Development and Validation of an Amphibious Drone-Based In-Situ SPE System for Environmental Water Monitoring

To improve the efficiency of aquatic environmental monitoring, an in-situ solid-phase extraction (SPE) system using amphibious (waterproof) drones was developed and validated using recovery testing with samples containing known concentrations of systemic insecticides in the laboratory and using real samples from natural water bodies. The system used a water-resistant linear actuator for continuous aspiration at 1–10 mL min−1 through a pre-washed hydrophilic–lipophilic balanced SPE cartridge. The system functioned properly during field sampling using vacuum-mode filtration to avoid overpressure, overcurrent, and contamination through repeated filtration. The recovery tests using 10 ng L−1 of each target analyte in ultra-pure water samples produced satisfactory recovery results of 89–96% (relative standard deviation < 10%). In the real sampling of water bodies, the developed system was able to detect target analytes of 0.9–180 ng L−1. The results are comparable to those obtained using in-situ manual SPE from boat sampling, irrespective of differences in the two aspiration systems. These findings suggest that the application of the developed drone-assisted in situ SPE system can improve the efficiency of real-sample monitoring of natural water bodies.

Authors

Institutions

Publication Details

Journal
Drones
Published
2025-09-15
DOI
https://doi.org/10.3390/drones9090649
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
0.00

Funders

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

Development and Validation of an Amphibious Drone-Based In-Situ SPE System for Environmental Water Monitoring

Takeshi Nagayoshi, M. Inoue, Hirokazu Madokoro, Makoto Yoshida et al.
Drones
Air Quality Monitoring and Forecasting
article

Development and Validation of an Amphibious Drone-Based In-Situ SPE System for Environmental Water Monitoring

Takeshi Nagayoshi, M. Inoue, Hirokazu Madokoro, Makoto Yoshida, Osamu Kiguchi, Hitoshi Osawa, Ken-ichi SAITOH, Nobumitsu Kurisawa, Takero Kikuchi, Shunsuke Watanabe
article en

Abstract

To improve the efficiency of aquatic environmental monitoring, an in-situ solid-phase extraction (SPE) system using amphibious (waterproof) drones was developed and validated using recovery testing with samples containing known concentrations of systemic insecticides in the laboratory and using real samples from natural water bodies. The system used a water-resistant linear actuator for continuous aspiration at 1–10 mL min−1 through a pre-washed hydrophilic–lipophilic balanced SPE cartridge. The system functioned properly during field sampling using vacuum-mode filtration to avoid overpressure, overcurrent, and contamination through repeated filtration. The recovery tests using 10 ng L−1 of each target analyte in ultra-pure water samples produced satisfactory recovery results of 89–96% (relative standard deviation < 10%). In the real sampling of water bodies, the developed system was able to detect target analytes of 0.9–180 ng L−1. The results are comparable to those obtained using in-situ manual SPE from boat sampling, irrespective of differences in the two aspiration systems. These findings suggest that the application of the developed drone-assisted in situ SPE system can improve the efficiency of real-sample monitoring of natural water bodies.

DronesVol. 9(9)
Akita Prefectural University (JP), Iwate Prefectural University (JP)
Akita Prefectural University
Openalex Percentile: Top 77%
Air Quality Monitoring and Forecasting
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.