Refined classification and characterization of atmospheric new-particle formation events using air ions

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Atmospheric new-particle formation (NPF) is a worldwide-observed phenomenon that affects the human health and the global climate. With a growing network of global atmospheric measurement stations, efforts towards investigating NPF have increased. In this study, we present an automated method to classify days into four categories including NPF events, non-events and two classes in between, which then ensures reproducibility and minimizes the hours spent on manual classification. We applied our automated method to 10 years of data collected at the SMEAR II measurement station in Hyytiala, southern Finland using a Neutral cluster and Air Ion Spectrometer (NAIS). In contrast to the traditionally applied classification methods, which categorize days into events and non-events and ambiguous days as undefined days, our method is able to classify the undefined days as it accesses the initial steps of NPF at sub-3 nm sizes. Our results show that, on similar to 24% of the days in Hyytiala, a regional NPF event occurred and was characterized by nice weather and favourable conditions such as a clear sky and low condensation sink. Another class found in Hyytiala is the transported event class, which seems to be NPF carried horizontally or vertically to our measurement location and it occurred on 17% of the total studied days. Additionally, we found that an ion burst, wherein the ions apparently fail to grow to larger sizes, occurred on 18% of the days in Hyytiala. The transported events and ion bursts were characterized by less favourable ambient conditions than regional NPF events and thus experienced interrupted particle formation or growth. Non-events occurred on 41% of the days and were characterized by complete cloud cover and high relative humidity. Moreover, for regional NPF events occurring at the measurement site, the method identifies the start time, peak time and end time, which helps us focus on variables within an exact time window to better understand NPF at a process level. Our automated method can be modified to work in other measurement locations where NPF is observed.
Originalspråkengelska
TidskriftAtmospheric Chemistry and Physics
Volym18
Utgåva24
Sidor (från-till)17883-17893
Antal sidor11
ISSN1680-7316
DOI
StatusPublicerad - 17 dec 2018
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@article{b96170603ff940b78126b971ad46bb02,
title = "Refined classification and characterization of atmospheric new-particle formation events using air ions",
abstract = "Atmospheric new-particle formation (NPF) is a worldwide-observed phenomenon that affects the human health and the global climate. With a growing network of global atmospheric measurement stations, efforts towards investigating NPF have increased. In this study, we present an automated method to classify days into four categories including NPF events, non-events and two classes in between, which then ensures reproducibility and minimizes the hours spent on manual classification. We applied our automated method to 10 years of data collected at the SMEAR II measurement station in Hyytiala, southern Finland using a Neutral cluster and Air Ion Spectrometer (NAIS). In contrast to the traditionally applied classification methods, which categorize days into events and non-events and ambiguous days as undefined days, our method is able to classify the undefined days as it accesses the initial steps of NPF at sub-3 nm sizes. Our results show that, on similar to 24{\%} of the days in Hyytiala, a regional NPF event occurred and was characterized by nice weather and favourable conditions such as a clear sky and low condensation sink. Another class found in Hyytiala is the transported event class, which seems to be NPF carried horizontally or vertically to our measurement location and it occurred on 17{\%} of the total studied days. Additionally, we found that an ion burst, wherein the ions apparently fail to grow to larger sizes, occurred on 18{\%} of the days in Hyytiala. The transported events and ion bursts were characterized by less favourable ambient conditions than regional NPF events and thus experienced interrupted particle formation or growth. Non-events occurred on 41{\%} of the days and were characterized by complete cloud cover and high relative humidity. Moreover, for regional NPF events occurring at the measurement site, the method identifies the start time, peak time and end time, which helps us focus on variables within an exact time window to better understand NPF at a process level. Our automated method can be modified to work in other measurement locations where NPF is observed.",
keywords = "NUCLEATION-MODE PARTICLES, CLOUD CONDENSATION NUCLEI, SIZE DISTRIBUTION DATA, AEROSOL-PARTICLES, NEUTRAL CLUSTER, GROWTH-RATES, SMEAR-II, HYYTIALA, SPECTROMETER, STATION, 1172 Environmental sciences, 114 Physical sciences, 116 Chemical sciences",
author = "Lubna Dada and Robert Chellapermal and Mazon, {Stephany Buenrostro} and Pauli Paasonen and Janne Lampilahti and Manninen, {Hanna E.} and Heikki Junninen and Tuukka Pet{\"a}j{\"a} and Veli-Matti Kerminen and Markku Kulmala",
year = "2018",
month = "12",
day = "17",
doi = "10.5194/acp-18-17883-2018",
language = "English",
volume = "18",
pages = "17883--17893",
journal = "Atmospheric Chemistry and Physics",
issn = "1680-7316",
publisher = "COPERNICUS GESELLSCHAFT MBH",
number = "24",

}

TY - JOUR

T1 - Refined classification and characterization of atmospheric new-particle formation events using air ions

AU - Dada, Lubna

AU - Chellapermal, Robert

AU - Mazon, Stephany Buenrostro

AU - Paasonen, Pauli

AU - Lampilahti, Janne

AU - Manninen, Hanna E.

AU - Junninen, Heikki

AU - Petäjä, Tuukka

AU - Kerminen, Veli-Matti

AU - Kulmala, Markku

PY - 2018/12/17

Y1 - 2018/12/17

N2 - Atmospheric new-particle formation (NPF) is a worldwide-observed phenomenon that affects the human health and the global climate. With a growing network of global atmospheric measurement stations, efforts towards investigating NPF have increased. In this study, we present an automated method to classify days into four categories including NPF events, non-events and two classes in between, which then ensures reproducibility and minimizes the hours spent on manual classification. We applied our automated method to 10 years of data collected at the SMEAR II measurement station in Hyytiala, southern Finland using a Neutral cluster and Air Ion Spectrometer (NAIS). In contrast to the traditionally applied classification methods, which categorize days into events and non-events and ambiguous days as undefined days, our method is able to classify the undefined days as it accesses the initial steps of NPF at sub-3 nm sizes. Our results show that, on similar to 24% of the days in Hyytiala, a regional NPF event occurred and was characterized by nice weather and favourable conditions such as a clear sky and low condensation sink. Another class found in Hyytiala is the transported event class, which seems to be NPF carried horizontally or vertically to our measurement location and it occurred on 17% of the total studied days. Additionally, we found that an ion burst, wherein the ions apparently fail to grow to larger sizes, occurred on 18% of the days in Hyytiala. The transported events and ion bursts were characterized by less favourable ambient conditions than regional NPF events and thus experienced interrupted particle formation or growth. Non-events occurred on 41% of the days and were characterized by complete cloud cover and high relative humidity. Moreover, for regional NPF events occurring at the measurement site, the method identifies the start time, peak time and end time, which helps us focus on variables within an exact time window to better understand NPF at a process level. Our automated method can be modified to work in other measurement locations where NPF is observed.

AB - Atmospheric new-particle formation (NPF) is a worldwide-observed phenomenon that affects the human health and the global climate. With a growing network of global atmospheric measurement stations, efforts towards investigating NPF have increased. In this study, we present an automated method to classify days into four categories including NPF events, non-events and two classes in between, which then ensures reproducibility and minimizes the hours spent on manual classification. We applied our automated method to 10 years of data collected at the SMEAR II measurement station in Hyytiala, southern Finland using a Neutral cluster and Air Ion Spectrometer (NAIS). In contrast to the traditionally applied classification methods, which categorize days into events and non-events and ambiguous days as undefined days, our method is able to classify the undefined days as it accesses the initial steps of NPF at sub-3 nm sizes. Our results show that, on similar to 24% of the days in Hyytiala, a regional NPF event occurred and was characterized by nice weather and favourable conditions such as a clear sky and low condensation sink. Another class found in Hyytiala is the transported event class, which seems to be NPF carried horizontally or vertically to our measurement location and it occurred on 17% of the total studied days. Additionally, we found that an ion burst, wherein the ions apparently fail to grow to larger sizes, occurred on 18% of the days in Hyytiala. The transported events and ion bursts were characterized by less favourable ambient conditions than regional NPF events and thus experienced interrupted particle formation or growth. Non-events occurred on 41% of the days and were characterized by complete cloud cover and high relative humidity. Moreover, for regional NPF events occurring at the measurement site, the method identifies the start time, peak time and end time, which helps us focus on variables within an exact time window to better understand NPF at a process level. Our automated method can be modified to work in other measurement locations where NPF is observed.

KW - NUCLEATION-MODE PARTICLES

KW - CLOUD CONDENSATION NUCLEI

KW - SIZE DISTRIBUTION DATA

KW - AEROSOL-PARTICLES

KW - NEUTRAL CLUSTER

KW - GROWTH-RATES

KW - SMEAR-II

KW - HYYTIALA

KW - SPECTROMETER

KW - STATION

KW - 1172 Environmental sciences

KW - 114 Physical sciences

KW - 116 Chemical sciences

U2 - 10.5194/acp-18-17883-2018

DO - 10.5194/acp-18-17883-2018

M3 - Article

VL - 18

SP - 17883

EP - 17893

JO - Atmospheric Chemistry and Physics

JF - Atmospheric Chemistry and Physics

SN - 1680-7316

IS - 24

ER -