Login / Signup

Use of Unmanned Surface Vehicles (USVs) in Water Chemistry Studies.

Georgios KatsourasElias DimitriouSotirios KaravoltsosStylianos SamiosAikaterini SakellariAngeliki MentzafouNikolaos TsalasMichael Scoullos
Published in: Sensors (Basel, Switzerland) (2024)
Unmanned surface vehicles (USVs) equipped with integrated sensors are a tool valuable to several monitoring strategies, offering enhanced temporal and spatial coverage over specific timeframes, allowing for targeted examination of sites or events of interest. The elaboration of environmental monitoring programs has relied so far on periodic spot sampling at specific locations, followed by laboratory analysis, aiming at the evaluation of water quality at a catchment scale. For this purpose, automatic telemetric stations for specific parameters have been installed by the Institute of Marine Biological Resources and Inland Waters of Hellenic Centre for Marine Research (IMBRIW-HCMR) within several Greek rivers and lakes, providing continuous and temporal monitoring possibilities. In the present work, USVs were deployed by the Athens Water and Sewerage Company (EYDAP) as a cost-effective tool for the environmental monitoring of surface water bodies of interest, with emphasis on the spatial fluctuations of chlorophyll α, electrical conductivity, dissolved oxygen and pH, observed in Koumoundourou Lake and the rivers Acheloos, Asopos and Kifissos. The effectiveness of an innovative heavy metal (HM) system installed in the USV for the in situ measurements of copper and lead was also evaluated herewith. The results obtained demonstrate the advantages of USVs, setting the base for their application in real-time monitoring of chemical parameters including metals. Simultaneously, the requirements for accuracy and sensitivity improvement of HM sensors were noted, in order to permit full exploitation of USVs' capacities.
Keyphrases
  • water quality
  • heavy metals
  • randomized controlled trial
  • systematic review
  • human health
  • risk assessment
  • health risk
  • machine learning
  • deep learning
  • health risk assessment
  • low cost
  • drinking water
  • organic matter