Department of Civil Engineering, Faculty of Science and Technology, Tokyo University of Science, Chiba, 278-8510, Japan.
Department of Civil and Environmental Engineering, Faculty of Engineering, Ehime University, Ehime, 790-8577, Japan.
Environ Pollut. 2023 Oct 15;335:122310. doi: 10.1016/j.envpol.2023.122310. Epub 2023 Aug 4.
Microplastics (MPs), plastic particles <5 mm in diameter, are emerging ubiquitous pollutants in natural environments, including freshwater ecosystems. As rivers facilitate efficient transport among landscapes, monitoring is crucial for elucidating the origin, dynamics, and fate of MPs. However, standardized methodologies for in situ sampling in freshwater environments remain undefined to date. Specifically, evaluating the sampling error of MP concentration estimates is crucial for comparing results among studies. This study proposes a novel method for computing confidence intervals (CIs) from a single estimate of numerical concentration (expressed in particles·m). MPs are expected to disperse according to purely random processes, such as turbulent diffusion, and to consequently exhibit a random distribution pattern that follows a Poisson point process. Accordingly, the present study introduced a framework based on the Poisson point process to compute CIs, which were validated using MP samples from two urban rivers in Chiba, Japan, obtained using a mesh with an opening size of 335 μm. Random number simulations revealed that the CIs were applicable when ≥10 MPs were present in a sample. Further, when ≥50 MPs were present in a sample, the sampling error (95% CI) was within ±30% of the concentration estimates. The proposed framework allows for the intercomparison of single river MP samples despite the lack of sample replicates. Further, the present study emphasizes that the volume of sampled river water is the only controllable parameter that can reduce the sampling error.
微塑料（MPs）是一种直径小于 5 毫米的塑料颗粒，是自然环境中新兴的普遍存在的污染物，包括淡水生态系统。由于河流促进了景观之间的高效运输，因此监测对于阐明 MPs 的来源、动态和命运至关重要。然而，目前尚未定义用于淡水环境的原位采样的标准化方法。具体来说，评估 MP 浓度估计值的采样误差对于比较研究结果至关重要。本研究提出了一种从单一数值浓度（以颗粒·m 表示）估计值计算置信区间（CI）的新方法。MPs 预计会根据纯粹的随机过程（如湍流扩散）分散，因此表现出遵循泊松点过程的随机分布模式。因此，本研究引入了一个基于泊松点过程的框架来计算 CI，并用日本千叶市两条城市河流中的 MPs 样本进行了验证，这些样本是使用开口尺寸为 335 微米的网格获得的。随机数模拟表明，当一个样本中存在≥10 个 MPs 时，CI 是适用的。此外，当一个样本中存在≥50 个 MPs 时，采样误差（95%CI）在浓度估计值的±30%范围内。尽管缺乏样本重复，但所提出的框架允许对单个河流 MPs 样本进行比较。此外，本研究强调，采样的河水体积是唯一可以减少采样误差的可控参数。
