ORCID Profile
0000-0003-2965-9557
Current Organisations
E O Lawrence Berkeley National Laboratory
,
Virginia Tech
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Publisher: American Chemical Society (ACS)
Date: 23-01-2020
Abstract: To understand water quality degradation during hypoxia, we need to understand sediment oxygen fluxes, the main oxygen sink in shallow hypolimnia. Kinetic models, which integrate diffusion and consumption of dissolved oxygen (DO) in sediments, usually assume a downward flux of DO from the sediment-water interface (SWI) with a zero-flux condition at the lower boundary of the oxic sediment layer. In this paper, we separately account for the oxidation of an upward flux of reduced compounds by introducing a negative flux of DO as a lower boundary condition. Using in situ measurements in two lakes, kinetic models were fit to DO microprofiles using zero-order and first-order kinetics with both zero and non-zero lower boundary conditions. Based on visual inspection and goodness-of-fit criteria, the negative-flux lower boundary condition, -0.25 g O
Publisher: American Geophysical Union (AGU)
Date: 2017
DOI: 10.1002/2016WR019279
Publisher: Wageningen University and Research
Date: 27-03-2023
DOI: 10.18174/SESMO.18563
Abstract: Models of socio-environmental or social-ecological systems (SES) commonly address problems requiring interdisciplinary scientific expertise and input from a heterogeneous group of stakeholders. In SES modelling multiple interactions occur on different scales among various phenomena. These scale phenomena include the technical, such as system variables, process detail, inputs and outputs, which most often require spatial, temporal, thematic and organisational choices. From a good practice and project efficiency perspective, the problem scoping and conceptual model formulation phase of modelling is the one to address well from the outset. During this phase, intense and substantive discussions should arise regarding appropriate scales at which to represent the different phenomena. Although the details of these discussions influence the path of model development, they are seldom documented and as a result often forgotten. We draw upon personal experience with existing protocols and communications in recent literature to propose preliminary guidelines for documenting these early discussions about the scale(s) of the studied phenomena. Our guidelines aim to aid modelling group members in building and capturing the richness of their rationale for scoping and scale decisions. The resulting transcripts are intended to promote transparency of modelling decisions and provide essential support for the justification of the final model for its intended use. They also facilitate adaptive modifications of the pathway of model development via retracing decisions and iterative reflection upon alternative scale options.
Publisher: Elsevier BV
Date: 02-2019
Publisher: Oxford University Press (OUP)
Date: 08-08-2021
Abstract: STOFFENMANAGER® and the Advanced REACH Tool (ART) are recommended tools by the European Chemical Agency for regulatory chemical safety assessment. The models are widely used and accepted within the scientific community. STOFFENMANAGER® alone has more than 37 000 users globally and more than 310 000 risk assessment have been carried out by 2020. Regardless of their widespread use, this is the first study evaluating the theoretical backgrounds of each model. STOFFENMANAGER® and ART are based on a modified multiplicative model where an exposure base level (mg m−3) is replaced with a dimensionless intrinsic emission score and the exposure modifying factors are replaced with multipliers that are mainly based on subjective categories that are selected by using exposure taxonomy. The intrinsic emission is a unit of concentration to the substance emission potential that represents the concentration generated in a standardized task without local ventilation. Further information or scientific justification for this selection is not provided. The multipliers have mainly discrete values given in natural logarithm steps (…, 0.3, 1, 3, …) that are allocated by expert judgements. The multipliers scientific reasoning or link to physical quantities is not reported. The models calculate a subjective exposure score, which is then translated to an exposure level (mg m−3) by using a calibration factor. The calibration factor is assigned by comparing the measured personal exposure levels with the exposure score that is calculated for the respective exposure scenarios. A mixed effect regression model was used to calculate correlation factors for four exposure group [e.g. dusts, vapors, mists (low-volatiles), and solid object/abrasion] by using ~1000 measurements for STOFFENMANAGER® and 3000 measurements for ART. The measurement data for calibration are collected from different exposure groups. For ex le, for dusts the calibration data were pooled from exposure measurements s led from pharmacies, bakeries, construction industry, and so on, which violates the empirical model basic principles. The calibration databases are not publicly available and thus their quality or subjective selections cannot be evaluated. STOFFENMANAGER® and ART can be classified as subjective categorization tools providing qualitative values as their outputs. By definition, STOFFENMANAGER® and ART cannot be classified as mechanistic models or empirical models. This modeling algorithm does not reflect the physical concept originally presented for the STOFFENMANAGER® and ART. A literature review showed that the models have been validated only at the ‘operational analysis’ level that describes the model usability. This review revealed that the accuracy of STOFFENMANAGER® is in the range of 100 000 and for ART 100. Calibration and validation studies have shown that typical log-transformed predicted exposure concentration and measured exposure levels often exhibit weak Pearson’s correlations (r is & .6) for both STOFFENMANAGER® and ART. Based on these limitations and performance departure from regulatory criteria for risk assessment models, it is recommended that STOFFENMANAGER® and ART regulatory acceptance for chemical safety decision making should be explicitly qualified as to their current deficiencies.
Publisher: Elsevier BV
Date: 12-2013
Publisher: University of California Press
Date: 2021
DOI: 10.1525/ELEMENTA.2020.00182
Abstract: The pathways taken throughout any model-based process are undoubtedly influenced by the modeling team involved and the decision choices they make. For interconnected socioenvironmental systems (SES), such teams are increasingly interdisciplinary to enable a more expansive and holistic treatment that captures the purpose, the relevant disciplines and sectors, and other contextual settings. In practice, such interdisciplinarity increases the scope of what is considered, thereby increasing choices around model complexity and their effects on uncertainty. Nonetheless, the consideration of scale issues is one critical lens through which to view and question decision choices in the modeling cycle. But separation between team members, both geographically and by discipline, can make the scales involved more arduous to conceptualize, discuss, and treat. In this article, the practices, decisions, and workflow that influence the consideration of scale in SESs modeling are explored through reflexive accounts of two case studies. Through this process and an appreciation of past literature, we draw out several lessons under the following themes: (1) the fostering of collaborative learning and reflection, (2) documenting and justifying the rationale for modeling scale choices, some of which can be equally plausible (a perfect model is not possible), (3) acknowledging that causality is defined subjectively, (4) embracing change and reflection throughout the iterative modeling cycle, and (5) regularly testing the model integration to draw out issues that would otherwise be unnoticeable.
Publisher: Elsevier BV
Date: 04-2018
Publisher: Elsevier BV
Date: 2021
Publisher: Wageningen University and Research
Date: 18-05-2023
DOI: 10.18174/SESMO.18509
Abstract: Stakeholder participation in social-ecological systems (SES) modeling is increasingly considered a desirable way to elicit erse sources of knowledge about SES behavior and to promote inclusive decision-making in SES. Understanding how participatory modeling processes function in the context of long-term adaptive management of SES may allow for better design of participatory processes to achieve the intended outcomes of inclusionary knowledge, representativeness, and social learning, while avoiding unintended outcomes. Long-term adaptive management contexts often include political influences -- attempts to shift or preserve power structures and authority, and efforts to represent the political and economic interests of stakeholders -- in the computer models that are used to shape policy making and implementation. In this research, we examine a period that included a major transition in the watershed model used for management of the Chesapeake Bay in the United States. The Chesapeake Bay watershed model has been in development since the 1980s, and is considered by many to be an exemplary case of participatory modeling. We use documentary analysis and interviews with participants involved in the model application and development transition to reveal a variety of ways in which participatory modeling may be subject to different kinds of political influences, some of which resulted in unintended outcomes, including: perceptions of difficulty updating the model in substantive ways, “gaming” of the model articipatory process by stakeholders, and increasing resistance against considering uncertainty in the system not captured by the model. This research suggests unintended or negative outcomes may be associated with both participatory decision-making and stakeholder learning even though they are so often touted as the benefits of participatory modeling. We end with a hypothesis that further development of a theory of computer model governance to bridge model impact and broader theories of environmental governance at the science-policy interface may result in improved SES modeling outcomes.
Publisher: MDPI AG
Date: 03-01-2021
DOI: 10.3390/W13010088
Abstract: Many researchers use one-dimensional (1-D) and three-dimensional (3-D) coupled hydrodynamic and water-quality models to simulate water quality dynamics, but direct comparison of their relative performance is rare. Such comparisons may quantify their relative advantages, which can inform best practices. In this study, we compare two 1-year simulations in a shallow, eutrophic, managed reservoir using a community-developed 1-D model and a 3-D model coupled with the same water-quality model library based on multiple evaluation criteria. In addition, a verified bubble plume model is coupled with the 1-D and 3-D models to simulate the water temperature in four epilimnion mixing periods to further quantify the relative performance of the 1-D and 3-D models. Based on the present investigation, adopting a 1-D water-quality model to calibrate a 3-D model is time-efficient and can produce reasonable results 3-D models are recommended for simulating thermal stratification and management interventions, whereas 1-D models may be more appropriate for simpler model setups, especially if field data needed for 3-D modeling are lacking.
Publisher: American Geophysical Union (AGU)
Date: 2018
DOI: 10.1002/2017WR021127
Publisher: American Chemical Society (ACS)
Date: 15-12-2020
Location: United States of America
No related grants have been discovered for John Little.