2013年苏州春季一次重污染天气的过程分析

研究了2013年3月在江苏范围内的一次重污染天气过程，重点分析苏州在此次污染过程中大气污染的变化特征。污染过程中，苏州市颗粒物浓度上升较为明显， PM10的小时质量浓度最高达548μg／m3， PM2．5质量浓度也达到197μg／m3，污染持续时间为2 d，3月8—9日当地空气质量均达到中度污染水平。根据后向轨迹模型、颗粒物离子浓度的分析，此次污染是由外来浮尘及苏州本地污染物排放所造成的区域霾污染影响所致。根据监测结果与实际污染特征，针对性地提出了对策和措施。     

The Metallic Composition of Aerosols at Three Monitoring Sites in Korea During Winter 2002

In the present study, the distribution patterns of various metals were analyzed and compared using PM samples collected concurrently from three monitoring sites located in Korea (Seoul, Busan, and Jeju island) in December 2002. As these sites can represent metal pollution with different degrees of anthropogenic activities, their concentration levels were distinguished in a systematic manner in the order of Jeju, Busan, and Seoul. By comparing the present data sets with those measured previously from other locations in Korea and around the world, we attempted to diagnose the general status of elemental pollution on the Korean peninsula. Through an application of different statistical approaches, the major processes controlling elemental levels were assessed for each of the three study sites. The results indicated the importance of both crustal and anthropogenic sources in all sites with their relative roles varying significantly from each other. The results of the metal analysis data, when examined in relation to back trajectory analysis, confirmed that their concentration changes are affected quite sensitively with air mass movement patterns. The overall results of this study consistently indicated the contribution of a strong anthropogenic source area (e.g., China) to the observed metal concentration levels in the study area, but the strengths of such signals vary considerably across the Korean peninsula.     

Evaluation of the temporal variations of air quality in Taipei City, Taiwan, from 1994 to 2003

Data collected from the five air-quality monitoring stations established by the Taiwan Environmental Protection Administration in Taipei City from 1994 to 2003 are analyzed to assess the temporal variations of air quality. Principal component analysis (PCA) is adopted to convert the original measuring pollutants into fewer independent components through linear combinations while still retaining the majority of the variance of the original data set. Two principal components (PCs) are retained together explaining 82.73% of the total variance. PC1, which represents primary pollutants such as CO, NO(x), and SO(2), shows an obvious decrease over the last 10 years. PC2, which represents secondary pollutants such as ozone, displays a yearly increase over the time period when a reduction of primary pollutants is obvious. In order to track down the control measures put forth by the authorities, 47 days of high PM(10) concentrations caused by transboundary transport have been eliminated in analyzing the long-term trend of PM(10) in Taipei City. The temporal variations over the past 10 years show that the moderate peak in O(3) demonstrates a significant upward trend even when the local primary pollutants have been well under control. Monthly variations of PC scores demonstrate that primary pollution is significant from January to April, while ozone increases from April to August. The results of the yearly variations of PC scores show that PM(10) has gradually shifted from a strong correlation with PC1 during the early years to become more related to PC2 in recent years. This implies that after a reduction of primary pollutants, the proportion of secondary aerosols in PM(10) may increase. Thus, reducing the precursor concentrations of secondary aerosols will be an effective way to lower PM(10) concentrations.     

Extension of the EUROS Integrated Air Quality Model to Fine Particulate Matter by Coupling to CACM/MADRID 2

The European Operational Smog (EUROS) integrated air quality modelling system has been extended to model fine particulate matter (PM). From an extended literature study, the Caltech Atmospheric Chemistry Mechanism and the Model of Aerosol Dynamics, Reaction, Ionisation and Dissolution were selected and recently coupled to EUROS. Currently, modelling of mass and chemical composition of aerosols in two size fractions (PM_2.5 and PM_10–2.5) is possible. The chemical composition is expressed in terms of seven components: ammonium, nitrate, sulphate, elementary carbon, primary inorganic compounds, primary organic compounds and secondary organic compounds. Calculated PM₁₀ concentrations and chemical composition are presented for two summer months of the year 2003 (1 July to 31 August).     

邢台市秋季PM2.5及水溶性离子污染特征

为研究邢台市秋季PM_2.5污染特征,于2017年10月15日~11月14日在邢台市区对PM_2.5样品进行了采集,并对其中水溶性离子（包括Cl^-、NO₃^-、SO₄^2-、NH₄⁺、Ca²⁺、Na⁺、Mg²⁺、K⁺）进行了分析.结果显示,观测期间邢台市ρ（PM_2.5）平均值为（130.0±74.9）μg/m³,其中水溶性离子质量浓度为（69.8±11.4）μg/m³,占ρ（PM_2.5）的53.3%,NO₃^-、SO₄^2-和NH₄⁺为主要离子,占水溶性离子比例达到了89.7%. 当污染加重,水溶性离子质量浓度随ρ（PM_2.5）增大而升高,且NO₃^-、NH₄⁺及SO₄^2-占比亦逐渐升高,但其他离子占比随之下降,Ca²⁺尤为明显,表明ρ（PM_2.5）升高时主要受二次无机转化影响;观测期间SOR（硫转化率）与NOR（氮转化率）的平均值分别为0.36和0.25,表明秋季SO₂与NO₂转化速率较强,二次无机污染严重,另外SOR及NOR与温度及相对湿度呈正相关,且SOR对二者更为敏感;邢台市秋季PM_2.5呈弱碱性,NH₄⁺主要以（NH₄）₂SO₄和NH₄NO₃的形式存在;ρ（NO₃^-）/ρ（SO₄^2-）平均值为2.13,表明移动源对秋季大气颗粒物的来源贡献较大;PMF分析结果表明,二次转化源、燃烧源及扬尘源为邢台市秋季PM_2.5中水溶性离子的主要来源.     

2018年春节期间京津冀地区污染过程分析

为了解2018年春节期间京津冀地区空气污染情况,利用近地面污染物浓度数据、激光雷达组网观测数据,结合WRF气象要素、颗粒物输送通量和HYSPLIT气团轨迹综合分析污染过程.结果表明,春节期间出现3次污染过程.春节前一次污染过程,各站点PM_2.5浓度均未超过200μg/m³;除夕夜,廊坊站点PM_2.5峰值浓度达到504μg/m³,是清洁天气的26倍;年初二~初五,各站点PM_2.5始终高于120μg/m³,且污染主要聚集在500m高度以下,北京地区存在高空传输,800m处最大输送通量达939μg/（m³·s）,此次重污染过程为一次典型的区域累积和传输过程.京津冀地区处于严格管控状态时,燃放烟花爆竹期间PM_2.5峰值浓度可达无燃放时PM_2.5峰值的3.2倍.为防止春节期间重污染现象的发生,需对静稳天气下燃放烟花炮竹采取预防对策.     

Regulated emission characteristics of in-use LNG and diesel semi-trailer towing vehicles under real driving conditions using PEMS

On-road driving emissions of six liquefied natural gas(LNG) and diesel semi-trailer towing vehicles(STTVs) which met China Emission Standard IV and V were tested using Portable Emission Measurement System(PEMS) in northern China.Emission characteristics of these vehicles under real driving conditions were analyzed and proved that on-road emissions of heavy-duty vehicles(HDVs) were underestimated in the past.There were large differences among LNG and diesel vehicles, which also existed between China V vehicles and China IV vehicles.Emission factors showed the highest level under real driving conditions, which probably be caused by frequent acceleration, deceleration, and start-stop.NOx emission factors ranged from 2.855 to 20.939 g/km based on distance-traveled and 6.719–90.557 g/kg based on fuel consumption during whole tests, which were much higher than previous researches on chassis dynamometer.It was inferred from tests that the fuel consumption rate of the test vehicles had a strong correlation with NOx emission, and the exhaust temperature also affected the efficiency of Selected Catalytic Reduction(SCR) aftertreatment system, thus changing the NOx emission greatly.THC emission factors of LNG vehicles were 2.012–10.636 g/km, which were much higher than that of diesel vehicles(0.029–0.185 g/km).Unburned CH_4 may be an important reason for this phenomenon.Further on-road emission tests, especially CH_4 emission test should be carried out in subsequent research.In addition, the Particulate Number(PN) emission factors of diesel vehicles were at a very high level during whole tests, and Diesel Particulate Filter(DPF)should be installed to reduce PN emission.     

加入SOA示踪的深圳大气PM2.5受体模型源解析

为厘清包括二次有机气溶胶（SOA）在内的深圳市区PM_2.5各种一次和二次来源贡献,本文于2017年9月2日~2018年8月29日在深圳市大学城点位开展PM_2.5样品采集,并进行化学组分和水溶性有机物（WSOM）质谱测量,共获得162组有效数据.观测期间深圳市大气PM_2.5平均质量浓度为26μg/m³,在传统PMF源解析的基础上加入羧基离子碎片（CO₂⁺）作为SOA的示踪物,加入水溶性有机氧（WSOO）用于计算各因子O/C,验证有机物解析效果.结果表明,SOA可以被独立解析出,其O/C明显高于其他一次污染源中有机物;机动车、二次硫酸盐、二次硝酸盐、SOA为最主要的4个源,对PM_2.5质量浓度的贡献分别为25%、23%、17%和10%,船舶、地面扬尘、老化海盐、建筑尘、生物质燃烧、燃煤和工业贡献均在5%以内.各个源的变化特征表明,机动车、二次硫酸盐、二次硝酸盐、SOA等源贡献呈现冬高夏低的季节特征,与冬季季风条件下源自内陆的污染传输密切相关.污染天气时,二次硝酸盐和SOA的贡献增加相对最显著,因此NOx和挥发性有机物是减排的关键.     

Concentrations and chemical compositions of PM10 during hazy and non-hazy days in Beijing

In order to study the concentrations of major components,characteristics and comparison in hazy and non-hazy days of PM_(10) in Beijing,aerosol samples were collected at urban site in Beijing from December 29,2014 to January 22,2015.Heavy metals like Zn,Pb,Mn,Cu,As,V,Cr and Cd were deeply studied considering their toxic effects on human being;nine water-soluble inorganic ions(SO_4~(2-),NO_3~-,NH_4~+,Na~+,K~+,Cl~-,Ca~(2+) and Mg~(2+)) and carbon fractions(OC and EC) were also analyzed.The concentrations of heavy metals were 1.03–1.98 times higher in hazy days than those in non-hazy days,mainly due to biomass burning and coal burning.The trends in total heavy metals concentrations were basically consistent with the trends in PM concentrations except for two obvious periods(12.29–12.30;1.14–1.15);but when air masses accumulated locally or around Beijing,trends in PM concentrations and heavy metals were opposite.The proportion for NO_3~-/SO_4~(2-) indicated that mobile sources such as automobiles were important reasons for haze in Beijing.Correlation between OC and EC during non-hazy days was strong(R~2= 0.95) but it was low(R~2= 0.67) during hazy days,and large variations for OC/EC values occurred in hazy days.The calculated mass concentration of SOC is 2.58 μg/m~3,which only accounted for 10.1% of the OC concentration.When air masses from the far north-west,they decreased PM concentration in Beijing and they were relatively clean;however,those from the near east,south-east and south of the mainland increased PM concentration and they were dirty.     

大兴安岭5种乔木树种燃烧释放PM2.5中水溶性元素特性

采用自主设计的生物质燃烧实验装置,在不同燃烧状态（明燃、阴燃）下,对大兴安岭林区5种典型乔木树种的不同部位（枝、叶、皮）燃烧释放PM_2.5中的水溶性元素特性进行研究.结果显示,不同树种间PM_2.5的排放因子差异显著,排放范围为（2.408±0.854）~（9.227±1.172）g/kg.5种乔木树种燃烧释放PM_2.5中主要检测到Mg、Ca、K等16 种元素,其中Ca、K、Zn、Mg 4种元素的排放因子明显大于其它元素.不同树种间元素排放因子差异较大,针叶树的排放因子一般高于阔叶树.除Cd元素外,不同器官间排放的元素总量无明显差异.不同树种不同器官燃烧释放PM_2.5中水溶性元素的占比顺序较为一致,其中Ca、K、Zn和Mg 4种元素的排放因子在枝、叶、皮中均较高.此外,燃烧状态对元素排放特征影响较大,Li、Mg、Ca等7种元素的排放因子均表现为明燃显著高于阴燃.