Xiekang Wang

Prediction of maximum daily ozone level using combined neural network and statistical characteristics

Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. As one of major pollutants, ozone, especially ground level ozone, is responsible for various adverse effects on both human being and foliage. Therefore, prediction of ambient ozone levels in certain environment, especially the ground ozone level in densely urban areas, is of great importance to urban air quality and city image. To date, though several ozone prediction models have been established, there is still a need for more accurate models to develop effective warning strategies. The development of such models is difficult because the meteorological variables and the photochemical reactions involved in ozone formation are very complex. The present work aims to develop an improved neural network model, which combines the adaptive radial basis function (ARBF) network with statistical characteristics of ozone in selected specific areas, and is used to predict the daily maximum ozone concentration level. The improved method is trained and testified by hourly time series data collected at three air pollutant-monitoring stations in Hong Kong during 1999 and 2000. The simulation results demonstrate the effectiveness and the reliability of the proposed method.

A study of ozone variation trend within area of affecting human health in Hong Kong

As far as the impact of air pollutants on human health being concerned, ozone is one of the main pollutants in atmosphere. In particular, the ground level ozone is responsible for a variety of adverse effects on both human being and plant life. To protect the humankind from such adverse health effects, early information and precautions of high ozone level need to be supplied in times. In this study, statistical characteristics of ground level ozone is analyzed according to the field monitoring data in mixed residential, commercial and industrial areas, e.g., Tsuen Wan area in Hong Kong. The study deals with the characteristics of hourly and daily mean ozone levels under different climatic conditions such as temperature, solar radiation, wind speed, and other pollutant concentration levels. The study aims to investigate the importance of meteorological factors and their impact on relevant pollutant concentration levels from chemical aspect. Further, reasons causing the spatial and temporal variations of ozone levels are discussed. All these results will provide a physical basis for accurately predicting ozone concentration in extensive, future research.

A preliminary study of ozone trend and its impact on environment in Hong Kong

As far as the impact of air pollutants on human health being is concerned, ozone (O3) is one of the most pollutant sources, and, in particular, the ground level ozone is responsible for a variety of adverse effects on both human being and plant life. To protect the population from such adverse health effects, early information and precautions about the high ozone level need to be ascertained. In this study, statistical characteristics of ground level ozone is analyzed according to field monitoring data in mixed residential, commercial and industrial areas, e.g., Tsuen Wan area in Hong Kong. The study deals with the characteristics of hourly and daily mean ozone levels under different climatic conditions such as temperature, solar radiation (SR), wind speed (WS), and other pollutant concentration levels. The study aims to investigate the importance of meteorological factors and their impact on relevant pollutant concentration levels from a chemical aspect. Further, reasons for the spatial and temporal variation of ozone levels are discussed. All these results will provide a physical basis for accurately predicting ozone concentration in similar research.

Optimal feed-forward neural networks based on the combination of constructing and pruning by genetic algorithms

The determination of the proper size of an artificial neural network (ANN) is recognized to be crucial, especially for its practical implementation in important issues such as learning and generalization. In the paper, an effective design method of neural network architectures is presented. The network is firstly trained by a dynamic constructive method until the error is satisfied. The trained network is then pruned by genetic algorithm (GA). The simulation results demonstrate the advantages in generalization and expandability of the proposed method.

Effects of turbulence models on the numerical simulation of flow in open channel junction

Separation zone and shear face generate most turbulence in confluence junctions. Flume experiment studies show the variation of a separation zone from the water surface to the bottom and impacts of the two circulation cells (secondary flow) on the sediment transportation in a confluence zone. The turbulence generated by the shear face is anisotropic, so it needs to be cautious to choose appropriate turbulence models in the simulation of confluence flow. This article utilizes the standard k − ϵ, RNG k − ϵ, and RSM turbulence models to simulate the secondary flow and separation zone in the confluence junction, and a quantificational comparison with the experiment data is provided.