成都大气污染物在焚烧秸秆时的溯源初步探究

在空气污染指数(API)的数据基础上,结合卫星遥感、气团后向轨迹及气象数据,通过初步分析秸秆焚烧对本地及区域范围输送对城市空气质量影响,研究不同空气污染类型(本地型、本地区域相结合型、输送型)特征,探索重污染条件的大气污染物溯源方法。     

Spatial and Temporal Variation of Ozone Concentrations at High Altitude Monitoring Sites in Germany

The present study deals with the characterisation and interpretation of hourly taken maximum ozone concentrations of each day at various high altitude monitoring sites in Germany and contributes to the understanding of ozone transport processes. The relation between long range transport of ozone as well as its precursors and high ozone concentrations is of special interest. Aim of this paper is to investigate the influence and importance of large scale meteorological circulation and source regions of anthropogenic ozone precursors to local ozone concentrations using 2-dimensional backward trajectories. Further, reasons for the spatial and temporal variation of ozone concentration levels will be shown. Investigating numerous cases a similar origin of air masses causing high ozone concentrations in Germany and uniform patterns at various sites could be identified.     

北方沙尘对江苏环境空气中可吸入颗粒物浓度的影响

利用卫星遥感数据、气象观测资料和江苏省环境空气自动站的可吸入颗粒物的监测数据,对春季中国北方的沙尘暴过程对江苏环境空气中可吸入颗粒物浓度的影响分析发现,沙尘暴所含的沙尘粒子在冷空气的引导下可能进入江苏,对环境空气中可吸入颗粒物浓度将会产生较大的影响;北方沙尘暴所携带的沙尘粒子是春季江苏空气污染的重要来源之一,是预报空气质量异常偏高的重要依据之一。     

Multi-objective optimization of air quality monitoring

A new method for multi-objective optimization of air quality monitoring systems based on satellite remote sensing of the troposphere is described in this work. The technique uses atmospheric turbidity as surrogate for air pollution loading. Through inverse chemical modeling and ancillary information the respective patterns of primary gaseous and particle pollutants are inferred. The optimization algorithm uses the resulting maps of ambient air pollution as input. It focuses on the gain of information with regard to human exposure to high pollution, potential impact on cultural heritage, compliance to ambient air quality standards, monitoring key point and area source emissions, as well as on the associated cost. Application of the method in Brescia, Italy showed its significant potential for improving the cost-effectiveness of air quality monitoring networks at the urban and regional scales.     

Relating Background NO2 Concentrations in Air to Air Mass History Using Non-Parametric Regression Methods: Application at Two Background Sites in Ireland

The concentration of nitrogen dioxide (NO₂) in background air varies temporally and spatially and is influenced by meteorological and anthropogenic factors. Background concentrations used in local air quality modelling studies have a significant effect on the accuracy of the overall result and when based on short-term monitoring data, variation in concentrations with air mass history is often unaccounted for. The current paper presents a powerful tool for the quantification and separation of local and regional air mass effects on background air quality. The origin of and the regions traversed by an air mass prior to reaching a receptor has been modelled using HYsplit-4. Trajectories (between 12 and 96?h duration) were defined based on the frequency with which they passed into 16 predefined compass quadrants and each represented as a vector. Using this vector as the predictor variable and the background concentration as the response variable, non-parametric regression using a Gaussian kernel function was carried out. A graphical output indicated the trajectory direction of maximum NO₂ concentration, while allowing distinction to be made between spurious and true peaks. In all cases, air mass history was found to have a statistically significant effect on NO₂ concentrations. Incorporating emissions data into the analysis local and regional effects were separated and quantified. It was found that emissions in the UK and Europe have a significant effect on background NO₂ concentrations in Ireland and in some instances supersede domestic emissions. The methods can be used to identify source regions, separate local and regional effects and improve predictions of background concentrations based on limited monitoring data. In particular, the results highlight the importance of considering air mass history when assessing background concentration levels for use in local air quality modelling studies.     

The impact assessment of Diwali fireworks emissions on the air quality of a tropical urban site, Hyderabad, India, during three consecutive years

Diwali is one of the largest festivals for Hindu religion which falls in the period October–November every year. During the festival days, extensive burning of firecrackers takes place, especially in the evening hours, constituting a significant source of aerosols, black carbon (BC), organics, and trace gases. The widespread use of sparklers was found to be associated with short-term air quality degradation events. The present study focuses on the influence of Diwali fireworks emissions on surface ozone (O₃), nitrogen oxides (NO _x ), and BC aerosol concentration over the tropical urban region of Hyderabad, India during three consecutive years (2009–2011). The trace gases are analyzed for pre-Diwali, Diwali, and post-Diwali days in order to reveal the festivity’s contribution to the ambient air quality over the city. A twofold to threefold increase is observed in O₃, NO _x , and BC concentrations during the festival period compared to control days for 2009–2011, which is mainly attributed to firecrackers burning. The high correlation coefficient (~0.74) between NO _x and SO₂ concentrations and higher SO₂/NO _x (S/N) index suggested air quality degradation due to firecrackers burning. Furthermore, the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation-derived aerosol subtyping map also confirmed the presence of smoke aerosols emitted from firecrackers burning over the region. Nevertheless, the concentration level of pollutants exhibited substantial decline over the region during the years 2010 and 2011 compared to 2009 ascribed to various awareness campaigns and increased cost of firecrackers.     

基于OPAQ的城市空气质量预报系统研究

空气质量预测在国内的关注度日益提高,传统的空气质量预测系统通常运用数值化学传输模型,利用物理方程来计算污染物的扩散、沉降和化学反应。而化学传输模型的预测准确性很大程度上需要依赖详细的污染源排放信息和气象模型的输出结果。基于统计模型的OPAQ空气质量预报业务系统,采用人工神经网络算法,可预测各污染物的日均值或日最大值。并对北京空气质量预报的结果进行了评价,OPAQ空气质量预报业务系统对空气质量预测的准确性较高,能够利用较低的计算资源得到较为准确的预测结果。与数值预报相比,OPAQ空气质量预报业务系统不需要大量的基础数据作为输入,可弥补数值预报的不足,并成为数值预报的有力补充。     

2017年上半年北京市及周边地区空气质量特征分析

依托北京市、廊坊市和保定市高密度的地面空气质量监测、气象要素监测以及PM_2.5化学组分监测和后向轨迹分析等手段，对2017年上半年三地的空气质量进行分析。研究发现：三地中北京市空气质量较好，保定市较差。分污染物来看，保定市SO₂浓度水平明显高于廊坊市和北京市，颗粒物PM₁₀和PM_2.5也呈现保定市最高、北京市最低的规律。从污染物日变化来看，CO、SO₂、NO₂、PM₁₀和PM_2.5呈双峰型分布，O₃呈单峰型分布。从区域整体分布规律来看，PM_2.5和SO₂呈现明显的"南高北低"特征。PM_2.5化学组分分析结果表明：1—4月燃煤对该区域空气质量的影响较大，5—6月机动车排放的影响更为凸显。后向轨迹分析结果表明：在2017年上半年到达北京市的气流中有24%来自于北京市南部，且这些气流多为低空传输，表明区域传输对于北京市空气质量具有一定的影响。     

臭氧数值预报模型综述

光化学大气质量模型在研究臭氧(O_3)污染以及O_3预报方面具有核心作用,是O_3污染防治决策者的有力工具。文章结合目前中国及国际区域尺度光化学大气质量预报模型的研究与应用,重点论述与O_3有关的大气化学过程在数值预报模型中的数学表达和计算方法,阐述大气物理与大气化学过程在主流大气质量数值预报模型中的实现方法及其优势和缺陷,介绍用于数值预报模型的大气物理过程和湍流参数化方案的最新进展。就当前O_3数值模拟的主要输入资料进行讨论,强调那些易被忽视但又显著影响模型预报能力和效果的诸多因素以及模型效果评估的重要性。结合O_3与复合型大气污染的关系,强调区域大气质量数值预报模型的发展趋势与方向以及在大气环境管理方面的意义和作用。     

基于卫星数据的灰霾污染遥感监测方法及系统设计

以中分辨率成像光谱仪为例,建立了卫星遥感参数和空气污染指标之间的联系,提出了基于卫星遥感的气溶胶光学厚度数据获得灰霾指数和灰霾污染时空气质量指数(AQI)等级的方法和相关的监测系统设计。结合在华北地区的监测结果,可以看出卫星遥感可以较好地反映灰霾污染程度的变化,有助于发现可能的传输通道,获得宏观的灰霾空间分布状况。在研究时段内,与地面空气质量监测站相比,88%的卫星遥感监测结果误差不大于1个AQI等级,平均的AQI等级偏差为0.7级,并分析了卫星遥感与地面监测之间差异的原因。     

基于Landsat 8卫星OLI影像遥感反演盐城市区域大气能见度状况

利用遥感软件ENVI 5.2的FLAASH大气校正模块,对盐城市2013—2014年共22景Landsat 8卫星OLI影像进行了区域大气能见度遥感反演,并与盐城市环境监测中心站的空气自动监测子站的PM10、PM2.5以及当地气象部门的能见度观测数据进行了对比。结果表明,OLI遥感影像可以对区域尺度大气能见度进行有效的观测,反演的区域性大气能见度水平与地面空气质量自动监测结果存在消长关系,与地面能见度数据有近70%的一致性。     

Air quality impact assessment

Air quality impact assessment (AQIA) is an important technique for determining the relative contribution to ground level pollutant concentrations of specific current or future source emissions at receptor sites. The principal activities in AQIA are air quality modelling and monitoring techniques. The choice of techniques which are applicable to a particular situation is intimately related to the problem to be assessed. A review of relevant modelling techniques has been presented, with emphasis placed on Gaussian plume models because of their ease of use and broad applicability. Alternative modelling techniques have been suggested when departures from the Gaussian form in the atmosphere occurs or when more detailed information on atmospheric chemistry, deposition or long range transport is required. The objectives and techniques of ambient air quality monitoring have been examined and the siting of instruments, duration and frequency of sampling, choice of monitoring rationale (fixed or mobile), minimization of sampling errors, data storage and analysis are discussed. Modelling and monitoring are essential to successful AQIA. An orderly approach to the principles and procedures involved in performing AQIA studies, using these two techniques in conjunction, has been presented.     

青海高原一次沙尘重污染天气成因分析

利用常规观测的卫星云图资料、地面资料、探空资料、地面污染物监测数据,结合拉格朗日粒子扩散模型(LPDM)污染源溯源方法,对2018年2月青海高原一次沙尘重污染天气的主要成因以及沙尘传输特征进行了分析。结果表明:此次重污染天气受高空低槽东移影响,在300~700 hPa形成了强烈的辐散下沉,槽后的高空急流随之东移。在其东移过程中,受高空急流动量下传及偏北气流中的冷空气共同作用,青海东部出现了大风沙尘天气。边界层中逆温层的存在是此次污染天气持续的重要原因之一,加之未出现明显降水,不利于大气污染物的扩散。通过运用LPDM对此次污染天气的运动轨迹进行分析来看,气团影响的模拟高度层距离地面100 m,气团层趋势一致。研究区地处青藏高原,海拔较高,0~100 m高度的气团足迹可以反映出PM 10污染气团的输送路径。同时,0~100 m是主要的人为源排放空间,也是对人类活动影响较大的区域。气团足迹与PM 10浓度的变化趋势一致,即青海东部沙尘污染主要是由河西走廊沙尘倒灌进入青海东部导致,这与天气学分析结果一致。     

2010年春季沙尘天气对北京市空气质量的影响及其天气类型分析

利用2010年3-5月北京市市区以及北京西北部、西部和东部3个不同方位边界的空气质量自动监测站监测数据,结合气象资料和激光雷达观测数据,分析了春季外来沙尘对北京市空气质量的影响。研究结果表明:2010年春季北京市出现15次外来沙尘天气,外来沙尘输送对北京市空气质量的影响天数为21 d,直接造成15 d空气质量超标,最严重的一次API指数达到最大值500。沙尘的天气形势特征以及输送路径的不同,对北京市空气质量的影响有明显差异。当低压中心过境时,沙尘天气影响最重,颗粒物浓度显著上升,气态污染物迅速下降,沙尘呈现自西北向东南输送的特征;低压底部过境与低压中心过境类似,但是沙尘强度略弱;除此之外,沙尘回流也可以直接造成北京市空气质量超标,颗粒物浓度和气态污染物浓度均表现出上升的变化趋势。     

大连市臭氧污染特征及典型污染日成因

通过对大连市区10个空气监测子站的监测数据进行分析,探讨了大连市臭氧污染的时空分布、气象条件对臭氧污染的影响,对臭氧污染日进行了归类分析。结果表明,大连市臭氧污染主要出现在4—10月。在强紫外辐射、高温、低湿、低压和低风速的气象条件下,监测点位的臭氧浓度较高。臭氧污染日的日变化分为单峰型、双峰型和夜间持续升高型3种类型。通过对2015年的一次高浓度臭氧污染过程的气象条件、污染物浓度和污染气团轨迹进行分析,发现臭氧浓度在夜间持续升高现象与区域输送密切相关。     

Stratospheric temperature monitoring using a vibrational Raman lidar. Part 1: aerosols and ozone interferences

Lidar measurements of temperature for the upper troposphere and lower stratosphere are commonly derived by the Raman technique. Lidar signals derived from vibrational Raman processes have been subjected to numerous simulation tests to examine their sensitivity to the presence of aerosols and ozone in the atmosphere. The influence of aerosols characteristics (wavelength dependence of aerosol extinction and particle phase function) and of ozone concentration on Raman temperature profiles is estimated. Simulations indicate large temperature deviations for post-volcanic conditions. For a Raman backscatter at 607 nm, bias is below 1 K for a total optical depth less than 9 x 10(-3) in the case of a stratospheric contamination and less than 6 x 10(-3) for a tropospheric contamination. The effect of aerosols depends on phase function and a few parameters such as altitude, optical depth and the shape of the high-altitude cloud. The wavelength dependence of aerosol extinction has some influence only for severe post-volcanic conditions (Scattering Ratio, SR >2). For a Raman backscatter at 387 nm, bias is larger and can be significant even in background aerosol conditions. Changes in the ozone density profile lead to significant Raman temperature deviations only for some specific conditions. Results suggest that both aerosol and ozone corrections are necessary to obtain an accuracy better than the 1 K requested for most atmospheric applications.     

Short-Term Forecasting of Nitrogen Dioxide (NO<Subscript>2</Subscript>) Levels Using a Hybrid Statistical and Air Mass History Modelling Approach

A novel hybrid model has been developed to support the provision of real-time air quality forecasts. Statistical techniques have been applied in parallel with air mass history modelling to provide an efficient and accurate forecasting system with the ability to identify high NO₂ events, which tend to be the episodes of most significance in Ireland. Air mass history modelling and k-means clustering are used to identify air mass types that lead to high NO₂ levels in Ireland. Trajectory matching techniques allow data associated with these air masses to be partitioned during model development. Non-parametric regression (NPR) has been applied to describe nonlinear variations in concentration levels with wind speed, direction and season and produce a set of linearized factors which, together with other meteorological variables, are employed as inputs to a multiple linear regression. The model uses an innovative integrated approach to combine the NPR with the air mass history modelling results. On validation, a correlation coefficient of 0.75 was obtained, and 91 % of daily maximum (hourly averaged) NO₂ predictions were within a factor of two of the measured value. High pollution events were well captured, as indicated by strong agreement between measured and modelled high percentile values. The model requires only simple input data, does not require an emission inventory and utilises very low computational resources. It represents an accurate and efficient means of producing real-time air quality forecasts and, when used in combination with forecaster experience, is a useful tool for identifying periods of poor air quality 24 h in advance. The hybrid approach outlined in this paper can easily be applied to produce high-quality forecasts of both NO₂ and additional pollutants at new locations/countries where historical monitoring data are available.     

Forecasting Air Quality Parameters Using Hybrid Neural Network Modelling

Urban air pollution has emerged as an acute problem in recent years because of its detrimental effects on health and living conditions. The research presented here aims at attaining a better understanding of phenomena associated with atmospheric pollution, and in particular with aerosol particles. The specific goal was to develop a form of air quality modelling which can forecast urban air quality for the next day using airborne pollutant, meteorological and timing variables.Hourly airborne pollutant and meteorological averages collected during the years 1995–1997 were analysed in order to identify air quality episodes having typical and the most probable combinations of air pollutant and meteorological variables. This modelling was done using the Self-Organising Map (SOM) algorithm, Sammon's mapping and fuzzy distance metrics. The clusters of data that were found were characterised by statistics. Several overlapping Multi-Layer Perceptron (MLP) models were then applied to the clustered data, each of which represented one pollution episode. The actual levels for individual pollutants could then be calculated using a combination of the MLP models which were appropriate in that situation.The analysis phase of the modelling gave clear and intuitive results regarding air quality in the area where the data had been collected. The resulting forecast showed that the modelling of gaseous pollutants is more reliable than that of the particles.     

卫星遥感在重大活动空气质量保障中的应用

以2016年G20峰会重大活动保障工作为例,简述了卫星遥感技术近年来在大气环境空气质量监测取得的进展以及重大活动空气质量保障会商机制和组织架构,重点分析了如何充分应用大气遥感产品服务于重大活动空气质量保障工作。指出,现有卫星遥感技术已具备服务重大活动空气质量保障工作的要求,其产品可应用于空气质量监测、生物质燃烧遥感监测,同时大气遥感产品相对客观,可以成为重大活动空气质量保障的客观参考标准,为今后类似保障工作提供借鉴。     

Air Issues amd Ecosystem Protection, a Canadian National Parks Perspective

Several case histories illustrate national park air issues and responses in Canada. These examples include: acidification studies and establishment of a multiparticipant monitoring programme at Kejimkujik; studies of smoke at campgrounds in Jasper, La Mauricie and Forillon, its effect on health, and the management of visitors and firewood supply to mitigate these risks; and estimates of emissions from through-traffic in Yoho. From these cases and from reviews of the secondary literature, we can identify air issues that affect the maintenance of ecological integrity in national parks. These issues are: forest fires and smoke management; defining goals for ecosystem restoration; representation of natural regional conditions; visitor health and amenity; acidification; pesticides; eutrophication from airborne nitrates; permafrost melting; and UV-B. In June 1995, an International Air Issues Workshop brought together representatives from Canadian and U.S. national parks and other selected agencies. They ranked the air issues affecting national parks, producing quite an eclectic list. From the most to least serious issue, they are: acidification, toxics, visibility impairment, UV-B, smoke management, oil and gas development, fugitive dust, global warming, overflights, light pollution, noise and odour. Note that atmospheric change is only one among a group of stresses affecting national parks. Of 28 stresses recognized as significant for national parks in 1992, acid precipitation ranked 8th and climate change 23rd. Petrochemicals, 17th, pesticides, 18th and heavy metals, 21st, may be partly airborne. The 1995 workshop made several recommendations applicable to Parks Canada, from which those related to research and monitoring needs have been extracted. The air monitoring needed most by national parks is of suspended particulate and visibility. This is in response to human health and amenity concerns and international treaty obligations. The long-term protection of natural sites in national parks provides opportunities for other agencies to monitor ambient air quality and ecosystem responses, for example through the installation of under-canopy monitoring towers. The air research most needed in national parks is the modelling of natural landscapes and vegetation complexes in response to climate change. This follows from the primary purpose of each national park, to maintain the ecological integrity of an area selected to represent a natural region. The principal air research opportunities for other agencies in national parks are probably intensive instrumentation and sampling over several years to examine the air-vegetation-soil transfers of nutrients, pollutants and radiation.