Characterization of airborne particles generated from metal active gas welding process

This study is focused on the characterization of particles emitted in the metal active gas welding of carbon steel using mixture of Ar + CO2, and intends to analyze which are the main process parameters that influence the emission itself. It was found that the amount of emitted particles (measured by particle number and alveolar deposited surface area) are clearly dependent on the distance to the welding front and also on the main welding parameters, namely the current intensity and heat input in the welding process. The emission of airborne fine particles seems to increase with the current intensity as fume-formation rate does. When comparing the tested gas mixtures, higher emissions are observed for more oxidant mixtures, that is, mixtures with higher CO2 content, which result in higher arc stability. These mixtures originate higher concentrations of fine particles (as measured by number of particles by cm 3 of air) and higher values of alveolar deposited surface area of particles, thus resulting in a more severe worker's exposure.

Determination of airborne nanoparticles in elderly care centres

According to numerous studies, airborne nanoparticles have a potential to produce serious adverse human health effects when deposited into the respiratory tract. The most important parts of the lung are the alveolar regions with their enormous surface areas and potential to transfer nanoparticles into the blood stream. These effects may be potentiated in case of the elderly, since this population is more susceptible to air pollutants in general and more to nanoparticles than larger particles. The main goal of this investigation was to determine the exposure of institutionalized elders to nanoparticles using Nanoparticle Surface Area Monitor (NSAM) equipment to calculate the deposited surface area (DSA) of nanoparticles into elderly lungs. In total, 193 institutionalized individuals over 65 yr of age were examined in four elderly care centers (ECC). The occupancy daily pattern was achieved by applying a questionnaire, and it was concluded that these subjects spent most of their time indoors, including the bedroom and living room, the indoor microenvironments with higher prevalence of elderly occupancy. The deposited surface area ranged from 10 to 46 μm2/cm3. The living rooms presented significantly higher levels compared with bedrooms. Comparing PM10 concentrations with nanoparticles deposited surface area in elderly lungs, it is conceivable that living rooms presented the highest concentration of PM10 and were similar to the highest average DSA. The temporal distribution of DSA was also assessed. While data showed a quantitative fluctuation in values in bedrooms, high peaks were detected in living rooms.

Determination of airborne nanoparticles in elderly care centres

According to numerous studies, airborne nanoparticles have a potential to produce serious adverse human health effects when deposited into the respiratory tract. The most important parts of the lung are the alveolar regions with their enormous surface areas and potential to transfer nanoparticles into the blood stream. These effects may be potentiated in case of the elderly, since this population is more susceptible to air pollutants in general and more to nanoparticles than larger particles. The main goal of this investigation was to determine the exposure of institutionalized elders to nanoparticles using Nanoparticle Surface Area Monitor (NSAM) equipment to calculate the deposited surface area (DSA) of nanoparticles into elderly lungs. In total, 193 institutionalized individuals over 65 yr of age were examined in four elderly care centers (ECC). The occupancy daily pattern was achieved by applying a questionnaire, and it was concluded that these subjects spent most of their time indoors, including the bedroom and living room, the indoor microenvironments with higher prevalence of elderly occupancy. The deposited surface area ranged from 10 to 46 mu m(2)/cm(3). The living rooms presented significantly higher levels compared with bedrooms. Comparing PM10 concentrations with nanoparticles deposited surface area in elderly lungs, it is conceivable that living rooms presented the highest concentration of PM10 and were similar to the highest average DSA. The temporal distribution of DSA was also assessed. While data showed a quantitative fluctuation in values in bedrooms, high peaks were detected in living rooms.

Emission of airborne ultrafine particles during welding of steel plates

The present study is focused on the characterization of ultrafine particles emitted in welding of steel using mixtures of Ar+CO2, and intends to analyze which are the main process parameters which may have influence on the emission itself. It was found that the amount of emitted ultrafine particles (measured by particle number and alveolar deposited surface area) are clearly dependent from the distance to the welding front and also from the main welding parameters, namely the current intensity and heat input in the welding process. The emission of airborne ultrafine particles seem to increase with the current intensity as fume formation rate does. When comparing the tested gas mixtures, higher emissions are observed for more oxidant mixtures, that is, mixtures with higher CO2 content, which result in higher arc stability. The later mixtures originate higher concentrations of ultrafine particles (as measured by number of particles by cm3 of air) and higher values of alveolar deposited surface area of particles, thus resulting in a more hazardous condition regarding worker's exposure. © 2014 Sociedade Portuguesa de Materiais (SPM). Published by Elsevier España, S.L. All rights reserved.

Assessment of exposure to airborne ultrafine particles in the urban environment of Lisbon, Porugal

The aim of this study was the assessment of exposure to ultrafine in the urban environment of Lisbon, Portugal, due to automobile traffic, and consisted of the determination of deposited alveolar surface area in an avenue leading to the town center during late spring. This study revealed differentiated patterns for weekdays and weekends, which could be related with the fluxes of automobile traffic. During a typical week, ultrafine particles alveolar deposited surface area varied between 35.0 and 89.2 mu m(2)/cm(3), which is comparable with levels reported for other towns such in Germany and the United States. These measurements were also complemented by measuring the electrical mobility diameter (varying from 18.3 to 128.3 nm) and number of particles that showed higher values than those previously reported for Madrid and Brisbane. Also, electron microscopy showed that the collected particles were composed of carbonaceous agglomerates, typical of particles emitted by the exhaustion of diesel vehicles. Implications: The approach of this study considers the measurement of surface deposited alveolar area of particles in the outdoor urban environment of Lisbon, Portugal. This type of measurements has not been done so far. Only particulate matter with aerodynamic diameters <2.5 (PM2.5) and >10 (PM10) mu m have been measured in outdoor environments and the levels found cannot be found responsible for all the observed health effects. Therefore, the exposure to nano- and ultrafine particles has not been assessed systematically, and several authors consider this as a real knowledge gap and claim for data such as these that will allow for deriving better and more comprehensive epidemiologic studies. Nanoparticle surface area monitor (NSAM) equipments are recent ones and their use has been limited to indoor atmospheres. However, as this study shows, NSAM is a very powerful tool for outdoor environments also. As most lung diseases are, in fact, related to deposition of the alveolar region of the lung, the metric used in this study is the ideal one.

Determiation of airborne nanoparticles from welding operations

The aim of this study is to assess the levels of airborne ultrafine particles emitted in welding processes (tungsten inert gas [TIG], metal active gas [MAG] of carbon steel, and friction stir welding [FSW] of aluminum) in terms of deposited area in pulmonary alveolar tract using a nanoparticle surface area monitor (NSAM) analyzer. The obtained results showed the dependence of process parameters on emitted ultrafine particles and demonstrated the presence of ultrafine particles compared to background levels. Data indicated that the process that resulted in the lowest levels of alveolar deposited surface area (ADSA) was FSW, followed by TIG and MAG. However, all tested processes resulted in significant concentrations of ultrafine particles being deposited in humans lungs of exposed workers.

Exposure to airborne ultrafine particles from cooking in portuguese homes

Cooking was found to be a main source of submicrometer and ultrafine aerosols from gas combustion in stoves. Therefore, this study consisted of the determination of the alveolar deposited surface area due to aerosols resulting from common domestic cooking activities (boiling fish, vegetables, or pasta, and frying hamburgers and eggs). The concentration of ultrafine particles during the cooking events significantly increased from a baseline of 42.7 mu m(2)/cm(3) (increased to 72.9 mu m(2)/cm(3) due to gas burning) to a maximum of 890.3 mu m(2)/cm(3) measured during fish boiling in water, and a maximum of 4500 mu m(2)/cm(3) during meat frying. This clearly shows that a domestic activity such as cooking can lead to exposures as high as those of occupational exposure activities.

Monitoring exposure to airborne ultrafine particles in Lisbon, Portugal

The aim of this study is to contribute to the assessment of exposure levels of ultrafine particles (UFP) in the urban environment of Lisbon, Portugal, due to automobile traffic, by monitoring lung-deposited alveolar surface area (resulting from exposure to UFP) in a major avenue leading to the town centre during late Spring, as well as in indoor buildings facing it. This study revealed differentiated patterns for week days and weekends, consistent with PM2.5 and PM10 patterns currently monitored by air quality stations in Lisbon. The observed ultrafine particulate levels could be directly related with the fluxes of automobile traffic. During a typical week, UFP alveolar deposited surface area varied between 35.0 and 89.2 mu m(2)/cm(3), which is comparable with levels reported for other towns such in Germany and United States. The measured values allowed the determination of the number of UFP per cm(3), which are comparable to levels reported for Madrid and Brisbane. In what concerns outdoor/indoor levels, we observed higher levels (32-63%) outdoor, which is somewhat lower than levels observed in houses in Ontario.

On the assessment of exposure to airborne ultrafine particles in urban environments

The aim of this study was to contribute to the assessment of exposure levels of ultrafine particles in the urban environment of Lisbon, Portugal, due to automobile traffic, by monitoring lung deposited alveolar surface area (resulting from exposure to ultrafine particles) in a major avenue leading to the town center during late spring, as well as in indoor buildings facing it. Data revealed differentiated patterns for week days and weekends, consistent with PM2.5 and PM10 patterns currently monitored by air quality stations in Lisbon. The observed ultrafine particulate levels may be directly correlated with fluxes in automobile traffic. During a typical week, amounts of ultrafine particles per alveolar deposited surface area varied between 35 and 89.2 mu m2/cm3, which are comparable with levels reported for other towns in Germany and the United States. The measured values allowed for determination of the number of ultrafine particles per cubic centimeter, which are comparable to levels reported for Madrid and Brisbane. In what concerns outdoor/indoor levels, we observed higher levels (32 to 63%) outdoors, which is somewhat lower than levels observed in houses in Ontario.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.