964 resultados para dichotomous sampler


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In this paper, source apportionment techniques are employed to identify and quantify the major particle pollution source classes affecting a monitoring site in metropolitan Boston, MA. A Principal Component Analysis (PCA) of paniculate elemental data allows the estimation of mass contributions for five fine mass panicle source classes (soil, motor vehicle, coal related, oil and salt aerosols), and six coarse panicle source classes (soil, motor vehicle, refuse incineration, residual oil, salt and sulfate aerosols). Also derived are the elemental characteristics of those source aerosols and their contributions to the total recorded elemental concentrations (i.e. an elemental mass balance). These are estimated by applying a new approach to apportioning mass among various PCA source components: the calculation of Absolute Principal Component Scores, and the subsequent regression of daily mass and elemental concentrations on these scores.

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The concentrations of the water-soluble inorganic aerosol species, ammonium (NH4+), nitrate (NO3-), chloride (Cl-), and sulfate (SO42-), were measured from September to November 2002 at a pasture site in the Amazon Basin (Rondnia, Brazil) (LBA-SMOCC). Measurements were conducted using a semi-continuous technique (Wet-annular denuder/Steam-Jet Aerosol Collector: WAD/SJAC) and three integrating filter-based methods, namely (1) a denuder-filter pack (DFP: Teflon and impregnated Whatman filters), (2) a stacked-filter unit (SFU: polycarbonate filters), and (3) a High Volume dichotomous sampler (HiVol: quartz fiber filters). Measurements covered the late dry season (biomass burning), a transition period, and the onset of the wet season (clean conditions). Analyses of the particles collected on filters were performed using ion chromatography (IC) and Particle-Induced X-ray Emission spectrometry (PIXE). Season-dependent discrepancies were observed between the WAD/SJAC system and the filter-based samplers. During the dry season, when PM2.5 (D-p <= 2.5 mu m) concentrations were similar to 100 mu g m(-3), aerosol NH4+ and SO42- measured by the filter-based samplers were on average two times higher than those determined by the WAD/SJAC. Concentrations of aerosol NO3- and Cl- measured with the HiVol during daytime, and with the DFP during day- and nighttime also exceeded those of the WAD/SJAC by a factor of two. In contrast, aerosol NO3- and Cl- measured with the SFU during the dry season were nearly two times lower than those measured by the WAD/SJAC. These differences declined markedly during the transition period and towards the cleaner conditions during the onset of the wet season (PM2.5 similar to 5 mu g m(-3)); when filter-based samplers measured on average 40-90% less than the WAD/SJAC. The differences were not due to consistent systematic biases of the analytical techniques, but were apparently a result of prevailing environmental conditions and different sampling procedures. For the transition period and wet season, the significance of our results is reduced by a low number of data points. We argue that the observed differences are mainly attributable to (a) positive and negative filter sampling artifacts, (b) presence of organic compounds and organosulfates on filter substrates, and (c) a SJAC sampling efficiency of less than 100%.

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In Brazil, sugarcane fields are often burned to facilitate manual harvesting, and this burning causes environmental pollution from the large amounts of soot released into the atmosphere. This material contains numerous organic compounds such as PAHs. In this study, the concentrations of PAHs in two particulate-matter fractions (PM2.5 and PM10) in the city of Araraquara (SE Brazil, with around 200,000 inhabitants and surrounded by sugarcane plantations) were determined during the sugarcane harvest (HV) and non-harvest (NHV) seasons in 2008 and 2009. The sampling strategy included four campaigns, with 60 samples in the NHV season and 220 samples in the HV season. The PM2.5 and PM10 fractions were collected using a dichotomous sampler (10 L min(-1), 24 h) with Teflon (TM) filters. The filter sets were extracted (ultrasonic bath with hexane/acetone (1:1 v/v)) and analyzed by HPLC/Fluorescence. The median concentration for total PAHs (PM2.5 in 2009) was 0.99 ng m(-3) (NHV) and 3.3 ng m(-3) (HV). In the HV season, the total concentration of carcinogenic PAHs (benz(a)anthracene, benzo(b)fluoranthene, benzo(k)fluoranthene, and benzo(a)pyrene) was 5 times higher than in the NHV season. B(a)P median concentrations were 0.017 ng m(-3) and 0.12 ng m(-3) for the NHV and HV seasons, respectively. The potential cancer risk associated with exposure through inhalation of these compounds was estimated based on the benzo[a]pyrene toxic equivalence (BaPeq), where the overall toxicity of a PAR mixture is defined by the concentration of each compound multiplied by its relative toxic equivalence factor (TEF). BaPeq median (2008 and 2009 years) ranged between 0.65 and 1.0 ng m(-3) and 1.2-1.4 ng m(-3) for the NHV and HV seasons, respectively. Considering that the maximum permissible BaPeq in ambient air is 1 ng m(-3), related to the increased carcinogenic risk, our data suggest that the level of human exposure to PAHs in cities surrounded by sugarcane crops where the burning process is used is cause for concern. (C) 2010 Published by Elsevier Ltd.

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This study aimed to determine the concentration and inorganic chemical composition of samples from airborne particulate matter inhaled in fine and coarse fractions. Aerosol samples were collected in 2013 and 2014, from sites located in the cities of Londrina and Maringa, in the state of Paraná, Brazil. The samples were collected daily (24h) in two campaigns: winter and summer. For the collection, was used a dichotomous sampler with quartz fiber filter with 47 mm in size and 2 µm porosity, 97% efficiency, retaining particles of up to 0.3 µm. Quantification of the airborne particulate matter mass was performed by gravimetry method. The results from Londrina to PM2.5 and MP2,5-10 represent, respectively, 29.2% and 70.8% of airborne particulate matter in the winter campaign (2013), 30.9% (PM2.5) and 69.1 % (MP2,5-10) in the summer campaign (2013), and 35.9% (PM2.5) and 64.1% (MP2,5-10) in the winter 2014 campaign. In the city of Maringa, the results presented the percentage of 42.0% (PM2.5) and 58.0% (MP2,5-10) for the winter season (2014), and 28.8% (PM2.5) and 71.2 % (MP2,5-10) for the summer season (2014). The PM2.5/PM10 ratio was on average 0.3, demonstrating that both cities are developing urban areas. Analysis of the major soluble inorganic species in water (NO3-, SO42- and Cl-) associated with MP2,5-10 were quantified by ion chromatography at the LACA Laboratory in the State University of Londrina, with the largest contribution found in all campaigns was to NO3-. The NO3-/SO42- ratios above 1.0 indicate the local traffic contribution. The analysis of metals associated with PM2.5 was carried out by mass spectrometry with inductively coupled plasma (ICP-MS) in the Federal University of Santa Catarina. The Zn, Pb, Cu and Mn concentrations found in all campaigns indicate the contribution of mobile sources to PM2.5. The concentration of BCe in PM2.5 was determined by reflectance, with higher BCe concentrations being found in winter campaigns. In general, Londrina presented the highest concentrations from the species analyzed when compared to Maringá. In addition, the analysis of the air mass trajectories indicated the transportation of pollutants coming mainly from fires in the southeastern region of the country.

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The estimation of the frequency of a sinusoidal signal is a well researched problem. In this work we propose an initialization scheme to the popular dichotomous search of the periodogram peak algorithm(DSPA) that is used to estimate the frequency of a sinusoid in white gaussian noise. Our initialization is computationally low cost and gives the same performance as the DSPA, while reducing the number of iterations needed for the fine search stage. We show that our algorithm remains stable as we reduce the number of iterations in the fine search stage. We also compare the performance of our modification to a previous modification of the DSPA and show that we enhance the performance of the algorithm with our initialization technique.

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We use reversible jump Markov chain Monte Carlo (MCMC) methods to address the problem of model order uncertainty in autoregressive (AR) time series within a Bayesian framework. Efficient model jumping is achieved by proposing model space moves from the full conditional density for the AR parameters, which is obtained analytically. This is compared with an alternative method, for which the moves are cheaper to compute, in which proposals are made only for new parameters in each move. Results are presented for both synthetic and audio time series.

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Both chemical and biological methods are used to assess the water quality of rivers. Many standard physical and chemical methods are now established, but biological procedures of comparable accuracy and versatility are still lacking. This is unfortunate because the biological assessment of water quality has several advantages over physical and chemical analyses. Several groups of organisms have been used to assess water quality in rivers and these include Bacteria, Protozoa, Algae, macrophytes, macroinvertebrates and fish. Hellawell (1978) provides an excellent review of the advantages and disadvantages of these groups, and concludes that macroinvertebrates are the most useful for monitoring water quality. Although macroinvertebrates are relatively easy to sample in shallow water (depth < 1m), quantitative sampling poses more problems than qualitative sampling because a large number of replicate sampling units are usually required for accurate estimates of numbers or biomass per unit area. Both qualitative and quantitative sampling are difficult in deep water (depth > 1m). The present paper first considers different types of samplers with emphasis on immediate samplers, and then discusses some problems in choosing a suitable sampler for benthic macroinvertebrates in deep rivers.