@article{ObiriNyarkoDuahKarikarietal.2021, author = {Obiri-Nyarko, Franklin and Duah, Anthony A. and Karikari, Anthony Y. and Agyekum, William A. and Manu, Evans and Tagoe, Ralph}, title = {Assessment of heavy metal contamination in soils at the Kpone landfill site, Ghana}, series = {Chemosphere : chemistry, biology and toxicology as related to environmental problems}, volume = {282}, journal = {Chemosphere : chemistry, biology and toxicology as related to environmental problems}, publisher = {Elsevier}, address = {Amsterdam [u.a.]}, issn = {0045-6535}, doi = {10.1016/j.chemosphere.2021.131007}, pages = {10}, year = {2021}, abstract = {Concentrations of lead (Pb), zinc (Zn), copper (Cu), mercury (Hg), and arsenic (As) in soils at the Kpone landfill site (Ghana) were determined using Atomic Absorption Spectrophotometry (AAS). Further analyses allowed establishing the degree of heavy metals (HMs) pollution, suitability of the soils for agriculture, sources of the HMs and their ecological and health risks. The site was divided into five zones, A, B, C, D, and E, and in all, seventeen (17) soil samples were collected. Average concentrations of Cu fell within the allowable range for agricultural soils in all the zones while average concentrations of Pb, Zn, Hg, and As exceeded the range in some or all the zones. Concentrations of the HMs generally exceeded their respective background value, with all zones showing very high degree of HMs contamination. The pollution load index (PLI) was 16.48, signifying extreme HMs pollution of the entire site. Multivariate statistical analyses revealed that Cu, Zn, and Pb in the soils originated from the deposited waste materials as well as traffic-related activities (e.g. wear and tear of tyres, brakes, and engines) at the site. Hg also originated from the deposited waste materials as well as cement production and oil and coal combustion activities in the study area, while As derived from industrial discharges and metal smelting activities. All the zones exhibited very high ecological risk. The carcinogenic and non-carcinogenic health risks posed by the HMs were also above acceptable levels, with children being more vulnerable than adults to these health risks.}, language = {en} } @article{MuehlenbruchKuxhausPencinaetal.2015, author = {M{\"u}hlenbruch, Kristin and Kuxhaus, Olga and Pencina, Michael J. and Boeing, Heiner and Liero, Hannelore and Schulze, Matthias Bernd}, title = {A confidence ellipse for the Net Reclassification Improvement}, series = {European journal of epidemiology}, volume = {30}, journal = {European journal of epidemiology}, number = {4}, publisher = {Springer}, address = {Dordrecht}, issn = {0393-2990}, doi = {10.1007/s10654-015-0001-1}, pages = {299 -- 304}, year = {2015}, abstract = {The Net Reclassification Improvement (NRI) has become a popular metric for evaluating improvement in disease prediction models through the past years. The concept is relatively straightforward but usage and interpretation has been different across studies. While no thresholds exist for evaluating the degree of improvement, many studies have relied solely on the significance of the NRI estimate. However, recent studies recommend that statistical testing with the NRI should be avoided. We propose using confidence ellipses around the estimated values of event and non-event NRIs which might provide the best measure of variability around the point estimates. Our developments are illustrated using practical examples from EPIC-Potsdam study.}, language = {en} } @article{GabsiHammersWirtzGrimmetal.2014, author = {Gabsi, Faten and Hammers-Wirtz, Monika and Grimm, Volker and Schaeffer, Andreas and Preuss, Thomas G.}, title = {Coupling different mechanistic effect models for capturing individual- and population-level effects of chemicals: Lessons from a case where standard risk assessment failed}, series = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog}, volume = {280}, journal = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0304-3800}, doi = {10.1016/j.ecolmodel.2013.06.018}, pages = {18 -- 29}, year = {2014}, abstract = {Current environmental risk assessment (ERA) of chemicals for aquatic invertebrates relies on standardized laboratory tests in which toxicity effects on individual survival, growth and reproduction are measured. Such tests determine the threshold concentration of a chemical below which no population-level effects are expected. How well this procedure captures effects on individuals and populations, however, remains an open question. Here we used mechanistic effect models, combining individual-level reproduction and survival models with an individual-based population model (IBM), to understand the individuals' responses and extrapolate them to the population level. We used a toxicant (Dispersogen A) for which adverse effects on laboratory populations were detected at the determined threshold concentration and thus challenged the conservatism of the current risk assessment method. Multiple toxicity effects on reproduction and survival were reported, in addition to effects on the F1 generation. We extrapolated commonly tested individual toxicity endpoints, reproduction and survival, to the population level using the IBM. Effects on reproduction were described via regression models. To select the most appropriate survival model, the IBM was run assuming either stochastic death (SD) or individual tolerance (IT). Simulations were run for different scenarios regarding the toxicant's effects: survival toxicity, reproductive toxicity, or survival and reproductive toxicity. As population-level endpoints, we used population size and structure and extinction risk. We found that survival represented as SD explained population dynamics better than IT. Integrating toxicity effects on both reproduction and survival yielded more accurate predictions of population effects than considering isolated effects. To fully capture population effects observed at high toxicant concentrations, toxicity effects transmitted to the F1 generation had to be integrated. Predicted extinction risk was highly sensitive to the assumptions about individual-level effects. Our results demonstrate that the endpoints used in current standard tests may not be sufficient for assessing the risk of adverse effects on populations. A combination of laboratory population experiments with mechanistic effect models is a powerful tool to better understand and predict effects on both individuals and populations. Mechanistic effect modelling thus holds great potential to improve the accuracy of ERA of chemicals in the future. (C) 2013 The Authors. Published by Elsevier B.V. All rights reserved.}, language = {en} } @misc{AugusiakVandenBrinkGrimm2014, author = {Augusiak, Jacqueline and Van den Brink, Paul J. and Grimm, Volker}, title = {Merging validation and evaluation of ecological models to 'evaludation': A review of terminology and a practical approach}, series = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog}, volume = {280}, journal = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0304-3800}, doi = {10.1016/j.ecolmodel.2013.11.009}, pages = {117 -- 128}, year = {2014}, abstract = {Confusion about model validation is one of the main challenges in using ecological models for decision support, such as the regulation of pesticides. Decision makers need to know whether a model is a sufficiently good representation of its real counterpart and what criteria can be used to answer this question. Unclear terminology is one of the main obstacles to a good understanding of what model validation is, how it works, and what it can deliver. Therefore, we performed a literature review and derived a standard set of terms. 'Validation' was identified as a catch-all term, which is thus useless for any practical purpose. We introduce the term 'evaludation', a fusion of 'evaluation' and 'validation', to describe the entire process of assessing a model's quality and reliability. Considering the iterative nature of model development, the modelling cycle, we identified six essential elements of evaludation: (i) 'data evaluation' for scrutinising the quality of numerical and qualitative data used for model development and testing; (ii) 'conceptual model evaluation' for examining the simplifying assumptions underlying a model's design; (iii) 'implementation verification' for testing the model's implementation in equations and as a computer programme; (iv) 'model output verification' for comparing model output to data and patterns that guided model design and were possibly used for calibration; (v) 'model analysis' for exploring the model's sensitivity to changes in parameters and process formulations to make sure that the mechanistic basis of main behaviours of the model has been well understood; and (vi) 'model output corroboration' for comparing model output to new data and patterns that were not used for model development and parameterisation. Currently, most decision makers require 'validating' a model by testing its predictions with new experiments or data. Despite being desirable, this is neither sufficient nor necessary for a model to be useful for decision support. We believe that the proposed set of terms and its relation to the modelling cycle can help to make quality assessments and reality checks of ecological models more comprehensive and transparent. (C) 2013 Elsevier B.V. All rights reserved.}, language = {en} } @article{GrimmAugusiakFocksetal.2014, author = {Grimm, Volker and Augusiak, Jacqueline and Focks, Andreas and Frank, Beatrice M. and Gabsi, Faten and Johnston, Alice S. A. and Liu, Chun and Martin, Benjamin T. and Meli, Mattia and Radchuk, Viktoriia and Thorbek, Pernille and Railsback, Steven Floyd}, title = {Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE}, series = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog}, volume = {280}, journal = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0304-3800}, doi = {10.1016/j.ecolmodel.2014.01.018}, pages = {129 -- 139}, year = {2014}, abstract = {The potential of ecological models for supporting environmental decision making is increasingly acknowledged. However, it often remains unclear whether a model is realistic and reliable enough. Good practice for developing and testing ecological models has not yet been established. Therefore, TRACE, a general framework for documenting a model's rationale, design, and testing was recently suggested. Originally TRACE was aimed at documenting good modelling practice. However, the word 'documentation' does not convey TRACE's urgency. Therefore, we re-define TRACE as a tool for planning, performing, and documenting good modelling practice. TRACE documents should provide convincing evidence that a model was thoughtfully designed, correctly implemented, thoroughly tested, well understood, and appropriately used for its intended purpose. TRACE documents link the science underlying a model to its application, thereby also linking modellers and model users, for example stakeholders, decision makers, and developers of policies. We report on first experiences in producing TRACE documents. We found that the original idea underlying TRACE was valid, but to make its use more coherent and efficient, an update of its structure and more specific guidance for its use are needed. The updated TRACE format follows the recently developed framework of model 'evaludation': the entire process of establishing model quality and credibility throughout all stages of model development, analysis, and application. TRACE thus becomes a tool for planning, documenting, and assessing model evaludation, which includes understanding the rationale behind a model and its envisaged use. We introduce the new structure and revised terminology of TRACE and provide examples. (C) 2014 Elsevier B.V. All rights reserved.}, language = {en} }