Modeling storm events to investigate the influence of the stream¿catchment interface zone on stream biogeochemistry

We formulate a new mixing model to explore hydrological and chemical conditions under which the interface between the stream and catchment interface (SCI) influences the release of reactive solutes into stream water during storms. Physically, the SCI corresponds to the hyporheic/riparian sediments. In the new model this interface is coupled through a bidirectional water exchange to the conventional two components mixing model. Simulations show that the influence of the SCI on stream solute dynamics during storms is detectable when the runoff event is dominated by the infiltrated groundwater component that flows through the SCI before entering the stream and when the flux of solutes released from SCI sediments is similar to, or higher than, the solute flux carried by the groundwater. Dissolved organic carbon (DOC) and nitrate data from two small Mediterranean streams obtained during storms are compared to results from simulations using the new model to discern the circumstances under which the SCI is likely to control the dynamics of reactive solutes in streams. The simulations and the comparisons with empirical data suggest that the new mixing model may be especially appropriate for streams in which the periodic, or persistent, abrupt changes in the level of riparian groundwater exert hydrologic control on flux of biologically reactive fluxes between the riparian/hyporheic compartment and the stream water.

Using toxicokinetic modeling approach to determine biological reference values (BRVs) and to assess human exposure to pesticides

Exposure to various pesticides has been characterized in workers and the general population, but interpretation and assessment of biomonitoring data from a health risk perspective remains an issue. For workers, a Biological Exposure Index (BEI®) has been proposed for some substances, but most BEIs are based on urinary biomarker concentrations at Threshold Limit Value - Time Weighted Average (TLV-TWA) airborne exposure while occupational exposure can potentially occurs through multiple routes, particularly by skin contact (i.e.captan, chlorpyrifos, malathion). Similarly, several biomonitoring studies have been conducted to assess environmental exposure to pesticides in different populations, but dose estimates or health risks related to these environmental exposures (mainly through the diet), were rarely characterized. Recently, biological reference values (BRVs) in the form of urinary pesticide metabolites have been proposed for both occupationally exposed workers and children. These BRVs were established using toxicokinetic models developed for each substance, and correspond to safe levels of absorption in humans, regardless of the exposure scenario. The purpose of this chapter is to present a review of a toxicokinetic modeling approach used to determine biological reference values. These are then used to facilitate health risk assessments and decision-making on occupational and environmental pesticide exposures. Such models have the ability to link absorbed dose of the parent compound to exposure biomarkers and critical biological effects. To obtain the safest BRVs for the studied population, simulations of exposure scenarios were performed using a conservative reference dose such as a no-observed-effect level (NOEL). The various examples discussed in this chapter show the importance of knowledge on urine collections (i.e. spot samples and complete 8-h, 12-h or 24-h collections), sampling strategies, metabolism, relative proportions of the different metabolites in urine, absorption fraction, route of exposure and background contribution of prior exposures. They also show that relying on urinary measurements of specific metabolites appears more accurate when applying this approach to the case of occupational exposures. Conversely, relying on semi-specific metabolites (metabolites common to a category of pesticides) appears more accurate for the health risk assessment of environmental exposures given that the precise pesticides to which subjects are exposed are often unknown. In conclusion, the modeling approach to define BRVs for the relevant pesticides may be useful for public health authorities for managing issues related to health risks resulting from environmental and occupational exposures to pesticides.

Variation in thermal performance and reaction norms among populations of Drosophila melanogaster.

The major goal of evolutionary thermal biology is to understand how variation in temperature shapes phenotypic evolution. Comparing thermal reaction norms among populations from different thermal environments allows us to gain insights into the evolutionary mechanisms underlying thermal adaptation. Here, we have examined thermal adaptation in six wild populations of the fruit fly (Drosophila melanogaster) from markedly different natural environments by analyzing thermal reaction norms for fecundity, thorax length, wing area, and ovariole number under ecologically realistic fluctuating temperature regimes in the laboratory. Contrary to expectation, we found only minor differences in the thermal optima for fecundity among populations. Differentiation among populations was mainly due to differences in absolute (and partly also relative) thermal fecundity performance. Despite significant variation among populations in the absolute values of morphological traits, we observed only minor differentiation in their reaction norms. Overall, the thermal reaction norms for all traits examined were remarkably similar among different populations. Our results therefore suggest that thermal adaptation in D. melanogaster predominantly involves evolutionary changes in absolute trait values rather than in aspects of thermal reaction norms.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Determination of the critical period of weed control in corn using a thermal basis

Field studies were conducted over 3 years in southeast Buenos Aires, Argentina, to determine the critical period of weed control in maize (Zea mays L.). The treatments consisted of two different periods of weed interference, a critical weed-free period, and a critical time of weed removal. The Gompertz and logistic equations were fitted to relative yields representing the critical weed-free and the critical time of weed removal, respectively. Accumulated thermal units were used to describe each period of weed-free or weed removal. The critical weed-free period and the critical time of weed removal ranged from 222 to 416 and 128 to 261 accumulated thermal units respectively, to prevent yield losses of 2.5%. Weed biomass proved to be inverse to the crop yield for all the years studied. When weeds competed with the crop from emergence, a large increase in weed biomass was achieved 10 days after crop emergence. However, few weed seedlings emerged and prospered after the 5-6 leaf maize stage (10-20 days after emergence).

Brain lactate kinetics: Modeling evidence for neuronal lactate uptake upon activation.

A critical issue in brain energy metabolism is whether lactate produced within the brain by astrocytes is taken up and metabolized by neurons upon activation. Although there is ample evidence that neurons can efficiently use lactate as an energy substrate, at least in vitro, few experimental data exist to indicate that it is indeed the case in vivo. To address this question, we used a modeling approach to determine which mechanisms are necessary to explain typical brain lactate kinetics observed upon activation. On the basis of a previously validated model that takes into account the compartmentalization of energy metabolism, we developed a mathematical model of brain lactate kinetics, which was applied to published data describing the changes in extracellular lactate levels upon activation. Results show that the initial dip in the extracellular lactate concentration observed at the onset of stimulation can only be satisfactorily explained by a rapid uptake within an intraparenchymal cellular compartment. In contrast, neither blood flow increase, nor extracellular pH variation can be major causes of the lactate initial dip, whereas tissue lactate diffusion only tends to reduce its amplitude. The kinetic properties of monocarboxylate transporter isoforms strongly suggest that neurons represent the most likely compartment for activation-induced lactate uptake and that neuronal lactate utilization occurring early after activation onset is responsible for the initial dip in brain lactate levels observed in both animals and humans.

Geochemistry of the thermal springs and fumaroles of Basse-Terre Island, Guadeloupe, Lesser Antilles

The purpose of this work was to study jointly the volcanic-hydrothermal system of the high-risk volcano La Soufriere, in the southern part of Basse-Terre, and the geothermal area of Bouillante, on its western coast, to derive an all-embracing and coherent conceptual geochemical model that provides the necessary basis for adequate volcanic surveillance and further geothermal exploration. The active andesitic dome of La Soufriere has erupted eight times since 1660, most recently in 1976-1977. All these historic eruptions have been phreatic. High-salinity, Na-CI geothermal liquids circulate in the Bouillante geothermal reservoir, at temperatures close to 250 degrees C. These Na-CI solutions rise toward the surface, undergo boiling and mixing with groundwater and/or seawater, and feed most Na-CI thermal springs in the central Bouillante area. The Na-Cl thermal springs are surrounded by Na-HCO3 thermal springs and by the Na-Cl thermal spring of Anse a la Barque (a groundwater slightly mixed with seawater), which are all heated through conductive transfer. The two main fumarolic fields of La Soufriere area discharge vapors formed through boiling of hydrothermal aqueous solutions at temperatures of 190-215 degrees C below the ``Ty'' fault area and close to 260 degrees C below the dome summit. The boiling liquid producing the vapors of the Ty fault area has SD and delta(18)O values relatively similar to those of the Na-CI liquids of the Bouillante geothermal reservoir, whereas the liquid originating the vapors of the summit fumaroles is strongly enriched in O-18, due to input of magmatic fluids from below. This process is also responsible for the paucity of CH;I in the fumaroles. The thermal features around La Soufriere dome include: (a) Ca-SO4 springs, produced through absorption of hydrothermal vapors in shallow groundwaters; (b) conductively heated, Ca-Na-HCO3 springs; and (c) two Ca-Na-Cl springs produced through mixing of shallow Ca-SO4 waters and deep Na-Cl hydrothermal liquids. The geographical distribution of the different thermal features of La Soufriere area indicates the presence of: (a) a central zone dominated by the ascent of steam, which either discharges at the surface in the fumarolic fields or is absorbed in shallow groundwaters; and (b) an outer zone, where the shallow groundwaters are heated through conduction or addition of Na-Cl liquids coming from hydrothermal aquifer(s).

Dewaterability of sewage sludge by ultrasonic, thermal and chemical treatments

Sludges resulting from wastewater treatment processes have a characteristically high water content, which complicates handling operations such as pumping, transport and disposal. To enhance the dewatering of secondary sludge, the effect of ultrasound waves, thermal treatment and chemical conditioning with NaOH have been studied. Two features of treated sludges were examined: their rheological behavior and their dewaterability. The rheological tests consisted of recording shear stress when the shear rate increases and decreases continuously and linearly with time, and when it increases and decreases in steps. Steady-state viscosity and thixotropy were obtained from the rheological tests, and both decreased significantly in all cases with increased treatment intensity. Centrifugation of ultrasonicated and thermally treated sludges allowed the total solid content to be increased by approximately 16.2% and 17.6%, respectively. These dewatered sludges had a lower viscosity and thixotropy than the untreated sludge. In contrast, alkali conditioning barely allowed the sludge to be dewatered by centrifugation, despite decreasing its viscosity and thixotropy.

Dosage optimization of treatments using population pharmacokinetic modeling and simulation.

Pharmacokinetic variability in drug levels represent for some drugs a major determinant of treatment success, since sub-therapeutic concentrations might lead to toxic reactions, treatment discontinuation or inefficacy. This is true for most antiretroviral drugs, which exhibit high inter-patient variability in their pharmacokinetics that has been partially explained by some genetic and non-genetic factors. The population pharmacokinetic approach represents a very useful tool for the description of the dose-concentration relationship, the quantification of variability in the target population of patients and the identification of influencing factors. It can thus be used to make predictions and dosage adjustment optimization based on Bayesian therapeutic drug monitoring (TDM). This approach has been used to characterize the pharmacokinetics of nevirapine (NVP) in 137 HIV-positive patients followed within the frame of a TDM program. Among tested covariates, body weight, co-administration of a cytochrome (CYP) 3A4 inducer or boosted atazanavir as well as elevated aspartate transaminases showed an effect on NVP elimination. In addition, genetic polymorphism in the CYP2B6 was associated with reduced NVP clearance. Altogether, these factors could explain 26% in NVP variability. Model-based simulations were used to compare the adequacy of different dosage regimens in relation to the therapeutic target associated with treatment efficacy. In conclusion, the population approach is very useful to characterize the pharmacokinetic profile of drugs in a population of interest. The quantification and the identification of the sources of variability is a rational approach to making optimal dosage decision for certain drugs administered chronically.