武汉市冬季灰霾期PM2.5中水溶性无机离子的特征

2018年1月,利用颗粒物采样器采集武汉市大气PM_2.5样品并进行水溶性无机离子（F^-、Cl^-、NO₃^-、SO₄^2-、Na⁺、NH₄⁺、K⁺、Mg²⁺、Ca²⁺）的分析.结果表明,NO₃^-、SO₄^2-、NH₄⁺是PM_2.5中最主要的3种水溶性无机离子,除Mg²⁺与Ca²⁺外,PM_2.5与WSⅡs （水溶性无机离子）之间的相关性显著,且移动源贡献占主导地位.阴阳离子平衡表明武汉市冬季灰霾期PM_2.5呈中性或弱酸性.通过混合单粒子拉格朗日综合轨迹模式模拟并采用分层聚类得出了4种主要的后向气流轨迹及相应的PM_2.5和水溶性离子浓度,结果表明区域传输对此次灰霾期影响较大.     

福建三明市春季PM2.5中有机碳、元素碳和水溶性离子特征分析

2014年3月13日至4月20日在福建三明市利用PM_2.5中流量采样器采集大气中PM_2.5膜样品,测定了PM_2.5的质量浓度,并用热/光碳分析仪和离子色谱分析了其组分变化特征.结果表明,三明市观测期间PM_2.5的平均质量浓度为73.61±0.73 μg/m³,有机碳(OC)和元素碳(EC)的平均质量浓度分别为7.26±1.00和5.63±0.27 μg/m³,水溶性离子中SO₄^2-、NH₄⁺、NO₃^-和Na⁺的质量浓度分别为18.08±12.19、4.18±3.56、2.77±1.16和2.73±0.23 μg/m³,总和占总水溶性离子的87.76%.结合后向轨迹分析了福建三明市的污染物来源特征.该地区OC/EC的平均比值小于2,SOC(二次有机碳)生成量很少,主要以一次有机污染物为主,OC、EC与K⁺的相关性分析表明OC、EC与K⁺的来源相近,可以判断OC、EC绝大部分来源是生物质燃烧产生的污染物.在水溶性离子分析中,观测期间NO₃^-/SO₄^2-为0.159±0.02,表明三明市主要以固定源为主,机动车辆等移动源贡献较少.     

南京市降水化学特征及其来源研究

为了解南京江北地区降水化学特征,分析了2011年3—6月共25个降水日的109个降水样品中的主要水溶性离子,并利用后向轨迹模式探讨了降水气团来源.结果表明:1)南京地区3—6月降水主要受南、北2种气团影响,北方气团降水的主要离子浓度高于南方气团降水.2)海盐示踪法和相关性分析显示,降水中NO₃^-和SO₄^2-主要来自燃煤、工业排放和汽车尾气;Ca²⁺主要来自地壳源;Cl^-主要来自海洋;海洋源和陆源对Mg²⁺和K⁺都有贡献,Mg²⁺的陆源贡献大于海洋源贡献,K⁺受海洋源的影响程度要低于Mg²⁺.3)南、北气团初期降水的各离子浓度高于总降水的各离子浓度,且初期降水的主要离子的富集系数高于总降水.这说明在降水初始阶段,雨水对南京大气中污染物(气态污染物和颗粒物)的云下冲刷去除作用较强,降水的离子浓度最高,局地源对降水离子的贡献较明显.     

冬季南京北郊大气气溶胶中水溶性阴离子特征

2009年冬季在南京北郊进行24 h采样,运用离子色谱法研究大气PM₁₀中水溶性阴离子的分布特征。结果表明:PM₁₀中阴离子的平均总质量浓度在白天和夜间分别为658.21、622.84 μg/m³;PM_2.1则分别为337.86、319.97 μg/m³,阴离子主要存在于细粒子中;主要水溶性阴离子均为SO₄^2-,且海盐对南京北郊大气PM₁₀和PM_2.1中的SO₄^2-质量浓度影响很小。SO₄^2-、Cl^-和F^-粒径谱分布相似,均呈双模态;NO₃^-和NO₂^-主要呈现单模态。SO₄^2-与NO₃^-、F^-与NO₃^-、SO₄^2-与Cl^-的相关系数均大于0.8,相关显著,说明其存在一定的同源性。NO₃^-/SO₄^2-的平均值在白天、夜间分别为0.058 2、0.048 4,说明南京北郊大气污染以固定源为主。分析NO₃^-、SO₄^2-前体物的转化率知道,采样期间SOR和NOR的平均值均大于10%,即SO₄^2-部分来源于SO₂的二次转化,而不是单一来源于一次污染物。     

THE PRELIMINARY STUDY ON THE PRECIPITATION CHEMISTRY AT THE CHINESE GREAT WALL STATION, ANTARCTICA IN 1998*

This paper reports the analysis results (including pH,conductivity and ion concentrations) of the precipitation samples collected at the Chinese Great Wall Station,Antarctica (62°13'S, 58°58'W,ASL10.0 m) in 1998.The average pH value and conductivity were 5.62 and 85.16 μS/cm,respectively.The pH value and conductivity of precipitation were higher during autumn, but lower during other seasons.The major ions in the precipitation were Cl^- and Na⁺,followed by SO₄^2-,Mg²⁺,Ca²⁺,K⁺,NO₃^-,NH₄⁺ with the lower concentrations in order.The positive correlation significantly existing between the major ions,except NO₃^- and NH₄⁺,indicated that those major ions might come from same sources.The fact that the relative abundances of ions in precipitation were very close to that of seawater of Antarctic Ocean indicated that marine aerosol was the dominant source of the ions of precipitation.However,there were yet other sources which may contribute to Ca²⁺ ion in the precipitation.The precipitation at the area was characterized by marine type chemically.     

苏南三市秋冬季PM2.5中水溶性离子和元素特征及源解析

2010年11月16日至12月17日在南京、常州、苏州三城市设置采样点,24 h采集大气PM_2.5样品,并测定其水溶性无机离子和元素的浓度,在此基础上讨论PM_2.5及无机组分的时空分布特征。结果表明,采样期间,PM_2.5污染较严重,且苏州最重,常州次之,南京最轻,南京、苏州、常州日均浓度分别是国家二级标准(75 μg/m³)的1.44、2.32、1.53倍;三市PM_2.5离子组分中,阴离子均以SO₄^2-和NO₃^-为主,阳离子以Ca²⁺和Mg²⁺为主;苏州Na⁺和Cl^-之间的相关性较高,其受到海盐输送影响较大;三城市PM_2.5中Ca是最主要元素,Al次之。运用主成分法分析南京、常州和苏州PM_2.5的来源可知,三城市PM_2.5受多个污染源影响,包括生物质燃烧、地表扬尘、五金工业及汽车尾气排放等。     

武汉市源成分谱特征研究

通过采集武汉市土壤风沙尘、建筑水泥尘、城市扬尘、餐饮源、生物质燃烧源、工业煤烟尘和电厂煤烟尘等7类源样品,并分析其碳组分、水溶性离子组分和无机元素组分,建立PM₁₀和PM_2.5源成分谱.研究表明,地壳元素Si、Ca、Al以及Fe等是土壤风沙尘的主要特征组分,其中Si是含量最高的成分,也是土壤风沙尘的标识组分.无组织建筑水泥尘中Si和Ca元素含量较高,将Ca元素作为无组织建筑水泥尘区别其他源类的重要元素,而有组织建筑水泥尘中OC、SO₄^2-含量比无组织建筑水泥尘高.城市扬尘中Ca的含量相对较高,表明城市扬尘受到建筑水泥尘影响较多.生物质燃烧源成分谱中OC的含量远高于成分谱中其他组分,另外Cl^-和K的平均含量也较高,K一般为生物质源的特征元素.     

武汉市大气污染源排放清单及分布特征研究

以武汉市为研究区域,基于实地调查获得典型行业污染源活动水平,以大气污染物排放清单编制技术指南为参考,利用排放因子法建立2014年武汉市大气污染源排放清单,并结合经纬度、人口密度分布、土地利用类型、道路长度等数据将排放清单进行了3 km×3 km网格化处理.结果表明,2014年武汉市SO₂、NO_x、PM₁₀、PM_2.5、CO、BC、OC、VOCs和NH₃排放量分别为10.3、17.0、16.3、7.1、63.1、0.6、0.4、19.8和1.6万t.固定燃烧源为SO₂排放的主要来源,其贡献率约64%;移动源为NO_x的主要来源,其贡献率约51%;颗粒物排放主要来源于扬尘源和工艺过程源;CO和VOCs主要来源于工艺过程源,BC和OC排放均以移动源和生物质燃烧源为主,NH₃排放主要来自农业源.污染物排放主要集中在青山区至新洲区一带.     

STUDY ON THE CHEMICAL CHARACTERISTICS OF POLLUTING FOG IN GUANGZHOU AREA IN SPRING

Samples of fog water collected in the area of Guangzhou during February, March and April of 2005 are used in this work to study the chemical composition of fog water in polluting fog there. Three typical episodes of polluting fog are analyzed in terms of ionic concentration and their possible sources. It is found that the concentration of various ions in fog water is much higher than those in rainwater. Fog not only blocks visual range but contains liquid particles that result in high degree of pollution and are very harmful to human health. SO₄⁼ is the anion with the highest concentration in fog water, followed by NO₃^-.For the cation, Ca⁺⁺ and NH₄⁺ are the highest in concentration. It is then known that rainwater is more acidic than fog water, indicating that ionic concentration of fog water is much higher than that of rainwater, but there are much more buffering materials in fog water, like NH₄⁺ and Ca⁺⁺. There is significant enrichment of Ca⁺⁺, SO₄⁼, and Mg⁺⁺ in fog water. In the Guangzhou area, fog water from polluting fog is mainly influenced continental environment and human activity. The episodes of serious fog pollution during the time have immediate relationships with the presence of abundant water vapor and large amount of polluting aerosol particles.     

PRELIMINARY STUDY OF VARIATION OF SO2 AND AEROSOL PARTICLE BACKGROUND CONCENTRATIONS IN THE EASTERN CHINA

The average concentrations of sulphur dioxide,sulfate aerosol and TSP were about 8-10 ppb,15.08 μg m^-3,and 241.40 μg m^-3 respectively,which were measured at the Lin'an regional background station during August-November,1991.The higher concentrations of SO₂ and SO₄^2- maybe acidify the rainfall.It has a great influence upon the human health and ecosystem.The simulated results indicate that the distributions of SO₂ and SO₄^2- were determined by local emission sources.Average aerosol particle number density was 2.0×10⁴ cm^-3.It shows that social development and human activities strongly affect the atmospheric background level.     

Source identification of PM2.5 in an arid Northwest U.S. City by positive matrix factorization

Spokane, WA is prone to frequent particulate pollution episodes due to dust storms, biomass burning, and periods of stagnant meteorological conditions. Spokane is the location of a long-term study examining the association between health effects and chemical or physical constituents of particulate pollution. Positive matrix factorization (PMF) was used to deduce the sources of PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) at a residential site in Spokane from 1995 through 1997. A total of 16 elements in 945 daily PM_2.5 samples were measured. The PMF results indicated that seven sources independently contribute to the observed PM_2.5 mass: vegetative burning (44%), sulfate aerosol (19%), motor vehicle (11%), nitrate aerosol (9%), airborne soil (9%), chlorine-rich source (6%) and metal processing (3%). Conditional probability functions were computed using surface wind data and the PMF deduced mass contributions from each source and were used to identify local point sources. Concurrently measured carbon monoxide and nitrogen oxides were correlated with the PM_2.5 from both motor vehicles and vegetative burning.     

Characteristics of the chemical composition and source apportionment of PM2.5 for a one-year period in Wuhan,China

In this study, 123 PM_2.5 filter samples were collected in Wuhan, Hubei province from December 2014 to November 2015. Water- soluble inorganic ions (WSIIs), elemental carbon (EC), organic carbon (OC) and inorganic elements were measured. Source apportionment and back trajectory was investigated by the positive matrix factorization (PMF) model and the hybrid single particle lagrangian integrated trajectory (HYSPLIT) model, respectively. The annual PM_2.5 concentration was 80.5?±?38.2 μg/m³, with higher PM_2.5 in winter and lower in summer. WSIIs, OC, EC, as well as elements contributed 46.8%, 14.8%, 6.7% and 8% to PM_2.5 mass concentration, respectively. SO₄^2?, NO₃^? and NH₄⁺ were the dominant components, accounting for 40.2% of PM_2.5 concentrations. S, K, Cl, Ba, Fe, Ca and I were the main inorganic elements, and accounted for 65.2% of the elemental composition. The ratio of NO₃^?/SO₄^2? was 0.86?±?0.72, indicating that stationary sources play dominant role on PM_2.5 concentration. The ratio of OC/EC was 2.9?±?1.4, suggesting the existence of secondary organic carbon (SOC). Five sources were identified using PMF model, which included secondary inorganic aerosols (SIA), coal combustion, industry, vehicle emission, fugitive dust. SIA, coal combustion, as well as industry were the dominant contributors to PM_2.5 pollution, accounting for 34.7%, 20.5%, 19.6%, respectively.

     

Source identification of PM2.5 particles measured in Gwangju, Korea

The UNMIX and Chemical Mass Balance (CMB) receptor models were used to investigate sources of PM_2.5 aerosols measured between March 2001 and February 2002 in Gwangju, Korea. Measurements of PM_2.5 particles were used for the analysis of carbonaceous species (organic (OC) and elemental carbon (EC)) using the thermal manganese dioxide oxidation (TMO) method, the investigation of seven ionic species using ion chromatography (IC), and the analysis of twenty-four metal species using Inductively Coupled Plasma (ICP)-Atomic Emission Spectrometry (AES)/ICP-Mass Spectrometry (MS). According to annual average PM_2.5 source apportionment results obtained from CMB calculations, diesel vehicle exhaust was the major contributor, accounting for 33.4% of the measured PM_2.5 mass (21.5 μg m^− 3), followed by secondary sulfate (14.6%), meat cooking (11.7%), secondary organic carbon (8.9%), secondary nitrate (7.6%), urban dust (5.5%), Asian dust (4.4%), biomass burning (2.8%), sea salt (2.7%), residual oil combustion (2.6%), gasoline vehicle exhaust (1.9%), automobile lead (0.5%), and components of unknown sources (3.4%). Seven PM_2.5 sources including diesel vehicles (29.6%), secondary sulfate (17.4%), biomass burning (14.7%), secondary nitrate (12.6%), gasoline vehicles (12.4%), secondary organic carbon (5.8%) and Asian dust (1.9%) were identified from the UNMIX analysis. The annual average source apportionment results from the two models are compared and the reasons for differences are qualitatively discussed for better understanding of PM_2.5 sources.Additionally, the impact of air mass pathways on the PM_2.5 mass was evaluated using air mass trajectories calculated with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backward trajectory model. Source contributions to PM_2.5 collected during the four air mass patterns and two event periods were calculated with the CMB model and analyzed. Results of source apportionment revealed that the contribution of diesel traffic exhaust (47.0%) in stagnant conditions (S) was much higher than the average contribution of diesel vehicle exhaust (33.4%) during the sampling period. During Asian dust (AD) periods when the air mass passed over the Korean peninsula, Asian dust and secondary organic carbon accounted for 25.2 and 23.0% of the PM_2.5 mass, respectively, whereas Asian dust contributed only 10.8% to the PM_2.5 mass during the AD event when the air mass passed over the Yellow Sea. The contribution of biomass burning to the PM_2.5 mass during the biomass burning (BB) event equaled 63.8%.     

苏州市大气PM_(2.5)中水溶性无机离子的源解析及其气象因子分析

为了探明PM_(2.5)中水溶性无机离子的来源和气象因子对其浓度变化的影响,利用2012年2、5、8和11月苏州市PM_(2.5)中水溶性无机离子浓度和本站气象观测数据,分析了苏州市水溶性无机离子的时间变化特征,解析了当地PM_(2.5)中水溶性无机离子的主要来源,探讨了气象因素对离子组分的影响。结果表明:(1)苏州市PM_(2.5)中水溶性无机离子年均浓度大小依次为:SO_4~(2-)NO_3~-NH_4~+Na~+Cl~-K~+Ca~(2+)Mg~(2+)F~-;SO_4~(2-)、NH_4~+和NO_3~-为PM_(2.5)中最重要的3种水溶性无机离子物种,其总和占PM_(2.5)总质量浓度的50.9%。各离子的季节浓度特征均为冬季最高、夏季最低。(2)通过运用主成分分析法对苏州市PM_(2.5)中水溶性无机离子进行来源分类解析,发现第一类为二次污染源和生物质燃烧,其贡献率为32.84;第二类为道路扬尘及工业排放,其贡献率为19.99%;第三类为海盐污染,其贡献率为18.43%。(3)通过水溶性无机离子与气象条件的相关性分析发现,风向、风速和温度与水溶性无机离子浓度的相关性较显著,这三者是颗粒物浓度变化的主要影响因子。(4)利用HYSPLIT后向轨迹模式对外来污染物进入苏州市的轨迹进行聚类分析后发现:因受季风气候影响,苏州市外来污染物的输入路径存在明显的季节性变化特征,其中夏半年输送主径源自海上,冬半年主径源自内陆。     

武汉市道路扬尘源排放清单及空间分布特征研究

参考AP-42方法的采样规范（USEPA,2011）,对武汉市13个城区的不同类型道路采集了137个扬尘样,并记录采样面积、车流情况、车道状况、地理位置、周围环境以及气象数据要素信息,得到了不同类型道路的积尘负荷,估算了其扬尘排放因子和排放量.结果表明：武汉总城区尘负荷由大到小顺序为支路 > 次干道 > 主干道 > 快速路,其中支路平均尘负荷为2.396 g/m²,快速路为0.852 g/m²,远城区平均尘负荷是主城区平均尘负荷的2倍左右.各类型道路不同粒径范围的道路交通扬尘排放因子大小顺序为支路 > 次干路 > 主干路 > 高速路,与尘负荷大小趋势一致.2016年道路交通扬尘源TSP的年排放量为156 931.4 t,PM₁₀的年排放量为39 868.7 t,PM_2.5的年排放量为11 574.8 t,其不确定性范围分别为-24.7%~31.4%、-31.3%~32.9%、-31.8%~30.5%.其中主干道扬尘排放量最大,其TSP、PM₁₀和PM_2.5的年排放量分别为64 447.1、16 372.9和4 753.4 t.     

2017年8—9月湖州市臭氧污染特征及其生成机制研究

近年来近地面臭氧问题日益凸显,成为影响空气质量持续改善的瓶颈.本研究基于2017年8—9月在湖州市城区开展的为期1个月的臭氧及其前体物挥发性有机物（VOCs）和氮氧化物（NO_x）在线观测数据,分析了臭氧及其前体物污染特征,利用正矩阵因子分析（PMF）解析了VOCs来源,并采用基于观测的模型（OBM）对臭氧生成机制进行研究.研究结果表明：1）观测期间湖州市VOCs平均体积分数为（24.78±9.10）×10^-9,其中占比最高的组成为烷烃、含氧VOCs （OVOCs）和卤代烃;2）在臭氧非超标时段,湖州市臭氧生成处于VOCs控制区,而在臭氧重污染期间湖州市处于以VOCs控制为主的过渡区;3）在臭氧超标时段,对臭氧生成潜势（OFP）贡献最大的是芳香烃（39.6%）,其次是烯烃（21.5%）和OVOCs （19.4%）,排名前三的关键组分为甲苯、乙烯和间/对二甲苯;4）源解析结果显示观测期间湖州市VOCs的主要来源是溶剂使用（27.0%）、交通排放（22.7%）、背景+传输（19.3%）、工业排放（16.9%）、汽油挥发（7.7%）和植物排放（6.4%）,重污染过程期间对OFP贡献最大的两类源是交通排放源和溶剂使用源,贡献百分比分别为35.1%和30.5%.因此,对交通排放和溶剂使用方面进行控制管理对湖州市大气臭氧污染防控有重要意义.     

Vertical Distribution Characteristics of PM<Subscript>2.5</Subscript> Observed by a Mobile Vehicle Lidar in Tianjin,China in 2016

We present mobile vehicle lidar observations in Tianjin, China during the spring, summer, and winter of 2016. Mobile observations were carried out along the city border road of Tianjin to obtain the vertical distribution characteristics of PM_2.5. Hygroscopic growth was not considered since relative humidity was less than 60% during the observation experiments. PM_2.5 profile was obtained with the linear regression equation between the particle extinction coefficient and PM_2.5 mass concentration. In spring, the vertical distribution of PM_2.5 exhibited a hierarchical structure. In addition to a layer of particles that gathered near the ground, a portion of particles floated at 0.6–2.5-km height. In summer and winter, the fine particles basically gathered below 1 km near the ground. In spring and summer, the concentration of fine particles in the south was higher than that in the north because of the influence of south wind. In winter, the distribution of fine particles was opposite to that measured during spring and summer. High concentrations of PM_2.5 were observed in the rural areas of North Tianjin with a maximum of 350 μg m^–3 on 13 December 2016. It is shown that industrial and ship emissions in spring and summer and coal combustion in winter were the major sources of fine particles that polluted Tianjin. The results provide insights into the mechanisms of haze formation and the effects of meteorological conditions during haze–fog pollution episodes in the Tianjin area.     

2009年秋季南京地区一次持续性灰霾天气过程研究

2009年10月14—27日,南京地区发生了一次持续性的灰霾天气过程。利用气象观测资料和污染物浓度监测资料,结合焚烧点监测、后向气流轨迹模拟,分析了颗粒物和气态污染物的浓度演变特征、气象要素特征及产生持续灰霾天气的可能原因。研究表明,该次过程中绝大部分时间能见度低于10 km,空气污染指数最大时达到195。地面PM2.5质量浓度有显著增长,在26日达到最大值为0.782 mg/m3。NO2质量浓度日均值在24日和27日超过了环境空气质量二级标准,其含量分别为0.094和0.099 mg/m3,对应NOx质量浓度分别为0.105和0.108 mg/m3。SO2质量浓度在22日达到峰值,最大值为0.161 mg/m3,平均值为0.083 mg/m3,低于环境空气质量二级标准。分析显示:近半个月内南京地区天气形势稳定,处于持续温度偏高、干燥无雨的状态,非常有利于灰霾天气的发生。卫星监测发现24、25、26日江淮之间中部均有火点,其中24日有50个着火点,25日增加为85个,26日减少为38个,表明有秸秆焚烧现象存在。从后向气流轨迹分析来看,在秸秆焚烧最为严重的3 d内,南京地区主要受到来自东到东北方向气流的影响,有利于秸秆焚烧形成的污染物经气流输送影响南京,造成严重灰霾天气。