Sensors for Emerging Water Contaminants: Overcoming Roadblocks to Innovation

风险分析(工程) 利益相关者 计算机科学 新兴技术 系统工程 工作流程 过程管理 业务 工程类 政治学 公共关系 数据库 人工智能
作者
Mohamed Ateia,Haoran Wei,Silvana Andreescu
出处
期刊:Environmental Science & Technology [American Chemical Society]
卷期号:58 (6): 2636-2651 被引量:117
标识
DOI:10.1021/acs.est.3c09889
摘要

Ensuring water quality and safety requires the effective detection of emerging contaminants, which present significant risks to both human health and the environment. Field deployable low-cost sensors provide solutions to detect contaminants at their source and enable large-scale water quality monitoring and management. Unfortunately, the availability and utilization of such sensors remain limited. This Perspective examines current sensing technologies for detecting emerging contaminants and analyzes critical barriers, such as high costs, lack of reliability, difficulties in implementation in real-world settings, and lack of stakeholder involvement in sensor design. These technical and nontechnical barriers severely hinder progression from proof-of-concepts and negatively impact user experience factors such as ease-of-use and actionability using sensing data, ultimately affecting successful translation and widespread adoption of these technologies. We provide examples of specific sensing systems and explore key strategies to address the remaining scientific challenges that must be overcome to translate these technologies into the field such as improving sensitivity, selectivity, robustness, and performance in real-world water environments. Other critical aspects such as tailoring research to meet end-users' requirements, integrating cost considerations and consumer needs into the early prototype design, establishing standardized evaluation and validation protocols, fostering academia-industry collaborations, maximizing data value by establishing data sharing initiatives, and promoting workforce development are also discussed. The Perspective describes a set of guidelines for the development, translation, and implementation of water quality sensors to swiftly and accurately detect, analyze, track, and manage contamination.
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