Abstract：
Long-term environmental loading of microplastics (MPs) causes alarming exposure risks for a variety of species worldwide, considered a planetary threat to the well-being of ecosystems. Robust quantitative estimates of MP extents and featured diversity are the basis for comprehending their environmental implications precisely, and of these methods, membrane-based characterizations predominate with respect to MP inspections. However, though crucial to filter-based MP quantification, aggregation statuses of retained MPs on these substrates remain poorly understood, leaving us a \"blind box\" that exaggerates uncertainty in quantitive strategies of preselected areas without knowing overview loading structure. To clarify this uncertainty and estimate their impacts on MP counting, using MP imaging data assembled from peer-reviewed studies through a systematic review, here we analyze the particle-specific profiles of MPs retained on various substrates according to their centre of mass with a fast-random forests algorithm. We visualize the formation of distinct galaxy-like MP aggregation-similar to the solar system and Milky Way System comprised of countless stars-across the pristine and environmental samples by leveraging two spatial parameters developed in this study. This unique pattern greatly challenges the homogeneously or randomly distributed MP presumption adopted extensively for simplified membrane-based quantification purposes and selective ROI (region of interest) estimates for smaller-sized plastics down to the nano-range, as well as the compatibility theory using pristine MPs as the standard to quantify the presence of environmental MPs. Furthermore, our evaluation with exemplified numeration cases confirms these location-specific and area-dependent biases in many imaging analyses of a selective filter area, ascribed to the minimum possibility of reaching an ideal turnover point for the selective quantitive strategies. Consequently, disproportionate MP schemes on loading substrates yield great uncertainty in their quantification processing, highlighting the prompt need to include pattern-resolved calibration prior to quantification. Our findings substantially advance our understanding of the structure, behavior, and formation of these MP aggregating statuses on filtering substrates, addressing a fundamental question puzzling scientists as to why reproducible MP quantification is barely achievable even for subsamples. This study inspires the following studies to reconsider the impacts of aggregating patterns on the effective counting protocols and target-specific removal of retained MP aggregates through membrane separation techniques.
摘要：
微塑料（MPs）的长期环境负荷导致全球各种物种面临令人震惊的暴露风险，被认为是对生态系统福祉的行星威胁。对MP范围和特征多样性的稳健定量估计是准确理解其环境影响的基础，在这些方法中，基于膜的表征在MP检查中占主导地位。然而,尽管对基于过滤器的MP量化至关重要，这些底物上保留的MP的聚集状态仍然知之甚少，给我们留下了一个“盲箱”，在不知道总体加载结构的情况下，夸大了预选区域的定量策略的不确定性。为了澄清这种不确定性并估计它们对MP计数的影响，使用从同行评审研究中收集的MP成像数据，通过系统评价，在这里，我们使用快速随机森林算法根据其质心分析保留在各种基材上的MPs的特定颗粒分布。我们通过利用本研究中开发的两个空间参数，在原始和环境样本中可视化了类似于太阳系和由无数恒星组成的银河系的独特星系状MP聚集的形成。这种独特的模式极大地挑战了广泛采用的均匀或随机分布的MP假设，用于简化的基于膜的定量目的和对更小尺寸塑料的选择性ROI（感兴趣区域）估计，直至纳米范围。以及使用原始MPs作为量化环境MPs存在的标准的相容性理论。此外，我们的评估与示例的数字案例证实了这些位置特定的和面积依赖的偏差在许多成像分析的选择性滤波器区域，归因于选择性定量策略达到理想周转点的最小可能性。因此,加载底物上的不成比例的MP方案在其量化处理中产生了很大的不确定性，强调在量化之前需要包括模式分辨校准。我们的发现大大推进了我们对结构的理解，行为，并在过滤基质上形成这些MP聚集状态，解决了一个令科学家困惑的基本问题，即为什么即使对子样本也几乎无法实现可重复的MP定量。本研究激发了以下研究，以重新考虑聚集模式对有效计数方案和通过膜分离技术对保留的MP聚集体的目标特异性去除的影响。
