Correction: Distributions of Autocorrelated First-Order Kinetic Outcomes: Illness Severity

[This corrects the article DOI: 10.1371/journal.pone.0129042.]

Pollution prevention technologies: A review and classification

Waste minimization practices, reported in the literature for major industries using or generating hazardous materials, were reviewed. Technologies are summarized briefly in this paper. The information was reorganized according to the function served by the material industrially, and the gene- ral chemical nature of the material. Ten basic functions were identified, as binding, pigmentation, reactants, reaction inhibition, catalysis, bleaching, mass deposition, mass removal, by-products, and end-products. The resulting perspective of this review is general with respect to industry, and with respect to waste phase, and considers productivity benefits along with prevention of risks associated with pollution. Simi- larities in waste reduction opportunities are evident between processes where hazardous materials perform similar functions, suggesting general approaches to industrial hazardous waste reduction

Correction: Distributions of Autocorrelated First-Order Kinetic Outcomes: Illness Severity.

[This corrects the article DOI: 10.1371/journal.pone.0129042.]

Bayesian Benefit-Risk Analysis for Sustainable Process Design

A Bayesian model for assessing uncertain and variable economics of proposed environmentally conscious and other manufacturing processes from available information is described in this paper. Economic risks may stem from environmental, health, and safety liabilities; potential product losses; and other intangibles. Benefits and costs may be uncertain due to lack of information and experience within the available time and budget, or may be inherently variable in nature. Bayesian inference allows the integrated use of available numeric data, related data, summary statistics, and professional judgment. The Principle of Maximum Entropy is used as a basis for incorporation of subjective information. A Bayesian Pareto incident-size distribution is used to model size distributions for individual costs. Sensitivity analysis is used to help identify primary elements of project economic risk. Probability distributions for planning period costs, exceedance probabilities, and other risk indicators for alternative paint-stripping projects were computed and compared for an example industrial facility. The relative importance of variability and uncertainty as a function of planning-period length is probed

Predictive Bayesian Dose-Response Assessment for Appraising Absolute Health Risk from Available Information

Currently, no general measure of population health response to untestable doses of chemicals and microbes has been established that accounts for uncertainty quantitatively and indicates relative toxicity or virulence directly. Untestable doses include those corresponding to the 2.74 × 10 −7 illnesses per exposure expressed in goals of the U.S. Environmental Protection Agency's Surface Water Treatment Rule, and doses of human bioterror agents. For example, it is shown that relative Benchmark Dose (BMD) values depend upon the level of confidence assumed. Because of the lack of scientific basis for this level, BMDs are not comparable among health stressors for untestable doses and stressors. In this paper a new predictive Bayesian method is proposed for absolute and relative dose-response assessment based on available information. Information may include toxicological judgment, epidemiological statistics, genetic information, related data, and numeric dose-response data. Results for rotavirus indicate a "safe dose" of 6.3 × 10 −7 focus-forming units/exposure, approximately one-half log above the dose corresponding to the maximum risk for any pathogen assuming a 100% infection rate. The result further indicates the limited value of data in refining the assessment, due to the inability of data to reduce variability. The method is suggested for assessing risks of new and existing chemicals and pathogens, as a basis for prioritizing expenditures for protection against environmental and terrorist threats

Distributions of Autocorrelated First-Order Kinetic Outcomes: Illness Severity

<div><p>Many complex systems produce outcomes having recurring, power law-like distributions over wide ranges. However, the form necessarily breaks down at extremes, whereas the Weibull distribution has been demonstrated over the full observed range. Here the Weibull distribution is derived as the asymptotic distribution of generalized first-order kinetic processes, with convergence driven by autocorrelation, and entropy maximization subject to finite positive mean, of the incremental compounding rates. Process increments represent multiplicative causes. In particular, illness severities are modeled as such, occurring in proportion to products of, e.g., chronic toxicant fractions passed by organs along a pathway, or rates of interacting oncogenic mutations. The Weibull form is also argued theoretically and by simulation to be robust to the onset of saturation kinetics. The Weibull exponential parameter is shown to indicate the number and widths of the first-order compounding increments, the extent of rate autocorrelation, and the degree to which process increments are distributed exponential. In contrast with the Gaussian result in linear independent systems, the form is driven not by independence and multiplicity of process increments, but by increment autocorrelation and entropy. In some physical systems the form may be attracting, due to multiplicative evolution of outcome magnitudes towards extreme values potentially much larger and smaller than control mechanisms can contain. The Weibull distribution is demonstrated in preference to the lognormal and Pareto I for illness severities versus (a) toxicokinetic models, (b) biologically-based network models, (c) scholastic and psychological test score data for children with prenatal mercury exposure, and (d) time-to-tumor data of the ED₀₁ study.</p></div

Simulated (o) and fitted Weibull (––) and lognormal (—) distributions of illness severity.

<p>Severities were simulated as (a) the results of the multiplicative model of illness severities of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129042#pone.0129042.e029" target="_blank">Eq 12</a>, and (b) the results of the simplified multiplicative model of illness severities of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129042#pone.0129042.e030" target="_blank">Eq 13</a>. [Conditions: all fractions, <i>f</i>_<i>X</i>, distributed standard exponential, <i>N</i> = 100,000; subsequent fractions correlated by copula; MLE parameter fits]</p

Definition diagram for a generalized, physiologically-based, first-order model of a liver-mediated toxicological pathway, including a terminal series of three generalized pharmacodynamic steps.

<p>Definition diagram for a generalized, physiologically-based, first-order model of a liver-mediated toxicological pathway, including a terminal series of three generalized pharmacodynamic steps.</p