Effective elution of RDX and TNT from particles of Comp B in surface soil

During live fire training exercises, large amounts of explosives are consumed. Low order detonations of high explosive payloads result in the patchy dispersal of particles of high explosive formulations over large areas of firing range soils. Dissolution of explosives from explosive formulation particles into soil pore water is a controlling factor for transport, fate, and effects of explosive compounds. We developed an empirical method to evaluate soils based on functionally defined effective dissolution rates. An automated Accelerated Solvent Extractor was used to determine the effective elution rates under controlled conditions of RDX and TNT from soil columns containing particles of Comp B. Contrived soils containing selected soil geosorbants and reactive surfaces were used to quantitatively determine the importance of these materials. Natural soils from training ranges of various soil types were also evaluated. The effects of geosorbants on effective elution rates were compound- and sorbent-specific. TNT elution was less than that of RDX and was greatly slowed by humic acid. Iron and iron-bearing clays reduced the effective elution rates of both RDX and TNT. This empirical method is a useful tool for directly generating data on the potential for explosives to leach from firing range soils, to identify general bulk soil characteristics that can be used to predict the potential, and to identify means to engineer soil treatments to mitigate potential transport.     

Antimony distribution and mobility in topsoils and plants (Cytisus striatus, Cistus ladanifer and Dittrichia viscosa) from polluted Sb-mining areas in Extremadura (Spain)

A study about topsoil antimony distribution and mobility from the soils to the biomass has been afforded in three abandoned Sb mining areas located at Extremadura. Physico-chemical characteristics of the soils and total antimony levels were measured in soils and autochthonous plant species (Cytisus striatus, Cistus ladanifer and Dittrichia viscosa). Comparison with corresponding values in reference areas isolated from the mining activities is discussed. Antimony mobility in the soils was estimated by measuring the water extractable fraction; low results were obtained for the three soil areas, with no statistical differences. Plant ability to accumulate antimony was estimated by use of plant accumulation coefficients (PAC). Seasonal (spring vs. autumn) effects on the antimony content in the plant species. Cytisus striatus from Mari Rosa mine presented antimony excluder characteristics, whereas Dittrichia viscosa specimens growing in San Antonio mine showed a significant antimony bioaccumulation.     

Complexing capacity of natural waters carrying a great amount of suspended matter

The cadmium binding properties of waters of the superior section of the Rio de la Plata estuarine were determined over a three-year period. Samples were collected at different hydrodynamic conditions. The complexing capacity was determined by square wave anodic stripping voltammetry (SWASV). Titration curve data were analyzed using a multivariable regression. Suspended particulate matter (SPM) was identified by XR diffraction and FTIR. These analyses showed that SPM principal components are clays (illite, montmorillonite and chlorite). The study was applied to the untreated, filtered and centrifuged fractions of each sample at the pH of the natural waters and at pH 1. The results show that the contribution of dissolved organic matter to the complexing capacity is negligible when compared with SPM. At natural pH, the complexing capacity of filtered and untreated fractions can be described by considering two kinds of binding sites. The associated conditional binding constants are independent of the concentration of suspended matter. Their average logarithms are ca. 6.5 and ca. 4.4. The total concentration of binding sites (S(T)) is in microM range, which is about three orders of magnitude higher than that reported for most of the studied estuaries. This difference is explained on the basis of the great amount of SPM. Hydrodynamic conditions produce variations in the concentration and composition of the SPM. At pH 1 samples still exhibit an important complexing capacity with only one binding site with log K(cond) ca. 5.4. These differences could be attributed to superficial modifications that take place at very low pH.     

Prediction of ozone concentrations in Oporto city with statistical approaches

The performance of three statistical methods: time-series, multiple linear regression and feedforward artificial neural networks models were compared to predict the daily mean ozone concentrations. The study here reported was based on data from one urban site with traffic influences and one rural background site. The studies were performed for the year 2002 and the respective four trimesters separately. In the multiple linear regression and feedforward artificial neural network models, the concentrations of ozone, the concentrations of its precursors (nitrogen oxides) and some meteorological variables for one and two days before the prediction day were used as predictors. For the application of these models in the validation step, the inputs of ozone concentration for one and two days before were replaced by the ozone concentrations predicted by the models. The results showed that time-series modelling was not profitable. In the development step, similar performances were obtained with multiple linear regression and feedforward artificial neural network. Better performance indexes were achieved with feedforward artificial neural network models in validation step. Concluding, feedforward artificial neural network models were more efficient to predict ozone concentrations.     

Rainfall trends in twentieth century over Kerala,India

Attempts were made to study temporal variation in monthly, seasonal and annual rainfall over Kerala, India, during the period from 1871 to 2005. Longterm changes in rainfall determined by Man-Kendall rank statistics and linear trend. The analysis revealed significant decrease in southwest monsoon rainfall while increase in post-monsoon season over the State of Kerala which is popularly known as the “Gateway of summer monsoon”. Rainfall during winter and summer seasons showed insignificant increasing trend. Rainfall during June and July showed significant decreasing trend while increasing trend in January, February and April. Hydel power generation and water availability during summer months are the concern in the State due to rainfall decline in June and July, which are the rainiest months. At the same time, majority of plantation crops are likely to benefit due to increase in rainfall during the post-monsoon season if they are stable and prolonged.     

Global sensitivity analysis of a process-based model for ammonia emissions from manure storage and treatment structures

The development of process-based models to estimate ammonia emissions from animal feeding operations (AFOSs) is sought to replace costly and time-consuming direct measurements. Critical to process-based model development is conducting sensitivity analysis to determine the input parameters and their interactions that contribute most to the variance of the model output. Global and relative sensitivity analyses were applied to a process-based model for predicting ammonia emissions from the surface of anaerobic lagoons for treating and storing manure. The objectives were to compare global sensitivity analysis (GSA) to relative (local) sensitivity analysis (RSA) on a process-based model for ammonia emissions. Based on the first-order coefficient, both GSA and RSA showed the model input parameters in order of importance in process model for ammonia emissions from lagoon surfaces were: (i) pH, (ii) lagoon liquid temperature, (iii) wind speed above the lagoon surface, and (iv) the concentration of ammoniacal nitrogen in the lagoon. The GSA revealed that interactions between model parameters accounted for over two-thirds of the model variance, a result that cannot be achieved using traditional RSA. Also, the GSA showed that parameter interactions involving liquid pH had more impact on the model output variance than the single parameters: (i) temperature, (ii) wind speed, or (iii) total ammoniacal nitrogen. This study demonstrates that GSA provides a more complete analysis of model input parameters and their interactions on the model output compared to RSA. A comprehensive tutorial regarding the application of GSA to a process model is presented.