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. 2022 Oct 1;310:119811. doi: 10.1016/j.envpol.2022.119811. Epub 2022 Aug 4.
Microplastics (MPs), plastic particles <5 mm in diameter, have become an emerging ubiquitous concern for the environment. Rivers are the primary pathways that transport MPs from the land to the ocean; however, standardized methodologies for in-situ sampling in freshwater environments remain undefined. Notably, uncertainties in MP sampling methods lead to errors in estimating MP discharge through rivers. In the present study, the inter-sample variance of plankton net-obtained MP concentrations for two urban rivers in Japan was investigated. Numerical concentrations, expressed in particles·m, revealed that variance s was proportional to the mean m of replicated estimates of numerical concentrations. A derived statistical model suggested that river MPs disperse according to purely random processes; that is, Poisson point processes. Accordingly, a method was established to project the "precision," the ratio of the standard error to m, of numerical concentrations based on the number of net sampling repetitions. It was found that the mean of two replicates maintained sufficient precision of <30% for conditions with high concentrations of ≥3 particles·m. Projected precisions under different levels of MP concentrations are also presented to help design future field campaigns.
微塑料（MPs）是指直径小于 5 毫米的塑料颗粒，已成为环境中一种新兴的普遍关注的问题。河流是将 MPs 从陆地输送到海洋的主要途径；然而，在淡水环境中进行原位采样的标准化方法仍未得到定义。值得注意的是，MP 采样方法的不确定性导致通过河流估算 MP 排放量存在误差。在本研究中，研究了日本两条城市河流中浮游生物网采集的 MP 浓度的样本间方差。以颗粒·m 表示的数值浓度表明，方差 s 与数值浓度重复估计值的平均值 m 成正比。得出的统计模型表明，河流中的 MPs 是根据纯粹的随机过程分散的，即泊松点过程。因此，建立了一种方法来根据网采样重复次数来预测数值浓度的“精度”，即标准误差与 m 的比值。结果发现，在浓度≥3 个颗粒·m 的高浓度条件下，两次重复的平均值保持了足够的精度<30%。还展示了不同 MP 浓度下的预测精度，以帮助设计未来的实地考察。
