Cambra López, M.; Hermosilla Gómez, T.; Aarnink, AJA.; Ogink, N. (2012). A methodology to select particle morpho-chemical characteristics to use in source apportionment of particulate matter from livestock houses. Computers and Electronics in Agriculture. 81:14-23. doi:10.1016/j.compag.2011.11.002
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/63889
Title:
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A methodology to select particle morpho-chemical characteristics to use in source apportionment of particulate matter from livestock houses
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Author:
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Cambra López, María
Hermosilla Gómez, Txomin
Aarnink, André Johannes Antonius
Ogink, Nico
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UPV Unit:
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Universitat Politècnica de València. Departamento de Ciencia Animal - Departament de Ciència Animal
Universitat Politècnica de València. Departamento de Ingeniería Cartográfica Geodesia y Fotogrametría - Departament d'Enginyeria Cartogràfica, Geodèsia i Fotogrametria
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Issued date:
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Abstract:
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[EN] Intensive poultry and pig houses are major point sources of particulate matter (PM) emissions. The
knowledge on the contribution of individual sources to PM in different fractions is essential to improve
PM reduction ...[+]
[EN] Intensive poultry and pig houses are major point sources of particulate matter (PM) emissions. The
knowledge on the contribution of individual sources to PM in different fractions is essential to improve
PM reduction from livestock houses. We developed a methodology to investigate which input data (particle
chemical, morphological or combined characteristics) were best to distinguish amongst specific
sources of airborne PM in livestock houses. We used a validation procedure with classification rules based
on decision trees and analyzed misclassification errors. The PM from two livestock species (poultry and
pigs), and in two different fractions (fine and coarse) was studied. Results showed the selection of the
best input data varied with the sources, which depend on livestock species. Using only particle chemical
characteristics resulted in higher overall classification accuracies (62–68%) than using only morphological
characteristics (40–64%) in poultry and pigs. Particle morphological characteristics can add value
when sources show distinctive and well defined morphologies or differ in size. Using combined chemical
and morphological resulted in the highest overall classification accuracies (average of 69% of particles
correctly assigned to their source) and lowest misclassification errors. This study provides a methodological
approach to assess input data and identifies the most effective characteristics to apportion PM in livestock
houses. These data are promising to determine the contribution of different sources to PM in
livestock houses and give insight in under- and overestimation errors in the source apportionment.
2011 Published by Elsevier B.V.
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Subjects:
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Atmospheric pollution
,
Animal housing
,
Dust
,
Expert systems
,
Image analysis
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Copyrigths:
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Reserva de todos los derechos
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Source:
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Computers and Electronics in Agriculture. (issn:
0168-1699
) (eissn:
1872-7107
)
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DOI:
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10.1016/j.compag.2011.11.002
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Publisher:
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Elsevier
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Publisher version:
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https://dx.doi.org/10.1016/j.compag.2011.11.002
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Thanks:
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We acknowledge the support of the Dutch Ministry of Agriculture, Nature and Food Quality that financed this study. We thank the Servicio de Microscopia Electronica (Universidad Politecnica de Valencia) for expert technical ...[+]
We acknowledge the support of the Dutch Ministry of Agriculture, Nature and Food Quality that financed this study. We thank the Servicio de Microscopia Electronica (Universidad Politecnica de Valencia) for expert technical assistance during SEM analysis.
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Type:
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Artículo
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