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Sensors 2017, 17(2), 343; doi:10.3390/s17020343

A Methodology to Monitor Airborne PM10 Dust Particles Using a Small Unmanned Aerial Vehicle

1
Environment Centre, Sustainable Mineral Institute, The University of Queensland, 4072 Brisbane, Australia
2
Science and Engineering Faculty, Queensland University of Technology (QUT), 4000 Brisbane, Australia
3
People Centre, Sustainable Mineral Institute, The University of Queensland, 4072 Brisbane, Australia
4
Advanced Environmental Dynamics Pty Ltd., Ferny Hills, 4055 Queensland, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 15 December 2016 / Revised: 26 January 2017 / Accepted: 4 February 2017 / Published: 14 February 2017
(This article belongs to the Special Issue UAV-Based Remote Sensing)

Abstract

Throughout the process of coal extraction from surface mines, gases and particles are emitted in the form of fugitive emissions by activities such as hauling, blasting and transportation. As these emissions are diffuse in nature, estimations based upon emission factors and dispersion/advection equations need to be measured directly from the atmosphere. This paper expands upon previous research undertaken to develop a relative methodology to monitor PM10 dust particles produced by mining activities making use of small unmanned aerial vehicles (UAVs). A module sensor using a laser particle counter (OPC-N2 from Alphasense, Great Notley, Essex, UK) was tested. An aerodynamic flow experiment was undertaken to determine the position and length of a sampling probe of the sensing module. Flight tests were conducted in order to demonstrate that the sensor provided data which could be used to calculate the emission rate of a source. Emission rates are a critical variable for further predictive dispersion estimates. First, data collected by the airborne module was verified using a 5.0 m tower in which a TSI DRX 8533 (reference dust monitoring device, TSI, Shoreview, MN, USA) and a duplicate of the module sensor were installed. Second, concentration values collected by the monitoring module attached to the UAV (airborne module) obtaining a percentage error of 1.1%. Finally, emission rates from the source were calculated, with airborne data, obtaining errors as low as 1.2%. These errors are low and indicate that the readings collected with the airborne module are comparable to the TSI DRX and could be used to obtain specific emission factors from fugitive emissions for industrial activities. View Full-Text
Keywords: PM10; monitoring; blasting; unmanned aerial vehicle (UAV); multi-rotor UAV; optical sensor PM10; monitoring; blasting; unmanned aerial vehicle (UAV); multi-rotor UAV; optical sensor
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Alvarado, M.; Gonzalez, F.; Erskine, P.; Cliff, D.; Heuff, D. A Methodology to Monitor Airborne PM10 Dust Particles Using a Small Unmanned Aerial Vehicle. Sensors 2017, 17, 343.

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