利用纵向全球粒径分布数据识别大气中新粒子的形成。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Simonas Kecorius, Leizel Madueño, Mario Lovric, Nikolina Racic, Maximilian Schwarz, Josef Cyrys, Juan Andrés Casquero-Vera, Lucas Alados-Arboledas, Sébastien Conil, Jean Sciare, Jakub Ondracek, Anna Gannet Hallar, Francisco J Gómez-Moreno, Raymond Ellul, Adam Kristensson, Mar Sorribas, Nikolaos Kalivitis, Nikolaos Mihalopoulos, Annette Peters, Maria Gini, Konstantinos Eleftheriadis, Stergios Vratolis, Kim Jeongeun, Wolfram Birmili, Benjamin Bergmans, Nina Nikolova, Adelaide Dinoi, Daniele Contini, Angela Marinoni, Andres Alastuey, Tuukka Petäjä, Sergio Rodriguez, David Picard, Benjamin Brem, Max Priestman, David C Green, David C S Beddows, Roy M Harrison, Colin O'Dowd, Darius Ceburnis, Antti Hyvärinen, Bas Henzing, Suzanne Crumeyrolle, Jean-Philippe Putaud, Paolo Laj, Kay Weinhold, Kristina Plauškaitė, Steigvilė Byčenkienė
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引用次数: 0

摘要

大气中新粒子的形成(NPF)是一种自然发生的现象,在这一过程中,通过气体到粒子的转化,会产生高浓度的 10 纳米以下粒子。在全球多种环境中都能观测到 NPF。虽然它对年度总粒子数浓度和超细粒子数浓度(分别为 PNC 和 UFP)有明显的影响,但只有有限的流行病学研究调查了这些粒子是否与不良健康影响有关。造成这种局限性的一个合理原因可能与 UFP 和 PNC 数据集中缺乏 NPF 识别器有关。直到最近,区域 NPF 事件通常都是通过人工从粒径分布等值线图中识别出来的。多年度和多站点数据集的 NPF 识别仍然是一项繁琐的任务。在这项工作中,我们引入了区域 NPF 识别器,该识别器是利用基于机器学习的自动算法创建的。区域 NPF 事件标签是为全球 65 个测量站点创建的,涵盖 1996 年至 2023 年。所讨论的数据集可用于未来与区域 NPF 相关的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Atmospheric new particle formation identifier using longitudinal global particle number size distribution data.

Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiological studies have investigated whether these particles are associated with adverse health effects. One plausible reason for this limitation may be related to the absence of NPF identifiers available in UFP and PNC data sets. Until recently, the regional NPF events were usually identified manually from particle number size distribution contour plots. Identification of NPF across multi-annual and multiple station data sets remained a tedious task. In this work, we introduce a regional NPF identifier, created using an automated, machine learning based algorithm. The regional NPF event tag was created for 65 measurement sites globally, covering the period from 1996 to 2023. The discussed data set can be used in future studies related to regional NPF.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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