处置库污泥工程特性测试研究

对某填埋场污泥库中取样的污泥进行了岩土工程特性测试,包括有机质含量、含水率、界限含水率、颗粒级配、渗透系数、压缩固结特性及抗剪强度等。试验结果表明,与常规淤泥相比,污泥库中经一段时间降解后污泥有机质含量及含水率较高,平均值分别为45%和520%;污泥在历时2 a的生物降解作用下,污泥含水率、有机质含量沿深度呈较为明显的减少趋势;污泥具有极高的压缩性,压缩系数a100-200高达7 MPa-1;污泥的固结表现为非线性大变形特性,其固结系数在10-5～10-6 cm2/s,比淤泥低1～2个数量级,固结系数随压力的增加而显著减少;污泥的抗剪强度参数较小,其黏聚力为0,内摩擦角为14.7°。有机质含量高是污泥高含水率、高液塑性指数、高压缩性,低渗透性、低固结系数及低抗剪强度的本质原因。以上成果可为污泥库的固化处置、稳定分析等提供必要的参数。     

秦岭勉略带中鞍子山蛇绿杂岩的地球化学：——古洋壳碎片的证据及意义

鞍子山蛇绿杂岩位于秦岭勉略带东段,它由蛇纹石化变质橄榄岩和ＭＯＲＢ型变质镁铁质岩组成。蛇纹石化变质橄榄岩主要是方辉橄榄岩和少量二辉橄榄岩,表了古洋幔的上部岩石;变质镁铁质岩为斜长角闪岩,其原岩是基性火成岩类,它多以亏损ＬＲＥＥ元素和具有很高的＾１４３Ｎｄ／＾１４４Ｎｄ值为特征,类似于现代洋壳中的洋中脊玄武岩（ＭＯＲＢ）,代表了古洋壳的残余部分。这表明鞍子山晕绿杂岩是勉略混杂岩带中又一个典型的蛇绿岩     

东北四海龙湾玛珥湖沉积物纹层计年与137Cs、210Pb测年

对东北四海龙湾玛珥湖SHLF6孔纹层沉积物的¹³⁷Cs放射性测量表明:¹³⁷Cs比活度的最大值出现在55cm处,对应于1963年世界原子弹试爆高峰期。纹层计年表明0～6cm共有35个纹层层偶。从75cm到65cm,¹³⁷Cs比活度从256±009dpm/g急剧增加到1868±017dpm/g,可能65cm对应于1954年。通过测量²²⁶Ra子核²¹⁴Pb和²¹⁴Bi（能量为295keV,352keV和609keV）放射的光子数获得²²⁶Ra比活度数据,然后求得过剩²¹⁰Pb_{比活度（²¹⁰Pbuns）。²¹⁰Pbuns比活度随深度增加而呈指数衰减,其异常波动可能与人类活动以及沉积速率变化有关,例如55cm处²¹⁰Pb比活度较高,与¹³⁷Cs的峰值对应,这可能与1963年前后人工核实验的高峰有关,因为核试验不仅产生¹³⁷Cs,而且可以产生208Pb和²¹⁰Pb;45cm处²¹⁰Pb比活度较低,而²²⁶Ra较高,可能与人类活动加剧,导致沉积速率增加有关。根据²¹⁰PbunsCRS模式,SHLF6孔0～19cm的平均沉积速率为20mg/cm²·a,或约为011cm/a。²¹⁰Pb测年数据与¹³⁷Cs时标及纹层计年均有很好的一致性。四海龙湾玛珥湖发育的纹层为年纹层,可以建立高分辨率时间序列。}     

青藏高原珠峰绒布河谷地区近地层湍流输送特征

利用2006年6月和2007年6月中国科学院HEST大气科学实验在珠峰绒布河谷地区获取的近地层湍流观测资料,分析了近地层湍流谱特征和方差统计特征,讨论了上下2层(2m和8m)基本气象要素和湍流通量的日变化特征.结果表明,珠峰地区湍流能谱基本上符合Monin-Obukov相似性理论在惯性副区的变化规律;由于山谷复杂地形和下垫面的影响,湍流方差统计值均小于高原其它地方;珠峰地区近地层感热通量白天下层大于上层,夜间相反;潜热通量一天内基本上上层大于下层.     

基于暴雨内涝模型的天津市西青区中小河流暴雨洪涝灾害评估

利用ARCGIS对天津市西青区的高精度地理信息数据、排水设施和排水方式进行预处理,以西青区下垫面和明渠河道的水流运动为模拟对象,建立天津市西青区中小河流暴雨洪涝仿真模型。以区内14条二级河道的水位变化作为模型的动态侧边界条件,针对2016年7月20日的大暴雨过程,设计了4个模拟方案,对河道高水位及暴雨造成的洪涝淹没过程进行评估,并将模拟结果与实际调查内涝灾情进行对比,结果表明,模型可以较客观地反映暴雨和河道水位变化对城镇造成的内涝灾害情况。     

基于压缩感知的地基红外云图云状识别

为了对地基全天空红外测云仪获得的云图进行分类,该文从压缩感知理论出发,提出了一种利用云图灰度稀疏性进行云状识别的新方法。首先运用典型云图样本构造冗余字典,然后通过梯度投影 (GPSR) 算法和正交匹配 (OMP) 算法求取测试样本在冗余字典中的l1范式最优解,最后利用残差法和稀疏比例法对云状进行判别并输出。采用压缩感知理论进行云状识别,降低了对特征提取技术的要求,为云状的自动识别提供了新思路,对典型波状云、层状云、积状云、卷云和晴空的总体识别率分别达到75%,91%,70%,85%和93%,平均识别率为82.8%。     

干旱区绿洲气候变化及影响——以新疆石河子为例

文章以新疆石河子气象局1961—2008年年均气温、年降水量和年均日照观测资料为研究对象,运用趋势拟合方法、Morlet小波和Yamamoto方法分析其趋势变化、周期及突变特性。结果表明:48年中,石河子年平均气温和年降水量呈递增趋势,年均日照呈微弱递减趋势,均有一定的周期性,在不同的年份发生了突变,最后分析了气候变化对农业生产的影响。     

用NOAA卫星监测甘肃森林草原火灾

本文介绍利用ＮＯＡＡ极轨气象卫星ＡＶＨＲＲ资料监测森林草原火点的原理和方法。对甘肃省１９９２年１０月至１９９６年１２月监测到的火点进行时空统计，分析了火灾监测中可能产生的误差原因，并提出相应改进措施。     

A comparison of the effects of interannual Arctic sea ice loss and ENSO on winter haze days: Observational analyses and AGCM simulations

This study compares the impacts of interannual Arctic sea ice loss and ENSO events on winter haze days in mainland China through observational analyses and AGCM sensitivity experiments. The results suggest that (1) Arctic sea ice loss favors an increase in haze days in central–eastern China; (2) the impact of ENSO is overall contained within southern China, with increased (reduced) haze days during La Niña (El Niño) winters; and (3) the impacts from sea ice loss and ENSO are linearly additive. Mechanistically, Arctic sea ice loss causes quasi-barotropic positive height anomalies over the region from northern Europe to the Ural Mountains (Urals in brief) and weak and negative height anomalies over the region from central Asia to northeastern Asia. The former favors intensified frequency of the blocking over the regions from northern Europe to the Urals, whereas the latter favors an even air pressure distribution over Siberia, Mongolia, and East Asia. This large-scale circulation pattern favors more frequent occurrence of calm and steady weather in northern China and, as a consequence, increased occurrence of haze days. In comparison, La Niña (El Niño) exerts its influence along a tropical pathway by inducing a cyclonic (anticyclonic) lower-tropospheric atmospheric circulation response over the subtropical northwestern Pacific. The northeasterly (southwesterly) anomaly at the northwestern rear of the cyclone (anticyclone) causes reduced (intensified) rainfall over southeastern China, which favors increased (reduced) occurrence of haze days through the rain-washing effect.     

A Climate Prediction Method Based on EMD and Ensemble Prediction Technique

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.