Assessing the economic feasibility of regional deep saline aquifer CO2 injection and storage: A geomechanics-based workflow applied to the Rose Run sandstone in Eastern Ohio, USA

Typical top-down regional assessments of CO₂ storage feasibility are sufficient for determining the maximum volumetric capacity of deep saline aquifers. However, they do not reflect the regional economic feasibility of storage. This is controlled, in part, by the number and type of injection wells that are necessary to achieve regional CO₂ storage goals. In contrast, the geomechanics-based assessment workflow that we present in this paper follows a bottom-up approach for evaluating regional deep saline aquifer CO₂ storage feasibility. The CO₂ storage capacity of an aquifer is a function of its porous volume as well as its CO₂ injectivity. For a saline aquifer to be considered feasible in this assessment it must be able to store a specified amount of CO₂ at a reasonable cost per ton of CO₂. The proposed assessment workflow has seven steps that include (1) defining the storage project and goals, (2) characterizing the geology and developing a geomechanical model of the aquifer, (3) constructing 3D aquifer models, (4) simulating CO₂ injection, (5,6) evaluating CO₂ injection and storage feasibility (with and without injection well stimulation), and (7) determining whether it is economically feasible to proceed with the storage project. The workflow was applied to a case study of the Rose Run sandstone aquifer in the Eastern Ohio River Valley, USA. We found that it is feasible in this region to inject 113 Mt CO₂/year for 30 years at an associated well cost of less than US $1.31/t CO₂, but only if injectivity enhancement techniques such as hydraulic fracturing and injection induced micro-seismicity are implemented.     

Nutrient dynamics between sediment and overlying water in the inner part of Izmir Bay, Eastern Aegean

In this article a concept is described in order to predict and map the occurrence of benthic communities within and near the German Exclusive Economic Zone (EEZ) of the North Sea. The approach consists of two work steps: (1) geostatistical analysis of abiotic measurement data and (2) calculation of benthic provinces by means of Classification and Regression Trees (CART) and GIS-techniques. From bottom water measurements on salinity, temperature, silicate and nutrients as well as from punctual data on grain size ranges (0–20, 20–63, 63–2,000 μ) raster maps were calculated by use of geostatistical methods. At first the autocorrelation structure was examined and modelled with help of variogram analysis. The resulting variogram models were then used to calculate raster maps by applying ordinary kriging procedures. After intersecting these raster maps with punctual data on eight benthic communities a decision tree was derived to predict the occurrence of these communities within the study area. Since such a CART tree corresponds to a hierarchically ordered set of decision rules it was applied to the geostatistically estimated raster data to predict benthic habitats within and near the EEZ.     

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.     

Variability of soil-water quality due to Tsunami-2004 in the coastal belt of Nagapattinam district, Tamilnadu

In this study, the Tsunami-caused deterioration of soil and groundwater quality in the agricultural fields of coastal Nagapattinam district of Tamilnadu state in India is presented by analyzing their salinity and sodicity parameters. To accomplish this, three sets of soil samples up to a depth of 30cm from the land surface were collected for the first six months of the year 2005 from 28 locations and the ground water samples were monitored from seven existing dug wells and hand pumps covering the study region at intervals of 3 months. The EC and pH values of both the soil and ground water samples were estimated and the spatial and temporal variability mappings of these parameters were performed using the geostatistical analysis module of ArcGIS((R)). It was observed that the spherical semivariogram fitted well with the data set of both EC and pH and the generated kriged maps explained the spatial and temporal variability under different ranges of EC and pH values. Further, the recorded EC and pH data of soil and ground water during pre-Tsunami periods were compared with the collected data and generated variability soil maps of EC and pH of the post-Tsunami period. It was revealed from this analysis that the soil quality six months after the Tsunami was nearing the pre-Tsunami scenario (EC< 1.5dSm(-1); pH<8), whereas the quality of ground water remained highly saline and unfit for irrigation and drinking. These observations were compared with the ground scenarios of the study region and possible causes for such changes and the remedial measures for taking up regular agricultural practices are also discussed.     

Spatial correlation between the prevalence of transmissible spongiform diseases and British soil geochemistry

Transmissible spongiform encephalopathies (TSEs) are a group of fatal neurological conditions affecting a number of mammals, including sheep and goats (scrapie), cows (BSE), and humans (Creutzfeldt-Jakob disease). The diseases are widely believed to be caused by the misfolding of the normal prion protein to a pathological isoform, which is thought to act as an infectious agent. Outbreaks of the disease are commonly attributed to contaminated feed and genetic susceptibility. However, the implication of copper and manganese in the pathology of the disease, and its apparent geographical clustering, have prompted suggestions of a link with trace elements in the environment. Nevertheless, studies of soils at regional scales have failed to provide evidence of an environmental risk factor. This study uses geostatistical techniques to investigate the correlations between the distribution of TSE prevalence and soil geochemical variables across the UK according to different spatial scales. A similar spatial pattern in scrapie and BSE occurrence is identified, which may be linked with increasing pH and total organic carbon, and decreasing iodine concentration. However, the pattern also resembles that of the density of dairy farming. Nevertheless, despite the low spatial resolution of the TSE data available for this study, the fact that significant correlations are detected indicates there is a possibility of a link between soil geochemistry, scrapie, and BSE. It is suggested that further investigations of the prevalence of TSE and environmental exposure to trace metals should take into account the factors affecting their bioavailability.     

地统计学在土壤重金属研究中的应用及展望

从采矿学与地质学研究中发展起来的地统计学是应用数理统计学的一个分支。与传统的统计学相比，地统计学可应用于土壤重金属研究中，能探索土壤重金属的空间分布特征及其变异规律。地统计学的基础理论与方法主要包括：区域化变量、半方差函数、克立格空间插值技术。半方差函数可以用来描述研究土壤重金属分布的空间相关性；而克立格插值可以对未采样区土壤重金属的含量进行无偏最优估计。在对地统计学理论进行简要阐述的基础上，回顾了近些年在土壤重金属研究的采样设计、空间结构分析、空间插值等方面的应用，并就其应用前景作了展望。     

区域PM2.5时空回归建模与预测

基于区域PM_(2.5)时空建模和预测的需要及PM_(2.5)浓度呈现明显的时空分布趋势的状况,以苏南地区2014年PM_(2.5)日监测数据为实验数据,使用回归克里格对区域PM_(2.5)进行时空建模和估值。利用最小二乘法建立了PM_(2.5)与时空位置的三元二次回归趋势模型,建模点趋势值与实测值间的平均误差接近于0,表明趋势模型拟合效果较好;拟合了样点残差的理论变异函数模型,表明该地区PM_(2.5)的空间和时间相关性范围分别为150 km和4 d;基于该模型,使用时空普通克里格对残差进行时空插值;插值结果与趋势项相加,得到PM_(2.5)回归克里格估值结果;通过对比不考虑趋势的时空普通克里格估值结果,发现考虑时空趋势的时空回归克里格法精度提高了1. 29%。对所提方法进行了创新性分析,并对不足之处进行了讨论。     

Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.     

Soil seed bank and seedling populations of Hordeum murinum and Cardaria draba in saffron fields

Geostatistical techniques were used to characterize the spatial relationship between Hordeum murinum and Cardaria draba seedling and soil seed bank over the entire growing season of 2004–2005 in three saffron (Crocus sativus) fields, located in Southern Khorasan (33°N latitude, 57°E longitude), Iran. The maps of H. murinum seed bank density corresponded moderately to those seedling density in a and strongly to those in b and c fields. The emergence percentage of C. draba was higher than for H. murinum in all fields. Semivariograms showed spatial autocorrelation in seed bank and seedling populations of H. murinum and C. draba in all fields. Grey-scale field maps of C. draba seed banks corresponded visually to maps of seedling populations and could have been used to target control efforts, but visual correspondence between H. murinum seed bank and seedling maps was poor.     

Spatial Variability of Soil Organic Carbon in Relation to Environmental Factors of a Typical Small Watershed in the Black Soil Region, Northeast China

A total of 292 soil samples were taken from surface soil (0–20 cm) of a typical small watershed–Tongshuang in the black soil region of Heilongjiang province, northeast China in June 2005 for examining the concentration of soil organic carbon (SOC). Spatial variability of SOC in relation to topography and land use was evaluated using classical statistics, geostatistics and geographic information system (GIS) analyses. The objective of this study was to provide a scientific basis for land management targeting at improving soil quality in this region. Classical statistical analysis results indicated that the variability of SOC was moderate (C _V = 0.30). Slope position and land use types were discriminating factors for its spatial variability. Geostatistics analyses showed that SOC had a strong spatial autocorrelation, which was mainly induced by structural factors. Mean concentration of SOC in surface soil was 2.27% in this watershed, which was a very low level in the northern black soil region of northeast China. In this small watershed, present soil and water conservation measures played an important role in controlling soil loss. But SOC's restoration was unsatisfactory. Nearly three-quarters of the area had worrisome productivity. How to improve SOC concentration targeting at soil fertility is a pressing need in the future.