Estimating the atmospheric aerosol number size distribution using deep learning

IF 3.5 Q3 ENVIRONMENTAL SCIENCES
Yusheng Wu, Martha Arbayani Zaidan, Runlong Cai, Jonathan Duplissy, Magdalena Okuljar, Katrianne Lehtipalo, Tuukka Petäjä and Juha Kangasluoma
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Abstract

The submicron aerosol number size distribution significantly impacts human health, air quality, weather, and climate. However, its measurement requires sophisticated and expensive instrumentation that demands substantial maintenance efforts, leading to limited data availability. To tackle this challenge, we developed estimation models using advanced deep learning algorithms to estimate the aerosol number size distribution based on trace gas concentrations, meteorological parameters, and total aerosol number concentration. These models were trained and validated with 15 years of ambient data from three distinct environments, and data from a fourth station were exclusively used for testing. Our estimative models successfully replicated the trends in the test data, capturing the temporal variations of particles ranging from approximately 10–500 nm, and accurately deriving total number, surface area, and mass concentrations. The model's accuracy for particles below 75 nm is limited without the inclusion of total particle number concentration as training input, highlighting the importance of this parameter for capturing the dynamics of smaller particles. The reliance on total particle number concentration, a parameter not routinely measured at all in air quality monitoring sites, as a key input for accurate estimation of smaller particles presents a practical challenge for broader application of the models. Our models demonstrated a robust generalization capability, offering valuable data for health assessments, regional pollution studies, and climate modeling. The estimation models developed in this work are representative of ambient conditions in Finland, but the methodology in general can be applied in broader regions.

Abstract Image

利用深度学习估计大气气溶胶数量大小分布
亚微米气溶胶数量大小分布对人类健康、空气质量、天气和气候有显著影响。然而,它的测量需要复杂和昂贵的仪器,需要大量的维护工作,导致有限的数据可用性。为了应对这一挑战,我们开发了使用先进深度学习算法的估计模型,以基于痕量气体浓度、气象参数和总气溶胶浓度来估计气溶胶数量大小分布。这些模型是用15年的三个不同环境的环境数据进行训练和验证的,第四个站点的数据专门用于测试。我们的估计模型成功地复制了测试数据中的趋势,捕获了大约10-500 nm范围内颗粒的时间变化,并准确地推导出总数、表面积和质量浓度。如果没有将总颗粒数浓度作为训练输入,该模型对75 nm以下颗粒的精度是有限的,这突出了该参数对于捕获较小颗粒动力学的重要性。总颗粒数浓度是空气质量监测点不经常测量的一个参数,但作为准确估计较小颗粒的关键输入,对模型的广泛应用提出了实际挑战。我们的模型显示出强大的泛化能力,为健康评估、区域污染研究和气候建模提供了有价值的数据。在这项工作中开发的估计模型代表了芬兰的环境条件,但一般的方法可以应用于更广泛的地区。
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CiteScore
2.90
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