Status of the Riparian Ecosystem in the Upper San Pedro River, Arizona: Application of an Assessment Model

A portion of Arizona’s San Pedro River is managed as a National Riparian Conservation Area but is potentially affected by ground-water withdrawals beyond the conservation area borders. We applied an assessment model to the Conservation Area as a basis for monitoring long-term changes in riparian ecosystem condition resulting from changes in river water availability, and collected multi-year data on a subset of the most sensitive bioindicators. The assessment model is based on nine vegetation bioindicators that are sensitive to changes in surface water or ground water. Site index scores allow for placement into one of three condition classes, each reflecting particular ranges for site hydrology and vegetation structure. We collected the bioindicator data at 26 sites distributed among 14 reaches that had similar stream flow hydrology (spatial flow intermittency) and geomorphology (channel sinuosity, flood-plain width). Overall, 39% of the riparian corridor fell within condition class 3 (the wettest condition), 55% in condition class 2, and 6% in the driest condition class. Condition class 3 reaches have high cover of herbaceous wetland plants (e.g., Juncus and Schoenoplectus spp.) along the perennial stream channel and dense, multi-aged Populus-Salix woodlands in the flood plain, sustained by shallow ground water in the stream alluvium. In condition class 2, intermittent stream flows result in low cover of streamside wetland herbs, but Populus-Salix remain abundant in the flood plain. Perennial wetland plants are absent from condition class 1, reflecting highly intermittent stream flows; the flood plain is vegetated by Tamarixa small tree that tolerates the deep and fluctuating ground water levels that typify this reach type. Abundance of herbaceous wetland plants and growth rate of Salix gooddingii varied between years with different stream flow rates, indicating utility of these measures for tracking short-term responses to hydrologic change. Repeat measurement of all bioindicators will indicate long-term trends in hydro-vegetational condition.     

基于元素毒性赋值的模糊数学环境质量评价

利用模糊数学方法,根据元素毒性的大小进行权重赋值,对成都经济区近地表大气尘进行地球化学环境质量评价。结果显示：不同城市中,成都、乐山、眉山的大气环境质量较好,处于一级清洁级别;眉山、雅安的大气环境质量处于二级安全级别;德阳的大气环境质量已经处于三级警戒级。不同地区中,绵阳和眉山的大气环境质量较好,处于一级清洁级别;德阳和雅安处于二级安全级别,成都和乐山处于三级警戒级.     

基于汶川8.0级强震记录的近场地震动特征分析

在汶川8.0级大地震中,国家数字强震动台网布设在龙门山断裂带及其周围地区的50多个台站获得了大于100 Gal的加速度记录。选取其中断层附近11个台站的加速度记录,分别进行了地震反应谱分析和基于正交化HHT法的能量分布特征分析,通过对竖向与水平向加速度峰值比、竖向与水平向加速度反应谱比值,以及能量分布和峰值系数的分析与比较,探讨了汶川地震的近场地震动特征。     

西安f7地裂缝发展变化的预测研究

论文首先分析和总结了影响西安地裂缝发展变化的主要人为因素。随后,利用三维非稳定渗流分析方法研究了人类活动对地下水位的影响;根据西安市新增固定资产投资的变化,预测了地面荷载的变化;以时间、地下水位和地面荷载为基本变量,建立了预测f 7地裂缝活动趋势的BP神经网络模型;最后,借助MATLAB语言进行编程,利用训练稳定的网络模型,预测了f 7地裂缝的活动趋势以及年平均的垂直位移沉降量,为进一步研究西安地铁2号线安全运行和防护提供参考依据。     

不同类型农户农地投入的影响因素

农户作为农业生产投入的微观主体,其农地投入行为对农业和农村经济发展影响重大.本文以湖北省江汉平原和鄂北岗地6个县(市、区)的473个农户抽样调查数据,构建逐步回归模型,分析不同类型的农户农地投入行为的影响因素.结果表明:①近年来,农户耕地的投入水平处于上升趋势,国家实施的惠农政策取得了积极成效;②相对于消费型农户而言,利润型农户的总投入水平较离;③目前农户农地投入主要是流动资本投入,固定资本投入较少,农业污染较为严重,农业机械化水平有较大的提升空间;④农户农业生产目标决定其农地投入行为:消费型农户与市场联系不太紧密,依据家庭消费需求进行生产投入决策;利润型农户与市场联系非常紧密,追求利润最大化,按照市场行情进行生产投入决策.⑤不同类型农户农地投入的影响因素存在差异:家庭特征和家庭资源禀赋是影响消费型农户农地投入的主要因素;农业生产要素市场、农产品市场和农业政策是影响利润型农户农地投入的主要因素.     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

汶川地震在江苏的强震动记录及地震影响场

2008年5月12日四川汶川发生M8.0大地震,江苏数字强震动台网固定观测台站、宿迁和溧阳断层观测存放台阵共获取该次地震有效强震动事件45个,计134条加速度记录。本文介绍了这些强震动记录分析处理的基本情况及其时域与频域的一些特点;编写SICalculate谱烈度计算程序和D2InterP二维插值程序,输入所获取的汶川地震26组实测强震动记录数据,得到该次地震在江苏地区的峰值加速度与谱烈度的等值线图,并就区域内地震影响场特征进行了初步研究。     

Sensitivity and Uncertainty of Ground‐Water Discharge Estimates for Semiarid Shrublands1

Abstract: We proposed a step‐by‐step approach to quantify the sensitivity of ground‐water discharge by evapotranspiration (ET) to three categories of independent input variables. To illustrate the approach, we adopt a basic ground‐water discharge estimation model, in which the volume of ground water lost to ET was computed as the product of the ground‐water discharge rate and the associated area. The ground‐water discharge rate was assumed to equal the ET rate minus local precipitation. The objective of this study is to outline a step‐by‐step procedure to quantify the contributions from individual independent variable uncertainties to the uncertainty of total ground‐water discharge estimates; the independent variables include ET rates of individual ET units, areas associated with the ET units, and precipitation in each subbasin. The specific goal is to guide future characterization efforts by better targeting data collection for those variables most responsible for uncertainty in ground‐water discharge estimates. The influential independent variables to be included in the sensitivity analysis are first selected based on the physical characteristics and model structure. Both regression coefficients and standardized regression coefficients for the selected independent variables are calculated using the results from sampling‐based Monte Carlo simulations. Results illustrate that, while as many as 630 independent variables potentially contribute to the calculation of the total annual ground‐water discharge for the case study area, a selection of seven independent variables could be used to develop an accurate regression model, accounting for more than 96% of the total variance in ground‐water discharge. Results indicate that the variability of ET rate for moderately dense desert shrubland contributes to about 75% of the variance in the total ground‐water discharge estimates. These results point to a need to better quantify ET rates for moderately dense shrubland to reduce overall uncertainty in estimates of ground‐water discharge. While the approach proposed here uses a basic ground‐water discharge model taken from an earlier study, the procedure of quantifying uncertainty and sensitivity can be generalized to handle other types of environmental models involving large numbers of independent variables.     

人工神经网络方法在拟建小区域环境质量评价中的应用

人工神经网络的评价方法用于小区域环境质量评价中,根据本地区特点因地制宜地选择环境质量参数,代入模型中进行环境质量评价及预测,对用于环境质量评价的BP人工神经网络模型进行了改进,即对网络模型的训练样本进行了扩充,从而提高了模型的抗干扰能力和准确性.将改进了的BP人工神经网络模型应用于四川省资阳市沱江二桥拟建项目小区域的大气、地表水环境质量评价中, 对该市小区域大气、地表水环境质量状况进行评价,评价结果表明,BP人工神经网络模型用于环境质量评价是可行的,且评价结论客观,评价模型普遍适用.     

锰资源开发利用中存在的问题和对策——以秀山锰矿为例

针对秀山锰矿开采秩序混乱、效率偏低、矸石处理、电解锰渣堆存和生产废水、锰深加工路线不健全等特点,结合其他地区锰资源的开发利用情况,提出了秀山锰矿应扩大开采规模,规范管理,矸石用于充填安全柱,加高加固锰渣尾矿库,锰渣的资源化利用等措施;提取生产废水中的金属元素,减少废水产生量,废水达标处理和循环利用降低污染;依靠发展和扩大锰产业链,加大科技投入,促进秀山的可持续发展.