RESUMEN
The present work describes a laboratory-on-a-drone (Lab-on-a-Drone) developed to perform in situ detection of contaminants in environmental water samples. Toward this goal, the system was mounted on an unmanned aerial vehicle (UAV) (drone) and remotely controlled via Wi-Fi to acquire a water sample, perform the electrochemical detection step, and then send the voltammetry data to a smartphone. This Lab-on-a-Drone system was also able to recharge its battery using a solar cell, greatly increasing the autonomy of the system, even in the absence of a power line. As a proof of concept, the Lab-on-a-Drone was employed for the detection of Pb2+ in environmental waters, using a simple electrochemical cell containing a miniaturized screen-printed boron-doped diamond electrode (SP-BDDE) as a working electrode, an Ag/AgCl as a reference electrode, and a graphite ink as a counter electrode. For quantification purposes, analytical curves were constructed covering a concentration range from 1.0 µg L-1 (4.83 nmol L-1) to 80.0 µg L-1 (386.10 nmol L-1), featuring a detection limit of 0.062 µg L-1 (0.30 nmol L-1). The Lab-on-a-Drone was applied to monitor a water reservoir in the Metropolitan Region of Recife, Brazil. To evaluate its performance regarding accuracy and precision, a reference method based on inductively coupled plasma optical emission spectrometry (ICP-OES) was applied, and the results obtained by both methods showed no statistical differences (t-test at 95% confidence level, n = 3). These results represent the first demonstration of the capabilities of an adapted UAV for the quantification of electroactive environmental contaminant using voltammetry, with real-time data transmission. Thus, the Lab-on-a-Drone makes it possible to reach difficult-to-access environmental reserves and to monitor potentially polluting activity in distant water bodies. Thus, this tool can be used by governments and non-profit organizations to monitor environmental waters using fast, low-cost, process autonomy with accurate and precise data useful to decision making.
