Detection and removal of emerging contaminants from water bodies: A statistical approach

Frontiers in analytical science Pub Date : 2023-04-04 DOI:10.3389/frans.2023.1115540

A. Banerjee, Surjit Singh, A. Ghosh

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Abstract

The integration of mathematical modelling in different scientific domains has increased dramatically in recent years. In general, modelling involves using programming languages, manipulating matrices, designing algorithms, and tracking functions and data to gain new insights and more quantitative and qualitative information about systems. These strategies have motivated researchers to investigate numerous approaches to accurately solve a variety of problems. In this direction, modelling and simulation have been used to create sensitive and focused detection methods for a variety of applications, including environmental control. New pollutants, including pesticides, heavy metals, and medications, are endangering wildlife by poisoning water supplies. As a result, numerous biosensors that use modelling for effective environmental monitoring have been documented in the literature. The most current model-inspired biosensors used for environmental monitoring will be discussed in this review study. Additionally, each analytical biosensor’s capabilities and degree of success will be discussed. Finally, present difficulties in this area will be highlighted.

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从水体中检测和去除新出现的污染物:一种统计方法

近年来，数学建模在不同科学领域的整合得到了极大的发展。一般来说，建模包括使用编程语言、操作矩阵、设计算法、跟踪函数和数据，以获得关于系统的新的见解和更多的定量和定性信息。这些策略促使研究人员研究许多方法来准确地解决各种问题。在这个方向上，建模和仿真已被用于为各种应用(包括环境控制)创建敏感和集中的检测方法。新的污染物，包括杀虫剂、重金属和药物，正在毒害水源，危及野生动物。因此，文献中记录了许多使用建模进行有效环境监测的生物传感器。本文将对目前用于环境监测的基于模型的生物传感器进行综述。此外，每种分析生物传感器的能力和成功程度将被讨论。最后，将强调目前在这方面的困难。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Frontiers in analytical science

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