1.Preface As the fast paced Chinese economy brings more and more direct and indirect affects into ourlives,environmental protection has become one ofthe most popular subjects of recent times.
A probabilistic analysis was performed on soil arsenic concentration data from 4 brownfield sites at Beijing （Chaoyang and Haidian Districts）, involved in environmental assessment studies. The available data sets we...A probabilistic analysis was performed on soil arsenic concentration data from 4 brownfield sites at Beijing （Chaoyang and Haidian Districts）, involved in environmental assessment studies. The available data sets were processed to provide a statistical characterization of the background populations and differentiate ＂anomalous data＂ from the natural range of variation of arsenic concentrations in soil. The site-specific background distributions and the existing wide-scale background values defined for the Beijing area were compared, discussing related implications for the definition of metal contamination soil screening levels （SSLs） in site assess- ment studies. The statistical analysis of As data sets discriminated site-specific background populations, encompassing 88% to 94% of the sample data, from oufliers values, associated with either subsoil natural enrichments or possible anthropogenic releases. Upper Baseline Concentration （UBC） limits （＋ 2σ level）, including most of the site-specific metal background variability, were derived based on the statistical characterization of the background populations. Sites in the Chaoyang South District area had UBC values in the range 10.4-12.6mg·kg^-1. These ranges provide meaningful SSL values to be adopted for As in local site assessment studies. Using the wide-scale background value for the Beijing area would have erroneously classified most of the areas in the subject sites as potentially contaminated.展开更多
The knowledge of the internal stability of granular soils is a key factor for the design of granular and filter for the geotechnical infrastructures such as dykes, barrages, weirs and roads embankment. To evaluate the...The knowledge of the internal stability of granular soils is a key factor for the design of granular and filter for the geotechnical infrastructures such as dykes, barrages, weirs and roads embankment. To evaluate the internal instability of granular soils different criteria are generally used in the practice. However, the results of these criteria on the same soil may lead to different evaluations of the internal instability. In this paper the common criteria used for the internal instability have been presented and compared as far as possible. It was found that the most internal instability criteria define a limit value for the secant slope of the grain size distribution curve of the granular soils. Based on this finding an own criterion for the evaluation of the internal instability of granular soil has been developed and compared to the common criteria. A very good agreement between some criteria was found. Furthermore, a site specific assessment for the evaluation of the internal instability of granular soil has been proposed in order to get more confidence in this evaluation.展开更多
As wind is the basis of all wind energy projects, a precise knowledge about its availability is needed. For ananalysis of the site-specific wind conditions, Virtual Meteorological Masts (VMMs) are frequently used. VMM...As wind is the basis of all wind energy projects, a precise knowledge about its availability is needed. For ananalysis of the site-specific wind conditions, Virtual Meteorological Masts (VMMs) are frequently used. VMMsmake use of site calibrated numerical data to provide precise wind estimates during all phases of a wind energyproject. Typically, numerical data are used for the long-term correlation that is required for estimating theyield of new wind farm projects. However, VMMs can also be used to fill data gaps or during the operationalphase as an additional reference data set to detect degrading sensors. The value of a VMM directly dependson its ability and precision to reproduce site-specific environmental conditions. Commonly, linear regressionis used as state of the art to correct reference data to the site-specific conditions. In this study, a frameworkof 10 different machine-learning methods is tested to investigated the benefit of more advanced methods ontwo offshore and one onshore site. We find significantly improving correlations between the VMMs and the reference data when using more advanced methods and present the most promising ones. The K-NearestNeighbors and AdaBoost regressors show the best results in our study, but Multi-Output Mixture of GaussianProcesses is also very promising. The use of more advanced regression models lead to decreased uncertainties;hence those methods should find its way into industrial applications. The recommended regression models canserve as a starting point for the development of end-user applications and services.展开更多
文摘1.Preface As the fast paced Chinese economy brings more and more direct and indirect affects into ourlives,environmental protection has become one ofthe most popular subjects of recent times.
文摘A probabilistic analysis was performed on soil arsenic concentration data from 4 brownfield sites at Beijing （Chaoyang and Haidian Districts）, involved in environmental assessment studies. The available data sets were processed to provide a statistical characterization of the background populations and differentiate ＂anomalous data＂ from the natural range of variation of arsenic concentrations in soil. The site-specific background distributions and the existing wide-scale background values defined for the Beijing area were compared, discussing related implications for the definition of metal contamination soil screening levels （SSLs） in site assess- ment studies. The statistical analysis of As data sets discriminated site-specific background populations, encompassing 88% to 94% of the sample data, from oufliers values, associated with either subsoil natural enrichments or possible anthropogenic releases. Upper Baseline Concentration （UBC） limits （＋ 2σ level）, including most of the site-specific metal background variability, were derived based on the statistical characterization of the background populations. Sites in the Chaoyang South District area had UBC values in the range 10.4-12.6mg·kg^-1. These ranges provide meaningful SSL values to be adopted for As in local site assessment studies. Using the wide-scale background value for the Beijing area would have erroneously classified most of the areas in the subject sites as potentially contaminated.
文摘The knowledge of the internal stability of granular soils is a key factor for the design of granular and filter for the geotechnical infrastructures such as dykes, barrages, weirs and roads embankment. To evaluate the internal instability of granular soils different criteria are generally used in the practice. However, the results of these criteria on the same soil may lead to different evaluations of the internal instability. In this paper the common criteria used for the internal instability have been presented and compared as far as possible. It was found that the most internal instability criteria define a limit value for the secant slope of the grain size distribution curve of the granular soils. Based on this finding an own criterion for the evaluation of the internal instability of granular soil has been developed and compared to the common criteria. A very good agreement between some criteria was found. Furthermore, a site specific assessment for the evaluation of the internal instability of granular soil has been proposed in order to get more confidence in this evaluation.
基金ts Digitale Windboje(FKZ 03EE3024)and“ADWENTURE”(FKZ 03EE2030)funded by the German Federal Ministry for Economic Affairs and Climate Action(BMWK)Other parts were funded by the BMBF project“MADESI”(FKZ 01IS18043B)+2 种基金by the Competence Center for AI and Labour(“kompAKI”,FKZ 02L19C150)The project also benefited from the Hessian Ministry of Higher Education,Research,Science and the Arts(HMWK)project“The Third Wave of AI”.The WRF simulations were performed on the HPC Cluster EDDY,located at the University of Oldenburg(Germany)and were funded by BMWK(FKZ 0324005)We would like to thank the Federal Maritime and Hydrographic Agency(BSH)for providing the met mast data of FINO2 and FINO3,and Engie SA for the SCADA data of R80736.Also we would like to acknowledge ECMWF for providing ERA5 data.
文摘As wind is the basis of all wind energy projects, a precise knowledge about its availability is needed. For ananalysis of the site-specific wind conditions, Virtual Meteorological Masts (VMMs) are frequently used. VMMsmake use of site calibrated numerical data to provide precise wind estimates during all phases of a wind energyproject. Typically, numerical data are used for the long-term correlation that is required for estimating theyield of new wind farm projects. However, VMMs can also be used to fill data gaps or during the operationalphase as an additional reference data set to detect degrading sensors. The value of a VMM directly dependson its ability and precision to reproduce site-specific environmental conditions. Commonly, linear regressionis used as state of the art to correct reference data to the site-specific conditions. In this study, a frameworkof 10 different machine-learning methods is tested to investigated the benefit of more advanced methods ontwo offshore and one onshore site. We find significantly improving correlations between the VMMs and the reference data when using more advanced methods and present the most promising ones. The K-NearestNeighbors and AdaBoost regressors show the best results in our study, but Multi-Output Mixture of GaussianProcesses is also very promising. The use of more advanced regression models lead to decreased uncertainties;hence those methods should find its way into industrial applications. The recommended regression models canserve as a starting point for the development of end-user applications and services.
