Management of air quality monitoring using principal component and cluster analysis—Part I: SO2 and PM10

Pires, José C.M.; Sousa, Sofia; Pereira, Maria do Carmo; Alvim-Ferraz, M.C.M.; Martins, F.G.

doi:10.1016/j.atmosenv.2007.10.044

Cited by 129 publications

(64 citation statements)

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“…In this study, also two different applications of cluster analysis (CA) were tested to assess the impacts of local circulation and regional-scale transports on PAHs levels. CA has been widely used in atmospheric sciences, e.g., to explore the structure of data and detect the most probable emission sources (Kavouras et al 2001;Contini et al 2010), to highlight group of samples on the basis of their chemical composition (Molinaroli et al 1999;Masiol et al 2010), to manage air quality networking (Pires et al 2008), to classify wind patterns in a region (Kaufmann and Whiteman 1999;Darby 2005), and to group similar back-trajectories and air pathways (Tamamura et al 2007;Ravindra et al 2008b).…”

Section: Responsible Editor: Leif Kronbergmentioning

confidence: 99%

GC-MS analyses and chemometric processing to discriminate the local and long-distance sources of PAHs associated to atmospheric PM2.5

Masiol

Centanni

Hofer

et al. 2012

Environ Sci Pollut Res

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Purpose This study presents a procedure to differentiate the local and remote sources of particulate-bound polycyclic aromatic hydrocarbons (PAHs). Methods Data were collected during an extended PM 2.5 sampling campaign (2009)(2010) carried out for 1 year in Venice-Mestre, Italy, at three stations with different emissive scenarios: urban, industrial, and semirural background. Diagnostic ratios and factor analysis were initially applied to point out the most probable sources. In a second step, the areal distribution of the identified sources was studied by applying the discriminant analysis on factor scores. Third, samples collected in days with similar atmospheric circulation patterns were grouped using a cluster analysis on wind data. Local contributions to PM 2.5 and PAHs were then assessed by interpreting cluster results with chemical data.Results Results evidenced that significantly lower levels of PM 2.5 and PAHs were found when faster winds changed air masses, whereas in presence of scarce ventilation, locally emitted pollutants were trapped and concentrations increased. This way, an estimation of pollutant loads due to local sources can be derived from data collected in days with similar wind patterns. Long-range contributions were detected by a cluster analysis on the air mass back-trajectories. Results revealed that PM 2.5 concentrations were relatively high when air masses had passed over the Po Valley. However, external sources do not significantly contribute to the PAHs load. Conclusions The proposed procedure can be applied to other environments with minor modifications, and the obtained information can be useful to design local and national air pollution control strategies.

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Section: Responsible Editor: Leif Kronbergmentioning

confidence: 99%

GC-MS analyses and chemometric processing to discriminate the local and long-distance sources of PAHs associated to atmospheric PM2.5

Masiol

Centanni

Hofer

et al. 2012

Environ Sci Pollut Res

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“…The Euclidean distance was used as a measure of the similarity between two objects. The clustering procedure adopted was the average linkage method (Otto 1998;Pires et al 2008).…”

Section: Multivariate Statistical Methodsmentioning

confidence: 99%

Optimization of River Water Quality Surveys by Multivariate Analysis of Physicochemical, Bacteriological and Ecotoxicological Data

Gomes

Pires

Figueiredo

et al. 2014

Water Resour Manage

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This study aims to optimize the water quality monitoring of a polluted watercourse (Leça River, Portugal) through the principal component analysis (PCA) and cluster analysis (CA). These statistical methodologies were applied to physicochemical, bacteriological and ecotoxicological data (with the marine bacterium Vibrio fischeri and the green alga Chlorella vulgaris) obtained with the analysis of water samples monthly collected at seven monitoring sites and during five campaigns (February, May, June, August, and September 2006). The results of some variables were assigned to water quality classes according to national guidelines. Chemical and bacteriological quality data led to classify Leça River water quality as "bad" or "very bad". PCA and CA identified monitoring sites with similar pollution pattern, giving to site 1 (located in the upstream stretch of the river) a distinct feature from all other sampling sites downstream. Ecotoxicity results corroborated this classification thus revealing differences in space and time. The present study includes not only physical, chemical and bacteriological but also ecotoxicological parameters, which broadens new perspectives in river water characterization. Moreover, the application of PCA and CA is very useful to optimize water quality monitoring networks, defining the minimum number of sites and their location. Thus, these tools can support appropriate management decisions.

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“…Statistical methods such as PCA and AHC have been used in many studies aimed at the management of water monitoring networks (Kannel et al 2007;Mendiguchia et al 2004;Shrestha and Kazama 2007;Singh et al 2004Singh et al , 2005) and air quality management (Gramsch et al 2006;Pires et al 2008a,b). The study of Gramsch et al (2006) determined the seasonal trends and spatial distribution of PM 10 and O 3 for Santiago de Chile, concluding that the city had four large sectors with dissimilar air pollution variations, whereas the study conducted by Pires et al (2008a) classified SO 2 sites in six groups and PM 10 sites in two groups of dissimilar variation in Oporto Metropolitan Area.…”

Section: Methods Of Data Analysismentioning

confidence: 99%

Statistical approach for the characterization of NO2 concentrations in Beirut

Afif

Dutot

Jambert

et al. 2009

Air Qual Atmos Health

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Measurement of ambient NO 2 concentrations using diffusion tube samplers is widespread in many countries. Results from a program of measuring ground-level concentration of NO 2 at 20 urban sites within the city of Beirut are presented. In addition, two curbside sites were implemented at different heights. Two-week sampling period measurements were performed over 41 periods for all sites. A study of the sites' behaviors was conducted using principal component analysis and agglomerative hierarchical clustering. Firstly, results indicate that urban sites are equivalent, and one global class is identified. Any further monitoring of the nitrogen dioxide concentration can be conducted by decreasing the number of urban sites. Secondly, nitrogen dioxide concentration levels present a seasonal variation, as expected. Maximum average concentration of 178 μg m −3 was observed during

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Management of air quality monitoring using principal component and cluster analysis—Part I: SO2 and PM10

Cited by 129 publications

References 0 publications

GC-MS analyses and chemometric processing to discriminate the local and long-distance sources of PAHs associated to atmospheric PM2.5

GC-MS analyses and chemometric processing to discriminate the local and long-distance sources of PAHs associated to atmospheric PM2.5

Optimization of River Water Quality Surveys by Multivariate Analysis of Physicochemical, Bacteriological and Ecotoxicological Data

Statistical approach for the characterization of NO2 concentrations in Beirut

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