An overview of biodiesel soil pollution: Data based on cytotoxicity and genotoxicity assessments

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mapping soil pollution by spatial analysis and fuzzy classification

Spatial analysis and fuzzy classification techniques were used to estimate the spatial distributions of heavy metals in soil. The work was applied to soils in a coastal region that is characterized by intense urban occupation and large numbers of different industries. Concentrations of heavy metals were determined using geostatistical techniques and classes of risk were defined using fuzzy classification. The resulting prediction mappings identify the locations of high concentrations of Pb, Zn, Ni, and Cu in topsoils of the study area. The maps show that areas of high pollution of Ni and Cu are located at the northeast, where there is a predominance of industrial and agricultural activities; Pb and Zn also occur in high concentrations in the northeast, but the maps also show significant concentrations of Pb and Zn in other areas, mainly in the central and southeastern parts, where there are urban leisure activities and trade centers. Maps were also prepared showing levels of pollution risk. These maps show that (1) Cu presents a large pollution risk in the north-northwest, midwest, and southeast sectors, (2) Pb represents a moderate risk in most areas, (3) Zn generally exhibits low risk, and (4) Ni represents either low risk or no risk in the studied area. This study shows that combining geostatistics with fuzzy theory can provide results that offer insight into risk assessment for environmental pollution.

The effects of soil pollution and its relation with morbidity in shoreline areas

Soil and subsoil pollution is not only significant in terms of environmental loss, but also a matter of environmental and public health. Solid, liquid and gaseous residues are the major soil contamination agents. They originate from urban conglomerates and industrial areas in which it is impossible to emphasize the chemical, petrochemical and textile industry; thermoelectric, mining, and ironmaster activities. The contamination process can thus be defined as a compound addition to soil, from what qualitative and or quantitative manners can modify soil's natural characteristics and use, producing baneful and deteriorative effects on human health. Studies have shown that human exposition to high concentration of some heavy metals found on soil can cause serious health problems, such as pulmonary or kidney complications, liver and nervous system harm, allergy, and the chronic exposition that leads to death. The present study searches for the correlation among soil contamination, done through a geochemical baseline survey of an industrial contamination area on the shoreline of Sao Paulo state. The study will be conducted by spatial analysis using Geographical Information Systems for mapping and regression analysis. The used data are 123 soil samples of percentage concentration of heavy metals. They were sampled and spatially distributed by geostatistics methods. To verify if there is a relation between heavy metals soil pollution and morbidity an executed correlation and regression analysis will be done using the pollution registers as the independent variables and morbidity as dependable variables. It is expected, by the end of the study, to identify the areas relation between heavy metals soil pollution and morbidity, moreover to be able to provide assistance in terms of new methodologies that could facilitate soil pollution control programs and public health planning. © 2010 WIT Press.

An overview of biodiesel soil pollution: Data based on cytotoxicity and genotoxicity assessments

Biodiesel production has received considerable attention in the recent past as a nonpolluting fuel. However, this assertion has been based on its biodegradability and reduction in exhaust emissions. Assessments of water and soil biodiesel pollution are still limited. Spill simulation with biodiesel and their diesel blends in soils were carried out, aiming at analyzing their cytotoxic and genotoxic potentials. While the cytotoxicity observed may be related to diesel contaminants, the genotoxic and mutagenic effects can be ascribed to biodiesel pollutants. Thus, taking into account that our data stressed harmful effects on organisms exposed to biodiesel-polluted soils, the designation of this biofuel as an environmental-friendly fuel should be carefully reviewed to assure environmental quality. (C) 2011 Elsevier B.V. All rights reserved.

Effects of metal pollution on soil macroinvertebrate burrow systems

A reduction in the numbers of macroinvertebrates present in soil may have a negative effect on soil structure, infiltration rates, and gas exchanges. Soil pollution by metal is known to have a detrimental effect on soil macrofauna. The aim of the present study was to evaluate (1) direct and indirect effects of soil pollution on soil macroinvertebrate bioturbation and (2) effects of the two macroinvertebrate communities found in a polluted and a nonpolluted area (one supposed sensitive, the other tolerant to metals) on burrow systems parameters. Macroinvertebrate porosity was studied using X-ray tomography. Three-dimensional reconstructions and characterisation of the burrow system were obtained using image analysis. Results showed that metal pollution principally affected the spatial distribution of macropores (more macropores were found near the soil surface) and the shape of the burrow system (branching rate was higher in the polluted soil), whereas soil macroinvertebrate composition principally affects burrow density parameters (the number of burrows was higher for the sensitive macroinvertebrate community).

Microbial Metabolic Potential Affected by Surplus Wastewater Irrigation in Tropical Soil Cultivated with Tifton 85 Bermuda Grass (Cynodon dactylon Pers. X C. niemfuensis Vanderyst)

Agricultural reuse of treated sewage effluent (TSE) is an environmental and economic practice; however, little is known about its effects on the characteristics and microbial function in tropical soils. The effect of surplus irrigation of a pasture with TSE, in a period of 18 months, was investigated, considering the effect of 0% surplus irrigation with TSE as a control. In addition, the experiment consisted of three surplus treatments (25%, 50%, and 100% excess) and a nonirrigated pasture area (SE) to compare the soil microbial community level physiological profiles, using the Biolog method. The TSE application increased the average substrate consumption of the soil microbial community, based on the kinetic parameters of the average well color development curve fitting. There were no significant differences between the levels of surplus irrigation treatments. Surplus TSE pasture irrigation caused minor increases in the physiological status of the soil microbial community but no detectable damage to the pasture or soil.

Availability of heavy metals in contaminated soil evidenced by chemical extractants

Heavy metals have been accumulating in Brazilian soils, due to natural processes, such as atmospheric deposition, or human industrial activities. For certain heavy metals, when in high concentrations in the soil, there is no specific extractant to determine the availability of these elements in the soil. The objective of the present study was to evaluate the availability of Cd, Cu, Fe, Mn, Pb and Zn for rice and soybeans, using different chemical extractants. In this study we used seven soil samples with different levels of contamination, in completely randomized experimental design with four replications. We determined the available concentrations of Cd, Cu, Fe, Mn, Pb and Zn extracted by Mehlich-1, HCl 0.1 mol L-1, DTPA, and organic acid extractants and the contents in rice and soybeans, which extracts were analyzed by ICP-OES. It was observed that Mehlich-1, HCl 0.1 mol L-1 and DTPA extractants were effective to assess the availability of Cd, Cu, Pb and Zn for rice and soybeans. However, the same was not observed for the organic acid extractant.

Sequential application of soil vapor extraction and bioremediation processes for the remediation of ethylbenzene-contaminated soils

Soil vapor extraction (SVE) is an efficient, well-known and widely applied soil remediation technology. However, under certain conditions it cannot achieve the defined cleanup goals, requiring further treatment, for example, through bioremediation (BR). The sequential application of these technologies is presented as a valid option but is not yet entirely studied. This work presents the study of the remediation of ethylbenzene (EB)-contaminated soils, with different soil water and natural organic matter (NOMC) contents, using sequential SVE and BR. The obtained results allow the conclusion that: (1) SVE was sufficient to reach the cleanup goals in 63% of the experiments (all the soils with NOMC below 4%), (2) higher NOMCs led to longer SVE remediation times, (3) BR showed to be a possible and cost-effective option when EB concentrations were lower than 335 mg kgsoil −1, and (4) concentrations of EB above 438 mg kgsoil −1 showed to be inhibitory for microbial activity.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Transfer of cadmium and barium from soil to crops grown in tropical soils

Phytotoxicity and transfer of potentially toxic elements, such as cadmium (Cd) or barium (Ba), depend on the availability of these elements in soils and on the plant species exposed to them. With this study, we aimed to evaluate the effect of Cd and Ba application rates on yields of pea (Pisum sativum L.), sorghum (Sorghum bicolor L.), soybean (Glycine max L.), and maize (Zea mays L.) grown under greenhouse conditions in an Oxisol and an Entisol with contrasting physical and chemical properties, and to correlate the amount taken up by plants with extractants commonly used in routine soil analysis, along with transfer coefficients (Bioconcentration Factor and Transfer Factor) in different parts of the plants. Plants were harvested at flowering stage and measured for yield and Cd or Ba concentrations in leaves, stems, and roots. The amount of Cd accumulated in the plants was satisfactorily evaluated by both DTPA and Mehlich-3 (M-3). Mehlich-3 did not relate to Ba accumulated in plants, suggesting it should not be used to predict Ba availability. The transfer coefficients were specific to soils and plants and are therefore not recommended for direct use in risk assessment models without taking soil properties and group of plants into account.

Biodiversity of Collembola in urban soils and their use as bioindicators for pollution

The objective of this work was to evaluate the effects of pollutants on the abundance and diversity of Collembola in urban soils. The research was carried out in three parks (Cişmigiu, Izvor and Unirea) in downtown Bucharest, where the intense car traffic accounts for 70% of the local air pollution. One site in particular (Cişmigiu park) was highly contaminated with Pb, Cd, Zn and Cu at about ten times the background levels of Pb. Collembola were sampled in 2006 (July, September, November) using the transect method: 2,475 individuals from 34 species of Collembola were collected from 210 samples of soil and litter. Numerical densities differed significantly between the studied sites.The influence of air pollutants on the springtail fauna was visible at the species richness diversity and soil pollution levels. Species richness was lowest in the most contaminated site (Cismigiu, 11 species), which presented an increase in springtails abundances, though. Some species may become resistant to pollution and occur in high numbers of individuals in polluted sites, which makes them a good bioindicator of pollutants.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Chemical attributes of a Hapludox soil after nine years of pig slurry application

The objective of this study was to evaluate the pig slurry application effects on chemical attributes of a Hapludox soil managed under no-tillage system. Treatments consisted of 50, 100 and 200 m³ ha-1 per year of pig slurry application, and a control with replacement of P and K exported through harvested grains. Attributes related to soil chemical reaction, exchange complex, and nutrient contents were determined in soil samples collected in the ninth year of experimentation from 0 - 0.025, 0.025 - 0.05, 0.05 - 0.10, 0.10 - 0.20, 0.20 - 0.40 and 0.40 - 0.60 m soil depths. The continuous application of high doses of pig slurry on the Oxisol surface under no-tillage acidifies the soil and increases Al, P, Cu, and Zn contents down to 0.2-m depth, and K levels down to 0.6-m depth.

Impacts of the application of swine wastewater in percolate and in soil cultivated with soybean

The aim of this study was to evaluate the possible impacts caused in the soil and in the percolate in lysimeters of drainage with application of different rates of swine wastewater (SW) during the cycle of soybean culture and to assess the productivity of it. The experiment was conducted at the Agricultural Engineering Experimental Center of UNIOESTE. The soil was classified as typical Distroferric Red Latosol. There were twenty-four drainage lysimeters in the area in which the soybean was cultivated, cultivar CD 214. Four SW depths (0; 100; 200 and 300 m³ ha-1) were applied to the soil seven days before the sowing in a single application combined with two mineral fertilizations in the sowing (with and without recommended fertilization during sowing), and three repetitions per treatment. It was realized three collections of percolate in each experimental portion, the first was conducted 40 days after sowing (DAS); the second at 72 DAS, and the third at the end of crop cycle (117 DAS). It was evaluated in the percolate the pH, calcium, magnesium, potassium, phosphorus, and total nitrogen. Based on the results, it was possible to observe that the level of K, P and N in the soil increased according tothe increase of SW rates. The levels of K and P in the percolate were higher for higher rates of SW. The productivity was not influenced by the application of SW or by fertilization.

Complexation of copper by sewage sludge-derived dissolved organic matter: Effects on soil sorption behaviour and plant uptake

The complexation of Cu by sewage sludge-derived dissolved organic matter (SSDOM) is a process by which the environmental significance of the element may become enhanced due to reduced soil sorption and, hence, increased mobility. The work described in this paper used an ion selective electrode procedure to show that SSDOM complexation of Cu was greatest at intermediate pH values because competition between hydrogen ions and Cu for SSDOM binding sites, and between hydroxyl ions and SSDOM as Cu ligands, was lowest at such values. Batch sorption experiments further showed that the process of Cu complexation by SSDOM provided an explanation for enhanced desorption of Cu from the solid phase of a contaminated, organic matter-rich, clay loam soil, and reduced adsorption of Cu onto the solid phase of a sandy loam soil. Complexation of Cu by SSDOM did not affect uptake of Cu by spring barley plants, when compared to free ionic Cu, in a sand-culture pot experiment. However, it did appear to lead to greater biomass yields of the plant; perhaps indicating that the Cu-SSDOM complex had a lower toxicity towards the plant than the free Cu ion.