Trends in eastern China agricultural fire emissions derived from a combination of geostationary (Himawari) and polar (VIIRS) orbiter fire radiative power products

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, PM2.5, 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.

Spatial and Temporal Distribution of Biomass Open Burning Emissions in the Greater Mekong Subregion

Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area products are widely used to assess the damaged area after wildfires and agricultural burning have occurred. This study improved the accuracy of the assessment of the burnt areas by using the MCD45A1 and MCD64A1 burnt area products with the finer spatial resolution product from the Landsat-8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) surface reflectance data. Thus, more accurate wildfires and agricultural burning areas in the Greater Mekong Subregion (GMS) for the year 2015 as well as the estimation of the fire emissions were reported. In addition, the results from this study were compared with the data derived from the fourth version of the Global Fire Emissions Database (GFED) that included small fires (GFED4.1s). Upon analysis of the data of the burnt areas, it was found that the burnt areas obtained from the MCD64A1 and MCD45A1 had lower values than the reference fires for all vegetation fires. These results suggested multiplying the MCD64A1 and MCD45A1 for the GMS by the correction factors of 2.11−21.08 depending on the MODIS burnt area product and vegetation fires. After adjusting the burnt areas by the correction factor, the total biomass burnt area in the GMS during the year 2015 was about 33.3 million hectares (Mha), which caused the burning of 109 ± 22 million tons (Mt) of biomass. This burning emitted 178 ± 42 Mt of CO2, 469 ± 351 kilotons (kt) of CH4, 18 ± 3 kt of N2O, 9.4 ± 4.9 Mt of CO, 345 ± 206 kt of NOX, 46 ± 25 kt of SO2, 147 ± 117 kt of NH3, 820 ± 489 kt of PM2.5, 60 ± 32 kt of BC, and 350 ± 205 kt of OC. Furthermore, the emission results of fine particulate matter (PM2.5) in all countries were slightly lower than GFED4.1s in the range between 0.3 and 0.6 times.

Indian agriculture, air pollution, and public health in the age of COVID

Emerging evidence supports the intuitive link between chronic health conditions associated with air pollution and the vulnerability of individuals and communities to COVID-19. Poor air quality already imposes a highly significant public health burden in Northwest India, with pollution levels spiking to hazardous levels in November and early December when rice crop residues are burned. The urgency of curtailing the COVID-19 pandemic and mitigating a potential resurgence later in the year provides even more justification for accelerating efforts to dramatically reduce open agricultural burning in India.

New eastern China agricultural burning fire emission inventory and trends analysis from combined geostationary (Himawari-8) and polar-orbiting (VIIRS-IM) fire radiative power products

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, PM2.5 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.

Determination of the Area Affected by Agricultural Burning

Agricultural burning is still a common practice around the world. It is associated with the high emission of air pollutants, including short-term climate change forcing pollutants such as black carbon and PM2.5. The legal requirements to start any regulatory actions to control them is the identification of its area of influence. However, this task is challenging from the experimental and modeling point of view, since it is a short-term event with a moving area source of pollutants. In this work, we assessed this agricultural burning influence-area using the US Environmental authorities recommended air dispersion model (AERMOD). We considered different sizes and geometries of burning areas located on flat terrains, and several crops burning under the worst-case scenario of meteorological conditions. The influence area was determined as the largest area where the short-term concentrations of pollutants (1 h or one day) exceed the local air quality standards. We found that this area is a band around the burning area whose size increases with the burning rate but not with its size. Finally, we suggested alternatives of public policy to regulate this activity, which is based on limiting the burning-rate in the way that no existing households remain inside the resulting influence-area. However, this policy should be understood as a transition towards a policy that forbids agricultural burning.

Did the Roman Empire affect European climate? A new look at the effects of land use and anthropogenic aerosol emissions

As one of the first transcontinental polities that led to widespread anthropogenic modification of the environment, the influence of the Roman Empire on European climate has been studied for more than 20 years. Recent advances in our understanding of past land use and aerosol-climate interactions make it valuable to revisit the way humans may have affected the climate of the Roman Era. Here we drive the global aerosol-enabled climate model ECHAM-HAM-SALSA with land use maps and novel estimates of anthropogenic aerosol emissions from the Roman Empire at its apogee to quantify the effect of humans on regional climate. In a factorial study, we used the HYDE and KK11 anthropogenic land cover change scenarios with three estimates (low, medium, high) of aerosol emissions from fuel combustion and burning of agricultural land. Land use effects on climate varied from no influence using the HYDE scenario to a significant warming over land of 0.15 K with KK11 relative to a no-land use control. This warming is primarily caused by regional decreases in turbulent fluxes, in contrast to previous studies that emphasised changes in albedo and evapotranspiration. Aerosol emissions from agricultural burning were greater than those from fuel consumption, but on the same order of magnitude. All emissions scenarios result in an enhanced cooling effect of clouds over a no-emissions control scenario. As a consequence, the land surface temperature averaged over our entire study domain decreased significantly by 0.17 K, 0.23 K, and 0.46 K for the low, the intermediate, and the high emissions scenarios, respectively. Cooling caused by aerosol emissions is largest over Central and Eastern Europe, while warming caused by land use occurs in parts of North Africa and the Middle East. Our results suggest that the influence of Roman Era anthropogenic aerosol emissions on European climate may have been as important as that of deforestation and other forms of land use. Our model may overestimate aerosol-effective radiative forcing, however, and our results are very sensitive to the inferred seasonal timing of agricultural burning practices and natural aerosol emissions over land (wildfire emissions and biogenic emissions). Nevertheless, it is likely that human influence on land and the atmosphere affected continental-scale climate during Classical Antiquity.