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在复杂的表面水中使用物理化学信息光谱变压器与UV-Vis-SWNIR光谱仪进行快速COD传感

Jiacheng Liu^1,2,3, Xiao Liu¹, Xueji Wang¹

¹Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China.

Environmental science & technology

|March 7, 2025

概括

一个新的深度学习模型,物理化学信息光谱变压器 (PIST),使用UV-vs-SWNIR光谱学准确地测量复杂水体中的化学氧气需求 (COD). 这一进步改善了水质传感和环境监测中的通用性.

科学领域:

环境科学环境科学
分析化学分析化学
数据科学数据科学数据科学

背景情况:

准确的水质监测对于可持续的资源管理至关重要.
测量化学氧气需求 (COD) 对于评估水的健康至关重要.
现有的光谱方法在复杂的水环境中面临挑战,因为光谱理解和概括性有限.

研究的目的:

引入一种新的深度学习模型,用于增强水质传感.
为了提高化学氧气需求 (COD) 测量在各种水体的准确性和通用性.
将物理化学知识纳入用于域适应的光谱分析.

主要方法:

物理化学信息的光谱变压器 (PIST) 模型的开发.
结合PIST与紫外线-可见-短波-近红外 (UV-vs-SWNIR) 光谱学.
使用长江和阳湖广泛的地表水光谱数据进行验证.

主要成果:

对于COD传感,PIST实现了0.9008的高确定系数 (R^2).
与SVR和CNN模型相比,根平均平方误差 (RMSE) 的显著减少为45.20%和29.38%.
在复杂的水环境中表现出显著的准确性和通用性.

结论:

PIST模型代表了利用光谱学和深度学习在水质感应方面取得的重大进展.
物理化学信息的整合增强了光谱编码和域适应.
PIST为快速,大规模的水质监测提供了强大而准确的解决方案.

关键词:

紫外线-对-SWNIR光谱学化学氧气需求 (COD) 是指化学测量方法化学测量方法深度学习是一种深度学习.基于物理化学信息的学习变压器变压器变压器变压器水质感应水质感应

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