A quadcopter unmanned aerial system (UAS)-based methodology for measuring biomass burning emission factors

Biomass burning (BB) emits large quantities of greenhouse gases (GHG) and aerosols that impact the 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 with airborne aerosol sensors. In this study, we address the main challenges associated with this approach: (1) the degree to which a limited number of samples is representative for the integral smoke plume and (2) the performance of the lightweight aerosol sensors. While aerosol measurements can be made continuously in a UAS set-up thanks to the lightweight analysers, the representativeness of our Tedlar bag filling approach was tested during prescribed burning experiments in the Kruger National Park, South Africa. We compared fire-averaged EFs from UAS-sampled bags for savanna fires with 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 with 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, although 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 λCombining double low line880 nm) and the wavelength corrected multi-angle absorption photometer (MAAP, R2 of 0.83 measuring at λCombining double low line637 nm). However, the high variability in observed BB mass absorption cross-section (MAC) values (5.2±5.1 m2g-1) suggested re-calibration may be required for individual fires. Overall, our results indicate that the proposed UAS set-up can obtain representative BB EFs for individual savanna fires if proper correction factors are applied and operating limitations are well understood.