An innovative autonomous robotic system for on-site detection of heavy metal pollution plumes in surface water.

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.

Development of a Heavy Metal Sensing Boat for Automatic Analysis in Natural Waters Utilizing Anodic Stripping Voltammetry.

Determination of the levels of heavy metal ions would support assessment of sources and pathways of water pollution. However, traditional spatial assessment by manual sampling and off-site detection in the laboratory is expensive and time-consuming and requires trained personnel. Aiming to fill the gap between on-site automatic approaches and laboratory techniques, we developed an autonomous sensing boat for on-site heavy metal detection using square-wave anodic stripping voltammetry. A fluidic sensing system was developed to integrate into the boat as the critical sensing component and could detect ≤1 µg/L Pb, ≤6 µg/L Cu, and ≤71 µg/L Cd simultaneously in the laboratory. Once its integration was completed, the autonomous sensing boat was tested in the field, demonstrating its ability to distinguish the highest concentration of Pb in an effluent of a galena-enriched mine compared to those at other sites in the stream (Osor Stream, Girona, Spain).

Multi-parameter calibration of a UV/Vis spectrometer for online monitoring of sewer systems.

UV/Vis spectrometers are powerful tools for online monitoring of wastewater constituents and processes. However, most studies only focus on typical parameters such as chemical oxygen demand (COD) and total suspended solids. This work presents a multi-parameter approach for calibration of a UV/Vis spectrometer for online monitoring of sewer systems. Parameters studied include soluble and total COD, nitrate, ammonium, sulphate and orthophosphate, as well as total dissolved sulphide, bisulphide and hydrogen sulphide, because they are one of the main causes for odour and corrosion in sewer systems. Two calibration methods are compared: multiple linear regression included in the manufacturer's software, and partial least square (PLS) computed using the pls package of the R library. Performance of the methods is evaluated for calibration and validation data sets employing four different criteria: relative root mean square error (RMSErel), RMSE-observations standard deviation ratio, Nash-Sutcliffe efficiency and percentage bias. A method-parameter dependency was revealed during the calibration phase but, when predicting new data, the PLS method showed higher robustness for almost all parameters. Both methods were able to predict concentration trends associated with sewer processes, some of which are strongly correlated to the sulphide species.