dc.contributor.author
Shahraiyni, Hamid Taheri
dc.contributor.author
Sodoudi, Sahar
dc.contributor.author
Kerschbaumer, Andreas
dc.contributor.author
Cubasch, Ulrich
dc.date.accessioned
2018-06-08T02:51:57Z
dc.date.available
2016-03-31T12:20:47.691Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/14020
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-18217
dc.description.abstract
The importance of air pollution monitoring networks in urban areas is well
known because of their miscellaneous applications. At the beginning of the
1990s, Berlin had more than 40 particulate matter monitoring stations,
whereas, by 2013, there were only 12 stations. In this study, a new and
free–of–charge methodology for the densifying of the PM10 monitoring network
of Berlin is presented. It endeavors to find the non–linear relationship
between the hourly PM10 concentration of the still–operating PM10 monitoring
stations and the shut–down stations by using the Artificial Neural Network
(ANN), and, consequently, the results of the shut–down stations were simulated
and re–constructed. However, input–variables selection is a pre–requisite for
any ANN simulation, and hence a new fuzzy–heuristic input selection has been
developed and joined to the ANN for the simulation. The hourly PM10
concentrations of the 20 shut–down stations were simulated and re–constructed.
The mean error, bias and absolute error of the simulations were 27.7%, –0.03
(μg/m3), and 7.4 (μg/m3), respectively. Then, the simulated hourly PM10
concentration data were converted to a daily scale and the performance of ANN
models which were developed for the simulation of the daily PM10 data were
evaluated (correlation coefficient >0.94). These appropriate results imply the
ability of the developed input selection technique to make the appropriate
selection of the input variables, and it can be introduced as a new input
variable selection for the ANN. In addition, a dense PM10 monitoring network
was developed by the combination of both the re–constructed (20 stations) and
the current (12 stations) stations. This dense monitoring network was applied
in order to determine a reliable mean annual PM10 concentration in the
different areas in Berlin in 2012.
en
dc.rights.uri
http://creativecommons.org/licenses/by/3.0/de/
dc.subject
Artificial neural networks
dc.subject
input variable selection
dc.subject
PM10 monitoring network
dc.subject.ddc
500 Naturwissenschaften und Mathematik::550 Geowissenschaften, Geologie::551 Geologie, Hydrologie, Meteorologie
dc.title
The development of a dense urban air pollution monitoring network
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation
Atmospheric Pollution Research. - 6 (2015), 5, S. 904–915
dcterms.bibliographicCitation.doi
10.5094/APR.2015.100
dcterms.bibliographicCitation.url
http://www.sciencedirect.com/science/article/pii/S1309104215301847
refubium.affiliation
Geowissenschaften
de
refubium.mycore.fudocsId
FUDOCS_document_000000024295
refubium.note.author
Der Artikel wurde in einer Open-Access-Zeitschrift publiziert.
refubium.resourceType.isindependentpub
no
refubium.mycore.derivateId
FUDOCS_derivate_000000006206
dcterms.accessRights.openaire
open access