TY - JOUR
T1 - Drones can reliably, accurately and with high levels of precision, collect large volume water samples and physio-chemical data from lakes
AU - Graham, C. T.
AU - O'Connor, I.
AU - Broderick, L.
AU - Broderick, M.
AU - Jensen, O.
AU - Lally, H. T.
N1 - Publisher Copyright:
© 2022
PY - 2022/6/10
Y1 - 2022/6/10
N2 - The rapid development and application of drone technology has included water sampling and collection of physiochemical data from lakes. Previous research has demonstrated the significant potential of drones to play a future pivotal role in the collection of such data from lakes that fulfil requirements of large-scale monitoring programmes. However, currently the utilisation of drone technology for water quality monitoring is hindered by a number of important limitations: i) the low rate of successful sample captured; ii) the relatively low volume of water sample retrieved for analyses of multiple water chemistry parameters; and critically iii) differences between water chemistry parameters when using a drone versus samples collected by boat. Here we present results comparing the water chemistry results of a large number of parameters (pH, dissolved oxygen concentration, temperature, conductivity, alkalinity, hardness, true colour, chloride, silica, ammonia, total oxidised nitrogen, nitrite, nitrate, ortho-phosphate, total phosphorous and chlorophyll) sampled via drone with samples collected by boat in a number of lakes. The drone water sampling method used here is the first to collect a sufficiently large volume of water to meet the monitoring requirements of large scale water monitoring programmes, 2 L, at a 100% success rate and most crucially, with water chemistry variables that are not significantly different to those taken using traditional boat water sampling. This study therefore shows that drone technology can be utilised to collect water chemistry data and samples from lakes in a reliable, more rapid and cost effective manner than traditional sampling using boats, that is safer for personnel and poses less of a biosecurity risk.
AB - The rapid development and application of drone technology has included water sampling and collection of physiochemical data from lakes. Previous research has demonstrated the significant potential of drones to play a future pivotal role in the collection of such data from lakes that fulfil requirements of large-scale monitoring programmes. However, currently the utilisation of drone technology for water quality monitoring is hindered by a number of important limitations: i) the low rate of successful sample captured; ii) the relatively low volume of water sample retrieved for analyses of multiple water chemistry parameters; and critically iii) differences between water chemistry parameters when using a drone versus samples collected by boat. Here we present results comparing the water chemistry results of a large number of parameters (pH, dissolved oxygen concentration, temperature, conductivity, alkalinity, hardness, true colour, chloride, silica, ammonia, total oxidised nitrogen, nitrite, nitrate, ortho-phosphate, total phosphorous and chlorophyll) sampled via drone with samples collected by boat in a number of lakes. The drone water sampling method used here is the first to collect a sufficiently large volume of water to meet the monitoring requirements of large scale water monitoring programmes, 2 L, at a 100% success rate and most crucially, with water chemistry variables that are not significantly different to those taken using traditional boat water sampling. This study therefore shows that drone technology can be utilised to collect water chemistry data and samples from lakes in a reliable, more rapid and cost effective manner than traditional sampling using boats, that is safer for personnel and poses less of a biosecurity risk.
KW - 2 L water sample
KW - Aquatic environments
KW - Unmanned aerial vehicle (UAV)
KW - Water chemistry
KW - Water framework directive (WFD)
UR - http://www.scopus.com/inward/record.url?scp=85126038686&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2022.153875
DO - 10.1016/j.scitotenv.2022.153875
M3 - Article
C2 - 35181365
AN - SCOPUS:85126038686
SN - 0048-9697
VL - 824
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 153875
ER -