COVID-19 pandemic microplastics environmental impacts predicted by deep random forest (DRF) predictive model

IF 6 3区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Environmental Sciences Europe Pub Date : 2024-11-07 DOI:10.1186/s12302-024-01019-z
Liping Chen, Arkan K. S. Sabonchi, Yaser A. Nanehkaran
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Abstract

Background

Microplastic pollution is a pressing issue with far-reaching environmental and public health consequences. This study delves into the intricacies of predicting microplastic pollution during the COVID-19 pandemic in Tehran, Iran.

Methods

The research introduces a rigorous comparative analysis that evaluates the predictive prowess of the Deep Random Forest algorithm and established benchmarks, such as Random Forest, Decision Trees, Gradient Boosting, AdaBoost, and Support Vector Machine. The evaluation process encompasses a meticulous 70–30 training–testing split of the main data set. Performance is assessed by analysis metrics, including ROC and statistical errors. The primary data set encompasses distinct categories, including household wastes, hospital wastes, clinics wastes, and unknown-originated susceptible waste which is categorized in Infected items, PPEs, SUPs, Test kits, Medical packages, Unknown-originated pandemic mircoplastic waste. Deliberately, this data set was partitioned into training and testing subsets, ensuring the robustness and reliability of subsequent analyses. Approximately 70% of the main database was allocated to the training data set, with the remaining 30% constituting the testing data set.

Results

The findings underscore the proposed algorithm’s supremacy, boasting an impressive AUC = 0.941. This exceptional score reflects the model’s precision in categorizing microplastics. These results have profound implications for environmental management and public health during pandemics.

Conclusions

The study positions the proposed model as a potent tool for microplastic pollution prediction, encouraging further research to refine predictive models and tap into new data sources for a more comprehensive understanding of microplastic dynamics in urban settings.

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用深度随机森林(DRF)预测模型预测 COVID-19 大流行病微塑料对环境的影响
背景微塑料污染是一个紧迫的问题,对环境和公众健康有着深远的影响。本研究深入探讨了在伊朗德黑兰 COVID-19 大流行期间预测微塑料污染的复杂性。 方法本研究引入了严格的比较分析,评估深度随机森林算法与随机森林、决策树、梯度提升、AdaBoost 和支持向量机等既定基准的预测能力。评估过程包括对主要数据集进行细致的 70-30% 训练-测试分离。性能通过分析指标进行评估,包括 ROC 和统计误差。主数据集包含不同类别,包括家庭废物、医院废物、诊所废物和来源不明的易感废物,其中来源不明的易感废物又分为受感染物品、个人防护设备、SUP、检测包、医疗包和来源不明的大流行性贻误性废物。该数据集被有意划分为训练子集和测试子集,以确保后续分析的稳健性和可靠性。约 70% 的主数据库分配给了训练数据集,其余 30% 构成了测试数据集。这一优异成绩反映了该模型在微塑料分类方面的精确性。这些结果对大流行病期间的环境管理和公共卫生有着深远的影响。结论这项研究将提出的模型定位为预测微塑料污染的有力工具,鼓励进一步研究完善预测模型,并利用新的数据源更全面地了解城市环境中的微塑料动态。
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来源期刊
Environmental Sciences Europe
Environmental Sciences Europe Environmental Science-Pollution
CiteScore
11.20
自引率
1.70%
发文量
110
审稿时长
13 weeks
期刊介绍: ESEU is an international journal, focusing primarily on Europe, with a broad scope covering all aspects of environmental sciences, including the main topic regulation. ESEU will discuss the entanglement between environmental sciences and regulation because, in recent years, there have been misunderstandings and even disagreement between stakeholders in these two areas. ESEU will help to improve the comprehension of issues between environmental sciences and regulation. ESEU will be an outlet from the German-speaking (DACH) countries to Europe and an inlet from Europe to the DACH countries regarding environmental sciences and regulation. Moreover, ESEU will facilitate the exchange of ideas and interaction between Europe and the DACH countries regarding environmental regulatory issues. Although Europe is at the center of ESEU, the journal will not exclude the rest of the world, because regulatory issues pertaining to environmental sciences can be fully seen only from a global perspective.
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