Monitoring of specific activities of long-lived radioactive elements along the Mini- Okoro/Oginigba Creek, Port Harcourt

Presence and concentrations of radionuclides could be as a result of natural and human activities. This study examined the associations and differences among soil, sediment and water specific activities of long-lived radioactive element (LLRE). Gamma spectroscopy was used to measure the concentration of the LLRE along the Mini Okoro/Oginigba Creek, Port Harcourt. Specific activities of three selected LLRE were derived. Correlation analysis was carried out to examine associations among the specific activities across different substrates. A strong and a significant negative correlation exists between the specific activities of Water 40K and Soil 232Th (r =-0.721, p<0.05); Water 238U and Soil 238U (r = -0.717, p<0.05) and Water 40K and Sediment 238U (r=-0.69, p<0.05). Comparison using Mann-Whitney U test shows that, soil and sediment are similar in their specific activities with Z values of -0.408, -1.209 and -1.021 (p > 0.05) for 40K, 232Th and 238U respectively. The concentration of solid samples (soil and sediment) is different from the liquid (water) samples. These associations can be attributed to some specific underlying factors. And in other to understand them there is need for more studies.

Chronic contamination assessment integrating biomarkers' responses in transplanted mussels-A seasonal monitoring

This study aimed to provide the first biomonitoring integrating biomarkers and bioaccumulation data in São Paulo coast, Brazil and, for this purpose, a battery of biomarkers of defense mechanisms was analyzed and linked to contaminants' body burden in a weigh-of-evidence approach. The brown mussel Perna perna was selected to be transplanted from a farming area (Caraguatatuba) to four possibly polluted sites: Engenho D'Agua, DTCS (Dutos e Terminais do Centro-Oeste de São Paulo) oil terminal (Sao Sebastiao zone), Palmas Island, and Itaipu (It; Santos Bay zone). After 3 months of exposure in each season, mussels were recollected and the cytochrome P4501A (CYP1A)- and CYP3A-like activities, glutathione-S-transferase and antioxidants enzymes (catalase, glutathione peroxidase, and glutathione reductase) were analyzed in gills. The concentrations of polycyclic aromatic hydrocarbons, linear alkylbenzenes, and nonessential metals (Cr, Cd, Pb, and Hg) in whole tissue were also analyzed and data were linked to biomarkers' responses by multivariate analysis (principal component analysisfactor analysis). A representation of estimated factor scores was performed to confirm the factor descriptions and to characterize the studied stations. Biomarkers exhibited most significant alterations all year long in mussels transplanted to It, located at Santos Bay zone, where bioaccumulation of organic and inorganic compounds was detected. This integrated approach using transplanted mussels showed satisfactory results, pointing out differences between sites, seasons, and critical areas, which could be related to land-based contaminants' sources. The influence of natural factors and other contaminants (e.g., pharmaceuticals) on biomarkers' responses are also discussed. (C) 2010 Wiley Periodicals, Inc. Environ Toxicol, 2012.

A Review of the Richland County DJJ Electronic Monitoring Program

The Electronic Monitoring Program for Richland County DJJ was selected as a topic to research because there were problems in the Richland County DJJ Office with equipment being lost, equipment being damaged, and staff failing to utilize the technology properly. If utilized correctly, Electronic Monitoring Technology can help reduce commitments of juveniles to secure facilities which will save the State of South Carolina incarceration costs. This paper explores the training and planning of electronic monitoring in Richland County.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.