To assess transport and ecotoxicological risks of metals, such as cadmium (Cd) in soils, models are needed for partitioning and speciation. We derived regression-based “partition-relations” based on adsorption and desorption experiments for main Australian soil types. First, batch adsorption experiments were carried out over a realistic range of dissolved Cd concentrations in agricultural soils in Australia. Results showed linear sorption relationships, implying the adequacy of using Kd values to describe partitioning. Desorption measurements were then carried out to assess in-situ Kd values and relate these to soil properties The best transfer functions for solid–solution partitioning were found for Kd values relating total dissolved Cd concentration to total soil Cd concentrations, accounting for the variation in pH, SOM contents and DOC concentrations. Model predictions compared well with measurements of an independent data set, but there was a tendency to underestimate dissolved Cd concentrations of highly polluted soils.

The influence of pH (6.0–9.0), natural organic matter (NOM) (0–10 mg C/L) and ionic strength (IS) (1.7–40 mM) on 14 nm CeO₂ NP aggregation and ecotoxicity towards the alga Pseudokirchneriella subcapitata was assessed following a central composite design. Mean NP aggregate sizes ranged between 200 and 10000 nm. Increasing pH and IS enhanced aggregation, while increasing NOM decreased mean aggregate sizes. The 48 h-EᵣC20s ranged between 4.7 and 395.8 mg CeO₂/L. An equation for predicting the 48 h-EᵣC20 (48 h-EᵣC20 = −1626.4 × (pH) + 109.45 × (pH)² + 116.49 × ([NOM]) − 14.317 × (pH) × ([NOM]) + 6007.2) was developed. In a validation study with natural waters the predicted 48 h-EᵣC20 was a factor 1.08–2.57 lower compared to the experimental values.

Clear association exists between ambient PM₁₀ concentrations and adverse health outcomes. However, determination of the strength of associations between exposure and illness is limited by low spatial-resolution of particulate concentration measurements. Conventional fixed monitoring stations provide high temporal-resolution data, but cannot capture fine-scale spatial variations. Here we examine the utility of biomagnetic monitoring for spatial mapping of PM₁₀ concentrations around a major industrial site. We combine leaf magnetic measurements with co-located PM₁₀ measurements to achieve inter-calibration. Comparison of the leaf-calculated and measured PM₁₀ concentrations with PM₁₀ predictions from a widely-used atmospheric dispersion model indicates that modelling of stack emissions alone substantially under-predicts ambient PM₁₀ concentrations in parts of the study area. Some of this discrepancy might be attributable to fugitive emissions from the industrial site. The composition of the magnetic particulates from vehicle and industry-derived sources differ, indicating the potential of magnetic techniques for source attribution.

Mercury biomagnification on the South River floodplain (Virginia, USA) was modeled at two locations along a river reach previously modeled for methylmercury movement through the aquatic trophic web. This provided an opportunity to compare biomagnification in adjoining trophic webs. Like the aquatic modeling results, methylmercury-based models provided better prediction than those for total mercury. Total mercury Food Web Magnification Factors (FWMF, fold per trophic level) for the two locations were 4.9 and 9.5. Methylmercury FWMF for the floodplain locations were higher (9.3 and 25.1) than that of the adjacent river (4.6). Previous speculation was not resolved regarding whether the high mercury concentrations observed in floodplain birds was materially influenced by river prey consumption by riparian spiders and subsequent spider movement into the trophic web of the adjacent floodplains. Results were consistent with a gradual methylmercury concentration increase from contaminated floodplain soil, to arthropod prey, and finally, to avian predators.

The experiments were done to investigate the effect of soil pH and organic matter content on EDTA-extractable heavy metal contents in soils and heavy metal concentrations in rice straw and grains. EDTA-extractable Cr contents in soils and concentrations in rice tissues were negatively correlated with soil pH, but positively correlated with organic matter content. The combination of soil pH and organic matter content would produce the more precise regression models for estimation of EDTA-Cu, Pb and Zn contents in soils, demonstrating the distinct effect of the two factors on the availability of these heavy metals in soils. Soil pH greatly affected heavy metal concentrations in rice plants. Furthermore, inclusion of other soil properties in the stepwise regression analysis improved the regression models for predicting straw Fe and grain Zn concentrations, indicating that other soil properties should be taken into consideration for precise predicting of heavy metal concentrations in rice plants.

This study measured concentrations of polycyclic aromatic hydrocarbons (PAHs) in surface sediments in the East China Sea (ECS) to investigate possible sources and fate of PAHs. Total concentration of PAHs in the sediments of the ECS ranged from 22 to 244ngg⁻¹, with the highest levels in the coastal area and outer shelf. The observed PAH results showed elevated levels in both inner and outer shelf areas, a finding that is different from predictions by an ocean circulation model, suggesting that terrestrial sources are important for PAH contaminations in the ECS, while sediment resuspension, tidal changes and lateral transport may be important in affecting the distribution of PAHs in the outer shelf. The distribution of PAHs in the surface sediments of the ECS is similar to the distribution of carbonaceous materials (e.g., particulate organic carbon and black carbon), suggesting that carbonaceous materials may strongly affect the distribution of PAHs.

Recent observations of cetacean mass strandings, coincident with anthropogenic sounds emissions, have raised concerns on the potential environmental impact of underwater noise. Cuvier’s beaked whale (Ziphius cavirostris) was reported in all the cited stranding events. Within the NATO Marine Mammal Risk Mitigation project (MMRM), multiple interdisciplinary sea trials have been conducted in the Mediterranean Sea with the objective of developing tools and procedures to mitigate the impact of underwater sound emissions. During these cruises, visual observations, passive acoustic detections and environmental data were collected. The aim of this study was to evaluate “a priori” predictions of Cuvier’s beaked whale presence in the Alboran Sea, using models developed in the Ligurian Sea that employ bathymetric and chlorophyll features as predictors. The accuracy of these predictions was found adequate and elements are given to account for the uncertainties associated to the use of models developed in areas different from their calibration site.

MOON (Mediterranean Operational Oceanography Network http://www.moon-oceanforecasting.eu) provides near-real-time information on oil-spill detection (ocean color and SAR) and predictions [ocean forecasts (MFS and CYCOFOS) and oil-spill predictions (MEDSLIK)]. We employ this system to study the Lebanese oil-pollution crisis in summer 2006 and thus to assist regional and local decision makers in Europe, regionally and locally. The MEDSLIK oil-spill predictions obtained using CYCOFOS high-resolution ocean fields are compared with those obtained using lower-resolution MFS hydrodynamics, and both are validated against satellite observations. The predicted beached oil distributions along the Lebanese and Syrian coasts are compared with in situ observations. The oil-spill predictions are able to simulate the northward movement of the oil spill, with the CYCOFOS predictions being in better agreement with satellite observations. Among the free MEDSLIK parameters tested in the sensitivity experiments, the drift factor appears to be the most relevant to improve the quality of the results.

Radiocarbon levels were recorded in Fucus vesiculosus samples collected on a monthly basis over a three-year period at a site on the east coast of Ireland. The resulting data was analysed using a numerical model which estimates the transit times from the Sellafield plant to the sampling location, and the mean availability time of ¹⁴C in seaweed. With the inclusion of a model parameter allowing for seasonal variability in uptake by the Fucus, good correlation was observed between the predicted and measured concentrations. Future temporal trends of ¹⁴C Fucus concentrations along the eastern Irish coastline were modelled with the application of three possible prospective discharge scenarios, predicting ¹⁴C Fucus concentrations to reduce to ambient background levels within 2.5-years of discharges being set to zero. Such projections may prove helpful in assessing the consequences of discharge management and policy making in the context of the OSPAR convention.

The occurrences of increased atmospheric nitrogen deposition (ADN) in Southeast Asia during smoke haze episodes have undesired consequences on receiving aquatic ecosystems. A successful prediction of episodic ADN will allow a quantitative understanding of its possible impacts. In this study, an artificial neural network (ANN) model is used to estimate atmospheric deposition of total nitrogen (TN) and organic nitrogen (ON) concentrations to coastal aquatic ecosystems. The selected model input variables were nitrogen species from atmospheric deposition, Total Suspended Particulates, Pollutant Standards Index and meteorological parameters. ANN models predictions were also compared with multiple linear regression model having the same inputs and output. ANN model performance was found relatively more accurate in its predictions and adequate even for high-concentration events with acceptable minimum error. The developed ANN model can be used as a forecasting tool to complement the current TN and ON analysis within the atmospheric deposition-monitoring program in the region.