Notably improved inversion of differential mobility particle sizer data obtained under conditions of fluctuating particle number concentrations

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

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The differential mobility particle sizer (DMPS) is designed for measurements of particle number size distributions. It performs a number of measurements while scanning over different particle sizes. A standard assumption in the data-processing (inversion) algorithm is that the size distribution remains the same throughout each scan. For a DMPS deployed in an urban area this assumption is likely to be violated most of the time, and the resulting size distribution data are unreliable. To improve the reliability, we developed a new algorithm using a statistical model in which the problematic assumption was replaced with more realistic smoothness assumptions, which were expressed through Gaussian process prior probabilities. We tested the model with data from a twin DMPS located at an urban background site in Helsinki and found that it provides size distribution data which are much more realistic. Furthermore, particle number concentrations extracted from the DMPS data were compared with data from a condensation particle counter. At 10 min resolution, the correlation for a period of 10 days was 0.984 with the new algorithm and 0.967 with the old one. Moreover, the time resolution was improved, and at 30 s resolution we obtained positive correlations for 89 % of the scans. Thus, the quality of the inverted data was clearly improved.
Alkuperäiskielienglanti
LehtiAtmospheric Measurement Techniques
Vuosikerta9
Sivut741-751
Sivumäärä11
ISSN1867-1381
DOI - pysyväislinkit
TilaJulkaistu - 29 helmikuuta 2016
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä, vertaisarvioitu

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title = "Notably improved inversion of differential mobility particle sizer data obtained under conditions of fluctuating particle number concentrations",
abstract = "The differential mobility particle sizer (DMPS) is designed for measurements of particle number size distributions. It performs a number of measurements while scanning over different particle sizes. A standard assumption in the data-processing (inversion) algorithm is that the size distribution remains the same throughout each scan. For a DMPS deployed in an urban area this assumption is likely to be violated most of the time, and the resulting size distribution data are unreliable. To improve the reliability, we developed a new algorithm using a statistical model in which the problematic assumption was replaced with more realistic smoothness assumptions, which were expressed through Gaussian process prior probabilities. We tested the model with data from a twin DMPS located at an urban background site in Helsinki and found that it provides size distribution data which are much more realistic. Furthermore, particle number concentrations extracted from the DMPS data were compared with data from a condensation particle counter. At 10 min resolution, the correlation for a period of 10 days was 0.984 with the new algorithm and 0.967 with the old one. Moreover, the time resolution was improved, and at 30 s resolution we obtained positive correlations for 89 {\%} of the scans. Thus, the quality of the inverted data was clearly improved.",
keywords = "114 Physical sciences",
author = "Bjarke Molgaard and Vanhatalo, {Jarno Petteri} and Aalto, {Pasi Pekka} and Prisle, {Nonne Lyng} and H{\"a}meri, {Kaarle Juhani}",
year = "2016",
month = "2",
day = "29",
doi = "10.5194/amt-9-741-2016",
language = "English",
volume = "9",
pages = "741--751",
journal = "Atmospheric Measurement Techniques",
issn = "1867-1381",
publisher = "COPERNICUS GESELLSCHAFT MBH",

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TY - JOUR

T1 - Notably improved inversion of differential mobility particle sizer data obtained under conditions of fluctuating particle number concentrations

AU - Molgaard, Bjarke

AU - Vanhatalo, Jarno Petteri

AU - Aalto, Pasi Pekka

AU - Prisle, Nonne Lyng

AU - Hämeri, Kaarle Juhani

PY - 2016/2/29

Y1 - 2016/2/29

N2 - The differential mobility particle sizer (DMPS) is designed for measurements of particle number size distributions. It performs a number of measurements while scanning over different particle sizes. A standard assumption in the data-processing (inversion) algorithm is that the size distribution remains the same throughout each scan. For a DMPS deployed in an urban area this assumption is likely to be violated most of the time, and the resulting size distribution data are unreliable. To improve the reliability, we developed a new algorithm using a statistical model in which the problematic assumption was replaced with more realistic smoothness assumptions, which were expressed through Gaussian process prior probabilities. We tested the model with data from a twin DMPS located at an urban background site in Helsinki and found that it provides size distribution data which are much more realistic. Furthermore, particle number concentrations extracted from the DMPS data were compared with data from a condensation particle counter. At 10 min resolution, the correlation for a period of 10 days was 0.984 with the new algorithm and 0.967 with the old one. Moreover, the time resolution was improved, and at 30 s resolution we obtained positive correlations for 89 % of the scans. Thus, the quality of the inverted data was clearly improved.

AB - The differential mobility particle sizer (DMPS) is designed for measurements of particle number size distributions. It performs a number of measurements while scanning over different particle sizes. A standard assumption in the data-processing (inversion) algorithm is that the size distribution remains the same throughout each scan. For a DMPS deployed in an urban area this assumption is likely to be violated most of the time, and the resulting size distribution data are unreliable. To improve the reliability, we developed a new algorithm using a statistical model in which the problematic assumption was replaced with more realistic smoothness assumptions, which were expressed through Gaussian process prior probabilities. We tested the model with data from a twin DMPS located at an urban background site in Helsinki and found that it provides size distribution data which are much more realistic. Furthermore, particle number concentrations extracted from the DMPS data were compared with data from a condensation particle counter. At 10 min resolution, the correlation for a period of 10 days was 0.984 with the new algorithm and 0.967 with the old one. Moreover, the time resolution was improved, and at 30 s resolution we obtained positive correlations for 89 % of the scans. Thus, the quality of the inverted data was clearly improved.

KW - 114 Physical sciences

U2 - 10.5194/amt-9-741-2016

DO - 10.5194/amt-9-741-2016

M3 - Article

VL - 9

SP - 741

EP - 751

JO - Atmospheric Measurement Techniques

JF - Atmospheric Measurement Techniques

SN - 1867-1381

ER -