Particle size and concentration of poly(epsilon-caprolactone) and adipate modified starch blend on mineralization in soils with differing textures

The objective of this study was to evaluate the effect of particle size and concentration of poly(F.-caprolactone) and adipate modified starch blend on mineralization in soils with differing textures, comparing it with polyethylene under the same experimental conditions. Two soil types were used: a Kandiudalfic Eutrudox with a clayey texture and an Arenic Hapludult with a sandy texture. The two different plastic specimens were incorporated in the form of plastic films with three increasing particle sizes and six doses, from 0 to 2.5 mg C g(-1) soil. Each plastic dose was incorporated into 200 g of soil placed in a hermetically closed jar at 28 degrees C, and incubated for a 120-day period to determine CO(2) evolution. Once again it was confirmed that polyethylene is almost non-biodegradable, in contrast to PCL/S, which can be defined as a biodegradable material. Soil texture affected the mineralization kinetics of the plastic specimens, with higher values for the clayey soil. No changes in soil microbial biomass-C or -N were observed by adding polyethylene and PCL/S to the soil. Also, no significant differences were observed on seed emergence and development of rice seedlings (Oryza sativa L.) in plastic modified soil. (C) 2009 Elsevier Ltd. All rights reserved.

Monitorization of alveolar deposited surface area of nanoparticles and ultrafine particles in different environments

Nanotechnology is an important emerging industry with a projected annual market of around one trillion dollars by 2015. It involves the control of atoms and molecules to create new materials with a variety of useful functions. Although there are advantages on the utilization of these nano-scale materials, questions related with its impact over the environment and human health must be addressed too, so that potential risks can be limited at early stages of development. At this time, occupational health risks associated with manufacturing and use of nanoparticles are not yet clearly understood. However, workers may be exposed to nanoparticles through inhalation at levels that can greatly exceed ambient concentrations. Current workplace exposure limits are based on particle mass, but this criteria could not be adequate in this case as nanoparticles are characterized by very large surface area, which has been pointed out as the distinctive characteristic that could even turn out an inert substance into another substance exhibiting very different interactions with biological fluids and cells. Therefore, it seems that, when assessing human exposure based on the mass concentration of particles, which is widely adopted for particles over 1 μm, would not work in this particular case. In fact, nanoparticles have far more surface area for the equivalent mass of larger particles, which increases the chance they may react with body tissues. Thus, it has been claimed that surface area should be used for nanoparticle exposure and dosing. As a result, assessing exposure based on the measurement of particle surface area is of increasing interest. It is well known that lung deposition is the most efficient way for airborne particles to enter the body and cause adverse health effects. If nanoparticles can deposit in the lung and remain there, have an active surface chemistry and interact with the body, then, there is potential for exposure. It was showed that surface area plays an important role in the toxicity of nanoparticles and this is the metric that best correlates with particle-induced adverse health effects. The potential for adverse health effects seems to be directly proportional to particle surface area. The objective of the study is to identify and validate methods and tools for measuring nanoparticles during production, manipulation and use of nanomaterials.

Task-based occupational exposure assessment and particle number concentration: two important data resources to perform risk assessment for occupational exposure to particles

Environment monitoring has an important role in occupational exposure assessment. However, due to several factors is done with insufficient frequency and normally don´t give the necessary information to choose the most adequate safety measures to avoid or control exposure. Identifying all the tasks developed in each workplace and conducting a task-based exposure assessment help to refine the exposure characterization and reduce assessment errors. A task-based assessment can provide also a better evaluation of exposure variability, instead of assessing personal exposures using continuous 8-hour time weighted average measurements. Health effects related with exposure to particles have mainly been investigated with mass-measuring instruments or gravimetric analysis. However, more recently, there are some studies that support that size distribution and particle number concentration may have advantages over particle mass concentration for assessing the health effects of airborne particles. Several exposure assessments were performed in different occupational settings (bakery, grill house, cork industry and horse stable) and were applied these two resources: task-based exposure assessment and particle number concentration by size. The results showed interesting results: task-based approach applied permitted to identify the tasks with higher exposure to the smaller particles (0.3 μm) in the different occupational settings. The data obtained allow more concrete and effective risk assessment and the identification of priorities for safety investments.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

Aircraft observations and model simulations of concentration and particle size distribution in the Eyjafjallajökull volcanic ash cloud

The Eyjafjallajökull volcano in Iceland emitted a cloud of ash into the atmosphere during April and May 2010. Over the UK the ash cloud was observed by the FAAM BAe-146 Atmospheric Research Aircraft which was equipped with in-situ probes measuring the concentration of volcanic ash carried by particles of varying sizes. The UK Met Office Numerical Atmospheric-dispersion Modelling Environment (NAME) has been used to simulate the evolution of the ash cloud emitted by the Eyjafjallajökull volcano during the period 4–18 May 2010. In the NAME simulations the processes controlling the evolution of the concentration and particle size distribution include sedimentation and deposition of particles, horizontal dispersion and vertical wind shear. For travel times between 24 and 72 h, a 1/t relationship describes the evolution of the concentration at the centre of the ash cloud and the particle size distribution remains fairly constant. Although NAME does not represent the effects of microphysical processes, it can capture the observed decrease in concentration with travel time in this period. This suggests that, for this eruption, microphysical processes play a small role in determining the evolution of the distal ash cloud. Quantitative comparison with observations shows that NAME can simulate the observed column-integrated mass if around 4% of the total emitted mass is assumed to be transported as far as the UK by small particles (< 30 μm diameter). NAME can also simulate the observed particle size distribution if a distal particle size distribution that contains a large fraction of < 10 μm diameter particles is used, consistent with the idea that phraetomagmatic volcanoes, such as Eyjafjallajökull, emit very fine particles.

Effect of the particle size of maize, rice, and sorghum in extruded diets for dogs on starch gelatinization, digestibility, and the fecal concentration of fermentation products

The influence of rice, maize, and sorghum raw material particle size in extruded dry dog food on the digestibility of nutrients and energy and the fecal concentration of fermentation products was investigated. Three diets with similar nutrient compositions were formulated, each with 1 starch source. Before incorporation into diets, the cereals were ground into 3 different particle sizes (approximately 300, 450, and 600 mu m); therefore, a total of 9 diets were in a 3 x 3 factorial arrangement (3 cereals and 3 particle sizes). Fifty-four beagle dogs (12.0 +/- 0.1 kg BW) were randomly assigned to the diets, with 6 dogs per diet. The digestibility was measured with the chromium oxide method. The data were evaluated with ANOVA considering the carbohydrate source, grinding effect, and interactions. The means were compared with the Tukey test and polynomial contrasts (P < 0.05). With the same grinding procedure, rice was reduced to smaller particles than other cereals. The cereal mean geometric diameter (MGD) was directly related to starch gelatini-zation (SG) during extrusion. For rice diets, the MGD and SG did not change nutrient digestibility (P > 0.05); only GE digestibility was reduced at the largest MGD (P < 0.01). For maize and sorghum diets, the total tract apparent nutrient digestibility was reduced for foods with greater MGD and less SG (P < 0.01). A linear reduction in nutrient digestibility according to cereal particle size was observed for sorghum (r(2) < 0.72; P < 0.01). Higher concentrations of fecal total shortchain fatty acids (SCFA) were observed for sorghum diets (P < 0.05) than for other diets. The rice diets led to the production of feces with less lactate (P < 0.05). The increase in raw material MGD did not influence fecal SCFA for rice diets, but for the dogs fed maize and sorghum foods, an increase in propionate and butyrate concentrations were observed as MGD increased (P < 0.05). In conclusion, for dogs fed different particle sizes of the cereal starches in the extruded diets, the digestibility and fecal characteristics were affected, and this effect was ingredient dependent.

Particle emission from heavy-duty engine fuelled with blended diesel and biodiesel

In this study, particulate matter (PM) were characterized from a place impacted by heavy-duty vehicles (Bus Station) fuelled with diesel/biodiesel fuel blend (B3) in the city of Londrina, Brazil. Sixteen priority polycyclic aromatic hydrocarbons (PAH) concentrations were analyzed in the samples by their association with atmospheric PM, mass size distributions and major ions (fluorite, chloride, bromide, nitrate, phosphate, sulfate, nitrite, oxalate; fumarate, formate, succinate and acetate; lithium, sodium, potassium, magnesium, calcium and ammonium). Results indicate that major ions represented 21.2% particulate matter mass. Nitrate, sulfate, and ammonium, respectively, presented the highest concentration levels, indicating that biodiesel may also be a significant source for these ions, especially nitrate. Dibenzo[a,h]anthracene and indeno[1,2,3,-cd]pyrene were the main PAH found, and a higher fraction of PAH particles was found in diameters lower than 0.25 mu m in Londrina bus station. The fine and ultrafine particles were dominant among the PM evaluated, suggesting that biodiesel decreases the total PAH emission. However, it does also increase the fraction of fine and ultrafine particles when compared to diesel.