This study aims to examine the ability of free floating aquatic plants to remove phosphorus and to predict the reduction of phosphorus from rice mill wastewater using soft computing techniques. A mesocosm study was conducted at the mill premises under normal conditions, and reliable results were obtained. Four aquatic plants, namely water hyacinth, water lettuce, salvinia, and duckweed were used for this study. The growth of all the plants was inhibited in rice mill wastewater due to low pH, high chemical oxygen demand, high conductivity, and high phosphorus concentration. Subsequently, a 1:1 ratio of mill water to tap water was used. A control was maintained to assess the aquatic plant technology. In this study, the aquatic plants reduced the total phosphorus content up to 80 % within 15 days. A comparison between three modeling techniques e.g. Artificial neural network (ANN), Random forest (RF) and M5P has been done considering the reduction rate of total phosphorus as predicted variable. In this paper, the data set has been divided in two parts, 70 % is used to train the model and residual 30 % is used for testing of the model. Artificial neural network shows promising results as compared to random forest and M5P tree modelling. The root mean square error (RMSE) for all the three models is observed as 0.0162, 0.0204 and 0.0492 for ANN, RF and M5P tree, respectively.

Defined as any substance in the air that may harm humans, animals, vegetation, and materials, air pollution poses a great danger to human health. It has turned into a worldwide problem as well as a huge environmental risk. Recent years have witnessed the increase of air pollution in many cities around the world. Similarly, it has become a big problem in Iran. Although ground-level monitoring can provide accurate PM2.5 measurements, it has limited spatial coverage and resolution. As a result, Satellite Remote Sensing (RS) has emerged as an approach to estimate ground-level ambient air pollution, making it possible to monitor atmospheric particulate matters continuously and have a spatial coverage of them. Recent studies show a high correlation between ground level PM2.5, estimated by RS on the one hand, and measurements, collected at regulatory monitoring sites on the other. As such, the present study addresses the relation between air pollution and satellite images. For so doing, it derives RS estimates, using satellite measurements from Landsat satellite images. Monitoring data is the daily concentration of PM2.5 contaminants, obtained from air pollution stations. The relation between the concentration of pollutants and the values of various bands of Landsat satellite images is examined through 19 regression models. Among them, the Ensembles Bagged Trees has the lowest Root-Mean-Square Error (RMSE), equal to 21.88. Results show that this model can be used to estimate PM2.5 contaminants, based on Landsat satellite images.

Forests in Europe face significant changes in climate, which in interaction with air quality changes, may significantly affect forest productivity, stand composition and carbon sequestration in both vegetation and soils. Identified knowledge gaps and research needs include: (i) interaction between changes in air quality (trace gas concentrations), climate and other site factors on forest ecosystem response, (ii) significance of biotic processes in system response, (iii) tools for mechanistic and diagnostic understanding and upscaling, and (iv) the need for unifying modelling and empirical research for synthesis. This position paper highlights the above focuses, including the global dimension of air pollution as part of climate change and the need for knowledge transfer to enable reliable risk assessment. A new type of research site in forest ecosystems ("supersites") will be conducive to addressing these gaps by enabling integration of experimentation and modelling within the soil-plant-atmosphere interface, as well as further model development. (C) 2011 Elsevier Ltd. All rights reserved.

Recent research in nitrogen exchange with the atmosphere has separated research communities according to N form. The integrated perspective needed to quantify the net effect of N on greenhouse-gas balance is being addressed by the NitroEurope Integrated Project (NEU). Recent advances have depended on improved methodologies, while ongoing challenges include gas-aerosol interactions, organic nitrogen and N2 fluxes. The NEU strategy applies a 3-tier Flux Network together with a Manipulation Network of global-change experiments, linked by common protocols to facilitate model application. Substantial progress has been made in modelling N fluxes, especially for N2O, NO and bi-directional NH3 exchange. Landscape analysis represents an emerging challenge to address the spatial interactions between farms, fields, ecosystems, catchments and air dispersion/deposition. European up-scaling of N fluxes is highly uncertain and a key priority is for better data on agricultural practices. Finally, attention is needed to develop N flux verification procedures to assess compliance with international protocols

Recent research in nitrogen exchange with the atmosphere has separated research communities according to N form. The integrated perspective needed to quantify the net effect of N on greenhouse-gas balance is being addressed by the NitroEurope Integrated Project (NEU). Recent advances have depended on improved methodologies, while ongoing challenges include gas-aerosol interactions, organic nitrogen and N2 fluxes. The NEU strategy applies a 3-tier Flux Network together with a Manipulation Network of global-change experiments, linked by common protocols to facilitate model application. Substantial progress has been made in modelling N fluxes, especially for N2O, NO and bi-directional NH3 exchange. Landscape analysis represents an emerging challenge to address the spatial interactions between farms, fields, ecosystems, catchments and air dispersion/deposition. European up-scaling of N fluxes is highly uncertain and a key priority is for better data on agricultural practices. Finally, attention is needed to develop N flux verification procedures to assess compliance with international protocols

Preservation of biodiversity and ecosystem services is critical for sustainable development and human well-being. However, an unprecedented erosion of biodiversity is observed and the use of plant protection products (PPP) has been identified as one of its main causes. In this context, at the request of the French Ministries responsible for the Environment, for Agriculture and for Research, a panel of 46 scientific experts ran a nearly 2-year-long (2020–2022) collective scientific assessment (CSA) of international scientific knowledge relating to the impacts of PPP on biodiversity and ecosystem services. The scope of this CSA covered the terrestrial, atmospheric, freshwater, and marine environments (with the exception of groundwater) in their continuity from the site of PPP application to the ocean, in France and French overseas territories, based on international knowledge produced on or transposable to this type of context (climate, PPP used, biodiversity present, etc.). Here, we provide a brief summary of the CSA’s main conclusions, which were drawn from about 4500 international publications. Our analysis finds that PPP contaminate all environmental matrices, including biota, and cause direct and indirect ecotoxicological effects that unequivocally contribute to the decline of certain biological groups and alter certain ecosystem functions and services. Levers for action to limit PPP-driven pollution and effects on environmental compartments include local measures from plot to landscape scales and regulatory improvements. However, there are still significant gaps in knowledge regarding environmental contamination by PPPs and its effect on biodiversity and ecosystem functions and services. Perspectives and research needs are proposed to address these gaps.

International audience | Preservation of biodiversity and ecosystem services is critical for sustainable development and human well-being. However, an unprecedented erosion of biodiversity is observed and the use of plant protection products (PPP) has been identified as one of its main causes. In this context, at the request of the French Ministries responsible for the Environment, for Agriculture and for Research, a panel of 46 scientific experts ran a nearly 2-year-long (2020-2022) collective scientific assessment (CSA) of international scientific knowledge relating to the impacts of PPP on biodiversity and ecosystem services. The scope of this CSA covered the terrestrial, atmospheric, freshwater, and marine environments (with the exception of groundwater) in their continuity from the site of PPP application to the ocean, in France and French overseas territories, based on international knowledge produced on or transposable to this type of context (climate, PPP used, biodiversity present, etc.). Here, we provide a brief summary of the CSA's main conclusions, which were drawn from about 4500 international publications. Our analysis finds that PPP contaminate all environmental matrices, including biota, and cause direct and indirect ecotoxicological effects that unequivocally contribute to the decline of certain biological groups and alter certain ecosystem functions and services. Levers for action to limit PPP-driven pollution and effects on environmental compartments include local measures from plot to landscape scales and regulatory improvements. However, there are still significant gaps in knowledge regarding environmental contamination by PPPs and its effect on biodiversity and ecosystem functions and services. Perspectives and research needs are proposed to address these gaps.

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]TED | International audience | The nitrifying/denitrifying activated sludge process removes several micropollutants from wastewater by sorption onto sludge and/or biodegradation. The objective of this paper is to propose and evaluate a lab-scale experimental strategy for the determination of partition coefficient and biodegradation constant for micropollutant with an objective of modelling their removal. Four pharmaceutical compounds (ibuprofen, atenolol, diclofenac and fluoxetine) covering a wide hydrophobicity range (log Kow from 0.16 to 4.51) were chosen. Dissolved and particulate concentrations were monitored for 4 days, inside two reactors working under aerobic and anoxic conditions, and under different substrate feed conditions (biodegradable carbon and nitrogen). We determined the mechanisms responsible for the removal of the target compounds: (i) ibuprofen was biodegraded, mainly under aerobic conditions by cometabolism with biodegradable carbon, whereas anoxic conditions suppressed biodegradation; (ii) atenolol was biodegraded under both aerobic and anoxic conditions (with a higher biodegradation rate under aerobic conditions), and cometabolism with biodegradable carbon was the main mechanism; (iii) diclofenac and fluoxetine were removed by sorption only. Finally, the abilities of our strategy were evaluated by testing the suitability of the parameters for simulating effluent concentrations and removal efficiency at a full-scale plant.

Composting has been demonstrated to be effective in degrading organic pollutants (OP) whose behaviour depends on the composting conditions, the microbial populations activated and interactions with organic matters. The fate of OP during composting involves complex mechanisms and models can be helpful tools for educational and scientific purposes, as well as for industrialists who want to optimise the composting process for OP elimination. A COP-Compost model, which couples an organic carbon (OC) module and an organic pollutant (OP) module and which simulates the changes of organic matter, organic pollutants and the microbial activities during the composting process, has been proposed and calibrated for a first set of OP in a previous study. The objectives of the present work were (1) to introduce the COP-Compost model from its convenient interface to a potential panel of users, (2) to show the variety of OP that could be simulated, including the possibility of choosing between degradation through co-metabolism or specific metabolism and (3) to show the effect of the initial characteristics of organic matter quality and its microbial biomass on the simulated results of the OP dynamic. In the model, we assumed that the pollutants can be adsorbed on organic matter according to the biochemical quality of the OC and that the microorganisms can degrade the pollutants at the same time as they degrade OC (by co-metabolism). A composting experiment describing two different 14C-labelled organic pollutants, simazine and pyrene, were chosen from the literature because the four OP fractions simulated in the model were measured during the study (the mineralised, soluble, sorbed and non-extractable fractions). Except for the mineralised fraction of simazine, a good agreement was achieved between the simulated and experimental results describing the evolution of the different organic fractions. For simazine, a specific biomass had to be added. To assess the relative importance of organic matter dynamics on the organic pollutants’ behaviour, a sensitivity analysis was conducted. The sensitivity analysis demonstrated that the parameters associated with organic matter dynamics and its initial microbial biomass greatly influenced the evolution of all the OP fractions, although the initial biochemical quality of the OC did not have a significant impact on the OP evolution