Global data-driven prediction of fire activity

Recent advancements in machine learning (ML) have expanded the potential use across scientific applications, including weather and hazard forecasting. The ability of these methods to extract information from diverse and novel data types enables the transition from forecasting fire weather, to predicting actual fire activity. In this study we demonstrate that this shift is feasible also within an operational context. Traditional methods of fire forecasts tend to over predict high fire danger, particularly in fuel limited biomes, often resulting in false alarms. By using data on fuel characteristics, ignitions and observed fire activity, data-driven predictions reduce the false-alarm rate of high-danger forecasts, enhancing their accuracy. This is made possible by high quality global datasets of fuel evolution and fire detection. We find that the quality of input data is more important when improving forecasts than the complexity of the ML architecture. While the focus on ML advancements is often justified, our findings highlight the importance of investing in high-quality data and, where necessary create it through physical models. Neglecting this aspect would undermine the potential gains from ML-based approaches, emphasizing that data quality is essential to achieve meaningful progress in fire activity forecasting.

Fire Intensity and spRead forecAst (FIRA): A Machine Learning Based Fire Spread Prediction Model for Air Quality Forecasting Application

Fire activities introduce hazardous impacts on the environment and public health by emitting various chemical species into the atmosphere. Most operational air quality forecast (AQF) models estimate smoke emissions based on the latest available satellite fire products, which may not represent real-time fire behaviors without considering fire spread. Hence, a novel machine learning (ML) based fire spread forecast model, the Fire Intensity and spRead forecAst (FIRA), is developed for AQF model applications. FIRA aims to improve the performance of AQF models by providing realistic, dynamic fire characteristics including the spatial distribution and intensity of fire radiative power (FRP). In this study, data sets in 2020 over the continental United States (CONUS) and a historical California fire in 2024 are used for model training and evaluation. For application assessment, FIRA FRP predictions are applied to the Unified Forecast System coupled with smoke (UFS-Smoke) model as inputs, focusing on a California fire case in September 2020. Results show that FIRA captures fire spread with R-squared (R 2) near 0.7 and good spatial similarity (∼95%). The comparison between UFS-Smoke simulations using near-real-time fire products and FIRA FRP predictions show good agreements, indicating that FIRA can accurately represent future fire activities. Although FIRA generally underestimates fire intensity, the uncertainties can be mitigated by applying scaling factors to FRP values. Use of the scaled FIRA largely outperforms the experimental UFS-Smoke model in predicting aerosol optical depth and the three-dimensional smoke contents, while also demonstrating the ability to improve surface fine particulate matter (PM2.5) concentrations affected by fires.

Forest fire emission estimates over South Asia using Suomi-NPP VIIRS-based thermal anomalies and emission inventory

Emission estimates of carbon-containing greenhouse gases (CO2, CH4) and aerosols (PM2.5) were made from forest fire across South Asia using Visible Infrared Imaging Radiometer Suite (VIIRS) based thermal anomalies and fire products. VIIRS 375 m I-band active fire product was selectively retrieved for the years 2012-2021 over forest cover across South Asia. Annual incidence of fire events across South Asia was 0.17 (±0.05) million (M) with robust spatio-temporal variation. Fire occurrences were mainly concentrated over the forest across Hindu Kush Himalayan region (HKH; 56%), Deccan Plateau (DP) and Central Highlands (CH; 34%). Monthly mean fire incidences emphasize February to May as a typical forest fire season, accounting 90% of annual fire counts. The highest fire pixel density (>1.5 km -2 yr-1) was noted over the tropical dry/moist deciduous and tropical semi-evergreen forests. Strong diurnal nature of fire radiative power (FRP) was evident with >85% of FRP linked to daytime retrieval. VIIRS based Fire Emission Inventory (VFEI, Version 0) was followed to constitute regional emissions of PM2.5 and green house gases from forest fire. Forest fire accounted a yearly emission of 91.58 (±14.76) and 0.25 (±0.04) Tg yr-1 CO2 and CH4 respectively, with 25.14 (±3.94) Tg of cumulative carbon release per year, i.e., roughly 1.3% of global fire-related carbon emission. Fire associated PM2.5 emission rate was 0.60 (±0.10) Tg yr-1, 95% of which emitted during peak fire season as was the case for carbon-containing gases. Forest fire across HKH (75%) and DP + CH (20%) predominately contribute to the regional carbon emission, while also accounting 68% (HKH) and 27% (DP + CH) of fire associated PM2.5 emission budget. With >70% of forest fires within South Asia being typically anthropogenic, forest fire appears to be a major sector of greenhouse gas and aerosols emissions, and necessitate planning and strict legalities to reduce emission load.

Investigation of size-segregated particulate matter and carbonaceous components in Phnom Penh, Cambodia

This study investigated the size-segregated carbonaceous components of particulate matter (PM), including ultrafine particles (UFP or PM0.1), across three distinct urban settings in Phnom Penh, Cambodia: an educational site (University of Health Sciences, UHS), an institutional site (Ministry of Environment, MoE), and a residential area near a landfill. A cascade impactor-type sampler equipped with an inertial filter was used to collect size-segregated particles down to UFPs. Carbonaceous species, including organic carbon (OC) and elemental carbon (EC), were analyzed using a thermal/optical carbon analyzer to determine their composition and ratios. The results indicated no significant differences in mass concentration between weekdays and weekends at all sites; however, PM profiles varied across the location. The UHS site displayed higher EC levels in UFP, with concentrations suggesting significant local vehicular emissions, underscored by a lower OC/EC ratio (2.39 ± 1.13) compared to other sites. In contrast, the landfill site exhibited higher OC components in finer particles, suggesting emissions from organic sources such as waste burning and cooking activities, evidenced by higher OC/EC ratios across all particle sizes (e.g., OC/EC ratio in UFP at 3.78 ± 0.98). The MoE site presented a balanced profile with moderate levels of both OC and EC, influenced by its proximity to natural dispersion factors like the Tonle Sap River. Additionally, air mass backward trajectory analysis integrated with hotspot data indicated transboundary influences, particularly from agricultural burning in surrounding provinces, including Vietnam. Therefore, both local and transboundary emissions influenced the PM levels in Phnom Penh city.

Seasonal anomaly of particulate matter concentration in an equatorial climate: Evaluating the transboundary impact from neighboring provinces on Padang City, Indonesia

This study investigated the anomalous seasonal variations in particulate matter (PM) concentrations-specifically PM2.5 and PM10-in Padang City, Indonesia, situated within the Equatorial climate zone. A one-year dataset of half-hourly PM measurements from January to December 2023, collected by the Air Quality Monitoring System (AQMS) managed by the Environmental Agency of West Sumatra (DLH), was utilized. Maps of hotspots and air mass backward trajectories were used to identify possible transboundary emissions affecting Padang City. Despite the region experiencing nearly continuous rainfall, significant elevations in PM levels were observed during the typically drier months of August to October. Specifically, PM2.5 levels peaked at 36.57 µg/m3 and PM10 at 39.58 µg/m3 in October, significantly higher than in other months and indicating a substantial deviation from the typical expectations for equatorial climates. These results suggest that the high PM concentrations are not solely due to local urban emissions or normal seasonal variations but are also significantly influenced by transboundary smoke from peatland fires and agricultural burning in neighboring provinces such as Bengkulu, Riau, Jambi, and South Sumatra. Backward trajectory analysis further confirmed the substantial impact of regional activities on degradation of air quality in Padang City. The study underscores the need for integrated air quality management that includes both local and transboundary pollution sources. Enhanced monitoring, public engagement, and inter-regional collaboration are emphasized as crucial strategies for mitigating the adverse effects of PM pollution in equatorial regions like Padang City.

Increasing frequency and intensity of the most extreme wildfires on Earth

Climate change is exacerbating wildfire conditions, but evidence is lacking for global trends in extreme fire activity itself. Here we identify energetically extreme wildfire events by calculating daily clusters of summed fire radiative power using 21 years of satellite data, revealing that the frequency of extreme events (≥99.99th percentile) increased by 2.2-fold from 2003 to 2023, with the last 7 years including the 6 most extreme. Although the total area burned on Earth may be declining, our study highlights that fire behaviour is worsening in several regions-particularly the boreal and temperate conifer biomes-with substantial implications for carbon storage and human exposure to wildfire disasters.

Diverging trends in aerosol sulfate and nitrate measured in the remote North Atlantic in Barbados are attributed to clean air policies, African smoke, and anthropogenic emissions

Sulfate and nitrate aerosols degrade air quality, modulate radiative forcing and the hydrological cycle, and affect biogeochemical cycles, yet their global cycles are poorly understood. Here, we examined trends in 21 years of aerosol measurements made at Ragged Point, Barbados, the easternmost promontory on the island located in the eastern Caribbean Basin. Though the site has historically been used to characterize African dust transport, here we focused on changes in nitrate and non-sea-salt (nss) sulfate aerosols from 1990-2011. Nitrate aerosol concentrations averaged over the entire period were stable at 0.59 μg m-3 ± 0.04 μg m-3, except for elevated nitrate concentrations in the spring of 2010 and during the summer and fall of 2008 due to the transport of biomass burning emissions from both northern and southern Africa to our site. In contrast, from 1990 to 2000, nss-sulfate decreased 30% at a rate of 0.023 μg m-3 yr-1, a trend which we attribute to air quality policies enacted in the United States (US) and Europe. From 2000-2011, sulfate gradually increased at a rate of 0.021 μg m-3 yr-1 to pre-1990s levels of 0.90 μg m-3. We used the Community Multiscale Air Quality (CMAQ) model simulations from the EPA's Air QUAlity TimE Series (EQUATES) to better understand the changes in nss-sulfate after 2000. The model simulations estimate that increases in anthropogenic emissions from Africa explain the increase in nss-sulfate observed in Barbados. Our results highlight the need to better constrain emissions from developing countries and to assess their impact on aerosol burdens in remote source regions.

Forest fire vulnerability in Nepal's chure region: Investigating the influencing factors using generalized linear model

The Chure region, among the world's youngest mountains, stands out as highly susceptible to natural calamities, particularly forest fires. The region has consistently experienced forest fire incidents, resulting in the degradation of valuable natural and anthropogenic resources. Despite its vulnerability, there have been limited studies to understand the relationship of various causative factors for the recurring fire problem. Hence, to comprehend the influencing factors for the recurring forest fire problem and its extent, we utilized generalized linear modeling under binary logistic regression to combine the dependent variable of satellite detected fire points and various independent variables. We conducted a variance inflation factor (VIF) test and correlation matrix to identify the 14 suitable variables for the study. The analysis revealed that forest fires occurred mostly during the three pre-monsoon periods and had a significant positive relation with the area under forest, rangeland, bare-grounds, and Normalized Difference Vegetation Index (NDVI) (P < 0.05). Consequently, our model showed that the probability of fire incidents decreases with elevation, precipitation, and population density (P < 0.05). Among the significant variables, the forest areas emerges as the most influencing factor, followed by precipitation, elevation, area of rangeland, population density, NDVI, and the area of bare ground. The validation of the model was done through the area under the curve (AUC = 0.92) and accuracy (ACC = 0.89) assessments, which showed the model performed excellently in terms of predictive capabilities. The modeling result and the forest fire susceptible map provide valuable insights into the forest fire vulnerability in the region, offering baseline information about forest fires that will be helpful for line agencies to prepare management strategies to further prevent the deterioration of the region.

Drought triggers and sustains overnight fires in North America

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 cycle1-3 and current fire management practices4,5. 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.

Pyrogeography in flux: Reorganization of Australian fire regimes in a hotter world

Changes to the spatiotemporal patterns of wildfire are having profound implications for ecosystems and society globally, but we have limited understanding of the extent to which fire regimes will reorganize in a warming world. While predicting regime shifts remains challenging because of complex climate-vegetation-fire feedbacks, understanding the climate niches of fire regimes provides a simple way to identify locations most at risk of regime change. Using globally available satellite datasets, we constructed 14 metrics describing the spatiotemporal dimensions of fire and then delineated Australia's pyroregions-the geographic area encapsulating a broad fire regime. Cluster analysis revealed 18 pyroregions, notably including the (1) high-intensity, infrequent fires of the temperate forests, (2) high-frequency, smaller fires of the tropical savanna, and (3) low-intensity, diurnal, human-engineered fires of the agricultural zones. To inform the risk of regime shifts, we identified locations where the climate under three CMIP6 scenarios is projected to shift (i) beyond each pyroregion's historical climate niche, and (ii) into climate space that is novel to the Australian continent. Under middle-of-the-road climate projections (SSP2-4.5), an average of 65% of the extent of the pyroregions occurred beyond their historical climate niches by 2081-2100. Further, 52% of pyroregion extents, on average, were projected to occur in climate space without present-day analogues on the Australian continent, implying high risk of shifting to states that also lack present-day counterparts. Pyroregions in tropical and hot-arid climates were most at risk of shifting into both locally and continentally novel climate space because (i) their niches are narrower than southern temperate pyroregions, and (ii) their already-hot climates lead to earlier departure from present-day climate space. Such a shift implies widespread risk of regime shifts and the emergence of no-analogue fire regimes. Our approach can be applied to other regions to assess vulnerability to rapid fire regime change.

Global distribution, trends and types of active fire occurrences

Fire occurrence is synonymous to terrestrial ecosystems and an important component of the Earth system. Climate change, vegetation characteristics, and human activity regulate fire occurrence and spread, however, fires also interact with them in multiple ways. Due to the complicated mechanisms of interactions between fire and land use or cover, the spatial distribution, change trends and land use or cover types of fire occurrences exist wide discrepancies in different regions or countries around the world. Therefore, the quantitative and spatial relationship and differences between fire and land use or cover at the global scale remain poorly understood systematically. Here, we combine active fire and land cover products during 2001-2020 to explore the spatio-temporal features, trends, and types of active fires from global to continental scales. Globally, the annual changes of monthly active fire occurrences kept a dramatic increase in first two or three years but a circuitous decrease since then. Most areas prevailingly experienced active fires for once to five times, a small part of areas clustered in Africa, Southeast Asia, and South America experienced active fires for over five times in the last 20-years. In particular, above 60 % of active fires (re-)occurred in forest and 20-25 % in cropland, whereas grassland and construction land only accounted for about 5 % and less than 2 % respectively. Driven by active fires, the conversion of forest to cropland accounted for nearly 60 % and the transition of cropland to forest (about 10 %) followed and formed an interactive circle. Our findings improve the understanding of fire-land cover change interactions, particularly agricultural expansion and forest loss driven by active fires. Future efforts on agricultural expansion, urban safety, carbon sequestration and biodiversity conservation should take the results of this research into account.

Stubble-burning activities in north-western India in 2021: Contribution to air pollution in Delhi

Stubble-burning in northern India is an important source of atmospheric particulate matter (PM) and trace gases, which significantly impact local and regional climate, in addition to causing severe health risks. Scientific research on assessing the impact of these burnings on the air quality over Delhi is still relatively sparse. The present study analyzes the satellite-retrieved stubble-burning activities in the year 2021, using the MODIS active fire count data for Punjab and Haryana, and assesses the contribution of CO and PM_2.5 from such biomass-burning activities to the pollution load in Delhi. The analysis suggests that the satellite-retrieved fire counts in Punjab and Haryana were the highest among the last five years (2016-2021). Further, we note that the stubble-burning fires in the year 2021 are delayed by ∼1 week compared to that in the year 2016. To quantify the contribution of the fires to the air pollution in Delhi, we use tagged tracers for CO and PM_2.5 emissions from fire emissions in the regional air quality forecasting system. The modeling framework suggests a maximum daily mean contribution of the stubble-burning fires to the air pollution in Delhi in the months of October-November 2021 to be around 30-35%. We find that the contribution from stubble burning activities to the air quality in Delhi is maximum (minimum) during the turbulent hours of late morning to afternoon (calmer hours of evening to early morning). The quantification of this contribution is critical from the crop-residue and air-quality management perspective for policymakers in the source and the receptors regions, respectively.

Influence of the Long-Range Transport of Siberian Biomass Burnings on Air Quality in Northeast China in June 2017

Biomass burning (BB) emits a large volume of trace gases and aerosols into the atmosphere, which can significantly affect the earth's radiative balance and climate and has negative impacts on air quality and even human health. In late June 2017, an intense BB case, dominated by forest and savanna fires, occurred in Siberia, and it affected the air quality of Northeast China through long-range transport. Here, multisatellite remote-sensing products and ground-based PM2.5 measurements are used to evaluate the influence of the Siberian smoky plume on Northeast China. The results show that the BB was intense at the early stage when the daily fire count and average fire radiative power exceeded 300 and 200 MW, respectively. The maximum daily fire count reached 1350 in Siberia, and the peak value of instantaneous fire radiative power was as high as 3091.5 MW. High concentrations of CO and aerosols were emitted into the atmosphere by the BB in Siberia. The maximum daily mean values of the CO column concentration and aerosol optical depth (AOD) increased by 3 × 1017 molec·cm2 and 0.5 compared with that during the initial BB stage. In addition, the BB released a large number of absorptive aerosols into the atmosphere, and the UV aerosol index (UVAI) increased by five times at the peak of the event in Siberia. Under the appropriate synoptic conditions and, combined with pyroconvection, the smoky plume was lifted into the upper air and transported to Northeast China, affecting the air quality of Northeast China. The daily mean values of CO concentration, AOD, and UVAI in Northeast China increased by 6 × 1017 molec·cm2, 0.5, and 1.4, respectively, after being affected. Moreover, the concentration of the surface PM2.5 in Northeast China approximately doubled after being affected by the plume. The results of this study indicate that the air quality of Northeast China can be significantly affected by Siberian BBs under favorable conditions.

Achieving Brazil's Deforestation Target Will Reduce Fire and Deliver Air Quality and Public Health Benefits

Climate, deforestation, and forest fires are closely coupled in the Amazon, but models of fire that include these interactions are lacking. We trained machine learning models on temperature, rainfall, deforestation, land-use, and fire data to show that spatial and temporal patterns of fire in the Amazon are strongly modified by deforestation. We find that fire count across the Brazilian Amazon increases by 0.44 percentage points for each percentage point increase in deforestation rate. We used the model to predict that the increased deforestation rate in the Brazilian Amazon from 2013 to 2020 caused a 42% increase in fire counts in 2020. We predict that if Brazil had achieved the deforestation target under the National Policy on Climate Change, there would have been 32% fewer fire counts across the Brazilian Amazon in 2020. Using a regional chemistry-climate model and exposure-response associations, we estimate that the improved air quality due to reduced smoke emission under this scenario would have resulted in 2,300 fewer deaths due to reduced exposure to fine particulate matter. Our analysis demonstrates the air quality and public health benefits that would accrue from reducing deforestation in the Brazilian Amazon.

Wild, tamed, and domesticated: Three fire macroregimes for global pyrogeography in the Anthropocene

Climate and natural vegetation dynamics are key drivers of global vegetation fire, but anthropogenic burning now prevails over vast areas of the planet. Fire regime classification and mapping may contribute towards improved understanding of relationships between those fire drivers. We used 15 years of daily active fire data from the MODIS fire product (MCD14ML, collection 6) to create global maps of six fire descriptors (incidence, size inequality, season length, interannual variability, intensity, and fire season modality). Using multiple correspondence analysis (MCA) and hierarchical agglomerative clustering, we identified three fire macroregimes: Wild, Tamed, and Domesticated, each of which splitting into prototypical and transitional regimes. Interpretation of the six fire regimes in terms of their main drivers relied on the global maps of anthromes and Köppen climate types. The analysis yielded a two-dimensional space where the principal dimension of variability is primarily defined by interannual variability in fire activity and fire season length, and the secondary axis is based mainly on fire incidence. The Wild fire macroregime occurs mostly in cold wildlands, where burning is sporadic and fire seasons are short. Tamed fires predominate in seasonally dry tropical rangelands and croplands with high fire incidence. Domesticated fires are characteristic of humid, warm temperate and tropical croplands and villages with low fire incidence. The Tamed and Domesticated fire macroregimes, representing managed burning, account for 86% of all active fires in our dataset and for 70% of the global burnable area. Fourteen percent of active fires were found in the cold wildlands, and in the rangelands and forests of steppe and desert climates of the Wild macroregime. These results highlight the extent of human control over global pyrogeography in the Anthropocene.

Wildfire plumes in the Western US are reaching greater heights and injecting more aerosols aloft as wildfire activity intensifies

By producing a first-of-its-kind, decadal-scale wildfire plume rise climatology in the Western U.S. and Canada, we identify trends toward enhanced plume top heights, aerosol loading aloft, and near-surface smoke injection throughout the American West. Positive and significant plume trends suggest a growing impact of Western US wildfires on air quality at the local to continental scales and support the notion that wildfires may have an increasing impact on regional climate. Overlap of identified trends with regions of increasing wildfire emissions and burn severity suggests a link to climate driven trends toward enhanced wildfire activity. Further, time series of plume activity point to a possible acceleration of trends over recent years, such that the future impacts to air quality and regional climate may exceed those suggested by a linear fit to the multi-decadal data. These findings have significant implications for human health and exacerbate concern for the climate-wildfire connection.

Fire Monitoring Algorithm and Its Application on the Geo-Kompsat-2A Geostationary Meteorological Satellite

Geo-Kompsat-2A (GK-2A) is the third new-generation geostationary meteorological satellite that orbits Asia and monitors China and its surrounding areas, following the Himawari-8 and Fengyun-4A satellites. The nadir point positioning and satellite channel parameters of the GK-2A are better than those of the Himawari-8 and FY-4A, which are more conducive to fire monitoring in China. In this study, a new fire detection algorithm is proposed based on GK-2A satellite data. That is, considering the large solar zenith angle correction for reflectance and the proportion information of background pixels in the existing spatial threshold method, fires under the different underlying surface types and solar radiation states can be automatically identified. Moreover, the accuracy of the Himawari-8 fire monitoring algorithm and the present algorithm of GK-2A is compared and analyzed through the ground truth fire spot data. The results show that compared with the original fire monitoring algorithm with fixed parameter thresholds, the brightness temperature difference of this algorithm is reduced by 0.55 K, and the correction coefficient is reduced by 0.6 times, the fire can be found earlier, and the monitoring sensitivity is improved. According to the practical fire case, the present fire monitoring algorithm of GK-2A has better monitoring accuracy than the fire monitoring algorithm of Himawari-8. The present fire monitoring algorithm of GK-2A can meet the fire monitoring requirements under different sun angles, different cloud cover ratios and vegetation ratios with good versatility.

Climatological Aspects of Active Fires in Northeastern China and Their Relationship to Land Cover

Biomass burning (BB) is a driving force for heavy haze in northeastern China (NEC) and shows distinct seasonal features. However, little is known about its climatological aspects, which are important for regional BB management and understanding BB effects on climate and environment. Here, the climatological characteristics of active fires and their dependence on land cover in NEC were studied using Moderate Resolution Imaging Spectroradiometer (MODIS) products. Moreover, the influence of meteorological factors on fire activities was explored. The number of fires was found to have increased significantly from 2003 to 2018; and the annual total FRP (FRPtot) showed a generally consistent variation with fire counts. However, the mean fire radiative power for each spot (FRPmean) decreased. Fire activity showed distinctive seasonal variations. Most fires and intense burning events occurred in spring and autumn. Spatially, fires were mainly concentrated in cropland areas in plains, where the frequency of fires increased significantly, especially in spring and autumn. The annual percentage of agricultural fires increased from 34% in 2003 to over 60% after 2008 and the FRPtot of croplands increased from 12% to over 55%. Fires in forests, savannas, and grasslands tended to be associated with higher FRPmean than those in croplands. Analysis indicated that the increasing fire count in NEC is mainly caused by agricultural fires. Although the decreasing FRPmean represents an effective management of BB in recent years, high fire counts and FRPtot in croplands indicate that the crop residue burning cannot be simply banned and a need instead for effective applications. More efforts should be made on clean utilization of straw. The accumulation of dry biomass, high temperature, and low humidity, and weak precipitation are conducive to the fire activities. This study provides a comprehensive analysis of BB in NEC and provides a reference for regional BB management and control.

Active fires show an increasing elevation trend in the tropical highlands

As an inherent element of the Earth's ecosystem, forest, and vegetation fires are one of the key contributors to and direct consequences of climate change. Given that topography is one of the key drivers of forest landscapes and fire behavior, it is important to clarify what the topographical characteristics and trends of global fire events are, particularly in the tropics. Here, we have investigated the variations in elevation of active fires at the continental to a global scale, including the tropics, the extra-tropics, the lowlands, and the highlands (greater than 200 m above sea level [asl]), using the available MODIS Collection 6 active fire products (2001-2019). The main conclusions are: (1) the annual totality (average of 4.5 million) of global active fire events decreased and over 97% of them occurred frequently below 1500 m asl. (2) The tropics and the highlands accounted for ~74% (±3%) and 71% (±2%) of global active fires, respectively, and 77% (±2%) were observed in the tropical highlands. (3) From the beginning of the 21st century, active fires in the highlands displayed an upward elevational trend, particularly in the tropics, while the opposite trend was observed for the lowlands. More importantly, the rate of the increasing elevation in the highlands had a greater magnitude than that of decreasing elevation in the lowlands. (4) Finally, the United Nations collaborative program on Reducing Emissions from Deforestation and Forest Degradation (UN-REDD) in Developing Countries seemed to slow down or even result in a reversal of the upward elevational trend of fire occurrences in the tropics for the partner countries, especially in the lowlands. In the context of global climate change and rampant fires, the trend of rising elevation for active fire occurrences, particularly in the tropical highlands, indicates that more vegetation burning events occur or will occur in hilly to mountainous areas, thus posing further threats to tropical forests and some important biodiversity refuges. More sustained efforts should be made by governments and the scientific community to instigate enhanced fire management practices and to conduct in-depth research programs.

Warming weakens the night-time barrier to global fire

Night-time provides a critical window for slowing or extinguishing fires owing to the lower temperature and the lower vapour pressure deficit (VPD). However, fire danger is most often assessed based on daytime conditions^1,2, capturing what promotes fire spread rather than what impedes fire. Although it is well appreciated that changing daytime weather conditions are exacerbating fire, potential changes in night-time conditions-and their associated role as fire reducers-are less understood. Here we show that night-time fire intensity has increased, which is linked to hotter and drier nights. Our findings are based on global satellite observations of daytime and night-time fire detections and corresponding hourly climate data, from which we determine landcover-specific thresholds of VPD (VPD_t), below which fire detections are very rare (less than 95 per cent modelled chance). Globally, daily minimum VPD increased by 25 per cent from 1979 to 2020. Across burnable lands, the annual number of flammable night-time hours-when VPD exceeds VPD_t-increased by 110 hours, allowing five additional nights when flammability never ceases. Across nearly one-fifth of burnable lands, flammable nights increased by at least one week across this period. Globally, night fires have become 7.2 per cent more intense from 2003 to 2020, measured via a satellite record. These results reinforce the lack of night-time relief that wildfire suppression teams have experienced in recent years. We expect that continued night-time warming owing to anthropogenic climate change will promote more intense, longer-lasting and larger fires.

Reconciling Assumptions in Bottom-Up and Top-Down Approaches for Estimating Aerosol Emission Rates From Wildland Fires Using Observations From FIREX-AQ

Accurate fire emissions inventories are crucial to predict the impacts of wildland fires on air quality and atmospheric composition. Two traditional approaches are widely used to calculate fire emissions: a satellite-based top-down approach and a fuels-based bottom-up approach. However, these methods often considerably disagree on the amount of particulate mass emitted from fires. Previously available observational datasets tended to be sparse, and lacked the statistics needed to resolve these methodological discrepancies. Here, we leverage the extensive and comprehensive airborne in situ and remote sensing measurements of smoke plumes from the recent Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to statistically assess the skill of the two traditional approaches. We use detailed campaign observations to calculate and compare emission rates at an exceptionally high-resolution using three separate approaches: top-down, bottom-up, and a novel approach based entirely on integrated airborne in situ measurements. We then compute the daily average of these high-resolution estimates and compare with estimates from lower resolution, global top-down and bottom-up inventories. We uncover strong, linear relationships between all of the high-resolution emission rate estimates in aggregate, however no single approach is capable of capturing the emission characteristics of every fire. Global inventory emission rate estimates exhibited weaker correlations with the high-resolution approaches and displayed evidence of systematic bias. The disparity between the low-resolution global inventories and the high-resolution approaches is likely caused by high levels of uncertainty in essential variables used in bottom-up inventories and imperfect assumptions in top-down inventories.

Estimating air pollutant emissions from crop residue open burning through a calculation of open burning proportion based on satellite-derived fire radiative energy

Crop residue open burning has substantial negative effects on air quality, human health, and climate change, and accurate and timely estimates of its air pollutant emissions are essential. Open burning proportion (OBP) is the key parameter in estimating the emission from the crop residue open burning by bottom-up method. However, the OBP is mainly obtained by field investigation, which consumes much time, manpower and financial resources, leading to the OBP data deficient seriously. In this study, the significant logarithmic relations were found between OBP and fire radiative energy (FRE), and then the FRE-based OBP estimation models were developed for different regions of China. The comparison between the FRE-based OBP and the field-investigated OBP illustrated the reliability of the developed models (r = 0.71, NMB = -8% and NME = 25%). The OBPs of different municipalities/provinces in mainland China from 2003 to 2018 were further calculated. The results showed that the estimated OBP variation exhibited fluctuating upward trend with annual mean growth rate of 3.7% from 2003 to 2014, while dramatically decreased with annual mean reduction rate of 5.9% from 2014 to 2018. The estimation accuracy of emission from open biomass burning can also be can be significantly improved by basing on the year-specific OBP, compared with the calculation based on fixed OBP. The annual PM_2.5 emissions would decrease 4.5%-25.9% and increase 6.6%-30.7% in the scenarios of a fixed OBP during 2003-2014 and 2014-2018, respectively. The developed models complemented the severely missing OBP data of mainland China for the first time. By combining the advantages of bottom-up approach and FRE method, the proposed FRE-based models can avoid their disadvantages, and can help to get more accurately and timely emissions from crop residue open burning.

Hurricanes as an enabler of Amazon fires

A teleconnection between North Atlantic tropical storms and Amazon fires is investigated as a possible case of compound remote extreme events. The seasonal cycles of the storms and fires are in phase with a maximum around September and have significant inter-annual correlation. Years of high Amazon fire activity are associated with atmospheric conditions over the Atlantic which favour tropical cyclones. We propose that anomalous precipitation and latent heating in the Caribbean, partly caused by tropical storms, leads to a thermal circulation response which creates anomalous subsidence and enhances surface solar heating over the Amazon. The Caribbean storms and precipitation anomalies could thus promote favourable atmospheric conditions for Amazon fire.

Fire Diurnal Cycle Derived from a Combination of the Himawari-8 and VIIRS Satellites to Improve Fire Emission Assessments in Southeast Australia

The violent and persistent wildfires that broke out along the southeast coast of Australia in 2019 caused a large number of pollutant emissions, which seriously affected air quality and the global climate. The existing two methods for estimating combustion emissions based on burned area and fire radiative power mainly use a medium resolution imaging spectrometer (MODIS) on the Aqua and Terra satellites. However, the low temporal resolution of MODIS and insensitivity to small fires lead to deviation in the estimation of fire emissions. In order to solve this problem, the Visible Infrared Imaging Radiometer Suite (VIIRS) with better performance is adopted in this paper, combined with the fire diurnal cycle information obtained by geostationary satellite Himawari-8, to explore the spatio-temporal model of biomass combustion emissions. Using this, a high-spatial- and -temporal-resolution fire emission inventory was generated for southeastern Australia from November 2019 to January 2020, which aims to fully consider the highly dynamic nature of fires and small fires (low FRP) that are much lower than the MODIS burned area or active fire detection limit, with emphasis on dry matter burned (DMB). We found that during the study period, the fire gradually moved from north to south, and the diurnal cycle of the fire in the study area changed greatly. The peak time of the fire gradually delayed as the fire moved south. Our inventory shows that the DMB in southeast Australia during the study period was about 146 Tg, with major burned regions distributed along the Great Dividing Range, with December 2019 being the main burning period. The total DMB we calculated is 0.5–3.1 times that reported by the GFAS (Global Fire Assimilation System) and 1.5 to 4 times lower than that obtained using the traditional “Burned Area Based Method (FINN)”. We believe that the GFAS may underestimate the results by ignoring a large number of small fires, and that the excessive combustion rate used in the FINN may be a source of overestimation. Therefore, we conclude that the combination of high-temporal-resolution and high-spatial-resolution satellites can improve FRE estimation and may also allow further verification of biomass combustion estimates from different inventories, which are far better approaches for fire emission estimation.

Large Air Quality and Public Health Impacts due to Amazonian Deforestation Fires in 2019

Air pollution from Amazon fires has adverse impacts on human health. The number of fires in the Amazon has increased in recent years, but whether this increase was driven by deforestation or climate has not been assessed. We analyzed relationships between fire, deforestation, and climate for the period 2003 to 2019 among selected states across the Brazilian Legal Amazon (BLA). A statistical model including deforestation, precipitation and temperature explained ∼80% of the variability in dry season fire count across states when totaled across the BLA, with positive relationships between fire count and deforestation. We estimate that the increase in deforestation since 2012 increased the dry season fire count in 2019 by 39%. Using a regional chemistry-climate model combined with exposure-response associations, we estimate this increase in fire resulted in 3,400 (95UI: 3,300-3,550) additional deaths in 2019 due to increased exposure to particulate air pollution. If deforestation in 2019 had increased to the maximum recorded during 2003-2019, the number of active fire counts would have increased by an additional factor of 2 resulting in 7,900 (95UI: 7,600-8,200) additional premature deaths. Our analysis demonstrates the strong benefits of reduced deforestation on air quality and public health across the Amazon.

Daily PM2.5 concentration estimates by county, ZIP code, and census tract in 11 western states 2008-2018

We created daily concentration estimates for fine particulate matter (PM2.5) at the centroids of each county, ZIP code, and census tract across the western US, from 2008-2018. These estimates are predictions from ensemble machine learning models trained on 24-hour PM2.5 measurements from monitoring station data across 11 states in the western US. Predictor variables were derived from satellite, land cover, chemical transport model (just for the 2008-2016 model), and meteorological data. Ten-fold spatial and random CV R2 were 0.66 and 0.73, respectively, for the 2008-2016 model and 0.58 and 0.72, respectively for the 2008-2018 model. Comparing areal predictions to nearby monitored observations demonstrated overall R2 of 0.70 for the 2008-2016 model and 0.58 for the 2008-2018 model, but we observed higher R2 (>0.80) in many urban areas. These data can be used to understand spatiotemporal patterns of, exposures to, and health impacts of PM2.5 in the western US, where PM2.5 levels have been heavily impacted by wildfire smoke over this time period.

Monthly Patterns of Ammonia Over the Contiguous United States at 2-km Resolution

Monthly, high-resolution (∼2 km) ammonia (NH3) column maps from the Infrared Atmospheric Sounding Interferometer (IASI) were developed across the contiguous United States and adjacent areas. Ammonia hotspots (95th percentile of the column distribution) were highly localized with a characteristic length scale of 12 km and median area of 152 km2. Five seasonality clusters were identified with k-means++ clustering. The Midwest and eastern United States had a broad, spring maximum of NH3 (67% of hotspots in this cluster). The western United States, in contrast, showed a narrower midsummer peak (32% of hotspots). IASI spatiotemporal clustering was consistent with those from the Ammonia Monitoring Network. CMAQ and GFDL-AM3 modeled NH3 columns have some success replicating the seasonal patterns but did not capture the regional differences. The high spatial-resolution monthly NH3 maps serve as a constraint for model simulations and as a guide for the placement of future, ground-based network sites.

Global Spatial and Temporal Variation of the Combined Effect of Aerosol and Water Vapour on Solar Radiation

This study aims to calculate the combined and individual effects of the optical thickness of aerosols (AOT) and precipitable water vapour (PWV) on the solar radiation reaching the Earth’s surface at a global scale and to analyse its spatial and temporal variation. For that purpose, a novel but validated methodology is applied to CERES SYN1deg products for the period 2000–2019. Spatial distributions of AOT and PWV effects, both individually and combined, show a close link with the spatial distributions of AOT and PWV. The spatially averaged combined effect results in a −13.9% reduction in irradiance, while the average AOT effect is −2.3%, and the PWV effect is −12.1%. The temporal analysis focuses on detecting trends in the anomalies. The results show overall positive trends for AOT and PWV. Consequently, significant negative overall trends are found for the effects. However, significant positive trends for the individual AOT and the combined AOT-PWV effects are found in specific regions, such as the eastern United States, Europe or Asia, indicating successful emission control policies in these areas. This study contributes to a better understanding of the individual and combined effects of aerosols and water vapour on solar radiation at a global scale.

About Validation-Comparison of Burned Area Products

This paper proposes a validation-comparison method for burned area (BA) products. The technique considers: (1) bootstrapping of scenes for validation-comparison and (2) permutation tests for validation. The research focuses on the tropical regions of Northern Hemisphere South America and Northern Hemisphere Africa and studies the accuracy of the BA products: MCD45, MCD64C5.1, MCD64C6, Fire CCI C4.1, and Fire CCI C5.0. The first and second parts consider methods based on random matrix theory for zone differentiation and multiple ancillary variables such as BA, the number of burned fragments, ecosystem type, land cover, and burned biomass. The first method studies the zone effect using bootstrapping of Riemannian, full Procrustes, and partial Procrustes distances. The second method explores the validation by using distance permutation tests under uncertainty. The results refer to Fire CCI 5.0 with the best BA description, followed by MCD64C6, MCD64C5.1, MCD45, and Fire CCI 4.1. It was also found that biomass, total BA, and the number of fragments affect the BA product accuracy.

Near Real-Time Automated Early Mapping of the Perimeter of Large Forest Fires from the Aggregation of VIIRS and MODIS Active Fires in Mexico

In contrast with current operational products of burned area, which are generally available one month after the fire, active fires are readily available, with potential application for early evaluation of approximate fire perimeters to support fire management decision making in near real time. While previous coarse-scale studies have focused on relating the number of active fires to a burned area, some local-scale studies have proposed the spatial aggregation of active fires to directly obtain early estimate perimeters from active fires. Nevertheless, further analysis of this latter technique, including the definition of aggregation distance and large-scale testing, is still required. There is a need for studies that evaluate the potential of active fire aggregation for rapid initial fire perimeter delineation, particularly taking advantage of the improved spatial resolution of the Visible Infrared Imaging Radiometer (VIIRS) 375 m, over large areas and long periods of study. The current study tested the use of convex hull algorithms for deriving coarse-scale perimeters from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections, compared against the mapped perimeter of the MODIS collection 6 (MCD64A1) burned area. We analyzed the effect of aggregation distance (750, 1000, 1125 and 1500 m) on the relationships of active fire perimeters with MCD64A1, for both individual fire perimeter prediction and total burned area estimation, for the period 2012–2108 in Mexico. The aggregation of active fire detections from MODIS and VIIRS demonstrated a potential to offer coarse-scale early estimates of the perimeters of large fires, which can be available to support fire monitoring and management in near real time. Total burned area predicted from aggregated active fires followed the same temporal behavior as the standard MCD64A1 burned area, with potential to also account for the role of smaller fires detected by the thermal anomalies. The proposed methodology, based on easily available algorithms of point aggregation, is susceptible to be utilized both for near real-time and historical fire perimeter evaluation elsewhere. Future studies might test active fires aggregation between regions or biomes with contrasting fuel characteristics and human activity patterns against medium resolution (e.g., Landsat and Sentinel) fire perimeters. Furthermore, coarse-scale active fire perimeters might be utilized to locate areas where such higher-resolution imagery can be downloaded to improve the evaluation of fire extent and impact.

Occurrence frequencies and regional variations in Visible Infrared Imaging Radiometer Suite (VIIRS) global active fires

Active fires are considered to be the key contributor to, and critical consequence of, climate change. Quantifying the occurrence frequency and regional variations in global active fires is significant for assessing carbon cycling, atmospheric chemistry, and postfire ecological effects. Multiscale variations in fire occurrence frequencies have still never been fully investigated despite free access to global active fire products. We analyzed the occurrence frequencies of Visible Infrared Imaging Radiometer Suite (VIIRS) active fires at national, pan-regional (tropics and extratropics) to global scales and at hourly, monthly, and annual scales during 2012-2017. The results revealed that the accumulated occurrence frequencies of VIIRS global active fires were up to 12,193 × 10⁴ , yet exhibiting slight fluctuations annually and with respect to the 2014-2016 El Niño event, especially during 2015. About 35.52% of VIIRS active fires occurred from July to September, particularly in August (13.06%), and typically between 10:00 and 13:00 Greenwich Mean Time (GMT; 42.96%) and especially at 11:00 GMT (17.65%). The total counts conform to a bimodal pattern with peaks in 5°-11°N (18.01%) and 5°-18°S (32.46%), respectively, alongside a unimodal distribution in terms of longitudes between 15°E and 30°E (32.34%). Tropical annual average of active fire (1,496.81 × 10⁴ ) accounted for 75.83%. Nearly 30% were counted in Brazil, the Democratic Republic of the Congo, Indonesia, and Mainland Southeast Asia (MSEA). Fires typically occurred between June (or August) and October (or November) with far below-average rainfall in these countries, while those in MSEA primarily occurred between February and April during the dry season. They were primarily observed between 00:00 and 02:00 GMT, between 12:00 and 14:00 within each Zone Time. We believed that VIIRS global active fires products are useful for developing fire detection algorithms, discriminating occurrence types and ignition causes via correlation analyses with physical geographic elements, and assessment of their potential impacts.

Effects of Climatic Warming and Wildfires on Recent Vegetation Changes in the Lake Baikal Basin

The vegetation changes in the area of the Russian part of the Lake Baikal water basin for the period 2010–2018 were investigated using MCD12C1 land cover. The decline in swamp systems area began in 2012 and continued until 2015, after which it partially recovered during the heavy rain season in 2018. During the period of 2010–2018, the area covered by forests did not exceed 20.3% of the Baikal basin of the total portion of the Baikal basin under study. Deforestation began in 2013 and continued until 2017. Over 2013–2018, the forest level decreased by 12.1% compared to the forest state in 2013. The analysis of summer rainfalls and aridity indexes was performed by using CRU TS and GPCC climatic datasets. It is shown that the interannual variations of precipitation and aridity changes are determined by the variability of the global circulation of moist air masses. The MCD64A1 (burned area) and MCD14ML (active fires) MODIS products were used for investigation of the influence of wildfires on vegetation changes. The spatial hotspot distributions and burned areas in general correspond to aridity zones, but they cannot explain the 20-fold increase in the number of wildfires. Most of the hotspot locations are away from settlements, roads, and loggings, in difficult-to-access mountainous areas, as well as in the low-inhabited areas of Siberia. We assume that the nature of such ignitions includes dry thunderstorms, pyrocumulus lightning, or remote impact.

Satellite-Observed Variations and Trends in Carbon Monoxide over Asia and Their Sensitivities to Biomass Burning

As the carbon monoxide (CO) total column over Asia is among the highest in the world, it is important to characterize its variations in space and time. Using Measurements of Pollution in the Troposphere (MOPITT) and Atmospheric InfraRed Sounder (AIRS) satellite data, the variations and trends in CO total column over Asia and its seven subregions during 2003–2017 are investigated in this study. The CO total column in Asia is higher in spring and winter than in summer and autumn. The seasonal maximum and minimum are in spring and summer respectively in the regional mean over Asia, varying between land and oceans, as well as among the subregions. The CO total column in Asia shows strong interannual variation, with a regional mean coefficient of variation of 5.8% in MOPITT data. From 2003 to 2017, the annual mean of CO total column over Asia decreased significantly at a rate of (0.58 ± 0.15)% per year (or −(0.11 ± 0.03) × 1017 molecules cm−2 per year) in MOPITT data, resulting from significant CO decreases in winter, summer, and spring. In most of the subregions, significant decreasing trends in CO total column are also observed, more obviously over areas with high CO total column, including eastern regions of China and the Sichuan Basin. The regional decreasing trends in these areas are over 1% per year. Over the entire Asia, and in fire-prone subregions including South Siberia, Indo-China Peninsula, and Indonesia, we found significant correlations between the MOPITT CO total column and the fire counts from the Moderate Resolution Imaging Spectroradiometer (MODIS). The variations in MODIS fire counts may explain 58%, 60%, 36%, and 71% of the interannual variation in CO total column in Asia and these three subregions, respectively. Over different land cover types, the variations in biomass burning may explain 62%, 52%, and 31% of the interannual variation in CO total column, respectively, over the forest, grassland, and shrubland in Asia. Extremes in CO total column in Asia can be largely explained by the extreme fire events, such as the fires over Siberia in 2003 and 2012 and over Indonesia in 2006 and 2015. The significant decreasing trends in MODIS fire counts inside and outside Asia suggest that global biomass burning may be a driver for the decreasing trend in CO total column in Asia, especially in spring. In general, the variations and trends in CO total column over Asia detected by AIRS are similar to but smaller than those by MOPITT. The two datasets show similar spatial and temporal variations in CO total column over Asia, with correlation coefficients of 0.86–0.98 in the annual means. This study shows that the interannual variation in atmospheric CO in Asia is sensitive to biomass burning, while the decreasing trend in atmospheric CO over Asia coincides with the decreasing trend in MODIS fire counts from 2003 to 2017.

Winter VOCs and OVOCs measured with PTR-MS at an urban site of India: Role of emissions, meteorology and photochemical sources

Within the outline of air quality studies at metropolitan city, the mixing ratios of seven selected volatile organic compounds (VOCs) were measured during December 2015 (winter) at an urban site of Pune. The measurement of VOCs was conducted using a proton transfer reaction-quadrupole mass spectrometer (PTR-QMS). The study represents daily variability of ambient VOCs and their various associated emission sources. Diurnal profiles have differed from one VOC to another as the result of their different origins and the influence of different meteorological parameters (i.e. solar radiation, temperature) and planetary boundary layer height (PBL-H). The hourly mixing ratios of Oxygenated-VOCs (OVOCs) and aromatics were in the ranges of 0.6-29 ppbv and 0.13-14 ppbv, respectively with OVOCs accounted for up to 75% of total measured VOCs. The role of long-range transport from the clear Thar Desert and polluted Indo-Gangetic Plain (IGP) was observed during the episodes of 1-15 and 17-31 December 2015, respectively. VOCs showed the strong diurnal variations with peaks during morning and evening hours and lowest in the afternoon. In the evening period, high levels of aromatics coincided with the lowest OVOCs suggests the role of fresh vehicular emissions. Emission ratios of various VOCs as a function of temperature showed the role of different sources including the biogenic and photochemical production as well as the anthropogenic sources, respectively. The higher emission ratio of Δmethanol/Δacetonitrile at the study site suggests the long range transport of biomass burning plumes from the Indo-Gangetic Plain (IGP) during the 17-31, Dec. 2015. In addition to the pattern of emission, the diurnal and day-to-day variations of VOCs were influenced by the local meteorological conditions and depth of planetary boundary layer (PBL-H).

Identification and characterization of spatio-temporal hotspots of forest fires in South Asia

Forest fire is considered as one of the major threats to global biodiversity and a significant source of greenhouse gas emissions. Rising temperatures, weather conditions, and topography promote the incidences of fire due to human ignition in South Asia. Because of its synoptic, multi-spectral, and multi-temporal nature, remote sensing data can be a state of art technology for forest fire management. This study focuses on the spatio-temporal patterns of forest fires and identifying hotspots using the novel geospatial technique "emerging hotspot analysis tool" in South Asia. Daily MODIS active fire locations data of 15 years (2003-2017) has been aggregated in order to characterize fire frequency, fire density, and hotspots. A total of 522,348 active fire points have been used to analyze risk of fires across the forest types. Maximum number of forest fires in South Asia was occurring during the January to May. Spatial analysis identified areas of frequent burning and high fire density in South Asian countries. In South Asia, 51% of forest grid cells were affected by fires in 15 years. Highest number of fire incidences was recorded in tropical moist deciduous forest and tropical dry deciduous forest. The emerging hotspots analysis indicates prevalence of sporadic hotspots, followed by historical hotspots, consecutive hotspots, and persistent hotspots in South Asia. Of the seven South Asian countries, Bangladesh has highest emerging hotspot area (34.2%) in forests, followed by 32.2% in India and 29.5% in Nepal. Study results offer critical insights in delineation of fire vulnerable forest landscapes which will stand as a valuable input for strengthening management of fires in South Asia.

The Uncharacteristic Occurrence of the June 2013 Biomass-Burning Haze Event in Southeast Asia: Effects of the Madden-Julian Oscillation and Tropical Cyclone Activity

One of the worst haze events to ever hit Peninsular Malaysia occurred in June 2013 due to smoke from Riau, Central Sumatra. While biomass-burning in the region is common, the early occurrence of a haze episode of this magnitude was uncharacteristic of the seasonality of extreme fire events, which usually occur between August and October in the Maritime Continent (MC). This study aims to investigate the phenomenology of the June 2013 haze event and its underlying meteorological forcing agents. The aerosol and meteorological environment during the event is examined using the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire hotspot detections and aerosol optical thickness retrievals, satellite-based precipitation retrievals, and meteorological indices. These datasets are then supported by a WRF-Chem simulation to provide a comprehensive picture of the event’s meteorology and aerosol transport phenomenology. While extreme fire events are more characteristic of El Nino years, the MODIS fire count over the MC in June for the years 2001–2015 was highest in 2013 when neutral El Nino/Southern Oscillation (ENSO) conditions prevailed. Although, the mean daily precipitation for June 2013 was below average for June 2003–2015. An early active tropical cyclone (TC) season occurred in 2013, and results show that the combined induced subsidence and flow enhancement due to TC Bebinca and the dry phases of a strong Madden–Julian Oscillation (MJO) event contributed to the event intensification. Results also show that Bebinca induced a decrease in surface relative humidity of at least 10% over Riau, where fire hotspots were concentrated.

Assessing Spatio-Temporal Variability of Wildfires and their Impact on Sub-Saharan Ecosystems and Air Quality Using Multisource Remotely Sensed Data and Trend Analysis

Globally, wildfires are considered the most commonly occurring disasters, resulting from natural and anthropogenic ignition sources. Wildfires consist of burning standing biomass at erratic degrees of intensity, severity, and frequency. Consequently, wildfires generate large amounts of smoke and other toxic pollutants that have devastating impacts on ambient air quality and human health. There is, therefore, a need for a comprehensive study that characterizes land–atmosphere interactions with regard to wildfires, critical for understanding the interrelated and multidimensional impacts of wildfires. Current studies have a limited scope and a narrow focus, usually only focusing on one aspect of wildfire impacts, such as air quality without simultaneously considering the impacts on land surface changes and vice versa. In this study, we use several multisource data to determine the spatial distribution, frequency, disturbance characteristics of and variability and distribution of pollutants emitted by wildfires. The specific objectives were to (1) study the sources of wildfires and the period they are prevalent in sub-Saharan Africa over a 9 year period, i.e., 2007–2016, (2) estimate the seasonal disturbance of wildfires on various vegetation types, (3) determine the spatial distribution of black carbon (BC), carbon monoxide (CO) and smoke, and (4) determine the vertical height distribution of smoke. The results show largest burned areas in December–January–February (DJF), June–July–August (JJA) and September–October–November (SON) seasons, and reciprocal high emissions of BC, CO, and smoke, as observed by Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). In addition, the results reveal an increasing trend in the magnitude of BC, and CO concentration driven by meteorological conditions such as low precipitation, low relative humidity, and low latent heat flux. Overall, this study demonstrates the value of multisource remotely sensed data in characterising long-term wildfire patterns and associated emissions. The results in this study are critical for informing better regional fire management and air quality control strategies to preserve endangered species and habitats, promote sustainable land management, and reduce greenhouse gases (GHG) emissions.

Anthromes displaying evidence of weekly cycles in active fire data cover 70% of the global land surface

Across the globe, human activities have been gaining importance relatively to climate and ecology as the main controls on fire regimes and consequently human activity became an important driver of the frequency, extent and intensity of vegetation burning worldwide. Our objective in the present study is to look for weekly cycles in vegetation fire activity at global scale as evidence of human agency, relying on the original MODIS active fire detections at 1 km spatial resolution (MCD14ML) and using novel statistical methodologies to detect significant periodicities in time series data. We tested the hypotheses that global fire activity displays weekly cycles and that the weekday with the fewest fires is Sunday. We also assessed the effect of land use and land cover on weekly fire cycle significance by testing those hypotheses separately for the Villages, Settlements, Croplands, Rangelands, Seminatural, and Wildlands anthromes. Based on a preliminary data analysis of the daily global active fire counts periodogram, we developed an harmonic regression model for the mean function of daily fire activity and assumed a linear model for the de-seasonalized time series. For inference purposes, we used a Bayesian methodology and constructed a simultaneous 95% credible band for the mean function. The hypothesis of a Sunday weekly minimum was directly investigated by computing the probabilities that the mean functions of every weekday (Monday to Saturday) are inside the credible band corresponding to mean Sunday fire activity. Since these probabilities are small, there is statistical evidence of significantly fewer fires on Sunday than on the other days of the week. Cropland, rangeland, and seminatural anthromes, which cover 70% of the global land area and account for 94% of the active fires analysed, display weekly cycles in fire activity. Due to lower land management intensity and less strict control over fire size and duration, weekly cycles in Rangelands and Seminatural anthromes, which jointly account for 53.46% of all fires, although statistically significant are weaker than those detected in Croplands.

Historical Black Carbon Reconstruction from the Lake Sediments of the Himalayan-Tibetan Plateau

Black carbon (BC) is one of the major drivers of climate change, and its measurement in different environment is crucial for the better understanding of long-term trends in the Himalayan-Tibetan Plateau (HTP) as climate warming has intensified in the region. We present the measurement of BC concentration from six lake sediments in the HTP to reconstruct historical BC deposition since the pre-industrial era. Our results show an increasing trend of BC concurrent with increased anthropogenic emission patterns after the commencement of the industrialization era during the 1950s. Also, sedimentation rates and glacier melt strengthening influenced the total input of BC into the lake. Source identification, based on the char and soot composition of BC, suggests biomass-burning emissions as a major contributor to BC, which is further corroborated by open-fire occurrence events in the region. The increasing BC trend continues to recent years, indicating increasing BC emissions, mainly from South Asia.

Correcting Swath-Dependent Bias of MODIS FRP Observations With Quantile Mapping

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 ∘ 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.

Long-Term Observed Visibility in Eastern Thailand: Temporal Variation, Association with Air Pollutants and Meteorological Factors, and Trends

The present study analyzed long-term observed visibility over Eastern Thailand, with a focus on urbanized/highly industrialized coastal areas. The temporal coverage spans 9 to 35 years for visibility data and 9 to 15 years for air quality data for the selected stations. Visibility shows strong seasonality and its degradation intensifies in the dry season. It shows a negative correspondence with PM10 and relative humidity, which is evident from different methods. Visibility has strong dependence on wind direction, suggesting the influence of local pollution sources. Back-trajectory results suggest important influences of long-range transport and humidity. Secondary aerosol formation has the potential to aggravate visibility based on a precursor-ratio method. The trends in average visibility at most stations in recent years show negative shift, decreasing direction, or persistence of relatively low visibility, possibly due to increase in air pollution. Contrast was found in the meteorologically adjusted trend (based on generalized linear models) in visibility and PM10, which is partly attributed to the role of fine particles. The study suggests that visibility degradation is a problem in Eastern Thailand and is affected by both air pollutants and meteorology. The study hopes to get attention from policymakers regarding issue of visibility degradation in the region.

Burned-Area Detection in Amazonian Environments Using Standardized Time Series Per Pixel in MODIS Data

Fires associated with the expansion of cattle ranching and agriculture have become a problem in the Amazon biome, causing severe environmental damages. Remote sensing techniques have been widely used in fire monitoring on the extensive Amazon forest, but accurate automated fire detection needs improvements. The popular Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64 product still has high omission errors in the region. This research aimed to evaluate MODIS time series spectral indices for mapping burned areas in the municipality of Novo Progresso (State of Pará) and to determine their accuracy in the different types of land use/land cover during the period 2000–2014. The burned area mapping from 8-day composite products, compared the following data: near-infrared (NIR) band; spectral indices (Burnt Area Index (BAIM), Global Environmental Monitoring Index (GEMI), Mid Infrared Burn Index (MIRBI), Normalized Burn Ratio (NBR), variation of Normalized Burn Ratio (NBR2), and Normalized Difference Vegetation Index (NDVI)); and the seasonal difference of spectral indices. Moreover, we compared the time series normalization methods per pixel (zero-mean normalization and Z-score) and the seasonal difference between consecutive years. Threshold-value determination for the fire occurrences was obtained from the comparison of MODIS series with visual image classification of Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) data using the overall accuracy. The best result considered the following factors: NIR band and zero-mean normalization, obtaining the overall accuracy of 98.99%, commission errors of 32.41%, and omission errors of 31.64%. The proposed method presented better results in burned area detection in the natural fields (Campinarana) with an overall accuracy value of 99.25%, commission errors of 9.71%, and omission errors of 27.60%, as well as pasture, with overall accuracy value of 99.19%, commission errors of 27.60%, and omission errors of 34.76%. Forest areas had a lower accuracy, with an overall accuracy of 98.62%, commission errors of 23.40%, and omission errors of 49.62%. The best performance of the burned area detection in the pastures is relevant because the deforested areas are responsible for more than 70% of fire events. The results of the proposed method were better than the burned area products (MCD45, MCD64, and FIRE-CCI), but still presented limitations in the identification of burn events in the savanna formations and secondary vegetation.

Variations in FINN Emissions of Particulate Matters and Associated Carbonaceous Aerosols from Remote Sensing of Open Biomass Burning over Northeast China during 2002–2016

Various particulate matters (PM) and associated carbonaceous aerosols released from open biomass burning (including open straw burning, grass and forest fires) are major sources of atmospheric pollutants. Northeast China is a central region with high forest and grass coverage, as well as an intensive agricultural area. In this study, the FINN (Fire INventory from Ncar) emission data was used to analyze the spatiotemporal variations of PM and associated carbonaceous aerosol component (PM2.5, PM10, OC and BC) emissions from open biomass burning in Northeast China from 2002 to 2016. The results show that the total amount of annual PM2.5, PM10, OC and BC emissions was estimated to be 59.0, 70.6, 31.5, and 4.3 kilotons, respectively, from open biomass burning over Northeast China, averaged from 2002 to 2016, with significant inter-annual variations in amplitudes from 28.0 to 122.3, 33.7 to 144.1, 15.0 to 65.0, and 2.1 to 8.6 kilotons. The regional PM2.5, PM10, OC and BC emissions showed significant seasonal variations with highest emissions in spring (with a seasonal peak in April), followed by autumn (with a seasonal peak in October), summer, and winter in Northeast China; high emissions were concentrated in the forests and grasslands with natural fires, as well as over agricultural areas with crop straw burning from human activities. The PM2.5, PM10, OC and BC emissions over forest areas presented decreasing trends, while the emissions over farmlands showed increasing trends in Northeast China during 2002–2016; this reflects on the dominance of biomass burning that shifted from forestland with natural fires to farmlands with increasing human activities. Three key meteorological drivers—strong near-surface wind speed, high air temperature and low relative humidity—were identified as having significant positive impacts on the inter-annual variations of PM2.5, PM10, OC and BC emissions from open biomass burning in Northeast China.

A Global Synthesis Inversion Analysis of Recent Variability in CO2 Fluxes Using GOSAT and In Situ Observations

The precise contribution of the two major sinks for anthropogenic CO₂ emissions, terrestrial vegetation and the ocean, and their location and year-to-year variability are not well understood. Top-down estimates of the spatiotemporal variations in emissions and uptake of CO₂ are expected to benefit from the increasing measurement density brought by recent in situ and remote CO₂ observations. We uniquely apply a batch Bayesian synthesis inversion at relatively high resolution to in situ surface observations and bias-corrected GOSAT satellite column CO₂ retrievals to deduce the global distributions of natural CO₂ fluxes during 2009-2010. Our objectives include evaluating bottom-up prior flux estimates, assessing the value added by the satellite data, and examining the impacts of inversion technique and assumptions on posterior fluxes and uncertainties. The GOSAT inversion is generally better constrained than the in situ inversion, with smaller posterior regional flux uncertainties and correlations, because of greater spatial coverage, except over North America and high-latitude ocean. Complementarity of the in situ and GOSAT data enhances uncertainty reductions in a joint inversion; however, spatial and temporal gaps in sampling still limit the ability to accurately resolve fluxes down to the sub-continental scale. The GOSAT inversion produces a shift in the global CO₂ sink from the tropics to the north and south relative to the prior, and an increased source in the tropics of ~2 Pg C y^-1 relative to the in situ inversion, similar to what is seen in studies using other inversion approaches. This result may be driven by sampling and residual retrieval biases in the GOSAT data, as suggested by significant discrepancies between posterior CO₂ distributions and surface in situ and HIPPO mission aircraft data. While the shift in the global sink appears to be a robust feature of the inversions, the partitioning of the sink between land and ocean in the inversions using either in situ or GOSAT data is found to be sensitive to prior uncertainties because of negative correlations in the flux errors. The GOSAT inversion indicates significantly less CO₂ uptake in summer of 2010 than in 2009 across northern regions, consistent with the impact of observed severe heat waves and drought. However, observations from an in situ network in Siberia imply that the GOSAT inversion exaggerates the 2010-2009 difference in uptake in that region, while the prior CASA-GFED model of net ecosystem production and fire emissions reasonably estimates that quantity. The prior, in situ posterior, and GOSAT posterior all indicate greater uptake over North America in spring to early summer of 2010 than in 2009, consistent with wetter conditions. The GOSAT inversion does not show the expected impact on fluxes of a 2010 drought in the Amazon; evaluation of posterior mole fractions against local aircraft profiles suggests that time-varying GOSAT coverage can bias estimation of flux interannual variability in this region.