采用数值风洞模型对热电厂烟塔合一大气污染扩散的研究

随着城市的快速建设,城市建筑的高度和体量不断增加,同时大气污染源的排放方式和排放状态也与从前发生了很大的变化,特别是热电厂采用烟塔合一排放方式的出现,对常规应用的稳态远距离以统计学为基础理论的高斯大气预测方法提出了挑战。目前国内外广泛使用的大气污染物预测模式——德国模式在烟塔合一排放方式的预测上存在着许多关键性问题,如大风下洗条件下,冷却塔附近空腔区的大小和范围、空腔区污染物最高地面浓度等无法给出准确的预测结果。为准确预测烟塔合一排放方式的大气污染物扩散情况,采用一种新的大气污染物扩散的预测模式——数值风洞模型进行模拟预测研究,预测结果表明,在烟塔合一排放方式下,大气污染物最高地面浓度随风速增加而增加,同时在冷却塔下风向存在负压区,污染物在该区域高浓度聚集。且在夏季6.0m/s风速下,冷却塔下风向最高地面浓度出现峰值,属于最不利的气象条件。数值风洞模型可利用图形化手段实现对空腔区产生、变化、破碎至再生成的全过程描述,从而建立了一种大气污染预测的重要手段。     

基于AERMOD模式的固定源对不同楼层大气污染预测研究

环境影响评价中,大气污染预测通常考虑地面的浓度影响,对高层住宅楼不同楼层影响的考虑较少.通过案例设计,模拟4种不同的污染排放情景,采用预测推荐模式AERMOD分别计算各情景下5个代表高度共20种组合情况的预测受体结果,并基于三维可视仪软件VOXLER直观显示各情景污染物浓度的空间连续分布情况,对小时最大浓度的分布规律利用分析工具CANOCO进行了分析.结果表明:(1)污染源高为15～80 m时,不同楼层间小时浓度分布差异性较大；污染源高为120～240 m时,楼层间差异性较小.(2)污染源高为15m时,小时最大浓度以50 m为分界线随楼层高度的增加先增后降；污染源高为80～240 m时,小时最大浓度随楼层高度的增加而增加.     

冬季沈阳市典型源排放PM_(10)浓度分布模拟分析

选取沈阳市7个典型的大气污染源2006年12月~2007年2月的PM10排放浓度资料,利用CALPUFF对PM10浓度月平均分布做模拟分析。模拟结果分析表明:冬季月平均PM10浓度分布的范围与风场、地形有直接的关系。地势平坦、风速大时,污染物扩散范围大,污染物浓度小;地势不平、风速小时,污染物扩散范围小,污染物浓度大。1月份是沈阳市冬季月平均大气污染最严重的月份,污染物分布主要集中在市区的北部、东部和南部地区,东部地区大气污染最为严重。     

冬季沈阳市典型源排放PM10浓度分布模拟分析

选取沈阳市7个典型的大气污染源2006年12月～2007年2月的PM10排放浓度资料，利用CALPUFF对PM10浓度月平均分布做模拟分析。模拟结果分析表明：冬季月平均PM10浓度分布的范围与风场、地形有直接的关系。地势平坦、风速大时，污染物扩散范围大，污染物浓度小；地势不平、风速小时，污染物扩散范围小，污染物浓度大。1月份是沈阳市冬季月平均大气污染最严重的月份，污染物分布主要集中在市区的北部、东部和南部地区，东部地区大气污染最为严重。     

城市街区流场和污染物扩散的数值模拟研究

利用天气预报(WRF)模式提供边界条件和初始条件驱动Fluent模式,模拟了榆中县城市区域的大气流动和污染物扩散。WRF模式在一定误差要求下对研究区域的基本气象要素的模拟准确率均在70%以上,较为准确地模拟气象要素实况,为Fluent模式提供了可靠的初始条件和边界条件;Fluent模式充分刻画了复杂城市下垫面的建筑物对污染物扩散的影响,较好地反映了不同区域污染物浓度差异;通过WRF-Fluent耦合模式的模拟,显示有建筑群存在的城市环境中,流场相对于环境风产生显著改变,进而反映建筑群中污染物的复杂扩散形态。结果表明,WRF-Fluent耦合模式作为一种研究城市建筑密集区域的大气流动状态和污染物扩散过程的方法是可行的。     

大气污染物扩散模式的应用研究综述

应用大气污染物扩散模式可以模拟不同尺度、气象、地形条件下工业污染物在大气中的输送与扩散特征,为大气监测、城市环境规划和空气质量预报等工作提供科学依据.归纳了目前广泛应用于模拟工业污染物扩散的模式,着重介绍了近年来国内外对这些模式的主要应用研究进展,比较了各模式在应用上的优缺点,并对大气污染物扩散模式的应用研究前景进行了讨论.     

AERMOD在国内环境影响评价中的实例验证与应用

AERMOD是美国环保局推出的新一代空气质量模式系统,它由AERMET(气象数据预处理器)、AERMAP(地形数据预处理器)和AERMOD(大气扩散模型)3部分组成.结合宁波市北仑区域大气环境影响评价,对该模式系统进行模式验证,并应用于实际预测评价.验证结果表明,在采用适当的模型参数时,该系统预测值与实际监测值具有很好的一致性,SO2、NO2日均最高浓度预测准确率分别达到64.3%和85.7%.最后结合实际预测评价工作,提出AERMOD模式系统在国内环境影响评价工作中的优势及不足.     

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.     

大气超细颗粒物观测和扩散模拟研究进展

总结了近年来不同地区对不同环境下大气超细颗粒物的观测和扩散模拟研究进展。大量的观测研究结果表明,大气超细颗粒物的时空分布、组成特征、形成和成长的特性因观测地区的不同而存在很大差异,受气象因素和局部污染源的影响很大;其来源主要包括固定、移动燃烧源的直接排放和大气中颗粒成核现象,前一种来源一般是局部的,而后一种来源则是区域性的。目前,大多数关于大气超细颗粒物扩散的模拟研究都是针对其质量浓度的,对其数浓度扩散的模拟研究主要集中在小范围(机动车排放烟云的研究方面),在城市区域范围上的研究和应用还很少。最后,探讨和展望了大气超细颗粒物今后的主要研究方向和研究中面临的挑战。     

燃煤锅炉烟囱高度复核计算方法应用实例分析

主要结合地区大气污染状况，气象条件，污染物排放参数及环境控制目标等因素，运用大气环境影响评价中大气扩散有关模式及理论，通过实例分析对燃煤锅炉烟囱设计高度进行复核计算，使锅炉排放污染物满足地区环境控制目标需要。     

Coupling of the Weather Research and Forecasting Model with AERMOD for pollutant dispersion modeling. A case study for PM10 dispersion over Pune,India

The prediction of spatial variation of the concentration of a pollutant governed by various sources and sinks is a complex problem. Gaussian air pollutant dispersion models such as AERMOD of the United States Environmental Protection Agency (USEPA) can be used for this purpose. AERMOD requires steady and horizontally homogeneous hourly surface and upper air meteorological observations. However, observations with such frequency are not easily available for most locations in India. To overcome this limitation, the planetary boundary layer and surface layer parameters required by AERMOD were computed using the Weather Research and Forecasting (WRF) Model (version 2.1.1) developed by the National Center for Atmospheric Research (NCAR). We have developed a preprocessor for offline coupling of WRF with AERMOD. Using this system, the dispersion of respirable particulate matter (RSPM/PM10) over Pune, India has been simulated. Data from the emissions inventory development and field-monitoring campaign (13–17 April 2005) conducted under the Pune Air Quality Management Program of the Ministry of Environment and Forests (MoEF), India and USEPA, have been used to drive and validate AERMOD. Comparison between the simulated and observed temperature and wind fields shows that WRF is capable of generating reliable meteorological inputs for AERMOD. The comparison of observed and simulated concentrations of PM10 shows that the model generally underestimates the concentrations over the city. However, data from this single case study would not be sufficient to conclude on suitability of regionally averaged meteorological parameters for driving Gaussian models like AERMOD and additional simulations with different WRF parameterizations along with an improved pollutant source data will be required for enhancing the reliability of the WRF–AERMOD modeling system.     

Sensitivity of two dispersion models (AERMOD and ISCST3) to input parameters for a rural ground-level area source

As of December 2006, the American Meteorological Society/U.S. Environmental Protection Agency (EPA) Regulatory Model with Plume Rise Model Enhancements (AERMOD-PRIME; hereafter AERMOD) replaced the Industrial Source Complex Short Term Version 3 (ISCST3) as the EPA-preferred regulatory model. The change from ISCST3 to AERMOD will affect Prevention of Significant Deterioration (PSD) increment consumption as well as permit compliance in states where regulatory agencies limit property line concentrations using modeling analysis. Because of differences in model formulation and the treatment of terrain features, one cannot predict a priori whether ISCST3 or AERMOD will predict higher or lower pollutant concentrations downwind of a source. The objectives of this paper were to determine the sensitivity of AERMOD to various inputs and compare the highest downwind concentrations from a ground-level area source (GLAS) predicted by AERMOD to those predicted by ISCST3. Concentrations predicted using ISCST3 were sensitive to changes in wind speed, temperature, solar radiation (as it affects stability class), and mixing heights below 160 m. Surface roughness also affected downwind concentrations predicted by ISCST3. AERMOD was sensitive to changes in albedo, surface roughness, wind speed, temperature, and cloud cover. Bowen ratio did not affect the results from AERMOD. These results demonstrate AERMOD's sensitivity to small changes in wind speed and surface roughness. When AERMOD is used to determine property line concentrations, small changes in these variables may affect the distance within which concentration limits are exceeded by several hundred meters.     

An Atmospheric Diffusion Model for Metropolitan Areas

An urban diffusion model, which does not require the use of an electronic computer, is presented. The main simplifying assumptions are that continuous pollutant sources are uniformly distributed over the urban area and vertical diffusion occurs until the effluent from each line source reaches the top of the mixing layer, after which the effluent is uniformly distributed through the mixing layer. After the appropriate vertical diffusion coefficient is specified, the calculated concentration is a function of source strength, linear dimension of the metropolis, mixing depth, and wind speed. The calculated concentration is interpreted either as a representative maximum concentration or, through integration, as the average concentration over the metropolitan area. When a representative pollutant concentration is known, the model may be used to determine the apparent “uniform” source strength.     

Recent trends of persistent organic pollutants in air in central Europe - Air monitoring in combination with air mass trajectory statistics as a tool to study the effectivity of regional chemical policy

We use air mass back trajectory analysis of persistent organic pollutant (POP) levels monitored at a regional background site, Ko?etice, Czech Republic, as a tool to study the effectiveness of emission reduction measures taken in the last decade in the region. The representativity of the chosen trajectory starting height for air sampling near ground was ensured by excluding trajectories starting at time of inversions lower than their starting height. As the relevant pollutant sources are exclusively located in the atmospheric boundary layer, trajectory segments above this layer were also excluded from the analysis. We used a linear time weight to account for the influence of dispersion and deposition on trace components abundances and to quantify the ground source loading, a continuous measure for the influence of surface emissions. Hexachlorocyclohexanes (HCHs), hexachlorobenzene (HCB), polychlorinated biphenyls (PCBs), DDT, and two time periods, the years 1997–1999 and 2004–2006, were studied. The pollutant levels transported to Ko?etice decreased for all substances except HCB. Except for lindane seasonal emissions were insignificant. Increasing emissions of HCB were at least partly linked to the 2002 floods in the Danube basin. Major emissions of 1997–1999 which decreased significantly were in France (lindane), western Poland, Hungary and northern ex-Yugoslavia (technical HCH), and the Czech Republic (DDT). Emissions remaining in 2004–2006 include HCB and DDT in the northern Czech Republic, HCB and PCBs in Germany. Besides changes in emission strength meteorological factors influence the level of transported pollutant concentrations. The prevailing air flow pattern limits the geographic coverage of this analysis to central Europe and parts of western Europe. However, no POP monitoring stations exist in areas suitable for a possible extension of the study area.     

Algorithms and analytical solutions for rapidly approximating long-term dispersion from line and area sources

Predicting long-term mean pollutant concentrations in the vicinity of airports, roads and other industrial sources are frequently of concern in regulatory and public health contexts. Many emissions are represented geometrically as ground-level line or area sources. Well developed modelling tools such as AERMOD and ADMS are able to model dispersion from finite (i.e. non-point) sources with considerable accuracy, drawing upon an up-to-date understanding of boundary layer behaviour. Due to mathematical difficulties associated with line and area sources, computationally expensive numerical integration schemes have been developed. For example, some models decompose area sources into a large number of line sources orthogonal to the mean wind direction, for which an analytical (Gaussian) solution exists. Models also employ a time-series approach, which involves computing mean pollutant concentrations for every hour over one or more years of meteorological data. This can give rise to computer runtimes of several days for assessment of a site. While this may be acceptable for assessment of a single industrial complex, airport, etc., this level of computational cost precludes national or international policy assessments at the level of detail available with dispersion modelling. In this paper, we extend previous work [S.R.H. Barrett, R.E. Britter, 2008. Development of algorithms and approximations for rapid operational air quality modelling. Atmospheric Environment 42 (2008) 8105–8111] to line and area sources. We introduce approximations which allow for the development of new analytical solutions for long-term mean dispersion from line and area sources, based on hypergeometric functions. We describe how these solutions can be parameterized from a single point source run from an existing advanced dispersion model, thereby accounting for all processes modelled in the more costly algorithms. The parameterization method combined with the analytical solutions for long-term mean dispersion are shown to produce results several orders of magnitude more efficiently with a loss of accuracy small compared to the absolute accuracy of advanced dispersion models near sources. The method can be readily incorporated into existing dispersion models, and may allow for additional computation time to be expended on modelling dispersion processes more accurately in future, rather than on accounting for source geometry.     

AERCOARE: An overwater meteorological preprocessor for AERMOD

AERCOARE is a meteorological data preprocessor for the American Meteorological Society and U.S Environmental Protection Agency (EPA) Regulatory Model (AERMOD). AERCOARE includes algorithms developed during the Coupled-Ocean Atmosphere Response Experiment (COARE) to predict surface energy fluxes and stability from routine overwater measurements. The COARE algorithm is described and the implementation in AERCOARE is presented. Model performance for the combined AERCOARE-AERMOD modeling approach was evaluated against tracer measurements from four overwater field studies. Relatively better model performance was found when lateral turbulence measurements were available and when several key input variables to AERMOD were constrained. Namely, requiring the mixed layer height to be greater than 25 m and not allowing the Monin Obukhov length to be less than 5 m improved model performance in low wind speed stable conditions. Several options for low wind speed dispersion in AERMOD also affected the model performance results. Model performance for the combined AERCOARE-AERMOD modeling approach was found to be comparable to the current EPA regulatory Offshore Coastal Model (OCD) for the same tracer studies. AERCOARE-AERMOD predictions were also compared to simulations using the California Puff-Advection Model (CALPUFF) that also includes the COARE algorithm. Many model performance measures were found to be similar, but CALPUFF had significantly less scatter and better performance for one of the four field studies. For many offshore regulatory applications, the combined AERCOARE-AERMOD modeling approach was found to be a viable alternative to OCD the currently recommended model.

Implications: A new meteorological preprocessor called AERCOARE was developed for offshore source dispersion modeling using the U.S. Environmental Protection Agency (EPA) regulatory model AERMOD. The combined AERCOARE-AERMOD modeling approach allows stakeholders to use the same dispersion model for both offshore and onshore applications. This approach could replace current regulatory practices involving two completely different modeling systems. As improvements and features are added to the dispersion model component, AERMOD, such techniques can now also be applied to offshore air quality permitting.     

Comparison of the industrial source complex and AERMOD dispersion models: case study for human health risk assessment

Air quality models are typically used to predict the fate and transport of air emissions from industrial sources to comply with federal and state regulatory requirements and environmental standards, as well as to determine pollution control requirements. For many years, the U.S. Environmental Protection Agency (EPA) widely used the Industrial Source Complex (ISC) model because of its broad applicability to multiple source types. Recently, EPA adopted a new rule that replaces ISC with AERMOD, a state-of-the-practice air dispersion model, in many air quality impact assessments. This study compared the two models as well as their enhanced versions that incorporate the Plume Rise Model Enhancements (PRIME) algorithm. PRIME takes into account the effects of building downwash on plume dispersion. The comparison used actual point, area, and volume sources located on two separate facilities in conjunction with site-specific terrain and meteorological data. The modeled maximum total period average ground-level air concentrations were used to calculate potential health effects for human receptors. The results show that the switch from ISC to AERMOD and the incorporation of the PRIME algorithm tend to generate lower concentration estimates at the point of maximum ground-level concentration. However, the magnitude of difference varies from insignificant to significant depending on the types of the sources and the site-specific conditions. The differences in human health effects, predicted using results from the two models, mirror the concentrations predicted by the models.     

Boundary Layer Emission Monitoring

A new methodology is described for determining the atmospheric emission rate of pollutants from large heterogeneous area sources, such as hazardous waste sites. The procedure hinges upon measuring average pollutant concentrations, at three or more different elevations, while traversing the plume downwind of the area source. A helium-filled tethersonde balloon is used to elevate the sampling lines to their appropriate height. During plume traversing the sampling rate is adjusted to be proportional to the sine of the angle between the wind vector and the direction of the traverse path. The average concentrations are corrected for any upwind, background concentration and then used to derive an average vertical concentration profile. This profile Is numerically integrated, with the wind velocity profile, over the pollutant boundary layer to yield the area source emission rate. The methodology was tested on several large industrial effluent lagoons and proved to be easy to use, robust, and precise.     

Chemical transport models

Background, aim, and scope  Improving the parameterization of processes in the atmospheric boundary layer (ABL) and surface layer, in air quality and chemical transport models. To do so, an asymmetrical, convective, non-local scheme, with varying upward mixing rates is combined with the non-local, turbulent, kinetic energy scheme for vertical diffusion (COM). For designing it, a function depending on the dimensionless height to the power four in the ABL is suggested, which is empirically derived. Also, we suggested a new method for calculating the in-canopy resistance for dry deposition over a vegetated surface. Materials and methods  The upward mixing rate forming the surface layer is parameterized using the sensible heat flux and the friction and convective velocities. Upward mixing rates varying with height are scaled with an amount of turbulent kinetic energy in layer, while the downward mixing rates are derived from mass conservation. The vertical eddy diffusivity is parameterized using the mean turbulent velocity scale that is obtained by the vertical integration within the ABL. In-canopy resistance is calculated by integration of inverse turbulent transfer coefficient inside the canopy from the effective ground roughness length to the canopy source height and, further, from its the canopy height. Results  This combination of schemes provides a less rapid mass transport out of surface layer into other layers, during convective and non-convective periods, than other local and non-local schemes parameterizing mixing processes in the ABL. The suggested method for calculating the in-canopy resistance for calculating the dry deposition over a vegetated surface differs remarkably from the commonly used one, particularly over forest vegetation. Discussion  In this paper, we studied the performance of a non-local, turbulent, kinetic energy scheme for vertical diffusion combined with a non-local, convective mixing scheme with varying upward mixing in the atmospheric boundary layer (COM) and its impact on the concentration of pollutants calculated with chemical and air-quality models. In addition, this scheme was also compared with a commonly used, local, eddy-diffusivity scheme. Simulated concentrations of NO₂ by the COM scheme and new parameterization of the in-canopy resistance are closer to the observations when compared to those obtained from using the local eddy-diffusivity scheme. Conclusions  Concentrations calculated with the COM scheme and new parameterization of in-canopy resistance, are in general higher and closer to the observations than those obtained by the local, eddy-diffusivity scheme (on the order of 15–22%). Recommendations and perspectives  To examine the performance of the scheme, simulated and measured concentrations of a pollutant (NO₂) were compared for the years 1999 and 2002. The comparison was made for the entire domain used in simulations performed by the chemical European Monitoring and Evaluation Program Unified model (version UNI-ACID, rv2.0) where schemes were incorporated.     

The role of a steel plant in north-west Italy to the local air concentrations of PCDD/Fs

Polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) are ubiquitous contaminants, mainly released into the environment during combustion processes (point sources), but also from other sources (traffic, uncontrolled combustion).This study aims at investigating the contribution of a steel plant in NW Italy (700 000 tons of steel year⁻¹) to the air concentrations of PCDDs/PCDFs at local level, through the analysis of measured, modelled and literature data. The study was carried out in an area of 600 km², using air quality data measured by the institutional monitoring network, data obtained from AERMOD simulations and literature data.The measured air concentrations were consistent with literature values for similar areas, and both the homologue profiles and PCA analyses showed a clear distinction between the monitoring stations and the source profiles.All the previous results were confirmed by the air dispersion model (AERMOD), that predicted PCDD/F air concentrations due to the steel plant from four to two orders of magnitude lower than those measured in the monitoring stations, highlighting the presence of other sources.This study outlines the limited influence of the source in the local PCDD/F air concentrations and at the same time the usefulness of a joint analysis of measured, literature and calculated data to correctly evaluate the role of a source to the local pollution. The study also highlights the usefulness of AERMOD as a complementary tool to define the correct placement of monitoring stations and to locate those areas expected to have the highest air concentrations deriving from a source.