A statistical model and national data set for partioning fish-tissue mercury concentration variation between spatiotemporal and sample characteristic effects

2004 ◽  
Author(s):  
Stephen P. Wente
Keyword(s):  
Statistical Model ◽  
Mercury Concentration ◽  
Fish Tissue ◽  
National Data ◽  
Data Set ◽  
Concentration Variation ◽  
Sample Characteristic

A Statistical Model of the Global Carbon Budget

2021 ◽  
Author(s):  
Eric Hillebrand ◽  
Mikkel Bennedsen ◽  
Siem Jan Koopman
Keyword(s):  
Statistical Model ◽  
Carbon Budget ◽  
Southern Oscillation ◽  
Annual Data ◽  
Science Data ◽  
Data Set ◽  
Global Carbon Budget ◽  
Atmospheric Co2 Concentrations ◽  
Atmospheric Concentrations ◽  
Global Carbon

<p>We propose a dynamic statistical model of the Global Carbon Budget (GCB) as represented in the annual data set made available by the Global Carbon Project (Friedlingsstein et al., 2019, Earth System Science Data 11, 1783--1838), covering the sample period 1959--2018. The model connects four main objects of interest: atmospheric CO2 concentrations, anthropogenic CO2 emissions, the absorption of CO2 by the terrestrial biosphere (land sink) and by the ocean and marine biosphere (ocean sink).<span>  </span>The model captures the global carbon budget equation, which states that emissions not absorbed by either land or ocean sinks must remain in the atmosphere and constitute a flow to the stock of atmospheric concentrations. Emissions depend on global economic activity as measured by World gross domestic product (GDP), and sink activity depends on the level of atmospheric concentrations and the Southern Oscillation Index (SOI). We use the model to determine the time series dynamics of atmospheric concentrations, to assess parameter uncertainty, to compute key variables such as the airborne fraction and sink rate, to forecast the GCB components from forecasts of World-GDP and SOI, and to conduct scenario analysis based on different possible future paths of World-GDP.</p>

Meteorological and hydrological conditions triggering rockfall events in Germany

2021 ◽  
Author(s):  
Katrin Nissen ◽  
Stefan Rupp ◽  
Björn Guse ◽  
Uwe Ulbrich ◽  
Sergiy Vorogushyn ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Soil Moisture ◽  
Statistical Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Daily Precipitation ◽  
Hydrologic Model ◽  
Data Set ◽  
Hydrological Conditions ◽  
Landslide Database

<p>In this study we present the results of a logistic regression model aimed at describing changes in probabilities for rockfall events in Germany in response to changes in meteorological and hydrological conditions.</p><p>The rockfall events for this study are taken from the landslide database for Germany (Damm and Klose, 2015). The meteorological variables we tested as predictors for the logistic regression model are daily precipitation from the REGNIE data set (Rauthe et al. 2013), hourly precipitation from the RADKLIM radar climatology (Winterrath et al., 2018) and temperature from the E-OBS data set (Cornes et al., 2018). As there is no observational soil moisture data set covering the entire country, we used soil moisture modelled with the state-of-the-art hydrological model mHM (Samaniego et al. 2010), which was calibrated using gauge measurements.</p><p>In order to select the best statistical model we tested a large number of physically plausible combinations of meteorological and hydrological predictors. Each model was checked using cross-validation. The decision on the final model was based on the value of the logarithmic skill score and on expert judgement.</p><p>The final statistical model includes the local percentile of daily precipitation, total relative soil moisture and freeze-thawing cycles in the previous weeks as predictors. It was found that daily precipitation is the most important parameter in the model. An increase of daily precipitation from its median to its 80th percentile approximately doubles the probability for a rockfall event. Higher soil moisture and the occurrence of freeze-thaw cycles also increase the probability for rockfall events. </p><p><br>Cornes, R. C. et al., 2018: An ensemble version of the E‐OBS temperature and precipitation data sets. Journal of Geophysical Research: Atmospheres, 123, 9391– 9409.</p><p>Damm, B., Klose, M., 2015. The landslide database for Germany: Closing the gap at national level. Geomorphology 249, 82–93</p><p>Rauthe, M. et al., 2013: A Central European precipitation climatology – Part I: Generation and validation of a high-reso-lution gridded daily data set (HYRAS), Vol. 22(3), p 235–256.</p><p>Samaniego, L. et al., 2010: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res., 46,W05523</p><p>Winterrath, T. et al., 2018: RADKLIM Version 2017.002: Reprocessed gauge-adjusted radar data, one-hour precipitation sums (RW), DOI: 10.5676/DWD/RADKLIM_RW_V2017.002.</p>

scholarly journals A meta-analysis and statistical modelling of nitrates in groundwater at the African scale

2016 ◽  
Vol 20 (6) ◽  
pp. 2353-2381 ◽  
Author(s):  
Issoufou Ouedraogo ◽  
Marnik Vanclooster
Keyword(s):  
Statistical Model ◽  
Nitrate Concentration ◽  
Regional Scale ◽  
Shallow Groundwater ◽  
Statistical Modelling ◽  
Nitrate Pollution ◽  
Nitrate Contamination ◽  
Data Set ◽  
Nitrate Concentrations ◽  
Groundwater Systems

Abstract. Contamination of groundwater with nitrate poses a major health risk to millions of people around Africa. Assessing the space–time distribution of this contamination, as well as understanding the factors that explain this contamination, is important for managing sustainable drinking water at the regional scale. This study aims to assess the variables that contribute to nitrate pollution in groundwater at the African scale by statistical modelling. We compiled a literature database of nitrate concentration in groundwater (around 250 studies) and combined it with digital maps of physical attributes such as soil, geology, climate, hydrogeology, and anthropogenic data for statistical model development. The maximum, medium, and minimum observed nitrate concentrations were analysed. In total, 13 explanatory variables were screened to explain observed nitrate pollution in groundwater. For the mean nitrate concentration, four variables are retained in the statistical explanatory model: (1) depth to groundwater (shallow groundwater, typically < 50 m); (2) recharge rate; (3) aquifer type; and (4) population density. The first three variables represent intrinsic vulnerability of groundwater systems to pollution, while the latter variable is a proxy for anthropogenic pollution pressure. The model explains 65 % of the variation of mean nitrate contamination in groundwater at the African scale. Using the same proxy information, we could develop a statistical model for the maximum nitrate concentrations that explains 42 % of the nitrate variation. For the maximum concentrations, other environmental attributes such as soil type, slope, rainfall, climate class, and region type improve the prediction of maximum nitrate concentrations at the African scale. As to minimal nitrate concentrations, in the absence of normal distribution assumptions of the data set, we do not develop a statistical model for these data. The data-based statistical model presented here represents an important step towards developing tools that will allow us to accurately predict nitrate distribution at the African scale and thus may support groundwater monitoring and water management that aims to protect groundwater systems. Yet they should be further refined and validated when more detailed and harmonized data become available and/or combined with more conceptual descriptions of the fate of nutrients in the hydrosystem.

scholarly journals Modeling the Effect of Scrap on the Electrical Energy Consumption of an Electric Arc Furnace

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1044
Author(s):  
Leo S. Carlsson ◽  
Peter B. Samuelsson ◽  
Pär G. Jönsson
Keyword(s):  
Energy Consumption ◽  
Statistical Model ◽  
Electrical Energy ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Melting Time ◽  
Arc Furnace ◽  
Total Energy Consumption ◽  
Data Set ◽  
Practical Usefulness

The melting time of scrap is a factor that affects the Electrical Energy (EE) consumption of the Electric Arc Furnace (EAF) process. The EE consumption itself stands for most of the total energy consumption during the process. Three distinct representations of scrap, based partly on the apparent density and shape of scrap, were created to investigate the effect of scrap on the accuracy of a statistical model predicting the EE consumption of an EAF. Shapley Additive Explanations (SHAP) was used as a tool to investigate the effects by each scrap category on each prediction of a selected model. The scrap representation based on the shape of scrap consistently resulted in the best performing models while all models using any of the scrap representations performed better than the ones without any scrap representation. These results were consistent for all four distinct and separately used cleaning strategies on the data set governing the models. In addition, some of the main scrap categories contributed to the model prediction of EE in accordance with the expectations and experience of the plant engineers. The results provide significant evidence that a well-chosen scrap categorization is important to improve a statistical model predicting the EE and that experience on the specific EAF under study is essential to evaluate the practical usefulness of the model.

scholarly journals Adding a new dimension to the weekend effect: an analysis of a national data set of electronic AKI alerts

QJM ◽  
2018 ◽  
Vol 111 (4) ◽  
pp. 249-255 ◽  
Author(s):  
J Holmes ◽  
T Rainer ◽  
J Geen ◽  
J D Williams ◽  
A O Phillips ◽  
...  
Keyword(s):  
Weekend Effect ◽  
National Data ◽  
Data Set

Cannabis psychosis, gender matters

2016 ◽  
Vol 33 (S1) ◽  
pp. S379-S379
Author(s):  
I. Hamilton ◽  
P. Galdas ◽  
H. Essex
Keyword(s):  
Gender Differences ◽  
Epidemiological Studies ◽  
Hospital Episode Statistics ◽  
Psychiatric Services ◽  
Data Sets ◽  
Gender Ratio ◽  
National Data ◽  
Data Set ◽  
British Crime Survey ◽  
Competing Interest

IntroductionDespite recent findings pointing toward cannabis psychosis as one area where gender differences may exist, there has been a widespread lack of attention paid to gender as a determinant of health in both psychiatric services and within the field of addiction.ObjectivesTo explore gender differences in treatment presentations for people with cannabis psychosis.AimsTo use national data sets to investigate gender differences.MethodsAnalysis of British Crime Survey data and a Hospital Episode Statistics data set were used in combination with data from previously published epidemiological studies to compare gender differences.ResultsMale cannabis users outnumber female users by 2:1, a similar gender ratio is found for those admitted to hospital with a diagnosis of schizophrenia or psychosis. However this ratio increases significantly for those admitted to hospital with a diagnosis of cannabis psychosis, with males outnumbering females by 4:1.ConclusionsThis research brings into focus the marked gender differences in cannabis psychosis. Attending to gender is important for research and treatment with the aim of improving understanding and providing gender sensitive services.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Federal leadership needed to realize national data set for cardiovascular care

2017 ◽  
Vol 189 (41) ◽  
pp. E1294-E1294
Author(s):  
Paul Dorian ◽  
Catherine Kells
Keyword(s):  
National Data ◽  
Data Set ◽  
Cardiovascular Care
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