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1.
Environ Res ; 142: 495-510, 2015 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-26277386

RESUMEN

Access to detailed comparisons in air quality variations encountered when commuting through a city offers the urban traveller more informed choice on how to minimise personal exposure to inhalable pollutants. In this study we report on an experiment designed to compare atmospheric contaminants inhaled during bus, subway train, tram and walking journeys through the city of Barcelona. Average number concentrations of particles 10-300 nm in size, N, are lowest in the commute using subway trains (N<2.5×10(4) part. cm(-3)), higher during tram travel and suburban walking (2.5×10(4) cm(-3)5.0×10(4) cm(-3)), with extreme transient peaks at busy traffic crossings commonly exceeding 1.0×10(5) cm(-3) and accompanied by peaks in Black Carbon and CO. Subway particles are coarser (mode 90 nm) than in buses, trams or outdoors (<70 nm), and concentrations of fine particulate matter (PM2.5) and Black Carbon are lower in the tram when compared to both bus and subway. CO2 levels in public transport reflect passenger numbers, more than tripling from outdoor levels to >1200 ppm in crowded buses and trains. There are also striking differences in inhalable particle chemistry depending on the route chosen, ranging from aluminosiliceous at roadsides and near pavement works, ferruginous with enhanced Mn, Co, Zn, Sr and Ba in the subway environment, and higher levels of Sb and Cu inside the bus. We graphically display such chemical variations using a ternary diagram to emphasise how "air quality" in the city involves a consideration of both physical and chemical parameters, and is not simply a question of measuring particle number or mass.


Asunto(s)
Contaminantes Atmosféricos/análisis , Aire , Exposición por Inhalación/análisis , Material Particulado/análisis , Transportes , Emisiones de Vehículos/análisis , Aire/análisis , Aire/normas , Monitoreo del Ambiente , Humanos , Metales Pesados/análisis , España , Transportes/normas , Urbanización , Caminata
2.
J Environ Monit ; 9(10): 1071-81, 2007 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-17909641

RESUMEN

Principal component analysis (PCA) coupled with a multilinear regression analysis (MLRA) was applied to PM(10) speciation data series (2002-2005) from four sampling sites in a highly industrialised area (ceramic production) in the process of implementing emission abatement technology. Five common factors with similar chemical profiles were identified at all the sites: mineral, regional background (influenced by the industrial estate located on the coast: an oil refinery and a power plant), sea spray, industrial 1 (manufacture and use of glaze components, including frit fusion) and road traffic. The contribution of the regional background differs slightly from site to site. The mineral factor, attributed to the sum of several sources (mainly the ceramic industry, but also with minor contributions from soil resuspension and African dust outbreaks) contributes between 9 and 11 microg m(-3) at all the sites. Source industrial 1 entails an increase in PM(10) levels between 4 and 5 microg m(-3) at the urban sites and 2 microg m(-3) at the suburban background site. However, after 2004, this source contributed less than 2 microg m(-3) at most sites, whereas the remaining sources did not show an upward or downward trend along the study period. This gradual decrease in the contribution of source industrial 1 coincides with the implementation of PM abatement technology in the frit fusion kilns of the area. This relationship enables us to assess the efficiency of the implementation of environmental technologies in terms of their impact on air quality.


Asunto(s)
Contaminación del Aire/análisis , Contaminación del Aire/prevención & control , Restauración y Remediación Ambiental/métodos , Industrias , Material Particulado/análisis , Elementos Químicos , Geografía , Residuos Industriales/análisis , Residuos Industriales/prevención & control , Material Particulado/química , Análisis de Componente Principal , España , Salud Urbana , Emisiones de Vehículos/análisis , Viento
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