Mark C. de Jong

Landscape fires can mobilise toxic metals and metalloids through smoke emissions and post-burn ash redistribution. Current emission estimates apply 'biome'-averaged emission factors that flatten realistic spatial variation. This study investigates how metal concentrations in organic soil fuels influence metal emissions in airborne particulates and accumulation in post-burn ash, including the potential for exacerbated levels of mobilisation from metal-contaminated landscapes. Controlled laboratory smouldering combustion experiments were conducted on organic soil samples from forests across Ontario, Canada, including arsenic- and iron-contaminated soils associated with historic mining and sintering activity in the Wawa Fume Kill region. Pre-burn soils, emitted particulates (PM2.5), and post-burn ash were analysed by ICP-OES to derive emission and enhancement factors. Metal particulate emission factors scaled with initial fuel concentrations, most strongly for arsenic, gallium, and barium (R2=0.83, 0.76, 0.67). Emission factors for arsenic, barium, and zinc varied by more than an order of magnitude across the observed soil concentration range. Contaminated soils produced substantially elevated emissions, with maximum arsenic and iron emission factors up to 400 and 397% greater, respectively, than mean background values. We introduce a parameterisation describing a 'latent metal release factor' linking fuel metal concentration to emissions. Applying this scaling to an industrially contaminated site near Yellowknife, Northwest Territories, Canada, produced arsenic emission estimates 130% higher than those derived using a mean ('uncontaminated') emission factor. Post-burn ash showed species-dependent enrichment, with metal concentrations increasing by factors of 2.1 - 5.7 relative to pre-burn soils. Ash enhancement was weakly and negatively correlated with the latent metal release factor, linking airborne and residual mobilisation. These findings demonstrate that organic soil metal content is an important control on airborne and residual metal mobilisation during fire. Incorporating spatially resolved fuel chemistry into emission models could improve predictions of human and ecosystem exposure, particularly in contaminated regions.

1. WildfireWildfire management and stewardship practices can vary dramatically across the globe as fire management entities operate under diverse socio-political, ecological, and technological cont...

Overnight fires are emerging in North America with previously unknown drivers and implications. This notable phenomenon challenges the traditional understanding of the ‘active day, quiet night’ model of the diurnal fire cycle^1,2,3 and current fire management practices^4,5. Here we demonstrate that drought conditions promote overnight burning, which is a key mechanism fostering large active fires. We examined the hourly diurnal cycle of 23,557 fires and identified 1,095 overnight burning events (OBEs, each defined as a night when a fire burned through the night) in North America during 2017–2020 using geostationary satellite data and terrestrial fire records. A total of 99% of OBEs were associated with large fires (>1,000 ha) and at least one OBE was identified in 20% of these large fires. OBEs were early onset after ignition and OBE frequency was positively correlated with fire size. Although warming is weakening the climatological barrier to night-time fires⁶, we found that the main driver of recent OBEs in large fires was the accumulated fuel dryness and availability (that is, drought conditions), which tended to lead to consecutive OBEs in a single wildfire for several days and even weeks. Critically, we show that daytime drought indicators can predict whether an OBE will occur the following night, which could facilitate early detection and management of night-time fires. We also observed increases in fire weather conditions conducive to OBEs over recent decades, suggesting an accelerated disruption of the diurnal fire cycle.

Satellite-derived land surface temperature (LST) data are most commonly observed in the longwave infrared (LWIR) spectral region. However, such data suffer frequent gaps in coverage caused by cloud cover. Filling these ‘cloud gaps’ usually relies on statistical re-constructions using proximal clear sky LST pixels, whilst this is often a poor surrogate for shadowed LSTs insulated under cloud. Another solution is to rely on passive microwave (PM) LST data that are largely unimpeded by cloud cover impacts, the quality of which, however, is limited by the very coarse spatial resolution typical of PM signals. Here, we combine aspects of these two approaches to fill cloud gaps in the LWIR-derived LST record, using Kenya (East Africa) as our study area. The proposed “cloud gap-filling” approach increases the coverage of daily Aqua MODIS LST data over Kenya from 90%. Evaluations were made against the in situ and SEVIRI-derived LST data respectively, revealing root mean square errors (RMSEs) of 2.6 K and 3.6 K for the proposed method by mid-day, compared with RMSEs of 4.3 K and 6.7 K for the conventional proximal-pixel-based statistical re-construction method. We also find that such accuracy improvements become increasingly apparent when the total cloud cover residence time increases in the morning-to-noon time frame. At mid-night, cloud gap-filling performance is also better for the proposed method, though the RMSE improvement is far smaller (<0.3 K) than in the mid-day period. The results indicate that our proposed two-step cloud gap-filling method can improve upon performances achieved by conventional methods for cloud gap-filling and has the potential to be scaled up to provide data at continental or global scales as it does not rely on locality-specific knowledge or datasets.

The boreal summers of 2019 and 2020 were witness to extensive high northern latitude wildfire activity, most notably within the Arctic Circle across eastern Russia. Near-real-time monitoring of the wildfire activity, based on satellite observations of active fires, showed widespread and persistent fires at a scale that had not been observed in the previous years that satellite observations are available. The European Centre for Medium-Range Weather Forecasts (ECMWF) through its operation of, and contribution to, different Copernicus Services is in a unique position to provide detailed information to monitor high-latitude wildfire activity, including their evolution and potential impacts, when they occur. Fire weather forecasts from the Copernicus Emergency Management Service (CEMS), and surface climate anomalies from the Copernicus Climate Change Service (C3S) both provide context to the environmental conditions required for wildfires to persist. Analyses based on observations of fire radiative power, along with analyses and forecasts of associated atmospheric pollutants, from the Copernicus Atmosphere Monitoring Service (CAMS) aid in quantifying the scale and intensity in near-real-time and the subsequent atmospheric impacts. We present an analysis of Arctic and high northern latitude wildfires during the summers of 2019 and 2020, reviewing the underlying meteorological/climatological conditions, the estimated emissions and transport of smoke constituents over the Arctic Ocean. We will show that the different datasets, while being relatively independent, show a strong correspondence and provide a wealth of information required to monitor and provide context for wildfire activity.

Correct specification of a target’s longwave infrared (LWIR) surface emissivity has been identified as one of the greatest sources of uncertainty in the remote sensing of land surface temperature (LST). Field and laboratory emissivity measurements are essential for improving and validating LST retrievals, but there are differing approaches to making such measurements and the conditions that they are made under can affect their performance. To better understand these impacts we made measurements of fourteen manmade and natural samples under different environmental conditions, both in situ and in the laboratory. We used Fourier transform infrared (FTIR) spectrometers to deliver spectral emissivities and an emissivity box to deliver broadband emissivities. Field- and laboratory-measured spectral emissivities were generally within 1–2% in the key 8–12 micron region of the LWIR atmospheric window for most samples, though greater variability was observed for vegetation and inhomogeneous samples. Differences between laboratory and field spectral measurements highlighted the importance of field methods for these samples, with the laboratory setup unable to capture sample structure or inhomogeneity. The emissivity box delivered broadband emissivities with a consistent negative bias compared to the FTIR-based approaches, with differences of up to 5%. The emissivities retrieved using the different approaches result in LST retrieval differences of between 1 and 4 °C, stressing the importance of correct emissivity specification.

Open burning of agricultural crop residues is widespread across eastern China, and during certain post-harvest periods this activity is believed to significantly influence air quality. However, the exact contribution of crop residue burning to major air quality exceedances and air quality episodes has proven difficult to quantify. Whilst highly successful in many regions, in areas dominated by agricultural burning, MODIS-based (MODIS: Moderate Resolution Imaging Spectroradiometer) fire emissions inventories such as the Global Fire Assimilation System (GFAS) and Global Fire Emissions Database (GFED) are suspected of significantly underestimating the magnitude of biomass burning emissions due to the typically very small, but highly numerous, fires involved that are quite easily missed by coarser-spatial-resolution remote sensing observations. To address this issue, we use twice-daily fire radiative power (FRP) observations from the “small-fire-optimised” VIIRS-IM FRP product and combine them with fire diurnal cycle information taken from the geostationary Himawari-8 satellite. Using this we generate a unique high-spatio-temporal-resolution agricultural burning inventory for eastern China for the years 2012–2015, designed to fully take into account small fires well below the MODIS burned area or active fire detection limit, focusing on dry matter burned (DMB) and emissions of CO₂, CO, PM_2.5, and black carbon. We calculate DMB totals 100 % to 400 % higher than reported by the GFAS and GFED4.1s, and we quantify interesting spatial and temporal patterns previously un-noted. Wheat residue burning, primarily occurring in May–June, is responsible for more than half of the annual crop residue burning emissions of all species, whilst a secondary peak in autumn (September–October) is associated with rice and corn residue burning. We further identify a new winter (November–December) burning season, hypothesised to be caused by delays in burning driven by the stronger implementation of residue burning bans during the autumn post-harvest season. Whilst our emissions estimates are far higher than those of other satellite-based emissions inventories for the region, they are lower than estimates made using traditional “crop-yield-based approaches” (CYBAs) by a factor of between 2 and 5. We believe that this is at least in part caused by outdated and overly high burning ratios being used in the CYBA, leading to the overestimation of DMB. Therefore, we conclude that satellite remote sensing approaches which adequately detect the presence of agricultural fires are a far better approach to agricultural fire emission estimation.

The NASA ESA Temperature Sensing Experiment (NET-Sense) is a NASA and ESA funded campaign in support of the Copernicus Land Surface Temperature Monitoring (LSTM) satellite mission.The LSTM mission would carry a calibrated, high spatial-temporal resolution thermal infrared imager whose data would be used to provide the land-surface temperature information required for such applications as evapotranspiration estimation at the European field-scale. The LSTM mission responds to priority requirements of the agricultural user community for improving sustainable agricultural productivity in a world of increasing water scarcity and variability.As part of the effort to LSTM mission development effort, the first non-US flights of NASA JPL’s state-of-the-art Hyperspectral Thermal Emission Spectrometer (HyTES) were conducted on a UK research aircraft in both the UK and Italy in June and July 2019. HyTES is an airborne thermal hyperspectral imager providing extremely high quality and radiometrically precise infrared radiances within 256 spectral channels across the spectral range 7.5 to 12 µm, with the primary aim to map LST and surface spectral emissivity. Flights in Italy were accompanied by the HyPLANT and TASI instruments, operated by FZ-Juelich, Germany installed aboard a second aircraft from CzechGlobe (CZ).We provide an overview of the NET-Sense campaign, example results from HyTES and comparisons to in situ LST and surface spectral emissivity data collected co-incident with the aircraft overflights using tower-mounted radiometers and portable FTIR spectrometers adapted for the purpose. We explain the integration of NET-Sense into the broader science strategy for the LSTM mission, and highlight planned activities for the coming years, including NET-Sense 2020 European campaign plans.

Open burning of agricultural crop residues is widespread across eastern China, and during certain post-harvest periods this activity is believed to significantly influence air quality. However, the exact contribution of crop residue burning to major air quality exceedances and air quality episodes has proven difficult to quantify. Whilst highly successful in many regions, in areas dominated by agricultural burning MODIS-based fire emissions inventories such as GFAS and GFED are suspected of significantly underestimating the magnitude of biomass burning emissions due to the typically very small, but highly numerous, fires involved that are quite easily missed by coarser spatial resolution remote sensing observations. To address this issue, we here use twice daily fire radiative power (FRP) observations from the ‘small fire optimised’ VIIRS-IM FRP product, and combine it with fire diurnal cycle information taken from the geostationary Himawari-8 satellite. Using this we generate a unique high spatio-temporal resolution agricultural burning inventory for eastern China for the years 2012–2015, designed to fully take into account small fires well below the MODIS burned area or active fire detection limit, focusing on dry matter burned (DMB) and emissions of CO₂, CO, PM_2.5 and black carbon. We calculate DMB totals 100 to 400 % higher than reported by GFAS and GFED4.1s, and quantify interesting spatial and temporal patterns previously un-noted. Wheat residue burning, primarily occurring in May–June, is responsible for more than half of the annual crop residue burning emissions of all species, whilst a secondary peak in autumn (Sept–Oct) is associated with rice and corn residue burning. We further identify a new winter (Nov–Dec) burning season, hypothesised to be caused by delays in burning driven by the stronger implementation of residue burning bans during the autumn post-harvest season. Whilst our emissions estimates are far higher than those of other satellite-based emissions inventories for the region, they are lower than estimates made using traditional ‘crop yield-based approaches’ (CYBA) by a factor of between 2 and 5 x. We believe that this is at least in part caused by outdated and overly high burning ratios being used in the CYBA approach, leading to the overestimation of DMB. Therefore we conclude that that satellite remote sensing approaches which adequately detect the presence of agricultural fires are a far better approach to agricultural fire emission estimation.