Environmetrics Special Issue: Modern Quantitative Methods for Environmental Risk Assessment

Features

  • Author: Statistics Views
  • Date: 02 Jan 2013

In our rapidly changing and evolving world, there are many activities and outcomes that influence each other and the surrounding environment. With all the technological advances and creation of new products our modern society produces, it is critical to assess the impact on our environment and ecosystems. By modeling and interpreting the uncertainty of potentially harmful or dangerous situations, we can reduce the detrimental effect and severity of these events. Quantitative risk assessment (QRA) provides a probabilistic methodology that evaluates potentially dangerous or hazardous situations and aids in decision making to create a safer and more secure environment.

This collection of articles in this Special Issue of Environmetrics on Modern Quantitative Methods for Environmental Risk Assessment, guest edited by Lelys Bravo de Guenni (Universidad Simón Bolívar, Caracas, Venezuela) and Susan J. Simmons (University of North Carolina Wilmington, USA), illustrates the breadth and complexity of modern-day QRA, and the variety of environmetric solutions being developed to address the underlying issues.

thumbnail image: Environmetrics Special Issue: Modern Quantitative Methods for Environmental Risk Assessment

Environmental risks originate from many different sources, such as climate change, air and water pollution and toxicity of chemicals, just to name a few. For example, extreme weather (high temperatures, intense rainfall and storms, etc.) is a potential consequence of climate change and continuously causes widespread damage in many parts of the world, distressing the most vulnerable populations and affecting agriculture and ecosystem services. Chlorofluorocarbon (CFC) in the atmosphere has depleted the ozone and increased the amount of ultraviolet radiation in the Earth’s atmosphere. Asbestos materials have increased the risk of developing lung cancer, mesothelioma and nonmalignant lung and pleural disorders. The implications of hazardous agents on the environment, ecosystem and human health are extensive. Herein, we highlight a number of important efforts in environmental quantitative risk assessment and their applications.

We begin this special issue with methodologies involving Bayesian spatio-temporal models describing trends and impacts of extreme weather. The first article investigates extreme temperatures across Europe in “Bayesian Spatial Extreme Value Analysis to Assess the Changing Risk of Concurrent High Temperatures across Large Portions of European Cropland” by Shaby and Reich. This article develops a hierarchical Bayesian spatial extreme value model that incorporates temporal information both marginally and in spatial coherence. The authors use this methodology to examine if the risk of extremely high temperatures across agricultural land in Europe has increased over the last century. The second article by Kottas et al. develops a nonparametric spatio-temporal Bayesian model that investigates extremes over a given threshold and assumes a non-homogeneous Poisson process. The methodology is applied to a rainfall data set from the Cape Floristic Region in South Africa. The third article, “Risk Management against Extremes in a Changing Environment: A Risk-layer Approach” by Hochrainer-Stigler and Pflug, develops a risk layer approach that demonstrates the usefulness in differentiating between more frequent and less frequent impacts on risk reduction and risk transfer under dynamic conditions. They apply this methodology to flood risks in the Tisza region of Hungary.

Warren et al. introduce a hierarchical Bayesian, spatio-temporal, multivariate probit regression model useful for identifying weeks during the first trimester of pregnancy that can impact development of cardiac congenital anomalies in their paper “Bayesian Spatial-temporal Model for Cardiac Congenital Anomalies and Ambient Air Pollution Risk Assessment”.

The fifth article, “Functional Clustering of Water Quality Data in Scotland” by Haggerty et al., develops a multivariate approach to cluster lakes based on temporal patterns of chlorophyll, alkalinity and phosphorus. The methodology is applied to a lake data set from the Scottish Environment Protection Agency (SEPA). The sixth article, “Estimating Brood-specific Reproductive Inhibition Potency in Aquatic Toxicity Testing” by Zhang et al. builds hierarchical models in a toxicity experiment that studies the effect of potentially harmful agents on reproduction of species. The models incorporate brood-specific information to better accommodate the changes produced by the agent.

Model uncertainty in benchmark dose estimation is explored in the article “The Impact of Model Uncertainty on Benchmark Dose Estimation” by West et al. The authors investigate eight ubiquitous models used in carcinogenic risk assessment and illustrate how model uncertainty can affect inference about the estimate for benchmark dose. A different approach to deal with model uncertainty in benchmark dose estimation is to not specify a parametric model at all and use a nonparametric approach. This is developed in “Nonparametric Estimation of Benchmark Doses in Environmental Risk Assessment” by Piegorsch et al. We conclude this special issue with “Ecological Risk-O-Meter: A Risk Assessor and Manager Software Tool for Better Decision-Making in Ecosystems” by Sahinoglu et al. This article assesses the environmental impact of various agents using a game-theory approach and takes into account potential solutions for risk reduction.

We hope this special issue will encourage further development of quantitative methods and analyses in this important, growing field of study.

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