基于多元统计分析的黄河水质评价方法

为探讨一种适用于大尺度、多断面和长时间的水质评价方法,用系统聚类分析将2000—2002年黄河6个监测断面的90个水质样本分为7组,并用判别分析验证了结果的可靠性。其主要程序为：利用多元方差分析对各断面多年水质监测样本进行空间尺度上的显著差异性分析,识别出具有显著差异的样本,然后通过系统聚类分析把上述样本进行聚类分组,最后应用判别分析方法对各组样本进行水质评价,此方法的特点为在不损失信息的前提下能大大减轻水质评价工作量,且客观可信、分辨率高,并能综合反映总体与个别特征。结果表明,黄河流域干流从上游到下游水质总体状况呈逐渐下降趋势,上游水质一般为Ⅰ-Ⅲ类,而中游和下游水质基本为Ⅳ-Ⅴ类和超Ⅴ类。     

象山港海水养殖区贝叶斯网络水质评价及预测

水质评价是水环境保护与管理的重要环节,传统的评价方法在处理评价中的不确定性、大量信息处理等方面存在局限性。贝叶斯网络可以有效地表达和分析不确定性问题,实现定性分析与定量分析的有机结合。以近10a来象山港海水养殖区的水质监测数据为样本数据,采用贝叶斯网络技术,建立反映各水质指标及水质级别之间相互关系和相互影响强度的贝叶斯网络模型。模型结构表明直接影响水质级别的水质指标为氨氮、化学需氧量、硝酸盐、无机磷和叶绿素a,而其他亚硝酸盐、无机氮等4个水质指标与水质级别存在间接的因果关系。对200条监测数据进行模型精度检验,结果表明,其预测精度达94.8%,Kappa指数为0.892,这说明采用贝叶斯网络技术对水质进行评价及预测是可行的。     

基于空间平衡法的县域耕地质量监测布样方法

县域监测样点布局是反映耕地质量等级变化的基础,样本点布设的质量直接影响到耕地质量监测的结果和精度。因此,该文提出了基于空间平衡法的县域耕地质量监测布样方法,对影响耕地质量监测成本和精度的主要因素进行分析,选取样本点距离道路远近、样本点所在位置坡度高低和自然质量各等别样本容量3个方面综合生成包含概率栅格图层,图层中的像元值指总体单元中一个单元相对于其他单元被抽中的相对概率,在此基础上,运用空间平衡算法对包含概率栅格层进行空间改造,抽样选取监测样点,以平均Kriging预测标准差和监测样本点距县级主要道路的平均距离作为优化评价准则,将该方法与传统抽样方法进行比较分析。以江西省吉安县为例,全县布设78个监测样点,结果表明,当样点数量相同时,该方法相较传统布样方法在抽样精度和抽样成本方面均有一定的优势,能有效地监测耕地质量变化,满足县域耕地质量监测的需求。     

Faecal Indicator Bacteria: Groundwater Dynamics and Transport Following Precipitation and River Water Infiltration

Faecal contamination of drinking water extracted from alluvial aquifers can lead to severe problems. River water infiltration can be a hazard for extraction wells located nearby, especially during high discharge events. The high dimensionality of river?Cgroundwater interaction and the many factors affecting bacterial survival and transport in groundwater make a simple assessment of actual water quality difficult. The identification of proxy indicators for river water infiltration and bacterial contamination is an important step in managing groundwater resources and hazard assessment. The time resolution of microbial monitoring studies is often too low to establish this relationship. A proxy-based approach in such highly dynamic systems requires in-depth knowledge of the relationship between the variable of interest, e.g. river water infiltration, and its proxy indicator. In this study, continuously recorded physico-chemical parameters (temperature, electrical conductivity, turbidity, spectral absorption coefficient, particle density) were compared to the counts for faecal indicator bacteria, Escherichia coli and Enterococcus sp. obtained from intermittent sampling. Sampling for faecal indicator bacteria was conducted on two temporal scales: (a) routine bi-weekly monitoring over a month and (b) intense (bi-hourly) event-based sampling over 3 days triggered by a high discharge event. Both sampling set-ups showed that the highest bacterial concentrations occurred in the river. E. coli and Enterococcus sp. concentrations decreased with time and length of flow path in the aquifer. The event-based sampling was able to demonstrate differences in bacterial removal between clusters of observation wells linked to aquifer composition. Although no individual proxy indicator for bacterial contamination could be established, it was shown that a combined approach based on time-series of physico-chemical parameters could be used to assess river water infiltration as a hazard for drinking water quality management.     

Surface Water Quality Assessment of Lis River Using Multivariate Statistical Methods

This study presents the application of multivariate statistical tools for the evaluation of spatial variations and the interpretation of water quality data obtained in a monitoring program of Lis river basin surface water, Portugal. Twenty-seven physicochemical and microbiological parameters were determined in six water sampling campaigns at 16 monitoring sites during the period from September 2003 to November 2006. Correlation analysis, principal component analysis, and cluster analysis were performed to evaluate the main water pollution sources and to characterize the spatial distribution of water pollution profiles in river basin. The results achieved with the statistical methodologies led to distinguish natural and anthropogenic pollution sources. Additionally, monitoring sites with similar water pollution profile were identified, indicating that some monitoring locations can be changed to improve the spatial characterization of water quality in the river basin. CBO, CQO, P, and N were identified as significant variables affecting spatial variations, namely in the Lis river middle reach. Besides the identification of main pollution sources, the applied statistical tools were able to identify spatial patterns of water pollution in Lis river basin, which further helps in the reassessment of the number and location of monitoring sites.     

Explaining and mapping total phosphorus content in French topsoils

Total phosphorus (TP) build‐up in agricultural soils represents both a threat to aquatic ecosystems and a valuable resource for future crop production, given the context of increasing food demand combined with the rapid depletion of the world's phosphate reserves. Therefore, it is crucially important (i) to identify the main factors controlling topsoil TP and (ii) to develop methods for mapping its spatial distribution. Multiple linear regression models were used with two distinct approaches to calculate TP and covariates linked to the P cycle. Firstly, covariates were selected from the Réseau de Mesures de la Qualité des Sols database, the French soil monitoring network, which consists of soil samples collected from 2158 sites on a 16‐km regular grid. Secondly, covariates were selected to map TP from spatially exhaustive datasets in France. The first approach explains 80% of variability in topsoil TP. The variables selected are linked to the autochthonous origin of P (parent material), to allochthonous origin (organic carbon and nitrogen contents) and to the retention capacity of soil (Al, Fe, Ca and clay contents). The predicted map obtained from the second approach provides a mean TP of 0.76 g/kg. This study demonstrates that creating national scale maps of TP, based on detailed soil sampling and many variables, is feasible and can be used to model the P cycle and P transfer processes. Such maps can be used in P erosion and transfer models over river basins, and therefore to predict P exports to surface waters.     

Evaluation of the Influence of Natural and Antrhopogenic Processes on Water Quality in Karstic Region

This paper presents a comprehensive study on the quality of surface and groundwater in an environmentally sensitive karstic watershed strongly pressed by urban growth. The objective of the study was to assess the integrated effects of human activities and natural characteristics of karstic environments on the quality of surface and groundwater using multivariate statistical techniques. Data from 18 physical, chemical, and microbiological water quality variables obtained throughout a hydrological year were analyzed using principal components analysis (PCA) and cluster analysis. The PCA was carried out individually for surface water and groundwater. Our goal was to study the behavior of the water quality variables at each of these systems, as well as to infer the importance of these variables on the dynamics of the water resources in the region. Our results for surface water showed that the 18 original variables could be reduced to five principal components which together accounted for 69% of the total variation in the data, whereas for groundwater, 70% of the total variation in the data was explained by five principal components. In order to evaluate the impacts of anthropic activities on the water quality at the monitoring locations, the cluster analysis was applied to the ten sampling points. The analysis identified five clusters, two of which are formed by points with low contamination levels and three where the anthropic interference is noticeable. The results suggest the existence of specific contamination sources in many points, including in the groundwater, and highlight the natural vulnerability of the karstic environments.     

Artificial neural network for estimating monthly reference evapotransiration under arid and semi arid environments

The objective of this study is to investigate the potential of artificial neural networks (ANNs) for estimating reference monthly evapotranspiration under arid and semi-arid environments. A simple leave one out data analysis was carried out; one neural network solution on six inputs and another six network solutions on five inputs for each monitoring station were done. Comparison of the results showed that the accuracy of ANNs is decreased when relative humidity, wind speed and solar or extraterrestrial radiation are excluded as input variables. The results also showed that monthly evapotranspiration could be computed with relatively good accuracy compared with local calibrated Hargreaves equation based on air temperature using trained ANNs at another location. We conclude, based on our overall results, that temperature-based method ANNs can be used with relatively good accuracy for water resource management, irrigation scheduling and management, and environmental assessment when data are not enough using trained ANNs from another location.     

利用Landsat TM遥感数据监测冬小麦开花期主要长势参数

为精准农业技术体系中的小麦农艺处方管理决策提供详尽的全局性信息,该文以2007－2009年试验实测数据为基础,以Landsat TM影像为遥感数据源,分析了试验样点开花期冬小麦主要长势参数与品质和产量间以及与卫星遥感变量间的相关性,分别建立及评价了TM影像遥感变量监测冬小麦开花期SPAD值、生物量、叶面积指数和叶片氮含量的模型。结果表明：冬小麦开花期,选用作物氮反射指数、近红外波段反射率和归一化植被指数这些遥感变量分别反演冬小麦SPAD值、生物量、叶面积指数和叶片氮含量是可行的;SPAD值、生物量、叶面积指数和叶片氮含量遥感监测模型的精度较高,均方根误差分别为3.12、216.5 kg/hm2、0.269和0.162,以此为基础,制作出具有实际农学意义的冬小麦开花期不同等级SPAD值、生物量、叶面积指数和叶片氮含量遥感监测专题图,实现了主要长势参数空间分布量化表达。基于卫星影像的农田面状信息获取技术克服了点状信息的不足,为农业生产管理决策及时提供信息支持,使该研究技术更利于大面积应用和推广。     

Preparation of a Composite Biosorbent Using Scenedesmus quadricauda Biomass and Alginate/Polyvinyl Alcohol for Removal of Cu(II) and Cd(II) Ions: Isotherms,Kinetics, and Thermodynamic Studies

Diatoms are good indicators of water quality in lotic systems. Unlike in the temperate region, the effect of substrate on diatom-based water quality assessment in tropical streams is not fully understood. The purpose of this study was to assess the effect of substrate on diatom-based multivariate water quality assessment. Epilithic, epiphytic, epipsammic and epipelic diatom community and water quality sampling was done four times at 10 sites during the dry season (2008 and 2009). Artificial substrates (brick and glasses) were also placed at the sampling sites during this period and sampled after 1 month. Cluster analysis was performed to show the main differences and similarities in community composition amongst substrates sampled and amongst sampling sites. The IndVal method was used to identify indicator species characterising different substrates. Canonical correspondence analyses (CCAs) were performed to relate the structure of diatom communities from different substrates to predictor variables. A gradient of increasing metal and organic pollution, eutrophication and ionic strength was observed from the agricultural/forested area to the urban area. Diatom community structure closely reflected this gradient, with communities from polluted sites (8, 9 and 10) being different from other communities. Polluted sites were associated with such species as Nitzschia palea, Plantago lanceolata, Achnanthes exigua, Caloneis hyaline, Cyclotella meneghiniana, Gomphonema parvulum, Fallacia monoculata, Luticola goeppertiana, Pinnularia microstauron, Pinnularia subcapitata and Sellaphora pupula. Indicator species analysis showed that common diatom species were not restricted to a single substrate, though preference was generally high for natural (especially macrophytes) compared to artificial substrates. Six CCAs corresponding to six substrates performed to relate diatom community structure to simultaneous effects of predictor variables explained ～50% of the diatom species variance in all cases and roughly separated highly polluted sites from the rest of the sites. This indicates that the results of diatom-based multivariate water quality assessment based on different substrates may be interchangeable. Only one substrate has to be collected at each site for water quality assessment surveys, thus avoiding unnecessary expensive and time-consuming oversampling. Given the limitations of artificial substrates, sampling of natural substrates is highly recommended.     

Satellite remote sensing for water erosion assessment: A review

Water erosion creates negative impacts on agricultural production, infrastructure, and water quality across the world. Regional-scale water erosion assessment is important, but limited by data availability and quality. Satellite remote sensing can contribute through providing spatial data to such assessments. During the past 30 years many studies have been published that did this to a greater or lesser extent. The objective of this paper is to review methodologies applied for water erosion assessment using satellite remote sensing. First, studies on erosion detection are treated. This comprises the detection of erosion features and eroded areas, as well as the assessment of off-site impacts such as sediment deposition and water quality of inland lakes. Second, the assessment of erosion controlling factors is evaluated. Four types of factors are discussed: topography, soil properties, vegetation cover, and management practices. Then, erosion mapping techniques are described that integrate products derived from satellite remote sensing with additional data sources. These techniques include erosion models and qualitative methods. Finally, validation methods used to assess the accuracy of maps produced with satellite data are discussed. It is concluded that a general lack of validation data is a main concern. Validation is of utmost importance to achieve regional operational monitoring systems, and close collaboration between the remote sensing community and field-based erosion scientists is therefore required.     

BP神经网络在渭河水环境质量评价中的应用

为准确和客观地评价渭河水环境质量状况,将改进算法的BP神经网络引入地表水环境质量评价领域,采用渭河2010年(9月7日至10月11日)水质指标监测数据,构建了渭河水环境质量评价模型。通过3个水质监测项目(氨氮、高锰酸盐指数、溶解氧)对渭河水质进行了评价,评价结果与环境保护部公布结果完全一致。结果表明,BP神经网络应用于水环境质量评价具有客观性、通用性和实用性,可以很好地解决评价因子与水质等级间复杂的非线性关系,评价方法简便可靠,预测精度高。     

基于模糊数学的黄河内蒙古段水环境质量评价

[目的]监测黄河内蒙古段5个监测地点,从上游到下游依次是三盛公、三湖河口、包头镫口、张立文尧、喇嘛湾,共24个监测断面,进而了解黄河内蒙古段水质现状。[方法]选择溶解氧、化学需氧量、氨氮、总磷、总氮5个评价参数,运用模糊数学综合评价方法进行综合评价。[结果]黄河内蒙古段水体中DO基本属于Ⅰ类水,其值在7.2~8.1mg/L之间;COD总体上介于Ⅲ和Ⅳ类水之间,其值在17~33mg/L之间;氨氮总体上接近Ⅲ类水,其值在0.5~1.3mg/L之间;TP总体上介于Ⅱ和Ⅲ类水之间,其值在0.12~0.28mg/L之间;TN基本超过Ⅳ类水,有的甚至超过Ⅴ类水,其值在1.4~2.7mg/L之间;主要污染物为COD和TN。其整体呈现Ⅲ和Ⅳ类水,其评价值在3.3~4.2mg/L之间。[结论]通过评价发现,黄河内蒙古段水质有逐渐恶化的趋势,主要原因是外源污染的汇入。     

基于Kriging估计误差的县域耕地等级监测布样方法

为了监测耕地的质量等级,通常采取抽样调查的方法.由于空间样本间存在不独立性等原因,传统抽样方法效率低、精度不高.为此,该文提出基于Kriging估计误差的布样方法,定义了反映Kriging估计情况的统计量作为评估监测网的标准,通过分析样本量与抽样精度的变化趋势确定最优样本容量,将调整过的方形格网作为监测网的基础,在泰森多边形限制下对监测网优化增密,并选用部分标准样地作为监测点.以北京市大兴区为例对该方法进行验证,结果表明,当监测点数同为48时,该文方法均方根误差小于简单随机抽样、分层抽样以及单一使用格网布样的方法,预测总体均值的相对误差为0.07%.因此,该文方法使用较少的监测点反映县域耕地等级的分布状况和变化趋势,能够满足县域耕地等级监测的需求.     

How geostatistics can help you

Abstract. Geostatistics is basically a technology for estimating the local values of properties that vary in space from sample data. Research and development in the last 15 years has shown it to be eminently suited for soil and ripe for application in soil survey and land management. The basic technique, ordinary kriging, provides unbiased estimates with minimum and known variance. Data for related variables can be incorporated to improve estimates using cokriging. By more elaborate analysis using disjunctive kriging the probabilities of deficiency and excess can be estimated to aid decision.
The variogram is crucial in all geostatistics; it must be estimated reliably from sufficient data at a sensible scale and modelled properly. Once obtained it can be used not only in the estimation itself but also to choose additional sampling sites, improve a monitoring network or design an optimal sampling scheme for a survey. It may also be used to control a multivariate classification so that the resulting classes are not too fragmented spatially to manage.     

Identifying Soil and Plant Nutrition Factors Affecting Yield in Irrigated Mature Pistachio Orchards

The main objective of this study was to evaluate the potential use of a hybrid Genetic Algorithm-Artificial Neural Network (GA–ANN) method for predicting pistachio yield and for identifying the determinant factors affecting pistachio yield in Rafsanjan region, Iran. A total of 142 pistachio orchards were selected randomly and soil samples were taken at three depths. Besides, water samples and leaves from branches without fruit were taken in each sampling point. Management information and pistachio yields were achieved by completing a questionnaire. Primarily, 58 variables affecting pistachio yield were measured, and then 26 out of them were selected by minimizing mean square error (MSE) using a feature selection (FS) method. The results showed that the accuracy of the method was acceptable. Furthermore, the sensitivity analysis showed that the main determinant features affecting the pistachio yield were the irrigation water amount, leaf phosphorus, soil soluble magnesium, electrical conductivity (EC), and leaf nitrogen.     

基于因子分析的Hopfield神经网络在水质评价的应用

针对Hopfield神经网络的过度拟合问题,在因子分析的基础上,结合Hopfield神经网络模型提出了因子分析—Hopfield神经网络模型。以东辽河为例,采用因子分析法确定7个水质评价因子,再建立5×7的Hopfield神经网络进行水质综合评价,并与单一的Hopfield网络和传统的内梅罗指数法的结果进行了比较。结果表明,因子分析—Hopfield神经网络明显优于单一的Hopfield神经网络,不仅在一定程度上弥补了因子分析在实际应用中没有实现水质分级的缺陷,而且有效地降低了Hopfield神经网络的过度拟和的程度,评价结果更为科学合理,为水质综合评价提供了一种新的方法,具有极好的应用前景。     

基于空间模拟退火算法的耕地质量布样及优化方法

耕地质量监测是保障耕地资源的永续利用,实现耕地产能提升、加强耕地资源的管理、保护、合理利用的重要措施,对实现持续粮食安全具有重要意义。该文提出了基于空间模拟退火算法的耕地质量布样优化方法,以空间模拟退火算法为基础生成一组最优样本,构成基础监测网络,在此基础上,通过多期耕地等级成果数据提取属性发生变化的分等因素和对应发生变化的区域,生成潜在变化区,并结合研究区实际情况辅以专家知识和异常监测点,对基础样本点进行增加、删除、替换等优化操作,生成最终监测样点。以北京市大兴区为例,最终确定布设55个监测样点,结果表明,该方法布设的样点在耕地质量预测方面的精度高于传统的随机抽样和分层抽样方法,能有效地预测县域耕地质量并监控耕地质量的变化情况。     

Uncertainty analysis for pedotransfer functions

Both empirical and process‐simulation models are useful in predicting the outcome of agricultural management on soil quality and vice versa, and pedotransfer functions have been developed to translate readily available soil information into variables that are needed in the models. The input data are subject to error, and consequently the transfer functions can produce varied outputs. A general approach to quantifying the resulting uncertainty is to use Monte Carlo methods. By sampling repeatedly from the assumed probability distributions of the input variables and evaluating the response of the model, the statistical distribution of the outputs can be estimated. Methods for sampling the probability distribution include simple random sampling, the sectioning method, and Latin hypercube sampling. The Latin hypercube sampling is applied to the quantification of uncertainties in pedotransfer functions of soil strength and soil hydraulic properties. Hydraulic properties predicted using recently developed pedotransfer functions are also used in a model to analyse the uncertainties in the prediction of soil‐water regimes in the field. The uncertainties of hydraulic properties in soil‐water simulation show that the model is sensitive to the soil's moisture state.     

基于径向基函数神经网络的地下水数值模拟模型的替代模型研究

近年来提出的替代模型方法是一种连接数值模拟模型与优化模型的有效途径,替代模型质量的好坏取决于采样方法和替代模型种类。以金泉工业园区地下水水源地为研究区,基于拉丁超立方抽样方法,结合研究区地下水数值模拟模型,获取输入(抽水量)输出(水位降深)数据集,运用人工神经网络方法,建立径向基函数神经网络模型,作为地下水数值模拟模型的近似替代模型。经验证,径向基函数神经网络模型输出得到的水位降深均值与模拟模型计算结果的拟合平均相对误差为0.038;水位降深剩余标准差的拟合平均相对误差为0.042。拟合平均相对误差较小,表明径向基函数神经网络模型能够有效地替代地下水数值模拟模型,为日后替代模型的深入研究提供了科学依据。