The leaching of organotin (OT) heat stabilizers from polyvinyl chloride (PVC) pipes used in residential drinking water systems may affect the quality of drinking water. These OTs, principally mono‐ and di‐substituted species of butyltins and methyltins, are a potential health concern because they belong to a broad class of compounds that may be immune, nervous, and reproductive system toxicants. In this article, we develop probability distributions of U.S. population exposures to mixtures of OTs encountered in drinking water transported by PVC pipes. We employed a family of mathematical models to estimate OT leaching rates from PVC pipe as a function of both surface area and time. We then integrated the distribution of estimated leaching rates into an exposure model that estimated the probability distribution of OT concentrations in tap waters and the resulting potential human OT exposures via tap water consumption. Our study results suggest that human OT exposures through tap water consumption are likely to be considerably lower than the World Health Organization (WHO) "safe" long‐term concentration in drinking water (150 μg/L) for dibutyltin (DBT)—the most toxic of the OT considered in this article. The 90th percentile average daily dose (ADD) estimate of 0.034 ± 2.92 × 10−4μg/kg day is approximately 120 times lower than the WHO‐based ADD for DBT (4.2 μg/kg day).
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. ; Australian Government Research Training Program ScholarshipAustralian GovernmentDepartment of Industry, Innovation and Science; Australian National University Hilda-John Endowment Fund; USDA Agricultural Research ServiceUnited States Department of Agriculture (USDA)USDA Agricultural Research Service [3072-22000-017-07-S]; National Centre for Groundwater Research and Training [MD2594]; National Science Foundation (NSF)National Science Foundation (NSF) [1937012]; National Socio-Environmental Synthesis Center [NSF DBI1639145] ; Published version ; The primary author (Takuya Iwanaga) is supported through an Australian Government Research Training Program Scholarship and a top-up scholarship from the Australian National University Hilda-John Endowment Fund. Hsiao-Hsuan Wang and Tomasz E. Koralewski acknowledge partial support from the USDA Agricultural Research Service provided through the Areawide Pest Management Program, "Areawide Pest Management of the Invasive Sugarcane Aphid in Grain Sorghum," project number 3072-22000-017-07-S. The Campaspe Integrated Model was developed as part of the Murray-Darling Basin Authority's partnership with the National Centre for Groundwater Research and Training under Contract No. MD2594. John Little acknowledges support from National Science Foundation (NSF) Award EEC 1937012. This work was also supported by the National Socio-Environmental Synthesis Center under funding received from the NSF DBI1639145.
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 example, for dusts the calibration data were pooled from exposure measurements sampled 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 <0.6) 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.
System-of-systems approaches for integrated assessments have become prevalent in recent years. Such approaches integrate a variety of models from different disciplines and modeling paradigms to represent a socioenvironmental (or social-ecological) system aiming to holistically inform policy and decision-making processes. Central to the system-of-systems approaches is the representation of systems in a multi-tier framework with nested scales. Current modeling paradigms, however, have disciplinary-specific lineage, leading to inconsistencies in the conceptualization and integration of socio-environmental systems. In this paper, a multidisciplinary team of researchers, from engineering, natural and social sciences, have come together to detail socio-technical practices and challenges that arise in the consideration of scale throughout the socioenvironmental modeling process. We identify key paths forward, focused on explicit consideration of scale and uncertainty, strengthening interdisciplinary communication, and improvement of the documentation process. We call for a grand vision (and commensurate funding) for holistic system-of-systems research that engages researchers, stakeholders, and policy makers in a multi-tiered process for the co-creation of knowledge and solutions to major socio-environmental problems. ; National Socio-Environmental Synthesis Center (SESYNC) under the National Science Foundation [DBI-1639145]; Australian Government Research Training Program (AGRTP) ScholarshipAustralian Government; ANU Hilda-John Endowment Fund; USDAUnited States Department of Agriculture (USDA); ARSUnited States Department of Agriculture (USDA)USDA Agricultural Research Service [58-3091-6-035]; Texas A&M AgriLife Research; Key Program of NSF of China [41930648]; NSFNational Science Foundation (NSF) [EEC 1937012] ; Published version ; This work was supported by the National Socio-Environmental Synthesis Center (SESYNC) under funding received from the National Science Foundation DBI-1639145. The primary author (Takuya Iwanaga) is supported through an Australian Government Research Training Program (AGRTP) Scholarship and a top-up scholarship from the ANU Hilda-John Endowment Fund. Hsiao-Hsuan Wang and Tomasz E. Koralewski acknowledge partial support from USDA, ARS Agreement No. 58-3091-6-035 with Texas A&M AgriLife Research, titled `Areawide pest management of the invasive sugarcane aphid in grain sorghum, regional population monitoring and forecasting.' Min Chen is supported by the Key Program of NSF of China (No. 41930648). John Little acknowledges partial support from NSF Award EEC 1937012. The authors would like to thank the three anonymous reviewers and Prof. Randall Hunt (USGS) for their constructive feedback and comments. The authors additionally thank Faye Duchin and Adrian Hindes for comments provided on an earlier draft. ; Public domain authored by a U.S. government employee
