Predicting Threshold Exceedance by Local Block Means in Soil Pollution Surveys

Soil contamination by heavy metals and organic pollutants around industrial premises is a problem in many countries around the world. Delineating zones where pollutants exceed tolerable levels is a necessity for successfully mitigating related health risks. Predictions of pollutants are usually required for blocks because remediation or regulatory decisions are imposed for entire parcels. Parcel areas typically exceed the observation support, but are smaller than the survey domain. Mapping soil pollution therefore involves a local change of support. The goal of this work is to find a simple, robust, and precise method for predicting block means (linear predictions) and threshold exceedance by block means (nonlinear predictions) from data observed at points that show a spatial trend. By simulations, we compared the performance of universal block kriging (UK), Gaussian conditional simulations (CS), constrained (CK), and covariance-matching constrained kriging (CMCK), for linear and nonlinear local change of support prediction problems. We considered Gaussian and positively skewed spatial processes with a nonstationary mean function and various scenarios for the autocorrelated error. The linear predictions were assessed by bias and mean square prediction error and the nonlinear predictions by bias and Peirce skill scores. For Gaussian data and blocks with locally dense sampling, all four methods performed well, both for linear and nonlinear predictions. When sampling was sparse CK and CMCK gave less precise linear predictions, but outperformed UK for nonlinear predictions, irrespective of the data distribution. CK and CMCK were only outperformed by CS in the Gaussian case when threshold exceedance was predicted by the conditional quantiles. However, CS was strongly biased for the skewed data whereas CK and CMCK still provided unbiased and quite precise nonlinear predictions. CMCK did not show any advantages over CK. CK is as simple to compute as UK. We recommend therefore this method to predict block means and nonlinear transforms thereof because it offers a good compromise between robustness, simplicity, and precisio

Characterising the local and intense water cycle during a cold air outbreak in the Nordic Seas

Air masses in marine cold air outbreaks (CAOs) at high latitudes undergo a remarkable diabatic transformation because of the uptake of heat and moisture from the ocean surface, and the formation of precipitation. In this study, the fundamental characteristics of the water cycle during an intense and persistent, yet archetypal basinwide CAO from Fram Strait into the Nordic seas are analyzed with the aid of the tracer-enabled mesoscale limited-area numerical weather prediction model COSMO. A water budget of the CAO water cycle is performed based on tagged water tracers that follow moisture picked up by the CAO at various stages of its evolution. The atmospheric dynamical factors and boundary conditions that shape this budget are thereby analyzed. The water tracer analysis reveals a highly local water cycle associated with the CAO. Rapid turnover of water vapor results in an average residence time of precipitating waters of about one day. Approximately one-third of the total moisture taken up by the CAO falls as precipitation by convective overturning in the marine CAO boundary layer. Furthermore, precipitation efficiency increases as the CAO air mass matures and is exposed to warmer waters in the Norwegian Sea. These properties of the CAO water cycle are in strong contrast to situations dominated by long-range moisture transport that occur in the dynamically active regions of extratropical cyclones. It is proposed that CAOs in the confined Nordic seas provide a natural laboratory for studying local characteristics of the water cycle and evaluating its representation in models.publishedVersio

Generalized cross-covariances and their estimation

Generalized cross-covariances describe the linear relationships between spatial variables observed at different locations. They are invariant under translation of the locations for any intrinsic processes, they determine the cokriging predictors without additional assumptions and they are unique up to linear functions. If the model is stationary, that is if the variograms are bounded, they correspond to the stationary cross-covariances. Under some symmetry condition they are equal to minus the usual cross-variogram. We present a method to estimate these generalized cross-covariances from data observed at arbitrary sampling locations. In particular we do not require that all variables are observed at the same points. For fitting a linear coregionalization model we combine this new method with a standard algorithm which ensures positive definite coregionalization matrices. We study the behavior of the method both by computing variances exactly and by simulating from various model

Some considerations on aggregate sample supports for soil inventory and monitoring

Soil monitoring and inventory require a sampling strategy. One component of this strategy is the support of the basic soil observation: the size and shape of the volume of material that is collected and then analysed to return a single soil datum. Many, but not all, soil sampling schemes use aggregate supports in which material from a set of more than one soil cores, arranged in a given configuration, is aggregated and thoroughly mixed prior to analysis. In this paper, it is shown how the spatial statistics of soil information, collected on an aggregate support, can be computed from the covariance function of the soil variable on a core support (treated as point support). This is done via what is called here the discrete regularization of the core-support function. It is shown how discrete regularization can be used to compute the variance of soil sample means and to quantify the consistency of estimates made by sampling then re-sampling a monitoring network, given uncertainty in the precision with which sample sites are relocated. These methods are illustrated using data on soil organic carbon content from a transect in central England. Two aggregate supports, both based on a 20 m 20 m square, are compared with core support. It is shown that both the precision and the consistency of data collected on an aggregate support are better than data on a core support. This has implications for the design of sampling schemes for soil inventory and monitoring

A Fractal Approach to Model Soil Structure and to Calculate Thermal Conductivity of Soils

Heat transport in soils depends on the spatial arrangement of solids, ice, air and water. In this study, we present a modified fractal approach to model the pore structure of soils and to describe its influence on the thermal conductivity. Three different fractal generators were sequentially applied to characterize a wide range of particle- and pore-size distributions. The given porosity and particle-size distribution of a clay, clay loam, silt loam and loamy sand were successfully modeled. The thermal conductivity of the fractal soil model was calculated using a network of resistors. We applied a renormalization approach to include the effects of smaller scale structures. The predictions were compared with the empirical Johansen' model (Johansen, 1975), that postulates a simple linear relationship between ice content and thermal conductivity. For high ice-saturated conditions, the calculated thermal conductivity agrees well with the empirical model. To describe partial ice saturation, we assumed that some pores were coated by ice films enclosing the air-filled center. In addition, we introduced a reduced heat exchange coefficient of the particles for unsaturated conditions. The ice-saturated and -unsaturated thermal conductivity calculated with this approach was very similar to that estimated by the empirical model. The variation of the thermal conductivities for different spatial arrangements of pores and particles in the prefractals were determined. Extreme values deviate more than 50% from the mean value

Besteht ein statistischer Zusammenhang zwischen der Hand- und Rumpfkraft bei gesunden Personen zwischen 40 und 79 Jahren? : eine deskriptive Querschnittstudie

Hintergrund: Morbus Bechterew ist eine chronisch entzündlich-rheumatische Erkrankung. Zur Evaluierung des Fitnesszustandes der Betroffenen werden jährlich Rumpf- und Handkraftassessments durchgeführt. Da die Bestimmung der Rumpfkraft zeitaufwändig ist, stellt sich die Frage, ob Rumpfkraftassessments durch schneller durchzuführende Handkraftassessments ersetzt werden können. Ziel: Diese Studie erforscht den statistischen Zusammenhang zwischen der Handund Rumpfkraft bei gesunden Personen. Methode: An der Querschnittsstudie nahmen 160 Personen, 80 Frauen und 80 Männer, teil. Die Kraft der ventralen, lateralen und dorsalen Rumpfmuskulatur wurde mit einer Rumpfkraft-Testbatterie und die Handkraft mittels Handdynamometer ermittelt. Der statistische Zusammenhang zwischen der Rumpf- und Handkraft wurde mittels Rangkorrelationskoeffizient nach Spearman kalkuliert. Ergebnisse: Bei den Frauen zeigt sich eine mittlere Korrelation zwischen ventraler Kette (rs=0.341, p=0.002, n=80) und Handkraft und jeweils eine starke Korrelation zwischen der lateralen (rs=0.457, p<0.001, n=80) beziehungsweise dorsalen Kette (rs=0.435, p<0.001, n=80) und der Handkraft. Bei den Männern zeigt sich eine schwache Korrelation zwischen lateraler Kette und Handkraft (rs=0.242, p<0.030, n=80). Die ventralen und dorsalen Ketten korrelieren nicht mit der Handkraft. Schlussfolgerung: Trotz teilweise mittlerer bis starker Korrelation zwischen Handund Rumpfkraft, kann über die gesamte Studie kein eindeutiger Zusammenhang zwischen den beiden Messgrössen aufgezeigt werden. Weitere Studien mit höherer Teilnehmerzahl sind zur Repräsentation der Normpopulation deshalb erforderlich.Background: Bekhterev's disease is a chronic inflammatory, rheumatic disease. To evaluate the fitness status of affected persons, annual hand grip and core strength assessments are performed. Since the core strength assessment is a time-consuming procedure, the question arises whether it can be replaced by the less time-consuming grip strength assessment. Aim: This study explores the statistical correlation between grip and core strength in healthy subjects. Methods: 80 women and 80 men participated in the cross-sectional study. The ventral, lateral, and dorsal core muscles were assessed using a core strength test battery whereas grip strength was assessed using a hand dynamometer. The statistical correlation was calculated using Spearman's rank correlation coefficient. Results: Females show a medium correlation between grip strength and the ventral chain (rs=0.341, p=0.002, n=80) and a strong correlation between grip strength and the lateral (rs=0.457, p<0.001, n=80) and dorsal chain (rs=0.435, p<0.001, n=80). In men, there is a weak correlation between grip strength and the lateral chain (rs=0.242, p<0.030, n=80). The ventral and dorsal chains do not correlate with the grip strength. Conclusion: No clear correlation can be shown across the entire study. Further studies with lager numbers of participants are needed to represent the norm population

The Impact of Cold-Air Outbreaks and Oceanic Lateral Fluxes on Dense-Water Formation in the Greenland Sea from a 10-Year Moored Record (1999–2009)

The Greenland Sea produces a significant portion of the dense water from the Nordic seas that supplies the lower limb of the Atlantic meridional overturning circulation. Here, we use a continuous 10-yr hydrographic record from moored profilers to examine dense-water formation in the central Greenland Sea between 1999 and 2009. Of primary importance for dense-water formation is air–sea heat exchange, and 60%–80% of the heat lost to the atmosphere during winter occurs during intense, short-lived events called cold-air outbreaks (CAOs). The long duration and high temporal resolution of the moored record has for the first time facilitated a statistical quantification of the direct impact of CAOs on the wintertime mixed layer in the Greenland Sea. The mixed layer development can be divided into two phases: a cooling phase and a deepening phase. During the cooling phase (typically between November and January), CAOs cooled the mixed layer by up to 0.08 K day−1, depending on the intensity of the events, while the mixed layer depth remained nearly constant. Later in winter (February–April), heat fluxes during CAOs primarily led to mixed layer deepening of up to 38 m day−1. Considerable variability was observed in the mixed layer response, indicating that lateral fluxes of heat and salt were also important. The magnitude and vertical distributions of these fluxes were quantified, and idealized mixed layer simulations suggest that their combined effect is a reduction in the mixed layer depth at the end of winter of up to several hundred meters.publishedVersio

Organic Wheat Farming Improves Grain Zinc Concentration

Zinc (Zn) nutrition is of key relevance in India, as a large fraction of the population suffers from Zn malnutrition and many soils contain little plant available Zn. In this study we compared organic and conventional wheat cropping systems with respect to DTPA (diethylene triamine pentaacetic acid)-extractable Zn as a proxy for plant available Zn, yield, and grain Zn concentration. We analyzed soil and wheat grain samples from 30 organic and 30 conventional farms in Madhya Pradesh (central India), and conducted farmer interviews to elucidate sociological and management variables. Total and DTPA-extractable soil Zn concentrations and grain yield (3400 kg ha-1) did not differ between the two farming systems, but with 32 and 28 mg kg-1 respectively, grain Zn concentrations were higher on organic than conventional farms (t = -2.2, p = 0.03). Furthermore, multiple linear regression analyses revealed that (a) total soil zinc and sulfur concentrations were the best predictors of DTPA-extractable soil Zn, (b) Olsen phosphate taken as a proxy for available soil phosphorus, exchangeable soil potassium, harvest date, training of farmers in nutrient management, and soil silt content were the best predictors of yield, and (c) yield, Olsen phosphate, grain nitrogen, farmyard manure availability, and the type of cropping system were the best predictors of grain Zn concentration. Results suggested that organic wheat contained more Zn despite same yield level due to higher nutrient efficiency. Higher nutrient efficiency was also seen in organic wheat for P, N and S. The study thus suggests that appropriate farm management can lead to competitive yield and improved Zn concentration in wheat grains on organic farms