Model study of tropospheric composition response to NOx and CO pollution

The effect of NO_x and CO pollution on other important atmospheric gases from 0–16 km in the northern temperate zonal belt is calculated using a 2-D (altitude–longitude) channel photochemical model with climatic zonal and vertical fixed transport. The geographical inhomogeneities of the NO_x and CO large-scale surface releases are modeled. The distinction between NO_x and CO fluxes from the oceanic and land surfaces and those from areas with various pollution source intensities is considered. NO_x and CO emissions from world transport aviation engine exhausts and the NO source from lightning discharges are also included. Model results are analyzed and compared with observational data for nitrogen compounds, carbon monoxide, and ozone. The dependence of the spatial and temporal hydroxyl distribution on the carbon monoxide (as the main destroyer of OH) concentration field is analyzed and discussed.     

1-D and 2-D personal computer programs for solute transport through porous media

One-dimensional (CXPMPM) and two-dimensional (TWODPLME) user-interactive analytical solute transport software for use on personal computers are described. The programs predict the spatial and temporal transport and fate of a pollutant introduced into a water-saturated sorbing homogeneous porous medium. Convection, dispersion, sorption and decay are considered. Linear equilibrium rules of sorption are assumed valid on the soil fractions (sand, clay and organics). First order loss of compound is included.In CXPMPM, the background contaminant distribution is described using up to 250 concentration steps. Up to 100 constant chemical sources, each with a different length and strength, can be modeled. Sources are also modeled as inlet boundary fluxes of three types: constant with an ending time, exponentially decreasing or bell-shaped.TWODPLME models up to 100 constant rectangular sources. Initial contaminant distributions are designated by setting the locations of up to 250 rectangular initial concentrations. Evaluation choices of temporal concentration histories at a single point or multiple point evaluations for a selected time are made. Popular graphics software are easily used to create spatial and temporal 2-D and 3-D plots of output data.     

Global distribution of tropospheric ozone and its precursors: a view from space

Satellite-borne tropospheric ozone measurements obtained from the tropospheric ozone residual (TOR) method, CO from the MOPITT (at 850 hPa level) measurements and NO₂ from the SCIAMACHY measurements for the three-year period 2003–2005 have been utilized to examine the distribution of the pollutant sources and long-range transport on a global scale. Elevated tropospheric ozone columns have been observed over regions of high NO₂ and CO concentrations in the northern and southern hemispheres. High levels of the tropospheric ozone column have been observed below about 5°S in the vicinity of the biomass burning regions and extend from continents out over the Atlantic during October. The seasonal distribution of tropospheric O₃ and its precursors in the southern hemisphere shows the strong correlation with the seasonal variation of biomass burning in Africa and South America. Northern hemisphere summer shows the widespread ozone and CO pollution throughout the middle latitudes. The inter-hemispheric gradient of ozone and CO found to be decreased during October. Large-scale transport of the ozone and CO over the Atlantic and Pacific Oceans has been clearly identified. Strong inter-continental transport has been observed to occur from west to east along with the mid-latitude winds in the northern hemisphere.     

Numerical modelling of transport,dispersion, and deposition — validation against ETEX-1, ETEX-2 and Chernobyl

A comprehensive, high-resolution, 3-dimensional tracer model, DREAM, has been developed for studying transport, dispersion, and deposition of air pollution caused by a single but very strong source. The model is based on a combination of a Lagrangian short-scale puff model and an Eulerian long-range transport model. The meteorological meso-scale model MM5V1 is used as a driver for the transport model. The tracer model and the numerical implementation of the model is described in this paper. The Eulerian transport model has been splitted into different submodels. The advantages of treating the different physical processes on different scales by using different numerical algorithms will be discussed. The model has been run and validated against measurements from the two ETEX releases (the European Tracer EXperiment) and the Chernobyl accident. 2-D and 3-D visualizations together with some comparisons of model results with measurements will be presented and discussed. The combined model is able to reproduce dosages within a factor of 2–3 in the worst case and arrival times within 2–3 h, compared to measurements from ETEX-1 and Chernobyl. This is within the currently achievable limits of accuracy in long-range dispersion modelling, according to the ETEX-1 experiment.     

Influence of turbulence and atmospheric chemistry on grid size with respect to location in modeling and simulation of photochemical smog formation and transport

In models for photochemical smog formation and transport, mixing is assumed to be instantaneous and consequently the reactions are assumed to be kinetically controlled. In this work, the Damkohler number is calculated to check the validity of this assumption. The results show that this number is above 50, especially near pollution point sources such as the power station stacks, so that the diffusion limits the reactions for such cases. Using the irregular or adaptive grids minimized the Damkohler number and diffusion limitations which indicates that the grids should be finer near the source and coarser in remote regions. Therefore, depending upon the atmospheric conditions and emission sources, one can determine the optimum grid sizes for modeling and simulation of photochemical smog.     

Modeling complex environmental data

Artificial neural networks (ANNs) are used to model the interactions that occur between ozone pollution, climatic conditions, and the sensitivity of crops and other plants to ozone. A number of generic methods for analysis and modeling are presented. These methods are applicable to the modeling and analysis of any data where an effect (in this case damage to plants) is caused by a number of variables that have a nonlinear influence. Multilayer perceptron ANNs are used to model data from a number of sources and analysis of the trained optimized models determines the accuracy of the model's predictions. The models are sufficiently general and accurate to be employed as decision support systems by United Nations Economic Commission for Europe (UNECE) in determining the critical acceptable levels of ozone in Europe. Comparison is made of the accuracy of predictions for a number of modeling approaches. It is shown that the ANN approach is more accurate than other methods and that the use of principal components analysis on the inputs can improve the model. The validation of the models relies on more than simply an error measure on the test data. The relative importance of the causal agents in the model is established in the first instance by summing absolute weight values. This indicates whether the model is consistent with domain knowledge. The application of a range of conditions to the model then allows predictions to be made about the nonlinear influences of the individual principal inputs and of combinations of two inputs viewed as a three-dimensional graph. Equations are synthesized from the ANN to represent the model in an explicit mathematical form. Models are formed with essential parameters and other inputs are added as necessary, in order of decreasing priority, until an acceptable error level is reached. Secondary indicators substituting for primary indicators with which they are strongly correlated can be removed. From the synthesized equations both known and novel aspects of the process modeled can be identified. Known effects validate the model. Novel effects form the basis of hypotheses which can then be tested.     

Models of self-organizing artificial neural networks to identify stationary industrial sources of air pollution

A problem of identifying one particular or a few possible pollution sources that are responsible for the deterioration of the air quality as a result of exceeding the standards of the maximum permissible emissions is considered. A model problem for a group of spatially divided stationary permanent industrial sources is solved. A statement identifying the problem and a method to solve it using two architectures of artificial neural networks, Kohonen’s networks for learning vector quantization with fixed and adaptive structures, as well as adaptive resonance theory network for analog inputs (ART-2), are presented. The method consists of clustering the data provided by self-learning algorithms (unsupervised learning). Estimation equations are given and operation algorithms of Kohonen’s and adaptive resonance theory networks at different life cycle stages are described. The results of the solution of the model problem that are obtained using each network is performed are comparatively analyzed.     

3TP: an application-Layer protocol for streaming 3-D models

This paper addresses the problem of streaming progressively compressed three-dimensional (3-D) models over lossy networks. Out of all encoded packets that can be transmitted, we intelligently choose a subset of packets to be transmitted using transport control protocol in order to meet a distortion constraint, while transmitting the remaining packets using user datagram protocol to minimize the end-to-end delay. We call this new application-layer protocol 3-D models transport protocol. We show the effectiveness of this protocol both experimentally and theoretically. We compare the performance of the proposed protocol with systems that do not optimize transmission according to the content of the encoded bitstream. When the maximum distortion is 30, measured using the Hausdorff distance, we achieve savings in delay time ranging from 39% to 68% for packet-loss rates between 1% and 19%.     

基于有界扰动分区的多模型自适应控制

对于含有界扰动的离散时间系统,将有界扰动变化范围分割成若干小区间,针对这些区间建立多个自适应模型,并针对每一个自适应模型建立相应的控制器.给定一个指标切换函数,基于多个自适应模型控制器和给定的指标切换函数构成多模型自适应控制器.可以证明多模型自适应控制器能够保证闭环系统是输入输出有界稳定的.仿真实例表明,当被控对象的有界扰动随时间变化的时候,采用单一模型自适应控制,系统输出很难跟踪设定值,而多模型自适应控制器却极大地改善了控制品质.     

Boundary layer development and meteorological parameters impact on the ground level ozone concentration over an urban area in a mountain valley (Sofia,Bulgaria)

The ecological problems caused by the increasing ozone concentration are not easily solved because ozone is not directly emitted by certain sources Its concentration depends on numerous dynamical and chemical processes. Stratosphere–troposphere exchange and subsequent ozone penetration into the boundary layer determine the contribution of so-called ‘natural’ ozone to ozone pollution near the ground. However, the main contribution to the concentration of this pollution is that of the anthropogenic ozone, which is generated as a result of complex photochemical reactions. The purpose of this research is the ground level ozone concentration behaviour to be studied during the stable boundary layer (SBL) and the residual layer (RL) destruction and the convective boundary layer (CBL) formation, so the influence of the temperature, the relative humidity and the height of the mixing layer (ML) as well as that of the ML formation in different areas of Sofia (42° 39′ N, 23° 23′ E, 591 m above sea level), Bulgaria, have to be determined. The ground level ozone concentration in the area of the Institute of Electronics changes synchronously with the development of the ML. The maximum values of the ground level ozone concentration are reached when the height of the ML reached its maximum and afterwards. The maximum growth of the ground level ozone concentration is around 11:00–12:30 h LST when a fast growth of the ML begins and the complete destruction of the RL is observed, that is, the two processes of ML growth and entrainment of aerosol and ozone from the higher layers of the atmospheric boundary layer are observed. The values of the ground level ozone concentration during the summer months are higher than those during the fall.     

Predicting daily ozone concentration maxima using fuzzy time series based on a two-stage linguistic partition method

Air pollution is a result of global warming, greenhouse effects, and acid rain. Especially in highly industrialization areas, air pollution has become a major environmental issue. Poor air quality has both acute and chronic effects on human health. The detrimental effects of ambient ozone on human health and the Earth’s ecosystem continue to be a national concern in Taiwan. The pollutant standard index (PSI) has been adopted to assess the degree of air pollution in Taiwan. The standardized daily air quality report provides a simple number on a scale of 0 to 500 related to the health effects of air quality levels. The report focuses on health and the current PSI subindices to reflect measured ozone (O₃) concentrations. Therefore, this study uses the O₃ attribute to evaluate air quality. In an effort to forecast daily maximum ozone concentrations, many researchers have developed daily ozone forecasting models. However, this continuing worldwide environmental problem suggests the need for more accurate models. This paper proposes two new fuzzy time series based on a two-stage linguistic partition method to predict air quality with daily maximum O₃ concentration: Stage 1, use the fuzzy time series based on the cumulative probability distribution approach (CPDA) to partition the universe of discourse into seven intervals; Stage 2, use two linguistic partition methods, the CPDA and the uniform discretion method (UDM), to repartition each interval into three subintervals. To verify the forecasting performance of the proposed methods in detail, the practical collected data is used as and evaluating dataset; five other methodologies (AR, MA, ARMA, Chen’s and Yu’s) are used as comparison models. The proposed methods both show a greatly improved performance in daily maximal ozone concentration prediction accuracy compared with the other models.     

Structural indexing: efficient 3-D object recognition

The authors present an approach for the recognition of multiple 3-D object models from three 3-D scene data. The approach uses two different types of primitives for matching: small surface patches, where differential properties can be reliably computed, and lines corresponding to depth or orientation discontinuities. These are represented by splashes and 3-D curves, respectively. It is shown how both of these primitives can be encoded by a set of super segments, consisting of connected linear segments. These super segments are entered into a table and provide the essential mechanism for fast retrieval and matching. The issues of robustness and stability of the features are addressed in detail. The acquisition of the 3-D models is performed automatically by computing splashes in highly structured areas of the objects and by using boundary and surface edges for the generation of 3-D curves. The authors present results with the current system (3-D object recognition based on super segments) and discuss further extensions     

Air damping in laterally oscillating microresonators: a numerical and experimental study

In this paper, we investigate computing air damping in a comb-drive resonator by numerically solving the three-dimensional (3-D) Stokes equation for the entire resonator using the FastStokes Solver. In addition, we used a recently developed computer microvision system to directly measure resonator frequency response. By comparing the measured results to those generated by one dimensional analytic models and by numerical solution of the 3-D Stokes' equation, we demonstrate that numerically solving the Stokes' equation is fast and also generates a model that matches quality the factor to within 10%. We also show that results based on one-dimensional (1-D) models mispredict quality factor by more than a factor of two. In addition, the detailed drag force distribution generated by the FastStokes solver is used to identify sources of errors in the 1-D models.     

Daily peak ozone forecast in Istanbul

In this study, we attempt to develop an ozone forecast model using two different approaches. The first approach is to use a multiple linear regression method and the second is to use a feed-forward artificial neural network. Models are developed for the ozone period of April through to September of the years 2002 and 2003 and verified for May to August 2004. In both models, 19 predictors are used. Calculated agreement indices (AI) for the model development period are 0.82 for the linear regression model and 0.88 for the artificial neural network model. On the other hand, AI values decrease to 0.53 and 0.64 for the validation period. Poor performance of the models in the validation phase might be due to the different maximum daily ozone averages of these two periods. While the average of maximum ozone values is 61.1 μg m^?3 in the model development phase, it is 42.2 μg m^?3 in the model validation phase.     

The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system

Mode choice modeling is probably the most important element of transportation planning. It affects the general efficiency of travel and the allocation of resources. The development of mode choice models has recently witnessed significant advances in many fields, such as passenger and freight transport. A large number of mathematical models have been used to model the traveler’s choice of mode and destination and the shipper’s choice of mode, shipment size and supply market, among others. Such models are not only becoming almost intractable but also data intensive, difficult to calibrate and update, and intransferable. These models cover a wide range of mathematical complexity and accuracy. This paper describes a new approach to mode choice of intercity freight transport modeling using artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) models. The new approach combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The approach is found to be highly adaptive and efficient in investigating non-linear relationships among different variables. The adaptive neuro-fuzzy inference system model is tested on the freight transport market in Turkey, Germany, France and Austria by using information on the freight flows and their attributes. The ANNs and ANFIS models are more successful in the representation of the non-linear behavior of mode choice of intercity freight transport compared to the classical models.     

Forecasting peak air pollution levels using NARX models

Air pollution has a negative impact on human health. For this reason, it is important to correctly forecast over-threshold events to give timely warnings to the population. Nonlinear models of the nonlinear autoregressive with exogenous variable (NARX) class have been extensively used to forecast air pollution time series, mainly using artificial neural networks (NNs) to model the nonlinearities. This work discusses the possible advantages of using polynomial NARX instead, in combination with suitable model structure selection methods. Furthermore, a suitably weighted mean square error (MSE) (one-step-ahead prediction) cost function is used in the identification/learning process to enhance the model performance in peak estimation, which is the final purpose of this application. The proposed approach is applied to ground-level ozone concentration time series. An extended simulation analysis is provided to compare the two classes of models on a selected case study (Milan metropolitan area) and to investigate the effect of different weighting functions in the identification performance index. Results show that polynomial NARX are able to correctly reconstruct ozone concentrations, with performances similar to NN-based NARX models, but providing additional information, as, e.g., the best set of regressors to describe the studied phenomena. The simulation analysis also demonstrates the potential benefits of using the weighted cost function, especially in increasing the reliability in peak estimation.     

Analysis of Clear-Sky Aerosol Optical Properties and Source Characteristics in Guangzhou

The basic optical properties of clear-sky aerosol of Guangzhou in 2015 were retrieved using CE318 sun photometer data,and their temporal variation characteristics were analyzed and local pollution sources were explored.based on the HYSPLIT backward-trajectory model,the characteristics of external transport sources of air pollution in Guangzhou were studied.The results show that：（1) in 2015,the annual average AOD in Guangzhou is 0.625,which is at a relatively high level.Among them,AOD is the largest in spring,followed by autumn,winter,and summer,with obvious seasonal variations;（2) the daily variation of aerosol basic optical properties are closely related to human activities,and road traffic pollution is the main source of atmospheric aerosol;（3)Air pollution affected by external transport has obvious seasonal variation characteristics,which is superimposed with local emissions,causing air pollution in Guangzhou to be worse,and marine particles transport occurs all year round;（4) the wavelength exponent of all seasons is greater than 1,and is mainly distributed within the interval of 0.8 to 1.4.The aerosol components are stable throughout the year,mainly with smaller particle size aerosols.In general,the aerosol type in the Guangzhou area is mainly a mix of urban-industrial and marine aerosols.     

3D多层次模型简化算法的研究

细节层次模型（LOD）是指对同一个场景或场景中的物体,使用具有不同细节的方法得到一组模型,供绘制时使用。建立LOD模型能很有效地降低数据量和复杂度,实现三维场景的实时处理。以研究多层次细节模型快速生成算法为目的,在分析当前多层次细节模型生成算法的基础上,构建了三角形面片的权值计算公式,设计和实现了基于面片删除操作的LOD算法,详细描述了算法的设计,给出了算法实现所需的关键公式,最后给出了算法的应用实例和对算法时间效率的分析。此算法的最大特点是在不同的层次细节模型之间快速地平滑过渡。从试验结果和分析可见,该算法可以满足大的3D模型快速生成和交互的需要,同时也证明了算法的正确性和实用性。