Pesticide bioconcentration factor estimate in apples.

This work objective was to estimate the bioconcentration factor (BCF) of thirty six pesticides used in the Brazilian integrated apple production systems (IAP), in order to select priority pesticides to be monitored in apples. A hypothetical apple orchard was assumed and the model applied was according to Paraíba (2007) [Pesticide bioconcentration modeling for fruit trees. Chemosphere (66:1468-1475)]. The model relates BCF with plant and pesticide characteristics. The octanol-water partition coefficients of pesticides and their degradation rates in the soil were used. The following plant variables were considered: growth rate, total dry biomass, daily water transpiration rate, and total volume of water necessary to produce one kg of fresh fruit per plant. The pesticide stem-water partition coefficient and the transpiration stream concentration factor were calculated using equations that relate each coefficient with the octanol-water partition coefficient. The pesticide BCF in fruits is an important indicator of the pesticide affinity to fruits, and helps to improve the integrated production systems.

Pesticide bioconcentration modelling for fruit trees.

The model presented allows simulating the pesticide concentration in fruit trees and estimating the pesticide bioconcentration factor in fruits of woody species. The model allows estimating the pesticide uptake by plants through the water transpiration stream and also the time in which maximum pesticide concentration occur in the fruits. The equation proposed presents the relationships between bioconcentration factor (BCF) and the following variables: plant water transpiration volume (Q), pesticide transpiration stream concentration factor (TSCF), pesticide stem-water partition coefficient (KWood,w), stem dry biomass (M) and pesticide dissipation rate in the soil-plant system (kEGS). The modeling started and was developed from a previous model ?Fruit Tree Model? (FTM), reported by Trapp and collaborators in 2003, to which was added the hypothesis that the pesticide degradation in the soil follows a first order kinetic equation. The model fitness was evaluated through the sensitivity analysis of the pesticide BCF values in fruits with respect to the model entry data variability.

Validation of neurobehavioral studies for evaluating the perinatal effects of single and mixture exposure to pesticides.

2009

Humified fraction of organic matter due to plant mixture cultivation.

The aim of this study was to assess the organic matter changes in quantity and quality, particularly of the humic fraction in the surface layer (0?20 cm), of a Typic Plinthustalf soil under different management of plant mixtures used as green manure for mango (Mangifera indica L.) crops. The plant mixtures, which were seeded between rows of mango trees, were formed by two groups of leguminous and non -leguminous plants. Prior to sowing, seeds were combined in different proportions and compositions constituting the following treatments: 100% non-leguminous species (NL); 100% leguminous species (L); 75% L and 25% NL; 50% L and 50% NL; 25% L and 75% NL; and 100% spontaneous vegetation, considered a control. The plant mixtures that grew between rows of mango trees caused changes in the chemical composition of the soil organic matter, especially for the treatments 50% L and 50% NL and 25% L and 75% NL, which increased the content of humic substances in the soil organic matter. However, the treatment 25% L and 75% NL was best at minimising loss of total organic carbon from the soil. The humic acids studied have mostly aliphatic characteristics, showing large amounts of carboxylic and nitrogen groups and indicating that most of the organic carbon was formed by humic substances, with fulvic acid dominating among the alkali soluble fractions.

Numeric model to estimate pesticide deposition and losses from aerial spraying.

Pesticides applications have been described by many researches as a very inefficient process. In some cases, there are reports that only 0.02% of the applied products are used for the effective control of the problem. The main factor that influences pesticides applications is the droplet size formed on spraying nozzles. Many parameters affects the dynamic of the droplets, like wind, temperature, relative humidity, and others. Small droplets are biologically more active, but they are affected by evaporation and drift. On the other hand, the great droplets do not promote a good distribution of the product on the target. In this sense, associated with the risk of non target areas contamination and with the high costs involved in applications, the knowledge of the droplet size is of fundamental importance in the application technology. When sophisticated technology for droplets analysis is unavailable, is common the use of artificial targets like water-sensitive paper to sample droplets. On field sampling, water-sensitive papers are placed on the trials where product will be applied. When droplets impinging on it, the yellow surface of this paper will be stained dark blue, making easy their recognition. Collected droplets on this papers have different kinds of sizes. In this sense, the determination of the droplet size distribution gives a mass distribution of the material and so, the efficience of the application of the product. The stains produced by droplets shows a spread factor proportional to their respectives initial sizes. One of methodologies to analyse the droplets is a counting and measure of the droplets made in microscope. The Porton N-G12 graticule, that shows equaly spaces class intervals on geometric progression of square 2, are coulpled to the lens of the microscope. The droplet size parameters frequently used are the Volumetric Median Diameter (VMD) and the Numeric Median Diameter. On VMD value, a representative droplets sample is divided in two equal parts of volume, in such away one part contains droplets of sizes smaller than VMD and the other part contains droplets of sizes greater that VMD. The same process is done to obtaining the NMD, which divide the sample in two equal parts in relation to the droplets size. The ratio between VMD and NMD allows the droplets uniformity evaluation. After that, the graphics of accumulated probability of the volume and size droplets are plotted on log scale paper (accumulated probability versus median diameter of each size class). The graphics provides the NMD on the x-axes point corresponding to the value of 50% founded on the y-axes. All this process is very slow and subjected to operator error. So, in order to decrease the difficulty envolved with droplets measuring it was developed a numeric model, implemented on easy and accessfull computational language, which allows approximate VMD and NMD values, with good precision. The inputs to this model are the frequences of the droplets sizes colected on the water-sensitive paper, observed on the Porton N-G12 graticule fitted on microscope. With these data, the accumulated distribution of the droplet medium volumes and sizes are evaluated. The graphics obtained by plotting this distributions allow to obtain the VMD and NMD using linear interpolation, seen that on the middle of the distributions the shape of the curves are linear. These values are essential to evaluate the uniformity of droplets and to estimate the volume deposited on the observed paper by the density (droplets/cm2). This methodology to estimate the droplets volume was developed by 11.0.94.224 Project of the CNPMA/EMBRAPA. Observed data of herbicides aerial spraying samples, realized by Project on Pelotas/RS county, were used to compare values obtained manual graphic method and with those obtained by model has shown, with great precision, the values of VMD and NMD on each sampled collector, allowing to estimate a quantities of deposited product and, by consequence, the quantities losses by drifty. The graphics of variability of VMD and NMD showed that the quantity of droplets that reachs the collectors had a short dispersion, while the deposited volume shows a great interval of variation, probably because the strong action of air turbulence on the droplets distribution, enfasizing the necessity of a deeper study to verify this influences on drift.