兰州春季沙尘过程PM10输送路径及其潜在源区

将2001-2008年分为沙尘天气相对多年和相对少年,计算兰州市春季逐日4个时次的4d气团后向轨迹。通过聚类分析得到春季到达兰州市区的主要气团轨迹组,结合可吸入颗粒物PM10日均质量浓度资料,通过计算潜在源贡献因子PSCF（potential source contribution function）和浓度权重轨迹CWT（concentration-weighted trajectory）,得到影响兰州市春季PMlo质量浓度的潜在源区以及不同源区对兰州市春季PM10质量浓度贡献的差异。结果表明,在沙尘天气相对多年,西路径和西北路径发生比例最高,分别占总轨迹的33％和19．4％,其中有50％以上为污染轨迹,是造成兰州市春季高质量浓度PM10污染的主要输送路径。沙尘天气相对少年的主要输送路径是西路径,其次是北路径,分别占23．6％和18％。影响兰州市春季大气PM10质量浓度的潜在源区分布在新疆塔里木盆地、吐鲁番盆地、青海柴达木盆地、甘肃河西走廊、内蒙古中部和西部的沙漠戈壁地区。     

西宁市区不同天气形势下的PM_(10)输送路径及潜在源区

针对2016年西宁市区稳定和沙尘两种不同天气形势,基于后向轨迹气团的聚类分析法、潜在源贡献因子法(PSCF)和浓度权重轨迹分析法(CWT),结合市环境监测站PM_(10)浓度质量资料,分析了西宁市区不同天气形势下不同来源区域PM_(10)质量浓度的贡献影响及其潜在源区。结果表明:西宁市区稳定天气PM_(10)均来自青海省境内,PM_(10)输送路径以西方和东方转向路径最多,占总轨迹数的34.78%和30.43%;西方路径主要从青海省格尔木市向东输送,东方转向路径则从西宁市西部地区向东转而向西输送,两者经过的地区均没有明显的沙源;PM_(10)的潜在贡献源区主要在西宁市区及其北部与大通县和互助县交界地区。沙尘天气PM_(10)输送路径除了以来自青海省海西州的西方路径为最多外,甘肃省河西走廊的东方转向路径也较多,占比分别达到42.11%和36.84%;西方路径PM_(10)主要从沙漠地带南疆—青海省海西州西部向东输送,东方转向路径PM_(10)则经河西走廊沙源地进入西宁市区;PM_(10)污染主要是PM_(10)由沙源地输送进入西宁市区聚集所造成。地形对PM_(10)的输送路径有较大的影响。     

长江三角洲西部地区PM2.5输送轨迹分类研究

长三角4个省会（直辖市）城市（上海、南京、合肥、杭州）中,合肥与南京的PM_2.5浓度演变有较高的一致性。应用聚类分析的方法对2013-2015年合肥非降水日（日降水量低于10 mm）100 m高度（代表近地层）和1000 m高度（代表边界层中上部）的72 h后向轨迹进行分类,结合合肥2013-2015年PM_2.5日均浓度资料,探讨近地层和边界层中上部输送轨迹与长三角西部PM_2.5浓度的关系。近地层和边界层中上部分别得到7组和6组不同的后向轨迹;不同输送轨迹对应的PM_2.5浓度、重污染（重度以上污染,PM_2.5日均浓度大于150 μg/m³）天数、能见度、地面风速、相对湿度等都有显著不同,尤其是在近地层。100 m高度,平均长度最短、来向偏东的轨迹组对应的PM_2.5浓度均值最高（约是组内均值最低值的2倍）、重污染天数最多,且占比最高（30%）,重污染日对应的气流在过去72 h下降高度均值仅28 m,明显低于其他PM_2.5污染等级日;来向偏西北、长度较短的轨迹组,PM_2.5浓度均值和重污染天数为第2高,这一类轨迹占比14%,气流到达本地前存在明显的下沉运动,反映了远距离输送加剧本地PM_2.5重污染的特征。这两类轨迹常对应PM_2.5日均浓度的上升。PM_2.5平均浓度最低的2个轨迹组分别是来自东北和西南的较长轨迹组,所占比例分别为6.4%和10.3%,这2类轨迹往往对应着PM_2.5日均浓度下降。1000 m高度的结果与100 m高度结果类似,但PM_2.5平均浓度的组间差异不及100 m高度,与2001-2005年PM₁₀浓度与输送轨迹的关系不同。对3 a中84个重污染日两个高度的后向轨迹进行聚类,近地层和边界层中上部各得到7类和6类PM_2.5重污染日的天气形势。近地层92%的重污染日对应的海平面气压形势场上,从华北到华东属于均压区,气压梯度小,轨迹来向以偏东到偏北方向为主,垂直方向延伸高度在950 hPa以下。1000 m高度,77%的重污染日属于相对较短的轨迹组,对应的850 hPa高度场特征为从中国西北（新疆）到东南受高压控制,长三角或位于高压底部,或位于两高压之间的均压区。这对PM_2.5浓度预报有较好的指示意义。     

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。     

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.     

2021年和2022年春季中国北方地区沙尘气象因素和沙源地条件异同

2021年春季中国北方地区共出现了4次沙尘暴或强沙尘暴,2022年同期仅出现1次沙尘暴。基于2015—2022年空气质量和多源气象数据,利用Lamb-Jenkinson分型法与Mann-Whitney U检验法开展了2021年和2022年春季沙尘源地条件和气象因素异同分析,得到以下结论:中国北方沙尘天气多发型分为NW-N型(气旋型)和E-NE型(高压型),NW-N型造成的PM₁₀极值更高、高浓度范围更广。气象因素而言,2022年春季有利于沙尘的天气型活动更频繁,与2021年春季沙尘日PM₁₀浓度差异主要集中在NW-N型,两段时期NW-N型活动频数、气旋强度接近,有利于沙尘天气的动力抬升条件接近。从沙源地条件而言,2021年前冬蒙古沙源地土壤温度“前冷后暖”导致融雪等水量峰值早至,加之大面积降水负距平且3月蒙古沙源地气旋偏强,干燥、稀松的沙源致使春季沙尘多发;2022年前冬蒙古沙源地土壤气温“前暖后冷”导致融雪期等水量、土壤含水量峰值晚至,深厚湿润的土壤条件不利于起沙。故蒙古沙源地条件差异是两个时期沙尘差异显著的主要原因。     

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。     

基于PSCF与CWT方法的赣江新区大气污染物潜在源区个例分析

利用TrajStat软件和GDAS全球同化气象数据,对江西省赣江新区2011—2020年四季72 h气团后向轨迹进行聚类分析,并结合PM2.5和O3逐小时浓度数据,运用潜在源贡献因子分析法（PSCF）和浓度权重轨迹分析法（CWT）分析了2016年12月2—10日一次污染天气过程中大气污染物输送对赣江新区上空污染物浓度的贡献。结果表明,赣江新区2011—2020年四季气团后向轨迹中占比最大的均为短支气流,其中春季的短支气流来源于东侧,其他季节均来源于东北方向的安徽省,夏季和冬季的长支气流与季风的季节性变化一致。在2016年12月2—10日的污染天气过程中,赣江新区的PM2.5潜在源区主要分布于江西省北部、湖北省东南部,O3潜在源区主要分布于江西省北部、湖北省南部和湖南省东北小部分地区;同时天气形势显示,赣江新区处在槽后脊前,湖北省东南部存在偏强西北风,为大气污染物向赣江新区的输送创造了条件。     

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%.     

大气PM2.5源解析“源清单化学质量平衡法(I-CMB)”模型的建立与应用

针对受体模型对大气PM2.5中二次无机、有机气溶胶不能给出有效源贡献的问题,建立了一种基于污染源清单的化学质量平衡(Inventory-Chemical Mass Balance,I-CMB)颗粒物源解析受体模型,代入北京市近年的污染物排放数据进行了解析应用。结果表明,燃煤是北京大气PM2.5的最大来源(占比约28.06%),其余依次为机动车(19.73%)、扬尘(17.88%)、工业(16.50%)、餐饮(3.43%)、植物(3.40%)。相比于传统的化学质量平衡法(Chemical Mass Balance,CMB),I-CMB的源解析过程对源成分谱的要求较低、抗干扰性更强,计算结果均衡、详尽,比较适合我国当前大气PM2.5控制的需求。     

Impact of a dust storm on characteristics of particle matter (PM) in Guangzhou, China

A strong dust-storm (23–25 April, 2009) occurred in the provinces of Inner Mongolia, Gansu, and Shanxi, North China. Cities along the storm path (from north to south: Xi’ning, Lanzhou, Chengdu, Changsha, and Guangzhou) all experienced a sharp increase in particle matter (PM₁₀) concentration. This is the first case that an Asian dust storm hit Guangzhou in Southern China. The impacts of dust storm on the characteristics of PM were investigated using samples collected in Guangzhou during 27–29 April, 2009. In addition, the mass concentration and chemical composition during a normal non-dust period (12–14 May, 2009) were compared with those in dust period. The results show that the concentration of PM₁₀ during the dust episode (0.231 mg m^?3) was twice higher than that in the non-dust episode (0.103 mg m^?3). Chemical analysis showed that concentrations of metal elements, enrichment factors of metal elements, and soluble ions during the dust episode were very different from those of non-dust. The total concentration of metal elements content in PM₁₀ was 53.5 μg m^?3 in the dust episode, which is about two times higher than that in non-dust episode (28.5 μg m^?3). Increases in concentrations of Na, Ti, Zn, Cu, and Cr ranged from zero to 100% during the dust episode. However, the enrichment factors in non-dust episode were higher than that in dust-storm period, indicating that the above five chemicals originated mainly from local sources in Guangzhou. The concentrations of K, Mg, Al, Fe, Mn, V, and Co increased by over 100% in the dust episode, indicating their origins of remote sources. In the dust period, some water-soluble ions increased in PM₁₀, but the main components in PM₁₀ were SO₄ ^?, NO₃ ^? and NH₄ ⁺. At last, we assessed the sources of dusts by analyzing synoptic situation and back trajectories of air mass in Guangzhou, and demonstrated that the main source of the dust storm was from Mongolia.     

Elemental composition of PM2.5 and PM10 at Mount Gongga in China during 2006

In order to investigate the chemical characteristics of atmospheric aerosols in a regional background site, PM_2.5 and PM₁₀ were collected at Mount Gongga Station once a week in 2006. The concentrations of fifteen elements including Na, Mg, Al, K, Ca, V, Fe, Ni, Cu, Zn, As, Ag, Ba, Tl, and Pb were detected by Inductively Coupled Plasma Mass Spectrometer (ICP-MS). The results showed that Na, Mg, Al, K, Ca, Fe were the major components of elements detected in PM_2.5 and PM₁₀, occupied 89.5% and 91.3% of all the elements. Crustal enrichment factor (EF) calculation indicated that several anthropogenic heavy metals (Ni, Cu, Zn, As, Ag, Tl, Pb) were transported long distances atmospherically. The concentrations of all elements (except Na) measured in PM_2.5 and PM₁₀ in spring and winter were higher than those in summer and autumn. The backward air mass trajectory analysis suggests that northeast India may be the source region of those pollutants.     

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.     

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.     

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。     

PM10 Observed at a Meteorological Station in Beijing: Historical Trend and Implications

Inhalable particles (PM10), with aerodynamic equivalent diameters that are generally 10 micrometers or smaller, are basic pollutants in many areas, especially in northern China, and thus the pollution from PM10 inhalable particulate matter is a growing concern for public health. Independent long-term observations are necessary to evaluate the efficacy of PM10 reduction actions. Variations in the PM10 concentration from 2006 to 2017 at an observation station (NJ) in Beijing were recorded and analyzed. The average value ±1 standard deviation of daily mean PM10 concentrations was 138.8 ±96.1 μg m-3 for 1307 days (accounting for 34.7% of the total days), showing PM10 concentration exceeding the National Ambient Air Quality Standard (NAAQS) 24-h average of 150 μg m-3. Particulate concentration depended upon various meteorological conditions as also observed in this work: at low wind speed (<4 m s-1), the concentrations of PM10 revealed a downward trend with -19 μg m-3 per unit of wind speed, but when wind speed rose (>4 m s-1), the values increased by 49 μg m-3 per unit of wind speed. In Beijing, air masses from northwest China, especially from the Gobi Desert and other desert areas, had net contributions to long-range transport of natural dust, enhancing the PM10 concentrations by up to 29%. Overall, PM10 mass concentration showed a significant downward trend with -8.0 μg/m3/yr from 2006 to 2017. Although with higher fluctuations in recorded data, similar downward trends derived from the ) in 2017 still exceeded the NAAQS standard. The results showed that there is still a long way to go to reduce PM10 in Beijing.     

Characteristics of Elemental Composition of PM2.5 in the Spring Period at Tongyu in the Semi-arid Region of Northeast China

Continuous observations of mass concentration and elemental composition of aerosol particles （PM2.5） were conducted at Tongyu, a semi-arid site in Northeast China in the spring of 2006. The average mass concentration of PM2.5 at Tongyu station was 260.9±274.4 μg m^-3 during the observation period. Nine dust events were monitored with a mean concentration of 528.0±302.7 μgm^-3. The PM2.5 level during non- dust storm （NDS） period was 111.65±63.37 μg m^-3. High mass concentration shows that fine-size particles pollution was very serious in the semi-arid area in Northeast China. The enrichment factor values for crust elements during the dust storm （DS） period are close to those in the NDS period, while the enrichment factor values for pollution elements during the NDS period are much higher than those in the DS period, showing these elements were from anthropogenic sources. The ratios of dust elements to Fe were relative constant during the DS period. The Ca/Fe ratio in dust aerosols at Tongyu is remarkably different from that observed in other source regions and downwind regions. Meteorological analysis shows that dust events at Tongyu are usually associated with dry, low pressure and high wind speed weather conditions. Air mass back-trajectory analysis identified three kinds of general pathways were associated with the aerosol particle transport to Tongyu, and the northwest direction pathway was the main transport route.