2017-08-11内蒙赤峰EF4级山地多发龙卷灾调与成因分析

2017年8月11日下午,三个EF4级龙卷袭击了内蒙古自治区赤峰市的地形复杂地区,造成5人死亡,58人受伤。这是1961年以来中国有记录的最强山地龙卷事件。首先给出了此次龙卷过程的灾情调查结果,接下来分析了此次龙卷母体风暴-龙卷超级单体产生的天气背景、关键环境参数以及多普勒天气雷达观测特征。本次龙卷事件发生在东北冷涡东南象限的地面锋前和干线向湿侧发展处,CAPE(对流有效位能)值为1 800 J/kg,0～6 km风垂直切变为12.9 m/s,0～1 km风垂直切变达到10.8 m/s;同时,0～1 km相对风暴螺旋度达到67.3 m²/s²,接近美国龙卷发生环境的中位数,有利于超级单体龙卷的发生。现场灾害调查发现,灾害路径具有多涡旋和不连续的特点,可能与当地的复杂地形有关。基于多普勒天气雷达相对径向速度图识别出三个龙卷涡旋特征(TVS),TVS径向速度差最大达到38 m/s。三个龙卷及对应TVS出自同一个超级单体的同一个中气旋,其中两个TVS出现时间重叠。     

登陆台风“摩羯”(1814)在山东引发龙卷的灾情调查与天气雷达识别

2018年8月13—14日,1814号台风“摩羯”（YAGI）由强热带风暴逐渐减弱成热带低压,在山东省境内造成强降水,并引发了系列龙卷。龙卷发生后,气象部门对龙卷进行了详细的实地灾情调查。通过对6处龙卷路径无人机航拍的高分辨率图像和现场勘察的建筑物损毁、树木折断、庄稼倒伏等状况的综合分析,判断发生在滨州市姜楼镇、东营市盐窝镇的龙卷达到EF2级,其他为EF0/EF1级。上述龙卷都发生在残余低压环流中心移动方向的右前方,且集中在残余低压环流外围偏北段雨带中的小型超级单体内;其中在滨州引发的龙卷距离残余低压环流中心最近,约150 km,在潍坊引发的龙卷距离残余低压环流中心最远,约400 km。这些小型超级单体在雨带中,自南向北或者自东南向西北方向移动,尺度都很小,发展高度较低,强反射率因子核位于风暴的底部,低层反射率因子的南端有入流缺口,呈钩状回波特征;低层径向速度产品有较强的正负速度对。用雷达系统原适配参数值计算表明,在调查的6次龙卷中,仅有1次龙卷发生前算出了中气旋（M）产品,2次算出龙卷涡旋特征（TVS）产品;用修改的适配参数值进行计算,在6次龙卷发生前都算出了M产品,4次算出TVS产品,优化适配参数可提前将弱的M和TVS识别出来,对龙卷的临近预警具有指导作用。     

广东某硫酸冶炼工业区土壤铊污染及评价

运用地累积指数法(Igeo)及富集指数法(EF)对广东某硫酸厂含铊黄铁矿冶炼废渣堆渣场周围土壤铊污染状况进行了调查和评价。结果表明,废渣周围土壤已受到了明显的土壤铊污染,铊污染物主要集中在土壤0~165cm范围,并且表现出沿垂直方向向下迅速降低的特点;土壤表层的铊吸附量未达到饱和状态,铊污染物将继续在土壤表层发生累积作用。Igeo与EF法对铊污染评价结果相似：废渣下伏土壤0~6cm范围铊污染达到中度污染水平,而在土壤约85~15cm以下为无至轻度污染水平;15cm以下没有受到土壤铊的污染;相对于下伏土壤剖面,废渣外围土壤污染更为严重,其中表层土壤0~1cm铊已经达到显著污染,土壤2~105cm范围内铊为中度污染,而在土壤约15cm以下铊为无至轻度污染。两种方法应用于工业区土壤铊污染水平分析是可行的,但是不能表明铊在土壤中存在状态、迁移能力以及生物有效性。     

J-band spectroscopy of cataclysmic variables

We present time-resolved, J ‐band (1.025–1.340 μm) infrared spectra of the short-period dwarf novae (DNe) WZ Sge and VY Aqr, and single spectra of the short-period DN EF Peg and the nova-like variable PX And. There is some evidence in the spectra of VY Aqr and EF Peg that we have detected the secondary star, both in the continuum slope and also through the possible presence of spectral features. The spectra of WZ Sge and PX And, on the other hand, show no evidence for the secondary star, with upper limits for its contribution to the J ‐band light of 10 and 20 per cent respectively. The spectral type of the secondary in WZ Sge is constrained to be later than M7.5V. Using skew mapping, we have been able to derive a value for the radial velocity semi-amplitude of the secondary star in VY Aqr of K _R =320±70 km s⁻¹, which in conjunction with K _W from Thorstensen & Taylor gives a mass ratio of q =0.15±0.04.     

江苏省生态足迹时间维度变化及其驱动因素分析——基于PLS方法对STIRPAT模型的修正

在分析江苏省生态足迹时间维变化规律的基础上,探讨江苏省生态足迹变化的重要驱动因素。1)1990-2007年,江苏省生态足迹以年均5.36%的速度从7 227.75万hm2升至13 817.76万hm2,而生态承载力以年均0.30%的速度从2 850.98万hm2降至2 703.58万hm2,生态足迹对区域生态赤字变化的贡献率达97.81%,成为江苏省生态赤字增加的主要原因;2)STIRPAT模型分析结果显示,人均GDP及其二次项、第一二产业产值占总产值比重及其二项式是江苏省生态足迹变化的重要驱动因素,总人口及城市化率指标却被排除在外,然而VIF值计算结果显示模型中各驱动因素间多重共线性明显;3)采用PLS方法修正STIRPAT模型,消除因素间的多重共线性问题,显示总人口、人均GDP及其二次项、第一二产业产值占总产值比重及其二次项、城市化率及其二次项都是江苏省生态足迹变化的重要驱动因素,且按VIP重要性排序为城市化率>人均GDP二次项>一二产业产值占总产值比重>人均GDP>城市化率二次项>总人口>一二产业产值占总产值比重二次项。两种方法中人均GDP二次项的系数均为正,表明江苏省生态足迹变化不存在环境EKC曲线的假说。该文进一步明确STIRPAT模型在分析环境压力的驱动因素中存在的缺陷,另一方面验证了基于PLS修正的STIRPAT模型的准确性与可行性。     

Concentration distribution and assessment of several heavy metals in sediments of west-four Pearl River Estuary

Grain size parameters, trace metals (Co, Cu, Ni, Pb, Cr, Zn, Ba, Zr and Sr) and total organic matter (TOM) of 38 surficial sediments and a sediment core of west-four Pearl River Estuary region were analyzed. The spacial distribution and the transportation procession of the chemical element in surficial sediments were studied mainly. Multivariate statistics are used to analyses the interrelationship of metal elements, TOM and the grain size parameters. The results demonstrated that terrigenous sediment taken by the rivers are main sources of the trace metal elements and TOM, and the lithology of parent material is a dominating factor controlling the trace metal composition in the surficial sediment. In addition, the hydrodynamic condition and landform are the dominating factors controlling the large-scale distribution, while the anthropogenic input in the coastal area alters the regional distribution of heavy metal elements Co, Cu, Ni, Pb, Cr and Zn. The enrichment factor (EF) analysis was used for the differentiation of the metal source between anthropogenic and naturally occurring, and for the assessment of the anthropogenic influence, the deeper layer content of heavy metals were calculated as the background values and Zr was chosen as the reference element for Co, Cu, Ni, Pb, Cr and Zn. The result indicate prevalent enrichment of Co, Cu, Ni, Pb and Cr, and the contamination of Pb is most obvious, further more, the peculiar high EF value sites of Zn and Pb probably suggest point source input.