Application of an environmental multimedia modeling system for health risk assessment: Key influencing factors and uncertainties research
Bibliographic record
Abstract
Abstract In this paper, an environmental multimedia modeling system (EMMS) that combines a risk assessment with a Monte Carlo method (MCM) was used to explore the interaction between groundwater and surface water with respect to chemical exposure and risk. The EMMS-MCM simulations incorporated several key influencing factors that are inherent in traditional predictions of subsurface and surface-water interactions, including soil permeability coefficients and parameters such as hydraulic conductivity, density, recharge, layers, and depth. These influencing factors are generally associated with the largest sources of uncertainty in modeling and pose significant challenges to water management and to the optimal allocation of water resources. A case study involving benzene at the Trail Road landfill site located in the Ottawa–Carlton area of Canada is presented to illustrate the use of the EMMS-MCM approach. The model results are verified by comparisons to the results of groundwater and surface-water investigations in the landfill. The EMMS-MCM results are evaluated using a risk quotient (RQ) risk assessment method to quantify environmental risk. The EMMS-MCM simulations can be used to support hazardous field work and contribute to environmental management by predicting the possible consequences of hazardous chemical contaminations in surface waters and groundwater. The integration of the EMMS-MCM and RQ approaches represents an appropriate tool for the accurate assessment of long-term pollutant risks and environmental management of surface- and groundwater resources. Integr Environ Assess Manag 2021;17:877–886. © 2021 SETAC KEY POINTS An environmental multimedia modeling system (EMMS) considering water allocation factors is discussed. How different types of pollutants pose varying health and ecological risks when different water allocation parameters (such as hydraulic conductivity, density, recharge, layer number, and depth) are applied. The EMMS-MCM model, combined with the risk quotient risk assessment methodology, provides an effective basis for pollution control decisions in interconnected groundwater and surface water systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".