Contributions to predicting contaminant leaching from secondary materials used in roads

Slags, coal ashes, and other secondary materials can be used in road construction. Both traditional and secondary materials used in roads may contain contaminants that may leach and pollute the groundwater. The goal of this research was to further the understanding of leaching and transport of contaminants from pavement materials. Towards this goal, a new probabilistic framework was introduced which provided a structured guidance for selecting the appropriate model, incorporating uncertainty, variability, and expert opinion, and interpreting results for decision making. In addition to the framework, specific contributions were made in pavement and embankment hydrology and reactive transport, Bayesian statistics, and aqueous geochemistry of leaching. Contributions on water movement and reactive transport in highways included probabilistic prediction of leaching in an embankment, and scenario analyses of leaching and transport in pavements using HYDRUS2D, a contaminant fate and transport model. Water flow in a Minnesota highway embankment was replicated by Bayesian calibration of hydrological parameters against water content data. Extent of leaching of Cd from a coal fly ash was estimated. Two dimensional simulations of various scenarios showed that salts in the base layer of pavements are depleted within the first year whereas the metals may never reach the groundwater if the pavement is built on adsorbing soils. Aqueous concentrations immediately above the groundwater estimated for intact and damaged pavements can be used for regulators to determine the acceptability of various recycled materials. Contributions in the aqueous geochemistry of leaching included a new modeling approach for leaching of anions and cations from complex matrices such as weathered steel slag. The novelty of the method was its simultaneous inclusion of sorption and solubility controls for multiple analytes. The developed model showed that leaching of SO4, Cr, As, Si, Ca, Mg, and V were controlled by corresponding soluble solids. Leaching of Pb was controlled by Pb(VO4)3 solubility at low pHs and by surface precipitation reactions at high pHs. Leaching of Cd and Zn were controlled by surface complexation and surface precipitation, respectively

Probabilistic modeling of one dimensional water movement and leaching from highway embankments containing secondary materials

Predictive methods for contaminant release from virgin and secondary road construction materials are important for evaluating potential long-term soil and groundwater contamination from highways. The objective of this research was to describe the field hydrology in a highway embankment and to investigate leaching under unsaturated conditions by use of a contaminant fate and transport model. The HYDRUS2D code was used to solve the Richards equation and the advection–dispersion equation with retardation. Water flow in a Minnesota highway embankment was successfully modeled in one dimension for several rain events after Bayesian calibration of the hydraulic parameters against water content data at a point 0.32 m from the surface of the embankment. The hypothetical leaching of Cadmium from coal fly ash was probabilistically simulated in a scenario where the top 0.50 m of the embankment was replaced by coal fly ash. Simulation results were compared to the percolation equation method where the solubility is multiplied by the liquid-to-solid ratio to estimate total release. If a low solubility value is used for Cadmium, the release estimates obtained using the percolation/equilibrium model are close to those predicted from HYDRUS2D simulations (10–4–10–2 mg Cd/kg ash). If high solubility is used, the percolation equation over predicts the actual release (0.1–1.0 mg Cd/kg ash). At the 90th percentile of uncertainty, the 10-year liquid-to-solid ratio for the coal fly ash embankment was 9.48 L/kg, and the fraction of precipitation that infiltrated the coal fly ash embankment was 92%. Probabilistic modeling with HYDRUS2D appears to be a promising realistic approach to predicting field hydrology and subsequent leaching in embankments

Vehicle detection and tracking using homography-based plane rectification and particle filtering

This paper presents a full system for vehicle detection and tracking in non-stationary settings based on computer vision. The method proposed for vehicle detection exploits the geometrical relations between the elements in the scene so that moving objects (i.e., vehicles) can be detected by analyzing motion parallax. Namely, the homography of the road plane between successive images is computed. Most remarkably, a novel probabilistic framework based on Kalman filtering is presented for reliable and accurate homography estimation. The estimated homography is used for image alignment, which in turn allows to detect the moving vehicles in the image. Tracking of vehicles is performed on the basis of a multidimensional particle filter, which also manages the exit and entries of objects. The filter involves a mixture likelihood model that allows a better adaptation of the particles to the observed measurements. The system is specially designed for highway environments, where it has been proven to yield excellent results

A new way of linking information theory with cognitive science

The relationship between the notion of *information* in information theory, and the notion of *information processing* in cognitive science, has long been controversial. But as the present paper shows, part of the disagreement arises from conflating different formulations of measurement. Clarifying distinctions reveals it is the context-free nature of Shannon's information average that is particular problematic from the cognitive point of view. Context-sensitive evaluation is then shown to be a way of addressing the problems that arise

Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data

A hierarchical framework for incorporating modes of climate variability into stochastic simulations of hydrological data is developed, termed the climate-informed multi-time scale stochastic (CIMSS) framework. A case study on two catchments in eastern Australia illustrates this framework. To develop an identifiable model characterizing long-term variability for the first level of the hierarchy, paleoclimate proxies, and instrumental indices describing the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO) are analyzed. A new paleo IPO-PDO time series dating back 440 yr is produced, combining seven IPO-PDO paleo sources using an objective smoothing procedure to fit low-pass filters to individual records. The paleo data analysis indicates that wet/dry IPO-PDO states have a broad range of run lengths, with 90% between 3 and 33 yr and a mean of 15 yr. The Markov chain model, previously used to simulate oscillating wet/dry climate states, is found to underestimate the probability of wet/dry periods >5 yr, and is rejected in favor of a gamma distribution for simulating the run lengths of the wet/dry IPO-PDO states. For the second level of the hierarchy, a seasonal rainfall model is conditioned on the simulated IPO-PDO state. The model is able to replicate observed statistics such as seasonal and multiyear accumulated rainfall distributions and interannual autocorrelations. Mean seasonal rainfall in the IPO-PDO dry states is found to be 15%-28% lower than the wet state at the case study sites. In comparison, an annual lag-one autoregressive model is unable to adequately capture the observed rainfall distribution within separate IPO-PDO states. Copyright © 2011 by the American Geophysical Union.Benjamin J. Henley, Mark A. Thyer, George Kuczera and Stewart W. Frank