Nonparametric construction of probability maps under local stationarity

The environmental contamination risk can be evaluated in a specific area by approximating the probability that the pollutant under study exceeds a critical value. This issue requires the estimation of the distribution function involved, which can be addressed by applying the indicator kriging methodology or by approximating the sill of the variogram of the underlying indicator process. These approaches demand an appropriate characterization of the indicator variogram, which in turn requires a previous specification of the trend function, if the latter is suspected to be non-constant. Since accuracy of the results will be strongly dependent on the adequate approximation of both functions, we suggest proceeding in a different way to avoid these requirements. Thus, in the current paper, two kerneltype estimators are proposed, based on first approximating the distribution at the sampled sites and then obtaining a weighted average of the resulting values, to derive a valid estimator at each (sampled or unsampled) location. Consistency of the kernel approaches is proved under rather general conditions, such as local stationarity and the existence of derivatives up to the second order of the distribution function. Numerical studies have been carried out to illustrate the performance of our proposals when compared to those procedures requiring the approximation of the indicator variogram. In a final step, the kernel-type estimation of the distribution function has been applied to map the risk of contamination by arsenic in the Central Region of Portugal. With this aim, biomonitoring data of arsenic concentrations were used to detect those zones with higher risk of arsenic accumulation, which is mainly located on the northern part of the region.The authors would like to thank the helpful suggestions and comments from the Editor, the Associate Editor, and the Reviewers. The authors are also grateful to Karen J. Duncan for her contribution in the language revision. The first author’s work has been partially supported by the Spanish National Research and Development Program project [TEC2015-65353-R], by the European Regional Development Fund (ERDF), and by the Galician Regional Government under project GRC 2015/018 and under agreement for funding AtlantTIC (Atlantic Research Center for Information and Communication Technologies). The second author acknowledges financial support from the Portuguese Funds through FCT-“Fundação para a Ciência e a Tecnologia,” within the Project UID/MAT/00013/2013.info:eu-repo/semantics/publishedVersio

Separation of Selected Heptacoordinated Derivatives of Goshchava-Silanates for HPLC

Optimum conditions of separation and determination was studied of two newly synthesized heptacoordinated benzyl derivatives from the group of Goshchava-silanates: Homo{O,O′,O″,O‴-oxalic acid's Si-[N-benzylaminiomethyl]-Si,Si-dihydroxysilanate} and Homo{O,O′,O″,O‴-oxalic acid's Si-[N-benzylaminiomethyl]-Si,Si-diethoxysilanate}. In the carried out investigation, we considered three stationary phases (octadecyl, octyl, phenylbutyl) and two mobile phases (acetonitrile and dichloromethane) in various intensities of flow. The best selectivity and the highest separation factor (α = 9.08) was obtained using the mobile phase acetonitrile (100%) and the phenylbutyl column. To reliably optimize the process of separation and determination of the compounds on the phenylbutyl column, the validation of prepared methodology was done

Selecting biological indicators for monitoring soils: a framework for balancing scientific opinion to assist policy development

Soils are one of the most important features of the natural capital of terrestrial ecosystems. There is a strong and increasing policy requirement for effective monitoring of soils at local, regional and national scales. However, it remains unclear which properties of soils are most appropriately monitored. This is partly due to the wide range of goods and services that soils provide, but also their inherent chemical, physical and biological complexity. Given that the biota plays such fundamental roles in the majority of ecosystem services provided by soils, biological properties are logical candidates as effective indicators, to complement other physico-chemical properties. A plethora of biological methods have been suggested as indicators for monitoring soils but few are used in national scale monitoring or are published as international standards. A framework for selecting ecologically-relevant biological indicators of soil quality for national-scale soil monitoring that covers the full range of ecological functions and services of soil was devised. The literature was surveyed to identify 183 candidate biological indicators which were then scored by experts and stakeholders against a wide range of scientific and technical criteria. The framework used the scores and weightings to then rank, prioritise and select the indicators. This semi-objective approach using a “logical-sieve” allowed repeated iterations to take account of end-user requirements and expert opinion. A ranked list of 21 indicators was produced that covered a range of genotypic-, phenotypic- and functional-based indicators for different trophic groups. Four of these were not deemed sufficiently robust for ready deployment in a national-scale monitoring scheme without further methodological development. The suite of indicators identified offers the strongest potential candidates for deployment in national-scale soil monitoring schemes provided standard operating procedures are defined and their inherent sensitivity, ability to discriminate between soil:land-use combinations, and provide ecologically interpretable signals is confirmed. The power of the approach adopted here is that it provides a clear record and audit trail on the decision-making process, enables different priorities to be set contingent on the nature of the desired monitoring, and can direct and allow the inclusion of further methods or indicators into the framework

Evaluation of a generalized use of the log Sum(k+1)AA descriptor in a QSRR model to predict peptide retention on RPLC systems.

At the current state of knowledge, the rational optimization of the chromatographic separation of peptides, as well as the identification of proteins in proteomics are challenges for analytical chemists. In this paper the generalized applicability of a recently derived descriptor log Sum(k+1)AA in a QSRR equation to model peptide retention in RP-LC systems was evaluated. For that purpose, two sets of peptides analyzed on dissimilar RP-LC systems were considered. A first set of 28 peptides was measured on 17 columns/systems, while a second of 70 peptides was eluted on four. The aim of this work was to confirm the usefulness of the partly experimental log Sum(k+1)AA descriptor for the prediction of peptides retention compared to the initially applied, fully experimental log SumAA descriptor. The verification of the predictive abilities of both QSRR models, applying either the initial or the alternative descriptor, was done by using the leave-one-out and leave-three-out cross-validation procedures. The results seem to demonstrate that the QSRR model with log Sum(k+1)AA, for which the retention measurement of only seven out of 20 existing amino acids is necessary, possesses similar or in some cases even better predictive abilities than that containing log SumAA.FLWINinfo:eu-repo/semantics/publishe