NOVA: an automated nutrients online variable analyzer for terrestrial input observation

Abstract The simultaneous measurement of ammoniacal nitrogen (NH 4 + −N) and total phosphate (TP) in terrestrial inputs is imperative for the effective monitoring of estuaries. Conventional laboratory analyses are laborious and time-consuming, and are also susceptible to degradation during transportation. In order to overcome these limitations, the nutrient online variable analyzer (NOVA) was developed. NOVA employs a single microfluidic syringe pump for the purpose of ensuring accurate control of inlet volume, and automates both sample dilution and tubing lubrication according to concentration range. This process serves to suppress errors that typically arise from multi-channel inconsistencies. Laboratory calibration and field deployments confirmed the robustness of NOVA, with correlation coefficients ( R 2 ) reaching 0.9993 for NH 4 + −N and 0.9995 for TP. The system demonstrated high adaptability and reproducibility under varying environmental conditions. Subsequent memory-effect tests demonstrated negligible carry-over, with T -values of −0.0133 for high-concentration samples and −0.0050 for low-concentration samples. The outcomes of this study demonstrate the capacity of NOVA to accurately and reliably quantify both NH 4 + −N and TP.

Authors

Institutions

Publication Details

Journal
Measurement Science and Technology
Published
2025-08-14
DOI
https://doi.org/10.1088/1361-6501/adfb9c
Primary Topic
Water Quality Monitoring Technologies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

NOVA: an automated nutrients online variable analyzer for terrestrial input observation

Feng Lyu, Zhisong Yang, S. S. Wei
Measurement Science and Technology
Water Quality Monitoring Technologies
article

NOVA: an automated nutrients online variable analyzer for terrestrial input observation

Feng Lyu, Zhisong Yang, S. S. Wei
article en

Abstract

Abstract The simultaneous measurement of ammoniacal nitrogen (NH 4 + −N) and total phosphate (TP) in terrestrial inputs is imperative for the effective monitoring of estuaries. Conventional laboratory analyses are laborious and time-consuming, and are also susceptible to degradation during transportation. In order to overcome these limitations, the nutrient online variable analyzer (NOVA) was developed. NOVA employs a single microfluidic syringe pump for the purpose of ensuring accurate control of inlet volume, and automates both sample dilution and tubing lubrication according to concentration range. This process serves to suppress errors that typically arise from multi-channel inconsistencies. Laboratory calibration and field deployments confirmed the robustness of NOVA, with correlation coefficients ( R 2 ) reaching 0.9993 for NH 4 + −N and 0.9995 for TP. The system demonstrated high adaptability and reproducibility under varying environmental conditions. Subsequent memory-effect tests demonstrated negligible carry-over, with T -values of −0.0133 for high-concentration samples and −0.0050 for low-concentration samples. The outcomes of this study demonstrate the capacity of NOVA to accurately and reliably quantify both NH 4 + −N and TP.

Measurement Science and TechnologyVol. 36(8)
Tongji University (CN), Shanghai Ocean University (CN)
Natural Science Foundation of Shanghai, National Natural Science Foundation of China, National Key Research and Development Program of China Stem Cell and Translational Research
Openalex Percentile: Top 79%
Water Quality Monitoring Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.