Abstract
Smart monitoring has been studied and developed in recent years to create faster, cheaper, and more user-friendly on-site methods. The present study describes an innovative technology for investigative monitoring of heavy metal pollution (Cu and Pb) in surface water. It is composed of an autonomous surface vehicle capable of semiautonomous driving and equipped with a microfluidic device for detection of heavy metals. Detection is based on the method of square wave anodic stripping voltammetry using carbon-based screen-printed electrodes (SPEs). The focus of this work was to validate the ability of the integrated system to perform on-site detection of heavy metal pollution plumes in river catchments. This scenario was simulated in laboratory experiments. The main performance characteristics of the system, which was evaluated based on ISO 15839 were measurement bias (Pb 75%, Cu 65%), reproducibility (in terms of relative standard deviation: Pb 11–18%, Cu 6–10%) and the limit of detection (4 µg/L for Pb and 7 µg/L for Cu). The lowest detectable change (LDC), which is an important performance characteristic for this application, was estimated to be 4–5 µg/L for Pb and 6–7 µg/L for Cu. The life span of an SPE averaged 39 measurements per day, which is considered sufficient for intended monitoring campaigns. This work demonstrated the suitability of the integrated system for on-site detection of Pb and Cu emissions from large and medium urban areas discharging into small water bodies.
Highlights
Water is of fundamental importance for the environment and life, and it determines the health of humans and animals (Silva Junior et al, 2016; Tuna et al, 2013)
The present paper describes one of the monitoring systems developed within that framework, which consists of an autonomous surface vehicle equipped with a microfluidic electrochemical heavy metal detection device, named integrated system
The following labels were used throughout the study: “cal1” indicated the calibration function obtained with linear regression, and “cal2” indicated the calibration function obtained with linear regression forced through zero
Summary
Water is of fundamental importance for the environment and life, and it determines the health of humans and animals (Silva Junior et al, 2016; Tuna et al, 2013). Both natural processes, such as weathering of soil, and anthropogenic activities, such as rapid industrialization, urbanization and agricultural activities, deeply influence the contamination of water resources with a variety and a large number of contaminants (Law et al, 2018; Li et al, 2015; Mohammed et al, 2016). The majority of heavy metal releases into water bodies in Europe are related to urban pollution coming from urban wastewater treatment plants (UWWTPs, 45.5%), from misconnections and cross-connections, untreated combined sewer overflows or road runoff (EEA, 2018). Heavy metals are considered indicators for the abovementioned emission sources, and their monitoring is of crucial importance in identifying the sources and pathways of potential pollutants in the aquatic environment
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