Estimation of PM10-bound As, Cd, Ni and Pb levels by means of statistical modelling: PLSR and ANN approaches

Air quality assessment regarding metals and metalloids using experimental measurements is expensive and time consuming due to the cost and time required for the analytical determination of the levels of these pollutants. According to the European Union (EU) Air Quality Framework Directive (Directive 2008/50/EC), other alternatives, such as objective estimation techniques, can be considered for ambient air quality assessment in zones and agglomerations where the level of pollutants is below a certain concentration value known as the lower assessment threshold. These conditions occur in urban areas in Cantabria (northern Spain). This work aims to estimate the levels of As, Cd, Ni and Pb in airborne PM10 at two urban sites in the Cantabria region (Castro Urdiales and Reinosa) using statistical models as objective estimation techniques. These models were developed based on three different approaches: partial least squares regression (PLSR), artificial neural networks (ANNs) and an alternative approach consisting of principal component analysis (PCA) coupled with ANNs (PCA-ANN). Additionally, these models were externally validated using previously unseen data. The results show that the models developed in this work based on PLSR and ANNs fulfil the EU uncertainty requirements for objective estimation techniques and provide an acceptable estimation of the mean values. As a consequence, they could be considered as an alternative to experimental measurements for air quality assessment regarding the aforementioned pollutants in the study areas while saving time and resources.The authors gratefully acknowledge the financial support from the Spanish Ministry of Economy and Competitiveness through the Project CMT2010-16068. The authors also thank the Regional Environment Ministry of the Cantabria Government for providing the PM10 samples at the Castro Urdiales and Reinosa sites

PCB-containing wood floor finish is a likely source of elevated PCBs in residents' blood, household air and dust: a case study of exposure

© 2008 Rudel et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Emission Factors for Gases and Particle-Bound Substances Produced by Firing Lead-Free Small-Caliber Ammunition

<div><p>Lead-free ammunition is becoming increasingly popular because of the environmental and human health issues associated with the use of leaded ammunition. However, there is a lack of data on the emissions produced by firing such ammunition. We report emission factors for toxic gases and particle-bound compounds produced by firing lead-free ammunition in a test chamber. Carbon monoxide, ammonia, and hydrogen cyanide levels within the chamber were analysed by Fourier transform infrared spectroscopy, while total suspended particles and respirable particles were determined gravimetrically. The metal content of the particulate emissions was determined and the associated organic compounds were characterized in detail using a method based on thermal desorption coupled to gas chromatography and mass spectrometry. The particulate matter (∼30 mg/round) consisted primarily of metals such as Cu, Zn, and Fe along with soot arising from incomplete combustion. Nitrogen-containing heterocyclic aromatic compounds such as carbazole, quinolone, and phenazine were responsible for some of the 25 most significant chromatographic peaks, together with PAHs, diphenylamine, and phthalates. Emission factors were determined for PAHs and oxygenated PAHs; the latter were less abundant in the gun smoke particles than in domestic dust and diesel combustion smoke. This may be due to the oxygen-deficient conditions that occur when the gun is fired. By using an electrical low pressure impactor, it was demonstrated that more than 90% of the particles produced immediately after firing the weapon had diameters of less than 30 nm, and so most of the gun smoke particles belonged to the nanoparticle regime.</p></div

Choosing the number of images and image position when analysing the UNC Passive Aerosol Sampler for occupational exposure assessment

The University of North Carolina passive aerosol sampler (UNC sampler) could be an alternative when measuring occupational dust exposure, but the time required for microscopic imaging of the sampler needs to be reduced to make it more attractive. The aims of this study were to 1) characterise the effect on precision when reducing imaging, in order to shorten analysis time and 2) assess if the position of the images makes a difference. Eighty-eight samplers were deployed in different locations of an open pit mine. Sixty images were captured for each UNC sampler, covering 51% of its collection surface, using scanning electron microscopy. Bootstrapped samples were generated with different image combinations, to assess the within-sampler coefficient of variation (CVws) for different numbers of images. In addition, the particle concentration relative to the distance from the centre of the sampler was studied. Reducing the number of images collected from the UNC sampler led to up to 8.3% CVws for ten images when calculating respirable fraction. As the overall CV has previously been assessed to 36%, the additional contribution becomes minimal, increasing the overall CV to 37%. The mean concentrations of the images were modestly related to distance from the centre of the sampler. The CVws changed from 8.26% to 8.13% for ten images when applying rules for the image collection based on distance. Thus, the benefit of these rules on the precision is small and the images can therefore be chosen at random. In conclusion, reducing the number of images analysed from 60 to 10, corresponding to a reduction of the imaged sampling area from 51% to 8.5%, results in a negligible loss in precision for respirable fraction dust measurements in occupational environments