Environmental Science and Pollution Research - Karlodinium veneficum is a toxic benthic globally distributed dinoflagellate which has direct impacts on human health and the environment. Early and... 相似文献
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