Gotowa bibliografia na temat „4-Dichlorophenoxyacetic Acid (2”

Uncertainty is pervasive in risk assessment. In ecotoxicological risk assessments, it arises from such sources as a lack of data, the simplification and abstraction of complex situations, and ambiguities in assessment endpoints (Burgman 2005; Suter 1993). When evaluating and managing risks, uncertainty needs to be explicitly considered in order to avoid erroneous decisions and to be able to make statements about the confidence that we can place in risk estimates. Although informative, previous approaches to dealing with uncertainty in ecotoxicological modelling have been found to be limited, inconsistent and often based on assumptions that may be false (Ferson & Ginzburg 1996; Suter 1998; Suter et al. 2002; van der Hoeven 2004; van Straalen 2002a; Verdonck et al. 2003a). In this thesis a Generalised Linear Modelling approach is proposed as an alternative, congruous framework for the analysis and prediction of a wide range of ecotoxicological effects. This approach was used to investigate the results of toxicity experiments on the effect of 2,4-Dichlorophenoxyacetic Acid (2,4-D) formulations and 4-Chlorophenol (4-CP, an associated breakdown product) on Daphnia carinata. Differences between frequentist Maximum Likelihood (ML) and Bayesian Markov-Chain Monte-Carlo (MCMC) approaches to statistical reasoning and model estimation were also investigated. These approaches are inferentially disparate and place different emphasis on aleatory and epistemic uncertainty (O'Hagan 2004). Bayesian MCMC and Probability Bounds Analysis methods for propagating uncertainty in risk models are also compared for the first time. For simple models, Bayesian and frequentist approaches to Generalised Linear Model (GLM) estimation were found to produce very similar results when non-informative prior distributions were used for the Bayesian models. Potency estimates and regression parameters were found to be similar for identical models, signifying that Bayesian MCMC techniques are at least a suitable and objective replacement for frequentist ML for the analysis of exposureresponse data. Applications of these techniques demonstrated that Amicide formulations of 2,4-D are more toxic to Daphnia than their unformulated, Technical Acid parent. Different results were obtained from Bayesian MCMC and ML methods when more complex models and data structures were considered. In the analysis of 4-CP toxicity, the treatment of 2 different factors as fixed or random in standard and Mixed-Effect models was found to affect variance estimates to the degree that different conclusions would be drawn from the same model, fit to the same data. Associated discrepancies in the treatment of overdispersion between ML and Bayesian MCMC analyses were also found to affect results. Bayesian MCMC techniques were found to be superior to the ML ones employed for the analysis of complex models because they enabled the correct formulation of hierarchical (nested) datastructures within a binomial logistic GLM. Application of these techniques to the analysis of results from 4-CP toxicity testing on two strains of Daphnia carinata found that between-experiment variability was greater than that within-experiments or between-strains. Perhaps surprisingly, this indicated that long-term laboratory culture had not significantly affected the sensitivity of one strain when compared to cultures of another strain that had recently been established from field populations. The results from this analysis highlighted the need for repetition of experiments, proper model formulation in complex analyses and careful consideration of the effects of pooling data on characterising variability and uncertainty. The GLM framework was used to develop three dimensional surface models of the effects of different length pulse exposures, and subsequent delayed toxicity, of 4-CP on Daphnia. These models described the relationship between exposure duration and intensity (concentration) on toxicity, and were constructed for both pulse and delayed effects. Statistical analysis of these models found that significant delayed effects occurred following the full range of pulse exposure durations, and that both exposure duration and intensity interacted significantly and concurrently with the delayed effect. These results indicated that failure to consider delayed toxicity could lead to significant underestimation of the effects of pulse exposure, and therefore increase uncertainty in risk assessments. A number of new approaches to modelling ecotoxicological risk and to propagating uncertainty were also developed and applied in this thesis. In the first of these, a method for describing and propagating uncertainty in conventional Species Sensitivity Distribution (SSD) models was described. This utilised Probability Bounds Analysis to construct a nonparametric 'probability box' on an SSD based on EC05 estimates and their confidence intervals. Predictions from this uncertain SSD and the confidence interval extrapolation methods described by Aldenberg and colleagues (2000; 2002a) were compared. It was found that the extrapolation techniques underestimated the width of uncertainty (confidence) intervals by 63% and the upper bound by 65%, when compared to the Probability Bounds (P3 Bounds) approach, which was based on actual confidence estimates derived from the original data. An alternative approach to formulating ecotoxicological risk modelling was also proposed and was based on a Binomial GLM. In this formulation, the model is first fit to the available data in order to derive mean and uncertainty estimates for the parameters. This 'uncertain' GLM model is then used to predict the risk of effect from possible or observed exposure distributions. This risk is described as a whole distribution, with a central tendency and uncertainty bounds derived from the original data and the exposure distribution (if this is also 'uncertain'). Bayesian and P-Bounds approaches to propagating uncertainty in this model were compared using an example of the risk of exposure to a hypothetical (uncertain) distribution of 4-CP for the two Daphnia strains studied. This comparison found that the Bayesian and P-Bounds approaches produced very similar mean and uncertainty estimates, with the P-bounds intervals always being wider than the Bayesian ones. This difference is due to the different methods for dealing with dependencies between model parameters by the two approaches, and is confirmation that the P-bounds approach is better suited to situations where data and knowledge are scarce. The advantages of the Bayesian risk assessment and uncertainty propagation method developed are that it allows calculation of the likelihood of any effect occurring, not just the (probability)bounds, and that the same software (WinBugs) and model construction may be used to fit regression models and predict risks simultaneously. The GLM risk modelling approaches developed here are able to explain a wide range of response shapes (including hormesis) and underlying (non-normal) distributions, and do not involve expression of the exposure-response as a probability distribution, hence solving a number of problems found with previous formulations of ecotoxicological risk. The approaches developed can also be easily extended to describe communities, include modifying factors, mixed-effects, population growth, carrying capacity and a range of other variables of interest in ecotoxicological risk assessments. While the lack of data on the toxicological effects of chemicals is the most significant source of uncertainty in ecotoxicological risk assessments today, methods such as those described here can assist by quantifying that uncertainty so that it can be communicated to stakeholders and decision makers. As new information becomes available, these techniques can be used to develop more complex models that will help to bridge the gap between the bioassay and the ecosystem.

In recent years there has been a great deal of scientific interest in processes that affect the fate of organic chemicals in the environment. one main reason for this increased interest is due to greater environmental concern over accidental or purposeful release of these chemicals into the environment by man. A major environmental concern is the increased use of pesticides over the last few years. In the thirty years prior to 1978 the use of pesticides has increased by a factor of forty (Ridgeway et al., 1978). Recently the use of herbicides has been increasing, but that of insecticides has stabilized (Willis, 1983). Detectable amounts of organic pesticides can be found in many areas of the biosphere. For toxic organic chemicals to be used safely, researchers must have a clear understanding of the fate and persistence of these chemicals when they are released into the environment. This understanding will also allow the development of new products that, when properly used, will not produce adverse effects to man or the environment (Weber, 1972). According to the Toxic Substance Control Act (TSCA) any new or expanded-use chemical that might be released into the environment must be tested for environmental hazard.

The teratogenic potential of commercial formulations of atrazine (40.8%) and 2,4-D was evaluated using FETAX (frog embryo teratogenic assay--Xenopus). Because these herbicides have been detected in ground and surface water, this study was designed to determine the adverse effects in buffer and natural water for both herbicides. All treatments showed a significant concentration-response effect on exposed embryos, except for the 2,4-D natural water sample. Atrazine (solubility of the commercial formula used 70 mg/L at 20 degrees C), compared to 2,4-D (solubility = 311 mg/L at pH = 1 and 25 degrees C), had a significantly greater teratogenic effect in both the buffer (atrazine EC50 = 33 mg/L, LC50 = 100 mg/L, TI = 3.03; 2,4-D EC50 = 245 mg/L, LC50 = 254 mg/L, TI = 1.04) and natural water samples (atrazine EC50 < 8 mg/L, LC50 = 126 mg/L; 2,4-D EC50 and LC50 > 270 mg/L). The 2,4-D EC50 and LC50 values for the buffer were similar at 245 mg/L and 254 mg/L. These similar values and the teratogenic index (TI) of 1.04 suggested that 2,4-D was more embryotoxic than teratogenic to frog embryos at high concentrations. Atrazine in natural water demonstrated a significantly greater EC50 (100% abnormality at 8 mg/L, the lowest test concentration) to frog embryos than the buffer experiment (EC50 = 33 mg/L). The extrapolated lowest observable adverse effect concentration (LOAEC) for the natural water experiment was 1.1 mg/L. These results suggest that atrazine toxicity is enhanced by the synergistic or additive effects of some component of the water or atrazine was already present in the sample. In contrast to atrazine, 2,4-D was less toxic in natural water than buffer. These results suggest that both atrazine and 2,4-D pose little threat, since their embryotoxicity and teratogenicity to frog embryos occur at high concentrations approaching their maximum solubility levels in water.

Photocatalyst such as Titanium Dioxide (TiO2) has been recently introduced as a nanoparticle into cementitious permeable pavements. Combining photocatalytic compounds within concrete permeable pavements can aid with depollution of several contaminants found in urban water streams and air impurities. This paper presents research carried out at the University of Greenwich, UK using photocatalytic concrete with varying percentages of TiO2 (0 %, 1% and 5%) to assess the levels depollution which can be achieved. Concrete samples were testing against the degradation of 2, 4-Dichlorophenoxyacetic Acid, a harmful chemical found in herbicides. This advanced oxidation process can aid in the reduction of urban pollution from an air and water perspective, improving sustainability for urban cities. Self-cleaning benefits of photocatalytic concrete permeable pavements can be used to keep urban infrastructure cleaner and more aesthetically pleasing. Experimental tests were carried out on the characterisation of inorganics through X-Ray diffraction and Fourier Transform Infrared Spectroscopy to ensure that the structures of the concrete samples were not altered by addition of the nanoparticles (photocatalyst). Experimental results were compared to that found in previous literature and confirmed that the addition of 5% TiO2 did not affect the structure of the concrete samples and can be a viable option used in urban infrastructure such as permeable pavements.