The past decade has seen episodes of increasingly severe air pollution across much of the highly populated Indo-Gangetic Plain (IGP), particularly during the post-monsoon season when crop residue burning (CRB) is most prevalent. Recent studies have suggested that a major, possibly dominant contributor to this air quality decline is that northwest (NW) Indian rice residue burning has shifted later into the post-monsoon season, as an unintended consequence of a 2009 groundwater preservation policy that delayed the sowing of irrigated rice paddy. Here we combine air quality modelling of fine particulate matter (PM_2.5) over IGP cities, with meteorology, fire and smoke emissions data to directly test this hypothesis. Our analysis of satellite-derived agricultural fires shows that an approximate 10 d shift in the timing of NW India post-monsoon residue burning occurred since the introduction of the 2009 groundwater preservation policy. For the air quality crisis of 2016, we found that NW Indian CRB timing shifts made a small contribution to worsening air quality (3% over Delhi) during the post-monsoon season. However, if the same agricultural fires were further delayed, air quality in the CRB source region (i.e. Ludhiana) and for Delhi could have deteriorated by 30% and 4.4%, respectively. Simulations for other years highlight strong inter-annual variabilities in the impact of these timing shifts, with the magnitude and even direction of PM_2.5 concentration changes strongly dependent on specific meteorological conditions. Overall we find post-monsoon IGP air quality to be far more sensitive to meteorology and the amount of residue burned in the fields of NW India than to the timing shifts in residue burning. Our study calls for immediate actions to provide farmers affordable and sustainable alternatives to residue burning to hasten its effective prohibition, which is paramount to reducing the intensity of post-monsoon IGP air pollution episodes.

The Global Fire Emissions Database (GFED)—currently by far the most widely used global fire emissions inventory—is primarily driven by the 500 m MODIS MCD64A1 burned area (BA) product. This product is unable to detect many smaller fires, and the new v4.1s of GFED addresses this deficiency by using a ‘small fire boost’ (SFB) methodology that estimates the ‘small fire’ burned area from MODIS active fire (AF) detections. We evaluate the performance of this approach in two globally significant agricultural burning regions dominated by small fires, eastern China and north-western India. We find the GFED4.1s SFB can affect the burned area and fire emissions data reported by GFED very significantly, and the approach shows some potential for reducing low biases in GFED’s fire emissions estimates of agricultural burning regions. However, it also introduces several significant errors. In north-western India, the SFB slightly improves the temporal distribution of agricultural burning, but the magnitude of the additional burned area added by the SFB is far too low. In eastern China, the SFB appears to have some positive effects on the magnitude of agricultural burning reported in June and October, but significant errors are introduced in the summer months via false alarms in the MODIS AF product. This results in a completely inaccurate ‘August’ burning period in GFED4.1s, where false fires are erroneously stated to be responsible for roughly the same amount of dry matter fuel consumption as fires in June and October. Even without the SFB, we also find problems with some of the burns detected by the MCD64A1 burned area product in these agricultural regions. Overall, we conclude that the SFB methodology requires further optimisation and that the efficacy of GFED4.1s’ ‘boosted’ BA and resulting fire emissions estimates require careful consideration by users focusing in areas where small fires dominate.

Deforestation and draining of the peatlands in equatorial SE Asia has greatly increased their flammability, and in September–October 2015 a strong El Niño-related drought led to further drying and to widespread burning across parts of Indonesia, primarily on Kalimantan and Sumatra. These fires resulted in some of the worst sustained outdoor air pollution ever recorded, with atmospheric particulate matter (PM) concentrations exceeding those considered “extremely hazardous to health” by up to an order of magnitude. Here we report unique in situ air quality data and tropical peatland fire emissions factors (EFs) for key carbonaceous trace gases (CO₂, CH₄ and CO) and PM_2.5 and black carbon (BC) particulates, based on measurements conducted on Kalimantan at the height of the 2015 fires, both at locations of “pure” sub-surface peat burning and spreading vegetation fires atop burning peat. PM_2.5 are the most significant smoke constituent in terms of human health impacts, and we find in situ PM_2.5 emissions factors for pure peat burning to be 17.8 to 22.3 g·kg⁻¹, and for spreading vegetation fires atop burning peat 44 to 61 g·kg⁻¹, both far higher than past laboratory burning of tropical peat has suggested. The latter are some of the highest PM_2.5 emissions factors measured worldwide. Using our peatland CO₂, CH₄ and CO emissions factors (1779 ± 55 g·kg⁻¹, 238 ± 36 g·kg⁻¹, and 7.8 ± 2.3 g·kg⁻¹ respectively) alongside in situ measured peat carbon content (610 ± 47 g-C·kg⁻¹) we provide a new 358 Tg (± 30%) fuel consumption estimate for the 2015 Indonesian fires, which is less than that provided by the GFEDv4.1s and GFASv1.2 global fire emissions inventories by 23% and 34% respectively, and which due to our lower EF_CH4 produces far less (~3×) methane. However, our mean in situ derived EF_PM2.5 for these extreme tropical peatland fires (28 ± 6 g·kg⁻¹) is far higher than current emissions inventories assume, resulting in our total PM_2.5 emissions estimate (9.1 ± 3.5 Tg) being many times higher than GFEDv4.1s, GFASv1.2 and FINNv2, despite our lower fuel consumption. We find that two thirds of the emitted PM_2.5 come from Kalimantan, one third from Sumatra, and 95% from burning peatlands. Using new geostationary fire radiative power (FRP) data we map the fire emissions’ spatio-temporal variations in far greater detail than ever before (hourly, 0.05°), identifying a tropical peatland fire diurnal cycle twice as wide as in neighboring non-peat areas and peaking much later in the day. Our data show that a combination of greatly elevated PM_2.5 emissions factors, large areas of simultaneous, long-duration burning, and very high peat fuel consumption per unit area made these Sept to Oct tropical peatland fires the greatest wildfire source of particulate matter globally in 2015, furthering evidence for a regional atmospheric pollution impact whose particulate matter component in particular led to millions of citizens being exposed to extremely poor levels of air quality for substantial periods.

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.

Active fire observations with satellite instruments exhibit a well-documented increase of the detection threshold with increasing pixel footprint size, i.e., distance from the sub-satellite point. This results in a viewing angle-dependent, negative bias in gridded representations of the observed Fire Radiative Power (FRP), which in turn is frequently being used for climate monitoring of biomass burning and for pyrogenic emission inventories. We present a method based on quantile mapping to alleviate this bias and apply it to the gridded-FRP from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instruments. The gridded-FRP observations are corrected with a correction function that depends on the satellite viewing angle and the magnitude of FRP in each grid cell. Assuming the fire observations at nadir to be the best representation of the truth, we derive a correction function by mapping cumulative distribution function (CDF) of off-nadir gridded-FRP to the CDF of near-nadir gridded-FRP. The method can be directly applied to correct the negative bias in gridded-FRP observations at a grid resolution of $1^{\circ }$ or more. The performance of the correction methodology is confirmed through comparisons with co-located Visible Infrared Imaging Radiometer Suite (VIIRS) satellite observations: After bias correction, the gridded-FRP observations from both satellites agree better than before, particularly over savanna, tropical forests, and extra-tropical forests. Experiments with the Global Fire Assimilation System (GFAS) show that the impacts of the bias-corrected MODIS/Aqua gridded-FRP observations and VIIRS/Suomi-NPP gridded-FRP observations on regional FRP analyses are comparable, which confirms the potential for improving fire emission inventories and climate monitoring based on FRP.

The international journal of Aerosol and Air Quality Research (AAQR) covers all aspects of aerosol science and technology, atmospheric science and air quality related issues.