Greenhouse gas emission in the whole process of forest fire including rescue: a case of forest fire in Beibei District of Chongqing

In the context of global high temperature, the harm of greenhouse gases (GHG) emissions caused by frequent forest fires to the environment cannot be ignored. Existing research only calculates the GHG generated by the burning of forest vegetation, ignoring the GHG generated by the fire-driven social rescue activities. Taking the forest fire in Beibei District, Chongqing City, China, as an example, this paper studies and establishes the GHG emission accounting method for the whole process of forest fire from ignition to fire extinguishing through three processes: vegetation burning, rescue transportation, and on-site fire extinguishing. It covers three GHG calculation types: biomass burning, traffic activity level comprehensive energy consumption, and machine energy consumption. Among them, the CO2 produced by the burning of coniferous forest, the support transportation of rescue teams in Yunnan province, and the motorcycle transportation at the fire extinguishing site accounted for a relatively high proportion in the corresponding processes, reaching 12,761.445 t, 118.750 t, and 1056.980 t, respectively. Finally, through data analysis, suggestions on GHG emission reduction related to forest tree regulation and optimization of rescue and fire extinguishing management are put forward, which provides a direction for future research on carbon reduction in the whole process of forest fire events.

Assessment of forest fire emissions in Uttarakhand State, India, using Open Geospatial data and Google Earth Engine

In the recent past, forest fires have increased due to the changing climate pattern. It is necessary to analyse and quantify various gaseous emissions so as to mitigate their harmful effects on air pollution. Satellite remote sensing data provides an opportunity to study the greenhouse gases in the atmosphere. The multispectral sensor of the Tropospheric Monitoring Instrument (Sentinel-5) is capable of recording the reflectance of wavelengths vital for measuring the atmospheric concentrations of methane, formaldehyde, aerosol, carbon monoxide, etc., at a spatial resolution of 0.01°. The present study utilized the Google Earth Engine (GEE) platform to study the emissions caused by forest fires in four districts of Uttarakhand State of India, which witnessed unprecedented fires in April-May 2021. All the datasets were ingested in GEE, which has the capability to analyse large datasets without the need to download them. The pre-fire period chosen was September 2020; the fire period was February-May 2021, and the post-fire period was June 2021. The variables chosen were aerosol absorbing index (AAI), carbon monoxide (CO) and nitrogen dioxide (NO2). The climate parameter temperature (Moderate Resolution Imaging Spectroradiometer Land Surface Temperature) and precipitation (from Climate Hazards Group InfraRed Precipitation (CHIRPS) Pentad) were also studied for the period mentioned. The results indicate a different trend for emissions in each district. For AAI, maximum emissions were noted in district Nainital followed by Almora, Tehri Garhwal and Garhwal. For CO emissions, the most affected district was Almora followed by Nainital, Garhwal and Tehri Garhwal. For NO2 emissions, the most affected district was Garhwal, followed by Nainital, Tehri Garhwal and Almora. Delta Normalized Burn Ratio was computed from Sentinel data (difference of pre-fire and post-fire images) to assess the burnt area severity. The Delta Normalized Burn Ratio values observed that the district with the most burnt area is Garhwal, followed by Nainital, Almora and Tehri Garhwal. The elevated temperatures and scanty rainfall patterns regulated the intensity and duration of forest fire. Monitoring the gaseous emissions as a consequence of forest fire in the GEE platform is much easier and more convenient at a regional level. Such data is much needed for mitigation measures to be implemented in time.

Mapping burn severity and monitoring CO content in Türkiye's 2021 Wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform

This study investigated forest fires in the Mediterranean of Türkiye between July 28, 2021, and August 11, 2021. Burn severity maps were produced with the difference normalised burned ratio index (dNBR) and difference normalised difference vegetation index (dNDVI) using Sentinel-2 images on the Google Earth Engine (GEE) cloud platform. The burned areas were estimated based on the determined burning severity degrees. Vegetation density losses in burned areas were analysed using the normalised difference vegetation index (NDVI) time series. At the same time, the post-fire Carbon Monoxide (CO) column number densities were determined using the Sentinel-5P satellite data. According to the burn severity maps obtained with dNBR, the sum of high and moderate severity areas constitutes 34.64%, 20.57%, 46.43%, 51.50% and 18.88% of the entire area in Manavgat, Gündoğmuş, Marmaris, Bodrum and Köyceğiz districts, respectively. Likewise, according to the burn severity maps obtained with dNDVI, the sum of the areas of very high severity and high severity constitutes 41.17%, 30.16%, 30.50%, 42.35%, and 10.40% of the entire region, respectively. In post-fire NDVI time series analyses, sharp decreases were observed in NDVI values from 0.8 to 0.1 in all burned areas. While the Tropospheric CO column number density was 0.03 mol/m² in all regions burned before the fire, it was observed that this value increased to 0.14 mol/m² after the fire. Moreover, when the area was examined more broadly with Sentinel 5P data, it was observed that the amount of CO increased up to a maximum value of 0.333 mol/m². The results of this study present significant information in terms of determining the severity of forest fires in the Mediterranean region in 2021 and the determination of the CO column number density after the fire. In addition, monitoring polluting gases with RS techniques after forest fires is essential in understanding the extent of the damage they can cause to the environment.

New Inventories of Global Carbon Dioxide Emissions through Biomass Burning in 2001–2020

Recently, the effect of large-scale fires on the global environment has attracted attention. Satellite observation data are used for global estimation of fire CO2 emissions, and available data sources are increasing. Although several CO2 emission inventories have already been released, various remote sensing data were used to create the inventories depend on the studies. We created eight global CO2 emission inventories through fires from 2001 to 2020 by combining input data sources, compared them with previous studies, and evaluated the effect of input sources on CO2 emission estimation. CO2 emissions were estimated using a method that combines the biomass density change (by the repeated fires) with the general burned area approach. The average annual CO2 emissions of the created eight inventories were 8.40 ± 0.70 Pg CO2 year−1 (±1 standard deviation), and the minimum and maximum emissions were 3.60 ± 0.67 and 14.5 ± 0.83 Pg CO2 year−1, respectively, indicating high uncertainty. CO2 Emissions obtained from four previous inventories were within ±1 standard deviation in the eight inventories created in this study. Input datasets, especially biomass density, affected CO2 emission estimation. The global annual CO2 emissions from two biomass maps differed by 60% (Maximum). This study assesses the performance of climate and fire models by revealing the uncertainty of fire emission estimation from the input sources.

Estimation of carbon dioxide emissions from the megafires of Australia in 2019-2020

Catastrophic fires occurred in Australia between 2019 and 2020. These fires burned vast areas and caused extensive damage to the environment and wildlife. In this study, we estimated the carbon dioxide (CO₂) emissions from these fires using a bottom-up method involving the improved burnt area approach and up-to-date remote sensing datasets to create monthly time series distribution maps for Australia from January 2019 to February 2020. The highest monthly CO₂ emissions in Australia since 2001 were recorded in December 2019. The estimated annual CO₂ emissions from March 2019 to February 2020 in Australia were 806 ± 69.7 Tg CO₂ year^-1, equivalent to 1.5 times its total greenhouse gas emissions (CO₂ equivalent) in 2017. New South Wales (NSW) emitted 181 ± 10.2 Tg CO₂ month^-1 in December 2019 alone, representing 64% of the average annual emissions of Australia from 2001-2018. The negative correlation observed between CO₂ emissions and precipitation for 2001-2020 was 0.51 for Australia. Lower than average precipitation and fires in high biomass density areas caused significant CO₂ emissions. This study helps to better assess the performance of climate models as a case study of one of the major events caused by climate.

Impact of wildfires on SO2 detoxification mechanisms in leaves of oak and beech trees

Frequency and intensity of wildfire occurrences are dramatically increasing worldwide due to global climate change, having a devastating effect on the entire ecosystem including plants. Moreover, distribution of fire-smoke can influence the natural environment over very long distances, i.e. hundreds of kilometres. Dry plant matter contains 0.1-0.9% (w/w) sulphur, which is mainly released during combustion into the atmosphere as sulphur dioxide (SO₂) resulting in local concentrations of up to 3000 nL L^-1. SO₂ is a highly hazardous gas, which enters plants mostly via the stomata. Toxic sulphite is formed inside the leaves due to conversion of SO₂. Plants as sessile organisms cannot escape from threats, why they evolved an impressive diversity of molecular defence mechanisms. In the present study, two recent wildfires in Germany were evaluated to analyse the effect of SO₂ released into the atmosphere on deciduous trees: the Meppen peat fire in 2018 and the forest fire close to Luebtheen in 2019. Collected leaf material from beech (Fagus sylvatica) and oak (Quercus robur) was examined with respect to detoxification of sulphur surplus due to the exposure to elevated SO₂. An induced stress reaction in both species was indicated by a 1.5-fold increase in oxidized glutathione. In beech leaves, the enzymatic activities of the sulphite detoxification enzymes sulphite oxidase and apoplastic peroxidases were increased 5-fold and a trend of sulphate accumulation was observed. In contrast, oaks did not regulate these enzymes during smoke exposure, however, the constitutive activity is 10-fold and 3-fold higher than in beech. These results show for the first time sulphite detoxification strategies of trees in situ after natural smoke exposure. Beech and oak trees survived short-term SO₂ fumigation due to exclusion of toxic gases and different oxidative detoxification strategies. Beeches use efficient upregulation of oxidative sulphite detoxification enzymes, while oaks hold a constitutively high enzyme-pool available.

Estimation of Forest Fire Emissions in Southwest China from 2013 to 2017

Forest fire emissions have a great impact on local air quality and the global climate. However, the current and detailed regional forest fire emissions inventories remain poorly studied. Here we used Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate monthly emissions from forest fires at a spatial resolution of 500 m × 500 m in southwest China from 2013 to 2017. The spatial and seasonal variations of forest fire emissions were then analyzed at the provincial level. The results showed that the annual average emissions of CO2, CO, CH4, SO2, NH3, NOX, PM, black carbon, organic carbon, and non-methane volatile organic compounds from forest fires were 1423.19 × 103, 91.66 × 103, 4517.08, 881.07, 1545.04, 1268.28, 9838.91, 685.55, 7949.48, and 12,724.04 Mg, respectively. The forest fire emissions characteristics were consistent with the characteristics of forest fires, which show great spatial and temporal diversity. Higher pollutant emissions were concentrated in Yunnan and Tibet, with peak emissions occurring in spring and winter. Our work provides a better understanding of the spatiotemporal representation of regional forest fire emissions and basic data for forest fire management departments and related research on pollution and emissions controls. This method will also provide guidance for other areas to develop high-resolution regional forest fire emissions inventories.

Development of a Smoke Dispersion Forecast System for Korean Forest Fires

Smoke from forest fires is a growing concern in Korea as forest structures have changed and become more vulnerable to fires associated with climate change. In this study, we developed a Korean forest fire smoke dispersion prediction (KFSDP) system to support smoke management in Korea. The KFSDP system integrates modules from different models, including a Korean forest fire growth prediction model, grid-based geographic information system (GIS) fuel loading and consumption maps generated by national forest fuel inventory data, and the Korean Weather Research and Forecasting Model, into a Gaussian plume model to simulate local- and regional-scale smoke dispersion. The forecast system is operated using grid-based fires and simulates a cumulative smoke dispersion of carbon monoxide (CO) and <2.5 µm and <10 µm particulate matter (PM2.5 and PM10, respectively) ground-level concentration contours at 30-min intervals during the fire in concert with weather forecasts. The simulated smoke dispersions were evaluated and agreed well with observed smoke spreads obtained from real forest fires in Korea, and the performance of the KFSDP system was also analyzed using “what-if” scenarios. This is the first study to develop an integrated model for predicting smoke dispersion from forest fires in Korea.

Modeling emissions for three-dimensional atmospheric chemistry transport models

Poor air quality is still a threat for human health in many parts of the world. In order to assess measures for emission reductions and improved air quality, three-dimensional atmospheric chemistry transport modeling systems are used in numerous research institutions and public authorities. These models need accurate emission data in appropriate spatial and temporal resolution as input. This paper reviews the most widely used emission inventories on global and regional scales and looks into the methods used to make the inventory data model ready. Shortcomings of using standard temporal profiles for each emission sector are discussed, and new methods to improve the spatiotemporal distribution of the emissions are presented. These methods are often neither top-down nor bottom-up approaches but can be seen as hybrid methods that use detailed information about the emission process to derive spatially varying temporal emission profiles. These profiles are subsequently used to distribute bulk emissions such as national totals on appropriate grids. The wide area of natural emissions is also summarized, and the calculation methods are described. Almost all types of natural emissions depend on meteorological information, which is why they are highly variable in time and space and frequently calculated within the chemistry transport models themselves. The paper closes with an outlook for new ways to improve model ready emission data, for example, by using external databases about road traffic flow or satellite data to determine actual land use or leaf area. In a world where emission patterns change rapidly, it seems appropriate to use new types of statistical and observational data to create detailed emission data sets and keep emission inventories up-to-date.

IMPLICATIONS

Emission data are probably the most important input for chemistry transport model (CTM) systems. They need to be provided in high spatial and temporal resolution and on a grid that is in agreement with the CTM grid. Simple methods to distribute the emissions in time and space need to be replaced by sophisticated emission models in order to improve the CTM results. New methods, e.g., for ammonia emissions, provide grid cell-dependent temporal profiles. In the future, large data fields from traffic observations or satellite observations could be used for more detailed emission data.

Atmospheric Aerosols: Composition, Transformation, Climate and Health Effects

Aerosols are of central importance for atmospheric chemistry and physics, the biosphere, climate, and public health. The airborne solid and liquid particles in the nanometer to micrometer size range influence the energy balance of the Earth, the hydrological cycle, atmospheric circulation, and the abundance of greenhouse and reactive trace gases. Moreover, they play important roles in the reproduction of biological organisms and can cause or enhance diseases. The primary parameters that determine the environmental and health effects of aerosol particles are their concentration, size, structure, and chemical composition. These parameters, however, are spatially and temporally highly variable. The quantification and identification of biological particles and carbonaceous components of fine particulate matter in the air (organic compounds and black or elemental carbon, respectively) represent demanding analytical challenges. This Review outlines the current state of knowledge, major open questions, and research perspectives on the properties and interactions of atmospheric aerosols and their effects on climate and human health.