Accurate measurements of the size distribution of atmospheric aerosol nanoparticles are essential to build an understanding of new particle formation and growth. This is particularly crucial at the sub-3 nm range due to the growth of newly formed nanoparticles. The challenge in recovering the size distribution is due its complexity and the fact that not many instruments currently measure at this size range. In this study, we used the particle size magnifier (PSM) to measure atmospheric aerosols. Each day was classified into one of the following three event types: a new particle formation (NPF) event, a non-event or a haze event. We then compared four inversion methods (stepwise, kernel, Hagen–Alofs and expectation–maximization) to determine their feasibility to recover the particle size distribution. In addition, we proposed a method to pretreat the measured data, and we introduced a simple test to estimate the efficacy of the inversion itself. Results showed that all four methods inverted NPF events well; however, the stepwise and kernel methods fared poorly when inverting non-events or haze events. This was due to their algorithm and the fact that, when encountering noisy data (e.g. air mass fluctuations or low sub-3 nm particle concentrations) and under the influence of larger particles, these methods overestimated the size distribution and reported artificial particles during inversion. Therefore, using a statistical hypothesis test to discard noisy scans prior to inversion is an important first step toward achieving a good size distribution. After inversion, it is ideal to compare the integrated concentration to the raw estimate (i.e. the concentration difference at the lowest supersaturation and the highest supersaturation) to ascertain whether the inversion itself is sound. Finally, based on the analysis of the inversion methods, we provide procedures and codes related to the PSM data inversion.

中文翻译：

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评估粒径放大镜反演方法以从大气测量中获得粒径分布

准确测量大气气溶胶纳米粒子的尺寸分布对于建立对新粒子形成和生长的理解至关重要。由于新形成的纳米颗粒的生长，这在亚3 nm范围内尤其重要。恢复大小分布的挑战是由于其复杂性以及当前没有很多仪器在此大小范围内进行测量这一事实。在这项研究中，我们使用了粒径放大器（PSM）来测量大气中的气溶胶。每天被分为以下三种事件类型之一：新粒子形成（NPF）事件，非事件或雾霾事件。然后，我们比较了四种反演方法（逐步，核，Hagen–Alofs和期望最大化），以确定它们恢复粒度分布的可行性。此外，我们提出了一种预处理测量数据的方法，并且我们引入了一个简单的测试来估计反演本身的功效。结果表明，这四种方法均能很好地逆转NPF事件。但是，在反转非事件或霾事件时，逐步方法和内核方法的效果很差。这是由于他们的算法以及以下事实：当遇到嘈杂的数据（例如，空气质量波动或低于3 nm的低颗粒浓度）并且在较大颗粒的影响下，这些方法高估了粒径分布，并在反演期间报告了人工颗粒。因此，使用统计假设检验在反演之前丢弃噪声扫描是实现良好尺寸分布的重要第一步。反演之后，理想的是将积分浓度与原始估算值进行比较（即 最低过饱和度和最高过饱和度时的浓度差），以确定反演本身是否合理。最后，在分析反演方法的基础上，我们提供了与PSM数据反演有关的过程和代码。