Soil contamination of heavy metals in the Katedan Industrial Development Area, Hyderabad, India

Studies on quantitative soil contamination due to heavy metals were carried out in Katedan Industrial Development Area (KIDA), south of Hyderabad, Andhra Pradesh, India under the Indo-Norwegian Institutional Cooperation Programme. The study area falls under a semi-arid type of climate and consists of granites and pegmatite of igneous origin belonging to the Archaean age. There are about 300 industries dealing with dyeing, edible oil production, battery manufacturing, metal plating, chemicals, etc. Most of the industries discharge their untreated effluents either on open land or into ditches. Solid waste from industries is randomly dumped along roads and open grounds. Soil samples were collected throughout the industrial area and from downstream residential areas and were analysed by X-ray Fluorescence Spectrometer for fourteen trace metals and ten major oxides. The analytical data shows very high concentrations of lead, chromium, nickel, zinc, arsenic and cadmium through out the industrial area. The random dumping of hazardous waste in the industrial area could be the main cause of the soil contamination spreading by rainwater and wind. In the residential areas the local dumping is expected to be the main source as it is difficult to foresee that rain and wind can transport the contaminants from the industrial area. If emission to air by the smokestacks is significant, this may contribute to considerable spreading of contaminants like As, Cd and Pb throughout the area. A comparison of the results with the Canadian Soil Quality Guidelines (SQGL) show that most of the industrial area is heavily contaminated by As, Pb and Zn and local areas by Cr, Cu and Ni. The residential area is also contaminated by As and some small areas by Cr, Cu, Pb and Zn. The Cd contamination is detected over large area but it is not exceeding the SQGL value. Natural background values of As and Cr exceed the SQGL values and contribute significantly to the contamination in the residential area. However, the availability is considerably less than anthropogenic contaminants and must therefore be assessed differently. The pre- and post-monsoon sampling over two hydrological cycles in 2002 and 2003 indicate that the As, Cd and Pb contaminants are more mobile and may expect to reach the groundwater. The other contaminants seem to be much more stable. The contamination is especially serious in the industrial area as it is housing a large permanent residing population. The study not only aims at determining the natural background levels of trace elements as a guide for future pollution monitoring but also focuses on the pollution vulnerability of the watershed. A plan of action for remediation is recommended.     

Soil and vegetation carbon stocks in Brazilian Western Amazonia: relationships and ecological implications for natural landscapes

The relationships between soils attributes, soil carbon stocks and vegetation carbon stocks are poorly know in Amazonia, even at regional scale. In this paper, we used the large and reliable soil database from Western Amazonia obtained from the RADAMBRASIL project and recent estimates of vegetation biomass to investigate some environmental relationships, quantifying C stocks of intact ecosystem in Western Amazonia. The results allowed separating the western Amazonia into 6 sectors, called pedo-zones: Roraima, Rio Negro Basin, Tertiary Plateaux of the Amazon, Javari-Juruá-Purus lowland, Acre Basin and Rondonia uplands. The highest C stock for the whole soil is observed in the Acre and in the Rio Negro sectors. In the former, this is due to the high nutrient status and high clay activity, whereas in the latter, it is attributed to a downward carbon movement attributed to widespread podzolization and arenization, forming spodic horizons. The youthful nature of shallow soils of the Javari-Juruá-Purus lowlands, associated with high Al, results in a high phytomass C/soil C ratio. A similar trend was observed for the shallow soils from the Roraima and Rondonia highlands. A consistent east–west decline in biomass carbon in the Rio Negro Basin sector is associated with increasing rainfall and higher sand amounts. It is related to lesser C protection and greater C loss of sandy soils, subjected to active chemical leaching and widespread podzolization. Also, these soils possess lower cation exchangeable capacity and lower water retention capacity. Zones where deeply weathered Latosols dominate have a overall pattern of high C sequestration, and greater than the shallower soils from the upper Amazon, west of Madeira and Negro rivers. This was attributed to deeper incorporation of carbon in these clayey and highly pedo-bioturbated soils. The results highlight the urgent need for refining soil data at an appropriate scale for C stocks calculations purposes in Amazonia. There is a risk of misinterpreting C stocks in Amazonia when such great pedological variability is not taken into account.     

Micromorphology and chemistry of airborne particles in Brussels during agriculture working periods in surrounding region

The main objective of our research was to compare the airborne particle micromorphology and chemistry in the Brussels environment during agriculture working periods in the surrounding farming region. We used specific methods and instrumentation that are adapted to the climate peculiarities of the Brussels region, the period of investigations (12 months) and the proposed objectives. For the agricultural works we defined the following six periods: before sowing, sowing, after sowing, before harvest, harvest and after harvest. The results indicate a possible temporal correlation between agricultural work periods and airborne particle concentration, micromorphology and chemistry in the Brabant-Brussels region. For wheat and corn plant-growth periods, the average particle size, defined as the area obtained by a planar projection of the particulate, showed important variations in time. For sugar beet and endive, the average area size variations are less important. The roughness and sphericity parameters for the growth periods of the four different plants also showed significant differences. Many of the larger particulates (> 10 microm) are aggregates of even finer particles coated with many still finer ones. The airborne particle chemistry averages (atomic percentage At%), showed that three constituents (Si, S and Fe) dominate all the samples (except for particles 3-10 microm in size, which contain a relatively large percentage of Al). Applying similar investigation methods to study the correlations between airborne particle dynamics in urban zones and the agriculture working periods in their surrounding regions could be of interest to better understand the complexity of the PM problematic.     

Effect of Trivalent and Hexavalent Chromium on Carbohydrate Metabolism of a Freshwater Field Crab, Barytelphusa guerini

The in vivo toxic impact of chromium in its two forms(trivalent and hexavalent) on glycogen metabolism in the tissuesof freshwater field crab Barytelphusa guerini was studied.In a sublethal long-term exposure of 30, 60 and 90 days, thedepletion of glycogen and glucose levels reflected in theactivity of glycogenphosphorylases `a' and `ab'. A tissuespecific and time-dependent variation in tissue glucose reservesparelleled the progressive accumulation of haemolymph sugars.Trivalent and hexavalent chromium induced alterations in metabolite levels and in enzyme activities in the tissues. Themagnitude of responses were found more in the hexavalent formthan in trivalent form.     

Migratory crustaceans as biomonitors of metal pollution in their nursery areas. The Lesina lagoon (SE Italy) as a case study

Lichens collected in an unpolluted forest (background) in November 2004, transplanted at 41 sampling sites along the Madina-Tetteh Quarshie road, retrieved in February 2005 and analysed were found to contain higher concentrations of Manganese (Mn), Vanadium (V), lead, Cadmium (Cd), Chromium (Cr) and Nickel (Ni) than in the background area. Observations showed that of all the heavy metals studied, Manganese concentrations were highest at all sampling points with maximum values around a traffic light, an intersection and car fitting/mechanic workshops. Significant correlations were found between Ni and Mn, Mn and Cr and Mn and V. Statistical analysis also revealed a relatively even dispersion of the studied elements on the eastern side of the road than on the western side.     

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.     

Prediction models of CO, SPM and SO2 concentrations in the Campo de Gibraltar Region, Spain: a multiple comparison strategy

The 'Campo de Gibraltar' region is a very industrialized area where very few air pollution studies have been carried out. Up to date, no model has been developed in order to predict air pollutant levels in the different towns spread in the region. Carbon monoxide (CO), Sulphur dioxide (SO(2)) and suspended particulate matter (SPM) series have been investigated (years 1999-2000-2001). Multilayer perceptron models (MLPs) with backpropagation learning rule have been used. A resampling strategy with two-fold crossvalidation allowed the statistical comparison of the different models considered in this study. Artificial neural networks (ANN) models were compared with Persistence and ARIMA models and also with models based on standard Multiple Linear Regression (MLR) over test sets with data that had not been used in the training stage. The models based on ANNs showed better capability of generalization than those based on MLR. The designed procedure of random resampling permits an adequate and robust multiple comparison of the tested models. Principal component analysis (PCA) is used to reduce the dimensionality of data and to transform exogenous variables into significant and independent components. Short-term predictions were better than medium-term predictions in the case of CO and SO(2) series. Conversely, medium-term predictions were better in the case of SPM concentrations. The predictions are significantly promising (e.g., d (SPM 24-ahead) = 0.906, d (CO 1-ahead) = 0.891, d (SO2 1-ahead) = 0.851).     

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.