Effective coastal Escherichia coli monitoring by unmanned aerial vehicles (UAV) thermal infrared images

Abstract

Coastal Escherichia coli (E. coli) significantly influence ocean safety and public health, thus requiring an effective E. coli pollution monitoring. However conventional detection relying on manual field sampling is time-consuming. Here, this study established an E. coli estimation model based on thermal remote sensing of unmanned aerial vehicles (UAV). This model was developed against one-year comprehensive field work in a representative sandy beach and further validated against 50 beaches in Hong Kong to evaluate its applicability. The estimated E. coli concentrations were in a reliable agreement with direct measurements. For this model, this study deployed the radon-222 (²²²Rn) as a bridging tracer to couple UAV thermal images and coastal E. coli concentrations. Coastal ²²²Rn can be reflected on the UAV thermal images, and there was a good positive correlation between the ²²²Rn activity and coastal E. coli concentration via one-year field data. Hence, coupling the ²²²Rn activity estimated from UAV thermal images and the relationship between ²²²Rn and E. coli, this study can readily monitor coastal E. coli by UAV. These findings highlighted that UAV technology is an effective approach to measure the E. coli concentrations and can further pave the way for an efficient coastal E. coli monitoring and public health risk warning.

Keywords: E. coli; Fecal pollution; Radon-222; Temperature; Unmanned aerial vehicles (UAV).