2013年12月上海市重度污染期间细颗粒物化学特征与输送轨迹影响

2013年12月我国中东部地区发生多场大范围高强度的颗粒物污染. 期间,本研究采用在线连续观测手段测量了上海市城区大气中气态污染物、颗粒物的质量浓度、细颗粒物的化学组分等,获得了浮尘污染、灰霾污染、雾霾污染、长距离传输的过境污染过程中颗粒物的污染特征变化. 观测结果显示,雾霾污染最为严重,PM₁₀和PM_2.5日均最大浓度分别达到536 μg ·m^-3和411 μg ·m^-3,PM_2.5/PM₁₀高达76.7%,高湿度加强了大气颗粒物中NO₃^-、SO₄^2-、NH₄⁺等二次组分的生成. 浮尘污染中PM_2.5的Ca²⁺浓度在所有污染过程中最高,且PM_2.5中一次组分比重明显上升. 长距离传输的过境污染中PM_2.5的SO₄^2-浓度最高,且增长速度很快. 同时本研究还采用Hysplit反向轨迹结合聚类分析方法,得到了不同污染过程中到达上海的主要气团轨迹,并结合上海城区在线观测的PM_2.5及其化学组分浓度数据,探讨了不同气团下PM_2.5组分特征差异和不同污染过程的大致来源. 结果表明,观测期间上海的气团轨迹可以聚类为六类. 其中,移动速度快的cluster6出现时,上海市不易出现颗粒物污染; 始于蒙古的cluster2和cluster3导致上海出现沙尘污染,该气团下PM_2.5/PM₁₀的比例都较低,且PM_2.5中Ca²⁺浓度较高. 移动缓慢的cluster5和cluster4有利于污染物的二次生成,静稳天气同时加剧了污染物的累积,加上他们经海上夹带水汽传输至上海,这些不利条件是导致上海出现严重污染的关键因素.     

青藏高原东南缘大气黑碳污染特征及其来源解析

黑碳是一种吸光物质,对全球变暖和区域气候变化具有重要作用;青藏高原又在气候、水资源、生物多样性、碳收支平衡等方面具有重要的生态作用。为了解青藏高原东南缘地区黑碳的污染特征及其来源,于云南省丽江市高美古地区进行春季(2018-03-15—2018-05-13)样品采集,采用热/光碳分析仪测定元素碳(EC)浓度,探究污染特征(EC、char-EC、soot-EC)、光学特征(b_abs、MAE),并基于正定矩阵因子分解法(PMF)和后向轨迹分析其来源。结果表明：春季EC浓度受生物质燃烧、旅游旺季等人为排放和沙尘等自然因素共同影响;EC与温差和太阳辐射呈正相关,与湿度呈显著负相关。Char-EC和soot-EC平均质量浓度分别为(0.35±0.20)μg·m^-3和(0.07±0.04)μg·m^-3,两者在EC中的占比分别为80.1%和19.9%。Char-EC/soot-EC比值均大于1,表明该区域受到生物质和煤炭燃烧影响较大。b_abs和MAE值在4月22日前后差异较大,主要与不同时期污染源占比有关。青藏高...     

南京地区细颗粒物污染输送影响及潜在源区

基于南京市空气质量数据和NCEP全球再分析资料,利用后向轨迹模式计算了2019年3月至2020年2月以南京城区为受体点的逐小时气团24 h后向轨迹,并将后向轨迹数据和PM_2.5浓度数据结合,进行轨迹聚类和潜在源区分析.结果表明,研究期间南京市ρ(PM_2.5)平均值为(36±20)μg·m^-3,超过国家二级标准限值的污染天数为17 d,ρ(PM_2.5)的季节变化特征明显：冬季(49μg·m^-3)>春季(42μg·m^-3)>秋季(31μg·m^-3)>夏季(24μg·m^-3),全年PM_2.5浓度和地面气压显著正相关,而跟气温、相对湿度、降水量和风速均为显著负相关关系;春季气团输送路径为7条,其余季节均为6条,其中,春季的西北路和东南偏南路,秋季东南路和冬季西南路是各季主要的污染输送路径,均具有传输距离短,气团移动慢的特点,说明静稳天气下本地累积是PM_2.5出...     

菏泽市PM2.5源方向解析研究

为了定量解析环境受体中不同方向PM_2.5的源贡献水平,利用“源方向解析”（source directional apportionment,SDA）法〔综合PMF（positive matrix factorization,正定矩阵因子）方法和后向轨迹模型〕对京津冀大气污染传输通道上某典型城市——菏泽市环境受体中PM_2.5进行来源解析,并分析不同方向的源贡献.结果表明,菏泽市环境受体中ρ（PM_2.5）变化范围为42.73~191.72 μg/m3,平均值为92.54 μg/m3.SO₄2-、NO₃-和NH₄+是菏泽市环境受体中PM_2.5的主要化学组分;ρ（SO₄2-）、ρ（NO₃-）和ρ（NH₄+）的平均值分别为29.78、22.11和7.91 μg/m3,三者之和占ρ（PM_2.5）的63.54%.PMF的计算结果显示,二次无机盐、机动车排放、扬尘、煤烟尘和建筑水泥尘是菏泽市环境受体中PM_2.5的贡献源类,分担率分别为32.61%、22.60%、19.54%、16.25%和9.00%.利用后向轨迹模型识别出PM_2.5贡献源类的4个潜在方向,分别为东南、正西、西北和正东.二次无机盐在4个方向的贡献分别为8.49%、5.01%、6.65%和12.88%;机动车排放分别为1.39%、4.44%、7.47%和8.22%;扬尘分别为4.95%、3.65%、4.12%和6.92%;煤烟尘分别为4.56%、1.93%、2.16%和7.28%;建筑水泥尘分别为2.22%、1.88%、1.27%和3.56%.研究显示,菏泽市PM_2.5污染较为严重,其中二次源、机动车和扬尘源是其主要贡献源类,并且来自菏泽市东部的各源类贡献均较高.      

邢台市颗粒污染物变化特征和后向轨迹分析

为深入了解邢台市PM_(10)、PM_(2.5)浓度变化情况和气流后向轨迹,对邢台市2013—2016年环境大气颗粒污染物监测数据进行了分析,同时利用HYSPLIT模型计算出逐日72 h后向气流轨迹。结果表明:邢台市的PM_(10)和PM_(2.5)质量浓度在2013—2016年间呈逐年下降趋势,PM_(10)和PM_(2.5)质量浓度高值出现在冬季(296μg/m~3和192μg/m~3),最低值出现在夏季(140μg/m~3和80μg/m~3),PM_(10)和PM_(2.5)质量浓度在日变化上均呈"双峰双谷"型分布;后向轨迹的季节聚类分析表明,春季大气颗粒物污染以粒径2.5～10μm的颗粒污染物为主,夏季、秋季和冬季的大气颗粒物污染以PM_(2.5)为主;逐日聚类分析表明,在路径为西北偏西向的、途经多个沙源地的气流影响下,邢台市的PM_(10)和PM_(2.5)质量浓度处于一个相对高值;来源于偏南向的气流由于化合反应,污染物积聚导致PM_(10)、PM_(2.5)质量浓度也处于相对高值;在来源于西北向和偏北向的、水汽含量相对较低的气流影响下,邢台市的PM_(10)、PM_(2.5)质量浓度出现一个明显的下降。     

基于后向轨迹对城市大气中二噁英长距离迁移来源的探讨

通过对广州某商住区大气中二噁英的季节性监测,结合后向轨迹的计算,对大气中PCDD/Fs的浓度及其长距离迁移来源进行分析.结果表明:大气中二噁英浓度有季节性变化特点,其趋势为冬季(14.4 pg·m^-3)>秋季(10.0 pg·m^-3)>春季(5.54 pg·m^-3)>夏季(3.88 pg·m^-3).同时,大气中PCDD/Fs单体特征也具有季节性特点,秋冬季节七氯代、八氯代PCDD/Fs百分比高于春秋两季,春夏两季低氯代单体百分比含量高于秋冬两季.追溯采样期间该城市大气的后向轨迹,发现秋冬两季到达广州的气团主要经过湖南、湖北和江西等北方或东北方的内陆省份,而春夏两季到达广州的气团主要经过我国东海和南海海域上空.而这种变化很可能是造成广州大气中二噁英浓度季节性变化的主要原因.     

Validation of the lagrangian particle dispersion model FLEXPART against large-scale tracer experiment data

A comprehensive validation of FLEXPART, a recently developed Lagrangian particle dispersion model based on meteorological data from the European Centre for Medium-Range Weather Forecasts, is described in this paper. Measurement data from three large-scale tracer experiments, the Cross-Appalachian Tracer Experiment (CAPTEX), the Across North America Tracer Experiment (ANATEX) and the European Tracer Experiment (ETEX) are used for this purpose. The evaluation is based entirely on comparisons of model results and measurements paired in space and time. It is found that some of the statistical parameters often used for model validation are extremely sensitive to small measurement errors and should not be used in future studies. 40 cases of tracer dispersion are studied, allowing a validation of the model performance under a variety of different meteorological conditions. The model usually performs very well under undisturbed meteorological conditions, but it is less skilful in the presence of fronts. The two ETEX cases reveal the full range of the model’s skill, with the first one being among the best cases studied, and the second one being, by far, the worst. The model performance in terms of the statistical parameters used stays rather constant with time over the periods (up to 117 h) studied here. It is shown that the method used to estimate the concentrations at the receptor locations has a significant effect on the evaluation results. The vertical wind component sometimes has a large influence on the model results, but on the average only a slight improvement over simulations which neglect the vertical wind can be demonstrated. Subgrid variability of mixing heights is important and must be accounted for.     

Analysis of a two-layer cloud system with RAMS model and comparison to airborne observations

A three-dimensional numerical model (Regional Atmospheric Modeling System—-RAMS) was used to study the formation and evolution of water forms in a two-layer cloud structure observed during a field campaign over Brest (France). The model performance in regular operations, using conventional meteorological data as initial and lateral boundary conditions, was also examined. Remote sensing observations of the cloud system and in-situ aircraft data, selected during the campaign, were used to validate the model outputs. The model simulations showed that the lower cloud formation was characterized by high number concentration of pristine ice and snow, while the concentration of aggregates, graupel and hail were considerably lower. Hydrometeors in liquid phase appeared demonstrating high number concentration and water content on the top of this layer. The upper cloud layer consisted only of frozen water substances in lower amounts. The qualitative and quantitative comparison of the model-calculated meteorological and microphysical fields to the available observational data revealed that the model reflected fairly well the cloud structure (e.g., the spatio-temporal variability of the cloud parameters, the geometry of the cloud system). However, there were deviations as far as the model underestimating the ice water content (IWC) and number concentration (N_t) fields is concerned, especially at the atmospheric layer between 2.5 and 4 km of altitude. These deviations of the model simulated quantities from the measured ones may be attributed either to the performance of the model’s microphysics scheme, to instrument inaccuracies and to the local disturbance caused by the aircraft.     

Determination and analysis of PM10 source apportionment during episodes of air pollution in Central Eastern European urban areas: The case of wintertime 2006

Source apportionment of air pollution due to particulate matter with an aerodynamic diameter <10 μm (PM₁₀) was investigated in Central Eastern European urban areas. A combination of four methods was developed to distinguish long-range transport (LRT) and regional transport (RT) from local pollution (LP) sources as well as to discern the involvement of traffic or residential sources in LP. Sources of PM₁₀ events of pollution were determined in January 2006 in representative Polish cities using monitored air quality and meteorological data, backward air mass trajectories, correlation and principal component analysis (PCA). Daily patterns of PM₁₀ levels show that several peak episodes were registered in Poland; January 21–30th being the most polluted days. Air mass back-trajectory analysis shows that all cities were under the influence of LRT from North-eastern origins (Russia–Belarus–Ukraine), most were also under LRT from Southern origin (Slovakia, Czech Republic), and northern cities were under national RT influence. PCA analysis shows that ion-sums of secondary inorganic aerosols account for LRT pollution while arsenic and chromium represents markers of RT (industrial) and LP (residential) sources of PM₁₀, respectively. Determination of several ratios (REG/UB, REG/TRAF, TRAF/UB) calculated between PM₁₀ levels measured at regional background (REG); urban background (UB) and traffic (TRAF) monitoring sites shows that, with ratios REG/UB ≥ 0.57, PM₁₀ episodes in both Szczecin and Warsaw bore a marked RT origin. The lower REG/UB ≤ 0.35 in the Southern cities of Cracow and Zabrze indicates that LP was the main contributor to the observed episodes. Only PM₁₀ episodes in Southern-western Poland (Jelenia Góra) were clearly of LP origin as characterized, by the lowest REG/UB ratio (<0.2). The high TRAF/UB ratios obtained for all cities (close to 1) indicate that there was a great uniformity of PM levels on an urban scale owing to the meteorologically stagnant conditions. A high correlation between PM₁₀, NO₂ and CO confirms that traffic emission represented a common and an important LP source of urban pollution in most Polish cities during January 2006. On the other hand PM₁₀ which is also highly correlated with SO₂ in 4 cities out of 6, indicates that coal combustion through domestic heating or industrial activities was also an important LP source of PM₁₀. Finally, extremely unfavourable meteorological conditions caused by the influence of a Siberian high-pressure system were found to be associated with the occurrence of severe PM₁₀ episodes of pollution.     

乌海市臭氧传输特征与潜在源区

乌海是典型的西北工业城市,O₃污染问题突出但缺乏研究.本文基于乌海市环境监测数据、气象数据分析了乌海市O₃污染特征.结果表明：乌海市O₃日最大8 h平均值（MDA8O₃）第90位百分数由2015年的131 μg·m^-3上升至2018年的162 μg·m^-3,超标天数由2015年的8 d上升到2018年的44 d,O₃超标日集中出现在4—8月;2018年4—8月乌海市近地面盛行南风,O₃浓度在近地面风向位于西南-南、东南、北-东北3个风向区间,风速处于2~7 m·s^-1的区间时最高.为进一步研究乌海O₃传输特征、潜在源区,本文基于NCEP再分析资料,使用后向轨迹、PSCF方法与CWT方法研究了2018年4—8月O₃的传输特征及潜在源区.O₃非污染过程中,74%的轨迹来自北方,气团移动速度快且途经的下垫面以沙漠为主,较为清洁.O₃污染过程中,来自南方的轨迹占比59%,贡献了76%的污染轨迹,这些轨迹传输距离较短、移动速度较慢且经过的下垫面多为城市及工业园区;PSCF分析与CWT分析的结果较为一致,O₃非污染过程的主要源区分布在乌海市以南.O₃污染过程的主要源区为鄂尔多斯西部、阿拉善东部、石嘴山、银川、吴忠、榆林西部,WPSCF值均大于0.5,WCWT值均大于120 μg·m^-3.乌海市O₃易受区域传输影响,O₃污染过程中其主要源区为距乌海400 km以内的上风向城市,O₃非污染过程则主要在200 km的范围内.分过程进行气团轨迹聚类分析和潜在源区分析有利于得到更为真实的污染物传输特征和潜在源区.