Sn(Ⅳ)掺杂纳米TiO2/AC降解橙黄G的动力学与机理研究

采用溶胶凝胶法制备了掺杂Sn(Ⅳ)的TiO2/AC光催化剂,以生物染料橙黄G为目标降解物,研究了多相光催化降解橙黄G的动力学规律.研究表明,该反应符合Langmuir-Hinshelwood动力学方程,速控步为吸附反应.同时,利用GC/MS联用仪探讨了橙黄的中间产物及其降解机理:橙黄G在强氧化性自由基·OH、·OOH、·O2-的作用下逐渐分解氧化为小分子有机醛、酮、酸,最后转化为CO2、NH4 、NO3-、NO2-、SO42-和H2O等无机小分子.     

修订版大气导则与现行大气导则推荐模式实例对比验证分析

介绍了现行大气导则推荐预测模式的不足及修订版大气导则推荐模式AERMOD与ADMS的特点,并分析了不同推荐模式的功能及适用范围.以美国环境保护署两个试验场(Clifty Creek(简单地形)和Epri Bowline(建筑物下洗))的数据资料为基础,分别用修订版大气导则推荐模式AERMOD与ADMS及现行大气导则推荐模式计算小时浓度、日均浓度及年均浓度,并采用统计法(最大值比较法、高端值比较法、相对偏差比较法)和图形法(Q-Q对比图)进行模式比较分析.结果表明,在简单地形下,3种预测模式小时浓度预测精度接近,修订版大气导则推荐模式在可靠性上要优于现行大气导则推荐模式;在考虑建筑物下洗的复杂条件下,AERMOD、ADMS模式的预测结果普遍比现行大气导则推荐模式要好;现行大气导则推荐模式无法应用于计算建筑物下洗的模拟条件.     

新书介绍

大气化学 J.Heicklen著的“大气化学”(Atmospheric Chemistry)于1977年在美国出版。全书分以下十章:1.大气结构;2.上层大气化学;3.电离层;4.大气污染物;5.碳氢化合物的氧化作用;6.光化学烟雾;7.O_3和单O_2的反应;8.SO_2化学;9.气溶胶化学;10控制方法。该书较为系统地全面论述了大气化学的各种有关问题,内容充实,观点明确,博引了近年来大气化学研究方面的很多文献资料,并把污染物在大气中的迁移、化学反应机理、污染状况发生的条件和频     

二氧化碳与碳反应动力学及其机理的研究

二氧化碳与碳反应动力学及其机理的研究在煤炭燃烧、固体燃料气化以及碳元素的循环等关系到大气环境污染及其控制等方面都还有现实意义。多年来许多人对这个反应进行着广泛的研究。但是由于这个反应的多阶段性和复杂性,致使对这个反应的机理及     

污水生物脱氮过程硝化阶段N_2O动力学模型

通过耦合导致N_2O产生的亚硝酰基(NOH)化学分解和氨氧化细菌(AOB)反硝化途径,构建了一种包含10个组分和7个生化过程的硝化阶段N_2O动力学模型。与此同时,利用MATLAB和Excel软件工具,完成了对所有动力学参数的相对灵敏度分析,并在此前提下实现了对模型关键参数的拟合,完成了对模型的模拟与验证过程,进而确定了一种不同于已有活性污泥模型计算软件的模型计算方法。并且通过对模型数据和实验数据之间相关系数(R~2)的考察,证明本模型不仅对硝化阶段含氮组分具有良好的模拟效果,同时也与机理研究相符。     

关于PM2.5的综述

综述了大气PM25的来源,样品采集分析,化学组成,病毒机理,对人类健康和大气能见度的影响,以及国内外的研究进展.     

多孔富铁组合填料化学除磷的化学动力学分析

研究了多孔富铁组合填料的动力学因素对化学除磷的影响,并通过化学除磷机理分析建立了化学动力学模型。实验结果显示,组合填料的粒径、配比、投加量及反应时间会对磷的去除效果产生显著影响,除磷优化条件为:组合填料粒径为1.25~1.6 mm,配比为1∶2,投加量为3.0 g,反应时间为60 min;在此条件下当进水总磷浓度为5.00 mg·L-1时,出水总磷浓度不大于0.5 mg·L-1。化学动力学分析表明,多孔富铁组合填料化学除磷速率对磷的浓度呈一级反应动力学模型。     

氧化镁基催化剂及脱硝性能研究

为控制燃烧烟气中NOx的污染,对共混合方法制备的氧化镁基催化剂进行烟气直接催化分解法脱硝实验研究,分析模拟烟气脱硝塔内温度及床层高度及氧气浓度、NO浓度和空速对脱硝效率的影响.研究表明:氧化镁基催化剂可以采用直接催化分解法对烟气脱硝,脱硝率85%～95%,氧化镁基催化吸附剂组成为氧化镁、固化剂、添加剂;脱硝的床层高度4～5 cm,脱硝反应温度130～170℃,烟气空速2 500～3 000 h-1;研究推测出氧化镁基催化剂存在活性缺陷,并对脱硝机理进行了初步分析.     

我国大气污染化学研究进展

本文综述了我国大气污染化学研究的概况。阐明了大气颗粒物（气溶胶）的表征研究，包括颗粒物的物理化学特性和环境化学行为；大气污染物的迁移、转化和归宿的规律，包括化学过程和大气化学模式的研究等。介绍了某些大气污染物的特殊分析测试方法和采样技术，对今后发展大气化学的展望和战略作了讨论。     

UV/O_3降解水样中COD的动力学建模

建立动力学模型可以对UV/O_3降解有机物的过程进行有效的分析和预测,帮助改进水处理工艺以提高有机物的降解效率和降低能源消耗。然而,已有的方法无法满足实际水样中未知有机物降解过程的建模需求。在化学反应机理的基础上,结合对有机物降解生成CO_2浓度演化规律的分析,建立了一种可预测未知有机物降解反应的数学模型。通过改变氧化消解的条件,从理论上检验了该模型的灵敏度;同时,实验采用了含有葡萄糖、尿素和邻苯二甲酸氢钾的混合溶液作为测试水样,考察了模型的准确性,实验值与预测值的趋势一致。     

Size Distribution Studies with the Aerosol Spectrometer

It is shown how the Goetz Aerosol Spectrometer can be used to determine the particle size distribution of a specific component of a mixed atmospheric aerosol. The method requires no micrographic counting, but does require that sensitive quantitative chemical analysis methods be available for the compounds of interest. The accuracy of the method and the sensitivity limitations are briefly discussed.     

Evaluation of the Meteo-France response in ETEX release 1

During ETEX Meteo-France applied part of its emergency response system for critical events developped in the framework of the World Meteorological Organization environmental emergency response program. The atmospheric transport model used to forecast the evolution of a passive tracer is an eulerian model called MEDIA. In real time this model is driven by meteorological data from ARPEGE, the operational numerical weather prediction model available at the Meteo-France operation center. The overall evaluation of the results show that the model can reproduce the cloud displacement, but there exists a stretching in the transport direction. In the ATMES-II phase, the results are closer to the observations when meteorological data from the European Center for Medium range Weather Forecast are used. A simulation using analyzed meteorological data from ARPEGE every 6 h slightly improve the results comparing with the real-time experiment. All the simulations we performed reveal that the quality of the atmospheric transport model is strongly dependent on the quality of the driving numerical weather prediction model.     

The effect of improved nowcasting of precipitation on air quality modeling

The predictive potential of air quality models and thus their value in emergency management and public health support are critically dependent on the quality of their meteorological inputs. The atmospheric flow is the primary cause of the dispersion of airborne substances. The scavenging of pollutants by cloud particles and precipitation is an important sink of atmospheric pollution and subsequently determines the spatial distribution of the deposition of pollutants. The long-standing problem of the spin-up of clouds and precipitation in numerical weather prediction models limits the accuracy of the prediction of short-range dispersion and deposition from local sources. The resulting errors in the atmospheric concentration of pollutants also affect the initial conditions for the calculation of the long-range transport of these pollutants. Customary the spin-up problem is avoided by only using NWP (Numerical Weather Prediction) forecasts with a lead time greater than the spin-up time of the model. Due to the increase of uncertainty with forecast range this reduces the quality of the associated forecasts of the atmospheric flow.In this article recent improvements through diabatic initialization in the spin-up of large-scale precipitation in the Hirlam NWP model are discussed. In a synthetic example using a puff dispersion model the effect is demonstrated of these improvements on the deposition and dispersion of pollutants with a high scavenging coefficient, such as sulphur, and a low scavenging coefficient, such as cesium-137. The analysis presented in this article leads to the conclusion that, at least for situations where large-scale precipitation dominates, the improved model has a limited spin-up so that its full forecast range can be used. The implication for dispersion modeling is that the improved model is particularly useful for short-range forecasts and the calculation of local deposition. The sensitivity of the hydrological processes to proper initialization implies that the spin-up problem may reoccur with changes in the model and increased model resolution. Spin-up should be an ongoing concern for atmospheric modelers.     

Neural network model for predicting peak photochemical pollutant levels

In this paper, an attempt is made for the 24-hr prediction of photochemical pollutant levels using a neural network model. For this purpose, a model is developed that relates peak pollutant concentrations to meteorological and emission variables and indexes. The analysis is based on measurements of O3 and NO2 from the city of Athens. The meteorological variables are selected to cover atmospheric processes that determine the fate of the airborne pollutants while special care is taken to ensure the availability of the required input data from routine observations or forecasts. The comparison between model predictions and actual observations shows a good agreement. In addition, a series of sensitivity tests is performed in order to evaluate the sensitivity of the model to the uncertainty in meteorological variables. Model forecasts are generally rather insensitive to small perturbations in most of the input meteorological data, while they are relatively more sensitive in changes in wind speed and direction.     

Identification of atmospheric boundary layer parameters by inverse problem

This paper deals with the use of inverse analysis techniques for the estimation of micro-meteorological parameters required for the characterization of atmospheric boundary layers. The physical problem is formulated in terms of a transient two-dimensional advection–diffusion equation. Concentration measurements of a tracer are assumed to be available for the inverse analysis. The analysis of the sensitivity coefficients and of the determinant of the information matrix reveals the most appropriate sensor locations and duration of the experiment for the estimation of the unknown parameters. The parameter estimation problem is solved with the Levenberg–Marquardt method of minimization of the least squares norm.     

Analysis of organic and inorganic species on the surface of atmospheric aerosol using time-of-flight secondary ion mass spectrometry (TOF-SIMS)

This work explores the utility of time-of-flight static secondary-ion mass spectrometry (TOF-SIMS) for the analysis of the surface organic layer on individual atmospheric aerosol particles. The surface sensitivity and minimal fragmentation available with TOF-SIMS suggest that it can be a powerful tool for the examination of the organic and inorganic species on the surface of individual particles. Cascade impactors were used to collect aerosol from summer 2000 Montana forest fires, winter snowmobile samples in Yellowstone National Park, Hawaiian lava and sea salt, from an Asian Dust event reaching Salt Lake City, Utah in April 2001 and from Salt Lake Valley summer urban aerosol. TOF-SIMS analysis and multivariate statistical techniques combined gave chemical and morphological information about the particles. Surfaces of the aerosol from forest fires, snowmobile exhaust, and sea salt were all dominated by aliphatic hydrocarbons and their amphiphilic derivatives. Each source showed a different organic chemical signature. The extent and composition of the organics layer which typically covers the surface of atmospheric particles are expected to effect all of the surface related aerosol properties such as health effects, the ability of the particle to activate and form cloud droplets, and the aggregation of particles as well as reactions between the particle and gas phase species.     

Neural Network Model for Predicting Peak Photochemical Pollutant Levels

ABSTRACT

In this paper, an attempt is made for the 24-hr prediction of photochemical pollutant levels using a neural network model. For this purpose, a model is developed that relates peak pollutant concentrations to meteorological and emission variables and indexes. The analysis is based on measurements of O₃ and NO₂ from the city of Athens. The meteorological variables are selected to cover atmospheric processes that determine the fate of the airborne pollutants while special care is taken to ensure the availability of the required input data from routine observations or forecasts. The comparison between model predictions and actual observations shows a good agreement. In addition, a series of sensitivity tests is performed in order to evaluate the sensitivity of the model to the uncertainty in meteorological variables. Model forecasts are generally rather insensitive to small perturbations in most of the input meteorological data, while they are relatively more sensitive in changes in wind speed and direction.     

First-order sensitivity analysis of models with time-dependent parameters: an application to PAN and ozone

A major limitation of the decoupled direct method for local sensitivity analysis (Dunker, 1984, Journal of Chemical Physics 81; 2385–2393; Gao, 1995, Ph.D. Thesis, University of Connecticut; McCroskey and McRae, 1987, Documentation for the direct decoupled sensitivity analysis method-DDM, Pittsburgh, PA) has been its restriction to the calculation of sensitivity coefficients for constant rate parameters. Realistic atmospheric simulations require that the rate parameters in chemical mechanisms, especially photolysis rate parameters and rate parameters strongly affected by temperature variations, vary diurnally during a simulation. For this reason a new conceptual framework has been devised where time-dependent rate parameters are expressed as products of time-varying profiles and time-independent multipliers. For computational convenience the nominal values of the time-independent multipliers are chosen to be unity. According to the new procedure the decoupled direct method is used to calculate the derivatives of the concentrations with respect to each time-independent multiplier. These derivatives represent the sensitivity of the concentrations to the time-varying profiles of the time-dependent rate parameters. Local sensitivity coefficients for O₃ and PAN were calculated for a moderately polluted scenario that was free of clouds and at a constant temperature using the Regional Atmospheric Chemistry Mechanism (RACM) (Stockwell et al., 1997, Journal of Geophysical Research 102, 25,847–25,879). Calculations were compared for simulations with constant as well as diurnally changing photolysis rate coefficients. The results show that sensitivity coefficients calculated using constant, average, rate parameter values may be significantly different from sensitivity coefficients calculated using time-varying rate parameters and therefore the relative importance of the mechanism's reactions may be different for the two calculations. The greatest differences in sensitivity coefficients were found for reactions with rates that have strong diurnal variations, such as photolysis, HO and NO₃ reactions. It was further found that diurnally varying reactions have cumulative effects on sensitivity coefficients during the simulation of an episode that are not present when constant rate parameters are used. These results have implications, not only for sensitivity analysis and modelling, but also for the use of measurements to validate chemical models.     

Dry Removal of Gaseous Fluorine Pollutants

The conservation equations governing buoyant plume rise are solved for the case of non-uniform wind conditions. A power law is selected to represent the actual wind profile. Analytical solutions are presented both for uniformly stable and neutral atmospheric conditions. These solutions are shown to be of the same form as those obtained in the simpler uniform case but with the plume rise now depending explicitly on the wind speed shear. A sensitivity analysis of the effects on plume rise of typical variation in wind shear and entrainment reveals that the two quantities have an almost equal effect therefore justifying the use of the present model. To simplify computations a “uniform wind” is introduced such that when used in conjunction with Briggs' equations the results become consistent with those of the present theory.