Identification of new particle formation events with deep learning

Joutsensaari, Jorma; Ozon, Matthew; Nieminen, Tuomo; Mikkonen, Santtu; Lähivaara, Timo; Decesari, Stefano; Facchini, M. Cristina; Laaksonen, Ari; Lehtinen, Kari E. J.

doi:10.5194/acp-18-9597-2018

Articles | Volume 18, issue 13

https://doi.org/10.5194/acp-18-9597-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-18-9597-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 13

Research article

|

09 Jul 2018

Research article |

| 09 Jul 2018

Identification of new particle formation events with deep learning

Jorma Joutsensaari, Matthew Ozon, Tuomo Nieminen, Santtu Mikkonen, Timo Lähivaara, Stefano Decesari, M. Cristina Facchini, Ari Laaksonen, and Kari E. J. Lehtinen

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Views and downloads (calculated since 29 Jan 2018)

Month	HTML	PDF	XML	Total
Jan 2018	233	86	4	323
Feb 2018	125	89	6	220
Mar 2018	50	36	1	87
Apr 2018	38	12	1	51
May 2018	48	32	1	81
Jun 2018	33	21	1	55
Jul 2018	334	111	4	449
Aug 2018	48	40	1	89
Sep 2018	42	48	3	93
Oct 2018	30	31	5	66
Nov 2018	21	31	0	52
Dec 2018	13	13	1	27
Jan 2019	20	24	2	46
Feb 2019	19	22	0	41
Mar 2019	21	11	1	33
Apr 2019	22	12	0	34
May 2019	22	22	0	44
Jun 2019	23	9	1	33
Jul 2019	20	15	2	37
Aug 2019	15	14	0	29
Sep 2019	23	18	1	42
Oct 2019	33	14	0	47
Nov 2019	12	8	0	20
Dec 2019	12	14	0	26
Jan 2020	12	15	0	27
Feb 2020	11	10	1	22
Mar 2020	15	10	1	26
Apr 2020	20	8	0	28
May 2020	31	18	0	49
Jun 2020	13	14	0	27
Jul 2020	28	23	4	55
Aug 2020	25	14	1	40
Sep 2020	22	12	0	34
Oct 2020	27	16	0	43
Nov 2020	38	13	0	51
Dec 2020	29	8	0	37
Jan 2021	34	20	1	55
Feb 2021	12	10	0	22
Mar 2021	17	23	2	42
Apr 2021	44	15	1	60
May 2021	48	19	2	69
Jun 2021	30	10	0	40
Jul 2021	34	20	1	55
Aug 2021	24	17	0	41
Sep 2021	28	19	1	48
Oct 2021	24	36	0	60
Nov 2021	48	38	0	86
Dec 2021	57	23	1	81
Jan 2022	26	15	2	43
Feb 2022	27	11	4	42
Mar 2022	28	40	1	69
Apr 2022	10	15	3	28
May 2022	22	11	0	33
Jun 2022	16	10	0	26
Jul 2022	17	7	0	24
Aug 2022	28	11	1	40
Sep 2022	11	10	0	21
Oct 2022	14	4	1	19
Nov 2022	37	17	0	54
Dec 2022	32	10	2	44
Jan 2023	20	6	1	27
Feb 2023	20	14	0	34
Mar 2023	15	13	0	28
Apr 2023	21	9	0	30
May 2023	23	10	1	34
Jun 2023	24	9	1	34
Jul 2023	23	12	1	36
Aug 2023	17	18	3	38
Sep 2023	40	15	1	56
Oct 2023	16	14	2	32
Nov 2023	10	5	0	15
Dec 2023	31	12	1	44
Jan 2024	37	9	1	47
Feb 2024	21	10	1	32
Mar 2024	33	19	5	57
Apr 2024	33	12	2	47
May 2024	30	12	1	43
Jun 2024	37	13	1	51
Jul 2024	39	12	1	52
Aug 2024	38	16	2	56
Sep 2024	17	5	0	22
Oct 2024	34	17	0	51
Nov 2024	41	14	6	61
Dec 2024	36	7	1	44
Jan 2025	36	13	3	52
Feb 2025	42	23	0	65
Mar 2025	31	12	2	45
Apr 2025	40	17	2	59
May 2025	65	13	0	78
Jun 2025	59	16	2	77
Jul 2025	45	28	2	75
Aug 2025	54	15	1	70
Sep 2025	72	17	2	91
Oct 2025	45	23	0	68
Nov 2025	44	26	1	71

Cumulative views and downloads (calculated since 29 Jan 2018)

Month	HTML	PDF	XML	Total
Jan 2018	233	86	4	323
Feb 2018	125	89	6	220
Mar 2018	50	36	1	87
Apr 2018	38	12	1	51
May 2018	48	32	1	81
Jun 2018	33	21	1	55
Jul 2018	334	111	4	449
Aug 2018	48	40	1	89
Sep 2018	42	48	3	93
Oct 2018	30	31	5	66
Nov 2018	21	31	0	52
Dec 2018	13	13	1	27
Jan 2019	20	24	2	46
Feb 2019	19	22	0	41
Mar 2019	21	11	1	33
Apr 2019	22	12	0	34
May 2019	22	22	0	44
Jun 2019	23	9	1	33
Jul 2019	20	15	2	37
Aug 2019	15	14	0	29
Sep 2019	23	18	1	42
Oct 2019	33	14	0	47
Nov 2019	12	8	0	20
Dec 2019	12	14	0	26
Jan 2020	12	15	0	27
Feb 2020	11	10	1	22
Mar 2020	15	10	1	26
Apr 2020	20	8	0	28
May 2020	31	18	0	49
Jun 2020	13	14	0	27
Jul 2020	28	23	4	55
Aug 2020	25	14	1	40
Sep 2020	22	12	0	34
Oct 2020	27	16	0	43
Nov 2020	38	13	0	51
Dec 2020	29	8	0	37
Jan 2021	34	20	1	55
Feb 2021	12	10	0	22
Mar 2021	17	23	2	42
Apr 2021	44	15	1	60
May 2021	48	19	2	69
Jun 2021	30	10	0	40
Jul 2021	34	20	1	55
Aug 2021	24	17	0	41
Sep 2021	28	19	1	48
Oct 2021	24	36	0	60
Nov 2021	48	38	0	86
Dec 2021	57	23	1	81
Jan 2022	26	15	2	43
Feb 2022	27	11	4	42
Mar 2022	28	40	1	69
Apr 2022	10	15	3	28
May 2022	22	11	0	33
Jun 2022	16	10	0	26
Jul 2022	17	7	0	24
Aug 2022	28	11	1	40
Sep 2022	11	10	0	21
Oct 2022	14	4	1	19
Nov 2022	37	17	0	54
Dec 2022	32	10	2	44
Jan 2023	20	6	1	27
Feb 2023	20	14	0	34
Mar 2023	15	13	0	28
Apr 2023	21	9	0	30
May 2023	23	10	1	34
Jun 2023	24	9	1	34
Jul 2023	23	12	1	36
Aug 2023	17	18	3	38
Sep 2023	40	15	1	56
Oct 2023	16	14	2	32
Nov 2023	10	5	0	15
Dec 2023	31	12	1	44
Jan 2024	37	9	1	47
Feb 2024	21	10	1	32
Mar 2024	33	19	5	57
Apr 2024	33	12	2	47
May 2024	30	12	1	43
Jun 2024	37	13	1	51
Jul 2024	39	12	1	52
Aug 2024	38	16	2	56
Sep 2024	17	5	0	22
Oct 2024	34	17	0	51
Nov 2024	41	14	6	61
Dec 2024	36	7	1	44
Jan 2025	36	13	3	52
Feb 2025	42	23	0	65
Mar 2025	31	12	2	45
Apr 2025	40	17	2	59
May 2025	65	13	0	78
Jun 2025	59	16	2	77
Jul 2025	45	28	2	75
Aug 2025	54	15	1	70
Sep 2025	72	17	2	91
Oct 2025	45	23	0	68
Nov 2025	44	26	1	71

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Short summary

New particle formation (NPF) in the atmosphere is globally an important source of aerosol particles. NPF events are typically identified and analyzed manually by researchers from particle size distribution data day by day, which is time consuming and might be inconsistent. We have developed an automatic analysis method based on deep learning for NPF event identification. The developed method can be easily utilized to analyze any long-term datasets more accurately and consistently.


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