空气中PM2.5问题的研究

主要研究空气污染中的PM2.5扩散问题.首先利用相关分析法讨论了PM2.5与SO_2,NO_2,CO,PM10,O_3的相关性,建立线性回归方程;然后建立一维的反应扩散方程,预测PM2.5浓度变化,并定量与定性分析西安市空气污染状况;再建立高斯烟羽模型,对持续高浓度PM2.5扩散情形进行拟合,并对污染物扩散范围进行预测,得到重度污染以及可能安全区域;最后通过建立最优化模型,得到较有经济效益的空气治理方案.     

空气中PM2.5的扩散和衰减模型

PM2.5的时空分布及其演变规律十分复杂.为刻画PM2.5的发生、扩散和衰减规律,提出点源、线源和面源叠加的多源模型描述区域内的多污染源对某一监测点的影响.考虑风力风速、太阳辐射强度、湿度等天气和季节因素以及重力、湍流扩散、分子扩散等对源强强度的影响,提出源衰减、湿沉积、化学迁移叠加衰减模型,用监测点的PM2.5浓度数据对污染源强度和衰减系数进行反演求解.针对西安市某些监测点处的PM2.5浓度突然增至数倍且延续数小时,建立污染扩散预测与评估方法,对提升前后污染源源强进行分析,给出重度污染区域,并用数据的人工统计定性验证模型的合理性.     

PM2.5扩散模型及预测研究

以武汉为例,以高斯扩散模型为基础研究PM2.5的扩散与衰减规律,充分考虑影响PM2.5扩散的因素,分析地面与建筑物边界反射、干沉积、雨洗湿沉积及湿度的影响,逐步改进高斯扩散模型,并引入时间t,计算当点源持续污染情况下,污染源上风和下风L公里处的浓度.通过数值仿真,得到距污染源下风向距离一定条件下污染扩散浓度的分布规律,预估突发情形下PM2.5的扩散距离及安全区域,结合三维图及平面图分析危险区及安全区.最后,结合小波理论及神经网络理论,提出小波神经网络的结构及算法,并通过Matlab实现了对PM2.5值的预测,并取得较高的预测拟合度.     

上海市PM2.5浓度的分析与预测

针对上海市PM2.5的浓度进行动态分析及预测.通过使用Page检验分析了上海市PM2.5浓度近几年的变化趋势;然后建立时间序列ARIMA模型对PM2.5浓度日数据进行拟合分析与预测.在此基础上通过引入影响PM2.5浓度的其他因素建立带时间序列误差的回归模型以及引入波动率因素建立带波动率方程的模型改进原时间序列ARIMA模型;通过比较样本外预测的效果,结果表明改进后的两个模型其结果均优于已知文献中的ARIMA模型.     

PM2.5相关因素及分布演变的研究

以武汉市和西安市的PM2.5检测点数据及气象数据作为基础,建立了相关性分析模型,灰色关联度模型,混合回归模型,高斯烟羽模型,分期治理最优化模型等模型,通过定量与定性分析的方法,从PM2.5的相关因素、分布与演变、控制管理三个方面,对两个市的PM2.5进行了深入的研究与探讨.     

空气中PM2.5的动态扩散及宏观预测

以PM2.5扩散、衰减模式为研究对象,分析探究了PM2.5的扩散规律、危机治理及其后5年的治理问题.首先通过主成分分析法,建立了PM2.5与其它污染物之间的多元非线性对数模型.同时引入相对湿度的影响因素对模型进行再度优化,提高了模型的拟合优度.运用统计学原理,得出采集点之间的PM2.5具有较高的协同性.另外分析了静态下PM2.5污染物颗粒的受力和漂移模式和从点源、面源两方面分析了PM2.5动态扩散模式,建立了PM2.5的扩散偏微分方程模型.根据建立的扩散模型,对突变的污染物浓度确定安全区域的范围.最后建立综合费用和专项费用的多目标优化模型,利用贝叶斯支持向量机方法对PM2.5进行宏观预测,并运用系统动力学理论对目标值进一步优化,并对不同治理模式进行对比分析.     

西安市PM2.5相关因素多元回归分析模型

为了探索空气污染的主要因素,对空气污染监测指标PM2.5与AQI中其他监测指标进行相关性分析,得到PM2.5与SO2、NO2、CO呈正相关,与O3和温度呈负相关,同时利用多元回归模型得到PM2.5与主因子的数量关系,给西安市PM2.5防控提供参考意见.     

基于时间序列分析的北京地区PM2.5浓度研究

基于中国环境监测总站公布的实时空气质量监测数据, 利用时间序列模型对PM2.5指标的数据进行了平稳性、纯随机性检验, 同时进行了模型阶数、未知参数估计以及模型显著性检验与优化. 最终在此基础上建立了指标预测的数学模型, 并对未来三天的PM2.5浓度值进行预测. 进一步地, 基于向量自回归(VAR)模型, 对北京市万寿西宫站PM2.5数据进行相关性分析, 研究空气中污染物O_{2}、NO_{2}、CO、O_{3}、PM10与PM2.5的动态影响关系. 研究发现当天的PM2.5浓度会受到前几天PM2.5、PM10、O_{3}、SO_{2}等污染物浓度的影响,其中PM10对PM2.5的影响最为明显且持续时间最长, O_{3}、SO_{2}对PM2.5浓度的影响在二、三期最为明显.     

基于时间序列的长沙市PM2.5的统计分析

通过对长沙市2015年AQI检测指标数值PM2.5与SO_2,NO_2,PM10,CO,O_3间相关性进行分析,得到PM2.5与SO_2,NO_2,PM10,CO间存在正相关关系,与O_3间为负相关关系.后建立自回归移动平均模型(ARMA)对长沙市2015年的PM2.5进行短期预测,得到最优模型为ARMA(3,2).最后对长沙治理PM2.5提出相关建议.     

基于改进高斯模型的哈尔滨市PM_(2.5)扩散问题实证分析

2013年初以来,国内发生大范围持续雾霾天气,我国城市PM_(2.5)的污染问题引起了社会各界的关注和重视.根据风场和气象变化对高斯扩散模型进行适当改进,并提出基于高斯模型的PM_(2.5)扩散预测模型.以哈尔滨市PM_(2.5)近期实测数据为基础进行实证分析,对市区内10个地区的PM_(2.5)扩散和浓度变化做出合理的预测.     

Chaos gray-coded genetic algorithm and its application for pollution source identifications in convection–diffusion equation

In order to reduce the computational amount and improve computational precision for nonlinear optimizations and pollution source identification in convection–diffusion equation, a new algorithm, chaos gray-coded genetic algorithm (CGGA) is proposed, in which initial population are generated by chaos mapping, and new chaos mutation and Hooke–Jeeves evolution operation are used. With the shrinking of searching range, CGGA gradually directs to an optimal result with the excellent individuals obtained by gray-coded genetic algorithm. Its convergence is analyzed. It is very efficient in maintaining the population diversity during the evolution process of gray-coded genetic algorithm. This new algorithm overcomes any Hamming-cliff phenomena existing in other encoding genetic algorithm. Its efficiency is verified by application of 20 nonlinear test functions of 1–20 variables compared with standard binary-coded genetic algorithm and improved genetic algorithm. The position and intensity of pollution source are well found by CGGA. Compared with Gray-coded hybrid-accelerated genetic algorithm and pure random search algorithm, CGGA has rapider convergent speed and higher calculation precision.     

An acceleration-scale model of IING’s diffusion based on force analysis

ABSTRACT

The diffusion of Internet-based Intangible Network Goods (IINGs) shows new characteristics completely different from that of traditional material products. This paper aims to establish new models to describe and predict IING’s diffusion at the aggregate level. Firstly, we transform the key factors affecting IING’s diffusion into driving forces, resistant forces, and variable forces. Secondly, we analyse the dynamic changes of these forces in different diffusion stages and obtain the acceleration model of IING’s diffusion. Then, since acceleration is the second derivative of scale, we further establish the scale model of IING’s diffusion. As the scale model can predict the number of IING’s adopters at a particular time and the acceleration model can explain the dynamic changes of scale, we combine them as the acceleration-scale model to describe IING’s diffusion. Finally, we make comparisons between the acceleration-scale model and the Bass model based on three cases. Different from the previous studies, we found that IING’s diffusion rate is asymmetric. The diffusion rate of successful IING is right skewed while the diffusion rate of failed IING is left skewed. The results also shows that the acceleration-scale model has a better predictive performance than the Bass model, no matter the diffusion is successful or failed     

建材中VOCs扩散系数和分配系数的模型研究

Bodala,运用静态双仓法建立的测定干建材中VOCs扩散系数和分配系数的数学模型是合理的,有一定的应用价值.但该模型假设低浓度仓中VOCs的初始浓度为零,限制了模型的应用范围.针对使用VOCs液体为污染源且建材透气性较好的条件,作出合理的假设,构建了VOCs在建材中扩散的数学模型.采用LaplaceCarson变换详细求解模型,得出计算VOCs扩散系数的方法,并运用两组数据对模型进行了比较分析,结果表明优化后的模型有一定的实用性和可行性.     

Lattice Boltzmann model for the bimolecular autocatalytic reaction–diffusion equation

A lattice Boltzmann model for the bimolecular autocatalytic reaction–diffusion equation is proposed. By using multi-scale technique and the Chapman–Enskog expansion on complex lattice Boltzmann equation, we obtain a series of complex partial differential equations, complex equilibrium distribution function and its complex moments. Then, the complex reaction–diffusion equation is recovered with higher-order accuracy of the truncation error. This equation can be used to describe the bimolecular autocatalytic reaction–diffusion systems, in which a rich variety of behaviors have been observed. Based on this model, the Fitzhugh–Nagumo model and the Gray–Scott model are simulated. The comparisons between the LBM results and the Alternative Direction Implicit results are given in detail. The numerical examples show that assumptions of source term can be used to raise the accuracy of the truncation error of the lattice Boltzmann scheme for the complex reaction–diffusion equation.     

An application of traveling wave analysis in economic growth model

A temporal–spatial economic growth model is established in this paper. As a useful tool, traveling wave analysis is used to analyze technological growth and diffusion. Numerical simulation shows that this model has perfect performance.     

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In order to solve the problem of testing multidimensional diffusion models, we develop a test statistic based on Multi-Dimensional Tail Condition Expectations (CTEs). Although it is almost impossible to estimate the transition density matrix of a multidimensional diffusion model directly, the transition density of each component can be estimated and each component can be combined by the CTE to establish a true multidimensional statistics. Finally, the performance of the test is evaluated through simulation.     

Moving collocation method for a reaction‐diffusion equation with a traveling heat source

We consider a reaction‐diffusion equation with a traveling heat source on an unbounded domain. The numerical simulation of the problem is difficult because of the moving singularity, the blow‐up phenomenon, and the delta function in the equation. Because we are only interested in the solution behavior near the heat source, we choose a bounded moving domain which contains the heat source and has the same speed as the source. Local absorbing boundary conditions are constructed on the boundaries of the moving domain. Then, we transform the moving domain to a fixed one. At last, a special moving collocation method is adopted. The new method is much simpler than the existing moving finite difference methods. Moreover, numerical experiments illustrate the accuracy and efficiency of our moving collocation method. © 2013 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2013