Evaluating response surface designs for uncertainty analysis and prescriptive applications of a large-scale water quality model

Environmental models are often too large and cumbersome for effective use in regulatory decision making or in the characterization of uncertainty. This paper describes and compares four response surfaces that could complement a large-scale water quality model, the U.S. National Water Pollution Control Assessment Model (NWPCAM), in simulation and regulatory decision support applications. Results show that a physically based reduced-form model that exploits the mathematical structure of the underlying water quality model is a better predictor of policy-relevant outputs than the polynomial expansions that are frequently used in response surface studies.     

Parameter values to model the soil ingestion pathway

Soil ingestion is an important exposure pathway for contaminants that are not otherwise very mobile in the environment. Health of both humans and animals can be affected. This paper summarizes the literature and recommends models and probabilistic parameter values for risk assessment applications. Models of the pathway require estimates of the amounts of soil ingested, the concentration of contaminants relative to the original soil, and the bioavailability in the gut of the contaminants ingested with soil. Using a lead-contaminated sandbox as an example, the modelling recommendations suggest that a child typically may consume 50 mg d^–1 of the sandbox soil, the soil ingested will have a tenfold higher lead concentration than the original soil, and the lead will be as bioavailable as if ingested as inorganic lead in water.The Canadian Government right to retain a non-exclusive, royalty free licence in and to any copyright is acknowledged.     

An assessment of cause and effect decision-making model for firm environmental knowledge management capacities in uncertainty

Environmental practices in knowledge management capability (EKMC) is a complex and uncertainty concept that is difficult to determine based on a firm’s real situation because measuring EKMC requires a set of qualitative and quantitative measurement. The objective of this study is to develop a cause and effect model in uncertainty using the fuzzy set theory and Decision Making Trial and Evaluation Laboratory (DEMATEL) method. A framework for evaluating EKMC is proposed. An approach of fuzzy linguistic is proposed to evaluate the firm EKMC. The evaluation results of EKMC obtained through the proposed approach are objective and unbiased due to two reasons. Firstly, the results are generated by a group of experts in the presence of motile attributes. Secondly, the fuzzy linguistic approach has more advantage to reduce distortion and losing of information. Through evaluating the result of EKMC, managers could judge the necessity to improve the EKMC and determine which criteria are the needed directions to improve. The managerial implication and conclusions are discussed.     

Representing uncertainty in climate change scenarios: a Monte-Carlo approach

Climate change impact assessment is subject to a range of uncertainties due to both incomplete and unknowable knowledge. This paper presents an approach to quantifying some of these uncertainties within a probabilistic framework. A hierarchical impact model is developed that addresses uncertainty about future greenhouse gas emissions, the climate sensitivity, and limitations and unpredictability in general circulation models. The hierarchical model is used in Bayesian Monte-Carlo simulations to define posterior probability distributions for changes in seasonal-mean temperature and precipitation over the United Kingdom that are conditional on prior distributions for the model parameters. The application of this approach to an impact model is demonstrated using a hydrological example. This revised version was published online in July 2006 with corrections to the Cover Date.     

Impact of model parameterization on predictive uncertainty of regional groundwater models in the context of environmental impact assessment

Parameterization strategy impacts models' outputs and the associated uncertainty. This is particularly true for transient regional groundwater models where parameters can only be weakly constrained by insufficient observations. However, this is rarely investigated under any particular model structure. This study bridges this gap using a regional groundwater model developed to understand the impact of coal seam gas extraction on groundwater systems in a probabilistic framework. Two different parameterization schemes were implemented for hydraulic conductivity and specific storage. The first method solely relies on the relationship between hydraulic properties and burial depths. The second more complex strategy allows more spatial variations of hydraulic parameters using pilot points. The study provides new insights and practical guidance on the application of groundwater modelling for environmental impact assessment. The results suggest that the choice of model parameterization has a significant influence on predictive uncertainty. The model using the simple parameterization provides predictions with a much wider range than the model with a more sophisticated parameterization. This is because that the lowly parameterized model tends to generate more extreme effective hydraulic parameter fields unless the parameterization simplification converts the inverse problem to a (close to) well-posed problem that rarely exists for applied regional groundwater modelling. The potential impact of model parameterization should be discussed explicitly in groundwater modelling applications to support decision making to avoid misinterpretation of the modelling results.     

Health,safety and environmental unit performance assessment model under uncertainty (case study: steel industry)

Performance assessment is a critical objective of management systems. As a result of the non-deterministic and qualitative nature of performance indicators, assessments are likely to be influenced by evaluators’ personal judgments. Furthermore, in developing countries, performance assessments by the Health, Safety and Environment (HSE) department are based solely on the number of accidents. A questionnaire is used to conduct the study in one of the largest steel production companies in Iran. With respect to health, safety, and environment, the results revealed that control of disease, fire hazards, and air pollution are of paramount importance, with coefficients of 0.057, 0.062, and 0.054, respectively. Furthermore, health and environment indicators were found to be the most common causes of poor performance. Finally, it was shown that HSE management systems can affect the majority of performance safety indicators in the short run, whereas health and environment indicators require longer periods of time. The objective of this study is to present an HSE-MS unit performance assessment model in steel industries. Moreover, we seek to answer the following question: what are the factors that affect HSE unit system in the steel industry? Also, for each factor, the extent of impact on the performance of the HSE management system in the organization is determined.     

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Understanding the spatial soil salinity aids farmers and researchers in identifying areas in the field where special management practices are required. Apparent electrical conductivity measured by electromagnetic induction instrument in a fairly quick manner has been widely used to estimate spatial soil salinity. However, methods used for this purpose are mostly a series of interpolation algorithms. In this study, sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were applied for assessing the prediction accuracy and uncertainty of soil salinity with apparent electrical conductivity as auxiliary variable. Results showed that the spatial patterns of soil salinity generated by SGS and SGCS algorithms showed consistency with the measured values. The profile distribution of soil salinity was characterized by increasing with depth with medium salinization (EC_e 4–8 dS/m) as the predominant salinization class. SGCS algorithm privileged SGS algorithm with smaller root mean square error according to the generated realizations. In addition, SGCS algorithm had larger proportions of true values falling within probability intervals and narrower range of probability intervals than SGS algorithm. We concluded that SGCS algorithm had better performance in modeling local uncertainty and propagating spatial uncertainty. The inclusion of auxiliary variable contributed to prediction capability and uncertainty modeling when using densely auxiliary variable as the covariate to predict the sparse target variable.     

Regulation of pesticide discharge into surface and groundwater under uncertainty: A model for environmental risk‐profitability tradeoffs and policy selection

The presence of contaminants in environmental media as well as the desire to maintain a high level of economic activity has led to an important and difficult decision‐making problem for both public policy decision makers and the general public. In one sense, all of the interested parties are likely to be concerned about the potential health risks posed by the presence of contaminants in environmental media and the need to design/implement policies for their mitigation and/or removal. At the same time, however, there also appear to be concerns about the cost of these policies. These costs could be measured in terms of the potential losses in economic activity that are likely to occur when a policy is adopted. The policy can be selected from a range of alternatives, with the choice being driven in part by the stakeholders represented in the policy decision problem. In this case, two general goals might be considered: minimizing environmental risk and minimizing the economic impact of the policy considered. These two objectives are likely to be viewed as conflicting goals, and the nature of the tradeoffs between them must be taken into account in the policy selection process. This paper presents the development of a zero–one weighted goal programming model that can be used to select a preferred policy that minimizes surface and groundwater contamination as well as the economic costs of environmental policy selection. A safety rule model is developed first and then extended to the zero–one weighted goal programming formulation. The stochastic aspects of these structures are emphasized throughout. The paper also addresses a number of issues related to implementation of the model.     

Development and sensitivity analysis of a global drinking water quality index

The UNEP GEMS/Water Programme is the leading international agency responsible for the development of water quality indicators and maintains the only global database of water quality for inland waters (GEMStat). The protection of source water quality for domestic use (drinking water, abstraction etc) was identified by GEMS/Water as a priority for assessment. A composite index was developed to assess source water quality across a range of inland water types, globally, and over time. The approach for development was three-fold: (1) Select guidelines from the World Health Organisation that are appropriate in assessing global water quality for human health, (2) Select variables from GEMStat that have an appropriate guideline and reasonable global coverage, and (3) determine, on an annual basis, an overall index rating for each station using the water quality index equation endorsed by the Canadian Council of Ministers of the Environment. The index allowed measurements of the frequency and extent to which variables exceeded their respective WHO guidelines, at each individual monitoring station included within GEMStat, allowing both spatial and temporal assessment of global water quality. Development of the index was followed by preliminary sensitivity analysis and verification of the index against real water quality data.     

A review of uncertainty research in impact assessment

This paper examines uncertainty research in Impact Assessment (IA) and the focus of attention of the IA scholarly literature. We do so by first exploring ‘outside’ the IA literature, identifying three main themes of uncertainty research, and then apply these themes to examine the focus of scholarly research on uncertainty ‘inside’ IA. Based on a search of the database Scopus, we identified 134 journal papers published between 1970 and 2013 that address uncertainty in IA, 75% of which were published since 2005. We found that 90% of IA research addressing uncertainty focused on uncertainty in the practice of IA, including uncertainty in impact predictions, models and managing environmental impacts. Notwithstanding early guidance on uncertainty treatment in IA from the 1980s, we found no common, underlying conceptual framework that was guiding research on uncertainty in IA practice. Considerably less attention, only 9% of papers, focused on uncertainty communication, disclosure and decision-making under uncertain conditions, the majority of which focused on the need to disclose uncertainties as opposed to providing guidance on how to do so and effectively use that information to inform decisions. Finally, research focused on theory building for explaining human behavior with respect to uncertainty avoidance constituted only 1% of the IA published literature. We suggest the need for further conceptual framework development for researchers focused on identifying and addressing uncertainty in IA practice; the need for guidance on how best to communicate uncertainties in practice, versus criticizing practitioners for not doing so; research that explores how best to interpret and use disclosures about uncertainty when making decisions about project approvals, and the implications of doing so; and academic theory building and exploring the utility of existing theories to better understand and explain uncertainty avoidance behavior in IA.