Chemical characterization and source apportionment of PM10 and PM2.5 in the metropolitan area of Costa Rica, Central America

Herrera-Murillo, Jorge; Román, Susana Rodríguez; Marín, José Félix Rojas; Ramos, Arturo Campos; Jiménez, Salvador Blanco; Gonzalez, Beatriz Cardenas; Baumgardner, Darrel

doi:10.5094/apr.2013.018

Cited by 36 publications

(9 citation statements)

References 37 publications

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“…Sea Salt = Na + ssCl + ssMg + ssK + ssCa + ssSO Secondary inorganic aerosol (SIA) contribution was calculated as the sum of non-sea salt nss-SO4 2− (calculated by subtracting ss-SO4 2− from total SO4 2− ), NO3 − and NH4 + [8,36].…”

Section: Reconstruction Of the Chemical Compositionmentioning

confidence: 99%

“…The latter was the sum of Na + concentration and fractions of the concentrations of Cl − , Mg 2+ , K + , Ca 2+ and SO 4 2− based on the standard sea water composition and ignoring atmospheric transformation [8]. Secondary inorganic aerosol (SIA) contribution was calculated as the sum of non-sea salt nss-SO 4 2− (calculated by subtracting ss-SO 4 2− from total SO 4 2− ), NO 3 − and NH 4 + [8,36].…”

Section: Reconstruction Of the Chemical Compositionmentioning

confidence: 99%

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Chemical Composition of PM10 at Urban Sites in Naples (Italy)

et al. 2016

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Here, we report the chemical characterization and identification of the possible sources of particulate matter (fraction PM 10 ) at two different sites in Naples. PM 10 concentration and its chemical composition were studied using the crustal enrichment factor (EF) and principal component analysis (PCA). In all of the seasons, the PM10 levels, were significantly higher (p < 0.01) in the urban-traffic site (denominated NA02) than in the urban-background site (denominated NA01). In order to reconstruct the particle mass, the components were classified into seven classes as follows: mineral dust (MD), trace elements (TE), organic matter (OM), elemental carbon (EC), sea salt (SS), secondary inorganic aerosol (SIA) and undetermined parts (unknown (UNK)). According to the chemical mass closure obtained, the major contribution was OM, which was higher (p < 0.01) during summer than in other seasons. In both sites, a good correlation (R 2 > 0.8) was obtained between reconstructed mass and gravimetric mass. PCA analysis explained 76% and 79% of the variance in NA01 and NA02, respectively. The emission sources were the same for both sites; but, the location of the site, the different distances from the sources and the presence and absence of vegetation proved the different concentrations and compositions of PM 10 .

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Section: Reconstruction Of the Chemical Compositionmentioning

confidence: 99%

Section: Reconstruction Of the Chemical Compositionmentioning

confidence: 99%

Chemical Composition of PM10 at Urban Sites in Naples (Italy)

et al. 2016

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“…where w fk is elements in the matrix W. The PMF is usually used in source apportionment for particle, such as PM 10 and PM 2.5 [61,[66][67][68], but seldom used in other environmental medias.…”

Section: Unsupervised MLmentioning

confidence: 99%

“…2À and certain amount of Fe, residual oil combustion, bringing Ni and V, fresh sea salt, including Cl À , Na and Mg, were the first three source. The others are crustal, or dust aerosols originated, organic carbon and sulfate, secondary sulfate, secondary nitrate, and heavy fuels [66].…”

Section: Te Apportionment In Air and Particlesmentioning

confidence: 99%

Data Mining for Source Apportionment of Trace Elements in Water and Solid Matrix

Shan¹,

Shi²

2021

Trace Metals in the Environment - New Approaches and Recent Advances

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Trace elements migrate among different environment bodies with the natural geochemical reactions, and impacted by human industrial, agricultural, and civil activities. High load of trace elements in water, river and lake sediment, soil and air particle lead to potential to health of human being and ecological system. To control the impact on environment, source apportionment is a meaningful, and also a challenging task. Traditional methods to make source apportionment are usually based on geochemical techniques, or univariate analysis techniques. In recently years, the methods of multivariate analysis, and the related concepts data mining, machine learning, big data, are developing fast, which provide a novel route that combing the geochemical and data mining techniques together. These methods have been proved successful to deal with the source apportionment issue. In this chapter, the data mining methods used on this topic and implementations in recent years are reviewed. The basic method includes principal component analysis, factor analysis, clustering analysis, positive matrix fractionation, decision tree, Bayesian network, artificial neural network, etc. Source apportionment of trace elements in surface water, ground water, river and lake sediment, soil, air particles, dust are discussed.

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“…PMF offers a viable alternative given the absence of a local source profile library for air pollutants especially in most developing countries such as Nigeria. PMF have been applied successfully in the studies of aerosol sources of many cities; Toronto, Canada [7]; Atlanta, USA [8] Pittsburg, USA [9]; Beijing, China [10]; Bangladesh [11] and Costa Rica [12]. An Trace Metals and Source Identification of Air-Borne Particulate Matter Pollution in A Nigerian Megacity achieved using ''Gent'' stacked filter sampler [14] capable of segregating both size fractions.…”

Section: Introductionmentioning

confidence: 99%