AirQuality Lab-on-a-Drone: A Low-Cost 3D-Printed Analytical IoT Platform for Vertical Monitoring of Gaseous H 2 S.
Vanderli Garcia LealHabdias A Silva-NetoSidnei Gonçalves da SilvaWendell Karlos Tomazelli ColtroJoão Flávio da Silveira PetruciPublished in: Analytical chemistry (2023)
The measurement of gaseous compounds in the atmosphere is a multichallenging task due to their low concentration range, long and latitudinal concentration variations, and the presence of sample interferents. Herein, we present a quadcopter drone deployed with a fully integrated 3D-printed analytical laboratory for H 2 S monitoring. Also, the analytical system makes part of the Internet of Things approach. The analytical method applied was based on the reaction between fluorescein mercuric acetate and H 2 S that led to fluorescence quenching. A 5 V micropump at a constant airflow of 50 mL min -1 was employed to deliver constant air into a flask containing 800 μL of the reagent. The analytical signal was obtained using a light-emitting diode and a miniaturized digital light detector. The method enabled the detection of H 2 S in the range from 15 to 200 ppbv, with a reproducibility of 5% for a sampling time of 10 min and an limit of detection of 9 ppbv. All devices were controlled using an Arduino powered by a small power bank, and the results were transmitted to a smartphone via Bluetooth. The proposed device resulted in a weight of 300 g and an overall cost of ∼50 USD. The platform was used to monitor the concentration of H 2 S in different intervals next to a wastewater treatment plant at ground and vertical levels. The ability to perform all analytical steps in the same device, the low-energy requirements, the low weight, and the attachment of data transmission modules offer new possibilities for drone-based analytical systems for air pollution monitoring.
Keyphrases
- wastewater treatment
- liquid chromatography
- air pollution
- low cost
- body mass index
- physical activity
- weight loss
- healthcare
- high throughput
- antibiotic resistance genes
- weight gain
- magnetic resonance
- microbial community
- computed tomography
- single cell
- single molecule
- machine learning
- electronic health record
- health information
- energy transfer