11 Mar 2022
11 Mar 2022
Status: a revised version of this preprint was accepted for the journal AMT.

An Unmanned Aerial System (UAS) based methodology for measuring biomass burning emission factors

Roland Vernooij1, Patrik Winiger1, Martin Wooster2,3, Tercia Strydom4, Laurent Poulain5, Ulrike Dusek6, Mark Grosvenor2, Gareth Roberts7, Nick Schutgens1, and Guido van der Werf1 Roland Vernooij et al.
  • 1Department of Earth Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
  • 2King’s College London, Environmental Monitoring and Modelling Research Group, Department of Geography, London, UK
  • 3National Centre for Earth Observation (NERC), UK
  • 4South African National Parks (SANParks), Scientific Services, Skukuza, South Africa
  • 5Atmospheric Chemistry Department (ACD), Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
  • 6Centre for Isotope Research (CIO), Energy and Sustainability Research Institute Groningen (ESRIG), Groningen the Netherlands
  • 7Geography and Environmental Science, University of Southampton, Southhampton, UK

Abstract. Biomass burning (BB) emits large quantities of greenhouse gases (GHG) and aerosols that impact climate and adversely affect human health. Although much research has focused on quantifying BB emissions on regional to global scales, field measurements of BB emission factors (EFs) are sparse, clustered and indicate high spatio-temporal variability. EFs are generally calculated from ground- or aeroplane measurements with respective potential biases towards smouldering or flaming combustion products. Unmanned aerial systems (UAS) have the potential to measure BB EFs in fresh smoke, targeting different parts of the plume at relatively low cost. We propose a light-weight UAS-based method to measure EFs for carbon monoxide (CO), carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), as well as PM2.5 (TSI Sidepak AM520) and equivalent black carbon (eBC, microAeth AE51) using a combination of a sampling system with Tedlar bags which can be analysed on the ground and airborne aerosol sensors. In this study, we address the main uncertainties associated with this approach (1) the degree to which taking a limited number of samples is representative for the integral smoke plume and including (2) the reliability of the lightweight aerosol sensors. This was done for prescribed burning experiments in the Kruger national park, South Africa where we compared fire-averaged EF from UAS-sampled bags for savanna fires to integrated EFs from co-located mast measurements. Both measurements matched reasonably well with linear R2 ranging from 0.81 to 0.94. Both aerosol sensors are not factory calibrated for BB particles and therefore require additional calibration. In a series of smoke chamber experiments, we compared the lightweight sensors to high-fidelity equipment to empirically determine specific calibration factors (CF) for measuring BB particles. For the PM mass concentration from a TSI Sidepak AM520, we found an optimal CF of 0.27, using a scanning mobility particle sizer and gravimetric reference methods, albeit that the CF varied for different vegetation fuel types. Measurements of eBC from the Aethlabs AE51 aethalometer agreed well with the multi-wavelength aethalometer (AE33) (linear R2 of 0.95 at λ = 880 nm) and the wavelength corrected Multi-Angle Absorption Photometer (MAAP, R2 0.83 measuring at λ = 637 nm). However, the high variability in observed BB mass absorption cross-section (MAC) values (5.2 ± 5.1 m2 g-1) suggested re-calibration may be required for individual fires. Overall, our results indicate that the proposed UAS setup can obtain representative BB EFs for individual savanna fires if proper correction factors are applied and operating limitations are well understood.

Roland Vernooij et al.

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Roland Vernooij et al.

Roland Vernooij et al.


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Short summary
Landscape fires are one of the largest emitters of greenhouse gases (GHG) and aerosols. Previous studies have indicated emission factors to be highly variable. Improving fire emission estimates, and understanding future climate- and human-induced changes in fire regimes requires in-situ measurements. We present a drone-based method that enables the collection of a large amount of high-quality emission factor measurements that moreover do not have the bias of aircraft or surface measurements.