Approaches for achieving long-term accuracy and precision of δ18O and δ2H for waters analyzed using laser absorption spectrometers.

Environ Sci Technol

Isotope Hydrology Section, International Atomic Energy Agency , Vienna International Center, Vienna, Austria , A-1400.

Published: January 2014

The measurement of δ(2)H and δ(18)O in water samples by laser absorption spectroscopy (LAS) are adopted increasingly in hydrologic and environmental studies. Although LAS instrumentation is easy to use, its incorporation into laboratory operations is not as easy, owing to extensive offline data manipulation required for outlier detection, derivation and application of algorithms to correct for between-sample memory, correcting for linear and nonlinear instrumental drift, VSMOW-SLAP scale normalization, and in maintaining long-term QA/QC audits. Here we propose a series of standardized water-isotope LAS performance tests and routine sample analysis templates, recommended procedural guidelines, and new data processing software (LIMS for Lasers) that altogether enables new and current LAS users to achieve and sustain long-term δ(2)H and δ(18)O accuracy and precision for these important isotopic assays.

Download full-text PDF

Source
http://dx.doi.org/10.1021/es403354nDOI Listing

Publication Analysis

Top Keywords

accuracy precision
8
laser absorption
8
δ2h δ18o
8
approaches achieving
4
achieving long-term
4
long-term accuracy
4
precision δ18o
4
δ18o δ2h
4
δ2h waters
4
waters analyzed
4

Similar Publications

In the medical field, endoscopic video analysis is crucial for disease diagnosis and minimally invasive surgery. The Endoscopic Foundation Models (Endo- FM) utilize large-scale self-supervised pre-training on endoscopic video data and leverage video transformer models to capture long-range spatiotemporal dependencies. However, detecting complex lesions such as gastrointestinal metaplasia (GIM) in endoscopic videos remains challenging due to unclear boundaries and indistinct features, and Endo-FM has not demonstrated good performance.

View Article and Find Full Text PDF

To enhance the intelligent classification of computer vulnerabilities and improve the efficiency and accuracy of network security management, this study delves into the application of a comprehensive classification system that integrates the Memristor Neural Network (MNN) and an improved Temporal Convolutional Neural Network (TCNN) in network security management. This system not only focuses on the precise classification of vulnerability data but also emphasizes its core role in strengthening the network security management framework. Firstly, the study designs and implements a neural network model based on memristors.

View Article and Find Full Text PDF

Modernizing power systems into smart grids has introduced numerous benefits, including enhanced efficiency, reliability, and integration of renewable energy sources. However, this advancement has also increased vulnerability to cyber threats, particularly False Data Injection Attacks (FDIAs). Traditional Intrusion Detection Systems (IDS) often fall short in identifying sophisticated FDIAs due to their reliance on predefined rules and signatures.

View Article and Find Full Text PDF

The widespread adoption of high-resolution computed tomography (CT) screening has led to increased detection of small pulmonary nodules, necessitating accurate localization techniques for surgical resection. This review examines the evolution, efficacy, and safety of various localization methods for small pulmonary nodules. Studies focusing on localization techniques for pulmonary nodules ≤30 mm in diameter were included, with emphasis on technical success rates and complication profiles.

View Article and Find Full Text PDF

Prediction of Pharmacoresistance in Drug-Naïve Temporal Lobe Epilepsy Using Ictal EEGs Based on Convolutional Neural Network.

Neurosci Bull

January 2025

Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, College of Pharmaceutical Sciences, The Second Affiliated Hospital of Zhejiang Chinese Medical University (Xinhua Hospital), Zhejiang Chinese Medical University, Hangzhou, 310053, China.

Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its early prediction is important for prevention and diagnosis.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!