Determination of toxic elements in waters and sediments from River Subin in the Ashanti Region of Ghana

Waters and sediments of Subin River, which flows through the industrial and commercial areas of Kumasi in the Ashanti region of Ghana, were geochemically investigated to ascertain heavy metal pollution levels due to anthropogenic activities. The study shows preoccupying pollution levels that constitute a threat to public and ecological systems. The waters of Subin River are neutral to slightly basic, inferred from pH values of 6.89–7.65). Electric conductivity (EC) of the waters ranges from 822 to 1,821 μs/cm and the range of total dissolved solids (TDS) is from 409 to 913 mg/l. Toxic elements contents of sediments and waters from 10 sites along the river were analysed by instrumental neutron activation analysis (INAA), and Al, As, Cd, Cr, Cu and Zn were determined. The concentrations of Al, As, Cd, Cr, Cu and Zn in the waters range between 4.02–15.18, 0.007–0.16, 0.002–0.05, 0.001–0.019, 1.32–7.04 and 4.28–10.2 mg/l, respectively. The contamination factors (CF) computed for the elements indicate that with the exception of sampling site S10, the sediments are polluted with Cd. Chromium contamination in the sediments is observed at S6 and S7, where the CF values were 1.39 and 1.52, respectively. The pollution load indices (PLI) were low (<1) and ranged from 0.14 to 0.75, suggesting that the overall sediment column of the river is not polluted.     

The Need for Scale Sensitive Approaches in Spatially Explicit Land Use Change Modeling

This paper introduces some of the issues that are relevant to the spatially explicit modeling of land use systems. A short overview is given of the ways and means in which a number of different land use change models describe the land use system. Specific attention is given to the empirical modeling approach used in the CLUE (Conversion of Land Use and its Effects) modeling framework. This approach is demonstrated for three case studies in China, Ecuador and the Atlantic Zone of Costa Rica. These case-studies illustrate the methodology for multi-scale analysis of land use driving factors and their application in spatially explicit modeling exercises. Model functioning, performance and limitations are discussed. The presented case-studies indicate that empirical modeling results can contribute to a better theoretic imbedding of land use change research in scale sensitive and integrated theories.     

Environmetrics to evaluate marine environment quality

The environmetric data analysis of analytical datasets from sediment and benthic organisms samples collected from different sampling sites along the coast of Black Sea near to City of Varna, Bulgaria has given some important indications about the bioindication properties of both type of samples. Various multivariate statistical methods like cluster analysis, principal components analysis, source apportioning modeling and partial least square (PLS) modeling were used in order to classify and interpret the parameters describing the chemical content of the coastal sediments (major components, heavy metals and total organic carbon) and benthic organisms (heavy metals). It has been shown that seriously polluted coastal zones are indicated in the same way by all benthic species, although some specificity could be detected for moderate polluted regions' e.g. polychaeta accumulated preferably Co, Cr, Cu, and Pb; crustacea - As, Cd, and Ni; mollusca - Zn. The identified latent factors responsible for the dataset structure are clearly indicated and apportioned with respect to their contribution to the total mass or total concentration of the species in the samples. The linear regression and PLS models indicated that a reliable forecast about the relation between naturally occurring chemical components and polluting species accumulated in the benthic organisms is possible.     

Concentration of heavy metals in Karanja reservoir, Bidar district, Karnataka, India

In this study, some heavy metals concentrations (Zn, Pb, Fe, Mn, Cu, Ni) and other physico-chemical parameters were studied during October 2001 to September 2003 in Karanja reservoir, Bidar district. Water quality parameters were collected monthly basis whereas heavy metals were analyzed by Atomic Adsorption Spectrometer (AAS). Heavy metals have shown within the permissible limits, except Fe and Ni were recorded higher values in southwest monsoon, where as Mn has showed higher concentration in northeast monsoon in and summer. All other physico-chemical parameters are with in the permissible limit. Water is moderately hard and reservoir is productive.     

Development of an integrated sensor to measure odors

Odorous air samples collected from several sources were presented to an olfactometer, an electronic nose, a hydrogen sulfide (H(2)S) detector and an ammonia (NH(3)) detector. The olfactometry measurements were used as the expected values while measurements from the other instrumentation values became input variables. Five hypotheses were established to relate the input variables and the expected values. Both linear regression and artificial neural network analyses were used to test the hypotheses. Principal component analysis was utilized to reduce the dimensionality of the electronic nose measurements from 33 to 3 without significant loss of information. The electronic nose or the H(2)S detector can individually predict odor concentration measurements with similar accuracy (R (2) = 0.46 and 0.50, respectively). Although the NH(3) detector alone has a very poor relationship with odor concentration measurements, combining the H(2)S and NH(3) detectors can predict odor concentrations more accurately (R (2) = 0.58) than either individual instrument. Data from the integration of the electronic nose, H(2)S, and NH(3) detectors produce the best prediction of odor concentrations (R (2) = 0.75). With this accuracy, odor concentration measurements can be confidently represented by integrating an electronic nose, and H(2)S and NH(3) detectors.     

Passive sampling of glycol ethers and their acetates in indoor air

This study examined the performances of a thermal desorbable radial diffusive sampler for the weekly measurement of eight glycol ethers in indoor air and described the results of an application of this method carried out as part of HABIT'AIR Nord - Pas de Calais program for the air monitoring of these compounds in sixty homes located in northern France. The target compounds were the four glycol ethers banned from sale to the public in France since the 1990s (i.e. 2-methoxy ethanol, 2-ethoxy ethanol and their acetates) and four other glycol ethers derivatives of which the use have increased considerably (i.e. 1-methoxy-2-propanol, 2-butoxy ethanol and their acetates).A test program was carried out with the aim of validating the passive sampling method. It allowed the estimation of all the parameters of a method for each compound (calibration, analytical precision, desorption efficiency, sampling rate in standard conditions, detection limit and stability of sample before and after exposure), the examination of the influence of environmental factors on the sampling rate by some exposure chamber experiments and the assessment of the uncertainty of the measurements.The results of this evaluation demonstrated that the method has turned out to be suitable for six out of eight glycol ethers tested. The effect of the environmental factors on the sampling rates was the main source of measurement uncertainty. The measurements done in sixty homes revealed a relative abundance of 1-methoxy-2-propanol that was found in more than two thirds of homes at concentration levels of 4.5 microg m(-3) on average (a maximum value of 28 microg m(-3)). 1-methoxy-2-propanol acetate and 2-butoxy ethanol were also detected, but less frequently (in 19% of homes) and with the concentrations below 12 microg m(-3). The highest levels of these glycol ethers appear to be in relation to the emissions occurring at the time of cleaning tasks.     

Inputs of chemicals from recreational activities into the Norwegian coastal zone

Wastewater treatment works effluent is often considered to be one of the most important point sources of a wide range of anthropogenic contaminants to aquatic systems, however, this paper discusses other potential sources. With the aim of establishing the important sources of UV filters, insect repellent and biocides to the aquatic environment, samples were collected from sites with direct (bathing areas and marinas) and indirect (sites receiving wastewater effluent) human influences. Sunscreens containing UV filters are used in large volumes during the summer months and often applied shortly before a person enters the water for swimming activities. The results presented here demonstrate that washing directly from the skin is an important point source of 4 UV filters to the Oslofjord. The insect repellent, diethyl toluamide, was also measured and it was concluded that washing from the skin was not such an important point source into the fjord. Concentrations of the biocide Irgarol 1051 were also measured and were elevated in the small boat marina and surrounding enclosed area. This work demonstrates that man recreational water-based activities are a diffuse source of some contaminants into coastal and fjord environments and this study provides an initial assessment of the levels being released.     

Spatial variation in soil carbon in the organic layer of managed boreal forest soil—implications for sampling design

We studied within-site spatial variation of the carbon stock in the organic layer of boreal forest soil. A total of 1,006 soil samples were taken in ten forest stands (five Scots pine stands and five Norway spruce stands). Our results indicate that the spatial autocorrelation disappears at a distance of 75-225 cm. This spatial autocorrelation should be taken into account in the sampling design by locating the sampling points at adequate intervals. With a sample size of over 20-30 samples per site, additional soil samples do not notably improve the precision of the site mean estimate. An adequate sample size is dependent on the purpose of sampling and on the site-specific soil variation. Our results on the dependence between sample size and precision of the mean estimates can be applied in designing efficient soil monitoring in boreal coniferous forests.