Adsorption of arsenic (V) on kaolinite and on kaolinite-humic acid complexes. Role of humic acid nitrogen groups

In order to investigate the influence of organic matter on arsenic retention, we used batch experiments at pH 7 to determine the adsorption of As(V) on three different solids: a crude, purified, Ca-exchanged kaolinite and two kaolinites coated with humic acids (HAs) having different nitrogen contents. We first examined the adsorption of each HA onto kaolinite, and then used the HA-kaolinite complexes to study As(V) adsorption. The results clearly show an influence of the HA coating on As adsorption. For example, with low initial As concentrations the solid/liquid partition coefficient (R(d)) for both HA complexes is greater than that for the crude kaolinite. We found that increasing the initial As concentrations decreased the R(d) values of the HA-coated kaolinites until finally they were the same as the crude kaolinite R(d) values. This suggests that adsorption occurs first on the HA sites and then, once the HA sites are saturated, on the remaining kaolinite sites. We also noted that the more reactive HA-kaolinite complex was the one with the highest N/C ratio. Comparing the amount of amine groups in the HA-kaolinite complexes with the total amount of adsorbed As indicates that the HA amine groups, due to their positive charge at pH 7, play a key role in the adsorption of As onto organic matter.     

RASTER-BASED HYDROLOGIC MODELING OF SPATIALLY-VARIED SURFACE RUNOFF1

ABSTRACT: The proliferation of watershed databases in raster Geographic Information System (GIS) format and the availability of radar-estimated rainfall data foster rapid developments in raster-based surface runoff simulations. The two-dimensional physically-based rainfall-runoff model CASC2D simulates spatially-varied surface runoff while fully utilizing raster GIS and radar-rainfall data. The model uses the Green and Ampt infiltration method, and the diffusive wave formulation for overland and channel flow routing enables overbank flow storage and routing. CASC2D offers unique color capabilities to display the spatio-temporal variability of rainfall, cumulative infiltrated depth, and surface water depth as thunderstorms unfold. The model has been calibrated and independently verified to provide accurate simulations of catchment response to moving rainstorms on watersheds with spatially-varied infiltration. The model can accurately simulate surface runoff from flashfloods caused by intense thunderstorms moving across partial areas of a watershed.     

Examining the Factors Affecting Air Pollution Emission Growth in China

In this study, a structural decomposition method was applied to research the factors affecting the changes in air pollution emissions in China. Based on 1995–2009 data from the World IO Database, we combine China’s (Import) Noncompetitive IO Table and the Environmental Account Table. The results indicate that emission intensities represent the most important factor for reducing air pollution emissions in China. In contrast, economies of scale and the intermediate input product structure constitute the major causes for acceleration in the growth of air pollution emissions in China. From the perspective of final demand, the economic scale effect caused by investment demand is the main reason for this accelerated growth in China’s air pollution emissions in recent years. Consumption-driven economic growth is cleaner, while investment-driven economic growth is dirtier. This study constructed a structure decomposition model based on the input-output tables, which is suitable for studying the driving forces of various economic indicators, such as energy, carbon dioxide, and economic growth. At the same time, this method is helpful for analyzing the factors that influence changes in economic indicators that result from different economic pull modes, such as the final demand mode. However, the model does have limitations; for example, it does not consider the difference between general trade and processing trade in exports.     

A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment

In this paper, we report an application of neural networks to simulate daily nitrate-nitrogen and suspended sediment fluxes from a small 7.1 km² agricultural catchment (Melarchez), 70 km east of Paris, France. Nitrate-nitrogen and sediment losses are only a few possible consequences of soil erosion and biochemical applications associated to human activities such as intensive agriculture. Stacked multilayer perceptrons models (MLPs) like the ones explored here are based on commonly available inputs and yet are reasonably accurate considering their simplicity and ease of implementation. Note that the simulation does not resort on water quality flux observations at previous time steps as model inputs, which would be appropriate, for example, to predict the water chemistry of a drinking water plant a few time steps ahead. The water quality fluxes are strictly mapped to historical mean flux values and to hydro-climatic variables such as stream flow, rainfall, and soil moisture index (12 model input candidates in total), allowing its usage even when no flux observations are available. Self-organizing feature maps based on the network structure established by Kohonen were employed first to produce the training and the testing data sets, with the intent to produce statistically close subsets so that any difference in model performance between validation and testing has to be associated to the model and not to the data subsets. The stacked MLPs reached different levels of performance simulating the nitrate-nitrogen flux and the suspended sediment flux. In the first instance, 2-input stacked MLP nitrate-nitrogen simulations, based on the same-day stream flow and on the 80-cm soil moisture index, have a performance of almost 90% according to the efficiency index. On the other hand, the performance of 3-input stacked MLPs (same-day stream flow, same-day historical flux, and same-day stream flow increment) reached a little more than 75% according to the same criterion. The results presented here are deemed already promising enough, and should encourage water resources managers to implement simple models whenever appropriate.     

Using OpenMI and a Model MAP to Integrate WaterML2 and NetCDF Data Sources into Flood Modeling of Genoa,Italy

Extreme hydrometeorological events such as flash floods have caused considerable loss of life and damage to infrastructure over recent years. Flood events in the Mediterranean region between 1990 and 2006 caused over 4,500 fatalities and cost over €29 billion in damage, with Italy one of the worst affected countries. The Distributed Computing Infrastructure for Hydro‐Meteorology (DRIHM) project is a European initiative aiming at providing an open, fully integrated eScience environment for predicting, managing, and mitigating the risks related to such extreme weather phenomena. Incorporating both modeled and observational data sources, it enables seamless access to a set of computing resources with the objective of providing a collection of services for performing experiments with numerical models in meteorology, hydrology, and hydraulics. The purpose of this article is to demonstrate how this flexible modeling architecture has been constructed using a set of standards including the NetCDF and WaterML2 file formats, in‐memory coupling with OpenMI, controlled vocabularies such as CF Standard Names, ISO19139 metadata, and a Model MAP (Metadata, Adaptors, Portability) gateway concept for preparing numerical models for standardized use. Hydraulic results, including the impact to buildings and hazards to people, are given for the use cases of the severe and fatal flash floods, which occurred in Genoa, Italy in November 2011 and October 2014.     

Fate of metal-associated POM in a soil under arable land use contaminated by metallurgical fallout in northern France

Organic matter is a major metal-retaining constituent in soils. Among the diversity of organic components in soils, particulate organic matter (POM) accumulates large amounts of metals, but the fate of such metal-associated POM is unknown. We studied different POM size fractions and their corresponding mineral size-fractions isolated from the surface horizon of a soil affected by metallurgical fallout. Analyses of total and EDTA extractible metal contents performed on all size fractions demonstrated that with decreasing POM size, larger metal concentrations were observed but they were less extractable. Micromorphological study revealed the occurrence of opaque parts in decaying POM fragments and their individualization as fine, irregularly shaped opaque fragments in the soil matrix. This work suggested a mutual sequestration of metal pollutants and organic carbon as micro-meter sized, metal-enriched organic particles derived from POM, representing an original pathway for natural attenuation of risk related to metal contaminated soils.     

Faecal contamination of water and sediment in the rivers of the Scheldt drainage network

The INSHORE system (INtegrated System for High Operational REsolution in shore monitoring) is a land-base survey system designed and developed for the specific task of monitoring the evolution in time of sandy shores. This system was developed with two main objectives: (1) to produce highly accurate 3D coordinates of surface points (in the order of 0.02 to 0.03 m); and (2) to be extremely efficient in surveying a beach stretch of several kilometres. Previous tests have demonstrated that INSHORE systems fulfil such objectives. Now, the usefulness of the INSHORE system as a survey tool for the production of Digital Elevation Models (DEMs) of sandy shores is demonstrated. For this purpose, the comparison of DEMs obtained with the INSHORE system and with other relevant survey techniques is presented. This comparison focuses on the final DEM accuracy and also on the survey efficiency and its impact on the costs associated with regular monitoring programmes. The field survey method of the INSHORE system, based on profile networks, has a productivity of about 30 to 40 ha/h, depending on the beach surface characteristics. The final DEM precision, after interpolation of the global positioning system profile network, is approximately 0.08 to 0.12 m (RMS), depending on the profile network's density. Thus, this is a useful method for 3D representation of sandy shore surfaces and can permit, after interpolation, reliable calculations of volume and other physical parameters.     

Pesticide risk mitigation by vegetated treatment systems: a meta-analysis

Pesticides entering agricultural surface waters threaten water quality and aquatic communities. Recently, vegetated treatment systems (VTSs) (e.g., constructed wetlands and vegetated ditches) have been proposed as pesticide risk mitigation measures. However, little is known about the effectiveness of VTSs in controlling nonpoint source pesticide pollution and factors relevant for pesticide retention within these systems. Here, we conducted a meta-analysis on pesticide mitigation by VTSs using data from the scientific literature and the European LIFE ArtWET project. Overall, VTSs effectively reduced pesticide exposure levels (i.e., the majority of pesticide retention performances was >70%). A multiple linear regression analysis of 188 retention performance cases identified the two pesticide properties, organic carbon sorption coefficient value and water-phase 50% dissipation time, as well as the VTS characteristics overall plant coverage and hydraulic retention time for targeting high efficacy of pesticide retention. The application of a Tier I risk assessment (EU Uniform Principle) revealed a higher toxicity reduction for hydrophobic and nonpersistent insecticides compared with less sorptive and not readily degradable herbicides and fungicides. Overall, nearly half (48.5%) of all pesticide field concentrations ( = 130) failed Tier I standard risk assessment at the inlet of VTSs, and 29.2% of all outlet concentrations exceeded conservative acute threshold levels. We conclude that VTSs are a suitable and effective risk mitigation strategy for agricultural nonpoint source pesticide pollution of surface waters. Further research is needed to improve their overall efficacy in retaining pesticides.