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.      

Sources and characteristics of fine particles over the Yellow Sea and Bohai Sea using online single particle aerosol mass spectrometer

Marine aerosols over the East China Seas are heavily polluted by continental sources. During the Chinese Comprehensive Ocean Experiment in November 2012, size and mass spectra of individual atmospheric particles in the size range from 0.2 to 2.0 μm were measured on board by a single particle aerosol mass spectrometer(SPAMS). The average hourly particle number(PN)was around 4560 ± 3240 in the South Yellow Sea(SYS), 2900 ± 3970 in the North Yellow Sea(NYS), and 1700 ± 2220 in the Bohai Sea(BS). PN in NYS and BS varied greatly over 3 orders of magnitude, while that in SYS varied slightly. The size distributions were fitted with two log-normal modes. Accumulation mode dominated in NYS and BS, especially during episodic periods. Coarse mode particles played an important role in SYS. Particles were classified using an adaptive resonance theory based neural network algorithm(ART-2a). Six particle types were identified with secondary-containing, aged sea-salt, soot-like, biomass burning, fresh sea-salt,and lead-containing particles accounting for 32%, 21%, 18%, 16%, 4%, and 3% of total PN,respectively. Aerosols in BS were relatively enriched in particles from anthropogenic sources compared to SYS, probably due to emissions from more developed upwind regions and indicating stronger influence of continental outflow on marine environment. Variation of source types depended mainly on origins of transported air masses. This study examined rapid changes in PN, size distribution and source types of fine particles in marine atmospheres. It also demonstrated the effectiveness of high-time-resolution source apportionment by ART-2a.     

Sources and characteristics of fine particles over the Yellow Sea and Bohai Sea using online single particle aerosol mass spectrometer

Marine aerosols over the East China Seas are heavily polluted by continental sources. During the Chinese Comprehensive Ocean Experiment in November 2012, size and mass spectra of individual atmospheric particles in the size range from 0.2 to 2.0 μm were measured on board by a single particle aerosol mass spectrometer (SPAMS). The average hourly particle number (PN) was around 4560 ± 3240 in the South Yellow Sea (SYS), 2900 ± 3970 in the North Yellow Sea (NYS), and 1700 ± 2220 in the Bohai Sea (BS). PN in NYS and BS varied greatly over 3 orders of magnitude, while that in SYS varied slightly. The size distributions were fitted with two log-normal modes. Accumulation mode dominated in NYS and BS, especially during episodic periods. Coarse mode particles played an important role in SYS. Particles were classified using an adaptive resonance theory based neural network algorithm (ART-2a). Six particle types were identified with secondary-containing, aged sea-salt, soot-like, biomass burning, fresh sea-salt, and lead-containing particles accounting for 32%, 21%, 18%, 16%, 4%, and 3% of total PN, respectively. Aerosols in BS were relatively enriched in particles from anthropogenic sources compared to SYS, probably due to emissions from more developed upwind regions and indicating stronger influence of continental outflow on marine environment. Variation of source types depended mainly on origins of transported air masses. This study examined rapid changes in PN, size distribution and source types of fine particles in marine atmospheres. It also demonstrated the effectiveness of high-time-resolution source apportionment by ART-2a.     

Sources and characteristics of fine particles over the Yellow Sea and Bohai Sea using online single particle aerosol mass spectrometer

Marine aerosols over the East China Seas are heavily polluted by continental sources. During the Chinese Comprehensive Ocean Experiment in November 2012, size and mass spectra of individual atmospheric particles in the size range from 0.2 to 2.0 μm weremeasured onboardby a single particle aerosol mass spectrometer (SPAMS). The average hourly particle number (PN) was around 4560 ± 3240 in the South Yellow Sea (SYS), 2900 ± 3970 in the North Yellow Sea (NYS), and 1700 ± 2220 in the Bohai Sea (BS). PN in NYS and BS varied greatly over 3 orders of magnitude, while that in SYS varied slightly. The size distributions were fitted with two log-normal modes. Accumulation mode dominated in NYS and BS, especially during episodic periods. Coarse mode particles played an important role in SYS. Particles were classified using an adaptive resonance theory based neural network algorithm (ART-2a). Six particle types were identified with secondary-containing, aged sea-salt, soot-like, biomass burning, fresh sea-salt, and lead-containing particles accounting for 32%, 21%, 18%, 16%, 4%, and 3% of total PN, respectively. Aerosols in BS were relatively enriched in particles from anthropogenic sources compared to SYS, probably due to emissions from more developed upwind regions and indicating stronger influence of continental outflowonmarine environment. Variation of source types dependedmainly on origins of transported air masses. This study examined rapid changes in PN, size distribution and source types of fine particles in marine atmospheres. It also demonstrated the effectiveness of high-time-resolution source apportionment by ART-2a.     

鹤山大气超级站旱季单颗粒气溶胶化学特征研究

利用单颗粒气溶胶飞行时间质谱等仪器在鹤山大气超级站开展综合观测,结合ART-2a自适应共振神经网络聚类算法,将2013年11月4日~2013年12月30日期间监测到的1637330个细颗粒分成9类: EC-Fresh颗粒、EC-Nitrate/Sulfate颗粒、K-EC颗粒、Ca-EC颗粒、ECOC颗粒、OC-Levoglucosan颗粒、OC-Nitrate/Sulfate颗粒、K-Nitrate/Sulfate颗粒和Metal-rich颗粒.结果表明:该大气超级站所在地区旱季霾日有利于与水溶性二次无机组分混合的EC-Nitrate/Sulfate颗粒、K-Nitrate/Sulfate颗粒的累积;晴朗天更有利于二次有机组分在气溶胶颗粒中生成,雨天受当地排放源的影响显著,含有较高EC-Fresh和K-EC颗粒.相关性的研究发现,EC-Nitrate/Sulfate颗粒与能见度有良好的相关性,它们对霾的形成有至关重要的作用.     

南京江北新区大气单颗粒来源解析及混合状态

利用单颗粒气溶胶飞行时间质谱（SPAMS）于2015年12月1~31日对南京江北新区大气单颗粒进行了测量,共采集到同时含有正负离子谱图的颗粒747.8万个.结果表明,监测期间南京江北新区总体空气质量较差,污染天气占比为49.2%,SPAMS所捕获的颗粒数与PM_2.5质量浓度的相关性达到0.83,因此颗粒物数浓度在一定程度上能够用来反映大气污染状况,监测点主要污染源包括燃煤源以及机动车尾气源,工业工艺源污染占比居第3位,3种源的总贡献率达到63.5%.从整体上看,PM_2.5质量浓度的升高大多伴随着燃煤及机动车尾气占比的升高,EC、混合碳（ECOC）与OC在生物质燃烧、扬尘、汽车尾气排放、燃煤燃烧以及工业源中均与NO₂^-、NO₃^-以及SO₄^-有较高的混合程度.     

桂林市细颗粒物典型排放源单颗粒质谱特征研究

利用单颗粒气溶胶飞行时间质谱仪(SPAMS)对桂林市四类PM2.5典型污染源排放颗粒物的粒径和化学成分进行分析,结果表明:除扬尘颗粒外,其他几类源排放颗粒物的粒径集中在0.2~1.25μm之间.几类污染源排放颗粒物的化学成分有明显区别,柴油车以EC颗粒为主,占总电离颗粒的74.7%,汽油车以富钙颗粒为主,占总电离颗粒的86.7%,燃煤排放以富铁、富铬颗粒为主,二者合计占了总电离颗粒的59.9%,生物质燃烧以含左旋葡聚糖颗粒为主,占总电离颗粒的44.4%,扬尘颗粒以富铝颗粒为主,占总电离颗粒的69.4%.这些特征为大气颗粒物的源解析提供了可能.     

利用SPAMS研究华北乡村站点(曲周)夏季大气单颗粒物老化与混合状态

利用单颗粒气溶胶飞行时间质谱(SPAMS)于2013年6月30日~7月8日对华北地区乡村站点曲周大气单颗粒粒径及其化学组成进行了在线测量,共采集到同时含有正负离子谱图的颗粒230 152个,其粒径主要集中在0.2~2.0μm.结果表明,该地区的大气颗粒物主要分为8类:元素碳(EC,55.5%)、有机碳(OC,10.7%)、钠,钾等碱金属颗粒(alkalis,17.4%)、其他金属颗粒(other metals,1.7%)、富铁颗粒(Fe-rich,6.3%)、富铅颗粒物(Pb-rich,3.1%)、沙尘颗粒(dust,4.8%),other颗粒(0.8%),观测得到的8类气溶胶颗粒中绝大部分包含46NO-2、62NO-3、80SO-3、96SO-4、97HSO-4等二次组分离子,说明这些颗粒都经历了不同的老化过程或与二次组分进行了不同程度的混合.从气溶胶类型的谱分布看,各类型颗粒数浓度峰值基本出现在700~800 nm之间,dust、Fe颗粒主要集中在粗粒径段,EC颗粒子类研究表明随着表面不断吸附NH+4、NO-3、SO2-4等二次组分,EC颗粒逐步演化成老化程度较低的NO-3吸附型EC(ECN)和严重老化的SO2-4吸附型EC(ECS)混合态,两者日变化呈现明显的负相关性,也可能随着二次有机物在EC表面吸附,形成OC/EC混合态.     

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 characterization and source apportionment of atmospheric submicron particles on the western coast of Taiwan Strait, China

Taiwan Strait is a special channel for subtropical East Asian Monsoon and its western coast is an important economic zone in China. In this study, a suburban site in the city of Xiamen on the western coast of Taiwan Strait was selected for fine aerosol study to improve the understanding of air pollution sources in this region. An Aerodyne high-resolution time-of-flight aerosol mass spectrometer(HR-To F-AMS) and an Aethalometer were deployed to measure fine aerosol composition with a time resolution of 5 min from May 1to 18, 2015. The average mass concentration of PM1 was 46.2 ± 26.3 μg/m~3 for the entire campaign. Organics(28.3%), sulfate(24.9%), and nitrate(20.6%) were the major components in the fine particles, followed by ammonium, black carbon(BC), and chloride. Evolution of nitrate concentration and size distribution indicated that local NOx emissions played a key role in high fine particle pollution in Xiamen. In addition, organic nitrate was found to account for 9.0%–13.8% of the total measured nitrate. Positive Matrix Factorization(PMF)conducted with high-resolution organic mass spectra dataset differentiated the organic aerosol into three components, including a hydrocarbon-like organic aerosol(HOA) and two oxygenated organic aerosols(SV-OOA and LV-OOA), which on average accounted for 27.6%,28.8%, and 43.6% of the total organic mass, respectively. The relationship between the mass concentration of submicron particle species and wind further confirmed that all major fine particle species were influenced by both strong local emissions in the southeastern area of Xiamen and regional transport through the Taiwan Strait.     

基于船载走航气溶胶质谱技术的海洋气溶胶研究

利用船载走航单颗粒气溶胶质谱仪(SPAMS)对东南沿海海洋气溶胶进行在线观测,获得了近岸海区大气气溶胶化学组成分布特性,并对气溶胶谱特征及其来源进行分析.结果表明:从近岸到远海,气溶胶的浓度呈下降趋势;在近岸海区气溶胶主要受港口及码头周边的汽车、船舶及燃煤等工业排放源的影响;随着离陆地距离的增加,海源性气溶胶的比重上升;海洋气溶胶在观测范围内呈单峰分布,峰值粒径在0.5μm左右,大多数颗粒主要集中在0.2~0.8μm的粒径段;近岸气溶胶元素碳谱峰强,正谱图中K~+元素具有很强的谱峰,负离子谱中HSO_4~-谱峰信号明显,但缺少NO_3~-和NO_2~-谱峰信号;远海气溶胶中元素碳的谱峰强度明显降低,正谱图中Na~+的谱峰很强,并出现Mg~+、Ca~+和NaCl~+的谱峰信号;负离子谱出现了明显的海洋生物特征气溶胶甲基磺酸MSA~-、CN~-、O~-和HSO_4~-等的谱峰信息.表明该观测海区的气溶胶组成复杂,同时受陆源排放气溶胶和海源气溶胶的影响.     

Chemical characterization and source identification of PM2.5 in Luoyang after the clean air actions

Luoyang is a typical heavy industrial city in China, with a coal-dominated energy structure and serious air pollution. Following the implementation of the clean air actions, the physicochemical characteristics and sources of PM_2.5 have changed. A comprehensive study of PM_2.5 was conducted from October 16, 2019 to January 23, 2020 to evaluate the effectiveness of previous control measures and further to provide theory basis for more effective policies in the future. Results showed that the aerosol pollution in Luoyang in autumn and winter is still serious with the average concentration of 91.1 μg/m³, although a large reduction (46.9%) since 2014. With the contribution of nitrate increased from 12.5% to 25.1% and sulfate decreased from 16.7% to 11.2%, aerosol pollution has changed from sulfate-dominate to nitrate-dominate. High NO₃⁻/SO₄²⁻ ratio and the increasing of NO₃⁻/SO₄²⁻ ratio with the aggravation of pollution indicating vehicle exhaust playing an increasingly important role in PM_2.5 pollution in Luoyang, especially in the haze processes. Secondary inorganic ions contributed significantly to the enhancement of PM_2.5 during the pollution period. The high value of Cl⁻/Na⁺ and EC concentration indicate coal combustion in Luoyang is still serious. The top three contributor sources were secondary inorganic aerosols (33.3%), coal combustion (13.6%), and industrial emissions (13.4%). Close-range transport from the western and northeastern directions were more important factors in air pollution in Luoyang during the sampling period. It is necessary to strengthen the control of coal combustion and reduce vehicle emissions in future policies.     

成都市夏冬季大气胺颗粒物的单颗粒质谱研究

基于单颗粒气溶胶质谱（SPAMS）对成都市夏冬两季大气胺颗粒进行了综合观测,结合ART-2a算法及人工合并,2个季节大气中的胺颗粒都可分为7类,各类颗粒贡献的季节差异明显,燃烧源颗粒（如EC）在夏季贡献较大,而老化的有机碳颗粒（OCa）贡献在冬季显著升高.两季节平均质谱差减进一步证明夏、冬季分别以燃烧源和老化的胺颗粒为主.因高温分解,夏季胺颗粒占比在正午出现明显的低值,而下午的燃烧活动（如生物质燃烧）对该占比提升作用明显;冬季胺颗粒占比在白天显著高于夜间.随污染加重,胺颗粒数在夏冬两季均快速增加,其中夏季EC颗粒升高最为明显,污染最重时贡献可达47%;冬季升高最明显的是老化程度更高的OCa颗粒,当PM_2.5浓度达到200μg/m³以上时,其贡献比例可达37%.因此,由于污染源和气候条件的差异,成都市大气胺颗粒形成机制和理化特征季节差异巨大.     

利用SPAMS研究南宁市四季细颗粒物的化学成分及污染来源

为研究南宁市四季大气颗粒物的化学成分及污染来源,利用单颗粒气溶胶质谱仪(single particle aerosol mass spectrometer,SPAMS)对南宁市2015年四季大气细颗粒物进行观测.SPAMS所测得4个观测阶段大气细颗粒物数浓度与细颗粒物质量浓度线性相关系数均在0.75以上,在一定程度上颗粒物数浓度可反映大气污染状况.南宁市四季大气细颗粒物平均质谱图体现出冬、春两季二次反应生成的污染物质较多.利用自适应共振神经网络分类方法对细颗粒物化学成分进行分类,南宁市细颗粒物各化学成分数浓度占比和污染来源在四季均有差异,且化学成分能体现污染来源.冬季元素碳最高,对应较高的燃煤源;秋季有机碳最高,对应较高的机动车尾气源;夏季富钾颗粒、左旋葡聚糖和矿物质较高,对应较高的生物质燃烧源和扬尘源;春季富钠颗粒和重金属略高.在污染升高过程,生物质燃烧源和燃煤源等贡献较大.     

基于SPAMS的天津市夏季环境受体中颗粒物的混合状态及来源

天津位于京津冀区域,近年来面临的颗粒物污染问题受到广泛关注,研究其大气环境中颗粒物的化学组成及来源具有重要意义.为明确天津市夏季环境受体中颗粒物的混合状态及可能来源,于2017年7月利用单颗粒气溶胶质谱仪(single particle aerosol mass spectrometer,SPAMS)在津南区采集到成功电离有粒径及完整质谱信息颗粒209 887个,利用ART-2a对有质谱数据的颗粒按照质谱特征的相似性进行聚类共获得369个颗粒物类别,随后按照类别的化学组成(质谱谱图)的相似性进行人工合并获得19个颗粒物类别,包括:K-EC(0.20%)、K-EC-Sec(0.18%)、K-NO₃-PO₃(12.00%)、K-NO₃-SiO₃(2.98%)、K-Sec(0.16%)、EC(39.60%)、EC-Sec(3.46%)、EC-HM-Sec(3.93%)、HEC(1.49%)、HEC-Sec(1.38%)、OC-Amine-Sec(3.58%)、OC-Sec(0.36%)、OCEC-Sec(...     

运用单颗粒气溶胶质谱技术初步研究广州大气矿尘污染

自主研发的单颗粒气溶胶飞行时间质谱仪(Single Particle Aerosol Mass Spectrometer,SPAMS)采用空气动力学透镜、双激光测径系统以及双极飞行时间质量分析器,并用其对广州市大气细颗粒物进行在线分析,实现了矿尘颗粒物的空气动力学直径和化学组成的同时检测. 在连续4 d的采样中,共采集到249 057个粒径在0.2～1.2 μm且同时含有正负质谱信息的颗粒物,其中矿尘颗粒物占7.9%. 结果表明:矿尘颗粒物的正离子成分以Ca2+,K+>/sup>,Fe2+和Na+>/sup>为主,同时还含有Li+>/sup>,Mg2+,V>/sup>5+,Ba2+和Ti4+等;负离子成分以NO₃->/sup> 和NO₂->/sup>为主,另外还含有HSO₄->/sup>,SiO₃->/sup>和PO₃->/sup>等. 在广州市大气细颗粒物中,矿尘贡献不如含碳颗粒物和生物质燃烧颗粒物,且在矿尘颗粒物中贡献较大的几类(如含钙、含铁、含钠钾颗粒物等)大多是老化的成分.      

Characteristics of PM2.5 pollution in Beijing after the improvement of air quality

Following the implementation of the strictest clean air policies to date in Beijing, the physicochemical characteristics and sources of PM_2.5 have changed over the past few years. To improve pollution reduction policies and subsequent air quality further, it is necessary to explore the changes in PM_2.5 over time. In this study, over one year (2017–2018) field study based on filter sampling (TH-150C; Wuhan Tianhong, China) was conducted in Fengtai District, Beijing, revealed that the annual average PM_2.5 concentration (64.8 ± 43.1 μg/m³) was significantly lower than in previous years and the highest PM_2.5 concentration occurred in spring (84.4 ± 59.9 μg/m³). Secondary nitrate was the largest source and accounted for 25.7% of the measured PM_2.5. Vehicular emission, the second largest source (17.6%), deserves more attention when considering the increase in the number of motor vehicles and its contribution to gaseous pollutants. In addition, the contribution from coal combustion to PM_2.5 decreased significantly. During weekends, the contribution from EC and NO₃^? increased whereas the contributions from SO₄^2?, OM, and trace elements decreased, compared with weekdays. During the period of residential heating, PM_2.5 mass decreased by 23.1%, compared with non-heating period, while the contributions from coal combustion and vehicular emission, and related species increased. With the aggravation of pollution, the contribution of vehicular emission and secondary sulfate increased and then decreased, while the contribution of NO₃^? and secondary nitrate continued to increase, and accounted for 34.0% and 57.5% of the PM_2.5 during the heavily polluted days, respectively.     

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.      

在线单颗粒气溶胶质谱SPAMS对细颗粒物中主要组分提取方法的研究

在对美国气溶胶飞行时间质谱仪(aerosol time-of-flight mass spectrometer,ATOFMS)和中国单颗粒气溶胶质谱仪(single particle aerosol mass spectrometer,SPAMS)前期研究调研的基础上,总结并比较了国内外飞行时间质谱(time-of-flight mass spectrometry,TOF-MS)研究中含硫酸盐、硝酸盐、铵盐、有机碳(OC)和元素碳(EC)等5种主要组分颗粒的典型提取条件.结合SPAMS单颗粒谱图的特点,本研究通过限定示踪离子,对5种主要组分的提取方法进行改进,提出了新的SPAMS组分示踪离子提取方法.以北京市2013年1月SPAMS实测数据为例,对不同提取方法的结果进行对比分析.并将本研究方法 SPAMS主要组分结果与离线滤膜样品的实验数据进行对比,验证了本研究所提出的优化方案的合理性.同时指出SPAMS在未来的应用研究中需考虑的因素及进一步深入的研究方向.     

天津市城市扬尘及土壤尘单颗粒质谱特征

为探究城市扬尘及不同类型的土壤尘单颗粒质谱特征,使用SPAMS（单颗粒气溶胶质谱仪）对天津城市扬尘及不同类型土壤尘（菜地、果园、林地、农田）进行分析.结果表明:天津城市扬尘粒径在0.5~1.0 μm之间的占比较高;4种土壤尘在不同粒径段的分布略有差异,整体而言小粒径段（0.2~1.0 μm）占比低于大粒径段（1.0~2.0 μm）,说明土壤尘更易分布在大粒径段.在小粒径段,天津城市扬尘颗粒含有更多的碳组分及硫酸盐,大粒径段含有较多的金属、氯、硝酸盐、磷酸盐及硅酸盐组分.与城市扬尘相比,土壤尘中23Na+、46NO₂-峰强显著降低,39K+、35Cl-、42CNO-峰强增高,通过其峰强比值分布可将城市扬尘与土壤尘明显区分.与城市扬尘相比,土壤尘中含碳颗粒显著减少,同时出现钾类颗粒.城市扬尘作为一种混合源,含碳颗粒可能受多种排放源（机动车、燃煤等）影响,而土壤尘中钾类颗粒可能与农作物施肥有关.两种源类含27Al+、26CN-、35Cl-、42CNO-、46NO₂-、76SiO₃-、79PO₃-的颗粒占比均高于50%,其中40Ca+、46NO₂-和62NO₃-颗粒在天津城市扬尘中占比更高,76SiO₃-颗粒在土壤尘中占比更高,表明天津城市扬尘中更多的颗粒含有钙和硝酸盐组分,土壤尘中含硅酸盐颗粒较多.与机动车尾气、生物质燃烧等排放的颗粒物相比,天津城市扬尘及土壤尘中含97HSO₄-颗粒占比较低,表明天津城市扬尘及土壤尘颗粒中含有更少的硫酸盐,可作为将天津城市扬尘、土壤尘与其他源类区分的指标.