重庆市典型城区PM2.5含碳气溶胶季节变化和来源解析

于2015年10月、12月和2016年3月、8月在重庆大学A区采集秋、冬、春、夏4个季节PM_2.5样品,观察其微观形貌,分析含碳气溶胶及其碳组分的浓度水平,并探讨其季节变化及进行来源解析.结果表明,重庆沙坪坝区PM_2.5中有机碳（OC）、元素碳（EC）、烟灰（char）和烟炱（soot）的年均质量浓度分别为20.66、6.16、5.42和0.74 μg·m^-3.OC季节变化显著,冬季最高,夏季最低;EC秋季最高,冬季最低,但与其它季节相差不大;char表现为秋季 > 春季 > 冬季 > 夏季;soot表现为秋季 > 夏季 > 春季 > 冬季.正定矩阵因子（PMF）解析出3个因子,分别代表生物质/煤燃烧和道路扬尘的混合源（52.7%）、汽油机动车排放源（22.9%）和柴油机动车排放源（24.4%）.机动车尾气是秋、春和夏3个季节含碳气溶胶的主要来源,冬季主要受煤炭/生物质燃烧和道路扬尘混合源的影响.秋季污染事件可能是因为本地及周边城市汽油车通行量增加,冬季污染事件可能是本地煤炭/生物质燃烧排放增加和周边农村地区输入的共同作用,春季污染事件可能与来自西北方向的沙尘长距离传输有关.     

Potential and Shortcomings of Numerical Weather Prediction Models in Providing Meteorological Data for Urban Air Pollution Forecasting

The last decade progress in numericalweather prediction (NWP) modelling and studies of urbanatmospheric processes for providing meteorological data forurban air pollution forecasting is analysed on examples ofseveral European meteorological centres. Modern nested NWP models are utilising land-use databasesdown to 1 km resolution or finer, and are approaching thenecessary horizontal and vertical resolution suitable forcity scale. The recent scientific developments in the fieldof urban atmospheric physics and the growing availabilityof high-resolution urban surface characteristics datapromise further improvements of the capability of NWPmodels for this aim. A strategy to improve NWP data forthe urban air pollution forecasting is suggested.     

呼包鄂地区冬季、夏季PM2.5污染特征

基于PMF模型源解析结果分析了呼包鄂地区冬季、夏季主要污染源及污染特征.物质重构结果表明,有机物(19. 9%～44. 6%)、地壳物质(9. 7%～46. 2%)在呼包鄂地区受体PM_(2.5)中占有较大比重.源解析结果表明,冬季各类源对PM_(2.5)的分担率大小依次为:二次源(26. 7%)燃煤源(26. 1%)机动车源(19. 1%)扬尘源(18. 1%),夏季则为二次源(26. 7%)扬尘源(22. 3%)燃煤源(16. 6%)机动车源(15. 1%) SOC(8. 7%),可以看出二次源在呼包鄂地区冬季和夏季均为首要贡献源类,冬季燃煤源、夏季扬尘源对呼包鄂地区的影响较大.分析冬季、夏季典型污染过程,对应于源解析结果,冬季主要污染源为二次源、燃煤源,夏季为二次源.     

青岛市臭氧污染与非污染期间VOCs化学特征及来源解析

青岛市夏季臭氧浓度水平高且污染事件频发,开展臭氧污染过程和非污染时期的挥发性有机物(VOCs)及其臭氧生成潜势(OFP)的精细化来源解析研究,对于有效降低沿海城市的大气臭氧污染,持续改善环境空气质量将会发挥重要的作用.因此,利用青岛市2020年夏季(6～8月)小时分辨率的在线VOCs监测数据,分析臭氧污染过程和非臭氧污染时期环境VOCs的化学特征,并通过正定矩阵因子分解(PMF)模型进行了环境VOCs及其OFP的精细化来源解析研究.结果表明,青岛市夏季环境ρ(TVOCs)平均值为93.8μg·m^-3,臭氧污染过程相较于非臭氧污染时期TVOCs浓度上升了49.3%,其中芳香烃浓度增加最显著,增加了59.7%.夏季环境VOCs总的OFP达到246.3μg·m^-3,臭氧污染过程相较于非臭氧污染时期环境VOCs的总OFP增加了43.1%;其中烷烃增加最多,增加了58.8%.间-乙基甲苯和2,3-二甲基戊烷是臭氧污染过程中OFP增加幅度最大的物种.青岛市夏季环境VOCs的主要贡献源为柴油车(11.2%)、溶剂使用(4.7%)、液化石油气及天然气(27....     

雾霾期北京大气中多环芳烃污染特征及暴露风险评估

雾霾对我国尤其是华北平原地区造成了极大的困扰,其发生常以颗粒物浓度急剧增长为特征,给人群健康带来了极大的风险。为进一步阐释雾霾的形成过程及其健康效应,在冬季雾霾期对北京城区大气颗粒态及气态中18种多环芳烃(PAHs)进行了连续测定,同步监测颗粒物、痕量气体污染物以及气象参数的变化,并对PAHs的浓度、组成、气粒分配等大气行为以及其与气象因素的作用机制进行了探讨。北京城区大气气相和颗粒物相中ΣPAHs浓度分别为585 ng·m~(-3)和705 ng·m~(-3)。雾霾发生时,PM_(2.5)浓度升高了3.6倍,PAHs浓度升高了2.6倍,18种PAHs同系物的浓度均随PM_(2.5)的浓度线性增加,其线性相关性受PAHs来源以及氧化活性的影响;夜间较重质量数的PAHs相对比例增加,主要受日间交通源以及夜间燃烧源贡献强度影响。受颗粒物组成以及湿度的影响,雾霾天气下PAHs颗粒相分配率降低。进一步评估了北京城区人群的PAHs吸入健康效应,冬季雾霾频繁发生下其对人群癌症风险为6.2×10~(-5)。     

广西PM2.5时空分布特征及污染天气类型

研究区域ρ（PM_2.5）的时空分布特征和污染天气类型的关系是开展大气污染防治和空气质量预报预警的关键支撑技术之一.基于2015—2016年广西14个城市环境空气质量日监测数据和相关气象资料,分析了2015—2016年广西空气质量概况和污染的基本特征,采用EOF（经验正交函数）分析和后向轨迹聚类分析方法表征了广西ρ（PM_2.5）时空分布模态,统计了广西两年间24次区域范围（3个及以上连片城市）大气轻度及以上污染过程,分析了不同污染过程的天气类型和空气质量变化特点.结果表明:PM_2.5是广西大气污染首要污染物,ρ（PM_2.5）年均值呈北高南低的区域特征,月际变化基本呈正V字型分布;EOF分析和后向轨迹聚类分析显示,广西ρ（PM_2.5）的时空结构主要有3种模态,其方差贡献率分别为78.9%、5.7%和3.7%,基本反映了广西ρ（PM_2.5）变化的时空模态的主要特征,桂林和玉林两年间的后向轨迹聚类很好地解释了第二和第三模态的南北浓度和东西浓度异常反相位分布特征;广西14个城市两年间PM_2.5区域性污染天气类型主要有10种,其中污染天气类型中占比较大的是弱冷高压脊型（24.4%）、均压场型（20.2%）、高压后部型（16.1%）和高压后部配合西南暖低压型（8.5%）,是引发广西大范围大气污染的典型天气类型.研究显示,广西大气污染具有地域性、季节性和南北输送特征,污染过程的天气形势变化具有一定规律性.      

An assessment of air pollution episodes in the urban area of athensfor the period 1984–1994

Air pollution episodes in the greater Athens area for the period 1984–1994 were examined to assess the type, frequency and repetitivity of air pollution in the examined area. The assessment depends strongly on the definition of an air pollution episode (APE), namely if it is the narrow or broad one. Results from the statistical analysis of the data record‐on the basis of the broad definition of the APE‐show a declining trend in the number of APEs especially for non‐photochemical species, a sharp increase of APEs from 1987 to 1993 due to high ozone levels and a similarly sharp decrease thereafter, and a decreasing trend of nitrogen dioxide episodes from the year 1992 thereon. Interannual examination of APEs shows that they occur in higher numbers during the period November to January and in the period May to July. Finally an examination of the number of days for which urgent‐short term‐measures were taken to reduce high levels of atmospheric pollution, shows a decreasing trend from 1989, with the exception of 1994 where the sharp increase in the number of days is due to the application of the urgent measures at a precautionary mode, i.e. when pollution loads exceed the alert levels, air pollution increases in rapid rates and meteorological conditions are supportive for an enhanced APE.     

Forecasting Daily Maximum Ozone Concentrations in the Athens Basin

In the work ozone data from the Liossion monitoring station of the Athens/PERPA network are analysed. Data cover the months May to September for the period 1987–93. Four statistical models, three multiple regression and one ARIMA (0,1,2), for the prediction of the daily maximum 1-hour ozone concentrations are developed. All models together, with a persistence forecast, are evaluated and compared with the 1993's data, not used in the models development. Validation statistics were used to assess the relative accuracy of models. Analysis, concerning the models' ability to forecast real ozone episodes, was also carried out. Two of the three regression models provide the most accurate forecasts. The ARIMA model had the worst performance, even lower than the persistence one. The forecast skill of a bivariate wind speed and persistence based regression model for ozone episode days was found to be quite satisfactory, with a detection rate of 73% and 60% for O3 >180 g m-3 and O3 >200 g m-3, respectively.     

The influence of meteorological conditions and stringent emission control on high TSP episodes in istanbul

Istanbul has faced serious air pollution problems since the mid-80s. This is mainly due to particulate air pollution coming from poor quality lignite in areas, which are heavily populated and industrialized. As a consequence of severe air pollution problems, stringent control on the emissions in the city started in the year of 1994. In this work, in order to study the relationship between emissions and meteorological conditions, an assessment of air pollution episodes and air pollution potential in the city is presented for the terms at the changed emission schedule as the influence of an emission reduction strategy. The influence of meteorological conditions on the TSP (total suspended particulates) levels is considered for two consecutive winter periods. On this occasion, the city has faced different TSP levels and episode characteristics depending on stringent emission reductions covering the banned, poor-quality lignite and fuel switching. For this purpose, climatological conditions and air quality analyses were performed.     

德州、北京重污染过程PM2.5中PAHs污染特征及来源分析

为探讨华北地区秋冬季重污染过程PM_2.5（细颗粒物）中PAHs（多环芳烃）的污染水平、分布特征及来源,分别采集2018年11月17日—2019年1月19日德州市和北京市PM_2.5样品,利用气相色谱-质谱法测量两个站点6次重污染过程中26种PAHs浓度水平,分析PAHs污染特征、分子组成分布及其来源,并利用毒性当量因子估算了PAHs毒性.结果表明:①6次重污染过程中,德州站点∑₂₆PAHs浓度为62~191 ng/m3,北京站点为61~129 ng/m3.②单位质量PM_2.5中PAHs的浓度北京站点更高.③两个站点PAHs分子组成分布较为一致,萘、蒽、芴等低分子量的PAHs浓度较低,高分子量PAHs浓度较高,浓度最高的分别为苯并[b]荧蒽、苯并[a]芘、苯并[a]蒽和甲基荧蒽等.④特征比值结果显示,PAHs来源包括柴油车尾气、燃煤和生物质燃烧,德州站点受生物质燃烧影响更为显著.⑤毒性当量计算结果表明,德州站点毒性当量浓度（TEQ）高于北京站点,6次重污染过程中两个站点PAHs的TEQ平均值在6.5~17.2 ng/m3之间,低于国内其他一些地区,但苯并[a]芘的浓度在5.2~13.1 ng/m3之间,超过了GB 3095—2012《环境空气质量标准》日均值的标准限值（2.5 ng/m3）,对人体健康存在潜在危害.研究显示:秋冬季重污染过程中,北京站点单位质量PM_2.5中PAHs的浓度较高,两个点位PAHs分子组成分布特征及来源较为相似,且均对人体健康存在潜在危害;应进一步加强对PAHs浓度水平的控制,这不仅有利于持续改善PM_2.5污染,也有助于减轻人体潜在的健康风险.