The effects of sludge burning on the PCDD/PCDF content of ashes from pulp and paper mill hog fuel boilers

The results of a study designed to quantify the levels of PCDD/Fs in boiler and control device ashes resulting from burning sludge of bleached mill origin, admixed with bark, are summarized.     

Statistical investigation of polychlorinated dibenzo-p-dioxins and dibenzofurans in the ambient air of Houston, Texas

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (dioxins and furans) are persistent, bioaccumulative and highly toxic chemical constituents that appear in the environment at very low and difficult to measure levels. Although dioxins and furans are widely recognized as toxic contaminants needing regulation, their temporal and spatial concentration profiles and the factors impacting their partitioning and congener distribution are only moderately understood. This paper presents the results from one of the most extensive ambient air dioxin and furan sampling efforts conducted in a geographic area to date. The data consist of monthly ambient concentrations of 17 of the most toxic congeners of dioxins and furans collected at five locations in Houston, Texas over a calendar year. The results showed a total annual mean dioxin and furan congener concentration of 1047 fg/m3 and an annual mean summation operatorI-TEQ concentration of 15 fg I-TEQ/m3 (16 fg WHO-TEQ/m3). These results, when compared with proximate data from the USEPA National Dioxin Ambient Monitoring Network (NDAMN), revealed significant (at the P0.05 level) differences between Houston and the NDAMN sites, suggesting the existence of local dioxin sources. A temporal analysis of the data showed that the congener concentration distribution varies over time, depending on the level of chlorination, type of congener, and temperature. The results also suggested that the fluctuations of congener concentrations and I-TEQ concentrations are closely correlated with minimum relative humidity, mean temperature, and mean NOx concentration.     

Lead reduces shell mass in juvenile garden snails (Helix aspersa)

In an earlier paper examining inherited tolerance to Pb, the shell growth of laboratory-bred offspring of Helix aspersa from contaminated sites was compared with that ofjuveniles from naive populations on dosed and undosed diets. Eight-week-old snails were fed either 500 microg g(-1) Pb or a control food in competitive trials between two populations. In the first series of trials, a parental history of exposure to Pb did not confer any advantage to either of two populations (BI and MI) competing with a na?ve population (LE). whether Pb was present in the diet or not. However, in the analysis of their metal concentrations reported here, LE are found to retain higher levels of Pb in the soft tissues than either BI or MI. Compared to their siblings on the unleaded diet, dosed LE and BI juveniles had lower soft tissue concentrations of Ca and Mg. Although the growth in shell height is unaffected by diet, LE and BI juveniles build lighter shells on the Pb-dosed diet, achieving around 75% of the shell mass of their controls. In contrast, the shell weights of dosed MI juveniles are depressed by only 15% and show no change in the essential metal concentrations of their soft tissues. A second experiment using five populations fed only the dosed food show that the shell weight/soft tissue weight ratios are comparable to the dosed snails of the previous experiment. Building a lighter shell thus appears to be the common response of all Helix populations to a high Pb diet, at least amongst juveniles. The reduction in its mass means that less Ca and Mg is added to the shell and, along with the lowered soft tissue concentrations observed in some populations. may be a consequence of an increased effort to excrete Pb. The possibility that the MI population shows a genotypic adaptation. perhaps as some form of modification of its Ca metabolism, is briefly discussed.     

Weak-wind mesoscale meandering in the nocturnal boundary layer

This study examines the strength and statistical behavior of mesoscale motions on time scales up to 1 h using eight data sets over different surface types. The mesoscale motions include internal gravity waves, microfront-like structures, horizontal modes, and a complex variety of other signatures, perhaps resulting from superposition of different modes. With weak large-scale flow, the mesoscale motions lead to meandering of the wind direction, as found in previous studies. However, the meandering often takes the form of sudden wind shifts rather than oscillation of wind direction. The relative strength and impact of such mesoscale motions are examined in terms of the constancy of the wind vector, the within-record standard deviation of the wind direction and the ratio of a meso-velocity scale to the speed of the large-scale flow. The strength of the mesoscale flow varies by an order of magnitude between nights at a given site and varies systematically between sites. The statistics of the vertical structure of such motions are examined for two of the data sets, both with sonic anemometers at seven levels.     

Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.