基于3D激光扫描技术的绕腰法采空区资源回收方法研究

针对大多数矿山采空区残留资源回收探测中存在人员、设备进入难,精度低、危险高等问题,我们通过采用三维激光扫描技术恰恰克服了以上困难。应用三维激光自动扫描新技术对采空区进行精密探测,能够安全准确地获取采空区的三维形态和矿石空间分布位置,再通过三维建模软件 Surpac 建立三维可视化实体模型,为回收残留矿石资源提供重要依据;最后通过应用绕腰法综合回收残留矿石,不仅能够减少采准工程量,并把采准与采矿工作很好地结合起来,而且达到了能够高效安全地回采采空区残留矿石目的。工程实例的成功应用,表明此方法在给矿山和国家带来良好的经济社会效益的同时,能够在同类矿山中推广应用。     

基于改进成功流法的消防系统可靠性研究

在多信号输入系统的可靠性研究中,传统GO法存在计算复杂、易出现误差和遗漏的缺点。将卡诺图化简法引入传统GO运算,利用其“合并同项,简化运算”的特点构建研究多信号输入系统可靠性的新GO模型。将其应用于预作用喷水灭火系统,对具有多启动方式的给水泵进行可靠性分析,并与事件树运算结果比较,证明新GO模型适用于具有多信号输入系统的可靠性研究。研究表明,通过采取措施降低多信号元器件故障概率可以提高系统整体可靠性。     

基于熵权-TOPSIS的陕西省各市区道路交通安全评价

为评价陕西省各市区道路交通安全状况,选取了十亿地区生产总值死亡人数、平均每起事故死亡人数、致死率、万车死亡率和十万人口死亡率5个相对评价指标,应用熵权-TOPSIS法对陕西省10个地级市的道路交通安全水平进行了综合评价。结果表明,陕西省各市区道路交通安全水平与地区生产总值、人口数量、机动化水平等社会经济因素具有较强的相关性,不同市区道路交通安全水平存在明显的差异。     

基于PCA-ELM模型的露采爆破振动对民房破坏的预测分析

针对露天采矿爆破振动对民房破坏的预测问题,采用主成分分析(PCA)和极限学习机(ELM)方法,选取爆破振幅、主频率、主频率持续时间、灰缝强度、砖墙面积率、房屋高度、屋盖形式、圈梁立柱、施工质量、场地条件10个主要影响因素。引入相关性分析在主成分分析过程中,对相关性高的指标进行降维,把得到的3个综合因子和爆破振幅、主频率、主频率持续时间、砖墙面积率作为输入变量,构建露天煤矿PCA-ELM预测模型。选取露天矿实际爆破过程中测量的100组数据作为模型训练样本,用另外20组数据作为测试样本进行预测。结果表明:对民房破坏影响因素中灰缝强度、房屋高度、屋盖形式、圈梁立柱、施工质量、场地条件之间具有较高的关联度。该模型处理高维数据时较传统的ELM算法具有预测精度高、稳定性好等特点,可准确预测爆破振动对民房的破坏程度,误判率为1/20。     

超深水水下分离器摆锤法下放风险分析

针对我国摆锤法下放安装风险分析研究空白的现状,基于工作安全分析与作业条件危险性评估法对水下分离器下放过程进行风险分析,考虑重力、海流力、浮力、阻力等多种复杂载荷,依据摆锤法下放步骤,分析水下分离器摆锤法下放过程受力情况,建立水下分离器下放过程受力关系。分析摆动过程中缆绳动张力随下放深度变化情况,为风险分析提出的有关水下分离器所受浮力、安装绳的动张力等参数的确定提供理论依据。最终建立水下分离器摆锤法下放风险评估体系,为我国摆锤安装法的工程应用提供技术支持与安全保障。     

Assessment of heavy metal pollution and health risks in a main river in Huayuan County,Xiangxi, Hunan Province北大核心CSCD

To study heavy metal pollution and assess the health risk of river water in Huayuan County, Xiangxi, Hunan Province, 11 water samples were collected from the Huayuan River and Brother Rivers in August and December 2016. Heavy metal (Pb, Zn, Cr, Cu, Fe, and Ni) concentrations were determined from the samples. The health risk assessment model recommended by the U.S. Environmental Protection Agency (USEPA) was applied to assess the health risk of heavy metals in the main surface waters of Huayuan County. The results indicated that the concentrations of heavy metals (Pb, Zn, Cr, Cu, Fe, and Ni) of surface water in the research area were 2.57 × 10-3, 4.66 × 10-4, 1.65 × 10-3, 6.27 × 10-4, 0.19, and 8.50 × 10-4 mg/L, respectively. The health risk of surface waters with heavy metals was high. Therefore, the chemical carcinogenic substance (Cr) health risk index was five or six times higher than that of chemical non-carcinogens (Pb, Zn, Cu, and Ni). The average health risk indices of non-carcinogenic substances were in the order Pb > Cu > Zn > Ni. The correlation and principal component analysis of surface water showed that the six heavy metal elements were composed of three main components in the main surface waters of the county. The first principal component was comprised of Fe and Ni (33.28%), which was mainly from internal pollution. The second component was comprised of Cu and Cr (26.98%), which was primarily due to industrial waste water, rainwater leaching mineral waste produced by heavy metal mining, and smelting enterprises. The third component, resulting from geochemical pollution, was Zn (17.10%). The health risk indices triggered by heavy metal in surface waters was high. Heavy metal pollutants in the research area need to be controlled in the order Cr, Pb, Cu, Zn and Ni. © 2018 Science Press. All rights reserved.     

乙酰丙酮荧光法在线监测环境空气中的甲醛

我国现行环境空气中甲醛的标准测定方法普遍难以满足当前大气监测中对痕量浓度水平甲醛的监测需要。在乙酰丙酮分光光度法的基础上改进得到的乙酰丙酮荧光法具有更低的检出限和更高的灵敏度,适用于大气中痕量浓度水平甲醛的监测。基于乙酰丙酮荧光法设计了甲醛在线监测系统,并对其主要性能指标进行了评估测试。测试结果显示,该系统的吸收效率为98.9%~100.1%,检出限为0.045 4×10^-9,精密度为0.44%,线性误差为-0.55%,线性范围为0.0~80.0×10^-9,响应时间为282 s,单日零点漂移为-0.04×10^-9~1.33×10^-9,单日跨度漂移为-0.90×10^-9~3.45×10^-9。测试结果表明,其各项性能指标均能满足当前对大气环境中痕量浓度水平甲醛的监测需求。     

Inverse uncertainty characteristics of pollution source identification for river chemical spill incidents by stochastic analysis

Identifying source information after river chemical spill occurrences is critical for emergency responses. However, the inverse uncertainty characteristics of this kind of pollution source inversion problem have not yet been clearly elucidated. To fill this gap, stochastic analysis approaches, including a regional sensitivity analysis method, identifiability plot and perturbation methods, were employed to conduct an empirical investigation on generic inverse uncertainty characteristics under a well-accepted uncertainty analysis framework. Case studies based on field tracer experiments and synthetic numerical tracer experiments revealed several new rules. For example, the release load can be most easily inverted, and the source location is responsible for the largest uncertainty among the source parameters. The diffusion and convection processes are more sensitive than the dilution and pollutant attenuation processes to the optimization of objective functions in terms of structural uncertainty. The differences among the different objective functions are smaller for instantaneous release than for continuous release cases. Small monitoring errors affect the inversion results only slightly, which can be ignored in practice. Interestingly, the estimated values of the release location and time negatively deviate from the real values, and the extent is positively correlated with the relative size of the mixing zone to the objective river reach. These new findings improve decision making in emergency responses to sudden water pollution and guide the monitoring network design.

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基于涡度相关技术的农田生态系统碳收支评估

以涡度相关技术为主要观测手段,连续观测冬小麦和水稻生态系统主要生长季净生态系统CO₂交换（NEE）的变化规律,评估两种农田生态系统CO₂的源/汇功能.结果表明,整个观测期间,两种作物生态系统CO₂浓度的日变化曲线呈现白天低、晚上高的"一峰一谷"型,冬小麦生态系统变化较为平缓,而水稻生态系统变化则比较剧烈.冬小麦和水稻生态系统白天30 min CO₂通量的平均值分别为-13.4 μmol·m^-2·s^-1和-12.9 μmol·m^-2·s^-1,通量最高值分别出现冬小麦的孕穗期与水稻的开花期.此外,两种作物生长季CO₂通量表现出"U"形曲线的日变化特点,白天以吸收CO₂为主,冬小麦和水稻生态系统分别于12：00和11：30达到吸收峰值;夜间CO₂通量变化较为稳定,表现为呼吸排放CO₂.两种农田生态系统均表现为碳汇,冬小麦与水稻生态系统净碳交换分别为188.2 g·m^-2与233.8 g·m^-2.     

基于监测及Kriging方法的京津冀地区大气污染物暴露分布研究

为了实现充分利用已有环境监测站点数据进行人群精细化暴露评估的目的,同时解决某些待测人群社区周边无监测站点时数据的选择问题,以保定市作为大气高污染研究城市,基于现场监测和Kriging（克里金插值）空间分析方法,明确了在研究大气污染物人群暴露时,某一个固定监测站污染物数据的代表性问题.研究表明:对于大气中φ（SO₂）、φ（NO₂）、颗粒物及其组分,空气质量监测点位的代表性一般为5~6 km;对于φ（CO）、φ（O₃）和φ（VOCs）,它们在城市不同地区的空间分布更为均匀,空气质量监测点位的代表性范围更大.通过使用Radial Basis Functions（径向基函数,RBF）、Local Polynomial Interpolation（局部多项式插值,LPI）、Inverse Distance Weighting（反距离权重插值,IDW）、Kriging、Kernel Smoothing（内核平滑插值,KS）和Diffusion Kernel（内核扩散插值,DK）等6种空间分析方法对大气污染物浓度进行预测发现,Kriging方法对大气污染物浓度预测时可使预测值和实测值间的偏差小于10%,准确度最高.因此,在进行某城市某点位的污染物人群暴露浓度预测时,若该点位周边5 km以内有空气质量监测点位,则可用该点位的监测值代替;若5 km以内没有空气质量监测点位,则可基于最近监测点位的污染物浓度进行Kriging空间插值,从而获得该点位的污染物暴露水平.