Geostatistical Interpolation Research Articles

Mapping oak shoot browning in SW Spain using online imagery as virtual prospecting tool

Oak shoot browning is spatially widespread throughout the region of Extremadura (SW Spain) following an aggregative distribution pattern, with a much higher prevalence in cork than in holm oak. Shoot browning is considered a potential contributing or inciting factor to oak decline in SW Iberia. Two causal agents are typically involved, Coraebus florentinus Herbst and a fungal complex in the genus Diplodia Fries (teleomorph Botryosphaeria De Cesati & De Notaris). Our goals were to map oak shoot browning incidence in Extremadura and to explore geographical/environmental factors potentially involved using a novel image-based prospecting method. We used online imagery provided by Google Maps/Google Street View. A virtual sampling protocol permitted to inspect, characterise and georeference forest stands and damaged oaks throughout > 5000 km of navigation. Geostatistical interpolation analyses (Indicator Kriging algorithm) and validation processes were conducted to generate probability maps of shoot browning incidence. We prospected 6478 forest stands in which 3410 oaks with shoot browning symptoms were identified and georeferenced. Damage had a clustered distribution pattern both at the regional scale (IK-based probability maps) and the stand scale (fitting to negative binomial function). Factors such as oak species, stand type (pure vs. mixed), treetop orientation, tree aspect, altitude, ground slope and some interactions among them significantly affected shoot browning incidence. We provide the first integrating overview on the distribution and prevalence of oak shoot browning at a regional scale in SW Spain and we also define the effects of some geographical/ecological factors involved.

ProxInLAS, a software program for detecting coal layers and estimating parameters of layers, using geophysical well-logs

The present paper describes the algorithm and working method for detection and estimation of proximate parameters of coal beds based on digital well-log data. Designed and developed by the authors in Visual Studio using C#, ProxInLAS is used to define a threshold value on each log to distinguish between coal and non-coal layers, followed by estimating each of the coal proximate parameters by importing the data pertaining to a particular borehole: the reference borehole. Inputs of the software include the reference borehole data, target functions for accepting a layer as coal, and the methods employed to estimate the proximate parameter. The software relies mainly on two algorithms, a detection algorithm for detecting coal layers, and an estimation algorithm for estimating proximate parameters of the detected layers. Loaded by the well-log and core-sampling data, the detection algorithm calculates a set of threshold values to distinguish between the coal and non-coal layers based on the frequency distribution function of the well-log values near coal beds. ProxInLAS offers four methods for estimating proximate parameters: (1) a method based on the regression between the considered parameter and a particular log, (2) a method based on a combination of several linear relationships, (3) radial basis function (RBF) method, and (4) geostatistical interpolation method. In addition to numeric records, the software can present its outputs graphically to provide a simple and instant view of the results. The case study used to evaluate the performance of ProxInLAS.

Geostatistical interpolation by quantile kriging

Abstract. The widely applied geostatistical interpolation methods of ordinary kriging (OK) or external drift kriging (EDK) interpolate the variable of interest to the unknown location, providing a linear estimator and an estimation variance as measure of uncertainty. The methods implicitly pose the assumption of Gaussianity on the observations, which is not given for many variables. The resulting “best linear and unbiased estimator” from the subsequent interpolation optimizes the mean error over many realizations for the entire spatial domain and, therefore, allows a systematic under-(over-)estimation of the variable in regions of relatively high (low) observations. In case of a variable with observed time series, the spatial marginal distributions are estimated separately for one time step after the other, and the errors from the interpolations might accumulate over time in regions of relatively extreme observations. Therefore, we propose the interpolation method of quantile kriging (QK) with a two-step procedure prior to interpolation: we firstly estimate distributions of the variable over time at the observation locations and then estimate the marginal distributions over space for every given time step. For this purpose, a distribution function is selected and fitted to the observed time series at every observation location, thus converting the variable into quantiles and defining parameters. At a given time step, the quantiles from all observation locations are then transformed into a Gaussian-distributed variable by a 2-fold quantile–quantile transformation with the beta- and normal-distribution function. The spatio-temporal description of the proposed method accommodates skewed marginal distributions and resolves the spatial non-stationarity of the original variable. The Gaussian-distributed variable and the distribution parameters are now interpolated by OK and EDK. At the unknown location, the resulting outcomes are reconverted back into the estimator and the estimation variance of the original variable. As a summary, QK newly incorporates information from the temporal axis for its spatial marginal distribution and subsequent interpolation and, therefore, could be interpreted as a space–time version of probability kriging. In this study, QK is applied for the variable of observed monthly precipitation from raingauges in South Africa. The estimators and estimation variances from the interpolation are compared to the respective outcomes from OK and EDK. The cross-validations show that QK improves the estimator and the estimation variance for most of the selected objective functions. QK further enables the reduction of the temporal bias at locations of extreme observations. The performance of QK, however, declines when many zero-value observations are present in the input data. It is further revealed that QK relates the magnitude of its estimator with the magnitude of the respective estimation variance as opposed to the traditional methods of OK and EDK, whose estimation variances do only depend on the spatial configuration of the observation locations and the model settings.

Geostatistical interpolation of non-stationary seismic data

The problem of sparsely collected seismic data is one of the main issues in reflection seismology, because most advanced data processing techniques require a dense and regular seismic data grid. We present a geostatistical seismic data interpolation technique based on sequential stochastic simulations with local structural anisotropies. This technique, contrary to conventional existing data-driven seismic interpolation approaches based on sparsity, prediction filters, or rank-reduction, predicts the value of seismic amplitudes at non-sampled locations by exploiting the statistics of the recorded amplitudes, which are used as experimental data for the geostatistical interpolation in the original data domain. Local mean and variance are computed on-the-fly to define intervals of the global conditional distribution function, from where amplitude values are stochastically simulated. The parameters to define subsets of experimental data from which mean and variance are calculated are given by local variogram models, which in turn are obtained from a local dip and azimuth estimation in the t-x-y domain. The geostatistical seismic data interpolation technique is applied to synthetic and real 2D and 3D datasets in both post- and pre-stack domains. Besides being computationally cheaper than other methods, because the interpolation is carried out directly in the original data domain, the proposed technique provides a local quantitative analysis of the reliability of the interpolated seismic samples, which can be exploited in following processing steps.

Geostatistical Methods in Water Distribution Network Design - A Case Study

Abstract Modeling of the loads of water supply networks and their subsequent forecasting is an element necessary for making optimum decisions in the process of planning the development and operation of the water supply networks. The results of this modeling are decisive for the selection of the diameters of the pipelines and their arrangement on the water demand area. This study presents the results of estimation of average values of loads for the selected investment variants. The aim of the article is to present the possibility of simulations and analyses of the geostatistical interpolation methods. Data input in the model regarded the fragment of the real water supply network administered by the Municipal Water and Sewerage Company in Warszawa. Results of the computer analyses for the presented investment variants were related to the operating data of the water supply network and the data on water demand for the years 2014-2017 and 2018-2025. The aim of this paper is to present the advantages of GIS for the water supply systems and to prove that using the appropriate IT system, with provision of proper data processing, may lead to decisions which are optimum in view of the established, often very complex criteria.

Assessing and transferring soil health information in a hilly terrain

Spatial variability of soil health related variables in a hilly terrain may be high, and its characterization may require many samples. Our research compares deterministic and geostatistical interpolation methods in two hilly areas in India. The soil in the study area was acidic, without salts and with sufficient organic carbon content. Hence, three soil parameters – pH, Electrical Conductivity (EC) and Total Organic Carbon (TOC) were considered. The optimal sampling scheme was designed using Spatial Simulated Annealing (SSA) with the minimized kriging variance as a criterion. This resulted in 96 locations in the first area and 7 locations in the second area. It was explored how spatial information from one area could be used in a second, topographically similar area. The study focused on pH as the key variable for soil health. Regression kriging performed best for all the soil variables at the surface and sub-surface levels. Bayesian kriging allows one to use prior information and hence was used to transfer from the first to the second area. A mean error of 0.15, a root mean square error of 0.28 and a residual variance equal to 0.73 respectively were observed. We conclude that with modern interpolation methods important information on soil health can be collected even with sparse amounts of data.

A new spatial precipitation interpolation method based on the information diffusion principle

In this paper, a spatial precipitation interpolation method based on the information diffusion principle is presented to reduce data or information uncertainties from point observations. To estimate the core parameters (i.e., diffusion coefficients) in the diffusion function, the empirical diffusion coefficient (EDC) and optimal diffusion coefficient (ODC) based on the particle swarm optimization (PSO) algorithm are introduced. The methodology was compared with other common interpolators including the inverse distance weighting (IDW) and three geostatistical interpolators. Three measures of interpolation quality were used to assess the accuracy of the results for all methods via the cross-validation procedure and a range of comparative split-sampling tests. The dataset chosen in the study comprises annual and seasonal precipitation means (2013–2017) measured at 80 rain gages in Linan City (East China). The findings of this study could be concluded as follows: (a) in general, the interpolation methods provided similar spatial distributions for annual precipitation, and the differences among the methods were small when using a dense observation network; (b) cokriging (CoOK), detrended universal kriging (DUK) and the ODC had the smallest errors in the cross-validation analysis; and (c) the EDC presented nearly the same accuracy as the ODC with low density of observation network, while the ODC was a better interpolator as the number of observations increased.

Combination of multifractal geostatistical interpolation and spectrum–area (S–A) fractal model for Cu–Au geochemical prospects in Feizabad district, NE Iran

Exploration geochemical surveys seek to delimit exploration targets through the analyses of geochemical exploration data. Different methods have been applied in the delineation of geochemical anomalies including frequency-based and frequency space–based methods. The success of the latter methods depends on the modeling of the spatial distribution of geochemical data. However, selection of an appropriate method for modeling the spatial distribution of geochemical data remains a challenge. The main objective of this study is to address the foregoing challenge through a comparative study of inverse distance weighting (IDW), ordinary Kriging (OK), multifractal IDW (MIDW), and multifractal Kriging (MK) surface interpolation techniques. Initially, a set of composite sediment geochemical data from Feizabad district, NE Iran, was subjected to multivariate geochemical analysis by which a multielement geochemical signature, representing Cu–Au-related mineralization, was derived. Four above-mentioned interpolation techniques were applied to model the spatial distribution of the derived geochemical signature. The effectiveness of four interpolated models was compared by success-rate curves through which the MK model was recognized to be the superior model. The spectrum–area (S–A) fractal model was then applied on the MK model to decompose the anomalous component. The t student spatial statistics method was employed to determine proper threshold values by which the anomalous component could be discretized. The resultant crisp model was considered as the map of exploration targets.

Prioritized area mapping for multiple stakeholders through geospatial modelling: A focus on marine plastics pollution in Hong Kong

Sources of plastics pollution, transport and deposition in Hong Kong are all subject to uncertainty and this characteristic must be mapped if stakeholders are to be made aware of their need for greater involvement in ongoing marine spatial planning and management. We employ geostatistical interpolation as the mapping approach, to enable a wider scale assessment of the spatial distribution of three quantities of interest, denoted by plastics waste, ports shipping intensity and a notional measure we refer to as ecological insecurity to be determined. We generate map layer representations for each of these variables to concentrate the focus of a stakeholder onto their regions of most concern and employ associated uncertainty maps to highlight prioritized regions identified to be above a designated level of certainty. We refer to the resulting map as a Prioritized Area Map (PAM). The PAM approach to characterize marine plastics waste links otherwise disparate stakeholders to ecosystem-based-management, thereby balancing ecological, socio-economic and governance principles across temporal and spatial scales. The PAM approach serves as a generic scoping tool to help assess any marine spatial planning problem.

Spatial distribution of bats richness in Mexico at different taxonomic levels: biogeographical and conservation implications

Due to the potential of bats to travel long distances, the great diversity of habitats in which they are found, and their biological peculiarities, bats typically have been excluded in biogeographic analyses. However, like other mammals, bats show common distributional patterns strongly influenced by their biotic and abiotic interactions, aspects that have been poorly analyzed mainly due to technical and methodological limitations. Such occurrence data are limited to a subset of a species’ range, the scale of occurrence records, characteristics of the life history ( e.g ., territory size, vagility) of the taxa being studied, and issues of environmental variable selection and, therefore, the modeling methods and the evaluation of them. In this work, the objectives were to: 1) Evaluate the spatial variation in bat richness in Mexico, both for the order as a whole and at the family taxonomic level. 2) Quantify overlapping species occurrences and corresponding richness values in order to statistically model and generate bat richness maps at different taxonomic levels. 3) Explore the relationship of bat richness with environmental factors and with the biogeographic regions proposed for Mexico. 4) Identify areas of interest with high richness. The information available on the Global Biodiversity Information Facility database for bat species with distribution in Mexico was downloaded, and clearly erroneous records deleted. Using the Kriging Ordinary utility (KO), inside the QGIS software, a geostatistical analysis interpolation data was used to determine patterns of richness. The relationship of bat richness to climate, vegetation, and the biotic provinces proposed for Mexico was also explored. Spatial variation in richness was evaluated at the taxonomic levels of order and family. Based on this determination, areas of interest were identified for their high richness. When considering all species of bats the highest diversity is in the central part of the West, the Northeast zone and on both sides of the Isthmus of Tehuantepec. However, there are differences present at the family level. Deciduous tropical forest vegetation type presents high bat richness, followed by pine-oak forest and evergreen tropical forest. Temperate sub-humid, warm sub-humid and warm-humid climate types correspond to the highest bat richness of both order and family. Collection localities are not uniformly distributed over the country, most of them being concentrated in the west, east and south of the country, while there are very few records in the north and central plateau, and this is reflected in the richness concentration per cell (values from 0 to 52 species per cell). Protection of the four areas with an overall high concentration of bat richness detected in this analysis should be a priority during the development of conservation strategies.

Vs30 Mapping and Soil Classification in The Southern Part of Kulon Progo Using Rayleigh Wave Ellipticity Inversion

Shear wave velocity from the ground surface to a depth of 30 meters (Vs30) is a parameter to determine dynamic characteristics of the soil, which can be used to assess the level of seismic hazard. Thus, Vs30 mapping has an important role in seismic hazard mitigation efforts. Vs30 can be determined by Multichannel Analysis of Surface Waves (MASW) and Spatial Autocorrelation (SPAC) methods. A simpler alternative can be done by using Rayleigh wave ellipticity. The main objective of this research is to map Vs30 in the southern part of Kulon Progo using Rayleigh wave ellipticity inversion. In this study, Rayleigh wave ellipticity inversion was performed on 42 microtremor single measurement data, scattered in the southern part of Kulon Progo. The inversion results are used to estimate the value of Vs30 and classify the soil type at the measurement points, referring to SNI 1726:2012. A Vs30 distribution map and soil type classification are obtained by applying the geostatistical interpolation method. The mapping result showed that most of the southern part of Kulon Progo has a relatively low Vs30 value. These values are in the range of 180-342 m/s, which categorized as stiff soil (SD). In this region, some parts located in the hilly and transition zones have relatively high shear wave velocities in the range of 357-578 m/s and included in the category of very dense soil/ soft rock (SC) types

The effect of outdoor air pollutants and greenness on allergic rhinitis incidence rates: a cross-sectional study in Seoul, Korea

ABSTRACTAir pollution poses a serious threat to human health in Asia. This study analyzes the association of air pollutants and greenness with incidence rates of allergic rhinitis in Seoul at the administrative district level to gain insight into district-level urban policies to improve public health. A spatial regression model is constructed to investigate the correlation between allergic rhinitis incidence rates and five air pollutants measured at 128 air pollution monitoring stations around Seoul: sulfur dioxide (SO2), particulate matter less than 10 μm (PM10), ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). The allergic rhinitis incidence data are derived from the National Health Insurance Service’s database that includes the number of allergic rhinitis-related clinic visits by the patients over 20 years of age and living in Seoul. A kriging geostatistical interpolation was used to estimate average air pollution level of 423 administrative districts. To assess pollen concentrations that can affect allergic rhinitis, the average normalized difference vegetation index (NDVI) is measured based on the urban greenness. The model, controlling for built environment and socio-economic attributes, identifies the possibility of a weak association between allergic rhinitis incidence rates and carbon monoxide levels. The NDVI value is negatively correlated with allergic rhinitis incidence rates, implying a complicated aspect in relation to the effect of urban greenness.

Constructing the Apparent Geological Model by Fusing Surface Resistivity Survey and Borehole Records.

We constructed an apparent geological model with resistivity data from surface resistivity surveys. We developed a data fusion approach by integrating dense electrical resistivity measurements collected with Schlumberger arrays and wellbore logs. This approach includes an optimization algorithm and a geostatistic interpolation method. We first generated an apparent formation factor model from the surface resistivity measurements and groundwater resistivity records with an inverse distance method. We then converted the model into a geology model with the optimized judgment criteria from the algorithms relating the apparent formation factors to the borehole geology. We also employed a non-parametric bootstrap method to analyze the uncertainty of the predicted sediment types, and the predictive uncertainties of clay, gravel, and sand were less than 5%. Overall, our model is capable of capturing the spatial features of the sediment types. More importantly, this approach can be arranged in a self-updated sequence to enable adjustments to the model to accommodate newly collected core records or geophysical data. This approach yields a more detailed apparent geological model for use in future groundwater simulations, which is of benefit to multi-discipline studies.

Age and forming conditions of groundwater in the middle Pliocene aquifer in the Mekong Delta, Vietnam, based on the results of carbon radioactive isotope study

The results of studying and assessment of the impact of climate change on the groundwater in the Mekong delta have indicated that the exploitation of quaternary aquifers is not enough to solve the task of water supply in the region. Therefore, in the exploitation plan of groundwater in the Mekong Delta, the greatest prospects are bound with Pliocene aquifers. So, by using of carbon radioactive isotope to determine the age and forming conditions of groundwater in the middle Pliocene aquifer in the Mekong Delta, the important information for planning exploitation and zoning of areas subject to restrained exploitation of groundwater is retrieved. The determination of the age with the use of carbon radioactive isotope study in the complex with the geostatistical interpolation techniques allowed creation age map of middle-Pliocene aquifer with high accuracy nad determination the recharge area on the north-east of the Mekong delta (South-Eastern area). From the recharge area, groundwater flows in the south-west direction at a flow velocity of 8 m/year and discharges into the sea (Siam Golf and Eastern sea). It is the first time by using geostatistical interpolation «cokriging» of two parameters (age - key parameter and depth of sampling - extra parameter). The tasks to improve the accuracy of specialized maps in Vietnam have been solved. The age map has been made with the correlation parameter 0,9, that justifies the high efficiency of the studies. Furthermore, results of the correlation between age and concentration chlorine of groundwater in the middle-Pliocene aquifer (n22) allowed making a suggestion, that formation of mineralized water is a result of mixing of meteoric water with the sedimentation waters of initial sea genesis, which were formed during Flandrian transgression which began about 21000 years ago and ended 4200 years ago.

Generation of Monthly Precipitation Climatologies for Costa Rica Using Irregular Rain-Gauge Observational Networks

Precipitation climatologies for the period 1961–1990 were generated for all climatic regions of Costa Rica using an irregular rain-gauge observational network comprised by 416 rain-gauge stations. Two sub-networks were defined: a high temporal resolution sub-network (HTR), including stations having at least 20 years of continuous records during the study period (157 in total); and a high spatial resolution sub-network (HSR), which includes all HTR-stations plus those stations with less than 20 years of continuous records (416 in total). Results from the kriging variance reduction efficiency (KRE) objective function between the two sub-networks, show that ordinary kriging (OK) is unable to fully explain the spatio-temporal variability of precipitation within most climatic regions if only stations from the HTR sub-network are used. Results also suggests that in most cases, it is beneficial to increase the density of the rain-gauge observational network at the expense of temporal fidelity, by including more stations even though their records may not represent the same time step. Thereafter, precipitation climatologies were generated using seven deterministic (IDW, TS2, TS2PARA, TS2LINEAR, TPS, MQS and NN) and two geostatistical (OK and KED) interpolation methods. Performance of the various interpolation methods was evaluated using cross validation technique, selecting the mean absolute error (MAE) and the root-mean square error (RMSE) as agreement metrics. Results suggest that IDW is marginally superior to OK and KED for most climatic regions. The remaining deterministic methods however, considerably deviate from IDW, which suggests that these methods are incapable of properly capturing the true-nature of spatial precipitation patterns over the considered climatic regions. The final generated IDW climatology was then validated against the Global Precipitation Climatology Centre (GPCC), Climate Research Unit (CRU) and WorldClim datasets, in which overall spatial and temporal coherence is considered satisfactory, giving assurance about the use this new climatology in the development of local climate impact studies.

Correction to: Analysis of deterministic and geostatistical interpolation techniques for mapping meteorological variables at large watershed scales

The widely scattered pattern of meteorological stations in large watersheds and remote locations, along with a need to estimate meteorological data for point sites or areas where little or no data have been recorded, has encouraged the development and implementation of spatial interpolation techniques. The various interpolation techniques featured in GIS software allow for the extraction of this new information from spatially distinct point data. Since no one interpolation method can be accurate in all regions, each method must be evaluated prior to each geographically distinct application. Many methods have been used for interpolating minimum temperature ( $$T_{ \min }$$ ), maximum temperature ( $$T_{ \max }$$ ) and precipitation data; however, only a few methods have been used in the Zayandeh-Rud River basin, Iran, and no comparison of methods has ever been carried out in the area. The accuracies of six spatial interpolation methods [Inverse Distance Weighting, Natural Neighbor (NN), Regularized Spline, Tension Spline, Ordinary Kriging, Universal Kriging] were compared in this study simultaneously, and the best method for mapping monthly precipitation and temperature extremes was determined in a large semi-arid watershed with high temperature and rainfall variation. A cross-validation technique and long-term (1970–2014) average monthly $$T_{ \min }$$ , $$T_{ \max }$$ and precipitation data from meteorological stations within the basin were used to identify the best interpolation method for each variable dataset. For $$T_{ \min }$$ , Kriging (Gaussian) proved to be the most accurate interpolation method (MAE = 1.827 °C), whereas, for $$T_{ \max }$$ and precipitation the NN method performed best (MAE = 1.178 °C and 0.5241 mm, respectively). Accordingly, these variable-optimized interpolation methods were used to define spatial patterns of newly generated climatic maps.

西北内陆盐湖盆地土壤重金属Cr、Hg、As空间分布特征及潜在生态风险评价

PDF HTML阅读 XML下载 导出引用 引用提醒 西北内陆盐湖盆地土壤重金属Cr、Hg、As空间分布特征及潜在生态风险评价 DOI: 10.5846/stxb201711292142 作者: 作者单位: 内蒙古农业大学水利与土木建筑工程学院,内蒙古水资源保护与利用重点实验室;内蒙古农业大学水利与土木建筑工程学院,内蒙古农业大学水利与土木建筑工程学院,内蒙古水资源保护与利用重点实验室;内蒙古农业大学水利与土木建筑工程学院,内蒙古水资源保护与利用重点实验室;内蒙古农业大学水利与土木建筑工程学院,内蒙古水资源保护与利用重点实验室;内蒙古农业大学水利与土木建筑工程学院,美国欧道明大学,诺福克 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金（51469019） Spatial distribution characteristics and potential ecological risk assessment of Cr, Hg, and As in soils of the Salt Lake Basin in northwest China Author: Affiliation: Inner Mongolia agricultural university institute of water conservancy and civil engineering,Hohhot/Huhhot/Hollyhock,Inner Mongolia Key Laboratory of Water Resources Protection and Utilization,Hohhot/Huhhot/Hollyhock;Inner Mongolia agricultural university institute of water conservancy and civil engineering,Hohhot/Huhhot/Hollyhock,Inner Mongolia agricultural university institute of water conservancy and civil engineering,Hohhot/Huhhot/Hollyhock,Inner Mongolia Key Laboratory of Water Resources Protection and Utilization,Hohhot/Huhhot/Hollyhock;Inner Mongolia agricultural university institute of water conservancy and civil engineering,Hohhot/Huhhot/Hollyhock,Inner Mongolia Key Laboratory of Water Resources Protection and Utilization,Hohhot/Huhhot/Hollyhock;Inner Mongolia agricultural university institute of water conservancy and civil engineering,Hohhot/Huhhot/Hollyhock,Inner Mongolia Key Laboratory of Water Resources Protection and Utilization,Hohhot/Huhhot/Hollyhock;Inner Mongolia agricultural university institute of water conservancy and civil engineering,Hohhot/Huhhot/Hollyhock,Old Dominion University,Norfolk,Virginia,USA Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:选取位于西北内陆的吉兰泰盐湖盆地为研究对象，按照表层（0-10 cm）、50 cm层（50-60 cm）和100 cm层（100-110 cm）分层土壤取样，系统采集了120个样品，测定了重金属Cr、Hg和As，以及主要化学成分的含量，以地统计学插值绘图揭示了吉兰泰盐湖盆地Cr、Hg和As的空间分布特征，以单因子指数法、内梅罗指数法和潜在生态风险指数法评价解析了盐湖盆地Cr、Hg和As的污染及生态风险状况，以统计相关检验和主成分分析法进行了盐湖盆地Cr、Hg和As的源分析。结果表明：1）盐湖盆地土壤中Cr、Hg、As总体具有相似的空间分布特征，Cr在西北部的巴音乌拉山、乌兰布和沙漠地区和西南台地地区的含量较高，Hg仅在西南低山台地地区、以及东北局部区域的含量较高，As在盐湖附近、东南贺兰山、巴音乌拉山西北部和乌兰布和沙漠地区含量较高；2）基于内蒙古土壤质量背景值，土壤污染分布次序为As > Hg > Cr，其中Hg和As中度污染以上累计比重达到为45%，而Cr仅有7.5%的轻微污染；研究区无清洁土壤，仅有尚清洁土壤占2.5%，开始受到污染和中度污染以上土壤比重为50%和42.5%；土壤潜在风险次序为Hg > As > Cr，Hg整体处于很强生态风险水平；轻微生态风险占到40%，由中度、强度到极强在20%以内，很强生态风险程度略超20%，大部地区处于轻微生态风险状况；3）基于国家土壤环境质量二级标准值，土壤中Cr、Hg、As无论是单因子污染、综合污染，还是潜在生态风险指数均未出现超标区域，不存在Cr、Hg、As污染或潜在生态风险，但相对内蒙古整个地区来说，盐湖盆地Cr、Hg、As含量较高；4）研究区土壤中Cr主要来源于土壤母质形成过程中的自然来源，Hg和As主要来源于工业排放、交通污染源等人类活动，均受到人类活动和自然环境变化的综合影响。 Abstract:The Jilantai Salt Lake Basin in the northwest inland area of China was selected as the study area. A total 120 soil samples from soil surface, 50 and 100 cm depth were collected to test for Cr, Hg, As, and the concentration of the main chemicals. The spatial distribution characteristics of Cr, Hg, and As were identified through geo-statistical interpolation. The pollution degree, pollution distribution, and ecological risk of Cr, Hg, and As were analyzed by a single factor index method, Nemero index method, and a potential ecological risk index. A source analysis of Cr, Hg, and As was performed by a statistical correlation test and a principal component analysis. There were similar spatial distribution characteristics for the concentrations of Cr, Hg, and As in the soils of the Salt Lake Basin. Cr was higher in the Bayinwula mountain area, the Wulanbuhe desert area, and the hilly area of the southwest study area; Hg was higher only in the northeast local area and the hilly area of the southwest study area; and As was higher in near the lake, the Helan mountain area, the Bayinwula mountain area, and the Wulanbuhe desert area than in the other areas. Based on the background value of soil quality for inner Mongolia, the order of soil pollution is As > Hg > Cr, in which Hg and As was at a moderate level of pollution above the cumulative proportion of 45%, but Cr was only at a slight level of pollution at 7.5%. There was no clean soil in the study area; cleaner soil only accounted for 2.5%. The beginning stages of contamination to moderate pollution in the soil are above 50% and 42.5%, respectively. The soil risk order is Hg > As > Cr. Broadly for the study area, Hg was at a very strong ecological risk level. A slight ecological risk accounted for 40%. Moderate to strong risk were within 20%. Most areas were in a slight ecological risk situation. Based on the Chinese national standard value of soil environmental quality and the assessment results from the single factor pollution, comprehensive pollution, and the potential ecological risk index, Cr, Hg, and As in the soil do not appear in a polluted area. Although there is no Cr, Hg, and As pollution or potential ecological risk, the concentration of Cr, Hg, and As in the study area soil from inner Mongolia was higher than that in the other areas. In the study area, Cr may be mainly derived from a natural source of soil parental formation, where Hg and As may be mainly derived from industrial emissions, traffic pollution, and other human-related activities. All the heavy metals are subject to integrated impacts from human activities and changes to the natural environment. 参考文献 相似文献 引证文献

Spatial Enhancement of DEM Using Interpolation Methods: A Case Study of Kuwait’s Coastal Zones

Digital elevation models (DEMs) are essential tools utilized in several branches of science, including environmental, geological, and geospatial studies. Unfortunately, high-accuracy DEM data such as LiDAR are not publicly available, and the coverage is limited. Therefore, the use of alternative methods, such as interpolation techniques (i.e., kriging, inverse distance weighting, radial basis functions), is greatly advantageous for the production of enhanced DEMs. The results of this study show that interpolated DEMs had minimal errors (RMSE = 1.44) with an increase of about 28% from the original DEM. However, the spatial resolution of interpolated DEM data was enhanced significantly by 83%. The deterministic interpolation methods provided more accurate estimations for producing DEMs in the coastal zones of Kuwait than geostatistical interpolation methods. The reference elevation data were collected using GPS and accurate topographic maps (1:25,000), and elevation points from the interpolated DEM were matched significantly (R2 = 0.88; R2 = 94, respectively). Given the lack of accurate DEM data, the interpolated DEM produced in this study are held in high regard and highly recommended for use in the coastal zone of Kuwait.

Geostatistical Merging of a Single-Polarized X-Band Weather Radar and a Sparse Rain Gauge Network over an Urban Catchment

Optimal Quantitative Precipitation Estimation (QPE) of rainfall is crucial to the accuracy of hydrological models, especially over urban catchments. Small-to-medium size towns are often equipped with sparse rain gauge networks that struggle to capture the variability in rainfall over high spatiotemporal resolutions. X-band Local Area Weather Radars (LAWRs) provide a cost-effective solution to meet this challenge. The Clermont Auvergne metropolis monitors precipitation through a network of 13 rain gauges with a temporal resolution of 5 min. 5 additional rain gauges with a 6-minute temporal resolution are available in the region, and are operated by the national weather service Météo-France. The LaMP (Laboratoire de Météorologie Physique) laboratory’s X-band single-polarized weather radar monitors precipitation as well in the region. In this study, three geostatistical interpolation techniques—Ordinary kriging (OK), which was applied to rain gauge data with a variogram inferred from radar data, conditional merging (CM), and kriging with an external drift (KED)—are evaluated and compared through cross-validation. The performance of the inverse distance weighting interpolation technique (IDW), which was applied to rain gauge data only, was investigated as well, in order to evaluate the effect of incorporating radar data on the QPE’s quality. The dataset is comprised of rainfall events that occurred during the seasons of summer 2013 and winter 2015, and is exploited at three temporal resolutions: 5, 30, and 60 min. The investigation of the interpolation techniques performances is carried out for both seasons and for the three temporal resolutions using raw radar data, radar data corrected from attenuation, and the mean field bias, successively. The superiority of the geostatistical techniques compared to the inverse distance weighting method was verified with an average relative improvement of 54% and 31% in terms of bias reduction for kriging with an external drift and conditional merging, respectively (cross-validation). KED and OK performed similarly well, while CM lagged behind in terms of point measurement QPE accuracy, but was the best method in terms of preserving the observations’ variance. The correction schemes had mixed effects on the multivariate geostatistical methods. Indeed, while the attenuation correction improved KED across the board, the mean field bias correction effects were marginal. Both radar data correction schemes resulted in a decrease of the ability of CM to preserve the observations variance, while slightly improving its point measurement QPE accuracy.

Sampling Processes and Intensities for the Geostatistical Modeling of an Unevenly Aged Forest

ABSTRACT The aim of this study was to estimate the basal area, through traditional sampling processes and geostatistical interpolation, and compare it to the forest census carried out in 25 ha of a Mixed Ombrophilous Forest remnant. The sampling simulations were structured for the systematic sampling, two stages, and simple random, in intensities 5, 11, and 22% of the potential sample units of 25 × 25 m. The increase in sample intensity for the systematic sampling and two stages revealed a higher level of spatial behavior details of the basal area, whereas when comparing the census with sampling, it was observed that the geostatistical interpolation shows ability to improve the accuracy of the basal area estimation.