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。     

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.     

青岛市秋冬季霾期PM1及其含碳组分理化特征及来源研究

对2017年11月1日—2018年1月31日与2018年11月1日—2019年1月31日连续两年青岛市大气PM₁进行监测,获取了PM₁中含碳组分的变化趋势,结合国控站点监测数据和气象条件,分析了秋冬季PM₁来源.结果表明：2017、2018年秋冬季观测期间PM₁日均质量浓度分别为40.58±25.98、42.55±25.05 μg/m³;霾日质量浓度分别为84.71±16.70、81.52±18.39 μg/m³.与2017年相比,2018年同期PM₁质量浓度增长4.85%,霾日下降3.76%.2017年霾日PM₁中OC、EC质量浓度分别为13.67±3.95、3.95±1.02 μg/m³,2018年分别为16.48±6.34、3.34±1.16 μg/m³.与2017年相比,2018年霾日OC质量浓度增长20.56%,EC下降15.44%.2017、2018年霾日SOC质量浓度分别是非霾日的1.28和2.15倍,表明霾污染发生时易发生有机碳二次转化.含碳组分主成分分析均解析出3个因子.因子1解释变量均最大,分别为58.98%、67.14%,其表征含碳组分主要源于生物质燃烧、燃煤、道路扬尘及汽油车尾气等排放源.由后向气流轨迹分析得出,2017、2018年秋冬季气团轨迹多起源于内蒙古,经河北、天津、山东等省市抵达青岛.     

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.     

南京市城市不同功能区PM10和PM2.1质量浓度的季节变化特征

使用Anderson-Ⅱ型9级撞击采样器测量了南京市鼓楼商业区、江北工业区、钟山风景区和宁六高速公路交通源春、夏、秋三季的大气气溶胶质量浓度。分析结果表明:南京市PM_2.1和PM₁₀的质量浓度存在明显的季节变化,秋季>春季>夏季;ρ_PM10春季为167.47 μg/m³,夏季为 85.99 μg/m³,秋季为238.99 μg/m³;ρ_PM2.1春季为59.66 μg/m³,夏季为42.80 μg/m³,秋季为100.15 μg/m³。不同季节中ρ_PM10和ρ_PM2.1均存在较好的相关性,夏季相关性最好,相关系数为0.952;秋季次之,相关系数为0.783;春季相对较差,相关系数为0.613。城市不同功能区之间ρ_PM2.1和ρ_PM10的质量浓度值差异很大,交通源>工业区>商业区>风景区。城市不同功能区的质量浓度谱分布基本一致,均为双峰型分布,峰值分别位于0.43~0.65 μm/m³和9.0~10.0 μm/m³。南京市春、夏、秋三个季节大气粒子质量浓度谱为双峰分布,粒子主要集中在0.43~3.3 μm/m³的粒径段。江北工业区ρ_PM10和ρ_PM2.1质量浓度的相关系数为0.814,略高于鼓楼商业区的0.797。     

南京市城市不同功能区PM10和PM2.1质量浓度的季节变化特征

使用Anderson-Ⅱ型9级撞击采样器测量了南京市鼓楼商业区、江北工业区、钟山风景区和宁六高速公路交通源春、夏、秋三季的大气气溶胶质量浓度。分析结果表明:南京市PM_2.1和PM₁₀的质量浓度存在明显的季节变化,秋季>春季>夏季;ρ_PM10春季为167.47 μg/m³,夏季为 85.99 μg/m³,秋季为238.99 μg/m³;ρ_PM2.1春季为59.66 μg/m³,夏季为42.80 μg/m³,秋季为100.15 μg/m³。不同季节中ρ_PM10和ρ_PM2.1均存在较好的相关性,夏季相关性最好,相关系数为0.952;秋季次之,相关系数为0.783;春季相对较差,相关系数为0.613。城市不同功能区之间ρ_PM2.1和ρ_PM10的质量浓度值差异很大,交通源>工业区>商业区>风景区。城市不同功能区的质量浓度谱分布基本一致,均为双峰型分布,峰值分别位于0.43~0.65 μm/m³和9.0~10.0 μm/m³。南京市春、夏、秋三个季节大气粒子质量浓度谱为双峰分布,粒子主要集中在0.43~3.3 μm/m³的粒径段。江北工业区ρ_PM10和ρ_PM2.1质量浓度的相关系数为0.814,略高于鼓楼商业区的0.797。     

2007和2008年夏季北京奥运馆大气PM10与PM2.5质量浓度变化特征

为了监测北京奥运主场馆附近大气颗粒物的污染状况以及评估奥运污染源减排措施对北京大气颗粒物质量浓度变化的影响,利用颗粒物在线监测仪器TEOM于2007年和2008年夏季,在奥运主场馆附近的中国科学院遥感应用研究所办公楼楼顶对大气颗粒物PM₁₀和PM_2.5进行了连续同步观测。结果表明,2007年夏季监测点附近大气PM₁₀与PM_2.5质量浓度的平均值分别为153.9和71.2μg·m^-3,而2008年夏季PM₁₀与PM_2.5质量浓度的平均值分别为85.2和52.8μg·m^-3。与奥运前一年同时段相比,奥运时段大气PM₁₀和PM_2.5的质量浓度分别下降44.5%和25.1%。对比分析奥运前后的2次典型污染过程发现,空气相对湿度的增加和偏南气流输送的共同影响易造成大气颗粒物的累积增长,而降雨的湿清除作用和偏北气流则会使大气颗粒物浓度迅速降低。在相近的气象条件下,奥运前后的污染过程中,大气细粒子的日均增长速率分别为25.1和13.9μg·m^-3·d^-1,而大气粗粒子的日均增长速率分别为20.8和2.2μg·m^-3·d^-1,奥运时段污染累积过程中大气粗、细粒子的增长速率分别显著低于和略低于奥运前同时段污染过程中颗粒物的增长速率。污染源减排措施的实施是奥运期间大气颗粒物质量浓度降低的主要原因,从控制效果来看,奥运期间实施的污染源减排措施对大气粗粒子的控制效果明显好于大气细粒子。     

成都市PM2.5污染特征及其与地面气象要素的关系分析

为了解成都市PM_2.5污染特征及其与地面气象要素的关系,利用环境空气质量监测资料和地面气象观测资料,分析了PM_2.5质量浓度的季节、月和日变化特征,并分不同空气质量等级分析空气质量与地面气象要素的关系。结果表明：PM_2.5质量浓度具有明显的季节、月和日变化特征,且成都市区6个监测站的变化趋势比较一致;成都市相对湿度较大,地面风速较小,约62%的样本分布在相对湿度80%~100%,约85%的样本分布在地面风速0~2 m·s^-1,地面风速对成都市PM_2.5的水平输送、扩散、稀释不利;降水对PM_2.5的清除量随PM_2.5初始浓度、降雨持续时间和累积降雨量增加而增大。     

南京北郊2011年春季气溶胶粒子的散射特征

利用南京北郊2011年春季积分浊度仪的观测资料,结合PM_2.5质量浓度、能见度和常规气象资料,分析了南京北郊春季气溶胶散射系数的变化特征、散射系数与PM_2.5质量浓度和能见度的关系。结果表明,观测期间气溶胶散射系数平均值为311.5±173.3 Mm^-1,小时平均值出现频率最高的区间为100~200 Mm^-1;散射系数的日变化特征明显,总体为早晚大,中午及午后小。散射系数与PM_2.5质量浓度的变化趋势基本一致,但与能见度呈负相关关系。霾天气期间散射系数日平均值为700.5±341.4 Mm^-1,最高值达到近1 900 Mm^-1;结合地面观测资料、NCEP/NCAR再分析资料和后向轨迹模式分析显示,霾期间气块主要来自南京南部和东南方向。     

唐山市PM2.5和O3行业来源解析

京津冀位于华北平原腹地,面临着严重的空气污染问题,尤其是河北省的重点工业城市唐山,长期位于全国空气质量最差的前十名。为改善空气质量,过去的十多年间我国颁布实施了多项污染防治计划,但唐山的PM_2.5和夏季O₃浓度仍超国家标准。为此,使用WRF(Weather Research and Forecasting Model)-CMAQ(Community Multiscale Air Quality Model)模型量化了唐山市2020年PM_2.5和O₃浓度的行业贡献并分析其协同控制可行性。工业源对唐山市PM_2.5浓度贡献最大,约占45％,其次是居民源约占16％。冬季能源、居民源和农业源占比为全年最高,分别达17％、19％和11％。O₃浓度的背景值约占一半以上,4月占比最高。在非背景值中,唐山O₃浓度最大来源为工业源,约占53％,其次是交通源,约占22％。生物源、交通源和能源行业的贡献在7月有所上升,分别约10％、27％和20％。不同污染情景下对唐山市PM_2.5和O₃的来源比较发现,工业和能源是其最重要的共同来源。     

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%.     

Seasonal characteristics and sources of carbonaceous components and elements of PM10 (2010–2019) in Delhi,India

In this study we present the seasonal chemical characteristics and potential sources of PM₁₀ at an urban location of Delhi, India during 2010?2019. The concentrations of carbonaceous aerosols [organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC) and water insoluble organic carbon (WIOC)] and elements (Al, Fe, Ti, Cu, Zn, Mn, Pb, Cr, F, Cl, Br, P, S, K, As, Na, Mg, Ca, B, Ni, Mo, V, Sr, Zr and Rb) in PM₁₀ were estimated to explore their possible sources. The annual average concentration (2010–2019) of PM₁₀ was computed as 227?±?97 µg m^?3 with a range of 34?734 µg m^?3. The total carbonaceous aerosols in PM₁₀ was accounted for 22.5% of PM₁₀ mass concentration, whereas elements contribution to PM₁₀ was estimated to be 17% of PM₁₀. The statistical analysis of OC vs. EC and OC vs. WSOC of PM₁₀ reveals their common sources (biomass burning and/or fossil fuel combustion) during all the seasons. Enrichment factors (EFs) of the elements and the relationship of Al with other crustal metals (Fe, Ca, Mg and Ti) of PM₁₀ indicates the abundance of mineral dust over Delhi. Principal component analysis (PCA) extracted the five major sources [industrial emission (IE), biomass burning?+?fossil fuel combustion (BB?+?FFC), soil dust, vehicular emissions (VE) and sodium and magnesium salts (SMS)] of PM₁₀ in Delhi, India. Back trajectory and cluster analysis of airmass parcel indicate that the pollutants approaching to Delhi are mainly from Pakistan, IGP region, Arabian Sea and Bay of Bengal.

     

Geochemistry of regional background aerosols in the Western Mediterranean

The chemical composition of regional background aerosols, and the time variability and sources in the Western Mediterranean are interpreted in this study. To this end 2002–2007 PM speciation data from an European Supersite for Atmospheric Aerosol Research (Montseny, MSY, located 40 km NNE of Barcelona in NE Spain) were evaluated, with these data being considered representative of regional background aerosols in the Western Mediterranean Basin. The mean PM₁₀, PM_2.5 and PM₁ levels at MSY during 2002–2007 were 16, 14 and 11 µg/m³, respectively. After compiling data on regional background PM speciation from Europe to compare our data, it is evidenced that the Western Mediterranean aerosol is characterised by higher concentrations of crustal material but lower levels of OM + EC and ammonium nitrate than at central European sites. Relatively high PM_2.5 concentrations due to the transport of anthropogenic aerosols (mostly carbonaceous and sulphate) from populated coastal areas were recorded, especially during winter anticyclonic episodes and summer midday PM highs (the latter associated with the transport of the breeze and the expansion of the mixing layer). Source apportionment analyses indicated that the major contributors to PM_2.5 and PM₁₀ were secondary sulphate, secondary nitrate and crustal material, whereas the higher load of the anthropogenic component in PM_2.5 reflects the influence of regional (traffic and industrial) emissions. Levels of mineral, sulphate, sea spray and carbonaceous aerosols were higher in summer, whereas nitrate levels and Cl/Na were higher in winter. A considerably high OC/EC ratio (14 in summer, 10 in winter) was detected, which could be due to a combination of high biogenic emissions of secondary organic aerosol, SOA precursors, ozone levels and insolation, and intensive recirculation of aged air masses. Compared with more locally derived crustal geological dusts, African dust intrusions introduce relatively quartz-poor but clay mineral-rich silicate PM, with more kaolinitic clays from central North Africa in summer, and more smectitic clays from NW Africa in spring.     

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.     

Influence of traffic emissions on the composition of atmospheric particles of different sizes – Part 1: concentrations and elemental characterization

Epidemiological studies initially considered the impact of total solid particles on human health, but according to the acquired knowledge about the worse effect of smaller particles, those studies turned to consider the impact of PM₁₀. However, for the last decade PM_2.5 began to be more important, once as they are smaller they can penetrate deeper in the lungs, being possible their trapping in alveoli and worse effects on human health. Therefore, more information on PM_2.5 should be provided namely concerning the levels and elemental composition. Considering the relevance of traffic on the emission of particles of small sizes, this work included the detailed characterization of PM₁₀ and PM_2.5, sampled at two sites directly influenced by traffic, as well as at two reference sites, aiming a further evaluation of the influence of PM₁₀ and PM_2.5 on public health. The specific objectives were to study the influence of traffic emission on PM₁₀ and PM_2.5 characteristics, considering concentration, size distribution and elemental composition. PM₁₀ and PM_2.5 samples were collected using low-volume samplers; the element analyses were performed by particle induced X-ray emission (PIXE). At the sites influenced by traffic emissions PM₁₀ and PM_2.5 concentrations were 7–9 and 6–7 times higher than at the background sites. The presence of 17 elements (Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn and Pb) was determined in both PM fractions; particle metal contents were 3–44 and 3–27 times higher for PM₁₀ and PM_2.5, respectively, than at the backgrounds sites. The elements originated mostly from anthropogenic activities (S, K, V, Mn, Ni, Zn and Pb) were predominantly present in PM_2.5, while the elements mostly originated from crust (Mg, Al, Si and Ca) predominantly occurred in PM_2.5–10. The results also showed that in coastal areas sea salt spray is an important source of particles, influencing PM concentration and distributions (PM₁₀ increased by 46%, PM_2.5/PM₁₀ decreased by 26%), as well as PM compositions (Cl in PM₁₀ was 11 times higher).     

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.

     

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。     

Extreme Particulate Levels at a Western Pacific Coastal City: The Influence of Meteorological Factors and the Contribution of Long-Range Transport

The 4-year data sets (1998–2001) of PM₁₀ and other gaseous pollutants at four rural and urban monitoring sites provided by Environmental Protection Department of Hong Kong have been analyzed for days of extremely high and low PM₁₀ levels. The annual means of PM₁₀ concentrations are between 37 and 57 μg/m³. The level of high PM₁₀ concentration is defined from the comparison of local and international standards. Episode days are mainly controlled by different meteorological conditions: the continental outflow, the land-sea breeze/weak synoptic forcings and the approaching tropical cyclones. Integrated approaches have been used to distinguish between the predominantly “territory wide” and “long-range transport” (LRT) episode days. Case studies of these types of episodes are presented and the number of episode days per year for each type has been estimated. It is found that the LRT contributions are significant and account for ∼66% of the PM₁₀ episode days. Very high correlations between CO and PM₁₀ concentrations, and between SO₂ and PM₁₀ concentrations, can be found during the “territory wide” episode days which implies the important contributions of fossil fuel combustion to the PM₁₀ episodes. The number of “low level” PM₁₀ days per year has decreased by a factor near 3 from 1998 to 2001. Precipitation scavenging is the major process causing low levels of PM₁₀, irrespective of the associated weather systems. The regional annual background level is ∼9 μg/m³. With the exception of seasalt components, the average elemental concentrations of major inorganic species are similar for all of the sites during LRT events and constitute representative compositions of PM₁₀ during such events.     

春季中国东部气溶胶化学组成及其分布的模拟研究

本文利用区域空气质量模式RAQMS（Regional Air Quality Model System）,对2009年春季中国东部气溶胶主要化学成分及其分布进行了模拟研究。与泰山站观测资料的对比结果显示,模式能比较合理地反映气溶胶浓度的逐日变化特征。整体上,模式对无机盐气溶胶的模拟好,分别高估和低估黑碳和有机碳气溶胶浓度,其原因与排放源、二次有机气溶胶化学机制和模式分辨率的不确定性有关。模拟结果显示,春季气溶胶浓度高值主要集中于华北、四川东部、长江中下游等地区。受东南亚生物质燃烧和大气输送的影响,中国的云南和广西等地区有机碳浓度高于中国其他地区。中国西北部沙尘浓度较高,而且向东输送并影响到中国东部和南方部分地区。中国东部的华北、四川东部、长江中下游等地PM_2.5（空气动力学直径在2.5微米以下的颗粒物）污染严重,4月平均PM_2.5浓度超过了我国日平均PM_2.5浓度限值。中国东部泰山站的观测和模拟结果都显示近地面硝酸盐浓度超过硫酸盐,中国北部对流层中硝酸盐的柱含量也大于硫酸盐,而在中国南部则相反,这一方面与春季中国云量 南多北少的分布特征以及云内液相化学反应有关,另一方面也与南北温差对气溶胶形成的影响有关。就整个中国东部而言,虽然硫酸盐的柱含量（46 Gg）仍大于硝酸盐（42 Gg）,但比较接近,反映出我国氮氧化物排放迅速增加的趋势。春季中国地区对流层中PM₁₀（空气动力学直径在10微米以下的颗粒物）及其化学成分柱含量分别为:990.8 Gg（PM₁₀）,52.6 Gg（硫酸盐）,48.2 Gg（硝酸盐）,32.1 Gg（铵盐）,22.9 Gg（黑碳）和74.1 Gg（有机碳）,有机碳（OC）中一次有机碳（POC）和二次有机碳（SOC）分别占60%和40%,中国东部PM₁₀中人为气溶胶和沙尘分别占30%和70%,反映了春季沙尘对我国大气气溶胶的重要贡献。     

Terra和Aqua卫星MODIS 3 km AOD与北京PM_(2.5)对比分析

本文利用2014年全年北京市12个空气质量监测站的逐小时PM_(2.5)地面观测资料,以及Terra卫星和Aqua卫星的MODIS 3 km气溶胶光学厚度(AOD)产品,分析了地面PM_(2.5)和两颗卫星AOD的时空分布特征,并在时空匹配的基础上,建立了AOD与PM_(2.5)浓度之间的回归模型。结果表明:PM_(2.5)浓度在城区高、郊区低,最低值位于定陵站,城区站和郊区站的逐时PM_(2.5)浓度的日变化分别呈"双峰型"和"单峰型";两颗卫星AOD数值也均是城区高、郊区低,沿山区的边界有明显的AOD梯度,且城区上午Terra卫星的AOD高于下午Aqua卫星的AOD,而郊区上、下午的AOD基本相同;Aqua卫星AOD与PM_(2.5)的确定系数(R2)较Terra卫星AOD与PM_(2.5)的确定系数平均高0.11,且城区站点两颗卫星AOD与PM_(2.5)相关性均较郊区站点AOD与PM_(2.5)相关性偏高;综合来看,Aqua卫星的AOD与城区的PM_(2.5)相关系数最高,即Aqua卫星的AOD更适于监测和反演城区地面的PM_(2.5)。