Nitrous oxide is a potent greenhouse gas, with a global warming potential 298 times greater than that of CO2. In agricultural soils, N2O emissions are influenced by a large number of environmental characteristics and crop management techniques that are not systematically reported in experiments. Random Forest (RF) is a machine learning method that can handle missing data and ranks input variables on the basis of their importance. We aimed to predict N2O emission on the basis of local information, to rank environmental and crop management variables according to their influence on N2O emission, and to compare the performances of RI: with several regression models. RF outperformed the regression models for predictive purposes, and this approach led to the identification of three important input variables: N fertilization, type of crop, and experiment duration. This method could be used in the future for prediction of N2O emissions from local information. (c) 2013 Elsevier Ltd. All rights reserved.

Environ Pollut 859TF Times Cited:2 Cited References Count:50 | International audience | An investigation was undertaken to identify the most significant soil parameters that can be used to predict Cd, Pb, and Zn bioaccessibility in smelter-contaminated agricultural soils. A robust model was established from an extended database of soils by using : a training set of 280 samples to select the main soil parameters, to define the best population to be taken into account for the model elaboration, and to construct multivariate regression models, and a test set of 110 samples to validate the ability of the regression models. Total carbonate, organic matter, sand, P2O5, free Fe-Mn oxide, and pseudo total Aland trace element (TE) contents appeared as the main variables governing TE bioaccessibility. The statistical modeling approach was reasonably successful, indicating that the main soil factors influencing the bioaccessibility of TEs were taken into account and the predictions could be applicable for further risk evaluation in the studied area.

Polycyclic aromatic hydrocarbons (PAHs) are important organic pollutants in the aquatic environment due to their persistence and bioaccumulation potential both in organisms and in sediments. Benzo(a)anthracene (BaA) and phenanthrene (PHE), which are in the priority pollutant list of the U.S. EPA (Environmental Protection Agency), are selected as model compounds of the present study. Bioaccumulation and depuration experiments with local Mediterranean mussel species, Mytilus galloprovincialis were used as the basis of the study. Mussels were selected as bioindicator organisms due to their broad geographic distribution, immobility and low enzyme activity. Bioaccumulation and depuration kinetics of selected PAHs in Mytilus galloprovincialis were described using first order kinetic equations in a three compartment model. The compartments were defined as: (1) biota (mussel), (2) surrounding environment (seawater), and (3) algae (Phaeodactylum tricornutum) as food source of the mussels. Experimental study had been performed for three different concentrations. Middle concentration of the experimental data was used as the model input in order to represent other high and low concentrations of selected PAHs. Correlations of the experiment and model data revealed that they are in good agreement. Accumulation and depuration trend of PAHs in mussels regarding also the durations can be estimated effectively with the present study. Thus, this study can be evaluated as a supportive tool for risk assessment in addition to monitoring studies.

Various sources supply PAHs that accumulate in soils. The methodology we developed provided an evaluation of the contribution of local sources (road traffic, local industries) versus remote sources (long range atmospheric transport, fallout and gaseous exchanges) to PAH stocks in two contrasting subcatchments (46–614 km²) of the Seine River basin (France). Soil samples (n = 336) were analysed to investigate the spatial pattern of soil contamination across the catchments and an original combination with radionuclide measurements provided new insights into the evolution of the contamination with depth. Relationships between PAH concentrations and the distance to the potential sources were modelled. Despite both subcatchments are mainly rural, roadside areas appeared to concentrate 20% of the contamination inside the catchment while a local industry was found to be responsible for up to 30% of the stocks. Those results have important implications for understanding and controlling PAH contamination in rural areas of early-industrialized regions.

Measurement of solute-transport parameters through soils for a wide range of solute- and soil-types is time-consuming, laborious, expensive and practically impossible. So, indirect methods for estimating the transport parameters by pedo-transfer functions are now advancing. This study developed and evaluated an Artificial Neural Network (ANN) model for estimating the transport velocity (V), dispersion coefficient (D) and retardation factor (R) of NaAsO₂, Pb(NO₃)₂, Cd(NO₃)₂, C₉H₉N₃O₂ and CaCl₂ from the basic soil properties. Breakthrough data of the solutes were measured in 14 agricultural soils of Bangladesh by time-domain reflectometry (TDR) in repacked soil columns under unsaturated steady-state water-flow conditions. The transport parameters of the chemicals were determined by analyzing the solute breakthrough data. Bulk density (γ), organic carbon (OC), clay (C) content, pH, median grain diameter (D₅₀) and uniformity coefficient (Cᵤ) of the soils were determined. An ANN model for V, D and R was developed by using data of eight soils, validated/tested with the data of five soils and verified with the data of one soil. Clay content and bulk density of the soils were the most sensitive input variables to the ANN model followed by other soil properties (OC, C, pH, D₅₀ and Cᵤ). The model reliably predicted V, D and R with relative root-mean-square error (RRMSE) of 0.028–0.363, mean error (ME) of – 0.00004 to 0.0005, bias error (BOE%) of 0–0.003 and modeling efficiency (EF) of >0.99. Thus, the ANN model can significantly enhance prediction of pollution transport through soils in terms of cost and effort.

It has been hypothesised that, if ingested, plastic debris could act as vector for the transfer of chemical contaminants from seawater to organisms, yet modelling suggest that, in the natural environment, chemical transfer would be negligible compared to other routes of uptake. However, to date, the models have not incorporated consideration of the role of gut surfactants, or the influence of pH or temperature on desorption, whilst experimental work has shown that these factors can enhance desorption of sorbed contaminants several fold. Here, we modelled the transfer of sorbed organic contaminants dichlorodiphenyltrichloroethane (DDT), phenanthrene (Phe) and bis-2-ethylhexyl phthalate (DEHP) from microscopic particles of polyvinylchloride (PVC) and polyethylene (PE) to a benthic invertebrate, a fish and a seabird using a one-compartment model OMEGA (Optimal Modelling for EcotoxicoloGical Applications) with different conditions of pH, temperature and gut surfactants. Environmental concentrations of contaminants at the bottom and the top of published ranges were considered, in combination with ingestion of either 1 or 5% by weight of plastic. For all organisms, the combined intake from food and water was the main route of exposure for Phe, DEHP and DDT with a negligible input from plastic. For the benthic invertebrate, predictions including the presence of contaminated plastic resulted in very small increases in the internal concentrations of DDT and DEHP, while the net change in the transfer of Phe was negligible. While there may be scenarios in which the presence of plastic makes a more important contribution, our modelling study suggests that ingestion of microplastic does not provide a quantitatively important additional pathway for the transfer of adsorbed chemicals from seawater to biota via the gut.

Increases in dissolved organic carbon (DOC) fluxes may relate to changes in sulphur and nitrogen pollution. We integrated existing models of vegetation growth and soil organic matter turnover, acid–base dynamics, and organic matter mobility, to form the ‘MADOC’ model. After calibrating parameters governing interactions between pH and DOC dissolution using control treatments on two field experiments, MADOC reproduced responses of pH and DOC to additions of acidifying and alkalising solutions. Long-term trends in a range of acid waters were also reproduced. The model suggests that the sustained nature of observed DOC increases can best be explained by a continuously replenishing potentially-dissolved carbon pool, rather than dissolution of a large accumulated store. The simulations informed the development of hypotheses that: DOC increase is related to plant productivity increase as well as to pH change; DOC increases due to nitrogen pollution will become evident, and be sustained, after soil pH has stabilised.

Guidelines provided by the OECD and EPPO allow the use of single-species tests performed in greenhouses to assess the risk of herbicides to non-target terrestrial plant communities in the field. The present study was undertaken to investigate the use of greenhouse data to determine effects of herbicides with a different mode of action on the biomass, seed production and emergence of field-grown plants. In addition, a single species approach was compared with a mixed species approach. Effects on the biomass of greenhouse and field-grown plants were found to be related at different effect levels, indicating that it might be possible to translate results from greenhouse studies to field situations. However, the use of single-species tests may not be valid. The response of a single plant species to sublethal herbicide dosages differed to the response of the same species grown in a mixture with other species. The use of single-species greenhouse tests in the ecological risk assessment of crop protection products may only be valid for single species in the field, not for vegetations. Keywords: Non-crop terrestrial plants; Tepraloxydim; Greenhouse; Field; Biomass

Increasing heavy metal pollution in wetland ecosystems around the world pose significant health risks to waterbirds, especially the endangered species. We analyzed heavy metal pollution in bird foraging area of Caohai wetland in Guizhou Plateau (China), and used an integrated exposure risk model for assessing heavy metal (Cd, Pb, Cr, Ni, Zn, Sb) exposure risk in birds from the soil, water, plants and benthic invertebrates. There is considerable variation in the extent of heavy metal contamination across the different sampling sites, and Cd and Sb are the main contaminants. The mussel Anodonta showed greater heavy metal accumulation (except for Zn) compared to the snail species C. cathayensis. The different plant species also varied in terms of amount for accumulated heavy metals. The phytophagous together with omnivorous birds were exposed to Cd, Pb, Zn and Ni through plants rather than the soil, whereas the maximum Zn exposure in the omnivorous and carnivorous birds was through consumption of benthic invertebrates. Furthermore, the phytophagous black-necked cranes (Grus nigricollis) were less risk to heavy metal exposure compared to the omnivorous bar-headed goose (Anser indicus) and carnivorous ruddy shelducks (Tadorna ferruginea). The exposure risk of Cr (8.1) was highest, followed by Pb (5.1), Zn (3.8), Sb (1.0), Cd (0.33) and Ni (0.28). The heavy metal assessment heavy metal exposure risk for migratory birds should take into account the exposure from food and soil. Our findings provide new insights into developing measures to minimize heavy metal contamination in migratory birds.

Distinct cropland acidification has been reported in China due to nitrogen (N) fertilizer overuse. However, the impacts on food production and thereby on food security are largely unknown. Yield losses in the period 1980–2050 were therefore assessed by simulating soil pH changes combined with derived pH-yield relationships for wheat, maize and rice. If the N fertilizer input continues to increase at 1% annually, the predicted average soil pH decline is about one unit and relative yield losses are expected to increase from approximately 4%–24% during 2010–2050. If the N fertilizer increase stops in 2020 (N2020), the expected losses are approximately 16% in 2050, which is comparable to a scenario of 100% crop residue return (100%RR). However, if 30% of the N fertilizer is replaced by manure N (30%MR), the losses reduce to near 5% in 2050. Soil acidification was predicted to reverse and expected losses are only 2.5% in 2050 in a combined scenario of N2020, 100%RR and 30%MR. Our results illustrate the potential food insecurity induced by cropland acidification and address the necessity of mitigation.