Phosphate removal from water by naturally occurring shale,sandstone, and laterite: The role of iron oxides and of soluble species

Aqueous phosphate removal by three geomaterials from Ivory Coast was evaluated to determine their potential application as low-cost phosphate adsorbents in wastewater treatment. Batch experiments showed that phosphate uptake strongly depended on pH. Laterite and sandstone dissolution was less pronounced compared to shale. A correlation between concentrations of aqueous cation species released from shale and phosphate uptake was observed. The kinetics were well described using the pseudo-second-order model. Isotherms displayed a saturation level on shale, while phosphate uptake continuously increased for laterite and sandstone. The removal efficiency decreased in the following ranking order: laterite > sandstone > shale. Laterite was also the most efficient adsorbent in column experiments. The high phosphate removal efficiency of laterite (8.3 mg PO₄ g^?1) was attributed to the presence of superparamagnetic low grain sizes of goethite. Laterite is a particularly promising material for further investigation in wastewater treatment technology such as constructed wetlands.     

A GPS-Supported Gravity Survey in the Amazon of Ecuador

The growing importance and effectiveness of the Global Positioning System (GPS) in positioning geophysical surveys is now well established. In Ecuador's Amazon basin the use of GPS was the determining factor in the successful completion of a gravity survey carried out in 1994. Over 500 gravity stations, spaced from 1 to 5 km apart and positioned by GPS, were collected in the jungle environment in the country's southeast, where the preexisting data, of variable and unknown quality, were mostly confined to areas along the major rivers. This station densification, which includes the reoccupation of selected points, contributed to increase the gravity coverage; it also allowed us to evaluate and correct 1100 preexisting data points in the area, so that the different surveys can be merged. The composite data set is used for a geologic analysis of the study area, where previously unknown structures are discovered. ? 1999 John Wiley & Sons, Inc.     

Mitigating the effects of sand dunes on seismic data from the Rub al Khali basin,Saudi Arabia

The Rub Al Khali region in Saudi Arabia is characterized by the presence of sand dunes separated by salt flats, also called Sabkhas. In general, the elevation of dunes in this region varies between 90 and 250 m above sea level. The presence of these sand dunes, along with the rapidly changing surface topography poses challenges for seismic data acquisition and processing. The high contrast in acoustic impedance between the dune base and the underlying formation often results in amplification of seismic waves that are recorded at stations located on the surface of sand dunes. Attempts to address the issue using conventional surface-consistent amplitude scaling methods without reducing these amplification effects generally fail, thus compromising the suitability of the processed data for amplitude versus offset analysis. In this study, we propose a new reference site technique to reduce the effects of sand dune amplification, enabling the production of data sets that are suitable for amplitude versus offset processing. The proposed technique uses a deterministic approach to derive surface-consistent, frequency-dependent de-amplification functions for shots and stations located on the dunes only. Two-dimensional synthetic and field data examples show that the technique significantly reduces the effects of sand dune amplification.     

Daily reservoir inflow forecasting using artificial neural networks with stopped training approach

In this paper, an early stopped training approach (STA) is introduced to train multi-layer feed-forward neural networks (FNN) for real-time reservoir inflow forecasting. The proposed method takes advantage of both Levenberg–Marquardt Backpropagation (LMBP) and cross-validation technique to avoid underfitting or overfitting on FNN training and enhances generalization performance. The methodology is assessed using multivariate hydrological time series from Chute-du-Diable hydrosystem in northern Quebec (Canada). The performance of the model is compared to benchmarks from a statistical model and an operational conceptual model. Since the ultimate goal concerns the real-time forecast accuracy, overall the results show that the proposed method is effective for improving prediction accuracy. Moreover it offers an alternative when dynamic adaptive forecasting is desired.     

Spatially constrained clustering of ecological units to facilitate the design of integrated water monitoring networks in the St. Lawrence Basin

Water monitoring networks are generally classified into surface water, precipitation, groundwater or water quality monitoring networks. The design of these networks typically occurs in isolation from each other. We present a regionalization approach to identify homogeneous subregions of large basins that are suitable as areas for the optimization of an integrated water monitoring network. The study area, which comprises a portion of the St. Lawrence Basin, was spatially divided using ecological units. For each ecological unit, 21 attributes were derived including both environmental and hydrological indicators. A spatially constrained regionalization technique was applied to define the final regions. A scree plot was used to determine the number of regions. The sensitivity of the technique to the correlation in the attribute data was removed by utilizing principal component analysis to reduce correlation between attribute data. During regionalization, the component values were weighted by their proportion of the total variance explained. The four regions in the final configuration had areas from 19% to 31% of the total area, 63,597 km². For the St. Lawrence Basin, this approach is effective for defining homogeneous regions that can be used in further research on the optimization of integrated water monitoring networks. The approach is portable to other regions and can incorporate any set of attribute data that is valuable to the regionalization objective.