Reseach Of The Urban Air Quality Prediction Model And Of The Data Visualization

As the rapid development of industrialization and urbanization, the city’s production and consumption have been expanding, it led the urban energy, transportation to grow continue sly. China is a developing country; the energy structure is not reasonable, coal is as the main fuel of the electricity, heat. Thus the atmospheric environment pollution. problems that the repairable particulate matter, carbon monoxide, sulfur dioxide, nitrogen oxides in the air as the main pollutants have become more serious and bring a great social and economic impact on resources, environment, people’s lives and socio-economic and that will also threat meanwhile the foundation of the sustainable development.To develop the air quality monitoring system, forecasting, data analysis and visualization of urban air quality and air pollution sources, will let us master the emissions data of the air pollution sources and the concentration data of a variety of air pollutants at different spatial area.That also can make us understand the factors which impact the urban air quality and grasp the air quality change’s trend in time and space dimension. It has very important theoretical significance and practical value in urban planning and construction, pollution control, environmental management and public undertakings.Many provinces and municipalities in China have carried out the large-scale, urban areas covered ambient air quality monitoring systems and the key sources of air pollution monitoring and control systems by in "Tenth Five-Year Plan" Planning and the "Eleventh Five-Year Plan" period. By using these systems, we can obtain a large number of basis data and the real-time monitoring data of the ambient air qualities and key air pollution sources. How to take advantage of these huge amounts of data, to study and evaluate the urban environment air quality indicators, to analyze and forecast the ambient air quality trends, to study the spatial and temporal correlation between air quality and air emissions of various air pollution sources scientifically will be the important work recently.With the supported of the National High Technology Research and Development program of China, that the title is "The Massive Amounts of Data in Dynamic Scene Visualization Technology and A Remote System to Achieve" and the Scientific and Technological Project of Shandong province that the title is "Urban Air Pollution Emergency Response Simulation and Visualization and System Implementation" and the project of Shandong Science and Technology that the title is "The Sustainable Development in Shandong Province Science and Technology Demonstration Projects-Jinan Ambient Air Quality and Air Pollution Monitoring, Early Warning and Monitoring Network Technology Research and Development", and the project scientific and technological of Jinan City that the title is "Jinan Smoke Pollution On-line Monitoring Systems Research, Development and Demonstration" and other research projects, this paper focus on the urban environment based on the neural network prediction model of air quality, urban air quality forecasting data visualization methods, and the reverent of the urban air pollution emission monitoring data and the air quality monitoring data based on Jinan’s environmental air quality monitoring system and Jinan’s air pollution emission monitoring and control systems to build some predictive models based on the neural network,to implement the visualization of the data and to get the correlation of the air quality monitoring data and the air pollution emission monitoring data.This paper studies the construction method of artificial neural network prediction model with the regional air pollution emissions monitoring data as input, ambient air monitoring stations within the region of the predictive value of pollutant concentration data as output. It proposed a BP neural network prediction model based on a rough set theory, which reduct attributes in a selected area of the original ambient air pollution emission monitoring data, construct of the hidden layer neuron node, of the neural network, determine the initial connection weights between neural nodes and make the initial network topology, obtain the network parameters through training by iterative BP algorithm and complete the construction of predictive models. The paper also proposed a neural network prediction model based on the RAN (Resource Allocation Network), it can meet the minimum requirements of neural network error structure using neural network RAN distance criterion and error criterion, and can generate dynamically the hidden nodes of the neural network and adjust the parameters dynamically. These two models have faster network training speed and higher prediction accuracy than the classical BP neural network. How to visualize the city’s ambient air quality forecasting data has been studied in this paper. We generate the air pollutants concentration isosurface in a specified level of the space using the interpolation method of RBF(Radial Basis Function) for a given air quality forecast regional pollutant’s concentrations in the data values in time and space dimensions. Using Marching Cubes algorithm we render the three-dimensional isosurface of pollutants concentration data on the regional dimension in the whole city space. Through the study of regional air pollutant concentration data in spatial area, we establish a pollution concentration data grid cube and how to render the overall diffusion effect for the certain pollutants concentrations data in the specified space area of the urban also be studied in this paper. Firstly, an abstract particle associated with the certain pollutant concentration data of every grid cube in the space is defined, Then pretreated all pollutant concentration data in every grid to form a normalized data set to be rendered, use the abstract particle to construct the corresponding relationship between pollutant concentration of a grid and the particle radius, After that we use the Rayleigh and Mie scattering models to render the all particles in the space and get the Visual effects as haze. The rendering results show that the rendering of the translucent material in spatial region has better visual effect. The paper studies the correlation of the urban air pollution emissions monitoring data and the urban air quality monitoring data and how to visualize it based on Gaussian point source dispersion model. Under the certain meteorological conditions we calculate the contribution rate by selecting some sources of pollutant emissions monitoring data to the air quality monitoring sites of the urban. Established a correlation matrix, analyzed the correlation of two data sets. The paper also studied the visualization technology to visualize the correlation relation of emissions monitoring data of the pollutant sources and that of the air quality monitoring sites.