sensors Article
Development and Validation of a UAV Based System for Air Pollution Measurements Tommaso Francesco Villa 1 , Farhad Salimi 2 , Kye Morton 3 , Lidia Morawska 1, * and Felipe Gonzalez 3 1 2 3
*
International Laboratory for Air Quality and Health (ILAQH), Queensland University of Technology (QUT), 2 George St, Brisbane QLD 4000, Australia;
[email protected] Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania 7000, Australia;
[email protected] Australian Research Centre for Aerospace Automation (ARCAA), Queensland University of Technology (QUT), 2 George St, Brisbane QLD 4000, Australia;
[email protected] (K.M.);
[email protected] (F.G.) Correspondence:
[email protected]; Tel.: +61-7-3138-2616
Academic Editor: Hans Tømmervik Received: 23 November 2016; Accepted: 19 December 2016; Published: 21 December 2016
Abstract: Air quality data collection near pollution sources is difficult, particularly when sites are complex, have physical barriers, or are themselves moving. Small Unmanned Aerial Vehicles (UAVs) offer new approaches to air pollution and atmospheric studies. However, there are a number of critical design decisions which need to be made to enable representative data collection, in particular the location of the air sampler or air sensor intake. The aim of this research was to establish the best mounting point for four gas sensors and a Particle Number Concentration (PNC) monitor, onboard a hexacopter, so to develop a UAV system capable of measuring point source emissions. The research included two different tests: (1) evaluate the air flow behavior of a hexacopter, its downwash and upwash effect, by measuring air speed along three axes to determine the location where the sensors should be mounted; (2) evaluate the use of gas sensors for CO2 , CO, NO2 and NO, and the PNC monitor (DISCmini) to assess the efficiency and performance of the UAV based system by measuring emissions from a diesel engine. The air speed behavior map produced by test 1 shows the best mounting point for the sensors to be alongside the UAV. This position is less affected by the propeller downwash effect. Test 2 results demonstrated that the UAV propellers cause a dispersion effect shown by the decrease of gas and PN concentration measured in real time. A Linear Regression model was used to estimate how the sensor position, relative to the UAV center, affects pollutant concentration measurements when the propellers are turned on. This research establishes guidelines on how to develop a UAV system to measure point source emissions. Such research should be undertaken before any UAV system is developed for real world data collection. Keywords: UAV remote gas sensing; downwash effect; air quality; hexacopter; optical sensor; air pollution; particle number concentration monitor
1. Introduction Unmanned Aerial Vehicles (UAVs), carrying onboard sensors, can be used to directly measure shipping emissions, emissions from industrial stacks or ground vehicles when it is too difficult or dangerous to use both manned aircrafts [1] and ground level stations [2]. However, accurate sampling of small plumes emitted by combustion sources such as trucks, petrol locomotives, ships and dredgers, industrial and even domestic chimneys demands appropriate location of the air sensor intakes onboard the UAV. Therefore, the use of UAVs for air pollution measurement, particularly at slow speeds or stationary flights, can only be effective if the location point of the air sensor intake is optimized, such Sensors 2016, 16, 2202; doi:10.3390/s16122202
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that sampling of gaseous and particulate matter pollutants is done before the propellers of the UAV mix or disperse the plume. The air is deflected by the action of the UAV propellers in motion, when producing lift, and this is called the downwash and upwash effect. It is the transport of gas to the gas sensors that is the critical process, and accurate sampling relies on assessing the contribution of rotor disturbance to this process. Only two studies were found [3,4] to investigate the sensor mounting point onboard a multirotor UAV to measure air pollution. The first study designed three different experiments to identify the best location of a CO2 gas sensor intake onboard a quadrotor and how to bring the air to the sensor [3]. The three experiments included: an active system using an additional fan, a semi-active system using the airflow generated by the rotors, and a passive system, without any auxiliary device directing the airflow. Significant differences between the three different gas transport solutions were found and no system was capable of measuring the reference gas concentration (0.5% by volume of CO2 ). Overall, the authors [3] established that the position of the tube inlet strongly affected the gas measurement results and wind resistance caused by the enlarged drone. The second study [4] investigated the airflow of a small quadcopter using a Computational Flow Dynamic (CFD) simulation and found that the maximum air speed was at the rotor perimeters and that the minimum was at the center of the UAV. The researchers proved this by attaching the UAV to a cardan joint to measure air speed above and below the platforms. Above the UAV center was the best mounting point. The paper failed, however, to provide information about the height of the cardan joint, ground effect, or mounting point along aside the UAV, which was a quadcopter (Parrot AR drone 2.0) for hobby use with both a limited payload (2 kgs payload), and more in-flight stability and maneuverability compared to quadrotors, making them suitable for UAV and air quality studies where the capability to carry different sensors and maintain an in-flight fixed position are needed [5]. However, a comprehensive study on sensor location on such platforms has not been done yet. The system developed and evaluated in this research consists of a multi-rotor UAV, four gas sensors for CO, CO2 , NO and NO2 , a DISCmini (Portable PNC monitor, Testo AG, Lenzkirsch, Germany), temperature and humidity sensors as well as a real-time visualization interface. All sensors are integrated with an Arduino MEGA 2560 microcontroller board. 2.2. System Architecture Figure 1 shows an overview of the system, the gas sensing payload and the PNC monitor, while the UAV system architecture is shown in Figure S1 in the Supplementary Material. The system presented in Figure 1 comprises: (a) the UAV system components including DJI S800, DISCmini and gas sensors, Arduino board, telemetry, and RC receiver, and (b) Ground Control Components including RC transmitter for the pilot, and GSO (ground station + telemetry link). Figure S1 (Supplementary Material) shows UAV system hardware and software, including the ground control station, with the connection types between the different UAV components highlighted.
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The The UAV UAV pilot pilot or or the the UAV UAV ground station operator operator (GSO) (GSO) can can communicate communicate with with the the UAV UAV wirelessly, wirelessly, using a radio controller (RC) (RC) transmitter transmitter or or aa computer, computer, respectively. The The purple purple arrows arrows show show pilot pilot and and GSO GSO inputs. inputs.
(a)
(b)
Figure 1. 1. (a) (a) Unmanned Unmanned aerial aerial vehicle vehicle (UAV) (UAV) system system components components including including gas gas sensors, sensors, DISCmini, DISCmini, Figure Arduino MEGA RCRC receiver; (b) UAV Ground Control Components for bothfor manual Arduino MEGA 2560, 2560,battery batteryand and receiver; (b) UAV Ground Control Components both (using RC transmitter) and autonomous (using PC) operations. manual (using RC transmitter) and autonomous (using PC) operations.
2.3. UAV UAV 2.3. The UAV thisthis research is a modified hexacopter S800 EVO manufactured by DJI (Shenzhen, The UAVused usedin in research is a modified hexacopter S800 EVO manufactured by DJI China) [6]. China) The frame mm in 800 width 320 mm in 320 height is made of is composite (Shenzhen, [6]. measures The frame800 measures mmand in width and mmand in height and made of materials. materials. The built-in damping allows the allows multi-rotor to be assembled without additional composite The built-in system damping system the multi-rotor to be assembled without frames andframes dampers. The UAV The weighs 3.7weighs kg with maximum take-off weight of weight 8 kg. The additional and dampers. UAV 3.7a kg with a maximum take-off of 8S800 kg. is designed to operate theunder 20 kgthe all 20 upkg weight of UAS Air System) The S800 is designed tounder operate all up(AUW) weightclass (AUW) class (Unmanned of UAS (Unmanned Air as defined the Civil Australia to (CASA) reduce operation and avoid System) as by defined by Aviation the Civil Safety Aviation Safety (CASA) Australia to reducecosts, operation costs,being and subjectbeing to thesubject tightertoCASA regulations larger UAVs Different maycountries have different avoid the tighter CASAfor regulations for [7]. larger UAVs countries [7]. Different may aviation regulations which should be considered design ofina the UAV system. example, inFor the have different aviation regulations which should in bethe considered design of aFor UAV system. US, the Federal Aviation Administration (FAA) considers small UAVs those with a weight less than example, in the US, the Federal Aviation Administration (FAA) considers small UAVs those with a 55 lb (25less kg)than [8]. 55 lb (25 kg) [8]. weight The UAV UAV uses a 16000 16,000mAh mAhLiPo LiPo66cell cellbattery, battery, which which provides provides aa hover hover time time of of approximately approximately The 20 min with no with thethe payload used in this study has 20 no additional additionalpayload. payload.However, However,the theflight flighttime time with payload used in this study beenbeen calculated to be about Themin. hovering motor power consumption is 800 W operating has calculated to be 12–13 about min. 12–13 The hovering motor power consumption is 800with W a minimumwith take-off weight. Motors run weight. in conjunction with 38 cm × 13 cm propellers of 13 g each. operating a minimum take-off Motors run in conjunction with 38 cm × 13 cm propellers of 13 g each. 2.4. Sensor Selection
2.4. Sensor Selection Gas sensors are classified according to their operational principles with the most common being thermal, electrochemical, conductometric, and optical sensors [9,10]. Gas mass, sensors are classifiedpotentiometric, according to amperometric, their operational principles with the most common PM sensors differ in terms of monitoring PM in the range of PM10 (mass concentrationand of particles being thermal, mass, electrochemical, potentiometric, amperometric, conductometric, optical with aerodynamic diameter
