Autonomous In Situ Measurements of Noncontaminant Water Quality Indicators and Sample Collection with a UAV

Lab-on-a-Drone: remote voltammetric analysis of lead in water with real-time data transmission

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.

Modelling of the Energy Depletion Process and Battery Depletion Attacks for Battery-Powered Internet of Things (IoT) Devices

The Internet of Things (IoT) is transforming almost every industry, including agriculture, food processing, health care, oil and gas, environmental protection, transportation and logistics, manufacturing, home automation, and safety. Cost-effective, small-sized batteries are often used to power IoT devices being deployed with limited energy capacity. The limited energy capacity of IoT devices makes them vulnerable to battery depletion attacks designed to exhaust the energy stored in the battery rapidly and eventually shut down the device. In designing and deploying IoT devices, the battery and device specifications should be chosen in such a way as to ensure a long lifetime of the device. This paper proposes diffusion approximation as a mathematical framework for modelling the energy depletion process in IoT batteries. We applied diffusion or Brownian motion processes to model the energy depletion of a battery of an IoT device. We used this model to obtain the probability density function, mean, variance, and probability of the lifetime of an IoT device. Furthermore, we studied the influence of active power consumption, sleep time, and battery capacity on the probability density function, mean, and probability of the lifetime of an IoT device. We modelled ghost energy depletion attacks and their impact on the lifetime of IoT devices. We used numerical examples to study the influence of battery depletion attacks on the distribution of the lifetime of an IoT device. We also introduced an energy threshold after which the device's battery should be replaced in order to ensure that the battery is not completely drained before it is replaced.

A study on water quality parameters estimation for urban rivers based on ground hyperspectral remote sensing technology

The purpose of this research is to seek a better inversion algorithm. And on this basis, it explores the feasibility of using hyperspectral monitoring technology instead of laboratory physical and chemical index test and evaluates the prediction effect of inversion model on water quality change. So as to be more convenient, more economical and extensive monitoring methods for water quality monitoring of urban internal river are provided. This paper takes the water samples collected in Fuyang River in downtown Handan as the research object and obtains original spectral data of the samples by the ASD FieldSpec 4 field hyperspectral spectrometer. After the smoothing filter pretreatment by the Savitzky-Golay (SG) method and specified mathematical transformations, the modeling spectral indicators of various water quality parameters are selected and determined by calculating the maximum mean of absolute values for correlation coefficients of various spectral indicators and measured values in the wavelength range from 400 to 950 nm. By introducing partial least squares (PLS), random forest (RF), and Lasso (least absolute shrinkage and selection operator), six water quality parameter fitting models were constructed including turbidity (Turb), suspended substance (SS), chemical oxygen demand (COD), NH4-N, total nitrogen (TN), and total phosphorus (TP), which are also testified and evaluated through hyperspectral data. The results show that different spectral transformation methods highlight different information inversion effects. The first derivative of reciprocal logarithm of spectral data after SG smoothing has a good modeling effect on four water quality parameters including Turb, COD, NH₄-N, and TP; and the first derivative of smoothed spectral data has a good modeling effect on both water quality parameters of SS and TN. Among the three models, the PLS model has a good prediction effect, with the [Formula: see text] for COD, TN, and TP ranging from 0.74 to 0.80, while that for Turb and SS shows relatively poorer prediction effect, followed by even worse effect on HN₄-H. Both machine learning algorithms of RF and Lasso have respectively obtained the best prediction models for different water quality parameters. The Lasso model has a [Formula: see text] value above 0.8 for water body organic pollutants COD, TN, and TP, and the decrease value for [Formula: see text] and [Formula: see text] is below 0.1, which indicates that the model has high prediction accuracy and strong generalization ability, but the results of SS and NH₄-N do not meet the expected accuracy. In the inversion model of RF for COD, [Formula: see text] is higher than [Formula: see text], which shows excellent performance, and has certain prediction ability for SS and NH₄-N. The RF model and Lasso model complement each other effectively in applicability and prediction accuracy. Compared with the traditional regression model PLS, machine learning has obvious overall advantages, making it more suitable for classified inversion prediction of urban river water quality parameters.

Drones can reliably, accurately and with high levels of precision, collect large volume water samples and physio-chemical data from lakes

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.

In situ voltammetric analysis of 2,4-dichlorophenoxyacetic acid in environmental water using a boron doped diamond electrode and an adapted unmanned air vehicle sampling platform

In the present work a voltammetric method was developed for in situ detection of 2,4-dichlorophenoxyacetic acid (2,4-D) in environmental water samples, using a compact and lightweight electrochemical cell using a fused deposition modeling (FDM) 3D printer with biodegradable polylactic acid filament, and a boron-doped diamond electrode (BDDE). The samples were collected by an adapted unmanned aerial vehicle (UAV) with a micropump and a miniature solenoid valve powered by an open source microcontroller. After optimizing the supporting electrolyte, pH and parameters of the square wave voltammetry (SWV) a linear analytical curve for 2,4-D in 0.5 mol L^-1 Na₂SO₄ (pH = 2.0 regulated using 0.5 mol L^-1 H₂SO₄ solution) in a concentration range from 100 nmol L^-1 to 911 nmol L^-1 with 34 nmol L^-1 as the limit of detection was obtained. The same samples in situ analyzed by SWV were sent to the laboratory for gas chromatography-mass spectrometry (GC-MS) analysis; and there was no statistical difference from the concentration of 2,4-D in any of the samples at a 95% confidence level. Therefore, the method developed for quantification of 2,4-D in water provides an important environmental monitoring tool since it enables access to difficult areas in a fast, practical and safe way. This is the first time that an adapted UAV with these features has been used to collect environmental water for in situ electrochemical analysis as a screening tool to alert the presence of environmental hazard compounds, such as 2,4-D. Thus, this method can be used by environmental and sanitary control agencies to monitor or to supervise environmental water quality with response in real time.

Temperature Profiling of Waterbodies with a UAV-Integrated Sensor Subsystem

Evaluation of thermal stratification and systematic monitoring of water temperature are required for lake management. Water temperature profiling requires temperature measurements through a water column to assess the level of thermal stratification which impacts oxygen content, microbial growth, and distribution of fish. The objective of this research was to develop and assess the functions of a water temperature profiling system mounted on a multirotor unmanned aerial vehicle (UAV). The buoyancy apparatus mounted on the UAV allowed vertical takeoff and landing on the water surface for in situ measurements. The sensor node that was integrated with the UAV consisted of a microcontroller unit, a temperature sensor, and a pressure sensor. The system measured water temperature and depth from seven pre-selected locations in a lake using autonomous navigation with autopilot control. Measurements at 100 ms intervals were made while the UAV was descending at 2 m/s until it landed on water surface. Water temperature maps of three consecutive depths at each location were created from the measurements. The average surface water temperature at 0.3 m was 22.5 °C, while the average water temperature at 4 m depth was 21.5 °C. The UAV-based profiling system developed successfully performed autonomous water temperature measurements within a lake.

MIMR Criterion Application: Entropy Approach to Select the Optimal Quality Parameter Set Responsible for River Pollution

Surface water quality has a vital role when defining the sustainability of the ecological environment, public health, and the social and economic development of whole countries. Unfortunately, the rapid growth of the worldwide population together with the current climate change have mostly determined fluvial pollution. Therefore, the employment of effective methodologies, able to rapidly and easily obtain reliable information on the quality of rivers, is becoming fundamental for an efficient use of the resource and for the implementation of mitigation measures and actions. The Water Quality Index (WQI) is among the most widely used methods to provide a clear and complete picture of the contamination status of a river stressed by point and diffuse sources of natural and anthropic origin, leading the policy makers and end-users towards a more and more correct and sustainable management of the water resource. The parameter choice is one of the most important and complex phases and recent statistical techniques do not seem to show great objectivity and accuracy in the identification of the real water quality status. The present paper offers a new approach, based on entropy theory and known as the Maximum Information Minimum Redundancy (MIMR) criterion, to define the optimal subset of chemical, physical, and biological parameters, describing the variation of the river quality level in space and time and thus identifying its pollution sources. An algorithm was implemented for the MIMR criterion and applied to a sample basin of Northeast Italy in order to verify its reliability and accuracy. A comparison with the Principal Component Analysis (PCA) showed how the MIMR is more suitable and objective to obtain the optimal quality parameters set, especially when the amount of investigated variables is small, and can thus be a useful tool for fast and low-cost water quality assessment in rivers.

Adaptive Water Sampling Device for Aerial Robots

Water quality monitoring and predicting the changes in water characteristics require the collection of water samples in a timely manner. Water sample collection based on in situ measurable water quality indicators can increase the efficiency and precision of data collection while reducing the cost of laboratory analyses. The objective of this research was to develop an adaptive water sampling device for an aerial robot and demonstrate the accuracy of its functions in laboratory and field conditions. The prototype device consisted of a sensor node with dissolved oxygen, pH, electrical conductivity, temperature, turbidity, and depth sensors, a microcontroller, and a sampler with three cartridges. Activation of water capturing cartridges was based on in situ measurements from the sensor node. The activation mechanism of the prototype device was tested with standard solutions in the laboratory and with autonomous water sampling flights over the 11-ha section of a lake. A total of seven sampling locations were selected based on a grid system. Each cartridge collected 130 mL of water samples at a 3.5 m depth. Mean water quality parameters were measured as 8.47 mg/L of dissolved oxygen, pH of 5.34, 7 µS/cm of electrical conductivity, temperature of 18 °C, and 37 Formazin Nephelometric Unit (FNU) of turbidity. The dissolved oxygen was within allowable limits that were pre-set in the self-activation computer program while the pH, electrical conductivity, and temperature were outside of allowable limits that were specified by Environmental Protection Agency (EPA). Therefore, the activation mechanism of the device was triggered and water samples were collected from all the sampling locations successfully. The adaptive water sampling with Unmanned Aerial Vehicle-assisted water sampling device was proved to be a successful method for water quality evaluation.