Triolein embedded cellulose acetate membrane (TECAM) was used for passive sampling of the fraction of naphthalene, phenanthrene, pyrene and benzo[a]pyrene in 18 field-contaminated soils. The sampling process of PAHs by TECAM fitted well with a first-order kinetics model and PAHs reached 95% of equilibrium in TECAM within 20 h. Concentrations of PAHs in TECAM (CTECAM) correlated well with the concentrations in soils (r2 = 0.693-0.962, p < 0.001). Furthermore, concentrations of PAHs determined in the soil solution were very close to the values estimated by CTECAM and the partition coefficient between TECAM and water (KTECAM-w). After lipid normalization nearly 1:1 relationships were observed between PAH concentrations in TECAMs and earthworms exposed to the soils (r2 = 0.591–0.824, n = 18, p < 0.01). These results suggest that TECAM can be a useful tool to predict bioavailability of PAHs in field-contaminated soils.

Soils of allotments are often contaminated by heavy metals and persistent organic pollutants. In particular, lead (Pb) and polycyclic aromatic hydrocarbons (PAHs) frequently exceed legal intervention values (IVs). Allotments are popular in European countries; cities may own and let several thousand allotment plots. Assessing soil contamination for all the plots would be very costly. Soil contamination in allotments is often linked to gardening practice and historic land use. Hence, we predict the risk of IV exceedance from attributes that characterize the history and management of allotment areas (age, nearby presence of pollutant sources, prior land use). Robust logistic regression analyses of data of Swiss allotments demonstrate that the risk of IV exceedance can be predicted quite precisely without costly soil analyses. Thus, the new method allows screening many allotments at small costs, and it helps to deploy the resources available for soil contamination surveying more efficiently. The contamination of allotment soils, expressed as frequency of intervention value exceedance, depends on the age and further attributes of the allotments and can be predicted by logistic regression.

This work summarizes the results of a study of atmospheric wet and dry deposition fluxes of Deisopropyl-atrazine (DEA), Desethyl-atrazine (DET), Atrazine, Terbuthylazine, Alachlor, Metolachlor, Diflufenican, Fenoxaprop-p-ethyl, Iprodione, Isoproturon and Cymoxanil pesticides conducted in Strasbourg, France, from August 2000 through August 2001. The primary objective of this work was to calculate the total atmospheric pesticide deposition fluxes induced by atmospheric particles. To do this, a modified one-dimensional cloud water deposition model was used. All precipitation and deposition samples were collected at an urban forested park environment setting away from any direct point pesticide sources. The obtained deposition fluxes induced by atmospheric particles over a forested area showed that the dry deposition flux strongly contributes to the total deposition flux. The dry particle deposition fluxes are shown to contribute from 4% (DET) to 60% (cymoxanil) to the total deposition flux (wet + dry). A modified one-dimensional cloud water deposition model is used to estimate the deposition fluxes of pesticides in the particle phase and compare the relative importance of dry and wet depositions.

There are strong drivers to increasingly adopt bioremediation as an effective technique for risk reduction of hydrocarbon impacted soils. Researchers often rely solely on chemical data to assess bioremediation efficiently, without making use of the numerous biological techniques for assessing microbial performance. Where used, laboratory experiments must be effectively extrapolated to the field scale. The aim of this research was to test laboratory derived data and move to the field scale. In this research, the remediation of over thirty hydrocarbon sites was studied in the laboratory using a range of analytical techniques. At elevated concentrations, the rate of degradation was best described by respiration and the total hydrocarbon concentration in soil. The number of bacterial degraders and heterotrophs as well as quantification of the bioavailable fraction allowed an estimation of how bioremediation would progress. The response of microbial biosensors proved a useful predictor of bioremediation in the absence of other microbial data. Field-scale trials on average took three times as long to reach the same endpoint as the laboratory trial. It is essential that practitioners justify the nature and frequency of sampling when managing remediation projects and estimations can be made using laboratory derived data. The value of bioremediation will be realised when those that practice the technology can offer transparent lines of evidence to explain their decisions. Detailed biological, chemical and physical characterisation reduces uncertainty in predicting bioremediation.

A three-year study was conducted to examine mercury in sport fish from the Sacramento-San Joaquin Delta. More than 4000 fish from 31 species were collected and analyzed for total mercury in individual muscle filets. Largemouth bass and striped bass were the most contaminated, averaging 0.40 μg/g, while redear sunfish, bluegill and rainbow trout exhibited the lowest (<0.15 μg/g) concentrations. Spatial variation in mercury was evaluated with an analysis of covariance model, which accounted for variability due to fish size and regional hydrology. Significant regional differences in mercury were apparent in size-standardized largemouth bass, with concentrations on the Cosumnes and Mokelumne rivers significantly higher than the central and western Delta. Significant prey-predator mercury correlations were also apparent, which may explain a significant proportion of the spatial variation in the watershed. Regional differences in sport fish mercury were found in the Sacramento-San Joaquin Delta.

Higher-tier environmental risk assessments on “down-the-drain” chemicals in river networks can be conducted using models such as GREAT-ER (Geography-referenced Regional Exposure Assessment Tool for European Rivers). It is important these models are evaluated and their sensitivities to input variables understood. This study had two primary objectives: evaluate GREAT-ER model performance, comparing simulated modelled predictions for LAS (linear alkylbenzene sulphonate) with measured concentrations, for four rivers in the UK, and investigate model sensitivity to input variables. We demonstrate that the GREAT-ER model is very sensitive to variability in river discharges. However it is insensitive to the form of distributions used to describe chemical usage and removal rate in sewage treatment plants (STPs). It is concluded that more effort should be directed towards improving empirical estimates of effluent load and reducing uncertainty associated with usage and removal rates in STPs. Simulations could be improved by incorporating the effect of river depth on dissipation rates.

The goal of this work was to study quantitatively lead bioaccumulation from a lead-doped nutrient medium by using a living aquatic macrophytes Pistia stratiotes. Several sets of aquatic plants with approximately 30 g weight were grown in greenhouse conditions and in hydroponic solutions supplied with a non-toxic Pb²⁺ concentration. The synchrotron radiation total X-ray fluorescence spectrometry was used to determine the metal concentrations in dry plants and hydroponic media as a function of time. Four different non-structural bioaccumulation models were applied to describe the process dynamics and to estimate the accumulated lead maximum capacity and rate constants. According to the experimental data, both biosorption and bioaccumulation mechanisms can be considered. Due to the low desorption rate constant, the experimental data were well described by the irreversible kinetic model. The results concerning modeling of living macrophytes' metal bioaccumulation kinetics can be used to predict the heavy metal removal dynamics from wastewaters in artificial wetlands.

The adsorption of two taste- and odor-causing compounds, namely MIB (2-methyl isoborneol—C₁₁H₂₀O) and geosmin (C₁₂H₂₂O) on activated carbon was investigated in this study. The impact of adsorbent pore size distribution on adsorption of MIB and geosmin was evaluated through single solute and multicomponent adsorption of these compounds on three types of activated carbon fibers (ACFs) and one granular activated carbon (GAC). The ACFs (ACC-15, ACC-20, and ACC-25) with different degrees of activation had narrow pore size distributions and specific critical pore diameters whereas the GAC (F-400) had a wider pore size distribution and lesser microporosity. The effect of the presence of natural organic matter (NOM) on MIB and geosmin adsorption was also studied for both the single solute and binary systems. The Myers equation was used to evaluate the single solute isotherms as it converges to Henry's law at low coverage and also serves as an input for predicting multicomponent adsorption. The single solute adsorption isotherms fit the Myers equation well and pore size distribution significantly influenced adsorption on the ACFs and GAC. The ideal adsorbed solute theory (IAST), which is a well-established thermodynamic model for multicomponent adsorption, was used to predict the binary adsorption of MIB and geosmin. The IAST predicted well the binary adsorption on the ACFs and GAC. Binary adsorption isotherms were also conducted in the presence of oxygen (oxic) and absence of oxygen (anoxic). There were no significant differences in the binary isotherm between the oxic and anoxic conditions, indicating that adsorption was purely through physical adsorption and no oligomerization was taking place. Binary adsorptions for the four adsorbents were also conducted in the presence of humic acid to determine the effect of NOM and to compare with IAST predictions. The presence of NOM interestingly resulted in deviation from IAST behavior in case of two adsorbents, ACC-15 and F-400.

Long-term flights or the establishment of permanent bases in space provide serious challenges for life support systems. Plants are essential companion life forms for such space missions, where human habitats must mimic the cycles of life on earth to generate and recycle food, oxygen and water. Nowadays, the chemical-mechanical recycling systems used in the international space station are much more compact, less labour intensive and more reliable than plant-based systems, but these systems would be too expensive for the long-term human exploration. In order to improve living conditions for humans and plants, we need an accurate characterisation of the mass transfer phenomena related to condensation of humid air. We are interested in developing an experimental protocol, which would help us to establish a theoretical model describing the heterogeneous transfers along a wall or a plant in an air-conditioned environment. Initially, we started in dry conditions by measuring the velocity profiles within the boundary layer that develop on a horizontal or a vertical flat plate in a wind tunnel. The velocity ranged from 0.5 to 2.5 m s⁻¹. Existing coupled heat and mass transfer measurement results relevant to our applications are discussed.

The problem of air pollution is a frequently recurring situation and its management has social and economic considerable effects. Given the interaction of the numerous factors involved in the raising of the atmospheric pollution rates, it should be considered that the relation between the intensity of emission produced by the polluting source and the resulting pollution is not immediate. The aim of this study was to realise and to compare two support decision system (neural networks and multivariate regression model) that, correlating the air quality data with the meteorological information, are able to predict the critical pollution events. The development of a back-propagation neural network is presented to predict the daily PM₁₀ concentration 1, 2 and 3 days early. The measurements obtained by the territorial monitoring stations are one of the primary data sources; the forecasting of the major weather parameters available on the website and the forecasting of the Saharan dust obtained by the “Centro Nacional de Supercomputaciòn” website, satellite images and back trajectories analysis are used for the weather input data. The results obtained with the neural network were compared with those obtained by a multivariate linear regression model for 1 and 2 days forecasting. The relative root mean square error for both methods shows that the artificial neural networks (ANN) gives more accurate results than the multivariate linear regression model mostly for 1 day forecasting; moreover, the regression model used, in spite of ANN, failed when it had to fit spiked high values of PM₁₀ concentration.