Classification of rivers based on water quality assessment using factor analysis in Taizi River basin, northeast China

A method for river classification based on water quality assessment (WQA) was introduced using factor analysis (FA) in this paper. Sixty-nine sampling sites and 20 water quality parameters in Taizi River basin were selected for monitoring and analysis. Five factors were determined in FA, denoted as general, hardness, trophic, nitrogen pollution, and physical factors. The total factor scores (TFSs) of the WQA results from all sampling sites were calculated by the eigenvalue and factor score of each factor. The TFSs of 69 sites were interpolated with the measure of inverse distance weighted in the river buffer zone generated by ArcGIS 9.2 software to form a continuous spatial distribution along river channels. All streams were divided into five classes marked “excellent”, “good”, “fair”, “poor”, and “seriously polluted”. The classification result showed that the water quality of Taizi River basin deteriorated gradually from the mountain area to the plain area. Sewage and intensive human activities contributed to the deterioration of water quality since towns and farmland were dotted densely along the river basin.     

The monitoring and prediction of mining subsidence in the Amaga,Angelopolis, Venecia and Bolombolo Regions,Antioquia, Colombia

Amaga, Angelopolis, Venecia and Bolombolo are small towns located in Antioquia, in the Central Cordillera of the Colombian Andes. Mining has been practised in this region for a period of at least 100 years. This mining has mainly been small-scale, poorly mechanised and restricted to shallow room and pillar workings. Recently, the semi-mechanisation of some mines has enabled coal to be extracted using longwall mining methods. However, this has resulted in subsidence that has caused severe damage to structures, residential property, and agricultural land, and also induced landslides. In the British Isles, there are several reliable methods that can be used to predict the likelihood and magnitude of mining subsidence. The British Coal Corporation and the University of Nottingham have developed one such method, the “Subsidence With Influence Function Technique (SWIFT).” Based on mining subsidence observations undertaken in the coalfields of Britain over a period of approximately 50 years. The SWIFT program was used to predict the magnitude of subsidence, above a longwall panel, at the Industrial Hullera mine in Colombia. The results were then compared with subsidence profiles obtained from precise levelling and field monitoring. In each case, the SWIFT program overestimated the magnitude of mining subsidence by 0.17–0.20 m. However, the morphology of the subsidence profile, area-of-influence and location of maximum subsidence were similar. This overestimation of the predicted subsidence was attributed to the occurrence of strong, igneous rocks, such as rhyolite sills, in the Colombian coal measures. These strong, competent horizons act as cantilever beams during subsidence, causing bed separation and therefore reducing the magnitude of subsidence. In spite of these differences, mining subsidence can be predicted with a reasonable degree of accuracy and precision using the SWIFT technique, provided the software is calibrated and used in conjunction with local expertise.     

Combined analyses of chemometrics and kriging for identifying groundwater contamination sources and origins at the Masan coastal area in Korea

Hydrogeochemical analyses including the basic statistics of chemical components, Piper??s trilinear diagram, and Mazor??s compositional bivariate diagram revealed that the main source and origin of groundwater contamination was seawater intrusion in the study area. However, the other sources and origins of groundwater contamination could be found by the combined analyses of chemometrics and kriging. Cluster analysis was helpful for the classification on the basis of the contamination characteristics of groundwater quality; however, it was not sufficient for the apportionment of groundwater contamination sources. Factor analysis (FA) determined three factors with 81.07% in total variance: Factor 1 for seawater contamination, Factor 2 for nitrate contamination, and Factor 3 for iron contamination. Factor analysis determined the sources of groundwater contamination; however, it could not discover the origins of contaminants except Factor 1. In backward stepwise mode, discriminant analysis decreased the number of parameters from 18 to 6 in discriminating the contaminant type with 96.2% correctness. TDS, Ca, NO₃, Mn, Fe, and Br were the most significant parameters for the discrimination of contaminants. Kriging analysis was very useful for the understanding of correlation and similarity between contaminants and factors of FA, and for the investigation of contaminant origins. It also showed that the similarity between factor scores and contaminant concentrations was proportional to the magnitudes of factor loadings for contaminants. This study represented that the combined analyses of chemometrics and kriging were very indispensable to the identification of groundwater contamination sources and origins, as well as for the spatial classification and assessment of groundwater quality.     

Stream sediment geochemical exploration for gold in Central Eastern Desert,Egypt: Application of the concentration-number fractal model,factor analysis,and geochemical mineralization probability index

Geochemical exploration by stream sediment sampling using bulk leach extractable gold (BLEG) technique and applying concentration-number (C-N) fractal model, factor analysis (FA), and geochemical mineralization probability index (GMPI) resulted in the recognition of new Au occurrences around the Sukari gold mine in the central Eastern Desert of Egypt. The geochemical data of 128 stream sediment samples collected from the study area was used for delineating the geochemical anomalies and characterizing the dispersion trains of ore and associated elements (Au, Ag, As, Sb, Cu, Pb, Zn, Mo). Statistical analysis of the geochemical data applying the C-N fractal modeling enabled us to identify significant anomaly and background populations of the investigated elements and to construct reliable geochemical anomaly maps. Factor analysis using centered log-ratios (CLR), to address the problem of closed compositional data, revealed significant element associations for mineralization (Au, As, Mo, Zn, Ba), country rock compositions (Rb, Li, Be, Sn, Bi for granite, and Co, Cr, Ni for mafic rocks), and element mobility (e.g. Sb, Zr, and Ag). Weak and moderate Au anomalies that cannot be detected by factor score maps can be delineated clearly by using the C-N fractal method and GMPI distribution map. Our study revealed that Ag, As, and Sb are the main pathfinder elements for gold mineralization in arid to semiarid regions exemplified by the Sukari gold district. Silver can be used as a “direct” pathfinder, whereas As and Sb are “indirect” pathfinders for Au in such regions. The spatial distribution of Au and Ag anomalies indicate that gold mineralization in the Sukari district is structurally controlled. However, the spatial distribution of Cu, Pb, Zn, and Mo is controlled by mineralogical and lithological factors and is not related to any significant base metal deposits.     

Assessment of groundwater quality using multivariate statistical techniques in Hashtgerd Plain, Iran

Multivariate statistical techniques, such as cluster analysis (CA), factor analysis (FA), principal component analysis (PCA), and discriminant analysis (DA), were applied for the evaluation of variations and the interpretation of a large complex groundwater quality data set of the Hashtgerd Plain. In view of this, 13 parameters were measured in groundwater of 26 different wells for two periods. Hierarchical CA grouped the 26 sampling sites into two clusters based on the similarity of groundwater quality characteristics. FA based on PCA, was applied to the data sets of the two different groups obtained from CA, and resulted in three and five effective factors explaining 79.56 and 81.57% of the total variance in groundwater quality data sets of the two clusters, respectively. The main factors obtained from FA indicate that the parameters influencing groundwater quality are mainly related to natural (dissolution of soil and rock), point source (domestic wastewater) and non-point source pollution (agriculture and orchard practices) in the sampling sites of Hashtgerd Plain. DA provided an important data reduction as it uses only three parameters, i.e., electrical conductivity (EC), magnesium (Mg²⁺) and pH, affording more than 98% correct assignations, to discriminate between the two clusters of groundwater wells in the plain. Overall, the results of this study present the effectiveness of the combined use of multivariate statistical techniques for interpretation and reduction of a large data set and for identification of sources for effective groundwater quality management.     

Factor analysis in geochemical exploration

Soil samples collected in the vicinity of a small sulfide mineralization were analyzed for Cu, Zn, Cd, Pb, Ag, Mn, Ni, Co, Cr, and Mo. Factor analysis was used to study the interrelationships among these variables. Selective removal of some samples prior to factor analysis proved to be an important tool in exposing “hidden” relationships between samples, variables, and factors. It has been demonstrated that factor analysis is a suitable technique for data reduction in geochemical exploration since factor scores can be used, instead of raw variables, for interpretation of field observations and location of anomalies.     

Assessment of groundwater quality using multivariate statistical techniques in the Azmak Spring Zone,Mugla, Turkey

About 150 coastal spring outlets discharging from a karstified carbonate rock aquifer constitute the Azmak streamflow which is slightly brackish with 3000 mg/l of total dissolved solids. In this study, multivariate statistical methods were applied including the use of factor analysis, correlation analysis and cluster analysis to evaluate groundwater quality of Azmak Spring Zone using eight variables (Ca, Mg, Na, K, Cl, SO₄, EC₂₅ and B) at 19 water points sampled in the dry and wet seasons. Hydrochemical analysis results revealed that for majority of the sampling points, the abundance of cations and anions were ordered as Na?+?K?>?Mg?>?Ca and Cl?>?SO₄?>?HCO₃?+?CO₃, respectively. Factor analysis results indicated that three factors explain 98% and 91% of the total variance in the dry and wet seasons, respectively. Factor 1 was found to be associated with the seawater, factor 2 indicated the effect of fresh water and factor 3 was defined to reflect the effect of seasonal fresh surface water contribution. Cluster analysis results indicated that two main groups and four subgroups could be defined with respect to the ratio of the seawater contribution. Cluster A (A1 and A2) represents the waters affected by seawater while waters less affected by the seawater intrusion are grouped in cluster B (B1 and B2).     

Groundwater Quality assessment using Water Quality Index (WQI) in parts of Varanasi District,Uttar Pradesh,India

Either naturally occurring process or human activities may have a significant impact on the quality of sub-surface waters which further limit its use. Multivariate statistical techniques such as factor analysis (FA), cluster analysis (CA) were applied for the evaluation of spatial variations and the interpretation of ground water quality data around Bacheli and Kirandul area. The major anions, cations and heavy metals were determined for each of 20 samples collected in pre-monsoon seasons. Hydrochemical parameters like EC, pH, TDS, TH, TA, Na⁺, K⁺, Ca²⁺, Cl^-, F^-, SO₄^2-, As, Sb, Se, Pb, Cd, Zn, Cu were estimated in pre monsoon and post monsoon seasons. Different geochemical controls of the investigated parameters were also assessed. Factor 1 explains 33.47% of the total variance and indicates atmospheric controls and silicate mineral weathering process. Factor 2 explains 13.83% of total variance, indicating silicate mineral weathering process resulting in elevated pH. Generally, water types tend towards magnesium-bicarbonate-chloride.     

Statistical interpretation on seasonal variations of groundwater quality in Ramanathapuram coastal tract,Tamil Nadu,India

Groundwater availability depends on its accessibility, as well as on its quality. Factor analysis (FA) has been used to analyze quality problems and provide strategies for water resources exploitation. The present study demonstrated the use of factor analysis to evaluate temporal variations in groundwater quality and find latent sources of water pollution in coastal areas of Ramanathapuram District, Tamil Nadu, India. The data set included data of eleven water quality parameters viz., pH, electrical conductivity, salinity, total dissolved solids, total alkalinity, calcium hardness, magnesium hardness, total hardness, chloride and fluoride for two different seasons (pre- and post-monsoon) in 2012. FA of the two seasons resulted in two latent factors accounting for 80.38 % of total variance for pre-monsoon (summer) and 73.03 % for post-monsoon (winter) in the water quality data sets. The results obtained from FA prove that the groundwater quality in winter is better than that of summer. Langelier Saturation Index was used to find out scaling and corrosive tendency of the groundwater samples for the study area. Karl Pearson correlation matrix was used to study the correlation between the studied water quality parameters. Hence, the analysis suggests that FA techniques are useful tools for identification of influence of various quality parameters on overall nature of the groundwater.     

Evaluation of the processes affecting vertical water chemistry in an alluvial aquifer of Mankyeong Watershed, Korea, using multivariate statistical analyses

Vertical variations of redox chemistry and groundwater quality were investigated in an alluvial aquifer beneath an agricultural area, in which deep groundwaters are free of NO₃, Fe, and Mn problems that are frequently encountered during the development of alluvial groundwaters. This study was performed to identify and evaluate vertical chemical processes attenuating these chemical species in the study area. For this study, the processes affecting groundwater chemistry were identified by factor analysis (FA) and the groundwater samples collected from six multilevel samplers were hierarchically classified into three different redox zones by cluster analysis (CA) based on the similarity of geochemical features. FA results indicated three major factors affecting the overall water chemistry: agricultural activities (factor 1), redox reactions (factor 2), and remnant seawater (factor 3). The groundwater quality in the study area was revealed to be controlled by a series of different redox reactions, resulting in different redox zones as a function of depth. It was also revealed that the low Fe and Mn levels in the groundwater of the deeper part are associated with sulfate reduction, which led to precipitation of Fe as iron sulfide and adsorption of Mn on it.     

Sensitivity analysis for the GIS-based mapping of the ground subsidence hazard near abandoned underground coal mines

Ground subsidence around abandoned underground coal mines can cause much loss of life and property. We analyze factors that can affect ground subsidence around abandoned mines in Jeongahm in Kangwon-do by sensitivity analysis in geographic information system (GIS). Spatial data for the subsidence area, topography and geology and various ground engineering data were collected and used to make a factor raster database for a ground subsidence hazard map. To determine the importance of extracted subsidence-related factors, frequency ratio model and sensitivity analysis were employed. Sensitivity analysis is a method for comparing the combined effects of all factors except one. Sensitivity analysis and its verification showed that using all factors provided 91.61% accuracy. The best accuracy was achieved by not considering the groundwater depth (92.77%) and the worst by not considering the lineament (85.42%). The results show that the distance from the lineament and the distance from the drift highly affected the occurrence of ground subsidence, and the groundwater depth, land use and rock mass rating had the least effects. Thus, we determined causes of ground subsidence in the study area and this information could help in the prediction of ground subsidence in other areas.     

山西阳泉矿区刘村采煤塌陷机理及数值模拟

选取山西阳泉矿区单煤层开采引发的强烈地面塌陷作为研究对象,在详细介绍刘村采煤塌陷所处地质环境背景及其发育变形特征的基础上,根据区域岩体工程地质特征,将其划分为12层岩组;运用关键层、复合关键层理论对采煤塌陷机理进行了分析,获取Hoek-Brown岩体力学参数;采用Flac5.0 Extrusion对刘村采煤塌陷坑进行了反演模拟。数值模拟反映各阶段采动裂缝在地表的发育分布情况并计算了最终沉降量,覆盖层裂缝自然修复周期为2个月,基岩裂缝自然修复周期为3个月;采动裂缝最终在平面上呈“θ”形,塌陷中心1和塌陷中心2最终沉降量分别达到4.5 m和4 m,塌陷面积是工作面面积的1.85倍。模拟结果与调查监测数据高度吻合,客观地反映了地表变形和深部覆岩塌陷的发展变化过程,为塌陷机理分析起到了帮助作用。该套岩体力学参数与模拟方法适用于阳泉矿区采煤塌陷精准预测。     

应用地理探测器改进地面沉降危险性评估模型的研究

地面沉降危险性评估对受地面沉降灾害影响的城市有着重要的参考意义。目前应用较广泛的因子叠加模型和模糊层次分析模型存在着评估指标权重的确定人工干预较强,影响评估结果的问题。本文采用地理探测器分别对因子叠加模型和模糊层次分析模型进行改进,根据各评估因子的分布与沉降分布的关联性定量地计算各项评估指标的权重。本文利用改进后的三个模型:地理探测器--最大值因子叠加模型、地理探测器--加权因子叠加模型以及地理探测器--模糊层次分析模型分别进行地面沉降危险性评估,并与最大值因子叠加模型、加权因子叠加模型、模糊层次分析模型进行对比,结果表明:改进后的模型总体评估准确度分别为75.23%、89.56%、76.38%,均高于未改进模型的60.51%、53.73%、65.05%。     

Assessment of water quality and identification of pollution sources of three lakes in Kashmir, India, using multivariate analysis

Multivariate statistical techniques, such as cluster analysis, principal component analysis (PCA) and factor analysis (FA) were applied to evaluate and interpret the water quality data set for 13 parameters at 10 different sites of the three lakes in Kashmir, India. Physicochemical parameters varied significantly (p?<?0.05) among the sampling sites. Hierarchical cluster analysis grouped 10 sampling sites into three clusters of less polluted, moderately polluted and highly polluted sites, based on similarity of water quality characteristics. FA/PCA applied to data sets resulted in three principal components accounting for a cumulative variance of 69.84, 65.05 and 71.76% for Anchar Lake, Khushalsar Lake and Dal Lake, respectively. Factor analysis obtained from principal components (PCs) indicated that factors responsible for accelerated eutrophication of the three lakes are domestic waste waters, agricultural runoff and to some extent catchment geology. This study assesses water quality of three lakes through multivariate statistical analysis of data sets for effective management of these lakes.     

Climatic classification of the Tibet Autonomous Region using multivariate statistical methods

A climatic regionalization of the Tibet (Xizang) Autonomous Region (TAR) is developed based on a multivariate analysis of temperature and precipitation records of two data sets from the periods 1971–1980 and 1980–1989. For these two decades 15 selected variables of the 29 meteorological stations — in total more than 50,000, partly handwritten original data — were computed. Three different statistical approaches, Cluster Analysis (CA), Factor Analysis (FA) and Discriminant Analysis (DA) were followed up. The vast expanse (1.2 million km²) and the pronounced relief of the TAR (between 500 m and 8,848 m in elevation), the low density and unequal distribution of meteorological stations, as well as uncertainties inherent in the selection of statistical methods, restricted and hampered the separation of coherent climatic subregions using multivariate statistics. However, a solution comprising of nine clusters, representing the different climatic conditions of Tibet, was found.The results of the multivariate statistical analyses were compared with traditional classification schemes published by the Chinese Academy of Sciences (CAS). The Chinese classifications from the years 1982 (CAS 82) and 1984 (CAS 84) are here presented to the scientific community of the western countries for the first time (Fig 2 and 3). The main aim of these Chinese climatic classifications in Tibet was the delineation of areas with the best climatic conditions for agricultural land-use. In comparison, the aim of this paper was to establish a new classification of Tibet's climate, mainly based on measurements of climatic elements analyzed with multivariate statistical procedures following up the approach of McGregor (1993). The Factor Analysis (FA) included the temperature factor, which explains 46% of the total variance, whereas 32% is explained by the winter moisture and 12% by the moisture factor.For temperatur zonation and, in particular, for the distribution of precipitation, the high mountain topography of Tibet is interpreted as the determining factor. The new climatic classification derived from multivariate statistics allows a more dynamic interpretation of the climatic cluster pattern. First, it shows the channelized influence of the summer monsoon along the southeast-northwest- and east-west-trending broad valleys north of the Gulf of Bengal. This is also obvious in the pattern of the temperature and moisture factors. Second, it can be supposed that a general south-trending influence of the winter monsoon, in combination with a northwest-trending influence from the Bay of Bengal leads to more irregular relief- and wind-dependent pattern, which can be estimated from the distribution of the winter moisture factor.     

淮南采煤沉陷区积水来源的氢氧稳定同位素证据

淮南是我国东部重要的能源基地,由于长期地下采煤,地表形成大面积的采煤沉陷区并积水,造成严重地质灾害。针对于此,部分学者提出利用采煤沉陷区建立"平原水库"解决周边地区干旱年份农田缺水问题的设想。然而,一方面,由于煤层上覆几百米厚的新生代沉积,采煤塌陷形成的沉陷裂隙是否沟通了不同含水层之间的水力联系,并因此改变了这个地区的地下水系统,成为区域水资源评价需要了解的一个重要科学问题;另一方面,建立"平原水库"需要有稳定的补给水源,采煤形成的沉陷裂隙如果沟通了地下不同深度含水层的水力联系,是否使地下水成为塌陷区除降雨外的重要补给来源,这就成为评价"平原水库"水资源潜力的重要参考依据。氢氧稳定同位素是示踪天然水体水来源的重要手段,笔者在淮南矿区采集了旱季和雨季的浅层地下水、河水、雨水、沉陷区的积水等不同水体的水样23件,分析了其氢氧稳定同位素组成并与深层地下水进行对比。结果表明:雨季和旱季,该地区采煤沉陷区积水的氢氧稳定同位素组成都非常接近大气降水的氢氧稳定同位素组成,而与深层地下水的氢氧稳定同位素组成相差较大,说明采煤沉陷区的积水来源主要是大气降水补给。采煤沉陷区的沉陷裂隙贯穿了整个新生代地层,使地表水发生下渗与在深部与深层地下水发生不同程度的混合,而深层地下水尚不是"平原水库"的稳定补给源。     

Geochemical application of spark source mass spectrography—III. Element sensitivity,precision and accuracy

Developments in the spark source mass spectrographic technique of trace element analysis have improved the precision of the method by about a factor of three. The main improvements have stemmed from corrections for interferences and corrections to the internal standard. The magnitude of interference corrections for the REE depends on the relative rather than the absolute abundances, being negligible for flat REE patterns and large for heavy REE depleted patterns. Precise determination of the photoplate intensity-density relationship is also considered to be important.Our data for BCR-1 fell within ± 3% of the “best estimate” of the “true” values. A set of relative sensitivity values for 31 elements show a factor of 25 between the most sensitive element (Rb) and the least sensitive (Ir). The relative element sensitivities depend on elemental boiling point, ionisation potential, and nuclide mass.     

Estimation of land subsidence caused by loss of smectite-interlayer water in shallow aquifer systems

Traditionally, land subsidence that results from groundwater over-pumping has often been described by the theory of consolidation. The mechanism of land subsidence due to the dehydration of clay minerals is not well addressed. A model of the “hydration state of smectite”, and a “solid solution model of smectite dehydration”, incorporating a thermodynamic solid solution model and laboratory results concerning clay-water systems of swelling pressure, hydration state and basal spacing in smectite interlayer, are employed to examine the effect of the release of water from the smectite interlayer on land subsidence in the coastal area of the Chou-Shui River alluvial fan and the Yun Lin offshore industrial infrastructure complex in Taiwan. The results indicate that 9.56–22.80% of the total cumulative land subsidence to a depth of 300 m is consistent with smectite dehydration following the over-pumping of groundwater. This dehydration-related land subsidence occurred to a depth of 0–60 m, with subsidence due to smectite dehydration accounting for 6.20–13.32% of the primary consolidation. Additionally, the total amount of subsidence resulting from both smectite dehydration and primary consolidation is consistent with the subsidence observed in the field. This study reveals that smectite dehydration appears to be important in assessing and predicting land subsidence in shallow aquifer systems.     

九龙江河口枯季沉降颗粒物及其地球化学组成研究

2009年3月28—29日在九龙江河口区采用自制沉积物捕获器分层收集沉降颗粒物,分别进行了扫描电镜、激光粒度和ICP—MS分析,并研究了沉降颗粒物的垂直通量。结果表明：各站各层沉降颗粒物类型以生物和矿物颗粒为主,絮凝体类型各站各层有所不同。各站各层之间粒度参数的变化主要是受絮凝作用的影响所致,沉降颗粒物垂直通量在各海域...     

Uncertainty in differential settlements of bridge foundations and retaining walls

ABSTRACT

In the bridge design specifications of the American Association of State Highway and Transportation Officials (AASHTO) using the Load and Resistance Factor Design (LRFD) method, the loads and resulting force effects are given two-letter designations, e.g. “SE” for “force effects due to settlement”. The SE load factor is used to develop factored values of the induced force effects such as moments and shears in a bridge structure due to foundation movements. In 2018 AASHTO committees voted to adopt calibrated values of the SE load factors that account for the uncertainty in predicted foundation movements from different analytical methods. However, additional uncertainty can occur in the differential settlements. This paper explores the additional uncertainty in differential settlements of bridge foundations and retaining walls using the datasets for two analytical methods that were used by AASHTO to develop the SE load factors for foundation settlement. Normalised probability exceedance charts (NPECs), that integrate the concept of reliability index and data correlation, have been developed and their application in bridge and wall design process is discussed for a variety of scenarios. Guidance is provided for the practical implementation of differential settlement in bridge analysis through an example problem.