2014年1月北京市大气重污染过程单颗粒物特征分析

利用在线单颗粒物气溶胶质谱仪(SPAMS)对2014年1月北京市典型大气重污染过程进行了连续监测,分析了具有正负离子质谱信息的颗粒物共2248225个.同时,利用ART-2a神经网络分类方法并结合Matlab统计分析,将具有质谱信息的颗粒物归为10类,分别为:矿尘类颗粒物(Dust)、元素碳颗粒物(EC)、有机碳颗粒物(OC)、元素碳和有机碳混合颗粒物(ECOC)、钠钾颗粒物(NaK)、富钾颗粒物(K)、含氮有机物(KCN)、高分子有机物(MOC,Macromolecular OC)、多环芳烃类颗粒物(PAHs)和重金属类颗粒物(Metal).结合PM_2.5质量浓度数据和HYSPLIT 4.0后向轨迹模型结果,将观测时间段划分为3个典型污染过程和1个清洁过程.结果显示,重污染期间OC、MOC和PAHs为最主要的颗粒物类型.最后,本文还比对分析了污染过程和清洁期间颗粒物的混合状态,结果表明,污染过程中硫酸盐和硝酸盐较清洁期间更容易与碳质颗粒物结合.     

华北平原灰霾天气下大气气溶胶的单颗粒分析

为分析灰霾期间单颗粒气溶胶化学组成和混合状态,于2014年12月9日—2015年1月10日,使用单颗粒气溶胶质谱仪（SPAMS）表征华北平原郑州市中牟县的气溶胶颗粒.结果表明:灰霾期（H1:20141213T19:00—20141215T10:00;H2:20150102T10:00—20150106T03:00）和清洁期（C1:20141215T18:00—20141217T18:00;C2:20141231T16:00—20150101T20:00）大气颗粒物种类相同,主要分为有机碳（OC）、元素碳（EC）、生物质燃烧颗粒（BB）、元素碳有机碳（ECOC）、钾二次颗粒（K-Secondary）、矿尘（Dust）以及重金属颗粒（HM）7类.C1时间段,ECOC颗粒占比最高,占总颗粒数的49.8%;其次是OC和EC颗粒物,二者分别占总颗粒数的16.5%和10.8%.H1时间段,K-Secondary颗粒的占比（31.3%）最高;其次是OC和EC颗粒,二者分别占总颗粒数的23.1%和20.2%.清洁期与灰霾期质谱差分结果表明,清洁期颗粒物中含有C₃H+、C₄H₃+、C₅H₃+等有机碳碎片峰,而灰霾期颗粒物中NO₃-、HSO₄-、NO₂-等组分的信号强度显著大于清洁期.混合状态分析表明,从清洁期到灰霾期的过程中,主要颗粒物与NO₃-和HSO₄-的混合程度显著增强.清洁期与灰霾期单颗粒化学组成与混合状态的对比分析表明,清洁期新鲜排放的含碳气溶胶在灰霾期不断老化,单颗粒中二次无机组分增加,气溶胶整体老化严重.此外,灰霾期（H2）EC颗粒占总颗粒数的比例增至18.1%,并且与NO₃-、HSO₄-二次组分的混合状态增强,使平均能见度降低为4.0 km.研究显示,郑州大气能见度主要受化学组分、颗粒物混合状态和污染物质量浓度的影响.      

利用SPAMS研究西安市重污染天气细颗粒物污染特征及来源

为了对西安市冬季重污染过程中的细颗粒物进行动态源解析,于2016年12月5-22日,利用SPAMS（单颗粒气溶胶质谱仪）在西安市城市运动公园开展连续观测.将观测期分为4个阶段,结合气象条件对不同阶段细颗粒物的污染特征进行分析比较.依据质谱特征,将所采集到的颗粒分为EC（元素碳）、OC（有机碳）、ECOC（混合碳）、HM（重金属）、LEV（左旋葡聚糖）、SiO₃（矿尘）、K（钾）、Na（钠）、HOC（有机大分子）及Other（其他）类.结果表明:观测期间所采集到的OC类颗粒物数量最多,在重污染阶段OC、K和EC类颗粒物占颗粒总数的70%以上,是重污染天气的主要组成颗粒.在雾霾消散期,OC、LEV和SiO₃类颗粒是主要类型颗粒物.根据颗粒物的化学类型及离子特征,利用PMF（正交矩阵因子分解）模型法得到6种污染源贡献率分别为27.7%（燃煤源）、22.3%（二次污染源）、20.4%（交通源）、10.4%（生物质燃烧源）、9.7%（工艺过程源）、6.5%（扬尘源）及3.0%（其他未知源）.研究显示:在重污染阶段,燃煤源与交通源占比大幅上升,与二次污染源共同造成了此次重污染天气;在雾霾消散期,扬尘源及生物质燃烧源成为大气细颗粒物的主要污染源.      

Highly time-resolved aerosol characteristics during springtime in Weizhou Island

A single particle aerosol mass spectrometer was deployed to measure the changes of single particle species and sizes during March 2015 in Weizhou Island of the Beibu Gulf, Guangxi province, South China. In this campaign, a total of 3,100,597 particles were sized, and 25.8%particles with both positive and negative mass spectrum were collected and 24.8%characterized in combination with the ART-2 a neural network algorithm. The distribution of sized particles was mainly in from 520 to 600 nm, and the diameters ranging from 340 to1000 nm accounted for above 90%. Eight types of particles were classified: Elemental Carbon containing(EC), Organic Carbon containing(OC), EC and OC combined containing particles,Na containing particles, K containing particles(K), Levoglucosan containing particles,mineral containing particles, and Heavy Metal containing particles(HM). EC, OC and K were the major containing particles, which accounted for 84.3% in the eight types particles. The relative ratio and size distribution of the three types were EC(48.1%, 620 nm), OC(12.7%,440 nm), and K(23.5%, 600 nm), respectively. The three types of particles were a bit increasing ratios compared with those in clean periods during haze pollution periods.Combined with the back-trajectory results from the Hysplit-4 model and local pollution sources revealed that the ambient air quality on the Weizhou Island may be influenced by biomass burning in the Indochina Peninsula(biomass burning in the Indochina Peninsula)from the transportation on higher level atmospheric layer and by mainland of south China located northeast of Weizhou Island on the ground.     

基于SPAMS的太原市典型生活区停暖前后PM2.5来源及组成

为分析太原市采暖期和非采暖期PM_2.5的特征,利用单颗粒气溶胶质谱仪（SPAMS）分析太原市典型生活区采暖期（2016年3月11-18日）和非采暖期（2016年4月1-7日）PM_2.5的来源及组成.结果表明:① 采暖期（停暖前）颗粒物有机碳、硫酸盐和多环芳烃等信号强度大于非采暖期（停暖后）,而元素碳、硝酸盐、铵盐等反之.② 为了尽可能排除气象因素的影响,选取风向（东南风）、风级（二级）相同时段的颗粒物进行分析,停暖前后颗粒物主要化学组分为有机碳、混合碳和元素碳,采暖前有机碳占比（达51.9%）最高,非采暖期元素碳占比（32.6%）最高.采暖期有机碳、高分子有机物和左旋葡聚糖占比明显高于非采暖期,元素碳、矿物质和重金属反之.③ 停暖前后首要的两类污染源为燃煤和机动车尾气,二者贡献率之和分别高达70.1%和67.4%,可见本地主要受这两类源的影响.燃煤在采暖期为首要污染源,并且贡献比例高于非采暖期,而机动车尾气在非采暖期为首要污染源,且比例明显高于采暖期.研究显示,采暖和非采暖期虽然首要污染源有所差异,但在污染过程中,机动车尾气源的贡献比例均高于优良时段,说明无论是采暖期还是非采暖期,除燃煤排放的影响外,机动车尾气的影响也需得到重视,建议加强机动车燃油品质的升级,使用清洁煤,并在重污染时段采取相应的管控措施.      

两种典型污染时段鹤山市大气细颗粒污染特征及来源

PM_2.5和O₃浓度超标是我国大气污染的主要特征,研究两种典型污染时段的细颗粒化学组成、混合状态和来源对治理大气污染具有重要意义.2016年11月10—20日广东省鹤山市先后出现了PM_2.5和O₃超标的污染事件.污染期间,采用SPAMS（单颗粒气溶胶质谱仪）对细颗粒进行实时采样分析,共采集到有正负化学组成信息的颗粒422 944个,占总颗粒数的19.2%.基于单颗粒质谱数据特征,使用自适应共振神经元网络算法（ART-2a）,对单颗粒数据进行自适应分类.颗粒物划分为OC（有机碳）、EC（元素碳）、ECOC（元素-有机碳混合）、HOC（高分子有机碳）、Pb-rich（富铅）、Si-rich（富硅）、LEV（左旋葡聚糖）、K-Secondary（钾二次）、Na-rich（海盐）和HM（重金属）颗粒共10类.结果表明:两个PM_2.5污染时段EC颗粒和K-Secondary颗粒的占比高,EC颗粒分别占46.5%和61.1%,K-Secondary颗粒分别占14.3%和10.3%;O₃污染时段EC颗粒占比（39.4%）最高,其次是OC颗粒占比17.0%;两种污染时段OC组分与HSO₄-和NO₃-的混合程度都有明显的上升,说明污染有利于有机气溶胶的老化.由源解析结果可知,PM_2.5污染时段,细颗粒主要来源于燃煤、机动车尾气和扬尘,而O₃污染时段细颗粒主要来源于燃煤、生物质燃烧和扬尘;此外,两种污染时段燃煤源对细颗粒的贡献都有较大提升.研究显示,控制燃煤源的排放对污染物的降低有着重要影响.      

Chemical characteristics of PM2:5 during a typical haze episode in Guangzhou

The chemical characteristics (water-soluble ions and carbonaceous species) of PM2:5 in Guangzhou were measured during a typical haze episode. Most of the chemical species in PM2:5 showed significant di erence between normal and haze days. The highest contributors to PM2:5 were organic carbon (OC), nitrate, and sulfate in haze days and were OC, sulfate, and elemental carbon (EC) in normal days. The concentrations of secondary species such as, NO3??, SO4 2??, and NH4 + in haze days were 6.5, 3.9, and 5.3 times higher than those in normal days, respectively, while primary species (EC, Ca2+, K+) show similar increase from normal to haze days by a factor about 2.2–2.4. OC/EC ratio ranged from 2.8 to 6.2 with an average of 4.7 and the estimation on a minimum OC/EC ratio showed that SOC (secondary organic carbon) accounted more than 36.6% for the total organic carbon in haze days. The significantly increase in the secondary species (SOC, NO3??, SO4 2??, and NH4 +), especially in NO3??, caused the worst air quality in this region. Simultaneously, the result illustrated that the serious air pollution in haze episodes was strongly correlated with the meteorological conditions. During the sampling periods, air pollution and visibility had a good relationship with the air mass transport distance; the shorter air masses transport distance, the worse air quality and visibility in Guangzhou, indicating the strong domination of local sources contributing to haze formation. High concentration of the secondary aerosol in haze episodes was likely due to the higher oxidation rates of sulfur and nitrogen species.     

沈阳市冬季不同污染程度PM2.5中OC和EC污染特征

于2018年12月~2019年1月对沈阳市PM_2.5进行持续在线浓度监测,使用有机碳/元素碳分析仪对PM_2.5中有机碳（OC）和元素碳（EC）的质量浓度进行分析,研究了不同污染程度下PM_2.5及其碳组分的污染特征和来源.结果表明,沈阳地区冬季碳组分污染较为严重,不同污染程度下的总碳气溶胶（TCA）约占PM_2.5的36.3%~42.8%.中/重度污染天气下PM_2.5、OC和EC的平均质量浓度达到148.6,29.6,6.6μg/m³,是清洁天的3.1~3.3倍.PM_2.5、OC和EC的日变化均表现为早晚高、午后低,任一时刻其浓度均为中/重度污染>轻度污染>清洁天.不同污染程度下的OC/EC值均大于2,其中污染天比值分布在2.1~25.3区间内,表明燃煤和机动车尾气排放是污染天碳质气溶胶的主要来源.二次有机碳（SOC）随污染程度增加表现出升高趋势,清洁天、轻度污染和中/重度污染下其平均浓度依次为2.9,6.5,10.6μg/m³.后向轨迹聚类结果表明,沈阳地区冬季污染天主要受偏北和西北方向气团影响.     

Single particle analysis of ambient aerosols in Shanghai during the World Exposition, 2010: two case studies

ATSI Model 3800 aerosol time-of-flight mass spectrometer (ATOFMS) was deployed for single-particle analysis in Shanghai during the World Exposition (EXPO), 2010. Measurements on two extreme cases: polluted day (1st May) and clean day (25th September) were compared to show how meteorological conditions affected the concentration and composition of ambient aerosols. Mass spectra of 90496 and 50407 particles were analyzed respectively during the two sampling periods. The ART-2a neural network algorithm was applied to sort the collected particles. Seven major classes of particles were obtained: dust, sea salt, industrial, biomass burning, organic carbon (OC), elementary carbon (EC), and NH₄-rich particles. Number concentration of ambient aerosols showed a strong anti-correlation with the boundary layer height variation. The external mixing states of aerosols were quite different during two sampling periods because of different air parcel trajectories. Number fraction of biomass burning particles (43.3%) during polluted episode was much higher than that (21.6%) of clean time. Air parcels from the East China Sea on clean day diluted local pollutant concentration and increased the portion of sea salt particle dramatically (13.3%). The large contribution of biomass burning particles in both cases might be an indication of a constant regional background of biomass burning emission. Mass spectrum analysis showed that chemical compositions and internal mixing states of almost all the particle types were more complicate during polluted episode compared with those observed in clean time. Strong nitrate signals in the mass spectra suggested that most of the particles collected on polluted day had gone through some aging processes before reaching the sampling site.     

城区与县区不同空气质量下碳组分污染特征对比分析

为对比城区与相邻县区不同空气质量下的碳组分污染特征,分别在成都市和仁寿县采集霾期及非霾期PM_(2.5)有效样品共计88个,确定其相应质量和各碳组分浓度[有机碳(OC)、元素碳(EC)和二次有机碳(SOC)等],并进行各碳组分之间的相关性及主成分分析.结果表明,不同空气质量下的城区污染物浓度均高于县区.OC和EC密切相关,非霾期的相关性系数较霾期大.与城区相比,霾期县区的SOC/PM_(2.5)较大,说明其受二次有机物污染更为明显;但城区非霾期二次气溶胶占比明显高于霾期,表明霾期的一次排放是城区大气污染的主要原因.燃煤、机动车排放和生物质燃烧均是两个区域PM_(2.5)的主要来源.