An intercomparison of semiempirical diffusion models under low wind speed,stable conditions

The performances of some diffusion models are analysed using concentration data measured at ground level up to 400 m from the emission point, in a series of diffusion tests conducted by U.S. National Oceanic and Atmospheric Administration (NOAA) under inversion conditions with light winds. All tested models are simple semiempirical formulae based on the Gaussian formulation, with different assumptions concerning dispersion parameters; each model utilizes a minimum set of information, i.e. vertical stability category, mean wind speed and standard deviation of the horizontal wind direction over the considered averaging time, σ_θ.Results show that for cases with very low wind speed and large plume spread, explicit consideration of diffusion along the mean wind direction, which is neglected in the standard plume model, significantly improves model results; moreover, when σ_θ is very large (greater than 50–60°), the analysis suggests that standard deviations of the horizontal wind speed may significantly differ from the estimates commonly found in the literature.     

Building wake dispersion at an Arctic industrial site: Field tracer observations and plume model evaluations

Ten multi-hour atmospheric dispersion SF₆ tracer experiments were conducted during October and November of 1987 near a large oil gathering facility in the Prudhoe Bay, Alaska, oilfield reservation. The purpose of this study was to investigate dispersion under arctic conditions and in situations where building-generated airflow disturbances dominate downwind distributions of ground level pollutant concentrations. This was accomplished with a network of micrometeorological instruments, portable syringe tracer samplers, continuous tracer analyzers, and infrared visualization of near source plume behavior.Atmospheric stability and wind speed profiles at this arctic site are influenced by the smooth (surface roughness = 0.03 cm), snow covered tundra surface which receives negligible levels of solar isolation in winter. The dispersion of pollutants emitted from sources within the oil gathering facility, however, is dominated by the influence of nearby buildings when high winds generate elevated ground level concentrations. An order of magnitude increase in maximum ground level concentration was observed as wind speeds increased from 5 to 8 m s⁻¹ and another order of magnitude increase was observed as winds increased from 8 to 16 m s⁻¹. Variation in maximum concentrations was also observed with changes in wind direction. Vertical plume diffusion (σ_z) near the buildings was a factor of 2–3 greater than that observed in open terrain and was dependent on both wind speed and the projected building width and location of nearby buildings. Wind tunnel tracer distributions for east winds agree with field observations but also indicate that a significant increase in plume downwash occurs with other wind directions. Concentration distributions were calculated using several versions of the Industrial Source Complex (ISC) model. Model estimates of ground level concentrations were within a factor of three depending on wind direction. The model predictions are extremely sensitive to the ratio of plume height to vertical plume diffusion which is significantly influenced by a complex aerodynamic wake in the field.     

BP网络框架下MODIS气溶胶光学厚度产品估算中国东部PM2.5

近年来随着中国经济的快速发展,中国区域的大气污染情况日趋严重,大气污染监测与治理已刻不容缓.由于卫星遥感具有较广的空间覆盖、成本低等优点,卫星遥感反演气溶胶光学厚度(AOD)产品被普遍认为是地面PM2.5浓度的重要指标,且已被广泛地应用于地面PM2.5遥感监测.利用2007～2008年的MODIS/Terra气溶胶光学厚度产品,考虑中国东部地区5个大气成分站点风速、风向、温度、湿度和边界层高度等气象数据,构建后向(BP)神经网络,提出了基于MODIS AOD产品估算PM2.5的模型.利用5个大气成分站点PM2.5观测数据对模型进行散点拟合和时间序列拟合验证,结果表明:①从PM2.5观测值与估算值的散点回归分析来看,PM2.5估算值与观测值相关系数最好的为庐山站(R=0.6),其它4个站次之,但其相关系数均在0.4(中强相关)以上;②从PM2.5观测值与估算值的时间序列比对分析来看,PM2.5估算值和观测值差值随时间变化而变化,且存在明显的日际振荡现象,但经相邻5 d滑动平均处理,5个站点的PM2.5估算值与观测值相关系数得到普遍提升,滑动后的相关系数RMA均在0.7以上(除郑州外),庐山RMA达到0.83.结果表明在BP网络框架下,基于MODIS AOD产品估算PM2.5的模型能较好地应用于PM2.5监测.     

An improved method for predicting seasonal and annual shadowing from cooling tower plumes

An improved model developed at Argonne National Laboratory and the University of Illinois (ANL/UI) for predicting long-term shadowing due to cooling tower plumes is presented, and its assumption are compared with those used in previous models. The model is based on a method for the selection of representative categories of similar plumes developed by Dunn and Policastro (Dunn, 1980, Proc. IAHR Cooling Tower Workshop, San Francisco; Policastro et al., 1984, Report EPRI CS-3403-CCM). At a given site this method reduces the large number of meteorological data cases in a season or year to a much smaller number (≈35) of representative cases, each of which will have a predicted plume substantially different from the others. Plume predictions for the reduced set of category representative cases are then made with the validated ANL/UI plume model. With category representative plume shape, wind speed, wind direction and sun angles available for each hour, full effects of sun angles for the latitude and longitude of the site to be studied are included. The ANL/UI model yields seasonal and annual isopleths of hours of additional shadowing or of percentage reduction in total and direct solar energy arriving at the ground on a horizontal surface. Results for two hypothetical sites with 500 MWe generating capacity are presented and contrasted, one at Syracuse, NY, and the other at Spokane, WA.     

基于255 m气象塔天津地区污染天气高空风特征研究

基于2016年4月—2017年3月天津地区地面、255 m气象塔和风廓线监测数据,结合数值模拟,研究天津污染天气分析中高空风特征,以期进一步提高污染天气预报准确率.结果表明:高空风速和风向分析对污染天气趋势判断有重要作用,如冠层以上高度风速、300~1500 m风向对PM2.5污染程度的指示效果好于近地面同类数据;在选取高空风速指标时,应尽量避免边界层顶附近高度风速数据选取,如使用300 m和600 m风速和作为指标要好于300、600和900 m风速和作为指标.而其是否有利于污染扩散判断的临界阈值为10~15 m·s-1,小于10 m·s-1时水平扩散条件不利于污染物扩散,大于15 m·s-1时有利于污染物扩散.分析高空风向时,需要考虑输送高度和Ekman螺线的影响,与地面不同,300~1500 m高空风分析时,有利于出现污染天气的风向为西风、西南风和南风,而地面仅为南风和西南风;当1500 m高度呈现东风、偏东风和东南风时,天津地区受来自渤海的气流影响明显,污染气象条件有利于污染物扩散,空气质量以良好为主.     

A new wind tunnel technique for investigating gas diffusion behind a structure

We predicted the diffusion of exhaust gas without thermal buoyancy from the top of a structure in down-wash under the influence of a structure by means of wind tunnel. We reproduced a wind directional fluctuation σ_A of about 10° in a wind tunnel.The present experimental method and experimental results are introduced here. Wind directional fluctuation was simulated by means of turntable equipment which rotated the model according to probability of wind direction in the field. In measuring concentration, NH₃ as tracer gas was used and a sampling method was adopted. The tracer gas was continuously sampled while rotating the model. As a result the sampled gas concentration was integrated with the weight according to the probability of each wind direction.We compared results of this wind tunnel experiment with the field test and the reliability of this prediction method was confirmed.     

榆中县城大气污染物扩散的观测实验与数值模拟

结合榆中县外场观测实验资料,利用计算流体动力学CFD (Fluent)软件对榆中县城区的流场和污染物扩散形态及规律进行了模拟研究.根据实际测量数据,利用Gambit软件建立了榆中县城区的三维城市建筑模型,导入Fluent进行模拟计算,对模拟结果与观测数据进行了对比分析,结果显示Fluent对流场和污染物扩散的模拟具有较好的一致性;具体而言,对风向、风速的模拟值与观测值的总体相似系数分别为0.876和0.843,对0.5m和27m高度处污染物模拟值和观测值的相关系数分别为0.71和0.72.因此,Fluent软件可用于城市污染扩散的模拟研究,也可以根据其模拟结果指导空气质量检测点的布设.     

Comparison of three dry deposition models applied to field measurements in the Southern Bight of the North Sea

Dry deposition velocities have been calculated using three different approaches. Turbulent wind profile theory has been used to predict the drag coefficient, wind speed and friction velocity at 10 m height when the wind speed is measured at a higher altitude. The resulting parameters were introduced in a two-layer deposition model. The second approach was the well-known model of Slinn and Slinn (1980, Atmospheric Environment14, 1013–1016), whereas the third corresponded to the model published by Williams (1982, Atmospheric Environment16, 1933–1938). Results point to clear differences. However, in a field experiment carried out at the Southern Bight of the North Sea, all three approaches show relatively comparable results. The role played by the size distribution of atmospheric particulate matter is essential. In our case any of the three models could have given satisfactory outcomes taking into account the wide spread of the experimental results cited in the literature for the same airshed.     

晋东南地区冬季PM2.5污染输送路径分析

利用2017~2019年晋城市和长治市冬季PM_2.5逐时浓度资料、地面风场数据等,结合HYSPLIT轨迹模型和中尺度数值模式WRFV4.2分析了晋东南地区冬季PM_2.5污染的特征和传输特点.结果表明,晋城市冬季PM_2.5污染程度高于长治市.受地形影响,晋城市地面盛行偏南风、偏北风和西北风,污染方向主要为偏南风和偏北风;长治市近地面盛行偏南风,该风向污染频率最高.影响晋城市和长治市污染的潜在源区主要分布在偏西、东北和东南方向,偏西气流来自陕西省中部,东北气流来自河北省西南部,东南气流来自河南省中东部.污染经过晋东南地区主要影响山西省中南部和北京南部.通过数值模拟流场,结合潜在源区和影响区域的分析结果,在均压场或高压后部的天气形势下,晋东南地区污染输送路径包括来自东北方向（河北省西南部一带）的气流,沿长治市东北部的滏口陉向晋东南地区输送污染物及沿太行山东麓向南在晋豫交界处的太行陉发生转折向晋东南地区输送污染物;来自东南方向（河南北部及东部）的气流输送和来自偏西方向（陕西中南部）的气流输送.污染物经过晋东南地区向北输送至山西省中南部,部分经过山西省中东部的井陉输送至北京南部.     

街道峡谷型交叉口内气态污染物扩散的数值模拟

对大气边界层内大气湍流和建筑物对道路交叉口处机动车排放气态污染物扩散的影响进行了研究.在计算区域内建立了三维街道峡谷型道路交叉口及其内部机动车排放的模型,并在中性层结条件下,采用CFD(Computational Fluid Dynamics)稳态κ-ε湍流模型和被动标量的输运方程模拟了模型内外的流场和CO浓度场.结果表明:① 在相同高度条件下,交叉口处与处于下风向的街区内CO浓度明显高于其他街区;②风向对污染物的输运起决定性作用,在不同高度平面内CO浓度最大值均出现在平行风向的街区内;③外部大气湍流的驱动使得垂直风向的街区内产生强烈涡旋,涡旋的输运作用导致相同高度下上风向CO浓度较高;④交叉口处气流的掺混导致气流速度降低,使得平行风向的街区内CO可以向两侧垂直风向的街区内扩散,起到了稀释交叉口处地面附近CO浓度的作用.模拟结果与风洞实验结果符合较好,验证了方法的可靠性.      

Hydrogen peroxide levels in Los Angeles: A screening-level evaluation

The major factors affecting the diurnal variation of gaseous hydrogen peroxide were studied using a one-dimensional vertical gas-phase chemistry/transport screening model. The model which included diurnal variations of the inversion layer, surface emissions, dry deposition of air pollutants, and meteorological conditions such as solar radiation, temperature, relative humidity and wind speed, was used to evaluate the influence of the ratio of NMHC/NO_x, the emission rates of NO_x and NMHC, the deposition velocity of H₂O₂, and the height of inversion layer on the ground level gaseous H₂O₂ concentrations. The model was found to yield reasonable agreement with field data from the Carbonaceous Species Methods Comparison Study at Glendora, California, in 11–21 August 1986. Specifically, model predictions and field results all indicated that during clear skies, ambient H₂O₂ concentration was highest at about early afternoon when O₃ concentration was highest and NO_x was lowest. It was concluded that the predicted gaseous H₂O₂ concentration is most sensitive to the emission rate of NO_x and the ratio of NHMC/NO_x, but it is less sensitive to the deposition velocity, height of inversion layer, and the emission rate of NMHC.     

空气污染潜势-统计结合预报模型的建立及应用

建立了一个空气污染潜势预报和统计预报相结合的模型,该模型以特征气象因子和大气扩散清除因子为基础,并考虑不同因子的权重,定义空气污染潜势指数APPI.所考虑的因子包括:地面风速、混合层高度、混合层内平均风速、风向日变化、稳定度级数、垂直扩散系数、SO2干沉降速率、NO2干沉降速率、PM10干沉降速率、降水时长、地面天气形势.进一步利用统计方法建立空气污染指数API与APPI之间的关系.利用南京地区2009~2010年气象资料计算APPI,通过3项式拟合得到API与APPI的统计方程.结果表明,拟合得到的API与实际API相关系数为0.67,具有显著的相关性,且等级准确率为76.7%.进一步利用2011年1~12月中尺度气象模式WRF预报的气象场开展实况预报.研究表明,24h预报、48h预报、回顾预报的逐月等级正确率分别为44.4%~87.5%,46.4%~100%和63.0%~80.0%,年均等级正确率为60.6%,62.4%.和73.1%.若定义预报API与实际API相差±20以内为正确,则24h预报、48h预报、回顾预报的正确率分别为58.1%, 59.4%和63.8%.在IBM x3500并行集群服务器上计算,48h预报需要机时3h.可见,该模型具有较好的预报性能, 相对数值模型计算效率很高.     

污染条件下成都市PM_(2.5)干沉降速率的模拟分析

利用成都市三瓦窑、沙河铺国控环监站2015年9月—2016年8月逐时PM_(2.5)监测数据,结合同期双流国际机场公布的地面气象要素(风场、温度、湿度和压强)以及温江站风速探空资料,首先统计分析了成都市风场、温度和湿度的基本特征,然后计算了污染条件下PM_(2.5)干沉降速率,并建立了适用于不同季节的GIFM模型和多元回归预测模型。结果表明:基本气象要素场的配置以及特殊地形导致了成都市PM_(2.5)干沉降环境恶劣,同时四季差异较大;污染条件下PM_(2.5)干沉降速率约为0.02~0.1 cm/s,表现为冬季<秋季<年<春季<夏季,四季的主要影响因子也不同,秋冬为湿度和压强,春季为温度,夏季为风速,且湿沉降强度过大时,会出现PM_(2.5)干沉降速率的"虚高"现象;GIFM模型和多元回归模型均能很好地预测污染条件下的PM_(2.5)干沉降速率,其预测能力均是夏季最好,冬季最差,春秋次之,通过对比分析表明GIFM模型的预测能力在各季节均优于多元回归模型。     

风域视角京津冀生态廊道空间格局识别

在风域视角下,分析大尺度生态廊道形成的关键因子,构建最小累计阻力模型和通风效益评测模型,以京津冀为研究区,利用气象、遥感及其他基础地理数据等,阐述研究区近地面风场特征,识别区域内生态廊道空间格局,结果表明：研究区全域近地面多年平均风速为2.07m/s.冬季,研究区多年平均风速呈张家口坝上—北京—天津沿海高两侧低格局,主导风向为偏北风;夏季,多年平均风速呈现西北部和东南部高,中东部和西南部低的格局,主导风向为偏南风.冬季,在研究区筛选出34个社会经济源地,识别出5条一级生态廊道,走向从西北向东南或从北向南,7条二级生态廊道,走向主要从西向东;夏季,在研究区筛选出68个生态源地,识别出5条一级生态廊道,走向从东南向西北或从南向北,6条二级生态廊道,走向主要从东向西.合并精简冬夏连通性能较好的生态廊道,优选出5条一级生态廊道,总长度3073.04km,以南北向为主,5条二级生态廊道,总长度1582.06km,以东西向为主,初步形成京津冀“五纵五横”生态廊道格局.     

济南市冬季一次典型重污染过程分析

为掌握济南市重污染天气发生规律,从而更好地为重污染天气预报预警和大气污染防治提供参考,采用空气质量监测数据、气象观测资料、雷达探测资料及轨迹模式模拟相结合的方法,对济南市2016年12月31日-2017年1月7日的持续性重污染过程,从污染演变过程、环流背景分析、气象要素特征和区域污染传输等多方面分析其形成原因及主要影响因素.结果表明:此次重污染过程期间首要污染物为颗粒物,ρ（PM₁₀）平均值为318 μg/m3,ρ（PM_2.5）平均值为200 μg/m3;地面风速在0.6~1.8 m/s范围内,风力均为1~2级,相对湿度为68%~95%,平均相对湿度为81%.在重污染过程中,从地面至800 m左右高度始终维持较强逆温层,逆温频次高达91.1%,污染边界层高度较低,大部分时间都在500 m以下.采用情景模拟分析方法计算得到,区域输送对济南市PM_2.5的贡献率为20%~35%.研究显示:此次重污染过程是在区域性污染背景下由本地不利的扩散条件造成的,静稳大气形势提供有利的环流背景,平流雾、辐射雾交替产生,持续性的高湿加重了污染程度;近地面的静风、高湿,垂直方向的双逆温层甚至多逆温层的结构是影响此次重污染过程的重要气象要素;区域性污染传输对此次重污染天气的发展有显著贡献,污染初期主要来自河北省中南部的输送,随着污染加重,有来自偏南、偏东方向的局地气团输送.      

The stem-II acid deposition and photochemical oxidant model—II. A diagnostic analysis of mesoscale acid deposition

The MesoSTEM model is used to analyse data obtained during the Mesoscale Acid Deposition Study. The Mesoscale Acid Deposition Study was designed to investigate acid deposition on the urban-suburban scale by sampling well defined frontal storms around the city of Philadelphia using a surface based network. The MesoSTEM model was developed to provide diagnostic analysis of these data. The mesoSTEM model is a combination of the Mesoscale Atmospheric Simulation System (MASS) predictive mesoscale dynamic meteorological model and the STEM-II model, a three-dimensional Eulerian acid deposition model. The MesoSTEM model is described in this paper and applied to the analysis of the 2 May 1985 storm. Elevated sulfate and nitrate deposition is predicted downwind of the major source areas and nitrate deposition exceeds that for sulfate. However, much of the NO_x and SO_x emitted on the mesoscale escapes the storm and becomes available for long-range transport and subsequent deposition.     

Dry deposition inferential measurement techniques—I. Design and tests of a prototype meteorological and chemical system for determining dry deposition

A simple filterpack system and a supporting meteorological and surface-condition monitoring system have been deployed in a trial network to test methods by which dry deposition rates can be estimated on a routine basis, using average air concentration data and site-specific deposition velocities. The filterpack used in this dry deposition inferential measurement (DDIM) sampler differs from standard filterpacks by the use of a heated horizontal settling tube at the inlet. This modification is intended to protect the SO₂ sampling system from adverse effects associated with filter liquefaction at high humidities, while at the same time providing a simple means for eliminating large particles from the sampled air. Tests indicate that the sampler works well for SO₂ and for particulate species, and that HNO₃ concentrations are underestimated by about 25%. The DDIM approach differs from previous network measurement programs in that the data sets are designed to permit extension from observations at a subset of research sites to less intensive routine measurement sites. Hence a major goal is the definition of a suitable set of supporting data from which dry deposition rates can be inferred using air concentration data. Local vegetation characteristics are noted. At present, the additional variables that are monitored are solar radiation, wind speed, wind direction standard deviation, temperature, humidity, surface wetness, and precipitation. Observers report changes in the surrounding surface, such as the presence of snow or unusual drought.     

辽宁省辽中降水化学特征与边界层流场的相关性

于2007年2月—2008年1月在辽宁省辽中县进行了酸雨观测,获取了辽中站降水中主要化学离子组成,并观测了同期的地面气象条件,结合不同气象流场分析方法,分析降水化学特征与边界层流场的关系. 结果表明:①降水酸碱性与风向有一定关系,偏南风易出现酸性降水,而偏北风易出现碱性降水,降水的酸碱性与850～925 hPa(约700～1 500 m)高度上的风向相关性较强,与近地面风向的相关性不明显;②降水的酸碱性和离子组成与研究区所处天气系统的相对位置关系显著,同是偏北气流,如研究区位于大陆反气旋东部,气流来源于西北地区,Ca2+浓度较高,反之,如位于海洋气旋的西部,由于气流来源于东南海上,故Na+浓度相对较高.      

Simulation of dispersion in moderately complex terrain—Part C. A dispersion model for operational use

An operational dispersion model for use in areas with complex terrain is presented. The model uses mean and turbulence quantities simulated with the fluid dynamic model presented in Part A. A large number of wind and turbulence fields are simulated with the fluid dynamic model. These simulations are put into a database and can be used in the calculations of dispersion with the operational model. To get relevant meteorological data for the model a Doppler sodar and a 10 m high mast with a temperature profile and wind and wind direction at one level are used. The model calculates a trajectory for the plume centerline from the simulated wind field, and approximates the concentration field with a bi-Gaussian distribution. For convective conditions the mixing height and the surface heat flux, used as input for the model, are being determined from the sodar measurements through relations related to the temperature structure parameter C_T² and the standard deviation of the vertical velocity. The horizontal and vertical standard deviations for the plume are determined by using the simulated turbulence quantities from the dynamic model and Eulerian velocity spectra. Simulations with the model is compared with dispersion measurements performed in an area in the southern Sweden, the Vänersborg-Trollhättan region. The geographical area is characterized by topographical features on the meso-γ-scale, i.e. 2–20 km. Thus there are forested hills, a relatively flat agricultural area and an extended lake area within the model domain. The terrain height relief is typically 80 m. The simulations show, in general, good agreement with the measured data both for unstable and stable stratifications.     

北京夏季典型臭氧污染分布特征及影响因子

为研究北京地区O₃分布特征及其影响因子,利用AML-3车载式大气环境污染激光雷达系统(下称AML-3)对北京地区2011年5月7日—6月9日的φ(O₃)进行观测. 通过AML-3自带的污染物地面观测系统和差分吸收激光雷达,分析近地面、高空φ(O₃)时空分布特征,并将φ(O₃)与温度、风速及风向3个气象要素进行相关分析. 结果表明:近地面φ(O₃)日变化明显,06:00左右为低谷,下午14:00左右达到峰值. 高空φ(O₃)的空间分布很不均匀,上层气流易使O₃富集层向下输送造成污染,同时稳定边界层对大气扩散的不利影响也是形成O₃污染的重要原因. φ(O₃)的日变化趋势与温度的日变化趋势呈显著正相关,R(相关系数)为0.74;上下层湍流交换使风速与近地面φ(O₃)呈正相关,而水平扩散使二者呈负相关;通过分析风向的分布规律发现,东北风易造成北京地区O₃污染.